公开(公告)号：US08762219B2

公开(公告)日：2014-06-24

申请号：US13232444

申请日：2011-09-14

Applicant: Michael Swinson ,  Param Pash Kaur Dhillon ,  Xingchu Liu

Inventor： Michael Swinson ,  Param Pash Kaur Dhillon ,  Xingchu Liu

IPC: G06Q30/00 ,  G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q30/02 ,  G06Q30/0613

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US20130006876A1

公开(公告)日：2013-01-03

申请号：US13173357

申请日：2011-06-30

Applicant: Michael Swinson ,  Param Pash Kaur Dhillon ,  Thomas J. Sullivan ,  Xingchu Liu

Inventor： Michael Swinson ,  Param Pash Kaur Dhillon ,  Thomas J. Sullivan ,  Xingchu Liu

IPC: G06Q99/00

CPC classification number: G06Q30/0206 ,  G06Q30/02 ,  G06Q30/0205

Abstract: Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

Abstract translation: 公开了通过地理特定模型来聚合和分析车辆价格的实施例。 可以在各种地理特定级别（例如ZIP-Code级别）收集数据以提供更大的数据分辨率。 考虑的数据集可以包括基于车辆交易的分界点数据集和数据集。 分界点数据集可以基于影响购车行为的消费者市场因素。 车辆交易​​可以分类为具有与车辆相似特征的其他车辆的数据集。 然后可以基于与特定车辆的相似特征将数据集合应用到地理特定的统计定价模型以产生车辆的价格估计。

公开(公告)号：US20120066092A1

公开(公告)日：2012-03-15

申请号：US13232444

申请日：2011-09-14

Applicant: Michael Swinson ,  Param Pash Kaur Dhillon ,  Xingchu Liu

Inventor： Michael Swinson ,  Param Pash Kaur Dhillon ,  Xingchu Liu

IPC: G06Q30/00

CPC classification number: G06Q30/02 ,  G06Q30/0613

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US10296929B2

公开(公告)日：2019-05-21

申请号：US13173357

申请日：2011-06-30

Applicant: Michael Swinson ,  Param Pash Kaur Dhillon ,  Thomas J. Sullivan ,  Xingchu Liu

Inventor： Michael Swinson ,  Param Pash Kaur Dhillon ,  Thomas J. Sullivan ,  Xingchu Liu

IPC: G06Q30/02

Abstract: Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.