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Using classified text or images and deep learning algorithms to identify risk of product defect and provide early warning

机译:使用分类的文本或图像以及深度学习算法来识别产品缺陷的风险并提供预警

摘要

Deep learning is used to identify specific, potential risks to an enterprise (of which product liability is the prime example here) while such risks are still internal electronic communications. The system involves mining and using existing classifications of data (e.g., from an internal litigation database, or from external sources such as customer complaints, and/or warranty claims) to train one or more deep learning algorithms, and then examining the enterprise's internal electronic communications with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to risks and take action in time to prevent the risks from resulting in harm to the enterprise or others.
机译:深度学习用于识别企业的特定潜在风险(此处以产品责任为主要示例),而此类风险仍是内部电子通信。该系统涉及挖掘和使用现有的数据分类(例如,来自内部诉讼数据库或来自外部源,例如客户投诉和/或保修索赔),以训练一种或多种深度学习算法,然后检查企业的内部电子设备。与受过训练的算法进行通信,以生成计分输出,从而使企业人员能够警惕风险并及时采取措施,以防止风险对企业或他人造成伤害。

著录项

  • 公开/公告号US9754205B1

    专利类型

  • 公开/公告日2017-09-05

    原文格式PDF

  • 申请/专利权人 NELSON E. BRESTOFF;

    申请/专利号US201715406431

  • 发明设计人 NELSON E. BRESTOFF;

    申请日2017-01-13

  • 分类号G06F15/18;G06N3/08;G06Q10/06;

  • 国家 US

  • 入库时间 2022-08-21 13:42:42

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