Abstract: Methods, systems, and computer programs are presented for detecting a mission changes in a conversation. A user utterance from a user device is received. The user utterance is part of a conversation with an intelligent assistant. The conversation includes preceding user utterances in pursuit of a first mission. It is determined that the user utterance indicates a mission change from the first mission to a second mission based on an application of a machine-learned model to the user utterance and the preceding user utterances. The machine-learned model has been trained repeatedly with past utterances of other users over a time period, the determining based on a certainty of the indication satisfying a certainty threshold. Responsive to the determining that the user utterance indicates the mission change from the first mission to a second mission, a reply to the user utterance is generated to further the second mission rather than the first mission.

Abstract: Methods, systems, and computer programs are presented for detecting a mission changes in a conversation. A user utterance from a user device is received. The user utterance is part of a conversation with an intelligent assistant. The conversation includes preceding user utterances in pursuit of a first mission. It is determined that the user utterance indicates a mission change from the first mission to a second mission based on an application of a machine-learned model to the user utterance and the preceding user utterances. The machine-learned model has been trained repeatedly with past utterances of other users over a time period, the determining based on a certainty of the indication satisfying a certainty threshold. Responsive to the determining that the user utterance indicates the mission change from the first mission to a second mission, a reply to the user utterance is generated to further the second mission rather than the first mission.

Abstract: Methods, systems, and computer programs are presented for detecting a mission changes in a conversation. A user utterance from a user device is received. The user utterance is part of a conversation with an intelligent assistant. The conversation includes preceding user utterances in pursuit of a first mission. It is determined that the user utterance indicates a mission change from the first mission to a second mission based on an application of a machine-learned model to the user utterance and the preceding user utterances. The machine-learned model has been trained repeatedly with past utterances of other users over a time period, the determining based on a certainty of the indication satisfying a certainty threshold. Responsive to the determining that the user utterance indicates the mission change from the first mission to a second mission, a reply to the user utterance is generated to further the second mission rather than the first mission.

Abstract: Methods and systems for automatically synthesizing product information from multiple data sources into an on-line catalog are disclosed, and in particular, for automatically synthesizing the product information based on attribute-value pairs. Information for a product may be obtained, via entity extraction, feed ingestion, and other mechanisms, from a plurality of structured and unstructured data sources having different taxonomies and schemas. Product information may additionally or alternatively be obtained or derived based on popularity data. The product information may be cleansed, segmented and normalized. The product information may be clustered so closest products, attribute names and attribute values are associated. A representative value for an attribute name may be determined, and the on-line catalog may be updated so that entries are comprehensive, meaningful and useful to a catalog user.

Abstract: Systems, methods and computer-storage media are provided for identifying query formulation suggestions in response to receiving a search query. A portion of a search query is received. Query formulation suggestions are identified by semantically analyzing the search query. The query formulation suggestions are used to further formulate the received search query. The query formulation suggestions include semantic-pattern-based query suggestions that are derived from semantic query patterns, one or more entities, and information associated with these entities. The query formulation suggestions are transmitted for presentation.

Abstract: Methods and systems for automatically synthesizing product information from multiple data sources into an on-line catalog are disclosed, and in particular, for automatically synthesizing the product information based on attribute-value pairs. Information for a product may be obtained, via entity extraction, feed ingestion, and other mechanisms, from a plurality of structured and unstructured data sources having different taxonomies and schemas. Product information may additionally or alternatively be obtained or derived based on popularity data. The product information may be cleansed, segmented and normalized. The product information may be clustered so closest products, attribute names and attribute values are associated. A representative value for an attribute name may be determined, and the on-line catalog may be updated so that entries are comprehensive, meaningful and useful to a catalog user.

Abstract: Systems, methods and computer-storage media are provided for identifying query formulation suggestions in response to receiving a search query. A portion of a search query is received. Query formulation suggestions are identified by semantically analyzing the search query. The query formulation suggestions are used to further formulate the received search query. The query formulation suggestions include semantic-pattern-based query suggestions that are derived from semantic query patterns, one or more entities, and information associated with these entities. The query formulation suggestions are transmitted for presentation.

Abstract: Methods and systems for automatically synthesizing product information from multiple data sources into an on-line catalog are disclosed, and in particular, for automatically synthesizing the product information based on attribute-value pairs. Information for a product may be obtained, via entity extraction, feed ingestion, and other mechanisms, from a plurality of structured and unstructured data sources having different taxonomies and schemas. Product information may additionally or alternatively be obtained or derived based on popularity data. The product information may be cleansed, segmented and normalized. The product information may be clustered so closest products, attribute names and attribute values are associated. A representative value for an attribute name may be determined, and the on-line catalog may be updated so that entries are comprehensive, meaningful and useful to a catalog user.

Abstract: As a result of the system, effective advertisements may be created using only the structured data from the seller. The difficulty of determining keywords to bid on is eliminated as the system determines the keywords from the structured data. In addition, the structure data is used to create the text and images for an advertisement. The seller supplies the sales data and the system creates advertisement based on the sales data. In addition, the system may be able to create and adjust bids on keywords to better optimize sales.

Abstract: A method, system, and medium are provided for determining optimal sales rebate rates. Historical data, including sales data, price data, and rebate data are received, along with ongoing current data from current rebate transactions. Changes across the spectrum of data are determined and calculations are used to obtain an optimal sales rebate rate for one of more products or services utilizing statistical models, including but not limited to, a linear rebate rate model and a logarithmic-linear rebate rate model for one or more products or services. A mathematical analysis determines the appropriate model to use to obtain the optimal sales rebate rate. The optimal sales rebate rate may be applied to computing or non-computing environments, in whole or as a combination of both computing and non-computing environments.