Patents by Inventor Mehmet Levent Koc
Mehmet Levent Koc has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12386643Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.Type: GrantFiled: April 10, 2024Date of Patent: August 12, 2025Assignee: GOOGLE LLCInventors: Sylvanus Garnet Bent, III, Xiaolan Zhou, Mehmet Levent Koc, Wei Luo
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Knowledge Graphs for Dynamically Generating Content Using a Machine-Learned Content Generation Model
Publication number: 20250061312Abstract: Example aspects of the present disclosure provide an example method. In some implementations, the example method can include receiving request data indicating a request for content. In some implementations, the example method can include determining a request context associated with the request data, wherein the request context is based on account data for a user device associated with the request. In some implementations, the example method can include determining, based on the request and the request context, a data object from a knowledge graph, wherein the data object comprises a subject and one or more attributes for the subject. In some implementations, the example method can include generating, using a machine-learned content generation model, content descriptive of the subject, the content generated based on the request, the request context, and the data object.Type: ApplicationFiled: July 5, 2024Publication date: February 20, 2025Inventors: Matthias Heiler, Sylvanus Garnet Bent, III, Mehmet Levent Koc, Snehal Sunilkumar Motarwar, Aravindan Raghuveer, Saachi Grover, Nidhi Gupta, Preksha Nema, Durga Deepthi Singh Sharma, Abhinav Khandelwal -
Publication number: 20240256311Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.Type: ApplicationFiled: April 10, 2024Publication date: August 1, 2024Inventors: Sylvanus Garnet Bent, III, Xiaolan Zhou, Mehmet Levent Koc, Wei Luo
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Patent number: 11983553Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.Type: GrantFiled: October 18, 2022Date of Patent: May 14, 2024Assignee: GOOGLE LLCInventors: Sylvanus Garnet Bent, III, Xiaolan Zhou, Mehmet Levent Koc, Wei Luo
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Publication number: 20240126576Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.Type: ApplicationFiled: October 18, 2022Publication date: April 18, 2024Inventors: Sylvanus Garnet Bent, III, Xiaolan Zhou, Mehmet Levent Koc, Wei Luo
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Publication number: 20240126997Abstract: Example embodiments of the present disclosure provide for an example method that includes obtaining via a conversational campaign assistant interface, by a custom language model, natural language input. The method includes generating, by the custom language model, an output comprising a predicted user intent. The method includes determining actions to perform and determining a natural language response. The method includes transmitting, to an action component, the action data structure comprising executable instructions that cause the action component to automatically perform operations associated with completing the action. The method includes transmitting to the conversation campaign assistant interface, the response data structure comprising the natural language response to be provided for display to a user via the conversational campaign assistant interface.Type: ApplicationFiled: May 23, 2023Publication date: April 18, 2024Inventors: Sylvanus Garnet Bent, III, Mehmet Levent Koc, Wei Luo, Xiaolan Zhou
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Publication number: 20200372359Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: ApplicationFiled: August 12, 2020Publication date: November 26, 2020Inventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Joseph Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
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Patent number: 10762422Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: GrantFiled: December 29, 2016Date of Patent: September 1, 2020Assignee: Google LLCInventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
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Publication number: 20170300814Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: ApplicationFiled: December 29, 2016Publication date: October 19, 2017Inventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng