# US 12,111,886 B2 · Data collection and retraining in edge video devices > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,886 B2 - Document ID: US12111886B2 - Status: Granted - Kind: B2 - Application number: 17/515,977 - Filed: 2021-11-01 - Published: 2023-05-04 - Granted: 2024-10-08 - Earliest claimed: 2021-11-01 - Assignees: Western Digital Technologies, Inc. - Applicants: Western Digital Technologies, Inc. - Inventors: Damien Kah; Qian Zhong; Shaomin Xiong; Toshiki Hirano - Web page: https://patentgrep.com/patent/US12111886B2 ## CPC - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/82: using neural networks - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06V 10/764: using classification, e.g. of video objects - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/776: Validation; Performance evaluation - G06V 20/40: in video content (extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44) - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture may identify objects according to a confidence threshold of a model. The confidence threshold may be monitored over time, and the model may be updated if the confidence threshold drops below an acceptable level. The data for retraining is ideally generated substantially internal to the camera. A filter is generated to process the entire field data set stored on the camera to create a field data subset also stored on the camera. The field data subset may be run through the model to generate cases that may be used in further monitoring, training, and updating of the model. ## Claims ### Claim 1 A device, comprising: an artificial intelligence logic configured to: evaluate a validation data set with a first production detector; use annotation of an output of the first production detector to generate a surrogate data set; train a filter with the surrogate data set; evaluate an entire field data set with the filter to generate a field data subset; evaluate the field data subset with the first production detector to generate a final field data subset; add the final field data subset to the validation data set, updated a training data set with the validation data set; train the second production detector with the updated training set; and replace the first production detector with the second production detector, wherein the field data subset is run through a second production detector to generate a locally stored portion of the final field data set. ### Claim 2 (depends on 1) The device of claim 1, wherein the annotation of the output of the first production detector is, at least in part, performed manually. ### Claim 3 (depends on 1) The device of claim 1, wherein the annotation of the output of the first production detector is performed computationally. ### Claim 4 (depends on 1) The device of claim 1, wherein the device further comprises an optics module and the field data set comprises video data obtained via the optics module. ### Claim 5 (depends on 1) The device of claim 1, wherein the field data set comprises surveillance video. ### Claim 6 A system, comprising: a computational system configured to: evaluate a validation data set with a first production detector; use annotation of an output data of the first production detector to generate a surrogate data set; and train a filter with the surrogate data set; and a device, comprising: an artificial intelligence logic configured to: receive the trained filter; evaluate an entire field data set with the filter to generate a field data subset; evaluate the field data subset with the first production detector to generate a final field data subset; add the final field data subset to the validation data set; update a training data set with the validation data set; train the second production detector with the updated training set; and replace the first production detector with the second production detector, wherein the field data subset is run through a second production detector to generate a locally stored portion of the final field data set. ### Claim 7 (depends on 6) The system of claim 6, wherein the annotation of the output of the first production detector is, at least in part, performed manually. ### Claim 8 (depends on 6) The system of claim 6, wherein the annotation of the output of the first production detector is performed computationally. ### Claim 9 (depends on 6) The system of claim 6, wherein the device further comprises an optics module and the entire field data set comprises video data obtained via the optics module. ### Claim 10 (depends on 6) The system of claim 6, wherein the entire field data set comprises surveillance video. ### Claim 11 A method, comprising: evaluating a validation data set with a first production detector; annotating output data of the first production detector to generate a surrogate data set; training a filter with the surrogate data set; evaluating an entire field data set with the filter to generate a field data subset, wherein a portion of the entire field data set comprises surveillance video; evaluating the field data subset with the first production detector to generate a final field data set; adding the final field data to the validation data set; updating a training data set with the validation data set; training the second production detector with the updated training set; and replacing the first production detector with the second production detector, wherein the field data subset is run through a second production detector to generate a locally stored portion of the final field data set.