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 .
Claim Status
Claims 1-20 were currently pending in the application filed October 26, 2023. As of the remarks and amendments filed February 20 2026, claims 1, 4-11, and 13-20 are amended. Claims 2-3 and 12 are cancelled and no claims are added. Accordingly, claims 1, 4-11, and 13-20 are currently pending in the application.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/26/2023 and 05/02/2024 have been considered by the Examiner.
Drawings
The objections to the drawings are removed in response to the remarks and arguments filed February 20 2026.
Specification
The objections to the specifications are removed in response to the remarks and arguments filed February 20 2026.
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, 4-11, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts without significantly more. The claims recite statutory categories:
Regarding claim 1, under step 1 the claim recites an apparatus and falls under a statutory category. Under step 2A prong 1, the claim recites limitation “input the first medical image and the second medical image in the memory into a gene mutation classification model to generate a first gene mutation classification result and a second genetic mutation classification result” , and “judge consistency of the gene classification model based on validity of gene mutations by: calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene; and judging consistency of the gene mutation classification model based on the first gene mutation classification result and the second gene mutation classification result the model consistency score, the model consistency score evaluating validity of the gene mutations.” These limitations amount to a mental process of classifying genes and judging the consistency of that classification. These limitations also amount to mathematical process due to the calculation of the model consistency score. These steps could practically be performed in the human mind. A human oncologist could review gene mutation classifications from two time points and determine whether the changes are consistent with medical principles. This is an observation, evaluation, or judgment of data – clearly within the "mental processes" grouping of abstract ideas under the July 2024 US PTO guidance.
Under step 2A prong 2, the claim recites additional limitation “acquire a first medical image and a second medical image acquired in at least a first-time phase and a second time phase.” This amounts to mere data gathering which fails to integrate the claim into a practical application. Under step 2B, the claim fails to recite any additional limitations which amount to significantly more than the abstract idea.
Regarding claim 19, the independent method claim follows the same logic as claim 1 above and recites no additional limitations.
Regarding claim 20, the independent claim follows the same logic as claim 1 above and recites additional limitation “A non-transitory computer-readable storage medium storing computer executable instructions, wherein the instructions, if executed by a processor, cause the processor to perform a method.” This amounts to generic computer processing components which fail to integrate the claim into a practical application or amount to significantly more.
Regarding claim 4, the claim adds limitations “ the validity includes that: it is medically valid that a tumor acquires a new gene mutation while continuing an existing mutation; it is medically valid that a gene mutation of a tumor continues with increasing complexity; and extinction of a gene mutation in a tumor is rare and not medically valid, and the change represents, for each gene, acquisition of a mutation, continuation of no mutation, continuation of a mutation, and extinction of a mutation.” This amounts to a mental process and fails to remedy the abstract idea of claim 1.
Regarding claim 5, the claim adds limitations judge the consistency by “evaluating a degree of conformance of a change between the first gene mutation classification result and the second gene mutation classification result for each gene to the validity based on the change and a duration for which a mutation represented by the change has continued.” This amounts to a mental process and fails to remedy the abstract idea of claim 1.
Regarding claim 6, the claim adds limitations “if the change is continuation of no mutation or continuation of a mutation, calculate the consistency score to have a positive value corresponding to a duration for which no mutation or the mutation has continued.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 7, the claim adds limitations “if the change is extinction of a mutation, calculate the consistency score to have a negative value corresponding to a duration for which the mutation has continued.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 8, the claim adds limitations “if the change is acquisition of a mutation, calculate the consistency score based on a predetermined positive value.” This amounts to mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 9, the claim adds limitations “if the change is acquisition of a mutation and a gene that has acquired the mutation satisfies a predetermined co-occurrence relationship, calculate the consistency score to have a value corresponding to a sum of the positive value and a fixed value.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 10, the claim adds limitations “if the change is acquisition of a mutation and a gene that has acquired the mutation does not satisfy a predetermined exclusivity relationship, calculate the consistency score to have a value corresponding to the positive value minus a fixed value.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 11, the claim adds limitations “if a result of a gene test indicates presence of a mutation while the change is continuation of no mutation or extinction of a mutation, calculate the consistency score to have a predetermined maximum negative value.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 13, the claim adds limitations “withhold judgment of the consistency if gene mutation treatment is performed between the first-time phase and the second time phase.” This amounts to a mental process and fails to remedy the abstract idea of claim 1.
Regarding claim 14, the claim adds limitations “generate the first gene mutation classification result and the second gene mutation classification result for each of a plurality of gene mutation classification models, judge the consistency for each of the plurality of gene mutation classification models, and select a gene mutation classification model having said consistency from among the plurality of gene mutation classification models based on a result of judgment of the consistency.” This amounts to a mental process and fails to remedy the abstract idea of claim 1.
Regarding claim 15, the claim adds limitations “generate a medical image by inputting, into an image generation model, a medical image acquired in a time phase corresponding to that of a missing one of the first medical image and the second medical image, and acquire the generated medical image as the missing one of the first medical image and the second medical image.” This amounts to a data gathering and fails to remedy the abstract idea of claim 1.
Regarding claim 16, the claim adds limitations “calculate the consistency score by collating a third gene mutation classification result corresponding to a first image feature amount of the first medical image with the first gene mutation classification result and collating a fourth gene mutation classification result corresponding to a second image feature amount of the second medical image with the second gene mutation classification result.” This amounts to mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 17, the claim adds limitations “calculate the consistency score with a fixed value added if collation results both indicate a match.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
Regarding claim 18, the claim adds limitations “calculate the consistency score with a fixed value subtracted if at least one of collation results indicates a mismatch.” This amounts to a mathematical concept and fails to remedy the abstract idea of claim 1.
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.
Claims 1, 19 and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Art 1 (JP-2019537714-A) in view of Saalbach (EP-3859743-A1) and Quinn (US20220411876A1).
Regarding claim 1, Art 1 teaches:
A medical information processing apparatus comprising a memory, and a processor: (Art 1, [0014]; “a processor (e.g., of a network or Internet host server) and a memory … above.”)
acquire, from an image database (Art 1, [0015];” the medical images in the database”), a first medical image and a second medical image acquired in at least a first time phase and a second time phase; (Art 1, [0034]; “receiving and storing, over time, a plurality of medical images of the first patient, each obtained at a different time (e.g., at a different visit to one or more doctors), to obtain the series of medical images of the first patient.”)
store the first medical image and the second medical image to the memory (Art 1, [0058]; “And a memory having … over time, a plurality of medical images for each of the one or more patients;”)
input the first medical image and the second medical image in the memory (Art 1, [0058]; “ a memory”) into a gene mutation classification model to generate a first gene mutation classification result and a second genetic mutation classification result; (Art 1, [0137]; “ (i) receiving and storing sets of medical images in a database; (ii) accessing one or more of the medical images for transmission to the user for display on a user computing device; (iii) automatically analyzing, by the processor, the medical images to compute a risk index (e.g., BSI) and/or to generate a risk map; (iv) generating a radiologist report for a patient according to the patient images and/or risk index/ risk map; and applying a machine learning algorithm to update a process for the automatic analysis of function (iii). ” )
Art 1 fails to teach:
judge consistency of the gene classification model based on validity of gene mutations by: calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene;
judge consistency of the gene mutation classification model based on the first gene mutation classification result and the second gene mutation classification result the model consistency score,
Saalbach teaches:
judge consistency of the gene classification model (Saalbach, [0005]; “calculating a likelihood score for each medical condition outputs …the machine learning model;”) based on validity of gene mutations by:
evaluating validity of the gene mutations (Saalbach, [0005]; “evaluated based upon the calculated likelihood score and a severity of the medical condition outputs.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach. The motivation for the combination is to judge the consistency of the gene classification model based on the validity of the gene mutations. (Saalbach, page 1; fig 2)
Saalbach fails to teach:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene; and
judging consistency of the gene mutation classification model based on the model consistency score
Quinn teaches:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene (Quinn, [0004];” based on a first mutant allele fraction (MAF) and a second MAF, an MAF ratio, determining, for the subject, a weighted mean of the MAF ratios, determining, based on the weighted mean of the MAF ratios”)
judging consistency of the gene mutation classification model based on the model consistency score(Quinn, [0004]; “an MAF ratio”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach and Quinn. The motivation for the combination is to calculate the classification result based on the gene mutations (Quinn, [0004]; “determining, for the subject, a weighted mean of the MAF ratios”)
Regarding claim 19, Art 1 teaches:
A medical information processing method comprising: (Art 1, [0014]; “ the processor to perform one or more of functions (i) to (v) as follows: (i) receive and store medical images [e.g., comprising one or more of the following: targeted PET images, targeted SPECT images, computed tomography (CT) images, magnetic resonance (MR) images, ultrasound (US) images, gamma camera (i.e. scintillation camera) images, and combinations, fusions, or derivatives of any of the above] in a database [e.g., wherein the targeted PET/SPECT/gamma camera image(s) are obtained using one or more ”)
acquire a first medical image and a second medical image acquired in at least a first-time phase and a second time phase; (Art 1, [0034]; “receiving and storing, over time, a plurality of medical images of the first patient, each obtained at a different time (e.g., at a different visit to one or more doctors), to obtain the series of medical images of the first patient. ”)
store the first medical image and the second medical image to the memory (Art 1, [0058]; “And a memory having … over time, a plurality of medical images for each of the one or more patients;”)
input the first medical image and the second medical image in the memory (Art 1, [0058]; “ a memory”) into a gene mutation classification model to generate a first gene mutation classification result and a second genetic mutation classification result; (Art 1, [0137]; “ (i) receiving and storing sets of medical images in a database; (ii) accessing one or more of the medical images for transmission to the user for display on a user computing device; (iii) automatically analyzing, by the processor, the medical images to compute a risk index (e.g., BSI) and/or to generate a risk map; (iv) generating a radiologist report for a patient according to the patient images and/or risk index/ risk map; and applying a machine learning algorithm to update a process for the automatic analysis of function (iii). ” )
Art 1 fails to teach:
judge consistency of the gene classification model based on validity of gene mutations by: calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene;
judge consistency of the gene mutation classification model based on the first gene mutation classification result and the second gene mutation classification result the model consistency score,
Saalbach teaches:
judge consistency of the gene classification model (Saalbach, [0005]; “calculating a likelihood score for each medical condition outputs …the machine learning model;”) based on validity of gene mutations by:
evaluating validity of the gene mutations (Saalbach, [0005]; “evaluated based upon the calculated likelihood score and a severity of the medical condition outputs.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach. The motivation for the combination is to judge the consistency of the gene classification model based on the validity of the gene mutations. (Saalbach, page 1; fig 2)
Saalbach fails to teach:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene; and
judging consistency of the gene mutation classification model based on the model consistency score
Quinn teaches:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene (Quinn, [0004];” based on a first mutant allele fraction (MAF) and a second MAF, an MAF ratio, determining, for the subject, a weighted mean of the MAF ratios, determining, based on the weighted mean of the MAF ratios”)
judging consistency of the gene mutation classification model based on the model consistency score(Quinn, [0004]; “an MAF ratio”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach and Quinn. The motivation for the combination is to calculate the classification result based on the gene mutations (Quinn, [0004]; “determining, for the subject, a weighted mean of the MAF ratios”)
Regarding claim 20, Art 1 teaches:
A non-transitory computer-readable storage medium storing computer-executable instructions, wherein the instructions, if executed by a processor, cause the processor to perform a method comprising: (Art 1, [0014]; “ Each resource provider 702 may include computing resources. In some implementations, computing resources may include any hardware and / or software used to process data. For example, a computing resource may include hardware and / or software capable of implementing an algorithm, a computer program, and / or a computer application.”)
acquire a first medical image and a second medical image acquired in at least a first-time phase and a second time phase; (Art 1, [0034]; “receiving and storing, over time, a plurality of medical images of the first patient, each obtained at a different time (e.g., at a different visit to one or more doctors), to obtain the series of medical images of the first patient. ”)
store the first medical image and the second medical image to the memory (Art 1, [0058]; “And a memory having … over time, a plurality of medical images for each of the one or more patients;”)
input the first medical image and the second medical image in the memory (Art 1, [0058]; “ a memory”)into a gene mutation classification model to generate a first gene mutation classification result and a second genetic mutation classification result; (Art 1, [0137]; “ (i) receiving and storing sets of medical images in a database; (ii) accessing one or more of the medical images for transmission to the user for display on a user computing device; (iii) automatically analyzing, by the processor, the medical images to compute a risk index (e.g., BSI) and/or to generate a risk map; (iv) generating a radiologist report for a patient according to the patient images and/or risk index/ risk map; and applying a machine learning algorithm to update a process for the automatic analysis of function (iii). ” )
Art 1 fails to teach:
judge consistency of the gene classification model based on validity of gene mutations by: calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene;
judge consistency of the gene mutation classification model based on the first gene mutation classification result and the second gene mutation classification result the model consistency score,
Saalbach teaches:
judge consistency of the gene classification model (Saalbach, [0005]; “calculating a likelihood score for each medical condition outputs …the machine learning model;”) based on validity of gene mutations by:
evaluating validity of the gene mutations (Saalbach, [0005]; “evaluated based upon the calculated likelihood score and a severity of the medical condition outputs.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach. The motivation for the combination is to judge the consistency of the gene classification model based on the validity of the gene mutations. (Saalbach, page 1; fig 2)
Saalbach fails to teach:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene; and
judging consistency of the gene mutation classification model based on the model consistency score
Quinn teaches:
calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene (Quinn, [0004];” based on a first mutant allele fraction (MAF) and a second MAF, an MAF ratio, determining, for the subject, a weighted mean of the MAF ratios, determining, based on the weighted mean of the MAF ratios”)
judging consistency of the gene mutation classification model based on the model consistency score(Quinn, [0004]; “an MAF ratio”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Art 1 with Saalbach and Quinn. The motivation for the combination is to calculate the classification result based on the gene mutations (Quinn, [0004]; “determining, for the subject, a weighted mean of the MAF ratios”)
Claims 4-11 are rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach, and Quinn as applied to claim 1 above, and further in view of Topham (“Circulating Tumor DNA Identifies Diverse Landscape of Acquired Resistance to Anti-Epidermal Growth Factor Receptor Therapy in Metastic Colorectal Cancer”), Bailey (“Tracking Cancer Evolution through Disease Course”), and Pascual (“ESMO recommendations on the use of circulating tumour DNA assays for patient with cancer: a report from the ESMO Precision Medicine Working Group”)
Regarding Claim 4, the combination of Art 1, Saalbach and Quinn fails to teach:
the validity includes that: it is medically valid that a tumor acquires a new gene mutation while continuing an existing mutation, it is medically valid that a gene mutation of a tumor continues with increasing complexity; and extinction of a gene mutation in a tumor is rare and not medically value,
the change represents, for each gene, acquisition of a mutation, continuation of not mutation, continuation of a mutation, and extinction of a mutation.
Topham teaches:
the validity includes that: it is medically valid that a tumor acquires a new gene mutation while continuing an existing mutation (Topham, Fig 6, “ctDNA identifies multiple, independent resistance mutations acquired in patients receiving anti-EGFR therapy”)
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Before the time of filing, it would have been obvious to combine Topham with Art 1, Saalbach, and Quinn. The motivation for the combination is to evaluate the medical validity of the mutation being acquired while continuing an existing mutation. (Topham, page 485, paragraph 3, “Acquired mutations appeared as multiple concurrent subclonal alterations,”)
Art 1, Saalbach, Quinn and Topham fail to teach:
it is medically valid that a gene mutation of a tumor continues with increasing complexity; and extinction of a gene mutation in a tumor is rare and not medically value,
the change represents, for each gene, acquisition of a mutation, continuation of not mutation, continuation of a mutation, and extinction of a mutation.
Bailey teaches
it is medically valid that a gene mutation of a tumor continues with increasing complexity; (Bailey, page 7 paragraph 2; “WGD (whole genome duplication event) has been linked to poor prognosis and increased subclonal diversity.”)
Before the time of filing, it would have been obvious to combine Bailey with Art 1, Saalbach, Quinn, and Topham. The motivation for the combination is to evaluate the medical validity of the mutation behavior becoming complex. (Bailey, page 8, paragraph 3, “A survival analysis demonstrated that tumours with extensive chromosomal complexity and minimal intratumour heterogeneity of driver mutations were associated with poor outcome”)
Art 1, Saalbach, Quinn, Topham and Bailey fail to teach:
extinction of a gene mutation in a tumor is rare and not medically value,
the change represents, for each gene, acquisition of a mutation, continuation of not mutation, continuation of a mutation, and extinction of a mutation.
Pascual Teaches:
extinction of a gene mutation in a tumor is rare and not medically value, (Pascual, page 754 Col 2 paragraph 1, “False negatives -non-identification of a variant of interest when actually present in the tumour”)
the change represents, for each gene, acquisition of a mutation (Pascual, Page 759 Col 1 Para 1; “longitudinal ctDNA analysis to detect the early emergence of mutations of resistance before clinical progression.”)
continuation of not mutation, continuation of a mutation, (Pascual, page 759 Col 1 Paragraph 2; “A tumour-agnostic strategy can identify the emergent molecular heterogeneity of the tumour if a broad gene panel is used”)
and extinction of a mutation. (Pascual, page 753 Col 2 Paragraph 3; the potential for discordance with tumour testing, especially in cases where a variant is not detected in plasma DNA”)Before the time of filing, it would have been obvious to combine Art 1, Saalbach, Quinn, Topham, Bailey and Pascual. The motivation for the combination is to evaluate the medical validity of all the mutation behavior like acquisition of mutation, continuation or no continuation and extinction of mutation. (Pascual, page 750 paragraph 1, “detection of molecular residual disease or molecular relapse, has high evidence of clinical validity in anticipating future relapse in many cancers.”)
Regarding Claim 5, the combination of Art 1, Saalbach, Quinn, Topham, Bailey, and Pascual teaches:
wherein the processor is configured and calculate the model consistency score for the gene mutation classification model (Quinn, [0004];” based on a first mutant allele fraction (MAF) and a second MAF, an MAF ratio, determining, for the subject, a weighted mean of the MAF ratios, determining, based on the weighted mean of the MAF ratios”) by calculating a consistency score for each gene and summing up the consistency scores for respective genes, the consistency score for each gene (Quinn, [0245]; “ determining an MAF ratio (step 1503 ), determining a weighted mean of the MAF ratios (step 1504 ), determining a confidence interval associated with the weighted mean of the MAF ratios (step 1505 ), and outputting the weighted mean of the MAF ratios and the confidence interval (step 1506 ).”)being calculated by evaluating a degree of conformance (Pascual, page 753 paragraph 2, “variants being biological true positives in patients being evaluated with a ctDNA test (e.g. a patient with advanced carcinoma who has a JAK2 V617F mutation in ctDNA may have an undiagnosed myeloproliferative disorder”) of a change between the first gene mutation classification result and the second gene mutation classification result for each gene to the validity based on the change and a duration for which a mutation represented by the change has continued. (Quinn, [0010]; “determining, by the computer, mutant allele frequencies (MAFs) for a plurality of variants from sequence information generated from targeted nucleic acids associated with one or more cancer types in samples obtained from the subject at first and second time points to produce sets of first and second MAFs for each variant in the plurality of variants. The method also includes (b) calculating, by the computer, a ratio of the first and second MAFs for each variant in the plurality of variants to produce a set of MAF ratios and a corresponding standard deviation for each MAF ratio in the set of MAF ratios”
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to specify the importance of validating and verifying to confirm mutation behaviors are accurately being represented. (Pascual, Page 750 paragraph; “For patients with advanced cancer, validated and adequately sensitive ctDNA assays have utility in identifying actionable mutations”)
Regarding Claim 6, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teaches:
wherein the processor is configured to, if the change is continuation of no mutation or continuation of a mutation, calculate the consistency score to have a positive value corresponding to a duration for which no mutation or the mutation has continued. (Quinn, [0175]; “A maximum MAF may be determined as the maximum or largest MAF of all somatic variants present or observed in a given sample. In some embodiments, maximum MAF can be considered as tumor fraction of a given sample.”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. In this case, focus is on continuation of mutation behavior having a high score representing accuracy level same concept as claim 6. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
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Regarding Claim 7, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teach:
wherein the processor is configured to, if the change is extinction of a mutation, calculate the consistency score to have a negative value corresponding to a duration for which the mutation has continued. (Quinn, [0183]; “In some embodiments, one or more somatic variants having MAFs that are less than about 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, or 0.9% at the first and/or second time points may be excluded from further analysis.”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. In this case, focus is on extinct mutation behavior having a high score representing accuracy level same concept as claim 7. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
Regarding Claim 8, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teach:
wherein the processor is configured to, if the change is acquisition of a mutation, calculate the consistency score based on a predetermined positive value. (Quinn, [0203]; “a CHIP filter 300 may estimate, at step 301, a scaled likelihood function P.sub.i(R.sub.i) for each mutation/variant in a sample, where i=1, . . ., I.sub.mv is the index for each unique qualifying mutation/variants observed across the two time points for a given sample, assuming a total of I.sub.mv qualifying mutation/variants are observed”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. In this scenario, which is mutation being acquired, Quinn looks into acquired mutation which is observed across different time point same as claim 8. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
Regarding Claim 9, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teach:
wherein the processor is configured to, if the change is acquisition of a mutation and a gene that has acquired the mutation satisfies a predetermined co-occurrence relationship, calculate the consistency score to have a value corresponding to a sum of the positive value and a fixed value. (Quinn, [0149]; “The combination of genomic location may be selected based on data indicating that, for a particular cancer or set of cancers, a majority of subjects have one or more tumor markers in one or more of the selected regions”
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. Quinn discloses the existence of “one or more tumor markers” similar to a mutation to be in a predetermined co-occurrent relationship mentioned in claim 9 as well. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
Regarding Claim 10, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teach:
wherein the processor is configured to, if the change is acquisition of a mutation and a gene that has acquired the mutation does not satisfy a predetermined exclusivity relationship, calculate the consistency score to have a value corresponding to the positive value minus a fixed value. (Quinn, [0149]; “Alternately, tumor markers may be shown to occur independently in two or more regions in subjects having a cancer such that, combined, a tumor marker in the two or more regions is present in a majority of a population of subjects having a cancer”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. Quinn teaches markers occurring independently in two or more region similar to mutation in a predetermined exclusive relationship. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
Regarding Claim 11, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn teach:
wherein the processor is configured to, if a result of a gene test indicates presence of a mutation while the change is continuation of no mutation or extinction of a mutation, calculate the consistency score to have a predetermined maximum negative value. (Quinn, [0157], “The panel can allow for detection of tumor markers in sequenced cfDNA at a frequency in a sample as low as 0.01% to 0.0001%.”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Pascual with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the reference is to assign a scores based on the medical validity of the mutation behavior. Quinn talks about the low frequency of tumor detected by cfDNA which is same as low scores of mutations being extinct over time in claim 11. (Quinn, [0175], “The mutant allele fraction (MAF) represents the number of mutant molecules divided by the total number of molecules (e.g., molecular coverage) at a specific genomic position:”)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach and Quinn as applied to claim 1 above, and further in view of Hou (“Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer”).
Regarding Claim 13, the combination of Art 1, Saalbach and Quinn fails to teach:
wherein the processor is configured to withhold judgment of the consistency if gene mutation treatment is performed between the first-time phase and the second time phase.
Hou teaches:
wherein the processor is configured to withhold judgment of the consistency if gene mutation treatment is performed between the first-time phase and the second time phase. (Hou, Page 2 Col 2 Para 3; “The inclusion criteria for patients included the following: (a) pathologically confirmed cervical squamous cell cancer (CSCC); (b) radical hysterectomy and pelvic lymphadenectomy performed; (c) without any prior treatment before surgical resection”)
Before the time of filing, it would have been obvious to combine Art 1, Saalbach and Quinn with Hou. The motivation for the combination is to prevent previous treatment from affecting the evaluation of the classification model same as prior PCA treatment excluded from altering the AI model performance. (Hou, page 2 col 1 para 1; Moreover, radical trachelectomy, an emerging fertility-sparing treatment for cervical cancer, was not eligible for patients with LNM (9). Therefore, accurate prediction of LNM is crucial for treatment strategy decision and predicting prognosis of patients with cervical cancer.)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach and Quinn as applied to claim 1 above, and further in view of Fujii Satoshi (US-20230274428-A1).
Regarding Claim 14, the combination of Art 1, Saalbach, and Quinn fail to teach:
wherein the processor is configured to generate the first gene mutation classification result and the second gene mutation classification result for each of a plurality of gene mutation classification models, judge the consistency for each of the plurality of gene mutation classification models, and select a gene mutation classification model having said consistency from among the plurality of gene mutation classification models based on a result of judgment of the consistency
Fujii teaches:
wherein the processor is configured to generate the first gene mutation classification result and the second gene mutation classification result for each of a plurality of gene mutation classification models, judge the consistency for each of the plurality of gene mutation classification models, and select a gene mutation classification model having said consistency from among the plurality of gene mutation classification models based on a result of judgment of the consistency. (Fujii, [0089], “the model highest in performance based on the five evaluation indicators is determined as a feature prediction model.”)
Before the time of filing, it would have been obvious to combine Art 1, Saalbach and Quinn with Fujii. The motivation for the combination is to be able to select the gene classification model which accurately depicts the behavior of mutation the best. (Fujii, [0090]; “the performance of the determined gene mutation prediction model is validated, and the gene mutation prediction model is adopted if its performance fulfills the criterion.”)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach and Quinn as applied to claim 1 above, and further in view of Nao (US-11526991-B2).
Regarding Claim 15, the combination of Art 1, Saalbach, and Quinn fail to teach:
wherein the processor is configured to generate a medical image by inputting, into an image generation model, a medical image acquired in a time phase corresponding to that of a missing one of the first medical image and the second medical image, and acquire the generated medical image as the missing one of the first medical image and the second medical image
Nao teaches:
wherein the processor is configured to generate a medical image by inputting, into an image generation model, a medical image acquired in a time phase corresponding to that of a missing one of the first medical image and the second medical image, and acquire the generated medical image as the missing one of the first medical image and the second medical image. (Nao, Col 4 Line 18; “outputs the ultrasonic image generated by the image processing unit “)
Before the time of filing, it would have been obvious to combine Art 1, Saalbach, with Nao. The motivation for the combination is to be able regenerate images from an image generation unit. (Nao, Col 4 line 36;” an ultrasonic image output by an ultrasonic imaging apparatus 10 including the imaging unit 100 is transmitted to an image processing apparatus 21 by wired or wireless communication, and a result thereof is transmitted again, if necessary, to an inside of the ultrasonic imaging apparatus 10.”)
Claim 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach, Quinn, Topham, Bailey, Pascual and Quinn as applied to claim 5 above, and further in view of Yulan (US-20240331416-A1, effectively filed 11/11/2022)
Regarding Claim 16, the combination of Art 1, Saalbach, Topham, Bailey, Pascual and Quinn fail to teach:
wherein the processor is configured to calculate the consistency score by collating a third gene mutation classification result corresponding to a first image feature amount of the first medical image with the first gene mutation classification result and collating a fourth gene mutation classification result corresponding to a second image feature amount of the second medical image with the second gene mutation classification result.
Yulan teaches:
wherein the processor is configured to calculate the consistency score by collating a third gene mutation classification result corresponding to a first image feature amount of the first medical image with the first gene mutation classification result and collating a fourth gene mutation classification result corresponding to a second image feature amount of the second medical image with the second gene mutation classification result. (Yulan, [0006], “
According to another aspect of the present disclosure, a method of processing medical data is provided, including: acquiring first medical text data and second medical image data; obtaining a second image feature according to the second medical image data; inputting the first medical text data into the second feature extraction network to obtain a first text feature; fusing the second image feature with the first text feature to obtain a first fusion feature; and obtaining a first survival information according to the first fusion feature..”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Yulan with Art 1, Saalbach, Topham, Bailey and Quinn. The motivation statement to combine the references is to disclose how the combination of the second medical image data into fourth feature extraction network is same as collating fourth gene mutation classification result to second image feature amount. (Yulan, [0007], “inputting the second medical text data into a fourth feature extraction network to obtain second text features;”)
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Art 1, Saalbach, Quinn, Topham, Bailey, Pascual, Quinn, and Yulan as applied to claim 16 above, and further in view of Goto (JP-2011086202-A)
Regarding Claim 17, the combination of Art 1, Saalbach, Topham, Bailey, Pascual, Quinn and Yulan fails to teach:
wherein the processor is configured to calculate the consistency score with a fixed value added if collation results both indicate a match.
Goto teaches:
wherein the processor is configured to calculate the consistency score with a fixed value added if collation results both indicate a match. (Goto, [0024]; “a matching score indicating the degree of similarity is calculated based on the comparison result.”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Goto with Art 1, Saalbach, Topham, Bailey, Quinn and Yulan. The motivation statement to combine the references is to disclose how the collation results are calculated whether it is a match or a mismatch (Goto, [0058], “The matching score calculation unit 14a is a processing unit that compares the feature points and the like of the inputted face image with those of all the registered face images included in the registered information 13a, and calculates a score representing the degree of similarity based on the comparison result. The matching score calculation unit 14a also performs a process of registering the calculated score in the matching score 13b.”)
Regarding Claim 18, the combination of Art 1, Saalbach, Topham, Bailey, Pascual, Quinn, Yulan, and Goto teaches:
wherein the processor is configured to calculate the consistency score with a fixed value subtracted if at least one of collation results indicates a mismatch. (Goto, [0024]; “a matching score indicating the degree of similarity is calculated based on the comparison result.”)
Before the time of filing, it would have been obvious to one of ordinary skill to combine Goto with Art 1, Saalbach, Topham, Bailey, Quinn and Yulan. The motivation statement to combine the references is to disclose how the collation results are calculated whether it is a match or a mismatch (Goto, [0058], “The matching score calculation unit 14a is a processing unit that compares the feature points and the like of the inputted face image with those of all the registered face images included in the registered information 13a, and calculates a score representing the degree of similarity based on the comparison result. The matching score calculation unit 14a also performs a process of registering the calculated score in the matching score 13b.”)
Response to Arguments
Applicant's arguments filed on February 20 2026 have been fully considered but they are not persuasive.
35 U.S.C. 101
The examiner respectfully disagrees to the applicant’s first argument regarding the functions of the processor and memory. The Examiner appreciates Applicants notations of perceived technical advantage, however, to overcome the 101 rejection Applicants must show where each and every claimed limitation is integrated into a practical application with reference to the support in the specification to the cited application and technical advantage. Applicants are kindly encouraged to refer to MPEP section 2016.04(a)(2) and 2106.05(a).
The Examiner most respectfully disagrees with Applicants' assertion that acquiring and storing images and inputting the images into a trained model is not a mental process. Applicants are invited to consult MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022] section C. III. A. where examples are specifically recited of mental processes. Applicants may provide citations from the original specification, or may provide further evidence of the impracticality of performance in the mind. Because no specific citations have been provided from the specification or from examples likened to the presently claimed subject matter, the rejection is respectfully sustained.
35 U.S.C. 103
The Examiner most respectfully disagrees with Applicants' assertion that “…None of the cited references discloses such a processor.” As cited in the present rejection, Art 1 teaches the processor. Therefore, the rejection is respectfully sustained.
The Examiner most respectfully disagrees with Applicants' assertion that “There is no suggestion of a processor that judges consistency of the gene classification mode by calculating a model consistency score based on a change between the first gene mutation classification result and the second gene mutation classification result for each gene; and judging consistency of the gene mutation classification model based on the model consistency score, the model consistency score evaluating the validity of the gene mutations.”
In the present rejection, Baker and Saalbach are not being made to teach the processor, Art 1 is used to teach the processor element of claims 1-20.
Saalbach teaches evaluating the validity of a gene classification model by calculating a likelihood score which reflects the validity of classification outcomes. A person of ordinary skill in the art would recognize that likelihood score represents a model consistency score since it evaluates whether the classification results are valid across gene mutations. (Saalbach, [0005]; “calculating a likelihood score for each medical condition outputs …the machine learning model;”)
Along with Saalbach, Quinn teaches changes between first mutant allele versus second mutant allele, hence reflecting the changes between the first and second classification results. (Quinn, [0004];” based on a first mutant allele fraction (MAF) and a second MAF, an MAF ratio, determining, for the subject, a weighted mean of the MAF ratios, determining, based on the weighted mean of the MAF ratios”)
Therefore, at this time rejections under 35 U.S.C. 103 are maintained.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIVANGI SARKAR whose telephone number is (571)272-7262. The examiner can normally be reached M-F: 7:30-5:00.
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/SHIVANGI SARKAR/Examiner, Art Unit 2666 /EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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