Abstract: A computer-implemented method of voice-to-text tagging for transcription of a human voice signal by one of an automatic speech recognition system, or a natural conversation, includes generating a speech-to-text verbal transcript of the verbal vocalizations of an audio signal at a verbal timestamp of the audio signal. A voice-to-text non-verbal transcript of the one or more non-verbal vocalizations of the audio signal is generated at a non-verbal timestamp of the audio signal. The non-verbal transcript and the verbal transcript are combined to generate an enhanced transcript.

Abstract: Techniques for personalizing an AI automated conversational system are provided. In one aspect, a method for personalizing an automated conversational system includes: making predictions of a familiarity of a user with concepts needed to understand a standard output utterance based on the familiarity of an aggregate of users and a background knowledge model of the concepts and related concepts, wherein the standard output utterance assumes that the concepts are known; and giving, by the automated conversational system, an output utterance that is tailored to the user given the predictions. For instance, the automated conversational system can give the standard output utterance to the user when it is predicted that the user is familiar with the concepts, or a nonstandard output utterance when it is it is predicted that the user is unfamiliar with at least one of the concepts.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: A tool for providing dynamic context-based presentation adjustments across one or more computer devices. The tool receives a presentation from at least one of a plurality of user devices. The tool collects real-time contextual data associated with the presentation from at least one of the plurality of user devices. The tool analyzes the real-time contextual data utilizing a training model. The tool determines one or more adjustment actions based, at least in part, on the analysis of the real-time contextual data. The tool modifies the presentation in real-time using the one or more adjustment actions.

Abstract: A computer-implemented method of voice-to-text tagging for transcription of a human voice signal by one of an automatic speech recognition system, or a natural conversation, includes generating a speech-to-text verbal transcript of the verbal vocalizations of an audio signal at a verbal timestamp of the audio signal. A voice-to-text non-verbal transcript of the one or more non-verbal vocalizations of the audio signal is generated at a non-verbal timestamp of the audio signal. The non-verbal transcript and the verbal transcript are combined to generate an enhanced transcript.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate word entity disambiguation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a language model component that employs an artificial intelligence model to generate a profile vector of an entity based on one or more binary values representing profile data of the entity and a word vector of a word entity in a dialogue based on one or more second word entities adjacent to the word entity in the dialogue. The computer executable components can further comprise a dialogue management component that disambiguates the word entity based on the profile vector and the word vector.

Abstract: Embodiments of the present invention provide a method comprising receiving a task set comprising multiple tasks, receiving operational information identifying one or more operating characteristics of multiple drones, and obtaining an initial heuristic ordering of the multiple tasks based on the operational information and the climate information. Each task has a corresponding task location. The method further comprises scheduling the multiple tasks to obtain a final ordering of the multiple tasks. The final ordering represents an order in which the multiple tasks are scheduled, and the final ordering may be different from the initial heuristic ordering.

Abstract: A method and system of automatically identifying topics of a conversation are provided. An electronic data package comprising a sequence of utterances between conversation entities is received by a computing device. Each utterance is classified to a corresponding social action. One or more utterances in the sequence are grouped into a segment based on a deep learning model. A similarity of topics between adjacent segments is determined. Upon determining that the similarity is above a predetermined threshold, the adjacent segments are grouped together. A transcript of the conversation including the grouping of the adjacent segments is stored in a memory.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: An example operation may include one or more of identifying a first conferred asset exchange request from a first user account and a second conferred asset exchange request from a second user account which are capable of being used to settle each other, requesting an intermediary blockchain to perform a conferred asset settlement transaction for the first and second conferred asset exchange requests, determining that first conferred assets of the first user account and second conferred assets of the second user account have been transferred to temporary intermediary trading addresses, respectively, and releasing the first conferred assets to the second user account and the second loyalty assets to the first user account, in response to the determining.

Abstract: One embodiment provides a method comprising maintaining a weather model based on predicted weather conditions for an air traffic control zone. A hash table comprising multiple hash entries is maintained. Each hash entry comprises a timestamped predicted weather condition for a cell in the zone. A flight plan request for a drone is received. The request comprises a planned flight path for the drone. For at least one cell on the planned flight path, same latitude or same longitude cells, whichever is most closely orthogonal to a direction of the planned flight path, are heuristically probed. Weather conditions for the at least one cell are estimated based on predicted weather conditions for the same latitude or same longitude cells. An executable flight plan is generated if the planned flight path is feasible based on the estimated weather conditions; otherwise, a report including an explanation of infeasibility is generated instead.

Abstract: A method and system of automatically identifying topics of a conversation are provided. An electronic data package comprising a sequence of utterances between conversation entities is received by a computing device. Each utterance is classified to a corresponding social action. One or more utterances in the sequence are grouped into a segment based on a deep learning model. A similarity of topics between adjacent segments is determined. Upon determining that the similarity is above a predetermined threshold, the adjacent segments are grouped together. A transcript of the conversation including the grouping of the adjacent segments is stored in a memory.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate word entity disambiguation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a language model component that employs an artificial intelligence model to generate a profile vector of an entity based on one or more binary values representing profile data of the entity and a word vector of a word entity in a dialogue based on one or more second word entities adjacent to the word entity in the dialogue. The computer executable components can further comprise a dialogue management component that disambiguates the word entity based on the profile vector and the word vector.

Abstract: One embodiment provides a method that includes obtaining a default language corpus. A second language corpus is obtained based on a second language preference. A first transcription of an utterance is received using the default language corpus and natural language processing (NLP). At least one problem word in the first transcription is determined based on an associated grammatical relevance to neighboring words in the first transcription. Upon determining that a first probability score is below a first threshold, an acoustic lookup is performed for an audible match for the problem word in the first transcription based on an associated acoustical relevance. Upon determining that a second probability score is below a second threshold, it is determined whether a match for the problem word exists in the secondary language corpus. Upon determining that the match exists in the secondary language corpus, a second transcription for the utterance is provided.

Abstract: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Abstract: An example operation may include one or more of identifying a first conferred asset exchange request from a first user account and a second conferred asset exchange request from a second user account which are capable of being used to settle each other, requesting an intermediary blockchain to perform a conferred asset settlement transaction for the first and second conferred asset exchange requests, determining that first conferred assets of the first user account and second conferred assets of the second user account have been transferred to temporary intermediary trading addresses, respectively, and releasing the first conferred assets to the second user account and the second loyalty assets to the first user account, in response to the determining.

Abstract: Embodiments are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluation based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

Abstract: One embodiment provides a method comprising maintaining a weather model based on predicted weather conditions for an air traffic control zone. A hash table comprising multiple hash entries is maintained. Each hash entry comprises a timestamped predicted weather condition for a cell in the zone. A flight plan request for a drone is received. The request comprises a planned flight path for the drone. For at least one cell on the planned flight path, same latitude or same longitude cells, whichever is most closely orthogonal to a direction of the planned flight path, are heuristically probed. Weather conditions for the at least one cell are estimated based on predicted weather conditions for the same latitude or same longitude cells. An executable flight plan is generated if the planned flight path is feasible based on the estimated weather conditions; otherwise, a report including an explanation of infeasibility is generated instead.

Abstract: One embodiment provides a method comprising maintaining a weather model based on predicted weather conditions for an air traffic control zone. A hash table comprising multiple hash entries is maintained. Each hash entry comprises a timestamped predicted weather condition for a cell in the zone. A flight plan request for a drone is received. The request comprises a planned flight path for the drone. For at least one cell on the planned flight path, same latitude or same longitude cells, whichever is most closely orthogonal to a direction of the planned flight path, are heuristically probed. Weather conditions for the at least one cell are estimated based on predicted weather conditions for the same latitude or same longitude cells. An executable flight plan is generated if the planned flight path is feasible based on the estimated weather conditions; otherwise, a report including an explanation of infeasibility is generated instead.