Abstract: Some implementations process structured calendar data of an electronic calendar for a first user, to generate a natural language representation of the structured calendar data. Versions of those implementations further, in response to receiving a query determined to be relevant to the electronic calendar, prime a large language model (LLM) using a priming input (e.g., process the priming input using the LLM), where the priming input is based on the natural language representation of the structured calendar data. Following priming of the LLM using the priming input, some of those versions process, using the LLM, query input that is based on the query, to generate a LLM output and determine, based on the LLM output, a response to the query. The response can include a natural language response that can be rendered.

Abstract: Implementations relate to determining a general routine when an automated assistant is not configured to fulfill a user request. When the user submits a request to an automated assistant to perform a routine and the automated assistant is not configured to fulfill the request, the user demonstrates the actions that are included in the routine. The automated assistant generates a routine based on the actions of the user and stores the routine with the request that was initially submitted by the user. In some implementations, a general routine can include one or more parameters and the user provides a value for the parameters with the request. General routines can additionally be generated based on previous routines performed by the user and/or other users.

Abstract: Some implementations process structured calendar data of an electronic calendar for a first user, to generate a natural language representation of the structured calendar data. Versions of those implementations further, in response to receiving a query determined to be relevant to the electronic calendar, prime a large language model (LLM) using a priming input (e.g., process the priming input using the LLM), where the priming input is based on the natural language representation of the structured calendar data. Following priming of the LLM using the priming input, some of those versions process, using the LLM, query input that is based on the query, to generate a LLM output and determine, based on the LLM output, a response to the query. The response can include a natural language response that can be rendered.