Patents by Inventor Kevin Gidney
Kevin Gidney 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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Publication number: 20250124311Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: ApplicationFiled: September 20, 2024Publication date: April 17, 2025Applicant: DocuSign International (EMEA) LimitedInventor: Kevin Gidney
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Publication number: 20250103970Abstract: A computing system remotely trains a public ensemble model of an artificial intelligence model management system. The system receives, by the model management system, an encrypted representation of a private data value from a client system. The encrypted representation includes annotation information provided by the client system. The system determines, using the encrypted representation and the annotation information, a data value cluster that corresponds to the private data value. Data value clusters are generated using encrypted representations of a private data values provided by client systems. The system obtains, based on the assigned data value cluster, an encrypted representation of a model. The model is trained remotely by the client system using the private data value. The system adds the encrypted representation of the model to the public ensemble model. The public ensemble model is generated using a plurality of encrypted representations of models remotely trained by the client systems.Type: ApplicationFiled: September 10, 2024Publication date: March 27, 2025Applicant: DocuSign, Inc.Inventor: Kevin Gidney
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Patent number: 12124972Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: GrantFiled: July 28, 2023Date of Patent: October 22, 2024Assignee: DocuSign International (EMEA) LimitedInventor: Kevin Gidney
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Patent number: 12093798Abstract: A computing system remotely trains a public ensemble model of an artificial intelligence model management system. The system receives, by the model management system, an encrypted representation of a private data value from a client system. The encrypted representation includes annotation information provided by the client system. The system determines, using the encrypted representation and the annotation information, a data value cluster that corresponds to the private data value. Data value clusters are generated using encrypted representations of a private data values provided by client systems. The system obtains, based on the assigned data value cluster, an encrypted representation of a model. The model is trained remotely by the client system using the private data value. The system adds the encrypted representation of the model to the public ensemble model. The public ensemble model is generated using a plurality of encrypted representations of models remotely trained by the client systems.Type: GrantFiled: October 5, 2020Date of Patent: September 17, 2024Assignee: DocuSign, Inc.Inventor: Kevin Gidney
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Patent number: 11995525Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.Type: GrantFiled: July 18, 2022Date of Patent: May 28, 2024Assignee: DocuSign International (EMEA) LimitedInventor: Kevin Gidney
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Publication number: 20230368050Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: ApplicationFiled: July 28, 2023Publication date: November 16, 2023Inventor: Kevin Gidney
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Patent number: 11755935Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: GrantFiled: September 12, 2022Date of Patent: September 12, 2023Assignee: DOCUSIGN INTERNATIONAL (EMEA) LIMITEDInventor: Kevin Gidney
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Publication number: 20230004841Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: ApplicationFiled: September 12, 2022Publication date: January 5, 2023Inventor: Kevin Gidney
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Publication number: 20230004868Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.Type: ApplicationFiled: July 18, 2022Publication date: January 5, 2023Inventor: Kevin Gidney
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Patent number: 11468345Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: GrantFiled: January 9, 2019Date of Patent: October 11, 2022Assignee: DOCUSIGN INTERNATIONAL (EMEA) LIMITEDInventor: Kevin Gidney
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Patent number: 11416767Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.Type: GrantFiled: January 9, 2019Date of Patent: August 16, 2022Assignee: DOCUSIGN INTERNATIONAL (EMEA) LIMITEDInventor: Kevin Gidney
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Publication number: 20220108225Abstract: A computing system remotely trains a public ensemble model of an artificial intelligence model management system. The system receives, by the model management system, an encrypted representation of a private data value from a client system. The encrypted representation includes annotation information provided by the client system. The system determines, using the encrypted representation and the annotation information, a data value cluster that corresponds to the private data value. Data value clusters are generated using encrypted representations of a private data values provided by client systems. The system obtains, based on the assigned data value cluster, an encrypted representation of a model. The model is trained remotely by the client system using the private data value. The system adds the encrypted representation of the model to the public ensemble model. The public ensemble model is generated using a plurality of encrypted representations of models remotely trained by the client systems.Type: ApplicationFiled: October 5, 2020Publication date: April 7, 2022Inventor: Kevin Gidney
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Publication number: 20200143267Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.Type: ApplicationFiled: January 9, 2019Publication date: May 7, 2020Inventor: Kevin Gidney
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Publication number: 20190332966Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.Type: ApplicationFiled: January 9, 2019Publication date: October 31, 2019Inventor: Kevin Gidney
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Patent number: 10402496Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.Type: GrantFiled: June 1, 2018Date of Patent: September 3, 2019Assignee: SEAL SOFTWARE LTD.Inventor: Kevin Gidney
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Patent number: 10185712Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.Type: GrantFiled: October 2, 2017Date of Patent: January 22, 2019Assignee: Seal Software Ltd.Inventor: Kevin Gidney
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Publication number: 20180276199Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.Type: ApplicationFiled: June 1, 2018Publication date: September 27, 2018Inventor: Kevin Gidney
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Patent number: 9996528Abstract: An electronic documents verification system (and method) detects related contracts, and analyzes contents in the related contracts including a primary contract and associated amendments from raw input data. One embodiment of a disclosed configuration includes a system (and method) for identifying clauses used in the related contracts. The system (and method) extracts features including key references or descriptions within each contract. Additionally, the system (and method) groups the related contracts, and establishes linkages of the related contracts based on the extracted features. Furthermore, the system (and method) analyzes contents in the related contracts based on advanced policy group including a plurality of policy groups.Type: GrantFiled: July 24, 2014Date of Patent: June 12, 2018Assignee: Seal Software Ltd.Inventor: Kevin Gidney
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Publication number: 20180024992Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.Type: ApplicationFiled: October 2, 2017Publication date: January 25, 2018Inventor: Kevin Gidney
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Patent number: RE49576Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.Type: GrantFiled: October 30, 2020Date of Patent: July 11, 2023Assignee: DOCUSIGN INTERNATIONAL (EMEA) LIMITEDInventor: Kevin Gidney