An agent system includes a first memory and a first processor coupled to the first memory. The first processor analyzes contents of a verbal question, and carries out pre-processing that replaces vocabulary, which is used in the contents of the question, with homogenized vocabulary, and generates response information based on results of analysis. In a case in which there exists substitution vocabulary that has replaced original vocabulary in the pre-processing, the first processor changes the response information such that it can be recognized that the substitution vocabulary in the response information is synonymous with the original vocabulary, and outputs the response information.

An agent system includes a first memory and a first processor coupled to the first memory. The first processor analyzes contents of a verbal question, and carries out pre-processing that replaces vocabulary, which is used in the contents of the question, with homogenized vocabulary, and generates response information based on results of analysis. In a case in which there exists substitution vocabulary that has replaced original vocabulary in the pre-processing, the first processor changes the response information such that it can be recognized that the substitution vocabulary in the response information is synonymous with the original vocabulary, and outputs the response information.

A syntax analyzing device includes: a syntax analyzing unit that analyzes syntax of a sentence received by a receiving unit, thereby acquiring a first analysis result, which is an analysis result having one or more elements constituting the sentence and parts of speech of the respective one or more elements and has one or more binary trees each having the parts of speech or the elements as nodes; a category acquiring unit that acquires categories of the respective one or more elements constituting the sentence; a category inserting unit that acquires a second analysis result in which the categories of the elements are respectively inserted between the elements and the parts of speech of the elements, which respectively correspond to the one or more categories, and constituting the first analysis result; and a learning unit that outputs the second analysis result acquired by the category inserting unit.

A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

The method for identifying noise samples, includes: obtaining an original sample set; obtaining a target sample set by adding masks to original training corpora in the original sample set using a preset adjustment rule; performing mask prediction on a plurality of target training corpora in the target sample set using a pre-trained language model to obtain a first mask prediction character corresponding to each target training corpus; matching the first mask prediction character corresponding to each target training corpus with a preset condition; and according to target training corpora of which first mask prediction characters do not match the preset condition in the target sample set, determining corresponding original training corpora in the original sample set as noise samples.

The method for identifying noise samples, includes: obtaining an original sample set; obtaining a target sample set by adding masks to original training corpora in the original sample set using a preset adjustment rule; performing mask prediction on a plurality of target training corpora in the target sample set using a pre-trained language model to obtain a first mask prediction character corresponding to each target training corpus; matching the first mask prediction character corresponding to each target training corpus with a preset condition; and according to target training corpora of which first mask prediction characters do not match the preset condition in the target sample set, determining corresponding original training corpora in the original sample set as noise samples.

A two way communication assembly includes a display housing that is positionable on a support surface such that the display housing is visible to a pair of users. A pair of displays and a pair of qwerty keyboards is each integrated into opposite sides of the display housing. A translation unit is integrated into the display housing and the translation unit stores a database comprising a plurality of languages spoken around the world. The translation unit translates language between the qwerty keyboards to facilitate a patient who speaks a first language to communicate with a caregiver that speaks a second language. In this way the translation unit facilitates the caregiver to communicate with the patient.