Flexible and scalable artificial intelligence and analytics platform with advanced content analytics and data ingestion

    公开(公告)号:US12236288B2

    公开(公告)日:2025-02-25

    申请号:US18339245

    申请日:2023-06-22

    Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository. In this way, ingested metadata can be leveraged across the platform, for instance, for trend analysis, mood detection, model building, etc.

    FLEXIBLE AND SCALABLE ARTIFICIAL INTELLIGENCE AND ANALYTICS PLATFORM WITH ADVANCED CONTENT ANALYTICS AND DATA INGESTION

    公开(公告)号:US20190279101A1

    公开(公告)日:2019-09-12

    申请号:US16296015

    申请日:2019-03-07

    Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository. In this way, ingested metadata can be leveraged across the platform, for instance, for trend analysis, mood detection, model building, etc.

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