Abstract: A spill resistant beverage container assembly for spill free beverage consumption includes a cup and a lid that is complementary to a top of the cup so that the lid is positioned to be selectively coupled to the cup to sealably close the top. The lid comprises a sidewall that is coupled to and extends between an upper plate and a lower plate to define an interior space. An aperture is positioned in the lid proximate to a circumference of the lid. A detection and access module that is coupled to the lid and positioned in the interior space is configured to detect motion in proximity to the aperture, positioning the detection and access module to open the aperture so that the aperture is configured to allow sipping of a beverage that is positioned in the cup.

Abstract: A spill resistant beverage container assembly for spill free beverage consumption includes a cup and a lid that is complementary to a top of the cup so that the lid is positioned to be selectively coupled to the cup to sealably close the top. The lid comprises a sidewall that is coupled to and extends between an upper plate and a lower plate to define an interior space. An aperture is positioned in the lid proximate to a circumference of the lid. A detection and access module that is coupled to the lid and positioned in the interior space is configured to detect motion in proximity to the aperture, positioning the detection and access module to open the aperture so that the aperture is configured to allow sipping of a beverage that is positioned in the cup.

Abstract: A speech and natural language-based virtual assistant parses user utterances and analyzes them in the context of recent prior actions to detect sentiment and indicators of satisfaction or dissatisfaction. Indicators are stored in a database in association with the prior command and resulting action. Databases can include timestamps, clarifications made by users, and a knowledge graph of facts. Machine learning, applied to the database, train models to deliver improved results in future user engagements.

Abstract: A system, method, and non-transitory computer readable medium provide for a visual display of a user interface for a voice-based virtual assistant system. After displaying a transcription of user speech and performing requested actions, the system allows the user to provide, by speech or manual input, an indication of satisfaction or dissatisfaction. For transcription errors, the user is presented an opportunity to correct the transcription text. The system can present several transcription hypotheses to the user, and allow the user to choose among them, or to edit one of them, as the intended transcription. A back-end server system uses the corrected transcription to train a machine learning model to perform more accurate speech recognition or provide more useful actions for future users. A system can save one or more speech recognition transcription hypotheses and check corrected results against the other transcriptions to further improve models.

Abstract: Virtual assistants provide results in response to user commands and analyze user utterances in response to the result. The analysis can interpret words, recognized from the utterance, as being negative indicators that imply user dissatisfaction. Virtual assistants request follow-up information from users. Analysis also interprets words as indicators of clarification and collect information to add to a knowledgebase. Machine learning algorithms use recognized words to train a behavioral model to improve results. Virtual assistants also infer, from replacement of words in successive commands, that earlier commands had word recognition errors and infer, from addition of words, that earlier commands had interpretation errors. Virtual assistants act locally or as devices in communication with servers.