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Behind market intelligence is a normalization engine

What looks like market intelligence is often just a pipeline: WO2015183098A1 describes crawling many business sources, filtering for relevance, extracting entities, mapping them to standardized IDs, then storing the normalized data for presentation^[1]^[2].

🧵 1/5

The patent is explicit that the goal is to put data from multiple business sources into the same structure so users can summarize, compare, see differences, and analyze statistics and trends across sources^[3].

🧵 2/5

In WO2015183098A1, NLP and machine learning are supporting tools in extraction and mapping, not the product moat. The defensible layer is standardized unique IDs that collapse equivalent terms onto one entity^[4]^[5].

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That normalization is what makes outputs comparable. Once entities share a common structure, the system can surface weighted sums, differences, statistics, and trends across sources^[6]^[7].

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The deeper value is auditable lineage, not just traceability: WO2015183098A1 says the normalized records and source relationships make the links back to the underlying sources clearer and more traceable in the common database^[8].

🧵 5/5

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