DETAILED ACTION
This is the first action regarding application number 17/791,392 filed 09/17/2025 with preliminary amendment filed 09/17/2025. Claims 2, 4-5, 8, 10-12 and 14-15 have been canceled and claims 1, 3, 6 and 7 have been amended. Claims 1, 3, 6-7, 9 and 13 have been examined and are pending.
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 .
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.
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.
Claim 1, 3, 6-7, 9, 13 rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea or mental process.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites enumerates a potential contradiction set… by traversing the BDD which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set with respect to specific rules or constraints using a binary decision diagram. See 2106.04.(a)(2).III.C.
The claim recites in which literals that contradict the background knowledge in a solution candidate set are combined which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites execute the abduction by searching for a solution hypothesis from the solution candidate set by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis while reducing computational cost which are categized into mathematical concepts grouping of Mathematical Calculations (MPEP 2106.04(a)(2)(I)(C)) and Mathematical Relationships (MPEP 2106.04(a)(2)(I)(A)) .
The claim recites enumerate BDD variables that have potential to have a boolean value determined by the solution candidate set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set where the elements are judged to have a potential to have a value that can be determined or used in another set. See 2106.04.(a)(2).III.C.
The claim recites search for a boolean assignment that leads to contradiction within a range of the BDD variables which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging a set of data for possible states that shouldn’t exist and using reasoning to determine contradictions. See 2106.04.(a)(2).III.C.
The claim recites search for a combination of literals corresponding to the assignment from the solution candidate set when the boolean assignment exists which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user searching for combination of variables based on another set of variables. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
at least one memory storing instructions and at least one processor c (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
acquire a contradiction determination function(which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g))
having a binary decision diagram as a data structure that compactly expresses a combination of logical formulas that contradict background knowledge(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) and (d) in claim 1 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible
Regarding claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the potential contradiction enumeration unit determines whether all combinations of literals in the solution candidate set contradict the background knowledge using the contradiction determination function which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites enumerates combinations of literals that contradict the background knowledge which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set with respect to a background knowledge constraint. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites enumerating a potential contradiction set… by traversing the BDD which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set with respect to specific rules or constraints using a binary decision diagram. See 2106.04.(a)(2).III.C.
The claim recites in which literals that contradict the background knowledge in a solution candidate set are combined which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites executing the abduction by searching for a solution hypothesis from the solution candidate set by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis while reducing computational cost which are categized into mathematical concepts grouping of Mathematical Calculations (MPEP 2106.04(a)(2)(I)(C)) and Mathematical Relationships (MPEP 2106.04(a)(2)(I)(A)) .
The claim recites enumerating BDD variables that have potential to have a boolean value determined by the solution candidate set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set where the elements are judged to have a potential to have a value that can be determined or used in another set. See 2106.04.(a)(2).III.C.
The claim recites searching for a boolean assignment that leads to contradiction within a range of the BDD variables which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging a set of data for possible states that shouldn’t exist and using reasoning to determine contradictions. See 2106.04.(a)(2).III.C.
The claim recites searching for a combination of literals corresponding to the assignment from the solution candidate set when the boolean assignment exists which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user searching for combination of variables based on another set of variables. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
executed by a computer(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
acquiring a contradiction determination function(which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g))
having a binary decision diagram as a data structure that compactly expresses a combination of logical formulas that contradict background knowledge(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) and (d) in claim 6 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites enumerating a potential contradiction set… by traversing the BDD which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set with respect to specific rules or constraints using a binary decision diagram. See 2106.04.(a)(2).III.C.
The claim recites in which literals that contradict the background knowledge in a solution candidate set are combined which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites executing the abduction by searching for a solution hypothesis from the solution candidate set by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis while reducing computational cost which are categized into mathematical concepts grouping of Mathematical Calculations (MPEP 2106.04(a)(2)(I)(C)) and Mathematical Relationships (MPEP 2106.04(a)(2)(I)(A)) .
The claim recites enumerating BDD variables that have potential to have a boolean value determined by the solution candidate set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user listing elements of a set where the elements are judged to have a potential to have a value that can be determined or used in another set. See 2106.04.(a)(2).III.C.
The claim recites search for a boolean assignment that leads to contradiction within a range of the BDD variables which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging a set of data for possible states that shouldn’t exist and using reasoning to determine contradictions. See 2106.04.(a)(2).III.C.
The claim recites searching for a combination of literals corresponding to the assignment from the solution candidate set when assignment exists which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user searching for combination of variables based on another set of variables. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
non-transitory computer-readable recording medium that includes a program recorded(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
acquiring a contradiction determination function(which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g))
having a binary decision diagram as a data structure that compactly expresses a combination of logical formulas that contradict background knowledge(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) and (d) in claim 6 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible
Claim 9 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 3 found in claim 9.
Claim 13 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 3 found in claim 13.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 6-7, 9, 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Biane et al.(Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery), henceforth known as Biane.
Regarding claim 1:
Biane discloses at least one memory storing instructions; and at least one processor(Biane, Page 1581, Col. 2, footnote 5, “Performed on a HP EliteBook 820 G2 Intel Core (2.30 GHz i5-5300U CPU with 16 Gb of memory)”)
Biane discloses acquires a contradiction determination function having a binary decision diagram (BDD) as a data structure(Biane, Figure 3 and “To circumvent this limitation, we have developed an alternative method based on prime implicants computation using reduced ordered Binary Decision Diagrams (ROBDDs). ROBDDs enable a compact and canonical representation of Boolean functions... This method, called BDD-NECESSITY, consists in representing the formula as a Positive Reduced Ordered Binary Decision Diagram (PROBDD) and computing the prime implicants on this representation” where a Binary Decision Diagram is considered a data structure that processes information) that compactly expresses a combination of logical formulas(Biane, Page 1581, Col. 2, Figure 3, where f is a logical formula being used in a data structure is considered expressing a logical formula) that contradict background knowledge(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a potential solution C is found when the formula f is consistent with background knowledge Φ is considered expressing a combination of logical formulas that contradict background knowledge.), enumerate a potential contradiction set(Biane, Page 1578, Col. 1, Paragraph 7, “Inferring a core corresponds to the determination of control parameters producing an expected effect. In logic finding causes from effects is an abduction problem” where identifying core sets that contradict or support the goal formula is considered enumerating potential contradiction sets), in which literals that contradict the background knowledge in a solution candidate set are combined, by traversing the BDD(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a cube C(solution candidate set) and when the background knowledge Φ is compared to the formula f with Φ ^ C being satisfied is considered combining the background knowledge in a solution candidate set as formula f is checked against Φ for satisfaction/no contradictions)
Biane discloses executes the abduction(Biane, page 1579, Col. 2, Paragraph 1, “… we have introduced the principle of the abductive inference of cores for drug target discovery” where the use of abductive inference of cores is considered executing abduction) by searching for a solution hypothesis from the solution candidate set (Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP). A 0 -1 ILP problem is formulated as:
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where y is the unknown vector of size r, m, v vectors, W matrix are the parameters of the problem and h the number of variables” where ILP-CORE computing the set of all cores(i.e. minimal cubes) via minimization of the weighted sum while satisfying the constraints and boolean formula is considered creating an inference based on solution candidate sets and cores/cubes are solution hypothesis that are searched for) by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis(Biane, Page 1577, Col. 2, Paragraph 2, “To avoid a contradictory freeze to 0 and 1 simultaneously, the constraint Φ = d0i ∨ d1i is added ensuring the mutual exclusion of the parameter activities” where Φ = d0i ∨ d1i is an inconsistency rule that is used as a constraint to find contradictions) while reducing computational cost(Biane, Page 1581, Col. 2, Paragraph 1, “this supplementary reduction rule reduces the size of the representation of the formula without impairing the computation of cores.”)
Biane discloses enumerate BDD variables that have potential to have a boolean value determined by the solution candidate set(Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP)” where a core is a minimal cube and computing all sets of cores K* using 0 – 1 ILP using formula f is considered enumerating BDD variables to boolean values determined by a solution candidate set), search for a boolean assignment that leads to contradiction within a range of the BDD variables(Biane, Page 1581, Col. 2, Paragraph 2, “Then, the prime implicants are computed on this representation of the formula … The principle underlying the algorithm is that all paths of a ROBDD from the root to the terminal node 1 can be seen as cubes constituting a disjoint cover of the function. A set of implicants of the function can then be found by computing all paths such that a variable is present as its negative literal in the implicant if the path go through its outgoing edge labeled low, as its positive literal if the path go through its outgoing edge labeled high and is absent of the cube if the path does not go through a node representing the variable” where computing all paths when finding implicants with the collection of background knowledge(control parameters, constraints and TN-Actions that provide causal relationships between variables in the network) and logical formulas leading to an inconsistent/conflicting literal assignments or no solution found is considered searching for a boolean assignment that leads to a contradiction in the systems representation as there is no consistent path to the terminal nodes) and search for a combination of literals (Biane, page 1580, Col. 1, Paragraph 6, “By definition of abduction, if there exists an implicant C* of a formula f which is consistent with a theory, then C* is satisfiable and thus also f. A formula in CNF is satisfiable if and only if all its clauses are satisfiable and a clause is satisfiable if and only if at least one of its literal is satisfiable. Therefore, an implicant of this formula is a cube formed by taking at least one literal from each clause” where an implicant taking at least one literal from each clause in a formula is considered searching for a combination of literals as it requires checking different combinations of literal assignments to check for satisfaction) corresponding to the assignment from the solution candidate set when the boolean assignment exists(Biane, page 1581, Col. 2, Paragraph 2, “Therefore, computing the prime implicants from this representation consist in recursively dividing the PROBDD into three sets: the low set, corresponding to the prime implicants containing the negative literal, the high set, containing the positive literal and the don’t care set that do not contain the variable” where computing the prime implicate requires identifying all possible cubes that are satisfactory and all combinations that are not satisfactory(i.e. contradictory) is considered searching combinations of literals that correspond to a contradictory assignment from the solution candidate set)
Regarding claim 3:
The rejection of Biane discloses the learning apparatus of claim 1 is incorporated and further:
Biane discloses determine wherein the potential contradiction enumeration unit determines whether all combinations of literals in the solution candidate set contradict the background knowledge using the contradiction determination function(Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP)” where a core is a minimal cube and computing all sets of cores K* is considered determining all combination of literals in a solution candidate set that contradict background knowledge) and enumerate combinations of literals that contradict the background knowledge(Biane, Page 1580, Col. 2, Paragraph 1,“
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be the set of variables occurring both positively and negatively in Lf , then we have the following constraints excluding at least the positive or the negative literal for the variables of P:
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” where the process of ensuring no logical contradiction by ensuring a literal and it’s negation are both selected in the same solution candidate set is considered enumerating combinations of literals that contradict the background knowledge)
Regarding claim 6:
Biane discloses executed by a computer(Biane, Page 1581, Col. 2, footnote 5, “Performed on a HP EliteBook 820 G2 Intel Core (2.30 GHz i5-5300U CPU with 16 Gb of memory)”)
Biane discloses acquiring a contradiction determination function having a binary decision diagram as a data structure(Biane, Figure 3 and “To circumvent this limitation, we have developed an alternative method based on prime implicants computation using reduced ordered Binary Decision Diagrams (ROBDDs). ROBDDs enable a compact and canonical representation of Boolean functions... This method, called BDD-NECESSITY, consists in representing the formula as a Positive Reduced Ordered Binary Decision Diagram (PROBDD) and computing the prime implicants on this representation” where a Binary Decision Diagram is considered a data structure that processes information) that compactly expresses a combination of logical formulas(Biane, Page 1581, Col. 2, Figure 3, where f is a logical formula being used in a data structure is considered expressing a logical formula) that contradict background knowledge(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a potential solution C is found when the formula f is consistent with background knowledge Φ is considered expressing a combination of logical formulas that contradict background knowledge.), enumerating a potential contradiction set(Biane, Page 1578, Col. 1, Paragraph 7, “Inferring a core corresponds to the determination of control parameters producing an expected effect. In logic finding causes from effects is an abduction problem” where identifying core sets that contradict or support the goal formula is considered enumerating potential contradiction sets), in which literals that contradict the background knowledge in a solution candidate set are combined, by traversing the BDD(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a cube C(solution candidate set) and when the background knowledge Φ is compared to the formula f with Φ ^ C being satisfied is considered combining the background knowledge in a solution candidate set as formula f is checked against Φ for satisfaction/no contradictions)
Biane discloses executing the abduction(Biane, page 1579, Col. 2, Paragraph 1, “… we have introduced the principle of the abductive inference of cores for drug target discovery” where the use of abductive inference of cores is considered executing abduction) by searching for a solution hypothesis from the solution candidate set (Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP). A 0 -1 ILP problem is formulated as:
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where y is the unknown vector of size r, m, v vectors, W matrix are the parameters of the problem and h the number of variables” where ILP-CORE computing the set of all cores(i.e. minimal cubes) via minimization of the weighted sum while satisfying the constraints and boolean formula is considered creating an inference based on solution candidate sets and cores/cubes are solution hypothesis that are searched for) by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis(Biane, Page 1577, Col. 2, Paragraph 2, “To avoid a contradictory freeze to 0 and 1 simultaneously, the constraint Φ = d0i ∨ d1i is added ensuring the mutual exclusion of the parameter activities” where Φ = d0i ∨ d1i is an inconsistency rule that is used as a constraint to find contradictions) while reducing computational cost(Biane, Page 1581, Col. 2, Paragraph 1, “this supplementary reduction rule reduces the size of the representation of the formula without impairing the computation of cores.”)
Biane discloses enumerating BDD variables that have potential to have a boolean value determined by the solution candidate set(Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP)” where a core is a minimal cube and computing all sets of cores K* using 0 – 1 ILP using formula f is considered enumerating BDD variables to boolean values determined by a solution candidate set), searching for a boolean assignment that leads to contradiction within a range of the BDD variables(Biane, Page 1581, Col. 2, Paragraph 2, “Then, the prime implicants are computed on this representation of the formula … The principle underlying the algorithm is that all paths of a ROBDD from the root to the terminal node 1 can be seen as cubes constituting a disjoint cover of the function. A set of implicants of the function can then be found by computing all paths such that a variable is present as its negative literal in the implicant if the path go through its outgoing edge labeled low, as its positive literal if the path go through its outgoing edge labeled high and is absent of the cube if the path does not go through a node representing the variable” where computing all paths when finding implicants with the collection of background knowledge(control parameters, constraints and TN-Actions that provide causal relationships between variables in the network) and logical formulas leading to an inconsistent/conflicting literal assignments or no solution found is considered searching for a boolean assignment that leads to a contradiction in the systems representation as there is no consistent path to the terminal nodes) and searching for a combination of literals (Biane, page 1580, Col. 1, Paragraph 6, “By definition of abduction, if there exists an implicant C* of a formula f which is consistent with a theory, then C* is satisfiable and thus also f. A formula in CNF is satisfiable if and only if all its clauses are satisfiable and a clause is satisfiable if and only if at least one of its literal is satisfiable. Therefore, an implicant of this formula is a cube formed by taking at least one literal from each clause” where an implicant taking at least one literal from each clause in a formula is considered searching for a combination of literals as it requires checking different combinations of literal assignments to check for satisfaction) corresponding to the assignment from the solution candidate set when the boolean assignment exists(Biane, page 1581, Col. 2, Paragraph 2, “Therefore, computing the prime implicants from this representation consist in recursively dividing the PROBDD into three sets: the low set, corresponding to the prime implicants containing the negative literal, the high set, containing the positive literal and the don’t care set that do not contain the variable” where computing the prime implicate requires identifying all possible cubes that are satisfactory and all combinations that are not satisfactory(i.e. contradictory) is considered searching combinations of literals that correspond to a contradictory assignment from the solution candidate set)
Regarding claim 7:
Biane discloses A non-transitory computer-readable recording medium(Biane, Page 1581, Col. 2, footnote 5, “Performed on a HP EliteBook 820 G2 Intel Core (2.30 GHz i5-5300U CPU with 16 Gb of memory)”)
Biane discloses acquiring a contradiction determination function having a binary decision diagram as a data structure(Biane, Figure 3 and “To circumvent this limitation, we have developed an alternative method based on prime implicants computation using reduced ordered Binary Decision Diagrams (ROBDDs). ROBDDs enable a compact and canonical representation of Boolean functions... This method, called BDD-NECESSITY, consists in representing the formula as a Positive Reduced Ordered Binary Decision Diagram (PROBDD) and computing the prime implicants on this representation” where a Binary Decision Diagram is considered a data structure that processes information) that compactly expresses a combination of logical formulas(Biane, Page 1581, Col. 2, Figure 3, where f is a logical formula being used in a data structure is considered expressing a logical formula) that contradict background knowledge(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a potential solution C is found when the formula f is consistent with background knowledge Φ is considered expressing a combination of logical formulas that contradict background knowledge), and enumerating a potential contradiction set(Biane, Page 1578, Col. 1, Paragraph 7, “Inferring a core corresponds to the determination of control parameters producing an expected effect. In logic finding causes from effects is an abduction problem” where identifying core sets that contradict or support the goal formula is considered enumerating potential contradiction sets), in which literals that contradict the background knowledge in a solution candidate set are combined, by traversing the BDD(Biane, Page 1578, Col. 1, Paragraph 7,“In propositional logic, a cube C is an abductive explanation of a formula f formalizing the facts with respect to another formula f representing the theory if and only if: C ^ Φ ╞ f and C is consistent with Φ (i.e., Φ ^ C is satisfied)” where Φ is the background knowledge, f is the input formula that is being tested and a cube C(solution candidate set) and when the background knowledge Φ is compared to the formula f with Φ ^ C being satisfied is considered combining the background knowledge in a solution candidate set as formula f is checked against Φ for satisfaction/no contradictions)
Biane discloses executing the abduction(Biane, page 1579, Col. 2, Paragraph 1, “… we have introduced the principle of the abductive inference of cores for drug target discovery” where the use of abductive inference of cores is considered executing abduction) by searching for a solution hypothesis from the solution candidate set (Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP). A 0 -1 ILP problem is formulated as:
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where y is the unknown vector of size r, m, v vectors, W matrix are the parameters of the problem and h the number of variables” where ILP-CORE computing the set of all cores(i.e. minimal cubes) via minimization of the weighted sum while satisfying the constraints and boolean formula is considered creating an inference based on solution candidate sets and cores/cubes are solution hypothesis that are searched for) by adding the enumerated potential contradiction set as a constraint to a constrained combinatorial optimization problem to ensure consistency of the solution hypothesis(Biane, Page 1577, Col. 2, Paragraph 2, “To avoid a contradictory freeze to 0 and 1 simultaneously, the constraint Φ = d0i ∨ d1i is added ensuring the mutual exclusion of the parameter activities” where Φ = d0i ∨ d1i is an inconsistency rule that is used as a constraint to find contradictions) while reducing computational cost(Biane, Page 1581, Col. 2, Paragraph 1, “this supplementary reduction rule reduces the size of the representation of the formula without impairing the computation of cores.”)
Biane discloses enumerating BDD variables that have potential to have a boolean value determined by the solution candidate set(Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP)” where a core is a minimal cube and computing all sets of cores K* using 0 – 1 ILP using formula f is considered enumerating BDD variables to boolean values determined by a solution candidate set), searching for a boolean assignment that leads to contradiction within a range of the BDD variables(Biane, Page 1581, Col. 2, Paragraph 2, “Then, the prime implicants are computed on this representation of the formula … The principle underlying the algorithm is that all paths of a ROBDD from the root to the terminal node 1 can be seen as cubes constituting a disjoint cover of the function. A set of implicants of the function can then be found by computing all paths such that a variable is present as its negative literal in the implicant if the path go through its outgoing edge labeled low, as its positive literal if the path go through its outgoing edge labeled high and is absent of the cube if the path does not go through a node representing the variable” where computing all paths when finding implicants with the collection of background knowledge(control parameters, constraints and TN-Actions that provide causal relationships between variables in the network) and logical formulas leading to an inconsistent/conflicting literal assignments or no solution found is considered searching for a boolean assignment that leads to a contradiction in the systems representation as there is no consistent path to the terminal nodes) and searching for a combination of literals (Biane, page 1580, Col. 1, Paragraph 6, “By definition of abduction, if there exists an implicant C* of a formula f which is consistent with a theory, then C* is satisfiable and thus also f. A formula in CNF is satisfiable if and only if all its clauses are satisfiable and a clause is satisfiable if and only if at least one of its literal is satisfiable. Therefore, an implicant of this formula is a cube formed by taking at least one literal from each clause” where an implicant taking at least one literal from each clause in a formula is considered searching for a combination of literals as it requires checking different combinations of literal assignments to check for satisfaction) corresponding to the assignment from the solution candidate set when the boolean assignment exists(Biane, page 1581, Col. 2, Paragraph 2, “Therefore, computing the prime implicants from this representation consist in recursively dividing the PROBDD into three sets: the low set, corresponding to the prime implicants containing the negative literal, the high set, containing the positive literal and the don’t care set that do not contain the variable” where computing the prime implicate requires identifying all possible cubes that are satisfactory and all combinations that are not satisfactory(i.e. contradictory) is considered searching combinations of literals that correspond to a contradictory assignment from the solution candidate set)
Regarding claim 9, The rejection of claim 6 incorporated in claim 9, and further, claim 9 is rejected under the same rationale as set forth in the rejection of claim 3.
Regarding claim 13, The rejection of claim 7 incorporated in claim 13, and further, claim 13 is rejected under the same rationale as set forth in the rejection of claim 3.
Response to Amendment
Applicant's arguments filed 09/17/2025 have been fully considered but they are not persuasive. A breakdown for the arguments can be found below.
101:
Applicant appears to argue on page 7-9 that the claims contain features that have technical complexity that cannot be performed as mental process, even with the use of pen and paper. Further, applicant appears to argue the claims integrate into practical application and provides an improvement by including contradictory sets efficiently enumerated from a BDD as constraints to the optimization problem. The improvement results in a reduced CPU load and improved processing speed which provides an improvement in the function of a computer itself (memory efficiency and computational speed).
Examiner respectfully disagrees as Examiner’s review of the arguments indicates an improvement to be provided by the claimed abstract idea of using contradictory sets(derived from binary decision diagrams) as a constraint to the an optimization problem, which does not result in an improvement in technology as examiner has identified the additional elements together and as a whole as corresponding to those limitations which are not indicative of practical application or significantly more as executing the abduction by searching for a solution hypothesis from the solution candidate set by adding the enumerated potential contradiction set as a constraint is a mental/abstract idea and a computer may still recite a mental process(please see MPEP 2106.04(a)(2).III.C)). Further, the claims as presented do not result or highlight an improvement in neural networks or hardware processors. Examiner notes MPEP 2106.05(a) which provides the requirements for how an improvement to the functioning of a computer or to any other technology or technical field is evaluated. Applicant’s example does not explain how the additional elements reflect this improvement, or how the improvement is affected by any claimed additional elements.
103:
Applicant appears to argue on pages 10-13 that Biane does not disclose “at least one memory storying instructions… enumerate BDD variables that have potential to have a boolean value determined by the solution candidate set, search for a boolean assignment that leads to contradiction within a range of the BDD variables, and search for a combination of literals corresponding to the assignment from the solution candidate set when the boolean assignment exists”
Examiner respectfully disagrees as Biane discloses at least one memory storying instructions at Biane, Page 1581, Col. 2, footnote 5, “Performed on a HP EliteBook 820 G2 Intel Core (2.30 GHz i5-5300U CPU with 16 Gb of memory)” that enumerates BDD variables that have potential to have a boolean value determined by the solution candidate set at Biane, Page 1579, Col. 2, Paragraph 4, “The method, called ILP-CORE, operates on a formula f in CNF and computes the set of all the cores K* using 0 - 1 Integer Linear Programming (0 -1 ILP)” where a core is a minimal cube and computing all sets of cores K* using 0 – 1 ILP using formula f is considered enumerating BDD variables to boolean values determined by a solution candidate set as it requires an examination of each variable one by one and uses those cubes/cores to determine a solution. Further, Biane discloses searching for a boolean assignment that leads to contradiction within a range of the BDD variables at Biane, Page 1581, Col. 2, Paragraph 2, “Then, the prime implicants are computed on this representation of the formula … The principle underlying the algorithm is that all paths of a ROBDD from the root to the terminal node 1 can be seen as cubes constituting a disjoint cover of the function. A set of implicants of the function can then be found by computing all paths such that a variable is present as its negative literal in the implicant if the path go through its outgoing edge labeled low, as its positive literal if the path go through its outgoing edge labeled high and is absent of the cube if the path does not go through a node representing the variable” where computing all paths when finding implicants with the collection of background knowledge(control parameters, constraints and TN-Actions that provide causal relationships between variables in the network) and logical formulas leading to an inconsistent/conflicting literal assignments or no solution found is considered searching for a boolean assignment that leads to a contradiction in the systems representation as there is no consistent path to the terminal nodes with searching for a combination of literals at Biane, page 1580, Col. 1, Paragraph 6, “By definition of abduction, if there exists an implicant C* of a formula f which is consistent with a theory, then C* is satisfiable and thus also f. A formula in CNF is satisfiable if and only if all its clauses are satisfiable and a clause is satisfiable if and only if at least one of its literal is satisfiable. Therefore, an implicant of this formula is a cube formed by taking at least one literal from each clause” where an implicant taking at least one literal from each clause in a formula is considered searching for a combination of literals as it requires checking different combinations of literal assignments to check for satisfaction that correspond to the assignment from the solution candidate set when the boolean assignment exists at Biane, page 1581, Col. 2, Paragraph 2, “…computing the prime implicants from this representation consist in recursively dividing the PROBDD into three sets: the low set, corresponding to the prime implicants containing the negative literal, the high set, containing the positive literal and the don’t care set that do not contain the variable” where computing the prime implicate requires identifying all possible cubes that are satisfactory and all combinations that are not satisfactory(i.e. contradictory) is considered searching combinations of literals that correspond to a contradictory assignment from the solution candidate set.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122