DCT
1:19-cv-06617
Rondevoo Tech LLC v. Keen Eye LLC
I. Executive Summary and Procedural Information
- Parties & Counsel:
- Plaintiff: Rondevoo Technologies, LLC (California)
- Defendant: Keen Eye, LLC (New York)
- Plaintiff’s Counsel: Ranallo Law Office; Sand, Sebolt & Wernow Co., LPA
- Case Identification: 1:19-cv-06617, E.D.N.Y., 11/23/2019
- Venue Allegations: Venue is alleged to be proper in the Eastern District of New York because Defendant is a New York corporation that resides in the district through a regular and established place of business.
- Core Dispute: Plaintiff alleges that Defendant’s "Keen Eye tool" for image analysis infringes three patents related to generating special-purpose, user-trained image analysis algorithms.
- Technical Context: The technology concerns computer-aided image analysis, where software is trained by an expert user to automatically identify and quantify specific features in complex images, a process critical in fields like medical pathology and materials science.
- 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
| Date | Event |
|---|---|
| 2001-04-25 | Priority Date (’854, ’266, ’879 Patents) |
| 2006-08-08 | Issue Date (U.S. Patent No. 7,088,854) |
| 2007-08-07 | Issue Date (U.S. Patent No. 7,254,266) |
| 2014-04-01 | Issue Date (U.S. Patent No. 8,687,879) |
| 2019-11-23 | Complaint Filing Date |
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 of using computer systems to accurately and consistently classify and count objects, or "entities," within complex digital images, such as histological sections used in medical research. Existing systems are described as lacking a mechanism to effectively incorporate the nuanced expertise of a human analyst (’854 Patent, col. 1:36-47; col. 2:1-8).
- The Patented Solution: The invention proposes a system that generates a special-purpose "product algorithm" for image analysis. This is achieved by first using an "evolving algorithm" that interacts with a human user. The user provides "judgment" to help the algorithm learn how to partition an image's data points (e.g., pixels) and identify specific entities. This trained, or "evolved," algorithm is then stored and can be used to automatically classify entities in new images without further user input (’854 Patent, Abstract; col. 6:7-35).
- Technical Importance: This approach sought to standardize the classification of complex image data, enabling large-scale, reproducible quantitative analysis where previously only qualitative conclusions based on a few sample images were practical (’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, a computer program product, include computer code configured to:
- Obtain an image with a plurality of chromatic data points.
- Generate an "evolving algorithm" that partitions these data points into an entity based on a "user's judgment."
- Store an instance of this evolving algorithm as a "product algorithm," which can then automatically classify instances of that entity in a second image according to the user's original judgment.
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: Like its parent, the ’854 Patent, this patent addresses the need for a system that can incorporate human expertise into automated image analysis to overcome the limitations of purely computational methods (’266 Patent, col. 2:1-8).
- The Patented Solution: This patent claims a method that formalizes the iterative training process. It describes obtaining a product algorithm by using a "training mode" where a first user provides iterative feedback (e.g., presenting entities, getting feedback, executing the algorithm, getting approval) to refine an evolving algorithm. The key final step is "providing said product algorithm to at least one second user" so they can apply the expert-trained algorithm to a different set of images (’266 Patent, Claim 1; col. 10:11-55).
- Technical Importance: The invention facilitates the scalable deployment of expert-trained classification models, allowing the knowledge of one expert to be captured and distributed for standardized use by others (’266 Patent, col. 2:1-8).
Key Claims at a Glance
- The complaint asserts independent Claim 1 (Compl. ¶18).
- The essential elements of Claim 1, a method, include:
- Obtaining a product algorithm for image analysis that is configured to recognize an entity via a "training mode."
- The training mode utilizes iterative input from a first user, involving steps of presenting entity identifications, obtaining feedback and approval, and storing the resulting evolving algorithm.
- Providing the stored 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 addresses the automated quantification of image data by claiming a non-transitory computer program product. The invention involves creating a "locked evolving algorithm" through an iterative training process with a user, who provides feedback and approval to refine the algorithm, which is then stored for later use on image sets (’879 Patent, Abstract; Compl. ¶22).
Asserted Claims
The complaint asserts independent Claim 1 (Compl. ¶23).
Accused Features
The "Keen Eye tool" is accused of being a non-transitory computer program product that implements this iterative training, locking, and storage process for creating image analysis algorithms (Compl. ¶24, 41-50).
III. The Accused Instrumentality
Product Identification
The accused instrumentality is Defendant’s "Keen Eye tool," also referred to as the "Accused System" (Compl. ¶25).
Functionality and Market Context
The complaint alleges the Keen Eye tool "enables image analysis based on product algorithms" (Compl. ¶25). Plaintiff alleges that the tool is a computer program product and/or method that performs the functions recited in the asserted claims, including generating special-purpose algorithms through a training mode that uses iterative input from a user to automate the quantification of image data (Compl. ¶14, 19, 24). The complaint states Defendant is in the business of "providing image computing solutions and services" via its website but does not provide further detail on the product's market position (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 following analysis is based on the narrative allegations in the body of the complaint.
’854 Patent Infringement Allegations
| Claim Element (from Independent Claim 1) | Alleged Infringing Functionality | Complaint Citation | Patent Citation |
|---|---|---|---|
| obtain at least one image having a plurality of chromatic data points; | The Accused System uses, practices, or is a computer program product obtaining an image having a plurality of chromatic data points. | ¶29 | col. 7:56-62 |
| 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 | The Accused System is configured to generate an evolving algorithm that partitions chromatic data points into an entity identified in accordance with a user's judgment. | ¶30 | col. 6:7-10 |
| 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 Accused System is configured to store a first instance of the evolving algorithm as a product algorithm that enables automatic classification of an entity in a second image. | ¶31 | col. 6:25-35 |
’266 Patent Infringement Allegations
| Claim Element (from Independent Claim 1) | Alleged Infringing Functionality | Complaint Citation | Patent Citation |
|---|---|---|---|
| obtaining a product algorithm for analysis of a first set of image data wherein said product algorithm is configured to recognize at least one entity... via a training mode that utilizes iterative input to an evolving algorithm... | The Accused System obtains a product algorithm for image analysis that recognizes an entity via a training mode using iterative user input. | ¶34 | col. 5:27-35 |
| presenting a first set of said at least one entity to said user for feedback as to the accuracy of said first set of identified entities; | The Accused System presents a first set of an entity to a user for feedback. | ¶35 | col. 10:15-20 |
| obtaining said feedback from said user; | The Accused System obtains feedback from the user. | ¶36 | col. 10:45-48 |
| executing said evolving algorithm using said feedback; | The Accused System executes the evolving algorithm using the feedback. | ¶37 | col. 10:40-44 |
| storing said evolving algorithm as a product algorithm; | The Accused System stores the evolving algorithm as a product algorithm. | ¶38 | col. 10:40-44 |
| providing said product algorithm to at least one second user so that said at least one second user can apply said product algorithm against a second set of image data... | The Accused System provides the algorithm to a second user for application against a second set of image data. | ¶39 | col. 10:45-55 |
Identified Points of Contention
- Technical Questions: The complaint makes conclusory allegations that the Accused System performs each claimed step but provides no specific factual detail about "how" it operates. A primary point of contention will be the evidentiary basis for these allegations. For example, what evidence shows that the Keen Eye tool performs the specific, multi-step iterative training loop recited in Claim 1 of the ’266 Patent (presenting, getting feedback, executing, presenting again, getting approval, etc.)?
- Scope Questions: The infringement case may depend on the scope of "user's judgment" (’854 Patent) and the "iterative input" of the "training mode" (’266 Patent). A question for the court will be whether the specific user interactions implemented in the Keen Eye tool meet the definitions of these claim terms as understood in light of the patent specifications.
V. Key Claim Terms for Construction
The Term: "evolving algorithm" (asserted in '854 and '266 Patents)
- Context and Importance: This term is the core of the claimed invention, representing the trainable software component. The outcome of the dispute may hinge on whether the accused product's learning mechanism constitutes an "evolving algorithm" as claimed.
- Intrinsic Evidence for Interpretation:
- Evidence for a Broader Interpretation: The specification suggests the term is not limited to a single implementation, 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. 6:7-10).
- Evidence for a Narrower Interpretation: The specification heavily details a process where the algorithm "evolves" through direct, iterative user feedback and can be "locked in place to yield a first product algorithm" (’854 Patent, col. 6:18-30). A party could argue the term is limited to algorithms that are refined through this specific user-driven feedback loop rather than any generic machine learning process.
The Term: "user's judgment" (asserted in '854 Patent)
- Context and Importance: This term defines the human input that drives the "evolving algorithm." Practitioners may focus on this term because its construction will determine what kind of user interaction is required to meet the claim limitation.
- Intrinsic Evidence for Interpretation:
- Evidence for a Broader Interpretation: The patent states that if user input is provided, "the system utilizes such input to aid the process of entity identification," which could encompass simple actions like selecting a sample area of an image (’854 Patent, col. 8:30-34).
- Evidence for a Narrower Interpretation: The specification describes a specific feedback process where a "verification message is displayed to the user for purpose of obtaining input from the user that reflects the user's judgment about the accuracy of a classification" (’854 Patent, col. 10:15-20). This suggests "judgment" may require a more deliberative confirmation or correction of the algorithm's output.
VI. Other Allegations
- Indirect Infringement: The complaint does not allege indirect infringement.
- Willful Infringement: The complaint alleges that Defendant has had knowledge of the patents-in-suit "at least as of the service of the present Complaint" (Compl. ¶53) and includes a prayer for enhanced damages (Compl. p. 14, ¶f). This forms a basis for potential post-suit willful infringement.
VII. Analyst’s Conclusion: Key Questions for the Case
- A central evidentiary question will be whether the "Keen Eye tool" actually operates in the manner alleged. Given the complaint's lack of specific technical facts, the case will depend on discovery to reveal if the accused product performs the specific, multi-step iterative training, feedback, and storage processes recited in the asserted claims.
- The case will also turn on a question of claim construction scope: will terms like "evolving algorithm" and "user's judgment" be interpreted broadly to cover a wide range of modern machine learning systems with user interaction, or narrowly to require the specific, multi-stage feedback and "locking" procedures that are repeatedly described in the patents' detailed descriptions?