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    • 72. 发明授权
    • Attribute prioritized configuration using a combined configuration-attribute data model
    • 使用组合配置属性数据模型进行属性优先配置
    • US09342628B1
    • 2016-05-17
    • US14338401
    • 2014-07-23
    • Versata Development Group, Inc.
    • Brian K ShowersBrandon M BeckNathan E. Little
    • G06F17/30
    • G06F17/30979G06F17/3053
    • A combined configuration rules-attribute configuration system uses an integrated configuration model to efficiently identify and attribute prioritize valid configuration answers. Submitting an attribute-based configuration query to the combined configuration rules-attribute configuration system allows the query to be answered in a single step. The combined configuration rules and attribute data guide product configuration processing and minimize configuration processing by, for example, calculating only the valid configuration answers that are candidates for the preferred valid answer(s). Thus, the combined configuration rules-attribute configuration system can minimize the number of valid configuration answers to be considered for presentation to a client system or other user of the combined configuration rules-attribute configuration system.
    • 组合的配置规则属性配置系统使用集成配置模型来有效地识别和归类有效配置答案的优先级。 将基于属性的配置查询提交给组合的配置规则属性配置系统允许在一个步骤中回答查询。 组合的配置规则和属性数据指导产品配置处理并且通过例如仅计算作为优选有效答案的候选的有效配置答案来最小化配置处理。 因此,组合的配置规则属性配置系统可以最小化被考虑用于呈现给组合配置规则属性配置系统的客户端系统或其他用户的有效配置答案的数量。
    • 78. 发明授权
    • Scoring recommendations and explanations with a probabilistic user model
    • US10719869B1
    • 2020-07-21
    • US16105858
    • 2018-08-20
    • Versata Development Group, Inc.
    • Thomas H. Dillon
    • G06Q30/00G06Q30/06G06Q30/02
    • A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and/or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and/or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and/or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).