Similarity matching of products based on multiple classification schemes

Abstract

Improved capabilities are described for identifying a first classification scheme associated with product attributes of a first grouping of products, identifying a second classification scheme associated with product attributes of a second grouping of products, and receiving a record of data relating to an item, the classification of which is uncertain. It may also involve receiving a dictionary of attributes associated with products and assigning the item to at least one of the classification schemes based on probabilistic matching among the attributes in the classification schemes, the attributes in the dictionary of attributes and the known attributes of the item.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 12 / 023,310 filed on Jan. 31, 2008, which is a continuation-in-part of U.S. patent application Ser. No. 12 / 021,263 filed on Jan. 28, 2008. These applications claim the benefit of the following U.S. provisional applications: App. No. 60 / 886,798 filed on Jan. 26, 2007; App. No. 60 / 886,801 filed on Jan. 26, 2007; App. No. 60 / 887,573 filed on Jan. 31, 2007; App. No. 60 / 891,508 filed on Feb. 24, 2007; App. No. 60 / 891,936 filed on Feb. 27, 2007; and App. No. 60 / 952,898 filed on Jul. 31, 2007.[0002]Each of the above applications is incorporated by reference herein in its entirety.BACKGROUND[0003]1. Field[0004]The disclosure relates to methods and systems for analyzing data, and more particularly to methods and systems for aggregating, projecting, and releasing data.[0005]2. Description of Related Art[0006]Currently, there exists a large variety of data sources, such as census ...

AI Technical Summary

Benefits of technology

The patent describes a system and method for identifying different products and their attributes based on a database of known attributes. This can be done by assigning the attributes of an item to one of the known attributes in the database. The system uses probability matching based on the attributes and the known attributes of the item. The method can be used for various product attributes such as nutritional levels, brand, product category, and physical attributes like flavor or scent. The technical effect of the patent is to enable better identification and classification of products based on their attributes.

Problems solved by technology

In practice, going back and changing the a priori decisions can lift these constraints, but this requires an arduous and computationally complex restructuring and reprocessing of data.

In practice, configuring a system to apply the releasability rules is an error-prone process that requires extensive manual set up and results in a brittle mechanism that cannot adapt to on-the-fly changes in data, dimensions, third parties, rules, aggregations, projections, user queries, and so on.

Existing methods allow an analyst to choose a projection methodology and thereby affect the statistical qualities of the output, but this does not satisfy the analyst's desire to directly dictate the statistical qualities.

Information systems are a significant bottle neck for market analysis activities.

The architecture of information systems is often not designed to provide on-demand flexible access, integration at a very granular level, or many other critical capabilities necessary to support growth.

Thus, information systems are counter-productive to growth.

Hundreds of market and consumer databases make it very difficult to manage or integrate data.

Restatements of data hierarchies waste precious time and are very expensive.

Navigation from among views of data, such as from global views to regional to neighborhood to store views is virtually impossible, because there are different hierarchies used to store data from global to region to neighborhood to store-level data.

Analyses and insights often take weeks or months, or they are never produced.

Currently, market analysis, business intelligence, and the like often use rigid data cubes that may include hundreds of databases that are impossible to integrate.

This may make it almost impossible to navigate from global uses that are used, for example, to develop overall company strategy, down to specific program implementation or customer-driven uses.

These ad hoc analytic tools and infrastructure are fragmented and disconnected.

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