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 .
This action is in response to the application and preliminary amendment filed 1/24/2023. In the amendment, claims 1-17 were cancelled and claims 18-37 were added. Thus, claims 18-37 are pending and have been examined. Claims 18-37 are rejected.
Priority
The present application is a national stage entry (under 35 U.S.C. § 371) of international application no. PCT/JP2020/028900 filed 07/28/2020.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statement filed 1/24/2023, which complies with 37 CFR 1.97. As such, the information disclosure statement has been placed in the application file and the information referred to therein has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(3) because Figures 10, 11A-C, 14, 16A, 18 and 24 include letters which do not measure at least .32 cm. (1/8 inch) in height (see, e.g., most of the characters in elements 62-64 in FIGs. 10 and 11A-C, including the lowercase characters, most of the lowercase characters in element 652 of FIG. 14, most of the lowercase characters in element 66 of FIG. 16A, most of the lowercase characters in element 56 of FIG. 18, and most of the lowercase characters in element 73 of FIG. 24). See MPEP 507 (A) and 37 CFR 1.84(p)(3): Numbers, letters, and reference characters must measure at least .32 cm. (1/8 inch) in height.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency.
Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claims 18-33 recite a “non-transitory computer readable medium”; however, the specification does not provide proper antecedent basis for this claimed subject matter. With referenced to the block diagram of FIG. 28, paragraph 188 mentions “The program 97 is recorded in a portable recording medium 96.” However, the specification is silent regarding any “non-transitory computer readable medium” as recited in claims 18-33.
As such, claims 18-33 do not appear to have support in the originally filed specification. There does not appear to be any discussion of any “non-transitory computer readable medium”. The specification fails to mention, let alone describe or discuss any “non-transitory computer readable medium” as recited in claims 18-33. Appropriate correction is required.
Independent claims 25 and 27 both recite “a review request relevant to the update of the integrated DB” (see line 2 of these claims); however, the specification does not provide proper antecedent basis for this claimed subject matter. Paragraphs 155 and 161 of the specification mention “control unit 21 is capable of setting which expert is requested to review the draft report, on the basis of the specialty area recorded in the specialty area field of the expert DB.” and “system 10 that requests the expert to review the information recorded in the integrated DB 52.” However, the specification is silent regarding any “review request relevant to the update of the integrated DB” as recited in claims 25 and 27.
As such, claims 25 and 27 do not appear to have support in the originally filed specification. There does not appear to be any discussion of any request for a review or “review request” that is relevant to an update or modification of an integrated database (DB), much less any description or discussion of any “review request relevant to the update of the integrated DB” as recited in claims 25 and 27. As such, claims 25 and 27 do not appear to have support in the originally filed specification. Appropriate correction is required.
The second sentence of paragraph 25 states “a BAM format, a SAM format, or a CRAM format.”, the last sentence of paragraph 26 states “a VCF format or a BCF format.” and the first sentence of paragraph 28 states “a FASTQ format and the reference sequence are given, the file in the FASTQ format can be converted into the file in the BAM format, the SAM format, the CRAM format, and the VCF format”. The acronyms BAM, BCF, FASTQ, SAM and VCF should be spelled out the first time they are used. Although the terms BAM, BCF, FASTQ, SAM and VCF may be well understood in the art, the first time these acronyms and terms are used, applicant should fully spell out their full meanings to avoid possible confusion. Examiner notes that while these acronyms and terms are mentioned elsewhere in applicant’s specification, they are not defined or spelled out. However, it appears that BAM, BCF, FASTQ, SAM and VCF refer to various data file formats. Appropriate correction is required.
Claim Objections
Claims 21-29 and 30-34 are objected to because of the following informalities:
Independent claim 21 recites “acquiring, by the processor, training data in which genome data obtained by reading a base sequence included in a specimen” in lines 3-4. This recitation is grammatically incorrect and appears to be missing the word “is” between “data” and “obtained”. Appropriate correction is required.
Independent claims 22, 30 and 34 each recite “an integrated DB” (see, e.g., line 4 of claim 22 and the last limitation of claim 30, and line 5 of claim 34). The acronym DB should be spelled out the first time it appears in the claims. It appears that DB stands for database (see, e.g., the recitation of “an integrated database (DB) 52” in paragraph 12 of the specification). Although the term DB may be well understood in the art, the examiner suggests that the first time the “DB” acronym is used, applicant fully spell out the full meaning to avoid possible confusion. Appropriate correction is required.
Claim 34 recites “a specimen” in lines 3 and 4. It appears that the second recitation of “a specimen” in line 4 should read “[[a]] the specimen” to clearly refer to the previously-introduced “specimen” from line 3 (see, e.g., the recitation of “the specimen in line 6). Appropriate correction is required.
Claims 23-29 and 31-33, which depend directly or indirectly from claims 22 and 30, respectively, are objected to based on their respective dependencies from claims 22 and 30.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 18-37 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Independent claims 18, 21, 30, 35, 36 and 37 recite, using respective similar language, “a learning model for outputting a prediction relevant to the genetic mutation” (see, lines 6-7 of claim 18, lines 5-6 of claims 21 and 30, lines 5-6 of claims 35 and 36, and line 6 of claim 37). The term “a prediction relevant to the genetic mutation” is a relative term which renders the claims indefinite. This term is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification repeats the claim language in stating “learning model for outputting a prediction relevant to the genetic mutation based on the specimen” and provides general examples with reference to the high-level block diagrams of FIGs. 5 and 26 in stating “the learning model 53 is a learned model for outputting the prediction relevant to the genetic mutation according to the specimen.” and “the learning model 53 that outputs the prediction relevant to the genetic mutation.” (see, e.g., paragraphs 6, 17 and 181). Thus, the specification fails to describe or define what is meant by this term. In particular, it is unclear what metrics or standards are used for ascertaining the requisite degree of relevance for the claimed “prediction relevant to the genetic mutation”. Thus, the specification does not provide a standard for determining the requisite degree of relevance and optimality for the claimed “prediction relevant to the genetic mutation” in claims 18, 21, 30 and 35-37. For the purposes of determining patent eligibility and comparison with the prior art, the Examiner is interpreting “a prediction relevant to the genetic mutation” as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation. Appropriate correction is required.
Independent claims 22, 30 and 34 recite “an analysis result relevant to a specimen” and “medical information relevant to the genetic mutation” (see, lines 4 and 6 of claim 22, lines 9 and 11 of claim 30, lines 4 and 6-7 of claim 34). Lines 2-3 of dependent claim 24 also recites “medical information relevant to the genetic mutation”. The terms “an analysis result relevant to a specimen” and “medical information relevant to the genetic mutation” are relative terms which render the claims indefinite. In particular, it is unclear what metrics or standards are used for ascertaining the requisite degree of relevance for the claimed “analysis result relevant to a specimen” and “medical information relevant to the genetic mutation”. These terms are not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification repeats the claim language and provides general examples with reference to the flowchart and block diagrams of FIGs. 3, 25 and 27 in stating “a DB in which medical information relevant to the genetic mutation”, “a result relevant to the germline mutation is an estimation result”, “control unit 21 selects the medical information … and determines whether the information is the information relevant to the genetic mutation” and “output unit 85” and “output unit 87 outputs the report in which an analysis result relevant to the specimen and the version of the integrated DB 52 are recorded in association with each other, on the basis of the genetic mutation” (see, e.g., paragraphs 18, 47, 169 and 183-184). Therefore, the specification does not provide a standard for determining the requisite degree of relevance and optimality for the claimed “an analysis result relevant to a specimen” and “medical information relevant to the genetic mutation” in claims 22, 30 and 34. For the purposes of determining patent eligibility and comparison with the prior art, the Examiner is interpreting “an analysis result relevant to a specimen” as any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample and is interpreting and “medical information relevant to the genetic mutation” as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation. Appropriate correction is required.
Independent claim 22 also recites “where the date and the report output request at the date are received” (see, the last limitation of claim 22). There is insufficient antecedent basis for the term “the date” in this claim. Applicant previously introduced “an acquisition date” in line 7 of claim 22. However, as the claim subsequently recites “the date” instead of “the acquisition date”, it is unclear if the subsequently-recited “the date” refers to a date the report output request is/was received, a date value that is received, and/or to the previously-introduced “acquisition date”. For examination purposes, “the date” is being interpreted as any date, including, but not limited to a date the report output request is/was received, a date value that is received, and/or to the previously-introduced “acquisition date”. Appropriate correction is required.
Independent claim 34 also recites “a date in the past, a report output request at the date” (see, line 9 of claim 34). Applicant previously introduced “an acquisition date” in line 7 of claim 34. However, as the claim subsequently recites “the date” instead of “the date in the past” or “the acquisition date”, it is unclear if the subsequently-recited “the date” refers to the date the report output request is/was received, a date value that is received, the previously-introduced “date in the past”, and/or to the previously-introduced “acquisition date”. For examination purposes, “the date” is being interpreted as any date, including, but not limited to a date the report output request is/was received, a date value that is received, the previously-introduced “date in the past” and/or to the previously-introduced “acquisition date”. Appropriate correction is required.
Claims 25 and 27 both recite “a review request relevant to the update of the integrated DB” (see line 2 of these claims). The term “a review request relevant to the update of the integrated DB” is a relative term which renders the claims indefinite. This term is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Paragraphs 155 and 161 mention “control unit 21 is capable of setting which expert is requested to review the draft report, on the basis of the specialty area recorded in the specialty area field of the expert DB.” and “system 10 that requests the expert to review the information recorded in the integrated DB 52.” However, as noted above in the objection to the specification, the original specification fails to mention any request for a review or “review request”, much less any “review request relevant to the update of the integrated DB”. Thus, the specification fails to describe or define what is meant by this term. In particular, it is unclear what metrics or standards are used for ascertaining the requisite degree of relevance for the claimed “review request relevant to the update of the integrated DB”. Thus, the specification does not provide a standard for determining the requisite degree of relevance and optimality for the claimed “review request relevant to the update of the integrated DB” in claims 25 and 27. For the purposes of determining patent eligibility and comparison with the prior art, the Examiner is interpreting “a review request relevant to the update of the integrated DB” as any request for review or review request that is appropriate, relevant, salient or corresponding to the update of the integrated database (DB). Appropriate correction is required.
Also, claims 19-21, 23-29 and 31-33, which each depend directly or indirectly from claims 18, 22 and 30, respectively, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claims 18, 22 and 30.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 18, 21 and 35-37 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haque et al. (U.S. Patent Application Pub. No. 2016/0371431 A1, hereinafter “Haque”, cited in applicant’s information disclosure statement filed 1/24/2023).
Regarding independent claim 18, Haque discloses the invention as claimed including a non-transitory computer readable medium including program instructions which when executed by a processor causing a computer to execute a process (see, e.g., paragraph 20, “Further provided herein is a non-transitory computer-readable storage medium comprising computer-executable instructions for carrying out any of the methods described herein. Also provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein” [i.e., non-transitory computer-readable medium including instructions causing a processor of a computer to carry out/execute a process/method]) comprising:
acquiring, by the processor, training data in which genome data obtained by reading a base sequence included in a specimen and a genetic mutation according to the specimen are recorded in association with each other, for a plurality of genetic tests performed in the past (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 46, “the test genetic sequence variant is a human genetic sequence variant”, 56, “The simulated genetic sequence variants can be generated, for example, by mutating a base in the genetic sequence” [i.e., data obtained by reading a base sequence included in a human specimen] and 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., acquiring/receiving training data sets with labeled sequence variants with a base/benign genetic sequence in a specimen and pathogenic genetic sequence/genetic mutation recorded/saved in association with each other]); and
generating, by the processor, a learning model for outputting a prediction relevant to the genetic mutation1 based on the specimen in a case where the genome data obtained by reading the base sequence included in the specimen is input by setting the genome data as input and the genetic mutation as output (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] after training based on input benign data set/base data/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., generated/trained machine learning model outputs a prediction of a probability that the test genetic sequence variant is pathogenic/genetically mutated]).
Regarding independent claim 21, Haque discloses the invention as claimed including a non-transitory computer readable medium including program instructions which when executed by a processor causing a computer to execute a process (see, e.g., paragraph 20, “Further provided herein is a non-transitory computer-readable storage medium comprising computer-executable instructions for carrying out any of the methods described herein. Also provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein” [i.e., non-transitory computer-readable medium including instructions causing a processor of a computer to carry out/execute a process/method]) comprising:
acquiring, by the processor, training data in which genome data obtained by reading a base sequence included in a specimen (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 46, “the test genetic sequence variant is a human genetic sequence variant”, and 56, “The simulated genetic sequence variants can be generated, for example, by mutating a base in the genetic sequence” [i.e., acquiring/receiving training data by reading a base sequence included in a human specimen]); and
inputting, by the processor, the genome data to a learning model that outputs a prediction relevant to a genetic mutation2 upon input of genome data (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] based on input benign data set/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., machine learning model outputs a prediction of a probability that the input test genetic sequence variant is pathogenic/genetically mutated]); and
outputting, by the processor, the prediction output from the learning model, on the basis of the input genome data (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score” based on the input benign data set/genome sequence and paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 after training. … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting a genetic mutation prediction output]).
Regarding independent claim 35, Haque discloses the invention as claimed including an information processing device comprising: a processor executing program code to perform (see, e.g., paragraph 20, “provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein.”):
acquiring, by the processor, genome data obtained by reading a base sequence included in a specimen (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 46, “the test genetic sequence variant is a human genetic sequence variant” [i.e., acquiring/receiving genetic/genome data sets with labeled sequence variants by reading a base benign genetic sequence included in a human specimen]);
inputting, by the processor, the genome data to a learning model that outputs a prediction relevant to a genetic mutation3 upon input of genome data (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] based on input benign data set/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., machine learning model outputs a prediction of a probability that the input test genetic sequence variant is pathogenic/genetically mutated]); and
outputting, by the processor, the prediction output from the learning model, on the basis of the genome data (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score” based on the input benign data set/genome sequence and paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 after training. … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting a genetic mutation prediction output]).
Regarding independent claim 36, Haque discloses the invention as claimed including an information processing method for causing a processor of an information processing apparatus to perform processing (see, e.g., paragraphs 9, “provided is a computer-implemented method for predicting pathogenicity of a test genetic sequence variant, the method comprising, at an electronic device having at least one processor and memory, training a machine learning model based on training data” [i.e., an information processing method causing a processor of an electronic device/apparatus to perform processing]) for:
acquiring, by the processor, genome data obtained by reading a base sequence included in a specimen (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 46, “the test genetic sequence variant is a human genetic sequence variant” [i.e., acquiring/receiving genetic/genome data sets with labeled sequence variants by reading a base benign genetic sequence included in a human specimen]);
inputting, by the processor, the genome data to a learning model that outputs a prediction relevant to a genetic mutation4 upon input of genome data (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] based on input benign data set/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., machine learning model outputs a prediction of a probability that the input test genetic sequence variant is pathogenic/genetically mutated]); and
outputting, by the processor, the prediction output from the learning model, on the basis of the input genome data (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score” based on the input benign data set/genome sequence and paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 after training. … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting a genetic mutation prediction output]).
Regarding independent claim 37, Haque discloses the invention as claimed including a method for generating a learning model for causing a processor of an information processing apparatus to perform processing (see, e.g., paragraph 9, “provided is a computer-implemented method for predicting pathogenicity of a test genetic sequence variant, the method comprising, at an electronic device having at least one processor and memory, training a machine learning model based on training data” [i.e., a method for generating a machine learning model for causing a processor of an electronic device/apparatus to perform processing]) for:
acquiring, by the processor, training data in which genome data obtained by reading a base sequence included in a specimen sampled in the past and a genetic mutation according to the specimen are recorded in association with each other (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 33, “Area-under-the curve (AUC) values .. the AUCs generated by dataset bootstrap sampling”, 46, “the test genetic sequence variant is a human genetic sequence variant”, 56, “The simulated genetic sequence variants can be generated, for example, by mutating a base in the genetic sequence” [i.e., data obtained by reading a base sequence included in a human specimen previously sampled] and 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., acquiring/receiving training data sets with labeled sequence variants with a base/benign genetic sequence in a specimen and pathogenic genetic sequence/genetic mutation recorded/saved in association with each other]); and
generating, by the processor, a learning model for outputting a prediction relevant to the genetic mutation5 based on the specimen in a case where the genome data obtained by reading the base sequence included in the specimen is input by setting the genome data as input and the genetic mutation as output (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] after training based on input benign data set/base data/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., generated/trained machine learning model outputs a prediction of a probability that the test genetic sequence variant is pathogenic/genetically mutated]).
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.
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 19, 22-28, 30-31 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Haque in view of Chen et al. (U.S. Patent Application Pub. No. 2014/0278133 A1, hereinafter “Chen”, cited in applicant’s information disclosure statement filed 1/24/2023).
Regarding claim 19, as discussed above, Haque discloses the computer readable medium of claim 18.
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein the learning model outputs a predicted position of a mutated base.
In the same field, analogous art Chen teaches wherein the learning model outputs a predicted position of a mutated base (see, e.g., paragraphs 38, “the disease/variant data structures 310 may include … the genomic location and details about the genomic variants”, 40 “The variant analysis module 320 may … identify the genomic location of variants”, 45, “module … may use machine learning techniques … to predict likelihood of rare diseases.”, 49, “customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” and 62, “the chromosome view of the disease prediction report may display the location of relevant variants” [i.e., machine learning model outputs a predicted location/position of a disease mutation/mutated base]).
Haque and Chen are analogous art because they are both directed to systems and techniques for using machine learning for analyzing genetic sequences and genomic variants (See, e.g., Haque, Abstract, and Chen, Abstract and paragraphs 19 and 43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Haque to incorporate the teachings of Chen to provide techniques and systems that “use machine learning techniques … to predict likelihood of rare diseases” where a “validation step 350 may … verify that an identified variant that is predicted to cause one or more rare or common disease is not an artifact created by a sequencing error” and the systems and techniques generate a “customized report 360 for the physician [that] may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” (See, e.g., Chen, paragraphs 45 and 48-49). Doing so would have allowed Haque to be able to use Chen’s techniques and system “in order to accurately and inexpensively validate the existence of the identified variants”, as suggested by Chen (See, e.g., Chen, paragraph 48).
Regarding independent claim 22, Haque discloses the invention as claimed including a non-transitory computer readable medium including program instructions which when executed by a processor causing a computer to execute a process (see, e.g., paragraph 20, “Further provided herein is a non-transitory computer-readable storage medium comprising computer-executable instructions for carrying out any of the methods described herein. Also provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein” [i.e., non-transitory computer-readable medium including instructions causing a processor of a computer to carry out/execute a process/method]) comprising:
outputting, by the processor … an analysis result relevant to a specimen6 (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score”/analysis result relevant to an input benign data set/genome sequence and paragraphs 46, “the test genetic sequence variant is a human genetic sequence variant”, and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting an analysis result/genetic mutation prediction related to/relevant to a specimen]) and a version of an integrated DB are recorded in association with each other, on the basis of a genetic mutation detected from the specimen (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., on the basis of a genetic mutation/pathogenic sequence detected from the specimen] and 100-101, “labeled benign data set was obtained from the 1000 Genomics project by filtering the database for genetic sequence variants”, “the known pathogenic sequence variant testing data set was obtained from the Human Gene Mutation Database” [i.e., and a version of an integrated DB/database with the benign data set recorded/saved in association with genetic sequence variants]), and the integrated DB in which medical information relevant to the genetic mutation7 acquired from a plurality of information sources, and an acquisition date and basis information of the medical information are integrated in association with each other (see, e.g., Table 2 – listing Clinvar, HGMD, and 1000G databases which contain mutation data from a plurality of sources and are integrated with each other as of a data acquisition date, and paragraph 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., an acquisition date for the integrated DB in which medical information/genetic sequence data relevant to the mutation/pathogenic genetic sequence are integrated and associated with each other]); and
outputting, by the processor, … the analysis result relevant to the specimen8 (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score”/analysis result relevant to an input benign data set/genome sequence and paragraphs 46, “the test genetic sequence variant is a human genetic sequence variant”, 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting an analysis result/genetic mutation prediction related to/relevant to the specimen]) and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., on the basis of a genetic mutation/pathogenic sequence detected from the specimen] and 100-101, “labeled benign data set was obtained from the 1000 Genomics project by filtering the database for genetic sequence variants”, “the known pathogenic sequence variant testing data set was obtained from the Human Gene Mutation Database” [i.e., and a version of an integrated DB/database with the benign data set recorded/saved in association with genetic sequence variants]) and the integrated DB at a date in the past (see, e.g., paragraph 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., and the integrated DB at a date in the past]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose outputting … in a case where a report output request is received, a report in which an analysis result relevant to a specimen … and the integrated DB in which medical information relevant to the genetic mutation acquired from a plurality of information sources, … and basis information of the medical information are integrated in association with each other; and
outputting … in a case where the date and the report output request at the date are received, the report in which the analysis result relevant to the specimen and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen.
In the same field, analogous art Chen teaches outputting … in a case where a report output request is received, a report in which an analysis result relevant to a specimen9 (see, e.g., paragraphs 21, “DNA samples may be obtained from a plurality of patients” [i.e., a specimen/sample], 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting”, 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” and 51, “FIG. 4 is an illustrative user interface that may be … presented to a user to allow the user to generate customized variant analysis and disease likelihood reports including information regarding validation of such analysis and/or reports.” [i.e., when a report output request is received via the user interface from an audience member/user/physician, generate a report with an analysis result relevant to a specimen/sample]) … and the integrated DB in which medical information relevant to the genetic mutation10 acquired from a plurality of information sources, … and basis information of the medical information are integrated in association with each other (see, e.g., paragraphs 36, “extracting information related to disease-related genomic variants from a plurality of databases 305. … removing the low-quality data and irrelevant information from information received from the plurality of databases 305 may be included in the construction of the one or more disease/variant data structures 310”, 39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” 67, “the data stores in this disclosure may be implemented using a relational database” [i.e., an integrated database/DB/data structure with medical information relevant to the genetic mutation/disease acquired from a plurality of databases/information sources] and 38-39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” [i.e., and basis information of the medical information are integrated in association with each other]); and
outputting … in a case where the date and the report output request at the date11 are received, the report in which the analysis result relevant to the specimen and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen, and the integrated DB (see, e.g., FIGs. 4, 6A-D and 9A-B depicting user interfaces for requesting and outputting “DISEASE LIKELIHOOD REPORT” and a “Clinical Report” with analysis results relevant to the specimen as of a date, and paragraphs 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting” [i.e., output the report when a query/report output request at a date is received, query the integrated database/DB], 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” and 51, “user interface … presented to a user to allow the user to generate customized variant analysis and disease likelihood reports including information regarding validation of such analysis and/or reports.” [i.e., when the report output request is received via the user interface on a date from an audience member/user/physician, output the report with the analysis result relevant to the specimen/sample based on the genetic mutation detected from the specimen/sample]).
Haque and Chen are analogous art because they are both directed to systems and techniques for using machine learning for analyzing genetic sequences and genomic variants (See, e.g., Haque, Abstract, and Chen, Abstract and paragraphs 19 and 43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Haque to incorporate the teachings of Chen to provide techniques and systems that “use machine learning techniques … to predict likelihood of rare diseases” where a “validation step 350 may … verify that an identified variant that is predicted to cause one or more rare or common disease is not an artifact created by a sequencing error” and the systems and techniques generate a “customized report 360 for the physician [that] may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” (See, e.g., Chen, paragraphs 45 and 48-49). Doing so would have allowed Haque to be able to use Chen’s techniques and system “in order to accurately and inexpensively validate the existence of the identified variants”, as suggested by Chen (See, e.g., Chen, paragraph 48).
Regarding claim 23, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 22.
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein the report includes medical information extracted from the integrated DB by setting the genetic mutation detected from the specimen as a key.
In the same field, analogous art Chen teaches wherein the report includes medical information extracted from the integrated DB by setting the genetic mutation detected from the specimen as a key (see, e.g., FIGs. 4, 6A-D and 9A-B depicting “DISEASE LIKELIHOOD REPORT” including Possible Diseases based on Sequencing and Validation Methods, and a “Clinical Report” including medical information Disease Risks, Carrier Status, Traits, for a patient extracted from the integrated DB/database and paragraphs 16, “FIG. 9A is an embodiment of a disease prediction report template that may be generated and presented to a user with warnings of a probability of disease, which may include a bar chart representation of mutations and associated disease risk.” and 56, “FIG. 6B is an embodiment of a report including information such as variant, disease association, likelihood of disease and affected gene. … report 650 may include details about a particular variant … which includes a mutation” [i.e., the report includes medical information from the integrated DB extracted by setting the detected mutation from the patient sample/specimen as a key]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding claim 24, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 23.
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein, in a case where the integrated DB is updated by adding the medical information relevant to the genetic mutation, an additional report is output on the basis of the genetic mutation detected from the specimen, and the updated integrated DB.
In the same field, analogous art Chen teaches wherein, in a case where the integrated DB is updated by adding the medical information relevant to the genetic mutation12, an additional report is output on the basis of the genetic mutation detected from the specimen, and the updated integrated DB (see, e.g., FIGs. 4, 6A-D and 9A-B depicting “DISEASE LIKELIHOOD REPORT” sub-reports including Possible Diseases based on Sequencing and Validation Methods, and an additional “Clinical Report” and “DISEASE PREDICTION” report including added medical information relevant to the genetic mutation - Disease Risks, Carrier Status for a patient based on updated data from the integrated DB/database and paragraphs 37, “disease/variant data structures 310 may be set up to be automatically updated when new releases are available for the plurality of databases 305.” and 58, “FIG. 6D is an embodiment of details related to a particular genomic variant of a patient. … more detailed information regarding a potentially disease-related variant may be explored” [i.e., an additional report includes medical information from the updated integrated DB relevant to the genetic mutation/disease-related variant]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding claim 25, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 24.
Haque further discloses an incentive with respect to the received review result is recorded in association with the expert (see, e.g., paragraphs 3-4, “more informative and available diagnostic testing promises to not only benefit patients, but also improve the efficiency of the health care system overall.”, “in each patient, sequencing will reveal new genetic sequence variants and the clinician must determine if these newly-observed genetic sequence variants are likely to be pathogenic. These classifications drive all further risk calculations and medical counseling.” and 53, “values based on the results of the processes described herein can be saved for subsequent use. Additionally, a non-transitory computer-readable medium can be used to store” [i.e., an incentive/benefit with respect to a diagnostic testing review result is recorded/saved/stored in association with the clinician/expert]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein a review request relevant to the update of the integrated DB is transmitted to an expert, a review result with respect to the transmitted review request is received.
In the same field, analogous art Chen teaches wherein a review request relevant to the update of the integrated DB13 is transmitted to an expert (see, e.g., FIGs. 6A-D and 9A – depicting a DISEASE LIKELIHOOD REPORT and Clinical Report for a patient displayed/transmitted to a physician where a “VIEW EXPERTS LIST” request can be made, and paragraphs 20, “systems and methods may be used to make high-confidence variant-based likelihood of disease analysis and predictions to clinicians, researchers” [i.e., review request for disease analysis and predictions transmitted to clinicians, researchers – experts], 49, “reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. … if the audience of the report is a physician [i.e., an expert], the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence, and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” [i.e., including information relevant/related to an updated of the integrated DB/database] and 62, “a list of experts 930 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review request to the reporting module that is relevant to an update of the DB is displayed/transmitted to an expert/physician]), a review result with respect to the transmitted review request is received (see, e.g., FIGs. 6A-D with interactive DISEASE LIKELIHOOD REPORT and Clinical Report interfaces for receiving review requests and results, and FIG. 9B – where a “VIEW EXPERTS LIST” request can be made and paragraphs 64, “a list of experts 960 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review result regarding a transmitted/displayed review request is received]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding claim 26, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 22.
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein, in a case where the integrated DB is updated by adding the medical information relevant to the genetic mutation, an additional report is output on the basis of the genetic mutation detected from the specimen, and the updated integrated DB.
In the same field, analogous art Chen teaches wherein, in a case where the integrated DB is updated by adding the medical information relevant to the genetic mutation14, an additional report is output on the basis of the genetic mutation detected from the specimen, and the updated integrated DB (see, e.g., FIGs. 4, 6A-D and 9A-B depicting “DISEASE LIKELIHOOD REPORT” sub-reports including Possible Diseases based on Sequencing and Validation Methods, and an additional “Clinical Report” and “DISEASE PREDICTION” report including added medical information relevant to the genetic mutation - Disease Risks, Carrier Status for a patient based on updated data from the integrated DB/database and paragraphs 37, “disease/variant data structures 310 may be set up to be automatically updated when new releases are available for the plurality of databases 305.” and 58, “FIG. 6D is an embodiment of details related to a particular genomic variant of a patient. … more detailed information regarding a potentially disease-related variant may be explored” [i.e., an additional report is output/displayed based on medical information from the updated integrated DB relevant to the genetic mutation/disease-related variant]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding claim 27, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 26.
Haque further discloses an incentive with respect to the received review result is recorded in association with the expert (see, e.g., paragraphs 3-4, “more informative and available diagnostic testing promises to not only benefit patients, but also improve the efficiency of the health care system overall.”, “in each patient, sequencing will reveal new genetic sequence variants and the clinician must determine if these newly-observed genetic sequence variants are likely to be pathogenic. These classifications drive all further risk calculations and medical counseling.” and 53, “values based on the results of the processes described herein can be saved for subsequent use. Additionally, a non-transitory computer-readable medium can be used to store” [i.e., an incentive/benefit with respect to a diagnostic testing review result is recorded/saved/stored in association with the clinician/expert]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein a review request relevant to the update of the integrated DB is transmitted to an expert, a review result with respect to the transmitted review request is received.
In the same field, analogous art Chen teaches wherein a review request relevant to the update of the integrated DB15 is transmitted to an expert (see, e.g., FIGs. 6A-D and 9A – depicting a DISEASE LIKELIHOOD REPORT and Clinical Report for a patient displayed/transmitted to a physician where a “VIEW EXPERTS LIST” request can be made, and paragraphs 20, “systems and methods may be used to make high-confidence variant-based likelihood of disease analysis and predictions to clinicians, researchers” [i.e., review request for disease analysis and predictions transmitted to clinicians, researchers – experts], 49, “reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. … if the audience of the report is a physician [i.e., an expert], the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence, and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” [i.e., including information relevant/related to an updated of the integrated DB/database] and 62, “a list of experts 930 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review request to the reporting module that is relevant to an update of the DB is displayed/transmitted to an expert/physician]), a review result with respect to the transmitted review request is received (see, e.g., FIGs. 6A-D with interactive DISEASE LIKELIHOOD REPORT and Clinical Report interfaces for receiving review requests and results, and FIG. 9B – where a “VIEW EXPERTS LIST” request can be made and paragraphs 64, “a list of experts 960 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review result regarding a transmitted/displayed review request is received]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding claim 28, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 22.
Haque further discloses an incentive with respect to the received review result is recorded in association with the expert (see, e.g., paragraphs 3-4, “more informative and available diagnostic testing promises to not only benefit patients, but also improve the efficiency of the health care system overall.”, “in each patient, sequencing will reveal new genetic sequence variants and the clinician must determine if these newly-observed genetic sequence variants are likely to be pathogenic. These classifications drive all further risk calculations and medical counseling.” and 53, “values based on the results of the processes described herein can be saved for subsequent use. Additionally, a non-transitory computer-readable medium can be used to store” [i.e., an incentive/benefit with respect to a diagnostic testing review result is recorded/saved/stored in association with the clinician/expert]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein a review request for the report is transmitted to an expert, a review result with respect to the transmitted review request is received.
In the same field, analogous art Chen teaches wherein a review request for the report is transmitted to an expert (see, e.g., FIGs. 6A-D and 9A – depicting a DISEASE LIKELIHOOD REPORT and Clinical Report for a patient displayed/transmitted to a physician where a “VIEW EXPERTS LIST” request can be made, and paragraphs 20, “systems and methods may be used to make high-confidence variant-based likelihood of disease analysis and predictions to clinicians, researchers” [i.e., review request for disease analysis and predictions transmitted to clinicians, researchers – experts], 49, “reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. … if the audience of the report is a physician [i.e., an expert], the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence, and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” and 62, “a list of experts 930 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review request for a report from the reporting module is displayed/transmitted to an expert/physician]), a review result with respect to the transmitted review request is received (see, e.g., FIGs. 6A-D with interactive DISEASE LIKELIHOOD REPORT and Clinical Report interfaces for receiving review requests and results, and FIG. 9B – where a “VIEW EXPERTS LIST” request can be made and paragraphs 64, “a list of experts 960 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review result regarding a transmitted/displayed review request is received]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 22.
Regarding independent claim 30, Haque discloses the invention as claimed including a non-transitory computer readable medium including program instructions which when executed by a processor causing a computer to execute a process (see, e.g., paragraph 20, “Further provided herein is a non-transitory computer-readable storage medium comprising computer-executable instructions for carrying out any of the methods described herein. Also provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein” [i.e., non-transitory computer-readable medium including instructions causing a processor of a computer to carry out/execute a process/method]) comprising:
acquiring, by the processor, genome data obtained by reading a base sequence included in a specimen (see, e.g., paragraphs 7, 42, “receiving training data comprising a first data set comprising labeled benign genetic sequence variants, and a second data set comprising simulated genetic sequence variants, the simulated genetic sequence variants comprising an unlabeled mixture of benign genetic sequence variants and pathogenic genetic sequence variants”, 46, “the test genetic sequence variant is a human genetic sequence variant” [i.e., acquiring/receiving training data sets with labeled sequence variants by reading a base benign genetic sequence included in a human specimen]);
inputting, by the processor, the acquired genome data to a learning model that outputs a prediction relevant to a genetic mutation16 upon input of genome data (see, e.g., FIG. 1 – depicting generating a “machine learning model” to “generate output score” [i.e., output genetic mutation prediction] based on input benign data set/genome sequence, and paragraphs 7, 43 “training a machine learning model based on the training data, … annotating the test genetic sequence variant with the one or more features; and predicting a probability that the test genetic sequence variant is pathogenic based on the machine learning model after training.” and 58, “methods of predicting pathogenicity based on the genetic sequence variant training data set used to train the machine learning model.” [i.e., machine learning model outputs a prediction of a probability that the input test genetic sequence variant is pathogenic/genetically mutated]);
acquiring, by the processor, the prediction relevant to the genetic mutation output from the learning model, on the basis of the input genome data (see, e.g., FIG. 1 – depicting acquiring an “output score” from a “machine learning model” based on the input benign data set/genome sequence and paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 after training. … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., acquiring a genetic mutation prediction output from the learning model]); and
outputting, by the processor … an analysis result relevant to the specimen17 (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score”/analysis result relevant to an input benign data set/genome sequence and paragraphs 46, “the test genetic sequence variant is a human genetic sequence variant”, and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting an analysis result/genetic mutation prediction related to/relevant to a specimen]) and a version of an integrated DB are recorded in association with each other, on the basis of the acquired prediction (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 97, “obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., on the basis of the acquired output score/probability/prediction] and 100-101, “labeled benign data set was obtained from the 1000 Genomics project by filtering the database for genetic sequence variants”, “the known pathogenic sequence variant testing data set was obtained from the Human Gene Mutation Database” [i.e., and a version of an integrated DB/database with the benign data set recorded/saved in association with genetic sequence variants]), and the integrated DB in which medical information relevant to the genetic mutation18 acquired from a plurality of information sources, and an acquisition date and basis information of the medical information are integrated in association with each other (see, e.g., Table 2 – listing Clinvar, HGMD, and 1000G databases which contain mutation data from a plurality of sources and are integrated with each other as of a data acquisition date, and paragraph 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., an acquisition date for the integrated DB in which medical information/genetic sequence data relevant to the mutation/pathogenic genetic sequence are integrated and associated with each other]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose outputting … a report in which an analysis result relevant to the specimen … and the integrated DB in which medical information relevant to the genetic mutation acquired from a plurality of information sources, and an acquisition date and basis information of the medical information are integrated in association with each other.
In the same field, analogous art Chen teaches outputting … a report in which an analysis result relevant to the specimen (see, e.g., paragraphs 21, “DNA samples may be obtained from a plurality of patients” [i.e., a specimen/sample], 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting”, 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” and 51, “FIG. 4 is an illustrative user interface that may be … presented to a user to allow the user to generate customized variant analysis and disease likelihood reports including information regarding validation of such analysis and/or reports.” [i.e., output a report to a user interface with an analysis result relevant to a specimen/sample]) … and the integrated DB in which medical information relevant to the genetic mutation19 acquired from a plurality of information sources, and an acquisition date and basis information of the medical information are integrated in association with each other (see, e.g., paragraphs 36, “extracting information related to disease-related genomic variants from a plurality of databases 305. … removing the low-quality data and irrelevant information from information received from the plurality of databases 305 may be included in the construction of the one or more disease/variant data structures 310”, 39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” 67, “the data stores in this disclosure may be implemented using a relational database” [i.e., an integrated database/DB/data structure with medical information relevant to the genetic mutation/disease acquired from a plurality of databases/information sources] and 38-39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” [i.e., and basis information of the medical information are integrated in association with each other]).
Haque and Chen are analogous art because they are both directed to systems and techniques for using machine learning for analyzing genetic sequences and genomic variants (See, e.g., Haque, Abstract, and Chen, Abstract and paragraphs 19 and 43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Haque to incorporate the teachings of Chen to provide techniques and systems that “use machine learning techniques … to predict likelihood of rare diseases” where a “validation step 350 may … verify that an identified variant that is predicted to cause one or more rare or common disease is not an artifact created by a sequencing error” and the systems and techniques generate a “customized report 360 for the physician [that] may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” (See, e.g., Chen, paragraphs 45 and 48-49). Doing so would have allowed Haque to be able to use Chen’s techniques and system “in order to accurately and inexpensively validate the existence of the identified variants”, as suggested by Chen (See, e.g., Chen, paragraph 48).
Regarding claim 31, as discussed above, Haque in view of Chen teaches the computer readable medium of claim 30.
Haque further discloses an incentive with respect to the received review result is recorded in association with the expert (see, e.g., paragraphs 3-4, “more informative and available diagnostic testing promises to not only benefit patients, but also improve the efficiency of the health care system overall.”, “in each patient, sequencing will reveal new genetic sequence variants and the clinician must determine if these newly-observed genetic sequence variants are likely to be pathogenic. These classifications drive all further risk calculations and medical counseling.” and 53, “values based on the results of the processes described herein can be saved for subsequent use. Additionally, a non-transitory computer-readable medium can be used to store” [i.e., an incentive/benefit with respect to a diagnostic testing review result is recorded/saved/stored in association with the clinician/expert]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein a review request for the report is transmitted to an expert (see, e.g., FIGs. 6A-D and 9A – depicting a DISEASE LIKELIHOOD REPORT and Clinical Report for a patient displayed/transmitted to a physician where a “VIEW EXPERTS LIST” request can be made, and paragraphs 20, “systems and methods may be used to make high-confidence variant-based likelihood of disease analysis and predictions to clinicians, researchers” [i.e., review request for disease analysis and predictions transmitted to clinicians, researchers – experts], 49, “reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. … if the audience of the report is a physician [i.e., an expert], the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence, and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” and 62, “a list of experts 930 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review request for a report from the reporting module is displayed/transmitted to an expert/physician]), a review result with respect to the transmitted review request is received (see, e.g., FIGs. 6A-D with interactive DISEASE LIKELIHOOD REPORT and Clinical Report interfaces for receiving review requests and results, and FIG. 9B – where a “VIEW EXPERTS LIST” request can be made and paragraphs 64, “a list of experts 960 pertaining to a particular disease area may be generated and displayed to a user if a user wishes to see the list.” [i.e., a review result regarding a transmitted/displayed review request is received]).
The motivation to combine Haque and Chen is the same as discussed above with respect to claim 30.
Regarding independent claim 34, Haque discloses the invention as claimed including an information processing device comprising: a processor executing program code to perform (see, e.g., paragraph 20, “provided is a system comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for carrying out any of the methods disclosed herein.”):
receiving, by the processor, a genetic mutation detected from a specimen (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., receiving an output indicating a genetic mutation/pathogenic sequence detected from a specimen]);
outputting, by the processor, … an analysis result relevant to a specimen20 (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score”/analysis result relevant to an input benign data set/genome sequence and paragraphs 46, “the test genetic sequence variant is a human genetic sequence variant”, 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting an analysis result/genetic mutation prediction related to/relevant to the specimen]) and a version of an integrated DB are recorded in association with each other, on the basis of a genetic mutation detected from the specimen (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., on the basis of a genetic mutation/pathogenic sequence detected from the specimen] and 100-101, “labeled benign data set was obtained from the 1000 Genomics project by filtering the database for genetic sequence variants”, “the known pathogenic sequence variant testing data set was obtained from the Human Gene Mutation Database” [i.e., and a version of an integrated DB/database with the benign data set recorded/saved in association with genetic sequence variants]), and the integrated DB in which medical information relevant to the genetic mutation21 acquired from a plurality of information sources, and an acquisition date and basis information of the medical information are integrated in association with each other (see, e.g., Table 2 – listing Clinvar, HGMD, and 1000G databases which contain mutation data from a plurality of sources and are integrated with each other as of a data acquisition date, and paragraph 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., an acquisition date for the integrated DB in which medical information/genetic sequence data relevant to the mutation/pathogenic genetic sequence are integrated and associated with each other]);
receiving, by the processor, a date in the past (see, paragraphs 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., receiving a date in the past]), a … output request at the date22, and the genetic mutation detected from the specimen (see, e.g., paragraphs 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting”, 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” [i.e., receiving a query/report request for customized reporting – an output request at a date] and 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., and the genetic mutation/pathogenic sequence detected from the specimen]); and
outputting, by the processor, … the analysis result relevant to the specimen23 (see, e.g., FIG. 1 – depicting a “machine learning model” that outputs a “output score”/analysis result relevant to an input benign data set/genome sequence and paragraphs 46, “the test genetic sequence variant is a human genetic sequence variant”, 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.” and 97, “the trained machine learning model as described herein is applied to a test genetic sequence variant to obtain an output score. The output score is a predicted probability that the test genetic sequence variant is pathogenic.” [i.e., outputting an analysis result/genetic mutation prediction related to/relevant to the specimen]) and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen (see, e.g., paragraphs 50, “At step 160, an output score is generated based on the machine learning model 135 … the output score relates to the probability that the test genetic sequence variant is pathogenic.”, 57, “the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation” [i.e., on the basis of a genetic mutation/pathogenic sequence detected from the specimen] and 100-101, “labeled benign data set was obtained from the 1000 Genomics project by filtering the database for genetic sequence variants”, “the known pathogenic sequence variant testing data set was obtained from the Human Gene Mutation Database” [i.e., and a version of an integrated DB/database with the benign data set recorded/saved in association with genetic sequence variants]), and the integrated DB at a date in the past (see, e.g., paragraph 103, “The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. … the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database (as of February 2014” [i.e., and the integrated DB at a date in the past]).
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose outputting …a report in which an analysis result relevant to a specimen … and the integrated DB in which medical information relevant to the genetic mutation acquired from a plurality of information sources, … and basis information of the medical information are integrated in association with each other; and
outputting … the report in which the analysis result relevant to the specimen and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen, and the integrated DB.
In the same field, analogous art Chen teaches outputting …a report in which an analysis result relevant to a specimen24 (see, e.g., paragraphs 21, “DNA samples may be obtained from a plurality of patients” [i.e., a specimen/sample], 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting”, 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence” and 51, “FIG. 4 is an illustrative user interface that may be … presented to a user to allow the user to generate customized variant analysis and disease likelihood reports including information regarding validation of such analysis and/or reports.” [i.e., outputting a report with an analysis result relevant to a specimen/sample]) … and the integrated DB in which medical information relevant to the genetic mutation acquired from a plurality of information sources, … and basis information of the medical information are integrated in association with each other (see, e.g., paragraphs 36, “extracting information related to disease-related genomic variants from a plurality of databases 305. … removing the low-quality data and irrelevant information from information received from the plurality of databases 305 may be included in the construction of the one or more disease/variant data structures 310”, 39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” 67, “the data stores in this disclosure may be implemented using a relational database” [i.e., an integrated database/DB/data structure with medical information relevant to the genetic mutation/disease acquired from a plurality of databases/information sources] and 38-39, “the disease/variant data structure 310 may classify the disease involved into two or more categories” [i.e., and basis information of the medical information are integrated in association with each other]); and
outputting … the report in which the analysis result relevant to the specimen25 and the version of the integrated DB are recorded in association with each other, on the basis of the genetic mutation detected from the specimen, and the integrated DB (see, e.g., FIGs. 4, 6A-D and 9A-B depicting user interfaces for requesting and outputting “DISEASE LIKELIHOOD REPORT” and a “Clinical Report” with analysis results relevant to the specimen, and paragraphs 36, “process of database query, variant analysis, statistical prediction of likelihood of disease, validation, and customized reporting” [i.e., output the report when a query/report output request at a date is received, query the integrated database/DB], 49, “the reporting module may create one or more customized report 360 based on the particular needs of the audience of the report. For example, if the audience of the report is a physician, the customized report 360 for the physician may include information such as: likelihood of rare/common diseases, which may be ranked by the likelihood value; variant information such as variant location, reference genomic sequence, variant genomic sequence and so forth; results of validation; sequencing parameters; alignment parameters; and/or validation parameters.” and 51, “user interface … presented to a user to allow the user to generate customized variant analysis and disease likelihood reports including information regarding validation of such analysis and/or reports.” [i.e., outputting the report with the analysis result relevant to the specimen/sample based on the genetic mutation detected from the specimen/sample]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Haque in view of McClelland et al. (U.S. Patent Application Pub. No. 2014/0011861 A1, hereinafter “McClelland”, cited in applicant’s information disclosure statement filed 1/24/2023).
Regarding claim 20, as discussed above, Haque discloses the computer readable medium of claim 18.
Although Haque substantially discloses the claimed invention, Haque does not explicitly disclose wherein the learning model outputs a prediction of a tumor content in the specimen.
In the same field, analogous art McClelland teaches wherein the learning model outputs a prediction of a tumor content in the specimen (see, e.g., paragraphs 10-11, “The prostate tissue sample may not include tumor cells”, “method for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject [i.e., a specimen]; (b) measuring expression levels for one or more prostate cell-type predictor genes in the sample; (c) determining the percentages of tissue types in the sample based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; … identifying the subject as having prostate cancer” and 95, “the methods provided herein allow segregation of molecular tumor and nontumor markers” [i.e., machine learning model outputs a prediction of a tumor/prostate cancer in the specimen]).
Haque and McClelland are analogous art because they are both directed to systems and techniques for analyzing genetic sequences and genomic variants (See, e.g., Haque, Abstract, and McClelland, Abstract and paragraphs 31, 54 and 153).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Haque to incorporate the teachings of McClelland to provide a “method for identifying a subject as having or not having prostate cancer … based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; and (f) if the classifier falls into a predetermined range of prostate cancer classifiers, identifying the subject as having prostate cancer” where “the methods … allow segregation of molecular tumor and nontumor markers” (See, e.g., McClelland, paragraphs 11 and 95). Doing so would have allowed Haque to be able to use McClelland’s classifier techniques, system and “method for identifying a subject as having or not having prostate cancer” where “The classifier predicted the tumor status of patients with an average accuracy of 97.4%”, as suggested by McClelland (See, e.g., McClelland, paragraphs 11 and 221).
Allowable Subject Matter
Upon overcoming of all the objections and rejections as discussed above in items 7-18, claims 29 and 32-33 are objected to as being dependent upon a rejected base claim (i.e., claims 22 and 30, respectively), but would be allowable if amended to address the above-noted objections and rejections under 35 U.S.C. 112(b), 102 and 103, and rewritten in independent form including all of the limitations of the base claims (i.e., claim 22 in the case of claim 29, and claim 30 in the case of claims 32-33) and any intervening claims (i.e., claim 28 in the case of claim 29, and claim 31 in the case of claims 32-33).
For example, with regard to dependent claims 29 and 32-33, the prior art of record does not anticipate, nor do they render obvious in any reasonable combination to one of ordinary skill in the art at the time of Applicants' invention, the combination of recited limitations of claims 29 and 32-33, their respective base claims, independent claims 21 and 30, and their respective intervening claims, dependent claims 28 and 31.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
The references listed on form PTO-892 are all generally related to techniques, methods and systems for using machine learning models and artificial intelligence for analyzing genetic data.
For example, non-patent literature Wu et al. ("Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing”, Clinical chemistry 66.1 (Jan 2020): 239-246, hereinafter “Wu”), discloses “a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens.” (see, Abstract).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANDY K BALDWIN whose telephone number is (571)270-5222. The examiner can normally be reached on Mon - Fri 9:00-6:00.
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/RANDALL K. BALDWIN/Primary Examiner, Art Unit 2125
1 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
2 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
3 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
4 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
5 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
6 As indicated above in the section 112(b) rejection of this claim, “an analysis result relevant to a specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
7 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
8 As indicated above in the section 112(b) rejection of this claim, “the analysis result relevant to a specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
9 As indicated above in the section 112(b) rejections of this claim, “an analysis result relevant to a specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
10 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
11 As indicated above in the section 112(b) rejections of this claim, “the date” is being interpreted as any date, including, but not limited to a date the report output request is/was received, a date value that is received, and/or to the previously-introduced “acquisition date”.
12 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
13 As indicated above in the section 112(b) rejections of this claim, “a review request relevant to the update of the integrated DB” has been interpreted as any request for review or review request that is appropriate, relevant, salient or corresponding to the update of the integrated database (DB).
14 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
15 As indicated above in the section 112(b) rejections of this claim, “a review request relevant to the update of the integrated DB” has been interpreted as any request for review or review request that is appropriate, relevant, salient or corresponding to the update of the integrated database (DB).
16 As indicated above in the section 112(b) rejection of this claim, “a prediction relevant to the genetic mutation” has been interpreted as any prediction that is appropriate, relevant, salient or corresponding to the genetic mutation.
17 As indicated above in the section 112(b) rejections of this claim, “an analysis result relevant to the specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
18 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
19 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
20 As indicated above in the section 112(b) rejection of this claim, “the analysis result relevant to a specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
21 As indicated above in the section 112(b) rejections of this claim, “medical information relevant to the genetic mutation” has been interpreted as any medical information, data or record that is appropriate, relevant, salient or corresponds to the genetic mutation.
22 As indicated in the section 112(b) rejection of this claim above, “the date” is being interpreted as any date, including, but not limited to a date the report output request is/was received, a date value that is received, the previously-introduced “date in the past” and/or to the previously-introduced “acquisition date”.
23 As indicated above in the section 112(b) rejections of this claim, “an analysis result relevant to the specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
24 As indicated above in the section 112(b) rejections of this claim, “an analysis result relevant to the specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.
25 As indicated above in the section 112(b) rejections of this claim, “an analysis result relevant to the specimen” has been interpreted any analysis result or output that is appropriate, relevant, salient or corresponds to a specimen or sample.