DETAILED ACTION
Applicant’s response, filed 10/13/2025, has been fully considered. 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 .
Claim Status
Claim 7 is cancelled.
Claims 12 and 13 are newly added.
Claims 1-6 and 8-13 are currently pending and examined on the merits.
Claims 1-6 and 8-13 are rejected.
Information Disclosure Statement
The information disclosure statements submitted on 09/20/2021 and 02/15/2023 are in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Priority
The instant application is a 371 of PCT/US2020/022905 filed on 3/16/2020, which claims priority to U.S. Provisional Application 62/821,609 filed on 3/21/2019. At this point in examination, the effective filing date of claims 1-6 and 8-13 is 3/21/2019.
Claim Rejections - 35 USC § 101
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 1-6 and 8-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-6 and 8-13 are directed to a method (process) for predicting whether a wound will heal or will not heal. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1 and 6 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
training a machine-learning algorithm utilizing at least: gene-expression values for at least m genes from a first clinical encounter for each of a plurality of training subjects, wherein the gene expression values are derived from a sample of debrided wound tissue from each of the plurality of training subjects (i.e., mental processes, mathematical concepts);
applying the previously trained machine-learning algorithm to gene-expression values for a corresponding set of m genes from a new subject having a wound, wherein the gene-expression values are derived from a sample of debrided wound tissue collected from the new subejct (i.e., mental processes, mathematical concepts).
Dependent claim 6 further recites:
proposing an optimum wound treatment for the new subject based on the gene expression values from the new subject (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 2-5 and 8-13 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1 and 6 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claim 1 recites the additional non-abstract elements of data gathering:
presenting a prediction of whether the wound will heal generated by the previously trained artificial neural network machine-learning algorithm (claim 1).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-6 and 8-13 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claim 1 contains additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claim 1: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
[Step 2B: NO]
Therefore, claims 1-6 and 8-13 are patent ineligible under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments, see pages 5-11, filed 10/13/2025, with respect to claims 1 and 12-13, have been fully considered but they are not persuasive.
Applicant asserts that the use of debrided wound tissue for analysis in claim 1 is not a mental process or a mathematical concept (pg. 5, para. 2 of Applicant’s Remarks). This argument is not persuasive as this is not an active step recited in the claims and is merely extra information about the source of the data used in training and applying the machine learning algorithm.
With respect to the Applicant’s argument that claim 1 is not directed to a mental process and is not directed to a mathematical concept (pg. 5, para. 3 of Applicant’s Remarks), this argument is not persuasive because claim 1 encompasses the limitations identified as training a machine learning algorithm and applying the previously trained machine learning algorithm, which are mental processes and mathematical concepts.
With respect to the Applicant’s argument that the analysis of gene expression using a sample of debrided wound tissue is not a mental process or mathematical concept (pg. 5-6, para. 3-4 of Applicant’s Remarks), this argument is not persuasive. It is noted that the claims do not actively obtain a sample of debrided wound tissue from each subject, but rather utilize the gene expression values that were already derived from the samples. Furthermore, the source of the data gathered does not materially affect the data itself. The gene expression values are the product resulting from a process. “[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself.” See MPEP 2113.
With respect to the Applicant’s argument that the steps of utilizing gene expression values derived from debrided wound tissue samples and applying a machine learning algorithm to gene expression values for a corresponding set of m genes from a new subject having a wound integrate the judicial exception into a practical application (pg. 6, para. 2-3 of Applicant’s Remarks), this argument is not persuasive. The gene expression values gathered from wound tissue samples of each subject is a set of generically described gene expression values that were produced by a process. This is merely extra information about the source of the data used in training and applying the machine learning algorithm. Furthermore, the steps of training a machine learning algorithm and applying the previously trained machine learning algorithm were identified as mental processes and mathematical concepts and do not contain additional elements that can integrate the claim into a practical application. See MPEP 2106.04(II).
With respect to the Applicant’s argument that the use of debrided wound tissue for obtaining gene expression values represents a significant advancement and is an inventive concept (pg. 6-7, para. 3-4 of Applicant’s Remarks), this argument is not persuasive. An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As set forth above, the additional elements were identified as data gathering/extra solution activity and the use of debrided wound tissue is merely extra information about the source of the data used in training and applying the machine learning algorithm.
Applicant asserts that storing the sample of debrided wound tissue in RNA-stabilizing solution is not a mental process or a mathematical concept (pg. 7-8, para. 4-5 of Applicant’s Remarks). This argument is not persuasive as this is not an active step recited in the claims and is merely extra information about the source of the data used in training and applying the machine learning algorithm.
With respect to the Applicant’s argument that storing the sample of debrided wound tissue in RNA-stabilizing solution integrates the judicial exception into a practical application (pg. 8-9, para. 3-4 of Applicant’s Remarks), this argument is not persuasive. This is merely extra information about the source of the data used in training and applying the machine learning algorithm.
With respect to the Applicant’s argument that storing the sample of debrided wound tissue in RNA-stabilizing solution represents a significant advancement and is an inventive concept (pg. 9, para. 2 of Applicant’s Remarks), this argument is not persuasive. An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As set forth above, the additional elements were identified as data gathering/extra solution activity and the storage of debrided wound tissue is merely extra information about the source of the data used in training and applying the machine learning algorithm.
Applicant asserts that the previous treatment and 40% or less reduction in wound size is not a mental process, mathematical concept, or a natural product (pg. 9-10, para. 3-5 of Applicant’s Remarks). This argument is not persuasive as this is not an active step recited in the claims and is merely extra information about the source of the data used in applying the machine learning algorithm.
With respect to the Applicant’s argument that the previous treatment and 40% or less reduction in wound size integrates the judicial exception into a practical application (pg. 10, para. 3-4 of Applicant’s Remarks), this argument is not persuasive. This is merely extra information about the source of the data used in applying the machine learning algorithm.
With respect to the Applicant’s argument that the previous treatment and 40% or less reduction in wound size represents a significant advancement and is an inventive concept (pg. 10-11, para. 5 of Applicant’s Remarks), this argument is not persuasive. An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As set forth above, the additional elements were identified as data gathering/extra solution activity and the previous treatment and 40% or less reduction in wound size is merely extra information about the source of the data used in applying the machine learning algorithm.
Therefore, the rejection to claims 1-6 and 8-13 under 35 USC § 101 is maintained with modifications as necessitated by amendment of the claims, filed 10/13/2025.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-3, 8, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ferraro et al. (Integrative Biology, 2017, 9(4), 328-338), in view of Spiller et al. [US20170247758A1], as provided in the IDS filed 9/20/2021, and Casciani et al. (Proceedings of the 17th Southern Biomedical Engineering Conference, IEEE, 1998), as provided in the 892 filed 6/11/2025.
With respect to claim 1:
Regarding the recited training a machine-learning algorithm utilizing at least gene expression values, Ferraro et al. discloses gene expression of selected genes in mixed samples used as training data for predicting sample composition in mixed samples of M1 and M2 macrophages with predictive models including linear regression, logistic regression, support vector regression, and neural net models (pg. 3, para. 2, lines 6-10). This teaches training a machine learning model with gene expression data.
Regarding the recited applying the previously trained machine-learning algorithm to gene-expression values for a corresponding set of m genes from a new subject having a wound, wherein the gene-expression values are derived from a sample of debrided wound tissue collected from the new subject, Ferraro et al. discloses inputting gene expression values for genes of interest from debrided wound tissue samples of 8 new subjects into trained predictive models (pg. 7, para. 2, lines 1-10, pg. 7, para. 3, lines 3-11, pg. 27-28, Tables 5 and 6). This teaches applying a trained machine learning model to gene expression values for genes derived from samples of debrided wound tissue of new subjects.
Ferraro et al. does not disclose gene-expression values for at least m genes from a first clinical encounter for each of a plurality of training subjects, wherein the gene expression values are derived from a sample of debrided wound tissue from each of the plurality of training subjects.
However, Spiller et al. discloses gene expression data of 10 different genes in debrided wound tissue samples from participants, where the samples can be obtained during an initial medical encounter concerning the wound (pg. 2, col. 2, para. [0016], lines 2-3; pg. 17, col. 2, para. [0142], lines 1-11; pg. 18, col. 1, para. [0145]-[0146]). This teaches gene expression values for genes derived from samples of debrided wound tissue from a first clinical encounter.
Ferraro et al. does not disclose a clinical diagnosis of a wound for each of the associated training subjects at a second, temporally later clinical encounter.
However, Spiller et al. discloses dividing participants into healing and nonhealing groups based on whether their wound was completely healed within 70 days from an initial visit (pg. 18, col. 1, para. [0144], lines 14-23). This teaches a clinical diagnosis for each participant at a later clinical encounter.
Ferraro et al. and Spiller et al. do not disclose presenting a prediction of whether the wound will heal generated by the previously trained artificial neural network machine-learning algorithm.
However, Casciani et al. discloses calculating a healed or non-healed result for diabetic foot ulcers based on patient demographics and specific Dermagraft product parameters using trained Neural Net Analyses models (pg. 132, col. 1, para. 1, lines 11-20, pg. 132, col. 1, para. 3, lines 1-6). This teaches presenting a prediction of whether a wound will heal or not generated from a neural network model.
It would have been prima facie obvious to one of ordinary skill in the art to combine the machine learning algorithm using gene expression values disclosed by Ferraro et al. with gene expression values derived from debrided wound tissue samples disclosed by Spiller et al. and wound healing prediction disclosed by Casciani et al. One would be motivated to combine the machine learning algorithm with gene expression data from debrided wound tissue samples and wound healing prediction because given that debrided tissue was used as the tissue source, and since wound debridement is already a standard part of wound care, Spiller et al.’s approach has great potential to be easily incorporated in wound care regimen (pg. 22-23, col. 2, para. [0187], lines 4-8). This means gene expression values derived from debrided wound tissue samples can be easily incorporated into a machine learning algorithm for wound healing prediction. Casciani et al. discloses a dichotomous accuracy (heal/not heal) of 93% was achieved in the final network analysis (pg. 132, col. 1, para. 2, lines 20-22). This means that incorporating wound healing prediction into the machine learning model will be highly accurate. There is a likelihood of success, since all methods explore wound healing and are well known in the field of clinical sciences.
With respect to claim 2:
Spiller et al. and Casciani et al. do not disclose wherein the machine learning algorithm is an artificial neural network, a support vector machine, a binary classifier or series of binary classifiers or a decision tree.
However, Ferraro et al. discloses predictive models including linear regression, logistic regression, support vector regression, and neural net models (pg. 3, para. 2, lines 6-10). This teaches machine learning models such as a support vector machine.
With respect to claim 3:
Ferraro et al. and Casciani et al. do not disclose wherein m is selected from the group consisting of 10, 50, 100, 500 and 1000.
However, Spiller et al. discloses a selection of 10 different genes (pg. 17, col. 2, para. [0142], lines 1-6; pg. 19, col. 2, para. [0161], lines 1-5). This teaches m selected as 10 from the group.
With respect to claim 8:
Ferraro et al. and Casciani et al. do not disclose wherein the sample of debrided wound tissue is collected at the first clinical encounter, stored in RNA-stabilizing solution and frozen until analysis.
However, Spiller et al. discloses debrided tissue samples can be obtained during an initial medical encounter concerning the wound, stored in RNALATER solution and frozen in -80
°
C
until further analysis (pg. 2, col. 2, para. [0016], lines 2-3; pg. 18, col. 1, para. [0145]). This teaches debrided wound tissue collected at a first clinical encounter, stored in RNA-stabilizing solution, and frozen until analysis.
With respect to claim 10:
Ferraro et al. and Casciani et al. do not disclose wherein the wound is a diabetic ulcer.
However, Spiller et al. discloses recruiting thirteen patients with chronic diabetic foot ulcers to undergo wound debridement for the study (pg. 18, col. 1, para. [0144]-[0145]). This teaches the wound is a diabetic ulcer.
With respect to claim 11:
Ferraro et al. and Casciani et al. do not disclose wherein the wound is a diabetic foot ulcer, a diabetic ulcer of the leg, a venous ulcer or a pressure ulcer.
However, Spiller et al. discloses recruiting thirteen patients with chronic diabetic foot ulcers to undergo wound debridement for the study (pg. 18, col. 1, para. [0144]-[0145]). This teaches the wound is a diabetic foot ulcer.
With respect to claim 12:
Ferraro et al. and Casciani et al. do not disclose wherein the sample of debrided wound tissue is stored in RNA-stabilizing solution until analysis.
However, Spiller et al. discloses obtaining debrided wound tissue samples, storing it in RNALATER solution, and freezing it in -80
°
C
until further analysis (pg. 18, col. 1, para. [0145]). This teaches collecting and storing debrided wound tissue samples in RNA-stabilizing solution, subsequently frozen until analysis.
Claims 4-5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ferraro et al. (Integrative Biology, 2017, 9(4), 328-338), Spiller et al. [US20170247758A1], and Casciani et al. (Proceedings of the 17th Southern Biomedical Engineering Conference, IEEE, 1998) as applied to claims 1-3, 8, and 10-12 above, in view of Borys et al. (Acta diabetologica, 2018, 56, 115-120), as provided in the 892 filed 6/11/2025.
Ferraro et al., Spiller et al., and Casciani et al. are applied to claims 1-3, 8, and 10-12 above.
With respect to claim 4:
Ferraro et al., Spiller et al., and Casciani et al. do not disclose wherein the plurality of training subjects comprises: a first subject group receiving a first wound treatment, and a second plurality of subjects receiving a second wound treatment.
However, Borys et al. discloses two cohorts of patients that are treated with either negative pressure wound therapy (NPWT) or standard therapy (pg. 115, Abstract, Methods). This teaches two subject groups receiving two different wound treatments.
It would have been prima facie obvious to one of ordinary skill in the art to modify the machine learning algorithm predicting wound healing disclosed by Ferraro et al., Spiller et al., and Casciani et al. to incorporate subject groups receiving two separate wound treatments disclosed by Borys et al. One would be motivated to incorporate two cohorts of patients treated with different wound treatments because Borys et al. discovered that gene expression in patients with diabetic foot ulcers after treatment with NPWT showed some genes functionally involved in wound healing, which are potentially valuable molecular mechanisms for clinical application and development of new therapies (pg. 115, Abstract). These results show that patients receiving wound treatments will be a significant element in the machine learning model for predicting wound healing because of their gene expression data demonstrating involvement in wound healing. There is a likelihood of success, since these methods assess either wounds or wound treatments and are well known in the field of clinical sciences.
With respect to claim 5:
Ferraro et al., Spiller et al., and Casciani et al. do not disclose wherein: the training step further utilizes gene expression values associated with the first and second wound treatment for the associated training subjects.
However, Borys et al. discloses a heatmap illustrating gene expression levels corresponding to patients treated with standard therapy and individuals treated with negative pressure wound therapy (NPWT) (pg. 115, Abstract, Methods; pg. 118, Fig. 1, lines 1-8). This teaches gene expression values associated with two separate wound treatments for patients.
Ferraro et al., Spiller et al., and Borys et al. do not disclose wherein: the applying step further provides a candidate wound treatment as an input to the previously trained machine-learning algorithm.
However, Casciani et al. discloses Neural Net Analyses (NNA) models using specific Dermagraft product parameters and patient demographics as input (pg. 132, col. 1, para. 1, lines 8-20; pg. 132, col. 1, para. 2, lines 1-10). This teaches providing wound treatment parameters as an input to neural networks.
With respect to claim 9:
Ferraro et al., Casciani et al., and Borys et al. do not disclose wherein the gene expression values are derived by quantitative real-time polymerase chain reaction or by using a multiplex or high throughput gene expression analysis platform.
However, Spiller et al. discloses measuring gene expression data using quantitative polymerase chain reaction (pg. 8, col. 2, para. [0105], lines 1-3; pg. 17, col. 2, para. [0143], lines 13-15). This teaches deriving gene expression values by quantitative real-time polymerase chain reaction.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ferraro et al. (Integrative Biology, 2017, 9(4), 328-338), Spiller et al. [US20170247758A1], and Casciani et al. (Proceedings of the 17th Southern Biomedical Engineering Conference, IEEE, 1998) as applied to claims 1-3, 8, and 10-12 above, in view of Jiang et al. [US20120315637A1].
Ferraro et al., Spiller et al., and Casciani et al. are applied to claims 1-3, 8, and 10-12 above.
With respect to claim 6:
Ferraro et al., Spiller et al., and Casciani et al. do not disclose proposing an optimum wound treatment for the new subject based on the gene expression values from the new subject.
However, Jiang et al. discloses using gene expression to determine classification and outcome of a particular wound and match the treatment accordingly, ensuring that individuals receive treatment tailored to their wound status (pg. 1, col. 1, para. [0003]; pg. 1-2, col. 2, para. [0012]-[0015]). This teaches proposing an optimum wound treatment for a subject based on their gene expression.
It would have been prima facie obvious to one of ordinary skill in the art to modify the machine learning algorithm predicting wound healing disclosed by Ferraro et al., Spiller et al., and Casciani et al. to incorporate proposing an optimum wound treatment disclosed by Jiang et al. One would be motivated to incorporate determining optimum wound treatments based on gene expression because the method disclosed by Jiang et al. is relatively straightforward to perform and provides an accurate indication of the likely outcome, before or during treatment, of a wound (pg. 1, col. 2, para. [0011], lines 1-5). This means the determination of optimum wound treatments in the machine learning model will be straightforward to perform and accurate. There is a likelihood of success, since all methods explore wound healing and are well known in the field of clinical sciences.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ferraro et al. (Integrative Biology, 2017, 9(4), 328-338), Spiller et al. [US20170247758A1], and Casciani et al. (Proceedings of the 17th Southern Biomedical Engineering Conference, IEEE, 1998) as applied to claims 1-3, 8, and 10-12 above, in view of Ud-Din et al. (PLoS ONE, 2015, 10(4), 1-22).
Ferraro et al., Spiller et al., and Casciani et al. are applied to claims 1-3, 8, and 10-12 above.
With respect to claim 13:
Ferraro et al., Spiller et al., and Casciani et al. do not disclose wherein, prior to sample collection, the new subject had previously underwent wound treatment resulting in a 40% or less reduction in wound size.
However, Ud-Din et al. discloses assessing wound healing after electrical stimulation in acute cutaneous wounds inducing angiogenesis, where wound diameter had 11.1%, 16%, and 16.2% decreases on days 10, 14, and 90, respectively (pg. 10, para. 2; pg. 19, para. 1). This teaches a subject previously undergoing electrical stimulation induced angiogenesis resulting in <40% reduction in wound diameter.
It would have been prima facie obvious to one of ordinary skill in the art to modify the machine learning algorithm predicting wound healing disclosed by Ferraro et al., Spiller et al., and Casciani et al. to incorporate subjects previously undergoing wound treatment resulting in <40% reduction in wound size disclosed by Ud-Din et al. One would be motivated to incorporate patients having undergone wound treatment because electrical stimulation accelerates acute cutaneous wound healing as evidenced by the reduction in wound diameter (pg. 19, para. 1, lines 1-2). This means incorporating subjects that have previously underwent wound treatment resulting in <40% reduction in wound size will allow the machine learning model to predict if there is accelerated wound healing or not. There is a likelihood of success, since all methods explore wound healing and are well known in the field of clinical sciences.
Response to Arguments
Applicant’s arguments, see pg. 11-21, filed 10/13/2025, with respect to the rejection(s) of claim(s) 1-13 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ferraro et al. (Integrative Biology, 2017, 9(4), 328-338).
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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