Clinc, Inc.

University of Michigan

Pocketnest Inc.

Clinic, Inc.

Zeroshotbot, Inc.

Trueselph, Inc.

Cline, Inc.

Tobu Corporation

Jason Mars

Lingjia Tang

Michael A Laurenzano

Johann Hauswald

Parker Hill

Yiping Kang

Yunqi Zhang

Jonathan K Kummerfeld

Stefan Larson

Andrew Lee

Kevin Leach

Jessica Willis

Chris Wascha

Eldon Marks

Anish Mahendran

Michael Laurenzano

Joseph J Peper

Patrick McLaughlin

Sean Stapleton

Christopher Clarke

A system for query-enhanced digital agent selection within a multi-digital agent architecture is described. The system may include one or more processors, a graphical user interface module rendering an interactive agent querying interface for receiving unrefined query data, and a dynamic query enhancement microservice for transforming the unrefined query data to enhanced query data. The system may further include a multi-agent arbiter communicably interposed between the dynamic query enhancement microservice and a set of digital agents, the multi-agent arbiter for selecting a digital agent of the set to receive an agent selection control signal from the multi-agent arbiter and for forwarding the enhanced query data to the selected digital agent. The selected digital agent may generate a response to the unrefined query data based on a processing of the enhanced query data against data accessed within a computer data source to which the selected digital agent is specifically permissioned.

Multi-digital agent architecture with query-enhanced digital agent selection mechanism

A system and method for accuracy-enhanced processing of query data within a multi-digital agent architecture may include receiving unrefined query data including textual data; retrieving historical user dialogue data from a computer database associated with the unrefined query data; transforming the unrefined query data to refined query data based on the retrieved historical user dialogue data; generating at least one digital agent classification inference using the refined query data; routing the refined query data to a digital agent based on the at least one digital agent classification inference; generating, by the digital agent, a digital response to the unrefined query data; and returning the digital response to an interactive graphical user interface.

Systems and methods for accuracy-enhanced processing of query data within a multi-digital agent architecture

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.

Systems and methods for collecting and processing memory-related data and synthesizing memory-based narratives

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, a human-imprinted media response tethered to the target embedding representation.

Systems and methods for generation and deployment of a human-personified virtual agent using pre-trained machine learning-based language models and a video response corpus

System and method of building a zero-shot training, machine learning-based virtual dialogue agent includes identifying a response corpus comprising a plurality of distinct response samples; providing the response corpus to a virtual agent generator; generating, by one or more pre-trained machine learning language models, a distinct embeddings inference for each of the plurality of distinct response samples; forming an embeddings-based architecture for response generation based on the distinct embeddings inference for each of the plurality of distinct response samples, wherein the embeddings-based architecture includes a mapping of the distinct embeddings inference for each of the plurality of distinct response samples to an n-dimensional space; instantiating a virtual dialogue agent based on receiving user stimuli; and computing, using the embeddings-based architecture, a response inference to the user stimuli, wherein the computed response inference is based on identifying one distinct embeddings inference of a distinct response sample of the response corpus.

Systems and methods for zero-shot, fast-generation and implementation of an intelligent virtual dialogue agent using one or more pre-trained machine learning-based language models and a response corpus

A system and method of configuring a graphical control structure for controlling a machine learning-based automated dialogue system includes configuring a root dialogue classification node that performs a dialogue intent classification task for utterance data input; configuring a plurality of distinct dialogue state classification nodes that are arranged downstream of the root dialogue classification node; configuring a graphical edge connection between the root dialogue classification node and the plurality of distinct state dialogue classification nodes that graphically connects each of the plurality of distinct state dialogue classification nodes to the root dialogue classification node, wherein (i) the root dialogue classification node, (ii) the plurality of distinct classification nodes, (iii) and the transition edge connections define a graphical dialogue system control structure that governs an active dialogue between a user and the machine learning-based automated dialogue system.

Systems and methods for intelligently configuring and deploying a control structure of a machine learning-based dialogue system

Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.

System and method for implementing an artificially intelligent virtual assistant using machine learning

Systems and methods for building a response for a machine learning-based dialogue agent includes implementing machine learning classifiers that predict slot segments of the utterance data based on an input of the utterance data; predict a slot classification label for each of the slot segments of the utterance data; computing a semantic vector value for each of the slot segments of the utterance data; assessing the semantic vector value of the slot segments of the utterance data against a multi-dimensional vector space of structured categories of dialogue, wherein the assessment includes: for each of a distinct structured categories of dialogue computing a similarity metric value; selecting one structured category of dialogue from the distinct structured categories of dialogue based on the computed similarity metric value for each of distinct structured categories; and producing a response to the utterance data.

Systems and methods for automatically categorizing unstructured data and improving a machine learning-based dialogue system

Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.

Systems and methods for automatically detecting and repairing slot errors in machine learning training data for a machine learning-based dialogue system

Systems and methods for synthesizing training data for multi-intent utterance segmentation include identifying a first corpus of utterances comprising a plurality of distinct single-intent in-domain utterances; identifying a second corpus of utterances comprising a plurality of distinct single-intent out-of-domain utterances; identifying a third corpus comprising a plurality of distinct conjunction terms; forming a multi-intent training corpus comprising synthetic multi-intent utterances, wherein forming each distinct multi-intent utterance includes: selecting a first distinct in-domain utterance from the first corpus of utterances; probabilistically selecting one of a first out-of-domain utterance from the second corpus and a second in-domain utterance from the first corpus; probabilistically selecting or not selecting a distinct conjunction term from the third corpus; and forming a synthetic multi-intent utterance including appending the first in-domain utterance with one of the first out-of-domain utterance from the second corpus of utterances and the second in-domain utterance from the first corpus of utterances.

Systems and methods for machine learning-based multi-intent segmentation and classification

Systems and methods for constructing an artificially diverse corpus of training data includes evaluating a corpus of utterance-based training data samples, identifying a slot replacement candidate; deriving distinct skeleton utterances that include the slot replacement candidate, wherein deriving the distinct skeleton utterances includes replacing slots of each of the plurality of distinct utterance training samples with one of a special token and proper slot classification labels; selecting a subset of the distinct skeleton utterances; converting each of the distinct skeleton utterances of the subset back to distinct utterance training samples while still maintaining the special token at a position of the slot replacement candidate; altering a percentage of the distinct utterance training samples with a distinct randomly-generated slot token value at the position of the slot replacement candidate; and constructing the artificially diverse corpus of training samples based on a collection of the percentage of the distinct utterance training samples.

Systems and methods for constructing an artificially diverse corpus of training data samples for training a contextually-biased model for a machine learning-based dialogue system

A system and method for intelligently configuring a machine learning-based dialogue system includes a conversational deficiency assessment of a target dialog system, wherein implementing the conversational deficiency assessment includes: (i) identifying distinct corpora of mishandled utterances based on an assessment of the distinct corpora of dialogue data; (ii) identifying candidate corpus of mishandled utterances from the distinct corpora of mishandled utterances as suitable candidates for building new dialogue competencies for the target dialogue system if candidate metrics of the candidate corpus of mishandled utterances satisfy a candidate threshold; building the new dialogue competencies for the target dialogue system for each of the candidate corpus of mishandled utterances having candidate metrics that satisfy the candidate threshold; and configuring a dialogue system control structure for the target dialogue system based on the new dialogue competencies, wherein the dialogue system control structure governs an operation of an automated dialogue agent.

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Ann Arbor, MI, United States of America

Jason Mars