# US 2019/0378060 A1 · Inferential Media Tagging Method and System > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0378060 A1 - Document ID: US20190378060A1 - Status: Published application - Kind: A1 - Application number: 16/531,026 - Filed: 2019-08-03 - Published: 2019-12-12 - Earliest claimed: 2004-05-20 - Assignees: ManyWorlds, Inc. - Inventors: Steven Dennis Flinn; Naomi Felina Moneypenny - Web page: https://patentgrep.com/patent/US20190378060A1 ## CPC - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - G06F 16/60: of audio data - G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations - G06Q 10/0633: Workflow analysis - G06Q 30/0206: {Price or cost determination based on market factors} - G06Q 30/0269: {based on user profile or attribute} - G06Q 30/0631: {Recommending goods or services} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An inferential media tagging method and system automatically performs inferences from audio content that is associated with a media instance through the application of, for example, Bayesian-based algorithms or computer-implemented neural networks. Recommended objects are generated based, at least in part, on the inferences, and are delivered to users. The recommended objects may be further generated based upon inference tuning controls and inferences of preferences from usage behaviors and may be delivered in a temporal sequence. User behaviors associated with users interacting with the recommended objects are accessed and elements of the media instance are selected for delivery to users based on the user behaviors. ## Claims ### Claim 1 A computer-implemented method, comprising: accessing automatically information comprising audio content that is associated with a media instance; performing automatically one or more inferences by analyzing the audio content; generating automatically a plurality of recommended objects based, at least in part, upon the one or more inferences; delivering automatically the plurality of recommended objects to one or more users; accessing automatically one or more usage behaviors associated with at least one user of the one or more users interacting with at least one recommended object of the plurality of recommended objects; selecting automatically an element of the media instance based on the one or more usage behaviors; and delivering automatically the element of the media instance to a user of the one or more users. ### Claim 2 (depends on 1) The method of claim 1, further comprising: performing automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a Bayesian-based algorithm. ### Claim 3 (depends on 1) The method of claim 1, further comprising: performing automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a computer-implemented neural network. ### Claim 4 (depends on 1) The method of claim 1, further comprising: generating automatically the plurality of recommended objects, wherein the recommended objects are further generated in accordance with an inference of a preference that is based on a plurality of usage behaviors associated with one or more users. ### Claim 5 (depends on 4) The method of claim 4, further comprising: generating automatically the plurality of recommended objects, wherein the recommended objects are further generated in accordance with the inference of the preference, wherein the inference of the preference is based upon the application of a plurality of inference weightings that are determined in accordance with usage behavior priority rules that are applied to the plurality of usage behaviors. ### Claim 6 (depends on 1) The method of claim 1, further comprising: selecting automatically the element of the media instance based on the one or more usage behaviors, wherein the element of the media instance comprises video content that is associated with the audio content. ### Claim 7 (depends on 1) The method of claim 1, further comprising: selecting automatically the element of the media instance based on the one or more usage behaviors, wherein the element of the media instance comprises an element of the audio content. ### Claim 8 A computer-implemented system comprising one or more processors configured to: access automatically information comprising audio content that is associated with a media instance; perform automatically one or more inferences by analyzing the audio content; generate automatically a plurality of recommended objects based, at least in part, upon the one or more inferences; deliver automatically the plurality of recommended objects to one or more users; access automatically one or more usage behaviors associated with at least one user of the one or more users interacting with at least one recommended object of the plurality of recommended objects; select automatically an element of the media instance based on the one or more usage behaviors; and deliver automatically the element of the media instance to a user of the one or more users. ### Claim 9 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: perform automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a Bayesian-based algorithm. ### Claim 10 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: perform automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a computer-implemented neural network. ### Claim 11 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: perform automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed in accordance with an inference tuning control. ### Claim 12 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: generate automatically the plurality of recommended objects, wherein each of the plurality of recommended objects comprises text. ### Claim 13 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: select automatically the element of the media instance based on the one or more usage behaviors, wherein the element of the media instance comprises video content that is associated with the audio content. ### Claim 14 (depends on 8) The computer-implemented system of claim 8 further comprising the one or more processors configured to: select automatically the element of the media instance based on the one or more usage behaviors, wherein the element of the media instance comprises an element of the audio content. ### Claim 15 A computer-implemented system comprising one or more processors configured to: access automatically information comprising audio content that is associated with a media instance; perform automatically one or more inferences by analyzing the audio content; generate a plurality of recommended objects based, at least in part, upon the one or more inferences; deliver automatically the plurality of recommended objects to one or more users; access automatically a plurality of usage behaviors associated with at least one user of the one or more users, wherein the plurality of usage behaviors comprise behaviors associated with the at least one of the plurality of users navigating the media instance; generate automatically a recommendation that is based upon an inference of a preference that is derived from the plurality of usage behaviors; and deliver automatically the recommendation to a user. ### Claim 16 (depends on 15) The computer-implemented system of claim 15 further comprising the one or more processors configured to: perform automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a Bayesian-based algorithm. ### Claim 17 (depends on 15) The computer-implemented system of claim 15 further comprising the one or more processors configured to: perform automatically the one or more inferences by analyzing the audio content, wherein the one or more inferences are performed by application of a computer-implemented neural network. ### Claim 18 (depends on 15) The computer-implemented system of claim 15 further comprising the one or more processors configured to: deliver automatically the plurality of recommended objects to the one or more users, wherein the plurality of recommended objects are arranged in a temporal-based sequence. ### Claim 19 (depends on 15) The computer-implemented system of claim 15 further comprising the one or more processors configured to: generate automatically the recommendation that is based upon the inference of a preference that is derived from the plurality of usage behaviors, wherein the inference of the preference is based upon the application of a plurality of inference weightings that are determined in accordance with usage behavior priority rules that are applied to the plurality of usage behaviors. ### Claim 20 (depends on 15) The computer-implemented system of claim 15 further comprising the one or more processors configured to: deliver automatically an explanation for delivering the recommendation, wherein the explanation is in a natural language format and comprises reasoning for the delivery of the recommendation.