AUTOMATIC VALIDATION OF COMPUTER-GENERATED CODE METHOD AND APPARATUS

    公开(公告)号:US20250103308A1

    公开(公告)日:2025-03-27

    申请号:US18475960

    申请日:2023-09-27

    Abstract: Techniques for automatically generating a natural language (NL) translation of computer code are disclosed. In one embodiment, a computer-implemented method is disclosed comprising receiving, from a user, a code translation request in connection with code generated by a code generation system based on natural language (NL) input, analyzing the computer-generated code and generating a natural language (NL) translation of the computer-generated code based on the analysis, generating a graphical user interface (GUI) comprising the NL input, the NL translation of the computer-generated code and GUI control elements for receiving input from the user in connection with at least one of the NL input and the NL translation; causing the GUI to be displayed at a client device of the user, and receiving input from the user via at least one GUI control element and causing performance of at least one operation in response to the input.

    METHOD AND SYSTEM FOR DETERMINING ALPHA AND BETA VALUES OF A CANDIDATE RELATIVE TO A CLASS

    公开(公告)号:US20240420014A1

    公开(公告)日:2024-12-19

    申请号:US18335353

    申请日:2023-06-15

    Inventor: Eric BAX Max BEECH

    Abstract: Disclosed are systems and methods for determining a beta value of at least one candidate relative to a class. The method comprises receiving historical data for the at least one candidate and historical data for the class; determining a first set of regression coefficients for the historical data for the at least one candidate, and a second set of regression coefficients for the historical data for the class; and calculating the beta value for the at least one candidate relative to the class based on the first set of regression coefficients and the second sets of regression coefficients. The method may further include generating an indication that the beta value for the candidate is one of positive, negative, or zero; and transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the candidate.

    AUTOMATED CITATIONS AND ASSESSMENT FOR AUTOMATICALLY GENERATED TEXT

    公开(公告)号:US20250005266A1

    公开(公告)日:2025-01-02

    申请号:US18345508

    申请日:2023-06-30

    Abstract: In some implementations, the techniques described herein relate to a method including: parsing, by a processor, a generated text to identify statements included within a generated text; querying, by the processor, a remote data source to identify sources for each statement in the statements; determining, by the processor, trustworthiness values for each statement, a trustworthiness value for a given statement determined by computing trustworthiness labels for each source corresponding to a given statement: generating, by the processor, a label for the generated text based on an aggregated trustworthiness of each of the statements; and displaying, by the processor, the generated text and the label within a user interface displayed to a user.

    AUTOMATIC PRIVACY-AWARE MACHINE LEARNING METHOD AND APPARATUS

    公开(公告)号:US20220417226A1

    公开(公告)日:2022-12-29

    申请号:US17899939

    申请日:2022-08-31

    Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

    METHOD AND SYSTEM FOR DETERMINING AND PROVIDING PRODUCT AVAILABILITY

    公开(公告)号:US20240320615A1

    公开(公告)日:2024-09-26

    申请号:US18187940

    申请日:2023-03-22

    CPC classification number: G06Q10/087

    Abstract: In some aspects, the techniques described herein relate to a method including obtaining purchase history data for a plurality of users within a group, the purchase history corresponding to at least one product; determining a first product availability corresponding to the at least one product based on the purchase history data; transmitting, to a User Equipment (UE) of a user of the plurality of users, a notification including the first product availability; obtaining updated purchase history data, the updated purchase history data corresponding to the at least one product; determining a second product availability corresponding to the at least one product based on the updated purchase history data; determining a product availability change based on the first product availability and the second product availability; and modifying user rights of the user based on the product availability change.

    AUTOMATIC PRIVACY-AWARE MACHINE LEARNING METHOD AND APPARATUS

    公开(公告)号:US20240187388A1

    公开(公告)日:2024-06-06

    申请号:US18443380

    申请日:2024-02-16

    CPC classification number: H04L63/0428 G06N20/00

    Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

    AUTOMATIC PRIVACY-AWARE MACHINE LEARNING METHOD AND APPARATUS

    公开(公告)号:US20220255904A1

    公开(公告)日:2022-08-11

    申请号:US17172679

    申请日:2021-02-10

    Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

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