Rondevoo Tech LLC v. Leica Biosystems Imaging Inc

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Rondevoo Technologies, LLC (California)
  • Defendant: Leica Biosystems Imaging, Inc. (Delaware)
  • Plaintiff’s Counsel: Stamoulis & Weinblatt LLC; Sand, Sebolt & Wernow Co., LPA
• Case Identification: 1:19-cv-02067, D. Del., 10/30/2019
• Venue Allegations: Plaintiff alleges venue is proper because Defendant is a Delaware corporation and resides in the district through a regular and established place of business.
• Core Dispute: Plaintiff alleges that Defendant’s Aperio Image Analysis system infringes three patents related to methods and systems for generating special-purpose, trainable image analysis algorithms.
• Technical Context: The technology concerns automating the quantification of features within digital images, a process critical in fields like histology and materials science where manual analysis is time-consuming and subjective.
• 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 Aug. 8, 2006)

The Invention Explained

• Problem Addressed: The patent describes the difficulty, inconsistency, and tedium of having human experts manually quantify entities (e.g., cells, plaques, material defects) in complex digital images. Existing computer systems are identified as being limited, difficult to use, and unable to "learn" from expert knowledge to improve their analytical skill over time (’854 Patent, col. 1:36-46; col. 2:6-14).
• The Patented Solution: The invention provides a system that generates a custom analysis algorithm by learning from an expert user's input. A user provides judgments on an image (e.g., identifying examples of an entity), which the system uses to create an "evolving algorithm." This algorithm is refined and then can be stored as a final "product algorithm" to automatically classify and count entities in subsequent images, effectively embedding the expert's knowledge into a reusable, automated tool (’854 Patent, Abstract; col. 3:15-49).
• Technical Importance: This technology sought to standardize and automate quantitative image analysis in scientific fields, replacing subjective, labor-intensive manual counting with a reproducible, computer-driven method (’854 Patent, col. 2:26-35).

Key Claims at a Glance

• The complaint asserts independent Claim 1 (’854 Patent, col. 32:20-34; Compl. ¶13).
• Claim 1 is for 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 said plurality of chromatic data points within said at least one image into at least one entity identified in accordance with a user's judgment; and
  • store a first instance of said evolving algorithm as a product algorithm wherein said product algorithm enables the automatic classification of instances of said at least one entity within at least one second image in accordance with said judgment of said user.
• 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 Aug. 7, 2007)

The Invention Explained

• Problem Addressed: Sharing the same specification as the ’854 Patent, this patent addresses the same problem of automating expert-level image quantification (’266 Patent, col. 1:36-46).
• The Patented Solution: This patent claims a method that formalizes an iterative training process. The method involves obtaining a product algorithm created through a "training mode" where a first user provides feedback on presented entity classifications. The system executes the evolving algorithm with this feedback, presents a second set of results, obtains final approval, and stores the algorithm. A key aspect is the final step of providing this trained algorithm to a second user for application on different images (’266 Patent, col. 31:22-41).
• Technical Importance: The claimed method formalizes the interactive feedback loop between an expert and the system, detailing a structured process for capturing and disseminating expert knowledge for automated analysis (’266 Patent, col. 6:15-29).

Key Claims at a Glance

• The complaint asserts independent Claim 1 (’266 Patent, col. 31:22-41; Compl. ¶18).
• Claim 1 is for a method comprising:
  • obtaining a product algorithm configured to recognize an entity via a training mode that uses iterative input from a first user;
  • The training mode itself comprises the steps of: presenting a first set of entities for feedback, obtaining the feedback, executing the evolving algorithm with the feedback, presenting a second set of entities, obtaining approval, and storing the evolving algorithm as a product algorithm.
  • providing said 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.

Multi-Patent Capsule: U.S. Patent No. 8,687,879

• Patent Identification: U.S. Patent No. 8,687,879, "Method and apparatus for generating special-purpose image analysis algorithms," Issued April 1, 2014.
• Technology Synopsis: As a continuation in the same patent family, the ’879 Patent claims a "non-transitory computer program product" for automating image quantification. The claims recite generating a "locked evolving algorithm" through a similar interactive training mode involving user feedback and approval, with the final algorithm being stored for subsequent use on other image sets. The language appears tailored to address patent-eligibility standards prevalent at the time of its prosecution (’879 Patent, Abstract; col. 29:5-32).
• Asserted Claims: Independent Claim 1 (Compl. ¶23).
• Accused Features: The complaint alleges the "Aperio Image Analysis" system embodies the claimed product, including the generation and storage of a "locked evolving algorithm" based on user training (Compl. ¶¶24-25).

III. The Accused Instrumentality

Product Identification

• Defendant's "Aperio Image Analysis" system (the "Accused System") (Compl. ¶25).

Functionality and Market Context

• The complaint alleges the Accused System "enables image analysis based on product algorithms" (Compl. ¶25). It is presented as a computer program product and/or method used for image computing solutions (Compl. ¶4, ¶14). The complaint provides minimal technical detail on the Accused System's operation, instead making conclusory allegations that it performs the claimed steps and incorporating by reference non-public claim chart exhibits (Compl. ¶¶26, 32, 40).
• No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint makes narrative infringement allegations by mapping claim elements to the Accused System's functionality, citing non-public exhibits for support.

’854 Patent Infringement Allegations

’266 Patent Infringement Allegations

Identified Points of Contention

• Scope Questions: The patents describe the "evolving algorithm" in the context of sophisticated processes like Bayesian classifiers and neural networks that are iteratively refined (’854 Patent, col. 6:7-14). A central question for the court will be one of claim scope: does the term "evolving algorithm" as used in the patents read on the accused system's specific functionality for creating and saving analysis routines, or is it limited to the more complex, machine-learning-style examples disclosed in the specification?
• Technical Questions: The claims require specific, interactive steps like "iterative input," "feedback," and "user's judgment" to generate the algorithm. Since the complaint lacks specific technical evidence (like screenshots or workflow descriptions), a key factual question will be what proof exists that the Accused System's user interface and workflow actually perform this claimed iterative training loop, as opposed to a more direct, one-time configuration of analysis parameters by a user.

V. Key Claim Terms for Construction

• The Term: "evolving algorithm"

  • Context and Importance: This term appears in all asserted independent claims and is foundational to the patents' novelty. Practitioners may focus on this term because its construction will determine whether a standard user-configurable analysis tool falls within the scope of the claims, or if the claims are limited to more complex, learning-based systems.
  • Evidence for a Broader Interpretation: The specification states the system "utilizes of a set of evolving algorithms (e.g., Bayes' Theorem, a neural network, or any other image classification algorithm) to evaluate image data" (’854 Patent, col. 6:7-10). Plaintiff may argue this "e.g." language supports a broad definition covering any algorithm that is changed or refined by user input.
  • Evidence for a Narrower Interpretation: The specification repeatedly links the term to specific, complex machine learning techniques and an iterative refinement process where "user input during the evaluation can modify the evolving product algorithm" (’854 Patent, col. 6:21-23). Defendant may argue the term is limited by these examples to algorithms that actively "learn" rather than ones that are merely "configured."

• The Term: "user's judgment"

  • Context and Importance: This term defines the nature of the user input required to create the claimed "evolving algorithm." Its construction is critical to distinguishing between simple user configuration and the expert-driven training process described in the patents.
  • Evidence for a Broader Interpretation: Claim 1 of the ’854 Patent broadly recites an entity "identified in accordance with a user's judgment" (’854 Patent, col. 32:28-29). Plaintiff may argue this covers any user selection that defines an entity, such as selecting a region of interest or setting a color threshold.
  • Evidence for a Narrower Interpretation: The specification frames the invention as a way to automate "expert classification" and incorporate the knowledge of a "trained histologist" (’854 Patent, col. 3:17-18, col. 2:63-65). Defendant may argue "judgment" requires a higher-level, semantic input (e.g., a user correcting a misclassification) rather than simply adjusting parameters.

VI. Other Allegations

• Indirect Infringement: The complaint does not contain allegations of indirect infringement. It pleads only direct infringement under 35 U.S.C. § 271 (Compl. ¶52, ¶54).
• Willful Infringement: The complaint alleges that Defendant has had knowledge of its infringement "at least as of the service of the present Complaint" (Compl. ¶53). This allegation supports a claim for post-filing willful infringement but does not plead pre-suit knowledge. The prayer for relief seeks enhanced damages (Compl. p. 13, ¶f).

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

• A core issue will be one of definitional scope: can the term "evolving algorithm," which is described in the patent specification with examples like iterative Bayesian classifiers and neural networks, be construed to cover the specific methods by which the Accused System allows users to create, save, and reuse analysis configurations?
• A key evidentiary question will be one of functional operation: given the complaint's conclusory allegations, what evidence will emerge in discovery to demonstrate that the Accused System's workflow actually performs the specific, multi-step interactive training loop—including "iterative input," "feedback," and "user's judgment"—as required by the asserted claims?