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 . A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/22/2026 has been entered.
Remarks
This Office Action is responsive to Applicants' Amendment filed on June 22, 2026, in which claims 11-23 and 25 are currently amended. Claim 26 is newly added. Claims 21-26 are currently pending.
Response to Arguments
The previous rejections to claims 11-25 under 35 U.S.C. § 112(b) are hereby withdrawn, as necessitated by applicant's amendments and remarks made to the rejections.
Applicant’s arguments with respect to rejection of claims 11-26 under 35 U.S.C. 101 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on pp. 8-10 of the Remarks submitted 6/22/2026 that the claims are not directed towards a mental process because they "requires steps such as manufacturing that need to be physically implemented", Examiner respectfully disagrees. The claims do not require physical manufacturing, nor would one of ordinary skill in the art interpret a method of manufacturing as a physical process when none of the implementation details of the manufacturing method actually require a physical structure. The instant specification explicitly states that ([¶0061] "The decision support system is often a computer 10 or part of the latter." [¶0035] "the computer 14 is an electronic computer suitable for handling and/or transforming data represented as electronic or physical quantities in registers of the computer 10 and/or memories into other similar data corresponding to physical data in the register memories or other types of displays, transmission devices or storage devices.") and that "part of the latter" includes ([¶0052] "program instructions, when executed by the data processing unit 20, lead to the execution of steps of the generation method"), where program instructions are not interpreted as physical but rather as software-per-se or as signal-per-se ([¶0055] " the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.") which are both judicial exceptions. A "calculator" is not defined at all in the instant specification, nor would one of ordinary skill in the art automatically limit a calculator to being a physical device (nearly every standard personal computer comes with a built-in software calculator). The use of the term "manufacturing" does not exempt the claims from this interpretation which is plainly set forth in the instant specification.
With respect to Applicant's arguments on pp. 8-10 of the Remarks submitted 6/22/2026 that the claims are directed towards a technical improvement, Examiner respectfully disagrees. The claims almost wholly recite a judicial exception and Examiner notes (MPEP 2106.05(a) "It is important to note, the judicial exception alone cannot provide the improvement."). The additional elements of the claims are seen as mere instructions to apply the judicial exception using generic computer components and do not integrate the judicial exception into a practical application (MPEP 2106.07(a)(II) "employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application"). For at least these reasons and those further detailed below, Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 USC 101.
Applicant’s arguments with respect to rejection of claims 11-26 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on pp. 11-12 of the Remarks submitted 6/22/2026 that "Pedro does not disclose or suggest (1) the formulation of a problem in the form a neural network and a set of constraints that the neural network must satisfy", Examiner respectfully disagrees. The arguments appear to be directed towards the instant claim limitation "transcribing the initial problem in a form of a neural network and of a set of constraints to be satisfied by the neural network, so as to obtain a transcribed neural network". Applicant's argument has been considered, but is not persuasive on the present claim language. Examiner does not read Pedro as disclosing only isolated "constraints" divorced from the neural-network formulation. Pedro first defines the decision-making problem over alternatives-"a fictitious decision making problem is built" and "the alternatives are located as a grid" ([p. 554 §2.1]) and then explicitly uses that same grid/problem formulation as the basis for ANN modeling. Pedro further explains that the partial-ranking procedure "creates a partition of the set A," that "many alternatives will have the same ranking," and that "a partial sorting is strictly necessary" for "building a regression model using ANNs" ([p. 554]). Pedro then identifies the regression model: "the regression tool chosen is an artificial neural network (ANN)," ([p. 554]) and later states that the grid alternatives are used "as input" and the ranking level of each alternative "as output" to train the ANN that approximates the utility function. Thus, Pedro does not merely disclose constraints in the abstract, it discloses formulating the initial decision problem as an ANN regression problem constrained or structured by the partial ranking/partition of alternatives that the ANN is trained to reproduce. The instant claim does not define "constraints" as limited to any particular formal constraint type in any way that would make the current interpretation in view of Pedro unreasonable.
With respect to Applicant's arguments on pp. 11-12 of the Remarks submitted 6/22/2026 that "Pedro does not disclose or suggest [...] (2) determining and physically manufacturing the determined function", Examiner notes that the instant claims never recite "physically manufacturing the determined function". Applicant's argument appears to read limitations into the claim that are not present. Under the broad language actually claimed, the relevant function is the trained neural network's decision-support function and the "calculator or part thereof", which is broad enough to encompass the ANN/functionality that receives alternatives as inputs and produces ranking, utility, or preference outputs. Pedro discloses that same operative chain ([p. 551 §1.1] "given a problem, with its set of possible solutions (the alternatives)" [p. 552 §1] "As a consequence, a regression of utility function values may be meaningful, and may help to guide a search for the preferred alternative from a set of alternatives" [p. 553 §1.1] "The value of each alternative is assigned by a decision-maker, that formally corresponds to a preference function P. The best alternative x∗ ∈ A is the one which has attributes f(x∗) ∈ B that maximize the function P(f(x)) = P(x) in the set A"). For at least these reasons and those further detailed below, Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 USC 102.
Claim Objections
Claim 11 objected to because of the following informalities: "ta distribution" should read "a distribution". Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 11-22 and 26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 11, “manufacturing a function determined to obtain the decision support system” is grammatically indefinite. It’s unclear if this should be read as “manufacture a determined function to obtain the decision support system” or “manufacture something having the determined function to obtain the decision support system”. As these interpretations are contradictory the scope of the claim cannot be reasonably determined. In the interest of further examination the claim is interpreted as “manufacture a determined function to obtain the decision support system”.
Regarding claim 11, "wherein the decision support system comprises a calculator or a part thereof" appears to be introducing new matter. The instant specification does not once recite "a calculator" and it's unclear if "a calculator" is broader, narrower, or completely different from the disclosed "calculation unit" which is only implicitly tied to the decision support system. For at least these reasons the claim is seen as introducing new matter.
Claims 12-22 and 26 are rejected with respect to their dependence on rejected claim 11.
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 11-22 and 26 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 11, "wherein the decision support system comprises a calculator or a part thereof" is indefinite because the instant specification provides no definitional support for the term "calculator" and never uses that term in describing the invention. Instead, the specification refers to a "calculation unit," "computing unit," and "data processing unit," without indicating that any of these components is a "calculator" or that "calculator" is intended to have the same meaning. Consequently, a person of ordinary skill in the art would be left to speculate whether "calculator" refers to a standalone hardware device, a software module, a processor configured to perform arithmetic operations, a calculation engine, or some other structure. Because the metes and bounds of the claimed "calculator" cannot be determined with reasonable certainty from the claim language read in light of the specification, the scope of the claim is indefinite under 35 U.S.C. §112(b). In the interest of further examination, "calculator" is interpreted as anything that performs any calculation.
Claims 12-22 and 26 are rejected with respect to their dependence on rejected claim 11.
Claim Rejections - 35 USC § 101
101 Rejection
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 11-26 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 11: Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 11 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass neural network processing, including the following:
manufacturing a multiple-criteria decision support system (observation, evaluation, and judgement)
providing an initial problem and training data solving the initial problem for particular cases, the initial problem being a problem of evaluating the quality of an existing system or future physical system, where the initial problem is a problem chosen from: a choice of a best alternative among a set of alternatives, a distribution of alternatives among preference classes, a storage of alternatives in order of preference, and the provision of an evaluation score of an alternative (observation, evaluation, and judgement),
transcribing of the initial problem in a form of a neural network and of a set of constraints to be satisfied by the neural network, so as to obtain a transcribed neural network (observation, evaluation and judgement)
determining of the function performed by the trained neural network (observation, evaluation, and judgement)
manufacturing a function determined to obtain a decision support system (observation, evaluation, and judgement. The claim is interpreted as stating that the physical implementation is determined, the determination comprised by a method.)
wherein the decision support system comprises a calculator or a part thereof (observation, evaluation, and judgement)
Therefore, claim 11 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 11 recites additional elements “training of the transcribed neural network using training data, so as to obtain a trained neural network solving the initial problem”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 11 is directed to a judicial exception.
Step 2B Analysis: Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 11 amount to no more than mere instructions to apply the judicial exception using a generic computer component.
For the reasons above, claim 11 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claims 12-22. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 12 recites additional observation, evaluation, and judgement “wherein the transcribed neural network includes a set of neural sub-networks, the transcribing including a formulation of the set of constraints to be satisfied by the neural network in the form of sub-constraints to be satisfied by each neural sub-network.”
Dependent claim 13 recites additional observation, evaluation, and judgement “wherein each neural sub-network includes hidden layers, a number of hidden layers being less than or equal to 5.”
Dependent claim 14 recites additional observation, evaluation, and judgement “the number of hidden layers is less than or equal to 3”.
Dependent claim 15 recites additional observation, evaluation, and judgement “wherein the sub-constraints to be satisfied by a neural sub-network are selected from the group consisting of: a monotonicity of a variation of output of the neural sub-network as a function of inputs of the neural sub-network, the output of the neural sub-network being comprised between a minimum value and a maximum value, the output of the neural sub-network being equal to the minimum value when all inputs of the neural sub-network are equal to the minimum value, and the output of the neural sub-network being equal to the maximum value when all the inputs of the neural sub-network are equal to the maximum value, and each sub-network being suitable for implementing weights, one constraint being that the weights are positive and that a sum of the weights is equal to 1”.
Dependent claim 16 recites additional observation, evaluation, and judgement “wherein the transcribed neural network includes a set of neural sub-networks arranged in a tree structure, each neural sub-network being a first neural sub-network or a second neural sub-network, each first neural sub-network performing a respective aggregation function, and each second neural sub-network performing a respective marginal utility function”.
Dependent claim 17 recites additional observation, evaluation, and judgement and mathematical calculations and relationships “wherein the respective aggregation function is a variable aggregation function selected from the group consisting of: a weighted sum of variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and ordered weighted average”
Dependent claim 18 recites additional observation, evaluation, and judgement “wherein the respective marginal utility function is a monotone function or a function having three parts, a monotone first part, a constant second part and a monotone third part, the monotonicity of the first part being different from the monotonicity of the third part”
Dependent claim 19 recites additional instructions to apply the judicial exception using generic computer components “wherein the training includes: a first training with the set of constraints of the transcription making the training of an intermediate neural network possible, a second training of the set of constraints by setting the neural network to the intermediate neural network, so as to obtain a trained set of constraints, and an adjustment of the trained neural network according to the difference between the set of constraints of the transcription and the trained set of constraints, so as to obtain an adjusted neural network, the trained neural network being the adjusted neural network” which recites generic neural network training
Dependent claim 20 recites additional mathematical calculations and relationships “wherein the training comprises employing at least one technique selected from the list consisting of batch gradient descent, stochastic gradient descent and mini-batch gradient descent”
Dependent claim 21 recites additional mathematical calculations and relationships “the training comprises the use of a weighted sum of sigmoids”
Dependent claim 22 recites additional observation, evaluation, and judgement “A decision support system generated by implementing a generation method according to claim 11”
Dependent claim 26 recites additional instructions to apply the judicial exception using generic computer components “The manufacturing method according to claim 11, wherein the method enables manufacturing on an array of field programmable gates (FPGA).”
Regarding claim 22, the decision support system under broadest reasonable interpretation is interpreted as being directed towards signals-per-se. This interpretation is supported by the instant specification at ([¶0035] "the computer 14 is an electronic computer suitable for handling and/or transforming data represented as electronic or physical quantities in registers of the computer 10 and/or memories into other similar data corresponding to physical data in the register memories or other types of displays, transmission devices or storage devices."). There is no indication that the decision support system cannot be a transitory signal, therefore the claim is rejected as signal-per-se. The claim can similarly be interpreted as being directed towards software-per-se ([¶0061] "The decision support system is often a computer 10 or part of the latter." [¶0035] "the computer 14 is an electronic computer suitable for handling and/or transforming data represented as electronic or physical quantities in registers of the computer 10 and/or memories into other similar data corresponding to physical data in the register memories or other types of displays, transmission devices or storage devices.") and that "part of the latter" includes ([¶0052] "program instructions, when executed by the data processing unit 20, lead to the execution of steps of the generation method"), where program instructions are not interpreted as physical but rather as software-per-se or as signal-per-se ([¶0055] "the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.").
Regarding Claim 23: Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a multiple-criteria decision support system which in view of the instant specification may be program instructions or transitory signals ([¶0061] "The decision support system is often a computer 10 or part of the latter." [¶0035] "the computer 14 is an electronic computer suitable for handling and/or transforming data represented as electronic or physical quantities in registers of the computer 10 and/or memories into other similar data corresponding to physical data in the register memories or other types of displays, transmission devices or storage devices.") and that "part of the latter" includes ([¶0052] "program instructions, when executed by the data processing unit 20, lead to the execution of steps of the generation method"), where program instructions are not interpreted as physical but rather as software-per-se or as signal-per-se ([¶0055] "the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.") which are both judicial exceptions.
Step 2A Prong One Analysis: Claim 23 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass neural network processing, including the following:
Manufacturing of a physical implementation of a neural network comprising a set of neural sub-networks arranged in a tree structure, each neural sub-network being a first neural sub-network or a second neural sub-network (observation, evaluation, and judgement. Describes a generic neural network which can readily be “manufactured” entirely in the mind with or without the assistance of tools such as pen and paper)
each first neural sub-network performs a respective aggregation function, the respective aggregation function being a variable aggregation function selected from the group consisting of: a weighted sum of the variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and the ordered weighted average (observation, evaluation, and judgement and mathematical calculations and relationships)
each second neural sub-network performs a respective marginal utility function, the marginal utility function preferentially being a monotone function or a function comprising three parts, namely a monotone first part, a constant second part and a monotone third part, and wherein the monotonicity of the first part being different from the monotonicity of the third part (observation, evaluation, and judgement),
Therefore, claim 23 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 23 recites additional elements (describes a generic neural network). However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Therefore, claim 23 is directed to a judicial exception.
Step 2B Analysis: Claim 23 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 23 amount to no more than mere instructions to apply the judicial exception using a generic computer component.
For the reasons above, claim 23 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claims 24-25. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 24 recites additional observation, evaluation, and judgement and mathematical calculations and relationships “wherein the respective aggregation function is a variable aggregation function selected from the list consisting of: a weighted sum of the variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and the ordered weighted average.”
Dependent claim 25 recites additional observation, evaluation, and judgement “wherein the marginal utility function is a monotone function or a function having three parts, a monotone first part, a constant second part and a monotone third part, the monotonicity of the first part being different from the monotonicity of the third part.”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 11-26 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 11-15, 19, and 22 are rejected under U.S.C. §103 as being unpatentable over the combination of Pedro (“Modeling Decision-Maker Preferences through Utility Function Level Sets”, 2011).
Regarding claim 11, Pedro teaches A method for manufacturing a multiple-criteria decision support system, the manufacturing method comprising:([Abstract] "In this paper, we present a method based on the multi attribute utility theory to approximate the decision-maker preference function")
providing an initial problem ([p. 554 §2.1 Step 1] "In this domain, a fictitious decision-making problem is built, in which the alternatives are located as a grid. The queries to the DM are presented over this grid. The grid is constructed to find a representation for the utility function U in the desired domain. The number of alternatives in the grid is related to the quality of the approximation U" decision making problem interpreted as synonymous with initial problem.)
and training data solving the initial problem for particular cases, ([p. 556] "We use the alternatives within the grid as input and the ranking level of each alternative as output to train the ANN ˆU which approximates U" [p. 558] "Figure 4 presents the normalized surface and level sets of function Uˆ, modeled by the ANN. Considering the grid of alternatives and creating 30 sets, each one with 50 random alternatives, the KTD average value in 30 runs was 0.02. These sets were created to find the KTD value in different sets, beyond the training set" [p. 556] "We use a multi-objective optimization approach to balance the training data error")
the initial problem being a problem of evaluating the quality of an existing system or future physical system([p. 554 §2.1 Step 1] "In this domain, a fictitious decision-making problem is built, in which the alternatives are located as a grid. The queries to the DM are presented over this grid. The grid is constructed to find a representation for the utility function U in the desired domain. The number of alternatives in the grid is related to the quality of the approximation U" Pedron evaluates how well the ANN-generated approximation models the existing utility function U.)
where the initial problem is a problem selected from the group consisting of: the choice of the best alternative among a set of alternatives, the distribution of alternatives among preference classes, the storage of alternatives in order of preference, and the provision of an evaluation score of an alternative,([p. 551 §1.1] "given a problem, with its set of possible solutions (the alternatives)" [p. 552 §1] "As a consequence, a regression of utility function values may be meaningful, and may help to guide a search for the preferred alternative from a set of alternatives" [p. 553 §1.1] "The value of each alternative is assigned by a decision-maker, that formally corresponds to a preference function P. The best alternative x∗ ∈ A is the one which has attributes f(x∗) ∈ B that maximize the function P(f(x)) = P(x) in the set A")
transcribing the initial problem in a form of a neural network ([p. 555] "the regression tool chosen is an artificial neural network (ANN)")
and of a set of constraints to be satisfied by the neural network, so as to obtain a transcribed neural network,([p. 555 §2.2 Step 2] "This procedure creates a partition of the set A in at least p disjunct subsets. As the number of pivots is less than the number of alternatives, many alternatives will have the same ranking, providing a partial sorting. A total sorting could be obtained through a total ranking, but these results would be useless for the purpose of building a regression model using ANNs. For this reason, a partial sorting is strictly necessary" [p. 555 §2.3 Step 3] "In this paper, the regression tool chosen is an artificial neural network (ANN)" partial sorting for ANN interpreted as synonymous with transcription in the form of a neural network.)
training of the transcribed neural network using the training data, so as to obtain a trained neural network solving the initial problem, ([p. 566 §2.3 Step 3] "We use the alternatives within the grid as input and the ranking level of each alternative as output to train the ANN Uˆ which approximates U")
determining the function performed by the trained neural network, and([p. 562] "Given a decision making problem, a partial ranking is built, leading to a partial sorting for the alternatives; - With a set of alternatives and the partial ranking process, the resulting function is obtained by the ANN")
manufacturing a function determined to obtain the decision support system.([p. 562] "With this approximation, no more queries to the decision-maker are necessary in further instances of the same decision problem")
wherein the decision support system comprises a calculator or a part thereof ([Abstract] "In this paper, we present a method based on the multi attribute utility theory to approximate the decision-maker preference function" Utility function approximation interpreted as a calculator).
Regarding claim 12, Pedro teaches the manufacturing method according to claim 11, wherein the transcribed neural network includes a set of neural sub-networks, (Pedro [p. 556] "The ANN architecture chosen for the application was a multilayer perceptron (MLP), with one hidden layer with 30 neurons" Neurons interpreted as a neural sub-networks. Layer interpreted as set of neural sub-networks.)
the transcribing including a formulation of the set of constraints to be satisfied by the neural network in a form of sub-constraints to be satisfied by each neural sub-network.(Pedro [pp. 555-556 §2.3] "The key element of this paradigm is the structure of the information processing system, which is composed of a large number of highly interconnected processing elements (neurons) working together to solve specific problems").
Regarding claim 13, Pedro teaches the manufacturing method according to claim 12, wherein each neural sub-network includes hidden layers, a number of hidden layers being less than or equal to 5.(Pedro [p. 556] "The ANN architecture chosen for the application was a multilayer perceptron (MLP), with one hidden layer with 30 neurons" Layer interpreted as set of neural sub-networks.).
Regarding claim 14, Pedro teaches the manufacturing method according to claim 13, wherein the number of hidden layers is less than or equal to 3.(Pedro [p. 556] "The ANN architecture chosen for the application was a multilayer perceptron (MLP), with one hidden layer with 30 neurons").
Regarding claim 15, Pedro teaches the manufacturing method according to claim 12, wherein the sub-constraints to be satisfied by a neural sub-network are selected from the group consisting of: a monotonicity of a variation of output of the neural sub-network as a function of inputs of the neural sub-network, the output of the neural sub-network being comprised between a minimum value and a maximum value, the output of the neural sub-network being equal to the minimum value when all inputs of the neural sub-network are equal to the minimum value, and the output of the neural sub-network being equal to the maximum value when all the inputs of the neural sub-network are equal to the maximum value, and each sub-network being suitable for implementing weights, one constraint being that the weights are positive and that the sum of the weights is equal to 1.(Pedro [p. 554 §2.1] "The domain for Uˆ is inferred from the domain of the instance i of the decision-making problem, i.e., from the available alternatives Ai. The domain is defined as the box constructed considering the minimum and maximum values for the available alternatives Ai in each problem dimension" Output layer neuron output interpreted as alternative which is explicitly constrained by a minimum and maximum.).
Regarding claim 19, Pedro teaches the manufacturing method according to claim 11, wherein the training includes: a first training with the set of constraints of the transcription making training of an intermediate neural network possible, a second training of the set of constraints by setting the neural network to the intermediate neural network, so as to obtain a trained set of constraints, and an adjustment of the trained neural network according to a difference between the set of constraints of the transcription and the trained set of constraints, so as to obtain an adjusted neural network, the trained neural network being the adjusted neural network.(Pedro [p. 556 §2.3] "The ANN architecture chosen for the application was a multilayer perceptron (MLP), with one hidden layer with 30 neurons. We use a multi-objective optimization approach to balance the training data error and the weight vector norm, to avoid underfitting and overfitting. The selection of the most appropriate solution within the Pareto-optimal set is performed by minimum validation error" Error interpreted as difference between set of constraints of the transcription and the trained set of constraints.).
Regarding claim 22, Pedro teaches A decision support system generated by implementing the manufacturing method according to claim 11.(Pedro [Abstract] "In this paper, we present a method based on the multi attribute utility theory to approximate the decision-maker preference function [...] Multicriteria decision analysis").
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 16, 17, 18, 23, 24, and 25 are rejected under U.S.C. §103 as being unpatentable over the combination of Pedro and Chen (“Integration of genetic algorithms and neural networks for the formation of the classifier of the hierarchical Choquet integral”, 2020).
Regarding claim 16, Pedro teaches the manufacturing method according to claim 11, wherein the transcribed neural network includes a set of neural sub-networks arranged in a tree structure, each neural sub-network being a first neural sub-network or a second neural sub-network,(Pedro See neurons in FIG. 1 architecture which shows each neuron arranged in a "tree" structure with respect to the instant specification ([¶0169] "The tree structure is a tree structure when there is a single path between a particular vertex to all other vertices, and each non-leaf vertex has at least two child nodes.")).
However, Pedro doesn't explicitly teach each first neural sub-network performing a respective aggregation function, and
each second neural sub-network performing a respective marginal utility function.
Chen, in the same field of endeavor, teaches each first neural sub-network performing a respective aggregation function, and([p. 55 §4.6] "The network structure of the NN used is depicted in Fig. 5. The input layer is composed of sub-Choquet integrals, which are determined by GAs and HLMS. Then, we add neurons (e.g., hierarchical sub-Choquet integrals) and rectified linear unit (ReLU) activation functions in the hidden layers" [p. 47] "For all A, B E N, if A (B then m(A) < m(B) (monotonicity)" neuron sub-Choquet integral interpreted as subnetwork performing a respective aggregation function)
each second neural sub-network performing a respective marginal utility function.([p. 55 §4.6] "The network structure of the NN used is depicted in Fig. 5. The input layer is composed of sub-Choquet integrals, which are determined by GAs and HLMS. Then, we add neurons (e.g., hierarchical sub-Choquet integrals) and rectified linear unit (ReLU) activation functions in the hidden layers" [p. 47] "For all A, B E N, if A (B then m(A) < m(B) (monotonicity)" Relu is monotonic.).
Pedro as well as Chen are directed towards using neural networks for multi-criteria decision making. Therefore, Pedro as well as Chen are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Pedro with the teachings of Chen by using neurons for sub-Choquet integrals and applying a Relu activation function. Relu is a well-known and common activation function in the art which would have been obvious to use by one of ordinary skill in the art before the effective filing date of the claimed invention. This is explicitly reinforced by Chen who provides as additional motivation for combination ([p. 59] "the selected features are transformed to form different sub-Choquet integrals (which form the input of NN). Next, compared to the NN, the proposed model is explainable, as the neurons are viewed as the black box in NN. In contrast, the neurons here are represented by subChoquet integrals. Furthermore, the accuracy criterion also indicates that the proposed model was significantly better than the naïve Bayes and decision tree approaches. Finally, the objective of the proposed method is accuracy, which is more intuitive than others in the classification problem"). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 17, the combination of Pedro, and Chen teaches the manufacturing method according to claim 16, wherein the respective aggregation function is a variable aggregation function selected from the group consisting of: a weighted sum of variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and an ordered weighted average.(Pedro [p. 551] "an aggregation of the multiple criteria is performed using some pre-defined aggregation function [...] It is worthy to notice that an important existing approach for the problem of representing non-linear dependencies between different criteria in decision problems is based on Choquet integrals" [...] Amongst MAUT-based methods, we can cite: Smarts and Smarter [6], Weighted Sum Model [7], Weighted Product Model [12]").
Regarding claim 18, the combination of Pedro, and Chen teaches the manufacturing method according to claim 16, wherein the respective marginal utility function is a monotone function or a function having three parts, a monotone first part, a constant second part and a monotone third part, the monotonicity of the first part being different from the monotonicity of the third part.(Chen [p. 55 §4.6] "The network structure of the NN used is depicted in Fig. 5. The input layer is composed of sub-Choquet integrals, which are determined by GAs and HLMS. Then, we add neurons (e.g., hierarchical sub-Choquet integrals) and rectified linear unit (ReLU) activation functions in the hidden layers" [p. 47] "For all A, B E N, if A (B then m(A) < m(B) (monotonicity)" Relu is monotonic.).
Regarding claim 23, Pedro teaches A multiple-criteria decision support system comprising manufacturing of a neural network comprising a set of neural sub-networks arranged in a tree structure, each neural sub-network being a first neural sub-network or a second neural sub-network,(See neurons in FIG. 1 architecture which shows each neuron arranged in a "tree" structure with respect to the instant specification ([¶0169] "The tree structure is a tree structure when there is a single path between a particular vertex to all other vertices, and each non-leaf vertex has at least two child nodes."))
wherein each first neural sub-network performs a respective aggregation function, the respective aggregation function being a variable aggregation function selected from the group consisting of: a weighted sum of variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and an ordered weighted average, and([p. 551] "an aggregation of the multiple criteria is performed using some pre-defined aggregation function [...] It is worthy to notice that an important existing approach for the problem of representing non-linear dependencies between different criteria in decision problems is based on Choquet integrals" [...] Amongst MAUT-based methods, we can cite: Smarts and Smarter [6], Weighted Sum Model [7], Weighted Product Model [12]").
However, Pedro does not explicitly teach wherein each second neural sub-network performs a respective marginal utility function, the marginal utility function being a monotone function or a function comprising three parts, namely a monotone first part, a constant second part and a monotone third part, wherein monotonicity of the first part is different from monotonicity of the third part.
Chen, in the same field of endeavor, teaches wherein each second neural sub-network performs a respective marginal utility function, the marginal utility function being a monotone function or a function comprising three parts, namely a monotone first part, a constant second part and a monotone third part, wherein monotonicity of the first part is different from monotonicity of the third part.([p. 55 §4.6] "The network structure of the NN used is depicted in Fig. 5. The input layer is composed of sub-Choquet integrals, which are determined by GAs and HLMS. Then, we add neurons (e.g., hierarchical sub-Choquet integrals) and rectified linear unit (ReLU) activation functions in the hidden layers" [p. 47] "For all A, B E N, if A (B then m(A) < m(B) (monotonicity)" Relu is monotonic.).
Pedro as well as Chen are directed towards using neural networks for multi-criteria decision making. Therefore, Pedro as well as Chen are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Pedro with the teachings of Chen by using neurons for sub-Choquet integrals and applying a Relu activation function. Relu is a well-known and common activation function in the art which would have been obvious to use by one of ordinary skill in the art before the effective filing date of the claimed invention. This is explicitly reinforced by Chen who provides as additional motivation for combination ([p. 59] "the selected features are transformed to form different sub-Choquet integrals (which form the input of NN). Next, compared to the NN, the proposed model is explainable, as the neurons are viewed as the black box in NN. In contrast, the neurons here are represented by subChoquet integrals. Furthermore, the accuracy criterion also indicates that the proposed model was significantly better than the naïve Bayes and decision tree approaches. Finally, the objective of the proposed method is accuracy, which is more intuitive than others in the classification problem"). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 24, the combination of Pedro, and Chen teaches The multiple-criteria decision support system according to claim 23, wherein the respective aggregation function is a variable aggregation function selected from the group consisting of: a weighted sum of the variables, a Choquet integral, a 2-additive Choquet integral, a weighted sum of combinations of min and max functions between k variables, for k at least equal to 2, a multi-linear model, a generalized additive independence function, and the ordered weighted average. (Pedro [p. 551] "an aggregation of the multiple criteria is performed using some pre-defined aggregation function [...] It is worthy to notice that an important existing approach for the problem of representing non-linear dependencies between different criteria in decision problems is based on Choquet integrals" [...] Amongst MAUT-based methods, we can cite: Smarts and Smarter [6], Weighted Sum Model [7], Weighted Product Model [12]").
Regarding claim 25, the combination of Pedro, and Chen teaches The multiple-criteria decision support system according to claim 23, wherein the respective marginal utility function is a monotone function or a function comprising three parts, namely a monotone first part, a constant second part and a monotone third part, the monotonicity of the first part being different from the monotonicity of the third part.(Chen [p. 55 §4.6] "The network structure of the NN used is depicted in Fig. 5. The input layer is composed of sub-Choquet integrals, which are determined by GAs and HLMS. Then, we add neurons (e.g., hierarchical sub-Choquet integrals) and rectified linear unit (ReLU) activation functions in the hidden layers" [p. 47] "For all A, B E N, if A (B then m(A) < m(B) (monotonicity)" Relu is monotonic.).
Claims 20 and 21 are rejected under U.S.C. §103 as being unpatentable over the combination of Pedro and Guo (“An interpretable machine learning framework for modelling human decision behavior”, 2019).
Regarding claim 20, Pedro teaches the manufacturing method of according to claim 11.
However, Pedro doesn't explicitly teach, wherein the training comprises employing at least one technique selected from the group consisting of batch gradient descent, stochastic gradient descent and mini-batch gradient descent.
Guo, in the same field of endeavor, teaches the manufacturing method of according to claim 11, wherein the training comprises employing at least one technique selected from the group consisting of batch gradient descent, stochastic gradient descent and mini-batch gradient descent.([p. 13 §3.2] "We can adopt a variety of optimization methods to minimize Eq.(8), such as Stochastic Gradient Descent (SGD)").
Pedro as well as Guo are directed towards using neural networks for multi-criteria decision making. Therefore, Pedro as well as Guo are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Pedro with the teachings of Guo by using stochastic gradient descent for training the neural network. Stochastic gradient descent is a common training algorithm which would have been obvious to use to one of ordinary skill in the art before the effective filing date of the claimed invention. This is explicitly reinforced by Guo who provides as additional motivation for combination ([p. 13 §3.2] "With the proposed model, the DM can know what attributes are more important for the prediction, what values of an attribute are positively or negatively associated to the prediction, and where the convexity and concavity of the function are changed"). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 21, Pedro teaches the manufacturing method according to claim 11.
However, Pedro doesn't explicitly teach, wherein the training comprises using a weighted sum of sigmoids.
Guo, in the same field of endeavor, teaches the manufacturing method according to claim 11, wherein the training comprises using a weighted sum of sigmoids.([p. 13 §3.2] "where σ(·) is a sigmoid function. To estimate the parameters, we minimize the mean square error (MSE)" See Eqn. 7 and 8).
Pedro as well as Guo are directed towards using neural networks for multi-criteria decision making. Therefore, Pedro as well as Guo are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Pedro with the teachings of Guo by using stochastic gradient descent for training the neural network. Stochastic gradient descent is a common training algorithm which would have been obvious to use to one of ordinary skill in the art before the effective filing date of the claimed invention. This is explicitly reinforced by Guo who provides as additional motivation for combination ([p. 13 §3.2] "With the proposed model, the DM can know what attributes are more important for the prediction, what values of an attribute are positively or negatively associated to the prediction, and where the convexity and concavity of the function are changed"). This motivation for combination also applies to the remaining claims which depend on this combination.
Claim 26 is rejected under U.S.C. §103 as being unpatentable over the combination of Pedro and Umuroglu (“FINN: A Framework for Fast, Scalable Binarized Neural Network Inference”, 2016).
Regarding claim 26, Pedro teaches The manufacturing method according to claim 11.
However, Pedro doesn't explicitly teach, wherein the method enables manufacturing on an array of field programmable gates (FPGA).
Umuroglu, in the same field of endeavor, teaches The manufacturing method according to claim 11, wherein the method enables manufacturing on an array of field programmable gates (FPGA).([p. 2 §2.3] "A great deal of prior work on mapping neural networks to hardware exist both for FPGAs and as ASICs" [p. 4] "Figure 4: Generating an FPGA accelerator from a trained BNN").
Pedro as well as Umuroglu are directed towards neural networks. Therefore, Pedro as well as Umuroglu are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Pedro with the teachings of Umuroglu by implementing Pedro’s neural network on an FPGA. Umuroglu provides as additional motivation for combination ([p. 2 §2.3] "A great deal of prior work on mapping neural networks to hardware exist both for FPGAs and as ASICs").
Conclusion
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
Prosecution Insights