Contract analysis

I. Executive Summary This memorandum analyzes the four prior art references identified by Helios Precision Diagnostics, Inc. ("Helios") in its invalidity contentions, which were served with its Answer and Counterclaims on May 12, 2025, and further elaborated in Helios's responses to GenomeDx's First Set of Interrogatories (Nos. 1–15), served on September 8, 2025. The purpose of this memorandum is to assess the streng

1. U.S. Patent No. 10,438,271 ("the '271 Patent"), titled "Methods for Predicting Prostate Cancer Outcomes Using Multi-Gene Expression Classifiers," issued October 8, 2019.

2. U.S. Patent No. 11,204,893 ("the '893 Patent"), titled "Genomic Classifier Systems and Methods for Post-Surgical Cancer Prognosis," issued December 21, 2021.

3. U.S. Patent No. 11,891,647 ("the '647 Patent"), titled "Improved Methods for Genomic Risk Stratification in Prostate Cancer Using Combined Biomarker and Clinical Variable Models," issued February 6, 2024. Prior Art References Under Analysis. Helios identifies four prior art references in support of its invalidity theories:

1. Nakamura et al., U.S. Patent No. 9,617,340 ("Nakamura"), issued April 4, 2017.

2. Gagnon, WO 2018/065413 ("Gagnon"), published April 12, 2018.

3. Thornberry et al., "Machine Learning Approaches to Genomic Risk Stratification in Prostate Cancer," Journal of Computational Oncology, Vol. 14, No. 3, pp. 287–301 (March 2015) ("Thornberry").

4. Klein et al., "A 17-Gene Assay to Predict Prostate Cancer Aggressiveness," European Urology, Vol. 66, No. 1, pp. 45–52 (2014) ("Klein"). High-Level Conclusions. After detailed analysis, it is my assessment that Helios's invalidity theories under §§ 102 and 103 face significant weaknesses. None of the four references individually anticipates the asserted claims of any of the three patents-in-suit. The obviousness c

Section 101 Invalidity. This memorandum does not address Helios's separate invalidity theory under 35 U.S.C. § 101 (patent eligibility), which is the subject of a separate memorandum. I note, however, that the Court's January 13, 2026 Order denying Helios's Motion to Dismiss under § 101 — in which Judge Lee held that § 101 challenges were premature without the benefit of full claim construction — is a favorable proce

II. Overview of the Patents-in-Suit and Asserted Claims This section provides an overview of each patent-in-suit, including its filing and priority dates, named inventors, prosecution history, and the specific claims asserted in this litigation. A detailed understanding of each patent's claims and prosecution history is essential to the prior art analysis, as the scope of the claims defines the universe of prior art

III. Detailed Analysis of Prior Art Reference 1: Nakamura et al. (U.S. Patent No. 9,617,340) A. Overview of Nakamura Title: "Gene Expression Profiling for Cancer Prognosis" Issue Date: April 4, 2017 Named Inventors: Toshihiro Nakamura, Jun Watanabe, Hideki Yamamoto Assignee: Osaka Genomics Research Institute Co., Ltd. Nakamura is the most significant prior art reference identified by Helios and is the focus of Helios

1. Gene Panel Size and Composition. The strongest distinguishing argument is that Nakamura's gene panel does not satisfy the "at least 22 genes selected from a predefined gene set" limitation. This argument has two components. First, Nakamura's 18 marker genes fall short of the 22-gene minimum if reference genes are excluded from the count — and there are strong reasons to exclude them, as the reference genes are use

2. Classifier Type. Nakamura's elastic net model is a penalized linear regression technique, not a non-linear ensemble machine-learning classifier of the type described in the '271 Patent's specification. GenomeDx should argue — and seek a claim construction — that "trained machine-learning classifier" encompasses methods such as random forests, support vector machines, and gradient-boosted decision trees (all specif

3. Post-Radical Prostatectomy Clinical Context. The '271 Patent's Claim 1 is specifically directed to "prognosing the likelihood of clinical recurrence following radical prostatectomy." Nakamura does not describe this clinical context. Nakamura's examples are drawn from breast cancer and colorectal cancer, and although the specification mentions prostate cancer in a list of potential applications, it does not describ

4. FFPE-Specific Methods (Claim 3). For dependent Claim 3, GenomeDx can argue that Nakamura does not disclose the FFPE-specific sample preparation and normalization methods required by the claim. Nakamura's passing reference to FFPE tissue does not constitute a disclosure of the specific normalization protocols necessary for reliable expression profiling from degraded FFPE-derived RNA. Prosecution History Support. It

IV. Detailed Analysis of Prior Art Reference 2: Gagnon (WO 2018/065413) A. Overview of Gagnon Title: "Multi-Gene Prognostic Assays for Urological Cancers" Publication Date: April 12, 2018 Applicant: Centre de Recherche du CHU de Québec (Québec, Canada) Named Inventor: Marc-André Gagnon Gagnon is a PCT (Patent Cooperation Treaty) international patent application published by the World intellectual Property Organizatio

1. Field-of-Use Differences. Gagnon covers "urological cancers" broadly — bladder, kidney, and prostate — without specifically teaching the post-radical prostatectomy prognosis context that is central to the GenomeDx patents. The prostate cancer examples in Gagnon appear to involve biopsy-based analysis rather than post-surgical specimens, which implicates different biological processes and clinical endpoints. A POSI

2. Absence of Machine-Learning Classifier. Gagnon does not disclose the use of machine-learning classifiers. Its prognostic methods are based on Cox regression and Kaplan-Meier analysis, which are established survival analysis tools but are not "machine-learning classifiers" as that term is used in the GenomeDx patents and understood in the field. The absence of machine-learning methodology in Gagnon means it cannot

3. Absence of Combined Genomic-Clinical Model. Gagnon does not teach the integration of genomic risk scores with clinical covariates. This is the defining feature of the '647 Patent, and Gagnon's failure to teach it means the Nakamura + Gagnon combination is insufficient to render the '647 Patent's claims obvious — a conclusion the Examiner reached during prosecution.

4. Gene Panel Differences. Gagnon's 24-gene prostate cancer panel shares only 11 genes with the '271 Patent's predefined gene set. This limited overlap undermines Helios's argument that a POSITA would have been motivated to select genes from Gagnon's panel for use in the claimed methods, because the specific gene selection is an inventive contribution.

5. Prosecution History of the '647 Patent. The Examiner's § 103 rejection of the '647 Patent based on Nakamura + Gagnon was overcome by Dr. Mehta's Rule 1.132 declaration, which established that the combined genomic-clinical model achieved unexpected improvements in predictive accuracy. This is powerful evidence against Helios's obviousness theory. While prosecution history does not bind a federal court, it is entitl

V. Detailed Analysis of Prior Art Reference 3: Thornberry et al. A. Overview of Thornberry Citation: Thornberry, R.M., Chen, L., and Patel, S.K., "Machine Learning Approaches to Genomic Risk Stratification in Prostate Cancer," Journal of Computational Oncology, Vol. 14, No. 3, pp. 287–301 (March 2015). Publication Date: March 2015 Prior Art Status: Thornberry was published in March 2015, which predates the filing dat

1. General Methodological Review vs. Specific Implementation. Thornberry is a review article that discusses the theoretical applicability of machine learning methods to genomic data. It does not disclose a specific, implementable classifier for prostate cancer prognosis. The distinction between knowing that a class of algorithms could theoretically be applied to a type of data and actually developing a specific, vali

2. No Specific Gene Panel. Thornberry does not identify a specific panel of 22 or more genes selected from a predefined gene set. The article uses publicly available full-transcriptome datasets without curating a specific gene panel. Gene selection — the process of identifying, from thousands of candidate genes, a specific panel of genes that collectively provide clinically meaningful prognostic information — is a cr

3. No Sample Preparation or Normalization Methods. Thornberry's computational experiments are performed on pre-existing gene expression datasets. The article does not address the laboratory steps of obtaining biological samples, extracting RNA from FFPE tissue, or normalizing expression data using a reference gene panel. These are specific technical steps recited in the claims that are absent from Thornberry's disclo

4. No Combined Genomic-Clinical Model. Thornberry does not describe integrating genomic risk scores with clinical covariates to generate a composite risk metric, as required by the '647 Patent. Thornberry's analysis is purely genomic.

5. Motivation to Combine. To combine Thornberry with Nakamura and/or Gagnon, Helios must show why a POSITA would have been motivated to select specific machine learning methods from Thornberry's survey, apply them to specific gene expression data from Nakamura's or Gagnon's disclosure, and arrive at the specific claimed methods — with a reasonable expectation of success. The mere fact that machine learning methods ex

6. Hindsight Reconstruction. The combination of Thornberry with Nakamura and/or Gagnon requires selecting specific teachings from each reference and combining them in a specific way to arrive at the claimed inventions. This kind of multi-reference combination, guided by the claims of the patent rather than by the teachings of the prior art itself, is precisely the type of hindsight reasoning that Graham v. John Deere