International Business Machines Corporation

Zhichao Li

Su Liu

Manjunath Ravi

Kai Liu

Yaoguang Wei

Li Li

Ben Gibbs

Diwesh Pandey

Gustavo Enrique Tellez

Riaz Yousuf Hussain

Matthew A Cooke

A method for playing music includes identifying a plurality of users of a corresponding plurality of electronic devices that are currently located within a selected area, retrieving music listening data for the plurality of users, building a playlist for the selected area based on the music listening data, filtering the playlist according to at least one host preference to produce a filtered playlist, and playing the filtered playlist within the selected area. The selected area may be a geo-fenced area. Examples of host preferences include genre, artist, tempo, mood and demographic. The playlist may include songs that are commonly selected by the plurality of users or conform to genres commonly preferred by the plurality of users. A corresponding system and computer program product for executing the above method are also disclosed herein.

Playing user preferred music in a selected area

Predicting power usage of a chip may include receiving placement data describing a placement, within the chip, of a plurality of logical components of the chip; providing the placement data as an input to a neural network; and determining, by the neural network, based on the placement data, a predicted power usage of the chip.

Predicting power usage of a chip

A computer-implemented method includes recognizing, by a computer device, a word as a new learned word for a user; registering, by the computer device, the new leaned word in a user's new learned word list as a registered new learned word; associating, by the computer device, the registered new learned word with related known words in a user's known word library, the known word library including words known to the user; tracking, by the computer device, uses of the related known words by the user; identifying, by the computer device, a used sentence used by the user that contains one of the related known words; and suggesting, by the computer device, to the user a new sentence that replaces the one of the related known words in the used sentence with the new learned word.

Adaptive vocabulary improvement

Provided are systems, methods, and media for building a prioritized group trip object to generate a social trip plan recommendation. An example method includes receiving a static trip grouping scheme object for a group of users. Generating an interactive customized scheme object (ICSO) for each user based, at least in part, on the received static trip grouping scheme object and on a set of user preferences of the user such that a plurality of ICSOs are generated for the group of users. Merging the plurality of ICSOs to generate a prioritized group trip ICSO. Generating a recommended trip plan for the group based, at least in part, on the prioritized group trip ICSO.

Customized trip grouping based on individualized user preferences

A method, computer program product, and system for providing cross-lingual search results that retain cultural context include a processor(s) monitoring (with user permission) computing activities performed by the user, via a client. The processor(s) analyzes the computing activities to identify data comprising elements relevant to the user and relationships between the elements and the user. The processor(s) generates a data structure comprising the data. The processor(s) generates a search interface for obtaining entries in a first language to perform a query; the entries are search parameters embedded with a portion of the elements. The processor(s) obtains an entry in the first language. The processor(s) extracts the portion of the elements from the entry (cultural context for the entry). The processor(s) determines target languages for data responsive to the query. The processor(s) translates the search parameters and the cultural context into the target languages and executes searches on databases.

Context-sensitive cross-lingual searches

A computer-implemented method, according to one embodiment, includes: collecting information which corresponds to internet browsing activity, receiving a request to create a bookmark for a first website, and creating keyword and density pairs (KDPs) for the first website using the information collected prior to receiving the request to create the bookmark. Creating the KDPs includes: extracting a plurality of keywords from the first website, and calculating a density value for each of the plurality of keywords based on the information collected prior to receiving the request to create the bookmark. Furthermore, each keyword and respective density value are added to a hash map as a KDP entry. Other systems, methods, and computer program products are described in additional embodiments.

Automatically categorizing bookmarks from customized folders and implementation based on web browsing activity

An approach is provided in which a system includes a GPU cluster. The GPU cluster includes multiple GPU nodes, that each includes a GPU core and a corresponding local cache. The GPU cluster also includes a shared memory and an internal bus that maintains cache coherency between the shared memory and the local caches included in the GPU nodes.

Cache coherency adopted GPU shared memory

A method, computer program product, and a system where a processor(s), subject to obtaining permission, monitors browsing activity of an application browsing sources, including analyzing the sources to identify keywords and calculate densities of the keywords in content accessed at the sources. The processor(s) retains the keywords and the densities of the keywords in the content accessed at the sources in an historical record. The processor(s) downloads a resource of data and metadata from a source. The processor(s) updates the metadata of the resource based on keywords and densities of the keywords in the resource and source. The processor(s) determines a destination for the downloaded resource, based on selecting the destination comprising a highest percentage of attributes matching the updated metadata.

Automated content-based and context-based file organizational structuring

Cross-charging among a set of wireless charging IoT devices using prioritizing management rules is disclosed. Device battery status data is received from each wireless charging device in the set of wireless charging devices. A device usage pattern is generated based on user profile data for each wireless charging device in the set of wireless charging devices. At least one priority management rule (PMR) is assigned to at least one wireless charging device in the set of wireless charging devices based on the device battery status data and user profile data from each wireless charging device in the set of wireless charging devices. At least one cross-charging instruction is transmitted to at least one wireless charging device in the set of wireless charging devices based on the at least one PMR.

Cross-charging among IOT devices with prioritizing management rules

A computer-implemented method includes gathering battery factors from a plurality of devices and generating a priority level for each of the devices. The method also includes generating priority management rules for the plurality of devices based at least in part on the battery factors, priority levels, device battery metrics, and device usage patterns. The method includes initiating a charging event based at least in part on the priority management rules. A computer program product includes a computer readable storage medium. The computer readable storage medium includes program instructions, the program instructions executable by a computer. The program instructions cause the computer to perform the foregoing method. A system includes a processor and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Virtual battery management

Techniques for reprioritizing waitlisted callers using biometric feedback. A biometric aspect of a calling user is monitored in real time via a sensor. A digital output characterizing an emotional state of the user is generated. The digital output is encoded and transmitted to a server via multi-frequency signaling. The server decodes the digital output and reprioritizes the calling user relative to other calling users in the waitlist, in order to expedite the calling user being serviced.

Reprioritizing waitlisted callers based on real-time biometric feedback

An integrated circuit physical design tool includes an interconnection congestion predictor that generates a congestion table for an integrated circuit design. Interconnection congestion hotspots are identified based on the congestion table. The proximity of interconnection congestion hotspots to each other is determined, and penalization values are computed based on the proximity of interconnection congestion hotspots to each other. The congestion table is then updated to reflect the penalization values due to proximity of interconnection congestion hotspots. Routability of the interconnection congestion hotspots is then predicted based on the updated congestion table. The updated congestion table may also be used by multiple physical design optimization tools, including placement, global routing, and detail routing.

Predicting routability of interconnects

A method for predicting post-placement timing-analysis results includes obtaining, for a logic design, logic-synthesis data and logic-planning data. The method also includes inputting, into a neural network, the logic-synthesis data and logic-planning data. The neural network is trained to correlate logic-synthesis data and logic-planning data with post-placement timing-analysis results. The method also includes receiving, from the neural network, predicted post-placement timing-analysis results.

Prediction of closure feasibility in microprocessor design

A computer-implemented method according to one embodiment includes receiving voice input, parsing the voice input for determining words in the voice input, recognizing at least some of the words, identifying an accent of a speaker of the voice input, evaluating a word accent of at least some of the recognized words based on the identified accent of the speaker and analysis of the recognized words, generating an evaluated accent score for each of the at least some of the recognized words based on the evaluated word accent, identifying at least one of the recognized words as an accent word based on the evaluated accent score thereof, and outputting a recommended correct pronunciation of the accent word or words. In one approach, a score of the identified accent word is updated in a personal accent word list of the speaker using the evaluated accent score.

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Austin, TX, United States of America

Zhichao Li