# US 11,308,332 B1 · Intelligent content rating determination using multi-tiered machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,332 B1 - Document ID: US11308332B1 - Status: Granted - Kind: B1 - Application number: 16/856,744 - Filed: 2020-04-23 - Granted: 2022-04-19 - Earliest claimed: 2018-04-09 - Assignees: Amazon Technologies, Inc. - Applicants: Amazon Technologies, Inc. - Inventors: Hooman Mahyar; Muhammad Yahia; Harshal Dilip Wanjari - Web page: https://patentgrep.com/patent/US11308332B1 ## CPC - H04N 21/251: {Learning process for intelligent management, e.g. learning user preferences for recommending movies (details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08)} - G06F 18/41: {Interactive pattern learning with a human teacher} - G06F 40/216: using statistical methods - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 40/30: Semantic analysis - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)} - G06V 20/47: {Detecting features for summarising video content} - H04N 21/233: Processing of audio elementary streams {(monitoring, identification or recognition of audio in broadcast systems H04H60/58)} - H04N 21/23418: {involving operations for analysing video streams, e.g. detecting features or characteristics (television picture signal circuitry for scene change detection H04N5/147; filtering for image enhancement G06T5/00; methods or arrangements for recognising scenes G06V20/00; arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59)} - H04N 21/26603: {for automatically generating descriptors from content, e.g. when it is not made available by its provider, using content analysis techniques} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model. ## Claims ### Claim 1 A method comprising: determining, by one or more computer processors coupled to memory, presence of a first feature in a first frame of a video; determining presence of a second feature in an audio file associated with the video; determining presence of a third feature in a text file associated with the video; generating a predicted content rating for the video using a machine learning model, wherein the predicted content rating is based at least in part on respective lengths of time associated with the first feature, the second feature, and the third feature; determining a confidence score for the predicted content rating based at least in part on presence and associated length of time of one or more of the first feature, the second feature, or the third feature in a set of training data for the machine learning model; determining that the confidence score satisfies a publishing threshold for a particular content rating; and causing the predicted content rating to be published. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining a frequency of mature content in the video; wherein generating the predicted content rating for the video comprises generating the predicted content rating for the video based at least in part on the first feature, the second feature, the third feature, and the frequency of mature content. ### Claim 3 (depends on 1) The method of claim 1, further comprising: using feedback data for the predicted content rating to retrain the machine learning model. ### Claim 4 (depends on 1) The method of claim 1, wherein generating the predicted content rating for the video comprises selecting a first predicted content rating for the video from a first set of content ratings for a first entity using the machine learning model. ### Claim 5 (depends on 4) The method of claim 4, further comprising: selecting a second predicted content rating for the video from a second set of content ratings for a second entity using the machine learning model. ### Claim 6 (depends on 1) The method of claim 1, wherein generating the predicted content rating for the video comprises generating a first predicted content rating for video content of the video using the machine learning model, the method further comprising: generating a second predicted content rating for audio content of the video using the machine learning model, wherein the second predicted content rating is based at least in part on the second feature. ### Claim 7 A method comprising: determining, by one or more computer processors coupled to memory, presence of a first feature in a first frame of a video; determining presence of a second feature in an audio file associated with the video; determining presence of a third feature in a text file associated with the video; generating a first predicted content rating for video content of the video using a machine learning model, wherein the first predicted content rating is based at least in part on a first length of time associated with the first feature; generating a second predicted content rating for audio content of the video using the machine learning model, wherein the second predicted content rating is based at least in part on a second length of time associated with the second feature; determining that a first confidence score for the first predicted content rating is greater than a second confidence score for the second predicted content rating; and causing the first predicted content rating to be published. ### Claim 8 (depends on 7) The method of claim 7, further comprising: determining a frequency of mature content in the video; wherein generating the first predicted content rating for the video comprises generating the predicted content rating for the video based at least in part on the first feature and the frequency of mature content. ### Claim 9 (depends on 7) The method of claim 7, further comprising: determining that the first confidence score for the first predicted content rating satisfies a publishing threshold. ### Claim 10 (depends on 9) The method of claim 9, wherein determining that the first confidence score satisfies the publishing threshold comprises determining that the first confidence score satisfies the publishing threshold for a particular content rating. ### Claim 11 (depends on 7) The method of claim 7, further comprising: using feedback data for the predicted content rating to retrain the machine learning model. ### Claim 12 (depends on 7) The method of claim 7, wherein generating the first predicted content rating for the video comprises selecting a first predicted content rating for the video from a first set of content ratings for a first entity using the machine learning model. ### Claim 13 (depends on 12) The method of claim 12, further comprising: selecting a second predicted content rating for the video from a second set of content ratings for a second entity using the machine learning model. ### Claim 14 A method comprising: determining, by one or more computer processors coupled to memory, presence of a first feature in a first frame of a video; determining presence of a second feature in an audio file associated with the video; determining presence of a third feature in a text file associated with the video; generating a predicted content rating for the video using a machine learning model, wherein the predicted content rating is based at least in part on respective lengths of time associated with the first feature, the second feature, and the third feature; generating a category heat map having graphical representations indicative of the presence and associated length of time of the first feature, the second feature, and the third feature; and causing presentation of the category heat map. ### Claim 15 (depends on 14) The method of claim 14, further comprising: determining a frequency of mature content in the video; wherein generating the predicted content rating for the video comprises generating the predicted content rating for the video based at least in part on the first feature, the second feature, the third feature, and the frequency of mature content. ### Claim 16 (depends on 14) The method of claim 14, wherein generating the predicted content rating for the video comprises selecting a first predicted content rating for the video from a first set of content ratings for a first entity using the machine learning model. ### Claim 17 (depends on 16) The method of claim 16, further comprising: selecting a second predicted content rating for the video from a second set of content ratings for a second entity using the machine learning model. ### Claim 18 (depends on 14) The method of claim 14, further comprising: using feedback data for the predicted content rating to retrain the machine learning model.