Rondevoo Tech LLC v. Definiens Inc

I. Executive Summary and Procedural Information

• Parties & Counsel:
  • Plaintiff: Rondevoo Technologies, LLC (California)
  • Defendant: Definiens, Inc. (Delaware)
  • Plaintiff’s Counsel: STAMOULIS & WEINBLATT LLC; SAND, SEBOLT & WERNOW CO., LPA
• Case Identification: 1:19-cv-02326, D. Del., 12/21/2019
• Venue Allegations: Plaintiff alleges venue is proper because Defendant is a Delaware corporation and thus resides in the district for purposes of patent venue under TC Heartland.
• Core Dispute: Plaintiff alleges that Defendant’s Definiens Image Analysis software infringes three patents related to systems and methods for generating special-purpose image analysis algorithms through user-guided training.
• Technical Context: The technology at issue pertains to computer-aided image analysis, where software is trained by an expert user to automatically classify and quantify objects or "entities" within complex digital images, such as those used in medical pathology.
• Key Procedural History: The complaint does not mention any prior litigation, inter partes review (IPR) 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 addresses the difficulty and inconsistency of using computer systems to classify and count objects ("entities") within complex digital images, a task that is particularly challenging when entity characteristics are subtle or varied, as is common in scientific and medical imaging ('854 Patent, col. 1:36-46). This limitation hinders the ability to obtain reliable quantitative data from images ('854 Patent, col. 2:27-35).
• The Patented Solution: The invention is a computer program product that generates a custom "product algorithm" for image analysis. The system first creates an "evolving algorithm" that partitions an image's data points (e.g., pixels) into entities based on input and "judgment" from a user. This trained, evolving algorithm is then stored as a "product algorithm," which can be used to automatically classify entities in subsequent images, effectively capturing and automating the user's expertise ('854 Patent, Abstract; Fig. 2).
• Technical Importance: The technology aimed to standardize image classification, allowing the expertise of a skilled analyst (e.g., a pathologist) to be embodied in a reusable software tool, thereby enabling more consistent and scalable quantitative analysis across different users and research studies ('854 Patent, col. 3:1-8).

Key Claims at a Glance

• The complaint asserts independent Claim 1 (Compl. ¶13).
• The essential elements of Claim 1 are:
  • A computer program product for generating special-purpose image analysis algorithms comprising a computer usable medium with program code configured to:
  • obtain at least one image with a plurality of chromatic data points;
  • generate an "evolving algorithm" that partitions the chromatic data points into at least one 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, a divisional of the '854 Patent, addresses the same core problem: the need for a reproducible and automated method for expert quantification of image data to overcome the limitations of manual analysis ('266 Patent, col. 2:27-46).
• The Patented Solution: The invention details a specific method for creating the product algorithm through a "training mode." In this mode, the system iteratively interacts with a "first user," presenting sets of identified entities for feedback, executing the evolving algorithm with that feedback, and obtaining final approval. The resulting "product algorithm" is then stored and provided to a "second user" to apply to different image data, effectively transferring the first user's expertise ('266 Patent, Abstract; col. 5:17-40).
• Technical Importance: This approach formalizes the capture and dissemination of expert knowledge by defining a structured, iterative training process and the subsequent transfer of the resulting algorithm to other, potentially non-expert, users ('266 Patent, col. 2:1-4).

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, comprising:
  • obtaining a product algorithm configured to recognize an entity via a "training mode" that uses iterative input to an "evolving algorithm" from a first user;
  • The training mode itself comprises the steps of: presenting a first set of entities for feedback, obtaining feedback, executing the algorithm, presenting a second set of entities for feedback, obtaining approval, and storing the evolving algorithm as a product algorithm;
  • providing the product algorithm to a "second user" for application 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.

• Technology Synopsis: This patent, a continuation in the same family, claims a non-transitory computer program product that automates image quantification using a "locked evolving algorithm." The method for generating this locked algorithm is substantially similar to that described in the '266 Patent, involving a training mode with iterative user feedback, presenting entity sets, obtaining feedback and approval, and storing the resulting algorithm for subsequent use ('879 Patent, Abstract, col. 32:28-56). The "locked" terminology suggests the creation of a finalized, non-editable algorithm for deployment.
• Asserted Claims: The complaint asserts independent Claim 1 (Compl. ¶23).
• Accused Features: The "Definiens Image Analysis software" is accused of being a non-transitory computer program product that performs the claimed steps of generating and using a locked evolving algorithm (Compl. ¶¶24-25).

III. The Accused Instrumentality

Product Identification

• The "Definiens Image Analysis software" (the "Accused System") (Compl. ¶25).

Functionality and Market Context

• The complaint alleges that the Accused System "enables image analysis based on product algorithms" and is part of Defendant's business of "providing image computing solutions and services" (Compl. ¶¶4, 25). The complaint does not provide specific technical details about the operation of the Accused System beyond conclusory allegations that it performs the steps recited in the asserted claims. No probative visual evidence provided in complaint.

IV. Analysis of Infringement Allegations

The complaint references non-limiting claim charts in Exhibits D, E, and F to support its infringement allegations; however, these exhibits were not filed with the complaint (Compl. ¶¶26, 32, 40). The infringement theory is therefore based on the narrative paragraphs, which are highly conclusory and directly track the language of the asserted claims.

For the '854 Patent, Plaintiff alleges the Accused System is a computer program product that obtains an image, generates an "evolving algorithm" based on "user's judgement," and stores it as a "product algorithm" for automated classification (Compl. ¶¶27-31). For the '266 Patent, Plaintiff alleges the Accused System performs a method involving a "training mode" with iterative feedback from a "first user" to create a product algorithm that is then provided to a "second user" (Compl. ¶¶33-39). The allegations for the '879 Patent are substantially similar, focusing on the creation of a "locked evolving algorithm" (Compl. ¶¶41-50). In all instances, the complaint lacks specific factual allegations detailing how the Accused System performs these functions.

• Identified Points of Contention:
  • Evidentiary Questions: The primary point of contention will be evidentiary. The complaint makes only bare allegations that the Accused System performs each claimed step. A key question for the court will be whether Plaintiff can produce evidence to substantiate these claims, such as technical documentation, source code, or expert testimony showing the accused software operates in the specific manner recited by the patents.
  • Scope Questions: A likely area of dispute will be the mapping of the Accused System's features to claim limitations. For example, does the defendant's software have a specific, structured "training mode" that is formally "provided" to a "second user" as required by the '266 Patent? Or does it merely have general configuration settings that any single user can adjust, which may not meet the claim's specific workflow? The distinction between a general-purpose configurable tool and the patents' specific training-and-deployment model will be critical.

V. Key Claim Terms for Construction

• The Term: "evolving algorithm"

  • Context and Importance: This term appears in all three patents and is foundational to the claimed invention. Its construction will be critical because the infringement case depends on whether the accused software's method of incorporating user input constitutes an "evolving algorithm." Practitioners may focus on this term because its meaning will distinguish between a patented iterative learning process and a standard, non-infringing software configuration feature.
  • Intrinsic Evidence for a Broader Interpretation: The specification suggests the term can encompass various known technologies, 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. 6:8-11).
  • Intrinsic Evidence for a Narrower Interpretation: The specification describes a specific process where the algorithm is modified by user input and can be "locked in place to yield a first product algorithm" ('854 Patent, col. 6:22-31). This may support an argument that the term requires a distinct, stateful process of refinement and finalization, not just any system that allows parameter adjustment.

• The Term: "user's judgment"

  • Context and Importance: This term from the '854 Patent is central to how the algorithm is trained. The dispute may turn on whether the input provided by a user of the Accused System qualifies as "judgment." Practitioners may focus on this term to differentiate between a high-level, expert classification decision (e.g., "this is a diseased cell") and a low-level parameter setting (e.g., "set brightness threshold to 50").
  • Evidence for a Broader Interpretation: The specification refers generally to a "user skilled at the identification of a particular object and/or entity," which could support construing any classification-guiding input from such a user as "judgment" ('854 Patent, col. 3:42-45).
  • Evidence for a Narrower Interpretation: The claims and specification frame the invention in the context of "expert quantification" ('854 Patent, col. 3:15-18). This could support a narrower construction requiring a semantic-level classification decision from the user, rather than simple interaction with software controls.

VI. Other Allegations

• Indirect Infringement: The complaint does not contain any allegations of indirect or contributory infringement. It alleges only direct infringement (Compl. ¶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 provides a basis for post-filing willfulness and supports the prayer for enhanced damages under 35 U.S.C. § 285 (Prayer for Relief, ¶f). No facts supporting pre-suit knowledge are alleged.

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

• A key question will be one of evidentiary sufficiency: Can Plaintiff produce technical evidence beyond its conclusory allegations to show that the Accused System's software architecture and workflow mirror the specific, multi-step processes of the asserted claims, including the creation of distinct "evolving" and "product" algorithms through iterative user feedback?
• A central issue will be one of technical and definitional mapping: Does the accused software's functionality, which likely includes user-configurable analysis tools, meet the specific claim limitations of a "training mode" that is used by a "first user" and then "provided" to a "second user," or is there a fundamental operational difference between the accused product and the patents' specific training-and-deployment workflow?
• The case may also turn on a question of claim scope: Can the term "evolving algorithm", as defined and used in the patents, be construed to read on the accused software's method for incorporating user preferences, or will the court construe the term more narrowly to require a specific learning and locking mechanism not present in the accused product?