# US 2026/0094431 A1 · Share sensor data feature vectors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0094431 A1 - Document ID: US20260094431A1 - Status: Published application - Kind: A1 - Application number: 19/332,814 - Filed: 2025-09-18 - Published: 2026-04-02 - Earliest claimed: 2024-09-30 - Assignees: ObjectVideo Labs, LLC - Applicants: ObjectVideo Labs, LLC - Inventors: Ariel Amato - Web page: https://patentgrep.com/patent/US20260094431A1 ## CPC - G06V 10/96: Management of image or video recognition tasks - G06V 10/62: relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking - G06V 10/806: {of extracted features} - G06V 10/82: using neural networks - G06V 10/95: {structured as a network, e.g. client-server architectures} - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing sensor data feature vectors. One of the methods includes detecting, by a device at a property, an object of interest; determining, by the device, whether a first feature vector that likely represents the object of interest and was received from a different device is available; in response to determining that the first feature vector that likely represents the object of interest and was received from the different device is available, obtaining, by the device, the first feature vector that likely represents the object of interest and was received from the different device; performing, by the device, an analysis task for the object of interest at least using the first feature vector that likely represents the object of interest and was received from the different device; and performing an action using a result of the analysis task. ## Claims ### Claim 1 A computer-implemented method, comprising: detecting, by a device at a property, an object of interest; determining, by the device, whether a first feature vector that likely represents the object of interest and was received from a different device is available; in response to determining that the first feature vector that likely represents the object of interest and was received from the different device is available, obtaining, by the device, the first feature vector that likely represents the object of interest and was received from the different device; performing, by the device, an analysis task for the object of interest at least using the first feature vector that likely represents the object of interest and was received from the different device; and performing, by the device, an action using a result of the analysis task. ### Claim 2 (depends on 1) The method of claim 1, comprising: generating, by the device, a second feature vector for the object of interest using sensor data captured by a sensor coupled to the device, wherein performing the analysis task for the object of interest uses: (i) the second feature vector for the object of interest generated using the sensor data captured by the sensor coupled to the device; and (ii) the first feature vector that likely represents the object of interest and was received from the different device. ### Claim 3 (depends on 2) The method of claim 2, wherein performing the analysis task for the object of interest uses a combined feature vector generated using the first feature vector and the second feature vector. ### Claim 4 (depends on 2) The method of claim 2, wherein performing the analysis task for the object of interest comprises: sequentially providing the first feature vector and the second feature vector as input to an artificial intelligence model to cause the artificial intelligence model to a) store in memory an intermediate value generated from a first input, and use the intermediate value to process a second input and b) generate an output for the analysis task; and obtaining the output for the analysis task after the artificial intelligence model processes all of the input. ### Claim 5 (depends on 2) The method of claim 2, wherein the first feature vector and the second feature vector represent the object of interest from different perspectives. ### Claim 6 (depends on 2) The method of claim 2, wherein performing the analysis task for the object of interest comprises: processing the first feature vector and the second feature vector using an artificial intelligence model trained to determine whether the first feature vector and the second feature vector likely represent a same object; and obtaining the result of the analysis task indicating whether the first feature vector and the second feature vector likely represent the same object. ### Claim 7 (depends on 1) The method of claim 1, comprising receiving the first feature vector in response to a detection of an event by the different device. ### Claim 8 (depends on 1) The method of claim 1, comprising: in response to the device detecting an event, requesting the first feature vector; and in response to requesting the first feature vector, receiving the first feature vector. ### Claim 9 (depends on 1) The method of claim 1, wherein performing, by the device, the action using the result of the analysis task comprises: determining, by the device, that the result of the analysis task is likely related to a monitoring system action; and in response to determining that the result of the analysis task is likely related to the monitoring system action, sending, by the device, the result of the analysis task to a monitoring system. ### Claim 10 (depends on 1) The method of claim 1, wherein performing, by the device, the action using the result of the analysis task comprises: determining, by the device, that the result of the analysis task is not likely related to a monitoring system action; and in response to determining that the result of the analysis task is not likely related to the monitoring system action, deleting, by the device, the first feature vector from a memory of the device. ### Claim 11 A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: detecting, by a device at a property, an object of interest; determining, by the device, whether a first feature vector that likely represents the object of interest and was received from a different device is available; in response to determining that the first feature vector that likely represents the object of interest and was received from the different device is available, obtaining, by the device, the first feature vector that likely represents the object of interest and was received from the different device; performing, by the device, an analysis task for the object of interest at least using the first feature vector that likely represents the object of interest and was received from the different device; and performing, by the device, an action using a result of the analysis task. ### Claim 12 (depends on 11) The system of claim 11, comprising: generating, by the device, a second feature vector for the object of interest using sensor data captured by a sensor coupled to the device, wherein performing the analysis task for the object of interest uses: (i) the second feature vector for the object of interest generated using the sensor data captured by the sensor coupled to the device; and (ii) the first feature vector that likely represents the object of interest and was received from the different device. ### Claim 13 (depends on 12) The system of claim 12, wherein performing the analysis task for the object of interest uses a combined feature vector generated using the first feature vector and the second feature vector. ### Claim 14 (depends on 12) The system of claim 12, wherein performing the analysis task for the object of interest comprises: sequentially providing the first feature vector and the second feature vector as input to an artificial intelligence model to cause the artificial intelligence model to a) store in memory an intermediate value generated from a first input, and use the intermediate value to process a second input and b) generate an output for the analysis task; and obtaining the output for the analysis task after the artificial intelligence model processes all of the input. ### Claim 15 (depends on 12) The system of claim 12, wherein the first feature vector and the second feature vector represent the object of interest from different perspectives. ### Claim 16 (depends on 12) The system of claim 12, wherein performing the analysis task for the object of interest comprises: processing the first feature vector and the second feature vector using an artificial intelligence model trained to determine whether the first feature vector and the second feature vector likely represent a same object; and obtaining the result of the analysis task indicating whether the first feature vector and the second feature vector likely represent the same object. ### Claim 17 (depends on 11) The system of claim 11, comprising receiving the first feature vector in response to a detection of an event by the different device. ### Claim 18 (depends on 11) The system of claim 11, comprising: in response to the device detecting an event, requesting the first feature vector; and in response to requesting the first feature vector, receiving the first feature vector. ### Claim 19 (depends on 11) The system of claim 11, wherein performing, by the device, the action using the result of the analysis task comprises: determining, by the device, that the result of the analysis task is likely related to a monitoring system action; and in response to determining that the result of the analysis task is likely related to the monitoring system action, sending, by the device, the result of the analysis task to a monitoring system. ### Claim 20 One or more computer storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: detecting, by a device at a property, an object of interest; determining, by the device, whether a first feature vector that likely represents the object of interest and was received from a different device is available; in response to determining that the first feature vector that likely represents the object of interest and was received from the different device is available, obtaining, by the device, the first feature vector that likely represents the object of interest and was received from the different device; performing, by the device, an analysis task for the object of interest at least using the first feature vector that likely represents the object of interest and was received from the different device; and performing, by the device, an action using a result of the analysis task.