Robotics & Machine Learning Daily News2024,Issue(Jul.1) :168-172.

Patent Issued for Utilizing machine learning models to generate interactive digi tal text threads with personalized digital text reply options (USPTO 12010075)

利用机器学习模型生成具有个性化数字文本回复选项的交互式数字文本线程的专利(USPTO 12010075)

Robotics & Machine Learning Daily News2024,Issue(Jul.1) :168-172.

Patent Issued for Utilizing machine learning models to generate interactive digi tal text threads with personalized digital text reply options (USPTO 12010075)

利用机器学习模型生成具有个性化数字文本回复选项的交互式数字文本线程的专利(USPTO 12010075)

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摘要

记者从发明者提供的背景信息中获得了以下引述:“近年来,利用计算设备与各种客户机和客户机设备交互的常规系统有了显著的改进。例如,传统系统可以利用计算机实现的ED聊天机器人来引导客户机通过各种选择并识别所需的信息或服务。尽管传统系统可以自主地与客户机交互,这些传统系统在实现计算设备的准确性、效率和灵活性方面存在许多问题。“例如,传统系统在引导客户端/客户端设备到相关资源方面往往是不准确的。例如,基于与菜单选项的交互,传统系统经常将客户端路由到不准确的终端路径,而不能提供所需的信息或资源。实际上,由于常规系统广泛地概括了菜单和子菜单选项,这导致系统将客户端路由到不适用和不准确的信息。

Abstract

Reporters obtained the following quote from the background information supplied by the inventors: “Recent years have seen significant improvements in convention al systems for utilizing computing devices to interact with various clients and client devices. For example, conventional systems can utilize computerimplement ed chat bots to guide clients through various options and identify desired infor mation or services. Although conventional systems can autonomously interact with clients, these conventional systems have a number of problems in relation to ac curacy, efficiency, and flexibility of implementing computing devices. “For instance, conventional systems are often inaccurate in guiding clients/clie nt devices to pertinent resources. For example, based on interactions with menu options, conventional systems often route clients to an inaccurate terminal path that fails to provide the needed information or resources. Indeed, because conv entional systems broadly generalize menu and sub-menu options this leads the sys tem to route clients to information that is inapplicable and inaccurate.

Key words

Business/Chime Financial Inc/Cyborgs/Emerging 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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