Microsoft Technology Licensing, LLC

Intel Corporation

Anantha Deepthi Uppala

Chetan Bansal

Mark D Yarvis

Ryen William White

Patrick Pantel

Nikrouz Ghotbi

Mark James Encarnacion

Nirupama Chandrasekaran

Vanessa Graham Murdock

Milind S Pandit

Mathieu Etienne Jacques Audouin

Kara E Jackson

David J Cobbley

Andrew S Dickinson

A technique is described herein for capturing signals that indicate when any calling BOT delegates control to a called BOT, or when a calling BOT is preconfigured to contact a called BOT (e.g., as conveyed by a manifest file associated with the calling BOT). The technique can leverage these signals to facilitate the selection of BOTs. For example, the technique can use the signals to improve searches performed by a search engine and/or recommendation engine. The technique can also use the signals to generate metadata items that describe the properties of the available BOTs.

Capturing and leveraging signals reflecting BOT-to-BOT delegation

A system is configured to replay a selected conversation with a selected agent. The selected conversation may be selected from a plurality of previously conducted conversations with other agents. The selected agent may be selected from a plurality of available agents. The system determines various tasks, named entities, and user preferences from the selected conversation. During a replay of the selected conversation with the selected agent, the system generates responses to messages received from the selected agent based on the determined tasks, named entities, and user preferences. The system also allows a user to select whether the replayed conversation should be conducted in a passive mode or in an active mode. In a passive mode, the system generally conducts the replayed conversation autonomously whereas, in an active mode, the system requests user input before sending a response to the selected agent.

Replay of conversations for task completion and delegation

Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).

Automated quick task notifications via an audio channel

A technique is described herein for providing implicit quality signals over a span of time that reflect quality of service provided by a collection of BOTs to a group of users. The technique can then leverage these implicit quality signals in various application-phase uses. In one use, an abandonment-determination component can leverage the implicit quality signals to provide an output result which indicates whether a current user has abandoned use of a current BOT with which he or she has been interacting, or is about to abandon use of that current BOT. In another use, a search engine or a recommendation engine can use the implicit quality signals to help identify an appropriate BOT for use by the current user. The implicit quality signals can include: one of more user-behavior implicit signals; one of more BOT-behavior implicit signals; and/or one of more transaction-summary implicit signals.

Providing and leveraging implicit signals reflecting user-to-BOT interaction

Methods, systems, and storage media for providing sizing information is described. In embodiments, a computing device may obtain purchase information associated with individual users. Each of the individual users may correspond to at least one user device of a plurality of user devices. The purchase information may indicate a purchase of an item by the individual users. The computing device may obtain feedback information associated with the item that is provided by a subset of the individual users. The computing device may generate sizing information for the item based on the purchase information and the feedback information. The computing device may generate a recommendation for the item. Other embodiments may be described and/or claimed.

Online clothing e-commerce systems and methods with machine-learning based sizing recommendation

Technologies for conversational control of a home automation system include a computing device in communication with one or more home automation devices. The computing device receives text input indicative of a spoken user request including one or more parameters associated with a home automation command. The computing device matches the text input against a home automation language model including several predefined dialog patterns. The computing device resolves parameters of the command using a context model. The computing device may resolve the location, device, or requested device state of the command. The computing device generates a text response and a home automation command using the language model, and then presents the text response to the user and performs the home automation command using the home automation devices. In some embodiments, the computing device may control an application automation system or a data exploration system. Other embodiments are described and claimed.

Growing community of inventors

Mercer Island, WA, United States of America

Anantha Deepthi Uppala