Mountech IP LLC v. Motorola Mobility LLC

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Mountech IP LLC (Texas)
  • Defendant: Motorola Mobility, LLC (Delaware)
  • Plaintiff’s Counsel: Chong Law Firm PA; Sand, Sebolt & Wernow Co., LPA
• Case Identification: 1:20-cv-00867, D. Del., 06/28/2020
• Venue Allegations: Venue is alleged to be proper in the District of Delaware because Defendant is a Delaware corporation.
• Core Dispute: Plaintiff alleges that Defendant’s smartphones, which include predictive text systems, infringe two patents related to methods for context-based, automatic completion of data entry.
• Technical Context: The patents address methods for improving the speed and accuracy of text input on electronic devices, a technology of significant market importance for smartphones and other portable electronics.
• Key Procedural History: The complaint does not allege any prior litigation, inter partes review proceedings, or licensing history related to the patents-in-suit.

Case Timeline

II. Technology and Patent(s)-in-Suit Analysis

U.S. Patent No. 7,991,784 - “Automatic Dynamic Contextual Data Entry Completion System,” issued Aug. 2, 2011 (’784 Patent)

The Invention Explained

• Problem Addressed: The patent identifies that prior art automated word completion systems were burdensome and inefficient for devices with small keyboards, as they were typically based on simple dictionaries or a user's most recently used words, lacking true contextual understanding (’784 Patent, col. 1:36-54).
• The Patented Solution: The invention proposes a method where the system analyzes "documents" (e.g., emails, notes) stored on a device to compute "contextual associations" between words based on their co-occurrence (’784 Patent, col. 2:27-31). When a user begins typing, the system uses these pre-computed associations to suggest relevant word completions that are specific to the context of the sentence or document being written, rather than just suggesting globally frequent words (’784 Patent, col. 2:31-38; Fig. 1).
• Technical Importance: This approach sought to make text entry more intelligent and efficient by dynamically learning from a user's own data to predict their intended words in a specific context.

Key Claims at a Glance

• The complaint asserts independent claim 1 and reserves the right to assert other claims (Compl. ¶¶ 13, 55).
• Claim 1 recites a method with the essential steps of:
  • Computing "contextual associations" between character strings by identifying pertinent documents, creating a list of strings from those documents, and creating an "interrelationship" between strings based on their occurrence.
  • In response to user input, identifying a selectable character string from the contextual associations that can complete the input "in context."
  • Providing the selectable string to the user.
  • Receiving the user's selection and completing the input.

U.S. Patent No. 8,311,805 - “Automatic Dynamic Contextual Data Entry Completion System,” issued Nov. 13, 2012 (’805 Patent)

The Invention Explained

• Problem Addressed: Similar to its parent, the ’805 Patent addresses the lack of a "true context based system that is dynamic and automatic" in prior art text entry systems (’805 Patent, col. 2:8-11).
• The Patented Solution: This patent refines the contextual completion concept by introducing a specific scoring and ranking mechanism. The method computes "relationship scores" for character strings, which are derived from "co-occurrence scores" stored in a single matrix (’805 Patent, col. 19:28-34). When presenting a completion, the system calculates an "overall ranking score" which is a function of both the "relationship score" and "at least one other score" (such as word frequency), to determine the most likely intended word (’805 Patent, col. 15:42-61).
• Technical Importance: The invention aims to improve the precision of contextual predictions by employing a multi-factor scoring system to rank potential word completions.

Key Claims at a Glance

• The complaint asserts independent claim 1 and dependent claim 2 (Compl. ¶¶ 23-24).
• Claim 1 recites a method with the essential steps of:
  • Computing "relationship scores" for character strings, where the scores are a function of "co-occurrence scores" between pairs of strings stored in a "single matrix."
  • Identifying a selectable character string based on an "overall ranking score" that is computed as a function of the "relationship score" and "at least one other score."
  • Providing the identified string to the user for selection.
• Claim 2 further specifies that each "relationship score represents the contextual association between an individual character string and another character string based upon co-occurrence."

III. The Accused Instrumentality

Product Identification

The complaint identifies the "Motorola G6 Forge" as the Accused Product, specifically targeting its predictive text system (Compl. ¶28).

Functionality and Market Context

The complaint alleges that the Accused Product's predictive text system performs a method of completing incomplete character strings input by a user (Compl. ¶28). This system allegedly establishes a "context" for an incomplete string based on the "previous appearance of charter [sic] strings in adjacent fashion" and suggests selectable words for completion (Compl. ¶28). The functionality is alleged to work by computing "contextual associations between multiple character strings based upon occurrence of character strings relative to each other" in documents like notes and messages present on the device (Compl. ¶30).

IV. Analysis of Infringement Allegations

The complaint references, but does not include, claim chart exhibits. The following tables are constructed from the narrative infringement allegations provided in the body of the complaint.

’784 Patent Infringement Allegations

’805 Patent Infringement Allegations

Identified Points of Contention

• Scope Questions: The case may turn on how broadly the court construes the computational steps. For example, a question exists whether the term "computing contextual associations" in the ’784 Patent is limited to the specific vector-space models detailed in the specification, or if it can read on other context-aware algorithms (’784 Patent, col. 7:10-31).
• Technical Questions: A central factual question will be whether the accused predictive text system actually performs the claimed multi-step methods. For the ’805 Patent, the complaint does not specify what the alleged "at least one other score" is, raising the question of what evidence Plaintiff will offer to prove the accused system computes its "overall ranking score" using this claimed two-part function.

V. Key Claim Terms for Construction

The Term: "contextual associations" (’784 Patent, Claim 1)

• Context and Importance: This term is the core of the ’784 invention. Its construction will determine what type of relationship between words is required for infringement. Practitioners may focus on whether this term requires a specific mathematical process or can encompass any system that considers surrounding text.
• Intrinsic Evidence for Interpretation:
  • Evidence for a Broader Interpretation: The specification describes the term generally as associations "based on context" which is "derived from the co-occurrence of individual words in documents" (’784 Patent, col. 4:41-43), potentially supporting a broader definition.
  • Evidence for a Narrower Interpretation: The patent provides detailed examples of computing associations using a specific method involving document similarity, grouping, and creating word lists from those groups, which could support a narrower construction limited to that process (’784 Patent, col. 5:26-col. 8:67).

The Term: "overall ranking score computed as a function of a relationship score and at least one other score" (’805 Patent, Claim 1)

• Context and Importance: This term distinguishes the ’805 Patent by adding a specific ranking mechanism. The dispute will likely center on whether the accused system uses a multi-factor score as required, or a different ranking logic.
• Intrinsic Evidence for Interpretation:
  • Evidence for a Broader Interpretation: The claim language "at least one other score" is open-ended. The term "function" is also broad, suggesting any mathematical combination of the two score types could suffice.
  • Evidence for a Narrower Interpretation: The specification provides a specific example where the "other score" is a "frequency score" and the "overall score" is computed using a specific formula (f=...relationship score x frequency score or + frequency score) (’805 Patent, col. 15:53-61). This may support an interpretation that requires the "other score" to be distinct from the co-occurrence-based relationship score, and for the two to be combined in a defined way.

VI. Other Allegations

• Indirect Infringement: The complaint makes a conclusory allegation of induced infringement, stating Defendant encourages infringement, but does not plead specific facts such as instructions in user manuals or marketing materials that would teach users to perform the claimed methods (Compl. ¶50).
• Willful Infringement: Plaintiff requests enhanced damages but only alleges that Defendant had knowledge of the patents "at least as of the service of the present Complaint" (Compl. ¶48; p. 16, ¶f). This allegation, on its own, would typically only support a claim for post-filing, not pre-filing, willfulness.

VII. Analyst’s Conclusion: Key Questions for the Case

1. A central issue will be one of claim construction and scope: Will the court define the core computational terms—"contextual associations" (’784) and "overall ranking score" (’805)—as being limited to the specific matrix and vector-based embodiments detailed in the patents, or will they be construed more broadly to cover any predictive text algorithm that considers word co-occurrence and frequency?

2. A key challenge for the plaintiff will be one of evidentiary proof: Given the proprietary nature of smartphone software, what evidence can be obtained and presented to demonstrate that Motorola's predictive text system performs the specific, multi-step processes of creating matrices, computing distinct "relationship scores," and combining them with "at least one other score" as affirmatively required by the claims, as opposed to using a different, albeit also "contextual," machine learning model?