How Computers Find Similar Text Using Compact Data Structures

This patent describes a method for efficiently identifying similar text records, like documents or product reviews, by using special compact data structures that store text terms probabilistically and then analyzing them with machine learning.

What it covers

This system (Claim 1) takes a piece of text, such as a product review, and uses a "hashing-based function" to map its words (e.g., "excellent") to specific spots in a "probabilistic data structure." This data structure acts like a compact, fuzzy summary of many other text records. When a word is mapped, the system updates an entry in this structure to indicate the word's presence. Importantly, these entries can represent multiple words (Claim 1), making the structure very efficient. After updating, the system applies a "dimensionality reduction algorithm" to simplify the data, then feeds this into a "similarity detection algorithm" to figure out how much the new text is like other texts it has seen. For example, it could find customer reviews that discuss similar product features.

What it doesn't cover

• —Does not cover systems that store every single word explicitly in a traditional database for similarity comparison, as it relies on probabilistic storage where entries can represent more than one text term.
• —Does not cover similarity detection that doesn't use a probabilistic data structure as the initial input for further analysis.
• —Does not cover text analysis methods that do not involve applying a hashing-based function to text terms to update the data structure.
• —Does not cover systems that omit the step of applying a dimensionality reduction algorithm on the probabilistic data structure before generating similarity indications.
• —Does not cover combining data structures without using bit-level Boolean operations or vector instructions, as specified in Claim 3.

The clever bit

The novelty lies in using probabilistic data structures, where multiple terms can share entries, as the direct input for machine learning algorithms like dimensionality reduction and similarity detection. This allows for highly scalable text analysis without needing to store full text or traditional, large term-frequency matrices.

Why it matters

This patent is important for processing huge amounts of text data efficiently, which is common in cloud services and e-commerce. By using probabilistic data structures, it allows for faster and more resource-friendly analysis of customer reviews, product descriptions, or documents. This efficiency helps companies quickly identify trends, recommend products, or moderate content without needing vast storage for every single word.

Real-world examples

• 1.Amazon product recommendation systems
• 2.Customer review analysis for sentiment and trends
• 3.Content moderation for online platforms
• 4.Document clustering in large datasets
• 5.Spam detection in email services