Abstract: Systems and methods for collecting and processing memory-related data and synthesizing memory-based narratives that includes collecting memory input data from one or more users, constructing a memory graph comprising a hierarchical data structure including one or more graphical nodes for storing the memory input data, assimilating the memory graph into a semantic collage of distinct memory graphs, generating a mnemonic narrative artifact comprising memory input data from related memory graphs, and surfacing the mnemonic narrative artifact via a user interface to one or more users.

Abstract: Systems and methods for collecting and processing memory-related data and synthesizing memory-based narratives that includes collecting memory input data from one or more users, constructing a memory graph comprising a hierarchical data structure including one or more graphical nodes for storing the memory input data, assimilating the memory graph into a semantic collage of distinct memory graphs, generating a mnemonic narrative artifact comprising memory input data from related memory graphs, and surfacing the mnemonic narrative artifact via a user interface to one or more users.

Abstract: A system and method for implementing a machine learning-based virtual dialogue agent includes computing an input embedding based on receiving a user input; computing, via a pre-trained machine learning language model, an embedding response inference based on the input embedding; searching, based on the embedding response inference, a response imprintation embedding space that includes a plurality of distinct embedding representations of potential text-based responses to the user input, wherein each of the plurality of distinct embedding representations is tethered to a distinct human-imprinted media response, and searching the response imprintation embedding space includes: searching the response imprintation embedding space based on an embedding search query, and returning a target embedding representation from the response imprintation embedding space based on the searching of the response imprintation embedding space; and executing, via a user interface of the machine learning-based virtual dialogue agent,

Abstract: A system and method for implementing a machine learning-based virtual dialogue agent includes computing an input embedding based on receiving a user input; computing, via a pre-trained machine learning language model, an embedding response inference based on the input embedding; searching, based on the embedding response inference, a response imprintation embedding space that includes a plurality of distinct embedding representations of potential text-based responses to the user input, wherein each of the plurality of distinct embedding representations is tethered to a distinct human-imprinted media response, and searching the response imprintation embedding space includes: searching the response imprintation embedding space based on an embedding search query, and returning a target embedding representation from the response imprintation embedding space based on the searching of the response imprintation embedding space; and executing, via a user interface of the machine learning-based virtual dialogue agent,