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    • 3. 发明授权
    • Performant relevance improvements in search query results
    • 搜索查询结果中的性能相关性改进
    • US07529736B2
    • 2009-05-05
    • US11123277
    • 2005-05-06
    • Sanjeev KatariyaQi YaoJun LiuAdwait RatnaparkhiBradley R. Green
    • Sanjeev KatariyaQi YaoJun LiuAdwait RatnaparkhiBradley R. Green
    • G06F17/30
    • G06F17/30864Y10S707/99932Y10S707/99933
    • Property store information and an aggregation of a plurality of ranking mechanisms, including a learning mechanism, are leveraged to provide performant query results with increased user relevancy. The learning mechanism permits query feedback to be accepted to facilitate in optimizing user relevance. This mechanism can also be incorporated with traditional Information Retrieval (IR) components, each supplying independent ranking to a relevance aggregation function that determines relevancy at a high level. This precludes diminishing the value of query feedback that occurs when the data is fed into traditional IR algorithms. By allowing the query feedback to maintain its proper weighting and utilizing scope and bias capabilities of the property store information, relevance increases in a highly performant manner.
    • 利用属性存储信息和包括学习机制在内的多个排名机制的聚合来提供具有增加的用户相关性的性能查询结果。 学习机制允许接受查询反馈以便于优化用户相关性。 这种机制还可以与传统的信息检索(IR)组件相结合,每个组件都提供独立排名,以确定相关性聚合功能,从而确定高水平的相关性。 这排除了当将数据馈送到传统IR算法时发生的查询反馈的值的减少。 通过允许查询反馈来维持其适当的权重并利用属性存储信息的范围和偏差能力,相关性以高性能的方式增加。
    • 4. 发明申请
    • Adaptive semantic platform architecture
    • 自适应语义平台架构
    • US20070203869A1
    • 2007-08-30
    • US11363747
    • 2006-02-28
    • William D. RamseySanjeev KatariyaJun LiuJianfeng GaoQi YaoZhanliang Chen
    • William D. RamseySanjeev KatariyaJun LiuJianfeng GaoQi YaoZhanliang Chen
    • G06N7/02
    • G06F17/279
    • An adaptive shared infrastructure that can be easily utilized to enable natural interaction between user(s) and machine system(s) is provided. Additionally, the novel innovation can provide interactive techniques that produce accurate intent-to-action mapping based upon a user input. Further, the innovation can provide novel mechanism by which assets (e.g., documents, actions) can be authored. The authoring mechanisms can enable the generation of learning models such that the system can infer a user intent based at least in part upon an analysis of a user input. In response thereto, the system can discover an asset, or group of assets based upon the inference. Moreover, the innovation can provide a natural language interface that learns and/or adapts based upon one or more user input(s), action(s), and/or state(s).
    • 提供了可以容易地利用以实现用户和机器系统之间的自然交互的自适应共享基础设施。 此外,新颖的创新可以提供基于用户输入产生准确的意图 - 动作映射的交互技术。 此外,创新可以提供可以创作资产(例如,文档,动作)的新颖机制。 创作机制可以实现学习模型的产生,使得系统至少部分地基于对用户输入的分析来推断用户意图。 作为响应,系统可以基于推论发现资产或资产组。 此外,创新可以提供基于一个或多个用户输入,动作和/或状态来学习和/或适应的自然语言界面。
    • 6. 发明申请
    • Adaptive semantic reasoning engine
    • 自适应语义推理引擎
    • US20070124263A1
    • 2007-05-31
    • US11290076
    • 2005-11-30
    • Sanjeev KatariyaQi YaoJun LiuWilliam RamseyJianfeng Gao
    • Sanjeev KatariyaQi YaoJun LiuWilliam RamseyJianfeng Gao
    • G06F15/18
    • G06F17/30663
    • Provided is an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts. The query can be broken down into tokens or a set of tokens. A task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one or more tasks. The token or token set(s) can be mapped into slots to retrieve one or more task result. A slot filling goodness may be determined that can include scoring each task search result and/or ranking the results in a different order than the order in which the tasks were retrieved. The token or token set(s), retrieved tasks, slot filling goodness, natural language query, context, search result score and/or result ranking can be feedback to the reasoning engine for further processing and/or machine learning.
    • 提供了一种自适应语义推理引擎,其接收可以包含一个或多个上下文的自然语言查询。 该查询可以分为令牌或一组令牌。 可以对令牌或令牌集执行任务搜索以对特定查询和/或上下文进行分类并检索一个或多个任务。 令牌或令牌集可被映射到插槽中以检索一个或多个任务结果。 可以确定插槽填充质量,其可以包括对每个任务搜索结果进行评分和/或以与检索任务的顺序不同的顺序对结果进行排序。 令牌或令牌集,检索任务,插槽填充良品,自然语言查询,上下文,搜索结果分数和/或结果排名可以反馈到推理引擎用于进一步处理和/或机器学习。
    • 8. 发明申请
    • Automatic task creation and execution using browser helper objects
    • 使用浏览器辅助对象自动创建和执行任务
    • US20070130186A1
    • 2007-06-07
    • US11294581
    • 2005-12-05
    • William RamseyQi YaoSanjeev KatariyaZhanliang Chen
    • William RamseyQi YaoSanjeev KatariyaZhanliang Chen
    • G06F7/00
    • G06F17/30864
    • A task system and method are provided. The system provides an automated approach for task creation, maintenance and/or execution. The system includes a browser that receives search results and at least one task associated with a query from a search engine. The system further includes a browser helper object that binds to the browser at runtime. The browser helper object provides information associated with a user's action with respect to the search results and/or at least one task. The information can be employed as feedback to update model(s) (e.g., query classification model(s) and/or slot-filling model(s)) of a semantic reasoning component that retrieves task based, at least in part, upon user query(ies).
    • 提供了一个任务系统和方法。 该系统为任务创建,维护和/或执行提供了一种自动化方法。 该系统包括接收搜索结果的浏览器和与搜索引擎的查询相关联的至少一个任务。 该系统还包括在运行时绑定到浏览器的浏览器助手对象。 浏览器辅助对象提供与用户对于搜索结果和/或至少一个任务相关联的操作的信息。 该信息可以用作反馈,以至少部分地基于用户来更新检索任务的语义推理组件的模型(例如,查询分类模型和/或插槽填充模型) 查询(ies)。
    • 9. 发明授权
    • Adaptive semantic reasoning engine
    • 自适应语义推理引擎
    • US07822699B2
    • 2010-10-26
    • US11290076
    • 2005-11-30
    • Sanjeev KatariyaQi Steven YaoJun LiuWilliam D. RamseyJianfeng Gao
    • Sanjeev KatariyaQi Steven YaoJun LiuWilliam D. RamseyJianfeng Gao
    • G06N5/00G06F17/00
    • G06F17/30663
    • Provided is an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts. The query can be broken down into tokens or a set of tokens. A task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one or more tasks. The token or token set(s) can be mapped into slots to retrieve one or more task result. A slot filling goodness may be determined that can include scoring each task search result and/or ranking the results in a different order than the order in which the tasks were retrieved. The token or token set(s), retrieved tasks, slot filling goodness, natural language query, context, search result score and/or result ranking can be feedback to the reasoning engine for further processing and/or machine learning.
    • 提供了一种自适应语义推理引擎,其接收可以包含一个或多个上下文的自然语言查询。 该查询可以分为令牌或一组令牌。 可以对令牌或令牌集执行任务搜索以对特定查询和/或上下文进行分类并检索一个或多个任务。 令牌或令牌集可被映射到插槽中以检索一个或多个任务结果。 可以确定插槽填充质量,其可以包括对每个任务搜索结果进行评分和/或以与检索任务的顺序不同的顺序对结果进行排序。 令牌或令牌集,检索任务,插槽填充良品,自然语言查询,上下文,搜索结果分数和/或结果排名可以反馈到推理引擎用于进一步处理和/或机器学习。