US 6,981,040 · Granted 2005-12-27

The Patent That Invented Your Personal Internet Assistant

Imagine if your computer quietly watched what you click on, what you like, and what you ignore — then used that knowledge to show you only the stuff you actually care about. That's what this patent describes: a system that learns your tastes and serves up personalized search results, news feeds, and web recommendations without you having to ask.

Curated and verified by the IsItPatented Editorial team

The plain-English version

What it protects

The claim covers a method for automatically learning a user's preferences by monitoring their computer activity, building a mathematical profile of their interests, and then using that profile to predict whether they'll like new documents or products. What's protected here is the specific process of collecting user behavior data, updating it continuously, feeding it into a learning model, and then applying that model to rank or filter content — whether that's search results, news articles, websites, or product recommendations.

Why it matters

This patent arrived in 2005 at a moment when the internet was still mostly static and unfiltered. It describes the foundational mechanics behind personalization algorithms that would eventually power recommendation engines across the web. The patent claims users can be grouped into clusters based on similar preferences, a technique that became central to how search engines, streaming platforms, and e-commerce sites operate today. It essentially codified the idea that learning from user behavior automatically could unlock value for both users and service providers.

Real-world use

Every time Netflix suggests a show you actually want to watch, or Google returns search results tailored to your past searches, a descendant of this patent's core idea is working in the background, tracking patterns and predicting your next move.

Original USPTO abstract

A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability. Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed; and personalized news, in which a third party server customized its interaction with the user. The User Model includes continually-updated measures of user interest in words or phrases, web sites, topics, products, and product features. The measures are updated based on both positive examples, such as documents the user bookmarks, and negative examples, such as search results that the user does not follow. Users are clustered into groups of similar users by calculating the distance between User Models.

Patent details

Publication number: US 6,981,040
Filing date: 2000-06-20
Grant date: 2005-12-27
Assignee: Utopy, Inc.
Inventor(s): KONIG YOCHAI, TWERSKY ROY, BERTHOLD MICHAEL R.
CPC class: G06N20/00

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