DETAILED ACTION
This Action is Responsive to Claims filed 04/09/2025.
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 .
Status of the Claims
Claims 1, 19, and 20 have been amended. Claim 18 has been previously cancelled. Claims 1-17 and 19-20 are currently pending.
Claim Interpretation
The Examiner has inherited this case after the Non-Final Office Actions dated 03/07/2025 and 12/03/2024. The Examiner has reviewed these previous Office Actions and taken the previous Examiner’s interpretations into consideration for the purposes of continuity.
Response to Arguments
Applicant's arguments filed 04/09/2025 regarding the 35 U.S.C. 101 Rejection of Claims 1-17 and 19-20 have been fully considered but they are not persuasive.
The Examiner finds the previous analysis of the interpretable abstract idea mental process steps proper, if not in verbiage, then at least in spirit. The generic recitation of “acquiring,” without structural limitation or implementation renders the acquisition of a first and second generic index practically performable by a human mind or with the aid of pen and paper. These steps are merely performed by generic computing components recited prior. Likewise, the generic “evaluate a reliability…” limitation is recited without structural limitation or implementation language, and is practically performed within the human mind or with the aid of pen and paper.
The Applicant argues (Pages 11-12), that the claimed limitations are not practically performed within the human mind. The Examiner respectfully disagrees with the Applicant. The “acquire…”, “acquire…”, and “evaluate…” steps are recited highly generically, and are only tied to a computing environment by recitation of highly generic computing components in the claims and the cited portion of the specification. The Examiner maintains the analysis under Step 2A – Prong 1.
The Applicant argues on subsequent Pages 12-14 that the Specification indicates, somehow, that the claimed steps could not be practically performed within the human mind or with the aid of pen and paper. The Applicant merely highlights several passages indicating a generically recited “acquisition unit” performs operations of values in an algorithmic fashion. This is insufficient structural detail or implementation to differentiate the claims’ computing components or preclude a human mind from performing said algorithmic steps.
The Examiner agrees with the Applicant’s assessment in regards to dependent claims 2-17, in that the limitations of said claims should not have been construed within the mathematical concepts grouping. The Claims have been reevaluated in the updated 35 U.S.C. 101 Rejection below.
In regards to Step 2A – Prong 2 of the analysis, the claimed limitations are practically performed within the human mind or with the aid of pen and paper but for the recitation of highly generic computing components. Without additional elements present in the claim to tie the alleged improvement to the functioning of computer to, the improvement must come from the set of algorithmic steps interpretable as abstract idea mental process step. Per MPEP 2106.05(a), the specific improvement cannot come from the abstract idea(s). The citations to the instant Specification merely address why performing the set of algorithmic steps with the particular data points would be beneficial to the evaluation accuracy, without indicating specific structure or implementation tying an additional element to the alleged improvement.
The Examiner acknowledges the previous deficiencies in the Step 2B analysis of the claims. The Claims have been reevaluated in the updated 35 U.S.C. 101 Rejection below.
Applicant's arguments, see Pages 26-28, filed 04/09/2025, regarding the outstanding 35 U.S.C. 103 Rejection(s) have been fully considered but they are not persuasive.
The Applicant alleges the cited references Zhao and Sunao do not read on the newly amended detail of the claimed second index. Based on the limited detail in the instant Specification and claims, the Examiner respectfully disagrees with the Applicant. There is insufficient detail in the instant Specification precluding the previous interpretation of Sunao from reading on the amendments. Especially Sunao Column 6, Lines 17-24, at least. If this is where the Applicant believes their novelty is best expressed, the Examiner encourages further more substantive amendments in this direction. See the updated 35 U.S.C. 103 Rejection below.
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 1-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1 (All Claims):
Claims 1-17 recite an evaluating device, which falls under the statutory category of a machine. Claim 19 recites an evaluation process, which falls under the statutory category of a process. Claim 20 recites a non-transitory computer-readable recording medium storing a program, which falls under the statuary category of a manufacture.
Step 2A – Prong 1 (Claim 1):
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “acquire a first index indicating a difference in data space between learning input data and actual operation input data;”, “acquire a second index indicating a difference in ignition tendency of neurons between a time of input of the learning input data in a learning model of a neural network and a time of input of the actual operation input data in the learning model of the neural network;”, and “evaluate a reliability of a prediction value output from the learning model with respect to the actual operation input data based on the first index and the second index.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. These limitations therefore fall within the mental process group.
The generic recitation of “acquire” does not preclude a human mind with or without the aid of pen and paper from generating, obtaining, or determining the claimed first and second indices. The acquisition of these indices is practically performed within the human mind or with the aid of pen and paper. The generic recitation of “evaluate” does not preclude a human mind with or without the aid of pen and paper from assessing the output of a machine learning model based on the first and second indices. The evaluation of a model’s output is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2 (Claim 1):
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “An evaluating device”, “memory”, “program”, and “processor” are recognized as generic computer components recited at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “learning input”, “operation input”, “ignition tendency of neurons”, “neural network”, and “prediction” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements recited in the limitation “…store a program…” is found to be mere pre- or post-solution extra-solution activity or data gathering (See MPEP 2106.05(g)).
Step 2B (Claim 1):
The only limitation on the performance of the described method is a limitation reciting “An evaluating device”, “memory”, “program”, and “processor” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements recited in the limitations “learning input”, “operation input”, “ignition tendency of neurons”, “neural network”, and “prediction” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements recited in the limitation “…store a program…” is found to be well-understood, routine, or conventional activity (see MPEP 2106.05(d)(II)(i)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 19 and 20
Claim 19 recites similar limitations to Claim 1, with the exception of “An evaluation method, comprising:” (generic computer components); therefore, both claims are similarly rejected.
Claim 20 recites similar limitations to Claim 1, with the exception of “A non-transitory computer readable recording medium storing a program for causing a computer to execute:” (generic computer components); therefore, both claims are similarly rejected.
Dependent Claims:
Claim 2 recites generic computer components and abstract idea mental process steps “evaluate the reliability as being high when the first index is less than a first threshold value and the second index is less than a second threshold value,” and “evaluates the reliability as being low when the first index is equal to or greater than the first threshold value and the second index is equal to or greater than the second threshold value.” The evaluation of a value such as the generic index against a threshold is practically performed within the human mind or with the aid of pen and paper.
Claim 3 recites generic computer components and abstract idea mental process steps “evaluate a prediction error of the learning model in a case where the first index is less than a first threshold value and the second index is equal to or greater than a second threshold value, or in a case where the first index is equal to or greater than the first threshold value and the second index is less than the second threshold value.” The evaluation of a value such as the generic error against a threshold is practically performed within the human mind or with the aid of pen and paper.
Claim 4 recites generic computer components and abstract idea mental process steps “change a calculation formula for the first index such that the first index is decreased when the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and the prediction error is evaluated as being less than a reference value.” Generically changing a calculation formula in response to the evaluation of values is practically performed within the human mind or with the aid of pen and paper.
Claim 5 recites generic computer components and abstract idea mental process steps “such that the second index is increased when the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and the prediction error is evaluated as being equal to or greater than a reference value.” (The evaluation of a value such as the generic index against a threshold is practically performed within the human mind or with the aid of pen and paper) and instructions to apply said evaluation of data in “…adjust a structure of the neural network…” (Steps 2A – Prong 2 and Step 2B, see MPEP 2016.05(f)).
Claim 6 recites generic computer components and abstract idea mental process steps “change a calculation formula for the first index such that the first index is increased when the first index is less than the first threshold value, the second index is equal to or greater than the second threshold value, and the prediction error is evaluated as being equal to or greater than a reference value.” Generically changing a calculation formula in response to the evaluation of values is practically performed within the human mind or with the aid of pen and paper.
Claim 7 recites generic computer components and abstract idea mental process steps “such that the second index is decreased when the first index is less than the first threshold value, the second index is equal to or greater than the second threshold value, and the prediction error is evaluated as being less than a reference value.” (The evaluation of a value such as the generic index against a threshold is practically performed within the human mind or with the aid of pen and paper) and instructions to apply said evaluation of data in “…adjust a structure of the neural network…” (Steps 2A – Prong 2 and Step 2B, see MPEP 2016.05(f)).
Claim 8 recites generic computer components and abstract idea mental process steps “a case where the first index is equal to or greater than a first threshold value and the second index is equal to or greater than a second threshold value, a case where the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and a prediction error of the learning model is evaluated as being equal to or greater than a reference value, or a case where the first index is less than the first threshold value, the second index is equal to or greater than the second threshold value, and the prediction error is evaluated as being equal to or greater than the reference value.” (The evaluation of a value such as the generic index against a threshold is practically performed within the human mind or with the aid of pen and paper) and instructions to apply said evaluation of data in “…execute re- learning or executes output of a notification prompting for re-learning in one or more of:” (Steps 2A – Prong 2 and Step 2B, see MPEP 2016.05(f)).
Claim 9 recites generic computer components and an abstract idea mental process step “calculate the second index based on a neuron coverage indicating a degree of ignition of all of the plurality of neurons included in the neural network.” The generic calculation of the claimed second index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 10 recites generic computer components and abstract idea mental process steps “calculate the second index based on one or more of: a degree of ignition in each of the plurality of neurons included in the neural network, a degree of ignition of the neurons in a layer of the neural network including a plurality of layers, or a degree of diversity of ignition patterns of the plurality of neurons.” The generic calculation of the claimed second index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 11 recites generic computer components and abstract idea mental process steps “calculate the second index based on a difference in neuron coverage indicating a degree of ignition of all of the plurality of neurons and a difference in ignition patterns of the plurality of neurons.” The generic calculation of the claimed second index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 12 recites generic computer components and abstract idea mental process steps “calculate the second index based on a difference in an ignition frequency of each of the plurality of neurons.” The generic calculation of the claimed second index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 13 recites generic computer components and abstract idea mental process steps “calculate the first index based on an Euclidean distance in the data space between the learning input data and the actual operation input data.” The generic calculation of the claimed first index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 14 recites generic computer components and, refinements to the data operated on, and abstract idea mental process steps “calculate the first index by adding weighting based on a degree of importance to each type of the input data of the learning input data and the actual operation input data.” The generic calculation of the claimed first index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 15 recites generic computer components and abstract idea mental process steps “use a dropout method to represent a distribution of output values in a case where the learning input data is input and calculate the first index based on a variance value in a case where the actual operation input data is input in the distribution.” The generic calculation of the claimed first index based on the claimed values is practically performed within the human mind or with the aid of pen and paper.
Claim 16 recites generic computer components and abstract idea mental process steps “determine a center value of a distribution in the data space of the learning input data, set a deviation or variance value from the center value as the first threshold value for acceptability determination of the first index, and evaluate the reliability.” The determination of a center value, setting of values, and evaluation of reliability is practically performed within the human mind or with the aid of pen and paper.
Claim 17 recites generic computer components and abstract idea mental process steps “evaluate the reliability with a second threshold value for acceptability determination of the second index being an increase in width corresponding to a neuron coverage in a case where the learning input data is input.” Evaluation against a generic threshold based on the claimed variables is practically performed within the human mind or with the aid of pen and paper.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Examiner’s Note: In order to maintain continuity, the Examiner has reviewed the previous Rejection(s) and reiterated them here. Indication of further explanation or mapping made by the current Examiner will be marked as such.
Claims 1-2, 4-12, 14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (CN 2020/10029453) in view of Sunao (EP 0450522).
Regarding currently amended independent Claim 1, Zhao teaches an evaluating device, comprising: a memory configured to store a program; and a processor configured to execute the program and control the evaluating device to acquire a first index indicating a difference in data space between learning input data and actual operation input data (Zhao, S4; Zhao teaches gathering optimal learning data by collecting actuaI data from practicaI operation in the environment during learning);
and evaluate a reliability of a prediction value output from the learning model with respect to the actual operation input data based on the first index (Zhao, S52 paragraph 2; Zhao goes into more detail here describing that data points from the simulation environment and the actual environment are compared to improve the next operation, thus improving reliability).
However, Zhao does not teach acquiring a second index indicating a difference in ignition tendency of neurons between a case when the learning input data is input in a learning model of a neural network and a case when the actual operation input data is input in the learning model of the neuraI network.
Sunao teaches a memory configured to store a program; and a processor configured to execute the program and control the evaluating device acquire a second index indicating a difference in ignition tendency of neurons between a time of input of the learning input data in a learning model of a neuraI network and a time of input of the actuaI operation input data in the learning model of the neural network (Sunao, page 4 column 5 lines 12 - 36; Sunao goes into depth about examining the ignition conditions of the input layer of neurons comparing the difference between learning phases and actual activity to improve accuracy. Sunao teaches in the summary of the Invention paragraph 3 deciding the weight of synapses by observing the ignition patterns of an input layers and output layers).
Examiner’s Note: Based on the limited detail in the instant Specification regarding the newly maended “time of input,” the Examiner reads on the limitation fairly broadly. Sunao Column 6, Lines 17-24, teaches “The increase of synapse weight on one learning changes as in Fig. 4 according to the number of learning. The learning of whole system is a executed gradually by plural learning, and simultaneously, fine adjustment is carried out for fine change. The learning speed is developed by rapid increase of weight in the beginning.” Which the Examiner submits broadly reads on said indices being dependent on a learning state or iteration the model exists in, which seems, to the Examiner, what the amendment points to.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to provide the ignition of neurons. Doing so would enable precise control of learning behaviors.
Regarding Claim 2, which is dependent on claim 1, Zhao teaches the processor is further configured to control the evaluating device to evaluate the reliability as being high when the first index is less than a first threshold value and the second index is less than a second threshold value, and evaluates the reliability as being low when the first index is equaI to or greater than the first threshold value and the second index is equal to or greater than the second threshold value (Zhao, S52 paragraph 2; This claim is understood to be a mathematical way to verify accuracy, Zhao teaches comparing reference nodes against actual result to improve accuracy).
Regarding Claim 4, which is dependent on claim 3, Zhao teaches the processor is further configured to control the evaluating device to change a calculation formula for the first index such that the first index is decreased when the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and the prediction error is evaluated as being less than a reference value (Zhao, section 3 paragraph 1; Zhao discusses that the solution can be adjusted by the error between the actual operation data and the reference data, thus changing the formula based on the data received).
Regarding Claim 5, which is dependent on claim 3, Zhao teaches the processor is further configured to control the evaluating device to adjust a structure of the neuraI network such that the second index is increased when the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and the prediction error is evaluated as being equal to or greater than a reference value (Zhao S52 paragraph 2, lines 2-4; Zhao teaches that the node operation point/threshold can be adjusted to improve the operation efficiency).
Regarding Claim 6, which is dependent on claim 3, Zhao teaches the processor is further configured to control the evaluating device to change a calculation formula for the first index such that the first index is increased when the first index is less than the first threshold value, the second index is equal to or greater than the second threshold value, and the prediction error is evaluated as being equal to or greater than a reference value (Zhao, S52 paragraph 2; Zhao also has an error model between the actual data and the ideal data/threshold).
Regarding Claim 7, which is dependent on claim 3, Zhao teaches the processor is further configured to control the evaluating device to adjust a structure of the neuraI network such that the second index is decreased when the first index is less than the first threshold value, the second index is equaI to or greater than the second threshold value, and the prediction error is evaluated as being less than a reference value (Zhao, S52 paragraph 2; Zhao teaches that the node operation point/threshold can be adjusted to improve the operation efficiency).
Regarding Claim 8, which is dependent on claim 1, Zhao teaches the processor is further configured to control the evaluating device to execute re-learning or executes output of a notification prompting for re-learning in one or more of (Zhao, S33 Paragraph 4; Zhao explains that with error messages correction will be made to the model, thus relearning to improve accuracy);
a case where the first index is equal to or greater than a first threshold value and the second index is equal to or greater than a second threshold value, a case where the first index is equal to or greater than the first threshold value, the second index is less than the second threshold value, and a prediction error of the learning model is evaluated as being equal to or greater than a reference value, or a case where the first index is less than the first threshold value, the second index is equal to or greater than the second threshold value, and the prediction error is evaluated as being equal to or greater than the reference value (Zhao, S52 paragraph 2; This claim is understood to be a mathematical way to verify accuracy, Zhao teaches comparing reference nodes against actual results to improve accuracy).
Regarding Claim 9, which is dependent on claim 1, Sunao teaches the processor is further configured to control the evaluating device to calculate the second index based on a neuron coverage indicating a degree of ignition of all of the plurality of neurons included in the neural network (Sunao, page 3 column 4 lines 18 - 39; Sunao shows that a learning phase is successful when the output of the neuron ignition pattern is correct).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to provide the ignition pattern of neurons. Doing so would enable the output of active neurons versus inactive neurons in a network.
Regarding Claim 10, which is dependent on claim 1, Sunao teaches the processor is further configured to control the evaluating device to calculate the second index based on one or more of:
a degree of ignition in each of the plurality of neurons included in the neural network (Sunao, page 3 column 4 lines 7 -17; Sunao teaches the process for getting an output that displays the ignition pattern of neurons),
a degree of ignition of the neurons in a layer of the neural network including a plurality of layers (Sunao, page 3 column 4 lines 40 - 51; Here it is shown that a comparison of ignition is made between a middle layer and input layer),
or a degree of diversity of ignition patterns of the plurality of neurons (Sunao, page 3 column 4 lines 21-24; Sunao teaches learning by looking for specific patterns of neuron ignition).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to provide the ignition pattern of neurons. Doing so would allow for improved accuracy using the ignition patterns of neurons.
Regarding Claim 11, which is dependent on claim 1, Sunao teaches the second processor is further configured to control the evaluating device to calculate the second index based on a difference in neuron coverage indicating a degree of ignition of all of the plurality of neurons and a difference in ignition patterns of the plurality of neurons (Sunao, page 3 column 4 lines 18 - 39; Sunao explains that different ignition patterns display based on the input, thus showing neuron coverage and comparing different ignition patterns).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to provide the ignition pattern of neurons. Doing so would allow for improved accuracy using the ignition patterns of neurons.
Regarding Claim 12, which is dependent on claim 1, Sunao teaches the processor is further configured to control the evaluating device to calculate the second index based on a difference in an ignition frequency of each of the plurality of neurons (Sunao, page 3 column 4 lines 18 - 39; Sunao explains that different ignition patterns display based on the input, thus showing neuron coverage and comparing different ignition frequency).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to provide the ignition frequency of neurons. Doing so would allow for improved accuracy using the ignition frequency of neurons.
Regarding Claim 14, which is dependent on claim 1, Sunao teaches the learning input data and the actual operation input data each include a plurality of types of input data and the processor is further configured to control the evaluating device to calculate the first index by adding weighting based on a degree of importance to each type of the input data of the learning input data and the actual operation input data (Sunao, page 3 column 4 lines 36 - 39; Sunao explains the use of applying weight to the layers based on the ignition patterns).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Sunao to add weights to the layers of neuron activity to facilitate desired learning patterns.
Regarding Claim 16, which is dependent on claim 1, Zhao teaches the processor is further configured to control the evaluating device to determine a center value of a distribution in the data space of the learning input data, set a deviation or va ria nee value from the center value as the first threshold value for acceptability determination of the first index, and evaluate the reliability (Zhao, section 3 paragraph 1; This paragraph teaches finding the center point of node discernment and using a correction ring to determine accuracy of the node activation).
Regarding Claim 17, which is dependent on claim 1, Zhao teaches the processor is further configured to control the evaluating device to evaluate the reliability with a second threshold value for acceptability determination of the second index being an increase in width corresponding to a neuron coverage in a case where the learning input data is input (Zhao, S33 paragraph 7; This paragraph teaches that a correction ring radius can be enlarged in order to increase reliability of the model).
Regarding original independent claim 19, it is an evaluation method of claim 1 and is rejected on the same grounds presented above.
Regarding original independent claim 20, it is a non-transitory computer readable medium of claim 1 and is rejected on the same grounds presented above.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (CN 2020/10029453) in view of Sunao (EP 0450522) as applied to claim 1 above, and further in view of Wee (US 2019/0196419).
Regarding Claim 3, which is dependent on claim 1, Wee teaches an processor is further configured to control the evaluating device to evaluate a prediction error of the learning model in a case where the first index is less than a first threshold value and the second index is equal to or greater than a second threshold value, or in a case where the first index is equal to or greater than the first threshold value and the second index is less than the second threshold value (Wee, page 4 paragraph 66; While not explicitly "first" and "second" in broadest reasonable terms the terms expert inputs and optimal inputs can be used to mean the same and the error between the two index are used to predict an error).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Wee to determine the deviation of error by comparing two indexes.
Regarding Claim 13, which is dependent on claim 1, Wee teaches the processor is further configured to control the evaluating device to calculate the first index based on an Euclidean distance in the data space between the learning input data and the actual operation input data (Wee, pages 3 -4 paragraph 55; Wee teaches the use of Euclidean distance to measure the error between nodes).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhao to incorporate the teaching of Wee for measuring distance using the accepted method for measuring distance the Euclidean metric.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao (CN 2020/10029453) in view of Sunao (EP 0450522) as applied to claim 1 above, and further in view of Han (Learning both Weights and connection for Efficient Neural Networks).
Regarding Claim 15, which is dependent on claim 1, Han teaches the processor is further configured to control the evaluating device first acquisition unit is configured to use a dropout method to represent a distribution of output values in a case where the learning input data is input and calculate the first index based on a variance value in a case where the actual operation input data is input in the distribution (Han, section 3.2; teaches the utilization of the "dropout method" that is well known in the art to prevent over-fitting and can be applied to retraining).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invent to have modified Zhao to incorporate the teaching of Han to provide more accurate distribution of active neurons using the dropout method as recognized by Han.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
Prosecution Insights