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公开(公告)号:US20240354322A1
公开(公告)日:2024-10-24
申请号:US18632900
申请日:2024-04-11
Applicant: Palantir Technologies Inc.
Inventor: Anirvan Mukherjee , Craig De Souza , Edgar Gomes de Araujo , Johannes Beil , Jessica Winssinger , Michael Zullo , Rushad Heerjee , Shubhankar Sachdev
IPC: G06F16/33 , G06F18/2415
CPC classification number: G06F16/3344 , G06F18/2415
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.
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公开(公告)号:US20240354584A1
公开(公告)日:2024-10-24
申请号:US18632958
申请日:2024-04-11
Applicant: Palantir Technologies Inc.
Inventor: Anirvan Mukherjee , Craig De Souza , Edgar Gomes de Araujo , Johannes Beil , Jessica Winssinger , Michael Zullo , Rushad Heerjee , Shubhankar Sachdev
IPC: G06N3/0895
CPC classification number: G06N3/0895
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for creating and/or updating an ontology. A computer-implemented method may include: receiving tabular data from one or more data sources; generating an interactive graphical representation of at least a portion of the tabular data and connections between the portion of the tabular data; providing, via a user interface, the interactive graphical representation; receiving a user operation via the user interface, updating an ontology and/or generating transformations for adding data objects into a database.
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公开(公告)号:US20240354436A1
公开(公告)日:2024-10-24
申请号:US18505912
申请日:2023-11-09
Applicant: Palantir Technologies Inc.
Inventor: Anirvan Mukherjee , Craig De Souza , Edgar Gomes de Araujo , Johannes Beil , Jessica Winssinger , Michael Zullo , Rushad Heerjee , Shubhankar Sachdev
CPC classification number: G06F21/6227 , G06F16/3344 , G06F16/3347
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for searching a large corpus of data. A computer-implemented method may include: receiving a first user input comprising a natural language query; vectorizing the first user input into a query vector; executing, using the query vector, a similarity search in a document search model to identify one or more similar document portions, where the document search model includes a plurality of vectors corresponding to a plurality of portions of a set of documents; generating a first prompt for a large language model (“LLM”), the first prompt including at least the first user input, and the one or more similar document portions; transmitting the first prompt to the LLM; receiving a first output from the LLM in response to the first prompt; and providing, via a user interface, the first output from the LLM.
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公开(公告)号:US20250110786A1
公开(公告)日:2025-04-03
申请号:US18794966
申请日:2024-08-05
Applicant: Palantir Technologies Inc.
Inventor: Max-Philipp Schrader , Sriram Krishnan , Megha Arora , Pavlo Tyshevskyi , Johannes Beil , Alexander Edwards , David Kebudi , Montgomery Evans , Anirvan Mukherjee
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods for automatically solving computational tasks or problems. A computer-implemented method may include: providing an agent service that utilizes a plurality of agents to process one or more tasks; receiving, by a first agent, a request to handle a first task; obtaining, by the first agent, a first accessory to handle the first task; assigning, by the first agent, at least a portion of the first task to a second agent; sharing, by the first agent, the first accessory to the second agent; and processing, by the second agent, at least the portion of the first task using the first accessory to generate a processing result.
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公开(公告)号:US20240427783A1
公开(公告)日:2024-12-26
申请号:US18828385
申请日:2024-09-09
Applicant: Palantir Technologies Inc.
Inventor: Elliot Hirsch , Johannes Beil , Lauren Brown , Nicolas Prettejohn , Paul Baseotto
IPC: G06F16/2458 , G06F16/248
Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
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公开(公告)号:US12299022B2
公开(公告)日:2025-05-13
申请号:US18632900
申请日:2024-04-11
Applicant: Palantir Technologies Inc.
Inventor: Anirvan Mukherjee , Craig De Souza , Edgar Gomes de Araujo , Johannes Beil , Jessica Winssinger , Michael Zullo , Rushad Heerjee , Shubhankar Sachdev
IPC: G06F16/00 , G06F16/334 , G06F18/2415 , G06N3/0895
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.
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公开(公告)号:US20250110753A1
公开(公告)日:2025-04-03
申请号:US18794904
申请日:2024-08-05
Applicant: Palantir Technologies Inc.
Inventor: Johannes Beil , Pavlo Tyshevskyi , Max-Philipp Schrader , Sriram Krishnan , Michael Zullo , Rushad Heerjee , Anirvan Mukherjee
Abstract: Computer-implemented systems and methods are disclosed, including systems and methods for automatically solving problems. A computer-implemented method may include: by an agent service configured to interact with an LLM to complete a run: providing an LLM with access to a state machine, executing an initial state of the state machine with the LLM, determining a subsequent state of the state machine based on at least an initial LLM output, and executing the subsequent state of the state machine.
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公开(公告)号:US12111839B1
公开(公告)日:2024-10-08
申请号:US18333975
申请日:2023-06-13
Applicant: Palantir Technologies Inc.
Inventor: Elliot Hirsch , Johannes Beil , Lauren Brown , Nicolas Prettejohn , Paul Baseotto
IPC: G06F7/00 , G06F16/2458 , G06F16/248
CPC classification number: G06F16/2468 , G06F16/248
Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
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公开(公告)号:US11720580B1
公开(公告)日:2023-08-08
申请号:US17683986
申请日:2022-03-01
Applicant: Palantir Technologies Inc.
Inventor: Elliot Hirsch , Johannes Beil , Lauren Brown , Nicolas Prettejohn , Paul Baseotto
IPC: G06F7/00 , G06F16/2458 , G06F16/248
CPC classification number: G06F16/2468 , G06F16/248
Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
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