Abstract: Implementations disclosed herein optimize performance of phone calls where users interact with interactive voice response (IVR) systems. By utilizing dynamic wait time statistics and intelligent call management techniques, these implementations minimize wait times and reduce telephone network traffic. Call optimization systems can analyze real-time data to provide users with real-time wait time information to inform users decision-making prior to placing a call or at the time of placing a call. Implementations can present dynamic wait time statistics that consider personalized user experiences to provide more accurate information about any particular IVR navigation path that a user can select from. Selection and execution of a suggested IVR path can result in additional wait time data that can be utilized for refining wait time statistics for users.

Abstract: Implementations disclosed herein optimize performance of phone calls where users interact with interactive voice response (IVR) systems. By utilizing dynamic wait time statistics and intelligent call management techniques, these implementations minimize wait times and reduce telephone network traffic. Call optimization systems can analyze real-time data to provide users with real-time wait time information to inform users decision-making prior to placing a call or at the time of placing a call. Implementations can present dynamic wait time statistics that consider personalized user experiences to provide more accurate information about any particular IVR navigation path that a user can select from. Selection and execution of a suggested IVR path can result in additional wait time data that can be utilized for refining wait time statistics for users.

Abstract: Implementations are directed to a cloud-based automated assistant that can handle incoming telephone calls on behalf of a user. In some implementations, processor(s) of a remote server can receive an indication of an incoming telephone call that is directed to a client device and via a cloud-based automated assistant executing at least in part at the remote server (e.g., that is remote from the client device). The processor(s) can determine a next action to be implemented by the cloud-based automated assistant and cause the next action to be implemented by the cloud-based automated assistant. In some implementations, the indication of the incoming telephone call can be directed to the cloud-based automated assistant (e.g., without the client device routing the incoming telephone call to the cloud-based automated assistant). In other implementations, the indication of the incoming telephone call is routed to the cloud-based automated assistant via the client device.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.