DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is responsive to the application filed on 06/14/2022 and a restriction selection of claim 1-12 and on 04/26/2026. Claims 1-12 are pending in the present application and Claims 13-19 are withdrawn.
Priority
3. The present application claims priority under 35 U.S.C. §119 to TW patent Application No. 110137963 filed on 10/13/2021. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Information Disclosure Statement
4. As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 06/17/2022, 08/25/2022, 10/28/2022, 01/13/2023, 06/10/2025, 07/17/2025, 08/21/2025, 12/05/2025, 01/08/2026 and 03/11/2026 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
5. 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-12 falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1,
At step 2A, prong 1, Does the claim recite a judicial exception?
(a) setting a current population, the current population comprising a plurality of compositional pattern-producing networks (CPPNs) (This step involves creating mathematical model parts that falls within mathematical concepts category of abstract ideas.)
(b) establishing a corresponding recurrent Bayesian network for each CPPN in the current population, to obtain a set of recurrent Bayesian networks corresponding to the current population (This step involves creating mathematical models’ parameters and relationship that falls within mathematical concepts category of abstract ideas.)
(c) evolving the current population by using an evolutionary algorithm and a fitness function to obtain a next population, and setting the next population as the current population (This step involves computing mathematical optimization and model evaluation that falls within mathematical concepts category of abstract ideas.)
(d) determining whether a termination condition is met according to the fitness function and the set of recurrent Bayesian networks corresponding to the current population (This step involves data manipulation and model optimization process that falls within the mathematical concept category of abstract ideas.)
(e) repeatedly performing steps (b), (c), and (d) in response to the termination condition being not met, and selecting a solution network in the current population as the task network based on the fitness function in response to the termination condition being met (This step involves iterative mathematical optimization and selecting as result of the optimization that falls within the mathematical concept category of abstract ideas.)
The claim recites a judicial exception, a mathematical concept and mathematical optimization applied in the field of machine learning. The claim recites mathematical relationships, data manipulation using math and data analysis calculations which falls within the Mathematical Concepts” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
No, As shown above with respect to integration of the abstract idea into a practical application, the additional element of:
A construction system for recurrent Bayesian networks, comprising a processor, the processor being configured to perform the following steps to generate a task network (claim 1) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A construction method for recurrent Bayesian networks, performed by a processor, to generate a task network, the construction method for recurrent Bayesian networks (claim 6)
A computer-readable recording medium with a stored program, the stored program (claim 11) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A non-transitory computer program product, storing at least one instruction, the at least one instruction, when executed by a processor, causing the processor to perform the method (claim 12) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
The additional elements as disclosed above alone or in combination do not
integrate the judicial exception into practical application as they are generic computer functions in combination with limitations that are generally linking the use of the judicial exception to a particular technological environment or field of use that are implemented to perform the disclosed abstract idea above. Thus, the claim is directed towards the abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, As shown above with respect to integration of the abstract idea into a practical application, the additional element of A construction system for recurrent Bayesian networks, comprising a processor, the processor being configured to perform the following steps to generate a task network (claim 1) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A construction method for recurrent Bayesian networks, performed by a processor, to generate a task network, the construction method for recurrent Bayesian networks (claim 6)
A computer-readable recording medium with a stored program, the stored program (claim 11) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
A non-transitory computer program product, storing at least one instruction, the at least one instruction, when executed by a processor, causing the processor to perform the method (claim 12) the computer performs steps comprising which is a generic computer component on which to implement the abstract idea (see MPEP 2106.05(f));
Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, the claim is not patent eligible.
The dependent claims respectively recite a judicial exception in limitations of: “wherein step (b) comprises: selecting a current CPPN in the current population; and establishing, by using an output generated by the current CPPN corresponding to an offspring node and a parent node corresponding to the offspring node in the corresponding recurrent Bayesian network, a conditional probability table of the parent node corresponding to the offspring node in the corresponding recurrent Bayesian network.”(claims 2 and 7), “wherein the evolutionary algorithm is neuro-evolution of augmenting topologies.” (claims 3 and 8), “wherein the termination condition is steps (b), (c), and (d) are repeatedly for a default number of times, or, according to the fitness function, the current population comprises a candidate network allowing a fitness value of the candidate network to be greater than a preset fitness value.” (claims 4 and 9), “wherein step (a) comprises: randomly generating an initial population according to a parameter; and setting the initial population as the current population, wherein the initial population comprising the CPPNs.” (claim 5 and 10). These additional limitations (in claims 2-5 and 7-10) also constitute concepts performed in the human mind which fall within the “Mental Processes” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code (in claims 2-5 and 7-10), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent 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 of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
Examiner Comments
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.
Claim Rejections - 35 USC § 103
6. 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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Rawal (US 20190180187 B1, 2019-06-13) in view of Commons (US 20250238673 A1, 2025-07-24)
Regarding independent Claim 1,
RAWAL teaches a construction system for recurrent Bayesian networks comprising a processor, the processor (see RAWAL: Abstract: “A system and method for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database.”), being configured to perform the following steps to generate a task network:
(a). setting a current population, the current population comprising a plurality of [RNN] compositional pattern-producing networks (CPPNs) (see RAWAL: Fig.9, [0086], “The candidate genome pool database 902 is initialized by a population initialization module (current population), which creates an initial set of candidate genomes in the population.”)
(c.) evolving the current population by using an evolutionary algorithm (see RAWAL: Fig.9 [0117], “a module comprises submodules, parameters, and hyperparameters that can be evolved using genetic algorithms (GAs).” … [0092], After the candidate genome pool database 902 has been updated, a procreation module evolves a random subset of them. Only genomes in the elitist pool 912 are permitted to procreate.), and a fitness function to obtain a next population (see RAWAL: Fig.9, [0081], “The training system 900 operates according to fitness function 904, which indicates to the training system 900 how to measure the fitness of a genome.”), and setting the next population as the current population (see RAWAL: Fig.9, [0081], “The fitness function 904 is specific to the environment and goals of the particular application. For example, the fitness function may be a function of the predictive value of the genome as assessed against the training data 918—the more often the genome correctly predicts the result represented in the training data, the more fit the genome is considered.”)
(d). determining whether a termination condition is met according to the fitness function (see RAWAL: Fig.9, [0113], “The process continues repeatedly. In some implementations, a control module iterates the candidate testing module, the competition module, and the procreation module until after the competition module yields a candidate pool of genomes not yet discarded but which satisfy a convergence condition. The convergence condition can be defined as an optimal output of the fitness function 904, according to some definition. The convergence condition may be, for example, a recognition that the candidate pool is no longer improving after each iteration.”), and the set of [RNN] recurrent Bayesian networks corresponding to the current population (see RAWAL: Fig.9, [0090], “a Meta-LSTM 946 can be used to estimate the performance of one or more RNN(s) without running the validation data 928 through the one or more RNN(s) 40 times (epochs).”), and
(e). repeatedly performing steps (b), (c), and (d) in response to the termination condition being not met (see RAWAL: Fig.9, [0113], “he speciation module and the candidate testing module operate again on the updated candidate genome pool database 902. The condition process continues repeatedly. In some implementations, a control module iterates the candidate testing module, the competition module, and the procreation module until after the competition module yields a candidate pool of genomes not yet discarded but which satisfy a convergence condition. The convergence condition can be defined as an optimal output of the fitness function 904, according to some definition. The convergence condition may be, for example, a recognition that the candidate pool is no longer improving after each iteration.”), and selecting a solution network in the current population as the task network based on the fitness function in response to the termination condition being met (see RAWAL: Fig.11, [0140], “Once the fitness of the blueprints and the included super modules is updated, the blueprints are sent to the blueprint competition module 1102 where certain low fitness blueprints are discarded, as discussed above. Following that, the blueprints that are not discarded are subject to procreation at the blueprint procreation module 1122, as discussed above. This is the first evolution loop at the blueprint level.”)
RAWAL does not teach the system comprising:
(a). a plurality of compositional pattern-producing networks (CPPNs)
(b). establishing a corresponding recurrent Bayesian network for each CPPN in the current population, to obtain a set of recurrent Bayesian networks corresponding to the current population.
However, Commons teaches the system comprising:
(a). a plurality of compositional pattern-producing networks (CPPNs) (see Commons: Fig.1, [0059], “Compositional pattern-producing networks (CPPNs) are a variation of ANNs which differ in their set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others. Furthermore, unlike typical ANNs, CPPNs are applied across the entire space of possible inputs so that they can represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and can be sampled for a particular display at whatever resolution is optimal.”)
(b). establishing a corresponding recurrent Bayesian network for each CPPN in the current population, to obtain a set of recurrent Bayesian networks corresponding to the current population (see Commons: Fig.1, [0036], “Training a neural network model essentially means selecting one model from the set of allowed models (or, in a Bayesian framework, determining a distribution over the set of allowed models) that minimizes the cost criterion. There are numerous algorithms available for training neural network models; most of them can be viewed as a straightforward application of optimization theory and statistical estimation. Most of the algorithms used in training artificial neural networks employ some form of gradient descent.”)
Because both RAWAL and Commons are in the same/similar field of constructing neural networks, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of RAWAL to include the system establishing a corresponding recurrent Bayesian network for each CPPN in the current population, to obtain a set of recurrent Bayesian networks corresponding to the current population as taught by Commons. One would have been motivated to make such a combination in order to provide improved network architecture.
Regarding Claim 2,
As shown above, RAWAL and Commons teaches all the limitations of claim 1. Vona further teaches the system wherein:
step (b) (see Commons: Fig.1, [0059], “Training a neural network model essentially means selecting one model from the set of allowed models (or, in a Bayesian framework, determining a distribution over the set of allowed models) that minimizes the cost criterion.”) comprises:
selecting a current CPPN in the current population (see Commons: Fig.1, [0059], “Compositional pattern-producing networks (CPPNs) are a variation of ANNs which differ in their set of activation functions and how they are applied.”); and
establishing, by using an output generated by the current CPPN corresponding to an offspring node and a parent node corresponding to the offspring node in the corresponding recurrent Bayesian network, a conditional probability table of the parent node corresponding to the offspring node in the corresponding recurrent Bayesian network (see Commons: Fig.1, [0038], “The feedforward neural network was the first and arguably simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.”)
Because both RAWAL and Commons are in the same/similar field of constructing neural networks, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of RAWAL to include the system establishing, by using an output generated by the current CPPN corresponding to an offspring node and a parent node as taught by Commons. One would have been motivated to make such a combination in order to provide improved network architecture.
Regarding Claim 3,
As shown above, RAWAL and Commons teaches all the limitations of claim 2. Vona further teaches the system wherein:
the evolutionary algorithm is neuro-evolution of augmenting topologies (see Rawal: Fig.11, [0134], “lustrates various modules that can be used to implement the functionality of the training system 900 in FIG. 9. In particular, FIG. 11 shows a first evolution at the level of blueprints that comprise the supermodules and a second evolution at the level of supermodules.)
Regarding Claim 4,
As shown above, RAWAL and Commons teaches all the limitations of claim 1. Vona further teaches the system wherein:
the termination condition is steps (b), (c), and (d) are repeatedly for a default number of times, or, according to the fitness function, the current population comprises a candidate network allowing a fitness value of the candidate network to be greater than a preset fitness value (see Rawal: Fig.9, [0146], “The server combines these two estimates with the genome's fitness estimate at the time it was sent to the two clients to calculate an updated server-centric fitness estimate for the genome. The combined results represent the genome's fitness evaluated over 700 samples. In other words, the distributed system, in accordance with this example, increases the experience level of a genome from 500 samples to 700 samples using only 100 different training samples at each client. A distributed system, in accordance with the technology disclosed, is thus highly scalable in evaluating its genomes.”
Regarding Claim 5,
As shown above, RAWAL and Commons teaches all the limitations of claim 1. Vona further teaches the system wherein:
step (a) (see RAWAL: Fig.9, [0086], “The candidate genome pool database 902 is initialized by a population initialization module, which creates an initial set of candidate genomes in the population. These genomes can be created randomly, or in some implementations a priori knowledge is used to seed the first generation.”), comprises:
randomly generating an initial population according to a parameter (see Rawal: Fig.9, [0086], “The candidate genome pool database 902 is initialized by a population initialization module, which creates an initial set of candidate genomes in the population. These genomes can be created randomly, or in some implementations a priori knowledge is used to seed the first generation.”), and
setting the initial population as the current population, wherein the initial population comprising the CPPNs (see Rawal: Fig.9, [0086], “In another implementation, genomes from prior runs can be borrowed to seed a new run. At the start, all genomes are initialized with a fitness function 904 that are indicated as undefined.”)
Regarding independent Claim 6,
Claim 6 is a method claim and has similar/same claim limitations as Claim 1 and is rejected under the same rationale
Regarding Claim 7,
Claim 7 is a method claim and has similar/same claim limitations as Claim 2 and is rejected under the same rationale.
Regarding Claim 8,
Claim 8 is a method claim and has similar/same claim limitations as Claim 3 and is rejected under the same rationale.
Regarding Claim 9,
Claim 9 is a method claim and has similar/same claim limitations as Claim 4 and is rejected under the same rationale.
Regarding Claim 10
Claim 10 is a method claim and has similar/same claim limitations as Claim 5 and is rejected under the same rationale.
Regarding independent Claim 11,
Claim 11 is a computer-readable recording medium claim and has similar/same claim limitations as Claim 1 and is rejected under the same rationale.
Regarding independent Claim 12,
Claim 11 is a non-transitory computer program product claim and has similar/same claim limitations as Claim 1 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20250193214 A1
HERCOCK; Robert
Title: NEURAL NETWORK CONSTRUCTION
Description: The method may also include encoding sets of merchant system data into sets of different types of data, and inputting the sets into different machine learning models to generate predictions of different types of merchant fraud.
US 20240242091 A1
Dalibard, Valentin Clement
Title: Enhancing population-based training of neural networks
Description: The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known methods of constructing neural networks..
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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