# US 12,120,193 B2 · Communications channels in media systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,120,193 B2 - Document ID: US12120193B2 - Status: Granted - Kind: B2 - Application number: 18/306,470 - Filed: 2023-04-25 - Published: 2023-08-17 - Granted: 2024-10-15 - Earliest claimed: 2016-12-23 - Assignees: DISH Technologies L.L.C. - Applicants: DISH Technologies L.L.C. - Inventors: Nicholas Brandon Newell; Christopher Day - Web page: https://patentgrep.com/patent/US12120193B2 ## CPC - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit - G10L 15/30: Distributed recognition, e.g. in client-server systems, for mobile phones or network applications - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - G10L 25/63: for estimating an emotional state - H04L 67/306: User profiles - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A computing device is programmed to receive, first and second user indicia of interest for media content. The computing device is further programmed to receive first user data related to a media content item. Based at least in part on the first and second user indicia of interest and the first user data, the computing device is further programmed to generate output that the first user recommend the media content item to the second user. ## Claims ### Claim 1 A method comprising: receiving, from a first user device associated with a first user, one or more first keywords of interest for a media content item; identifying a second user, for the first user to recommend the media content item to, based on a correlation value indicating a degree of association between the one or more first keywords of interest of the first user and one more second keywords of interest of the second user; in response to the correlation value being above a threshold, establishing a communication link between the first user device and a second user device associated with the second user; monitoring a communication between the first user and the second user correlating to the media content item; determining a feedback score of the second user for the media content item based on monitoring facial expressions of the second user during the communication correlating to the media content item; and updating the correlation value between the one or more first keywords of interest of the first user and the one more second keywords of interest of the second user based on the feedback score of the second user for the media content item. ### Claim 2 (depends on 1) The method of claim 1, further comprising: generating, based on the correlation value, output to the first user device that the first user recommend the media content item to the second user. ### Claim 3 (depends on 1) The method of claim 1, further comprising: determining a period of time since the first user and the second user have communicated; and generating, based on the period of time, an output to the first user device that the first user recommend the media content item to the second user. ### Claim 4 (depends on 1) The method of claim 1, further comprising: monitoring the communication between the first user and the second user by: analyzing one or more segments of the communication; and assigning a segment score to the one or more segments, wherein the segment score indicates a positive emotion or negative emotion associated with the media content item. ### Claim 5 (depends on 4) The method of claim 4, wherein the segment score is based on (a) words or expressions within the one or more segments, (b) voice quality of the second user within the one or more segments, or (c) facial expressions of the second user during the one or more segments. ### Claim 6 (depends on 4) The method of claim 4, further comprising: determining a first partial segment score of the segment score based on words or expressions with the one or more segments; determining a second partial segment score based on a voice quality of the second user within the one or more segments; and determining a third partial segment score based on facial expressions of the second user during the one or more segments. ### Claim 7 (depends on 6) The method of claim 6, further comprising: determining the segment score based on a combination of the first partial segment score, the second partial segment score, and the third partial segment score. ### Claim 8 A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising: receiving, from a first user device associated with a first user, one or more first keywords of interest for a media content item; identifying a second user, for the first user to recommend the media content item to, based on a correlation value indicating a degree of association between the one or more first keywords of interest of the first user and one more second keywords of interest of the second user; in response to the correlation value being above a threshold, establishing a communication link between the first user device and a second user device associated with the second user; monitoring a communication between the first user and the second user correlating to the media content item; determining a feedback score of the second user for the media content item based on monitoring facial expressions of the second user during the communication correlating to the media content item; and updating the correlation value between the one or more first keywords of interest of the first user and the one more second keywords of interest of the second user based on the feedback score of the second user for the media content item. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: generating, based on the correlation value, output to the first user device that the first user recommend the media content item to the second user. ### Claim 10 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: determining a period of time since the first user and the second user have communicated; and generating, based on the period of time, an output to the first user device that the first user recommend the media content item to the second user. ### Claim 11 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: monitoring the communication between the first user and the second user by: analyzing one or more segments of the communication; and assigning a segment score to the one or more segments, wherein the segment score indicates a positive emotion or negative emotion associated with the media content item. ### Claim 12 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the segment score is based on (a) words or expressions within the one or more segments, (b) voice quality of the second user within the one or more segments, or (c) facial expressions of the second user during the one or more segments. ### Claim 13 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the operations further comprise: determining a first partial segment score of the segment score based on words or expressions with the one or more segments; determining a second partial segment score based on a voice quality of the second user within the one or more segments; and determining a third partial segment score based on facial expressions of the second user during the one or more segments. ### Claim 14 (depends on 13) The non-transitory computer-readable medium of claim 13, wherein the operations further comprise: determining the segment score based on a combination of the first partial segment score, the second partial segment score, and the third partial segment score. ### Claim 15 A system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform a process comprising: receiving, from a first user device associated with a first user, one or more first keywords of interest for a media content item; identifying a second user, for the first user to recommend the media content item to, based on a correlation value indicating a degree of association between the one or more first keywords of interest of the first user and one more second keywords of interest of the second user; in response to the correlation value being above a threshold, establishing a communication link between the first user device and a second user device associated with the second user; monitoring a communication between the first user and the second user correlating to the media content item; determining a feedback score of the second user for the media content item based on monitoring facial expressions of the second user during the communication correlating to the media content item; and updating the correlation value between the one or more first keywords of interest of the first user and the one more second keywords of interest of the second user based on the feedback score of the second user for the media content item. ### Claim 16 (depends on 15) The system according to claim 15, wherein the process further comprises: generating, based on the correlation value, output to the first user device that the first user recommend the media content item to the second user. ### Claim 17 (depends on 15) The system according to claim 15, wherein the process further comprises: determining a period of time since the first user and the second user have communicated; and generating, based on the period of time, an output to the first user device that the first user recommend the media content item to the second user. ### Claim 18 (depends on 15) The system according to claim 15, wherein the process further comprises: monitoring the communication between the first user and the second user by: analyzing one or more segments of the communication; and assigning a segment score to the one or more segments, wherein the segment score indicates a positive emotion or negative emotion associated with the media content item. ### Claim 19 (depends on 18) The system according to claim 18, wherein the segment score is based on (a) words or expressions within the one or more segments, (b) voice quality of the second user within the one or more segments, or (c) facial expressions of the second user during the one or more segments. ### Claim 20 (depends on 18) The system according to claim 18, wherein the process further comprises: determining a first partial segment score of the segment score based on words or expressions with the one or more segments; determining a second partial segment score based on a voice quality of the second user within the one or more segments; and determining a third partial segment score based on facial expressions of the second user during the one or more segments.