Abstract: Computerized data processing and electronic file management methods of organizing and indexing electronic records in an electronic database for categorizing new products that are being added to an existing database of product offerings and computerized digital data processing methods of transferring digital information between a plurality of computers and employing computer instructions to categorize new products that are being added to an existing database of product offerings. Multiple classification models classify a description of a particular product and the classifications are compared, and if found to be equivalent, are added to the existing database of product offerings. If the classifications from the models are not equivalent, then the description is sent to multiple people for classification and the classifications from the people are compared, and if found to be equivalent, are added to the existing database of product offerings.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat.

Abstract: Systems and methods are disclosed herein for performing classification of documents or performing other tasks based on rules. The rules may include context rules that define a mapping that relates a value and context in a document to an attribute to which the value corresponds. Products are selected for labeling with attributes by identifying patterns, e.g. values and contexts that are not covered by a current rule set. Those products having a highest score are selected for labeling in a crowd sourcing forum, where the score is based on the number of non-covered patterns and a frequency of occurrence of the non-covered patterns in a document corpus. Proposed rules are generated for frequently occurring patterns and submitted to analysts for one or both of completion and validation. Proposed rules may include a proposed attribute for a frequently occurring value and corresponding context.

Abstract: Systems and methods are disclosed herein for generating a normalized record from an import record, the normalized record having attribute-value pairs corresponding to a native schema. In import records, a plurality of attribute-value are identified each having an attribute label not found in a native schema. One or more attribute labels in the native schema having as possible values one or more values corresponding to the values of the plurality of attribute-value pairs are also identified. The computer system generates one or more normalization rules relating one or more attribute labels of the plurality of attribute-value pairs to at least a portion of the one or more attribute labels in the native schema. Normalization rules may be validated by crowdsourcing. Normalization rules may be applied by identifying implicated rules by classifying the import record and identifying rules applicable to the classification.

Abstract: Computerized data processing and electronic file management methods of organizing and indexing electronic records in an electronic database for categorizing new products that are being added to an existing database of product offerings and computerized digital data processing methods of transferring digital information between a plurality of computers and employing computer instructions to categorize new products that are being added to an existing database of product offerings. Multiple classification models classify a description of a particular product and the classifications are compared, and if found to be equivalent, are added to the existing database of product offerings. If the classifications from the models are not equivalent, then the description is sent to multiple people for classification and the classifications from the people are compared, and if found to be equivalent, are added to the existing database of product offerings.

Abstract: Systems and methods are disclosed herein for obtaining a structured listing of attributes and corresponding values based on an unstructured document, such as a product description in a product record. Putative values are identified in the document and corresponding candidate attributes are identified in a taxonomy. Attribute-value pairs are then evaluated with respect to a plurality of rules. Attribute-value pairs and outputs of the one or more rules are evaluated using a machine-learning algorithm, such as a decision tree, in order to determine which attribute-value pairs to retain. Retained attribute-value pairs are stored and used to respond to search queries and facilitate comparison of products. Attributes selected may also be used to update a product template.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat. High confidence classifications may be identified using an accuracy model trained to relate an accuracy percentage to a confidence score output by the classification model.

Abstract: The present disclosure extends to methods, systems, and computer program products for automatically determining key words within item information with product types, and classifying new items within product types within a merchant's database.

Abstract: Systems and methods are disclosed herein for generating a normalized record from an import record, the normalized record having attribute-value pairs corresponding to a native schema. In import records, a plurality of attribute-value are identified each having an attribute label not found in a native schema. One or more attribute labels in the native schema having as possible values one or more values corresponding to the values of the plurality of attribute-value pairs are also identified. The computer system generates one or more normalization rules relating one or more attribute labels of the plurality of attribute-value pairs to at least a portion of the one or more attribute labels in the native schema. Normalization rules may be validated by crowdsourcing. Normalization rules may be applied by identifying implicated rules by classifying the import record and identifying rules applicable to the classification.

Abstract: Systems and methods are disclosed herein for obtaining a structured listing of attributes and corresponding values based on an unstructured document, such as a product description in a product record. Putative values are identified in the document and corresponding candidate attributes are identified in a taxonomy. Attribute-value pairs are then evaluated with respect to a plurality of rules. Attribute-value pairs and outputs of the one or more rules are evaluated using a machine-learning algorithm, such as a decision tree, in order to determine which attribute-value pairs to retain. Retained attribute-value pairs are stored and used to respond to search queries and facilitate comparison of products. Attributes selected may also be used to update a product template.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat. High confidence classifications may be identified using an accuracy model trained to relate an accuracy percentage to a confidence score output by the classification model.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a merchant database with new products in an optimized manner using both computer based classification models and human involvement in a smart crowd source environment.

Abstract: The present disclosure extends to methods, systems, and computer program products for automatically determining key words within item information with product types, and classifying new items within product types within a merchant's database.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a database with new products by classifying the new products within a hierarchy, and then using the hierarchy to improve the classification by including other product types within the classification for the new products.

Abstract: Systems and methods are disclosed herein for performing classification of documents or performing other tasks based on rules. The rules may include context rules that define a mapping that relates a value and context in a document to an attribute to which the value corresponds. Products are selected for labeling with attributes by identifying patterns, e.g. values and contexts that are not covered by a current rule set. Those products having a highest score are selected for labeling in a crowd sourcing forum, where the score is based on the number of non-covered patterns and a frequency of occurrence of the non-covered patterns in a document corpus. Proposed rules are generated for frequently occurring patterns and submitted to analysts for one or both of completion and validation. Proposed rules may include a proposed attribute for a frequently occurring value and corresponding context.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a merchant database with new product items and placing the new product items within a hierarchy of existing merchant product offerings. In operation, the new product is represented by a title and description that can be semantically classified using a plurality of classification models and reviewed by users for accuracy.