Rondevoo Tech LLC v. Proscia Inc

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Rondevoo Technologies, LLC (California)
  • Defendant: Proscia, Inc. (Delaware)
  • Plaintiff’s Counsel: Stamoulis & Weinblatt LLC; Sand, Sebolt & Wernow Co., LPA
• Case Identification: 1:19-cv-02327, D. Del., 12/21/2019
• Venue Allegations: Venue is alleged to be proper in the District of Delaware because the Defendant is a Delaware corporation and therefore resides in the district. The complaint also alleges Defendant has a regular and established place of business in the district.
• Core Dispute: Plaintiff alleges that Defendant’s Concentriq Image Analysis platform infringes three patents related to methods and systems for generating special-purpose, user-trained image analysis algorithms.
• Technical Context: The technology concerns computer-aided image analysis, where software is trained by an expert (e.g., a pathologist) to automatically identify, classify, and quantify specific features or "entities" within complex digital images.
• Key Procedural History: The patents-in-suit belong to the same patent family. U.S. Patent No. 7,254,266 is a divisional of the application that resulted in U.S. Patent No. 7,088,854. U.S. Patent No. 8,687,879 is a continuation in a chain of applications leading back to the '266 Patent. No other procedural events are mentioned in the complaint.

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 impracticality of having human experts manually quantify features in large numbers of complex images, such as histological slides. This manual process is noted as being labor-intensive, inconsistent, and a barrier to reproducible, large-scale scientific research ('854 Patent, col. 2:1-26).
• The Patented Solution: The invention proposes a system that generates a "product algorithm" capable of automating image analysis. This is achieved by first using an "evolving algorithm" which is trained based on a user's "judgment" in identifying specific entities within a sample image. The system learns from the user's classifications of chromatic (color) data points, and the resulting trained "product algorithm" can then be stored and used to automatically classify entities in new images ('854 Patent, Abstract; col. 6:1-35).
• Technical Importance: The technology aims to encapsulate the knowledge of a trained expert into a reusable software tool, thereby standardizing and automating quantitative image analysis for scientific and medical applications ('854 Patent, col. 3:1-7).

Key Claims at a Glance

• The complaint asserts at least Claim 1 (Compl. ¶13, ¶14).
• The essential elements of independent Claim 1 are:
  • A computer program product with code configured to:
  • obtain at least one image having a plurality of chromatic data points;
  • generate an evolving algorithm that partitions the chromatic data points into an entity identified in accordance with a user's judgment; and
  • store a first instance of the evolving algorithm as a product algorithm that enables automatic classification of the entity in a second image.

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: The patent addresses the same problem as its parent '854 Patent: the need for an improved technology to obtain quantitative data from images that overcomes the limitations of manual expert analysis ('266 Patent, col. 2:27-35).
• The Patented Solution: This invention claims a specific method for automating image quantification that focuses on an iterative training process. The method involves obtaining an initial product algorithm, presenting classification results to a first user for feedback, using that feedback to execute and refine an "evolving algorithm," and repeating the feedback loop. Once the first user grants approval, the refined algorithm is stored and can be provided to a second user for application against new images ('266 Patent, Abstract; col. 10:13-48).
• Technical Importance: The claimed method formalizes the interactive feedback loop between an expert and the system, detailing a process for creating, validating, and distributing a trained algorithm for use by others ('266 Patent, col. 14:45-58).

Key Claims at a Glance

• The complaint asserts at least Claim 1 (Compl. ¶18, ¶19).
• The essential elements of independent Claim 1 are:
  • A method for automating expert quantification of image data, comprising:
  • obtaining a product algorithm for analysis of a first set of image data;
  • The algorithm recognizes an entity via a training mode using iterative input from a first user, which comprises steps of: presenting an entity to the user for feedback, obtaining feedback, executing the evolving algorithm with the feedback, presenting a second set of entities for feedback, obtaining approval, and storing the evolving algorithm as a product algorithm; and
  • providing the product algorithm to a second user to apply against a second set of image data.

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

Issued April 1, 2014

• Patent Identification: U.S. Patent No. 8,687,879, "Method and apparatus for generating special-purpose image analysis algorithms," issued April 1, 2014 (Compl. ¶20).
• Technology Synopsis: The '879 patent claims a non-transitory computer program product that generates a "locked evolving algorithm." The invention centers on a training mode where a first user provides iterative input and feedback to refine the algorithm, which is then approved, stored as a product algorithm, and saved for subsequent use on other image sets ('879 Patent, Abstract). The claimed subject matter is highly similar to the method of the '266 patent but is framed as a computer program product.
• Asserted Claims: Independent Claim 1 (Compl. ¶23).
• Accused Features: The "Concentriq Image Analysis" system is alleged to be a computer program product that generates a "locked evolving algorithm" through the claimed iterative training mode involving user feedback, approval, and storage (Compl. ¶24-25, ¶41-50).

III. The Accused Instrumentality

Product Identification

• The "Concentriq Image Analysis" solution, referred to as the "Accused System" (Compl. ¶25).

Functionality and Market Context

• The complaint describes the Accused System as a solution that "enables image analysis based on product algorithms" (Compl. ¶25).
• Based on the infringement allegations, the system is a software product or method that Defendant makes, uses, sells, or imports, which allegedly obtains images, generates and stores algorithms based on user input, and uses these algorithms for automatic classification (Compl. ¶14, ¶19, ¶24).
• The complaint alleges Defendant is in the business of "providing image computing solutions and services," but provides no further details on the market context of the Accused System (Compl. ¶4).
• No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint references claim chart exhibits that are not provided with the filing (Compl. ¶26, ¶32, ¶40). The analysis below is based on the narrative infringement allegations in the body of the complaint.

'854 Patent Infringement Allegations

'266 Patent Infringement Allegations

Identified Points of Contention

• Factual Specificity: The complaint's infringement allegations are conclusory and largely mirror the claims' language without providing specific facts about how the Accused System operates. A central point of contention will be whether the Plaintiff can produce discovery evidence that maps the actual functionality of the "Concentriq Image Analysis" system to the specific claim elements, such as the "iterative input" and "user's judgment" limitations.
• Technical Questions: The allegations raise the question of whether the Accused System's learning architecture functions as the claimed "evolving algorithm." It is unclear from the complaint what technical mechanism in the Accused System corresponds to the process of generating, iteratively refining with user feedback, and then "storing" a "product algorithm" for later use by another user.

V. Key Claim Terms for Construction

The Term: "evolving algorithm"

• Context and Importance: This term is the technological core of the asserted claims. The outcome of the infringement analysis depends heavily on whether the learning mechanism in the Accused System meets the definition of an "evolving algorithm." Practitioners may focus on this term because its construction will define the boundary between general-purpose machine learning and the specific process claimed by the patents.
• Intrinsic Evidence for a Broader Interpretation: The specification states the system may utilize "a set of evolving algorithms (e.g., Bayes' Theorem, a neural network, or any other image classification algorithm) to evaluate image data," which may support a construction covering a wide range of learning systems ('854 Patent, col. 6:7-10).
• Intrinsic Evidence for a Narrower Interpretation: The patent repeatedly describes the "evolving algorithm" as one that is modified by direct "user input during the evaluation" and can be "locked in place to yield a first product algorithm" ('854 Patent, col. 6:22-31). This may support a narrower construction requiring an interactive training and finalization process, as opposed to any algorithm that learns from data.

The Term: "user's judgment"

• Context and Importance: This term defines the nature of the input that trains the "evolving algorithm." The validity of the infringement allegation hinges on whether the user interaction with the Accused System constitutes "judgment" as claimed.
• Intrinsic Evidence for a Broader Interpretation: The plain meaning of the term could be argued to encompass any user input that guides or corrects the system, such as selecting or deselecting areas in an image.
• Intrinsic Evidence for a Narrower Interpretation: The specification sets the context as one where "a user skilled at the identification of a particular object and/or entity" trains the system, and gives the example of a "neuropathologist" ('854 Patent, col. 3:41-44; col. 3:59-61). This may support a construction requiring a level of expert cognitive input, not merely mechanical feedback.

VI. Other Allegations

Willful Infringement

• The complaint alleges that the Defendant has had knowledge of its infringement "at least as of the service of the present Complaint" (Compl. ¶53). The prayer for relief seeks enhanced damages, suggesting a claim for post-filing willfulness only (Compl. Prayer for Relief ¶f). No facts are alleged that would support a claim of pre-suit willfulness.

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

• A core issue will be one of evidentiary sufficiency: Can the Plaintiff, beyond the conclusory recitations in its complaint, produce concrete technical evidence showing that the "Concentriq Image Analysis" system actually performs the specific, multi-step process of creating and deploying a user-trained "evolving algorithm" as detailed in the patent claims?
• The case will likely also turn on a definitional scope question: Will the term "evolving algorithm," as used in the patents from the early 2000s, be construed broadly enough to read on the architecture of a modern AI-driven image analysis platform, or will it be limited by the specification to the specific interactive, user-driven training-and-locking sequence described in the embodiments?