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 .
DETAILED ACTION
Claims 1,3,6,12-14 are amended and claims 2, 4, 5, 7 are canceled. Thus claims 1,3,6,8-14 are pending. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot in view of new grounds of rejection. This action is a Final Rejection.
Claim Objections
Claims 1,1, 14 and 14 are objected to because of the following informalities: see below Appropriate correction is required.
Claims 1, 13, and 14 as amended contain “Near training instance”, “near the selected training instances” etc. what is it “near a decision boundary”, 0037 of the spec.? How does one know what “near” is as it’s a relative term. For the purpose of examination, the examiner will interpret literally.
Claims 1, 13, 14, “the plurality of prediction results is large,” and “results is large” and “not to derive large variation”, what is large? To different people large would be different bounds. The examiner will interpret large generically.
Claims 1,13 and 14- “vote entropy”, although the specification mentions voting, and entropy, it’s not clear what is vote entropy. Applicant should clarify along the lines of lines of the specification. The examiner will interpret as entropy and prediction results.
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.
Claim(s) 1,3,6,8-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over
US Patent Publication to 20190279102 Cataltepe in view of US Patent Publication
20210117718 to Badjatiya
As per claim 1, Cataltepe discloses;
an acquisition process of acquiring a plurality of training instances; a training process of training, with use of the plurality of training instances, a machine learning model group that includes a plurality of machine learning models each of which outputs a prediction result while using instances as input,
Cataltepe (0226, training instances and models, see also 3c)
wherein the training includes dividing the machine learning model group into a plurality of machine learning model groups,
Cataltepe (00212, instances grouped together)
extracting, for each of the plurality of machine learning model groups, a training instance group that is a part of the plurality of training instances, and training each of the plurality of machine learning model groups with use of the extracted training instance group;
Cataltepe (0165)
evaluating, for each of the plurality of machine learning model groups, C (0212)
variation in a plurality of prediction results for each training instance that has not been used in training of the machine learning model group among the plurality of training instances;
Cataltepe (0210)
a selection process of selecting, among the plurality of training instances for which the variation is evaluated, a training instance for which a result of the evaluation of variation indicates that variation in the plurality of prediction results is large, CATALTEPE(0129-32, as noted in the object, “large” is relative, 0213, model variance could be high, is that results are large?)
which derives variation in a plurality of prediction results obtained by using the machine learning model group which has been trained; and a generation process of generating a synthetic instance by combining, among the plurality of training instances, two or more training instances including the training instance which has been
Cataltepe (0234, create synthetic instance
selected, wherein the generating includes selecting a near training instance that is present, in a feature quantity space, near the selected training instance and generating the synthetic instance by combining the selected training instance and the near training instance;
Cataltepe (0234, create synthetic instance, and 0144 enough to the old models for “near” training instance)
Here Cataltepe does not explicitly disclose what Badjatiya teaches; what
wherein the evaluation of variation includes at least one of evaluation based on vote entropy or evaluation based on a proportion of prediction results that indicate a same label among the plurality of prediction results;
Badjatiya (0013, entropy, “or” is a choice, evaluation based on entropy)
ex-post evaluating, for the generated synthetic instance,
variation in prediction results by using the trained machine learning model group and deleting the generated synthetic instance in a case where the generated synthetic instance is evaluated not to derive large variation in prediction results;
and adding, to the plurality of training instances, a synthetic instance remaining after the ex- post evaluating and causing the training, the selecting, and the generating to be carried out again, wherein one or more synthetic instances generated by the generating are used to train a machine learning model to be trained.
Badjatiya (Here the ex post evaluating appears to be to determine results that have less variation, more predictable results, 0029-30)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the AI teachings of Cataltepe and Badjatiya for the motivation of improving “the accuracy of the data classification” (0003)
Claims 1, 14 are similar to claim 13.
As per claim 3 Cataltepe discloses; The information processing apparatus according to claim 1, wherein: in the selection process, the at least one processor selects, among the plurality of training instances, two or more training instances each of which derives variation in the plurality of prediction results; and in the generation process, the at least one processor generates the synthetic instance by combining the two or more training instances which have been selected. Cataltepe (0231)
As per Claim 6 Cataltepe discloses; The information processing apparatus according to, claim 1,wherein: in the generation means process, the at least one processor generates a plurality of synthetic instances, and integrates, into a single synthetic instance, two synthetic instances that satisfy a similarity condition among the plurality of synthetic instances.
Cataltepe (0102, models who’s outputs can be combined)
As per claim 8 Cataltepe discloses; The information processing apparatus according to, claim 1,wherein: the machine learning model group includes a machine learning model which is to be trained using the synthetic instance. Cataltepe (0231, synthetic instance)
As per Claim 9 Cataltepe discloses; The information processing apparatus according to 8, claim 1,wherein: at least two machine learning models included in the machine learning model group use machine learning algorithms which are different from each other.
Cataltepe (fig. 3E and 0164, “one or more machine learning algo’s could be more than one
As per Claim 10 Cataltepe discloses; The information processing apparatus , claim 1,wherein: the machine learning model group uses a single machine learning algorithm.
Cataltepe (fig. 3E and 0164, “one or more machine learning algo’s could be just one)
As per Claim 11 Cataltepe discloses; The information processing apparatus according, claim 1,wherein: at least one machine learning model in the machine learning model group is a decision tree.
Cataltepe (0177 decision tree)
As per Claim 12 Cataltepe discloses; The information processing apparatus according claim 1, further comprising: wherein: the at least one processor further carries out a label assignment means for process of assigning a label to each of at least one of or all of the plurality of training instances and the synthetic instance. Cataltepe (0144)
Response to Arguments
Claims 1,3,6,12-14 are amended and claims 2, 4, 5, 7 are canceled. Thus claims 1,3,6,8-14 are pending. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot in view of new grounds of rejection. This action is a Final Rejection.
Claims rejections -35 USC 101 – moot
Claims rejections -35 USC 102(a)- Moot in view of updated grounds of rejection resulting from amendment plus objections above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure from IP.com.
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods, ArXiv.org, 2020
Machine Learning for Synthetic Data Generation: A Review, ARXIV.org 2023
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 BRUCE I EBERSMAN whose telephone number is (571)270-3442. The examiner can normally be reached 8:00 am - 5:00 pm Monday-Friday.
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, Michael W Anderson can be reached at 571-270-0508. 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.
/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693
Prosecution Insights