Elanso

I don’t always have time to dig as deeply as I like into some of the patent filings that I uncover each week, and end up not writing about some interesting things that the search engines come out with.

Instead of not mentioning those at all, I think I might try to do a weekly post pointing out some of the most interesting of those each week, like the following. If you find something interesting about any of them that you would like to share in the comments, please be my guest…

Google

Google explores providing better data for advertisers, especially those with multiple web sites focusing upon broad geographical areas, in the following patent application:

Large-Scale Aggregating and Reporting of Ad Data
Invented by Weipeng Yan
US Patent Application 20080120165
Published May 22, 2008
Filed November 20, 2006

Abstract

Statistical data relating to one or more parameters associated with an advertisement may be gathered. The statistical data may be filtered to a Universal Resource Locator (URL) or domain level. The statistical data may be aggregated and evaluated, including applying a filter to the statistical data. The filtered data may be delivered to an advertiser. The advertiser may receive the filtered data in a report and modify their advertising campaign in accordance with the report.

Yahoo

A Google programming model, described in MapReduce: Simplified Data Processing on Large Clusters -PDF (Slides), is at the heart of the following patent application from Yahoo:

Map-Reduce with Merge to Process Multiple Relational Datasets
Invented by Hung-Chih Yang, Ali Dasdan, Ruey-Lung Hsiao
Assigned to Yahoo
US Patent Application 20080120314
Published May 22, 2008
Filed November 16, 2006

Abstract

A method of processing relationships of at least two datasets is provided. For each of the datasets, a map-reduce subsystem is provided such that the data of that dataset is mapped to corresponding intermediate data for that dataset. The intermediate data for that dataset is reduced to a set of reduced intermediate data for that dataset.

Data corresponding to the sets of reduced intermediate data are merged, in accordance with a merge condition. In some examples, data being merged may include the output of one or more other mergers. That is, generally, merge functions may be flexibly placed among various map-reduce subsystems and, as such, the basic map-reduce architecture may be advantageously modified to process multiple relational datasets using, for example, clusters of computing devices.

The description of the next patent application starts off telling us that “Marketing is the art of reaching the right people with the right messages at the right time.” It looks at the clustering of customers by demographic information.

Guided Cluster Attribute Selection
Invented by Glen Anthony Ames, David A. Burgess, Joshua Ethan Miller Koran, and Amit Umesh Shanbhag
US Patent Application 20080120307
Published May 22, 2008
Filed November 20, 2006

Abstract

A method for selecting at least one target customer attribute from a plurality of customer attributes, wherein each customer attribute represents a unique customer characteristic is provided. The plurality of customer attributes is presented for selection. A selection of at least one target customer attribute selected from the plurality of customer attributes is received.

For each cluster of a plurality of clusters, indicated the statistic of customers belonging to that cluster who possess each and every one of the at least one target customer attribute. The plurality of clusters comprises a plurality of customers and each customer of the plurality of customers belongs to at least one cluster of the plurality of clusters.

Yahoo explores ways to help make it easier for people to fill out forms online. Reminded me of the Autofill function on Google’s toolbar.

Automatic online form filling using semantic inference
Invented by Amit Goyal, Gajendra Nishad Kamat, and Shouvick Mukherjee
US Patent Application 20080120257
Published May 22, 2008
Filed February 5, 2007

Abstract

A machine learning based automated online form-filling technique provides for automatically completing user input controls based on previously stored information. An associative parser is used to identify and associate characteristics related to form controls with the corresponding form controls. The characteristics of the user input controls are input into a machine learning based semantic inference engine that was trained for the purpose of identifying the type of information that is supposed to be input into various user input controls.

The semantic inference engine operates to label the controls in a manner that describes the meaning of the control, i.e., the type of information that should be automatically input into the corresponding controls. Consequently, the user input controls can be automatically filled in with previously stored user profile information associated with the corresponding labels.

Microsoft

Text annotation and tagging of digitial objects, like photographs, might be helpful in organizing those objects into a hierarchical structure if done intelligently.

Deriving hierarchical organization from a set of tagged digital objects
Invented by Sami Khoury
US Patent Application 20080120310
Published May 22, 2008
Filed November 17, 2006

Abstract

A method of and system for deriving hierarchical structure from a set of digital objects is presented. The set of digital objects, such as digital images for example, may include a plurality of digital objects each associated with a data tag (such as a date, location, and/or text description, for example) that is part of a flat property set that does not encode the hierarchical relationships of the data objects. The set of digital objects may be filtered by a query and a query engine.

Each data tag of each digital object may be compared on the basis of a relationship, such as mathematical equality, proximity in value, and/or similarity of text strings, for example. A hierarchical data structure may be established that includes a parent data segment and a child data segment, each labeled with a related data tag and populated with one or more related digital objects. The hierarchical data structure may be represented by a tree or by nested folders.