Clinc, Inc.

Pocketnest Inc.

University of Michigan

Yiping Kang

Jason Mars

Lingjia Tang

Michael A Laurenzano

Johann Hauswald

Parker Hill

Yunqi Zhang

Jonathan K Kummerfeld

Jessica Willis

Chris Wascha

Trevor N Mudge

Joseph Michael Pusdesris

Andrea Pellegrini

Benjamin Allen Vandersloot

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

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 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

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.

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

Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.

Systems and methods for intelligently curating machine learning training data and improving machine learning model performance

A data processing apparatus has a memory rename table for storing memory rename entries each identifying a mapping between a memory address of a location in memory and a mapped register of a plurality of registers. The mapped register is identified by a register number. In response to a store instruction, the store target memory address of the store instruction is mapped to a store destination register and so the data value is stored to the store destination register instead of memory. A memory rename entry is provided in the table to identify the mapping between the store target memory address and store destination target register. In response to a load instruction, if there is a hit in the memory rename table for the load target memory address then the loaded value can be read from the mapped register instead of memory.

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Ann Arbor, MI, United States of America

Yiping Kang