# US 12,217,474 B2 · Detection of moment of perception > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,474 B2 - Document ID: US12217474B2 - Status: Granted - Kind: B2 - Application number: 18/485,266 - Filed: 2023-10-11 - Published: 2024-04-11 - Granted: 2025-02-04 - Earliest claimed: 2019-08-21 - Assignees: Apple Inc. - Applicants: Apple Inc. - Inventors: Hessam Bagherinezhad; Carlo Eduardo Cabanero Del Mundo; Anish Jnyaneshwar Prabhu; Peter Zatloukal; Lawrence Frederick Arnstein - Web page: https://patentgrep.com/patent/US12217474B2 ## CPC - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 20/00: Machine learning - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - G06V 10/764: using classification, e.g. of video objects - G06V 10/82: using neural networks - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 40/20: Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16) - G06T 2207/20081: Training; Learning - G06V 20/44: {Event detection} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates a moment of perception of the determined motion. ## Claims ### Claim 1 A method comprising: receiving an input video comprising a plurality of image frames including an object of interest; determining, based on the plurality of image frames, a motion associated with the object of interest; and determining, using a computing system and based at least in part on one or more factors corresponding to the input video, at least one of the plurality of image frames that indicates detection of the determined motion. ### Claim 2 (depends on 1) The method of claim 1, wherein the one or more factors of the input video comprise at least one of: (i) an environmental context of the input video, (ii) one or more attributes of the object of interest, (iii) one or more attributes of the motion associated with the object of interest, or (iv) a metadata associated to the input video. ### Claim 3 (depends on 2) The method of claim 2, wherein the environmental context of the input video comprises a climate determined from the input video. ### Claim 4 (depends on 2) The method of claim 2, wherein the one or more attributes of the object of interest comprise a temperature of the object of interest in the input video. ### Claim 5 (depends on 1) The method of claim 1, wherein the motion associated with the object of interest is based at least in part on a motion performed by one or more portions of the object of interest. ### Claim 6 (depends on 1) The method of claim 1, wherein the object of interest comprises at least one of: (i) a human, (ii) an animal, or (iii) an object. ### Claim 7 (depends on 1) The method of claim 1, wherein the object of interest comprises a combination of one or more humans, animals, or objects. ### Claim 8 (depends on 1) The method of claim 1, wherein the one or more factors comprise captions included in the input video. ### Claim 9 A system, comprising: a processor; and a memory device containing instructions which, when executed by the processor, cause the processor to: receive an input video comprising a plurality of image frames including an object of interest; determine, based on the plurality of image frames, a motion associated with the object of interest; and determine, based at least in part on one or more factors corresponding to the input video, at least one of the plurality of image frames that indicates detection of the determined motion. ### Claim 10 (depends on 9) The system of claim 9, wherein the one or more factors of the input video comprise at least one of: (i) an environmental context of the input video, (ii) one or more attributes of the object of interest, (iii) one or more attributes of the motion associated with the object of interest, or (iv) a metadata associated to the input video. ### Claim 11 (depends on 10) The system of claim 10, wherein the environmental context of the input video comprises a climate determined from the input video. ### Claim 12 (depends on 10) The system of claim 10, wherein the one or more attributes of the object of interest comprise a temperature of the object of interest in the input video. ### Claim 13 (depends on 9) The system of claim 9, wherein the motion associated with the object of interest is based at least in part on a motion performed by one or more portions of the object of interest. ### Claim 14 (depends on 9) The system of claim 9, wherein the object of interest comprises at least one of: (i) a human, (ii) an animal, or (iii) an object. ### Claim 15 (depends on 9) The system of claim 9, wherein the object of interest comprises a combination of one or more humans, animals, or objects. ### Claim 16 (depends on 9) The system of claim 9, wherein the one or more factors comprise captions included in the input video. ### Claim 17 A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving an input video comprising a plurality of image frames including an object of interest; determining, based on the plurality of image frames, a motion associated with the object of interest; and determining, using a computing system and based at least in part on one or more factors corresponding to the input video, at least one of the plurality of image frames that indicates detection of the determined motion. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the one or more factors of the input video comprise at least one of: (i) an environmental context of the input video, (ii) one or more attributes of the object of interest, (iii) one or more attributes of the motion associated with the object of interest, or (iv) a metadata associated to the input video. ### Claim 19 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the environmental context of the input video comprises a climate determined from the input video. ### Claim 20 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the one or more attributes of the object of interest comprise a temperature of the object of interest in the input video.