DCT

2:25-cv-11772

Unaliwear Inc v. Garmin Ltd

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Key Events
Complaint

I. Executive Summary and Procedural Information

  • Parties & Counsel:
  • Case Identification: 2:25-cv-11772, C.D. Cal., 12/12/2025
  • Venue Allegations: Plaintiff alleges venue is proper in the Central District of California based on Defendants transacting business, committing acts of infringement, and maintaining regular and established places of business within the district, including research and development facilities in Goleta and Diamond Bar, California.
  • Core Dispute: Plaintiff alleges that Defendant’s wearable products, including the Garmin Forerunner line, infringe two patents related to systems for monitoring a wearer’s activity, learning their behavioral patterns, and providing assistance when deviations are detected.
  • Technical Context: The technology resides in the field of wearable health monitors and mobile personal emergency response services (mPERS), a market focused on providing safety and independence for vulnerable populations through automated monitoring and alerts.
  • Key Procedural History: The complaint notes the filing of a "companion action in the International Trade Commission" (ITC), indicating a parallel proceeding that often seeks an exclusion order to block the importation of infringing products into the United States.

Case Timeline

Date Event
2013-09-19 Priority Date for ’410 and ’193 Patents
2018-08-14 U.S. Patent No. 10,051,410 Issues
2020-06-16 U.S. Patent No. 10,687,193 Issues
2025-12-12 Complaint Filed

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

U.S. Patent No. 10,051,410 - "Assist Device and System"

The Invention Explained

  • Problem Addressed: The patent describes a need for systems that can monitor elderly, ill, or infirm persons to detect falls or deviations from normal life patterns in a manner that is "unobtrusive, dignified," and socially acceptable, thereby allowing them to live independently for longer (’410 Patent, col. 1:20-36).
  • The Patented Solution: The invention is a wearable device and server system that learns a user's individual activity patterns over time. The wearable device gathers sensor data (e.g., location, motion) and sends it to a server, which analyzes the data to create and update personalized "behavioral rules" (’410 Patent, col. 2:3-18). These rules, which can include models of the user's sleep/wake cycles, home area boundaries, and typical activity levels, are then transmitted back to the wearable device. The device uses these rules locally to check the wearer's current activity, and if a deviation is detected (e.g., a potential fall or wandering), it can offer assistance (’410 Patent, col. 2:36-54).
  • Technical Importance: This approach represents a shift from reactive, user-initiated alert systems (e.g., "panic buttons") to a proactive system that uses personalized, learned data to automatically identify potential distress situations.

Key Claims at a Glance

  • The complaint asserts independent claim 1 and reserves the right to assert additional claims (Compl. ¶35).
  • Claim 1 of the ’410 Patent requires, in essence:
    • A wearable device with a processor, physiologic sensor, user interface, network interface, and memory.
    • The device is programmed to collect activity data and provide it to a server.
    • The server processes the data to create or update "behavioral rules" based on historical wearer data like location mapping, sleep/wake cycles, and physiological measurements.
    • The device receives these behavioral rules back from the server.
    • The device provides assistance to the wearer when a check of its current activity against the received behavioral rules indicates a potential need for assistance.

U.S. Patent No. 10,687,193 - "Assist Device and System"

The Invention Explained

  • Problem Addressed: The ’193 Patent, a continuation-in-part of the application leading to the ’410 Patent, addresses the same technical problem of unobtrusive monitoring for vulnerable individuals (’193 Patent, col. 1:33-49).
  • The Patented Solution: This patent refines the concept of the ’410 Patent, describing the learned model as a "parameterized rule-based custom data model" (’193 Patent, Abstract). A remote computer creates or updates this model based on the wearer's activity data. The wearable device receives this custom model and compares it to newly collected activity data to determine if the wearer's activity is inconsistent with their established patterns, which may trigger an offer of assistance (’193 Patent, col. 2:25-39).
  • Technical Importance: The claimed "parameterized rule-based custom data model" suggests a more sophisticated and formally structured approach to modeling user behavior than the "behavioral rules" of the parent patent, potentially enabling more complex and adaptive learning.

Key Claims at a Glance

  • The complaint asserts independent claim 1 and reserves the right to assert additional claims (Compl. ¶49).
  • Claim 1 of the ’193 Patent requires, in essence:
    • A wearable device with a processor, physiologic sensor, user interface, network interface, and memory.
    • The device is programmed to collect physical activity data related to the times and locations of the wearer's activity.
    • The device provides this data to a remote computer for processing to create or update a "parameterized rule-based custom data model."
    • The device receives this custom data model from the remote computer.
    • The device communicates with the wearer when a comparison of current activity data against the model indicates the activity is not consistent with the model.

III. The Accused Instrumentality

Product Identification

The complaint identifies "Garmin wearable products," with a specific example being the "Garmin Forerunner 970" (Compl. ¶5, ¶11).

Functionality and Market Context

  • The complaint alleges the Accused Products are wearable devices that feature "activity tracking technology including for automatic fall detection" (Compl. ¶8). It asserts that these products and their associated systems perform the functions claimed in the Asserted Patents, namely collecting user-specific data, processing it to understand motion patterns, and using that understanding to provide alerts or assistance (Compl. ¶1-3).
  • No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint references, but does not include, claim chart exhibits detailing the infringement allegations (Compl. ¶32, ¶46). The narrative theory of infringement is summarized below.

  • ’410 Patent Infringement Allegations

    • The complaint alleges that the Accused Products are wearable devices that meet the structural limitations of claim 1. It further alleges that the Garmin system, comprising the wearable device and associated services, collects a wearer's activity data over time. This data is allegedly used to create personalized "behavioral rules" for the user. These rules are then allegedly used by the device to analyze subsequent activity to detect deviations, such as a fall, and provide assistance, thereby infringing the method steps of claim 1 (Compl. ¶30-32).

  • ’193 Patent Infringement Allegations

    • The infringement theory for the ’193 Patent is analogous. The complaint alleges that the Garmin system collects user activity data and processes it to create a "parameterized rule-based custom data model" of the user's behavior. This model is then allegedly used on the wearable device to monitor the user's activity for inconsistencies, which if detected, may trigger an alert or offer of assistance from the device, thereby mapping onto the elements of claim 1 of the ’193 Patent (Compl. ¶44-46).

  • Identified Points of Contention:

    • Scope Questions: The dispute may center on whether Garmin's technology for fall detection and activity monitoring constitutes the "behavioral rules" or "parameterized rule-based custom data model" described in the patents. The question for the court will be whether the specific architecture claimed—a remote server creating a model that is then sent back to the wearable device for local comparison—is practiced by the Garmin system.
    • Technical Questions: A key factual question will be where and how the learning and analysis occur in the Garmin ecosystem. Does the Garmin device send sensor data to a server for model generation and then receive a custom model back, as the claims require? Or does the learning occur entirely on-device or in a way that does not involve the specific server-device feedback loop recited in the claims?

V. Key Claim Terms for Construction

  • The Term: "behavioral rules" (from ’410 Patent, claim 1)

    • Context and Importance: This term is central to the claimed invention of the ’410 Patent. Its construction will determine whether Garmin’s algorithmic approach to activity analysis falls within the scope of the claim. Practitioners may focus on this term because its definition could either be broad enough to encompass various machine learning models or be limited to more explicit, human-readable rule sets.
    • Intrinsic Evidence for Interpretation:
      • Evidence for a Broader Interpretation: The specification describes the purpose of the rules as enabling the server to "learn activity patterns" and monitor health, which could support a broad interpretation covering any learned model of user behavior (’410 Patent, col. 2:32-34).
      • Evidence for a Narrower Interpretation: The specification provides specific examples of what historical data is used to create the rules, including "location mapping of the wearer over time, identification of a home area, location boundaries, [and] sleep/wake cycles" (’410 Patent, col. 25:29-32). This language may be used to argue that the term is limited to rules explicitly derived from these enumerated data types.

  • The Term: "parameterized rule-based custom data model" (from ’193 Patent, claim 1)

    • Context and Importance: This term is the core of the asserted claim in the ’193 Patent and appears more technically specific than "behavioral rules." The infringement analysis will depend heavily on whether Garmin's system can be characterized as creating and using a model with these specific attributes.
    • Intrinsic Evidence for Interpretation:
      • Evidence for a Broader Interpretation: The general description still focuses on learning a user's activity patterns, suggesting the term describes a functional outcome rather than a specific implementation. "Parameterized" could be argued to cover any model with adjustable variables (’193 Patent, Abstract).
      • Evidence for a Narrower Interpretation: The combination of "parameterized," "rule-based," and "custom" suggests a specific technical structure. A defendant could argue that this language requires a model with distinct, adjustable parameters and an explicit rule structure, potentially excluding other machine learning approaches like certain types of neural networks that may not be easily characterized as "rule-based" (’193 Patent, col. 2:29-34).

VI. Other Allegations

  • Indirect Infringement: The complaint alleges that Defendants induce infringement by providing "user manuals and online instruction materials" that instruct customers on how to use the Accused Products in an infringing manner (Compl. ¶34, ¶48).
  • Willful Infringement: Willfulness allegations are based on Defendants’ alleged knowledge of the Asserted Patents. The complaint alleges that this knowledge stems from Defendants' "full access to study Plaintiff's public IP portfolio," as well as from the filing of the complaint and the parallel ITC action (Compl. ¶29, ¶36, ¶43, ¶50). This suggests bases for both pre- and post-suit knowledge.

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

  • A core issue will be one of architectural equivalence: Does the Garmin system practice the specific server-device interaction claimed in the patents? The case will likely require a detailed examination of whether Garmin's wearable devices receive a personalized behavioral model ("behavioral rules" or a "parameterized rule-based custom data model") from a remote server, which is then used for local comparison against new sensor data, or if its system operates on a different architectural principle.
  • A central question will be one of definitional scope: The outcome may depend on the construction of the key claim terms "behavioral rules" and "parameterized rule-based custom data model." The court's interpretation will determine whether these terms are broad enough to read on the specific machine learning and data analysis techniques employed in Garmin's modern wearable devices, or if they are limited to the specific embodiments and structures described in the patent specifications.