Techniques for filtering reconnaissance data.

Abstract

A system for identifying desired information from collected sensor data includes a collection device and a processing module. The collection device collects the sensor data, coarsely filters the sensor data according to predefined rules, generates filter-matched data, and securely transmits the filter-matched data to the processing module. The processing module finely filters the filter-matched data, generates desired information, provides the desired information to an authorized actor, and deletes the filter-matched data.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 202,954 (Attorney Docket No. AA610), filed July 1, 2021, and entitled “SURVEILLANCE DATA FILTRATION TECHNIQUES,” which is incorporated by reference in its entirety herein.

[0002] FIELD OF THE DISCLOSURE This disclosure relates generally to electronic search or reconnaissance and, more specifically, to identifying locations of interest to law enforcement agencies and filtering collected data associated therewith. [Background technology]

[0003] Autonomous vehicles associated with law enforcement agencies can, for example, determine if a motor vehicle has violated a traffic law, follow the vehicle, and electronically issue a ticket or warning to the violator. Autonomous vehicles can be trained to find good hiding places to catch traffic violators, point their cameras to accurately monitor traffic, identify vehicles, analyze incoming data against a traffic law database, and communicate with a central computing system and reconnaissance cameras. Such autonomous vehicles can record collected data and transmit the records to government agencies. Autonomous vehicles can also assess the environment and the people in it to reduce risk to law enforcement personnel. For example, a drone can observe and interact with a suspect, capture images of the suspect and associated documents of interest, perform image comparisons, perform text extraction and classification, correlate the text with image identification, and communicate the data to a base station.

[0004] The transmitted images can be encrypted, for example, to address privacy concerns. Privacy issues with the collection of image data sets, for example for reconnaissance or medical data, may be addressed using learnable encryption algorithms. It is also possible to provide a high level of protection for people's privacy while photographing and recording to investigate incidents such as crimes and terrorist acts. For example, cameras may be placed in private vehicles, and images from such cameras may then be stored for a short period of time, for example, one to two weeks, in case they are needed by law enforcement.

[0005] What many such systems have in common is that they provide data to law enforcement, and therefore all data collected pertains to suspects about whom law enforcement has a right to collect data. However, some such systems, for example, at least autonomous traffic monitoring and environmental assessments, may involve collecting a range of data that is not related to any person or thing about whom law enforcement has a right to collect data. Systems that can identify the location of objects and items of interest may not be able to do so while also protecting the privacy of objects and items that are not of interest.

[0006] The background described above is intended merely to provide a contextual overview of some existing problems and is not intended to be exhaustive. Summary of the Invention [Means for solving the problem]

[0007] In the following description, numerous specific details are set forth to provide a thorough understanding of various aspects and arrangements. However, those skilled in the relevant art will recognize that the techniques described herein may be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations may not be shown or described in detail to avoid obscuring certain aspects.

[0008] References throughout this specification to "an aspect," "an arrangement," or "a configuration" indicate that a particular feature, structure, or characteristic is being described. Thus, the appearances of phrases such as "in one aspect," "in one arrangement," "in a configuration," or similar phrases in various places throughout this specification do not necessarily refer to the same aspect, feature, configuration, or arrangement, respectively. Further, particular features, structures, and / or characteristics may be combined in any suitable manner.

[0009] As used in this disclosure and claims, the terms "component," "system," "platform," "layer," "selector," "interface," and the like, are intended to refer to computer-related entities or entities related to an operable device with one or more specific functionalities, where the entities may be either hardware, a combination of hardware and software, software, or software in execution. By way of example, and not limitation, a component may be 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 illustration, and not limitation, both an application running on a server and the server itself may be a component. One or more components may reside within a process and / or thread of execution, and a component may be localized on one computer and / or distributed between two or more computers. In addition, a component may execute from various computer-readable media, device-readable storage devices, or machine-readable media having various data structures stored thereon. A component may communicate via local and / or remote processes, such as according to a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or with other systems via signals across a network such as the Internet). As another example, a component may be a device with specific functionality provided by mechanical parts operated by electrical or electronic circuitry, which may be operated by a software or firmware application executed by a processor, which may be internal or external to the device and executes at least a portion of the software or firmware application.As yet another example, a component may be a device that provides specific functionality without mechanical parts, through electronic components, which may include a processor therein for executing, at least in part, software or firmware that provides the functionality of the electronic component.

[0010] As used herein, terms such as "store," "storage," "data store," "data storage," "database," and the like, refer to a memory component, an entity embodied in memory, or a component that comprises memory. It will be understood that the memory components described herein can be either volatile memory or non-volatile memory, or can include both volatile and non-volatile memory.

[0011] In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any of the natural inclusive enumerations. That is, if X employs A, X employs B, or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing cases. Furthermore, the articles "a" and "an" as used in this disclosure and claims should generally be construed to mean "one or more," unless otherwise specified or clear from the context to be directed to the singular form.

[0012] The words "exemplary" and / or "demonstrative," as used herein, mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by the disclosed examples. In addition, any aspect or design described herein as "exemplary" and / or "demonstrative" should not necessarily be construed as preferred or advantageous over other aspects or designs, or is not meant to exclude equivalent exemplary structures and techniques known to those skilled in the art. Further, to the extent that "includes," "has," "contrains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open-ended transitional term, without excluding any additional or other elements.

[0013] As used herein, the terms "infer" or "inference" generally refer to the process of inferring about or inferring states of a system, environment, user, and / or intent from a set of observations as captured via events and / or data. The captured data and events can include user data, device data, environmental data, data from sensors, application data, implicit data, explicit data, etc. Inference can be employed, for example, to identify specific contexts or actions, or can generate a probability distribution over states of interest based on a consideration of data and events.

[0014] The disclosed subject matter can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof, to control a computer to implement the disclosed subject matter. The term "article of manufacture," as used herein, is intended to cover any computer-readable device, machine-readable device, computer-readable carrier, computer-readable medium, or computer program accessible from a machine-readable medium. For example, computer-readable media include, but are not limited to, magnetic storage devices, such as hard disks, floppy disks, magnetic stripes, optical disks (e.g., compact disks (CDs), digital video disks (DVDs), Blu-ray disks, and the like). TM (BD), smart cards, flash memory devices (e.g., cards, sticks, key drives), virtual devices that emulate storage devices, and / or any combination of the above computer readable media.

[0015] Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The illustrated embodiments of the present disclosure may be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0016] A computing device may include at least a computer-readable storage medium, a machine-readable storage medium, and / or a communication medium. A computer-readable or machine-readable storage medium may be any available storage medium that can be accessed by a computer, including both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, a computer-readable or machine-readable storage medium may be implemented in association with any method or technology for storage of information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0017] A computer-readable storage medium may include, but is not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disk (BD), or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, solid-state drive or other solid-state storage device, or other tangible and / or non-transitory medium that may be used to store the desired information. In this regard, it is to be understood that the terms "tangible" or "non-transitory" herein as applied to storage, memory, or computer-readable medium, as modifiers, exclude only the propagating transient signal itself, and do not exclude any standard storage, memory, or computer-readable medium that is not limited to the propagating transient signal itself.

[0018] A computer-readable storage medium can be accessed by one or more local or remote computing devices for various operations on information stored by the medium, for example, via access requests, queries, or other data reading protocols.

[0019] The system bus, as may be used herein, may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The database, as may be used herein, may include a basic input / output system (BIOS), which may be stored in a non-volatile memory such as a ROM, EPROM, or EEPROM, and contains basic routines that help transfer information between elements in the computer, such as during startup. The RAM may also include a high-speed RAM, such as a static RAM, for caching data.

[0020] As used herein, a computer can operate in a network environment using logical connections via wired and / or wireless communications to one or more remote computers. The remote computer can be a workstation, a server, a router, a personal computer, a portable computer, a microprocessor-based entertainment appliance, a peer device, or other common network node. The logical connections depicted herein can include wired / wireless connectivity to a local area network (LAN) and / or a larger network, e.g., a wide area network (WAN). Such LAN and WAN networking environments are commonplace in offices and companies, facilitating enterprise-wide computer networks, such as intranets, any of which can connect to a global communications network, e.g., the Internet.

[0021] When used in a LAN networking environment, the computer can be connected to the LAN through a wired and / or wireless communication network interface or adapter. The adapter can facilitate wired or wireless communication to the LAN, which may also include a wireless access point (AP) disposed thereon for communicating with the adapter in a wireless mode.

[0022] When used in a WAN networking environment, the computer can include a modem or be connected to a communication server on the WAN via other means for establishing communications over the WAN, such as via the Internet. The modem, which can be internal or external and a wired or wireless device, can be connected to the system bus via an input device interface. In a networked environment, the program modules depicted herein for the computer or portions thereof can be stored in a remote memory / storage device.

[0023] When used within a networked environment, either a LAN or a WAN, a computer can access a cloud storage system or other network-based storage system in addition to or instead of an external storage device. Generally, the connection between the computer and the cloud storage system can be established via a LAN or a WAN, for example, via an adapter or modem, respectively. Upon connecting the computer to the associated cloud storage system, the external storage interface can, with the aid of the adapter and / or modem, manage the storage provided by the cloud storage system as would other types of external storage devices. For example, the external storage interface can be configured to provide access to cloud storage sources as if they were physically connected to the computer.

[0024] As employed herein, the term "processor" may refer to virtually any computing processing unit or device, including, but not limited to, a single-core processor, a single-core processor with software multithreading execution capability, a multi-core processor, a multi-core processor with software multithreading execution capability, a multi-core processor with hardware multithreading technology, a vector processor, a pipeline processor, a parallel platform, and a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a combined programmable logic device (CPLD), a state machine, a discrete gate or transistor logic, a discrete hardware component, or any combination thereof, designed to perform the functions described herein. The processor may utilize nanoscale architectures, such as, but not limited to, molecular and quantum dot-based transistors, switches, and gates, to optimize space usage or improve the performance of user equipment. A processor may also be implemented as a combination of computing processing units. For example, the processors may be implemented together as one or more processors, closely coupled, loosely coupled, or located remotely from one another. Multiple processing chips or multiple devices may share performance of one or more functions described herein, and similarly, storage may be provided across multiple devices.

[0025] As an overview, various arrangements are described herein. For convenience of explanation, methods (or algorithms) are depicted and described as a series of steps or actions. It should be understood and appreciated that the various arrangements are not limited by the illustrated actions and / or by the order of the actions. For example, actions may occur in various orders and / or in parallel and with other actions not presented or described herein. Furthermore, not all illustrated actions may be required to implement a method. In addition, the method may alternatively be represented as a series of interrelated states via a state diagram or events. In addition, the methods described hereinafter can be stored on an article of manufacture (e.g., a machine-readable storage medium) to facilitate transporting and transferring such methodologies to a computer.

[0026] According to an aspect, a system of the present teachings collects data, such as, but not limited to, sensor data, identifies data such as, but not limited to, images of interest, and removes data that is not of interest. In an aspect, the system encrypts the incoming data. In an aspect, the encryption occurs in-place. In an aspect, all incoming data is encrypted. The system includes, but is not limited to, data filtering. In an aspect, data is filtered to reduce the amount of data required to be transmitted from the collection device to the processing device. In an aspect, the collection device and the processing device are operatively coupled by electronic communication means. In an aspect, the collection device and the processing device are not physically co-located. In an aspect, the collection device and the processing device are co-located. In an aspect, the collection device and the processing device share a processor or multiple processors. In an aspect, the data filtering includes multiple passes. In an aspect, there is a first pass data filter and a second pass data filter. The present teachings contemplate additional filter passes or a single filter pass. In an aspect, the system includes a rule set processor that can process a filter rule set. In certain aspects, the rule set processor processes the filter rule set after at least one criterion is satisfied. For example, but not limited to, the filter rule set is processed after the sender of the filter rule set is verified to be an authorized entity. In certain aspects, the system removes data that does not satisfy the selection criteria. The selection criteria may include, but are not limited to, dynamically determined criteria, default criteria, and / or user established criteria. In certain aspects, the system operates in multiple modes. In certain aspects, the system automatically selects a mode based on, for example, data collected by a collection device. In certain aspects, a user selects a mode. In certain aspects, a default mode is selected based on a desired application. In some configurations, the system includes a general mode, a subject mode, and a scene mode. Other modes are also envisioned and may be accommodated by the present teachings.

[0027] In one aspect, the general mode can be likened to a reconnaissance camera in a store from which sensor data can be retrieved when it is determined that some object of interest may have occurred during a specific time frame. In the general mode, the system collects all sensor data, as indicated by, for example, a rule set, a default set of sensors, or a user-supplied set of sensors. For example, a user may choose to collect LIDAR data, camera data, audio data, and chemical data because a particular application may require point cloud and image data to be collected in a certain geography and in a certain time frame, along with audio and chemical data. Additionally or alternatively, the general mode may indicate that, by default, all available sensors are activated and all available sensor data is collected. In one aspect, in the general mode, the system pre-processes the incoming data by filtering the data according to preselected criteria, such as, for example, but not limited to, blurriness, signal-to-noise ratio, data quality, temporal filtering, or detection of defined objects. In some arrangements, further filtering is performed according to a preselected processing recipe for the general data or according to specific rules that may be provided to the system. In general mode, in some aspects, the data remaining after filtering is encrypted and optionally encoded and then provided to the user for review. In some aspects, for example, when a person of interest is being sought, in general mode, the user or system owner may be a private sector user and any remaining data is made accessible to the system owner and made available for further subject level filtering and analysis if such is desired. In some aspects, in general mode, the user may be a public sector user and any remaining data is made available for further subject level filtering and analysis as authorized and configured, but not made available for public use.

[0028] In some aspects, the general mode may be used to perform background subtraction techniques. Such background subtraction techniques are used for scenarios in which geographic areas are specified with objects that are permitted or expected to be present, and the system detects exceptions within such areas and notifies a user that an exception has occurred. Such notification may include providing image data and / or other data representative of the exception. For example, the system may be configured to specify areas that have restrictions or requirements, and optionally provide a warning, e.g., when a car is parked in a location where it should not be parked.

[0029] In one aspect, the target mode enables the user to apply image recognition to the collected data. For example, if a particular person needs to be located, a type of machine learning model used for this purpose is trained to sort through the collected data and look for the person in the image. Those skilled in the art will appreciate that one of the many examples of a model suitable for face recognition is a neural network model called Deep Face. An exemplary face recognition process follows four steps: (1) face detection, where one or more faces in an image or video are located and marked with a bounding box; (2) face matching, where the detected face (and its location, size, and pose) is normalized to match a database, for example, regarding geometry and photometry; (3) feature extraction, where features are extracted from the matched face that can be used for recognition; and (4) feature matching of the feature vector representing the face against one or more known faces in a prepared database (e.g., a database of registered users). In one aspect, the system has separate modules or programs for each of the four steps, or combines some or all of the steps into a single process. Similarly, when an object such as a license plate needs to be located, a type of machine learning model used for this purpose is trained to sort through the collected data and look for the specific license plate. Those skilled in the art will appreciate that one of many examples of models suitable for license plate detection and identification is sold by Plate Recognizer. In an exemplary license plate detection and identification process, a first object detection model is used to recognize images of cars or other vehicles in multiple images and / or videos. In one aspect, a detection model is used to identify license plates in images of cars or other vehicles. The detection model does not have to be a machine learning model. If a machine learning model is used, for example, a neural network such as YOLO or SSD (both defined below) can be trained to detect license plates.Optical Character Recognition (OCR) may be performed on the detected license plate and convert the image to text. In one aspect, in object mode, where data collection and data processing are accomplished in processors that are geographically remote (with respect to each other), at the data collection location, the data undergoes a first pass filtering process. The first pass filtering process sorts through the data, for example, identifying bounding boxes for objects and sorting out objects from the remainder of the data, which are omitted. The data within the bounding boxes is transmitted from the data collection module to a processing module that subjects the data to further filtering. In one aspect, the processing module selects a machine learning model that would be appropriate for the selected object type, such that when the collected data arrives at the processing module, an object-specific machine learning model is applied to the data. Potential matches are located and non-matches are removed. Potential matches are provided, for example, to a user interface of a handheld device, tablet, or laptop, a log file, and / or a user interface of a local or remote system operator desktop. In various configurations, multiple filtering passes are performed within the same processor. Additionally or alternatively, multiple filtering passes are performed at the data collection location. Additionally or alternatively, multiple filtering passes are performed remotely from the data collection location. Additionally or alternatively, multiple filtering passes are performed in various processors local to and remote from the data collection location.

[0030] Various machine learning models are known to those skilled in the art and may include, as non-limiting and non-exhaustive examples, clustering, dimensionality reduction, ensemble methods, neural networks (e.g., convolutional neural network models), and deep learning, transfer learning, reinforcement learning, natural language processing, and word embedding. Many suitable techniques for object detection and recognition are readily understood by those skilled in the art and include, as non-limiting examples, region-based convolutional neural networks (R-CNN), Faster R-CNN, Faster R-CNN, region-based fully convolutional networks (R-FCN), histogram of oriented gradients (HOG), single-shot detector (SSD), spatial pyramid pooling (SPP-net), and You Only Look Once (YOLO).

[0031] In one embodiment, in target mode, the system according to the present disclosure receives an electronic authorization from an authorized entity that can be used to create and / or extend a filter rule set. The new filter rule set can in effect authorize the autonomous vehicle to be on the lookout for a vehicle of interest, e.g., a Black Hummer H1 with a particular license plate, by passing the filter rule set to a collection device. The collection device may be incorporated within the autonomous vehicle of the present teachings. Additionally or alternatively, the collection device may be located remotely, or at least partially remotely, from the autonomous vehicle and communicatively coupled thereto. As the autonomous vehicle collects data, the data is compared against the list of vehicles contained in the filter rule set, e.g., all active authorizations. In one aspect, data associated with vehicles matching those listed in the active authorizations is stored, passed to the processing module, and possibly made available to law enforcement agencies based on further processing performed by the processing module. When a vehicle of interest is detected, the law enforcement agency listed on the authorization is notified that the vehicle has been detected and provided with the date / time of such detection. In one aspect, law enforcement agencies can securely view and potentially download images of vehicles of interest.

[0032] In some configurations, the system according to the present disclosure may operate in a scene mode. The scene mode enables the user to apply scene of interest rules to the collected data. In a similar aspect to that of operation in the object mode, a particular scene of interest (similar to a particular object of interest) may appear from the perspective of the data collector at any time. The criteria for recognizing a scene of interest may not be specific to a particular location, but instead may provide general characteristics of what such a scene may be. For example, if a machine learning model is trained with general characteristics of arson scenes, the data collector will recognize possible arson scenes. In the scene mode, the trained machine learning model is used either in the data collector or in an arrangement where two data processing paths are available in the data collector and also in the processing system, for example, one in the data collector and the other in the processing system. The scenes identified by the machine learning model are provided to the user, the computer, a log file, and / or various types of displays, and the remaining data is deleted.

[0033] In an aspect, a system of the present teachings includes a collection processor configured to receive sensor data and to encrypt the received sensor data. In various aspects, the collection processor and one or more sensors are mounted, for example, on an autonomous vehicle, a utility pole, and / or a drone, and / or the collection processor and the sensor are carried by a human or an animal. In an aspect, the collection processor is configured to execute coded instructions stored in its memory or in a memory coupled to the collection processor to filter the sensor data. In an aspect, data that is filtered out is deleted. In an aspect, sensor data for which the filter indicates a likely match, for example, a probability of matching at or above a predetermined threshold probability, is retained. To perform the filtering, the collection processor in some configurations locates features in the received sensor data and, if possible, matches those features to any of the items in a list of possible features of interest, for example, a trigger list, provided according to a filter rule set. In an aspect, the trigger list is created from rules established, for example, but not limited to, by law enforcement and / or local government officials. In one aspect, the trigger list includes a subset of the full set of rules so that processing requirements on the collection processor can be reduced or minimized to reduce power consumption. In one embodiment, the trigger list includes face data of suspected individuals or missing persons, and the collection processor looks for a match between the face data in the trigger list and the received sensor data and / or filtered sensor data. Those skilled in the art will readily appreciate that many types of sensor data can be collected and that the trigger list can include triggers for many kinds of data. For example, if the trigger is an object, object detection can be performed using techniques according to various methods known to those skilled in the art, including, but not limited to, R-CNN, Faster R-CNN, Faster R-CNN, R-FCN, HOG, SSD, SPP-net, and YOLO.Other types of sensor signals and / or other types of data may be detected and collected, such as, by way of non-limiting example, LIDAR signals, radar signals, ultrasound, optical camera data, audio data (e.g., voice or music data, etc.), chemical data, infrared signals, magnetic or near-field waveforms, electromagnetic or radio frequency waveforms, point cloud data, bitmaps, alphanumeric data (e.g., vehicle license plate numbers, etc.), video data, detected faces, and other types of detected objects.

[0034] In an aspect, the collection processor is configured to ensure that collected data is maintained in a secure state, regardless of whether the data is stored locally or transmitted to a remote location, such as, for example, a processing station. For example, the collection processor is configured to encrypt all data as it is received. In an aspect, the collection processor is configured to re-encrypt, within the transmission chain, decrypt data, i.e., certain elements of the data selected for transmission to the processing station. As will be appreciated by those skilled in the art, various known encryption techniques may be used, such as, by way of non-limiting example, Advanced Encryption System (AES) 256-bit encryption. Additionally or alternatively, the collection module authenticates the data, for example, by ensuring that the data is digitally signed before it is transmitted to enable the receiver to prove the identity of the sender upon receipt of the data. Additionally or alternatively, the collection module associates metadata, such as time and / or location data, with the transmitted data. Additionally or alternatively, the data to be transmitted to the processing station is compressed to conserve bandwidth. Compression may be accomplished using any of a variety of known standardized methods, such as, by way of non-limiting examples, vocoding for audio data, ITU-T H.264, HEVC, or VVC for video data, Huffman coding, lossless compression, or lossy compression, as one of ordinary skill in the art would understand. Additionally or alternatively, a checksum is applied to the data to be transmitted to the processing station to enable a receiver at the processing station to detect any errors caused by the transmission medium. Additionally or alternatively, the data to be transmitted to the processing station is encoded such that the receiver can decode the data to correct errors caused by the transmission medium.As one skilled in the art would appreciate, the error correction encoding may be accomplished using a variety of known techniques, including, by way of non-limiting examples, block coding, e.g., Reed-Solomon coding, convolutional encoding, turbo encoding, low-density parity check (LDPC) encoding, or polar encoding. One skilled in the art would also appreciate that the error correction encoding format employed may depend on the communication protocol being used for transmission. In some aspects, when compression, encryption, and error correction encoding are performed on data prior to transmission of the data, the data may first be compressed, then the compressed data may be encrypted, and then the encrypted compressed data may be error correction encoded.

[0035] In some arrangements, the collection processor performs feature extraction and matching using one or more of various known machine learning models, with computations performed on the autonomous vehicle (e.g., at an edge node, not in the cloud). In some aspects, a feature-matched target set of such features is predefined in a rule set. The collection module applies tracking during object detection, including, for example, temporal filtering of the objects. Such filtering includes a first type of filtering and a second type of filtering that may be performed by a remote processor at a processing station (e.g., in the cloud). In some aspects, the first type of filtering includes coarse-grained filtering relative to the second type of filtering. In some aspects, the collection module utilizes a two-dimensional (2D) bounding box tracking mechanism, such as, for example, generalized union intersection (GIOU) tracking, to track objects and their bounding boxes from each collected image frame to subsequent collected image frames. In other arrangements, the collection module tracks objects using a Joint Probabilistic Data Association Filter (JDAF) algorithm, a linear velocity prediction technique, and / or a combination of the two. The collection module, in some aspects, crops one or more or all sections of the image within a bounding box of the object of interest as defined by the rule set. The collection module transmits data to a remotely located processing station or to a co-located processing station, at least one such cropped image associated with the detected and tracked object of interest. In one aspect, the collection module compresses, encrypts, and / or encodes the cropped image prior to transmitting the cropped image.

[0036] Systems according to some aspects of the present disclosure include a processing module (e.g., at or in a processing station) configured to receive and process filters or triggers used to sort collected data. In some aspects, the processing module receives inputs such as, for example, but not limited to, triggers and accompanying permissions to search for the triggers. Triggers include, for example, but not limited to, objects, images, odors, sounds, scenes, and / or other sensory data. Additionally or alternatively, the processing module receives such identification information as, for example, but not limited to, one or more locations of scenes of interest, one or more durations of sounds of interest, and / or one or more notifications, such as, for example, communications from a collection device or from a human, or automated messages, including notifications of matches or likely matches between collected image data and scenes or objects of interest. In some examples, the processing module is configured to receive and execute instructions provided via inputs received from an end-user application. The end-user application is, in some aspects, used to request rule sets to be applied by the processing module. In various embodiments, the processing module itself includes a manager program. The manager program, in some embodiments, is a cloud-based manager. The manager program is designed to maintain, among other things, one or more of data privacy, data security, chain of custody control, and / or audit trails (e.g., time or date stamps, etc.). In one aspect, the manager program is rule set agnostic, i.e., the manager program is not limited to processing rule sets encoded in any one specific format.In some configurations, the manager program includes instructions that enable the provision of application programming interfaces (APIs) for privacy, chain of custody, and / or audit rules to be configured by users, such as, by way of non-limiting examples, law enforcement, government agencies, national security agencies, or corporate customers with special privacy needs.

[0037] In various configurations, the processing module and the collection module (e.g., collection processor) communicate with each other, for example, through a gateway. In one aspect, the gateway establishes a web service between the collection module and the gateway and establishes communication between the collection module and the gateway using the web service, as would be understood by one skilled in the art. In some configurations, the gateway is a web server of the web service and the collection module is a web client of the web service. In one aspect, data is communicated between the collection module and the gateway using the web service.

[0038] The processing module in various arrangements is configured to decrypt the data received from the collection module using an encryption key associated with the encrypted data received from the collection module. In addition, the processing module is optionally configured to re-encrypt the decrypted data received from the collection module. In some aspects, the processing module verifies and tracks the chain of custody and / or digital signature associated with the incoming data and / or validates the digital signature of some or all of the outgoing data transmitted by the processing module. In various arrangements, the processing module receives the rule set and, based at least in part thereon, prepares one or more subsets of the rule set (e.g., a trigger list) for use by the collection processor and transmits it to the collection module. In various aspects, the processing module applies a fine-grained filter to the coarsely filtered data received from the collection module and applies the rule set to the filtered data. The filtered results, including at least the matched data and a match notification, are transmitted, for example, to an authorized individual, a log file, a cloud-based system, a laptop, a handheld device, a desktop, and / or a tablet.

[0039] The processing module according to various aspects uses any of a variety of machine learning models to subject the data received from the collection module to a finer, e.g., more detailed, version of feature matching than the relatively rough feature matching calculation performed by the collection module. For example, the processing module determines data that meets trigger requirements described in a rule set for a filter. In some aspects, the processing module includes at least an extraction algorithm and a recognition algorithm. In some aspects, the extraction algorithm is used to decrypt and / or decode the sensor data previously collected and filtered by the collection module. In some aspects, the extraction algorithm includes at least a neural network algorithm configured to receive the decoded and filtered sensor data and generate a bounding box containing the desired sensor data. In some aspects, the extraction algorithm processes the detected sensor data provided by the neural network algorithm. In some aspects, the recognition algorithm subjects the detected sensor data to further processing, such as, for example, but not limited to, an optical property recognition algorithm. Additionally or alternatively, the recognition algorithm filters the resulting data, e.g., text matching between text generated from the data and a trigger value, such as a license plate number, described in the filter rule set, and classifies the text as matching / non-matching. In some aspects, the processing module stores the results from further processing. In some aspects, the storage is secure. In some aspects, the processing module encrypts the storage area so that the data is only accessible by authorized users based on the received trigger data. In some aspects, the processing module sends a notification when a match is found between the data and the trigger database, by sending a text to an authorized user, as a non-limiting example. In some aspects, the processing module allows authorized users to view and download the matched data and the location of the collection of matched data.In some aspects, the viewing is secure. In some aspects, data that does not provide a match according to the trigger list is not made available to authorized individuals or other parties to protect the privacy of those not of interest. Such data may be deleted or such data may be encrypted and stored. It will be appreciated by those skilled in the art that a system according to the present disclosure need not be limited to detecting and / or recognizing only facial features. It will be appreciated by those skilled in the art that machine learning models can be trained to recognize many types of objects, including, but not limited to, license plates, automobiles, animals, and consumer goods, for example.

[0040] The system according to the present disclosure may be used in a variety of different ways, each raising privacy and security concerns that are addressed by the system's architecture. For example, when the system is being used to locate a person for whom a search authorization is outstanding, the system is run by an autonomous vehicle, for example, and the system collects a substantial number of images of non-suspect individuals. The exemplary system prevents the location and / or activities of non-suspect individuals from being made available to law enforcement. Filtering and encryption by the collection and processing modules may enable images that are substantially similar in appearance to a suspect to be provided to law enforcement. On the other hand, the system according to the present disclosure may be used to scan an environment generally, without searching for any particular individual or item of interest. In the act of scanning the environment, the system may detect evidence that a crime has been committed or may detect a crime being committed. The exemplary system ensures that the images captured are not disqualified as evidence and / or that the captured images provided to authorities do not include data that may implicate innocent people. A system according to the present disclosure accomplishes the foregoing by evaluating incoming data based on rules established by, for example, but not limited to, authorities.

[0041] As would be readily appreciated by one of ordinary skill in the art, AES-256 encryption may be used within collection modules and / or processing modules according to the present disclosure when symmetric key encryption is required, such as, for example, encryption of data at rest for data stores such as object, relational, directory, and / or search data stores. The AES-256 encryption algorithm may also be used to encrypt message blocks exchanged between collection and processing modules over the network. Such messages may also, or alternatively, be encrypted using TLS 1.2 encryption used for the computer network channel.

[0042] Also, as would be understood by one of ordinary skill in the art, messages containing data exchanged between a collection module and a processing module according to the present disclosure may be digitally signed and a cryptographic hash may be generated for each such message. The recipient of the message may use the sender's public key certificate to decrypt the message. The cryptographic hash may be generated again at the recipient. Both cryptographic hashes may be compared to verify the authenticity of the message. If the two cryptographic hashes match, the message may be determined to be valid. RS-2048 encryption may be used for the digital signatures.

[0043] In some aspects, access to data requires user authentication and authorization. For example, multi-factor authentication using at least two strong authentication codes may be desirable in situations involving law enforcement. Authenticated users may be authorized to view specific data as defined, for example, using role-based access controls that limit access based on subject, time frame, geography, and / or various other parameters. [Brief description of the drawings]

[0044] Non-limiting and non-exhaustive aspects of the present disclosure are described with reference to the following figures, in which like reference numerals refer to like parts throughout the various views unless otherwise specified.

[0045] [Figure 1] FIG. 1 is a flowchart depicting flows and steps according to various aspects of the present disclosure.

[0046] [Diagram 2] FIG. 2 is a flowchart depicting flows and actions according to various aspects of the disclosure.

[0047] [Diagram 3] FIG. 3 is a schematic block diagram of a system according to various aspects of the present disclosure.

[0048] [Figure 4] FIG. 4 is a message flow diagram depicting message flows according to various aspects of the disclosure.

[0049] [Diagram 5] FIG. 5 is a schematic block diagram of an exemplary system of the present teachings.

[0050] [Figure 6] FIG. 6 is a flowchart depicting flows and steps according to various aspects of the disclosure.

[0051] [Figure 7] FIG. 7 is a flowchart depicting flows and steps according to various aspects of the disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0052] Detailed Description In some configurations, the system may perform first pass processing, assuming that the first and second path processors will transfer data between each other, according to the method depicted in FIG. 1. In some aspects, second pass processing may be performed according to the method depicted in FIG. 2. With reference to FIG. 1, in action 1851, if it is not time to transmit data, e.g., if a desired amount of data has not been collected, or if a time limit for collecting data has not expired, or if some other known criteria for ceasing data collection has not been met, flow control proceeds to action 1852. In action 1852, if there is no more data to be processed, the first pass processing ends. If there is more data to be processed in action 1852, the control flow proceeds to action 1857. In action 1857, the system receives (e.g., from the second path processor) a desired mode. The mode may be established by a system user, be a default mode, be determined by an application selection made by a user, be determined by a set of one or more sensors, or selected in any of a variety of other suitable manners. The mode may be set by a system user, although the mode may alternatively be dynamically determined by the data collection system. Control flow then proceeds to action 1859.

[0053] In action 1859, the system activates one or more sensors based on the determined mode and receives and encrypts data from the one or more sensors. If the mode was previously determined and the sensors are activated, the system continues to receive and encrypt data. Control flow then proceeds to action 1861. In action 1861, if the mode is a general mode, control flow proceeds to action 1863. In action 1863, the system accumulates sensor data received from the activated sensors. In some configurations, the system supports dynamic adjustment of the set of activated sensors based at least on, for example, the occurrence of a sensor failure or whether at least a portion of the collected sensor data indicates that other sensors should be activated. If in action 1861, it is determined that the mode is not a general mode, control flow proceeds to action 1865.

[0054] In action 1865, if the mode is an object mode, the control flow proceeds to action 1867. In action 1867, the system determines the type of object desired. As a non-limiting example, the desired object may be a human, an animal, or an object. The control flow then proceeds to action 1869, where the system may select a trained machine learning model, in some configurations, the selection may be based at least in part on the type of object. The control flow then proceeds to action 1871, where the system applies the selected, trained machine learning model to the sensor data. In some aspects, in a first pass of processing, the selected machine learning model is trained to identify sensor data that generally meets the characteristics of the object, but may not more specifically meet the characteristics of the object (e.g., more detailed, fine, or granular characteristics). In some aspects, the first pass processing and the second pass processing are combined, enabling the system to identify specific objects in a single pass. Further, as one skilled in the art would understand, based on the type of subject, it is possible to adjust the first pass processing relative to the second pass processing (or vice versa) to achieve optimal results. In other words, the relative coarseness of the first pass filtering and the relative fineness of the second pass filtering may be adjusted or tuned relative to one another as desired. An example of first pass processing when the subject is a human is to identify all sensor data that meets a criteria of human-likeness according to a trained machine learning model and delete the remaining portion of the data. If in action 1865 it is determined that the mode is not a subject mode, the control flow proceeds to action 1873.

[0055] At action 1873, if the mode is scene mode, control flow proceeds to action 1875. At action 1875, the system may determine the type of scene desired. In some arrangements, the possible scenes of interest, as a non-limiting example, the general characteristics of a crime scene, may be known in advance. In other aspects, the possible scenes of interest, as a non-limiting example, the general characteristics of a crime scene, may be provided by a system user. Control flow then proceeds to action 1877, where the system selects one or more machine learning models based, at least in part, on the type of scene of interest. Control flow then proceeds to action 1871, where the system applies one or more selected machine learning models trained to identify specific types of scenes to the collected sensor data. Additionally or alternatively, applying multiple machine learning models to the collected data occurs as part of second pass filtering.

[0056] In action 1851, if a desired amount of data has been collected, or if a data collection time limit has expired, or if some other criteria for ceasing data collection has been met, flow control proceeds to action 1853. In action 1853, the system encodes the data generated by filtering, for example, in some configurations, by filtering with one or more machine learning models. Control flow then proceeds to action 1855, where the system transmits the data to a second path processor. Control flow then returns to action 1851, where a timer for ceasing data collection may be reset, and data collection and processing continues, if applicable.

[0057] In some aspects, if a second pass is required, the system performs the second pass according to the method depicted in FIG. 2. It will be understood by those skilled in the art that the first pass and second pass processes may be performed by a single processor executing coded instructions. In some configurations, the second pass processor interfaces with a system user such that the user interacts with the second pass processor, for example, via an application. The user interface may be optional and may be driven, at least by default, by a recipe and / or dynamically determined criteria.

[0058] With reference to FIG. 2, in some configurations, in action 1951, the system determines a data collection interval. The data collection interval may be a default value, or may be defined by a system user, or may be dynamically determined based on, as a non-limiting example, the number of sensors available or the number of types of sensors available, or the number of each type of sensor available. Control flow then proceeds to action 1953, where the system determines a desired mode. In various aspects, the system user may set the desired mode, or the desired mode may be dynamically determined by the system, or the desired mode may be established, at least in part, based on information requested by the system user. Control flow then proceeds to action 1955, where the system receives rules governing which portions of the collected data are relatively more significant. For example, the rules may include designation of images of persons of interest or scenes of interest. The rules may be established, for example, by law enforcement or other interested authorities or individuals. Control flow then proceeds to action 1957.

[0059] In action 1957, the system selects one or more machine learning models based at least on the determined mode. Additionally or alternatively, the one or more machine learning models are selected based at least in part on the received rules or other criteria that one skilled in the art would understand. In some configurations, models other than machine learning models are deployed. The control flow then proceeds to action 1959. In action 1959, the system trains one or more selected machine learning models based on the received rules. For example, if specific people are the subject of interest, the selected machine learning models can be trained to find matches between collected data and images of the subject of interest. In configurations where multiple processors are deployed, the control flow proceeds to action 1961, where the system provides the desired mode to, for example, a first path processor coupled to or associated with the data collection module. The control flow then proceeds to action 1963, where the system commands the first path processor to begin data collection. Control flow then proceeds to action 1965, where the system determines whether the data collection time interval has expired. If the data collection time interval has expired, control flow proceeds to action 1967. In action 1967, the second path processor receives data from the first path processor, and, if applicable, the second path processor either decodes the received data and / or decrypts the received data or encrypts the received data and / or encodes the received data for transmission or provision. Control flow then proceeds to action 1969.

[0060] In action 1969, the system determines whether the operating mode is a general mode, e.g., as opposed to an object mode or a scene mode. If the operating mode is determined to be a general mode, the control flow proceeds to action 1971. In action 1971, the system provides the data to a system user for evaluation, or, depending at least in part on the application, the system subjects the data to further processing or filtering. If in action 1969, the operating mode is determined to be not a general mode, i.e., the operating mode is determined to be one of an object mode or a scene mode, the control flow proceeds to action 1973. In action 1973, the system applies the trained machine learning model to the data and generates matched data by determining whether there are any matches in the data, as a subset of the data, to specific desired objects, to scenes of interest to the system user, or to scenes dictated by the received rules (as a non-limiting example, scenes indicating that a crime has been or is being committed). The control flow then proceeds to action 1975, where the system deletes any or all data that is not matched data. Control flow then proceeds to action 1971. In some aspects, after the data is evaluated by a system user, additional data is collected. In some aspects, the operating mode and / or rules are changed before data collection is resumed.

[0061] With reference to FIG. 3, a system 100 according to various aspects may selectively process images based on predetermined criteria. The system 100 includes at least an acquisition module 131 and a processing module 147. In some aspects, the acquisition module 131 is coupled to the processing module 147 by a communication medium 119. The communication medium 119 may be a wired connection, such as, for example, Ethernet, or the communication medium 119 may be a wireless connection, such as, for example, WiFi or a cellular or wide area band network, among others. In some arrangements, data may be encrypted in transit across the communication medium 119. In some configurations, the acquisition module 131 and / or the processing module 147 are implemented as a processor (not shown) that executes coded instructions stored in a memory (not shown) accessible thereby and / or integral thereto. In some aspects, the acquisition module 131 and the processing module 147 are located remotely from one another. In other aspects, the acquisition module 131 and the processing module 147 are co-located. In some aspects, the collection module 131 and the processing module 147 are implemented as a single processor.

[0062] In one arrangement, the collection module 131 includes a feature detector 105, a feature matching processor 107 coupled to or integral with the feature detector 105, a data filter 109 coupled to or integral with the feature matching processor 107, a data compressor 111 coupled to or integral with the data filter 109, a data-at-rest encryption module 113 coupled to or integral with the data compressor 111, and a transmission chain 115 coupled to or integral with the data-at-rest encryption module 113. The data filter 109 performs a first filter type on the received data. In some configurations, the data-at-rest encryption module 113 encrypts and digitally signs the data such that a receiver may verify the identity of the sender and / or determine if the received data has been altered. The transmission chain module 115 provides forward error correction encoding and / or modulation of the filtered, encrypted and digitally signed sensor data for transmission over the communication medium 119. In certain aspects, the transmission chain module 115 provides forward error correction encoding and / or modulation of the metadata (e.g., timestamp and / or GPS location) for transmission over the communication medium 119. In some embodiments, the data recipient uses the metadata (e.g., GPS location and timestamp) to verify whether the collection module 131 was present at the indicated location at the indicated time. Any or all of the feature detector 105, feature matching processor 107, data filter 109, data compressor 111, data-at-rest encryption module 113, and / or transmission chain 115 may be implemented as one or more processors, microcontrollers, or state machines executing code stored in hardware (e.g., ASICs or FPGAs), software or firmware modules, or in memory.The collection module 131, as well as the feature detector 105, the feature matching processor 107, the data filter 109, the data compressor 111, the data-at-rest encryption module 113, and the transmission chain 115 may function substantially as described above for various features of Figures 1 and 2.

[0063] In one aspect, the processing module 147 includes one or more of a data decryption module 135, a vault processor 137 coupled to or integral with the data decryption module 135, a data filter 139 coupled to or integral with the vault processor 137, a rule set processor 141 coupled to or integral with the data filter 139, a data-at-rest encryption module 143 coupled to or integral with the rule set processor 141, and a signature processor 145 coupled to or integral with the data-at-rest encryption module 143. The data decryption module 135 performs the decryption using a public and private key. Those skilled in the art will appreciate that the private key is a key that resides at all times with the authorized entity that uses the key to decrypt the received data. The vault processor 137 keeps track of the custody chain. The data filter 139 performs a first type of filtering on the received data. Signature processor 145 may verify a digital signature associated with a packet of data prior to using the data to prove that the data originates from a trusted source. Any or all of data decryption module 135, storage processor 137, data filter 139, rule set processor 141, data-at-rest encryption module 143, and / or signature processor 145 may be implemented as one or more processors, microcontrollers, or state machines executing code stored in hardware (e.g., ASIC or FPGA), software or firmware modules, or in memory. Processing module 147, and feature data decryption module 135, storage processor 137, data filter 139, rule set processor 141, data-at-rest encryption module 143, and signature processor 145 function substantially as described above with respect to various aspects of Figures 1 and 2.

[0064] In an aspect, the collection module 131 is configured to receive data from one or more sensors 103. The types of sensors 103 that may be available to the collection module 131 may depend on the environment of the system 100. For example, the sensors 103 may be mounted on a device, such as, for example, a remote-controlled robot, or bot for short, autonomous bot, or autonomous vehicle (AV) 102 configured to execute the system 100, and / or the sensors 103 may include optical cameras, laser devices, ultrasonic sensors, weather sensors, LIDAR sensors, radar sensors, infrared sensors, and / or near-field sensors, etc. An exemplary AV is described, for example, in the system shown and described in U.S. Patent Application No. 16 / 926,522 (Attorney Docket No. AA291), filed July 10, 2020, and entitled "System and Method for Real Time Control of an Autonomous Device." In some configurations, the device on which the system 100 is mounted is mobile. Data received from the one or more sensors 103 is provided to the feature detector 105. The acquisition module 131 receives a list of triggers from the processing module 147 via the communication medium 119.

[0065] In one arrangement, a rule set provider source 123 is coupled to the processing module 147. The rule set provider source 123 provides one or more rule sets to the processing module 147. The rule set provider source 123 may be, for example, but not limited to, a data store under the control of a law enforcement agency or other local government that tracks objects of interest. Examples of objects of interest may include, but are not limited to, people, automobiles, and / or tangible devices. In one aspect, the rule set includes information about an object of interest that may be used by the system 100 to locate the object of interest. In one embodiment, the rule set is provided to a rule set processor 141, which selects therefrom a subset of rules, e.g., a trigger list, based at least in part, for example, but not limited to, the location of the system 100, the time of day, and / or any other factors that may make the selected subset of rules relatively more useful or applicable. In one aspect, the rule set processor 141 is coupled to the communication medium 119 via a transmitter (not shown) such that the one or more trigger lists are provided to the collection module 131, specifically to the feature detector 105 via a receiver (not shown). Those skilled in the art will appreciate that the one or more trigger lists may additionally or alternatively be provided directly to the collection module 131. In one aspect, the processing module 147 is configured to provide at least one of the matching notification and the matched sensor data to the application 133 for use by the authorized actor. In one arrangement, the signature processor 145 is configured to provide at least one of the matching notification and the matched sensor data to the application 133 for use by the authorized actor via a transmitter (not shown).

[0066] In some aspects, data is stored in a legally permissible location, e.g., U.S. data is not stored on a server located outside the U.S. In various aspects, data collected by one or more sensors 103 resides in RAM memory (not shown) until it must be provided to the feature detector 105. In some aspects, each data source retains its own private key that is used to encrypt data transmitted from that source. In some aspects, an authorized system user or entity uses the private key in combination with the public key to decrypt data received from an authorized data source. In various configurations, the chain of custody may include one or more of the following example actors: collection device at rest, data transfer mechanism, cloud-based receiver service, cloud-based detection filter service, cloud-based notification service, and end user (e.g., law enforcement agency).

[0067] FIG. 4 depicts a message flow 200 according to an exemplary aspect. It will be understood by those skilled in the art that any or all of the various entities shown in FIG. 4 may be either physical or logical entities, may be co-located with or remote from one another, and / or may be implemented as a single entity or processor. Those skilled in the art will also readily appreciate that any or all of the various messages shown in FIG. 4 (in the context of different entities rather than a single entity) may be transmitted / received via any known communication medium, including but not limited to wired (e.g., Ethernet) and wireless (e.g., WiFi, cellular, satellite) communication mediums. With reference to FIG. 4, the collection device 201 transmits an image message 202 to an object type filter module 203. The object type filter module 203 attempts to detect an object type, e.g., a face or license plate (determined as described below), within the received image message 202. If an object type is detected, the object type filter module 203 transmits a type-matched image message 204 to an object filter module 205. The object filter module 205 attempts to detect a particular object (determined as described below), such as a specific person's face or a license plate having a specific license plate number, in the received type-matched image message 204. If a particular object is detected, the object filter module 205 transmits the object-matched image message 206 to an authorized agent 207 (e.g., law enforcement or other local government).

[0068] Continuing to refer to FIG. 4, the authorized agent 207 transmits a rule set message 208 to the rule set manager module 209. The rule set manager module 209 attempts to validate (e.g., authenticate) the rule set (e.g., containing rules defining, for example, but not limited to, an object or item of interest, a set of criteria used to detect the object or item of interest, etc.) in the received message 208. If the rule set manager module 209 validates the rule set in the rule set message 208, the rule set manager module 209 transmits a validated rule set message 210 to the persistent storage module 211. The rule set manager module 209 also creates an object type filter based, at least in part, on the validated rule set. The rule set manager module 209 transmits a type filter message 212 to the persistent storage module 211. The rule set manager module 209 also creates an object filter based, at least in part, on the validated rule set. The rule set manager module 209 transmits an object filter message 214 to the persistent storage module 211. The persistent storage module 211 transmits the received type filter message 212 to the object type filter module 203, which uses the received type filter message 212 to detect object types within the received image message 202. The persistent storage module 211 also transmits the received object filter message 214 to the object filter module 205, which uses the received object filter message 214 to detect specific objects within the received type-matched image message 204.

[0069] 5, data arrives from sensors 301 to cause initial processor 303 to perform initial processing. In an aspect, sensors 301 include mobile and / or fixed sensors. In an aspect, mobile sensors are mounted on vehicles, such as, for example, but not limited to, wheeled vehicles and / or autonomous vehicles, and / or drones, or humans / animals. In an aspect, fixed sensors are mounted on immovable locations, such as, but not limited to, roadways, traffic lights, traffic signals, traffic signs, buildings, and / or monuments. Sensors 301 can include, but are not limited to, visual sensors, such as cameras, signal sensors, such as lidar and ultrasonic, audio sensors, tactile sensors, and others. For initial processing 303 to be performed, rule sets 305 are made available from a trigger list 307 that is used, for example, to match faces and license plates. Data processing, in some aspects, includes compression, encryption, and encoding, followed by feature detection, feature matching (using a rule set), and a first type of filtering, such as, but not limited to, coarse-grained filtering. Data is encrypted at rest and digitally signed before transmission, enabling the receiver to prove the sender's identity upon receipt. The data includes metadata, such as timestamp and GPS location. The processed data is transferred and / or stored for use anywhere. For data transfer, only human-readable data is sent upon matching. Otherwise, data that is encrypted, signed, and encoded in transit (raw) is sent. The processed data is received, for example, by a cloud processor 311, where the data is decrypted using a private key and tracked using a chain of custody strategy, digital signatures, and metadata. The data is further filtered, for example, fine-grained filters are applied. The cloud processor 311 receives a rule set 313, for example, from a local government, processes the rule set 313, and provides an updated rule set 319 to the trigger list 307. The cloud processor 311 provides the match notification and the matched data 317 to an authorized actor 315 .In the cloud processor 311, the data is encrypted at rest and the digital signature is verified before the data is used to prove that the data arrives from a trusted source. The cloud processor 311 and / or the initial processor 303 store the data. During data storage, the data is stored at rest in a legal location, e.g., US data cannot be stored on a server outside the US. The data used by the sensor 301 stay in RAM until they need to be transmitted, at which point they are encrypted. Each authorized data source has its own private key that is used to encrypt the date from its source. Each authorized user / entity has a unique public key that enables the user / entity to decrypt data received from the authorized data source. Exemplary actors along the data custody chain can include, but are not limited to, bots at rest, data forwarding mechanism 309, the cloud processor 311 (receivers, detection filters, and notification services), and end users, e.g., law enforcement agencies. For data integrity, each data source digitally signs each data packet with a unique key. Each data receiver verifies the digital signature of each data packet, verifying that the packet is from an authenticated and authorized sender and that the packet was not tampered with in transit. Each data receiver uses contextual data to verify the data, e.g., if a matched image is from GPS coordinates x,y, the data receiver verifies that the patrolling bot was present at that location at the time reported in the match result. Private keys exist with the authorized parties, which they are authorized to verify, and are required to decrypt the data.

[0070] 6 and 7, a method for determining desired information from a configuration of the present teachings is shown. The method in FIG. 6 is described in terms of a processing device associated with a collection device collecting sensor data and with a user or another processor providing search information. In FIG. 6, a method 600 for determining desired information when receiving rules from an authorized actor includes, but is not limited to, receiving at least one rule securely from the authorized actor (602). In one aspect, the authorized actor is a law enforcement officer and the rule is, for example, a license to locate a vehicle, the license including a description of the desired vehicle by make, model, and license plate number. The method 600 includes updating at least one rules database (604) with the at least one rule. In one aspect, the rules database includes, for example, information for detecting vehicles in general, specific makes / models / models of vehicles, license plates in general, and specific license plates. The rules provided by the law enforcement officer provide specific information about a particular vehicle. As rules are added to the rules database, the database is expanded so that searches for specific vehicles are possible. Method 600 includes a step of securely transmitting (606) the rules database to the collection device. The processing device and the collection device may be co-located, but security measures such as encryption at rest can ensure that privacy concerns about the rules and search data are not compromised. When the processing device and the collection device are communicating over a network, the step of securely transmitting messages, including, for example, legal authorizations, includes features such as encryption and man-in-the-middle. Method 600 includes a step of securely receiving (608) sensor data from the collection device. The collection device can encrypt in-place and encrypt its transmission of collected data, for example images of the vehicle. In one aspect, the collection device uses the rules database, particularly rules provided by law enforcement officials, to perform "rough" filtering of the collected data.This step reduces, among other things, the amount of data that is encrypted and transmitted by the collection device to the processing device. The coarse filter can, for example, filter out data that is not a vehicle. In one aspect, filtering out data is defined as deleting all data that does not meet the filter criteria. This step, although optional, protects the privacy of vehicle owners that are not associated with the license and protects law enforcement agencies from privacy violation complaints. The coarse filter can be adjusted to filter out data that is not a vehicle of the desired make / model / type and / or vehicles that do not have a license plate. The filter can be adjusted according to the processing capabilities of the collection device and, if applicable, the transmission rate of the communication link between the collection device and the processing device. The method 600 includes a step (610) of securely storing the received coarsely filtered data. In one aspect, no storage device is required. However, if the data is stored, encryption at rest protects the data from unauthorized access, thus protecting, for example, the privacy of the vehicle owner. Method 600 includes applying (612) a fine filter to the securely stored, coarsely filtered data. For example, if the data includes a vehicle of the desired make / model / type, the fine filter can further examine the data for a match to the desired license plate. Method 600 includes securely transmitting (614) the desired information to an authorized actor. In embodiments herein, a law enforcement officer is provided with the location of the desired vehicle, for example, through an encrypted transmission. In one aspect, other data that is not the desired information is permanently deleted from the storage area of ​​the processing device.

[0071] Referring now to FIG. 7, a method 700 for retrieving and providing desired information is executing within a collection device. The method 700 includes a step of securely receiving (702) at least one rule set database from a processing device that receives information from an authorized actor and uses the information to update the database. The method 700 includes a step of securely collecting and storing (704) sensor data associated with a preselected area associated with the location of the collection device, thus, in effect, location tagging the collected data. The method 700 includes a step of filtering (706) the collected data according to a "trigger" (coarse filtering) from the rule set database. As discussed herein, such filtering includes, for example, sorting vehicles from other data, but may include any threshold, sorting vehicle license plates from each other, or even locating a specific desired license plate location. Method 700 includes securely storing (708) the filtered data and securely transmitting (710) the filtered data to a processing device. In one aspect, the filtered data is securely transmitted but not stored. Thus, the data are either deleted when they do not meet the trigger criteria or deleted after or as they are securely transmitted to the processing device.

[0072] One or more computer systems can be configured to perform a particular operation or action by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes an action or causes the system to perform an action. One or more computer programs can be configured to perform a particular operation or action by including instructions that, when executed by a data processing device, cause the device to perform an action. One general aspect includes a method for identifying desired information from sensor data collected by a collection device. The method also includes the steps of securely receiving at least one rule from an authorized actor, determining at least one coarse filter based on the at least one rule, updating at least one rules database with the at least one rule, securely transmitting the at least one coarse filter to the collection device, and securely receiving sensor data from the collection device, the sensor data being filtered by the at least one coarse filter. The method also includes the steps of determining desired information by applying a fine filter to the filtered sensor data, the fine filter being based at least on the at least one rules database. The method also includes encrypting and transmitting the desired information to the authorized actor. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0073] Implementations may include one or more of the following features. The method may include encrypting in place and storing the received coarsely filtered sensor data. The coarse filter may include at least one feature of interest. The at least one feature of interest may include a height of the object. The at least one feature of interest may include a make of the vehicle. The at least one feature of interest may include a color of the vehicle. The authorized actor may include a law enforcement agency. The at least one rule may include a rule generated, at least in part, from a letter of authorization from the authorized actor. The desired information may include an identification of the object. The desired information may include a license plate number. The method may include securely deleting all the coarsely filtered sensor data after securely transmitting the desired information to the authorized actor. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0074] One general aspect includes a method for identifying desired information from sensor data collected by a collection device. The method also includes securely receiving at least one rules data set from a processing device, and securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device. The method also includes filtering the sensor data to determine desired information, the filtering based at least on at least one rules database, and securely storing the desired information. The method also includes securely transmitting the desired information to the processing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0075] Implementations may include one or more of the following features: The method may include compressing the desired information. The method may include encrypting the desired information. The method may include encoding the desired information. The filtering step may include filtering the sensor data to determine a human subject, the filtering step being based at least on at least one rules database. The filtering step may include filtering the sensor data to determine a license plate number, the filtering step being based at least on at least one rules database. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0076] One general aspect includes a system for identifying desired information from sensor data collected by a collection device. The system also includes at least one sensor mounted on an autonomous vehicle and a collection module executing on a processor, the collection module configured to securely receive at least one rules data set, the collection module configured to securely receive and store sensor data associated with a preselected area associated with a location of the autonomous vehicle, the collection module configured to filter based at least on the at least one rules database, the sensor data to determine the desired information, the collection module configured to securely store the desired information, and the collection module configured to securely transmit the desired information. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0077] Implementations may include one or more of the following features: A system, wherein the collection module is configured to compress the desired information. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0078] One or more computer systems can be configured to perform a particular operation or action by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes an action or causes the system to perform an action. One or more computer programs can be configured to perform a particular operation or action by including instructions that, when executed by a data processing device, cause the device to perform an action. One general aspect includes a system for identifying desired information from sensor data collected by a collection device, comprising a processing module, the processing module comprising: a processor for processing the sensor data; ... a processing module including processing module computer instructions for applying a fine filter to the coarsely filtered sensor data to determine desired information, the fine filter being based at least on at least one rules database; securely transmitting the desired information to an authorized actor; and securely deleting all intermediate coarsely filtered sensor data; and a collection module configured to execute on the collection device, the collection module securely receiving at least one rules data set from the processing device executing the processing module computer instructions; securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device; coarsely filtering the sensor data;The method includes a collection module including collection module computer instructions for determining intermediate coarsely filtered sensor data, where the coarse filtering is based at least on at least one rule database, securely storing the intermediate coarsely filtered sensor data, and securely transmitting the intermediate coarsely filtered sensor data to a processing module; a user interface configured to receive the at least one rule from an authorized actor; and a communication gateway configured to enable secure communication between the user interface, the processing module, and the collection module. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0079] Implementations may include one or more of the following features: A system, wherein the collection module is configured to delete all sensor data after securely transmitting the coarsely filtered sensor data to the processing module. The secure communication may include encrypted communication. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0080] One general aspect includes a method for identifying desired information from sensor data collected by a collection device. The method also includes securely receiving at least one rules data set from a processing device, and securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device. The method also includes filtering the sensor data to determine desired information, the filtering based at least on at least one rules database, and securely storing the desired information. The method also includes securely transmitting the desired information to the processing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0081] Implementations may include one or more of the following features: The method may include compressing the desired information. The method may include encrypting the desired information. The method may include encoding the desired information. The filtering step may include filtering the sensor data to determine a human subject, the filtering step being based at least on at least one rules database. The filtering step may include filtering the sensor data to determine a license plate number, the filtering step being based at least on at least one rules database. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0082] One general aspect includes a system for identifying desired information from sensor data collected by a collection device. The system also includes at least one sensor mounted on an autonomous vehicle and a collection module executing on a processor, the collection module configured to securely receive at least one rules data set, the collection module configured to securely receive and store sensor data associated with a preselected area associated with a location of the autonomous vehicle, the collection module configured to filter based at least on the at least one rules database, the sensor data to determine the desired information, the collection module configured to securely store the desired information, and the collection module configured to securely transmit the desired information. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0083] Implementations may include one or more of the following features: A system, wherein a collection module is configured to compress the desired information. The collection module is configured to encrypt the desired information. The collection module is configured to encode the desired information. The desired information may include data associated with a human subject. The desired information may include data associated with a license plate. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0084] One general aspect includes a system for identifying desired information from sensor data collected by a collection device, the system including a processing module, the processing module comprising: securely receiving at least one rule from an authorized actor; updating at least one rule database with the at least one rule; securely transmitting the at least one rule database to the collection device; securely receiving intermediate coarsely filtered data from the collection device, the intermediate coarsely filtered data being coarsely filtered based at least on the at least one rule database; securely storing the received intermediate coarsely filtered sensor data; applying a fine filter to the securely stored intermediate coarsely filtered sensor data to determine desired information, the fine filter being based at least on the at least one rule database; securely transmitting the desired information to the authorized actor; and securely transmitting all intermediate coarsely filtered data to the authorized actor. a processing module including processing module computer instructions for securely deleting the filtered sensor data; and a collection module configured to execute on a collection device, the collection module including collection module computer instructions for securely receiving at least one rule data set from the processing device executing the processing module computer instructions; securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device; coarsely filtering the sensor data and determining intermediate coarsely filtered sensor data, the coarse filtering being based at least on at least one rule database; securely storing the intermediate coarsely filtered sensor data; and securely transmitting the intermediate coarsely filtered sensor data to the processing module; and a user interface configured to receive the at least one rule from an authorized actor.and a communication gateway configured to enable secure communication between the user interface, the processing module, and the collection module. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0085] Implementations may include one or more of the following features: A system, wherein the desired information may include data associated with a human subject. The desired information may include data associated with a license plate. The collection module is configured to delete all sensor data after securely transmitting the coarsely filtered sensor data to the processing module. The secure communication may include encrypted communication. The collection module is configured to encode the desired information. A method, wherein the at least one rule may include: The method includes a rule generated, at least in part, from a letter of authorization from an authorized actor, the authorized actor being a law enforcement agency. The method further includes: determining an identity of the subject based at least on results from the fine filter. The method further includes: determining a license plate number based at least on results from the fine filter. The method further includes: securely deleting all intermediate coarsely filtered sensor data after securely transmitting the desired information to the authorized actor. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0086] Implementations may include one or more of the following features: A method, wherein at least one rule may include: The method includes a rule generated, at least in part, from a letter of authorization from an authorized actor, the authorized actor being a law enforcement agency; The method further includes: determining an identity of the subject based at least on results from the fine filter; The method further includes: determining a license plate number based at least on results from the fine filter; The method further includes: securely deleting all intermediate coarsely filtered sensor data after securely transmitting the desired information to the authorized actor; Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0087] In various aspects, the system may enable law enforcement or relevant authorities using the system to instruct the system on what it should search for. The system may be actively patrolling, and to the extent the law enforcement agency has the authority, the law enforcement agency may tell the system to look for specific things, such as a particular face, sound, car model, license plate, etc. The system may also be told to include items such as location and / or time of day. The system may also be configured to note the absence in a detected scene of an item that would normally be present. As a non-limiting example, the system may detect the absence of trucks in a lot where only four trucks are parked, although five trucks are assumed to be parked there. The collection device of the system may in some configurations be collocated with, or housed in or on, or executed by, an autonomous vehicle. A non-limiting example of a suitable autonomous vehicle is described in U.S. Patent Application No. 16 / 435,007 (Attorney Docket No. AA001), filed June 7, 2019, and entitled "System and Method for Distributed Utility Service Execution," which is incorporated herein by reference in its entirety. The collection module may perform a relatively coarse-grained filter on the collected data and transmit only the desired data such that undesired data is not transmitted. The collection module may be configured to downsample the data in real-time as the data is being collected, such that, for example, within a set of collected images depicting the same item (and each adding little or no useful information to each other, e.g., each differing from its nearest neighbors by less than a threshold percentage or absolute delta), only one or a subset of such collected images are transmitted. Additionally, the collection module may be configured to compress the data to be transmitted to conserve bandwidth.The collection module may be configured to transmit a window of images around the matched image, responsive to detecting a matched image for a desired object or item, beginning with an image detected some time before the matched image is detected and ending with an image detected some time after the matched image is detected. The collection module may be configured to encrypt all data to be transmitted. The fine-grained filter may be implemented in a processing module to which the collection module transmitted data. As a non-limiting example, the processing module may be located in a cloud. The relative coarseness and relative fineness of the filtering may be adjusted relative to one another, for example, as needed for system optimization. Additionally or alternatively, the relative coarseness of the filtering implemented in the collection module may be adjusted, at least in part, based on the type of object desired and / or the privacy expectations associated with the desired object. Some types of objects, e.g., certain faces, may be more difficult to reliably detect than other types of objects, e.g., license plates. For example, some types of objects, e.g., certain faces, may be associated with a higher privacy expectation compared to other types of objects, e.g., license plates.

[0088] In at least one arrangement, the processing module may create a rule set. An authorized agent may be authenticated to a web-based application that enables the authorized agent to create new rule sets. Additionally or alternatively, a trusted external system may create new rule sets via a representational state transfer (REST) ​​application programming interface (API). In at least one arrangement, the rule set may include at least the following rule information: (i) a subject / rule identifier that associates the rule and any results with an agent or with the agent's system of record (e.g., license number, case number, etc.); (ii) subject type (e.g., person, vehicle license plate, etc.); (iii) compared subject (e.g., person image, license plate number); (iv) search effective date / time; (v) search expiration date / time; and (vi) search location. The web-based application may validate the information provided and create a filter object based on the subject type and the compared subject input. In some aspects, the person filter object may not contain an image of the subject. The collection module may detect any / all people, rather than specific people, and thus the facial recognition module may reside in the cloud, and thus the target image does not need to be sent to the collection module. In some aspects, the license plate filter object may contain an alphanumeric string. In some aspects, the information in the rule set about the target type may be in the form of a JavaScript Object Notation (JSON) object.

[0089] In at least one arrangement, the processing module may assign one or more rule sets to one or more autonomous devices, e.g., robots, or bots for short. The bots may incorporate a data collection module. The web-based application may select a list of active bots located within a search location defined in a database of current bot locations. In some aspects, the system may encrypt the filter package using an AES encryption algorithm at 256-bit strength. Additionally or alternatively, the web-based application may transmit the filter package to the selected bots using Transport Layer Security (TLS) 1.2.

[0090] In at least one arrangement, the collection module may collect images of detected objects matching the rule set's object type (e.g., person, license plate number). In some aspects, the system may coarsely filter objects based on the rule set. For example, if the rule set defines that an object of interest has license plate number x, the system may filter only vehicles (cars, trucks, bikes, etc.) among all detected objects. In some aspects, the system may provide a two-dimensional (2D) bounding box tracking mechanism, such as, for example, generalized intersection of union (GIOU) tracking, to track people or vehicles and their respective bounding boxes from one image frame to the next. In some aspects, the system may crop some or all sections of an image within a bounding box of a person, as a non-limiting example. The system may transmit at least one such cropped image associated with each detected and tracked object of type person or license plate number to a processing module. In some aspects, the system may encode and / or encrypt the cropped image data before transmitting the cropped image data to a processing module.

[0091] In at least one arrangement, the processing module may receive and process cropped images of objects detected and tracked within the location and time prescribed by the rule set. In some aspects, the system may decrypt and / or decode the cropped images generated by the collection module and provided to the processing module. The processing step may continue if the data is valid, i.e., the data can be decrypted and decoded. Invalid data, i.e., data that cannot be decrypted or decoded, may be deleted. In some aspects, the system may verify that the timestamp and / or GPS location of the cropped images are within the boundaries indicated by the search authorization, delete the invalid data, and store the valid data for further processing.

[0092] In at least one arrangement, the processing module may receive and process cropped images of license plates detected and tracked within the location and time defined by the rule set. In some aspects, the system may decrypt and / or decode the cropped images generated by the collection module and provided to the processing module. The system may provide at least one decoded cropped image as an input to a neural network algorithm that may generate at least one bounding box containing at least one license plate as an output. In some aspects, the system may further crop the detected license plates from each input to the neural network algorithm for further processing. The cropped images containing the license plates may, in some aspects, be subjected to an optical characteristic recognition algorithm to output the alphanumeric characters on the license plate as text. In some aspects, the system may perform text matching between the generated text and the license plate number described in the filter rule set to classify the text as a match or a non-match.

[0093] In at least one arrangement, the processing module may receive and process cropped images of the objects detected and tracked within the location and time defined by the rule set. In some aspects, the system may decrypt and / or decode the cropped images generated by the collection module and provided to the processing module. The system may provide at least one decoded, cropped image as an input to a neural network algorithm, which may generate at least one bounding box containing at least one facial image as an output. In some aspects, the system may further crop the detected facial image from each input to the neural network algorithm for further processing.

[0094] In at least one arrangement, the system may encrypt and store each cropped image containing matched text or a matched image.

[0095] In at least one arrangement, the authorized agent may view information about the matched object or the matching event. The processing module may notify the authorized agent about the matched object. In some aspects, the processing module may create and store an encrypted package containing GPS coordinates and / or timestamps and / or images and / or text associated with the matched object. The system may notify the authorized agent by sending an SMS message and / or / email message, a message containing a URL link to a web-based application. Additionally or alternatively, the system may notify the authority by sending a notification to a trusted third-party web service via a message queue.

[0096] In at least one arrangement, an authorized agent may view and / or download and / or retrieve the matched subject data. The agent may authenticate to a web-based application and thereby view and / or download the matched subject data. Additionally or alternatively, the processing module may send an access token to a trusted third party for use in retrieving the encrypted package.

[0097] In at least one arrangement, the facial recognition and / or text recognition modules may be periodically trained to maintain a threshold prediction accuracy rate over time. In some aspects, the prediction confidence threshold is initially set at a relatively low level that generates more false positives and is raised over time as the model becomes more accurate, generating fewer overall matches.

[0098] In at least one arrangement, a confidence level threshold for filtering data to determine whether to verify, withhold, or invalidate and discard the data may be set at 85%, for example. In some aspects, any data that cannot be decrypted or decoded may be discarded. In some aspects, any data for which the source, by way of non-limiting example a bot, cannot be authenticated may be discarded. In some aspects, any data that is outside the scope of a letter of authorization, for example, may be discarded.

[0099] In at least one arrangement, security measures may be maintained between the bot with the collection module and the bot's remote controller operator. As a non-limiting example, the remote control operator may be required to authenticate with a username and password to access the remote control console. In some aspects, the remote control console may include a web browser that may transmit a connection request, the request may be encrypted using an AES encryption algorithm at 256-bit strength, and the request may be transmitted using Transport Layer Security (TLS) 1.2. In some aspects, the identity of the remote control console and the bot may be authenticated by a connection broker prior to the creation of a peer-to-peer connection. As a non-limiting example, the remote control console's web browser may establish a secure peer-to-peer connection with the bot via WebRTC. In some aspects, video from the remote control console display may be streamed to the browser via WebRTC and may not be stored in the web browser or on the remote console or remote console host machine.

[0100] Those skilled in the art will understand that the methods described within this disclosure may be applied to computer systems configured to perform such methods, and / or computer readable media containing programs for performing such methods, and / or software and / or firmware and / or hardware (e.g., integrated circuits) designed to perform such methods. The raw data and / or results may be stored for future retrieval and processing, printed, displayed, transferred to another computer, and / or transferred anywhere. The communication links may be wired or wireless, including, as non-limiting examples, Ethernet, cellular or broadband networks, WiFi or local area networks, military communication systems, and / or satellite communication systems. Portions of the system may run on computers with, for example, varying numbers of CPUs. Other alternative computer platforms may be used.

[0101] As one skilled in the art would understand, the methods described in this disclosure may be implemented in whole or in part electronically. Signals representing actions taken by elements of the system of this disclosure and other disclosed configurations may travel via at least one live communication network. Control and data information may be electronically executed and stored on at least one computer readable medium. The system may be implemented to execute on at least one computer node in at least one live communication network. Common forms of computer readable media may include, for example, but are not limited to, floppy disks, flexible disks, hard disks, magnetic tapes or any other magnetic media, compact disks read only memory or any other optical media, punch cards, paper tapes or any other physical media with patterns of holes, random access memory, programmable read only memory, erasable programmable read only memory (EPROM), flash EPROM or any other memory chip or cartridge, or any other medium from which a computer may read.

[0102] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different existing techniques. For example, data, instructions, commands, information, signals, bits, symbols, or chips that may be referred to throughout this description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, ultrasound, projected capacitance, or any combination thereof.

[0103] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in connection with the arrangements disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the appended claims.

[0104] The various illustrative logic blocks, modules, and circuits described in connection with the arrangements disclosed herein may be implemented with or implemented using a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. A general purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0105] The actions of the methods or algorithms described in connection with the arrangements disclosed herein may be embodied directly in hardware, in software modules executed by a processor, or in a combination of the two. The software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. The storage medium may be coupled to the processor such that the processor can read information from the storage medium and write information to it. Alternatively, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a functional device such as, for example, a computer, a robot, a user terminal, a mobile phone or tablet, a car, or an IP camera. Alternatively, the processor and the storage medium may reside as discrete components in such functional devices.

[0106] The above description is not intended to be exhaustive or to limit the features to the precise form disclosed. Various alternatives and modifications can be devised by those skilled in the art without departing from the disclosure, and the general principles defined herein may be applied to other aspects without departing from the spirit or scope of the appended claims. Thus, the disclosure is intended to encompass all such alternatives, modifications, and variations. In addition, although several arrangements of the disclosure are shown in the drawings and / or discussed herein, the disclosure is not intended to be limited thereto, as the disclosure is intended to be as broad in scope as the art will permit, and the specification is intended to be read in the same manner. Thus, the above description should not be interpreted as limiting, but merely as examples of specific configurations. Also, those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto. Other elements, steps, actions, methods, and techniques that are not substantially different from those described above and / or in the appended claims are also intended to be within the scope of the disclosure. Thus, the appended claims are not intended to be limited to the arrangements shown and described herein, but are intended to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0107] The arrangements shown in the drawings are presented only to demonstrate certain embodiments of the present disclosure. Also, the drawings described are merely illustrative and non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn to a particular scale for illustrative purposes. In addition, elements shown in the drawings with the same number may be the same elements or similar elements, depending on the context.

[0108] When the term "comprising" is used in the present description and claims, it does not exclude other elements or steps. When an indefinite or definite article, such as "a", "an", or "the", is used when referring to a singular noun, it also includes the plural of that noun, unless something is specifically stated otherwise. Thus, the term "comprising" should not be interpreted as being limited to the items listed thereafter, as it does not exclude other elements or steps, and thus the scope of the expression "a device comprising items A and B" should not be limited to a device consisting of only components A and B. Furthermore, insofar as the terms "includes", "has", "possesses", and equivalents are used in the present description and claims, such terms, when employed as transitional terms in the claims, are intended to be inclusive in a manner similar to the term "comprising", so that they are interpreted as "comprising".

[0109] Moreover, the terms "first," "second," "third," and the like, whether used in the description or in the claims, are provided to distinguish between similar elements and not necessarily to describe an order or chronology. It is to be understood that the terms so used are interchangeable under appropriate circumstances (unless expressly disclosed otherwise) and that the disclosed embodiments described herein are capable of operation in sequences and / or arrangements other than those described or illustrated herein.

[0110] One or more computer systems can be configured to perform a particular operation or action by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes an action or causes the system to perform an action. One or more computer programs can be configured to perform a particular operation or action by including instructions that, when executed by a data processing device, cause the device to perform an action. One general aspect includes a method for identifying desired information from sensor data collected by a collection device. The method also includes the steps of securely receiving at least one rule from an authorized actor, updating at least one rules database with the at least one rule, securely transmitting the at least one rules database to the collection device, securely receiving sensor data from the collection device, the sensor data being at least coarsely filtered based on the at least one rules database, and securely storing the received coarsely filtered sensor data. The method also includes the steps of applying a fine filter to the securely stored filtered sensor data to determine desired information, the fine filter being based at least on the at least one rules database. The method also includes securely transmitting the desired information to the authorized actor. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0111] Implementations may include one or more of the following features: A method, wherein at least one rule may include a rule generated, at least in part, from a letter of authorization from an authorized actor, the authorized actor being a law enforcement agency. The method may include determining an identity of a subject based at least on results from the fine filter. The method may include determining a license plate number based at least on results from the fine filter. The method may include securely deleting all intermediate coarsely filtered sensor data after securely transmitting the desired information to the authorized actor. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0112] One general aspect includes a method for identifying desired information from sensor data collected by a collection device. The method also includes securely receiving at least one rules data set from a processing device, and securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device. The method also includes filtering the sensor data to determine desired information, the filtering based at least on at least one rules database, and securely storing the desired information. The method also includes securely transmitting the desired information to the processing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0113] Implementations may include one or more of the following features: The method may include compressing the desired information. The method may include encrypting the desired information. The method may include encoding the desired information. The filtering step may include filtering the sensor data to determine a human subject, the filtering step being based at least on at least one rules database. The filtering step may include filtering the sensor data to determine a license plate number, the filtering step being based at least on at least one rules database. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0114] One general aspect includes a system for identifying desired information from sensor data collected by a collection device. The system also includes at least one sensor mounted on an autonomous vehicle and a collection module executing on a processor, the collection module configured to securely receive at least one rules data set, the collection module configured to securely receive and store sensor data associated with a preselected area associated with a location of the autonomous vehicle, the collection module configured to filter based at least on the at least one rules database, the sensor data to determine the desired information, the collection module configured to securely store the desired information, and the collection module configured to securely transmit the desired information. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0115] Implementations may include one or more of the following features: A system, wherein a collection module is configured to compress the desired information. The collection module is configured to encrypt the desired information. The collection module is configured to encode the desired information. The desired information may include data associated with a human subject. The desired information may include data associated with a license plate. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0116] One general aspect includes a system for identifying desired information from sensor data collected by a collection device, the system including a processing module, the processing module comprising: securely receiving at least one rule from an authorized actor; updating at least one rule database with the at least one rule; securely transmitting the at least one rule database to the collection device; securely receiving intermediate coarsely filtered data from the collection device, the intermediate coarsely filtered data being coarsely filtered based at least on the at least one rule database; securely storing the received intermediate coarsely filtered sensor data; applying a fine filter to the securely stored intermediate coarsely filtered sensor data to determine desired information, the fine filter being based at least on the at least one rule database; securely transmitting the desired information to the authorized actor; and securely transmitting all intermediate coarsely filtered data to the authorized actor. a processing module including processing module computer instructions for securely deleting the filtered sensor data; and a collection module configured to execute on a collection device, the collection module including collection module computer instructions for securely receiving at least one rule data set from the processing device executing the processing module computer instructions; securely receiving and storing sensor data associated with a preselected area associated with a location of the collection device; coarsely filtering the sensor data and determining intermediate coarsely filtered sensor data, the coarse filtering being based at least on at least one rule database; securely storing the intermediate coarsely filtered sensor data; and securely transmitting the intermediate coarsely filtered sensor data to the processing module; and a user interface configured to receive the at least one rule from an authorized actor.and a communication gateway configured to enable secure communication between the user interface, the processing module, and the collection module. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0117] Implementations may include one or more of the following features: A system, wherein the collection module is configured to delete all sensor data after securely transmitting the coarsely filtered sensor data to the processing module. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0118] The claims are as follows:

Claims

1. A method for identifying desired information from sensor data collected by a collection device, the method comprising: securely receiving at least one rule from an authorized actor; determining at least one first filter based on the at least one rule; updating at least one rule database using the at least one rule; securely transmitting the at least one first filter to the collection device; securely receiving the sensor data from the collection device, wherein the sensor data is filtered by the at least one first filter; determining the desired information by applying at least one second filter to the filtered sensor data, wherein the at least one second filter is based at least on the at least one rule database; encrypting the desired information and transmitting it to the authorized actor and including. Method.

2. The method according to claim 1, further comprising encrypting in place and storing the received first-filtered sensor data.

3. The authorized actor is a law enforcement agency and including. The method according to claim 1.

4. The at least one rule is a rule generated at least in part from a certificate of authorization from the authorized actor and including. The method according to claim 1.

5. The desired information is identification of an object and including. The method according to claim 1.

6. The desired information is a license plate number and including. The method according to claim 1.

7. After securely transmitting the desired information to the authorized actor, securely deleting all the first-filtered sensor data and further including. The method according to claim 1.

8. A method for identifying desired information from sensor data collected by a collection device, the method comprising: securely receiving at least one rule dataset from a processing device; securely receiving and storing the sensor data associated with a preselected area associated with the location of the collection device; Filtering the sensor data to determine the desired information, wherein the filtering is at least based on the at least one rule data set, and Securely storing the desired information, and Securely transmitting the desired information to the processing device A method further comprising.

9. Compressing the desired information The method according to claim 8, further comprising.

10. Encrypting the desired information The method according to claim 8, further comprising.

11. Encoding the desired information The method according to claim 8, further comprising.

12. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, the system is executed on a processor, the processor is located within an autonomous vehicle, and the system is A collection module executed on the processor, the collection module being configured to securely receive at least one rule data set, the collection module being configured to securely receive and store the sensor data associated with a preselected area associated with the location of the autonomous vehicle, the collection module being configured to filter at least based on the at least one rule data set, the sensor data being for determining the desired information, the collection module being configured to securely store the desired information, and the collection module being configured to securely transmit the desired information. Collection module A system comprising.

13. The system according to claim 12, wherein the collection module is configured to compress the desired information.

14. The system according to claim 12, wherein the collection module is configured to encrypt the desired information.

15. The system according to claim 14, wherein the collection module is configured to encode the desired information.

16. The desired information is Data associated with a human subject The system according to claim 12, comprising.

17. The desired information is Data associated with a license plate The system according to claim 12, comprising.

18. A system for identifying desired information from sensor data collected by a collection device, the system comprising: A processing module, the processing module comprising: Securely receiving at least one rule from an authorized actor; Updating at least one rule database using the at least one rule; Securely transmitting the at least one rule database to the collection device; Securely receiving intermediate roughly filtered data from the collection device, the intermediate roughly filtered data being roughly filtered based at least on the at least one rule database; Securely storing the received intermediate roughly filtered sensor data; Applying a fine filter to the securely stored intermediate roughly filtered sensor data to determine the desired information, the fine filter being based at least on the at least one rule database; Securely transmitting the desired information to the authorized actor; Securely deleting all of the intermediate roughly filtered sensor data; A processing module including computer instructions for the processing module; A collection module configured to execute on the collection device, the collection module comprising: Securely receiving the at least one rule database from a processing device executing the computer instructions of the processing module; Securely receiving and storing the sensor data associated with a preselected area associated with the location of the collection device; Roughly filtering the sensor data to determine the intermediate roughly filtered sensor data, the roughly filtering being based at least on the at least one rule database; Securely storing the intermediate roughly filtered sensor data; Securely transmitting the intermediate roughly filtered sensor data to the processing module; A collection module including computer instructions for the collection module; A user interface configured to receive the at least one rule from the authorized actor; A communication gateway configured to enable secure communication between the user interface, the processing module, and the collection module A system comprising: The system, wherein the secure communication includes encrypted communication.

19. A method for identifying and processing desired information from sensor data collected by a collection device, the method comprising: Collecting the sensor data; Receiving a set of filter rules from an authorized entity; Filtering the sensor data and sorting out unnecessary information from the desired information based on a validity check of the set of filter rules and the authorized entity; Deleting the unnecessary information; Encrypting the desired information; Selecting an operation mode based on the desired information; Processing the desired information based on the operation mode A method comprising: The collection device includes an autonomous vehicle.

20. A method for identifying and processing desired information from sensor data collected by a collection device, the method comprising: Collecting the sensor data; Receiving a set of filter rules from an authorized entity; Filtering the sensor data and sorting out unnecessary information from the desired information based on a validity check of the set of filter rules and the authorized entity; Deleting the unnecessary information; Encrypting the desired information; Selecting an operation mode based on the desired information; Processing the desired information based on the operation mode A method comprising: Encrypting the desired information in place A method further comprising:

21. A method for identifying and processing desired information from sensor data collected by a collection device, the method comprising: Collecting the sensor data; Receiving a set of filter rules from an authorized entity; Filter the sensor data and sort out the unnecessary information from the desired information based on the filtering rule set and the validity check of the authorized entity; Delete the unnecessary information; Encrypt the desired information; Select an operation mode based on the desired information; Process the desired information based on the operation mode A method comprising: The processing comprises: Executing processing instructions within a processing device A method.

22. Further comprising coupling the processing device and the collection device by an electronic communication device The method according to claim 21.

23. The method according to claim 21, wherein the processing device and the collection device are physically separate.

24. The method according to claim 21, wherein the processing device and the collection device are physically co-located.

25. Further comprising configuring a processor shared by the processing device and the collection device The method according to claim 21.

26. Further comprising configuring a plurality of processors shared by the processing device and the collection device The method according to claim 21.

27. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely matching with a trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a matching between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor A system comprising The system, wherein the collection processor is configured to be mounted on an autonomous vehicle, a utility pole, and / or a drone, and / or a human and / or an animal Claim 28 A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising The system, wherein the collection processor is configured to execute encoded instructions stored in a memory associated with the collection processor **Claim 29** A system for identifying desired information from sensor data collected by at least one sensor, the at least one sensor being associated with a collection device, the system comprising A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising The collection processor is configured to execute encoded instructions stored in a memory coupled to the collection processor. A system. **Claim 30** A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, the collection processor being configured to encrypt the received sensor data, the collection processor being configured to filter the sensor data, the collection processor deleting the sensor data to be filtered out, the collection processor retaining the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set being received from an authorized entity, the collection processor being configured to search for a match between the trigger list information and the filtered sensor data, the collection processor being configured to maintain the sensor data in a secure state, the collection processor being configured to perform feature extraction and a match with the trigger list information. Collection processor A system comprising The likely match is The probability of a match at or above a predetermined threshold probability Including a system. **Claim 31** A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising The trigger list information Rules established by authorities of law enforcement agencies A system including

32. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising The trigger list information A subset of a complete set of rules A system including the same.

33. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system includes A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information, the collection processor A system comprising The trigger list information includes Facial data of a desired individual A system including the same.

34. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system includes A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information, the collection processor A system comprising The collection processor that executes instructions When the trigger list information includes an object, perform object detection A system including Claim 35 A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information, the collection processor A system comprising wherein the sensor data includes LIDAR, radar, ultrasonic, optical, audio, chemical, infrared, magnetic, near-field waveform, electromagnetic wave, radio frequency waveform, point cloud, bitmap, alphanumeric, video, detected face, and detected object A system.

36. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system is a collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information A system comprising wherein the secure state is encrypted data, and the sensor data is encrypted data that is encrypted when the sensor data is received A system.

37. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information, collection processor A system comprising The secure state is Digitally signed data, wherein the sensor data begins to be digitally signed before the sensor data is transmitted, digitally signed data Including the system.

38. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system is A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be removed by filtering, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising The collection processor is configured to execute on an autonomous vehicle, the system.

39. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be removed by filtering, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, the collection processor A system comprising A remote processor that is executed at a processing station remote from the collection processor The system further comprises.

40. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system comprises A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, a collection processor The system comprises The system further comprises A processing module configured to receive and process a filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, the processing module is configured to receive a permission associated with the trigger list information, and the permission is configured to enable the collection processor and the processing module to search the trigger list information, a processing module The system further comprises The processing module further comprises A manager program, wherein the manager program is configured to maintain one or more of data privacy, data security, custody chain control, and / or audit trail, a manager program The system comprises

41. The manager program further comprises A cloud-based manager The system according to claim 40, further comprising.

42. The system according to claim 40, wherein the manager program is configured to process the filter rule set encoded in a plurality of formats.

43. The manager program is configured with instructions enabling the provision of an application for programming an interface related to privacy, a custody chain, and / or an audit rule to be set by a user. The system according to claim 40, comprising.

44. The user includes police, government authorities, national security agencies, and corporate customers with special privacy requirements. The system according to claim 43.

45. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system includes a collection processor configured to receive the sensor data from the collection device, the collection processor being configured to encrypt the received sensor data, the collection processor being configured to filter the sensor data, the collection processor deleting the sensor data filtered out and retained the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set being received from an authorized entity, the collection processor being configured to search for a match between the trigger list information and the filtered sensor data, the collection processor being configured to maintain the sensor data in a secure state, and the collection processor being configured to perform feature extraction and matching with the trigger list information. A system comprising. The system A processing module configured to receive and process the filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, and the processing module is configured to receive a permission associated with the trigger list information, and the permission is configured to enable the collection processor and the processing module to search the trigger list information, processing module further comprising A gateway configured to establish a web service to enable communication between the processing module and the collection processor, wherein data is exchanged between the collection processor and the gateway using the web service, gateway further comprising, a system

46. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor comprising, a system The system A processing module configured to receive and process the filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, and the processing module is configured to receive a permission associated with the trigger list information, and the permission is configured to enable the collection processor and the processing module to search the trigger list information, processing module further comprising The system, wherein the processing module is configured to decrypt the received information from the collection processor using an encryption key associated with the encrypted data received from the collection processor.

47. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor comprising, a system The system A processing module configured to receive and process the filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, and the processing module is configured to receive the authority associated with the trigger list information, and the authority is configured to enable the collection processor and the processing module to search the trigger list information, processing module further comprising The system, wherein the processing module is configured to re-encrypt the received information that has been decrypted.

48. A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely match with the trigger list information associated with the filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor A system comprising The system A processing module configured to receive and process the filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, and the processing module is configured to receive the authority associated with the trigger list information, and the authority is configured to enable the collection processor and the processing module to search the trigger list information, processing module further comprising A system, wherein the processing module is configured to verify and track a storage chain and / or a digital signature associated with received data. **Claim 49** A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system comprises: A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered out, the collection processor retains the sensor data indicating a likely match with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a match between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and a match with the trigger list information. A system comprising: The system further comprises: A processing module configured to receive and process a filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, the processing module is configured to receive a right associated with the trigger list information, and the right is configured to enable the collection processor and the processing module to search the trigger list information. And further comprising: A system, wherein the processing module is configured to enable a digital signature of some or all of the outgoing data transmitted by the processing module. **Claim 50** A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system comprises: A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely matching with trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a matching between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor A system comprising The system is A processing module configured to receive and process a filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, and the processing module is configured to receive a right associated with the trigger list information, and the right is configured to enable the collection processor and the processing module to search the trigger list information, processing module Further comprising The processing module At least one extraction algorithm, wherein the extraction algorithm is configured to decrypt and / or decode sensor data collected and filtered by the collection processor, at least one extraction algorithm At least one recognition algorithm, wherein the at least one recognition algorithm is configured to subject the decrypted / decoded sensor data to further processing, such as, but not limited to, an optical character recognition algorithm, at least one recognition algorithm A system comprising

51. The extraction algorithm At least one neural network algorithm, wherein the at least one neural network algorithm is configured to receive the decoded sensor data and generate a bounding box containing the decoded sensor data The system according to claim 50, comprising the same. **Claim 52** The system according to claim 51, wherein the extraction algorithm is configured to process the decoded sensor data provided by the at least one neural network algorithm. **Claim 53** The system according to claim 50, wherein the recognition algorithm is configured to filter the processed decoded data. **Claim 54** The system according to claim 50, wherein the recognition algorithm is configured to perform text matching between text generated from the decoded data and values from the filter rule set. **Claim 55** The system according to claim 50, wherein the recognition algorithm is configured to store the results from the further processing, and the storage is secure. **Claim 56** A system for identifying desired information from sensor data collected by at least one sensor, wherein the at least one sensor is associated with a collection device, and the system comprises A collection processor configured to receive the sensor data from the collection device, wherein the collection processor is configured to encrypt the received sensor data, the collection processor is configured to filter the sensor data, the collection processor deletes the sensor data to be filtered and removed, the collection processor retains the sensor data indicating a likely matching with a trigger list information associated with a filter rule set, the filter rule set is received from an authorized entity, the collection processor is configured to search for a matching between the trigger list information and the filtered sensor data, the collection processor is configured to maintain the sensor data in a secure state, and the collection processor is configured to perform feature extraction and matching with the trigger list information, collection processor A system comprising The system is A processing module configured to receive and process a filter and / or the trigger list information, wherein the filter and the trigger list information are configured to sort the sensor data, the processing module is configured to receive a permission associated with the trigger list information, and the permission is configured to enable the collection processor and the processing module to search for the trigger list information, processing module Further comprising The processing module encrypts a storage area and is configured to make the information stored in the storage area accessible by an authorized user based on the trigger list information, system

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