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 .
Status of the Application
This action is a first action on the merits in response to the application filed on 05/09/2024.
Status of Claims
Claims 1-20 filed on 05/09/2024 are currently pending and have been examined in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/09/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-20 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of calculating loss value and comparing data.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “with respect to a plurality of output data obtained as a result of inputting a plurality of training data into a neural network, compare the plurality of output data with a plurality of pieces of correct answer information associated with the plurality of training data, to calculate a loss value for each of the plurality of output data; select, among the plurality of output data, relevant output data, the loss value for which meets a predetermined reference, and irrelevant output data, the loss value for which does not meet the predetermined reference; and create processed correct answer information by processing the correct answer information compared with the relevant output data, compare the relevant output data with the processed correct answer information to output a processed loss value, and update the neural network by using the processed loss value, or create processed training data by processing the training data associated with the relevant output data, input the processed training data into the neural network, to cause the neural network to output processed output data obtained as a result of classifying the processed training data, compare the processed output data with the correct answer information associated with the relevant output data, to output a processed loss value, and update the neural network by using the processed loss value”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite mathematical concept and data manipulation (calculating loss values and comparing analysis results). As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 11 and 16 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 11 and 16 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-10, 12-15, and 17-20 recite mathematical concept and data manipulation because the claimed elements describe a process for calculating loss values and comparing analysis results. As a result, claims 2-10, 12-15, and 17-20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “neural network update device comprising a processor comprising hardware, the processor being configured to:”. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 11 and 16 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A non-transitory recording medium recording a neural network update program” and claim 16 further recites “A neural network update method by using a neural network update device”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 11 and 16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-10, 12-15, and 17-20 do not include any additional elements beyond those recited by independent claims 1, 11, and 16. As a result, claims 2-10, 12-15, and 17-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “neural network update device comprising a processor comprising hardware, the processor being configured to:”. The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 11 and 16 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A non-transitory recording medium recording a neural network update program” and claim 16 further recites “A neural network update method by using a neural network update device”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 11 and 16 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-10, 12-15, and 17-20 do not include any additional elements beyond those recited by independent claims 1, 11, and 16. As a result, claims 2-10, 12-15, and 17-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
With respect to the prior arts search, none of the prior arts of record, taken individually or in any combination, teach, inter alia,
A neural network update device comprising a processor comprising hardware, the processor being configured to: with respect to a plurality of output data obtained as a result of inputting a plurality of training data into a neural network, compare the plurality of output data with a plurality of pieces of correct answer information associated with the plurality of training data, to calculate a loss value for each of the plurality of output data; select, among the plurality of output data, relevant output data, the loss value for which meets a predetermined reference, and irrelevant output data, the loss value for which does not meet the predetermined reference; and create processed correct answer information by processing the correct answer information compared with the relevant output data, compare the relevant output data with the processed correct answer information to output a processed loss value, and update the neural network by using the processed loss value, or create processed training data by processing the training data associated with the relevant output data, input the processed training data into the neural network, to cause the neural network to output processed output data obtained as a result of classifying the processed training data, compare the processed output data with the correct answer information associated with the relevant output data, to output a processed loss value, and update the neural network by using the processed loss value.
The prior art references most closely resembling the Applicant’s claimed invention is Matsumura Akira (JP 2020052475 A).
Akira teaches sorter is constructed by learning teacher data that includes a teaching class and a feature quantity vector in which the feature quantity axis is composed of a class confidence factor per class acquired from a plurality of class evaluation results obtained from a teacher image by each of a plurality of tentative auxiliary sorters prior to the current generation. See Abstract. Further, Akira teaches in the classification by the provisional auxiliary classifier 30, further, a representative value (for example, an average value, a median value, a weighted average value, etc.) of a plurality of evaluation values on a plurality of auxiliary feature amount axes is obtained for each class. Then, the class having the largest representative value among the plurality of classes is determined as the classification class of the teacher image. The classification class of each teacher image is stored in the auxiliary classifier correction unit 315. At this time, in the preferred processing in the provisional auxiliary classifier 30, when the maximum representative value is less than one threshold value, or when the difference (or ratio) between the maximum representative value and the second largest representative value is equal to another threshold value, for example, when the number is less than the above, an additional class indicating that the class to be classified is unknown is determined as the classification class. In the following description, in the above case, the teacher images in the teacher image group are classified into additional classes.
Reference (Binary Classification with a third 'uncertain' class label) teaches a data set is provided for training/testing a binary classifier. However, three labels are provided for each image in the data set: Image shows a cat, Image shows no cat, Undecided. The third class label (undecided) implies that the image is of bad quality, i.e., it is impossible to determine with confidence that the image shows either (1) a cat or (2) no cat. An example is a very blurry image.
The initial approach to solving the task was to discard the third (undecided) class label and train/test a binary classifier with the first (cat) and second (no cat) class labels, since the original task requires classifying an image as either (1) showing a cat or (2) showing no cat . However, this will reduce the size of the data set significantly.
None of the cited documents by the Examiner, taken individually or in combination, discloses or suggests the features in the independent claims as amended, nor could a person skilled in the art easily conceive of such features even in the light of common technical knowledge at the time of filing. The pending claims 1-20 are therefore distinguished from the prior arts.
Conclusion
The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure because they all use a network to track compliance with standards for a site using a score, track policies implementations, perform auditing, and train employees. Park et al. (US 20210166071 A1), processor-implemented neural network method comprising: determining, using a neural network, a feature vector based on a training image of a first class among a plurality of classes; determining, using the neural network, plural feature angles between the feature vector and class vectors of other classes among the plurality of classes; determining a margin based on a class angle between a first class vector of the first class and a second class vector of a second class, among the class vectors, and a feature angle between the feature vector and the first class vector; determining a loss value using a loss function including an angle with the margin applied to the feature angle and the plural feature angles; and training the neural network by updating, based on the loss value, either one or both of one or more parameters of the neural network and one or more of the class vectors
Kunisada Yasushi (JP 2022041434 A), An input unit for acquiring the learning data and the correct answer value of the learning data, A feature extraction unit that extracts features based on the training data and the first neural network, A similarity calculation unit that calculates and outputs the similarity between a part or all of the feature amount and the prototype that is the feature amount extracted from the predetermined original data, and An inference unit that outputs an inference value based on the similarity, An output control unit that controls the output unit so that the learning data and the original data of the prototype are output by the output unit. A recording control unit that records correction data of the similarity in a storage unit based on an operation input by the user, and a recording control unit. An evaluation unit that obtains an evaluation result based on the correct answer value, the inference value, the similarity, and the correction data. An update unit that updates the weight parameter of the first neural network based on the evaluation result, and an update unit.
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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