DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-13 are currently pending and under examination herein.
Claims 1-13 are rejected.
Priority
The application claims benefit under 35 U.S.C. §119 of foreign priority
10-2022-0053742, filed 04/29/2022, and 10-2023-0055651 filed 04/27/2023, are acknowledged. The certified copies have been received, however, there is no certified translation of record for the Korean language priority document, 10-2022-0053742, filed 04/29/2022. Therefore, the effective filing date is based on the English translation priority document, 10-2023-0055651, filed 04/27/2023, for supporting the claimed invention.
In this action, all claims 1-13 are examined for an effective filing date of 04/27/2023. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
Information Disclosure Statements, filed 05162023 and 03112026, have been considered. Signed copies of the IDS are included with this Office Action.
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Drawings
The Drawings submitted 04/28/2023 are accepted. The drawings as filed are suitable to the Examiner. Applicant is encouraged to review the submission in PAIR to ensure all details are readable, particularly FIGs 1A-C, 19-26, and 32.
Claim Objections
Claims are objected to because of the following informalities:
Claim 12 is written in independent form, however, refers to claim 1 which indicates a dependency and would necessitate correction to “12. The method…”
Claim 13 is written in independent form, however, refers to claim 12 which indicates a dependency and would necessitate correction to “13. The computer-readable recording medium…”
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 limitation(s) is/are:
“using deep learning… for predicting efficiency and an outcome of a base editor…” in claim 1;
“target sequence input unit configured to receive an input of target sequence data of the base editor…” in claim 1;
“an outcome prediction unit configured to obtain a base editing efficiency output value and a base editing outcome proportion output value by applying the target sequence data…” in claim 1;
"receiving an input of base conversion activity data of the base editor through an information input unit" in claim 2;
“performing deep learning based on a convolutional neural network (CNN) on the base conversion activity data” in claim 2;
“performing deep learning based on a CNN on the base editing outcome data” in claim 6;
“an output unit configured to output efficiency and an outcome proportion of the base editor” in claim 7;
Said deep learning, units and program are the generic placeholders, and each respective function is the specialized function.
“predicting efficiency and an outcome of a base editor’ in claim 1;
“configured to receive an input of target sequence data of the base editor” in claim 1;
“configured to obtain a base editing efficiency output value and a base editing outcome proportion output value by applying the target sequence data” in claim 1;
"receiving an input of base conversion activity data of the base editor” in claim 2;
“performing deep learning … on the base conversion activity data” in claim 2;
“performing deep learning … on the base editing outcome data” in claim 6; and
“to output efficiency and an outcome proportion of the base editor” in claim 7.;
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) 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 it/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 it/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.
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 1-13 are rejected under 35 USC 112 2nd or 112(b) as failing to particularly point out and distinctly claim the invention. Claims 1-13 fail to particularly point out and distinctly claim the subject matter which applicant regards as his invention.
Claim limitation “using deep learning… for predicting efficiency and an outcome of a base editor…” in claim 1; 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. The claim fails to particularly set forth and distinctly claim the structures/particular system or NN architecture required to perform deep learning. The claim fails to set forth using deep learning how to apply any unspecified NN to a generic base editor to achieve an efficiency and an outcome of the activity. The claim fails to identify what system components are relevant, necessary, and sufficient for the stated goals. The claim fails to particularly set forth how to incorporate the generically recited system for deep learning. A reading of the specification provides a variety of possibilities, however no specific definition of deep learning which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim limitation “target sequence input unit configured to receive an input of target sequence data of the base editor…” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description [0020: As used herein, the term "target sequence input unit" refers to a component that is included in a system] 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. The claim fails to particularly set forth and distinctly claim the structure required to receive an input. The claim fails to set forth the unit structure and how it receives and where it receives (from what database?) any unspecified target sequence data to a generic but appropriate base editor.
Claim limitation “outcome prediction unit configured to obtain a base editing efficiency output value and a base editing outcome proportion output value…” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description [0042: As used herein, the term "outcome prediction unit" refers to a component configured to predict] 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. The claim fails to particularly set forth and distinctly claim the structures required to predict an outcome. The claim fails to set forth the unit structure and how applying the target sequence data to obtain… A reading of the specification provides a variety of possibilities, however no specific definition of deep learning which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim limitation “information input unit …receiving an input of base conversion activity data of the base editor” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description [0025: the term "information input unit" refers to a component configured to receive base conversion activity data or base editing outcome data of a BE] 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. The claim fails to particularly set forth and distinctly claim the structures required for receiving an input. The claim fails to set forth the unit structure capable of receiving base conversion activity data (from what database?). A reading of the specification provides a variety of possibilities, however no specific definition of unit which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim limitation “convolutional neural network (CNN)” in claim 2 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. The claim fails to particularly set forth and distinctly claim the structures required for performing deep learning… on the base conversion activity data. The claim fails to set forth the NN structure for deep learning on base conversion activity data. A reading of the specification provides a variety of possibilities [0032: As used herein, the term "deep learning" refers to artificial intelligence (AI) technology… [0033] As used herein, the term "convolutional neural network (CNN)"], however no specific definition or system architecture of deep learning…CNN which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim limitation “a CNN” in claim 6 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. The claim fails to particularly set forth and distinctly claim the structures required for performing deep learning… on the base editing outcome data. The claim fails to set forth the CNN structure for deep learning on base editing outcome data. A reading of the specification provides a variety of possibilities [0032: As used herein, the term "deep learning" refers to artificial intelligence (AI) technology… [0033] As used herein, the term "convolutional neural network (CNN)"], however no specific definition of deep learning…CNN which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Claim limitation “output unit” in claim 7 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. The claim fails to particularly set forth and distinctly claim the structures required for output data. The claim fails to set forth the unit for outputting efficiency and an outcome proportion values. A reading of the specification provides a variety of possibilities, however no specific definition of said unit which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
MPEP 2181.II.B: “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).”
“To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.”
“Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
Therefore, claims 1-13 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
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.
Claims 1-13 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 claim(s) contains 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 inventor(s), at the time the application was filed, had possession of the claimed invention. This is a WRITTEN DESCRIPTION rejection.
As set forth above, the claims invoke 112 6th paragraph for the above steps related to output efficiency and an outcome proportion. The specification fails to specifically disclose the necessary and sufficient system components, NN functions, step-by-step procedures or structures required to compute or generate each specialized function and fails to specifically link those structures to the specialized functions of the claims. Merely stating the desired goal of the step is not a written description of the actual steps required to achieve the goal. The claim fails to particularly set forth and distinctly claim the structures required for performing deep learning… on the base editing outcome data. The claim fails to set forth the CNN structure for deep learning on base editing outcome data. A reading of the specification provides a variety of possibilities [0032: As used herein, the term "deep learning" refers to artificial intelligence (AI) technology… deep learning may be defined as a set of machine learning algorithms that attempt high-level abstractions (summarizing key content or functions in large amounts of data or complex materials) through a combination of several nonlinear transformation methods. [0033: As used herein, the term "convolutional neural network (CNN) refers to a technique of extracting features representing a part of provided information and achieving generalization through hierarchization of information. [0034] The generating of the base editing efficiency prediction model by performing the deep learning based on the CNN may further include… [0047: Computer programming languages capable of implementing the program of the disclosure include Python, C, C++, "], however no specific definition and architecture of deep learning…CNN which is necessary and sufficient is provided for one of skill to achieve the instant invention and would need additional information. While the specification recites a variety of generic algorithms and no drawings demonstrate a system architecture, the specific linkage of a particular set of steps and features which are necessary and sufficient to achieve each specialized function is lacking.
MPEP 2181.II.B: “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign.Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).”
“To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.”
“Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
“When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP § 2163.03, subsection VI.”
Claims 1-13 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.
The metes and bounds of claim 1 are unclear. The claim, while listing the input, target sequence data, fails to particularly point out what appropriate type of base editor, how the data is to be applied, what constitutes the functions in the base editing efficiency output and base editing outcome proportion output models to calculate their respective end values, as required.
Claim 5 recites the limitation “a CNN” in “deep learning based on a CNN”. There is insufficient antecedent basis for this limitation in claim 4 which is dependent on claim 3, “the CNN”. It is unclear whether CNN recited in claim 3 is the same as CNN in claim 5. Clarification is requested through clearer claim language.
Claim 6, which depends on claim 1, recites the limitation “a CNN” in “deep learning based on a CNN”. It is unclear whether CNN recited in claim 2 is the same as CNN in claim 6.
Claims 2 and 6 recite “information input unit” which receive respectively base conversion activity data and base editing outcome models, however, the minimally sufficient and necessary structure to provide these models to an outcome prediction unit in claim 1 is not claimed. There is insufficient system structure to recite operative coupling between the information input units and the outcome prediction unit.
The metes and bounds of claim 3 limitation “linking CRISPR associated protein 9 (Cas9) activity data” are unclear, as it is unclear where within claim 2, this limitation is to be applied, and what/how it is intended to be linked to the Cas9 activity data.
The metes and bounds of claim 4 are unclear, as it is unclear where within claim 3, this limitation is to be applied, and it is further unclear if the system of claim 1 is equipped to contain Cas9 or a cell library/oligonucleotides, or to perform introducing Cas9, or library deep sequencing. The instant system does not appear to provide the minimally sufficient and necessary components for completing claimed steps, as required.
The metes and bounds of claim 5 limitation “a correlation between indel frequencies of the Cas9” are unclear. It is unclear what CNN architecture is used for deep learning and the functions and criteria for determining a correlation between indel frequencies.
The metes and bounds of claim 12 are unclear, as it is unclear where within claim 1 this limitation is to be applied, and it is further unclear how it affects the system of claim 1. This method step does not appear to provide the minimally sufficient and necessary steps to the system for predicting efficiency and an outcome of a base editor by using deep learning.
The metes and bounds of “designing” step in claim 12 are unclear. The claim fails to particularly point out how a target sequence is determined or then produced for a generic base editor, and any steps or limitations for applying it to the instant system. This applies equally to claim 13.
Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture, or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
With respect to step (1): yes, the claims 1-12 are directed to a system/method for prediction & optimization of Cas9 base editor metrics, the answer is "yes".
The claimed invention (computer program product claims 13 is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because CRM/program does not specify a non-transitory computer-readable medium [0045-0046] . The MPEP 2106.03 (I) teaches examples of claims that are not directed to any of the statutory categories include: products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations; and transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave. Applicant should consider adding non-transitory, consistent with the instant specification [0045-0047], to all instances of claimed computer-readable medium.
With respect to step (2A)(1), the claims recite abstract ideas and natural correlations. To determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon, MPEP at 2106.03 teaches abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). In the instant application, the claims recite the following limitations that equate to an abstract idea with mental steps and mathematical concepts and natural correlations.
Abstract ideas include mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information) (MPEP 2106.04(a)(2). Laws of nature or natural phenomena include naturally occurring principles/ relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP2106(b)). Claims 1, 12 and 14 are independent, and all recite nearly identical limitations. Primarily claim 1 is referred to here for clarity. The claims directing to judicial exceptions are as follows:
The claims directing to judicial exceptions are as follows:
Mental processes:
Claim 1: predicting efficiency and an outcome of a base editor by using deep learning…
Claim 3: linking CRISPR associated protein 9 (Cas9) activity data.
Claim 4: analyzing efficiency of the Cas9 based on data obtained from the deep sequencing.
Claim 5: predicting an activity of the Cas9 based on a correlation between indel frequencies of the Cas9 in a particular target sequence …
Claim 7: an output unit configured to output efficiency and an outcome proportion of the base editor, which are predicted by the outcome prediction unit.
Claim 8: selected from a group consisting of SpCas9, VRQR variant, SpCas9-NG, SpCas9-NRRH, SpCas9- NRTH, SpCas9-NRCH, SpG, SpRY, and Sc++.
Claim 9: selected from a group consisting of YE1-BE4max, SsAPOBEC3B, ABE8e(V106W), ABE8.17- m+V106W, CGBE1, miniCGBE1, and APOBEC-nCas9-Ung.
Claim 12: designing a target sequence of the base editor; and
applying the designed target sequence to the system for predicting efficiency and an outcome of a base editor of claim
Mathematical concepts
Claim 1: applying the target sequence data that is input through the target sequence input unit, to a base editing efficiency prediction model and a base editing outcome proportion prediction model, respectively, and generate a base editing prediction score by multiplying the base editing efficiency output value by the base editing outcome proportion output value. (mathematical concept of mathematical calculations)
Claim 2: generating the base editing efficiency prediction model by performing deep learning based on a convolutional neural network (CNN) on the base conversion activity data that is input through the information input unit. (mathematical algorithm based on machine learning calculations/NN)
Claim 6: generating the base editing outcome proportion prediction model…performing deep learning based on a CNN on the base editing outcome data that is input through the information input unit. (mathematical algorithm based on machine learning calculations/NN)
Claims 10 and 11: wherein the base editing efficiency output/outcome proportion output value is calculated through Equation …(mathematical concept of mathematical calculations with equations)
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Natural phenomenon:
Claim 5: predicting an activity of the Cas9 based on a correlation between indel frequencies of the Cas9 in a particular target sequence …
Claim 5 takes genomic data, in combination with base editor metrics, to identify a designed target sequence. This is a correlation between naturally occurring genotype information, naturally occurring aspects of Cas9 activity, and a phenotype of most efficient. This is a naturally occurring correlation.
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas and natural correlation.
With respect to step 2A(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claim 1: to receive an input of target sequence data of the base editor…to obtain a base editing efficiency output value and a base editing outcome proportion output value
Claim 2: receiving an input of base conversion activity data of the base editor through an information input unit…
Claims 2-6: a convolutional neural network (CNN)…
Claim 4: introducing Cas9 into a cell library containing oligonucleotides containing a nucleotide sequence that encodes sgRNA and a target nucleotide sequence targeted by the sgRNA; performing deep sequencing by using DNA obtained from the cell library into which the Cas9 is introduced
Claim 5: genomic information is at least one transcription data selected from a group consisting of messenger RNA (mRNA), RNA sequencing (RNA-seq), and Clustered regularly interspaced short palindromic repeats (CRISPR).
Claim 6: receiving an input of base editing outcome data of the base editor through an information input unit;
Claims 1 and 13: system, a computer, computer-readable recording medium having recorded thereon a program
Claims 1 do not utilize the base editor designed target sequence in any real world or practical application, only to predict more data (efficiency/outcome of a base editor).
Claim 4 recites conventional laboratory steps of introducing Cas9 into a cell library containing oligonucleotides containing a nucleotide sequence that encodes sgRNA and a target nucleotide sequence targeted by the sgRNA; performing deep sequencing by using DNA obtained from the cell library into which the Cas9 is introduced and claim 2-6 recite conventional deep learning tools of CNN (Liu et al. WO2021030666A1; Kurt et al. 2021: CRISPR C-to-G base editors for inducing targeted DNA transversions in human cells", Nat Biotechnol, 39(1): 1-24; Vinodkumar PK et al. 2021: Prediction of sgrna off-target activity in crispr/cas9 gene editing using graph convolution network. Entropy, 23(5), 608: 18 pages).
Claims 1-2, and 6 recite additional elements that are not an abstract idea but are data gathering steps. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Claims 1-6, and 13 also recite the additional non-abstract elements: system, CNN, computer-readable recording medium having recorded thereon a program. The claims do not describe any specific computational steps by which the computer, CNN, or system related parts perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer such as the computer readable recording media are used to implement these functions. The claims require nothing more than a minimally described, generic computer [0045: Provided is a computer-readable recording medium having recorded thereon a program for causing a computer to execute a method of predicting the efficiency and an outcome of a BE by using deep learning] and CNN [0032-033] to perform the functions that constitute the abstract idea (Vinodkumar PK et al. 2021: Prediction of sgrna off-target activity in crispr/cas9 gene editing using graph convolution network. Entropy, 23(5), 608: 18 pages). Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not recite integrate that abstract idea into a practical application. (see MPEP 2106.05(f)).
Claims 1-13 recite no additional element/limitation related to the natural law and so do not provide a particular limitation which would integrate the natural law into a practical application. To integrate a judicial exception into a practical application, the additional limitation must be specifically identified, and not merely instructions to apply the judicial exception. The additional limitation must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04(d)(2))
Dependent claims 2-11 have been analyzed. Dependent claims 2-11 are directed to further abstract limitations. Further abstract limitations cannot provide a practical application of the judicial exception as they are a part of that exception. Dependent claims 2-6 are also directed to additional steps of data gathering. Steps of data gathering do not provide a practical application for the judicial exception. Claim 13 is directed to additional computer limitations. These further limitations are still generically stated and require no more than a standard computer to perform them.
None of these dependent claims recite additional elements which would integrate a judicial exception into a practical application.
Finally, the (2B) analysis. Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering, instructions, and field of use limitations described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rest in whether or not the additional elements (or combination of elements) represent well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concept:
Claim 1: to receive an input of target sequence data of the base editor…to obtain a base editing efficiency output value and a base editing outcome proportion output value
Claim 2: receiving an input of base conversion activity data of the base editor through an information input unit…
Claims 2-6: a convolutional neural network (CNN)…
Claim 4: introducing Cas9 into a cell library containing oligonucleotides containing a nucleotide sequence that encodes sgRNA and a target nucleotide sequence targeted by the sgRNA; performing deep sequencing by using DNA obtained from the cell library into which the Cas9 is introduced
Claim 5: genomic information is at least one transcription data selected from a group consisting of messenger RNA (mRNA), RNA sequencing (RNA-seq), and Clustered regularly interspaced short palindromic repeats (CRISPR).
Claim 6: receiving an input of base editing outcome data of the base editor through an information input unit;
Claims 1 and 13: system, a computer, computer-readable recording medium having recorded thereon a program
Said steps that are “in addition” to the recited judicial exception in the instant claims represent those of mere data handling instructions or field of use limitations (target sequence data, receive an input, introducing Cas9 into a cell library, oligonucleotides, sgRNA) to implement in the recited judicial exception and do not impart meaning to said recited judicial exception, such that is applied in a practical manner. Further with respect to the additional elements in the instant claims, these steps direct to mere data gathering and handling (receive an input) to carry out the abstract idea without imposing any meaningful limitation on the abstract idea. Thereby these steps are insignificant extra-solutions activity steps and are insufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
With respect to claim 1: The additional element of data gathering does not rise to the level of significantly more than the judicial exception. Steps of “receive an input” of genomic information are merely steps of obtaining data from unspecified databases.
Claim 4 recites conventional laboratory steps of introducing Cas9 into a cell library containing oligonucleotides containing a nucleotide sequence that encodes sgRNA and a target nucleotide sequence targeted by the sgRNA; performing deep sequencing by using DNA obtained from the cell library into which the Cas9 is introduced and claim 2-6 recite conventional deep learning tools of CNN (Liu et al. WO2021030666A1; Kurt et al. 2021: CRISPR C-to-G base editors for inducing targeted DNA transversions in human cells", Nat Biotechnol, 39(1): 1-24; Vinodkumar PK et al. 2021: Prediction of sgrna off-target activity in crispr/cas9 gene editing using graph convolution network. Entropy, 23(5), 608: 18 pages).
Claims 1-2, and 6 recite additional elements that are not an abstract idea but are data gathering steps. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Claims 1-11 and 13 also recite the additional non-abstract elements: system, CNN, computer-readable recording medium having recorded thereon a program. The claims do not describe any specific computational steps by which the computer, CNN, or system related parts perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer such as the computer readable recording media are used to implement these functions. The claims require nothing more than a generic computer/CNN to perform the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not recite integrate that abstract idea into a practical application. (see MPEP 2106.05(f)).
Claims 1-13 recite no additional element/limitation related to the natural law and so do not provide a particular limitation which would integrate the natural law into a practical application. To integrate a judicial exception into a practical application, the additional limitation must be specifically identified, and not merely instructions to apply the judicial exception. The additional limitation must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04(d)(2))
Remaining claims have been analyzed. Dependent claims 2-11 are directed to further abstract limitations. Further abstract limitations cannot provide a practical application of the judicial exception as they are a part of that exception. Dependent claims 2-6 are also directed to additional steps of data gathering. Steps of data gathering do not provide a practical application for the judicial exception. Dependent claim 13 is directed to additional computer limitations. These further limitations are still generically stated and require no more than a standard computer to perform them.
None of these dependent claims recite additional elements which would integrate a judicial exception into a practical application.
With respect to claims 1 and 13, the computer related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The specification does not disclose any system architecture or hardware components. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
With respect to claims 1, 12, and 13: the additional limitations to the law of nature do not rise to the level of significantly more than the judicial exception. The additional limitations have all been shown to be routine, well-understood and conventional in the art. These limitations in addition to the law of nature do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. These additional limitations constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP 2106.05(b)&(c).)
Remaining claims have been analyzed with respect to step 2B. Dependent claims 4, and 8-9 relate to field of use limitations for data gathering discussed above. Claim 13 relates to additional computer components discussed above. Dependent claims 2-12 provide further abstract limitations. None of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception.
For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter (Step 2B: No). As such, claims 1-13 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.
(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.
Note: citations from the instant application are italicized in the following section.
Claims 1-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Song M et al (2020). Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nature biotechnology, 38(9), 1037-1043.
The independent claim is drawn to a system (as well as method and computer program product) of prediction & optimization of Cas9 base editor metrics. By using target sequence data of the base editor; using two base editing models/algorithms (an efficiency prediction model and an outcome proportion prediction model), the invention
generates a base editing prediction score (multiplying the base editing efficiency output value by the base editing outcome proportion output value),
With respect to claim 1, the prior art to Song et al. provide the overall system and program. Song provides means for a system relying on computer code automated analysis of base-editing frequencies and outcomes [p1045 Col 1: deep sequencing data were analyzed using in-house Python scripts, which were modified from previously used code] with a deep learning CNN [p1045 Col 2: CNNs are one of the most robust deep-learning architectures…have been used successfully in a variety of DNA sequence-related studies, which include the prediction of activities and outcomes of CRISPR nuclease, transcription factor, binding affinity, and DNA sequence accessibility]. A predictive analysis of base editor metrics is generated, based on a base editing efficiency prediction equation and a base editing outcome proportion prediction equation, to provide a base editing outcome prediction score [p1045 Col 2: The absolute frequency of base-edited outcomes can be calculated by multiplying the base-editing outcome proportion and the base-editing efficiency].
With respect to claim 2, Song provides a calculation of base-editing efficiencies at each position when any of the target nucleotides of ABE (that is, A) and CBE (that is, C) in the editable windows were converted to T and G, respectively. [p1045 Col 1: For analysis of base-editing efficiencies and outcomes, the reads sorted by the unique barcode sequences were aligned using an in-house Python script…]
With respect to claim 3, Song provides a deep learning CNN analysis based on Cas9 data:
[p1045 Col 2: Song’s CNN using deep reinforcement learning omitted the pooling layer for better performance and used early stopping based on the validation score.]
[p1045 Col 1: deep sequencing data were analyzed using in-house Python scripts. Each guide RNA [sgRNA] and target sequence pair was identified using the unique 15-nucleotide barcode sequence located upstream of the target sequence.
With respect to claim 4, Song provides a target-integrated lentiviral plasmid library and then high throughput/deep sequencing performed [p1044 Col 2: ABE and CBE delivery into the cell library. For delivery of ABE to the cell library, Lenti Split-ABE-N-Blast and Lenti Split-ABE-C-Hygro-eGFI were mixed…]
With respect to claims 5, Song provides a correlation between indel frequencies [p1045 Col 1: insertions or deletions located around the expected cleavage site of Cas9 nuclease (that is, the 8-nt region centered on the middle of the cleavage site) were considered to be nuclease-induced indels].
With respect to claim 6, Song provides the analysis of base-editing outcome proportions, aligned reads were recalculated according to the sequence outcomes in the base-editing window using an in-house Python script [p1045 Col 1: For analysis of base-editing efficiencies and outcomes, the reads sorted by the unique barcode sequences were aligned using an in-house Python script…]
. With respect to claim 7, Song provides a predictive analysis of base editor metrics based on a base editing efficiency prediction equation and a base editing outcome proportion prediction equation to generate a base editing outcome prediction score [p1045 Col 2: The absolute frequency of base-edited outcomes can be calculated by multiplying the base-editing outcome proportion and the base-editing efficiency].
With respect to claims 8-9, Song provides Cas9, such SpCas9, and multiple base editors.
With respect to claims 10-12, Song provides a predictive analysis of base editor metrics is generated, based on a base editing efficiency prediction equation and a base editing outcome proportion prediction equation, to provide a base editing outcome prediction score [p1045 Col 2: The absolute frequency of base-edited outcomes can be calculated by multiplying the base-editing outcome proportion and the base-editing efficiency].
With respect to claim 13, Song provides deep-learning-based computational modeling tools to predict the efficiencies and outcome frequencies of ABE- and CBE-directed editing at any target sequence, to facilitate modeling and therapeutic correction of genetic diseases by base editing [Abstract].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A. Instant claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 4, 6, 7-9, 11-16, and 18-20 of 18/007,241. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a species to ‘241 which is a system for predicting prime editing efficiency by using deep learning on input of data on prime editing efficiency of a prime editor.
B. Instant claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 6-9, and 11 of 18/319,071. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are generic to ‘071. ‘071 is a species of instant application which is also system for predicting an activity of small Cas9 using deep learning.
C. Instant claims 1-13 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 8, 11-12, 15-19, 24, and 26-27 of 19/109,301. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are generic to ‘301. ‘301 is a species of the instant application which is also for training a predictive model for prime editing efficiency, comprising: obtaining a dataset on a prime editing efficiency of pegRNAs according to cell types and prime editor types; and training the predictive model using the dataset by deep learning.
These are provisional nonstatutory double patenting rejections because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686
Prosecution Insights