Author: Olaf Kopp
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Website Representation Vector

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This patent describes a system and method for improving web search results by using vectors to represent websites. These vectors help classify and differentiate websites based on their relevance and content characteristics. The approach involves generating composite representations for sets of websites and comparing these to categorize a new or unclassified website accurately.

The Google patent  “Website Representation Vectors” used to classify websites based on expertise and authority.

Here’s a summary of the key points from the patent:

  • The patent application was filed in August 2018, and it covers a range of industries, including health and artificial intelligence sites as examples.
  • The patent application uses Neural Networks to understand patterns and features behind websites to classify those sites. This classification is based on different levels of expertise, with examples given such as doctors (experts), medical students (apprentices), and laypeople (nonexperts) in the health domain.
  • The patent application does not specifically define a “quality score”, but it mentions classifying websites based on whether they meet thresholds related to these scores. These scores could potentially relate to a range of quality measures of sites relative to other sites.
  • The patent also discusses how search queries from specific knowledge domains (covering specific topics) might return results using classified sites from the same knowledge domain. It aims to limit possible results pages based on classifications involving industry and expertise that meet sufficient quality thresholds.
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