Abstract: A method is provided for generating a ranked list of candidate responders. In some embodiments, the method includes receiving a question from a user and generating a question feature vector representing an intent of the question and a first skill set inferred from the question. The method also includes for one or more candidate responders, generating a candidate feature vector representing a skill set and questions associated with the respective candidate responder; computing a reputation score based on questions and user feedback associated with the respective candidate responder; and computing, based on the question feature vector, candidate feature vector, and reputation score, a probability score representing a prediction of the quality of an answer that would be provided by the respective candidate responder if the input question were routed to the respective candidate responder. The method further includes generating a ranked list of candidate responders using the computed probability scores.

Abstract: A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.

Abstract: A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.

Abstract: A method is provided for generating a ranked list of candidate responders. In some embodiments, the method includes receiving a question from a user and generating a question feature vector representing an intent of the question and a first skill set inferred from the question. The method also includes for one or more candidate responders, generating a candidate feature vector representing a skill set and questions associated with the respective candidate responder; computing a reputation score based on questions and user feedback associated with the respective candidate responder; and computing, based on the question feature vector, candidate feature vector, and reputation score, a probability score representing a prediction of the quality of an answer that would be provided by the respective candidate responder if the input question were routed to the respective candidate responder. The method further includes generating a ranked list of candidate responders using the computed probability scores.

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

Abstract: A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.

Abstract: A method is provided for generating a ranked list of candidate responders. In some embodiments, the method includes receiving a question from a user and generating a question feature vector representing an intent of the question and a first skill set inferred from the question. The method also includes for one or more candidate responders, generating a candidate feature vector representing a skill set and questions associated with the respective candidate responder; computing a reputation score based on questions and user feedback associated with the respective candidate responder; and computing, based on the question feature vector, candidate feature vector, and reputation score, a probability score representing a prediction of the quality of an answer that would be provided by the respective candidate responder if the input question were routed to the respective candidate responder. The method further includes generating a ranked list of candidate responders using the computed probability scores.

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

Abstract: A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.

Abstract: A method is provided for generating a ranked list of candidate responders. In some embodiments, the method includes receiving a question from a user and generating a question feature vector representing an intent of the question and a first skill set inferred from the question. The method also includes for one or more candidate responders, generating a candidate feature vector representing a skill set and questions associated with the respective candidate responder; computing a reputation score based on questions and user feedback associated with the respective candidate responder; and computing, based on the question feature vector, candidate feature vector, and reputation score, a probability score representing a prediction of the quality of an answer that would be provided by the respective candidate responder if the input question were routed to the respective candidate responder. The method further includes generating a ranked list of candidate responders using the computed probability scores.

Abstract: In the examples provided herein, a controversy detection system includes a classifier engine to classify each of a plurality of comments about a publication as controversial or non-controversial. The system also includes a controversy detector engine to determine, based on the classification of the plurality of comments, whether the publication is controversial or non-controversial.

Abstract: Delivery of an attachment as a summary in an email is disclosed. An attachment in an email to be sent by a sender is summarized to extract attachment highlights. The email is sent from the sender to a recipient by including in a body of the email the extracted attachment highlights and a link to the attachment.

Abstract: In the examples provided herein, a controversy detection system includes a classifier engine to classify each of a plurality of comments about a publication as controversial or non-controversial. The system also includes a controversy detector engine to determine, based on the classification of the plurality of comments, whether the publication is controversial or non-controversial.

Abstract: Event summarization can include extracting Content from an unfiltered social media content associated with an event. Event summarization can also include constructing a summary of the event based on the extracted content.

Abstract: Examples relate to integrating and extracting topics from content of heterogeneous sources. Observed words are identified in documents, which are received from the heterogeneous sources. Next, document metadata and source metadata are obtained from the heterogeneous sources. The document metadata is used to calculate word topic probabilities for the observed words, and the source metadata is used to calculate source topic probabilities for the observed words. A latent topic is then determined for one of the documents based on the observed words, the word topic probabilities, and the source topic probabilities.

Abstract: Examples relate to performance of tasks within organizations. In example implementations, a computing device receives a user profile of a plurality of users in an organization. The device may receive a task from a first user in the organization including an action to be performed by one user on behalf of another. In response, the device may receive a plurality of bids from other users in the organization, where each bid specifies a number of credits to be exchanged between the first user and a second user for performance of the task. The device may receive a selection of a particular bid from the first user. Upon performance of the task, the device may exchange the specified number of credits between the first user and the second user.

Abstract: Examples relate to integrating and extracting topics from content of heterogeneous sources. Observed words are identified in documents, which are received from the heterogeneous sources. Next, document metadata and source metadata are obtained from the heterogeneous sources. The document metadata is used to calculate word topic probabilities for the observed words, and the source metadata is used to calculate source topic probabilities for the observed words. A latent topic is then determined for one of the documents based on the observed words, the word topic probabilities, and the source topic probabilities.

Abstract: Analyzing plurality of participants of a computer-implemented social network. Data related to the plurality of participants is accessed. A query that includes a plurality of attributes associated with the plurality of participants is created. The plurality of attributes includes at least one social networking attribute and at least one business value attribute. The data related to the plurality of participants is evaluated based on the query and the plurality of attributes based on the data related to the plurality of participants is computed.