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    • 1. 发明申请
    • OPEN PRICING AND PRICING RULES
    • 开放价格和定价规则
    • US20150310570A1
    • 2015-10-29
    • US14263239
    • 2014-04-28
    • Duetto Research, Inc.
    • Marco BenvenutiCraig WeissmanPatrick BosworthNeelav RanaMichael SkinnerJayesh SureshchandraAnuj JenvejaDavid Su
    • G06Q50/14G06Q30/02
    • G06Q50/14G06Q30/0206G06Q30/0283
    • A pricing rules engine identifies a plurality of potential competitors for a travel property and identifies one or more rule conditions. The pricing rules engine performs a pricing correlation on the plurality of potential competitors according to the one or more rule conditions and determines, based on a result of the pricing correlation, at least one of the plurality of potential competitors to be included in a competitor set. When an offer price exceeds a rule threshold, the pricing rules engine adjusts a sub-rate price discount for the travel property according to rates charged by competitor travel properties in the competitor set. When the capacity for a given room type satisfies the rule threshold, the pricing rules engine adjusts a price modifier for the given room type according to rates charged by competitor travel properties in the competitor set.
    • 定价规则引擎识别旅行财产的多个潜在竞争对手,并识别一个或多个规则条件。 所述定价规则引擎根据所述一个或多个规则条件对所述多个潜在竞争对手执行定价相关性,并且基于所述定价相关性的结果确定所述多个潜在竞争者中的至少一个要被包括在竞争者组中 。 当报价超过规则阈值时,定价规则引擎根据竞争对手组中的竞争对手旅行资源收取的费率来调整旅行财产的子利率价格折扣。 当给定房间类型的容量满足规则阈值时,定价规则引擎根据竞争对手集合中的竞争对手旅行属性收取的费率来调整给定房间类型的价格修正。
    • 2. 发明申请
    • NOSQL ONLINE ANALYTICAL PROCESSING ARCHITECTURE
    • NOSQL在线分析处理架构
    • US20140337064A1
    • 2014-11-13
    • US14030677
    • 2013-09-18
    • Duetto Research, Inc.
    • Craig Weissman
    • G06Q30/06
    • G06Q30/0627
    • A breadth-first join module receives, at a runtime, a query for travel data, wherein the travel data is stored in a data store, the data store comprising parent and child data structures arranged in a hierarchy. The breadth-first join module identifies a first child data structure associated with the travel data, the first child data structure comprising a plurality of pointers, wherein each of the plurality of pointers is associated with one of a plurality of first level parent data structures in the data store and queries each of the plurality of first level parent data structures to resolve the plurality of pointers in the first child data structure, wherein the plurality of first level parent data structures comprise travel data objects associated with the plurality of pointers, and wherein at least one of the plurality of first level parent data structures comprises a pointer to a second level parent data structure. The breadth-first join module queries the second level parent data structure to resolve the pointer in the at least one first level parent data structure, wherein all of the plurality of pointers in the first child data structure are resolved prior to querying the second level parent data structure.
    • 广度优先连接模块在运行时接收旅行数据的查询,其中旅行数据被存储在数据存储器中,数据存储器包括以层次结构排列的父和子数据结构。 宽度优先连接模块识别与旅行数据相关联的第一子数据结构,第一子数据结构包括多个指针,其中多个指针中的每一个与多个第一级父数据结构中的一个相关联, 所述数据存储并查询所述多个第一级父数据结构中的每一个以解析所述第一子数据结构中的所述多个指针,其中所述多个第一级父数据结构包括与所述多个指针相关联的旅行数据对象,并且其中 多个第一级父数据结构中的至少一个包括指向第二级父数据结构的指针。 广度优先联接模块查询第二级父数据结构以解析至少一个第一级父数据结构中的指针,其中第一子数据结构中的所有多个指针在查询第二级父级之前被解析 数据结构。
    • 3. 发明申请
    • TRAVEL DEMAND FORECAST USING SHOPPING DATA
    • 使用购物数据的旅行需求预测
    • US20140067469A1
    • 2014-03-06
    • US13760414
    • 2013-02-06
    • DUETTO RESEARCH, INC.
    • Patrick BosworthMarco BenvenutiCraig WeissmanNeelav Rana
    • G06Q30/02
    • G06Q10/02G06Q30/0201G06Q30/0202G06Q50/14
    • A profit optimization module identifies historical transaction data associated with a travel property. The historical transaction data includes bookings made for a same day of the week as the day of arrival during a forecast period for a plurality of previous weeks. The profit optimization module also identifies lost business data associated with the travel property from the historical transaction data. The lost business data includes at least one of a regret or a denial. The profit optimization module forecasts a demand for bookings at the travel property on a day of arrival, wherein the demand for bookings is based on at least in part on the historical transaction data and the lost business data. In addition, the profit optimization module determines an offer price for a booking of a unit at the travel property, wherein the offer price is based on a capacity of the travel property and the forecasted demand for bookings at the travel property, and wherein the offer price is designed to increase a profit for the travel property.
    • 利润优化模块识别与旅行属性相关联的历史交易数据。 历史交易数据包括在多个前几周的预测期间内与当天的同一天的预订。 利润优化模块还从历史交易数据中识别与旅行财产相关联的丢失的业务数据。 丢失的业务数据包括遗憾或拒绝中的至少一个。 利润优化模块预测在抵达当天旅行财产的预订需求,其中预订的需求至少部分基于历史交易数据和丢失的业务数据。 此外,利润优化模块确定在旅行财产上预订单位的报价,其中所述报价基于所述旅行财产的能力和所述旅行财产预定的需求,并且其中所述报价 价格旨在增加旅游产业的利润。
    • 6. 发明授权
    • NoSql online analytical processing architecture
    • NoSql在线分析处理架构
    • US08903872B1
    • 2014-12-02
    • US14030677
    • 2013-09-18
    • Duetto Research, Inc.
    • Craig Weissman
    • G06F17/30G06Q30/06
    • G06Q30/0627
    • A breadth-first join module receives, at a runtime, a query for travel data, wherein the travel data is stored in a data store, the data store comprising parent and child data structures arranged in a hierarchy. The breadth-first join module identifies a first child data structure associated with the travel data, the first child data structure comprising a plurality of pointers, wherein each of the plurality of pointers is associated with one of a plurality of first level parent data structures in the data store and queries each of the plurality of first level parent data structures to resolve the plurality of pointers in the first child data structure, wherein the plurality of first level parent data structures comprise travel data objects associated with the plurality of pointers, and wherein at least one of the plurality of first level parent data structures comprises a pointer to a second level parent data structure. The breadth-first join module queries the second level parent data structure to resolve the pointer in the at least one first level parent data structure, wherein all of the plurality of pointers in the first child data structure are resolved prior to querying the second level parent data structure.
    • 广度优先连接模块在运行时接收旅行数据的查询,其中旅行数据被存储在数据存储器中,数据存储器包括以层次结构排列的父和子数据结构。 宽度优先连接模块识别与旅行数据相关联的第一子数据结构,第一子数据结构包括多个指针,其中多个指针中的每一个与多个第一级父数据结构中的一个相关联, 所述数据存储并查询所述多个第一级父数据结构中的每一个以解析所述第一子数据结构中的所述多个指针,其中所述多个第一级父数据结构包括与所述多个指针相关联的旅行数据对象,并且其中 多个第一级父数据结构中的至少一个包括指向第二级父数据结构的指针。 广度优先联接模块查询第二级父数据结构以解析至少一个第一级父数据结构中的指针,其中第一子数据结构中的所有多个指针在查询第二级父级之前被解析 数据结构。
    • 7. 发明申请
    • ACTUALS CACHE FOR REVENUE MANAGEMENT SYSTEM ANALYTICS ENGINE
    • 收费管理系统分析发动机的实际追踪
    • US20160132790A1
    • 2016-05-12
    • US14539164
    • 2014-11-12
    • Duetto Research, Inc.
    • Craig Weissman
    • G06Q10/02
    • G06Q10/02
    • At a data ingestion time, a travel sessionizer captures raw event data representing a plurality of bookings made by a plurality of users for a given day, identifies a plurality of cache entries corresponding to a travel property for the given day, wherein at least one of the plurality of bookings from the raw event data is for the travel property, and updates the plurality of cache entries corresponding to the travel property for the given data to reflect the at least one booking from the raw event data. At a runtime, the travel sessionizer receives a travel analytic query corresponding to the travel property, accesses at least one of the plurality of cache entries corresponding to the travel property for the given day, and executes the travel analytic query using travel data from the at least one of the plurality of cache entries.
    • 在数据摄取时间,旅行会话记录器捕获表示在给定日期由多个用户做出的多个预约的原始事件数据,识别对应于给定日期的旅行属性的多个高速缓存条目,其中至少一个 来自原始事件数据的多个预订用于旅行属性,并且更新与给定数据的旅行属性相对应的多个缓存条目,以反映来自原始事件数据的至少一个预订。 在运行时,旅行会话器接收对应于旅行属性的旅行分析查询,访问对应于给定日期的旅行属性的多个高速缓存条目中的至少一个,并且使用来自at的旅行数据执行旅行分析查询 多个缓存条目中的至少一个。