Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes of observation, judgement and evaluation. This judicial exception is not integrated into a practical application nor does it amount to significantly more as the additional elements amount to mere generic computer hardware performing generic functions to implement the abstract idea. See the analysis below for further details.
Claims 1, 4 and 7
Step 1: Claims 1-3 cite recites a method, claims 4—6 recited a non-transitory computer recording medium, and claims 7-9 recite a system. Therefore, the claims falls into the statutory categories.
Step 2A Prong 1: The claim recites, inter alia:
detecting, based on a result obtained by inputting the plurality of pieces of training data and a plurality of pieces of operation data to the inspector model, a change in the output result of the operation model caused by a change in distribution of the operation data in accordance with elapsed time. (This is a mental step of comparing the results of system over time. It is a user looking at results distribution at first time and determining a distribution. For example, the results are 50% in class A and 50% in class B. Then comparing it results a later time. For example, at the later time results show 75% in class A and 25% in class B. From this the determines or detects that change in output based on distribution of data. This is mental step of observation and evaluation.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
training an operation model as a monitoring target by inputting to the operation model and an inspector model each of a plurality of pieces of training data associated with a correct answer label defining a first class and a second class, such that an output result of the operation model obtained by inputting the training data approaches the correct answer label; (This amount to training a machine learning model using labeled data, as such it is “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
training the inspector model to learn a decision boundary between an area of the first class and an area of the second class to approach the output result of the operation model obtained by inputting the training data and an output result of the inspector model obtained by inputting the training data, (This amount to training a machine learning model using labeled data, as such it is “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
Operation model; inspector model; (Claims 1, 4 and 7) (These limitations are cited at high level of generality and result in mere instructions to implement an abstract idea on a computer using the computer as a tool, see MPEP 2106.05(f).)
a memory and a processor coupled to the memory; (Claim 7) (These limitations are cited at high level of generality and result in mere instructions to implement an abstract idea on a computer using the computer as a tool, 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 mere generic computer hardware performing generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements “training an operation model as a monitoring target by inputting to the operation model and an inspector model each of a plurality of pieces of training data associated with a correct answer label defining a first class and a second class, such that an output result of the operation model obtained by inputting the training data approaches the correct answer label; training the inspector model to learn a decision boundary between an area of the first class and an area of the second class to approach the output result of the operation model obtained by inputting the training data and an output result of the inspector model obtained by inputting the training data,” both are “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f), (Claims 1, 4 and 7). The “Operation model, inspector model (Claims 1, 4 and 7) ; a memory and a processor coupled to the memory (Claim 7);” are generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer hardware in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 2, 5 and 8
2A Prong 1:
Wherein the detecting the change includes calculating, based on the result obtained by inputting the plurality of pieces of training data to the inspector model, a first portion of training data included within a range that is arbitrary set from the decision boundary out of the plurality of pieces of training data, (This amounts to a mental step of observation, judgement and evaluation wherein a user determines the count or portion of data that is within a distance of boundary. For example, a user has a boundary of x = 20, and the range is +/- 2. So any training data with a value that is between 18 and 20 is set as part of the first portion.)
Calculating, based on the result obtained by inputting the plurality of pieces of operation data to the inspector model, a second proportion of operation data included within the range that is arbitrary set from the decision boundary out of the plurality of pieces of operation data, and (This amounts to a mental step of observation, judgement and evaluation wherein a user determines the count or portion of data that is within a distance of boundary. For example, a user has a boundary of x = 20, and the range is +/- 2. So any operation data with a value that is between 18 and 20 is set as part of the second proportion.)
Detecting the change in the output result of the operation model based on the first proportion and the second proportion. (This amounts to a mental step of judgment and evaluation wherein a user compares the proportion or amount of data in the first set to the second set, wherein an increase or decrease in portion mean a change in output that could greater or lesser accuracy of the model.)
2A Prong 2:
The claims do not recite any additional elements that integrate the judicial exception into practical application as it does not include any additional elements.
Step 2B
The claims do not recite any additional elements that amount to significantly more than the judicial exception as it does not include any additional elements.
Claim 3, 6 and 9
2A Prong 1:
generating a training data set by performing a process of determining whether the input data is associated with the first class or the second class, and a process of associated a determination result with the input data, the processing being performed on the plurality of pieces of data, (This amounts to a mental step of observation, judgement and evaluation wherein a user determines the count or portion of data that falls into a first class or second class by determining it value when compared to the boundary. If the boundary is x=20 any values less than 20 would be the first class and any value more than 20 would the second class. The act of associating a result with the input is a mental process of associated data.)
2A Prong 2:
The claims do not recite any additional elements that integrate the judicial exception into practical application, the additional elements are:
Inputting data to the operation model; the training includes training the inspector model to learn the decision boundary by using the training data set; wherein the training the decision boundary; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
Using a processor; (This amounts to using generic hardware 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 mere generic computer hardware performing generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B
The claims do not recite any additional elements that amount to significantly more than the judicial exception the additional elements are:
Inputting data to the operation model; the training includes training the inspector model to learn the decision boundary by using the training data set; wherein the training the decision boundary; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
Using a processor; (This amounts to using generic hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer hardware in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Response to Arguments
Applicant's arguments filed 27 October 2025 have been fully considered but they are not persuasive.
The applicant argues that claims do not contain an abstract idea but are directed to training a model based on knowledge distillation, and as such is a practical application that provides a tangible improvement to machine learning. The examiner respectfully traverses the applicant arguments and maintains that claims 1-9 cites an abstract idea without significantly more. The abstract idea cited in claim 1 is detecting a change in distribution of data based on results. This a mental process of user comparing the results data at first time (time T) to the results at second time (time T+1), and determining if there is change in distribution. For example, at time T, the user counts how many results are in class A and how many at claim B. At the second time (T+1) the user counts again how many results are in class A and how many are class B. If it the percentage of results in either claims has changed then there is change in distribution. This is a mental process of observation, judgement and evaluation. The remaining elements of claim 1 are training a machine an operation model using label training data, and training an inspector model using the training data and output data of the operation model. Both of these limitation are cited at high level of generality and result using generic computer components to executed the abstract idea, see MPEP 2106.05(f). As such the rejection under 35 USC 101 for being an abstract idea is maintained. As claims 4 and 7 are similar to that of claim 1 just different embodiments they too are rejected for the same reasons, along all claims dependent on claims 1, 4 and 7.
Conclusion
THIS ACTION IS MADE FINAL. 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 PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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, Abdullah Kawsar can be reached at 571-270-3169. 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.
/PAULINHO E SMITH/Primary Examiner, Art Unit 2127