Contextualized attributes for vectorized data
AssigneeThe Toronto-Dominion Back
InventorDino Paul D'Agostino
Life of this patent
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Abstract
A method that includes at least one of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database.
Claims20
An apparatus comprising:
- a memory storing instructions;
- and
- a processor coupled to the memory and configured to execute the instructions, wherein when executed, the instructions configure the processor to:
- receive conversation content from a communication session between a source device and a provider device,
- identify a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the conversation content,
- annotate the conversation content to include identifiers of the plurality of contextual attributes therein,
- convert the annotated conversation content into a vector based on execution of an additional LLM,
- add a label to the vector with identifiers of the plurality of contextual attributes used to annotate the conversation content, and
- store the labelled vector within a vector database based on a contextual attribute in the label.
A method comprising:
- receiving, by hardware processor, conversation content from a communication session between a source device and a provider device;
- identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) by the hardware processor on the conversation content;
- annotating, by the hardware processor, the conversation content to include identifiers of the plurality of contextual attributes therein;
- converting, by the hardware processor, the annotated conversation content into a vector based on execution of an additional LLM;
- adding, by the hardware processor, a label to the vector with identifiers of the plurality of contextual attributes used to annotate the conversation content;
- and
- storing, by the hardware processor, the labelled vector within a vector database based on a contextual attribute in the label.
A non-transitory computer-readable storage medium comprising instructions stored therein which when executed by a processor cause the processor to perform:
- receiving conversation content from a communication session between a source device and a provider device;
- identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the conversation content;
- annotating the conversation content to include identifiers of the plurality of contextual attributes therein;
- converting the annotated conversation content into a vector based on execution of an additional LLM;
- adding a label to the vector with identifiers of the plurality of contextual attributes used to annotate the conversation content;
- and
- storing the labelled vector within a vector database based on a contextual attribute in the label.
Dependencies are inferred from the claim text.