DETAILED ACTION
This action is responsive to the amendment filed 12/03/2025. Claims 1-2, 5-9, 12-16, and 19-20 are currently pending in the case. Claims 1, 5-8, 12-15, and 19-20 are currently amended. Claims 1, 8, and 15 are currently amended.
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 Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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 1-2, 5-9, 12-16, and 19-20 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.
Regarding claim 1, the claim recites “each of the statements of the plurality of statements” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation has been interpreted as “each statement of the plurality of statements”.
Claims 2 and 5-7 are rejected as being dependent on a rejected base claim without curing any of the deficiencies.
Regarding claim 8, the claim recites “each of the statements of the plurality of statements” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation has been interpreted as “each statement of the plurality of statements”.
Claims 9 and 12-14 are rejected as being dependent upon a rejected base claim without curing any of the deficiencies.
Regarding claim 15, the claim recites “each of the statements of the plurality of statements” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation has been interpreted as “each statement of the plurality of statements”.
Claims 16 and 19-20 are rejected as being dependent upon a rejected base claim without curing any of the deficiencies.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 Statutory Category: Claim 1 is directed to a method, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 1 recites, in part, “identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statements of the plurality of statements”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “identifying, …, a set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “predicts the set of queries based on a first user”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case judgment. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “determining, …, second probability distributions for each query of the set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites that the method is “processor-implemented”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “by an artificial intelligence algorithm” and “by the neural network”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the artificial intelligence algorithm receives the knowledge base”. This limitation amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, this limitation is insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: “processor-implemented”, “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional elements “by an artificial intelligence algorithm” and “by the neural network” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “wherein the artificial intelligence algorithm receives the knowledge base” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is incorporated, and further the claim recites: “wherein the first probability distributions … consist of a lower bound of probability and an upper bound of probability that the corresponding statement is true”. This limitation is a continuation of the “identifying a knowledge base of one or more statements and first probability distributions corresponding to each of the one or more statements” limitation identified as an abstract idea in the rejection of the parent claim. Further, the claim recites: “wherein the … second probability distributions consist of a lower bound of probability and an upper bound of probability that the corresponding statement is true”. This limitation is a continuation of the “determining logical inferences about and second probability distributions for queries from the one or more queries or statements from the one or more statements based on information in the knowledge base” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements 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 5, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein a query from the set of queries includes a premise that is assumed true in a context of the query”. This limitation is a continuation of the “identifying one or more queries” limitation identified as an abstract idea in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements 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, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein identifying the knowledge base is performed based on a natural language description of information”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein identifying the knowledge base is performed using a second neural network”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 8:
Step 1 Statutory Category: Claim 8 is directed to a system, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 8 recites, in part, “identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statement of the plurality of statements”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “identifying, …, a set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “predicts the set of queries based on a first user”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case judgment. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “determining, …, second probability distributions for each query of the set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “a computer system” and “a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “by an artificial intelligence algorithm” and “by the neural network”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the artificial intelligence algorithm receives the knowledge base”. This limitation amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, this limitation is insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: “a computer system”, “a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations”, “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional elements “by an artificial intelligence algorithm” and “by the neural network” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “wherein the artificial intelligence algorithm receives the knowledge base” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 9, the rejection of claim 8 is incorporated, and further claim 9 is substantially similar to claim 2 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 12, the rejection of claim 8 is incorporated, and further claim 12 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 13, the rejection of claim 8 is incorporated, and further claim 13 is substantially similar to claim 6 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 14, the rejection of claim 8 is incorporated, and further claim 14 is substantially similar to claim 7 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 15:
Step 1 Statutory Category: Claim 15 is directed to a machine, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 15 recites, in part, “identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statements of the plurality of statements”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “identifying, …, a set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “predicts the set of queries based on a first user”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case judgment. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “determining, …, second probability distributions for each query of the set of queries”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “a computer program product” and “one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “by an artificial intelligence algorithm” and “by the neural network”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “wherein the artificial intelligence algorithm receives the knowledge base”. This limitation amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, this limitation is insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: “a computer program product”, “one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations”, “training a neural network on historical instances of probabilistic inference from the knowledge base”, “updating the knowledge base using the second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional elements “by an artificial intelligence algorithm” and “by the neural network” generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “wherein the artificial intelligence algorithm receives the knowledge base” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 16, the rejection of claim 15 is incorporated, and further, claim 16 is substantially similar to claim 2 and claim 9 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 19, the rejection of claim 15 is incorporated, and further, claim 19 is substantially similar to claim 5 and claim 12 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 20, the rejection of claim 15 is incorporated, and further, claim 20 is substantially similar to claim 6 and claim 13 respectively, and is rejected in the same manner and reasoning applying.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8-9, 12-13, 15-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qian et al., Logical Credal Networks, 09/25/2021, https://arxiv.org/pdf/2109.12240, hereinafter referred to as “Qian” in view of Riegel et al., Logical Neural Networks, 06/23/2020, https://arxiv.org/pdf/2006.13155, hereinafter referred to as “Riegel”.
Regarding claim 1, Qian teaches identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statements of the plurality of statements (Qian, Page 3, Section 3.1, Lines 1-5, “an LCN is specified by a set of probability-assessment sentences in one of the following two forms:
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where q and r can be arbitrary propositional and finite-domain first-order logic formulas and
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”; Qian, Page 4, Example 1, Lines 1-3, “Consider the following LCN derived from the Smokers and Friends example of Richardson and Domingos (2006)”; See also Equations 12-15; The “logical credal network (LCN)” is considered to be the “knowledge base” and “q” and “q|r” are considered to be the “statements”, deriving the LCN is considered to be “identifying a knowledge base”);
Identifying, by an artificial intelligence algorithm, a set of queries (Qian, Page 4, Section 3.3, Lines 2-3, “inference in an LCN means computing upper and lower bounds on a probability of interest” The “probability of interest” is considered to be the “set of queries”, wherein the artificial intelligence algorithm receives the knowledge base and predicts the set of queries based on a first user (Qian, Page 6, Section 4, Lines 1-2, “Given an LCN and a subset of query (or MAP) variables”; The overall method is considered to be the “artificial intelligence algorithm” as a person of ordinary skill in the art would recognize that using LCNs is considered artificial intelligence, and thus the algorithm is an “artificial intelligence algorithm”, further, the “LCN” which is considered to be the “knowledge base” is given, and thus must have been received by the artificial intelligence algorithm; Qian, Page 7, Section 4.2, “We consider a realistic credit card fraud detection task based on the UCSD-FICO Data Mining Contest dataset (FICO UCSD 2009) which contains 100,000 transactions over a period of 98 days out of which 2,654 are fraudulent”; Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-8, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history”; In the credit card fraud detection experiment the queries are considered to be identified “based on a first user” because the account history is considered);
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determining, …, second probability distributions for each query from the set of queries (Qian, Page 6, Section 4, Lines 2-6, “Given an LCN and a subset of query (or MAP) variables, the task is to find the assignment to the query variables such that its posterior marginal probability interval has the largest upper bound (maximax) or, alternatively, the largest lower bound (maximin)”; Qian, Page 9, Section A, Paragraph 3, “Consider a query on the marginal probability of P(c). We formulate two optimization problems: (A.6 – A.22)…By maximizing and minimizing the objective function (A.22), we obtain the upper and lower bounds for P(c), which are 0.33 and 0 respectively”)
updating the knowledge base using the second probability distributions for each query in the set of queries (Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-9, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history through the following three logic rules from Li et al. (2020):
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"; Because the probabilistic inferences in this experiment rely on if the account has fraud history or not, the inferences on certain transactions must be added to the LCN, which is considered the knowledge base, in order to continue making accurate inferences)
Further, Qian teaches that the method is processor-implemented (Qian, Page 6, Section 4.1, Paragraph 2, Lines 7-10, “We use ten random seeds to generate ten sets of puzzles (each set having 729 puzzles on average) and report the mean and standard deviation of the accuracy (Table 1)”; A person of ordinary skill in the art would recognize that this must have been performed using a computer, providing evidence that the method is processor implemented).
Qian does not explicitly teach training a neural network on historical instances of probabilistic inference from the knowledge base, determining second probability distributions for each query of the set of queries by the neural network and training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base.
Riegel teaches training a neural network on historical instances of probabilistic inference from the knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”)
determining second probability distributions for each query of the set of queries by the neural network (Riegel, Page 5, Section 4, Lines 3-5, “LNN achieves this with multiple passes over the represented formulae, propagating tightened truth value bounds from neuron to neuron until computation necessarily converges”)
training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”; Riegel, Page 7, Section 6, Lines 3-5, “Loss functions for LNN may exploit its logical interpretability, in particular by penalizing contradiction, which can then be used to enforce even complicated logical requirements”; see also Riegel, Page 7, Section 6, Paragraph 2, Loss function E, Equations 6 and 7).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the logical inference method of Qian to include using a neural network to determine probability distributions for queries and training the neural network before and after that inference as taught by Riegel. The motivation to do so would have been that it increases resistance to inconsistent knowledge and incomplete knowledge, improving accuracy of the method (Riegel, Page 1, Abstract, Lines 6-10) and the neural network yields a highly interpretable disentangled representation (Riegel, Page 1, Abstract, Lines 1-4).
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein the first probability distributions and second probability distributions consist of a lower bound of probability and an upper bound of probability that the corresponding statement is true ((Qian, Page 3, Section 3.1, Lines 1-5, “an LCN is specified by a set of probability-assessment sentences in one of the following two forms:
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is the upper bound; Qian, Page 9, Section A, Paragraph 3, “Consider a query on the marginal probability of P(c). We formulate two optimization problems: (A.6 – A.22)…By maximizing and minimizing the objective function (A.22), we obtain the upper and lower bounds for P(c), which are 0.33 and 0 respectively”; Qian, Page 2, Section 2.3, Paragraph 2, Lines 5-9, “Following Nilsson (1986), computing the bounds on P(x ⊕ y) 2 results in the interval [0, 1]. Indeed, there exists a joint distribution over x, y such that (1) and (2) are satisfied and that x ⊕ y is always false, and there exists another such that x ⊕ y is always true” “always false” is represented in the probability interval as “0” and “always true” is represented in the probability interval as “1”, thus the probability distributions contain an upper and lower bound of probability that the corresponding statement is true).
Regarding claim 5, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein a query from the set of queries includes a premise that is assumed true in a context of the query (Qian, Page 9, Section A, Paragraph 3, “Consider a query on the marginal probability of P(c). We formulate two optimization problems: (A.6 – A.22)…By maximizing and minimizing the objective function (A.22), we obtain the upper and lower bounds for P(c), which are 0.33 and 0 respectively”; “c” is considered to be the “one or more premises”; Qian, Page 2, Footnote 1, “Throughout this paper, we use P (q) as shorthand notation for P (q is True) and P (q | r) for P (q is True | r is True)”).
Regarding claim 6, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein identifying the knowledge base is performed based on a natural language description of information (Qian, Page 4, Example 1, “Consider the following LCN derived from the Smokers and Friends example of Richardson and Domingos (2006). We abbreviate the predicates Friends(·, ·), Smokes(·) and Cancer(·) by F r(·, ·), Sm(·) and Ca(·), respectively. Predicate F r(·, ·) is symmetric. Equations 12-15, “Here, (12) states that friends of friends are likely friends; (13) states that, if two people are friends, they likely either both smoke or neither does; (14) and (15) state that smoking likely causes cancer”).
Regarding claim 8, Qian teaches A computer system, the computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations (Qian, Page 6, Section 4.1, Paragraph 2, Lines 7-10, “We use ten random seeds to generate ten sets of puzzles (each set having 729 puzzles on average) and report the mean and standard deviation of the accuracy (Table 1)”; A person of ordinary skill in the art would recognize that this must have been performed using a computer, providing evidence of processors, computer-readable storage media, storage medium, and program instructions) comprising:
identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statements of the plurality of statements (Qian, Page 3, Section 3.1, Lines 1-5, “an LCN is specified by a set of probability-assessment sentences in one of the following two forms:
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where q and r can be arbitrary propositional and finite-domain first-order logic formulas and
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”; Qian, Page 4, Example 1, Lines 1-3, “Consider the following LCN derived from the Smokers and Friends example of Richardson and Domingos (2006)”; See also Equations 12-15; The “logical credal network (LCN)” is considered to be the “knowledge base” and “q” and “q|r” are considered to be the “statements”, deriving the LCN is considered to be “identifying a knowledge base”);
identifying, by an artificial intelligence algorithm, a set of queries (Qian, Page 4, Section 3.3, Lines 2-3, “inference in an LCN means computing upper and lower bounds on a probability of interest” The “probability of interest” is considered to be the “set of queries”, wherein the artificial intelligence algorithm receives the knowledge base and predicts the set of queries based on a first user (Qian, Page 6, Section 4, Lines 1-2, “Given an LCN and a subset of query (or MAP) variables”; The overall method is considered to be the “artificial intelligence algorithm” as a person of ordinary skill in the art would recognize that using LCNs is considered artificial intelligence, and thus the algorithm is an “artificial intelligence algorithm”, further, the “LCN” which is considered to be the “knowledge base” is given, and thus must have been received by the artificial intelligence algorithm; Qian, Page 7, Section 4.2, “We consider a realistic credit card fraud detection task based on the UCSD-FICO Data Mining Contest dataset (FICO UCSD 2009) which contains 100,000 transactions over a period of 98 days out of which 2,654 are fraudulent”; Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-8, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history”; In the credit card fraud detection experiment the queries are considered to be identified “based on a first user” because the account history is considered);
…
determining, …, second probability distributions for each query from the set of queries (Qian, Page 6, Section 4, Lines 2-6, “Given an LCN and a subset of query (or MAP) variables, the task is to find the assignment to the query variables such that its posterior marginal probability interval has the largest upper bound (maximax) or, alternatively, the largest lower bound (maximin)”; Qian, Page 9, Section A, Paragraph 3, “Consider a query on the marginal probability of P(c). We formulate two optimization problems: (A.6 – A.22)…By maximizing and minimizing the objective function (A.22), we obtain the upper and lower bounds for P(c), which are 0.33 and 0 respectively”)
updating the knowledge base using the second probability distributions for each query in the set of queries (Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-9, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history through the following three logic rules from Li et al. (2020):
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"; Because the probabilistic inferences in this experiment rely on if the account has fraud history or not, the inferences on certain transactions must be added to the LCN, which is considered the knowledge base, in order to continue making accurate inferences)
Further, Qian teaches that the method is processor-implemented (Qian, Page 6, Section 4.1, Paragraph 2, Lines 7-10, “We use ten random seeds to generate ten sets of puzzles (each set having 729 puzzles on average) and report the mean and standard deviation of the accuracy (Table 1)”; A person of ordinary skill in the art would recognize that this must have been performed using a computer, providing evidence that the method is processor implemented).
Qian does not explicitly teach training a neural network on historical instances of probabilistic inference from the knowledge base, determining second probability distributions for each query of the set of queries by the neural network and training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base.
Riegel teaches training a neural network on historical instances of probabilistic inference from the knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”)
determining second probability distributions for each query of the set of queries by the neural network (Riegel, Page 5, Section 4, Lines 3-5, “LNN achieves this with multiple passes over the represented formulae, propagating tightened truth value bounds from neuron to neuron until computation necessarily converges”)
training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”; Riegel, Page 7, Section 6, Lines 3-5, “Loss functions for LNN may exploit its logical interpretability, in particular by penalizing contradiction, which can then be used to enforce even complicated logical requirements”; see also Riegel, Page 7, Section 6, Paragraph 2, Loss function E, Equations 6 and 7).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the logical inference method of Qian to include using a neural network to determine probability distributions for queries and training the neural network before and after that inference as taught by Riegel. The motivation to do so would have been that it increases resistance to inconsistent knowledge and incomplete knowledge, improving accuracy of the method (Riegel, Page 1, Abstract, Lines 6-10) and the neural network yields a highly interpretable disentangled representation (Riegel, Page 1, Abstract, Lines 1-4).
Regarding claim 9, the rejection of claim 8 is incorporated, and further, claim 9 is substantially similar to claim 2 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 12, the rejection of claim 8 is incorporated, and further, claim 12 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 13, the rejection of claim 8 is incorporated, and further, claim 13 is substantially similar to claim 6 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 15, Qian teaches A computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations (Qian, Page 6, Section 4.1, Paragraph 2, Lines 7-10, “We use ten random seeds to generate ten sets of puzzles (each set having 729 puzzles on average) and report the mean and standard deviation of the accuracy (Table 1)”; A person of ordinary skill in the art would recognize that this must have been performed using a computer, providing evidence of a computer program product with computer-readable storage media, and program instructions) comprising:
identifying a knowledge base of a plurality of statements and first probability distributions corresponding to each of the statements of the plurality of statements (Qian, Page 3, Section 3.1, Lines 1-5, “an LCN is specified by a set of probability-assessment sentences in one of the following two forms:
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where q and r can be arbitrary propositional and finite-domain first-order logic formulas and
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”; Qian, Page 4, Example 1, Lines 1-3, “Consider the following LCN derived from the Smokers and Friends example of Richardson and Domingos (2006)”; See also Equations 12-15; The “logical credal network (LCN)” is considered to be the “knowledge base” and “q” and “q|r” are considered to be the “statements”, deriving the LCN is considered to be “identifying a knowledge base”);
identifying, by an artificial intelligence algorithm, a set of queries (Qian, Page 4, Section 3.3, Lines 2-3, “inference in an LCN means computing upper and lower bounds on a probability of interest” The “probability of interest” is considered to be the “set of queries”, wherein the artificial intelligence algorithm receives the knowledge base and predicts the set of queries based on a first user (Qian, Page 6, Section 4, Lines 1-2, “Given an LCN and a subset of query (or MAP) variables”; The overall method is considered to be the “artificial intelligence algorithm” as a person of ordinary skill in the art would recognize that using LCNs is considered artificial intelligence, and thus the algorithm is an “artificial intelligence algorithm”, further, the “LCN” which is considered to be the “knowledge base” is given, and thus must have been received by the artificial intelligence algorithm; Qian, Page 7, Section 4.2, “We consider a realistic credit card fraud detection task based on the UCSD-FICO Data Mining Contest dataset (FICO UCSD 2009) which contains 100,000 transactions over a period of 98 days out of which 2,654 are fraudulent”; Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-8, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history”; In the credit card fraud detection experiment the queries are considered to be identified “based on a first user” because the account history is considered);
…
determining, …, second probability distributions for each query from the set of queries (Qian, Page 6, Section 4, Lines 2-6, “Given an LCN and a subset of query (or MAP) variables, the task is to find the assignment to the query variables such that its posterior marginal probability interval has the largest upper bound (maximax) or, alternatively, the largest lower bound (maximin)”; Qian, Page 9, Section A, Paragraph 3, “Consider a query on the marginal probability of P(c). We formulate two optimization problems: (A.6 – A.22)…By maximizing and minimizing the objective function (A.22), we obtain the upper and lower bounds for P(c), which are 0.33 and 0 respectively”)
updating the knowledge base using the second probability distributions for each query in the set of queries (Qian, Page 7, Section 4.2, Paragraph 2, Lines 6-9, “In addition to learning from training data, we provide additional knowledge regarding fraudulent transactions and account history through the following three logic rules from Li et al. (2020):
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"; Because the probabilistic inferences in this experiment rely on if the account has fraud history or not, the inferences on certain transactions must be added to the LCN, which is considered the knowledge base, in order to continue making accurate inferences)
Further, Qian teaches that the method is processor-implemented (Qian, Page 6, Section 4.1, Paragraph 2, Lines 7-10, “We use ten random seeds to generate ten sets of puzzles (each set having 729 puzzles on average) and report the mean and standard deviation of the accuracy (Table 1)”; A person of ordinary skill in the art would recognize that this must have been performed using a computer, providing evidence that the method is processor implemented).
Qian does not explicitly teach training a neural network on historical instances of probabilistic inference from the knowledge base, determining second probability distributions for each query of the set of queries by the neural network and training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base.
Riegel teaches training a neural network on historical instances of probabilistic inference from the knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”)
determining second probability distributions for each query of the set of queries by the neural network (Riegel, Page 5, Section 4, Lines 3-5, “LNN achieves this with multiple passes over the represented formulae, propagating tightened truth value bounds from neuron to neuron until computation necessarily converges”)
training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base (Riegel, Page 1, Section 1, Lines 10-12, “Inputs include a propositional or first-order logic (FOL) knowledge base (KB), including the usual training data (feature-value pairs) as a special case, and which variables should be predicted from which”; Riegel, Page 2, Section 2, Paragraph 2, Lines 1-2, “Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data”; Riegel, Page 7, Section 6, Lines 3-5, “Loss functions for LNN may exploit its logical interpretability, in particular by penalizing contradiction, which can then be used to enforce even complicated logical requirements”; see also Riegel, Page 7, Section 6, Paragraph 2, Loss function E, Equations 6 and 7).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the logical inference method of Qian to include using a neural network to determine probability distributions for queries and training the neural network before and after that inference as taught by Riegel. The motivation to do so would have been that it increases resistance to inconsistent knowledge and incomplete knowledge, improving accuracy of the method (Riegel, Page 1, Abstract, Lines 6-10) and the neural network yields a highly interpretable disentangled representation (Riegel, Page 1, Abstract, Lines 1-4).
Regarding claim 16, the rejection of claim 15 is incorporated, and further, claim 16 is substantially similar to claim 2 and claim 9 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 19, the rejection of claim 15 is incorporated, and further, claim 19 is substantially similar to claim 5 and claim 12 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 20, the rejection of claim 15 is incorporated, and further, claim 20 is substantially similar to claim 6 and claim 13 respectively, and is rejected in the same manner and reasoning applying.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Qian in view of Riegel in further view of Lu et al., Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning. 10/17/2022, In Proceedings of the 29th International Conference on Computational Linguistics, pages 5419–5431, Gyeongju, Republic, hereinafter referred to as “Lu”.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the proposed combination teaches identifying the knowledge base using arbitrary propositional and finite-domain first-order logic formulas (Qian, Page 3, Section 3.1, Lines 1-5, “an LCN is specified by a set of probability-assessment sentences in one of the following two forms:
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where q and r can be arbitrary propositional and finite-domain first-order logic formulas and
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”; Qian, Page 4, Example 1, Lines 1-3, “Consider the following LCN derived from the Smokers and Friends example of Richardson and Domingos (2006)”; See also Equations 12-15; The “logical credal network (LCN)” is considered to be the “knowledge base” and “q” and “q|r” are considered to be the “statements”, deriving the LCN is considered to be “identifying a knowledge base”).
The proposed combination does not teach identifying a knowledge base is performed using a second neural network.
Lu teaches identifying propositional logic using a second neural network (Lu, Page 1, Abstract, Lines 1-5, “Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL)”; Lu, Page 2, Section 2.2, Lines 2-5, “The backbone of the framework is dual reinforcement learning, which consists of two sub-modules: The prime module generates a logical expression given a natural language sentence”; See also, Figure 1, “Encoder” and “Decoder” are considered to be “a neural network”).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention to have modified the method of identifying a knowledge base of the proposed combination to include using a second neural network as taught by Lu. The motivation for doing so would have been that automating the generation of propositional logic would improve performance of the inference model (Lu, Page 1, Abstract, Lines 24-27, “Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced”).
Regarding claim 14, the rejection of claim 8 is incorporated, and further, claim 14 is substantially similar to claim 7 respectively, and is rejected in the same manner and reasoning applying.
Response to Arguments
Applicant’s amendments to specification paragraph 0085 with respect to objections to the drawings have been fully considered, and overcome the objections set forth in the nonfinal office action dated 09/03/2025. Consequently, the objections to the drawings have been withdrawn.
Applicant’s amendments to the claims with respect to 35 U.S.C. 112(b) indefiniteness rejections have been fully considered, and overcome the rejections set forth in the nonfinal office action dated 09/03/2025. Consequently, the rejections to the claims have been withdrawn. However, a new grounds of rejection has been made with regard to the amendments.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered but are unpersuasive.
Applicant first argues, on page 11, final paragraph – page 12, paragraph 1 of the response, that independent claims 1, 8, and 15 do not recite judicial exceptions. Applicant specifically points to “identifying, by an artificial intelligence algorithm, a set of queries, wherein the artificial intelligence algorithm receives the knowledge base and predicts the set of queries based on a first user”, “training a neural network on historical instances of probabilistic inference from the knowledge base”, “determining, by the neural network, second probability distributions for each query of the set of queries”, and “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base”. With regard to “wherein the artificial intelligence algorithm receives the knowledge base”, “training a neural network on historical instances of probabilistic inference from the knowledge base”, “training the neural network on historical instances of probabilistic inference including the second probability distributions for each query of the set of queries from the updated knowledge base”. Examiner agrees, and these limitations have been analyzed as additional elements in the updated 35 U.S.C. 101 rejection above. With regard to the remaining limitations point to by the applicant, examiner respectfully disagrees. Identifying a set of queries by predicting them based on a user can be practically performed in the human mind, and using an artificial intelligence algorithm to perform the judicial exception does not integrate the exception, as using an artificial intelligence algorithm merely generally links the judicial exception to a particular technological environment or field of use. Applicant also argues a human mind cannot perform “determining, by the neural network, second probability distributions for each query of the set of queries”. Examiner respectfully disagrees. Using a neural network merely generally links the judicial exception to a particular technological environment or field of use, and a human mind is capable of determining probability distributions for queries.
Applicant next argues, on page 12, paragraph 2 – page 14, paragraph 1 of the response, that independent claims 1, 8, and 15 are integrated into a practical application. Examiner respectfully disagrees. Applicant argues that claims 1, 8, and 15 “improve the technical field of computational logic by using a knowledge based composed of probabilistic logical statements, queries, and evidence to draw useful probabilistic inferences”. It is important to note an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. An improvement to drawing useful probabilistic inferences may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer. Applicant points to paragraph 12 of applicant’s specification; however, the cited material is not reflected in the claims. For an in depth analysis of each claim limitation, refer to the updated 35 U.S.C. 101 rejection seen above.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Applicant’s amendments to the claims with respect to the prior art rejections of the claims have been fully considered, and overcome the rejections set forth in the nonfinal office action dated 09/03/2025. However, a new grounds of rejection has been made in response to the amended claims. Refer to the updated 35 U.S.C. 103 rejection seen above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/M.C.S./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122