Abstract: Systems and methods are disclosed for analyzing a customer review of a product includes extracting product categories and predicates from the customer review; extracting product features from the customer review; extracting an activity with the product features from the customer review; performing sentiment analysis using a learning machine on the customer review; determining a life scene from the customer review; and analyzing a customer opinion from the customer review.

Abstract: Systems and methods are disclosed to automatically associate a product or a service with external content by characterizing the product from unstructured data sources including a product text or text from similar products; generating a label for the product or service; applying the label as a search engine; extracting signals relating to the product or service; and providing business intelligence for the product or service.

Abstract: An exemplary system that provides automated business intelligence from business data to improve operations of the business is disclosed. The system extracts signals from any unstructured data source. The system identifies anomalies in customer data and global trends for retail companies that present opportunities and crises to avoid and suggests optimal courses of action and estimated financial impact. The system also alerts individuals with opportunities and predicts customers' needs. The system extracts signals from any data source, structured or not, to alert the user of opportunities and anticipate customers' needs. The system determines the trends, what products are hits, the opportunities to pursue, and when to reach out to customers. This is done by collecting data from a multi-source data collection system.

Abstract: A preform is made by mixing cyclic olefin copolymer (COC) and light shielding pigment(s) with PET. The preform is stretch-blow moulded to produce a bottle which may be white and opaque. Light may be restricted from entering the bottle to thereby restrict degradation of the bottle contents from certain wavelengths of visible light.

Abstract: A method to determine alignment between first and second entities by collecting structured and unstructured data from sources including web search, social media, newspaper, and official sources of data; extracting entities and values; providing the entities and values text through multiple neural network text processing pipelines to an ensemblist density processing to generate the entities alignment values.

Abstract: A system and method for providing natural training to an AI/agent/bot. In one embodiment, the system and method include receiving a first input indicating a trainer's desire to provide knowledge or know-how to an artificially-intelligent agent; creating a first natural training session to capture the knowledge or know-how; receiving the knowledge or know-how from the trainer; sending a first response to the trainer, the first response requesting a first prompt that, when received by the artificially-intelligent agent, prompts the artificially-intelligent agent to use the knowledge or know-how captured in the first natural training session to respond; receiving a second input from the trainer including a first prompt; validating a capability of the artificially-intelligent agent to correctly respond to requests related to the knowledge or know-how captured by the first training session; and receiving additional.

Abstract: A system and method for providing a voice assistant including receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech recognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding.

Abstract: A system and method to receive user input from a human user in a communication session between the human user and a first machine; autonomously determine a sentiment metric of the user input from the human user based on one or more sentiment criteria, wherein the sentiment metric represents an attitude of the human user; autonomously determine a quality metric associated with the communication session; autonomously rank the communication session between human user and first machine based on one or more of the sentiment metric and the quality metric; and determine based on the ranking, to recommend human agent intervention in the communication session between the human user and the first machine.

Abstract: A system and method for providing a voice assistant including receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech recognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding.

Abstract: A system and method for providing a voice assistant including receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech recognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding.

Abstract: A system and method for providing natural training to an AI/agent/bot. In one embodiment, the system and method include receiving a first input indicating a trainer's desire to provide knowledge or know-how to an artificially-intelligent agent; creating a first natural training session to capture the knowledge or know-how; receiving the knowledge or know-how from the trainer; sending a first response to the trainer, the first response requesting a first prompt that, when received by the artificially-intelligent agent, prompts the artificially-intelligent agent to use the knowledge or know-how captured in the first natural training session to respond; receiving a second input from the trainer including a first prompt; validating a capability of the artificially-intelligent agent to correctly respond to requests related to the knowledge or know-how captured by the first training session; and receiving additional.

Abstract: A system and method for providing a voice assistant including receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech recognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding.