PTAB

IPR2025-00967

Google LLC v. Bootler LLC

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Petition
petition

1. Case Identification

2. Patent Overview

  • Title: System for Aggregating Food Delivery Service Data
  • Brief Description: The ’683 patent discloses computer-implemented methods and systems for aggregating, processing, and presenting service data from a plurality of food or beverage delivery services. The system acquires source data, such as restaurant menus, in various formats from different delivery service computers and transforms it into a searchable, aggregated data structure for use in a networked application.

3. Grounds for Unpatentability

Ground 1: Obviousness over Rahle and Rhodes - Claims 1-6 and 10-16 are obvious over Rahle in view of Rhodes.

  • Prior Art Relied Upon: Rahle (Application # 2013/0282486) and Rhodes (Patent 10,176,448).
  • Core Argument for this Ground:
    • Prior Art Mapping: Petitioner argued that Rahle disclosed a method for creating a "social graph," a searchable data structure, by aggregating information about entities like restaurants and their menu items from various sources. Rahle’s system represents restaurants as "page objects" and menu items as associated "sub-nodes." Petitioner contended Rhodes disclosed a conventional food delivery service that enables customers to order from various restaurants via a website, which presents restaurant menus. The combination of Rahle's data aggregation and structuring system with Rhodes's disclosure of a known data source (food delivery services) allegedly taught all limitations of the challenged claims. Specifically, Rahle's "external data gathering module" for acquiring data from third-party websites met the limitation of acquiring source data, and Rhodes taught that such websites could be those of food delivery services.
    • Motivation to Combine: A Person of Ordinary Skill in the Art (POSA) would combine Rahle and Rhodes to enhance the user experience of Rahle’s social networking system. Rahle explicitly taught gathering restaurant menu data from various "third-party website[s]" and "external systems" to provide more comprehensive information. A POSA would have recognized that delivery services like those in Rhodes were a valuable and often more up-to-date source of such menu data. The motivation was to capture data that might otherwise be missed or outdated if relying only on restaurant owners to update their information, thereby providing a "better understanding of" available food items.
    • Expectation of Success: A POSA would have had a reasonable expectation of success because adapting Rahle's flexible system, designed to gather data from external sources, to pull information from known delivery service websites as taught by Rhodes, would have been a straightforward implementation within the ordinary skill of a POSA.

Ground 2: Obviousness over Rahle, Rhodes, and Jin - Claims 1-16 are obvious over Rahle and Rhodes in view of Jin.

  • Prior Art Relied Upon: Rahle (Application # 2013/0282486), Rhodes (Patent 10,176,448), and Jin (Patent 6,651,057).

  • Core Argument for this Ground:

    • Prior Art Mapping: This ground built upon the Rahle and Rhodes combination by adding the teachings of Jin to address limitations related to training algorithms and identifying menu items. Petitioner asserted that Jin disclosed techniques for information retrieval that involve training a model to determine document relevance based on the frequency of keywords. This training process was used to implement Rahle’s more general disclosure of associating menu items ("sub-nodes") with restaurants ("page objects") by "identifying attributes of page objects that match existing sub-node objects." Specifically, claims 5-9, which recite training an algorithm using techniques like word frequency models to identify identical menu items across different services, were allegedly met by incorporating Jin's specific machine learning methods.
    • Motivation to Combine: A POSA seeking to implement Rahle's system for matching menu items would have been motivated to use Jin’s specific and robust techniques for identifying attribute matches. Jin's method of using training documents to generate a model for each topic (e.g., a menu item like a "burrito") was well-suited to automate and improve the accuracy of Rahle's matching process. The goal was to achieve a more "automatic, efficient, and robust system" for matching items, a benefit explicitly taught by Jin.
    • Expectation of Success: A POSA would have expected success in applying Jin's information retrieval techniques to the food-item matching task in Rahle-Rhodes, as Jin's methods were described as broadly applicable for matching topics to documents, and applying them to match a food item concept to a restaurant menu was a predictable application.

  • Additional Grounds: Petitioner asserted that claims 1-6 and 10-16 are obvious over Rahle, Rhodes, and Belousova (Patent 10,366,434), arguing Belousova’s disclosure of a "food taxonomy" and machine learning classifier provided further motivation and specific techniques for mapping menu items to master dishes. A final ground asserted that claims 1-16 are obvious over the four-way combination of Rahle, Rhodes, Belousova, and Jin.

4. Arguments Regarding Discretionary Denial

  • Petitioner argued that discretionary denial is unwarranted. The parallel district court litigation is in a very early motion-to-dismiss stage with no trial date set. Furthermore, Petitioner noted that none of the prior art references asserted in the petition were cited or considered during the original prosecution of the ’683 patent.

5. Relief Requested

  • Petitioner requests the institution of an inter partes review and the cancellation of claims 1-16 of Patent 10,445,683 as unpatentable under 35 U.S.C. §103.