accessiBe Ltd v. AudioEye Inc

1. Case Identification

• Case #: IPR2022-00148
• Patent #: 10,809,877
• Filed: December 1, 2021
• Petitioner(s): accessiBe Ltd.
• Patent Owner(s): AudioEye, Inc.
• Challenged Claims: 1-21

2. Patent Overview

• Title: Modular Systems and Methods for Selectively Enabling Cloud-Based Assistive Technologies
• Brief Description: The ’877 patent discloses systems for automatically remediating website accessibility issues. The claimed method involves accessing a website's code (HTML or DOM), detecting compliance issues such as input fields lacking descriptive attributes, and using contextual cues and remediation code to programmatically assign appropriate textual descriptions that can be read by assistive technologies.

3. Grounds for Unpatentability

Ground 1: Claims 1-21 are obvious over Springer, Nguyen, and Hendry.

• Prior Art Relied Upon: Springer (Application # 2004/0148568), Nguyen (a 2008 academic paper on extracting form labels), and Hendry (Application # 2013/0104029).
• Core Argument for this Ground:
  • Prior Art Mapping: Petitioner argued that Springer taught the core of the claimed invention. Springer disclosed a system of "checkers" and "fixers" to identify and correct web accessibility violations. Specifically, its "FormChecker" identified input fields lacking a descriptive label, and its corresponding "FormFixer" used a heuristic method—analyzing nearby contextual cues in the HTML "document tree" (a DOM)—to find and insert the most likely label. This system programmatically assigned descriptive attributes to enable audible descriptions for users of assistive technology. Nguyen was cited for its disclosure of using a machine learning (ML) algorithm, a type of artificial intelligence, to perform the same function as Springer's heuristic fixer but with greater accuracy. Hendry was cited for its teachings on distributed computing, disclosing that accessibility remediation steps could be performed on a local client, a remote server, or a combination of both.
  • Motivation to Combine: A POSITA would combine these references to improve upon Springer's system. The primary motivation was to replace Springer’s heuristic-based FormFixer with Nguyen's more accurate and robust ML-based approach for analyzing contextual cues and extracting labels. Nguyen explicitly stated its ML approach was superior to prior heuristic methods. A POSITA would also incorporate Hendry's distributed computing model to leverage the processing power and storage of remote servers, a known and beneficial architecture for web-based tools, while still executing the final remediation on the user's computer.
  • Expectation of Success: Petitioner asserted a POSITA would have a high expectation of success. Substituting Nguyen's ML algorithm for Springer's heuristic method was a predictable improvement, as Nguyen was designed for the exact same purpose of label extraction. The integration of Hendry's client-server architecture was a straightforward application of well-understood computing principles to gain known benefits.

Ground 2: Claims 1-21 are obvious over Lehota, Nguyen, and Hendry.

• Prior Art Relied Upon: Lehota (Application # 2010/0205523), Nguyen (a 2008 academic paper on extracting form labels), and Hendry (Application # 2013/0104029).
• Core Argument for this Ground:
  • Prior Art Mapping: Petitioner argued that Lehota disclosed a system that met the core limitations of the claims by using an "accessibility widget library" (e.g., JavaScript) to dynamically fix accessibility problems. Lehota’s system could be invoked to scan a website's DOM, find compliance issues such as "inadequate use of the 'Label' tag" for form elements, and modify the DOM by applying pre-existing scripts from the widget library to make the website accessible. While Lehota taught the framework for remediation, it did not detail the specific method used to determine the correct descriptive label for an untagged element.
  • Motivation to Combine: A POSITA would combine Lehota with Nguyen to provide a specific, improved method for determining appropriate labels within Lehota's existing framework. Because Lehota's widgets were already designed to generate labels for untagged fields, a POSITA would be motivated to incorporate Nguyen's superior ML-based context analysis and label extraction functionality to enhance the widget's performance. The motivation to further incorporate Hendry was the same as in Ground 1: to add the known benefits of a distributed client-server architecture for processing and data storage.
  • Expectation of Success: The combination was expected to succeed because Nguyen provided an improved and experimentally confirmed technique for a function already performed by Lehota's system. Integrating this functionality would be a predictable enhancement. As with Ground 1, adding Hendry's distributed computing model was a straightforward implementation of known principles.

4. Arguments Regarding Discretionary Denial

• Petitioner argued that discretionary denial under Fintiv would be inappropriate. The petition asserted that the parallel district court litigation was at a very early stage, with fact discovery having only recently opened and no substantive arguments on invalidity presented to the court. Petitioner further contended that the district court's trial date of September 2022 was tentative and highly likely to be rescheduled, minimizing any potential overlap with the Board’s Final Written Decision (FWD) deadline. Finally, the petition argued that it presented strong grounds for unpatentability, which weighs in favor of institution.

5. Relief Requested

• Petitioner requests institution of an inter partes review (IPR) for claims 1-21 of the ’877 patent and requests that those claims be found unpatentable and cancelled.