Computer-Implemented Method and Computing Device

Abstract

In some embodiments, the present disclosure provides an example method that may include receiving input data for a plurality of identified data records; receiving a plurality of pre-determined contract parameters associated with at least one logistics data provider of the plurality of logistics data providers; dynamically enriching the input data by aggregating current data, forecast data, and prediction data; calculating a respective risk probability value associated with each eligible provider of the plurality of providers; generating a respective dynamic data model associated with each eligible provider of the plurality of providers; dynamically determining a real-time pre-determined contract risk threshold for the identified data records; automatically modifying a real-time pre-determined contract risk threshold associated with the at least one eligible provider of the plurality of providers; and dynamically selecting respective data points for each eligible provider of the plurality of providers.

Description

[Technical Field]

[0001] The present disclosure relates generally to computerized systems and methods for analyzing data related to cargo transportation, and more particularly to computerized systems and methods for determining risk probabilities associated with the transportation of cargo and determining alternative actions to reduce the risks. [Background technology]

[0002] Risk assessments are used to identify risks and / or risk factors that could cause harm. For example, risk assessments associated with the transportation of cargo within a freight shipment have historically been based on events that occurred in the previous year related to the specific cargo shipment, and less than half of all shipments are currently uninsured. That is, risk assessments associated with any losses that occurred related to cargo within a freight shipment are attributable to circumstances during the transportation and delivery of the cargo that resulted in a loss of value for that specific cargo shipment. Similarly, risk assessments associated with any delays that occurred related to cargo within a freight shipment are attributable to circumstances during the transportation and delivery of the cargo that resulted in a delay for that specific cargo shipment. Generally, risk assessments associated with a freight shipment rely solely on the value of the cargo within the freight shipment and do not take into account other factors that may affect the freight shipment. For example, these other factors may include information about the cargo from a real-time perspective and information detailing other cargo shipments containing one or more characteristics similar to the freight shipment from a historical perspective.

[0003] For example, a risk assessment associated with the transportation of cargo included in a freight shipment can be used to determine the insurance premium associated with that shipment. For example, cargo insurance is typically offered and purchased on an annual basis. Typically, in these types of annual insurance provider offerings, the premium paid to the insurance provider is based on losses from the previous year (e.g., 1 year, 2 years, 5 years, 10 years, etc.). For example, the insurance provider uses the previous year's losses to determine the current year's premium. Often, the premium is the previous year's premium, with slight price adjustments. For example, current premiums are based in part on the insurance provider's revenue projections for the coming year. For example, for logistics delivery providers (e.g., United States Postal Service (USPS), UPS®, FedEx®, Flexport®, DHS®, carriers, etc.), the current annual premium providing cargo insurance coverage for cargo delivered in the current year is based on the carrier's revenue, not the value of the goods insured and its annual contract. For example, often, the current year's premium providing cargo insurance coverage for cargo delivered in the current year is paid in ten installments. Summary of the Invention

[0004] In some embodiments, the present disclosure provides an exemplary technically improved computer-based method that includes at least the following steps: at least one input interface receiving input data for identified data records by a logistics data provider; at least one processor receiving, from a plurality of pre-generated databases, a plurality of pre-determined contract parameters associated with at least one provider of the plurality of providers; at least one processor identifying at least one qualified provider of the plurality of providers based on a comparison of the input data with the plurality of pre-determined contract parameters associated with the identified data records; at least one processor calculating a respective risk probability value associated with each qualified provider of the plurality of providers based on the input data and the plurality of pre-determined contract parameters; and at least one processor calculating a respective risk probability value associated with each qualified provider of the plurality of providers based on the input data and the plurality of pre-determined contract parameters. generating, by the at least one processor, a respective dynamic data model associated with each eligible provider of the plurality of providers based on the input data and the respective determined risk probability values; dynamically determining, by the at least one processor, a predetermined contract risk threshold for the identified data record utilizing the respective dynamic data models and the respective determined risk probability values ​​associated with each eligible provider of the plurality of providers; automatically modifying, by the at least one processor, a real-time predetermined contract risk threshold associated with at least one eligible provider of the plurality of providers based on the respective model risk probability values; and dynamically selecting, by the at least one processor, a respective data point for each eligible provider of the plurality of providers in real time for the identified data record based on the respective model risk probability values ​​and the modified contract risk threshold.

[0005] In some embodiments, the present disclosure provides an exemplary technically improved computer-based system that includes at least the following components: at least one processor configured to execute software instructions that cause the at least one processor to: receive, via at least one input interface, input data for identified data records containing goods transported by a logistics data provider; determine, via the at least one processor, a plurality of commodity types associated with each identified data record database based on the input data; receive, via the at least one processor, a plurality of pre-determined contract parameters associated with at least one provider among the plurality of providers from a plurality of pre-generated databases accessible by the digital platform; identify, via the at least one processor, at least one qualified provider among the plurality of providers based on a comparison of the input data with the plurality of pre-determined contract parameters associated with the identified data records; and identify, via the at least one processor, a plurality of qualified providers among the plurality of providers based on a comparison of the input data with the plurality of pre-determined contract parameters associated with the identified data records. the at least one processor calculating a respective risk probability value associated with each eligible provider of the plurality of providers based on a comparison of the input data to a plurality of predetermined contract parameters; generating, by the at least one processor, a respective dynamic data model associated with each eligible provider of the plurality of providers based on the input data and the respective determined risk probability value; dynamically determining, by the at least one processor, a real-time predetermined contract risk threshold for the identified data record utilizing the respective dynamic data model and the respective determined risk probability value associated with each eligible provider of the plurality of providers; receiving, by the at least one processor, subsequent data associated with the identified data record or historical data from a plurality of pre-generated databases; and calculating, using the at least one processor, the respective model risk probability value for each of the one or more eligible insurance providers.generating, based on real-time or historical transportation data, at least one processor automatically revising a real-time, predetermined contract risk threshold associated with at least one qualified provider of the plurality of providers based on the respective model risk probability values; and at least one processor dynamically selecting, based on the respective model risk probability values ​​and the real-time, revised contract risk threshold for the identified data record. [Brief explanation of the drawings]

[0006] Various embodiments of the present disclosure are further described with reference to the accompanying drawings, in which like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed on illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art how to variously use one or more exemplary embodiments.

[0007] A better understanding of one or more embodiments of the present invention will be obtained when the following detailed description of the preferred embodiments is considered in conjunction with the following drawings.

[0008] [Figure 1] 1 illustrates an exemplary computer-based system architecture for implementing one or more methods according to embodiments of the present disclosure. [Figure 2] 1 illustrates an exemplary data processing system functioning on networked computing devices and / or servers providing a cloud infrastructure supporting the implementation of a PSCI platform that executes a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 3A-3B]FIG. 1 is a block diagram illustrating an example sensor that may be utilized in accordance with one or more embodiments of the present disclosure. [Figure 4A] FIG. 1 illustrates a schematic block diagram of an exemplary PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 4B] 1 depicts a flowchart of the operational steps of a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 4C] 1 depicts a flowchart of the operational steps of a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 4D] 1 illustrates a schematic diagram of exemplary supervised and unsupervised machine learning utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 4E] 1 illustrates an exemplary sample layer of an artificial neural network utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 4F] 1A-1C are flow diagrams illustrating respective methods of training an artificial intelligence device included in a simulation-based learning PSCI platform and using the trained artificial intelligence device. [Figure 4G] 1A-1C are flow diagrams illustrating respective methods of training an artificial intelligence device included in a simulation-based learning PSCI platform and using the trained artificial intelligence device. [Figure 4H] 1A-1C are flow diagrams illustrating respective methods of training an artificial intelligence device included in a simulation-based learning PSCI platform and using the trained artificial intelligence device. [Figure 5A]1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5B] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5C] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5D] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5E] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5F] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5G] 1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 5H]1 illustrates a list of insurer / underwriter product types and a list of insurer / underwriter transport types utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figures 5I-5J] 1 illustrates exemplary pre-underwriter PSCI insurance bracket values ​​and product price adjustment factor values ​​utilized by a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 6A] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 6B] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 6C] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 6D] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 6E] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 6F] 1 is a graphical representation of an example menu architecture for a PSCI platform executing a PSCI software platform in accordance with one or more embodiments of the present disclosure. [Figure 7A-1]1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7A-2] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7B] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7C] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7D] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7E] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 7F] 1 is a graphical representation depicting an example embodiment of user information collected in response to one or more user requests sent to a PSCI platform executing a PSCI software platform according to one or more embodiments of the present disclosure. [Figure 8]1 shows a PSCI system interactive data and logic flow diagram illustrating an example embodiment of functionality performed by a PSCI platform executing a PSCI software platform in an example embodiment of a PSCI system according to one or more embodiments of the present disclosure. [Figure 9] 1 is a flowchart illustrating operational steps for automatically modifying pre-determined contract risk thresholds in real time based on respective model risk probability values, in accordance with one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] Various detailed embodiments of the present disclosure are disclosed herein in conjunction with the accompanying drawings. However, it should be understood that the disclosed embodiments are merely exemplary. Additionally, each example given in connection with the various embodiments of the present disclosure is intended to be illustrative, not limiting.

[0010] Throughout the specification, the following terms have the meanings expressly associated therewith, unless the context clearly dictates otherwise. As used herein, the phrases "in one embodiment," "in one or more embodiments," and "in some embodiments" do not necessarily refer to the same embodiment, although this is possible. Furthermore, as used herein, the phrases "in another embodiment," "in one or more other embodiments," and "in some other embodiments" do not necessarily refer to different embodiments, although this is possible. That is, as noted below, various embodiments may be readily combined without departing from the scope or spirit of the present disclosure.

[0011] Additionally, the term "based on" is not exclusive and allows for based on additional unrecited factors unless the context clearly dictates otherwise. Additionally, throughout this specification, the meanings of "a," "an," "the," and "the" include plural references. The meaning of "in" includes "in," "on," and "adjacent to."

[0012] As described herein, one or more aspects and / or functions included in one or more embodiments may be performed dynamically and / or in real time.

[0013] As used herein, the term "dynamic" means that an event and / or action is triggered and / or occurs without any human intervention. In some embodiments, the event and / or action of the present invention may occur in real time and / or may be based on a predetermined periodicity of at least one of nanoseconds, nanoseconds, milliseconds, milliseconds, seconds, seconds, minutes, minutes, hourly, hours, daily, daily, weekly, monthly, yearly, etc.

[0014] As used in accordance with the above-described embodiments, the phrase "real-time" contemplates, for example, that an object on a display is acquired, processed, received, transmitted, and / or displayed at a sufficiently high data rate and sufficiently low delay that the object moves smoothly, e.g., without any user-noticeable judder or latency between the object's movement and the display's movement, or that the receipt of relevant data should occur at a sufficiently high data rate and sufficiently low delay relative to the acquisition of the relevant data, or that the use of the relevant data should occur at a sufficiently high data rate and sufficiently low delay relative to the receipt of the relevant data. For example, "real-time processing," "real-time computation," and "real-time execution" all relate to the performance of a computation during the actual time that a relevant physical process (e.g., a user operating an application on a mobile device) is occurring, and the results of the computation may be used in guiding the physical process.

[0015] As used herein, the terms "component" and "system" are intended to refer to any computer-based entity: hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of example, both an application running on a server and the server itself may be a component. One or more components may reside within a process or thread of execution, and a component may be localized on one computer and / or distributed between two or more computers.

[0016] Embodiments of the subject matter described herein may be implemented in a computing system including a back-end component, e.g., a data server, or a middleware component, e.g., an application server, or a front-end component, e.g., a client computer having a graphical user interface or web browser through which a user can interact with an implementation of the subject matter described herein, or any combination of one or more such back-end, middleware, or front-end components. In one or more embodiments, the components of the system may be interconnected by any form or medium of digital data communication, such as, for example, a communications network. Examples of communications networks include local area networks (“LANs”) and wide area networks (“WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad-hoc peer-to-peer networks). For example, specially programmed computing systems with associated devices may be configured to operate in a distributed network environment, communicating with each other via one or more suitable data communications networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communications protocols / modes. For example, as those skilled in the art will appreciate, many packet protocols exist. Some well-known packet protocols include, without limitation, IPX / SPX, X.25, AX.25, AppleTalk®, TCP / IP (e.g., HTTP), SNA, etc. Other suitable data communication protocols / modes include Near Field Communication (NFC), RFID, Narrowband Internet of Things (NBIOT), 3G, 4G, 5G, GSM®, GPRS, WiFi®, WiMax®, CDMA, satellite, ZigBee®, and other suitable communication modes. In one or more embodiments, NFC may refer to a near-field communication technology in which NFC-enabled devices are "swiped," "bumped," "tapped," or otherwise moved in close proximity to facilitate communication.

[0017] The operations described herein may be implemented as operations performed by a data processing apparatus on data stored in one or more computer-readable storage devices or on data received from other sources. The term "data processing system" encompasses all types of apparatus, devices, and machines for processing data, including, for example, a programmable processor, a computer, a server, a system on a chip, or a combination of the above. An apparatus may include, for example, special purpose logic circuitry such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, an apparatus may also include code that provides an execution environment for the computer program in question, such as code comprising processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or one or more combinations of these. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.

[0018] Embodiments of the present disclosure may be implemented in hardware, firmware, software, or any combination thereof. Embodiments of the present disclosure may also be implemented as instructions applied by a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include one or more processing resources such as a random access memory (RAM), a central processing unit (CPU), or hardware or software control logic, a read-only memory (ROM), a magnetic disk storage medium, an optical storage medium, a flash memory device, an electrical, optical, acoustic, or other form of propagated signal (e.g., a carrier wave, an infrared signal, a digital signal, etc.), and others.

[0019] As used herein, the terms "computer engine" and "engine" identify a combination of at least one software component and at least one hardware component (e.g., a library, software development kit (SDK), object, etc.) designed / programmed / configured to manage / control at least one other software component and / or other software and / or hardware components. The term "engine" refers to functional operations that may be embodied as a standalone component or as an integrated configuration of multiple subcomponents. Thus, an engine may be implemented, for example, as a single module or as multiple modules operating in cooperation with each other. Furthermore, an engine may be implemented as software instructions in a memory or individually in either hardware (e.g., electronic circuitry), firmware, software, or a combination thereof. In one embodiment, an engine includes instructions that control a processor to perform the functions described herein.

[0020] Examples of hardware elements used in one or more embodiments may include a processor, a microprocessor, a circuit, a circuit element (e.g., a transistor, a resistor, a capacitor, an inductor, etc.), an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a logic gate, a register, a semiconductor device, a chip, a microchip, a chipset, etc.

[0021] The term "terminal" as used herein may be referred to as a mobile station (MS), user equipment (UE), user terminal (UT), wireless terminal, access terminal (AT), terminal, subscriber unit, subscriber station (SS), server, wireless device, wireless communication device, wireless transmit / receive unit (WTRU), mobile node, mobile, or other terminology.

[0022] Various embodiments of the terminal may include a cellular telephone, a smartphone with wireless communication capabilities, a personal digital assistant (PDA) with wireless communication capabilities, a server, a wireless modem, a portable computer with wireless communication capabilities, a photography device such as a digital camera with wireless communication capabilities, a gaming device with wireless communication capabilities, a home appliance for storing and playing music with wireless communication capabilities, an Internet appliance capable of wireless Internet access and browsing, and a mobile unit or terminal that integrates these functions. Furthermore, the terminal may include, but is not limited to, a machine-to-machine (M2M) terminal and a machine-type communication (MTC) terminal / device. In this disclosure, the terminal may also be referred to as an electronic device.

[0023] A computer program (also known as a program, software, software program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted, declarative or procedural, and can be deployed in any form, such as a standalone program or as a module, component, subroutine, object, or separate unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple cooperating files (e.g., a file storing one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0024] The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows may be performed by, and an apparatus may be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0025] Processors suitable for the execution of a computer program include, by way of example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. In one or more embodiments, the one or more processors may be implemented as a multiple instruction set computer (CISC) or reduced instruction set computer (RISC) processor, an x86 instruction set compatible processor, a multi-core processor, or other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processors, dual-core mobile processors, or the like. Generally, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor that performs actions in accordance with the instructions and one or more memory devices that store instructions and data. Typically, a computer also includes one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data, or is operatively coupled to receive data from or transfer data to, or both, the one or more mass storage devices. However, a computer need not have such devices. Additionally, computers may be embedded in other devices, such as mobile phones, personal digital assistants (PDAs), portable audio or video players, game consoles, global positioning system (GPS) receivers, or portable storage devices (e.g., universal serial bus (USB) flash drives), to name just a few.

[0026] Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices, magnetic disks such as internal hard disks or removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0027] Various embodiments may be implemented using software components, hardware components, or a combination of both. Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (APIs), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. The decision whether an embodiment is implemented using hardware and / or software elements may depend on any number of factors, such as desired computational speed, power levels, thermal tolerances, processing cycle budgets, input data rates, output data rates, memory resources, data bus speeds, and other design or performance constraints.

[0028] One or more aspects of at least one embodiment can be implemented by representative instructions stored on a machine-readable medium representing various logic within a processor, which, when read by a machine, creates logic for performing the techniques described herein. Such representations, known as “IP cores,” can be stored on tangible machine-readable media and supplied to various customers or manufacturing facilities for loading into manufacturing machines that produce the logic or processors. In one or more embodiments, an exemplary specially programmed browser application can be configured to receive and display graphics, text, multimedia, and the like using virtually any web-based language, including, but not limited to, Standard Generalized Markup Language (SMGL) such as Hypertext Markup Language (HTML), Wireless Application Protocol (WAP), Handheld Device Markup Language (HDML) such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, and the like. In one or more embodiments, the user device can be specially programmed in any of Java, .NET, QT, C, C++, and / or other suitable programming languages. It should be noted that the various embodiments described herein may, of course, be implemented using any suitable hardware and / or computing software language (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.).

[0029] In one or more embodiments, one or more of the exemplary inventive computer-based systems of the present disclosure may be partially or fully incorporated into at least one personal computer (PC), laptop computer, ultra-laptop computer, tablet, touchpad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, cellular telephone / PDA combination, television, smart device (e.g., smartphone, smart tablet, or smart television), wearable device, mobile internet device (MID), messaging device, data communication device, etc.

[0030] To provide for user interaction, embodiments of the subject matter described herein may be implemented in a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, that displays information to the user, and a keyboard and pointing device, such as a mouse or trackball, that allows the user to provide input to the computer. Other types of devices may also be used to provide for user interaction. For example, feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user may be received in any form, including acoustic, speech, or tactile input. Additionally, a computer may interact with a user by sending documents to and receiving documents from a device used by the user, for example, by sending a web page to a web browser on the user's client device in response to a request received from the web browser.

[0031] In one or more embodiments, a computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communications network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., HTML pages) to a client device (e.g., for the purpose of displaying the data to and receiving input from a user interacting with the client device). Data generated at a client device (e.g., as a result of user interaction) can be received from the client device by the server. As used herein, the term "server" should be understood to mean a service point that provides processing, database, and communications facilities. By way of example and not limitation, the term "server" may refer to a single physical processor with associated communications, data storage, and database facilities, or may refer to a networked or clustered complex of processors and associated networks and storage devices, as well as an operating system and one or more database systems and application software that support the services provided by the server. A cloud server is an example.

[0032] In one or more embodiments, as detailed herein, one or more of the exemplary inventive computer-based systems of the present disclosure may obtain, manipulate, transfer, store, convert, generate, and / or output (e.g., from within and / or outside a particular application) any digital object and / or data unit, which may be in any suitable format, such as, but not limited to, a file, a contact, a task, an email, a tweet, a map, an entire application (e.g., a calculator), etc.In one or more embodiments, as detailed herein, one or more of the exemplary inventive computer-based systems of the present disclosure may be based on, for example, (1) Amiga® OS, AmigaOS4, (2) FreeBSD, NetBSD, OpenBSD, (3) Linux®, (4) Microsoft® Windows®, (5) OpenVMS, (6) OS X (Mac OS), (7) OS / 2, (8) Solaris, (9) Tru64 UNIX®, (10) VM, (11) Android®, (12) Bada®, (13) BlackBerry® OS, (14) Firefox® OS, (15) iOS, (16) Embedded Linux, (17) Palm OS, (18) Symbian®, (19) Tizen®, (20) WebOS, (21) Windows Mobile, (22) Windows Phone, (23) Adobe® AIR, (24) Adobe® AIR, (25) Adobe® AIR, (26) Adobe® AIR, (27) Adobe® AIR, (28) Adobe® AIR, (29) Adobe® AIR, (30) Adobe® AIR, (31) Adobe® AIR, (32) Adobe® AIR, (33) Adobe® AIR, (34) Adobe® AIR, (35) Adobe® AIR, (36) Adobe® AIR, (37) Adobe® AIR, (38) Adobe® AIR, (39) Adobe® AIR, (40) Adobe® AIR, (41) Adobe® AIR, (42) Adobe® AIR, (43) Adobe® AIR, (44) Adobe® AIR, (45) Adobe® AIR, (46) Adobe® AIR, (47) Adobe® AIR, (48) Adobe® AIR, (49) Adobe® AIR, (50) Adobe® AIR, (51) Adobe® AIR, (52) IDE®, (53) IDE®, (54) IDE®, (55) IDE®, (56) It may be implemented across one or more of a variety of computer platforms, such as, but not limited to, Flash, (25) Adobe Shockwave®, (26) Binary Runtime Environment for Wireless (BREW), (27) Cocoa® (API), (28) Cocoa Touch, (29) Java® Platform, (30) JavaFX, (31) JavaFX Mobile, (32) Microsoft XNA, (33) Mono, (34) Mozilla® Prism, XUL and XULRunner, (35) .NET Framework, (36) Silverlight®, (37) Open Web Platform, (38) Oracle® Database, (39) Qt, (40) SAP® NetWeaver®, (41) Smartface®, (42) Vexi®, and (43) Windows Runtime.

[0033] In one or more embodiments, the exemplary inventive computer-based systems and / or exemplary inventive computer-based devices of the present disclosure may be configured to utilize hardwired circuitry, which may be used in place of or in combination with software instructions, to implement features consistent with the principles of the present disclosure. That is, implementation of one or more embodiments consistent with the principles of the present disclosure is not limited to any particular combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways, such as a standalone software package, a software component such as a combination of multiple software packages, or a software package that is incorporated as a "tool" in a larger software product.

[0034] For example, exemplary software specially programmed in accordance with one or more principles of the present disclosure may be downloadable from a network (e.g., a website), as a standalone product, or as an add-in package for installation into an existing software application. For example, exemplary software specially programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application or as a web-enabled software application. For example, exemplary software specially programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

[0035] In one or more embodiments, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of the present disclosure may be configured to handle a large number of concurrent users, such as at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,0 ...,000,000-9,999), or at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999). ,999), at least 10 million (for example, but not limited to, 10,000,000 to 99,999,999), at least 100 million (for example, but not limited to, 100,000,000 to 999,999,999), at least 1 billion (for example, but not limited to, 1,000,000,000 to 10,000,000,000).

[0036] In one or more embodiments, the exemplary inventive computer-based systems and / or exemplary inventive computer-based devices of the present disclosure may be configured to output to a separate, specifically programmed graphical user interface implementation of the present disclosure (e.g., desktop, web app, etc.). In various embodiments of the present disclosure, the final output may be displayed on a display screen, which may be, but is not limited to, a computer screen, a mobile device screen, etc. In various implementations, the display may be a holographic display. In various implementations, the display may be a transparent surface that can receive a visual projection. Such projections may convey various forms of information, images, and / or objects. For example, such projections may be visual overlays for mobile augmented reality (MAR) applications.

[0037] As used herein, the terms "cloud," "Internet cloud," "cloud computing," "cloud architecture," and similar terms correspond to at least one of: (1) a large number of computers connected via a real-time communications network (e.g., the Internet); (2) providing the ability to run programs or applications simultaneously on a large number of connected computers (e.g., physical machines, virtual machines (VMs)); and (3) network-based services provided by virtual hardware (e.g., virtual servers) that appear to be provided by actual server hardware but are actually simulated by software running on one or more real machines (e.g., that can be moved around and scaled up (or down) on the fly without impacting end users).

[0038] In one or more embodiments, the exemplary inventive computer-based systems and / or exemplary inventive computer-based devices of the present disclosure may be configured to securely store and / or transmit data by utilizing one or more cryptographic techniques (e.g., private / public key pairs, Triple Data Encryption Standard (3DES)), block cipher algorithms (e.g., IDEA, RC2, RC5, CAST, Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL, RNG).

[0039] The above examples are, of course, illustrative and not limiting.

[0040] As used herein, the term "user" shall mean at least one user. In one or more embodiments, the terms "user," "subscriber," "consumer," or "customer" shall be understood to refer to a user of one or more applications described herein and / or a consumer of data provided by a data provider. By way of example and not limitation, the terms "user" or "subscriber" may refer to a person receiving data over the Internet in a browser session or data provided by a service provider, or may refer to an automated software application that receives the data and stores or processes it.

[0041] Under these insurance providers' offerings, installments are paid regardless of the actual revenue generated by the carrier. Additionally, once the price of the installments to be paid for the current year has been agreed upon by the logistics delivery provider and the insurance provider, the installments cannot be adjusted. As detailed herein, one or more of the disclosed embodiments use a computer-based technology solution to determine risk ratings associated with various activities. As detailed herein, those skilled in the art with the benefit of this disclosure will understand that the present disclosure is equally applicable to technology solutions that provide risk ratings for various activities, including risk ratings associated with construction activities, maintenance activities, and freight transportation activities. For example, the present disclosure is equally applicable to technology solutions that provide risk ratings for a predetermined abbreviated period (e.g., days, weeks, months, etc.) and / or a predetermined number (e.g., tens, hundreds, thousands, tens of thousands, hundreds of thousands, millions, etc.) and / or range (e.g., tens, hundreds, thousands, tens of thousands, hundreds of thousands, millions, etc.) of cargo insurance for individual cargo shipments.

[0042] In such cases, much innovation and improvement is needed to accurately determine risk assessments that take into account real-time and historical parameters.

[0043] In one or more embodiments, methods, apparatus, systems, and computer program products are disclosed for a per shipment cargo insurance (PSCI) computer-based platform using adaptive machine learning. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method including a neural network that can be trained and utilized to compare simulated pricing models to one or more associated benchmark pricing models. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method including a neural network that can be trained and utilized to determine one or more model risk probability values ​​and / or one or more model commodity price adjustment factors to be applied to one or more dynamic pricing models generated for one or more identified cargo shipments to dynamically adjust insurance premiums. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method including a machine learning module engine utilized to apply various machine learning algorithms, techniques, methods, etc. to tracking real-time shipment data and static historical shipment data compiled, for example, in one or more PSCI databases, third-party databases, network databases, and / or remote PSCI servers, to build models for optimizing dynamic insurance pricing models generated by a PSCI risk modeling engine that provides insurance quotes to users of a PSCI platform computer system (referred to herein as the PSCI platform). In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method utilizing the PSCI machine learning engine to discard low-quality and / or irrelevant data and simulate improved model cargo shipments from which risk probability values ​​can be generated, thereby conserving memory, reducing storage requirements, and reducing processing overhead.In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method including a machine learning module engine utilized to filter low-quality and / or irrelevant data from data utilized to simulate model cargo transportation, to increase the efficiency of the PSCI machine learning engine and thus increase the efficiency of a computer system including one or more processors configured to execute the PSCI machine learning engine. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide a method including a process performed by the PSCI machine learning engine and neural network that improves the operational efficiency of the PSCI platform computer system by filtering out low-quality and / or irrelevant data and avoiding processing of such low-quality and / or irrelevant data. For example, in one or more embodiments, such a process further increases the computational efficiency of the PSCI platform computer system by filtering out illogical data that requires additional processing cycles to analyze. For example, in one or more embodiments, removing low quality and / or irrelevant data from the simulated model freight transport generated by the PSCI machine learning engine and input to the neural network reduces storage requirements associated with the simulated model freight transport and data points obtained from the simulated model freight transport that are used as input to the neural network. For example, in one or more embodiments, the processes performed by the PSCI machine learning engine and neural network are directed to one or more improvements in the functionality of a computer, such as one or more server devices included in the PSCI platform computer system.

[0044] In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus including a neural network that can be trained and utilized to compare simulated pricing models to one or more associated benchmark pricing models. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus including a neural network that can be trained and utilized to determine one or more model risk probability values ​​and / or one or more model commodity price adjustment factors to be applied to one or more dynamic pricing models generated for one or more identified freight shipments to dynamically adjust insurance premiums. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus including a machine learning module engine that can be utilized to apply various machine learning algorithms, techniques, methods, etc. to tracked real-time shipment data and static historical shipment data compiled in, for example, one or more PSCI databases, third-party databases, network databases, and / or remote PSCI servers to build models to optimize dynamic insurance pricing models generated by a PSCI risk modeling engine that provides insurance quotes to users of a PSCI platform computer system (referred to herein as the PSCI platform). In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus that utilizes a PSCI machine learning engine to discard low quality and / or irrelevant data and simulate improved model cargo shipments from which risk probability values ​​can be generated, thereby conserving memory, reducing storage requirements, and reducing processing overhead.In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus including a machine learning module engine utilized to filter low-quality and / or irrelevant data from data utilized to simulate model cargo transportation, to increase the efficiency of the PSCI machine learning engine and, therefore, the efficiency of a computer system including one or more processors configured to execute the PSCI machine learning engine. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more embodiments provide an apparatus including a process performed by a PSCI machine learning engine and neural network that improves the operational efficiency of a PSCI platform computer system by filtering out low-quality and / or irrelevant data and avoiding processing of the low-quality and / or irrelevant data. For example, in one or more embodiments, such a process performed by the apparatus further increases the computational efficiency of a PSCI platform computer system by filtering out illogical data that requires additional processing cycles to analyze. For example, in one or more embodiments, removing low quality and / or irrelevant data from the simulated model freight transport generated by the PSCI machine learning engine and input to the neural network reduces the storage requirements associated with the simulated model freight transport and the data points obtained from the simulated model freight transport that are used as input to the neural network. For example, in one or more embodiments, the processes performed by the apparatus including the PSCI machine learning engine and neural network are directed to one or more improvements in the functionality of a computer, such as one or more server devices included in a PSCI platform computer system.

[0045] In one or more embodiments, optionally in combination with any of the embodiments disclosed herein, one or more embodiments provide a method that includes receiving, using an input interface provided by a PSCI platform, shipping details for an identified freight shipment including items to be shipped by a logistics transportation provider. In one or more embodiments, the method includes, using at least one processor included in the PSCI platform, determining a product type based on the shipping details indicating the type of items included in the identified freight shipment. In one or more embodiments, the method includes, using the at least one processor, receiving, from one or more databases accessible to the PSCI platform, one or more predetermined respective underwriter pre-contract criteria provided by one or more insurance providers. In one or more embodiments, the method includes, using the at least one processor, comparing the shipping details to the respective predetermined respective underwriter pre-contract criteria to identify one or more eligible insurance providers from the one or more insurance providers. In one or more embodiments, the method includes, using the at least one processor, determining, for each of the one or more eligible insurance providers, a respective risk probability value based on the shipping details and the respective underwriter pre-contract criteria. In one or more embodiments, the method includes generating, using at least one processor, a respective dynamic pricing model for each of the one or more eligible insurance providers based on the shipment details and the respective determined risk probability values. In one or more embodiments, the method includes generating, using at least one processor, a respective insurance policy premium for the identified freight shipment in real time for each of the one or more eligible insurance providers using the respective dynamic pricing model and the respective determined risk probability values, and receiving, using the at least one processor, one or more real-time or historical shipment data from one or more databases.In one or more embodiments, the method includes, using at least one processor, generating, for each of the one or more eligible insurance providers, a respective model risk probability value based on one or more real-time shipment data or historical shipment data. In one or more embodiments, the method includes, using at least one processor, dynamically modifying, for each of the one or more eligible insurance providers, a respective real-time insurance policy premium based on the respective model risk probability value if predetermined pre-determined premium modification criteria are met to determine a respective modified premium value. In one or more embodiments, the method includes, using at least one processor, determining, in real time, a respective insurance quote for the identified shipment of cargo for each of the one or more eligible insurance providers based on one of the respective real-time insurance policy premium or the respective modified insurance policy premium.

[0046] In one or more embodiments, optionally in combination with any of the embodiments disclosed herein, one or more embodiments utilize a PSCI platform that uses adaptive machine learning. In one or more embodiments, the device includes a user interface. In one or more embodiments, the device includes one or more non-transitory memories that include one or more accessible databases. In one or more embodiments, the device includes one or more processors. In one or more embodiments, the device includes one or more processors configured to receive, using an input interface provided by the PSCI platform, shipment details for an identified freight shipment that includes items to be transported by a logistics transportation provider. In one or more embodiments, the device includes one or more processors configured to determine, using at least one processor included in the PSCI platform, a product type based on the shipment details indicating the type of items included in the identified freight shipment. In one or more embodiments, the device includes one or more processors configured to receive, using at least one processor, respective predetermined underwriter pre-contract criteria provided by one or more insurance providers from one or more databases accessible to the PSCI platform. In one or more embodiments, the apparatus includes one or more processors configured to compare the transportation details to predetermined respective underwriter pre-contract criteria to identify one or more qualified insurance providers from the one or more insurance providers. In one or more embodiments, the apparatus includes one or more processors configured to determine, for each of the one or more qualified insurance providers, a respective risk probability value based on the transportation details and the respective underwriter pre-contract criteria. In one or more embodiments, the apparatus includes one or more processors configured to generate, for each of the one or more qualified insurance providers, a respective dynamic pricing model based on the transportation details and the respective determined risk probability value.In one or more embodiments, the apparatus includes one or more processors configured to generate, for each of one or more eligible insurance providers, a respective insurance policy premium in real time for the identified freight shipment using a respective dynamic pricing model and a respective determined risk probability value. In one or more embodiments, the apparatus includes one or more processors configured to receive one or more real-time or historical transportation data from one or more databases. In one or more embodiments, the apparatus includes one or more processors configured to generate, for each of one or more eligible insurance providers, a respective model risk probability value based on the one or more real-time or historical transportation data. In one or more embodiments, the apparatus includes one or more processors configured to dynamically modify, in real time, a respective insurance policy premium based on the respective model risk probability value for each of the one or more eligible insurance providers to determine a respective modified premium value when predetermined pre-determined premium modification criteria are met. In one or more embodiments, the apparatus includes one or more processors configured to determine, in real time, a respective insurance quote for each of one or more eligible insurance providers for the identified freight shipment based on the respective insurance policy premium or the respective modified insurance policy premium.

[0047] In one or more embodiments, optionally in combination with any of the embodiments disclosed herein, one or more embodiments utilize a PSCI platform and a computer program product including a computer program product embodied therein. In one or more embodiments, the computer program product executable by a processor included in the PSCI platform includes a computer-readable storage medium (not a transitory signal) having program code embedded therein. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to receive shipment details for an identified freight shipment including items to be transported by a logistics transportation provider. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to determine a product type based on the shipment details indicating the type of items included in the identified freight shipment. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to receive one or more predetermined respective underwriter pre-contract criteria provided by one or more insurance providers from one or more databases accessible to the PSCI platform. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to compare the shipment details to the predetermined respective underwriter pre-contract criteria to identify one or more eligible insurance providers from one or more insurance providers. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to determine a respective risk probability value for each of the one or more eligible insurance providers based on the shipment details and the respective underwriter pre-contract criteria. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform to generate a respective dynamic pricing model for each of the one or more eligible insurance providers based on the shipment details and the respective determined risk probability value.In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform for generating, in real time for a specified shipment, a respective insurance policy premium for each of one or more eligible insurance providers using the respective dynamic pricing model and the respective determined risk probability value. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform for receiving one or more real-time or historical transportation data from one or more databases. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform for generating, for each of the one or more eligible insurance providers, a respective modeled risk probability value based on one or more of the real-time or historical transportation data. In one or more embodiments, the program code is readable and executable by one or more processors included in the PSCI platform for dynamically modifying, in real time, a respective insurance policy premium for each of the one or more eligible insurance providers based on the respective modeled risk probability value when predetermined predetermined premium modification criteria are met to determine a respective modified premium value. In one or more embodiments, the method includes one or more processors configured to determine, for each of one or more eligible insurance providers, a respective insurance quote in real time for the identified shipment of cargo based on one of the respective insurance policy premiums or the respective modified insurance policy premiums.

[0048] Reference to features, advantages, or similar language throughout this specification, including the Abstract, does not imply that all of the features and advantages that may be realized by the present invention should or will be present in any single embodiment of the present invention. Rather, language referring to features and advantages is understood to mean that the particular feature, advantage, or characteristic described in connection with one embodiment is included in at least one embodiment of the present invention. That is, descriptions of features and advantages and similar language throughout this specification may, but do not necessarily, refer to the same embodiment.

[0049] Furthermore, the described features, advantages, and characteristics of an embodiment may be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize and appreciate that the invention may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that are not present in all disclosed embodiments.

[0050] These features and advantages of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.

[0051] FIG. 1 depicts a block diagram of an example architecture of one or more embodiments of system 10, including a computer-based Per Shipment Cargo Insurance (PSCI) system / platform 100, example user devices 102a-102n, one or more logistics transportation provider (e.g., shipping companies, carriers, cargo underwriters such as DHL, FedEx, USPS, etc.) computer systems (e.g., transportation logistics server devices 130a-130n) configured to interact with PSCI system / platform 100 via one or more data communications networks 110, and one or more insurer / underwriter (e.g., insurers, cargo underwriters) computer systems (e.g., insurer / underwriter server devices 180a-180n). In one or more embodiments, the PSCI system / platform 100, the user devices 102a-102n, the transportation logistics server devices 130a-130n, or a combination of the above, is configured to provide insurance policies for individual shipments at the point of sale to logistics transportation providers (e.g., DHL, FedEx, UPS, USPS) and / or shipping agents and / or other users of the PSCI system / platform 100.

[0052] In one or more embodiments, optionally in combination with any embodiment disclosed herein, an exemplary computer-based system may be configured to include a PSCI system / platform 100 implemented on one or more PSCI platform server devices 120a-120n. Each server device may be implemented on one or more computing devices, e.g., a cluster of multiple computers. In one or more embodiments, the exemplary inventive PSCI platform 100, which may include one or more PSCI platform server devices 120a-120n, is configured to execute exemplary inventive Transport Unit Cargo Insurance (PSCI) software. For example, the exemplary inventive PSCI software may provide a virtual machine, application programmable interface libraries, and / or other instructions that provide a standard environment for executing the PSCI software, which utilizes a real-time risk engine to determine insurance rates globally using internal and external data sources. For example, in one or more embodiments, risk models that consider weather, global disputes, and / or other factors that may affect transportation are adjusted in real time to offer optimal insurance rates to transportation logistics companies and other users of the PSCI system / platform 100. In this manner, the PSCI platform 100 provides the capability for individual cargo transportation to be guaranteed in an efficient and accurate manner.

[0053] One or more of the transportation logistics server devices 130a-130n may be implemented on one or more computing devices, such as a cluster of computers. In one or more embodiments, the one or more transportation logistics server devices 130a-130n may be configured to utilize the PSCI system / platform 100 to determine insurance costs associated with the transportation of cargo (e.g., per-shipment insurance) in real time and / or at the time of contract with the cargo owner (e.g., owner / shipper / consignee) transporting the cargo. For example, transportation logistics providers such as FedEx, DHL, UPS, USPS, etc. may utilize the PSCI platform 100 to offer per-shipment-based transportation insurance to their customers in real time and / or at the time of contract with the owner.

[0054] One or more insurer / underwriter server devices 180a-180n may be implemented on one or more computing devices, such as a cluster of computers. In one or more embodiments, one or more insurer / underwriter server devices 180a-180n are configured to use the PSCI platform 100 to provide insurance policies (e.g., unit shipment policies) for individual commodity type shipments for transportation by logistics transportation providers. These insurance policies may be accessed and used by the provided PSCI platform to provide cargo shippers with quotes for insurance coverage for one or more individual shipments in real time, near real time, or at the time the shipper contracts with a logistics transportation provider to transport the shipment. For example, transportation logistics providers such as FedEx, DHL, UPS, USPS, etc. may use the PSCI system / platform 100 to provide customers with quotes for insurance coverage for cargo shipments underwritten by one or more third-party insurers and / or underwriters in real time, near real time, or at the time of signing a contract to transport the shipment.

[0055] For example, in one or more embodiments, the PSCI platform server devices 120a-120n, the insurer / underwriter server devices 180a-180n, and / or the transportation logistics server devices 130a-130n are web servers (or a series of servers) running a network operating system. For example, one or more of the PSCI platform server devices 120a-120n, the insurer / underwriter server devices 180a-180n, and / or the transportation logistics server devices 130a-130n may include, but are not limited to, Microsoft Windows Server, Novell NetWare, or Linux. In one or more embodiments, one or more of the PSCI platform server devices 120a-120n, the insurer / underwriter server devices 180a-180n, and / or the transportation logistics server devices 130a-130n may be used for and / or provide cloud and / or network computing. 1, in some embodiments, the PSCI platform server devices 120a-120n, the insurer / underwriter server devices 180a-180n, and / or the transportation logistics server devices 130a-130n may have connections to external systems such as email, SMS messaging, text messaging, advertising content providers, etc. In one or more embodiments, any of the features of the PSCI platform server devices 120a-120n may be implemented in the transportation logistics server devices 130a-130n, and vice versa.

[0056] In one or more embodiments, one or more of the PSCI platform server devices 120a-120n, the insurer / underwriter server devices 180a-180n, and / or the transportation logistics server devices 130a-130n may be programmed to perform as, by way of non-limiting example, an authentication server, a search server, an email server, a social networking service server, an SMS server, an IM server, an MMS server, an exchange server, a photo sharing service server, an ad serving server, an insurance / underwriting service server, a financial / banking-related service server, a mobility service server, or a similarly suitable service-based server.

[0057] In one or more embodiments, optionally in combination with any of the above or below embodiments, for example, one or more of exemplary user devices 102a-102n, PSCI platform server devices 120a-120n, insurer / underwriter server devices 180a-180n, and / or transportation logistics server devices 130a-130n may include specially programmed software modules that may be configured to send, receive, and process information using scripting languages, email, remote procedure calls, tweets, short message service (SMS), multimedia message service (MMS), instant messaging (IM), Internet relay chat (IRC), mIRC, Jabber®, application programming interfaces, Simple Object Access Protocol (SOAP) methods, Common Object Request Broker Architecture (CORBA), Hypertext Transfer Protocol (HTTP), Representational State Transfer (REST), or any combination of the above. In one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more processors included in one or more PSCI platform server devices 120a-120n, insurer / underwriter server devices, and / or transportation logistics server devices 130a-130n execute the PSCI software programs disclosed herein to perform the functions and functionality, including the calculation criteria necessary to provide insurance policies for one or more particular shipments.

[0058] In one or more embodiments, a user of the PSCI system / platform 100 may use a user device 102a-102n having client software 104a-104n installed to use the PSCI system / platform 100 to perform various functions, including providing shipping information for one or more freight shipments, requesting cargo insurance for the associated freight, receiving freight insurance quotes for the associated freight, and / or accepting cargo insurance for the associated freight. In one or more embodiments, a user may use a user device 102a-102n having client software 104a-104n installed to access the PSCI software installed on one or more transportation logistics server devices 130a-130n to perform various functions, including providing shipping information for one or more freight shipments, requesting cargo insurance for the associated freight, receiving freight insurance quotes for the associated freight, and / or accepting cargo insurance for the associated freight. In one or more embodiments, a user of the PSCI system / platform 100 may use a transportation logistics server device 130a-130n having client software 134a-134n installed thereon to use the PSCI system / platform 100. In other embodiments, one or more user devices 102a-102n and / or transportation logistics server devices 130a-130n have client software installed thereon to enable the user to use the PSCI system / platform 100 to perform one or more functions described herein. In one or more embodiments, a user accesses and browses the Internet using a web browser, typically resident and executing on the user device 102. A web browser is a computer program or set of computer instructions that allows a shipper / user to retrieve and render hypermedia content from one or more server computers, such as one or more servers 130a-130n, available via the web.In one or more embodiments, users can interact with the PSCI system / platform 100 using respective client software on their respective user devices. In one or more other embodiments, users may have client software installed on their user devices configured to access the exemplary inventive PSCI system / platform 100, which includes an integrated API (e.g., PSCI API 401) configured to allow authorized users to input information (e.g., shipping information, shipping details, login / password information (e.g., authentication and other information disclosed herein) and request information from the PSCI platform 100). For example, an authorized user may be an account owner or authorized account user of an account on the PSCI system / platform 100. The PSCI system / platform 100 may have millions of accounts for individuals, businesses, or other entities (e.g., pseudonymous accounts, novelty accounts, etc.). For example, in one or more embodiments, the PSCI API 401 may be configured as integrated software implemented on a website or the World Wide Web, another website owner, or an additional intermediary server on one or more PSCI server devices 120a-120n. This integration software provides one or more graphic user interfaces (GUIs) that allow authorized users to input information that may be received by PSCI platform 100 and / or receive information provided by PSCI platform 100. For example, in one or more embodiments, PSCI API 401 may be used to facilitate integration and communication between PSCI platform 100 and one or more of a website, one or more user devices 102a-102n, one or more logistics transportation provider server devices 130a-130n, and / or one or more insurance provider / underwriter server devices 180a-180n.

[0059] In one or more embodiments, an insurance company and / or underwriter may utilize one or more of the insurer / underwriter server devices 180a-180n, on which client software 104a-104n is installed, to access the exemplary inventive PSCI platform 100 executing the exemplary PSCI software to perform various functions, including, for example, providing transportation information regarding one or more freight shipments and / or commodity types of cargo included in a freight shipment; providing insurance quotes regarding one or more freight shipments and / or commodity types of cargo included in a freight shipment; providing information utilized by the PSCI system / platform 100 to provide cargo insurance quotes for the associated shipments; providing pricing and premium information (e.g., commodity price fluctuation factors and / or risk probability values ​​described herein) and / or other information (e.g., what commodity types of cargo (i.e., commodity-type cargo) are covered by insurance, the desired premium required to provide insurance coverage for each commodity type of cargo, the geographic areas where that coverage is provided and / or excluded, maximum coverage available, company logos, etc.) (collectively referred to herein as insurer / underwriter information); and / or denying cargo insurance and / or other information for the associated shipments. In one or more embodiments, a user of an insurer / underwriter server device 180a-180n may access and browse the Internet using a web browser typically resident and executing on the insurer / underwriter server device 180a-180n to access the exemplary inventive PSCI platform 100 executing exemplary PSCI software for performing various functions, including providing insurer / underwriter information that the PSCI system / platform 100 utilizes to provide cargo insurance quotes for and / or deny cargo insurance for the associated cargo. A web browser is a computer program or set of computer instructions that allows a shipper / user to retrieve and render hypermedia content from one or more server computers, e.g., one or more of servers 180a-180n, available via the web.In one or more embodiments, a user (e.g., insurance provider, underwriter, etc.) may interact with the PSCI system / platform 100 using respective client software on each server device 180a-180n. A user may be an account owner or an authorized account user of an account on the PSCI system / platform 100. The PSCI system / platform 100 may have millions of accounts for individuals, businesses, or other entities (e.g., pseudonymous accounts, novelty accounts, etc.).

[0060] In one or more embodiments, the user devices 102a-102n may be any computing device capable of sending and receiving messages to and from other computing devices, such as the PSCI platform server devices 120a-120n and / or the transportation logistics server devices 130a-130n, via a network (e.g., a cloud network) such as the data communications network 110. For example, the user devices may be connected to the Internet via a network such as a mobile network, through an Internet Service Provider (ISP), or otherwise. In one or more embodiments, the user devices 102a-102n may be personal computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, etc. In one or more embodiments, the one or more user devices 102a-102n may include computing devices typically connected to a network using a wireless communication medium, such as a mobile phone, a smartphone, a pager, a walkie-talkie, a radio frequency (RF) device, an infrared (IR) device, a CB, an integrated device combining one or more of the above devices, or virtually any mobile computing device. For example, one or more of the user devices 102a-102n may include, but are not limited to, a laptop or desktop computer, a smartphone, or an electronic tablet. In one or more embodiments, one or more of the user devices 102a-102n may be a device capable of connecting using a wired or wireless communication medium, such as a PDA, a pocket PC, a wearable computer, a laptop, a tablet, a desktop computer, a netbook, a video game device, a pager, a smartphone, an ultra-mobile personal computer (UMPC), and / or other device equipped to communicate via a wired and / or wireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.).In one or more embodiments, one or more user devices 102a-102n may run one or more applications, such as an internet browser, mobile applications, voice calls, video games, video conferencing, and email, among others. In one or more embodiments, one or more user devices 102a-102n may be configured to send and receive web pages, etc. In some embodiments, an exemplary specially programmed browser application may be configured to receive and display graphics, text, multimedia, etc. using virtually any web-based language, including, but not limited to, a Standard Generalized Markup Language (SMGL) such as Hypertext Markup Language (HTML), Wireless Application Protocol (WAP), Handheld Device Markup Language (HDML) such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, etc. In one or more embodiments, the user devices 102a-102n may be specially programmed in any of Java, .NET, QT, C, C++, and / or other suitable programming languages. In one or more embodiments, one or more user devices 102a-102n may be specially programmed to include or run applications to perform a variety of possible tasks. Tasks may include, but are not limited to, messaging functions, browsing, searching, playing, streaming or displaying various types of content, including locally stored or uploaded messages, images and / or videos, and / or games.

[0061] In one or more embodiments, one or more user devices 102a-102n and / or one or more transportation logistics server devices 130a-130n are configured with software, referred to as a client or client software 104a-104n, that enables the operating user device to access the PSCI system / platform 100 and allows users to send, post, and / or receive information and / or interact with and enter information into data fields and submit that information in response to one or more information requests. For example, in one or more embodiments, a client residing on one or more user devices 102a-102n and / or one or more transportation logistics server devices 130a-130n may be a web browser or an HTML (HyperText Markup Language) document rendered by a web browser. In one or more embodiments, a client may be JavaScript code or Java code. In one or more embodiments, a client may be dedicated software, such as an installed app or application, designed specifically to operate with the PSCI system / platform 100. In one or more embodiments, the client may be or include, for example, a short messaging service (SMS) interface, an instant messaging interface, an email-based interface, or an API function-based interface.

[0062] In one or more embodiments, the one or more data communications networks 110 (and 270 disclosed with reference to FIG. 2 ) may include any combination of different types of suitable communications networks, such as, but not limited to, a broadcast network, a cable network, a public network (e.g., the Internet), a private network, a wireless network, a cellular network, or any other suitable private and / or public network. Furthermore, any one or more data communications networks 110 (and 270 disclosed with reference to FIG. 2 ) may have any suitable communications range associated therewith. For example, they may include a global network (e.g., the Internet), a metropolitan area network (MAN), a wide area network (WAN), a local area network (LAN), or a personal area network (PAN). Furthermore, any one or more data communications networks 110 may include any type of medium over which network traffic may be carried. This medium may include, but is not limited to, coaxial cable, twisted pair wire, optical fiber, hybrid fiber coaxial (HFC) medium, microwave terrestrial transceiver, radio frequency communications medium, white space communications medium, very high frequency communications medium, satellite communications medium, or any combination thereof.

[0063] In one or more embodiments, one or more data communications networks 110 (and 270 disclosed with reference to FIG. 2) can provide network access, data transfer, and / or other services to computing devices coupled thereto. In one or more embodiments, one or more data communications networks 110 (and 270 disclosed with reference to FIG. 2) can be used in conjunction with one or more types of wireless communications signals and / or systems following one or more wireless communications protocols. Such protocols include, for example, radio frequency (RF), infrared (IR), frequency division multiplexing (FDM), orthogonal FDM (OFDM), time division multiplexing (TDM), time division multiple access (TDMA), enhanced TDMA (E-TDMA), general packet radio service (GPRS), enhanced GPRS, code division multiple access (CDMA), wideband CDMA (WCDMA), CDMA2000, single carrier CDMA, multi-carrier CDMA, multi-carrier modulation (MDM), discrete multi-tone (DMT), Bluetooth, global positioning system (GPS), Wi-Fi, Wi-Max, ZigBee, ultra-wideband (FDM), global system for mobile communications (OFDM), 2G, 2.5G, 3G, 3.5G, 4G, fifth generation (5G) mobile networks, 3GPP, Long Term Evolution (TDM), LTE advanced, Enhanced Data Rate for GSM Evolution (TDMA), etc. Other embodiments may be used in various other devices, systems and / or networks.

[0064] In one or more embodiments, the PSCI system / platform 100 is configured to access one or more data communications networks 110, one or more databases 150(1)-(n), one or more logistics provider databases 160(a)-(n), and / or one or more third-party databases 170(a)-(n). For example, the network 110 and / or third-party databases 170(a)-(n) connected to a third-party insurance provider system via the network 110 can be accessed by the PSCI platform 100 to access one or more insurance policies directed to individual logistics transportation providers. The one or more databases 150(1)-(n), the logistics provider databases 160(a)-(n), and / or the third-party databases 170(a)-(n) can be any type of database, including a database managed by a database management system (DBMS). In one or more embodiments, an exemplary DBMS-managed database may be a database implemented in a database management system (DBMS) such as MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Microsoft Access, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMaker Pro, and / or any other type of database capable of organizing a collection of data. For example, the database may be relational, such as primary key-driven, or post-relational or in-memory. For example, if records include a primary key, such as a unique identifier, the records may be clustered within a set or sub-clustered into subsets based on the unique identifier, where each set or subset is also associated with a unique identifier generated specifically for that set or subset. It should be noted that although each record in a database may be a row containing multiple columns, other ways of organizing multiple records, such as unstructured data, may also be used.

[0065] 1, one or more embodiments of a system 10 are provided that include one or more telecommunications and / or telematics devices 140(a)-(n) configured to collect, store, and / or transmit data regarding objects and / or entities, such as, for example, shipments, operational data regarding equipment used to transport the shipments, operational data regarding the equipment, and / or sensor data (as described herein), in real time to a PSCI system / platform 100 via one or more data communications networks 110. For example, in one or more embodiments, one or more telecommunications and / or telematics devices 140(a)-(n) are configured to transmit sensor data generated by one or more sensor devices (e.g., 290, see FIG. 2) to one or more databases 150a-150n, 160a-160n, and / or 170a-170n accessible via a PSCI platform server device 120a-120n and / or a transportation logistics server device 130a-130n. For example, the sensor data may be received by remote sensors. The sensor data may include environmental, contextual, behavioral, psychological, and / or other freight transportation related data. The telecommunications and / or telematics devices 140(a)-(n) are configured to collect, store, and / or transmit / transmit telematics information regarding objects (i.e., inanimate or animate objects, such as cargo and / or personnel involved in a cargo shipment) and / or entities and / or environments (e.g., the geographic environment and / or packaging, containers, vessels, and / or vehicles in which cargo is housed or transported), and / or the context in which the objects and / or entities are housed or transported, in which the objects and / or entities are located or moved, and / or in which the objects and / or entities are affected.Such telematics information collected, stored, and / or transmitted by telecommunications and / or telematics devices 140(a)-(n) about objects and / or entities may include, but is not limited to, the status, behavior, value, movement, health, and / or safety of the objects and / or entities. Such telematics information about the environment may include, but is not limited to, weather conditions (e.g., clear, sunny, partly cloudy, cloudy, overcast, rain, drizzle, sleet, snow, storm, moisture, humidity, temperature, dew point, atmospheric pressure and density, wind, clouds, precipitation, evaporation, atmospheric stability, frost, radiation, and other weather parameters, etc.) and / or environmental conditions (e.g., moisture, humidity, air, water, temperature, pollution, emissions, waste, water transport conditions, cargo shock, rail transport conditions, road transport conditions, air transport conditions, etc.). Such telematics information about transportation context information may include, but is not limited to, the conditions of roads, railways, waterways, airways, etc. These are, for example, the depth of waterways, the condition of the road (e.g., slippery, wet, dry, surface condition, etc.), the condition of one or more street lights and / or stop lights and / or intersections located on the road, the condition of the airway (e.g., turbulence, visibility, humidity, etc.), the condition of the railway, the condition of the cargo, an estimate of the cargo, information regarding damage to the cargo and / or the method of transport (e.g., road, waterway, airway, railway, etc.) or other conditions of the mode of transport that may be used to transport the object and / or entity.

[0066] The term "telematics" is generally associated with the use of Global Positioning System (GPS) technology commonly used in automobile navigation systems, where telematics data is collected, stored, and extracted / transmitted from vehicle embedded platforms / hardware devices (e.g., devices such as sensors installed by the original equipment manufacturer (OEM) via on-board diagnostics (OBD) self-installation or mobile devices). This disclosure is not limited to such technology; rather, telematics devices 140(a)-(n) are intended to encompass and may include a broad array of devices, including, but not limited to, vehicle navigation systems and / or sensors used to collect, store, and transmit information about objects and / or entities. In one or more embodiments, telecommunications and / or telematics devices 140(a)-(n) may include, but are not limited to, mobile devices, GPS devices, etc., and may also include other devices associated with objects and / or entities, such as RFID tags, sensor devices, accelerometer devices, or small-scale radio transceivers, to track the movement, behavior, status, and / or health of the objects and / or entities.

[0067] Additionally, it is noted that in one or more embodiments, telecommunications and / or telematics devices 140a-140n may include devices that are not specifically associated with biological entities but are nevertheless capable of collecting, storing, and / or transmitting information about living beings or objects, such as transportation personnel tasked with managing, collecting, transporting, and / or storing cargo. Examples include surveillance or traffic cameras, microphones, optical and / or electromagnetic sensors, and / or multiple such devices operating in concert. Other examples of telecommunications and / or telematics devices 140a-140n that can collect, store, and / or transmit biometric information about individuals include, but are not limited to, blood pressure monitors, heart rate monitors, weight scales, alcohol analysis devices, skin elasticity monitors, wearable computing devices (e.g., smartwatch devices), etc. Telecommunications and / or telematics devices 140a-140n may include a single device or multiple devices operating individually or in concert to collect, store, and / or transmit information about the behavior or movement of objects and / or entities.

[0068] In one or more embodiments, the PSCI system / platform 100 may obtain telematics data from one or more third-party databases / services 170a-170n via one or more data communications networks 110. The obtained data may be utilized, as described herein with respect to analysis of the collected telematics data, to add one or more contextual aspects, including, but not limited to, global dispute data (e.g., intelligence, information, trends, etc.), traffic data (e.g., intelligence, information, trends, etc.), and / or weather data (e.g., intelligence, information, trends, etc.), relevant to the calculation and determination of unit of shipment insurance coverage, and / or information about the cargo itself (e.g., value, damage to the cargo, depreciation / increase in value, cargo attributes (weight, height, width, container / packaging / restraint / transport requirements, etc.)).

[0069] Those skilled in the art will appreciate that the hardware, firmware / software utilities, and software components depicted in the figures referenced herein, as well as their basic configurations, may vary. For example, the depicted components of PSCI platform 100 are not intended to be exhaustive, but rather representative to highlight some of the components utilized in particular implementations of the described embodiments. For example, different configurations of PSCI platform 100 may be provided and may include other devices / components that may be used in addition to or in place of the depicted hardware. Thus, the depicted examples are not intended to imply architectural or other limitations with respect to the presently described embodiments and / or the general invention.

[0070] Referring now to the figures, beginning with FIG. 2, a block diagram of an exemplary PSCI data processing system (PSCI DPS) operating as a network computing device and / or server providing a cloud infrastructure supporting implementation of a Transportation Unit Cargo Insurance (PSCI) framework according to one or more embodiments is shown. PSCI DPS 200 is an exemplary embodiment of PSCI platform 100 and includes one or more computing devices configured to execute the exemplary inventive PSCI software disclosed herein to perform one or more of the described features of various embodiments of the PSCI system / platform 100 disclosure disclosed with reference to FIG. 1. In one embodiment, PSCI DPS 200 may be any electronic device, such as, but not limited to, a desktop computer, a notebook computer, or a server. In one embodiment, PSCI DPS 200 may be one server in a cluster of multiple servers, where the multiple servers may be co-located in a single location, geographically distributed across multiple locations, or a combination thereof. Additionally, in one embodiment, PSCI DPS 200 may be implemented as a virtual machine sharing the hardware resources of a physical server.

[0071] In one or more embodiments, the exemplary PSCI DPS 200 includes one or more processors or central processing units (CPUs) 205 coupled to system memory 210, non-volatile storage 225, and input / output (I / O) controller 230 via a system interconnect 215. In one or more embodiments, the system interconnect 215 may be interchangeably referred to as a system bus. For example, in one or more embodiments, the memory 210 may be operatively coupled to one or more processors 105. The memory 210 may be a non-transitory medium configured to store various types of data. For example, the memory 210 may include one or more memory devices, including secondary storage, read-only memory (ROM), and / or random access memory (RAM). The secondary storage typically consists of one or more disk drives, optical drives, solid-state drives (SSDs), and / or tape drives and is used for non-volatile storage of data. In certain cases, if the allocated RAM is not large enough to hold all working data, the secondary storage may be used to store overflow data. Secondary storage may also be used to store programs, which may be loaded into RAM when selected for execution. ROM is used to store instructions and possibly data that is read during program execution. ROM is a non-volatile memory device that typically has a small memory capacity compared to the large memory capacity of secondary storage. RAM is used to store volatile data, and possibly instructions.

[0072] In one or more embodiments, one or more software and / or firmware modules may be loaded into system memory 210 (from storage 225 or other sources) during operation of PSCI DPS 200. Specifically, in the illustrated embodiment, system memory 210 is shown having several common modules therein, including firmware (F / W) 212, basic input / output system (BIOS) 214, operating system (OS) 216, and applications 218. In addition, system memory 210 includes PSCI utility 220, which, in alternative embodiments, may be implemented as one of applications 218 and / or as an executable component within firmware F / W 212 or operating system OS 216. The exemplary and inventive PSCI software and / or firmware modules in system memory 210 provide various functions when corresponding program code is executed by one or more processors (e.g., CPUs) 205 or secondary processing devices (not specifically shown) in the processing layer of PSCI DPS 200.

[0073] In one or more embodiments, the I / O controller 230 supports connection with and processing of signals from one or more connected input devices 232, examples of which are shown as a microphone 234, a keyboard 236, and a pointing device 238. The pointing / touch device 238 may be, for example, a mouse, a touchpad, or a stylus. As will be appreciated, the input devices may also include hardware buttons, a touchscreen 245, an infrared (IR) sensor, a fingerprint scanner, etc., as a non-exclusive list. In one or more embodiments, the I / O controller 230 also supports connection with and transmission of output signals to one or more connected output devices, including a display 244 and other output devices 248. The display 244 may include a touchscreen 245 that functions as a tactile input device. In one embodiment, the PSCI DPS 200 also includes a graphics processing unit (GPU) 246 that is communicatively or physically coupled to the display 244 and to the one or more processors 205. The GPU 246 controls the generation and presentation of a predetermined user interface (UI) that is provided during execution of the PSCI utility 220 by the CPU 205 .

[0074] Additionally, in one or more embodiments, one or more device interfaces 240, such as a Universal Serial Bus (USB), a Personal Computer Memory Card International Association (PCMIA) slot, an optical reader, a card reader, and / or a High-Definition Multimedia Interface (HDMI®), may be associated with the PSCI DPS 200. For example, the device interface 240 may be used to read or store data from a corresponding removable storage device (RSD) 242, such as a compact disc (CD), a digital video disc (DVD), a flash drive, or a flash memory card. In one or more embodiments, the device interface 240 may further include a general-purpose I / O interface, such as a system management bus (SMBus), an Inter-Integrated Circuit (I2C), and a Peripheral Component Interconnect (PCI) bus. According to one or more embodiments, the functional modules described herein as aspects of the present disclosure may be implemented as a computer program product. The computer program product includes a removable storage device 242 as a computer-readable storage medium that stores program code that, when executed by one or more processors, causes the one or more processors to implement various functions described herein, including, but not limited to, the features and functions presented with reference to Figures 1 through 8.

[0075] In one or more embodiments, the PSCI DPS 200 further includes a network interface device (NID) 260, which may include, for example, both wired and wireless network devices. The NID 260 enables the PSCI DPS 200 and / or components within the PSCI DPS 200 to communicate and / or interface with other devices, services, and components external to the PSCI DPS 200. In one or more embodiments, the PSCI DPS 200 may directly connect to one or more of these external devices via the NID 260, such as via a direct wired or wireless connection. In one or more embodiments, the PSCI DPS 200 connects to certain external devices, services, and / or components, such as an information server 275 and a cloud database 280, via an external network 270 using one or more communication protocols. In one or more embodiments, the PSCI DPS 200 connects to external sensors 290(a)-(n) via the external network 270. Network 270, as described herein, may be a local area network, a wide area network, a personal area network, etc., and the connection to network 270 and / or the connection between network 270 and PSCI DPS 200 may be wired or wireless (via access point 265), including a telecommunications network, a cellular network, a satellite link network, or a combination thereof. For purposes of explanation, network 270 is shown as a single collective component for simplicity. However, it is understood that network 270 may include one or more direct connections to other devices, as well as a complex set of interconnections, such as may exist in a wide area network such as the Internet. In one embodiment, the CSIST framework is accessible via the Internet (270) as a website having one or more domain names associated therewith.

[0076] In one aspect of the disclosure, PSCI utility 250 includes multiple functional modules that execute on CPU 205 to perform specific functions, and these functional modules utilize and / or generate specific data that is stored as information and / or data in storage 225 and / or cloud database 280. By way of example, storage 225 is shown to include PSCI database 251, which includes different data blocks, including, but not limited to, shipping data 252, freight data 253, maps 254, logistics provider data 255, equipment data 256, and underwriter data 257. Cloud database 280 is also shown to include a copy of PSCI database 251. In one or more embodiments, both the PSCI database 251 located in storage 225 and the cloud database 280 store relevant data utilized by the PSCI utility 220 to personalize a particular user interface (UI), provide data collected by one or more sensors 290, provide environmental, weather, global conflicts, trends, date, time, cultural events, mapping information and / or other data that may affect the origin, destination and / or route of a cargo shipment, and identify the location of trucks and / or shipments on a displayed map. Access to the PSCI database 251 and the remote cloud database 280 is provided via connection through the network 270.

[0077] In one or more embodiments, telecommunications and / or telematics device 295 is configured to access PSCI DPS 200 via network 270. Telecommunications device 295 may be any portable device, such as a mobile phone (including a "smartphone"), PDA, laptop computer, Blackberry, or other portable telematics device suitable for receiving and / or transmitting data to PSCI DPS 200 via network 270. As disclosed herein, in one or more embodiments, telematics device 140(a)-(n) may include, but is not limited to, mobile devices such as those disclosed above with respect to telecommunications devices, GPS devices, etc., as well as other devices associated with objects and / or entities, such as RFID tags, sensor devices, accelerometer devices, or small-scale radio transceivers to track the movement, behavior, status, and / or health of the objects and / or entities. For example, the data transmitted or received may include remote sensor data generated or transmitted by one or more sensor devices 290(a)-(n) disclosed herein. In one or more embodiments, data received by telecommunications and / or telematics device 295 is stored in storage 225 and / or on cloud database 280 and is accessible by one or more of application 218 and / or PSCI utility 220, for example, residing in PSCI database 251 in storage 225 and / or cloud database 280. In one or more embodiments, telecommunications and / or telematics device 295 may be a fixed transmitting device residing at a facility or known point along the transportation route.For example, in one or more embodiments, one or more telecommunications and / or telematics devices 295 may be configured to transmit remote sensor data generated and / or transmitted by one or more of the transportation sensor devices 310(a)-(n) described herein using any wireless communication mode, such as, but not limited to, NFC, RFID, Narrowband Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes.

[0078] In one or more embodiments, optionally in combination with any of the embodiments described herein, the exemplary inventive computer-based PSCI system may be configured to include a network of environmental and / or transportation sensors 290. In one or more embodiments, one or more telecommunications and / or telematics devices 295 transmit remote sensor data generated by one or more sensors 290 to storage 225 and / or cloud database 280 via network 270. In some embodiments, additional hardware-based devices may be connected to storage 225 and / or cloud database 280 to process the remote sensor data via one or more processors 205 of DPS 200, calculate quality metrics from the environmental and / or transportation data, and then store the quality metrics data in storage 251, PSCI database 251 in storage 225, and / or cloud database 280. In some embodiments, one or more processors 205 present in the exemplary inventive PSCI platform execute the exemplary inventive PSCI computer programs described herein present in system memory 210 to perform the functions disclosed herein (e.g., the functions disclosed with reference to FIGS. 1-8 ). In one or more embodiments, the network of remote sensors 290 is configured to operate in a distributed network environment, communicating via a suitable data communications network 270 (e.g., the Internet, a telecommunications network, a combination of suitable data communications networks, etc.) and utilizing at least one suitable data communications protocol (e.g., IPX / SPX, X.25, AX.25, AppleTalk, TCP / IP (e.g., HTTP), etc.). It should be noted that the embodiments described herein may, of course, be implemented using any suitable hardware and / or computing software language.In this regard, those skilled in the art are familiar with the types of computer hardware that may be used, the types of computer programming techniques that may be used (e.g., object-oriented programming), and the types of computer programming languages ​​that may be used (e.g., C++, Objective-C, Swift, Java, JavaScript). The above examples are, of course, illustrative and not limiting.

[0079] FIG. 3A is a block diagram illustrating an exemplary transportation sensor system 300 including one or more transportation sensor devices 310(a)-(n) according to one or more embodiments. In one or more embodiments, one or more of the illustrated transportation sensor devices 310(a)-(n) includes one or more cargo sensor devices 320 and / or one or more sensor devices 330, one or more processors 340 (e.g., signal processors, microprocessors), and one or more transmitters / receivers 350. In one or more embodiments, one or more of the illustrated transportation sensor devices 310(a)-(n) includes a power source 360. In one or more embodiments, one or more of the illustrated cargo sensor devices 320 are intended primarily for use associated with the transportation of cargo and / or for monitoring environmental conditions that may affect cargo during cargo management, handling, and / or transportation. In one or more embodiments, one or more of the illustrated sensor devices 330 are intended primarily for use associated with monitoring human activity, including personnel handling and / or transporting cargo, during cargo management, handling, and / or transportation. In one or more embodiments, one or more of the cargo sensor devices 320 are attached directly to the cargo. In one or more embodiments, the cargo sensor devices 320 monitor a predetermined condition at a distance from the cargo. In one or more embodiments, one or more of the sensor devices 330 are attached directly to shipper personnel as mountable and / or wearable sensor devices. In one or more embodiments, one or more of the sensor devices 330 monitor a predetermined condition at a distance from transport personnel.

[0080] Exemplary sensor units of the present disclosure may also include a control board (not shown) configured to allow communication between one or more of the sensor modules and one or more microprocessors (207) programmed to operate the sensor modules. Exemplary sensor units of the present disclosure may also include a battery (not shown). Exemplary sensor units of the present disclosure may also include data memory storage (208) (e.g., an SD card).

[0081] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more of the transportation sensor devices 310(a)-(n) may function as an environmental sensor or sensors. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more of the transportation sensor devices 310(a)-(n) may be any small sensor that monitors in real time or near real time any environment, cargo condition, and / or data that may affect a given cargo shipment, including, but not limited to, the external environment near stored cargo, cargo being prepared for shipment, cargo en route from a departure point to a destination point, cargo that has arrived at a destination point, or cargo at any time after cargo has been identified for shipment.For example, one or more cargo sensor devices 320 may detect temperature, pressure, climate, humidity, barometric pressure, pollution, vehicle exhaust, color, oxygen levels, pH, soot density, light, air particle density, air particle size, air pressure, gases, air particle shape, particulate matter, odor, air particle identity, volatile organic chemicals (VOCs), hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), carcinogens, toxins, electromagnetic energy (optical radiation, x-rays, gamma rays, microwaves, terahertz radiation, ultraviolet, infrared, radio waves, etc.), EMF energy, atomic energy (alpha particles, beta particles, gamma rays, etc.), gravity, light properties (intensity, frequency, flicker, phase, etc.), ozone, carbon monoxide, greenhouse gases, CO2, nitrogen, ozone, suboxides, sulfides, airborne pollution, airborne foreign particles, biological particles (viruses, bacteria, and toxins), signatures from chemical weapons, wind, air turbulence, sound and acoustic energy. The sensors may include, but are not limited to, sensors that monitor parameters in real time or near real time, including, but not limited to, sounds (both audible and inaudible to humans), ultrasonic energy, noise pollution, human voices, animal sounds, diseases excreted by other people, the breath and breath components of other people, toxins from other people, bacteria and viruses from other people, pheromones from other people, industrial and transportation sounds, allergens, animal hair, pollen, engine exhaust, steam and emissions, fuel, mineral or oil deposit signatures, snow, rain, thermal energy, hot surfaces, hot gases, solar energy, hail, ice, acceleration, shock, vibration, traffic, gyroscopic data (e.g., heading), the number of people in the vicinity of the shipment, the number of people the shipment encounters throughout the day, the sounds of people coughing and sneezing in the vicinity of the shipment, the volume and pitch of people speaking in the vicinity of the shipment, and the like, or combinations thereof.For example, one or more cargo sensor devices 320 may display weather conditions such as clear, sunny, partly cloudy, cloudy, overcast, rain, drizzle, sleet, snow, storm, moisture, humidity (e.g., instantaneous relative humidity, relative humidity interval value, absolute humidity), temperature (e.g., instantaneous temperature, temperature interval value), dew point (e.g., instantaneous dew point, dew point interval value), atmospheric pressure and density (e.g., pressure adjusted to sea level, surface pressure, altitude pressure, air density), wind (e.g., instantaneous wind speed, instantaneous wind direction, horizontal wind component, vertical wind component, interval wind speed, interval gust, interval wind direction), clouds (e.g., cloud cover, ceiling height, cloud base), precipitation (e.g., accumulated precipitation amount, precipitation type, precipitation probability, hail, supercooled liquid water, rain, snow, sleet), evaporation (e.g., accumulated evaporation), atmospheric stability ( These may include, but are not limited to, sensors that monitor, in real time or near real time, parameters such as meteorological parameters including, but not limited to, convective available potential energy (cape), ascent index, thunderstorm probability), frost (e.g., frost depth, soil frost, snow melt, new snow amount, snow depth, snow water equivalent, snow density, snowfall probability), radiation (e.g., radiation directed to instantaneous flux i.e., clear sky radiation, diffuse radiation, direct radiation, global radiation, and radiation due to stored energy i.e., stored energy (direct, diffuse, global radiation), clear sky radiation), and other meteorological parameters (e.g., geopotential height, water vapor mixing ratio, layer thickness).

[0082] In one or more embodiments, one or more of the transportation sensor devices 310(a)-(n) may function as sensors. For example, one or more of the transportation sensor devices 310(a)-(n) may be any small sensor that monitors, in real time or near real time, one or more functions and parameters of personnel responsible for handling, managing, or transporting cargo, whether stored, prepared for transport, en route from an origin to a destination, arrived at a destination, or any point after cargo has been identified for transport. For example, one or more sensor devices 330 may monitor respiratory rate, blood flow, heart rate, pulse rate, heartbeat signature, cardiopulmonary health, organ health, metabolism, electrolyte type and concentration, physical activity, calorie intake, calorie metabolism, metabolomics, physical and psychological stress levels and stress level indicators and psychological response to treatment, drug dose and activity (drug dosimetry), drug response, drug chemistry in the body, biochemistry, position and balance, body tension, nerve function, brain activity, EEG, blood pressure, intracranial pressure, hydration level, auscultation information, auscultation signals associated with pregnancy, response to infection, skin and core body temperature, eye muscle movement, blood volume, inspired and expired air volume, physical movement, physical and chemical composition of expired air, presence, identity and concentration of viruses and bacteria, foreign substances in the body, toxins in the body, heavy metals in the body, anxiety, fertility, ovulation, sex The devices may include, but are not limited to, sensors for real-time or near real-time monitoring of functions and / or parameters including, but not limited to, hormones, psychological mood, sleep patterns, hunger and thirst, hormone types and concentrations, cholesterol, lipids, blood panels, bone density, body fat density, muscle density, organs and weight, reflex responses, sexual arousal, mental and physical alertness, drowsiness, auscultation information, responses to external stimuli, swallowing volume, swallowing rate, illnesses, voice characteristics, tone, pitch and volume, vital signs, head tilt, allergic responses, inflammatory responses, autoimmune responses, mutagenic responses, DNA, proteins, blood protein concentrations, body hydration, blood water content, pheromones, body sounds, digestive system functions, cell regeneration responses, healing responses, stem cell regeneration responses, etc., and any combination thereof. For example, vital signs may include pulse rate, respiratory rate, blood pressure, pulse signature, body temperature, hydration, skin temperature, etc.For example, in one or more embodiments, one or more of the transport sensor devices 310(a)-(n) may include sensors that may include an impedance plethysmograph to measure changes in volume within an organ or body (typically due to fluctuations in the amount of blood or air contained therein). For example, one or more of the transport sensor devices 310(a)-(n) may include one or more sensor devices 330 that may include an impedance plethysmograph for monitoring blood pressure in real time or near real time.

[0083] In one or more embodiments, one or more of the transportation sensor devices 310(a)-(n) can measure and transmit sensor information in real time and / or over a period of time. For example, one or more cargo sensor devices 320 and / or one or more sensor devices 330 utilized in one or more of the transportation sensor devices 310(a)-(n) can be used to sense the above-mentioned functions and / or parameters over time, enabling time-dependent analysis of the environment in which the cargo is being transported, the condition of the cargo being transported, and the health of one or more monitored individuals in the transportation crew, as well as enabling comparisons of the individual's health to the environment. In one or more embodiments, the transportation sensor system 300 can be combined with proximity or location detection to enable analysis to identify locations of environmental stress and physical strain.

[0084] FIG. 3B is a block diagram of an exemplary sensor device 320 / 330 utilized in one or more embodiments of the cargo sensor device 320 and / or sensor 330, including, for example, one or more sensor modules 370(a)-(n). In one or more embodiments, the exemplary transportation sensor device 310(a)-(n) may also include a control board (not shown) configured to enable data communication exchange between the one or more sensor modules 370(a)-(n) and one or more microprocessors 340 programmed to operate the sensor modules 370(a)-(n). The sensor modules 370(a)-(n) may be connected via either a "hard" connection (such as an electrical cable) or a "soft" connection (such as a wireless connection). For example, the Bluetooth® protocol may be used to simultaneously connect one or more sensor modules 370(a)-(n), with each module communicating directly and wirelessly with one or more other sensor modules. For example, one or more of the sensor modules 370(a)-(n) utilized in one or more embodiments of the present disclosure may include one or more of a temperature measuring sensor module (e.g., a DHT11 by Adafruit, Inc., New York, NY), a humidity measuring sensor module (e.g., a DHT11 by Adafruit, Inc., New York, NY), an impact measuring sensor module, an acceleration measuring sensor module, a gyroscope module, an air pressure measuring sensor module, an air quality measuring sensor module (e.g., a particulate matter monitoring, MQ13 by Adafruit, Inc., New York, NY), a magnetic sensor module (e.g., a DRV5023 Digital-Switch Hall Effect Sensor by Texas Instruments, Inc., Dallas, TX), and / or other suitable sensor modules configured to measure the aforementioned environmental and / or parameters and conditions.One or more embodiments of the transport sensor device 310(a)-(n) may also include a control board (not shown) configured to enable a communication exchange between one or more of the sensor modules 370(a)-(n) and one or more microprocessors 340 programmed to operate the sensor modules 370(a)-(n). An example transport sensor device 310(a)-(n) of the present disclosure may also include a power source 360, such as a battery. An example sensor unit of the present disclosure may include data storage (355), such as an SD card, a micro SD card, or other suitable storage device.

[0085] In one or more embodiments, the sensor modules 370(a)-(n) wirelessly communicate with one or more of the portable telecommunications and / or telematics devices 295, preferably in an open architecture configuration such as Bluetooth or ZigBee, as described with reference to FIG. 2. The telecommunications and / or telematics devices 295 may be any portable device, such as a mobile phone (including a "smartphone"), a PDA, a laptop computer, a Blackberry, or other portable telemetric device. In one or more embodiments, the telecommunications and / or telematics devices 295 may be fixed transmitting devices located at facilities or known points along the transportation route. For example, one or more of the portable telecommunications and / or telematics devices 295 and one or more of the transportation sensor devices 310(a)-(n) may communicate telemetry both to and from each other using the sensor modules 370(a)-(n). One or more portable telecommunications and / or telematics devices 295 may be configured to receive sensor data from sensor modules 370(a)-(n) in the form of wireless signals transmitted from transmitter / receiver 350 and to transmit the wireless signals to PSCI platform 100 via network 270 as described herein.

[0086] The one or more processors 340 may include a signal processor that provides a means for converting digital or analog signals transmitted from one or more sensor devices 320, 330 into data that can be wirelessly transmitted by the transmitter / receiver 350. The signal processor 340 may be comprised of, for example, signal conditioners, amplifiers, filters, digital-to-analog and analog-to-digital converters, digital encoders, modulators, mixers, multiplexers, transistors, various switches, microprocessors, etc. In one or more embodiments, the signal processor 340 processes signals received by the transmitter / receiver 350 into signals that can be transmitted to one or more telecommunications and / or telematics devices 295 and heard or viewed by a user of the handheld device 295. The received signals may also include protocol information linking various telemetric devices together, and this protocol information may also be processed by the signal processor 340.

[0087] In one or more embodiments, the exemplary transportation sensor device includes a signal processor 340 that utilizes one or more compression / decompression algorithms (CODECs) used in digital media to process sensor data. In one or more embodiments, the exemplary transportation sensor devices 310(a)-(n) include a transmitter / receiver 350, which may be comprised of a variety of small electromagnetic transmitters. For example, a standard small antenna may be used in conjunction with a standard Bluetooth® protocol to transmit sensor data generated by one or more sensors 320, 330 to one or more telecommunications and / or telematics devices 295. Any type of electromagnetic antenna suitable for transmitting electromagnetic frequencies may be used to transmit sensor data to one or more telecommunications and / or telematics devices 295. For example, the receiver 350 may also include a suitable antenna. In one or more embodiments, the receive antenna 350 and the transmit antenna 350 are physically the same. In one or more embodiments, the transmitter / receiver 350 may be, for example, a non-line-of-sight (NLOS) optical scattering transmission system. For example, a non-line-of-sight (NLOS) light scattering transmission system may utilize short-wave (blue or UV) light radiation or "solar-blind" (deep UV) light radiation to promote light scattering to transmit and receive sensor data. In one or more embodiments, infrared wavelengths may also be utilized to transmit and receive sensor data.

[0088] In one or more embodiments, the exemplary transportation sensor device 310(a)-(n) includes a receiver / transmitter 350 that includes a sonic or ultrasonic transmitter for transmitting sensor data generated by one or more sensors 320, 330. For example, various sonic and ultrasonic receivers and transmitters are commercially available and may be utilized in accordance with one or more embodiments of the present invention. For example, the receiver / transmitter 350 may communicate audible speech information to one or more telecommunications and / or telematics devices 295. In one or more embodiments, encoded telemetric speech data received by a microphone utilized in the transportation sensor device may be decoded by the signal processing module 340 to generate an electrical signal that is transmitted by the transmitter 350 to one or more telecommunications and / or telematics devices 295.

[0089] In one or more embodiments, optionally in combination with any of the embodiments described herein, one or more of the transportation sensor devices 310(a)-(n) and / or sensor-associated devices utilized in the exemplary inventive computer-based systems of the present disclosure may be configured to transmit sensor data using any wireless communication mode, such as, but not limited to, NFC, RFID, Narrowband Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes. For example, in one or more embodiments, one or more of the transportation sensor devices 310(a)-(n) includes a transmitter 350 configured to transmit sensor data using one of the suitable communication modes disclosed herein. In one or more other embodiments, one or more of the transportation sensor devices 310(a)-(n) is configured to transmit sensor data to one or more mobile electronic devices included in a group of telecommunications and / or telematics devices 295 configured to transmit sensor data using one of the suitable communication modes disclosed herein. In these embodiments, the sensor data is transmitted to the PSCI platform 100 via a suitable network, such as network 270. In one or more embodiments, and optionally in combination with any of the embodiments described herein, the term "mobile electronic device" may refer to any portable electronic device, which may or may not have location tracking functionality enabled. For example, a mobile electronic device may include a mobile phone, a personal digital assistant (PDA), a Blackberry®, a pager, a smartphone, or any other reasonable mobile electronic device. For simplicity, the above variations may not be listed or may only be partially listed herein, but this is not meant to be limiting in any way. In one or more embodiments, sensor data collected from one or more transport sensor devices 310(a)-(n) assigned to a freight shipment and / or assigned to freight included in the freight shipment is aggregated and transmitted to an auxiliary transmitting device.The auxiliary transmitting device is a mobile electronic device, for example, included in the group of telecommunications and / or telematics devices 295, located near (e.g., within a few millimeters, centimeters, inches, feet, yards, or any suitable distance that allows the sensor to operate for its intended purpose) or within a transportation vehicle (e.g., a truck, airplane, ship, railcar, train, crate, package, or other transport vehicle). In one or more embodiments, the auxiliary transmitting device is configured to transmit the aggregated sensor data to PSCI platform 100 or to a database accessible by PSCI platform 100 using a satellite-based data transmission network.

[0090] In one or more embodiments, the transmitter / receiver 350 utilized in one or more transportation sensor devices 310(a)-(n) is configured to transmit sensor data processed by one or more processors 340 (e.g., signal processor 340) to the PSCI platform 100 over the network 270. In one or more embodiments, the sensor data is transmitted from one or more transportation sensor devices 310(a)-(n) to one or more telecommunications and / or telematics devices configured to transmit to the PSCI platform 100 over the network 270. In one or more embodiments, the sensor data generated by one or more transportation sensor devices 310(a)-(n) may be converted to an electrical signal and transmitted in real time. In one or more embodiments, the sensor data generated by one or more transportation sensor devices 310(a)-(n) may be converted to an electrical signal and transmitted in near real time. In one or more embodiments, the sensor data generated by one or more transportation sensor devices 310(a)-(n) is converted to an electrical signal and transmitted at one or more predetermined time intervals depending on the type of sensor data being transmitted. For example, the transmitter 350 may delay transmission until a predetermined amount of detection time has passed, until a predetermined amount of processing time has passed, etc. In some cases, the transmitter / receiver 350 is configured to transmit a signal to the PSCI platform 100 in response to information detected by the sensors 320, 330.

[0091] In one or more embodiments, one or more of the exemplary transport sensor devices 310(a)-(n) includes a power source 360. For example, the power source 360 ​​may be any portable power source that can fit within the transport sensor device 310 housing. In one or more embodiments, the power source 360 ​​is a portable, rechargeable lithium polymer or zinc-air battery. In one or more embodiments, the power source 360 ​​may be a portable, energy-harvesting power source that may be integrated into the transport sensor device 310 and function as a primary or secondary power source. For example, a solar cell module may be integrated into the transport sensor device 310 to collect and store solar energy. In one or more embodiments, piezoelectric devices or microelectromechanical systems (MEMS) may be integrated into the transport sensor device 310 and used to collect and store energy from physical movement, electromagnetic energy, and other forms of energy in the environment or from the user themselves. In one or more embodiments, thermoelectric or thermoelectric devices may be used to provide some power from thermal energy or temperature gradients. In one or more embodiments, a crank or winding mechanism may be used to store mechanical energy for electrical conversion or to convert mechanical energy to electrical energy, which may be used to power one or more transport sensor devices 310(a)-(n) immediately or for later use.

[0092] In one or more embodiments, one or more of the transport sensor devices 310(a)-(n) may be configured to capture optical parameters that may include at least one from the group consisting of visible light, infrared light, and ultraviolet light parameters (e.g., TSL2561 from Adafruit, Inc., New York, NY). For example, one or more of the transport sensor devices 310(a)-(n) may include an optical detection module that may include a light sensor that detects light levels or changes in levels. In one or more embodiments, optical detection may include, but is not limited to, a digital image capture device, such as a CCD or CMOS imager, that captures data related to infrared, visible, and / or ultraviolet light images. For example, one or more of the transport sensor devices 310(a)-(n) may include an optical detection module that may include an exemplary light sensor that may generate an indication of increased ambient light. This indication may indicate, for example, the opening of a door that should not be opened.

[0093] In one or more embodiments, optionally in combination with one or more embodiments described herein, the terms "proximity detection," "location detection," "location data," "location information," and "location tracking" are used herein to refer to any form of location tracking technology or method that may be used to provide the location of a mobile electronic device. Location tracking techniques or methods may include, for example, location information manually entered by the user such as, but not limited to, city, town, municipality, postal code, area code, cross street, or any other reasonable input for determining a geographic area; Global Positioning System (GPS); GPS accessed using Bluetooth; GPS accessed using any reasonable form of wireless and / or non-wireless communication; Wi-Fi server location data; Bluetooth based location data; network-based triangulation; Wi-Fi server information-based triangulation; Bluetooth server information-based triangulation; cell identity-based triangulation; enhanced cell identity-based triangulation; Uplink Time Difference of Arriva (U-TDOA)-based triangulation; Time of Arrival (TOA)-based triangulation; Angle of Arrival (AOR)-based triangulation; techniques and systems using geographic coordinate systems, such as, but not limited to, longitude and latitude-based, geodetic height-based, and Cartesian-based; radio frequency identification, such as, but not limited to, long-range RFID, short-range RFID; the use of any type of RFID tag, such as, but not limited to, active RFID tags, passive RFID tags, and battery-assisted passive RFID tags; and / or any other reasonable method for determining location. For simplicity, the above variations may not be listed or may only be partially listed, but this is in no way meant to be limiting.

[0094] In one or more embodiments, one or more of sensors 320, 330 includes a sensor equipped with GPS technology (e.g., a GPS-equipped sensor) configured to determine and / or store data used to track and / or determine the location of one of the cargo, the transport vehicle transporting the cargo, the container containing or supporting the cargo for transport, or the transport device. In one or more of these embodiments, the GPS sensor module is included in the group of sensor modules 370(a)-(n). In one or more embodiments, the GPS sensor module includes an antenna and a sensor. For example, the sensor can operate autonomously following application of operating power to transport sensor device 310, whereby the sensor can digitally sample signals from visible GPS satellites and store this data in storage 355 (e.g., a digital buffer). The sensor data, including satellite data, enables the determination of the global position and velocity of the cargo, the transport vehicle transporting the cargo, the container containing or supporting the cargo for transport, and / or the transport device.

[0095] In one or more embodiments, one or more of the transportation sensor devices 310(a)-(n) may include a location module. In these embodiments, the location module may include a wireless transceiver, a Global Navigation Satellite System (GNSS) receiver, a processor, and device memory. For example, the transmitter / receiver 350 may be configured as a wireless transceiver, and the one or more processors 340 may be configured to process GNSS sample data to determine the location of cargo included in the cargo shipment. In one or more embodiments, the location samples of one or more GPS sensor modules 370(a)-(n) may be obtained using GNSS signals received from GNSS satellites. In one or more embodiments, one or more of the location samples may be obtained by one or more GPS sensor modules 370(a)-(n), for example, using one or more base stations or base station towers. For example, the location samples of one or more GPS sensor modules 370(a)-(n) may be obtained by triangulating cellular data signals received from base stations. One or more of the location samples may also be obtained by one or more GPS sensor modules 370(a)-(n) using a wireless access point, such as wireless access point 265, having a known location. For example, the location samples of one or more GPS sensor modules 370(a)-(n) may be obtained by communicating with a wireless access point whenever one of the one or more GPS sensor modules 370(a)-(n) is within the vicinity of the wireless access point. For example, the location of a cargo in a cargo shipment may be established by using triangulation of signal samples or by using the signal strength of a cellular signal provided by a base station or tower (e.g., the signal strength indicates that the cargo in the cargo shipment is in proximity to the cellular signal origin).

[0096] In one or more embodiments, optionally in combination with any of the embodiments described herein, an exemplary inventive computer-based PSCI system of the present disclosure may be configured to provide insurance policies on a per-shipment basis utilizing one or more of the shipment details, sensor data, telematics data, real-time shipment data, historical shipment data, emerging trends, forecast data, data regarding weather conditions, traffic conditions, regional conflict dynamics, and / or other data (collectively referred to herein as Per-Shipment Cargo Risk Assessment (PSCRA) data) that may affect the shipment of cargo as disclosed herein. In one or more embodiments, optionally in combination with any of the embodiments described herein, an exemplary inventive computer-based PSCI system of the present disclosure may be configured to: receive a request from a transportation logistics provider (e.g., DHL, FedEx, UPS, USPS, etc.) to insure the shipment of cargo; access one or more databases (e.g., internal and / or external databases) to receive and process shipment details for cargo transported by the transportation logistics provider (e.g., DHL, FedEx, UPS, USPS, etc.); determine whether the shipment is suitable through application of an automated insurance suitability assessment rules engine; perform a risk analysis using relevant collected data (e.g., weather information, traffic information, local conflict information, information that may affect the cargo shipper's road, waterway, rail, and / or air routes, stored sensor data, information that may affect delivery times, etc.); and approve, deny, or request additional information regarding the cargo shipment based on the processed information. In one or more embodiments, an exemplary inventive computer-based PSCI system of the present disclosure may be configured to provide a real-time risk assessment based on PSCRA data according to one or more protocols and metrics. In one or more embodiments, an exemplary inventive computer-based PSCI system of the present disclosure can be configured to provide near real-time risk assessment based on PSCRA data according to one or more protocols and metrics.For example, once the PSCI system receives relevant shipping information from a user of the PSCI system via a user device 102 utilizing client software 104, one or more embodiments of the exemplary inventive computer-based PSCI system of the present disclosure are configured to process the relevant PSCRA data to provide a risk assessment (i.e., calculate a quantifiable expression of risk) for the cargo of a particular shipping request (i.e., on a per-shipment basis). In one or more embodiments, the exemplary inventive computer-based PSCI system of the present disclosure may be configured to use big data, AI, IoT, and / or unsupervised machine learning to calculate the risk for a particular shipment in real time. In one or more embodiments, the exemplary inventive computer-based PSCI system of the present disclosure may be configured to use big data, AI, IoT, and / or unsupervised machine learning to calculate the risk for a particular shipment in near real time. In one or more embodiments, the exemplary inventive computer-based PSCI system of the present disclosure may be configured to provide a cargo shipper with a real-time or near-real-time quote for an insurance policy for a particular shipping request based on the risk analysis. For example, in one or more embodiments, if a transportation client (e.g., a buyer of transportation services) agrees to the quote provided by the shipper, once the transportation client pays for the transportation services, including the insurance policy provided by the PSCI system, responsibility for the cargo included in the freight shipment passes to the transportation insurance company. If the transportation client rejects the freight insurance quote, responsibility for the cargo remains with the transportation client.

[0097] In one or more embodiments, optionally in combination with any of the embodiments disclosed herein, the PSCI system of the present disclosure can be configured to provide pre-underwriting insurance contracts to remove the human element, manual review process, and / or post-underwriting process from the process of deciding whether to insure a particular cargo shipment. For example, because manual underwriting processes that may cause delays (e.g., hours, days, weeks, etc.) between the time a cargo owner or carrier enters relevant shipment information (e.g., shipper name, shipping date, start / end points, product type, estimated cargo value, mode of transportation, or type of insurance coverage requested) into a logistics transportation provider's website and the time an insurance quote covering the cargo shipment is provided is not required, manual underwriting processes are removed from the decision-making and underwriting processes made by the PSCI software. For example, in one or more embodiments, the PSCI platform provides a decision to insure a particular cargo, provides an insurance quote, and issues premiums and / or coverage in real time or near real time.

[0098] In one or more embodiments, logistics transportation providers have an integrated, direct access solution to the PSCI platform. For example, a cargo owner and / or logistics transportation provider can request insurance for a specific cargo being transported by pressing a button on a graphical user interface (GUI) presented via a web browser. For example, a cargo owner and / or logistics transportation provider can request insurance for a specific cargo shipment while interacting within a single system (i.e., the PSCI system). For example, a logistics transportation provider can provide cargo owners with instant insurance quotes, offering a single payment solution for both cargo movement (e.g., the price of shipping cargo from point A to point B) and insurance coverage. In one or more embodiments, the PSCI software utilizes the shipping information and compares the shipping request with predefined underwriter risk ratings associated with the relevant logistics transportation provider. For example, the PSCI software determines in real time whether the insurance request for a specific cargo shipment is within an acceptable risk level, and the underwriter provides cargo insurance for the cargo transported in the manner specified in the shipping information. For example, in one or more embodiments, each logistics transportation provider has an individual cargo insurance policy accessible to the PSCI platform, which policy includes, for example, individual pricing, commodity price adjustment factors, and underwriter risk ratings. In one or more embodiments, by utilizing the individual policies, the PSCI platform, using the PSCI software disclosed herein, can automatically determine in real time or near real time whether insurance coverage should be provided or denied, and, if necessary, can automatically provide adjustments to the insurance quote based on various dynamically changing risks (e.g., various risks associated with the cargo itself, the mode of transportation, the transportation route, the cargo containers and / or restraints, etc.). For example, in one or more embodiments, the insurance quotes provided by the PSCI platform for individual insurance policies for one or more specific cargo shipments are all pre-underwritten, eliminating the need for a traditional underwriting process for each insurance policy request.The PSCI software instantly determines whether the insurance coverage request for one or more shipments is within the risk level accepted by the insurer. In one or more embodiments, the PSCI platform also provides direct compliance and sanctions checks for each insurance policy by integrating with one or more compliance and / or sanctions platforms. For example, in step 426, shipment details are sent to one or more PSCI rules engines 404 and / or a third-party insurance policy compliance and / or sanctions platform (e.g., ComplyAdvantage (www.complyadvantage)) to determine whether the particular shipment details are consistent with one or more federal, state, local, and / or regional (e.g., international, national, county, city, etc.) regulations and / or compliance rules.

[0099] In one or more embodiments, collected data, including sensor data regarding cargo shipments, is stored in one or more databases (internal and / or external) and converted into at least one of: i) at least one alert (e.g., an audible alert, a visual alert, etc.) to a predetermined interested party (e.g., an insured client, a shipper, etc.), and ii) at least one visual display (e.g., a graph tracking measured time-based quality metrics over time in real time, etc.). For example, in one or more embodiments, telematics devices 140 and / or one or more sensors 290 configured to measure temperature, humidity, shock, etc. may be utilized to receive real-time information regarding cargo scheduled for shipment. For example, a cargo container may be equipped with one or more sensors 290 and telecommunications and / or telematics devices 295 that measure the temperature, shock, humidity, and door status of the container and transmit that information in real time as the cargo moves anywhere in the world. Sensors 290 and telecommunications and / or telematics devices 295 may be configured to not only provide measurements regarding the container, but also its contents. In this aspect, the computer-based PSCI system can dynamically collect sensor data and utilize the sensor information to provide shipment-unit cargo insurance in real time or near real time via execution of PSCI software. For example, logistics transportation providers and insurance providers can access all policies (e.g., residing in one or more databases 170a-170n and / or 160a-160n) utilizing the PSCI platform via their respective computing devices (e.g., servers 130a-130n). In one or more embodiments, the PSCI platform also allows authorized users to track exposure risk for individual shipments in real time and / or visualize trends in real time using sensors and / or telematics devices as disclosed herein and / or one or more third-party databases storing real-time shipment data, as described with reference to step 416 of FIG. 4C.For example, an insurance provider using the PSCI platform may track exposure risk in real time to visualize trends for individual cargo shipments in real time. This allows the insurance provider to dynamically adjust the risk assessment of logistics transportation providers and / or cargo shipment brackets to reduce the risk associated with one or more insurance policies and / or individual cargo shipments, thereby reducing the risk of claims or increasing business. For example, if the PSCI platform determines, based on individual settings or factors, that too many container shipments containing beef originate in Europe with a destination port of Baltimore, an alert, flag, or other warning may be triggered, raising or lowering the risk assessment of these and future shipments containing similar shipment characteristics.

[0100] FIG. 4A illustrates a block diagram representation of an exemplary inventive PSCI software platform 400 executed by the exemplary PSCI platform 100 according to one or more embodiments. As presented herein, the PSCI software framework / platform 400 (referred to herein as the PSCI software platform 400) is generally a software and firmware entity (e.g., a functional engine for communicating modules that comprise the PSCI software platform 400). This entity is provided by one or more processors (e.g., one or more processors 105 present in one or more PSCI server devices 120a-120n) executing a PSCI utility 220, which may be provided as one of the applications 218 and / or as an executable component within firmware 212 or O / S 216 in alternative embodiments, on one or more PSCI server devices 120a-120n (e.g., the PSCI DPS 200), to implement certain functional aspects of the disclosure according to one or more embodiments. An exemplary architecture of one or more embodiments of a computer-based PSCI system 400 is disclosed with reference to FIGS. 4A-4H and 8. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI software platform 400 includes a PSCI API 401 (e.g., an application programming interface provided to one or more authorized user computer systems (e.g., one or more logistics transportation provider servers 130a-130n and / or user devices 102a-102n) and utilized to communicate / exchange information with the exemplary PSCI platform 100), a PSCI risk modeling engine 402, a PSCI user experience engine 403, a PSCI rules engine 404, a PSCI machine learning engine 405, a PSCI probability scoring engine 406, and a neural network 407, which are described herein below with reference to Figures 4B-4E.In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI software platform 400 may be configured as a dedicated app or dedicated plug-in software that facilitates the exchange of information between the PSCI platform 100 and one or more logistics transportation provider servers 130a-130n and / or user devices 102a-102n and / or one or more insurance provider / underwriter servers 180a-180n, as disclosed herein.

[0101] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI software platform 400 is communicatively coupled to one or more databases 150a-150n, 251 via a secure network 110, 270. For example, in one or more embodiments, one or more of the databases 150a-150n, 251 is configured to store information of authorized users (e.g., insurers, underwriters, logistics transportation providers, and / or customers of logistics transportation providers). For example, in one or more embodiments, the user information may include demographic information for the authorized users, information corresponding to user interactions regarding requests and / or responses presented to the users used to provide transportation insurance for one or more individual shipments, shipment information, requests for information, and / or other information presented to the users, etc. For example, in one or more embodiments, the user information may include each offer and / or acceptance and / or denial of insurance provided by an insurer and / or underwriter for one or more individual freight shipments via the PSCI platform, each quote provided by an insurer and / or underwriter that is offered and / or accepted and / or denied for one or more individual freight shipments, 1) each product type of cargo included in a freight shipment, e.g., as disclosed with reference to Figures 5A-5G, and / or 2) each product price adjustment factor and / or risk probability value provided by an insurer and / or underwriter for each transportation type used to transport cargo included in a freight shipment, e.g., as disclosed with reference to Figure 5H, and / or other information associated with the provision of insurance coverage, quotes, product price adjustment factors and / or risk probability values. For example, one or more of the above data may be collectively referred to as "user information" or "user data."

[0102] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI software platform 100 executing the exemplary inventive PSCI software includes one or more front-end servers dedicated to managing network connections with remote clients (e.g., authorized users, including, but not limited to, insurers, underwriters, logistics transportation providers, and / or customers of logistics transportation providers). In one or more embodiments, the front-end servers provide various interfaces for interacting with one or more of the different types of clients. For example, a web interface module within a front-end module included in one or more of the front-end servers provides client access to the PSCI platform when a client uses a web browser to access the platform. Similarly, a PSCI API interface provides client access to the PSCI platform 100 when a client invokes a PSCI API 401 made available by the PSCI platform for such purpose. In one or more embodiments, optionally in combination with any embodiment described herein, the exemplary computer-based PSCI software platform 400 may be configured to include an integrated PSCI API 401 (e.g., a PSCI application programming interface (API) enabling execution of PSCI application modules). The PSCI API 401 is configured to provide data (e.g., user information) to one or more PSCI server devices. In one or more embodiments, the PSCI API 401 provides offers for insurance quotes, pricing (e.g., premiums, PSCI technology solution pricing, and / or logistics transportation provider revenue participation balances, etc.) and / or other decisions (e.g., denial of insurance coverage) back to authorized users of the PSCI platform. In one or more embodiments, the PSCI API 401 provides insurance providers using one or more user devices and / or computer systems (e.g., one or more insurance carrier / underwriter servers 180a-180n).It accesses the PSCI platform 100 to provide information (e.g., information associated with the pre-underwriting process disclosed herein, insurance policy information disclosed herein, and other information necessary to perform the functions disclosed herein) and request information (e.g., insurance and contract information disclosed herein).

[0103] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk management and modeling engine 402 is configured to automatically calculate a risk profile specific to an individual shipment using one or more of the risk modeling methods and / or tools disclosed herein. In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk management and modeling engine 402 included in the exemplary inventive PSCI software platform 400 executing on the PSCI software platform 100 is configured to automatically access one or more of the PSCI databases 150a-150n, 251 to compile user information, compare multiple data points contained in the user information, select a premium based on the data points, and provide the same to the shipper via the PSCI API 401. For example, in one or more embodiments, data points utilized in the comparison process may include, but are not limited to, data regarding cargo details (e.g., shipper identification, shipment origin address, shipment destination address, shipment route, route segments included in the shipment route, transportation modes (e.g., road, air, water, rail, etc.) utilized to transport the cargo included in the cargo shipment during each route segment, geographic areas intersected by the transportation route, container types utilized to transport the cargo included in the cargo shipment, cargo commodity types, special requirements (e.g., refrigeration, perishables, temperature requirements, environmental requirements, light requirements, contextual requirements, etc.), restraints, logistics transportation provider identification, time required to transport the cargo, etc., potential risks associated with the cargo and / or the transportation of the cargo, available insurance policies provided by one or more authorized users of the PSCI platform 100 (e.g., insurers and / or underwriters) that have been pre-approved by insurance companies or underwriters for the shipment of the cargo, and the insurance premiums associated with each of the available policies.In one or more embodiments, real-time transportation data (e.g., data disclosed with reference to step 416 described herein) and / or historical transportation data (e.g., data disclosed with reference to step 418 described herein) can be utilized in addition to the user data described above to compile user information, compare multiple data points contained in the user information, and select insurance premiums based on the data points.

[0104] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, each logistics transportation provider stores individual cargo policies in one or more PSCI system databases, each cargo policy including information that can be used by the PSCI risk management and modeling engine 402 as a data point for the comparison processing function. In one or more embodiments, additional authorized users of the PSCI platform, including other third-party insurers and / or underwriters, store exemplary cargo insurance policies for individual cargo shipments in one or more PSCI system databases, each third-party cargo insurance policy including information that can be used by the PSCI risk management and modeling engine 402 as a data point for the comparison processing function. For example, information included in cargo insurance policies includes, but is not limited to, individual pricing, commodity price adjustment factors, and insurer risk ratings. For example, in one or more embodiments, the PSCI risk management and modeling engine 402 compares data points gathered using cargo insurance policies with data points gathered from user information and / or shipment information for the identified cargo shipment and provides an insurance policy quote to the shipper (e.g., logistics transportation provider) based on the comparison process. For example, in one or more embodiments, the PSCI risk management and modeling engine 402 may compare, for example, the type of cargo that is insurable (e.g., cargo of a particular commodity type as disclosed with reference to Figures 5A-5G), the type of transportation used to transport the cargo included in the cargo shipment (e.g., the particular transportation type as disclosed with reference to Figure 5H), and the insurance premium incurred by the cargo (i.e., commodity price adjustment factors and / or risk probability values, as described in more detail below), areas associated with individual cargo shipments that the insurer / underwriter does not cover or excludes from insurance coverage, and / or the maximum coverage that the insurer / underwriter provides for the identified cargo shipment.For example, in one or more embodiments, once the PSCI risk management and modeling engine 402 compares the data points with shipping and / or other information regarding the identified individual shipment, the PSCI risk management and modeling engine provides a real-time (or near real-time) coverage decision (e.g., accepts the insurance coverage option, declines the insurance coverage option, and / or requests further information from the shipper / logistics transportation provider) and, if accepted, provides an insurance quote to the shipper in real-time or near real-time via the PSCI API 401.

[0105] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk management and modeling engine 402 is configured to make coverage decisions in real time or near real time and to provide insurance quote adjustments based on various risks in real time or near real time. For example, the PSCI risk management and modeling engine 402 is configured to utilize real-time transportation data (disclosed with reference to step 416 below), including real-time sensor data and other real-time transportation data, to enable insurance providers to track exposure risks in real time to both dynamically modify insurance quotes for individual shipments in real time or near real time to account for emerging risks and to visualize certain trends that may affect the risk for individual shipments in real time. For example, by compiling and processing real-time sensor data and other real-time transportation data, insurance providers can dynamically adjust the risk levels (e.g., risk brackets) associated with insurance policies for individual shipments to reduce risk (e.g., by charging higher premiums for different types of cargo included in and / or in the transportation details associated with the individual shipments) or to increase business (e.g., lowering premiums for different types of cargo included in and / or in the transportation details associated with the individual shipments). In one or more embodiments, the PSCI risk management and modeling engine 402 provides insurance policy quotes to shippers (e.g., logistics carriers) based on a dynamic insurance pricing model. For example, if too many container shipments containing beef originate in Europe and have a destination port of Baltimore, Maryland, these data points can be utilized by the PSCI risk management and modeling engine 402 to trigger flags based on individual settings that may require dynamic adjustments to the insurer's premiums for the identified individual shipments or policies for the identified product type of cargo included in the individual shipments (e.g., referenced in Figures 5A through 5G as "meat and meat offal").

[0106] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI UX (user experience) engine 403 is configured to act as an authorization and validation gateway between the example PSCI platform 100 and a user's computer system and internal network (e.g., an intranet or other internal network). In one or more embodiments, the PSCI user experience engine 403 authorizes and validates users (e.g., insurance providers, underwriters, etc.) accessing the example PSCI platform 100 via the PSCI API 401, e.g., via an employer's intranet or other suitable network. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI user experience engine 403 may include an interface module, an identity verification module, and an access management module. For example, in one or more embodiments, these modules provide user control (e.g., data access), security / authentication, and authorization for users accessing and / or interacting with the example PSCI platform 100.

[0107] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the insurance risk model generated by PSCI risk modeling engine 402 may be continuously trained, for example, without limitation, by applying at least one machine learning technique used by PSCI machine learning engine 405 to data collected and / or compiled regarding shipments of cargo, the routes used to transport the cargo included in the cargo shipment, and / or the individual transportation logistics providers contracted with the shipper to transport the cargo. In one or more embodiments, optionally in combination with any embodiment disclosed herein, an exemplary neural network technique used by neural network 407 may be utilized. PSCI machine learning engine 405 and neural network 407 are described in detail herein with reference to FIGS. 4B and 4C . For example, in one or more embodiments, PSCI machine learning engine 405 includes exemplary neural network 407. In one or more other embodiments, exemplary neural network 407 is communicatively coupled to PSCI machine learning engine 405.

[0108] FIG. 4B is a flowchart disclosing an illustrative example of how one or more embodiments of the PSCI system utilize the collected and compiled data to provide insurance policies on a shipment-by-shipment basis in real time or near real time through execution of the PSCI software. In step 408, a client user utilizes a user device 102 to input shipping information about a shipment the client wishes to transport from an origin geographic location (e.g., a departure / collection address) to a destination geographic location (e.g., a delivery address). In one or more embodiments, one or more user devices 102a-102n are configured with software referred to as a client or client software 104a-104n. In operation, these may access one or more logistics transportation provider systems (e.g., servers 130a-130n) over a network (e.g., the Internet) to enable users to send and receive messages, view lists of content items, and interact with them over the network. In one or more embodiments, one or more user devices 102a-102n are configured with a client or client software 104a-104n. During operation, they may access one or more of the PSCI platform provider systems (e.g., servers 120a-120n) over a network (e.g., the Internet). In these embodiments, the PSCI platform provider system may communicatively interact with a logistics transportation provider system (e.g., a back-end computer system) such that user-provided data is transmitted to the logistics transportation provider and / or logistics transportation provider data is transmitted to the user, who may send and receive messages over the network, view and interact with lists of content items, and more. In this manner, the user device 102 may be utilized to transmit information, including, for example, transportation information about the shipment of cargo, to a logistics transportation provider system (e.g., one or more associated servers 130a-130n included within a specified logistics transportation provider computer system).In one or more embodiments, for example, the client on one or more user devices 102a-102n may be or include a web browser or an HTML (HyperText Markup Language) document rendered by a web browser. In one or more embodiments, for example, the client on one or more user devices 102a-102n may be or include JavaScript code or Java code. In one or more embodiments, for example, the client on one or more user devices 102a-102n may be or include specialized software, such as an installed app or application specifically designed to interface with a logistics transportation provider system (e.g., server 130a-130n). In one or more embodiments, for example, the client on one or more user devices 102a-102n may be or include a short messaging service (SMS) interface, an instant messaging interface, an email-based interface, or an API function-based interface.

[0109] In one or more embodiments, web browsers located on the user device and the logistics transportation provider system (e.g., servers 130a-130n) represent an initial procedure for constructing a graphical array in response to initial user input directed to the user's intent to transport cargo. In one or more embodiments, for example, the logistics transportation provider system includes one or more servers 130a-130n that provide various graphical user interfaces for interacting with different types of clients. These interfaces are operable to provide users with data fields available to input transportation information regarding cargo to be transported using a particular logistics transportation provider. The transportation information may include, but is not limited to, one or more of: source zip code, destination zip code, cargo weight, cargo type, estimated value of the cargo, desired delivery time, desired pickup time, transportation origin address and / or transportation destination address, and / or desired cargo insurance type to determine timing schedules and fees for each supported carrier. For example, the minimum transportation information required to complete a transportation order may include at least sufficient information to enable the logistics transportation provider to determine timing schedules and fees for fulfilling the transportation request. For example, the shipping information may also include additional special instructions, such as a guaranteed delivery time, and / or a specific mode of transportation (e.g., road, air, sea, rail, etc.), a specific cargo container (e.g., a container with appropriate lighting or darkness, refrigeration, appropriate temperature range, appropriate humidity range, appropriate impact range, etc.), if the user has requested such.

[0110] In one or more embodiments of step 409, the logistics transportation provider receives the transportation information from the customer (shipper) and a transportation contract is generated, for example, using one of the logistics transportation provider's backend servers (e.g., one or more server devices 130).

[0111] For example, in one or more embodiments, at step 410, the logistics transportation provider validates the transportation contract using its contract gateway system and verifies the transportation information included in the transportation contract. For example, in one or more embodiments, if at least the minimum transportation information required to fulfill a valid transportation request has not been provided to the logistics transportation provider at step 410, the logistics transportation provider system displays an error message prompting the user to enter more transportation information. For example, in one or more embodiments disclosed with reference to FIG. 1 , a graphical user interface (GUI) displayed on the user device 102 that enables the user to enter transportation information may display one or more graphic arrays that the user utilizes to provide all of the information necessary to complete the transportation request (e.g., origin address, delivery address, cargo type, payment information, etc.). In one or more embodiments, the graphical user interface (GUI) displayed on the user device 102 may display an error to the user indicating that insufficient transportation information has been provided and / or prompt the user for additional information. In one or more embodiments, the GUI and the display data associated with the GUI are all processed by a web browser of the client 104 resident on the user device 102.

[0112] In one or more embodiments, completing all other functions and processes necessary to complete the transportation request in step 410, i.e., the steps necessary for the PSCI software platform to engage a contract with the user to transport the cargo included in the freight shipment without granting the PSCI insurance policy to the logistics transportation provider, is performed by one or more of the logistics transportation provider systems, including one or more servers 130a-130n hosting the delivery service UI and / or delivery service backend functionality. For example, the additional information and processes necessary to complete and book the transportation request may include, but are not limited to, processing payment, providing one or more notifications for timing, tracking, shipping, payment, and / or delivery confirmation. For example, in one or more embodiments, in step 430, if the logistics transportation provider contract determines that one or more details / characteristics included in the transportation information provided by the user fall into a category that prevents the logistics transportation provider from fulfilling the terms of the transportation contract (e.g., if the type of cargo included in the freight shipment includes hazardous materials, if the type of cargo included in the freight shipment is perishable and the refrigeration requirements cannot be met by the transportation logistics provider, if the requested delivery time cannot be met, if the freight shipment is prohibited by one or more federal, state, local, and / or regional compliance regulations, etc.), the user is automatically sent a rejection message (e.g., display, warning, message, user error, etc.) indicating that the customer's transportation request has been rejected. In one or more embodiments, in step 432, the shipping information received by the user and the reasons for rejecting the shipping contract requested by the user are transmitted to the exemplary PSCI platform 100, stored in one or more databases (e.g., databases 150, 251) accessible to the exemplary inventive PSCI platform 100 executing the exemplary inventive PSCI software platform 400, and utilized by the PSCI machine learning engine 405 to improve the dynamic pricing model generated by the PSCI risk modeling engine 402 as detailed herein.

[0113] For example, in one or more embodiments, if in step 440 the logistics transportation provider contract determines that the details / characteristics included in the transportation information are such that the transportation contract can be fulfilled, the user is automatically sent an acknowledgment message (e.g., display, warning, message, user error, etc.) indicating that the customer's transportation request has been accepted.

[0114] In one or more embodiments, the logistics transportation provider requests a transportation unit insurance (PSCI) policy through the PSCI platform at step 442. For example, in one or more embodiments, the user requests a PSCI insurance policy at the time of booking at step 408. In one or more other embodiments, in response to the logistics transportation provider contract gateway approving the transportation request at step 440, the logistics transportation provider reservation system offers PSCI insurance for the shipment of cargo to the user, which may be requested or rejected. In one or more embodiments, the logistics transportation provider may offer the customer a PSCI insurance policy that operates to provide coverage for the shipment of cargo that the user has contracted with the logistics transportation provider to transport to a delivery destination (e.g., a delivery address). In one or more embodiments, PSCI software configured to provide insurance quotes for individual shipments of cargo in real time resides on the logistics transportation provider system (e.g., one or more servers 130a-130n). In one or more embodiments, the PSCI software resides on the PSCI platform 100 and is accessible using the PSCI API 401 over a network 110 hosted by a logistics transportation provider system (e.g., one or more associated servers 130a-130n).

[0115] In one or more embodiments, additional transportation information not provided by a user in creating a transportation contract for transporting cargo included in a cargo shipment without a PSCI insurance policy may be requested by a PSCI platform executing the PSCI software platform to provide a PSCI insurance quote to a shipper. For example, the transportation information may be provided to the PSCI software platform via a logistics transportation provider computer system (e.g., one or more associated servers 130a-130n). For example, the additional transportation information may include geographic mapping information for each portion (i.e., leg) of the planned transportation route to transport the cargo from the origin geographic location to the destination geographic location, the types of containers used on each portion of the route, the types of restraints used to secure the cargo on each portion of the route, the total weight of the cargo (e.g., weight including any packaging used to support / contain the cargo within the shipping container, the platform used to support the cargo, and the container used to support / contain the cargo within the shipping container), the center of gravity of the cargo, the type of cargo (perishable, perishable cargo type, etc.), the weight of the cargo on each portion of the route, ... This information may include, but is not limited to, route information, including the type of shipping container used to transport the goods, the mode of transportation (e.g., roadway, tractor-trailer, railroad, waterway (e.g., sea, ocean, river, canal crossing, lake, inlet, etc.), the type of vehicle (year make, model) used to transport the cargo on each portion of the route, customs and border control information, timing information regarding the transportation (e.g., total time from pickup to delivery of the cargo, time required for each portion of the route, weight information regarding the shipping container used to transport the cargo (e.g., pre-loaded weight of the shipping container, loaded weight of the shipping container, rated weight of the shipping container).

[0116] In step 442, information including, for example, at least a portion of the shipment information provided by a user via user device 102 and / or at least a portion of the information generated or provided by a logistics transportation provider system (e.g., one or more servers 130a-130n) is transmitted to the exemplary PSCI platform 100, including one or more processors resident in the exemplary PSCI platform 100 utilized to execute the exemplary inventive PSCI software platform, to determine whether the freight shipment is approved / denied for insurance coverage. In one or more embodiments, the PSCI software for determining whether the freight shipment is approved / denied for insurance coverage, and thus the processors for executing the PSCI software (processing layer), reside in one or more of servers 120a-120n in the PSCI platform 100. In one or more other embodiments, the PSCI software for determining whether the freight shipment is approved / denied for insurance coverage resides in the associated logistics transportation provider computer system (e.g., one or more servers 130a-130n). In one or more embodiments, the PSCI system 100 is a distributed system, where certain portions of the functionality performed by the PSCI software are performed by processors residing on one or more of the logistics transportation provider systems (e.g., one or more servers 130a-130n), and certain portions of the functionality performed by the PSCI software are performed by processors residing on one or more of the servers 120a-120n in the PSCI platform 100.

[0117] In step 444, transportation details including at least a portion of the transportation information (e.g., including at least a portion of shipping information provided by a user via user device 102 and / or transportation information generated and / or provided by a logistics transportation provider system) and / or other information needed to request insurance via the exemplary original PSCI platform 100 executing the exemplary original software platform 400 for the identified individual freight shipment are received by a PSCI processing layer (e.g., one or more software components included in the exemplary original software platform 400) that provides one or more functions described herein, such as determining whether the freight shipment is approved or denied for insurance coverage and, if approved, providing an insurance quote in real time or near real time. The functions performed by the exemplary PSCI platform 100 executing the exemplary original software platform 400 are described below with reference to FIG. 4C.

[0118] 4C is a flowchart disclosing an illustrative example of one or more embodiments of the PSCI software platform 400, referred to in FIG. 4B as the PSCI Contract Gateway, which may be utilized to provide insurance quotes for individual shipments to logistics provider shipping customers (e.g., shippers) in real time or near real time. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the exemplary PSCI computer-based platform includes one or more servers (e.g., PSCI platform servers 120a-120n) that provide one or more interfaces and / or gateways, e.g., PSCI gateways, for interacting with different types of clients. For example, in one or more embodiments, when a web browser resident on a user device 102 accesses the exemplary PSCI platform 100, a web interface module resident on the user device or client software provides the client access to the PSCI gateway. In one or more embodiments, when a logistics transportation provider system client invokes the PSCI API 401, the PSCI software platform 400 is made available via the PSCI API 401, enabling authorized users (e.g., logistics transportation providers) to utilize the exemplary PSCI platform to receive insurance quotes for individual cargo shipments in real time or near real time and to offer insurance policies to customers (e.g., shippers) from one or more insurers and / or underwriters.

[0119] In step 411, in one or more embodiments disclosed herein with reference to FIG. 4C , a logistics transportation provider computer system is utilized to provide the required information (i.e., shipment details) to the PSCI API 401, including at least a portion of the shipment information required for the exemplary PSCI platform 100 executing the PSCI software platform 400 to determine whether the cargo insurance coverage request for the identified individual cargo insurance policy should be approved or denied. In one or more embodiments, if the PSCI platform 100 executes the PSCI software platform 400 to determine whether the cargo insurance request should be approved or denied and the required information is not received by the PSCI API 401, the PSCI API 401 may provide an indicator (e.g., a message, an error message, a warning, etc.) to an authorized user (e.g., a logistics transportation provider, another authorized user) that more information is needed. For example, the necessary information provided to the PSCI API 401 to enable an insurance request to be sent to the PSCI platform may depend on one or more factors, including, but not limited to, the type of insurance requested, the amount of insurance coverage requested, the dates and times that insurance coverage begins and ends, the type of cargo, the carrier used to transport the cargo, and / or the transportation route used to transport the cargo. For example, in one or more embodiments, the PSCI API 401 requires at least the following information to enable an insurance request to be sent to the PSCI platform: 1) origin / collection address, 2) destination / delivery address, 3) shipper identity, 4) cargo type, 5) estimated value of the cargo, 6) cargo type, and 7) logistics transportation provider identity. In one or more other embodiments, the necessary information provided to the PSCI API 401 to enable an insurance request to be sent to the PSCI platform may be less or more, depending on the factors described above.

[0120] If, in step 412, the PSCI API 401 determines that it has the necessary information (i.e., shipment details) including at least a portion n of the shipment information, the shipment details are transmitted over the secure network 110, 270 to an example PSCI platform 100 executing the PSCI software platform 400. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, once the shipment details are received, the PSCI software platform 400 utilizes the shipment details to provide one or more of the following functions: 1) a user identity, authentication, and / or security analysis to determine whether a user has permission to access the PSCI platform; 2) a compliance analysis to determine whether the scheduled shipment complies with one or more federal, state, and local regulations and compliance rules; 3) a determination as to whether a request for insurance coverage for one or more individual cargo shipments is approved / denied; and / or 4) an insurance coverage quote, among other functions. In one or more embodiments, a compliance analysis is not required.

[0121] For example, in one or more embodiments, one or more of the servers 120a-120n present on the PSCI platform 100 execute a number of different services implemented by the PSCI software platform 400 installed and operating on one or more of the servers. The services performed by the PSCI platform software 400 are described as being performed by software-based engines (e.g., software modules and / or components). For example, in one or more other embodiments, a particular server present in, for example, a logistics transportation provider system (e.g., one or more logistics transportation provider servers 130a-130n) and / or the PSCI platform 100 (e.g., servers 120a-120n) may be dedicated to executing one or a few specific services and may install only the software module components necessary for that particular service. For example, some modules may be generally installed on one or more non-special-purpose servers present in the logistics transportation provider system and / or the PSCI platform 100. For example, the software for each module may be implemented in any convenient form, and portions of the modules may be distributed across multiple computers, with the operations of the modules being performed by multiple computers operating software that cooperates with each other to perform the operations. In some implementations, the operations of the modules are performed in part by special-purpose hardware. For example, in one or more embodiments, the PSCI software platform 400 is generally a software and / or firmware configuration provided by a processor executing, for example, software residing in the PSCI utility 220 and / or applications 210 residing on one or more servers 120a-120n, that implements particular functional aspects of the present disclosure in accordance with one or more embodiments.In one or more other embodiments, the PSCI software platform 400 is generally a software and / or firmware configuration provided by a processor executing PSCI software on one or more servers 130a-130n present in a logistics transportation provider system, and implements particular functional aspects of the present disclosure in accordance with one or more embodiments.

[0122] For example, in one or more embodiments, in step 414, the PSCI risk modeling engine 402 receives the shipment details via the PSCI API 401. For example, in one or more embodiments, optionally in combination with any embodiment described herein, the PSCI risk modeling engine 402 uses the shipment details to determine specific requirements for insurance coverage for the planned cargo shipment, such as freight commodity types for specific cargoes included in the freight shipment, estimated values ​​for specific cargoes included in the freight shipment, requested insurance values ​​for specific cargoes included in the freight shipment, shipping dates (estimated and actual) for the specific cargo shipment, transportation routes for the specific cargo shipment and possible exceptions to one or more of the transportation routes, refrigeration needs / requirements (reefer insurance needs), handling specifications, waypoints, etc.

[0123] At step 424, user access and interaction with the example PSCI platform 100 is managed by the PSCI user experience engine 403. For example, in one or more embodiments, optionally in combination with any embodiment described herein, the PSCI platform may utilize the PSCI user experience engine 403 to act as a gateway between the user's system (e.g., a logistics transportation provider computer system including one or more servers 130a-130n and one or more user devices 102a-102n) and the PSCI platform. For example, in one or more embodiments, optionally in combination with any embodiment described herein, the PSCI user experience engine 403 provides each user of the PSCI platform with a unique access token for authenticating and / or verifying the user's identity. For example, in step 424, this unique access token may be received by the user experience module as a query parameter (e.g., / api / endpoint?access_token=2df0e411-b4b1-4aa0-8dff-51b10d13b0e4) along with every request by the user to access the PSCI platform. For example, if the user's identity and / or authentication is not verified in step 424, the user is denied access to the PSCI platform, and in one or more embodiments, one or more of an error message, a denial message, and / or a warning is sent to the user via the secure network 110, 270.

[0124] In one or more embodiments, optionally in combination with any embodiment described herein, the PSCI user experience engine 403 utilized in step 424 includes one or more of an interface module, an identity verification module, and an access management module. These modules together provide user control (e.g., data access definition), security / authentication, and authorization. For example, the interface module provides a gateway for communication between the PSCI platform and a user computer system. This communication is established over a network, such as an employer's intranet or network 110, 270, through which users can access the PSCI platform. The interface module provides a user interface (e.g., one or more GUIs) through which users can be granted access to the PSCI platform. For example, specific access rights can be granted to specific logistics transportation providers. In other words, access can be limited to data used to provide insurance quotes for individual freight shipments and / or other functions disclosed herein.

[0125] In one or more embodiments, optionally in combination with any of the embodiments described herein, the interface module may provide a user with the ability to set and / or select one or more sign-on credentials. For example, a user may select and / or modify a username, password, etc. via the interface module. In one or more embodiments, optionally in combination with any of the embodiments described herein, the interface module may utilize biometric authentication to establish a user's identity. For example, in one or more embodiments, the interface module may be used to learn, train, or collect biometric data (e.g., retinal scan information, iris scan, fingerprint, facial recognition, etc.).

[0126] In one or more embodiments, optionally in combination with any of the embodiments described herein, an identity verification module may be utilized to verify a user. For example, authentication of a user may be specific to the identity and / or authority of a particular user to access the PSCI platform. For example, a user may be granted the ability to use the PSCI platform to access its data and / or data the user has provided to the PSCI platform in the past (e.g., the past hours, days, weeks, months, years, and / or decades). For example, a user may be granted the ability to access one or more agreements (e.g., insurance agreements, user agreements, confidentiality agreements, etc.) utilized by the PSCI platform and / or one or more agreements entered into with other service providers (e.g., insurance companies, freight forwarding companies, cargo management companies, etc.). For example, in one or more embodiments, once the user's identity is verified, data access may be granted to the PSCI platform, and the process of a customer being provided with an insurance policy using the PSCI software may begin. In one or more embodiments, optionally in combination with any of the embodiments described herein, authentication and / or identity verification may be achieved by any suitable authentication and / or identity verification mechanism. For example, in one or more embodiments, a username / password combination may be used. For example, in one or more embodiments, a challenge / response mechanism may be used to verify identity, whereby one or more questions (e.g., challenges) may be presented to the user. For example, in one or more embodiments, biometric authentication may be used to verify identity.

[0127] In one or more embodiments, optionally in combination with any of the embodiments described herein, the access management module can regulate which PSCI applications and / or services a user can access according to their verified identity and / or authentication. For example, once a user's identity and authentication are verified, the user can access the PSCI platform and send and receive data without having to re-enter their identity and / or authentication information. In one or more embodiments, optionally in combination with any of the embodiments described herein, the access management module can maintain information regarding which users can access which PSCI applications and / or services. For example, in one or more embodiments, the access management module can communicate with the interface module to establish which data can be accessed by which applications and / or services, e.g., based on defined policies, preferences, and / or rules.

[0128] In step 426, information including at least a portion of the shipment information is received by a PSCI automated insurance compliance assessment rules engine 404 (referred to herein as PSCI rules engine 404) in response to a user inputting the shipment information via one or more GUIs provided by the PSCI API 401 and transmitting the shipment information to the PSCI platform, and an automatic determination is made as to whether one or more shipment characteristics associated with the shipment of the cargo are in compliance with one or more federal, state, or local regulations and / or compliance regulations. For example, in one or more embodiments, if one or more details and / or characteristics of the shipment (e.g., weight, value, type, etc.) and / or details associated with the shipment of the cargo (e.g., means of transport, route (leg), weather, environment, context, territorial / regional / national activity, etc.) do not comply with predetermined rules and / or regulations applicable to one or more associated shipments of cargo, the PSCI rules engine 404 may include one or more of an error message, a negative message, and / or a warning being sent to the user.

[0129] For example, in one or more embodiments, the PSCI rules engine 404 automatically reviews and evaluates whether one or more details and / or characteristics of a shipment are in conformance with one or more federal, state, or local regulations and / or compliance regulations and / or other legal compliance requirements for entities related to freight insurance. In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI rules engine 404 stores rule data derived from legal compliance requirements, license data derived from regulatory requirements, system configuration data, and supplemental system insurance coverage data.

[0130] In one or more other embodiments, a third-party insurance compliance evaluation service (e.g., ComplyAdvantage (www.complyadvantage)) is utilized to review and evaluate whether one or more details and / or characteristics of the shipment are in conformance with one or more federal, state, or local regulations and / or compliance regulations and / or other legal compliance requirements for entities related to freight transportation insurance. For example, in one or more embodiments, the PSCI rules engine 404 automatically extracts insurance coverage data from at least a portion of the shipment information received by the PSCI API 401 from a user (e.g., a logistics transportation provider or a customer), generates an insurance compliance information file utilizing at least a portion of the shipment information, and transmits the insurance compliance information file over a secure communications network, such as networks 110, 270, to an automated compliance evaluation system server (not shown) for use by the third-party insurance compliance evaluation service. For example, in one or more embodiments, the automated compliance evaluation system has its own internal processes for auditing the insurance compliance information file for conformance with federal, state, and local regulatory compliance requirements. For example, in one or more embodiments, the insurance compliance information file is transmitted over a secure network to an API-based platform capable of processing high-quality data for compliance purposes. For example, in one or more embodiments, the ComplyAdvantage® API may be integrated into the PSCI software platform (e.g., the PSCI API) to enable automated screening of PSCI user insurance coverage claims that include at least a portion of the shipment information received by the PSCI platform for one or more individual freight shipments.For example, in one or more embodiments, once the shipment information is received by the PSCI platform via the PSCI API 401, an insurance compliance information file is automatically generated by the PSCI rules engine in step 414 and automatically transmitted over a secure network 110, 270 to an insurance compliance platform (e.g., ComplyAdvantage or another suitable compliance platform in step 426), which can screen the insurance compliance information file in real time or near real time against insurance regulations and / or compliance rules, sanctions lists, watch lists, politically exposed person / product lists, and / or adverse media. For example, in one or more embodiments, the insurance compliance information file is reviewed for legal compliance requirements imposed by federal, state, and / or local jurisdictions, as well as licensing requirements that the client insurance provider company and associated personnel must meet. For example, the results of the audit process are transmitted over the secure communications network 110, 270 and received by the exemplary PSCI platform 100 running the PSCI software, indicating areas of non-compliance. For example, in one or more embodiments, the PSCI rules engine 404 automatically sends one of an error, a message, and / or a warning to notify a user that one or more of the transportation characteristics for a cargo shipment are not in compliance and displays the reason for the non-compliance.

[0131] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, when the PSCI API 401 receives the shipping information at step 412 and transmits the information to the example PSCI platform 100 executing PSCI software at step 412, the PSCI rules engine 404 automatically extracts freight insurance data from the shipping information and performs an insurance compliance assessment review at step 426. In one or more embodiments, optionally in combination with any embodiment disclosed herein, in response to the example PSCI platform 100 executing PSCI software receiving the shipping information via the PSCI API 401 at step 414, the PSCI rules engine 404 automatically generates an insurance compliance information file using at least a portion of the shipping information and automatically transmits it to the insurance compliance platform. In one or more embodiments, optionally in combination with any embodiment disclosed herein, in response to the example PSCI platform 100 executing PSCI software receiving the shipping information via the PSCI API 401 at step 414, the PSCI rules engine 404 automatically transmits at least a portion of the shipping information to the insurance compliance platform such that an insurance compliance assessment is triggered. In one or more embodiments, for example, the PSCI rules engine 404 may automatically perform an initial insurance compliance assessment in step 426 in response to at least a portion of the transportation information and / or other information necessary to perform the insurance compliance assessment (e.g., results of the initial insurance compliance assessment) sent to the PSCI rules engine 404 in step 414.For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the insurance compliance evaluation performed by the third-party insurance compliance platform and / or the PSCI rules engine 404 may be automatically performed and / or triggered by a change in the state of the shipment information and / or any one or more characteristics of the shipment at any point during the transportation process (e.g., staging of the shipment at the shipping location and / or during transportation of the shipment included in the shipment) or at a milestone in the insurance coverage workflow of the PSCI API. In one or more embodiments, optionally in combination with any embodiment disclosed herein, the insurance compliance evaluation may be triggered upon request by a PSCI platform user.

[0132] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI rules engine 404 responds to the insurance compliance assessment request received by the logistics transportation provider in step 444 via the PSCI API 401 by sending an insurance compliance assessment request result message in step 426. With reference to FIGS. 4B and 4C , for example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, in response to a determination that a freight shipment does not comply with one or more regulatory / compliance rules / regulations, the PSCI rules engine 404 sends a message in step 426 that a request for insurance coverage for one or more associated freight shipments has been denied. For example, in one or more embodiments, in response to the PSCI rules engine 404 sending or causing the sending of a denial message, the PSCI API 401 automatically communicates a message (e.g., a message, alert, letter, instructions, etc.) to a PSCI platform user (e.g., the logistics transportation provider and / or other user) in step 450 indicating that the request for insurance coverage has been denied. For example, the message may include one or more reasons for the decision to deny coverage. For example, as shown in step 450 of FIG. 4B, if the type of cargo included in the cargo shipment transported by the logistics transportation provider falls into an automatic exclusion category for insurance coverage (e.g., a category that includes one or more types of cargo that may require special handling and transportation (e.g., refrigeration, shorter transit time than offered, etc.), may be considered hazardous, may be damaged for one or more reasons, and / or may be excluded for other reasons), then the cargo insurance request may be automatically denied. For example, in step 452, the customer (i.e., the shipper) remains responsible for the cargo included in the cargo shipment.

[0133] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI rules engine 404 responds to the insurance compliance assessment request received by the logistics transportation provider in step 444 via the PSCI API 401 by sending an insurance compliance assessment request result message in step 426. Referring to FIGS. 4B and 4C , for example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, in response to determining that a freight shipment complies with one or more regulatory / compliance rules / regulations, the PSCI rules engine 404 sends a message in step 426 that the request for insurance coverage for one or more associated freight shipments has been approved. For example, in one or more embodiments, in response to the PSCI rules engine 404 sending or causing the sending of the approval message, the PSCI API 401 automatically communicates a message (e.g., a message, alert, letter, instructions, etc.) to a PSCI platform user (e.g., the logistics transportation provider and / or other user) in step 450 indicating that the request for insurance coverage has been approved. For example, the message may include an insurance quote from an insurance provider for providing insurance coverage for the identified individual freight shipment. Once the customer (e.g., shipper) has paid the logistics transportation provider for the transportation and all associated costs, and the insurance policy and all associated costs, responsibility for the freight shipment is transferred to the insurance provider in step 448. For example, as shown in step 446 of FIG. 4B , a request for cargo insurance may be automatically approved if, for example, the type of cargo included in the freight shipment transported by the logistics transportation provider falls into a category for which the insurance provider is deemed able to provide coverage.

[0134] In one or more embodiments, optionally in combination with any embodiment disclosed herein, at least a portion of the information utilized by the insurance compliance platform and / or automated insurance compliance assessment rules engine to perform the insurance compliance assessment is transmitted to a machine learning engine as referenced in FIG. 4C and described herein, e.g., with reference to step 420.

[0135] In step 416, in one or more embodiments, real-time freight shipment data provided by the real-time tier information module for a given freight shipment is received and / or compiled and transmitted to the PSCI risk modeling engine 402 and / or transmitted to one or more PSCI databases 150a-150n, 251 accessible to the PSCI risk modeling engine 402. For example, in one or more embodiments, the sensor data may be used by the PSCI platform to dynamically affect insurance coverage quote offers for individual freight shipments. For example, real-time and / or near real-time sensor data may be used by the PSCI platform with respect to one or more individual freight shipments to provide quotes for insurance coverage offers and / or dynamically change insurance coverage quotes, deny coverage, provide additional coverage, change policy premiums for insurance coverage offers, or revoke denials of insurance coverage. For example, in one or more embodiments, dynamic changes to insurance coverage may be based on one or more weather-related events, environmental events, contextual events (e.g., traffic reports (e.g., congestion, accidents), road closures, State Department warnings), the perceived condition of the cargo, and / or one or more external factors that may affect the cargo during transit. For example, in one or more embodiments, the insurance policy premium may be changed by modifying one or more of the commodity price modification factors described herein, the risk probability values, and / or the total price offered to the shipper to provide insurance coverage for one or more individual shipments. For example, in one or more embodiments, one or more commodity price modification factors and / or risk probability values ​​used to determine the insurance policy premium may be dynamically modified by the PSCI risk modeling engine 402 based at least in part on real-time shipment data. For example, in one or more embodiments, one or more commodity price modification factors may be set by an authorized user (e.g., an insurance provider), and one or more risk probability values ​​used to determine the insurance policy premium may be dynamically modified by the PSCI risk modeling engine 402 based at least in part on real-time shipment data.For example, in one or more embodiments, the real-time transportation data may include, but is not limited to, sensor data (e.g., environmental, weather, cargo status / information and / or sensor data, equipment failure / malfunction, fire, theft, etc.). For example, the sensors may include sensor data collected and / or compiled from one or more sensors 290 and / or one or more telecommunications and / or telematics devices 140, 295. For example, in one or more embodiments, the system executing the PSCI software interfaces with internal databases and / or external proprietary and / or public databases to provide real-time transportation data. The real-time transportation data may relate to weather conditions, weather trends, weather forecasts, and any other data utilizing weather data and / or weather parameters described herein that may be used to obtain weather information that may affect a given cargo shipment. For example, in one or more embodiments, the system executing the PSCI software is provided by one or more internal PSCI servers accessing one or more internal or remote databases and / or one or more logistics transportation provider servers accessing one or more internal or remote databases. For example, in one or more embodiments, an exemplary inventive PSCI platform running PSCI software interfaces with internal and / or external proprietary and / or public databases. Real-time transportation data relates to current and / or trending news (e.g., data related to traffic accidents, State Department warnings, weather forecasts, regional and / or global conflicts, labor fluctuations (e.g., strikes), work stoppages, militia movements, violent and / or territorial and / or threatening skirmishes, tactics or conflicts, intimidation tactics, terrorist groups, organizations, movements and / or activities), any other data utilizing news data, and / or data that can be utilized to obtain current and / or trending news information that may affect a given cargo shipment.For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, real-time transportation data that may affect a given freight shipment may be provided as model data and / or observation data. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, real-time transportation data may be provided via a Platform as a Service (PaaS).

[0136] For example, in one or more embodiments, for a particular freight shipment including perishable cargo where a refrigerated truck is required to transport the cargo included in the freight shipment, one or more sensors and / or telematics devices, one or more sensors 290, and / or one or more telecommunications and / or telematics devices 140, 295 may track temperature control of the cargo storage area of ​​the refrigerated truck. In one or more embodiments, in step 416, sensor data related to the temperature of the refrigerated truck is received and compiled in real time or near real time by one or more databases (e.g., one or more PSCI databases 251 residing on the PSCI platform, one or more network databases 150a-150n, one or more third-party databases 1701-170n) and utilized by the PSCI risk modeling engine to determine whether insurance coverage should be provided, whether the policy premium for the insurance coverage should be increased or decreased, or whether the insurance coverage provided should be modified. For example, if sensor data from one or more sensors 290 and / or one or more telecommunications and / or telematics devices 140, 295 indicates that a cargo storage container included in a refrigerated truck was not pre-cooled before perishable cargo included in the cargo shipment was placed in the container, then in step 414, the PSCI risk modeling engine 402 may be configured to automatically use the sensor data to perform one or more functions, including, but not limited to, denying insurance coverage, providing a higher policy premium for insurance coverage, providing a higher insurance quote for insurance coverage, or modifying the terms of an existing insurance policy. For example, in one or more embodiments, each of the above functions may be performed without underwriter intervention, review, authorization, or approval.

[0137] In step 418, in one or more embodiments, historical transportation data provided by the historical layer information module for the given cargo shipment is received and / or compiled and transmitted to the PSCI risk modeling engine 402 and / or transmitted to one or more PSCI databases 150a-150n, 251 accessible to the PSCI risk modeling engine 402. In one or more other embodiments, the historical layer information module is included in the PSCI risk modeling engine 402 and configured to receive or compile historical transportation data that the PSCI risk modeling engine 402 uses to provide quotes for insurance coverage for the individual cargo shipment. For example, one or more product modifiers used to determine policy premiums may be dynamically modified by the PSCI risk modeling engine 402 based at least in part on the historical transportation data. For example, in one or more embodiments, historical transportation data, including data for shipments similar to the related cargo shipment, is compiled by the PSCI risk modeling engine 402. For example, historical shipping data may be compiled by the PSCI risk modeling engine 402 for any suitable period (e.g., 1-12 months prior to the shipping date, 1-5 years prior to the shipping date, 5-10 years prior to the shipping date, 10-20 years prior to the shipping date, etc.) necessary to provide an adequate amount of risk information to provide a model of insurance risk to assist in providing insurance quotes. For example, in one or more embodiments, the historical shipping data may include shipping information regarding previous similar cargo shipments over any suitable historical period (e.g., hours, days, months, years, decades, etc.) and / or claims made regarding cargo shipments over any suitable historical period (e.g., hours, days, months, years, decades, etc.) and / or historical sensor data over any suitable historical period (e.g., hours, days, months, years, decades, etc.). This data may include, but is not limited to, sensor data (e.g., environmental, weather, cargo status / information and / or sensor data, equipment failure / malfunction, fire, theft, etc.) disclosed herein previously collected from one or more sensors 290 and / or one or more telecommunications and / or telematics devices 140, 295 for the relevant cargo shipment and similar shipments compiled for any suitable period of time.For example, historical sensor data throughout the supply chain may be utilized by the PSCI risk modeling engine 402 in step 414 to model and / or determine loss history (freight shipment non-delivery history) for a particular stockpile, a particular shipper, a particular supplier (insured), a particular product, a particular transportation mode, a particular cargo container, a particular product packaging, and any combination thereof. For example, in one or more embodiments, historical transportation data includes historical weather data and / or historical environmental data is compiled for similar transportation routes that may be affected by one or more similar or related weather or environmental conditions. For example, historical weather data and / or historical environmental data may be compiled by the PSCI risk modeling engine 402 for any suitable period (e.g., 1-12 months prior to the shipment date, 1-5 years prior to the shipment date, 5-10 years prior to the shipment date, 10-20 years prior to the shipment date, etc.) necessary to provide an adequate amount of risk information for modeling insurance risk.

[0138] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 may, in steps 416 and 418, utilize real-time shipping data, historical shipping data, and / or any other suitable data and / or historical data (e.g., supply chain historical data) to generate insurance quotes for providing insurance coverage for individual shipments, model insurance risk for one or more individual shipments, and / or determine loss benchmarks and / or criteria (e.g., cargo shipment underachievement benchmarks) for any of a particular stockpile, a particular shipper, a particular supplier (insured), a particular product, a particular mode of transportation, a particular cargo container, a particular product packaging, and any combination thereof. In one or more embodiments, optionally in combination with any of the above or below embodiments, the historical shipping data compiled for a given shipment may be, without limitation, piracy history data, theft history data, terrorist history data, natural disaster history data, regional / global conflict history data, political risk history data, weather, news (e.g., strike announcements), etc. In one or more embodiments, the data collection component of the PSCI risk modeling engine 402 (e.g., the real-time tier information module and / or the historical tier information module) may also include one or more PSCI data crawlers configured to collect real-time and / or historical transportation data and store the data in one or more PSCI databases 150a-150n, 251 accessible to the PSCI risk modeling engine 402. In one example, the PSCI data crawler may include a web crawler that may collect relevant real-time and / or historical transportation data from the internet or any publicly accessible online source, for example, using identifying information (e.g., keywords) about one or more relevant individual freight shipments. In some embodiments, the data crawler may be automated using a bot. Additionally, the bot may use natural language processing to analyze webpage content and select relevant content.For example, the data collection component PSCI risk modeling engine 402 may monitor news feeds and match data including names, identities, fields, events, locations, and other relevant characteristics to one or more related freight shipments to identify potentially relevant material. In one or more embodiments, the data collection component PSCI risk modeling engine 402 utilizes a PSCI data crawler to collect relevant real-time and / or historical shipment data by analyzing textual content from various news feeds and to identify keywords associated with one or more related freight shipments.

[0139] For example, in one or more embodiments, in step 414, the PSCI risk modeling engine 402 utilizes at least a portion of the transportation details to determine one or more of the transportation mode (e.g., road, water, rail, air, and / or any combination of the foregoing) of the identified freight shipment, the commodity type of the identified freight shipment, the estimated value of the identified freight shipment, the requested actual shipping date and estimated actual shipping date of the identified freight shipment, and / or possible exceptions to one or more of the identified and / or requested transportation routes of the identified freight shipment. In one or more embodiments, for example, the PSCI risk modeling engine 402 uses at least a portion of the shipment details, historical or real-time shipment data received by the PSCI database, or one or more sources used and accepted by the relevant insurance industry (e.g., Kelly Blue Book®, internal policies, external policies, industry practices), or determines whether the declared estimated value of the identified cargo included in the freight shipment falls within an acceptable value range (e.g., 1%, 3.5%, 5%, 7.5%, 10%, or a predetermined tolerance range) of the estimated value of the identified cargo included in the freight shipment minus depreciation according to industry standards. For example, in one or more embodiments, the acceptable value range (measured in relevant monetary values ​​(e.g., U.S. dollars, euros, pounds sterling, etc.)) may depend on the range into which the estimated value of the identified cargo included in the freight shipment falls (e.g., 1% for estimated values ​​within a first value range, 3.5% for estimated values ​​within a second value range greater than the first value range, 5% for estimated values ​​within a third value range greater than both the second and first value ranges, etc.). For example, in one or more embodiments, in response to the PSCI risk modeling engine 402 determining that the declared estimated value of an identified shipment included in a freight shipment provided by a customer / shipper and / or logistics transportation provider is outside of a range of acceptable values, the PSCI user experience engine 403 has an indicator (e.g., a message, a text message, a warning, an error message, and / or any other suitable type of indicator) automatically sent to the transportation logistics provider and / or customer / shipper via the PSCI API 401.For example, indicators including a solution (e.g., the estimated value of the identified cargo included in the cargo shipment as determined by the PSCI risk modeling engine 402) may be automatically transmitted to the customer / shipper and / or transportation logistics provider.

[0140] For example, in one or more embodiments, in step 414, the PSCI risk modeling engine 402 may determine that a given estimated transit time (e.g., the transit time required to transport an identified cargo included in a freight shipment from its origin geographic location to an identified destination) included in the received shipment details is inaccurate. For example, in one or more embodiments, the PSCI risk modeling engine 402 uses one or more of the shipment details, historical and / or real-time transportation data received by the PSCI database, or at least a portion of sources used and accepted by the relevant insurance industry, to determine whether the given estimated transit time for the identified freight shipment is within an acceptable range of values. For example, in one or more embodiments, in response to the PSCI risk modeling engine 402 determining that the estimated transit time provided by the customer / shipper and / or logistics transportation provider is outside of an acceptable range of values, an indicator is automatically sent to the customer / shipper and / or logistics transportation provider. For example, in one or more embodiments, the PSCI user experience engine 403 includes indicators (e.g., messages, text messages, alerts, error messages, and / or any other suitable type of indicator) that are automatically sent to the logistics transportation provider and / or customer / shipper via the PSCI API 401. For example, an indicator including a solution (e.g., an estimated travel time and / or a proposed transportation mode and / or transportation route as determined by the PSCI risk modeling engine 402) is automatically sent to the customer / shipper and / or logistics transportation provider.

[0141] For example, in one or more embodiments, the PSCI risk modeling engine 402 uses one or more of the transportation details, historical or real-time transportation data received by the PSCI database, or at least a portion of sources used and accepted by the relevant insurance industry to determine whether a given transportation mode (e.g., road, water, air, rail, etc.) utilized in the identified cargo shipment is an acceptable transportation mode. For example, in one or more embodiments, in response to the PSCI risk modeling engine 402 determining that a given transportation mode (details provided by the customer / shipper and / or logistics transportation provider) is unacceptable based on acceptable criteria and / or given criteria (e.g., estimated time or journey, geographic locations of origin and destination, weather, local conflicts, risk of theft, risk of damage, unavailable resources (e.g., refrigerated containers / trucks, containers, restraints, fuel, personnel, and / or other details provided by the customer / shipper and / or logistics transportation provider), an indicator is automatically sent to the customer / shipper and / or logistics transportation provider. For example, in one or more embodiments, the PSCI user experience engine 403 includes an indicator (e.g., a message, a text message, an alert, an error message, and / or any other suitable type of indicator) that is automatically sent to the logistics transportation provider and / or the customer / shipper via the PSCI API 401. For example, in one or more embodiments, the transportation mode for one or more of the transportation legs in the transportation route provided by the customer / shipper and / or logistics transportation provider is an option (e.g., road, water, air, rail, etc.) that is acceptable and / or determined to be impossible or improbable based on given criteria (e.g., estimated time or journey, geographic location of origin and destination, weather, local conflict, risk of theft, risk of damage, unavailable resources, etc.).In response to a determination by the PSCI risk modeling engine 402 that the proposed route is limited to the specified number of routes, an indicator including a solution (e.g., adding other modes of transportation not included in the original transportation details, including one of rail, air, water, road, etc., to one or more of the route segments and / or proposed route, and / or adding proposed routes for routes that are included in the given route or that are substituted for identified routes) is automatically provided to the customer / shipper and / or transportation logistics provider via the PSCI API 401. For example, in one or more embodiments, the PSCI user experience engine 403 includes an indicator (e.g., a message, a text message, a warning, an error message, and / or any other suitable type of indicator) that is automatically sent to the logistics transportation provider and / or customer / shipper via the PSCI API 401.

[0142] In step 414, the PSCI risk and modeling engine 402 receives the shipment details provided by the identified logistics transportation provider via the PSCI API 401. For example, in one or more embodiments, the shipment details provided by the user device 102 and / or the logistics transportation provider computer system (e.g., one or more server devices 130a-130n) via the PSCI API 401 are received by the PSCI risk modeling engine 402, and risk characteristics specific to the individual cargo shipment are automatically calculated using one or more tools and / or methods (e.g., complex computer-generated models that predict the likelihood of loss).

[0143] In one or more embodiments, optionally in combination with any embodiment disclosed herein, in step 414, at least a portion of the real-time shipment data and / or at least a portion of the historical shipment data is provided to the PSCI risk modeling engine 402, which allows a user to calculate a risk profile specific to an individual shipment using one or more of the risk modeling methods and / or tools disclosed herein. For example, sensor data, real-time shipment data, and historical shipment data and real-time data (e.g., data related to specific shipment types, weather trends and / or forecasts, traffic trends and / or forecasts, local conflict data and / or forecasts, travel route condition information and / or forecasts, etc.) provided by one or more sensor devices 290(a)-(n) and / or one or more telecommunications and / or telematics devices 140, 295 are processed by the PSCI risk modeling engine 402 to automatically provide a real-time or near-real-time insurance policy quote to a shipper (e.g., a logistics transportation provider) based on a dynamic insurance pricing model.

[0144] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the dynamic insurance pricing model generated and utilized by the PSCI risk modeling engine 402 provides insurance quotes based on an estimated value of the shipment, based on a base level algorithm that is adjustable by a correction factor.

[0145] In one or more embodiments, optionally in combination with any embodiment disclosed herein, the dynamic insurance pricing model utilized by the PSCI risk modeling engine 402 to provide an insurance quote to the shipper may be based on a policy premium, a technology solution value, and a sales commission. For example, in one or more embodiments, the policy premium may be the amount paid to the insurance company for the insurance policy being purchased. For example, in one or more embodiments, the base policy premium may be calculated by the PSCI risk modeling engine 402 using a predetermined estimated value of the cargo included in the shipment (i.e., the insured value) multiplied by a "risk probability value." The base policy premium is referred to as the "Probable Maximum Loss (PML)" and is calculated using the formula: [Risk] = [Insured Value] * [Risk Probability Value]. For example, the "insured value" of the cargo included in a freight shipment may be provided by the customer / shipper at the time of booking the cargo with a logistics transportation provider or may be determined by the PSCI risk modeling engine using one or more sources (e.g., Kelly Blue Book®, etc.) used to determine the value of an asset (minus depreciation). In one or more embodiments, the estimated value of the cargo included in the freight shipment is the insured value. For example, in one or more embodiments, the "risk probability value" is a value less than 1 (e.g., 0.025) and is a measure of the probability that a claim will exist under the insurance policy.

[0146] For example, in one or more embodiments, the PSCI risk modeling engine 402 is configured to dynamically adjust the insurance pricing model according to the commodity price correction factor and the desired underwriter premium by dynamically adjusting the "risk probability value" based on one or more of the transportation details, real-time transportation data, and historical transportation data obtained from one or more sources, as described herein. For example, the "maximum acceptable probability" may be configured by the insurance provider and / or by the PSCI system for the insurance provider via the PSCI policy administration utilized to set the value of the PSCI insurance bracket identifier disclosed according to Table 2 provided herein. For example, the "maximum acceptable probability" is configured for each cargo commodity type described herein with reference to Figures 5A-5G. For example, in one or more embodiments, the base insurance policy premium is calculated based on the following exemplary formula (1): Basic insurance premium (risk) = [$ insurance value] * [risk probability value] This is equal to the PML that the PSCI risk modeling engine 402 may determine using

[0147] In one or more embodiments, the PSCI computer-based system utilizes technology solutions to streamline business processes and operations. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the exemplary PSCI computer-based platform 100 running the exemplary inventive PSCI software platform 400 uses artificial intelligence (AI) to automate one or more complex and high-risk processes, such as property valuation, fraud detection, claims verification, and / or premium processing. The cost for these technology solutions may be included in the insurance premium. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the base technology solution value may be calculated by [insurance value] * [risk probability value]. Similar to the base policy premium, the PSCI system is configured to dynamically adjust the "risk probability value" for the technology solution calculation. For example, in one or more embodiments, the base technology solution value may be calculated using the following exemplary formula (2): Technology Solution Value = [$Insurance Value] * [Risk Probability Value] can be determined by the PSCI risk modeling engine 402 using

[0148] In one or more embodiments, the PSCI computer-based system may include accruing one or more sales fees and / or commissions and / or sales costs (collectively referred to herein as sales fees). For example, the one or more sales fees may factor into the process of calculating, allocating, and adjusting sales fees awarded to sellers of PSCI-based insurance policies. For example, an asset manager using the PSCI platform may award one-time or recurring commissions to sellers of insurance policies as an incentive for offering and / or establishing PSCI insurance to their clients (e.g., customers and / or logistics transportation providers). For example, in one or more embodiments, the dynamic insurance pricing model utilized by the PSCI risk modeling engine 402, optionally in combination with any embodiment disclosed herein, determines a sales fee value based on a predetermined percentage of the sum of the policy premium and the technology solution value. For example, in one or more embodiments, the sales fee value may be calculated using the following exemplary formula (3): Sales Fee Value = Pre-determined percentage * (Policy fee + Technology solution value) may be determined by the PSCI risk modeling engine 402 using

[0149] For example, an exemplary calculation using equations (1), (2), and (3) provided herein generates a dynamic insurance pricing model based on the estimated value of the cargo included in the identified cargo shipment. For example, in one or more embodiments, the dynamic insurance pricing model calculates the following exemplary equation (4): Insurance quote = policy premium + technology solution value + sales fee value An insurance quote may be generated that is determined by:

[0150] In one or more embodiments, for example, to satisfy payment obligations under a transportation contract with a logistics transportation provider, a shipper (i.e., sender of goods) pays the transportation costs (e.g., cargo transportation costs) plus applicable value-added tax (VAT), insurance policy premiums (insurance policy premiums) plus applicable insurance premium tax (IPT), technology costs (technology solution value), and sales fees (sales fee value) plus applicable value-added tax (VAT) as provided by the logistics transportation provider. The taxes, if any, depend on the jurisdictional combination of the shipper, underwriter, specific insurance provider, and / or logistics transportation provider. For example, in one or more embodiments, the PSCI risk modeling engine 402 determines a shipper's receipt, which may itemize the costs and taxes described above, for example.

[0151] In one or more embodiments, the PSCI platform automatically provides shippers with instant insurance quotes for one or more individual cargo shipments in real time or near real time. For example, in one or more embodiments, the PSCI risk modeling engine 402 is configured to dynamically adjust the insurance pricing model according to the price correction factors of the goods being transported and the desired underwriter premium using one or more correction factors. For example, in one or more embodiments, optionally in combination with any of the embodiments described herein, the PSCI risk modeling engine 402 determines the insurance policy premium based on the value of the cargo included in the cargo shipment and the type of cargo being transported by the logistics transportation provider. For example, one or more correction factors can be used to affect the valuation of the cargo shipment. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the price of insurance coverage for an individual cargo shipment determined by the dynamically adjusting insurance pricing model may be based, at least in part, on one or more of: 1) the estimated price of the shipment (insurance value); 2) a product price adjustment factor, which may be a set or dynamically adjusted value based, for example, on the product type of the cargo being shipped, and used to adjust premium pricing to be competitive in the marketplace and maximize profits; 3) a risk probability value, which is a measure of the probability that the insurance policy will have a claim; and / or 4) additional parameters, for example, as previously agreed upon by the insurance provider and / or underwriting partner (as described with reference to Table 2). For example, in one or more embodiments, the product price adjustment factors used to differentiate the insurance premiums, technology solutions, and / or sales rates used by the PSCI risk modeling engine 402 to automatically determine insurance quotes and / or policy premiums for an individual cargo shipment (e.g., to set the value of the product price adjustment factor that adjusts prices based on the product type of the cargo being shipped to be competitive in the marketplace and maximize profits) may be the following as provided in Table 1: [Table 1]

[0152] In step 414, in one or more embodiments, the PSCI risk modeling engine 402 utilizes the example policy premium features, example technology solution features, and example sales rate features (and all applicable taxes), optionally in combination with any embodiment disclosed herein, to model insurance risks associated with the individual shipments and automatically determine insurance quotes and / or policy rates for the individual shipments in real time or near real time. In one or more embodiments, the PSCI risk modeling engine 402 is configured to modify the insurance pricing model by utilizing one or more product price adjustment factors referenced in Table 1 provided below when applying the example policy premium features, example technology solution features, and example sales rate features to the insurance pricing model. In one or more other embodiments, the PSCI risk modeling engine 402 is configured to modify the insurance pricing model by adjusting one or more risk probability values ​​while utilizing one or more set product price adjustment factors when applying the example policy premium features, example technology solution features, and example sales rate features to the insurance pricing model. For example, applying an increased product price adjustment factor of 1.05 in connection with the exemplary policy premium function would result in a five percent (5%) increase in the policy premium value, which is calculated using the following exemplary formula (5): Adjusted policy premium = 1.05*[$insured value]*[risk probability value] can be determined by

[0153] For example, a 1.05 increase in the product price adjustment factor associated with the exemplary technology solution feature will result in a five percent (5%) increase in the value of the technology solution value, which is calculated using the following exemplary equation (6): Adjusted Technology Solution Value = 1.05*[$Insurance Value]*[Risk Probability Value] can be determined by

[0154] For example, a decrease of 0.95 in the product price adjustment factor associated with the exemplary policy premium function would result in a five percent (5%) decrease in the policy premium value, which is expressed as follows in exemplary equation (7): Adjusted policy premium = 0.95*[$insured value]*[risk probability value] can be determined by

[0155] For example, a decrease of 0.95 in the product price adjustment factor associated with the exemplary technology solution feature would result in a five percent (5%) decrease in the value of the technology solution value, which is calculated using the following exemplary equation (8): Adjusted Technology Solution Value = 0.95*[$Insurance Value]*[Risk Probability Value] can be determined by

[0156] In one or more embodiments, the traditional underwriting process can be eliminated from the process of providing insurance coverage for cargo shipments by utilizing risk probability values ​​and / or commodity price adjustment factors included in the policy premium calculations performed by PSCI risk modeling engine 402. For example, in one or more embodiments, optionally in combination with any of the embodiments described herein, PSCI risk modeling engine 402 automatically determines the policy premium based on predetermined underwriter parameters, including, for example, the value of the cargo included in the cargo shipment and / or the commodity type of cargo being transported by the logistics transportation provider (as disclosed herein with reference to FIGS. 5A-5G ), and the transportation mode used to transport the cargo.

[0157] In one or more embodiments, the use of risk probability values ​​and / or commodity price correction factors included in the technology solution value calculations performed by the PSCI risk modeling engine 402 allows technology rates to be adjusted for each cargo commodity type to accommodate market demand without the need to change insurance premiums. For example, in one or more embodiments, the cost of these technology solutions used to provide real-time or near-real-time insurance quotes to provide insurance coverage for a specified cargo shipment as disclosed herein may be provided without the need to change insurance premiums. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the risk probability values ​​and / or commodity price correction factors used to calculate technology solution values ​​may be calculated independently of each other and independently of the risk probability values ​​and / or commodity price correction factors used to calculate policy premiums.

[0158] In step 444, the transportation information required by the PSCI risk modeling engine 402 to provide an insurance quote (shipment details) for the identified freight shipment is received via the PSCI API 401. For example, in one or more embodiments, the shipper declares the information necessary to determine the commodity types of the freight included in the individual freight shipment in step 408 shown in FIG. 4B at the time the shipper provides the details necessary to enter into a transportation contract with a logistics transportation provider. For example, in one or more embodiments, the transportation details are provided to the PSCI API 401, which transmits them to the exemplary PSCI platform 100 in step 442. For example, in one or more embodiments, the logistics transportation provider declares the information necessary to determine the commodity types of the freight included in the freight shipment in step 442 shown in FIG. 4B. In one or more other embodiments, insurance cargo contracts for the relevant logistics transportation providers are accessibly stored in one or more PSCI databases 150a-150n, 251, which the PSCI risk modeling engine 402 uses in combination with at least a portion of the shipment details received via the PSCI API 401 to automatically determine commodity shipment modifiers to be used to generate insurance pricing models for individual freight shipments. For example, in one or more embodiments, the PSCI risk modeling engine 402 in step 414 uses at least a portion of the shipment information provided by the shipper or one or more of the logistics transportation providers to determine the commodity types of the cargo included in the freight shipment and the transport types used to transport the cargo included in the freight shipment. In one or more embodiments, the PSCI risk modeling engine 402 uses the commodity types and transport types to adjust the risk probability values ​​and / or commodity price modifiers, which in turn adjust the minimum premium and / or base insurance price of the insurance policy. For example, the PSCI risk modeling engine 402 utilizes known product types and transport types that underwriters have pre-approved for insurance coverage, as well as underwriter-desired premiums and price factors associated with each of the product types and transport types.For example, the desired premium and price factors may be represented by risk probability values ​​and / or product price adjustment factors and used by PSCI risk modeling engine 402 to adjust the minimum premium and / or base insurance price of the insurance policy. For example, once the underwriter parameters are known, PSCI risk modeling engine 402 automatically applies the parameters to one or more individual shipments to provide a quote for insurance coverage in real time or near real time, i.e., a process that does not require underwriting evaluation, review, and / or approval.

[0159] For example, in one or more embodiments, an exemplary computer-based inventive PSCI platform (i.e., PSCI risk modeling engine 402) executing exemplary inventive PSCI software, optionally in combination with any embodiment described herein, receives underwriter parameters from a predetermined authorized user (e.g., insurance provider, underwriter, etc.), thereby enabling insurance policies to be sold to shippers in real time or near real time at the time the authorized user books transportation through a specified logistics transportation provider for one or more individual cargo shipments. For example, via insurance quotes provided in real time or near real time, a process that does not require underwriting evaluation, review, and / or approval prior to insurance is offered to shippers at the time the shipper enters into a transportation agreement with a logistics transportation provider.

[0160] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, authorized users (e.g., insurance companies (insurers), underwriters (underwriters)) may use a secure PSCI platform portal to provide information that is accessible and stored in one or more PSCI databases 150a-150n, 251 and used by the PSCI risk modeling engine 402 to offer insurance policies and provide insurance quotes to shippers in real time or near real time. For example, the information provided by one or more authorized users may include, but is not limited to, one or more of the following: 1) cargo product types insurable by the insurer / underwriter; 2) modes of transportation used to transport cargo insurable by the insurer / underwriter; 3) geographic areas the insurer / underwriter intends to insure and / or exclude insurance coverage; 4) maximum coverage provided by the insurer / underwriter for each cargo product type; 5) cargo insurance contracts entered into with one or more logistics transportation providers; 6) and / or other information necessary to offer transport unit cargo insurance to the shipper upon entering into a transportation contract with the logistics transportation provider; and 6) (optionally) the logo of the PSCI platform website.

[0161] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 provides a dynamic, automated, computer-based pre-underwriting solution for participant licensed insurance providers (e.g., insurance companies (insurers), underwriters (underwriters)) and / or licensed insurance providers that can be dynamically modified by the PSCI risk modeling engine based, for example, on PSCRA data. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the automated pre-underwriting process performed by the example inventive PSCI platform 100 executing the example inventive PSCI software platform 400 is based on one or more pre-underwriting PSCI insurance bracket values.

[0162] For example, in one or more embodiments, the insurance bracket values ​​and / or options associated with each PSCI insurance bracket identifier are provided by a licensed insurance provider (e.g., insurance entity, underwriting entity) via the PSCI API 401, stored in one or more PSCI databases 150a-150n, 251, and utilized by the PSCI risk modeling engine 402 to automatically execute an automated pre-underwriting process and provide insurance quotes for individual shipments in real time or near real time. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more of the PSCI insurance bracket identifiers provided in Table 2 are provided by a licensed insurance provider, respectively, for each cargo product type (i.e., product type), such as those provided in Figures 5A-5G. For example, in one or more embodiments, the list of one or more underwriter pre-contract criteria (referred to herein as PSCI insurance bracket identifiers) utilized by the PSCI risk modeling engine 402 to automatically execute an automated pre-underwriting process may include one or more of the following, as provided in Table 2: [Table 2]

[0163] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 automatically executes an automated pre-underwriting process for one or more eligible insurance providers using one or more of the PSCI insurance bracketing identifiers provided in Table 2 to determine insurance quotes that may be provided to a customer / shipper in response to receiving shipping details for the identified cargo shipment and / or to determine whether insurance may be provided by an eligible insurance provider. For example, in one or more embodiments, the PSCI insurance bracketing identifier "commodity type" (e.g., fish and crustaceans, mollusks and other aquatic invertebrates, grains, or other suitable cargo classifications, e.g., provided in Figures 5A-5G) is used to determine whether the types of cargo included in the cargo shipment are for which insurance may be provided and what types of cargo (e.g., see Figures 5A-5G) are included in the identified individual cargo shipment. For example, in one or more embodiments, the PSCI insurance bracket identifier "Maximum Value" (e.g., $200k per load, $300k per load, or other appropriate value provided by the insurance provider) is used to determine the maximum estimated value of cargo included in a specified individual freight shipment for which the insurance provider will provide insurance coverage. For example, in one or more embodiments, the PSCI insurance bracket identifier "Minimum Value" (e.g., $15k per load, $10k per load, or other appropriate value provided by the insurance provider) is used to determine the minimum estimated value of cargo included in a specified individual freight shipment for which the insurance provider will provide insurance coverage. For example, in one or more embodiments, the PSCI insurance bracket identifier "Maximum Duration" (e.g., 3 weeks, 8 weeks, or other appropriate duration provided by the insurance provider) is used to determine the maximum duration for which the insurance provider will provide insurance coverage for a freight shipment. For example, in one or more embodiments, the PSCI insurance bracket identifier "Transportation Mode" (e.g., Road Only, Road, Rail, Air, Ocean, etc.) is used to determine the type of transportation for which the insurance provider will provide insurance coverage for a specified freight shipment.For example, in one or more embodiments, the PSCI insurance bracket identifier "International" (e.g., No, Yes) is used to determine whether an insurance provider will provide insurance coverage for shipments whose route crosses international borders. For example, in one or more embodiments, the PSCI insurance bracket identifier "Asset Type" (e.g., Trailer Only, or other appropriate asset as provided by the insurance provider) is used to determine whether an insurance provider will provide insurance coverage for the type of shipping container that may be used to transport the cargo included in an identified individual shipment. For example, in one or more embodiments, the PSCI insurance bracket identifier "Tracking" (e.g., No, Yes) is used to determine whether tracking is required for an insurance provider to provide insurance coverage for an individual shipment. For example, in one or more embodiments, the PSCI insurance bracket identifier "Minimum Volume" (e.g., Trailer, Pallet, or other appropriate volume as provided by the insurance provider) is used to determine the minimum volume of cargo included in an individual shipment for which an insurance provider will provide insurance coverage. For example, in one or more embodiments, a PSCI insurance bracket identifier "minimum premium" (e.g., $20 per load, $100 per load, or other appropriate value provided by the insurance provider) is used to determine the minimum premium at which the insurance provider will provide insurance coverage for the identified cargo shipment. For example, in one or more embodiments, a PSCI insurance bracket identifier "maximum daily exposure" (e.g., $20 million, $100 million, or other appropriate value provided by the insurance provider) is used to determine the maximum daily exposure that the identified individual cargo shipment can incur for which the insurance provider will provide insurance coverage. For example, in one or more embodiments, a PSCI insurance bracket identifier "risk probability value" (e.g., 0.0014, 0.0007, or other appropriate value provided by the insurance provider), a value less than 1 (1<), representing the likelihood that a claim will exist, is used to determine the insurer's risk exposure or loss probability associated with the insurance policy for the identified individual cargo shipment.

[0164] For example, in one or more embodiments, the PSCI risk modeling engine 402 can use one or more of the PSCI insurance bracket values ​​(e.g., maximum, minimum, maximum duration, mode of transport, international, asset type, tracking, minimum volume, minimum premium, maximum daily exposure) disclosed with respect to Table 2 provided herein for one or more product types to identify eligible insurance providers for the identified cargo shipment, which are provided by one or more insurance providers that use the PSCI platform to provide insurance coverage for the individual cargo shipment. For example, in one or more embodiments, one or more eligible insurance providers (e.g., insurance providers for which one or more of the given pre-underwriting PSCI insurance bracket values ​​(e.g., maximum, minimum, maximum duration, mode of transport, international, asset type, tracking, minimum volume, minimum premium, and / or maximum daily exposure) can be specified for the identified cargo shipment) can be identified, and the PSCI risk modeling engine 402 can generate insurance quotes for the specific insurance providers using a dynamic pricing model. In one or more embodiments, the PSCI risk modeling engine 402 may also modify the quote generated for that particular insurance provider and / or generate one or more modified insurance quotes using the techniques disclosed herein. For example, in one or more embodiments, one or more of the modified insurance quotes may be based on one or more risk probability values ​​output by the neural network 407 using data provided by one or more of the PSCI risk modeling engine 402, the PSCI machine learning engine 405, and / or the PSCI probability scoring engine 406 (e.g., real-time transportation data, historical transportation data, sensor and / or telematic data, and / or other data related to the particular cargo shipment (e.g., different transportation modes)), as disclosed herein.

[0165] In one or more embodiments, the PSCI platform running the PSCI software platform provides one or more GUIs to each insurance provider (i.e., authorized user) who uses the PSCI platform to provide insurance coverage, thereby allowing the user to provide the values / options described above for each of the PSCI bracket identifiers. For example, FIGS. 5I and 5J illustrate exemplary GUIs provided by the PSCI platform that authorized users of the PSCI platform may use to manage a given user's rights. For example, in one or more embodiments, as shown in FIG. 5I, an authorized user (e.g., insurance provider, underwriter) may provide one or more of a product price adjustment factor (e.g., 1.1) and a value (e.g., 30) for the PSCI bracket identifier "minimum premium" in each data field included in the GUI. These data may be received by the PSCI risk modeling engine 102 and applied to the base premium contract value to generate a dynamic pricing model and provide insurance quotes for the specified cargo shipment. For example, in one or more embodiments, as shown in FIG. 5J, an authorized user (e.g., insurance provider, underwriter) can provide one or more commodity price adjustment factors for a given commodity type of cargo transported by the logistics transportation provider, providing information that the PSCI risk modeling engine 102 receives and applies to the base insurance premium policy value to generate a dynamic pricing model for the identified cargo shipment and provide an insurance quote.

[0166] For example, in one or more embodiments, utilization of one or more PSCI insurance bracketing identifiers by the PSCI risk modeling engine enables the example inventive PSCI platform 100 executing the example inventive software platform 400 to automatically provide insurance quotes to customers / shippers in real time or near real time in response to receiving transportation details for an identified individual freight shipment without going through an underwriting process. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, one or more of the alternative PSCI insurance bracketing identifiers, such as those provided in Table 2, can be provided to the PSCI risk modeling engine 402 automatically to perform an automated pre-underwriting process. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, if more than a predetermined number of insurance providers (e.g., <1, ≦3, or any other suitable number of insurance providers) have been identified to provide insurance coverage for an identified individual freight shipment, the PSCI risk modeling engine 402 can automatically perform an automated pre-underwriting process using one or more alternative PSCI insurance bracketing identifiers, such as those provided in Table 2, to determine insurance quotes that can be provided to customers / shippers in response to receiving transportation details for the identified individual freight shipment.

[0167] An exemplary list of commodity (i.e., cargo) types is shown in Figures 5A-5G. Based on these commodity types, risk probability values ​​and / or commodity price adjustment factors can determine the insurance products for which a shipper can obtain insurance coverage and the premiums required to provide that insurance coverage. For example, with reference to Figures 5A-5G, a licensed insurer / underwriter can utilize the secure PSCI platform portal to provide underwriter parameters for shipments of cargo included in individual freight shipments classified as a "live animals" commodity type. For example, in one or more embodiments, the insurer / underwriter may conclude that a risk exists for cargo included in a freight shipment that falls under the "live animals" commodity type such that a high risk probability value (e.g., a value <1) or a low risk probability value (e.g., a value <1) is appropriate. For example, in one or more embodiments, the insurer / underwriter may conclude that a risk exists for cargo included in a freight shipment that falls under the "live animals" commodity type such that a commodity price adjustment factor of 1.5 is appropriate. For example, in one or more embodiments, the PSCI risk modeling engine 402 is configured to access information from the identified insurance provider (e.g., insurance policies covering cargo included in a cargo shipment that falls within the "Live Animals" product type) and provide an insurance quote for an insurance policy that the insurer / underwriter offers to the shipper for a separate shipment of cargo that falls within the "Live Animals" product type at a rate of 1.5 times the base premium. For example, in one or more embodiments, the insurer / underwriter may conclude that the risk is a product price adjustment factor of 0.5. For example, in one or more embodiments, the PSCI risk modeling engine 402 is configured to offer an insurance quote for an insurance policy that the insurer / underwriter offers to the shipper for a separate shipment of cargo that falls within the "Live Animals" product type at a rate of 0.5 times (i.e., half) the base premium. For example, if the product price adjustment factor in the above example is "0" or "-1," then in one or more embodiments, the PSCI risk modeling engine 402 is configured to refuse to insure the separate shipment of cargo that falls within the "Live Animals" product type.

[0168] FIG. 5H illustrates an exemplary list of transportation types (e.g., road, sea, air, rail, etc.) that may be used to transport various types of goods, on which one or more of the risk probability value and / or commodity price modifier may be based. For example, in one or more embodiments, the transportation type is used to determine which types of transportation are available to transport the insurable goods, and the shipper may receive the insurance coverage and premiums needed to provide coverage for the selected transportation type to transport the goods. For example, with reference to FIG. 5H, an authorized insurer / underwriter may utilize a secure PSCI platform portal to provide underwriter parameters for a particular transportation mode (e.g., "road") used in a cargo shipment that corresponds to one of the provided transportation types. For example, in one or more embodiments, the insurer / underwriter may conclude that a risk exists for an individual cargo shipment that corresponds to the transportation type "road," i.e., when the cargo included in the cargo shipment is transported by road by a logistics transportation provider, such that a high risk probability value (e.g., a value > 1) or a low risk probability value (e.g., a value < 1) is appropriate. For example, in one or more embodiments, an insurer / underwriter may conclude that a risk exists for a particular commodity type of cargo included in a freight shipment transported by road by a logistics transportation provider such that a commodity price adjustment factor of 1.5 is appropriate. For example, in one or more embodiments, the PSCI risk modeling engine 402 may be configured to provide an insurance quote at 1.5 times the base premium for an insurance policy that the insurer / underwriter provides to a shipper for a particular commodity type of cargo included in a freight shipment using a transportation mode classified as the transportation type "road." For example, in one or more embodiments, an insurer / underwriter may conclude that a commodity price adjustment factor of 0.5 is appropriate for a risk under other commodity types of cargo included in a freight shipment classified as the transportation type "road."For example, if the commodity price adjustment factor in the above example is "0" or "-1," in one or more embodiments, the PSCI risk modeling engine 402 may be configured to deny insurance to an individual shipment of cargo utilizing a transportation mode classified as transportation type "road" (e.g., send or have an error message, text, message, alert, etc. sent to the shipper and / or other authorized users indicating the denial of insurance).

[0169] 6A-6F illustrate an exemplary graphical user interface (GUI) provided, for example, via a web page, an integrated API 401, an app, plug-in software, and / or other suitable methods disclosed herein, which may be utilized by authorized users (e.g., insurers, underwriters, customers / shippers, etc.) accessing the secure PSCI web-based platform via a portal. In one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI user experience engine 403 generates a GUI (e.g., the exemplary GUI disclosed with reference to FIGS. 6A-6F) that allows authorized users of the computer-based PSCI platform to upload / download user information to / from one or more PSCI databases 150a-150n, 251 disclosed herein. This GUI may be utilized by the PSCI risk modeling engine 402 to provide insurance coverage and / or one or more insurance quotes for individual shipments in real time or near real time. For example, once accessed, an Authorized User can provide information that the PSCI risk modeling engine 402 uses to automatically offer a shipper insurance quotes for insurance policies covering one or more individual shipments in real time or near real time during the shipment booking process with a logistics transportation provider. For example, in one or more embodiments, optionally in combination with any embodiment described herein, the PSCI platform provides access to a secure portal via the PSCI user experience engine 403. This portal allows authorized and authenticated user entities (e.g., insurance companies, underwriters, etc.) to access the PSCI platform over a network (e.g., the Internet) to provide and update certain information about the user. For example, in one or more embodiments, once an Authorized User accesses the secure PSCI web-based platform via the portal, the user can see an exemplary main menu shown in FIG. 6A.This includes predefined accessible categories such as Dashboard, User Management, Contract Listings, Pricing Modifiers and PSCI Modifiers, for example.

[0170] FIG. 6B illustrates an exemplary GUI provided by the PSCI platform that an authorized user of the PSCI platform can use to manage the rights of a given user. For example, an authorized user may enter a username and email address to perform various functions on the PSCI platform. In one or more embodiments, a new user can gain access to the PSCI platform by interacting with the “Create New User” option shown in the exemplary GUI illustrated in FIG. 6B. For example, in one or more embodiments, the types of information that may be displayed and / or updated on the PSCI portal include, but are not limited to, the user's name, the user's contact details, policy numbers for insurance policies associated with the user, the total number of policies for each user, the total value of policies for each user, the product types of cargo that the user currently insures or plans to insure, the transportation routes used for one or more shipments for which the user currently approves and / or plans to approve insurance coverage, the approved transportation routes for each product type of cargo, the user's current exposure, the user's collected and / or distributed premiums, risk probability values, product price correction factors (e.g., pricing correction factors), PSCI correction factors, user preferences, etc. For example, in one or more embodiments, the types of information that may be displayed and / or updated on the PSCI portal include, but are not limited to, values / options for each PSCI insurance bracket identifier, as disclosed herein with reference to Table 2.

[0171] For example, in one or more embodiments, in the pricing modifier section, a user may adjust minimum premiums, risk probability values, base prices, and / or pricing modifiers for different products. These, in turn, affect how a particular insurance policy premium is calculated. For example, in one or more embodiments, once insurance policies are provided to the PSCI platform, an Authorized User may view existing insurance policies by interacting with the “Policy List” option shown in the exemplary GUI illustrated in FIG. 6C. In one or more embodiments, an Authorized User may add an insurance policy to cover cargo included in a freight shipment that resides within one of the cargo product types listed in the product type lists illustrated in FIGS. 5A-5G. For example, in one or more embodiments, existing insurance policies belonging to an Authorized User may be provided to the user via the GUI by the PSCI 100 platform requesting them from one or more PSCI databases 150a-150n, 251, as illustrated in FIG. 6F. For example, FIG. 6F illustrates an exemplary GUI including a reference to an exemplary insurance policy belonging to an Authorized User utilizing the PSCI platform 100. This may include one or more of a reference identifier, a product type identifier (e.g., a reference identifier and description of the product), a premium associated with each insurance policy, and / or the date and time each policy was purchased. In one or more embodiments, the references may each include a link to one or more insurance policies associated with the reference.

[0172] For example, in one or more embodiments, optionally in combination with any embodiment described herein, Figure 6D illustrates an exemplary GUI that one or more logistics transportation providers may use to provide shipment details to the exemplary inventive PSCI platform 100. For example, in one or more embodiments, the exemplary GUI illustrated in Figure 6D is used to submit shipment details to the PSCI platform 100 (e.g., one or more PSCI server devices 120a-120n) and automatically receive one or more insurance quotes for insurance coverage for the identified cargo shipment. For example, as shown in FIG. 6D , the shipment details may include a “Start Date” representing the date (e.g., month, day, year) when the shipment begins, an “End Date” representing the date (e.g., month, day, year) when the shipment ends, a “Start Location” representing the geographic location (e.g., address, latitude and longitude pair, etc.) of the start of the shipment (e.g., where specific cargo included in the cargo shipment is picked up, the start location of the first transport leg, etc.), an “End Location” representing the geographic location (e.g., address, latitude and longitude pair, etc.) of the end of the shipment (e.g., where specific cargo included in the cargo shipment is delivered, the end location of the last transport leg, etc.), a “Product Type” representing the identification of the type of cargo included in the cargo shipment that can be insured by an insurance provider using the PSCI platform (e.g., the identification of the cargo product type given in the exemplary FIGS. 5A-5G ), a PSC The information may include one or more of the following: a "Product Description" representing a description of the goods for which an insurance provider using the PSCI platform may insure (e.g., a description of the goods identified by the cargo product type provided in exemplary FIGS. 5A-5G ); an "Insured Value" representing the estimated value of the goods included in the cargo shipment for which an insurance provider using the PSCI platform may insure; a "Shipment ID" representing any tracking code that can be used to identify the cargo shipment for which an insurance provider using the PSCI platform may insure; and an "Organization / Company Name," "First Name" and "Last Name," "Email," "Street Address," "City," "State," and "Zip Code" representing insurance policy customer details that can be used to provide insurance coverage for the specific cargo shipment. For example, in one or more embodiments, a subset of the shipment details set forth in FIG. 6D is required for the PSCI platform to provide one or more insurance quotes for the specific cargo shipment.For example, in one or more embodiments, the required information may include, for example, "start date," "end date," "start location," "end location," "product type," "insurance value," "first name," and "last name."

[0173] FIG. 6E illustrates an exemplary insurance quote received by an authorized user of the PSCI platform. For example, although only a single insurance quote is described with reference to FIG. 6E , the output of the PSCI platform may include one or more insurance quotes from which the authorized user or customer / shipper may then select (i.e., one or more insurance quotes may be provided to the customer / shipper from the logistics transportation provider for selection (e.g., acceptance / rejection)). For example, in one or more embodiments, optionally in combination with any embodiment described herein, in response to receiving shipping details, the exemplary inventive PSCI platform 100 executing the exemplary inventive software platform 400 provides one or more insurance quotes to the customer / shipper in real time to provide insurance coverage for the specific cargo shipment. In one or more embodiments, optionally in combination with any embodiment described herein, in response to receiving shipping details, the exemplary inventive PSCI platform 100 executing the exemplary inventive software platform 400 provides one or more insurance quotes to the customer / shipper in near real time to provide insurance coverage for the specific cargo shipment. For example, in one or more embodiments, as disclosed with reference to FIG. 6D , after a user (e.g., a customer / shipper, logistics transportation provider, or other authorized user) enters the required shipping details into data fields, the user submits the required shipping details to the PSCI platform (e.g., submits the shipping details to PSCI platform 100, presses a "Submit" button on the user's screen to cause PSCI platform 100 to submit the shipping details), and receives one or more insurance quotes in real time. In one or more embodiments, the one or more insurance quotes are provided in near real time.

[0174] For example, in one or more embodiments, optionally in combination with any embodiment described herein, the PSCI risk modeling engine 402 is configured to, once a request for insurance quotes for cargo shipments is received, automatically search for an optimal insurance quote (e.g., lowest price) based on underwriter parameters, generate insurance quotes for insurance coverage for one or more individual shipments, and transmit or cause the insurance quotes to be transmitted to the shipper for acceptance or rejection. When the customer / shipper / logistics transportation provider confirms the purchase of a transportation contract that includes the provision of insurance, a notification (e.g., email, text, alert, message, etc.) detailing the insurance coverage is transmitted to the shipper and / or logistics transportation provider. In one or more embodiments, optionally in combination with any embodiment described herein, the PSCI platform (e.g., via execution of the PSCI user experience module 403) may execute the example PSCI platform 400 to enable collection of all insurance payments to insurers, underwriters, etc., and spread the payments over a predetermined time period (e.g., daily, weekly, monthly, bimonthly, etc.). In one or more embodiments, the PSCI user experience module 403 can also provide authorized users with user reports on demand and / or within predetermined time periods detailing the number and types of insurance policies requested / approved / denied, the product types of cargo included in the cargo shipment for which insurance was requested / approved / denied, payments collected, insurance claims made, and / or other relevant information necessary for insurers and / or underwriters to provide the PSCI platform with underwriter parameters (e.g., information used by the PSCI risk modeling engine 402 to determine one or more risk probability values ​​and / or product price adjustment factors).For example, in one or more embodiments, each offer and / or acceptance and / or denial of insurance provided by an insurer and / or underwriter via the PSCI platform, each quote provided by an insurer and / or underwriter that is offered and / or accepted and / or denied for one or more individual cargo shipments, each risk probability value and / or product price correction factor provided by an insurer and / or underwriter for 1) each product type of cargo included in a cargo shipment, for example, as disclosed with reference to Figures 5A-5G, and 2) each shipment type, for example, as disclosed with reference to Figure 5H, and / or other information associated with the provision of insurance coverage, quotes and / or product price correction factors are stored accessible by one or more PSCI platform server devices 120a-120n in one or more databases 251 and / or third party databases 170a-170n and / or network databases 150a-150n and utilized by the PSCI risk modeling engine 402 to determine one or more risk probability values ​​and / or product price correction factors and / or provide insurance quotes for individual cargo shipments.

[0175] 7A illustrates an exemplary request format generated by the PSCI platform executing the PSCI software using at least a portion of the shipping information and / or user information provided by one or more of the provider insurers and / or underwriters and utilized to generate one or more insurance quotes for processing by the PSCI risk modeling engine 402. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, once the PSCI risk modeling engine 402 is provided with a valid request for insurance coverage quotes, the PSCI risk modeling engine 402 is configured to generate an array of quotes using at least a portion of the shipping information and / or user information provided by one or more of the provider insurers and / or underwriters. For example, FIG. 7A illustrates a chart describing one or more exemplary data fields, the type of field associated with each exemplary data field (e.g., array, string, date / time, policyholder, address), and a description of the information entered into one or more data fields of the generated array of quotes utilized by the PSCI risk modeling engine 402 to provide a shipper with a request for insurance quotes for the individual shipment of cargo. For example, one or more of the data fields in the insurance quote request generated by the PSCI risk modeling engine 402 may include one or more of the following: 1) "journeyLegs," a text field where input data represents legs included in the transportation route of the journey; 2) "startTime," data representing the start date and time of one or more legs in the transportation route of the journey; 3) "endTime," data representing the end date and time of one or more legs in the transportation route of the journey; 4) "startLocation," data (e.g., latitude / longitude data or address data) representing the start location of one or more legs in the transportation route of the journey; 5) "endLocation," data (e.g., latitude / longitude data or address data) representing the end location of one or more legs in the transportation route of the journey; 6) "transportTypes," data representing the type of transportation (e.g., truck, boat, plane, train, etc.) used to transport cargo included in the cargo of one or more legs in the transportation route of the journey;7) "transportFeatures", which is data representing special characteristics of the transportation route of the transportation leg (e.g., need for refrigeration, need for dark shipping containers, need for air freight, etc.) and / or the transportation means (e.g., truck, boat, plane, train, etc.) used to transport the cargo included in the cargo for one or more legs of the transportation route of the transportation leg; 8) "commodityType", which is data representing a commodity type or other description that describes and / or refers to the insured goods; 9) "commodityDescription", which is data representing a description or other identifier that describes and / or refers to the insured goods; 10) "insuredValue", which is data representing an estimated value of the insured goods; 11) "shipmentId", which is data representing the location of the insured goods (e.g., tracking code, GPS location, address, and / or any other information that can be used to determine the location of the cargo included in the cargo shipment); 12) "policyh", which is data representing a description and / or other identifier that describes and / or refers to the customer to whom an insurance policy is provided for the insured goods; 14) "surname" is data representing the last name of the customer to whom the insured product is to be offered, 15) "email" is data representing the email of the customer to whom the insured product is to be offered, and / or data representing a description and / or other identifier describing and / or referring to the email of the customer; 16) "telephone" is data representing the telephone number of the customer to whom the insured product is to be offered, and / or data representing a description and / or other identifier describing and / or referring to the telephone number of the customer; 17) address information of the customer to whom the insured product is to be offered, including number and / or street (e.g. data representing the number and street name), and / or area (e.g. data representing a city, town, village, post office town, or other area where the street address can be found), and / or area (data representing a political division such as a state, county, or sub-county where the area can be found);and / or a postal code (e.g., data representing a postal code, postcode, ZIP code, or other short code associated with an address by the postal system of that country), and / or a country (e.g., data representing a country name), and / or address information including data representing a description and / or other identifier that describes and / or references the customer's address information, including number and / or street, and / or area, and / or region, and / or postal code, and / or country.

[0176] FIG. 7B illustrates an exemplary response format generated by a PSCI platform executing PSCI software to provide one or more insurance quotes for cargo insurance to a shipper. For example, in one or more embodiments, the PSCI risk modeling engine 402 utilizes the quote array described with reference to FIG. 7A. A response to a cargo insurance request is generated using at least a portion of the data provided in the quote array. For example, in one or more embodiments, the PSCI platform executing PSCI software utilizes at least a portion of the data disclosed with reference to FIG. 7A in connection with the request format to determine whether one or more identified shipments qualify for insurance coverage, and provides one or more quotes for insurance coverage in real time or near real time in response to a positive determination (i.e., one or more individual shipments qualify for insurance coverage). For example, FIG. 7B illustrates a chart describing one or more exemplary response data fields, the type of field (e.g., string, Boolean, float, array) associated with each exemplary data field, and a description of the information entered into one or more data fields utilized by the PSCI risk modeling engine 402 to provide one or more quotes for insurance coverage in real time. For example, one or more of the data fields in the response format may include one or more of the following:1) "id", data representing a unique identification used to reference one or more insurance quotes for one or more individual shipments used by a PSCI platform executing PSCI software to perform additional functions; 2) "accepted", data representing a determination of whether the estimated risk associated with the provision of insurance coverage for one or more individual shipments is accepted; 3) "premium", data representing that in response to the estimated risk being accepted, the total amount of insurance premium to be paid by the shipper upon acceptance of the cargo insurance quote is determined; and 4) "amendment", data representing an array of strings describing steps that can be taken to obtain acceptance of the risk associated with the provision of insurance coverage for one or more individual shipments and / or to obtain one or more lower insurance premiums (e.g., steps that can be taken to reduce risk) to be paid by the shipper for the risk associated with the provision of insurance coverage for one or more individual shipments.

[0177] FIG. 7C is a chart disclosing an exemplary format of data that may be used to provide data representing one or more previous insurance quotes offered to a consignor for purchase. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402, or an authorized user of the PSCI platform utilizing a GUI provided by the PSCI API 401, can request data representing one or more previous insurance quotes offered to a consignor for purchase. For example, in one or more embodiments, the PSCI user experience engine 403 provides the authorized user with data representing one or more previous insurance quotes offered to a consignor for purchase in response to the request. For example, FIG. 7C shows a chart describing one or more exemplary data fields, the field type (e.g., string, Boolean, float, array) associated with each exemplary data field, and a description of the information to be entered into the one or more data fields used by a PSCI platform executing PSCI software to select from one or more previous quote requests for purchase and provide an offer (i.e., insurance quote). For example, one or more of the data fields in a previous quote request format may include, for example: 1) "id," which is data representing a unique identification used to reference an insurance quote for an estimated risk associated with an insured individual shipment. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 may utilize at least a portion of the requested data (e.g., one or more generated premiums) disclosed with respect to the response to a request for cargo insurance format with reference to FIG. 7B to generate one or more risk models, which may include one or more premiums for the identified individual freight shipment, and / or generate a purchase request including one or more insurance quote offers from a previous insurance quote request for purchase.For example, in one or more embodiments, the PSCI machine learning engine 405 and / or neural network 407 described herein can utilize at least a portion of the requested data (e.g., one or more generated insurance premiums) in its analysis and / or generated simulations.

[0178] FIG. 7D is a chart disclosing an exemplary format of data that may be used to provide data representing a list of risks purchased by or offered to a customer (e.g., a shipper) for individual freight shipments over a specific period of time (e.g., hours, days, weeks, months, years, decades, etc.). For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, an authorized user of the PSCI risk modeling engine 402 or the PSCI platform may utilize a GUI provided by the PSCI API 401 to request data representing a list of risks purchased by or offered to a customer (e.g., a shipper) for individual freight shipments over a specific period of time. For example, FIG. 7D shows a chart describing one or more exemplary data fields, the type of field associated with each exemplary data field (e.g., date, string, Boolean, float, array), and a description of the information to be entered in the one or more data fields that a PSCI platform executing PSCI software uses to generate information in response to a request from one or more authorized users for a list of insurance risks from a start date to an end date. For example, one or more of the data fields in the request may include, for example, the following: 1) "from," which is data representing the start date of the list of risks purchased by and / or offered to the shipper, and 2) "to," which is data representing the end date of the list of risks purchased by and / or offered to the shipper. For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 may use at least a portion of the requested data provided by the PSCI user experience engine 403 (e.g., a list of risks purchased by or offered to the customer for individual cargo shipments during a particular time) to generate one or more risk models, which may include, for example, one or more insurance premiums for the identified individual cargo shipments, and / or to generate a purchase request, which may include one or more insurance quote offers from previous insurance quote requests for purchase.For example, in one or more embodiments, the PSCI machine learning engine 405 and / or neural network 407 described herein may utilize at least a portion of the requested data in its analysis and / or generated simulations.

[0179] 7E is a chart disclosing an exemplary format of data that an exemplary inventive PSCI platform executing exemplary inventive PSCI software may generate in response to an authorized user providing a start date and an end date included in a user request for a list of risks purchased by or offered to a customer (e.g., a shipper) for individual cargo shipments over a specific period of time (e.g., hours, days, weeks, months, years, decades, etc.). For example, FIG. 7E illustrates a chart describing one or more exemplary data fields, the type of field (e.g., date, datetime, integer, string, Boolean, float, array) associated with each exemplary data field, and a description of the information that the PSCI user experience engine 403 generates in response to a request from one or more authorized users for a list of insurance risks from a start date to an end date. For example, one or more of the data fields in the request may include, for each insurance policy identified according to the request, e.g., the following: 1) "startTime", data representing the date on which the insurance contract for an individual cargo shipment became effective and the time when insurance coverage for the individual cargo shipment began; 2) "endTime", data representing the date on which the insurance contract for an individual cargo shipment ended and the time when insurance coverage for the individual cargo shipment ended; 3) "purchaseTime", data representing the date on which the insurance contract for an individual cargo shipment was purchased and the time when the insurance contract was purchased; 4) "commodityType", data representing the identification of the commodity type of the cargo included in the individual cargo shipment covered by the insured contract (e.g., by referencing one or more of the cargoes of the commodity type listed in the commodity list disclosed with reference to Figures 5A to 5G); 5) "insuredValue", data representing the total insured value of the cargo included in the individual cargo shipment covered by the insured contract; and 6) "travelType", data representing the identification of the transportation mode used for the individual cargo shipment classified as a transportation type (e.g., by referencing one or more transportation types listed in the transportation type list disclosed with reference to Figures 5A to 5H).For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 can utilize one or more of the insurance policies provided by the PSCI user experience engine 403 to generate one or more risk models that may include, for example, one or more insurance premiums for identified individual shipments, and / or generate a purchase request that includes one or more insurance quote offers from a previous insurance quote request for purchase. For example, in one or more embodiments, the PSCI machine learning engine 405 and / or neural network 407 described herein can utilize one or more of the insurance policies provided by the PSCI user experience engine 403 in its analysis and / or generated simulations.

[0180] 7F is a chart disclosing an exemplary format of data (e.g., delivery completion data) that an exemplary inventive PSCI platform executing exemplary inventive PSCI software may generate in response to notification from one or more authorized users (e.g., logistics transportation providers, insurers, underwriters, shippers, etc.) that delivery of cargo included in an individual cargo shipment insured by one of the PSCI insurance providers has been completed. For example, in one or more embodiments, the PSCI user experience engine 403 may be configured to receive information from the logistics transportation provider (e.g., identification of insurance initially purchased for the associated individual cargo shipment, policy number of the insurance policy provided to cover the cargo shipment, cargo identification used in the initial quote to identify the individual shipment of cargo) and generate data in the exemplary format disclosed with respect to FIG. 7F. This data is accessible and stored in one or more of the PSCI databases. 7F shows a chart describing one or more exemplary data fields, the type of field associated with each exemplary data field (e.g., string, date, datetime, integer, string, Boolean, float, array), and a description of the information entered into one or more data fields that a PSCI platform executing the PSCI software uses to generate information in response to a notification of a completed freight shipment. For example, one or more of the data fields included in the information generated in response to the notification may include, for example, 1) "offerId," data representing the identification of the insurance offer initially purchased, 2) "shipmentId," data representing the identification of the logistics transportation provider for the shipped cargo utilized in the initial insurance quote offered to the shipper, 3) "policyNumber," data representing the policy number of the insurance policy generated by the PSCI user experience engine 403 when the insurance policy was purchased by the shipper, and 4) "dateTime," data representing the date and time the delivery associated with the insured individual freight shipment was completed.For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI risk modeling engine 402 may utilize at least a portion of the delivery completion data to generate one or more risk models, which may include, for example, one or more insurance premiums for the identified individual shipments, and / or generate a purchase request that includes one or more insurance quote offers from a previous insurance quote request for purchase. For example, in one or more embodiments, the PSCI machine learning engine 405 and / or neural network 407 described herein may utilize at least a portion of the delivery completion data in its analysis and / or generated simulations.

[0181] Those skilled in the art having the benefit of this specification will understand that the exemplary equations (1)-(8) provided above provide the basic functionality of a dynamic insurance pricing model and are provided for a better understanding of the functionality of the dynamic insurance pricing model. For example, in one or more embodiments, optionally in combination with any of the embodiments described herein, in step 420, the dynamic insurance pricing model generated by the PSCI risk modeling engine 402 may be based, at least in part, on a Monte Carlo method of computational algorithm (e.g., a Solovay-Strassen type algorithm, a Baillie-PSW type algorithm, a Miller-Rabin type algorithm, and / or a Schreier-Sims type algorithm). For example, in one or more embodiments, optionally in combination with any of the embodiments described herein, in step 420, the dynamic insurance pricing model may take into account, for example, at least a portion of the shipment details provided by the shipper and / or logistics transportation provider, parameters provided by the insurer / underwriter that are used to determine how much risk the insurer / underwriter is willing to accept, one or more risk probability values, one or more product price adjustment factors, historical shipment data, real-time shipment data, user experience associated with one or more individual cargo shipments, product type, coverage type, and / or one or more quality indicators associated with the insured.In one or more embodiments, optionally in combination with any of the embodiments described herein, one or more example functions utilized in the dynamic insurance pricing model may be continuously trained by applying at least one machine learning technique, such as, but not limited to, an unsupervised machine learning technique, an artificial neural network (ANN), a decision tree, a random forest (RF), a boosted decision tree technique, bagging, a support vector machine (SVM), gradient boosting, a naive Bayes classifier, a K-nearest neighbor technique, a classification and regression tree, a deep long short-term memory (LSTM) technique, and / or other suitable techniques, to collected and / or compiled data related to one or more individual shipments, data including sensor data, historical shipment data, real-time shipment data, user experience associated with the shipment, product type, coverage type, and / or one or more quality metrics associated with the insured.

[0182] For example, in one or more embodiments, optionally in combination with any embodiment disclosed herein, the PSCI machine learning engine 405 utilized in step 420 uses machine learning techniques / processes / algorithms via a machine learning module to improve the functionality of the PSCI computer system executing the PSCI software platform 400 (e.g., one or more PSCI server devices 120a-120n utilized by the PSCI platform) by enabling the computer system to continuously mature and learn as it discovers one or more optimal and / or recommended risk probability values ​​for authorized users of the PSCI platform (e.g., insurers and / or underwriters), and / or one or more optimal and / or recommended product price correction factors for authorized users of the PSCI platform, and / or one or more reference risk probability values ​​and / or product price correction factors for experts or other authorized users. As described herein, the exemplary inventive PSCI platform 100 executes the exemplary inventive software platform 400 to generate insurance quotes for individual shipments in real time or near real time, for example, by generating one or more dynamic pricing models using the risk probability values ​​and product price correction factors.For example, as more and more users interact with the PSCI machine learning engine 405, the PSCI database (e.g., usernames, user contact details, policy numbers for insurance policies associated with the users, the total number of insurance policies for each user, the total value of each user's insurance policies, product types of cargo that the users are currently insuring or will insure pursuant to insurance policies provided by insurers / underwriters, transportation routes and route segments utilized for one or more logistics transportation providers, authorized transportation routes for transporting cargo of each product type, the users' current exposure, the users' collected and / or distributed premiums, risk probability values, etc.) may be updated. Accessibly stored repositories of data (including, but not limited to, historical shipping data and other data, including one or more of: commodity price modifiers (e.g., pricing modifiers), PSCI modifiers, and / or user preferences), data describing behaviors, interactions, patterns, etc. related to freight shipping are utilized by the PSCI machine learning engine 405 to generate freight shipping mod...

Claims

1. 1. A computer-implemented method comprising: at least one input interface receiving input data for a plurality of identified data records from a plurality of logistics data providers; receiving, by the at least one processor, a plurality of pre-determined contract parameters associated with at least one of a plurality of providers from a plurality of pre-generated databases; the at least one processor dynamically enriching the input data by aggregating current data, forecast data, and prediction data associated with the identified data records, the enriched input data being utilized to train a machine learning model associated with the identified data records; calculating, by the at least one processor, a first respective risk probability value associated with each qualified provider of the plurality of providers based on a comparison of the enriched input data to the plurality of predetermined engagement parameters; generating, by the at least one processor, a respective dynamic data model associated with each qualified provider of the plurality of providers based on the enriched input data and the first respective determined risk probability value; the at least one processor training a machine learning model with at least one data feedback loop by introducing training data as a plurality of variables to generate a first plurality of scenarios in real time, the training data being a representation of potential risks associated with the enriched input data expressed in the form of data points; the at least one processor dynamically simulating the first plurality of scenarios in real time to optimize a first respective dynamic probability risk value generated by the respective dynamic data model using the trained machine learning model; the at least one processor automatically updating the at least one feedback loop associated with the trained machine learning model based on at least one result of a real-time dynamic simulation of at least one scenario of the first plurality of scenarios; the at least one processor dynamically generating a second plurality of scenarios in real time based on the at least one feedback loop associated with the trained machine learning model; the at least one processor dynamically determining a real-time first predetermined contract risk threshold for the identified data record based in real-time on the second plurality of scenarios utilizing the respective dynamic data model and the first respective determined risk probability value associated with each eligible provider of a plurality of providers; the at least one processor automatically revising in real time the first predetermined contract risk threshold associated with the at least one qualified provider of the plurality of providers based on a second respective model risk probability value; the at least one processor dynamically selecting a respective data point for each qualified provider of the plurality of providers based on the second respective model risk probability value and a real-time second contract risk threshold for the identified data record; the at least one processor automatically executing at least two smart contracts between at least one entity and the at least one provider based on the dynamically selected respective data points for each eligible provider among the plurality of providers; A computer-implemented method for performing

2. The computer-implemented method of claim 1 , wherein the at least one processor resides in a PSCI platform.

3. generating, for each qualified provider of the plurality of providers, a respective dynamic data model base based on the first respective dynamic probabilistic risk value; utilizing a machine learning module to dynamically simulate a third plurality of scenarios to optimize the first respective dynamic probabilistic risk values ​​generated by the respective dynamic data models; The computer-implemented method of claim 1 , further comprising:

4. The computer-implemented method of claim 3, further comprising generating the trained machine learning module based on real-time simulation of the first plurality of scenarios and at least one of a plurality of modifications to the first respective dynamic probability risk values ​​generated by the respective dynamic data models.

5. further comprising a trained PSCI machine learning module; The trained PSCI machine learning module: an input layer; a processing layer; an output layer; at least one data feedback loop; A supervised learning layer, supervised learning layers including classification layers and regression layers; An unsupervised learning layer, Unsupervised learning layers including clustering layers 10. The computer-implemented method of claim 1, comprising:

6. The computer-implemented method of claim 1 , wherein dynamically enriching the input data comprises utilizing a statistical analysis module and a machine learning module to enhance the input data based on internal historical data and transportation data.

7. 10. The computer-implemented method of claim 1, further comprising utilizing a risk probability scoring engine to determine a reference risk probability value based on the enriched input data included in shipment details for an identified freight shipment.

8. 2. The computer-implemented method of claim 1, wherein the plurality of predetermined contract parameters comprises one or more of: product type, maximum value, minimum value, maximum duration, mode of transport, international, asset type, tracking, minimum volume, minimum premium, maximum daily exposure, or risk percentage.

9. 2. The computer-implemented method of claim 1, wherein the real-time dynamic modification of the first predetermined contract risk threshold comprises receiving, in real time, sensor data generated by one or more sensors based on one or more of weather data, transportation route data including data about a route used to transport the item, and item data including data about the item included in the identified freight shipment.

10. The computer-implemented method of claim 1 , further comprising transmitting at least one dynamically selected respective data point to each qualified logistics data provider of the plurality of logistics data providers.

11. The computer-implemented method of claim 1, wherein each of the at least one data point dynamically selected in response to the automatic modification to the first predetermined contract risk threshold includes an insurance policy providing insurance coverage for the identified cargo shipment.

12. 1. A computer-implemented method comprising: at least one input interface receiving input data for identified data records including items transported by a logistics data provider associated with the at least one computer system; the computer system includes a PSCI platform including the at least one input interface and at least one processor; receiving, by the at least one processor, a plurality of pre-determined contract parameters associated with at least one of a plurality of providers from a plurality of pre-generated databases accessible by the digital platform; verifying, by the at least one processor, the plurality of predetermined contract parameters associated with the at least one provider of the plurality of providers based on a plurality of services performed using the computer system; the at least one processor dynamically enriching the input data by aggregating current data, forecast data, and projection data associated with the identified data records and the plurality of verified pre-determined contract parameters associated with the identified data records; calculating, by the at least one processor, a first respective risk probability value associated with each eligible provider of the plurality of providers based on a comparison of the input data to the plurality of predetermined engagement parameters; generating, by the at least one processor, a respective dynamic data model associated with each qualified provider of the plurality of providers based on the input data and the first respective determined risk probability value; the at least one processor training a machine learning model with at least one data feedback loop by introducing training data as a plurality of variables to generate a first plurality of scenarios in real time, the training data being a representation of potential risks associated with the enriched input data expressed in the form of data points; the at least one processor dynamically simulating the first plurality of scenarios in real time to optimize a first respective dynamic probability risk value generated by the respective dynamic data model using the trained machine learning model; the at least one processor automatically updating the at least one feedback loop associated with the trained machine learning model based on at least one result of a real-time dynamic simulation of at least one scenario of the first plurality of scenarios; the at least one processor dynamically generating a second plurality of scenarios in real time based on the at least one feedback loop associated with the trained machine learning model; the at least one processor dynamically determining a real-time first predetermined contract risk threshold for the identified data record based in real-time on the second plurality of scenarios utilizing the respective dynamic data model and the first respective determined risk probability value associated with each eligible provider of a plurality of providers; receiving, by the at least one processor, subsequent data associated with the identified data records or historical data from the plurality of pre-generated databases; generating, by the at least one processor, for each qualified provider of the plurality of providers, a second respective model risk probability value based on the real-time transportation data or the subsequent data; the at least one processor automatically revising in real time the first predetermined contract risk threshold associated with the at least one qualified provider of the plurality of providers based on a second respective model risk probability value; the at least one processor dynamically selecting a respective data point for each qualified provider of the plurality of providers based on the second respective model risk probability value and a real-time second contract risk threshold for the identified data record; A computer-implemented method for performing

13. 13. The computer-implemented method of claim 12, wherein the plurality of services includes locating at least one sender, locating at least one recipient, determining validity of a solvency associated with the at least one sender, and determining validity of a solvency associated with the at least one recipient.

14. The computer-implemented method of claim 12 , wherein the plurality of providers includes a plurality of insurance providers.

15. The computer-implemented method of claim 14, further comprising generating the trained machine learning module based on real-time simulation of the first plurality of scenarios and at least one of a plurality of modifications to the first respective dynamic probability risk values ​​generated by the respective dynamic data models.

16. generating, for each qualified provider of the plurality of providers, a respective dynamic data model base based on the first respective dynamic probabilistic risk value; utilizing a machine learning module to dynamically simulate a third plurality of scenarios to optimize the first respective dynamic probabilistic risk values ​​generated by the respective dynamic data models; 13. The computer-implemented method of claim 12, comprising:

17. 13. The computer-implemented method of claim 12, wherein the real-time dynamic modification of the first predetermined contract risk threshold comprises receiving, in real time, sensor data generated by one or more sensors based on one or more of weather data, transportation route data including data about a route used to transport the item, and item data including data about the item included in the identified freight shipment.

18. 1. A computing device comprising: a non-transitory computer memory for storing software instructions; At least one processor and Including, When the at least one processor executes the software instructions, the computing device: at least one input interface receiving input data for a plurality of identified data records from a plurality of logistics data providers; receiving, by the at least one processor, from a plurality of pre-generated databases, a plurality of pre-determined contract parameters associated with at least one logistics data provider of the plurality of logistics data providers; the at least one processor dynamically enriching the input data by aggregating current data, forecast data, and prediction data associated with the identified data records; calculating, by the at least one processor, a first respective risk probability value associated with each qualified provider of the plurality of providers based on a comparison of the enriched input data to the plurality of predetermined engagement parameters; generating, by the at least one processor, a respective dynamic data model associated with each qualified provider of the plurality of providers based on the enriched input data and the first respective determined risk probability value; the at least one processor training a machine learning model with at least one data feedback loop by introducing training data as a plurality of variables to generate a first plurality of scenarios in real time, the training data being a representation of potential risks associated with the enriched input data expressed in the form of data points; the at least one processor dynamically simulating the first plurality of scenarios in real time to optimize a first respective dynamic probability risk value generated by the respective dynamic data model using the trained machine learning model; the at least one processor automatically updating the at least one feedback loop associated with the trained machine learning model based on at least one result of a real-time dynamic simulation of at least one scenario of the first plurality of scenarios; the at least one processor dynamically generating a second plurality of scenarios in real time based on the at least one feedback loop associated with the trained machine learning model; the at least one processor dynamically determining a real-time first predetermined contract risk threshold for the identified data record based in real-time on the second plurality of scenarios utilizing the respective dynamic data model and the first respective determined risk probability value associated with each eligible provider of the plurality of providers; the at least one processor automatically modifies the first pre-determined contract risk threshold associated with at least one qualified provider of the plurality of providers in real time based on a second respective model risk probability value; the at least one processor dynamically selecting a respective data point for each qualified provider among the plurality of providers based on the second respective model risk probability value and a real-time second contract risk threshold for the identified data record; the at least one processor automatically executing at least two smart contracts between at least one entity and the at least one provider based on the dynamically selected respective data points for each eligible provider among the plurality of providers; a computing device that is programmed to perform the steps of:

19. The computing device further comprises: generating, for each qualified provider of the plurality of providers, a respective dynamic data model base based on the first respective dynamic probabilistic risk value; utilizing a machine learning module to dynamically simulate a third plurality of scenarios to optimize the first respective dynamic probabilistic risk values; 20. The computing device of claim 18 programmed to:

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