DETAILED ACTION
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 .
Status of the Claims
The amended claim set received 04 May 2026 has been entered into the application.
Claims 1, 8, and 12 have been amended.
Claims 2-3, 5-7, 9-11, 13-14, and 19 are previously cancelled.
Claims 1, 4, 8, 12, 15-18, and 20-25 are pending.
Priority
Applicant claims priority to provisional application 62/715,079 received 06 August 2018 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06 May 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 15 March 2026 and modified in view of the amendments filed 04 May 2026. It is noted the amendments received 04 May 2026 necessitated new ground(s) of rejection.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 8, 12, 15-18, and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Following the flowchart of MPEP 2106
Step I - Process, Machine, Manufacture or Composition
Claims 1, 4, 8, and 20-22 are drawn to a computing device, so a machine.
Claims 12, 15-18, and 23-25 are drawn to a computer-implemented method, so a process.
2A Prong I - Identification of an Abstract Ideas
Claims 1 drawn is a computing device while claim 12 is drawn to a computer-implemented method. Claims 1 and 12 encompass similar limitations and are examined similarly.
Claims 1 and 12 recite:
obtain gene expression data corresponding to the solid tumor tissue sample
This step can be performed in the human mind to follow instructions to gather gene expression data and is therefore an abstract idea.
segmenting the H&E-stained histopathology image into a plurality of tiles
This step encompasses following instructions to segment the H&E-stained histopathology image into tiles and is therefore an abstract idea. This step encompasses performing mathematical concepts to divide a larger image into smaller more manageable “tiles” using formulas relating to original size, tile size, and overlap which reads on abstract idea. Moreover, it is known that tiling encompasses mathematical formulas such as Total Width = (Num Tiles Across) * (Tile Width), Total Height = (Num Tiles Down) * (Tile Height). To exemplify, in order to tile a 100x100 pixel image to cover a 300x200 area, one would need 3 across (300/100) and 2 down (200/100), making a 3x2 grid.
concatenating and integrating a set of outputs of the gene expression neural network and a set of outputs of the imaging feature neural network in an integrated neural network comprising a third set of neural network layers to (i) learn relationships between the gene expression data and the sets of imaging features. and {ii) produce an integrated neural network output
This step can be performed in the human mind by organizing and combining information (i.e., gene expression and image feature neural network output) to integrate the data into a third neural network layer used for 1) learning relationships between gene expression data and imaging features and 2) to produce an integrated neural network output and is therefore an abstract idea. This step describes the integrated neural network as comprising a third set of neural network layers that 1) learns relationships between gene expression data and image feature and produces am integrated neural network output. This step encompasses using neural network layers to learn relationship and produce output which encompasses mathematical concepts of mathematical relationships between gene expression data and image features and encompasses performing calculations to produce an integrated neural network output which reads on abstract ideas. Here, it is known in the art that neural networks produce output related to mathematical operations (i.e., outputs through interconnected mathematical operations like weighted sums, biases, and activation functions (e.g., sigmoid, ReLU) which reads on abstract ideas. This step encompasses performing mathematical concepts for concatenating and integrating of gene expression and image feature information/data (i.e., output) into a neural network which reads on abstract ideas/mathematical concepts. It is known in the art that concatenating and integrating neural network inputs fundamentally involves extensive mathematics, primarily linear algebra (matrix multiplication, vector addition) and calculus (for training via backpropagation), which allows networks to learn complex patterns from combined data by adjusting weights and biases through weighted sums and nonlinear transformations which reads on abstract ideas. For example, concatenation encompasses vector/matrix mathematics for joining input vectors (features) or outputs from different layers/models into a single, longer vector or larger matrix while integration encompasses mathematically merging information from various streams, often relying on matrix operations and specialized units. Here, the step encompasses concatenation/integrating information (i.e., gene expression data and imaging features), manipulating the data using mathematical functions (i.e., gene expression neural network and image feature neural network), and organizing this information into a new form (i.e., integrated neural network output) which encompasses mathematical concepts. See MPEP 2106.04(a)(2)(A)(iv). Furthermore, and under BRI, the terms “concatenating” and “integrating” are interpreted as combining data.
Claims 4, 16-18, 20, 22-23, and 25 are further drawn to limitations that describe the abstract ideas of claims 1 and 12 and are therefore abstract ideas.
2A Prong II - Consideration of Practical Application.
Claims 1 and 12 do not recite any additional element which integrate the recited judicial exception into a practical application. Here, in the instant case, the claims provide a method of data analysis for inputting information into a neural network to output a set of lymphocyte subgroups in a solid tumor sample. As such, practicing the claims merely results in the inputting information into a neural network to output information which is an extra-solution activity that does not integrate the judicial exception into a practical application.
Additionally, the claims only recite the solution (i.e., identifying lymphocyte subgroups in a solid tissue sample) which attempt to cover any solution for identifying lymphocyte subgroups with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e., identifying lymphocyte subgroups in a solid tumor sample) which does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f). For example, the claims 1 and 12 recites identifying lymphocyte subtype in a solid tumor tissue sample but claims 1 and 12 and their associated dependent claims do not recite specific genes in order correlating specific lymphocyte subtypes to identify lymphocyte subtypes. To exemplify, claim 18 recites “performing gene selection” but previous and subsequent claims do not encompass or identify any genes associating with any subtypes of lymphocytes. As a further example, claims 1 and 12 and their associated dependent claims do not contain any analysis steps which will be used for identifying lymphocyte subgroups based on gene expression data and image features as claims 1 and 12 are merely drawn to inputting information (i.e., gene expression and image feature data) into neural networks to output identified lymphocyte subtypes.
Furthermore, the neural network is equivalent to merely adding the words “apply it”. Here, the neural networks (NN’s) (i.e., gene expression, image feature, and prediction) are used to generally apply the abstract idea without limiting how the NN’s function. The NN’s are described at a high level such that it amounts to using a computer with a generic NN’s to apply the abstract idea. These limitations do not recite using the NN’s to make predictions but merely recites inputting data into neural network layers to output information. For example, claims 1 and 12 recite the third neural network layers learn relationships and produce output which describes the outcomes of the NN’s without any details about how the outcomes (i.e., learn relationships and produced output, outputting lymphocyte subtypes) are accomplished. See MPEP 2106.05(f).
Thus, such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv). Therefore, the claims do not utilize the obtained gene expression data and the histopathology image data and the results of the abstract ideas to construct a practical application such as treating a subject, making a tangible object, or improving upon an existing technology.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
2B Analysis - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of using sample (i.e., solid tumor) of claims 1, 8, 12 does not add significantly more than the recited judicial exception because using solid tumor or tissue samples for sources of nucleic acid data that is subsequently analyzed by abstract ideas is deemed conventional. See MPEP 2106.05(d)(II).
The recited additional element of using computer process, components, and equipment of claims 1, 12, and 15 does not add significantly more than the recited judicial exception because using computers to analyze abstract ideas is merely tangential to the claimed method and is deemed well-understood, routine, and conventional. See MPEP 2106.05(b), 2106.05(d)(II), and 2106.05(g).
The recited additional element of using extracting data of claims 1, 12, 21, and 24 does not add significantly more than the recited judicial exception because extracting data that is subsequently analyzed by abstract ideas is deemed a well-understood, routine, and conventional extra-solution activity. See MPEP 2106.05(d)(II).
The recited additional element of using data inputting of claims 1, 8 and 12 does not add significantly more than the recited judicial exception because inputting data into a computer for subsequently analysis is merely tangential to the claimed method perform and is deemed well-understood, routine, and conventional extra-solution activity. See MPEP 2106.05(b), 2106.05(d)(II), and 2106.05(g).
The recited additional element of data gathering of claims 1 and 12 does not add significantly more than the recited judicial exception because obtaining histopathology images from tissue is deemed a well-understood and routine insignificant extra-solution activity for providing RNA sequencing data that is subsequently analyzed by the abstract idea. See MPEP 2106.05(d)(II)(i) and (v). To provide evidence of conventionality of obtaining histopathology image and image data, Cooper et al. teaches an example of whole slide imaging and data analysis [page 518 fig 2 and page 519 fig 3] (The Journal of Pathology, 2018-04, Vol.244 (5), p.512-524) (Cited in the Office Action mailed 08 February 2025). To provide further evidence of conventionality, Kothari teaches analyzing whole slide image from ovarian serous carcinoma biopsy [page 1100 figure 2] (Journal of the American Medical Informatics Association: JAMIA, 2013-11) (Cited in the Office Action mailed 08 February 2025).
The recited additional element of data gathering of claims 15 does not add significantly more than the recited judicial exception because using RNA sequencing data source coupled to a communication network is deemed a well-understood and routine insignificant extra-solution activity for providing RNA sequencing data that is subsequently analyzed by the abstract idea. See MPEP 2106.05(d)(II)(i) and (v). To exemplify conventionality of using RNA sequencing data source coupled to a communication network, Hackl et al. (Hackl) computational genomic tools for dissecting tumor-immune cell interactions which reviews utilizing a computer coupled to a next generation sequencing (NGS) machine for processing RNA sequencing data [page 442 box 1] (Nature Reviews Genetics volume 17, pages441–458 (2016) (Cited in the Office Action mailed 08 February 2025)).
In conclusion and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments, filed 04 May 2026, have been fully considered but the rejection is maintained.
The applicant states the claims are not directed to a judicial exception and are integrate into a practical application [remarks, page 8 B]. The Applicant states the claims integrate the recited judicial exception because they recite a specific technological implementation that addresses concrete technical problems in the field of computational pathology and provides measurable improvements over conventional approaches [remarks, page 8 B]. The Applicant points to the specification 0005, 0006, 0105, 0091, and 0040 for guidance [remarks, pages 8-9 B]. The Applicant points to Desjardins for clarification. The Applicant states the instant claims are similar to the claims of Desjardins by reciting a specific technical context [remarks, page 9 B]. The Applicant states the claims integrate a four-stage neural network architecture with specialized pathways for each modality [remarks, page 9]. The Applicant states the claims provide a specific improvement to accurately identify lymphocyte subtypes [remarks, pages 8-9].
In response, and as noted in Step 2A Prong II of the 101 analyses above, the claims do not integrate the recited judicial exception into a practice application because the claims are drawn to merely applying the exception.
With respect to the four-stage neural network, the neural network (NN’s) (i.e., gene expression, image features, prediction, integrated output), as noted in Step 2A Prong I above, read on “using” NN’s but provide nothing more than mere instructions to implement abstract ideas on a generic computer in a computing environment. Here, the NN’s are described at a high level such that it amounts to using tangential computer elements with generic NN’s to apply the abstract idea. See MPEP 2106.04(a)(2)(III)(C) (1-3) and 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and 2106.05(f). Additionally, similar to the AI Guidance and for compact prosecution, the NN’s are used to generally apply the abstract idea in a computing environment without limiting how the NN’s function such that to integrate gene expression data and image data into an integrated output of lymphocyte subtypes. These limitations only recite the outcomes of “inputting the integrated neural network output into a prediction neural network comprising a fourth set of neural network layers to output a set of lymphocyte subtypes in the solid tumor tissue sample” and without any details about how set of lymphocytes are selected for output. As such, the utilization of the four stage NN does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
Thus, even though the specification [page 21 para 0090] mentions improvements to identifying subtype lymphocytes, the generic and tangential nature of the generically recited neural networks (NN’s) which is, therefore, insufficient to integrate the judicial exception into a practical application such as an improvement to technology.
With respect to Desjardins, the improvement was with how the machine learning model itself operates or "effectively learn new tasks in succession whilst protecting knowledge about previous tasks" regarding 'catastrophic forgetting' in continual learning systems. Here, the instant claims teach a method using computer processes for processing gene expression data and image feature data for outputting a set of lymphocyte subtypes from a solid tumor sample. In contrast, the claims of Desjardins addressed the technical problem of 'catastrophic forgetting' in continual learning systems. Therefore, the claims are not patent eligible under Step 2A Prong II.
The Applicant states the claims recite additional elements that amount to significantly more than the recites judicial exception [remarks, page 10 C]. The Applicant provides a list of the additional elements (a-f) in the claim. The Applicant states the order of the claims is unconventional. The Applicant points to the specification [page 0005 and 0046] for guidance. The Applicant states the specific texture and intensity features are not generic data but rather specific computational features configured to capture information about the tumor immune microenvironment from H&E-stained images. The Applicant points to the specification [0077-0078] for guidance. The Applicant states the architecture which processes gene expression data and imaging features is processed through separate specialize neural network pathways before being concatenated and integrated, is a specific technical implementation that is not routine or conventional [remark, page 11]. The Applicant states the architecture, in which "the modularized dense layers" are concatenated "to create an integrated set of features" (see paragraph [0078]), is a specific technical implementation for integrating heterogeneous biological data that is not routine or conventional in the art.” [remarks, page 11]. The Applicant states the specification demonstrates the ordered combination achieves quantitative improvements over conventional approaches. The Applicant points to specification [0091 and 0105] for guidance [remarks, page 11-12]. The Applicant states the claims provide a technical solution to the problem of accurately identifying lymphocyte subtypes in solid tumors.” [remarks, page 12].
In response, and regarding additional element as listed (a), (c), and (e), and as noted in Step 2A Prong I of the 101 analysis above, (a) obtaining gene expression data step reads on following instructions to gather gene expression data, (c) segmenting reads on following instructions to segment the H&E-stained histopathology image into tiles and mathematical concepts. the four-stage neural network reads on abstract ideas. Furthermore, as noted in Step 2A Prong I above, (e) the neural network also reads on abstract ideas. Also, for compact prosecution, even if the neural network is considered an additional element for integrating and outputting data, outputting data is deemed a conventional extra-solution activity. See MPEP 2106.05(g).
With respect to the additional elements of (b) and (f), these elements are extra-solution activities. Thus, utilizing a neural network does not provide an unconventional use of computer elements. Furthermore, regarding the combination of additional elements demonstrating a quantitative improvement, improvements to technology are evaluated under Step 2A Prong II of the 101 analyses.
Here, the combination of steps is merely inputting gathered gene expression data and image feature data into a computer system for evaluation to output a set of lymphocyte subset which results in mere data output. Regarding paragraphs [0091 and 0105], the paragraphs do not demonstrate that claimed combination achieves quantitative improvements over conventional approaches because paragraph [0091] does not recite the quoted language “Pearson correlation. Of note, improvements were preferentially observed…”, and paragraph [0105] does not disclose demonstrating an improved accuracy but mentions “demonstrate a generalizable and flexible framework for clinical RNA-seq and imaging adapted for use broadening widespread pathological reporting of the immune infiltrate in tumor biopsies and guiding patient treatment decisions.” Furthermore, the argument is not persuasive because the claimed steps, individually and as a whole, do not provide significantly more because inputting data is a well-known and conventional additional element for providing data that is subsequently analyzed or acted upon by the abstract idea. Additionally, concatenating and integrating data are considered abstract ideas as described in Step 2A Prong I of the 101 analyses above.
Moreover, the claims encompass routine and conventional additional elements which does not provide significantly more because the claims do not encompass additional elements outside of tangential computer elements, nucleic acid sequencing, slide images, and data inputting and outputting. Here, the claimed steps recite obtaining data (i.e., gene expression data and image feature data), extracting data, inputting data into a neural network, and producing integrated neural network output which encompasses routine and conventional additional elements. Furthermore, with respect to the neural networks, the limitations are evaluated under Step 2A Prong I of the 101 analyses above as encompassing abstract ideas. Additionally, it is noted that neural network layers can range from 3 to hundreds-thousands.
As such, the claims are drawn to gathering and analyzing data (i.e., gene expression and image feature data) using conventional techniques (i.e., computer elements and nucleic acid sequencing) for inputting the results into a prediction neural network which is insufficient to be patient eligible under Step 2B of the 101 analyses because the additional elements do not provide significantly more than the recited judicial exception.
Conclusion
Claims 1, 4, 8, 12, 15-18, and 20-25 are rejected.
No claims are allowed.
Finality
THIS ACTION IS MADE FINAL. 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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights