Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: A watercraft comprises a hull, a deck disposed on the hull, a motor connected to at least one of the hull and the deck, a propulsion system operatively connected to the motor, a command console disposed on the deck and operatively connected to the motor, an accelerator control lever operatively connected to the command console, and a control unit operatively connected to the command console and to the motor. The control unit receives, from the command console, a value representing a current position of the accelerator control lever, controls an operating parameter of the motor based at least in part on the current position of the accelerator control lever, receives, from the command console, a cruise control enabling signal, and in response to receiving the cruise control enabling signal, selectively engages a cruise control function of the watercraft by maintaining the operating parameter of the motor.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: A biodegradable polymer composition, according to the present invention, comprises polyhydroxybutyrate and poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) blended with thermoplastic starch, one or more compatibilizers selected from the group consisting of dihexyl sodium sulfosuccinate and maleic anhydride, and one or more additives selected from the group consisting of micro-crystalline cellulose and cellulose. Methods of producing a biodegradable polymer use processed cannabis waste as a carbon source.

Abstract: The disclosure herein describes providing action item information for a current activity based on similarity with past activities. Activity attributes indicative of an activity outcome are identified, and a random forest classifier based on the identified activity attributes is generated. The random forest classifier classifies an activity based on the activity attributes. Similarity factors associated with the current activity and past activities are calculated based on the random forest classifier. Based on the similarity factors, data value ranges of performance indicators of past activities associated with the activity outcome are determined. Based on comparing the determined data value ranges to performance indicator data values of the current activity, action item information associated with the performance indicator data values of the current activity is provided.