Abstract: Methods, systems, apparatuses, and computer-readable storage mediums described herein are directed to techniques for smart browser history searching. For example, a user may submit natural language-based search queries to a browser application, which searches for various textual features of web pages maintained by a browser's history, as well as various entity object types included on such web pages based on the search queries. The entity object types include various content included on the web pages, including, but not limited to, products, images, and videos. The browser application also searches for textual features and/or entity object types having a semantic similarity to the search terms of the search queries, thereby providing an advanced search that not only aims to locate web pages based on exact keywords, but also based on the intent and contextual significance of the search terms specified by the user.

Abstract: A computerized personal assistant communicatively couples to a computer database including a plurality of available skills for the computerized personal assistant. The computerized personal assistant recognizes a current context of the user. The computerized personal assistant operates a previously-trained machine learning classifier to assess a match confidence for a candidate skill, the match confidence indicating a quality of match between the current context and a reference context previously associated with the candidate skill. The computerized personal assistant executes instructions defining an assistive action associated with the candidate skill responsive to the match confidence exceeding a predefined match confidence threshold. The computerized personal assistant executes the instructions defining a complementary help action associated with the candidate skill responsive to the match confidence not exceeding the predefined match confidence threshold.

Abstract: A computerized personal assistant communicatively couples to a computer database including a plurality of available skills for the computerized personal assistant. The computerized personal assistant recognizes a current context of the user. The computerized personal assistant operates a previously-trained machine learning classifier to assess a match confidence for a candidate skill, the match confidence indicating a quality of match between the current context and a reference context previously associated with the candidate skill. The computerized personal assistant executes instructions defining an assistive action associated with the candidate skill responsive to the match confidence exceeding a predefined match confidence threshold. The computerized personal assistant executes the instructions defining a complementary help action associated with the candidate skill responsive to the match confidence not exceeding the predefined match confidence threshold.

Abstract: Providing a random factoid in response to a user trigger is provided. Aspects are directed to a device, method, and computer-readable medium to provide random factoids responsive to a triggering event. Various inputs such as user taps, gestures, voice commands, environment commands, etc., are monitored to recognize a triggering event associated with the random factoid system. Upon determining that the input is a triggering event, a random factoid is randomly selected and provided to the user. The factoid can be a fun fact or a helpful learning tip. In some examples, the random factoid system provides a contextual random factoid based one or more contextual factors, such as geographic location of the device, the user's interaction history, the user's demographic data, etc., thus providing an improved user experience.

Abstract: A computerized personal assistant communicatively couples to a computer database including a plurality of available skills for the computerized personal assistant. The computerized personal assistant recognizes a current context of the user. The computerized personal assistant operates a previously-trained machine learning classifier to assess a match confidence for a candidate skill, the match confidence indicating a quality of match between the current context and a reference context previously associated with the candidate skill. The computerized personal assistant executes instructions defining an assistive action associated with the candidate skill responsive to the match confidence exceeding a predefined match confidence threshold. The computerized personal assistant executes the instructions defining a complementary help action associated with the candidate skill responsive to the match confidence not exceeding the predefined match confidence threshold.

Abstract: A computerized personal assistant communicatively couples to a computer database including a plurality of available skills for the computerized personal assistant. The computerized personal assistant recognizes a current context of the user. The computerized personal assistant operates a previously-trained machine learning classifier to assess a match confidence for a candidate skill, the match confidence indicating a quality of match between the current context and a reference context previously associated with the candidate skill. The computerized personal assistant executes instructions defining an assistive action associated with the candidate skill responsive to the match confidence exceeding a predefined match confidence threshold. The computerized personal assistant executes the instructions defining a complementary help action associated with the candidate skill responsive to the match confidence not exceeding the predefined match confidence threshold.

Abstract: Conversations can be generated automatically based on any given persona. The conversations can be produced by a language generation model that automatically generates persona-based language in response to a message, wherein a persona identifies a type of person with role specific characteristics. After a language generation model is acquired, for example by identifying a predefined model or generating a new model, the language generation model can be provisioned for use by a conversational agent, such as a chatbot, to enhance the functionality of the conversational agent.

Abstract: Providing a random factoid in response to a user trigger is provided. Aspects are directed to a device, method, and computer-readable medium to provide random factoids responsive to a triggering event. Various inputs such as user taps, gestures, voice commands, environment commands, etc., are monitored to recognize a triggering event associated with the random factoid system. Upon determining that the input is a triggering event, a random factoid is randomly selected and provided to the user. The factoid can be a fun fact or a helpful learning tip. In some examples, the random factoid system provides a contextual random factoid based one or more contextual factors, such as geographic location of the device, the user's interaction history, the user's demographic data, etc., thus providing an improved user experience.