Abstract: An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

Abstract: A natural language understanding (NLU) framework includes a modeling and optimization system that enables enhanced understanding and explainability to the operation of the NLU framework. The NLU framework includes a configuration vector storing settings of various components that may be applied during NLU inference of an utterance, such as which components should be activated or deactivated, as well as which numerical values (e.g., threshold values, coefficients, weight values) that are used by these components during operation. By using this configuration vector to systematically disable and adjust numerical parameters of the components of the NLU framework, and then determining the performance of the NLU framework in these configurations, the modeling and optimization system determines relationships between, as well as the relative importance of, the components of the NLU framework.

Abstract: An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation. The system includes a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions including: performing rule-based generalization of the model to generate at least one generalized meaning representation of the model from the at least one original meaning representation of the model; performing rule-based refinement of the model to prune or modify the at least one generalized meaning representation of the model, or the at least one original meaning representation of the model, or a combination thereof; and after performing the rule-based generalization and the rule-based refinement of the model, using the model to extract intents/entities from a received user utterance.

Abstract: An agent automation system implements a virtual agent that is capable of learning new words, or new meanings for known words, based on exchanges between the virtual agent and a user in order to customize the vocabulary of the virtual agent to the needs of the user or users. The agent automation framework has access to a corpus of previous exchanges between the virtual agent and the user, such as one or more chat logs. New words and/or new meanings for known words are identified within the corpus and new word vectors are generated for these new words and/or new meanings for known words and added to refine a word vector distribution model. The refined word vector distribution model is then utilized by the agent automation system to interact with the user.

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

Abstract: Described are systems and methods for product recommendation refinement in a topic-based virtual storefront embedded in a topical community web page. A system and method may facilitate determining user and community member activity in the virtual storefront based on which weighted keywords are derived. A topic set containing various weighted keywords may be iteratively configured for extracting and ordering one or more products that are extracted from a plurality of heterogeneous sources. A server may be configured for refining the topic set by modifying a baseline session keyword weight or a baseline contextual keyword weight based on a total topic set weight, an elasticity parameter, core topic keyword weights associated with core topic keywords, session keyword weights, and/or contextual keyword weights; and identifying, based at least on the configured topic set, products to be presented to a user as product recommendations in the virtual storefront.

Abstract: An agent automation system includes a memory configured to store a corpus of utterances and a semantic mining framework and a processor configured to execute instructions of the semantic mining framework to cause the agent automation system to perform actions, wherein the actions include: detecting intents within the corpus of utterances; producing intent vectors for the intents within the corpus; calculating distances between the intent vectors; generating meaning clusters of intent vectors based on the distances; detecting stable ranges of cluster radius values for the meaning clusters; and generating an intent/entity model from the meaning clusters and the stable ranges of cluster radius values, wherein the agent automation system is configured to use the intent/entity model to classify intents in received natural language requests.

Abstract: A natural language understanding (NLU) framework includes a domain-aware vector encoding (DAVE) framework. The DAVE framework enables a designer to create a DAVE system having a domain-agnostic semantic (DAS) model and a corresponding trained vector translator (VT) model. The DAVE system uses the DAS model to generate domain-agnostic semantic vectors for portions of a user utterance, and then uses the VT model to translate the domain-agnostic semantic vectors into a domain-aware semantic vectors to be used by a NLU system of the NLU framework during a meaning search operation. The VT model is also designed to provide predicted intent classifications for the portions the user utterance. Both the NLU system and the DAVE system of the NLU framework are highly configurable and refer to various NLU constraints during operation, including performance constraints and resource constraints provided by a designer or user of the NLU framework.

Abstract: A natural language understanding (NLU) framework includes a modeling and optimization system that enables enhanced understanding and explainability to the operation of the NLU framework. The NLU framework includes a configuration vector storing settings of various components that may be applied during NLU inference of an utterance, such as which components should be activated or deactivated, as well as which numerical values (e.g., threshold values, coefficients, weight values) that are used by these components during operation. By using this configuration vector to systematically disable and adjust numerical parameters of the components of the NLU framework, and then determining the performance of the NLU framework in these configurations, the modeling and optimization system determines relationships between, as well as the relative importance of, the components of the NLU framework.

Abstract: Methods and Systems for displaying product recommendations at a virtual storefront by receiving product recommendations from a plurality of heterogeneous sources. The source servers send the product recommendations from their end that are then normalized and refined by a recommendation engine based on a plurality of factors ensuring that the products are presentable and sellable on the storefront. The recommendation collation is accomplished using a pipeline of stateless processors, thereby providing a highly scalable platform perfectly suited for cloud-based computational platforms.

Abstract: Present embodiment include a prosody subsystem of a natural language understanding (NLU) framework that is designed to analyze collections of written messages for various prosodic cues to break down the collection into a suitable level of granularity (e.g., into episodes, sessions, segments, utterances, and/or intent segments) for consumption by other components of the NLU framework, enabling operation of the NLU framework. These prosodic cues may include, for example, source prosodic cues that are based on the author and the conversation channel associated with each message, temporal prosodic cues that are based on a respective time associated with each message, and/or written prosodic cues that are based on the content of each message. For example, to improve the domain specificity of the agent automation system, intent segments extracted by the prosody subsystem may be consumed by a training process for a ML-based structure subsystem of the NLU framework.

Abstract: Present embodiment include a prosody subsystem of a natural language understanding (NLU) framework that is designed to analyze collections of written messages for various prosodic cues to break down the collection into a suitable level of granularity (e.g., into episodes, sessions, segments, utterances, and/or intent segments) for consumption by other components of the NLU framework, enabling operation of the NLU framework. These prosodic cues may include, for example, source prosodic cues that are based on the author and the conversation channel associated with each message, temporal prosodic cues that are based on a respective time associated with each message, and/or written prosodic cues that are based on the content of each message. For example, to improve the domain specificity of the agent automation system, intent segments extracted by the prosody subsystem may be consumed by a training process for a ML-based structure subsystem of the NLU framework.

Abstract: An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

Abstract: Described are systems and methods for product recommendation refinement in a topic-based virtual storefront embedded in a topical community web page. A system and method may facilitate determining user and community member activity in the virtual storefront based on which weighted keywords are derived. A topic set containing various weighted keywords may be iteratively configured for extracting and ordering one or more products that are extracted from a plurality of heterogeneous sources. A server may be configured for refining the topic set by modifying a baseline session keyword weight or a baseline contextual keyword weight based on a total topic set weight, an elasticity parameter, core topic keyword weights associated with core topic keywords, session keyword weights, and/or contextual keyword weights; and identifying, based at least on the configured topic set, products to be presented to a user as product recommendations in the virtual storefront.

Abstract: An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation.

Abstract: An agent automation system implements a virtual agent that is capable of learning new words, or new meanings for known words, based on exchanges between the virtual agent and a user in order to customize the vocabulary of the virtual agent to the needs of the user or users. The agent automation framework has access to a corpus of previous exchanges between the virtual agent and the user, such as one or more chat logs. New words and/or new meanings for known words are identified within the corpus and new word vectors are generated for these new words and/or new meanings for known words and added to refine a word vector distribution model. The refined word vector distribution model is then utilized by the agent automation system to interact with the user.

Abstract: Methods and Systems for displaying product recommendations at a virtual storefront by receiving product recommendations from a plurality of heterogeneous sources. The source servers send the product recommendations from their end that are then normalized and refined by a recommendation engine based on a plurality of factors ensuring that the products are presentable and sellable on the storefront. The recommendation collation is accomplished using a pipeline of stateless processors, thereby providing a highly scalable platform perfectly suited for cloud-based computational platforms.

Abstract: Systems and methods for product recommendation refinement in a topic-based virtual storefront embedded in a topical community web page. The systems and methods facilitate continuous monitoring of user activity and community member activity in the topic-based virtual storefront based on which one or more weighted keywords are derived. A topic set containing various weighted keywords is iteratively configured for extracting and ordering one or more products that are extracted from a plurality of heterogeneous sources.