Abstract: A user verification system is configured to identify likely interactors approaching points of interaction to ensure accurate interactions and to provide loyalty rewards. In an example, a interaction system installs a user verification device with a stereo camera at an interacting device to capture images of the user and to obtain data from the device. The user verification device uses the stereo camera to determine the location of the user to determine if the user is a likely interactor. The user verification device recognizes motions of the user that are indicative of an interactor, such as reaching across the counter or looking at the operator. The user verification device identifies or verifies the interactor based on the record data or the display data. If the user is the likely interactor, then the system conducts a hands-free interaction with the user account or provides loyalty rewards to the user account.

Abstract: A user verification system is configured to identify likely interactors approaching points of interaction to ensure accurate interactions and to provide loyalty rewards. In an example, a interaction system installs a user verification device with a stereo camera at an interacting device to capture images of the user and to obtain data from the device. The user verification device uses the stereo camera to determine the location of the user to determine if the user is a likely interactor. The user verification device recognizes motions of the user that are indicative of an interactor, such as reaching across the counter or looking at the operator. The user verification device identifies or verifies the interactor based on the record data or the display data. If the user is the likely interactor, then the system conducts a hands-free interaction with the user account or provides loyalty rewards to the user account.

Abstract: A user verification system is configured to identify likely interactors approaching points of interaction to ensure accurate interactions and to provide loyalty rewards. In an example, a interaction system installs a user verification device with a stereo camera at an interacting device to capture images of the user and to obtain data from the device. The user verification device uses the stereo camera to determine the location of the user to determine if the user is a likely interactor. The user verification device recognizes motions of the user that are indicative of an interactor, such as reaching across the counter or looking at the operator. The user verification device identifies or verifies the interactor based on the record data or the display data. If the user is the likely interactor, then the system conducts a hands-free interaction with the user account or provides loyalty rewards to the user account.

Abstract: Systems and methods are disclosed herein for extracting products referenced in a document. A document is analyzed to identify a product type that is referenced in the document. Attributes are extracted from the document. A set of candidate products are identified corresponding to the extracted attributes. A score is calculated for the candidate products and the products are further selected or filtered based on the score, whitelist rules, and blacklist rules in order to identify one or more inferred products referenced by the document. The whitelist and blacklist rules may take as inputs a domain, a user identifier, and keywords included in the document. A set of sufficient attributes may be identified for each product type. Selection of a candidate product may be based at least in part on the document including all of the attributes in the set of sufficient attributes.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Disclosed are methods for inferring interests of a user based on declared interests of the user. Text for which a user has expressed interest, e.g. “liked” is evaluated to identify at least one principal concept. A principal article for the principal concept is located in a taxonomy and the link structure of the taxonomy analyzed to identify candidate articles related to the principal article. The candidate articles are scored according to a plurality of metrics and these scored are weighted and combined for a final score. Candidate articles are selected for the score and recommendations are generated and recommendations generated based on the concepts of the selected candidate articles.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Systems and methods are disclosed herein for extracting products referenced in a document. A document is analyzed to identify a product type that is referenced in the document. Attributes are extracted from the document. A set of candidate products are identified corresponding to the extracted attributes. A score is calculated for the candidate products and the products are further selected or filtered based on the score, whitelist rules, and blacklist rules in order to identify one or more inferred products referenced by the document. The whitelist and blacklist rules may take as inputs a domain, a user identifier, and keywords included in the document. A set of sufficient attributes may be identified for each product type. Selection of a candidate product may be based at least in part on the document including all of the attributes in the set of sufficient attributes.

Abstract: Disclosed are methods for inferring interests of a user based on declared interests of the user. Text for which a user has expressed interest, e.g. “liked” is evaluated to identify at least one principal concept. A principal article for the principal concept is located in a taxonomy and the link structure of the taxonomy analyzed to identify candidate articles related to the principal article. The candidate articles are scored according to a plurality of metrics and these scored are weighted and combined for a final score. Candidate articles are selected for the score and recommendations are generated and recommendations generated based on the concepts of the selected candidate articles.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

Abstract: Disclosed are methods for returning to a user an answer to the question “what is <string>.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.