# US 2020/0401908 A1 · Curated data platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401908 A1 - Document ID: US20200401908A1 - Status: Published application - Kind: A1 - Application number: 16/809,196 - Filed: 2020-03-04 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Company, Ltd. - Inventors: Andres Ortega; Ashwin Chandra; David Ho Suk Chung - Web page: https://patentgrep.com/patent/US20200401908A1 ## CPC - G06N 3/08: Learning methods - G06F 18/2163: {Partitioning the feature space} - G06F 18/23: Clustering techniques - G06F 18/29: {Graphical models, e.g. Bayesian networks} - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N 5/02: Knowledge representation; Symbolic representation - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06V 10/762: using clustering, e.g. of similar faces in social networks - H04N 21/466: Learning process for intelligent management, e.g. learning user preferences for recommending movies {(services using the results of monitoring in broadcast systems H04H60/61)} - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method includes generating one or more graphs representing automatic content recognition (ACR) data associated with a computing device; identifying one or more paths representing at least a portion of the one or more graphs; training one or more models based on inputting the one or more paths into one or more machine-learning algorithms; producing one or more embeddings from the one or more models; and clustering the one or more embeddings to provide at least one cluster corresponding to a behavioral profile associated with the computing device. ## Claims ### Claim 1 A method comprising: generating, by a computing system, one or more graphs representing automatic content recognition (ACR) data associated with a computing device; identifying, by the computing system, one or more paths representing at least a portion of the one or more graphs; training, by the computing system, one or more models based on inputting the one or more paths into one or more machine-learning algorithms; producing, by the computing system, one or more embeddings from the one or more models; and clustering, by the computing system the one or more embeddings to provide at east one cluster corresponding to a behavioral profile associated with the computing device. ### Claim 2 (depends on 1) The method of claim 1, wherein the behavioral profile includes a persona, and wherein the persona is associated with at least two users. ### Claim 3 (depends on 2) The method of claim 2, further comprising determining one or more content recommendations based on behaviors associated with the persona. ### Claim 4 (depends on 1) The method of claim 1, wherein: the one or more graphs comprise comprises a plurality of nodes and a plurality of edges connecting the nodes; and the plurality of nodes comprises a node corresponding to the computing device and a plurality of nodes corresponding to an aspect. ### Claim 5 (depends on 4) The method of claim 4, further comprising determining a weight for each edge in the graph. ### Claim 6 (depends on 4) The method of claim 4, wherein identifying the one or more paths comprises traversing a predetermined number of nodes connected by at least some of the plurality of edges. ### Claim 7 (depends on 6) The method of claim 6, further comprising selecting the predetermined number of nodes based on a duration that a particular content was consumed. ### Claim 8 (depends on 6) The method of claim 6, further comprising: categorizing each of the plurality of nodes into a particular one of a plurality of concepts; determining a relationship between the plurality of concepts; and selecting the predetermined number of nodes based on the relationship between the plurality of concepts. ### Claim 9 (depends on 1) The method of claim 1, further comprising transforming the portion of the one or more graphs from a higher dimensional space to a lower-dimensional space. ### Claim 10 (depends on 1) The method of claim 1, further comprising determining a weighted average of the one or more embeddings, wherein clustering the embeddings comprises clustering the weighted average of the one or more embeddings with a weighted average of one or more embeddings associated with one or more other computing devices. ### Claim 11 (depends on 10) The method of claim 10, further comprising: assigning each cluster a persona, wherein each persona is associated with one or more computing devices; accessing, for a particular computing device, a persona with which that computing device is associated; and generating, based on the persona and the cluster to which that persona is assigned, one or more content recommendations for presentation on the particular computing device. ### Claim 12 (depends on 1) The method of claim 1, further comprising partitioning the ACR data into one or more time-bands. ### Claim 13 (depends on 12) The method of claim 12, further comprising: aggregating the ACR data of a particular one of the time bands over a predetermined period of time; and reaggregating the ACR data of the particular one of the time-bands after a predetermined amount of time has elapsed. ### Claim 14 A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to: generate one or more graphs representing automatic content recognition (ACR) data associated with a computing device; identify one or more paths representing at least a portion of the one or more graphs; train one or more models based on inputting the one or more paths into one or more machine-learning algorithms; produce one or more embeddings from the one or more models; and cluster the one or more embeddings to provide at least one cluster corresponding to a behavioral profile associated with the computing device. ### Claim 15 (depends on 14) The medium of claim 14, wherein the behavioral profile includes a persona, and wherein the persona is associated with at least two users. ### Claim 16 (depends on 15) The medium of claim 15, further comprising instructions that when executed cause the one or more processors to determine one or more content recommendations based on behaviors associated with the persona. ### Claim 17 (depends on 14) The medium of claim 14, wherein: the one or more graphs comprise comprises a plurality of nodes and a plurality of edges connecting the nodes; and the plurality of nodes comprises a node corresponding to the computing device and a plurality of nodes corresponding to an aspect. ### Claim 18 (depends on 14) The medium of claim 14, further comprising instructions that when executed cause the one or more processors to determine a weighted average of the one or more embeddings, wherein the instructions that cause the one or more processors to cluster the embeddings comprise instructions that cause the one or more processors to cluster the weighted average of the one or more embeddings with a weighted average of one or more embeddings associated with one or more other computing devices. ### Claim 19 (depends on 18) The medium of claim 18, further comprising instructions that when executed cause the one or more processors to: assign each cluster a persona, wherein each persona is associated with at one or more computing devices; access, for a particular computing device, a persona with which that computing device is associated; and generate, based on the persona and the cluster to which that persona is assigned, one or more content recommendations for presentation on the particular computing device. ### Claim 20 A system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to: generate one or more graphs representing automatic content recognition (ACR) data associated with a computing device; identify one or more paths representing at least a portion of the one or more graphs; train one or more models based on inputting the one or more paths into one or more machine-learning algorithms; produce one or more embeddings from the one or more models; and cluster the one or more embeddings to provide at least one cluster corresponding to a behavioral profile associated with the computing device.