The technology disclosed herein enables presentation of call identification information based on user presence. In a particular embodiment, a method includes receiving a communication request directed to an endpoint of a user and determining a presence status of the user from a presence system. In response to determining that the presence status indicates the user is not present, the method includes transferring the communication request to the endpoint and preventing the endpoint from presenting call identification information associated with the communication request.

A method and apparatus for automatically generating a call summary in call center environments is provided. The method includes identifying, from a transcript of a call between a first person and a second person, two or more consecutive mergeable turns of the first person from multiple consecutive turns of the first person, if the two or more consecutive mergeable turns of the first person are interjected by a turn of the second person. The two or more consecutive mergeable turns of the first person are merged into a single merged turn of the first person. In some embodiment, multiple entities, entity values and intents are determined, and each of multiple entity values is mapped to a corresponding entity from multiple entities. A call summary is generated based on the identified entity(ies) and corresponding entity value(s), and the identified intent(s).

Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

An adjustable hand brake lever assembly for a proportional electric braking system for a bicycle or other vehicle. The brake lever assembly may include a sensor assembly which is adapted for use with a standard brake lever assembly as used with mechanical cable braking systems. The sensor assembly may be replaced as a unit. The sensor assembly is adapted to allow for setting the reach adjustment, allowing a user to set a preferred initial position. The adjustable brake lever assembly may be coupled to a control electronic module and a regenerative braking system on a bicycle.

Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

An adjustable hand brake lever assembly for a proportional electric braking system for a bicycle or other vehicle. The brake lever assembly may include a sensor assembly which is adapted for use with a standard brake lever assembly as used with mechanical cable braking systems. The sensor assembly may be replaced as a unit. The sensor assembly is adapted to allow for setting the reach adjustment, allowing a user to set a preferred initial position. The adjustable brake lever assembly may be coupled to a control electronic module and a regenerative braking system on a bicycle.

Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

A system may determine to provide content in a multimedia content message to a user. The user may be associated with a recipient account that is associated with content type preferences that identify a plurality of permitted content types. Each content type, of the plurality of permitted content types, may identify a type of content that is permitted to be included in the multimedia content message. The system may obtain the content type preferences to determine the plurality of permitted content types. The system may determine one or more selected content types of the plurality of permitted content types. The one or more selected content types may be content types of content to be obtained by the system and included in the multimedia content message. The system may obtain content of the one or more selected content types. The system may provide the multimedia content message including the content.