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    • 3. 发明授权
    • Natural language processing system, method and computer program product useful for automotive data mapping
    • 自然语言处理系统,方法和计算机程序产品,适用于汽车数据映射
    • US09031967B2
    • 2015-05-12
    • US13406325
    • 2012-02-27
    • Michael SwinsonMikhail SemeniukXingchu Liu
    • Michael SwinsonMikhail SemeniukXingchu Liu
    • G06F17/30G06Q30/02G06F17/27
    • G06F17/30985G06F17/2795G06F17/30684G06Q30/0283
    • Natural language processing (NLP) approaches may be utilized to map two strings. The strings may come from sources utilizing different naming conventions. One example may be a data aggregator that collects used car transaction information. Another example may be a comprehensive database listing all possible manufacturer-defined vehicle options. A NLP system may operate to determine whether a source string is present in a target string and outputting a match containing the source string and the target string if the source string is present in the target string or computing a similarity factor if the source string is not present in the target string. The similarity factor representing a measure of similarity between two strings may be computed based on a plurality of parameters, including a Levenshtein edit distance parameter. The computed similarity can be used to find pricing information, including trade-in, sale, and list prices, across disparate naming conventions.
    • 自然语言处理(NLP)方法可用于映射两个字符串。 字符串可能来自使用不同命名约定的来源。 一个示例可以是收集二手车交易信息的数据聚合器。 另一个例子可能是列出所有可能的制造商定义的车辆选项的综合数据库。 如果源字符串存在于目标字符串中,则NLP系统可以操作以确定源字符串是否存在于目标字符串中并输出包含源字符串和目标字符串的匹配,或者如果源字符串不是,则输出相似因子 存在于目标字符串中。 可以基于包括Levenshtein编辑距离参数的多个参数来计算表示两个串之间的相似度的度量的相似性因子。 计算的相似性可以用于查找不同命名约定的定价信息,包括交易,销售和列表价格。
    • 4. 发明申请
    • NATURAL LANGUAGE PROCESSING SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT USEFUL FOR AUTOMOTIVE DATA MAPPING
    • 自动语言处理系统,用于汽车数据映射的方法和计算机程序产品
    • US20130226945A1
    • 2013-08-29
    • US13406325
    • 2012-02-27
    • Michael SwinsonMikhail SemeniukXingchu Liu
    • Michael SwinsonMikhail SemeniukXingchu Liu
    • G06F17/30
    • G06F17/30985G06F17/2795G06F17/30684G06Q30/0283
    • Natural language processing (NLP) approaches may be utilized to map two strings. The strings may come from sources utilizing different naming conventions. One example may be a data aggregator that collects used car transaction information. Another example may be a comprehensive database listing all possible manufacturer-defined vehicle options. A NLP system may operate to determine whether a source string is present in a target string and outputting a match containing the source string and the target string if the source string is present in the target string or computing a similarity factor if the source string is not present in the target string. The similarity factor representing a measure of similarity between two strings may be computed based on a plurality of parameters, including a Levenshtein edit distance parameter. The computed similarity can be used to find pricing information, including trade-in, sale, and list prices, across disparate naming conventions.
    • 自然语言处理(NLP)方法可用于映射两个字符串。 字符串可能来自使用不同命名约定的来源。 一个示例可以是收集二手车交易信息的数据聚合器。 另一个例子可能是列出所有可能的制造商定义的车辆选项的综合数据库。 如果源字符串存在于目标字符串中,则NLP系统可以操作以确定源字符串是否存在于目标字符串中并输出包含源字符串和目标字符串的匹配,或者如果源字符串不是,则输出相似因子 存在于目标字符串中。 可以基于包括Levenshtein编辑距离参数的多个参数来计算表示两个串之间的相似度的度量的相似性因子。 计算的相似性可以用于查找不同命名约定的定价信息,包括交易,销售和列表价格。
    • 10. 发明授权
    • System, method and program product for predicting best/worst time to buy
    • 用于预测最佳/最差时间购买的系统,方法和程序产品
    • US08762219B2
    • 2014-06-24
    • US13232444
    • 2011-09-14
    • Michael SwinsonParam Pash Kaur DhillonXingchu Liu
    • Michael SwinsonParam Pash Kaur DhillonXingchu Liu
    • G06Q30/00G06Q30/02G06Q30/06
    • G06Q30/02G06Q30/0613
    • In response to a user request for information on the best/worst days in an upcoming time period to buy a commodity, a vehicle data system may determine anticipated daily discounts applicable to the commodity. An example commodity may be a vehicle of a specific configuration. In one embodiment, characteristics of month, day of week, and day of month may be gathered and fed into a Best Day to Buy model to determine, for each day of the time period, a projected daily discount relative to a set price for the commodity. Additional input variables such as incentives and seasonal discounts may be included. From the computed daily discounts, the vehicle data system may determine the best day and/or the worst day to buy and report same to the user.
    • 响应于用户要求在即将到来的时间段内购买商品的最佳/最差天数的信息,车辆数据系统可以确定适用于商品的预期每日折扣。 示例商品可以是具体配置的载体。 在一个实施例中,可以收集月份,星期几和月份的特征,并且将其馈送到“最佳购买日”模型中,以确定每个时间段的每一天相对于 商品。 可能包括其他输入变量,如激励和季节性折扣。 从计算出的每日折扣中,车辆数据系统可以确定最佳的一天和/或最差的一天,以向用户购买和报告相同的日期。