Patents by Inventor Neelakantan Sundaresan

Neelakantan Sundaresan 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).

  • Publication number: 20250117819
    Abstract: Systems and methods for on demand local commerce are described. One example embodiment includes a device gathering location information and product interest associated with clients and client devices. The system may use location information in determining that the first plurality of client devices are within a first geographic area during a first time period, and may further use the interest information in calculating an interest level for a first product. A threshold may be identified and used in determining that the interest level for the first product exceeds the threshold. When the calculated interest level exceeds the threshold, a local commerce action is initiated. In various embodiments, the local commerce action may be a live on demand auction at a particular location, an offer associated with a geofenced area, a sales location recommendation to a merchant, or any other such local commerce action.
    Type: Application
    Filed: December 16, 2024
    Publication date: April 10, 2025
    Inventor: Neelakantan Sundaresan
  • Publication number: 20250111239
    Abstract: A deep learning model is trained to learn to generate a better-quality unit test case for a focal method through reinforcement learning using a reward score that considers static code quality properties of a best coding standard. The static code quality properties include an assertion in the predicted unit test case, an invocation of the focal method in the predicted unit test case, and a descriptive name for the predicted unit test case. A reward model is trained to compute a reward score for a model-predicted unit test case based on the static code quality properties. The reward score is used in a proximal policy optimization method to produce a policy loss that updates the parameters of the deep learning model towards generating a better-quality unit test case.
    Type: Application
    Filed: November 21, 2023
    Publication date: April 3, 2025
    Inventors: BENJAMIN STEENHOEK, ALEXEY SVYATKOVSKIY, NEELAKANTAN SUNDARESAN, MICHELE TUFANO
  • Patent number: 12266001
    Abstract: Techniques for mapping size information associated with a client to target brands, garments, sizes, shapes, and styles for which there is no standardized correlation. The size information associated with a client may be generated by modeling client garments, accessing computer aided drawing (CAD) files associated with client garments, or by analyzing a history of garment purchases associated with the client. Information for target garments may be generated in a similar fashion. A system may then create a standardized scale with a set of sizes for a target, and map a client base size to that standardized size scale. Similar matching and mapping may also be done with shape and style considerations. A recommendation based on the mapping may then be communicated to the client.
    Type: Grant
    Filed: June 26, 2023
    Date of Patent: April 1, 2025
    Assignee: eBay Inc.
    Inventors: Jonathan Su, Mihir Naware, Jatin Chhugani, Neelakantan Sundaresan
  • Publication number: 20250103325
    Abstract: A code review is automatically generated by a large language model given a prompt that includes code changes made to a source code program, an associated intent, and an extended context. The intent represents an issue with the code changes from a code reviewer's perspective and is predicted from a neural classifier given the code changes in a code diff format. The neural classifier is a neural encoder transformer model pre-trained on various code review datasets and fine-tuned on code diff hunks of code changes labeled with an intent.
    Type: Application
    Filed: September 23, 2023
    Publication date: March 27, 2025
    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: SHENGYU FU, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, SHUO ZHANG
  • Publication number: 20250088550
    Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.
    Type: Application
    Filed: September 26, 2024
    Publication date: March 13, 2025
    Inventors: Neelakantan Sundaresan, Atish Das Sarma, Si Si, Elizabeth Churchill
  • Patent number: 12248767
    Abstract: A deep learning model trained to learn to predict source code is tuned for a target source code generation task through reinforcement learning using a reward score that considers the quality of the source code predicted during the tuning process. The reward score is adjusted to consider code-quality factors and source code metrics. The code-quality factors account for the predicted source code having syntactic correctness, successful compilation, successful execution, successful invocation, readability, functional correctness, and coverage. The source code metrics generate a score based on how close the predicted source code is to a ground truth code.
    Type: Grant
    Filed: February 20, 2024
    Date of Patent: March 11, 2025
    Assignee: Microsoft Technology Licensing, LLC.
    Inventors: Shao Kun Deng, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
  • Publication number: 20250077533
    Abstract: A method and a system process a stream of data in parallel across a plurality of nodes. The log processing system has a log module, a query language module, and a query processing module. The log module receives and organizes the stream of data into a sequential and nested data structure. The query language operator module defines operators that operate on the sequential and nested data structure. The query processing module processes in parallel across a plurality of nodes a query based on an operator on the stream of data.
    Type: Application
    Filed: September 26, 2024
    Publication date: March 6, 2025
    Applicant: eBay Inc.
    Inventors: Gyanit Singh, Chi-Hsien Chiu, Neelakantan Sundaresan
  • Patent number: 12242372
    Abstract: A pre-trained neural code generation model generates repair code for a method containing a performance bug given a prompt including a code transformation instruction. The code transformation instruction guides the model on how to predict the repair code when the model has not been fine-tuned for the repair code task. The code transformation instruction is retrieved from abstract bug patterns derived from historical performance bug fixes found in commits to a source code repository. The augmentation of the code transformation instruction in the prompt to the pre-trained neural code generation model provides the model with a hint on how the repair code may be generated based on similar performance bug fixes.
    Type: Grant
    Filed: March 21, 2023
    Date of Patent: March 4, 2025
    Assignee: Microsoft Technology Licensing, LLC.
    Inventors: Spandan Garg, Neelakantan Sundaresan, Roshanak Zilouchian Moghaddam
  • Patent number: 12242822
    Abstract: Custom source code generation models are generated by tuning a pre-trained deep learning model by freezing the model parameters and optimizing a prefix. The tuning process is distributed across a user space and a model space where the embedding and output layers are performed in the user space and the execution of the model is performed in a model space that is isolated from the user space. The tuning process updates the embeddings of the prefix across the separate execution spaces in a manner that preserves the privacy of the data used in the tuning process.
    Type: Grant
    Filed: March 13, 2024
    Date of Patent: March 4, 2025
    Assignee: Microsoft Technology Licensing, LLC.
    Inventors: Colin Bruce Clement, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano, Andrei Zlotchevski
  • Publication number: 20250068665
    Abstract: A user query for information regarding data of a codebase is answered by a large language model given a prompt that includes examples of code segments from the codebase that are similar to the user query. The code segments from the codebase are associated with metadata that includes both natural language text and source code. The search for the examples of code segments from the codebase is based on embeddings of code segments and associated metadata that are closely similar to an embedding of the user query and context.
    Type: Application
    Filed: August 24, 2023
    Publication date: February 27, 2025
    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: SHUBHAM CHANDEL, COLIN BRUCE CLEMENT, SHENGYU FU, NEELAKANTAN SUNDARESAN
  • Publication number: 20250068419
    Abstract: A retrieval-augmented neural transformer model with chunk cross-attention predicts a code review given a proposed source code change, represented as a code diff hunk, and a set of historical code review comments. The code diff hunk represents proposed edits to a source code snippet with its surrounding context that has not been changed. The historical code review comments are associated with code edits that are semantically similar to the proposed source code changes. The code diff hunk is partitioned into chunks which are used to find semantically similar historical code review comments. The set of historical code review comments is aggregated and used to guide the model in makings its predictions.
    Type: Application
    Filed: November 14, 2024
    Publication date: February 27, 2025
    Inventors: SHENGYU FU, XIAOYU LIU, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY
  • Patent number: 12229533
    Abstract: A code completion tool uses a neural transformer model to generate candidate sequences to complete a line of source code. The neural transformer model is trained using a conditional language modeling objective on a large unsupervised dataset that includes source code programs written in several different programming languages. The neural transformer model is used within a beam search that predicts the most likely candidate sequences for a code snippet under development.
    Type: Grant
    Filed: August 9, 2023
    Date of Patent: February 18, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Alexey Svyatkovskiy, Shengyu Fu, Neelakantan Sundaresan, Shao Kun Deng
  • Publication number: 20250053394
    Abstract: A code insertion engine predicts one or more statements of a programming language to be inserted at an insertion point in between existing source code statements of a source code program being edited. The code insertion engine extracts the surrounding context of the insertion point which includes the source code immediately preceding and the source code immediately following the insertion point. The code insertion engine uses a neural expansion model and a neural selector model to predict the one or more statements most likely to be inserted into the insertion point that are syntactically and semantically consistent with the surrounding context of the existing program.
    Type: Application
    Filed: October 28, 2024
    Publication date: February 13, 2025
    Inventors: NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY
  • Publication number: 20250045049
    Abstract: An Artificial Intelligence (AI) driven pull request summarization system utilizes a large language model to classify the code changes of a pull request and to generate a summary of the changes contained in the pull request. The large language model predicts the pull request classification and summarization given a prompt that includes the top-k code changes in the pull request of a repository. The code changes are prioritized based on the most impact that a code change has on the files, methods and classes in the repository. Each of the top-k code changes is linked to a related open issue of the repository, if any. A suggested code reviewer for a code change is then selected from an author or commentator associated with the linked open issue.
    Type: Application
    Filed: October 13, 2023
    Publication date: February 6, 2025
    Inventors: NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO, SHUO ZHANG
  • Publication number: 20250045965
    Abstract: An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.
    Type: Application
    Filed: October 23, 2024
    Publication date: February 6, 2025
    Inventors: Anurag Bhardwaj, Robinson Piramuthu, Neelakantan Sundaresan
  • Publication number: 20250036778
    Abstract: A neural classifier model is used to detect cybersecurity vulnerabilities in the source code predicted by a deep learning code generation model having been trained on source code possibly containing security bugs. Upon the classifier model classifying a given source code snippet as likely containing a cybersecurity vulnerability, a proposed repair for the cybersecurity vulnerability is predicted from a neural decoder transformer model having been trained on non-vulnerable source code. The neural decoder transformer model is used to predict source code that repairs the cybersecurity vulnerability given the source code classified with a cybersecurity vulnerability.
    Type: Application
    Filed: October 16, 2024
    Publication date: January 30, 2025
    Inventors: AARON YUE-CHIU CHAN, COLIN BRUCE CLEMENT, YEVHEN MOHYLEVSKYY, NEELAKANTAN SUNDARESAN, ROSHANAK ZILOUCHIAN MOGHADDAM
  • Publication number: 20250036911
    Abstract: An automated system for resolving program merges uses neural transformers with attention. In one aspect, a neural encoder transformer model is trained from developer-resolved merge conflicts to learn to predict a resolution strategy that aids a developer in constructing a merged program. In a second aspect, a neural decoder transformer model is trained on the syntax and semantics of different source code programming languages to predict a merge resolution consisting of interleaved lines of source code from programs A, B, or O, where programs A and B contain changes to code base O.
    Type: Application
    Filed: October 11, 2024
    Publication date: January 30, 2025
    Inventors: CHRISTIAN BIRD, SHUVENDU K. LAHIRI, TODD DOUGLAS MYTKOWICZ, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY
  • Patent number: 12205130
    Abstract: Systems and methods for on demand local commerce are described. One example embodiment includes a device gathering location information and product interest associated with clients and client devices. The system may use location information in determining that the first plurality of client devices are within a first geographic area during a first time period, and may further use the interest information in calculating an interest level for a first product. A threshold may be identified and used in determining that the interest level for the first product exceeds the threshold. When the calculated interest level exceeds the threshold, a local commerce action is initiated. In various embodiments, the local commerce action may be a live on demand auction at a particular location, an offer associated with a geofenced area, a sales location recommendation to a merchant, or any other such local commerce action.
    Type: Grant
    Filed: March 9, 2023
    Date of Patent: January 21, 2025
    Assignee: EBAY INC.
    Inventor: Neelakantan Sundaresan
  • Patent number: 12197896
    Abstract: A code generation system uses a non-terminal expansion model and a non-terminal selector model to generate a code sketch to complete a partially-formed source code snippet. The non-terminal expansion model is a neural transformer model trained on a supervised dataset through reinforcement learning to learn to predict the production rule to expand for a given non-terminal symbol. The non-terminal selector model is trained through reinforcement learning to predict the non-terminal symbol to expand given a partial-code state. The models are used in a two-step beam search to generate the top candidate code sketches, where a candidate code sketch may contain a hole that represents an unexpanded non-terminal symbol.
    Type: Grant
    Filed: November 3, 2023
    Date of Patent: January 14, 2025
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: Miltiadis Allamanis, Daya Guo, Neelakantan Sundaresan, Alexey Svyatkovskiy
  • Publication number: 20250004918
    Abstract: A debugging tool identifies the smallest subset of an input sequence or rationales that influenced a neural language model to generate an output sequence. The debugging tool uses the rationales to understand why the model made its predictions and in particular, the particular input tokens that had the most impact on the output sequence. In the case of erroneous output, the rationales are used to alter the input sequence to avoid the error or to tailor a new training dataset to retrain the model to improve its performance.
    Type: Application
    Filed: September 9, 2024
    Publication date: January 2, 2025
    Inventors: COLIN BRUCE CLEMENT, DAVID ALBERTO NADER PALACIO, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO