Abstract: A method, computer system, and a computer program product for automatically generated question suggestions is provided. The present invention may include generating an intent-entity sequence representative of a current chat conversation with a user. The present invention may also include predicting, using a trained intent-entity prediction model, a next intent-entity sequence based on the generated intent-entity sequence representative of the current chat conversation. The present invention may further include converting the predicted next intent-entity sequence into at least one likely question suggestion to return back to the user.

Abstract: A method for countering adversarial attacks on deep neural networks is disclosed. In one embodiment, such a method includes observing, by a first system, actual traffic behavior within a transportation network. The method classifies, by a deep neural network of a second system, a traffic sign for regulating traffic within the transportation network. The method determines whether a conflict exists between the actual traffic behavior and expected traffic behavior based on the traffic sign. If a conflict is deemed to exist, the method adjusts training data of the deep neural network with respect to classifying the traffic sign. In certain embodiments, the first system includes one or more forerunner vehicles and the second system is an autonomous vehicle. The one or more forerunner vehicles may be configured to travel ahead of the autonomous vehicle when navigating the transportation network. A corresponding system and computer program product are also disclosed.

Abstract: A computer-implemented method, in accordance with one embodiment, includes obtaining observational data from an unmanned aerial vehicle (UAV) deployed in a working environment, the working environment including a plurality of entities carrying out tasks within the working environment. The data is analyzed to detect a disruption caused by the UAV to one or more of the entities within the working environment during the deployment. A parameter of a deployment is adjusted based on the analysis of the obtained data.

Abstract: A computer assists in training a user to speak with a target accent by determining improvable aspects of diagnostic input of a user speaking diagnostic phrases with the target accent. The computer selects, a focus phrase characterized by at least one of said improvable aspects. The computer records performance input of the user attempting to say the focus phrase in the target accent. The computer converts the performance input into output having a baseline voice of the and the target accent applied. The computer presents the output and determines teachable aspects of revised performance input from the user replicating the output. The computer converts selected aspects of the revised performance input into augmented teaching output in the user's voice with the target accent applied. The computer presents the augmented teaching output to the user.

Abstract: According to one embodiment, a method, computer system, and computer program product for dynamic acoustics adjustment is provided. The embodiment may include capturing contextual information of an environment surrounding an unmanned aerial vehicle (UAV). The embodiment may also include generating an environmental model using a cluster of machine learning techniques based on the captured contextual information. The embodiment may further include identifying one or more dominant sounds within a soundscape of the captured contextual information. The embodiment may also include calculating an impact of an operation of the UAV on one or more activities within the environment based on the generated environmental model and the one or more identified dominant sounds. The embodiment may further include, in response to determining the calculated impact affects an activity within the one or more activities, modifying the operation to minimize the impact on the soundscape.

Abstract: Generating questions by receiving user utterance data, determining an intent confidence vector for the user utterance data, predicting, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector, and generating a next question using the next user-intent confidence vector.

Abstract: Generating error event descriptions by receiving a set of error messages associated with an error event, generating a tokenization of at least one line of the set of error messages, providing the tokenization to an attention head according to a context of the tokenization, providing an output of the attention head as input to a generative model, generating a description of the error event according to the output, and providing the description to a user.

Abstract: A method, system, and computer program product for routing queries to answer resources based on component parts and intents of a received query is provided. The method receives a query from a user. The query is analyzed to identify a set of entities associated with the query and generate an utterance representing the query. The method generates an intent classification for the utterance and a vector for the query. The vector is generated based on the set of entities, the utterance, and the intent classification. The method determines an answer resource for the query based on the vector and the intent classification of the query. In response to determining the answer resource, the method provides an answer interface based on the query, the vector, and the intent classification. The answer interface dynamically provides a response to the query.

Abstract: A computer-implemented method, a computer system, and a computer program product for automatically generated conversation output is provided. The present invention may include training an answer-intent classifier to associate an intent expressed in an example question with an example answer that is responsive to the example question. The present invention may further include classifying, using the trained answer-intent classifier, a content transmitted to the trained answer-intent classifier with the intent expressed in the example question in response to determining, using the trained answer-intent classifier, that the content includes relevant information for generating the example answer that is responsive to the example question.

Abstract: Generating error event descriptions by receiving a set of error messages associated with an error event, generating a tokenization of at least one line of the set of error messages, providing the tokenization to an attention head according to a context of the tokenization, providing an output of the attention head as input to a generative model, generating a description of the error event according to the output, and providing the description to a user.

Abstract: A method can include obtaining notification data for a user, and obtaining a first set of images of the user from one or more cameras. The method can include identifying a first status of the user, and generating a set of notification options based on the first status. The method can include selecting a first notification option, and initiating a first notification by the first notification option. The method can include determining that the user does not acknowledge the first notification, and obtaining a second set of images from the one or more cameras. The method can include identifying a second status of the user, and selecting a second notification option of the set of notification options. The method can include initiating a second notification by the second notification option.

Abstract: A method, computer system, and a computer program product for automatically generated question suggestions is provided. The present invention may include generating an intent-entity sequence representative of a current chat conversation with a user. The present invention may also include predicting, using a trained intent-entity prediction model, a next intent-entity sequence based on the generated intent-entity sequence representative of the current chat conversation. The present invention may further include converting the predicted next intent-entity sequence into at least one likely question suggestion to return back to the user.

Abstract: An approach is disclosed for storing information tailored to a user and a condition to be accessed offline to fit on a local device. The information is retrieved from a set of online sources by a device. The retrieved information is tailored according to a criteria and the condition to form a curated data. An index is provided to access the curated data. The index is used to answer queries. Responsive to identifying a question without an answer found in the curated data when the device is off-line, searching for the answer when the device is online. Responsive to finding the answer online, pruning the curated data by eliminating lower importance data as needed to store higher importance data based on an availability of space allocated to store the curated data on the local storage device, and updating the curated data stored on the local device.

Abstract: In an approach to customizing meeting notes, a computer receives audio input of a virtual meeting, converts the audio input to text, and displays the text to a plurality of meeting participants. A computer receives highlighted phrases of the text from the plurality of meeting participants and determines a highlighting frequency of each of the highlighted phrases. A computer determines phrases with a highlighting frequency greater than a pre-defined threshold. A computer orders the phrases based on a chronological order of the phrases in the audio input. A computer determines preferences of a first meeting participant associated with a meeting summary. A computer generates a customized summary of the virtual meeting for the first meeting participant of the plurality of meeting participants based on the ordered phrases with a high frequency of highlighting and on the preferences. A computer transmits the customized summary to the first meeting participant.

Abstract: Generating questions by receiving user utterance data, determining an intent confidence vector for the user utterance data, predicting, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector, and generating a next question using the next user-intent confidence vector.

Abstract: In an approach for identifying when transferring a real-time conversation on a chatbot application to a customer support agent would be more valuable than outputting suggested queries, a processor classifies a query inputted into a real-time conversation as an intention. A processor predicts a set of next intentions of the user. A processor filters out one or more intentions from the set of next intentions of the user that do not further the real-time conversation. A processor generates a set of suggested queries the user can ask to further the real-time conversation from the subset of next intentions. A processor builds a suggestion evaluation contextual bandit model that determines whether the real-time conversation will be resolved successfully if transferred to a customer support agent. A processor trains the suggestion evaluation contextual bandit model against a set of annotated historical interactions. A processor outputs a response recommendation.

Abstract: A method, computer system, and a computer program product for customer representative matching is provided. The present invention may include receiving a chat transcript with one or more tagged triplets and one or more multi-dimensional success vectors. The present invention may include aggregating the one or more multi-dimensional success vectors. The present invention may include receiving at least one business priority. The present invention may include training a machine learning model to match a customer to at least one customer representative. The present invention may include querying the trained machine learning model to match the customer to the at least one customer representative. The present invention may include revealing a match.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for evaluating the pollination of one or more crops, the method comprising collecting pollination data, extracting one or more features from the collected data, and evaluating a current state of pollination of the one or more crops based on the extracted one or more features and one or more models.

Abstract: When a chatbot application is in a mode of live chat conversation with a user, a trained intent classifier determines an intent that underlies a first live question received by the trained intent classifier from the user. A trained next predictor receives the intent from the intent classifier. The trained next intent predictor generates a set of predicted next intents responsive to receiving the intent. A trained re-ranker selects at least one of the predicted next intents responsive to receiving the set of predicted next intents. A question selection engine sends at least one suggested question to the user responsive to receiving the at least one predicted next intent. As a result of the above, the chatbot application provides to the user at least one suggested next question the user may wish to ask in response to the first question from the user.

Abstract: A computer assists in training a user to speak with a target accent by determining improvable aspects of diagnostic input of a user speaking diagnostic phrases with the target accent. The computer selects, a focus phrase characterized by at least one of said improvable aspects. The computer records performance input of the user attempting to say the focus phrase in the target accent. The computer converts the performance input into output having a baseline voice of the and the target accent applied. The computer presents the output and determines teachable aspects of revised performance input from the user replicating the output. The computer converts selected aspects of the revised performance input into augmented teaching output in the user's voice with the target accent applied. The computer presents the augmented teaching output to the user.