Google Inc v. eDigital Corp

1. Case Identification

• Case #: IPR2015-01474
• Patent #: 8,311,524
• Filed: June 24, 2015
• Petitioner(s): Google Inc., Dropcam, Inc., and Nest Labs, Inc.
• Patent Owner(s): E.Digital Corporation
• Challenged Claims: 1, 3-5, 7, and 9

2. Patent Overview

• Title: Method of Automatically Providing Differing Levels of Information According to a Social Hierarchy
• Brief Description: The ’524 patent discloses a method for using a mobile device's optical and acoustic sensors to determine a user's context or status. This status is then selectively shared with different people in the user's social circle, with the level of detail provided depending on the recipient's position in a "predetermined social hierarchy."

3. Grounds for Unpatentability

Ground 1: Claims 1, 3-5, 7, and 9 are obvious over Jolliff in view of Jager.

• Prior Art Relied Upon: Jolliff (Application # 2009/0300525) and Jager (Application # 2009/0094179).
• Core Argument for this Ground:
  • Prior Art Mapping: Petitioner argued that Jolliff disclosed the core features of the challenged claims. Jolliff taught a system that uses sensor data (light, noise) to automatically update a user's "virtual world avatar," which represents their real-world status. Jolliff's "avatar selection logic table" stored sensor value ranges correlated with specific avatars and "authorization levels," which Petitioner mapped to the ’524 patent's "social templates" and "social hierarchy." Different avatars (representing differing levels of information) were shown to different groups (e.g., "Family," "Buddy List," "Boss") based on these authorization levels.
  • Motivation to Combine: Petitioner contended that while Jolliff taught most elements, its data structure was inefficient, as its logic table could contain duplicate sensor data entries corresponding to different hierarchies. A person of ordinary skill in the art (POSITA) would have been motivated to improve this inefficiency. Jager taught a similar context-aware system but with a more efficient data storage scheme that removed duplicate profiles to limit memory usage, ensuring each profile had a unique set of attributes (a "unique social signature"). A POSITA would combine Jager's efficient data structure with Jolliff's system to reduce memory usage and speed up processing.
  • Expectation of Success: Petitioner asserted success would be expected because the combination involved applying a known technique (Jager's efficient data storage) to a similar system (Jolliff) to yield a predictable improvement.
  • Key Aspects: Petitioner argued the combination also rendered dependent claims obvious. Jager's teaching of automatically adapting stored profiles based on differences between observed and stored data was alleged to teach the error detection and updating limitations of claim 3 and the creation of new templates in claim 4.

Ground 2: Claims 1, 3-5, 7, and 9 are obvious over Miluzzo in view of Robarts.

• Prior Art Relied Upon: Miluzzo (WO 2009/043020) and Robarts (Application # 2006/0004680).
• Core Argument for this Ground:
  • Prior Art Mapping: Petitioner argued that Miluzzo disclosed a method for "injecting sensed presence into social networking applications" by using a smartphone's microphone and camera to infer a user's status. This status was then shared with others according to "group membership policies," which provided different levels of disclosure. Petitioner asserted that Miluzzo did not explicitly detail how to infer status from sensor data or how to organize the privacy settings. Robarts was alleged to supply these missing details by teaching a system using "themes" (analogous to social templates) that contained sensor attributes (e.g., "Ambient_noise_level," "Ambient_light_level") and privacy settings based on social hierarchies (e.g., "Work, Family, Friends").
  • Motivation to Combine: A POSITA implementing Miluzzo's system would have sought a known method for organizing the sensor data and privacy policies to build its inference engine. Petitioner argued it would have been obvious to incorporate Robarts' well-defined "themes" and context recognition engine into Miluzzo's framework. This would amount to a simple substitution of a known component to predictably achieve Miluzzo's stated goal of context-aware information sharing.
  • Expectation of Success: Petitioner asserted a high expectation of success, as both references aimed to solve the same problem (context-aware information sharing based on sensor data), making Robarts' detailed implementation a natural and predictable solution to fill the gaps in Miluzzo's disclosure.

4. Key Claim Construction Positions

• "social template": Petitioner proposed the construction "data that associates a social signature with information provided to one or more levels of a social hierarchy." This broad construction was central to mapping the data structures in the prior art, such as Jolliff's "avatar selection logic table" and Robarts' "themes," to the claim language.
• "social hierarchy": Petitioner proposed construing this term as simply "groupings of people receiving different information." This interpretation avoided any requirement for a rigid or formal hierarchical structure, allowing prior art disclosures of different privacy groups (e.g., Family, Friends, Boss) to meet the limitation.

5. Relief Requested

• Petitioner requests the institution of an inter partes review and the cancellation of claims 1, 3-5, 7, and 9 of the ’524 patent as unpatentable under 35 U.S.C. §103.