Rondevoo Tech LLC v. Arterys Inc

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Rondevoo Technologies, LLC (California)
  • Defendant: Arterys, Inc. (California)
  • Plaintiff’s Counsel: Stamoulis & Weinblatt LLC; Sand, Sebolt & Wernow Co., LPA
• Case Identification: 1:19-cv-01694, D. Del., 09/10/2019
• Venue Allegations: Venue is alleged to be proper in the District of Delaware because Defendant has a regular and established place of business in the district.
• Core Dispute: Plaintiff alleges that Defendant’s "Arterys Imaging" cloud-based medical imaging platform infringes three patents related to systems and methods for generating special-purpose image analysis algorithms through user-guided machine learning.
• Technical Context: The technology concerns the automation of expert image analysis, particularly in complex domains like medical diagnostics, by creating reusable software algorithms trained on human judgment.
• Key Procedural History: The complaint does not mention 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. 7,088,854 - "Method and apparatus for generating special-purpose image analysis algorithms"

Issued August 8, 2006

The Invention Explained

• Problem Addressed: The patent describes the difficulty and inconsistency of quantifying features in complex images, such as histological samples in biomedical research. Existing computer systems are described as lacking the ability to effectively incorporate the nuanced expertise of a skilled human analyst to automate this process ('854 Patent, col. 2:3-28).
• The Patented Solution: The invention proposes a system where an "evolving algorithm" makes an initial attempt to classify entities in an image. This classification is presented to a user, who provides "judgment" (e.g., feedback on correctness). This feedback is used to refine the algorithm, which is then stored as a "product algorithm" capable of automatically classifying new images without further user input ('854 Patent, Abstract; col. 6:7-34). Figure 2 illustrates this core feedback loop for training the algorithm.
• Technical Importance: This approach aimed to codify the subjective knowledge of an expert into a reusable, objective software tool, enabling more scalable and standardized quantitative analysis in scientific and medical fields ('854 Patent, col. 3:1-6).

Key Claims at a Glance

• The complaint asserts independent claim 1 (Compl. ¶13).
• The essential elements of claim 1 are:
  • A computer usable medium with computer readable program code.
  • The code is configured to obtain at least one image with chromatic data points.
  • The code is configured to generate an "evolving algorithm" that partitions the data points into at least one entity "identified in accordance with a user's judgment."
  • The code is configured to store a first instance of the evolving algorithm as a "product algorithm" that enables automatic classification of entities in a second image.
• The complaint does not explicitly reserve the right to assert dependent claims.

U.S. Patent No. 7,254,266 - "Method and apparatus for generating special-purpose image analysis algorithms"

Issued August 7, 2007

The Invention Explained

• Problem Addressed: As a divisional of the application leading to the '854 Patent, the '266 Patent addresses the same fundamental problem of automating expert image quantification ('266 Patent, col. 2:3-28).
• The Patented Solution: This patent claims a method for creating a "product algorithm" through a "training mode." The training mode involves an iterative feedback process with a "first user" (an expert) to create an evolving algorithm. The key distinction is the final step: "providing said product algorithm to at least one second user" so they can apply the expert-trained tool to their own image data ('266 Patent, col. 5:2-18).
• Technical Importance: The invention facilitates the capture and dissemination of expert knowledge, allowing a non-expert ("second user") to perform sophisticated image analysis that would otherwise be beyond their skill set ('266 Patent, col. 6:21-34).

Key Claims at a Glance

• The complaint asserts independent claim 1 (Compl. ¶18).
• The essential elements of claim 1 are:
  • A method for automating expert quantification of image data using a product algorithm.
  • Obtaining a product algorithm configured to recognize an entity via a "training mode" that uses iterative input from a "first user" to create an "evolving algorithm."
  • The training mode comprises steps of presenting entities, obtaining feedback, executing the algorithm, presenting a second set of entities, obtaining approval, and storing the result as a product algorithm.
  • Providing the product algorithm to a "second user" to apply against a second set of image data.
• The complaint does not explicitly reserve the right to assert dependent claims.

U.S. Patent No. 8,687,879 - "Method and apparatus for generating special-purpose image analysis algorithms"

Issued April 1, 2014

• Technology Synopsis: This patent, part of the same family as the '854 and '266 patents, claims a non-transitory computer program product for automating image quantification. The invention centers on generating a "locked evolving algorithm" through a training mode that involves iterative user feedback, with the resulting "locked" algorithm then being stored for subsequent use on other image sets ('879 Patent, Abstract).
• Asserted Claims: The complaint asserts independent claim 1 (Compl. ¶23).
• Accused Features: The complaint alleges that the "Arterys Imaging" system embodies the claimed non-transitory product, specifically its functions for generating analysis algorithms via deep learning and training on user-provided feedback (Compl. ¶¶41-49).

III. The Accused Instrumentality

• Product Identification: The accused instrumentality is Defendant's "Arterys Imaging" solution, referred to as the "Accused System" (Compl. ¶25).
• Functionality and Market Context: The Accused System is described as a solution that "enables image analysis based on product algorithms" (Compl. ¶25). The complaint alleges the system functions by generating algorithms based on "user manual annotation of objects of interest," thereby training the algorithm through user feedback (Compl. ¶33, ¶43). This trained algorithm is then allegedly used to "automatically classify additional images" (Compl. ¶38). The system is offered as an "image computing solution and service" via Defendant's website, from which Defendant allegedly derives revenue (Compl. ¶4).
• Visual Evidence: No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint references claim chart exhibits that were not provided with the filed document. The following summary is based on the narrative allegations in the complaint body.

'854 Patent Infringement Allegations

'266 Patent Infringement Allegations

Identified Points of Contention

• Technical Questions: The complaint's allegations for the '266 Patent claim are somewhat general. A technical question is whether the Accused System's training process performs the specific, multi-step iterative loop recited in the claim (presenting a first set, getting feedback, executing, presenting a second set, getting approval), or if it employs a more generalized machine learning workflow that does not map directly onto these discrete steps.
• Scope Questions: Claim 1 of the '266 Patent requires providing the algorithm from a "first user" (trainer) to a "second user" (consumer). The complaint alleges this is met by storing the algorithm and running it on "all the data" (Compl. ¶38). This raises the question of whether this alleged functionality meets the two-user requirement, or if it describes a single-user workflow where a user trains an algorithm and then applies it themselves.

V. Key Claim Terms for Construction

• The Term: "evolving algorithm"

  • Context and Importance: This term is central to all asserted patents, defining the core technical component that is trained by user input. Its construction will determine whether the machine learning models used by the Accused System fall within the scope of the claims. Practitioners may focus on this term because the degree and manner of "evolution" required will be a key point of dispute.
  • Intrinsic Evidence for Interpretation:
    • Evidence for a Broader Interpretation: The specification suggests the term can encompass various known classification systems, stating the system may utilize "a set of evolving algorithms (e.g., Bayes' Theorem, a neural network, or any other image classification algorithm)" ('854 Patent, col. 6:8-10).
    • Evidence for a Narrower Interpretation: The claims and detailed descriptions consistently tie the "evolving algorithm" to a specific, structured process of receiving iterative feedback and judgment directly from a user to refine its parameters, suggesting it may be narrower than any generic, pre-trained AI model ('854 Patent, Fig. 2; col. 10:11-40).

• The Term: "user's judgment"

  • Context and Importance: This term defines the nature of the input that drives the "evolving algorithm". The infringement case may hinge on whether the "user manual annotation of objects of interest" alleged in the complaint (Compl. ¶33) constitutes the claimed "judgment."
  • Intrinsic Evidence for Interpretation:
    • Evidence for a Broader Interpretation: The term could be argued to cover any user input that guides the algorithm, including the outlining or labeling of features in an image.
    • Evidence for a Narrower Interpretation: The specification frames the input as a "verification message" where the user "makes a judgment about the correctness of the classification" ('854 Patent, col. 10:17-24). This language, along with phrases like "ratified by the user" (col. 10:37-38), may support a narrower construction requiring a qualitative assessment of the algorithm's output, rather than merely providing initial training data.

VI. Other Allegations

• Indirect Infringement: The complaint does not plead specific facts to support claims of induced or contributory infringement, focusing instead on allegations of direct infringement (Compl. ¶53).
• Willful Infringement: The complaint alleges that Defendant had knowledge of the patents-in-suit "at least as of the service of the present Complaint" (Compl. ¶52). This allegation supports a claim for post-filing willfulness only, as no facts suggesting pre-suit knowledge of the patents are asserted.

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

• A key evidentiary question will be one of functional specificity: does the accused "Arterys Imaging" platform's training functionality, which the complaint describes as being based on "user manual annotation" (Compl. ¶33), perform the specific, multi-step iterative feedback and approval process required by the asserted claims, or is there a material difference in the operational details of the learning process?
• The case will likely involve a dispute over definitional scope: can the claim term "user's judgment," which the patent specification links to a user's verification of a classification's correctness, be construed to cover the "manual annotation of objects of interest" (Compl. ¶33) allegedly performed in the accused system, a process that might be characterized as data labeling rather than expert validation?
• A central legal and factual question for the '266 Patent will be the distinction between user roles: does the accused system's alleged operation satisfy the claim requirement of providing an algorithm from a "first user" to a "second user," or does the evidence show a single-user workflow that falls outside the claimed two-party transfer of expertise?