Abstract: Systems and methods for automated, degradation-resistant, tuning of machine-learning (“ML”) models are provided. The systems and methods may identify an inoperative input utterance, retrieve a feature set associated with the inoperative input utterance, and generate an updated utterance-feature-intent (“UFI”) mapping based on the retrieved feature set. The systems and methods may retrain the ML model using the updated UFI mapping, and compare the accuracy of the system after the retraining and before the retraining. In a scenario where the accuracy of the system does not improve, the systems and methods may amplify the updated UFI mapping. In a scenario where the accuracy of the system does improve, the systems and methods may deploy the updated UFI mapping.

Abstract: Aspects of the disclosure relate to supervised machine-learning (“ML”) training platforms for artificial intelligence (“AI”) computer systems. The ML training platform may include isolated update testing. The isolated update testing may feature a plurality of environments with various levels of isolation. The ML training platform may also include bi-directional channels for controlled update propagation.

Abstract: A system for determining intents associated with human utterances at a voice response system is provided. The system may involve a machine learning classifier and a deep learning classifier. The machine learning classifier may receive labeled training data. The labeled training data may be sample utterances. Each sample utterance may be assigned an intent. The intent may be the label of the utterance. The intent may be included in a predefined plurality of intents. The machine learning classifier may include a feature engineering and extraction module. The feature engineering and extraction module may identify features specific to each intent. The machine learning classifier may label previously unlabeled utterances received in a production environment. The newly labeled utterances received in the production environment may be used as training data to train a deep learning classifier. After being trained, the deep learning classifier may receive and label previously unlabeled utterances.

Abstract: Systems and methods for automated, degradation-resistant, tuning of machine-learning (“ML”) models are provided. The systems and methods may identify an inoperative input utterance, retrieve a feature set associated with the inoperative input utterance, and generate an updated utterance-feature-intent (“UFI”) mapping based on the retrieved feature set. The systems and methods may retrain the ML model using the updated UFI mapping, and compare the accuracy of the system after the retraining and before the retraining. In a scenario where the accuracy of the system does not improve, the systems and methods may amplify the updated UFI mapping. In a scenario where the accuracy of the system does improve, the systems and methods may deploy the updated UFI mapping.

Abstract: Apparatus for mapping a user utterance onto a plurality of intents is provided. The apparatus may include an intent training database that includes a plurality of tokens and intents. The apparatus may include a processor. The processor may utilize a token-intent map to generate a token-row map and an intent-column map. The processor may map the plurality of tokens onto a token-intent matrix. The processor may generate a token-cognitive matrix, a cognitive-comprehension matrix and an intent-cognitive matrix from the decomposition. The cognitive-comprehension matrix may be the space of entanglement between the token-cognitive matrix and the intent-cognitive matrix. The processor may reduce the rank of the cognitive-comprehension matrix. The processor may compute a plurality of token vectors from a computation of the token-cognitive matrix and the cognitive-comprehension matrix.

Abstract: Apparatus for mapping a user utterance onto a plurality of intents is provided. The apparatus may include an intent training database that includes a plurality of tokens and intents. The apparatus may include a processor. The processor may utilize a token-intent map to generate a token-row map and an intent-column map. The processor may map the plurality of tokens onto a token-intent matrix. The processor may generate a token-cognitive matrix, a cognitive-comprehension matrix and an intent-cognitive matrix from the decomposition. The cognitive-comprehension matrix may be the space of entanglement between the token-cognitive matrix and the intent-cognitive matrix. The processor may reduce the rank of the cognitive-comprehension matrix. The processor may compute a plurality of token vectors from a computation of the token-cognitive matrix and the cognitive-comprehension matrix.

Abstract: Apparatus for mapping a user utterance onto a plurality of intents is provided. The apparatus may include an intent training database that includes a plurality of tokens and intents. The apparatus may include a processor. The processor may utilize a token-intent map to generate a token-row map and an intent-column map. The processor may map the plurality of tokens onto a token-intent matrix. The processor may generate a token-cognitive matrix, a cognitive-comprehension matrix and an intent-cognitive matrix from the decomposition. The cognitive-comprehension matrix may be the space of entanglement between the token-cognitive matrix and the intent-cognitive matrix. The processor may reduce the rank of the cognitive-comprehension matrix. The processor may compute a plurality of token vectors from a computation of the token-cognitive matrix and the cognitive-comprehension matrix.

Abstract: A system for extracting verifiable entities from a user-utterance received on an automated calling service is provided. The system may include a receiver configured to receive a user-utterance, a processor and a non-transitory computer-readable media comprising computer-executable instructions. The processor may be configured to execute the instructions which, canonicalize the user-utterance into a plurality of tokens, determine the number of tokens of the user-utterance, and generate, using a sliding-window protocol, a comprehensive number of n-gram sequences from the user-utterance. The processor may be configured to process a plurality of threads of execution that may include a series of actions executed on the n-gram sequences to identify and extract verified entities from the user-utterance.

Abstract: A system for extracting verifiable entities from a user-utterance received on an automated calling service is provided. The system may include a receiver configured to receive a user-utterance, a processor and a non-transitory computer-readable media comprising computer-executable instructions. The processor may be configured to execute the instructions which, canonicalize the user-utterance into a plurality of tokens, determine the number of tokens of the user-utterance, and generate, using a sliding-window protocol, a comprehensive number of n-gram sequences from the user-utterance. The processor may be configured to process a plurality of threads of execution that may include a series of actions executed on the n-gram sequences to identify and extract verified entities from the user-utterance.

Abstract: Apparatus for mapping a user utterance onto a plurality of intents is provided. The apparatus may include an intent training database that includes a plurality of tokens and intents. The apparatus may include a processor. The processor may utilize a token-intent map to generate a token-row map and an intent-column map. The processor may map the plurality of tokens onto a token-intent matrix. The processor may generate a token-cognitive matrix, a cognitive-comprehension matrix and an intent-cognitive matrix from the decomposition. The cognitive-comprehension matrix may be the space of entanglement between the token-cognitive matrix and the intent-cognitive matrix. The processor may reduce the rank of the cognitive-comprehension matrix. The processor may compute a plurality of token vectors from a computation of the token-cognitive matrix and the cognitive-comprehension matrix.

Abstract: Methods for analyzing a query is provided. Methods may include receiving a query from a user. Methods may include dissecting the query into a plurality of word segments. Methods may include determining, based on a predetermined ontology, which of the plurality of words satisfies a predetermined ontological threshold and which words do not satisfy the predetermined ontological threshold. Methods may include discarding the words that do not satisfy the predetermined ontological threshold. Methods may include determining a part of speech associated with each word based on the content of the query. Methods may include determining a concept associated with each word based on the content of the query. Methods may include displaying to the user word that satisfies the predetermined threshold alongside the associated part of speech and associated concept. Methods may include enabling the user to change each word, each concept and each part of speech.

Abstract: A method for utilizing a multi-intent matrix is provided. The method may be used to avoid a derived intent collision while determining correct intent of a human-voice-telephone query. The method may include receiving a telephone call at the voice response system. The method may include responding to the telephone call at the voice response system. The response may include prompting a human caller to utter a reason for the telephone call. The human caller may be associated with the telephone call. The method may include receiving a human-voice-telephone-query-utterance at the voice response system. The method may include translating the human-voice-telephone-query-utterance into a text string. The translating may occur at the voice response system or a translation module. The method may include analyzing the text string utilizing a group of parameters. The method may include re-analyzing the text string utilizing a group of parameters set to custom values.

Abstract: An apparatus for automatic completion of an entry into an input field is provided. The apparatus may include a webpage. The webpage may include a search box. The search box may include a user input field. The apparatus may further include a processor and a machine-readable memory. When a user begins to type a query into the user input box, the processor may be configured to complete, pending user confirmation, the user's query. The completion may be based, for example, on information stored on the machine-readable memory pertaining to the user. The information may be available to an authorized accessor of the machine-readable memory via a secure communications channel. However, the information may be restricted from the user's access via a communications channel associated with the user.

Abstract: A method for natural language search for variables is provided. The method may include searching an index using key words from a user's natural language question and the context of the user's question. The index may reference variables and/or web service calls in a domain model. The method may also include saving documents obtained in response to the search. The method may also include mapping each of the documents as a node into an object graph. Each node may be associated with a parent node, except when the node is a root node. The method may also include identifying the root node of each document. The method may also include identifying the path of each node from the node to the node's root node. The method may also include identifying matching paths. Each matching path may provide an answer to the user's question.

Abstract: Methods for analyzing a query is provided. Methods may include receiving a query from a user. Methods may include dissecting the query into a plurality of word segments. Methods may include determining, based on a predetermined ontology, which of the plurality of words satisfies a predetermined ontological threshold and which words do not satisfy the predetermined ontological threshold. Methods may include discarding the words that do not satisfy the predetermined ontological threshold. Methods may include determining a part of speech associated with each word based on the content of the query. Methods may include determining a concept associated with each word based on the content of the query. Methods may include displaying to the user word that satisfies the predetermined threshold alongside the associated part of speech and associated concept. Methods may include enabling the user to change each word, each concept and each part of speech.

Abstract: A method for natural language search for variables is provided. The method may include searching an index using key words from a user's natural language question and the context of the user's question. The index may reference variables and/or web service calls in a domain model. The method may also include saving documents obtained in response to the search. The method may also include mapping each of the documents as a node into an object graph. Each node may be associated with a parent node, except when the node is a root node. The method may also include identifying the root node of each document. The method may also include identifying the path of each node from the node to the node's root node. The method may also include identifying matching paths. Each matching path may provide an answer to the user's question.

Abstract: A method for natural language search for variables is provided. The method may include searching an index using key words from a user's natural language question and the context of the user's question. The index may reference variables and/or web service calls in a domain model. The method may also include saving documents obtained in response to the search. The method may also include mapping each of the documents as a node into an object graph. Each node may be associated with a parent node, except when the node is a root node. The method may also include identifying the root node of each document. The method may also include identifying the path of each node from the node to the node's root node. The method may also include identifying matching paths. Each matching path may provide an answer to the user's question.

Abstract: An apparatus for automatic completion of an entry into an input field is provided. The apparatus may include a webpage. The webpage may include a search box. The search box may include a user input field. The apparatus may further include a processor and a machine-readable memory. When a user begins to type a query into the user input box, the processor may be configured to complete, pending user confirmation, the user's query. The completion may be based, for example, on information stored on the machine-readable memory pertaining to the user. The information may be available to an authorized accessor of the machine-readable memory via a secure communications channel. However, the information may be restricted from the user's access via a communications channel associated with the user.

Abstract: Apparatus, including a processor, for performing a search based, at least in part, on MCC information, is provided. The processor may identify an MCC with greater than a threshold level of correlation to a user's query. The processor may be configured to search a machine-readable memory for documents pertaining to the MCC and/or pertaining to the user's query. The processor may respond to the user's query by providing a set of documents. The processor may provide a reciprocal query. The reciprocal query may be for obtaining a greater quantum of MCC information then was included in the user's query. The reciprocal query may prompt the user to choose from one or more of a plurality of MCCs that correlate to the subject matter area. The reciprocal query may prompt a user to select from a plurality of subject matter areas corresponding to the plurality of MCCs.

Abstract: A method for natural language search for variables is provided. The method may include searching an index using key words from a user's natural language question and the context of the user's question. The index may reference variables and/or web service calls in a domain model. The method may also include saving documents obtained in response to the search. The method may also include mapping each of the documents as a node into an object graph. Each node may be associated with a parent node, except when the node is a root node. The method may also include identifying the root node of each document. The method may also include identifying the path of each node from the node to the node's root node. The method may also include identifying matching paths. Each matching path may provide an answer to the user's question.