Vehicle asset benchmarking system

Abstract

A vehicle asset benchmarking system (10) configured to obtain vehicle telemetry data from a plurality of vehicles is provided, the vehicle asset benchmarking system (10) including a processing system including an interconnecting processor, an input / output interface, and a memory setting. The processor is configured to provide a GUI to a user, thereby enabling the user to provide a vehicle category including telemetry data of such a vehicle, organize a class pattern from vehicle telemetry data obtained from a plurality of vehicles, compare the provided vehicle category telemetry data to the class pattern by a statistical benchmarking algorithm, and determine whether the vehicle category telemetry data is in the class pattern. And providing a comparison report of such a benchmarking to a user, wherein the report indicates a condition of the vehicle category compared to the genus.

Description

Technical Field

[0001] The present invention relates broadly to the field of mechanical asset management and valuation, and more particularly to a vehicle asset benchmarking system and related methods for vehicle asset benchmarking. Background Art

[0002] The following discussion of the background art is intended only to assist in understanding the present invention. The discussion is not an acknowledgement or admission that any of the material referred to is or was ever part of the common general knowledge as of the priority date of the present application.

[0003] Generally, machinery asset management is the process of managing demand and directing the acquisition, use and disposal of mechanical assets to maximize their service delivery potential and manage risk and cost over their lifecycle. For machinery and related equipment such as vehicles, managing all relevant operational aspects of such machinery over its lifecycle generally results in maximizing the resale value of such assets.

[0004] For most vehicles, whether privately owned or fleet vehicles, it is desirable to maximize value through optimal mechanical asset management. Many different approaches have been taken in the past to such resale value management. For example, maintaining a repair history and usage records can provide a potential purchaser of a vehicle with some indication of the expected condition of said vehicle. Alternatively or additionally, many potential used vehicle buyers inspect the vehicle prior to purchase, but this approach can be fraught with uncertainty, particularly in situations where an unscrupulous owner may obfuscate any issues with the vehicle, including damage and / or defects, prior accidents, etc. Many potential purchasers may not have the mechanical or engineering knowledge to identify such issues during a pre-purchase inspection.

[0005] Many systems have been developed to monitor the usage of vehicles over a particular period of time. For example, US9087099B2 to Camacho et al. discloses a system and method for acquiring and maintaining driver and vehicle information in a centralized repository for the purpose of rating each driver and vehicle according to established criteria and standards. Such driver ratings generally indicate the relative desirability of a particular driver, taking into account the expected wear and tear incurred by the driver's use of the vehicle. Such conventional systems are not effective for car rental services and ride-sharing services such as Uber. TM , is of particular interest because rating a driver and his / her vehicle usage is relevant for insurance and billing purposes.

[0006] Another conventional system is described in US2018 / 0108189A of General Motors LLC, which discloses a system and method for providing a telematics-based vehicle value report. This prior art system is capable of wirelessly polling sensor data of a vehicle so that a manufacturer or vehicle, such as General Motors, compares these polled values ​​to a manufacturing baseline for the vehicle and provides a report of the polled vehicle compared to the baseline.

[0007] However, given their intended use, such conventional solutions focus primarily on driver performance and compare a specific vehicle to a manufacturer's baseline. In contrast, a poor driver may not affect the overall condition or quality of a vehicle at all, a baseline value for the vehicle is unavailable or irrelevant, i.e., where "old" vs. "new" comparisons are irrelevant, or the vehicle generates data when it is not specifically in use. For example, a low-quality but new vehicle may be used as a baseline for comparison, which is not actually useful as a benchmark against a used vehicle of the same type.

[0008] Applicants have therefore identified a need in the art for a method by which a potential buyer of a used vehicle or type of vehicle (or similar asset) can determine a qualitative and useful overview of the vehicle's condition so that statistical predictions of future performance, value, and reliability can be made, beyond a mere "old" vs. "new" baseline comparison. The present invention was conceived with this goal in mind. Summary of the invention

[0009] It will be understood by those skilled in the art that reference to vehicles herein is made in a non-exclusive sense and that such reference broadly includes reference to any suitable machinery that can be monitored for quality purposes, as described herein. For example, generator sets, aircraft, power plants, etc. are just some possible examples, and where relevant, broad references to vehicles may apply to these examples.

[0010] Those skilled in the art will also understand that references herein to the "Internet" generally refer to any suitable communications network, and generally include references to a global system of interconnected computer networks that use the Internet Protocol Suite (TCP / IP) to connect global processing devices. Such networks include any of the following, which may consist of private, public, academic, commercial, and government networks ranging from local to global in scope, connected by a wide range of electronic, wireless, and optical network technologies.

[0011] It should also be understood that references herein to "GUI" refer to a graphical user interface, which is a user interface that allows a user to interact with an electronic device such as a terminal, processing or computing system by manipulating graphical icons, visual indicators, text-based input command labels and / or text navigation including primary and / or secondary symbols, as known in the computer science field.

[0012] In addition, references herein to "GNSS" refer generally to any suitable global navigation satellite system capable of providing autonomous geospatial positioning, including GPS, GLONASS, Galileo, Beidou and other regional satellite systems. In addition, references herein to "telemetry data" include broad references to on-board diagnostic (OBD) and vehicle-related data, the scope of which is further limited herein.

[0013] According to a first aspect of the present invention, there is provided a vehicle asset benchmarking system configured to acquire vehicle telemetry data from a plurality of vehicles, the vehicle asset benchmarking system comprising a processing system including an interconnected processor, an input / output interface and a memory arrangement, the processor being configured to:

[0014] providing a GUI to a user via the Internet, thereby enabling the user to provide a vehicle category including telemetry data for such vehicle;

[0015] Organizing generic patterns from vehicle telemetry data obtained from multiple vehicles;

[0016] Comparing the provided vehicle category telemetry data with generic patterns through a statistical benchmarking algorithm; and

[0017] A comparison report of such benchmarking is provided to the user, wherein the report indicates the condition of the vehicle class compared to the generic class.

[0018] In an embodiment, the vehicle asset benchmarking system is configured to obtain the vehicle telemetry data by interacting with an original equipment manufacturer (OEM) database.

[0019] In an embodiment, the vehicle asset benchmarking system is configured to obtain the vehicle telemetry data by interacting with a vehicle service provider database.

[0020] In an embodiment, the vehicle asset benchmarking system is configured to obtain the vehicle telemetry data by directly interacting with an onboard vehicle computer system.

[0021] In an embodiment, the vehicle telemetry data is selected from a non-exhaustive group consisting of: vehicle on-board diagnostic (OBD) data, vehicle telematics, vehicle GNSS data, vehicle service history, and original equipment manufacturer (OEM) specifications.

[0022] In an embodiment, vehicle on-board diagnostic (OBD) data is selected from a non-exhaustive group consisting of: battery charge history, motor health, external temperature, coolant temperature, engine oil level, engine oil temperature, engine speed, acceleration rate, accelerometer readings, throttle position, steering angle, transmission fluid level and temperature, diagnostic trouble codes (DTCs), registered faults, crash location / severity, braking performance, service due date and history, oxygen and emission sensors, mechanical position sensors, vibration sensors, knock sensors, software update status, vehicle metadata, cybersecurity event data, GNSS location data, etc. It will be appreciated by those skilled in the art that such on-board diagnostic (OBD) data may include any monitorable aspect of a vehicle.

[0023] In an embodiment, the processor is configured to anonymize the vehicle telemetry data when generic patterns are collated.

[0024] In an embodiment, the GUI is provided via an application or "app" that can be installed on a user's mobile device and is configured to enable the mobile device to collect vehicle type, telemetry and / or on-board diagnostic (OBD) data from the vehicle.

[0025] Typically, generic patterns are collated by compiling patterns from comparative telemetry data from multiple similar categories of vehicles.

[0026] In an embodiment, a statistical benchmarking algorithm defines, extracts and populates multiple telemetry fields across categories, performs statistical analysis on each field to obtain a mean or median, and performs a comparison of each field to assign a comparison score across each category field to the vehicle telemetry data.

[0027] In an embodiment, the statistical benchmarking algorithm includes temporal data analysis for vehicle telemetry data and vehicle OBD data.

[0028] In an embodiment, the comparison report provides a comparison score for each comparison generic field of the vehicle.

[0029] In an embodiment, the benchmarking algorithm calculates the overall condition score of the vehicle by comparing the mean or median value of the generic field to the overall mean or median value of the vehicle's OBD data.

[0030] In an embodiment, the processor is configured to perform a "lifetime to expected failure" analysis by comparing generic patterns to vehicle telemetry data and providing a comparison between the two as part of a report.

[0031] In an embodiment, the processor provides the comparison report via the GUI.

[0032] According to a second aspect of the present invention, there is provided a method for a vehicle asset benchmarking system, comprising the following steps:

[0033] obtaining, by a processor, vehicle telemetry data from a plurality of vehicles;

[0034] providing a GUI to a user via the Internet, thereby enabling the user to provide a vehicle category including telemetry data for such vehicle;

[0035] collating, by a processor, categorized patterns from vehicle telemetry data obtained from a plurality of vehicles;

[0036] comparing, by a processor, the provided vehicle type telemetry data with a generic pattern using a statistical benchmarking algorithm; and

[0037] A comparison report of such benchmarking is provided to the user, wherein the report indicates the condition of the vehicle class compared to the generic class.

[0038] In an embodiment, the step of obtaining vehicle telemetry data includes interacting with an Original Equipment Manufacturer (OEM) database.

[0039] In an embodiment, the step of obtaining vehicle telemetry data comprises interacting with a vehicle service provider database.

[0040] In an embodiment, the step of obtaining vehicle telemetry data comprises interacting directly with an onboard vehicle computer system.

[0041] In an embodiment, the vehicle telemetry data is selected from a non-exhaustive group consisting of: vehicle on-board diagnostic (OBD) data, vehicle telematics, vehicle GNSS data, vehicle service history, and original equipment manufacturer (OEM) specifications.

[0042] In an embodiment, vehicle on-board diagnostic (OBD) data is selected from a non-exhaustive group consisting of: battery charge history, motor health, external temperature, coolant temperature, engine oil level, engine oil temperature, engine speed, acceleration rate, accelerometer readings, throttle position, steering angle, transmission fluid level and temperature, diagnostic trouble codes (DTCs), registered faults, crash location / severity, braking performance, service due date and history, oxygen and emission sensors, mechanical position sensors, vibration sensors, knock sensors, software update status, GNSS location data, etc. It will be appreciated by those skilled in the art that such on-board diagnostic (OBD) data may include any monitored aspect of the vehicle.

[0043] In an embodiment, the method comprises the step of anonymizing the vehicle telemetry data when generic patterns are collated.

[0044] In an embodiment, the step of providing a GUI includes providing an application or "app" that can be installed on the user's mobile device and is configured to enable the mobile device to collect vehicle classification and telemetry data from the vehicle.

[0045] Typically, the step of collating generic patterns includes compiling patterns from comparative telemetry data of a plurality of similar classes of vehicles.

[0046] In an embodiment, a statistical benchmarking algorithm defines, extracts and populates multiple telemetry fields across categories, performs statistical analysis on each field to obtain a mean or median, and performs a comparison of each field to assign a comparison score across each category field to the vehicle telemetry data.

[0047] In an embodiment, the statistical benchmarking algorithm includes temporal data analysis for vehicle telemetry data and vehicle OBD data.

[0048] In an embodiment, the step of providing the comparison report includes providing a comparison score for each comparison generic field of the vehicles.

[0049] In an embodiment, the benchmarking algorithm calculates the overall condition score of the vehicle by comparing the mean or median value of the generic field to the overall mean or median value of the vehicle's OBD data.

[0050] In an embodiment, the method includes the step of performing, by a processor, a "life to expected failure" analysis by comparing generic patterns to vehicle telemetry data and providing a comparison between the two as part of a report.

[0051] In an embodiment, the step of providing a report comprises providing the comparison report via a GUI.

[0052] According to a further aspect of the invention, there is provided a computer program product which, when executed by a suitable processing system, facilitates the performance of the method according to the second aspect of the invention above.

[0053] According to further aspects of the invention there is provided a vehicle asset benchmarking system and associated method for a vehicle asset benchmarking system substantially as described and / or illustrated herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The description will be made with reference to the accompanying drawings, in which:

[0055] Figure 1 is a diagrammatic overview representation of one example of a vehicle asset benchmarking system according to aspects of the present invention;

[0056] Figure 2 A functional block diagram of an example processing system that can be used to implement or realize Figure 1A specific embodiment of a vehicle asset benchmarking system;

[0057] Figure 3 An example network infrastructure is shown that can be used to implement or realize certain embodiments of a communication network, wherein Figure 1 The processing system may be arranged in signal communication; and

[0058] Figure 4 is a functional block diagram representation of method steps of a vehicle asset benchmarking method according to aspects of the present invention. DETAILED DESCRIPTION

[0059] The additional features of the present invention are more fully described in the following description of several non-limiting embodiments thereof. This description is only used to illustrate the present invention to those skilled in the art. It should not be construed as a limitation to the broad overview, disclosure or description of the present invention described above.

[0060] In the accompanying drawings used to illustrate the features of one or more example embodiments, the same reference numerals are used to identify the same components throughout. In addition, features, mechanisms, and aspects that are well known and understood in the art will not be described in detail, as such features, mechanisms, and aspects will be within the comprehension of those skilled in the art.

[0061] Broadly speaking, the present invention provides a vehicle asset benchmarking system 10 that is configured to obtain vehicle telemetry data from a plurality of vehicles. As described in more detail below, such system 10 includes a plurality of components and is networked with other processing systems via a suitable communication network, such as the Internet, to perform its functions as part of the present invention.

[0062] exist Figure 1 In the broad embodiment shown, a plurality of vehicles are divided into a plurality of categories consisting of classes, broadly defined as vehicles of the same or comparable type within a class. In the illustrated embodiment, for purposes of illustration, only two classes 14 and 18 are shown, each of which consists of specific classes 12 and 16, respectively. Thus, as indicated, class 14 consists of class 12, and class 18 consists of class 16. Of course, in practice it should be understood that any number of classes are possible, each of which consists of any number of classes.

[0063] For example, category 12 may include a vehicle type, such as a certain year and model of Volkswagen TM Golf TM to form a genus 14, and a category 16 may include another vehicle type, such as a certain year and model of Mazda TM MX-5 TM To form genus 18.

[0064] Broadly speaking, the vehicle asset benchmarking system 10 includes a processing system, as described in more detail below, which includes an interconnected processor, input / output interface, and memory arrangement. The vehicle asset benchmarking system 10 also interacts with various databases 20, 22, and 24, as described in more detail below, typically implemented through the processing system.

[0065] It should be understood that any reference to "mechanism" herein specifically includes any one or more of a computer program product for a local or distributed computing system, a computer-readable modulated carrier signal for interpretation by a local or distributed computing system, or a computer-readable instruction medium for enabling a local or distributed computing system to provide such "mechanism" in the context of the specification. In addition, such "mechanism" may further explicitly include any independent or combined hardware and / or software components provided in the following description, as understood by those skilled in the art.

[0066] Furthermore, in the field of computer programming, an application programming interface (API) is a set of subroutine definitions, communication protocols, and tools for building software. Typically, it is a set of well-defined communication methods between various components. The mechanism for facilitating any communication or interaction between the vehicle asset benchmarking system 10, databases 20, 22, and 24, and user devices 26 and 28 can be facilitated by a suitable API within the processing system 100 or network 200, which is obvious to those skilled in the art.

[0067] Therefore, now refer to the accompanying drawings Figure 2 and Figure 3 , shows a broad example of a processing system 100 that can be used in different configurations that are apparent to one skilled in the art to implement the vehicle asset benchmarking system 10, and databases 20, 22, and 24, and user devices 26 and 28, as described in more detail below. Similarly, Figure 2 A broad example of a network communication system 200 is shown in which various processing systems and computer systems may be placed in signal communication.

[0068] As background, in a typical network information or data communication system, a user may access one or more terminals that are capable of requesting and / or receiving information or data from a local or remote information source. In such a communication system, a terminal may be a processing system, a computer or computerized device, a personal computer (PC), a mobile, cellular or satellite phone, a mobile data terminal, a portable computer, a personal digital assistant (PDA), a pager, a thin client, or any other similar type of digital electronic device. The ability of such a terminal to request and / or receive information or data may be provided by software, hardware, and / or firmware. A terminal may include or be associated with other devices, such as a local data storage device, such as a hard disk drive or a solid state drive.

[0069] The information source may include a server or any type of terminal that may be associated with one or more storage devices capable of storing information or data, such as in one or more databases resident on the storage devices. The exchange of information (i.e., the request and / or receipt of information or data) between the terminal and the information source or other terminals is facilitated by a communication mechanism. The communication mechanism may be implemented by a physical cable, such as a metallic cable, such as a telephone line, a semiconductor cable, an electromagnetic signal, such as a radio frequency signal or an infrared signal, a fiber optic cable, a satellite link, or any other such medium or combination thereof connected to a network infrastructure.

[0070] The Internet, which is usually an enabling part of the communication network 200, is a large-scale interconnection of public and private networks. The network infrastructure can include devices such as telephone switches, base stations, bridges, routers, or any other such specialized network components that can facilitate the connection between the terminal and the information source. In general, the interconnected group consisting of terminals, communication mechanisms, infrastructure, and information sources is called a network. The network itself can have many forms. For example, it can be a computer network, a telecommunications network, a data communication network, a local area network (LAN), a wide area network (WAN), a wireless network, an interconnected network, an intranet, the Internet, etc.

[0071] Given this general background, Figure 2 The processing system 100 of the present invention generally includes at least one processor 102, or a processing unit or multiple processors, a memory 104, at least one input device 106, and at least one output device 108, which are coupled together via a bus or bus group 110. Generally, the processor 102 includes any suitable processor or microcontroller configured to receive input, perform logical and arithmetic operations on a suitable instruction set, and provide output as well as temporary and / or non-temporary electronic storage, such as the memory 104 and the storage device 114.

[0072] In some embodiments, the input device 106 and the output device 108 may be the same device, such as a touch screen. An interface 112 may also be provided for coupling the processing system 100 to one or more peripheral devices, such as the interface 112 may be a PCI card or a PC card. At least one storage device 114 may also be provided to accommodate at least one database 116. The memory 104 may be any form of storage device, such as a volatile or non-volatile memory, a solid-state storage device, a magnetic device, etc. The processor 102 may include more than one different processing device, such as processing different functions within the processing system 100.

[0073] The input device 106 receives input data 118 and may include, for example, a keyboard, a pointing device such as a pen-like device or a mouse, an audio receiving device such as a microphone for voice control activation, a data receiver or antenna such as a modem or a wireless data adapter, a data acquisition card, a touch screen for receiving tactile input, etc. The input data 118 may come from different sources, for example, keyboard commands combined with data received via a network or a dedicated global navigation satellite system (GNNS) sensor known in the art, etc. The output device 108 produces or generates output data 120 and may include, for example, a display device or monitor where the output data 120 is visible, a printer to print the output data 120, a port such as a USB port, a peripheral component adapter, a data transmitter or antenna such as a modem or a wireless network adapter, etc. The output data 120 may be different and derived from different output devices, for example, a visual display on a monitor together with data transmitted to a network.

[0074] The user can view the data output or interpretation of the data output on, for example, a touch screen, a display, or using a printer. Storage device 114 can be any form of data or information storage mechanism, such as volatile or non-volatile memory, solid-state storage device, magnetic device, etc.

[0075] In use, the processing system 100 is adapted to allow data or information to be stored in and / or retrieved from at least one database 116 via wired or wireless communication mechanisms. The interface 112 may allow wired and / or wireless communication between the processing unit 102 and peripheral components that may be used for specialized purposes. The processor 102 receives instructions as input data 118 via the input device 106, and may display the results of the processing or other output to a user by utilizing the output device 108. More than one input device 106 and / or output device 108 may be provided. It should be understood that the processing system 100 may be any form of terminal, server, dedicated hardware, etc.

[0076] As depicted, processing system 100 is typically part of a networked communication system 200, such as Figure 3As shown. Processing system 100 can be connected to network 202, such as the Internet or WAN. Input data 118 and output data 120 can be communicated to other devices through network 202. Other terminals, such as thin client 204, other processing systems 206 and 208, laptop 210, mainframe computer 212, PDA 214, pen computer 216, server 218, etc., can be connected to network 202. Various other types of terminals or configurations can be utilized. The transmission of information and / or data through network 202 can be implemented using wired communication mechanism 220 or wireless communication mechanism 222. Server 218 can facilitate data transmission between network 202 and one or more databases 224. Servers 218 and 266 and one or more databases 224 provide examples of databases 20, 22, and 24, for example.

[0077] Other networks may be in communication with the network 202. For example, the telecommunications network 230 may facilitate data transfer between the network 202 and a mobile or cellular telephone 232 or a PDA-type device 234 by utilizing wireless communication devices 236 and receiving / transmitting stations 238. The satellite communication network 240 may communicate with a satellite signal receiver 242 that receives data signals from a satellite 244, which in turn communicates remotely with a satellite signal transmitter 246. Terminals, such as additional processing systems 248, laptop computers 250, or satellite phones 252 may thus communicate with the network 202. A local network 260, such as a private network, a LAN, etc., may also be connected to the network 202. For example, the network 202 may be connected to an Ethernet 262 that connects a terminal 264, a server 266, and a printer 270 that controls the transfer of data to and / or from a database 268. Various other types of networks may be utilized.

[0078] The processing system 100 is adapted to communicate with other terminals, such as further processing systems 206, 208, by sending and receiving data 118, 120 to the network 202, thereby facilitating possible communications with other components of the network communication system 200. Thus, for example, the network 202, 230, 240 may form part of or be connected to the Internet, in which case the terminals 206, 212, 218 may be, for example, web servers, Internet terminals, etc. The network 202, 230, 240, 260 may be or form part of other communication networks, such as LAN, WAN, Ethernet, Token Ring, FDDI Ring, Star, etc. networks, or mobile telephone networks, such as GSM, CDMA or 3G, etc. networks, and may be fully or partially wired, including, for example, fiber optic or wireless, depending on the particular implementation.

[0079] In one embodiment, network communications between processing systems may be secured by a blockchain. As will be appreciated by those skilled in the art, a blockchain is a distributed electronic ledger, which is a publicly or privately accessible database that maintains a growing list of electronic data records that are hardened to prevent tampering and modification. A blockchain is typically composed of blocks of data structures, each of which contains batches of individual transactions. Each block contains a timestamp and information linking it to the previous block, typically through a hash value of the previous block. Linked blocks form a chain, with each additional block strengthening the block that preceded it. A blockchain is peer-to-peer over an open or private communications network, such as the Internet or a private network, and each user on the network can connect to the blockchain ledger, send new transactions to it, verify transactions, and create new blocks or immutable records.

[0080] Therefore, in the manner described above, the vehicle asset benchmarking system 10, the databases 20, 22 and 24, and the user devices 26 and 28 may generally be implemented by a suitable version of the processing system 100 described above, and according to Figure 3 The network mechanisms are networked together to perform the functions and provide the features broadly described herein.

[0081] Specifically, the vehicle asset benchmarking system 10 of the present invention is configured to obtain vehicle telemetry data from multiple vehicles, as shown in the figure numerals 12 and 16. In one embodiment, the vehicle asset benchmarking system 10 is configured to obtain vehicle telemetry data by interacting with an original equipment manufacturer (OEM) database 20. In one embodiment, the vehicle asset benchmarking system 10 is configured to obtain vehicle telemetry data by interacting with a vehicle service provider database 22. In yet another embodiment, the vehicle asset benchmarking system 10 is configured to obtain vehicle telemetry data by interacting with a third-party database 24, etc. In this way, any suitable telemetry data source inside or outside the vehicle can be used to collect information directly related to the vehicle status or provide information related to external variables that may affect the vehicle status.

[0082] Those skilled in the art will appreciate that in a preferred embodiment, the vehicle asset benchmarking system 10 may also obtain vehicle telemetry data directly from an onboard computer system. For example, vehicle telemetry data may be obtained directly from each vehicle configured to communicate with the Internet via a smartphone or the like having a suitable application linked to such vehicle.

[0083] Such vehicle telemetry data may take a variety of forms, including vehicle on-board diagnostic (OBD) data, vehicle telematics, vehicle GNSS data, vehicle service history, and original equipment manufacturer (OEM) specifications. As described above, different types of vehicle telemetry data may be obtained from different sources, for example, OEM specifications may be obtained from an OEM database 20, vehicle service history may be obtained from a vehicle service provider database 22, on-board diagnostic (OBD) data may be obtained from a third-party database 24, and so on.

[0084] Those skilled in the art further understand that vehicle on-board diagnostic (OBD) data may include any detected aspects of the vehicle, such as battery charging history, motor health, external temperature, coolant temperature, engine oil level, engine oil temperature, engine speed, acceleration rate, accelerometer readings, throttle position, steering angle, transmission oil level and temperature, diagnostic trouble codes (DTCs), registered faults, collision location / severity, braking performance, service due date and history, oxygen and emission sensors, mechanical position sensors, vibration sensors, knock sensors, software status, GNSS location data, etc. For example, GNSS location data can be used to determine the location where the vehicle is primarily used, such as in a mining area, city driving, long-distance driving, etc.

[0085] Additionally, for more modern vehicles, software status may be included as part of the (OBD) data and consist of aspects such as vehicle metadata, cybersecurity event data, software update status, etc. For example, in one embodiment, cybersecurity benchmarking may be performed for the purpose of selling a vehicle. Such cybersecurity OBD data may include vehicle metadata (data upload / download rates) and indicators of compromise (IOCs). In this way, software functionality and aspects of a connected / autonomous vehicle may be monitored and benchmarked, for example, analysis of metadata / IOCs may indicate that a vehicle for sale has been hacked and the buyer is able to see this as it relates to their safety (real world examples of malicious parties hacking vehicles and disabling brakes / changing vehicle speed) and / or privacy (real world examples of privacy violations through use of onboard microphones / external cameras, etc.).

[0086] Likewise, such benchmarking of vehicle cybersecurity OBD data can be used to monitor vehicle condition throughout the life of the vehicle. Such regular monitoring may help detect potential issues early and may aid in maintenance and upkeep to extend the life of the vehicle.

[0087] Such vehicle OBD data may be specific to the vehicle class, e.g., certain vehicle classes have comparable vehicle OBD data that is not directly comparable to other classes across classes. For example, electric vehicles may contain specific details as part of the vehicle OBD data that are not comparable to other classes.

[0088] The vehicle asset benchmarking system 10, as a processing system, typically includes a suitable processor that is configured to provide a GUI to a user, typically via the Internet, thereby enabling the user to provide a vehicle category including on-board diagnostic (OBD) data for such vehicle. In one embodiment, the GUI is provided via an application or "app" that can be installed on a user's mobile device and is configured to enable the mobile device to collect vehicle categories and on-board diagnostic (OBD) data from the vehicle. For example, a user (such as shown by mobile phone 26) can collect vehicle categories and on-board diagnostic (OBD) data directly from vehicle 12.1 and send such collected information to the vehicle asset benchmarking system 10. Alternatively or additionally, the GUI can be provided as a website, etc.

[0089] The vehicle asset benchmarking system 10 is then configured to collate a generic pattern from the vehicle telemetry data obtained from the plurality of vehicles, wherein the generic pattern consists of the same class as the vehicle 12.1. As described above, such vehicle telemetry data may take a variety of forms relevant to that particular class and may be obtained from a variety of sources. In one embodiment, the processor of the system 10 is configured to anonymize the vehicle telemetry data as the generic pattern is collated. Typically, the generic pattern is collated by compiling a pattern of comparative telemetry and OBD data from a plurality of similar classes of vehicles.

[0090] The vehicle asset benchmarking system 10 compares the provided vehicle type OBD data extensively with the collated category patterns through a statistical benchmarking algorithm. In one embodiment, the statistical benchmarking algorithm defines, extracts and populates multiple OBD fields across categories, performs statistical analysis on each field to obtain, for example, a mean or median, and performs a comparison of each field to assign a comparison score across each category field to the vehicle telemetry data. In one embodiment, the statistical benchmarking algorithm also includes time data analysis for vehicle telemetry data and vehicle OBD data, that is, interpreting specific vehicle telemetry data within a predetermined time period. It should be understood that other benchmarking options are appropriate, such as high and low values, outliers, data distribution, etc.

[0091] In one embodiment, the benchmarking algorithm calculates (i.e., considers) an overall condition score for a vehicle by comparing the mean, median, or other value of a generic field to the overall mean or median of such data, such as vehicle OBD data. It should be understood that the benchmarking algorithm can calculate similar condition scores for each generic field of the vehicle, thereby providing a more granular indication of each field for that particular vehicle, for example, comparing each subsystem of the vehicle, such as the power unit, exhaust / emission system, fuel system, software system, etc. In this manner, various types of statistically significant comparisons can be calculated as desired.

[0092] In further embodiments, the processor may also be configured to perform a "lifetime to expected failure" analysis by comparing generic patterns to vehicle OBD data and providing a comparison between the two as part of a report. For example, such a "lifetime to expected failure" may be performed to allow fleet managers to determine which vehicles are part of a fleet and allow budgeting for critical component replacements / failures based on real world data collected from similar types of vehicles of the same make / model. In this way, the system 10 may find specific applications for preventive maintenance of assets.

[0093] Finally, the vehicle asset benchmarking system 10 typically provides a comparison report to the user, wherein the report indicates how the vehicle types and categories across the mode fields compare. In one embodiment, the comparison report provides a comparison score for each comparison category field for the vehicle. Typically, the processor provides the comparison report via a GUI, but there may be variations here. For example, the report may be provided to a tablet 28, which may be a third party such as a car dealer, a potential buyer of the vehicle 12.1, etc. Such a status report may also form part of the vehicle service history, showing the benchmarking status at various stages of the vehicle's useful life, such as the benchmarking status at each scheduled service, etc.

[0094] Reference Figure 4 , the present invention also includes a related method 300 for vehicle asset benchmarking. The method 300 generally includes the following steps, 302: obtaining vehicle telemetry data from a plurality of vehicles by a processor; 304: providing a GUI to a user via the Internet, enabling the user to provide a vehicle category including on-board diagnostic (OBD) data of such vehicle; and 306: collating a generic pattern from the vehicle telemetry data obtained from the plurality of vehicles by the processor. The method 300 also includes the following steps, 308: comparing the provided vehicle category OBD data with the generic pattern by a statistical benchmarking algorithm by the processor, and 310: providing a comparison report of such benchmarking to the user, wherein the report indicates how the vehicle category compares to the generic pattern.

[0095] The telemetry-based analysis and benchmarking of the present invention can be used to provide a "Certified Pre-Owned" classification for used vehicles as well as third-party warranty products, and can also be used to generate price estimate / comparison reports (by combining this process with pricing information available for, or currently selling on the market for, the same / similar vehicles included in the comparison group).

[0096] For example, the technology can be used by used car dealers to negotiate fair prices when trading in old cars, and can also be used to ensure the quality of inventory. This can be provided by dealers to potential customers to ensure buyers' confidence in used cars.

[0097] In addition to such benchmarking / comparison analysis, the system 10 may also include a "lifetime to expected failure" analysis, i.e., a vehicle analysis of the expected failure rates / life expectancy of major components for a particular make / model, in the context of telemetry-based data (which may have been collected during a particular model launch). The resulting analysis may be used to predict the remaining time / mileage to component or system failure for a vehicle based on the average for that make / model, i.e., the category, which in turn is based on telemetry data collected from the vehicle population. Reports may be for systems / components that stand out, or for components that have proven troublesome for a particular make / model.

[0098] It should be appreciated that the system 10 may collate a temporal attribute pattern wherein the benchmarking algorithm is configured to calculate an overall condition score over a period of time, i.e., a temporal aspect as part of any statistical analysis of the OBD and related vehicle information. In the manner described, the present invention enables like-for-like comparisons of vehicles, i.e., the condition of a class of vehicles compared to a class, rather than an irrelevant "old" vs. "new" comparison when purchasing a used vehicle or related asset. For example, if a class of vehicles is known to have issues and require ongoing maintenance and servicing, it would not be helpful to compare the used class of said class to the overall class when it was new, whereas comparing the class to a statistically analyzed class, all of which have been "used" over time, would provide more insight into the condition of the class.

[0099] Applicants believe that it is particularly advantageous that the present invention provides a system and method that enables a vehicle as an asset to be compared to similar vehicles as a class, such that such comparison can be used to provide an accurate indication of the vehicle's condition. Specifically, vehicle telemetry data is comparable within a class, making the comparison an apples-to-apples comparison applicable to a particular class of vehicles. Such benchmarking can facilitate the assessment of the vehicle at any given time, as well as ongoing condition monitoring for maintenance purposes.

[0100] Alternative embodiments of the present invention may also be broadly considered to consist in the parts, elements and features mentioned or indicated herein, individually or collectively, any or all combinations of two or more parts, elements or features, and where specific integers mentioned herein have known equivalents in the art to which the present invention relates, such known equivalents are deemed to be incorporated herein as if individually set forth. In the example embodiments, well-known methods, well-known device structures and well-known technologies are not described in detail because these are readily understood by those skilled in the art.

[0101] In the context of describing various embodiments (especially in the context of claimed subject matter), the use of the terms "a / an", "said / the", and / or similar references should be interpreted to cover both the singular and the plural, unless otherwise specified herein or clearly contradicted by the context. The terms "comprising", "having", "including", and "containing" should be interpreted as open-ended terms (i.e., meaning "including but not limited to") unless otherwise specified. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. No language in the specification should be interpreted as indicating any non-claimed subject matter that is essential to the practice of the claimed subject matter.

[0102] It should be understood that references to "one example" or "example" or similar exemplary language (e.g., "such as") of the present invention herein are not exclusive. Thus, one example may illustrate certain aspects of the present invention, while other aspects are illustrated by different examples.

[0103] Any method steps, processes, and operations described herein should not be construed as necessarily requiring their execution in the particular order discussed or illustrated, unless specifically identified as an order of execution. It should also be understood that additional or alternative steps may be employed.

Claims

1. A vehicle asset benchmarking system, configured to acquire vehicle telemetry data from a plurality of vehicles, the vehicle asset benchmarking system comprising a processing system, the processing system comprising an interconnected processor, an input / output interface, and a memory arrangement, the processor being configured to: providing a GUI to a user via the Internet, thereby enabling said user to provide a vehicle category including telemetry data for such vehicle; Organizing generic patterns from vehicle telemetry data obtained from multiple vehicles; comparing the provided vehicle category telemetry data with the generic pattern via a statistical benchmarking algorithm; and providing said user with a comparison report of such benchmarking, wherein said report indicates the condition of said vehicle class compared to said generic class, The benchmarking algorithm calculates an overall condition score for the vehicle by comparing the mean or median of the generic fields in the pattern with the overall mean or median of the vehicle telemetry data.

2. The vehicle asset benchmarking system of claim 1, configured to obtain the vehicle telemetry data by interacting with an Original Equipment Manufacturer (OEM) database.

3. The vehicle asset benchmarking system according to claim 1 or 2, which is configured to obtain the vehicle telemetry data by interacting with a vehicle service provider database.

4. The vehicle asset benchmarking system according to any one of claims 1 to 3, which is configured to obtain the vehicle telemetry data by directly interacting with an onboard computer system.

5. The vehicle asset benchmarking system according to any one of claims 1 to 4, wherein: The vehicle telemetry data is selected from a non-exhaustive group consisting of: vehicle on-board diagnostic (OBD) data, vehicle telematics, vehicle GNSS data, vehicle service history, and original equipment manufacturer (OEM) specifications.

6. The vehicle asset benchmarking system according to any one of claims 1 to 5, wherein: The vehicle on-board diagnostic (OBD) data is selected from a non-exhaustive group consisting of: battery charging history, motor health, external temperature, coolant temperature, engine oil level, engine oil temperature, engine speed, acceleration rate, accelerometer readings, throttle position, steering angle, transmission fluid level and temperature, diagnostic trouble codes (DTCs), registered faults, crash location / severity, braking performance, service due date and history, oxygen and emission sensors, mechanical position sensors, vibration sensors, knock sensors, software update status, vehicle metadata, cybersecurity event data, etc.

7. The vehicle asset benchmarking system according to any one of claims 1 to 6, wherein: The processor is configured to anonymize the vehicle telemetry data when the generic patterns are collated.

8. The vehicle asset benchmarking system according to any one of claims 1 to 7, wherein: The GUI is provided via an application or “app” that is installable on the user's mobile device and configured to enable the mobile device to collect the vehicle type, telemetry and / or on-board diagnostic (OBD) data from the vehicle.

9. The vehicle asset benchmarking system according to any one of claims 1 to 8, wherein: The generic patterns are organized by compiling patterns from comparative telemetry data from multiple similar categories of vehicles.

10. The vehicle asset benchmarking system according to any one of claims 1 to 9, wherein: The statistical benchmarking algorithm defines, extracts and populates a plurality of OBD fields across generic categories, performs statistical analysis on each field to obtain a mean or median, and performs a comparison of each field to assign a comparison score across each generic field to the vehicle OBD data.

11. The vehicle asset benchmarking system according to any one of claims 1 to 10, wherein: The statistical benchmarking algorithm includes a temporal data analysis for the vehicle telemetry data and vehicle OBD data.

12. The vehicle asset benchmarking system according to any one of claims 1 to 11, wherein: The comparison report provides a comparison score for each comparison generic field of the vehicle.

13. The vehicle asset benchmarking system according to any one of claims 1 to 12, wherein: The processor is configured to perform a "lifetime to expected failure" analysis by comparing the generic pattern to the vehicle telemetry data and providing a comparison between the two as part of a report.

14. The vehicle asset benchmarking system according to any one of claims 1 to 13, wherein: The processor provides the comparison report via the GUI.

15. A method for vehicle asset benchmarking, the method comprising the following steps: obtaining, by a processor, vehicle telemetry data from a plurality of vehicles; providing a GUI to a user via the Internet, thereby enabling said user to provide a vehicle category including telemetry data for such vehicle; collating, by a processor, categorized patterns from vehicle telemetry data obtained from a plurality of vehicles; comparing, by a processor, the provided vehicle category telemetry data to the generic pattern by a statistical benchmarking algorithm, the benchmarking algorithm being configured to calculate an overall condition score for the vehicle by comparing an average or median value of a generic field in the pattern to an overall average or median value of the vehicle telemetry data; as well as A comparison report of such benchmarking is provided to the user, wherein the report indicates the condition of the vehicle class compared to the generic category.

16. The method of claim 15, wherein: The step of obtaining the vehicle telemetry data includes interacting with an Original Equipment Manufacturer (OEM) database.

17. The method according to claim 15 or 16, wherein: The step of obtaining the vehicle telemetry data includes interacting with a vehicle service provider database.

18. The method according to any one of claims 15 to 17, wherein: The step of obtaining the vehicle telemetry data includes directly interacting with an onboard vehicle computer system.

19. The method according to any one of claims 15 to 18, wherein: The vehicle telemetry data is selected from a non-exhaustive group consisting of: vehicle on-board diagnostic (OBD) data, vehicle telematics, vehicle GNSS data, vehicle service history, and original equipment manufacturer (OEM) specifications.

20. The method according to any one of claims 15 to 19, wherein: The vehicle on-board diagnostic (OBD) data is selected from a non-exhaustive group consisting of: battery charge history, motor health, external temperature, coolant temperature, engine oil level, engine oil temperature, engine speed, acceleration rate, accelerometer reading, throttle position, steering angle, transmission fluid level and temperature, diagnostic trouble codes (DTCs), registered faults, crash location / severity, braking performance, service due date and history, oxygen and emission sensors, mechanical position sensors, vibration sensors, knock sensors, software update status, etc.

21. A method as claimed in any one of claims 15 to 20 including the step of anonymising the vehicle telemetry data when the generic patterns are collated.

22. The method of any one of claims 15 to 21, wherein: The step of providing the GUI includes providing an application or "app" that can be installed on the user's mobile device and is configured to enable the mobile device to collect the vehicle type, telemetry and / or on-board diagnostic (OBD) data from the vehicle.

23. The method of any one of claims 15 to 22, wherein: The step of collating the generic patterns includes compiling patterns from comparative telemetry data of a plurality of similar categories of vehicles.

24. The method of any one of claims 15 to 23, wherein: The statistical benchmarking algorithm defines, extracts and populates a plurality of OBD fields across generic categories, performs statistical analysis on each field to obtain a mean or median, and performs a comparison of each field to assign a comparison score across each generic field to the vehicle OBD data.

25. The method of any one of claims 15 to 24, wherein: The statistical benchmarking algorithm includes a temporal data analysis for the vehicle telemetry data and vehicle OBD data.

26. The method of any one of claims 15 to 25, wherein: The step of providing the comparison report includes providing a comparison score for each comparison generic field of the vehicles.

27. A method as claimed in any one of claims 15 to 26, including the step of performing, by the processor, a "life to expected failure" analysis by comparing the generic pattern with the vehicle telemetry data and providing a comparison between the two as part of a report.

28. A computer program product which, when executed by a suitable processing system, facilitates the performance of the method of any one of claims 15 to 27.

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