Abstract
This publication describes methods and techniques, implemented on computing devices, directed at enhancing a user experience associated with copying and pasting items, including text, images, and other media. In aspects, a computing device includes a multi-item clipboard and a Clipboard Manager configured to utilize a machine-learned algorithm (“Sound Recognition Module”) to translate sound data to a categorical key (e.g., a value used to identify a certain component within a data-structure). While a user performs actions directed at copying an item (e.g., keystroking a keyboard combination), the Clipboard Manager can accept audible input from a user (e.g., a voice command). Once the user provides a discernible sound, the Sound Recognition Module translates the sound to a key. The Clipboard Manager then associates the key with the item, creating a key-value pair, and stores the key-value pair in the multi-item clipboard. To paste the item, the user can perform actions directed at pasting an item (e.g., keystroking a keyboard combination) and provide an associated sound. After providing the sound, the Sound Recognition Module translates the sound to a key. The Clipboard Manager can then search through the multi-item clipboard for a matching key, retrieve the associated value, and then output the item to a user-designated location.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Dahlstrom, Dana, "Computing-Device Clipboards Accessible via Audible Input", Technical Disclosure Commons, (March 18, 2021)
https://www.tdcommons.org/dpubs_series/4162