Robotics & Machine Learning Daily News2024,Issue(Nov.15) :128-131.

Patent Issued for Device, system and method for reducing bandwidth usage by perf orming provider object adjustments at an intermediation server based on historic al data (USPTO 12130806)

为通过根据历史AL数据在中介服务器上组织提供者对象调整来减少带宽使用的设备、系统和方法颁发的专利(USPTO 12130806)

Robotics & Machine Learning Daily News2024,Issue(Nov.15) :128-131.

Patent Issued for Device, system and method for reducing bandwidth usage by perf orming provider object adjustments at an intermediation server based on historic al data (USPTO 12130806)

为通过根据历史AL数据在中介服务器上组织提供者对象调整来减少带宽使用的设备、系统和方法颁发的专利(USPTO 12130806)

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摘要

新闻编辑从发明人提供的背景信息中获得了以下引文:“提供各种产品,例如包括与运输有关的货物和服务(例如航班、酒店预订通常需要不同的离散实体来交换定义不同类型的数据产品的各个方面。在旅游相关产品方面,这类实体的例子包括航空公司、旅行社、终端用户、预订系统MS等。尽管这些实体可能配置成根据标准化格式(例如,根据可扩展标记)交换数据基于语言e(xml)的trave l相关环境下新发布能力(NDC)标准然而,它们可以采用不同的机制来启动数据交换。然而,某些机制可能导致带宽利用率的增加和/或处理资源利用率的增加在系统组件之间交换有关各种产品调配的数据。

Abstract

News editors obtained the following quote from the background information suppli ed by the inventors:“The provision of various products, including for example t ravel-related goods and services (e.g., flights,hotel reservations, and the lik e) typically requires various discrete entities to exchange data defining various aspects of the products. Examples of such entities, in the context of travel-r elated products, includeairlines, travel agencies, end users, reservation syste ms, and the like. Although such entities may beconfigured to exchange data acco rding to a standardized format (e.g., according to the extensible MarkupLanguag e (XML)-based New Distribution Capability (NDC) standard in the context of trave l-relatedproducts), they may nonetheless employ different mechanisms to initiat e the exchange of data. However,certain mechanisms may result in increased band width usage and/or increased use of processing resourcesbetween components of a system that exchange data with regards to provisioning of various products.”

Key words

Amadeus S.A.S./Business/Cyborgs/Emerg ing Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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