Various embodiments provide an ability to automatically capture audio and/or video during a communication exchange between participants. At times, the automatic capture can be triggered when one or more characteristics are identified and/or observed during the communication exchange. In some cases, analyzing the communication exchange for characteristic(s) is based on previous input. Some embodiments train a machine-learning algorithm on desired characteristic(s) using multiple user-initiated video and/or audio clips. Alternately or additionally, some embodiments identify characteristic(s) in a communication exchange based on other properties not provided by user-input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for training a machine-learning algorithm to identify one or more moments of interest in communication exchanges, the computer-implemented method comprising: receiving, using the computer, at least one user-triggered training clip associated with a communication exchange as input to the machine-learning algorithm; training, using the computer, the machine-learning algorithm using one or more characteristics associated with the at least one user-triggered training clip; and aggregating, using the computer, the training associated with the at least one user-triggered training clip with other results from additional training clips effective to enable automatic capture of one or more moments of interest in the communication exchanges. 2. The computer-implemented method of claim 1, wherein training the machine-learning algorithm further comprises training the machine-learning algorithm at least by using a statistical analysis of the training clip. 3. The computer-implemented method of claim 1 further comprising receiving the training clip, training the machine-learning algorithm, and aggregating the training at a cloud-based service. 4. The computer-implemented method of claim 1, wherein, prior to receiving the training clip, the method further comprises: receiving, using the computer, at least one input associated with the communication exchange; receiving, using the computer, a user-triggered event; and generating, using the computer, the training clip from the at least one input based, at least in part, on the user-triggered event. 5. The computer-implemented method of claim 4, wherein generating the training clip further comprises generating the training clip using data associated with the at least one input captured prior to the user-triggered event and data associated with the at least one input captured after the user-triggered event. 6. The computer-implemented method of claim 4, wherein the at least one input comprises at least: audio associated with a participant in the communication exchange; or video associated with the participant in the communication exchange. 7. The computer-implemented method of claim 4, wherein the at least one input comprises timestamp information associated with the at least one input. 8. The computer-implemented method of claim 1, wherein the communication exchange comprises a Voice-over-Internet Protocol (VoIP) communication call. 9. The computer implemented method of claim 4, wherein the at least one input comprises audio and video inputs associated with at least two participants in the communication exchange. 10. A computer-implemented method for automatically capturing at least one moment of interest in a first communication exchange, the method comprising: receiving, using the computer, at least one input associated with the first communication exchange; analyzing, using the computer, the at least one input for at least one characteristic; identifying, using the computer, at least one portion of the at least one input as having the at least one characteristic; classifying, using the computer, the at least one portion as desirable; responsive to classifying the at least one portion as being desirable, automatically generating, using the computer, a capture trigger event; and generating, using the computer, a capture of the at least one portion of the input that is classified as being desirable based, at least in part, on the capture trigger event effective to automatically provide at least one moment of interest to a user. 11. The computer-implemented method of claim 10, wherein classifying the at least on portion as desirable further comprises using a binary classifier for at least part of the classifying. 12. The computer-implemented method of claim 10, wherein the analyzing for at least one characteristic further comprises analyzing for multi-modal characteristics. 13. The computer-implemented method of claim 10, wherein the analyzing for at least one characteristic is based, at least in part, on a machine-learning algorithm that has been trained on user-triggered training clips associated with at least one communication exchange other than the first communication exchange. 14. The computer-implemented method of claim 10, wherein generating the capture of the at least one portion of the input further comprises generating the capture using some data associated with the input captured prior to the capture trigger event and some data associated with the input captured after the capture trigger event. 15. The computer-implemented method of claim 10, wherein the at least one characteristic comprises at least one of: an audio quality; a video quality; a facial expression; an audio cue associated with a key word; or a turn-taking metric. 16. A device for automatically capturing a moment of interest in a communication exchange, the device comprising: a video camera for generating video images; a microphone for capturing audio sounds; at least one processor; and one or more computer-readable storage memories comprising processor instructions which, responsive to execution by at least one processor, are configured to implement, at least in part: an input module associated with a communication client module configured to receive real-time audio and input generated by the video camera and microphone as part of communication exchanges; a training module configured to: receive real-time audio and video input, via the communication client module, associated with at least a first communication exchange; receive a user-triggered event via the input module; generate a training clip from the real-time audio and video input based, at least in part, on the user-triggered event, by extracting the training clip from the real-time audio and video input associated with at least the first communication exchange; and train a machine-learning algorithm using at least the training clip; and a classification module configured to: receive real-time audio and video input, via the communication client module, associated with at least a second communication exchange; analyze the real-time audio and video input associated with at least the second communication exchange for one or more characteristics based, at least in part, on the machine-learning algorithm; classify the at least one portion of the real-time audio and video input associated with the at least second communication exchange as desirable; automatically generate a capture trigger event; and generate a capture of the at least one portion of the input that is classified as being desirable based, at least in part, on the capture trigger event. 17. The device of claim 16, wherein the input module is further configured to generate the user-triggered event based, at least in part, on receiving an input associated with a keyboard input. 18. The device of claim 16, wherein the communication client module comprises a Voice-over-Internet Protocol (VoIP) communication client module. 19. The device of claim 16, wherein the training module is further configured to: receive a plurality of training clips; train the machine-learning algorithm using each of the plurality of training clips; and aggregate each result from each training clip of the plurality of training clips together. 20. The device of claim 16, wherein the classification module is further configure to: identify single-modal characteristics effective to classify a portion of the real-time audio and video input associated with at least the second communication exchange as desirable; and identify multi-modal characteristics effective to classify a portion of the real-time audio and video input associated with at least the second communication exchange as desirable.