Abstract

Disclosed are methods and systems for utilizing a large language model (LLM) to generate a computing structure. An exemplary method includes: receiving unstructured data from a first source; receiving a computing library from a second source; receiving a set of computing prompts from a third source; receiving a set of computing structures from a fourth source; transmitting the unstructured data, the computing library, the set of computing prompts, and the set of computing structures to an LLM; receiving structured data from the LLM, wherein the structured data comprises the set of computing prompts, a set of answers associated with the set of computing prompts, and a computing structure; and transmitting the structured data to a system.

IPC Classes  ?

• G06F 16/21 - Design, administration or maintenance of databases

Abstract

Disclosed are methods and systems for generating structured data from unstructured data using metadata received from a large language model (LLM). An exemplary method includes: receiving computing prompts from a source; receiving a first vector embedding for a first computing prompt from an LLM; receiving a second vector embedding for a second computing prompt from the LLM; generating a computing prompt group comprising the first computing prompt and the second computing prompt; determining at least one first file from a vector database that corresponds with the first computing prompt; determining at least one second file from the vector database that corresponds with the second computing prompt; receiving processed data from the LLM based on the computing prompt group, the at least one first file, and the at least one second file; and generating structured data comprising the processed data and computing indicators.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems

Abstract

Disclosed are methods and systems for utilizing a large language model (LLM) to generate a computing structure. An exemplary method includes: receiving unstructured data from a first source; receiving a computing library from a second source; receiving a set of computing prompts from a third source; receiving a set of computing structures from a fourth source; transmitting the unstructured data, the computing library, the set of computing prompts, and the set of computing structures to an LLM; receiving structured data from the LLM, wherein the structured data comprises the set of computing prompts, a set of answers associated with the set of computing prompts, and a computing structure; and transmitting the structured data to a system.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/25 - Integrating or interfacing systems involving database management systems

Abstract

Disclosed are methods and systems for prompting a large language model (LLM) for vector embeddings and metadata to generate an indexed computing file. An exemplary method includes: receiving a file from a source; extracting data from the file; transmitting the data to an LLM; determining metadata associated with the file using the LLM; chunking the file, thereby generating a chunked file; transmitting the chunked file to the LLM; determining at least one vector embedding associated with the chunked file using the LLM; configuring a vector database; and inserting the metadata and the at least one vector embedding into the vector database, thereby generating an indexed computing file.

IPC Classes  ?

• G06F 16/13 - File access structures, e.g. distributed indices

Abstract

Methods and systems for producing and transmitting recommendations associated with a database using a large language model (LLM). An exemplary method includes receiving a set of computing prompts from a source. Receiving vector embeddings for the set of computing prompts from an LLM and determining using the vector embeddings, files from a database that correspond with a first computing prompt from the set of computing prompts or a second computing prompt from the set of computing prompts. The system receives first data from the LLM based on the first and second computing prompts, and the files from the database. The method generates second data comprising the first data and computing indicators. Recommendations associated with the database are generated based on the second data and transmitted to a system maintaining the database.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 18/2111 - Selection of the most significant subset of features by using evolutionary computational techniques, e.g. genetic algorithms
• G06F 40/30 - Semantic analysis

Abstract

Disclosed are methods and systems for generating structured data from unstructured data using metadata received from a large language model (LLM). An exemplary method includes: receiving computing prompts from a source; receiving a first vector embedding for a first computing prompt from an LLM; receiving a second vector embedding for a second computing prompt from the LLM; generating a computing prompt group comprising the first computing prompt and the second computing prompt; determining at least one first file from a vector database that corresponds with the first computing prompt; determining at least one second file from the vector database that corresponds with the second computing prompt; receiving processed data from the LLM based on the computing prompt group, the at least one first file, and the at least one second file; and generating structured data comprising the processed data and computing indicators.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/22 - IndexingData structures thereforStorage structures

Goods & Services

Software as a service (SAAS) services featuring software for enabling customers to maintain a single repository of all third party relationships, their firmagraphic data and risk profiles.

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Software as a service (SAAS) services featuring software for managing third party relationships, their firmagraphic data and risk profiles

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Developing training systems and learning methodologies for others

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Software as a service featuring software for governance, risk, and compliance management

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Software as a service featuring software in the field of governance, risk, and compliance.

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Software as a service featuring software in the field of governance, risk, and compliance.

Goods & Services

Software as a service featuring software for governance, risk, and compliance management

Goods & Services

Software as a service featuring software for governance, risk, and compliance management