Google LLC v. Uniloc 2017 LLC

1. Case Identification

• Case #: IPR2020-00170
• Patent #: 6,253,201
• Filed: December 3, 2019
• Petitioner(s): Google LLC
• Patent Owner(s): Uniloc 2017 LLC
• Challenged Claims: 1-14 and 17-20

2. Patent Overview

• Title: Image Retrieval Method and System
• Brief Description: The ’201 patent discloses methods and systems for matching a target image to a database of reference images. The technology operates by first partitioning images into a grid in a content-independent manner, then characterizing each partition by features like color, and finally creating indexed lists that group images by these partition-specific characteristics to find the closest match.

3. Grounds for Unpatentability

Ground 1: Claims 1-14 and 17-20 are obvious over Hull in view of Barber.

• Prior Art Relied Upon: Hull (Patent 5,465,353) and Barber (Patent 5,579,471).
• Core Argument for this Ground:

  • Prior Art Mapping: Petitioner argued that the combination of Hull and Barber rendered all challenged claims obvious under 35 U.S.C. §103. Hull taught a general document and image matching system using an efficient database architecture known as an “inverted index.” In Hull’s system, the database was organized by feature “descriptors” rather than by documents. Each descriptor was linked to a list of all documents containing that feature. A search involved extracting descriptors from a query image, retrieving the corresponding lists of document identifiers, and “accumulating votes” for each identifier. The identifier with the most votes was determined to be the best match.

  • Petitioner asserted that while Hull described a powerful search backend, it only broadly discussed its applicability to graphical images. Barber allegedly supplied the specific front-end image processing that Hull lacked. Barber taught a method of spatially partitioning an image into a content-independent grid and characterizing each partition by computing features such as color, texture, and edges. The proposed obvious combination involved using Barber’s method for partitioning and characterizing images to generate the specific feature descriptors (e.g., a feature value combined with its partition location) required by Hull’s inverted index and vote-counting search algorithm.

  • For dependent claims, Petitioner argued Barber taught characterizing partitions by color and edges (addressing claim 2), quantizing color characteristics into a predetermined set of bins to form an index value (addressing claim 3), and ranking the final image results to provide a sorted list (addressing claim 4). Further, Hull's disclosure on using redundant descriptors to overcome "quantization noise" was argued to make claims covering multiple, overlapping quantization sets obvious (addressing claims 9 and 20).

  • Motivation to Combine: A POSITA would combine these references for complementary reasons. Hull expressly stated its system was applicable to graphical images and would need feature extractors to "locate and characterize graphical features." This statement provided an explicit motivation for a skilled artisan to seek out known image characterization techniques, such as the one disclosed in Barber. Barber’s method, which focused on key image attributes like color and location and was computationally efficient, would have been an obvious and advantageous choice.

  • Conversely, a POSITA starting with Barber’s image characterization method would have been motivated to improve its search efficiency, particularly for large databases. Barber itself suggested implementing “indexing techniques” to increase search performance. Hull’s inverted index and vote-counting method was a well-known, efficient search architecture that directly addressed this need, making it a logical and obvious backend to integrate with Barber’s front-end processing.

  • Expectation of Success: Petitioner contended the combination would have yielded predictable results. Hull's search engine was designed to be agnostic to the specific type of descriptor it processed; it simply required a set of descriptors from a feature extractor to perform its vote-counting function. Barber's method produced discrete, well-defined descriptors (e.g., “predominantly blue in top-left partition”) perfectly suited for this purpose. The integration was a straightforward combination of a known image-processing front-end with a known search back-end. This would predictably result in an efficient and effective image retrieval system that matched images based on shared features in corresponding spatial partitions.

4. Relief Requested

• Petitioner requests institution of inter partes review and cancellation of claims 1-14 and 17-20 of Patent 6,253,201 as unpatentable.