Patents by Inventor Robin Abraham

Robin Abraham 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).

  • Patent number: 12218890
    Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.
    Type: Grant
    Filed: October 19, 2023
    Date of Patent: February 4, 2025
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Robin Abraham, Liang Du, Manqing Mao, Paishun Ting, Julia Chen, Jianzhe Lin, Yijian Xiang, Mingyang Xu, Wenhan Wang, Fahimeh Raja
  • Patent number: 12205202
    Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing an interactive graphing system to achieve improved dataset exploration utilizing an intelligent workflow and an interactive user interface. More specifically, the interactive graphing system facilitates generating updated network graphs that include inferred user influences based on implicit user action. Indeed, the interactive graphing system can automatically generate and present a user with an updated network graph that includes added, removed, or subsetted elements and relationships that are otherwise hidden from a user. Additionally, the interactive graphing system facilitates network graph exploration and processing of customized combined network graphs that join otherwise separate network graphs.
    Type: Grant
    Filed: June 6, 2022
    Date of Patent: January 21, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Harsh Shrivastava, Maurice Diesendruck, Robin Abraham
  • Publication number: 20250021596
    Abstract: A system for implementing object state distributions obtains a first object state distribution associated with a first image set. The first object state distribution includes a first plurality of object state vectors generated based upon (i) a first set of object type classifications associated with the first image set and (ii) a first set of image information based on the first image set. The system obtains a second object state distribution associated with a second image set. The second object state distribution comprises a second plurality of object state vectors generated based upon (i) a second set of object type classifications associated with the second image set and (ii) a second set of image information based on the second image set. The system determines a distance measure between the first object state distribution and the second object state distribution and assigns a label based upon the distance measure.
    Type: Application
    Filed: October 1, 2024
    Publication date: January 16, 2025
    Inventors: Maurice DIESENDRUCK, Robin ABRAHAM
  • Patent number: 12191004
    Abstract: This disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. The two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. Each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. The model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. New queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. The architecture and training of the model make it possible to perform semantic matching based on chemical structures. The model is highly accurate providing an average recall at K=5 of 95.9%.
    Type: Grant
    Filed: June 27, 2022
    Date of Patent: January 7, 2025
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Sudipto Mukherjee, Liang Du, Ke Jiang, Robin Abraham
  • Publication number: 20250006303
    Abstract: An ensemble machine learning model is used to visualize complex data relationships. Complex data is provided to multiple generator models that each independently create a graph representing relationships in the data. At least one of the generator models is a machine learning model that can be trained with a generator model loss function. The graphs produced by the separate generator models are combined by an ensemble model to create a consensus graph. The ensemble model may be implemented as an edge-selector neural network. The models are trained jointly with a ensemble model loss function that includes the loss functions of the generator models added as regularization terms. A visualization of the ensemble graph is created to aid a user in understanding the complex data relationships.
    Type: Application
    Filed: June 28, 2023
    Publication date: January 2, 2025
    Inventors: Harsh SHRIVASTAVA, Robin ABRAHAM
  • Publication number: 20240430216
    Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.
    Type: Application
    Filed: October 19, 2023
    Publication date: December 26, 2024
    Inventors: Robin ABRAHAM, Liang DU, Manqing MAO, Paishun TING, Julia CHEN, Jianzhe LIN, Yijian XIANG, Mingyang XU, Wenhan WANG, Fahimeh RAJA
  • Publication number: 20240428005
    Abstract: The present disclosure relates to methods and systems for automatically generating documents for a specific topic using large language models. The methods and systems receive an input query that identifies a topic for the document. The methods and systems automatically generate, using the large language models, a framework for the document with sections and subsections for the document. The methods and systems write the document, using the large language models, and provide references for the data sources used to obtain the data that the large language model used to write the document.
    Type: Application
    Filed: June 20, 2023
    Publication date: December 26, 2024
    Inventors: Robin ABRAHAM, Mingyang XU, Julia CHEN, Yijian XIANG, Manqing MAO, Jianzhe LIN, Paishun TING, Liang DU
  • Publication number: 20240428008
    Abstract: The present disclosure relates to methods and systems for using large language models to support research activities. The methods and systems include a copilot engine that creates input prompts to provide to the large language model to use in generating responses to input messages. The copilot engine infers an intent of the input messages and sends the intent with the input message in the input prompt to the large language model. The large language model generates different types of responses for different intents.
    Type: Application
    Filed: June 22, 2023
    Publication date: December 26, 2024
    Inventors: Robin ABRAHAM, Liang DU, Fahimeh RAJA, Wenhan WANG, Dustin James STEWART, Lipsa PATNAIK, Stuart Richard LONG, Timothy EARNHEART, Sam Daniel GAMMON, Sacha AROZARENA VALLADARE, Jedediah Miller SINGER, Henrique DANTAS
  • Patent number: 12169680
    Abstract: The present disclosure relates to methods and systems for converting Portable Document Format (PDF) documents to LaTeX files. The methods and systems use machine learning models to identify and extract PDF portions of a PDF document. The methods and systems create a LaTeX file for the PDF document using the PDF portions extracted by the machine learning models. The methods and systems provide an output with the LaTeX file for the PDF document. The LaTeX file is used to perform different actions on the PDF document.
    Type: Grant
    Filed: June 6, 2022
    Date of Patent: December 17, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Harsh Shrivastava, Sarah Panda, Liang Du, Robin Abraham
  • Patent number: 12159722
    Abstract: A relevance system ranks a set of medical studies based on a relevance of each medical study in the set of medical studies to a patient profile. The relevance system includes a relevance model. The relevance model determines a relevance of each medical study to the patient profile based on a semantic relationship score, a concept relationship score, and a term-occurrence score. The semantic relationship score is a measure of a similarity in semantic meaning of a medical study and a patient profile. The concept relationship score is a measure of the closeness of medical concepts in a medical study to medical concepts in a patient profile. The term-occurrence score is a measure of occurrences of terms in a medical study that also appear in a patient profile and the statistical significances of the terms.
    Type: Grant
    Filed: November 23, 2020
    Date of Patent: December 3, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Nut Limsopatham, Liang Du, Robin Abraham
  • Patent number: 12159110
    Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.
    Type: Grant
    Filed: June 6, 2022
    Date of Patent: December 3, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Harsh Shrivastava, Maurice Diesendruck, Robin Abraham
  • Patent number: 12158910
    Abstract: A system for implementing object state distributions obtains a first object state distribution associated with a first image set. The first object state distribution includes a first plurality of object state vectors generated based upon (i) a first set of object type classifications associated with the first image set and (ii) a first set of image information based on the first image set. The system obtains a second object state distribution associated with a second image set. The second object state distribution comprises a second plurality of object state vectors generated based upon (i) a second set of object type classifications associated with the second image set and (ii) a second set of image information based on the second image set. The system determines a distance measure between the first object state distribution and the second object state distribution and assigns a label based upon the distance measure.
    Type: Grant
    Filed: December 17, 2021
    Date of Patent: December 3, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Maurice Diesendruck, Robin Abraham
  • Publication number: 20240331235
    Abstract: A machine learning model is used to generate molecular images by text-to-image diffusion techniques based on natural language text inputs. The machine learning model is trained on combinations of molecule images and corresponding text such that representatives of both are embedded in latent space. Users provide natural language text describing molecular characteristics and the machine learning model generates an image of a molecule with those characteristics. Existing molecular images or those generated by the system can be further edited and refined with additional natural language text instructions. The system also uses machine vision techniques to understand the molecule represented by a molecular image and translate that image into other representations of the molecule.
    Type: Application
    Filed: March 31, 2023
    Publication date: October 3, 2024
    Inventors: J Brandon SMOCK, Robin ABRAHAM, Maurice DIESENDRUCK, Rohith Venkata PESALA
  • Patent number: 12086551
    Abstract: A computer implemented method determines differences between documents. The method includes parsing a first document and a second document into respective distinct instances of content. The distinct instances of content are classified into different categories. Category specific matching algorithms are applied to each of the respective instances of content to determine a similarity score for each of the respective instances of content. Semantic differences between the first document and the second document are analyzed as a function of the similarity scores. A characterization of the semantic differences is generated.
    Type: Grant
    Filed: June 23, 2021
    Date of Patent: September 10, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Robin Abraham, J Brandon Smock, Owen Stephenson Whiting, Henry Hun-Li Reid Pan
  • Patent number: 12072935
    Abstract: Machine learning to predict a layout type that each of a plurality of portions of a document appears in. This is done even though the computer-readable representation of the document does not contain information at the granularity of the prediction to be made that identifies which layout type that each of the plurality of document portions belongs in. For each of a plurality of the portions, the machine-learning system predicts the layout type that the respective portion appears in, and indexes the document using the predictions so as to result in a computer-readable index. The index represents a predicted layout type associated with each of the plurality of portions of the document. Thus, the index can be used to search based on position of a searched term within the document.
    Type: Grant
    Filed: September 8, 2021
    Date of Patent: August 27, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yao Li, Liang Du, Robin Abraham
  • Publication number: 20240211796
    Abstract: The present disclosure relates to utilizing an embedding space relationship query exploration system to explore embedding spaces generated by machine-learning models. For example, the embedding space relationship query exploration system facilitates efficiently and flexibly revealing relationships that are encoded in a machine-learning model during training and inferencing. In particular, the embedding space relationship query exploration system utilizes various embeddings relationship query models to explore and discover the relationship types being learned and preserved within the embedding space of a machine-learning model.
    Type: Application
    Filed: December 22, 2022
    Publication date: June 27, 2024
    Inventors: Maurice DIESENDRUCK, Leo Moreno BETTHAUSER, Urszula Stefania CHAJEWSKA, Rohith Venkata PESALA, Robin ABRAHAM
  • Publication number: 20240087683
    Abstract: A machine learning model trained with a triplet loss function classifies input strings into one of multiple hierarchical categories. The machine learning model is pre-trained using masking language modeling on a corpus of unlabeled strings. The machine learning module includes an attention-based bi-directional transformer layer. Following initial training, the machine learning model is refined by additional training with a loss function that includes cross-entropy loss and triplet loss. This provides a deep learning solution to classify input strings into one or more hierarchical categories. Embeddings generated from inputs to the machine learning model capture language similarities that can be visualized in a cartesian plane where strings with similar meanings are grouped together.
    Type: Application
    Filed: September 14, 2022
    Publication date: March 14, 2024
    Inventors: Pramod Kumar SHARMA, Andy Daniel MARTINEZ, Liang DU, Robin ABRAHAM, Saurabh Chandrakant THAKUR
  • Patent number: 11874868
    Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.
    Type: Grant
    Filed: June 14, 2021
    Date of Patent: January 16, 2024
    Assignee: Microsoft Tech LLC nology Licensing, LLC
    Inventors: Robin Abraham, Leo Betthauser, Ziyao Li, Jing Tian, Xiaofei Zeng, Maurice Diesendruck, Andy Daniel Martinez, Min Xiao, Liang Du, Pramod Kumar Sharma, Natalia Larios Delgado
  • Publication number: 20230420085
    Abstract: This disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. The two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. Each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. The model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. New queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. The architecture and training of the model make it possible to perform semantic matching based on chemical structures. The model is highly accurate providing an average recall at K=5 of 95.9%.
    Type: Application
    Filed: June 27, 2022
    Publication date: December 28, 2023
    Inventors: Sudipto MUKHERJEE, Liang DU, Ke JIANG, Robin ABRAHAM
  • Patent number: 11847166
    Abstract: Performing collaborative search engine searching. The method includes receiving user input at a user interface for performing a plurality searches on a first search engine. The method further includes receiving user input at the user interface applying one or more augmentation AI models to searches in the plurality of searches. The method further includes creating a shareable, executable package executable by one or more search engines based on the plurality of searches and the applied AI models that when executed by the search engines causes the search engines to apply the AI models to searches performed at the search engines.
    Type: Grant
    Filed: November 15, 2018
    Date of Patent: December 19, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Liang Du, Ranjith Narayanan, Robin Abraham, Vijay Mital