Patents Assigned to Chooch Intelligence Technologies Co.
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Publication number: 20240386578Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: ApplicationFiled: July 29, 2024Publication date: November 21, 2024Applicant: Chooch Intelligence Technologies Co.Inventors: Hakan Robert Gultekin, Emrah Gultekin
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Publication number: 20240354578Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: ApplicationFiled: July 1, 2024Publication date: October 24, 2024Applicant: Chooch Intelligence Technologies Co.Inventors: Hakan Robert Gultekin, Emrah Gultekin
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Patent number: 12051209Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: GrantFiled: April 19, 2021Date of Patent: July 30, 2024Assignee: CHOOCH INTELLIGENCE TECHNOLOGIES CO.Inventors: Hakan Robert Gultekin, Emrah Gultekin
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Patent number: 12026622Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: GrantFiled: June 6, 2022Date of Patent: July 2, 2024Assignee: CHOOCH INTELLIGENCE TECHNOLOGIES CO.Inventors: Hakan Robert Gultekin, Emrah Gultekin
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Patent number: 11354351Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: GrantFiled: January 31, 2019Date of Patent: June 7, 2022Assignee: CHOOCH INTELLIGENCE TECHNOLOGIES CO.Inventors: Hakan Robert Gultekin, Emrah Gultekin
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Publication number: 20210326646Abstract: Embodiments of the present invention train multiple Perception models to predict contextual metadata (tags) with respect to target content items. By extracting context from content items, and generating associations among the Perception models, individual Perceptions trigger one another based on the extracted context to generate a more robust set of contextual metadata. A Perception Identifier predicts core tags that make coarse distinctions among content items at relatively higher levels of abstraction, while also triggering other Perception models to predict additional perception tags at lower levels of abstraction. A Dense Classifier identifies sub-content items at various levels of abstraction, and facilitates the iterative generation of additional dense tags across integrated Perceptions.Type: ApplicationFiled: April 19, 2021Publication date: October 21, 2021Applicant: Chooch Intelligence Technologies Co.Inventors: Hakan Robert Gultekin, Emrah Gultekin