DETAILED ACTION
Applicant’s response, filed Aug 19 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Status
Claims 1-3, 5, 7-8, 14-16, and 18-28 are pending.
Claims 4, 6, 9-13, and 17 are canceled.
Claims 21-28 are newly added.
Claims 1-3, 5, 7-8, 14-16, and 18-28 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/264,465, filed Nov 23 2021.
However, 63/264,465 does not provide support for “clinical data includes one or more of age, medical history, cancer treatment history, family history, past biopsy information, or cytology information” in claims 5 and 18.
Accordingly, each of claims 1-3, 7-8, 14-16, and 19-20 are afforded the effective filing date of the Nov 23 2021, and claims 5 and 18 are afforded the effective filing date of Oct 31 2022.
Response to Applicant Arguments
At p. 11-12, section III, Applicant submits that amended claims 5 and 18 find support in US provisional application 63/264,465 because one of ordinary skill in the art would understand the terms "clinical outcome data" and "clinical information about the patient", as used in the '465 Provisional, to refer to conventional clinical data, such as would be found in electronic health records or medical histories, including the types of clinical data recited in claims 5 and 18.
It is respectfully submitted that this is not persuasive. As set forth in MPEP 2163(II)(A)(3)(a)(ii)(b), to be entitled to an earlier priority date each claim limitation must be expressly, implicitly, or inherently supported in the originally filed disclosure. When an explicit limitation in a claim "is not present in the written description whose benefit is sought it must be shown that a person of ordinary skill would have understood, at the time the patent application was filed, that the description requires that limitation," noting that a subgenus is not necessarily implicitly described by a genus encompassing it and a species upon which it reads (Regents of the Univ. of Minnesota v. Gilead Scis., Inc., 61 F.4th 1350, 1356-58, 2023 USPQ2d 269 (Fed. Cir. 2023); the court found the later-filed patent claims could not receive benefit under 35 U.S.C. 120, because the earlier-filed applications did not have ipsis verbis disclosure of the claimed subgenus and did not provide sufficient blaze marks to provide the later-filed claims with sufficient support under 35 U.S.C. 112(a)).
Therefore, although one of ordinary skill in the art may understand the terms "clinical outcome data" and "clinical information about the patient" to refer to conventional clinical data, such as would be found in electronic health records or medical histories, as submitted by Applicant, it is not considered that one of ordinary skill in the art would consider these terms to be expressly, implicitly, or inherently supported. Therefore, claims 5 and 18 do not find support in the provisional application.
Information Disclosure Statement
The information disclosure statement (IDS) filed on Aug 19 2026 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The replacement drawing sheets submitted Aug 19 2026 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Specification
The amendments to the specification submitted Aug 19 2026 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Claim Objections
The outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
Claim Rejections - 35 USC § 102
The outstanding rejections from the previous Office Action are withdrawn in view of the amendments submitted herein. Specifically, the prior art to Grossmann et al. (eLIFE, 2017, 6, p.e23421) does not teach the newly included limitation in claims 1, 14, and 21 of “wherein the unpopulated gene network graph comprises a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an individual gene and each edge represents an interaction between genes”, as submitted by Applicant at p. 14-16, Section VII.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claims 1-2, 5, 14-15, 18, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Grossmann et al. (eLIFE, 2017, 6, p.e23421; previously cited) in view of Cline et al. (Nature Protocols, 2007, 2(10), pp.2366-2382; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
Claim 1 discloses a method for processing digital medical images to populate gene network graphs. Claim 14 discloses a system for processing digital medical images to populate gene network graphs, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations. Claim 21 discloses a non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for processing digital medical images to populate gene network graphs,
The prior art to Grossmann discloses an analysis of two independent cohorts of patients with lung cancer, which identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors, where a number of imaging features showed predictive value for specific pathways (abstract). Grossmann teaches employing a process called radiomics, which uses computer algorithms to process imaging data (p. 2, par. 1-6). As Grossmann teaches using computer algorithms, it is considered that Grossmann fairly teaches the system of claim 14, which discloses a generic computer and algorithms (i.e., instructions). Grossmann, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
The method of claim 1 and the operations of claims 14 and 21 comprise:
receiving one or more digital medical images associated with a subject;
Grossmann teaches obtaining computed tomography (CT) scans (i.e., digital medical images) of patients with tumors (p. 14, par. 3-4).
providing an unpopulated gene network graph and the one or more digital medical images as input to a machine learning system, wherein the unpopulated gene network graph comprises a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an individual gene and each edge represents an interaction between genes, the machine learning system having been trained to: infer a gene expression level for each node of the plurality of nodes, based on the one or more digital medical images; and populate each node of the plurality of nodes with the inferred gene expression levels; and
Grossmann teaches obtaining a preranked gene set enrichment algorithm of an expert-curated set of pathways (i.e., an unpopulated gene network graph) (p. 15, par. 2). Grossmann teaches calculating a correlation between the expression of every gene across all patients (i.e., for each of the plurality of patients) and radiomic features extracted from the CT scans, and inputting the resulting gene ranks into the preranked gene set enrichment analysis algorithm to obtain normalized enrichment scores (i.e., populating the gene network graph) (p. 15, par. 2). Grossmann teaches fitting (i.e., trained) univariate logistic regression models (i.e., machine learning system) of every features to predict the normalized enrichment score sign of pathways (i.e., trained to populate gene network graphs) (p. 15, par. 5 through p. 16, par. 1).
As Grossman teaches that although previous studies have indicated that gene expression can be predicted by imaging features, no previous study before theirs had examined and validated radiomic models for specific pathways (p. 13, par. 2; see also p. 16, par. 3), which reads on a machine learning system trained to infer a gene expression level as instantly claimed. Grossmann also teaches fitting (i.e., training) univariate logistic regression models (i.e., machine learning system) of every features to predict the normalized enrichment score sign of pathways (i.e., infer populated gene network graphs) (p. 4, par. 7; p. 15, par. 5 through p. 16, par. 1).
Grossmann teaches that in order to test whether radiomic features can predict if a pathway is activated or deleted in individual patients, the strongest predictor (i.e., features extracted from digital medical images) of each module (i.e., populated gene network graph) of coherently expressed features and pathways are validated in a separate dataset (p. 4, par. 7; p. 16, par. 1), which reads on inputting the unpopulated gene network graph and digital medical images into a machine learning system that has been trained as instantly claimed.
Grossman does not teach an unpopulated gene network graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an individual gene and each edge represents an interaction between genes. See below for teachings by Cline regarding this limitation.
receiving, as output from the machine learning system, a populated gene network graph comprising the inferred gene expression.
Grossmann teaches validating the strongest predictor of each module (i.e., populated gene network graph) of coherently expressed features and pathways in a separate dataset (p. 4, par. 7; p. 16, par. 1). Grossmann teaches displaying radiomic-pathway association modules based on the normalized expression scores given in Z-scores (i.e., populated gene network graph) (Figure 3(A)).
Grossman does not teach an unpopulated gene network graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an individual gene and each edge represents an interaction between genes.
However, the prior art to Cline discloses Cytoscape, a software package for visualizing, modeling, and analyzing molecular and genetic interaction networks by analyzing the results of mRNA expression profiling in the context of an interaction network obtained for genes of interest (abstract). Cline teaches that the major steps performed by Cytoscape include (i) obtaining a gene or network, (ii), displaying the network, (iii), and integrating with gene expression (i.e., populating the network with gene expression levels) and other functional attributes. Cline teaches that Cytoscape represents nodes as genes which are connected with edges representing pairwise interactions (p. 2367, col. 1). Cline teaches that the module “Obtain network data” describes methods to build networks for genes of interest by querying protein interaction databases and text-mining data sources (p. 2367, col. 2, par. 2; p. 2368, col. 1, par. 4 through p. 2370, col. 1, par. 1; p. 2371-2373, Steps 1-4). As Cline does not teach that the obtained networks are built based on mRNA expression data, it is considered that the obtained networks of Cline are not associated with any gene expression data, or are unpopulated as instantly claimed. Cline teaches that the module “Annotate with attribute and expression data” shows how to link expression profile data to the network (p. 2367, col. 2, par. 2 through p. 2368, col. 1, par. 1; p. 2370, col. 1, par. 3; p. 2374, Steps 14-32).
Regarding claims 1, 14, and 21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann and Cline because each reference discloses methods for analyzing gene expression data and networks. It would have been obvious to one of ordinary skill in the art to modify the method of Grossmann to analyze networks comprised of nodes and edges rather than pathways taught therein because such networks comprised of nodes and edges are known in the art as a common tool for visualizing and exploring interactions between genes and proteins, as taught by Cline (p. 2367, col. 1). Further, Cline teaches that methods for populating such networks with gene expression data are also known in the art (p. 2367, col. 2, par. 2 through p. 2368, col. 1, par. 1; p. 2370, col. 1, par. 3; p. 2374, Steps 14-32). Therefore, the substitution of the network taught by Cline for the curated pathways taught by Grossmann is no more than the simple substitution of one known element for another which would result in the predictable result of using a machine learning system to infer gene expression values from medical images, as taught by Grossmann, and mapping those values to a network comprised of nodes and edges, as taught by Cline, rather than a heatmap or other format as taught by Grossmann.
Regarding claims 2, 15, and 22, Grossmann in view of Cline teaches claims 1, 14, and 21 as described above. Claims 2, 15, and 22 further add receiving clinical data associated with the subject; and providing the clinical data as additional input to the trained machine learning system.
Grossmann teaches that the data used in the study include clinical data (p. 14, par. 3). Grossmann teaches including the clinical factors in the prediction model (p. 16, par. 3).
Regarding claims 5, 18. and 23, Grossmann in view of Cline teaches claims 1-2, 14-15, and 21-22 as described above. Claims 5, 18. and 23 further add that the clinical data includes one or more of age, medical history, cancer treatment history, family history, past biopsy information, or past cytology information.
Grossmann teaches that the data used in the study include clinical data (p. 14, par. 3). Grossmann teaches including the clinical factors in the prediction model (i.e., the training data includes clinical data) (p. 16, par. 3). Grossmann teaches that the clinical data include smoking status (i.e., medical history) and cancer type, stage, and site (i.e., past biopsy or cytology information) (Table 1).
Regarding claim 24, Grossmann in view of Cline teaches claim 1 as described above. Claim 24 further adds identifying, based on the populated gene network graph, an aberrant oncogenic signaling pathway.
Grossmann teaches using radiomic, genetic, and clinical information in their model to determine prognostic biomarkers for lung cancer (title; abstract; p. 4, final par.; through p. 6; p. 9), which reads on identifying an aberrant oncogenic signaling pathway as instantly claimed.
B. Claims 3, 16, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Grossmann in view of Cline, as applied to claims 1-2 and 14 above, and in further view of Schmauch et al. (Nature Communications, 2020, 11:3877, p. 1-15; cited on the Mar 24 2023 IDS).
Regarding claims 3 and 16, Grossmann in view of Cline teaches claims 1 and 14 as described above. Claims 3 and 16 further add that the one or more digital medical images comprise digital whole slide images, digital multiplex immunofluorescent images, or digital multiplex immunohistochemistry images, which Grossmann doesn’t teach.
However, the prior art to Schmauch discloses HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone (abstract; entire document is relevant).
Regarding claim 28, Grossmann in view of Cline teaches claims 1-2 as described above. Claim 28 further adds the machine learning system comprises a multi-modal deep neural network configured to process both the one or more digital medical images and the clinical data associated with the subject, which Grossmann does not teach.
However, Schmauch teaches using a deep-learning neural network model based on a multitask supervised approach that is applied to every tile of the slide (p. 2, col. 1, par. 4; p. 12, col. 2, par. 5 through p. 13, col. 1, par. 2), which reads on a multi0modal deep neural network.
Regarding claims 3, 16, and 28, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann in view of Cline and Schmauch because both references disclose methods for predicting gene expression values from medical imaging data. The motivation to use would have been to whole slide images as taught by Schmauch rather than the CT images taught by Grossmann would have been to facilitate patient diagnosis and prediction of response to treatment and survival outcome, as taught by Schmauch (p. 2, col. 1, par. 2). Further, Grossmann motivates such a modification by investigating the agreement between the radiomic score produced by their method examining CT images and immunohistochemical staining of tumor tissues (p. 9; Figure 4). Therefore, the substitution of the whole slide images taught by Schmauch for the CT scans taught by Swearingen is no more than the simple substitution of one known element for another. Regarding claim 28, it would be obvious to one of ordinary skill in the art to additionally then use the multitask neural network taught by Schmauch to analyze the slides of Schmauch. As Grossmann teaches incorporating clinical data into their model, it would have been obvious to one of ordinary skill in the art to retain this analysis when using the multitask neural network taught by Schmauch in the method of Grossmann in view of Cline.
C. Claims 7, 19, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Grossmann in view of Cline, as applied to claims 1 and 14 above, and in further view of Swearingen et al. (IET Biometrics, 2018, 7(1):71-80; newly cited; corresponds to NPL 9 on the Oct 31 2022 IDS).
Regarding claims 7 and 19, Grossmann in view of Cline teaches claims 1 and 14 as described above. Claims 7 and 19 further add that the machine learning system has been trained to infer gene expression levels by using one or more label propagation techniques, which Grossmann does not teach.
However, the prior art to Swearingen discloses the use of a graph structure (i.e., network) to model the relationship between the biometric records in a database and a label propagation scheme to deduce missing values for both binary-valued biographic attributes (e.g. gender) as well as multi-valued biographic attributes (e.g. age group) (abstract; p. 71, col. 1, par. 4 through p. 72, col. 1, par. 2; p. 74, section 2.3; p. 75-76, section 4; Fig. 3).
Regarding claim 27, Grossmann in view of Cline teaches claim 1 as described above. Claim 27 further adds that the machine learning system comprises a convolutional neural network configured to extract features from the one or more digital medical images, and wherein the features are used to infer the gene expression levels, which Grossmann does not teach.
However, Swearingen teaches that convolutional neural networks have been used to learn features and classify images (p. 72, col. 2, par. 2-3).
Regarding claims 7, 19, and 27, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann in view of Cline and Swearingen because both references disclose methods for evaluating data as graph structures with nodes, or networks. The motivation to use label propagation to impute missing values would have been to induce labels, which includes multi-valued and binary attributes, on incomplete nodes using the similarity information between all the nodes in the graph, as taught by Sweringen (p. 71, col. 2, par. 4 through p. 72, col. 1, par. 1). Therefore, the nature of the problem to be solved, missing values in a network, as well as the need to impute those missing values to use those networks for further analysis, would have led one of ordinary skill in the art to choose an appropriate missing label imputation method. Therefore, it would have been obvious to use the label propagation method as taught by Swearingen in combination with the gene networks produced by Grossmann to impute any missing values. Regarding claim 27, it would have been obvious to one of ordinary skill to use a convolutional neural network to extract features from the one or more digital medical images, because Swearingen teaches that convolutional neural networks are known in the art to learn features from images. Therefore, the substitution of a convolutional neural network for the model taught by Grossmann is therefore no more than the simple substitution of one known element for another, that would have produced the predictable result of extracting features from an image.
D. Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grossmann in view of Cline, as applied to claims 1 and 14 above, and Swearingen, as applied to claims 7 and 19 above, and in further view of Li (Physics Letters A, 2019, 383(8):732-737; cited on the Oct 31 2022 IDS).
Regarding claims 8 and 20, Grossmann in view of Cline teaches claims 1, 4, 6, 9, 14, and 17-18 and, in further view of Swearingen, claims 7 and 19, as described above. Claims 8 and 20 further add that the one or more label propagation techniques include directed label propagation. Grossmann does not teach these limitations.
However, the prior art to Li discloses an extension of one of the most famous scalable algorithms, label propagation algorithm (LPA), to a directed case which can recognize the flow direction among nodes in a network (abstract).
Regarding claims 8 and 20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann in view of Cline and Swearingen with Li because each reference discloses methods for evaluating data as graph structures with nodes, or networks. The motivation to use a directed label propagation method would have been to employ a method which can recognize the flow direction among nodes in the network, as taught by Li. Therefore, the substitution of the directed label propagation method taught by Li for the label propagation method taught by Swearingen is no more than the simple substitution of one known element for another.
E. Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Grossmann in view of Cline, as applied to claims 1 above, and in further view of Wang et al. (Computational and Structural Biotechnology, 2020, 18:3335-3343; newly cited).
Regarding claim 25, Grossmann in view of Cline teaches claim 1 as described above. Claim 25 further adds that the machine learning system comprises a graph neural network configured to process node and edge relationships of the unpopulated gene network graph, which Grossmann does not teach.
However, the prior art to Wang discloses an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi supervised framework (abstract). Wang teaches that GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link (i.e., edge relationships) between the two nodes (abstract; entire document is relevant), which reads on processing node and edge relationships as instantly claimed
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann in view of Cline with Wang because each reference discloses methods for examining relationships between genes. The motivation to use the graph neural network to process node and edge relationships would have been to construct gene regulatory network by integrating topological neighbor propagation through the whole gene network, as taught by Wang (abstract).
F. Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Grossmann in view of Cline, as applied to claims 1 above, and in further view of Laleh et al. (Biorxiv, Aug 10 2021, doi.org/10.1101/2021.08.09.455633, p.1-33; newly cited).
Regarding claim 26, Grossmann in view of Cline teaches claim 1 as described above. Claim 26 further adds that the machine learning system comprises a transformer neural network configured to receive tile embeddings derived from the one or more digital medical images, which Grossmann does not teach.
However, the prior art to Laleh discloses a systematic comparison of different artificial intelligence methods for extracting visual information from digitized histopathology slides (i.e., digital medical images), including vision transformers as one of the examined methods (abstract). Laleh teaches that Vision Transformer models are transformer neural networks that take as input flattened 2D patches from an image (i.e., tile embeddings) (p. 11, par. 4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Grossmann in view of Cline with Laleh because Grossmann and Laleh both teach methods for using artificial intelligence to analyze medical images. The motivation to use the vision transformer would have been because it achieved the highest absolute performance in the Laleh’s comparison (p. 4, par. 1). Therefore, one of ordinary skill in the art would have been motivated to improve the method of Grossmann by extracting features using a vision transformer, as taught by Laleh.
Response to Applicant Arguments
With respect to Applicant’s arguments under 35 USC 103, the arguments have been fully considered but are moot in view of the new grounds of rejection set forth above as necessitated by claim amendment herein.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANNA NICOLE SCHULTZHAUS whose telephone number is (571)272-0812. The examiner can normally be reached on Monday - Friday 8-4.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
