Abstract: A customer is provided with a customized and guided experience when shopping in-person in a retail store, distribution center, or other shopping area or location. Proactive actions that guide a customer through the store in a customized way are determined taking the customer's intent, customer's history with the store, customer's purchase history, and customer's location within the store (among other things} as inputs and rendering to the user guidance as to how to navigate or interact with or within the store.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving a voice command from a user; transforming the voice command; transforming the voice command can include using a natural language understanding and rules execution engine into (a) an intent of the user to add recipe ingredients to a cart and (b) a recipe descriptor; determining a matching recipe from a set of ingested recipes based on the recipe descriptor; determining items and quantities associated with the items that correspond to a set of ingredients included in the matching recipe using a quantity inference algorithm; and automatically adding all of the items and the quantities associated with the items to the cart. Other embodiments are disclosed.

Abstract: A method for automatically rectifying in real-time anomalies in natural language processing systems. The method can include determining an output corresponding to a user request from a user device for a user based on a new request template or machine learning. The method further can include retrieving one or more entity rules corresponding to entity data of the user request. The method also can include overwriting entity information of the entity data corresponding to the one or more entity rules. Additionally, the method can include outputting the output. Furthermore, the method can include transmitting, to the user device, a response to the user. Other embodiments are disclosed.

Abstract: Systems and methods are disclosed for a voice/chatbot building system. The voice/chatbot builder may involve receiving an identified intent, receiving a task related to the identified intent, and receiving a response related to both the identified intent and the task. The identified intent, task, and response may form a first conversation. The first conversation may be linked to other conversations to establish contextual relationships among conversations and determine conversation priority. Voice/chatbot building may also train natural language processing machine learning algorithms.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance including at least one identified semantic component and at least one missing semantic component and generate a context stack including a set of context entries. Each of the context entries includes a root intent element, an entity list element, and a dialogue stack and each context entry in the set of context entries is associated with one of a user utterance or a system utterance. The computing device is further configured to review at least one context entry in the set of context entries to locate the at least one missing semantic element within the dialogue stack and generate an intent flow execution request including the at least one semantic element from the first speech data and the missing semantic element.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance and identify at least one semantic element within the user utterance. An intent associated with the at least one semantic element is identified and an intent flow associated with the identified intent is executed. The intent flow includes a set of tasks executed in a predetermined order. A system utterance is generated by instantiating a response template selected from a plurality of response templates associated with the executed intent.

Abstract: A system for generating compressed product titles that can be used in conversational transactions includes a computing device configured to obtain product title data characterizing descriptive product titles of products available on an ecommerce marketplace and to determine compressed product titles based on the product title data using a machine learning model that is pre-trained using a replaced-token detection task. The computing device also stores the compressed product titles for use during conversational transactions.

Abstract: A system for generating a response to a customer query includes a computing device configured to obtain a first dataset, including a plurality of first phrase-intent pairs associated with a first domain. Each first phrase-intent pair includes a first phrase and a corresponding first intent. The computing device is configured to retrieve a set of configuration rules to configure a plurality of environments. The computing device is also configured to configure a first environment using the first dataset and the set of configuration rules to determine a result user intent based on a requested query associated with the first domain. The first environment embeds the plurality of first phrase-intent pairs in a vector space based on the set of configuration rules. The computing device is configured to perform operations based on the first environment.

Abstract: A method for automatically rectifying in real-time anomalies in natural language processing systems. The method can include determining an output corresponding to a user request from a user device for a user based on a new request template or machine learning. The method further can include retrieving one or more entity rules corresponding to entity data of the user request. The method also can include overwriting entity information of the entity data corresponding to the one or more entity rules. Additionally, the method can include outputting the output. Furthermore, the method can include transmitting, to the user device, a response to the user. Other embodiments are disclosed.

Abstract: The disclosed subject matter relates to a system and method for facilitating automated conversations with users over a network. In some embodiment, a disclosed system includes a computing device operably connected to a database, and is configured to receive a plurality of incoming messages from a first user in an asynchronous manner; and identify a user request associated with the plurality of incoming messages. In these embodiments, the computing device determines that the plurality of incoming messages are all incoming messages that are associated with the user request; processes the plurality of incoming messages together; and generates at least one outgoing message as a response to the user request.

Abstract: A method for automatically rectifying in real-time anomalies in natural language processing systems. The method can include receiving command data from a user device of a user. The command data can correspond to a user request. The method further can include retrieving, from a new template database, a new request template corresponding to the user request. Additionally, the method can include retrieving, from the new template database, an output instruction corresponding to the new request template, when the new request template is found. The method further can include determining, by a machine learning system, the output instruction corresponding to the user request, when the new request template is not found. The method also can include transmitting the output instruction to a request processing system. The request processing system can be configured to perform the output instruction and transmit, to the user device, a response to the user request. Other embodiments are disclosed.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance including at least one identified semantic component and at least one missing semantic component and generate a context stack including a set of context entries. Each of the context entries includes a root intent element, an entity list element, and a dialogue stack and each context entry in the set of context entries is associated with one of a user utterance or a system utterance. The computing device is further configured to review at least one context entry in the set of context entries to locate the at least one missing semantic element within the dialogue stack and generate an intent flow execution request including the at least one semantic element from the first speech data and the missing semantic element.

Abstract: A system for generating a response to a customer query includes a computing device configured to obtain a first dataset, including a plurality of first phrase-intent pairs associated with a first domain. Each first phrase-intent pair includes a first phrase and a corresponding first intent. The computing device is configured to retrieve a set of configuration rules to configure a plurality of environments. The computing device is also configured to configure a first environment using the first dataset and the set of configuration rules to determine a result user intent based on a requested query associated with the first domain. The first environment embeds the plurality of first phrase-intent pairs in a vector space based on the set of configuration rules. The computing device is configured to perform operations based on the first environment.

Abstract: A system for generating compressed product titles that can be used in conversational transactions includes a computing device configured to obtain product title data characterizing descriptive product titles of products available on an ecommerce marketplace and to determine compressed product titles based on the product title data using a machine learning model that is pre-trained using a replaced-token detection task. The computing device also stores the compressed product titles for use during conversational transactions.

Abstract: Systems and methods are disclosed for a voice/chatbot building system. The voice/chatbot builder may involve receiving an identified intent, receiving a task related to the identified intent, and receiving a response related to both the identified intent and the task. The identified intent, task, and response may form a first conversation. The first conversation may be linked to other conversations to establish contextual relationships among conversations and determine conversation priority. Voice/chatbot building may also train natural language processing machine learning algorithms.

Abstract: The disclosed subject matter relates to a system and method for retrieving relevant information in response to a user query without devising intent of the query. The relevant information is contained within a semi-structured database which was populated from Q&A pairs, help web sites, product descriptions and other information from an organizations knowledge base and from which an inverted index is created. The semi-structured data base may be created automatically or entered manually. Upon receiving a user query, data segments are identified (and ranked) via the inverse index and the data segment most similar to the query is provided to a MRC model which reads the segments to determine the portion (span/snippet) of the data segment that addresses the query. This portion is provided to the user in response to the query.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance including at least one identified semantic component and at least one missing semantic component and generate a context stack including a set of context entries. Each of the context entries includes a root intent element, an entity list element, and a dialogue stack and each context entry in the set of context entries is associated with one of a user utterance or a system utterance. The computing device is further configured to review at least one context entry in the set of context entries to locate the at least one missing semantic element within the dialogue stack and generate an intent flow execution request including the at least one semantic element from the first speech data and the missing semantic element.

Abstract: The disclosed subject matter relates to a system and method for facilitating automated conversations with users over a network. In some embodiment, a disclosed system includes a computing device operably connected to a database, and is configured to receive a plurality of incoming messages from a first user in an asynchronous manner; and identify a user request associated with the plurality of incoming messages. In these embodiments, the computing device determines that the plurality of incoming messages are all incoming messages that are associated with the user request; processes the plurality of incoming messages together; and generates at least one outgoing message as a response to the user request.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance and identify at least one semantic element within the user utterance. An intent associated with the at least one semantic element is identified and an intent flow associated with the identified intent is executed. The intent flow includes a set of tasks executed in a predetermined order. A system utterance is generated by instantiating a response template selected from a plurality of response templates associated with the executed intent.

Abstract: Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive a user utterance and identify at least one semantic element within the user utterance. An intent associated with the at least one semantic element is identified and an intent flow associated with the identified intent is executed. The intent flow includes a set of tasks executed in a predetermined order. A system utterance is generated by instantiating a response template selected from a plurality of response templates associated with the executed intent.