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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Korean Application No. 10-2021-0102589. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55, filed on Sep 14th, 2022. Thus, the effective filing date of claims 1-12 is Aug 4th, 2021.
Claim Status
Claims 1-12 are currently pending and under exam herein.
Claims 1-12 are rejected.
Claims 6 and 12 are objected to.
Information Disclosure Statement
The information disclosure statement (IDS) was filed on Aug 3rd, 2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The Drawings filed on Aug 3rd, 2022 are accepted.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Please correct the embedded hyperlinks in para [0051], [0054], and [00109].
Claim Objections
Claims 6 and 12 are objected to because of the following informalities:
Acronyms pLI and LOEUF need to be spelled out/explained
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Claim 1: “loss of function prediction unit”
Claim 4: “a first characteristic score calculation unit”
Claim 4: “a second characteristic score calculation unit”
Claim 10: “a first characteristic score calculating operation”
Claim 10: “a second characteristic score calculating operation”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
In claim 1, the instant application recites “a loss of function prediction unit”, which uses the generic placeholder “unit” coupled with the function language “for calculating”. In addition, it is not preceded or followed by sufficient structure or materials for performing the claimed function. In looking at the Specification, the only additional details regarding the loss of function prediction unit (20) are that it includes a variable setting unit (210), a log linear model (220), and a LoF probability calculator (230) ([0094]). While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer elements relating to the system are not described. Please see below regarding issues under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) arising from this claim interpretation.
In claim 4, the instant application recites a “first characteristic score calculation unit” coupled with functional language “calculating”. However, it is not preceded or followed by sufficient structure or materials for performing the claimed function. In looking at the Specification, it seems that the first characteristic score is calculated by using an in-silico tool using a computer stimulation ([00103]). The Specification also explains how a pLI algorithm may be used to calculate the first characteristic score, which is a method to quantify a deviation between the theoretically observable number and the actual observable number of LoF genetic variants in general genomes ([0049]). The Specification also states that a LOEUF algorithm could be used, but fails to expand on the LOEUF algorithm and only references it as an algorithm similar to the pLI algorithm ([0052]). Lastly, the Specification states that to calculate the first characteristic score, a method of simply dividing the number of generic variants causing a LoF actually observed by the number of genetic variants causing a LoF theoretically expected may be used ([0055]). While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer elements relating to the in-silico tool are not described. Please see below regarding issues under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) arising from this claim interpretation.
In claim 4, the instant application recites a “second characteristic score calculation unit” coupled with functional language “calculating”. However, it is not preceded or followed by sufficient structure or materials for performing the claimed function. In looking at the Specification, it seems that the second characteristic score is calculated by using an in-silico toll using a computer stimulation ([00106]). However, the Specification is unclear about the specific algorithm that is utilized to calculate the second characteristic score. The Specification lists various algorithms with acronyms and abbreviation, and fails to disclose the specific steps to calculate the second characteristic score ([00107]). While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer elements relating to the in-silico tool are not described. Please see below regarding issues under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) arising from this claim interpretation.
In claim 10, the instant application recites a “first characteristic score calculating operation” coupled with functional language “calculating”. However, it is not preceded or followed by sufficient structure or materials for performing the claimed function. In looking at the Specification, it seems that the first characteristic score is calculated by using an in-silico tool using a computer ([00103]). The Specification also explains how a pLI algorithm may be used to calculate the first characteristic score, which is a method to quantify a deviation between the theoretically observable number and the actual observable number of LoF genetic variants in general genomes ([0049]). The Specification also states that a LOEUF algorithm could be used, but fails to expand on the LOEUF algorithm and only references it as an algorithm similar to the pLI algorithm ([0052]). Lastly, the Specification states that to calculate the first characteristic score, a method of simply dividing the number of generic variants causing a LoF actually observed by the number of genetic variants causing a LoF theoretically expected may be used ([0055]). Hence going forth, examiner will interpret the calculating operation to be as such: a method/operation of dividing the number of observed LoF genetic variants by the number of theoretical LoF genetic variants or a pLI algorithm.
In claim 10, the instant application recites a “second characteristic score calculating operation” coupled with functional language “calculating”. However, it is not preceded or followed by sufficient structure or materials for performing the claimed function. In looking at the Specification, it seems that the second characteristic score is calculated by using an in-silico toll using a computer ([00106]). However, the Specification is unclear about the specific algorithm that is utilized to calculate the second characteristic score. The Specification lists various algorithms with acronyms and abbreviation, and fails to disclose the specific steps to calculate the second characteristic score ([00107]). Hence, the Specification does not disclose adequate structure to perform the claimed function. See below regarding issues under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) arising from this claim interpretation.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6 and 10-12, are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
Claim 1 recites a loss of function prediction unit, that was interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In looking at the specification, the only additional details regarding the loss of function prediction unit (20) are that it includes a variable setting unit (210), a log linear model (220), and a LoF probability calculator (230) ([0094]). While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer/calculator elements relating to the system are not described. Hence, the specification fails to describe how one skilled in the art could create a loss of function prediction unit.
Claim 4 recites a first characteristic score calculation unit, that was interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In looking at the specification, it seems that the first characteristic score is calculated by using an in-silico tool using a computer stimulation ([00103]). The specification also explains how a pLI algorithm ([0049]), a LOEUF algorithm ([0052]), or a method of simply dividing the number of generic variants causing a LoF actually observed by the number of genetic variants causing a LoF theoretically expected ([0055]) may be used to calculate the first characteristic score. While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer elements relating to the in-silico tool using a computer stimulation are not described at all. Hence, the specification fails to describe how one skilled in the art could create the first characteristic score calculation unit.
Claim 4 recites a second characteristic score calculation unit, that was interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In looking into the specification, examiner found that the score is calculated by using an in-silico tool using a computer stimulation ([00106]). The specification also lists various algorithms in abbreviations that may be used for calculating the characteristic score ([00107]). However, the specification does not explain what these abbreviations stand for, what these algorithms are, the steps to the algorithms, how they determine a second characteristic score, and/or how it is implemented with the in-silico tool. Hence, the specification fails to describe how one skilled in the art would determine the second characteristic score and/or create the second characteristic score calculation unit.
Claim 10 similarly recites a second characteristic score calculating operation, that was interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In looking into the specification, examiner found that the score is calculated by using an in-silico tool using a computer stimulation ([00106]). The specification also lists various algorithms in abbreviations that may be used for calculating the characteristic score ([00107]). However, the specification does not explain what these abbreviations stand for, what these algorithms are, the steps to the algorithms, how they determine a second characteristic score, and/or how it is implemented with the in-silico tool. Hence, the specification again fails to describe how one skilled in the art would determine the second characteristic score.
Claims 2-3, 5-6 and 11-12 are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph due to their dependency on claims 1, 4, and 10 respectively.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 and 9-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the first equation". There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitations “the system according to claim 8” and "the first equation". There are insufficient antecedent basis for these limitations in the claim.
Claims 4-6 and 10-12 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, going forth due to their dependencies on claim 3 and 9 respectively.
Claim limitation “a loss of function prediction unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In looking at the specification, the only additional details regarding the loss of function prediction unit (20) are that it includes a variable setting unit (210), a log linear model (220), and a LoF probability calculator (230) ([0094]). While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer/calculator elements relating to the system are not described. Therefore, claim 1 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 1-6 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, going forth due to their dependencies on claim 1.
Claim limitations “a first characteristic score calculating unit” and “a second characteristic score calculating unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In looking at the specification, it seems that the first characteristic score is calculated by using an in-silico tool using a computer stimulation ([00103]). The specification also explains how a pLI algorithm ([0049]), a LOEUF algorithm ([0052]), or a method of simply dividing the number of generic variants causing a LoF actually observed by the number of genetic variants causing a LoF theoretically expected ([0055]) may be used to calculate the first characteristic score. While these details provide the algorithmic steps to the system/unit, the system components are still not fully described. Specifically, the computer elements relating to the in-silico tool using a computer stimulation are not described at all. Therefore, claim 4 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 5 and 6 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, going forth due to their dependencies on claim 4.
Claim limitations “a second characteristic score calculating unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In looking into the specification, examiner found that the scores are calculated by using an in-silico tool using a computer stimulation ([00106]). The specification also lists various algorithms in abbreviations that may be used for calculating the characteristic score ([00107]). However, the specification does not explain what these abbreviations stand for, what these algorithms are, the steps to the algorithms, how they determine a second characteristic score, how it is implemented with the in-silico tool, and/or what is the in-silico tool. Therefore, claim 4 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 5 and 6 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, going forth due to their dependencies on claim 4.
Claim limitation “a second characteristic score calculating operation” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In looking into the specification, examiner found that the score is calculated by using an in-silico tool using a computer stimulation ([00106]). The specification also lists various algorithms in abbreviations that may be used for calculating the characteristic score ([00107]). However, the specification does not explain what these abbreviations stand for, what these algorithms are, the steps to the algorithms, how they determine a second characteristic score, how it is implemented with the in-silico tool, and/or what is the in-silico tool. Therefore, claim 10 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 11 and 12 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, going forth due to their dependencies on claim 10.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 and 7 recites predicting a loss of function probability for genetic variants of a target gene (Abstract Idea; mathematical concept and/or mental process). Specifically, claims 1 and 7 recite calculating the loss of function probability through logistic regression with respect to a first probability (probability that the target gene will be intolerant of the loss of function) and a second probability (probability that the gene variant will be intolerant) (Abstract Idea; mathematical concept and/or mental process). The process of calculating a probability based on other probabilities through logistic regression is a mathematical concept that utilizes mathematical relations for calculation. In addition, based on the broadest reasonable interpretation, the calculation may be simple enough to do in the human mind, which would render the limitations a mental process as well.
Claim 1 recites a log linear model (220) and a LoF probability calculator (230) after 35 U.S.C. 112(f) claim interpretation (Abstract idea: mathematical concept and/or mental process). A log linear model is a mathematical model, which constitutes a mathematical concept. In addition, depending on the sample size, the calculations can also be carried out in the human mind. A probability calculator utilized mathematical relations to calculate probabilities, which again is a mathematical concept. Furthermore, based on the broadest reasonable interpretation, probabilities can also be calculated in the human mind.
Claim 2 and 8 recites that the genetic variant includes a protein truncated variant, while these dependent claims further limit the type of variant/data being processed, it does not change the fact that the processing steps are an abstract idea (mathematical concept and/or mental process)
Claim 3 and 9 recites a first equation to calculate the probability that the genetic variant will cause a loss of function expressed by:
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(Abstract Idea: mathematical concept and/or mental process). The claim further defines
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as the probability that the genetic variant will cause a loss of function, and
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as the first probability and
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as the second probability. Again, the process of calculating a probability based on other probabilities is a mathematical concept that utilizes mathematical relations for calculation based off of an equation. In addition, based on the broadest reasonable interpretation, this calculation may be simple enough to do in the human mind, hence, these limitations would also constitute a mental process.
Claim 4 and 10 recites calculating a first characteristic score (
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, where c and d are predetermined constants (Abstract Idea; mathematical concept and/or mental process). The process of first calculating a score based on known algorithms before plugging the score into equations to calculate a probability is a mathematical concept that utilizes mathematical equations and relations to calculate outputs. Furthermore, based on the simplicity of the algorithms and equations, this is also a process that can be done in the human mind, or with the assistance of a pen and paper.
Claim 5 and 11 recites a log linear model for the first equation from claim 3 as:
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as the log value of the second characteristic score and
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as the log value of the first characteristic score (Abstract idea: mathematical concept and/or mental process). The process of taking the logarithm of a known equation (the first equation of claim 3) is a mathematical concept that is well known and, in this case, could be also done in the human mind with pen and paper. In addition, the process of taking the logarithm of a known number (the first and second characteristic score) is also a mathematical concept that is well known, and in this case, could also be done in the human mind.
Claim 6 and 12 recites using a PLI algorithm or an LOEUF algorithm to calculate the first characteristic score (Abstract Idea: mathematical concept and/or mental process). Both algorithms utilize mathematical relations to calculate values that characterize how tolerable gene variants are, which constitute a mathematical concept. In addition, based on the sample size, the algorithms are also able to be carried out in the human mind as well, making these limitations a mental process as well.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1-6 recite performing some aspects of the analysis with a “system” or “unit” there are no additional limitations that indicate that this system requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-12 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exceptions into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1, 3, 5, 6, and 9 recites a system, which equates to a generic computing environment/computer
Claim 1 recites a loss of function prediction unit, which after 35 U.S.C. 112(f) claim interpretation includes a variable setting unit (210) which is a generic component in a computing environment/computer that allows the computer to take variables as input
Claim 4 recites a first and second characteristic score calculation unit, which under 35 U.S.C 112(f) claim interpretation, are made up of an in-silico tool, which again equates to a generic computing environment/computer
There are no limitations in the additional elements that indicate that the claimed system or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. In general, linking the use of an abstract idea to a particular technological environment, such as a computer, does not integrate the abstract idea into a practical application based on MPEP 2106.05(h). Therefore, claims 1-12 are directed to an abstract idea as the additional elements do not integrate the judicial exceptions into a practical application (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). In the instant application, claims 1-12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. As discussed above, there are no additional limitations to indicate that the claimed system requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-12 are not patent eligible.
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 1-2 and 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Curtis et al. (European Journal of Human Genetic Vol 27, pg. 114-124 published Sep 26th 2018). The limitations of the instant claim are italicized below.
With respect to claim 1, Curtis et al. teaches the use of a logistic regression approach to analyze risk scores of genetic variants to output a signed log p-value (SLP) to represent how the risk score predicts cases (diseased/loss of function) vs. control (non-diseased/normal) (pg. 115 left col para 2 and right col para 3, calculating a probability that a target genetic variant will cause a loss of function in a target gene through logistic regression). Curtis et al. then lists out the various components that contribute to the risk score such as weights based on VEP (Variant Effect Predictor), PolyPhen (Polymorphism Phenotyping), and SIFT (Sorting Intolerant from Tolerant) algorithms (pg. 115 left col para 2). SIFT is an algorithm for scoring genetic variants that outputs a score for from 0-1, with scores near 0 representing deleterious (intolerant) and scores near 1 representing tolerable (reference 19 of Curtis et al.: Kumar et al. Nature Protocols 2009; 4 pg. 1073-81, with respect to a first probability that the target gene will be intolerant of the loss of function). PolyPhen is an algorithm that utilizes conservation profiles to score whether a gene variant is damaging, where the score represents a probability that the variant is damaging (reference 18 of Curtis et al.: Adzhubei et al. Current Protocols in Human Genetic 2013; pgs. 7.20.1-7.20.41, a second probability that the target genetic variant contained in the target gene will be intolerant). Curtis et al. teaches that the software program contains a standard logistic regression model (pg. 115 right col para 1, a log linear model). In addition, Curtis et al. highlights that the variables within the program can be set to be varied or fixed values, based on prior knowledge (pg. 115 right col para 1, variable setting unit). Finally, Curtis et al. implied that all the calculations were done on a computer with software codes (pg. 123 left col para 2, a LoF probability calculator). These three components of Curtis et al.: the logistic regression model, the variable setting, and the computer that the program was carried out on, combine to form the overall invention (a loss of function prediction unit).
Regarding claim 2, Curtis et al. teaches the use of all variants in a gene (pg. 121 left col para 2). These variants include protein truncated variants as indicated by the VEP annotations of “feature truncation”, “protein altering variant” and “transcript ablation” in Table 1 (pg. 116 Table 1, wherein the target genetic variant includes a protein truncated variant, in which protein expressed by the variant of a gene is shorter than normal protein).
Concerning claim 7, Curtis et al. teaches a method of using logistic regression to analyze risk scores of genetic variants to output a signed log p-value (SLP) to represent how the risk score predicts cases (diseased/loss of function) vs. control (non-diseased/normal) (pg. 115 left col para 2 and right col para 3, calculating a probability that a target genetic variant will cause a loss of function in a target gene through logistic regression). Curtis et al. then lists out the various components that contribute to the risk score such as weights based on VEP (Variant Effect Predictor), PolyPhen (Polymorphism Phenotyping), and SIFT (Sorting Intolerant from Tolerant) algorithms (pg. 115 left col para 2). SIFT is an algorithm for scoring genetic variants that outputs a score for from 0-1, with scores near 0 representing deleterious (intolerant) and scores near 1 representing tolerable (reference 19 of Curtis et al.: Kumar et al. Nature Protocols 2009; 4 pg. 1073-81, with respect to a first probability that the target gene will be intolerant of the loss of function). PolyPhen is an algorithm that utilizes conservation profiles to score whether a gene variant is damaging, where the score represents a probability that the variant is damaging (reference 18 of Curtis et al.: Adzhubei et al. Current Protocols in Human Genetic 2013; pgs. 7.20.1-7.20.41, a second probability that the target genetic variant contained in the target gene will be intolerant).
With respect to claim 8, Curtis et al. teaches the use of all variants in a gene (pg. 121 left col para 2). These variants include protein truncated variants as indicated by the VEP annotations of “feature truncation”, “protein altering variant” and “transcript ablation” in Table 1 (pg. 116 Table 1, wherein the target genetic variant includes a protein truncated variant, in which protein expressed by the variant of a gene is shorter than normal protein).
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 3 and 9 are rejected under 35 U.S.C. 103 over Curtis et al. as applied to claim 1-2 and 7-8 above, and further in view of Pishro-Nik et al. (Introduction to Probability, Statistics, and Random Processes. Kappa Research LLC. Chapter 1.4.0 – 1.4.3, published Aug 24, 2014). The limitations of the instant claim are italicized below.
The limitations of claim 1-2 and 7-8 have been taught by Curtis et al. above.
With respect to claim 3, Pishro-Nik et al. teaches the concept of Conditional Probability, Independence, Law of Total Probability, and Bayes’ Rule that underlie the first equation. By plugging in the variable names of intolerant, tolerant, Loss of Function (LoF), and Gain of Function as B, BC, A, and AC into Pishro-Nik et al.’s Fig 1.23, one can derive the first equation of claim 3 based on conditional probability (1.4.0 Conditional Probability, Fig. 1.23). Through manipulating the equations based on simple mathematical concepts, Pishro-Nik et al. teaches that the P(B) is equivalent to the sum of P(B|A)P(A) and P(B|Ac)P(Ac). And assuming that P(B|Ac)~0 as gains of function mutation rarely result in intolerance, P(B) = P(B|A)P(A). In other words, P(Intolerant) = P(Intolerant|LoF)P(LoF), and P(LoF) = P(Intolerant)/P(Intolerant|LoF). See below for the diagram, and mathematical equations.
PNG
media_image1.png
542
1314
media_image1.png
Greyscale
Hence, based on the conditional probabilities equations taught in Pishro-Nik et al., one can arrive at the final equation of
P
(
L
o
F
|
V
a
r
i
a
n
t
)
=
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
(wherein the first equation is expressed by
P
L
o
F
=
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
, wherein
P
L
o
F
indicates the probability that the target genetic variant will cause a loss of function to the target gene,
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
is a first probability and
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
is a second probability )
Regarding claim 9, Pishro-Nik et al. teaches the concept of Conditional Probability, Independence, Law of Total Probability, and Bayes’ Rule that underlie the first equation. By plugging in the variable names of intolerant, tolerant, Loss of Function (LoF), and Gain of Function as B, BC, A, and AC into Pishro-Nik et al.’s Fig 1.23, one can derive the first equation of claim 3 based on conditional probability (1.4.0 Conditional Probability, Fig. 1.23). Through manipulating the equations based on simple mathematical concepts, Pishro-Nik et al. teaches that the P(B) is equivalent to the sum of P(B|A)P(A) and P(B|Ac)P(Ac). And assuming that P(B|Ac)~0 as gains of function mutation rarely result in intolerance, P(B) = P(B|A)P(A). In other words, P(Intolerant) = P(Intolerant|LoF)P(LoF), and P(LoF) = P(Intolerant)/P(Intolerant|LoF). See below for the diagram, and mathematical equations.
PNG
media_image1.png
542
1314
media_image1.png
Greyscale
Hence, based on the conditional probabilities equations taught in Pishro-Nik et al., one can arrive at the final equation of
P
(
L
o
F
|
V
a
r
i
a
n
t
)
=
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
(wherein the first equation is expressed by
P
L
o
F
=
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
, wherein
P
L
o
F
indicates the probability that the target genetic variant will cause a loss of function to the target gene,
P
(
i
n
t
o
l
e
r
a
n
t
|
L
o
F
)
is a first probability and
P
(
i
n
t
o
l
e
r
a
n
t
|
v
a
r
i
a
n
t
)
is a second probability ).
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to implement the conditional probability framework of Pishro-Nik et al. within the logistic model of Curtis et al. to create a more mathematically accurate representation between intolerance and loss of function variants. One of ordinary skill in the art would have been motivated to incorporate the probability equations of Pishro-Nik et al. in addition to the weighted sum equations of Curtis et al. to more accurately model the interdependence relationship between loss of function, intolerance, and pathogenicity. In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the conditional probability framework of Pishro-Nik et al. with Curtis et al.’s logistic regression model as it is merely adding standard mathematical techniques of probability to known logistic modeling systems to achieve the predicable result of generating a probability score.
Claims 4, 6, 10 and 12 are rejected under 35 U.S.C. 103 over Curtis et al. and Pishro-Nik et al. as applied to claims 1-3 and 7-9 above, and further in view of Lek et al. (Nature 536 pgs. 285-291, published Aug 17 2016). The claims of the instant application italicized below.
The limitations of claims 1-3 and 7-9 have been taught by Curtis et al. and Pishro-Nik et al. above.
With regards to claim 4, Curtis et al. teaches utilizing a known in-silico method of calculating a PolyPhen score that represents how damaging/pathogenic a gene variant is (pg.115 left col para 2, a second characteristic score calculation unit calculating a digitized second characteristic score corresponding to the degree that the target genetic variant has pathogenicity). Curtis et al. also teaches utilizing a known in-silico method of calculating a SIFT score that represents how intolerable a gene variant is (pg. 115 left col para 2, a first characteristic score calculation unit calculating a digitized first characteristic score corresponding to the degree that the target gene is intolerant of the loss of function). Curtis et al. then teaches calculating weights/probabilities for each variant based on these scores (pg. 115 left col para 2, wherein the first probability is expressed by
a
×
(
s
c
o
r
e
L
o
F
)
b
, wherein the second probabilities is expressed by
c
×
(
s
c
o
r
e
p
a
t
h
o
g
e
n
i
c
)
d
, wherein
s
c
o
r
e
L
o
F
is the first characteristic score,
s
c
o
r
e
L
o
F
is the second characteristic score, and wherein and a, b, c, and d are respectively predetermined constants).
Regarding claim 10, Curtis et al. teaches utilizing a known in-silico method of calculating a PolyPhen score that represents how damaging/pathogenic a gene variant is (pg.115 left col para 2, a second characteristic score calculating operation of calculating a digitized second characteristic score corresponding to the degree that the target genetic variant has pathogenicity). Curtis et al. also teaches utilizing a known in-silico method of calculating a SIFT score that represents how intolerable a gene variant is (pg. 115 left col para 2, a first characteristic score calculating operation of calculating a digitized first characteristic score corresponding to the degree that the target gene is intolerant of the loss of function). Curtis et al. then teaches calculating weights/probabilities for each variant based on these scores (pg. 115 left col para 2, wherein the first probability is expressed by
a
×
(
s
c
o
r
e
L
o
F
)
b
, wherein the second probabilities is expressed by
c
×
(
s
c
o
r
e
p
a
t
h
o
g
e
n
i
c
)
d
, wherein
s
c
o
r
e
L
o
F
is the first characteristic score,
s
c
o
r
e
L
o
F
is the second characteristic score, and wherein and a, b, c, and d are respectively predetermined constants).
However, Curtis et al. fails to disclose that the first characteristic score is based on a pLI (probability of being loss of function intolerant) algorithm (claim 4, 6, 10, and 12). Yet, this algorithm and pLI metric was well known in the art at the time of the effective filing date of the invention as demonstrated by Lek et al.
With respect to claim 4, Lek et al. teaches a pLI algorithm to calculate a pLI score for genetic variations in 60,706 humans (Abstract and pg. 288 right col para 1, a pLI algorithm).
Concerning claim 6, Lek et al. teaches a pLI algorithm to calculate a pLI score for genetic variations in 60,706 humans (Abstract and pg. 288 right col para 1, wherein the first characteristic score includes a score using at least one among a pLI algorithm and an LOEUF algorithm).
With respect to claim 10, Lek et al. teaches a pLI algorithm to calculate a pLI score for genetic variations in 60,706 humans (Abstract and pg. 288 right col para 1, a pLI algorithm).
Concerning claim 12, Lek et al. teaches a pLI algorithm to calculate a pLI score for genetic variations in 60,706 humans (Abstract and pg. 288 right col para 1, wherein the first characteristic score includes a score using at least one among a pLI algorithm and an LOEUF algorithm).
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to utilize the pLI algorithm and metric of Lek et al. within the logistic model of Curtis et al. and Pishro-Nik et al. to improve the prediction probability accuracy. One of ordinary skill in the art would have been motivated to incorporate the pLI algorithm and metric into the logistic regression model in order to account for the evolutionary context and haploinsufficiency of genetic variants as stated in Lek et al. (pg. 288 right col para 1). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the pLI algorithm and metric of Lek et al. within Curtis et al.’s logistic regression model as the use of pLI algorithms on genetic variants was well established, and the method of incorporating scores derived from other algorithms into the logistic regression of Curtis et al. has been demonstrated to be successful as shown by the score incorporation of SIFT, PolyPhen, and VEP.
Claims 5 and 11 are rejected under 35 U.S.C. 103 over Curtis et al., Pishro-Nik et al., and Lek et al. as applied to claims 1-4, 6-10, and 12 above, and further in view of Wang et al. (Genetic Epidemiology Vol 41 pgs. 790-800, published Oct 11, 2017). The claims of the instant application italicized below.
The limitations of claims 1-4, 6-10, and 12 have been taught by Curtis et al., Pishro-Nik et al., and Lek et al. above.
With respect to claims 5 and 10, Curtis et al. discloses using a logistic regression model to calculating the probability. However, Curtis et al. does not explicitly teach taking the logarithm of the equation. Yet, the process of taking a logarithm of an equation for a logistic model to predict phenotypes of genetic variants was well known in the art at the effective filing date of the instant application, as taught by Wang et al.
Regarding claim 5, Wang et al. discloses a logistic regression equation for a logistic regression model, where the output Y is the probability that the genetic variant is a case (diseased/loss of function) or control (normal/no loss of function) (pg. 3 para 4 – pg. 4 para 1). The equation (1) is expressed as:
l
o
g
i
t
P
Y
=
1
X
=
α
+
β
1
X
1
+
β
2
X
2
(pg. 4 para 1, wherein a log linear model for the first equation includes the following equation:
log
P
L
o
F
=
β
v
a
r
i
a
n
t
×
X
v
a
r
i
a
n
t
+
β
g
e
n
e
×
X
g
e
n
e
-
log
Z
). Wang et al. explains that X is the genotype data for the genetic variant, in this case with a codominant coding of
X
1
and
X
2
(pg. 3 para 4 – pg. 4 para 1, wherein
X
v
a
r
i
a
n
t
is a log value of the second characteristic score,
X
g
e
n
e
is a log value of the first characteristic score). Wang et al. further elaborates that
β
1
and
β
2
are the log-OR parameters (pg. 4 para 1, and
β
v
a
r
i
a
n
t
and
β
g
e
n
e
and Z are respectively predetermined constants).
Concerning claim 10, Wang et al. discloses a logistic regression equation for a logistic regression model, where the output Y is the probability that the genetic variant is a case (diseased/loss of function) or control (normal/no loss of function) (pg. 3 para 4 – pg. 4 para 1). The equation (1) is expressed as:
l
o
g
i
t
P
Y
=
1
X
=
α
+
β
1
X
1
+
β
2
X
2
(pg. 4 para 1, wherein a log linear model for the first equation includes the following equation:
log
P
L
o
F
=
β
v
a
r
i
a
n
t
×
X
v
a
r
i
a
n
t
+
β
g
e
n
e
×
X
g
e
n
e
-
log
Z
). Wang et al. explains that X is the genotype data for the genetic variant, in this case with a codominant coding of
X
1
and
X
2
(pg. 3 para 4 – pg. 4 para 1, wherein
X
v
a
r
i
a
n
t
is a log value of the second characteristic score,
X
g
e
n
e
is a log value of the first characteristic score). Wang et al. further elaborates that
β
1
and
β
2
are the log-OR parameters (pg. 4 para 1, and
β
v
a
r
i
a
n
t
and
β
g
e
n
e
and Z are respectively predetermined constants).
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to implement the logistic regression equation of Wang et al. within the logistic model of Curtis et al., Pishro-Nik et al., and Lek et al. to clarify the mathematical concept behind the logistic regression model. One of ordinary skill in the art would have been motivated to explicitly incorporate the logistic regression equation into the logistic regression model to further clarify the standard mathematical operationalization of the logistic regression model. In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the logistic regression equation of Wang et al. within Curtis et al.’s logistic regression model as logistic regression equations were well known components of logistic regression models and can be manipulate to take various inputs into account as demonstrated in Curtis et al.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENYU YANG whose telephone number is (571)272-0035. The examiner can normally be reached 8:30am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.Y./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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