Database query generation using natural language text

    公开(公告)号:US11520815B1

    公开(公告)日:2022-12-06

    申请号:US17877365

    申请日:2022-07-29

    申请人: Dsilo, Inc.

    IPC分类号: G06F16/33 G06F40/295

    摘要: Some embodiments may obtain a natural language question, determine a context of the natural language question, and generate a first vector based on the natural language question using encoder neural network layers. Some embodiments may access a data table comprising column names, generate vectors based on the column names, and determine attention scores based on the vectors. Some embodiments may update the vectors based on the attention scores, generating a second vector based on the natural language question, determine a set of strings comprising a name of the column names and a database language operator based on the vectors. Some embodiments may determine a values based on the determined database language operator, the name, using a transformer neural network model. Some embodiments may generate a query based on the set of strings and the values.

    DATABASE QUERY GENERATION USING NATURAL LANGUAGE TEXT

    公开(公告)号:US20230096857A1

    公开(公告)日:2023-03-30

    申请号:US18073815

    申请日:2022-12-02

    申请人: DSilo, Inc.

    IPC分类号: G06F16/33 G06F40/295

    摘要: Some embodiments may obtain a natural language question, determine a context of the natural language question, and generate a first vector based on the natural language question using encoder neural network layers. Some embodiments may access a data table comprising column names, generate vectors based on the column names, and determine attention scores based on the vectors. Some embodiments may update the vectors based on the attention scores, generating a second vector based on the natural language question, determine a set of strings comprising a name of the column names and a database language operator based on the vectors. Some embodiments may determine a values based on the determined database language operator, the name, using a transformer neural network model. Some embodiments may generate a query based on the set of strings and the values.

    DATABASE GENERATION FROM NATURAL LANGUAGE TEXT DOCUMENTS

    公开(公告)号:US20230037077A1

    公开(公告)日:2023-02-02

    申请号:US17877321

    申请日:2022-07-29

    申请人: Dsilo, Inc.

    摘要: Some embodiments may perform operations of a process that includes obtaining a natural language text document and use a machine learning model to generate a set of attributes based on a set of machine-learning-model-generated classifications in the document. The process may include performing hierarchical data extraction operations to populate the attributes, where different machine learning models may be used in sequence. The process may include using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model augmented with a pooling operation to determine a BERT output via a multi-channel transformer model to generate vectors on a per-sentence level or other per-text-section level. The process may include using a finer-grain model to extract quantitative or categorical values of interest, where the context of the per-sentence level may be retained for the finer-grain model.

    Database generation from natural language text documents

    公开(公告)号:US11580150B1

    公开(公告)日:2023-02-14

    申请号:US17877321

    申请日:2022-07-29

    申请人: Dsilo, Inc.

    摘要: Some embodiments may perform operations of a process that includes obtaining a natural language text document and use a machine learning model to generate a set of attributes based on a set of machine-learning-model-generated classifications in the document. The process may include performing hierarchical data extraction operations to populate the attributes, where different machine learning models may be used in sequence. The process may include using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model augmented with a pooling operation to determine a BERT output via a multi-channel transformer model to generate vectors on a per-sentence level or other per-text-section level. The process may include using a finer-grain model to extract quantitative or categorical values of interest, where the context of the per-sentence level may be retained for the finer-grain model.

    SELF-EXECUTING PROTOCOL GENERATION FROM NATURAL LANGUAGE TEXT

    公开(公告)号:US20230038529A1

    公开(公告)日:2023-02-09

    申请号:US17877264

    申请日:2022-07-29

    申请人: Dsilo, Inc.

    IPC分类号: G06F40/279 G06F40/186

    摘要: A process includes obtaining a document, determining a set of vectors based on a count of n-grams of the document, and determining a first set of information based on the document using a first set of neural networks. The process includes selecting a text section of the natural language document using a second set of neural networks and a code template of a plurality of code templates based on the text section based on the first set of information and the text section. The process includes determining an entity identifier, a value of a conditional statement, a second set of information, and a third set of information based on the text section, the first set of information, and the code template. The process includes generating a first set of program code based on the entity identifier, the value, the second set of information, and the third set of information.