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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5, 7-10, 12, 14, 15 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Baidya et al. U.S. PGPub 2020/0004921 (hereinafter “Baidya”).
Regarding claim 1, Baidya discloses a method for an augmented decision tree analysis in a machine learning algorithm (MLA) for a manufacturing system (e.g. ¶41), the method comprising: inputting data (e.g. test data) containing data acquired during operation (e.g. ¶36-37 and 41-44); amending the input data (e.g. update test data) with feature information (e.g. ¶36-37 and 41-44); and applying the input data in a decision tree analytics model with leaves, each of the leaves of the decision tree associated to a label giving information about an operational condition of the manufacturing system and branches of the decision tree that represent conjunctions of feature information that lead to a labeled state (e.g. ¶42, 52, 55, 83, 89, 93, 97, 103, 107, 116, 120 and 124), wherein there is at least one simulation model (e.g. forest model) that shows dependencies between the label and the input data (e.g. ¶42, 52, 55, 83, 89, 93, 97, 103, 107, 116, 120 and 124), and wherein one or more simulation models of the at least one simulation model replace at least one part of at least one of the branches (via new/updated decision tree) of the decision tree (e.g. ¶55 and 61).
Regarding claims 3 and 14, Baidya discloses the method of claim 1, wherein the data is presented in a tabular form (e.g. via multiple vectors), with measurement sets in rows and correlated feature information, label, or a combination thereof in column (e.g. ¶36, 43 and 57).
Regarding claims 5 and 15, Baidya discloses the method of claim 1, further comprising detecting an anomaly, the detecting of the anomaly comprising: generating, using a simulation model, example anomaly data (e.g. quality/defect data) that is compared to process data that has been collected during operation (e.g. ¶52); or inputting collected process data to the simulation model while comparing an output to other measurement channels or time frames of the collected process data.
Regarding claims 7 and 17, Baidya discloses the method of claim 1, further comprising determining cause of anomalies using the at least one simulation model, the determining of the cause of anomalies comprising identifying signals that influence anomalies by a cause analysis (e.g. ¶36 and 45-46).
Regarding claims 8 and 18, Baidya discloses the method claim 1, wherein the at least one simulation model is used to simulate future behavior and predict expected values (e.g. ¶40-46, 49, 52, 56-58, 62-63 and 83).
Regarding claims 9 and 19, Baidya discloses the method of claim 1, further comprising varying simulation model parameters (e.g. via improved machine learning system) of at least one simulation model, such that an error function defined through simulated and measured output values is minimized (e.g. minimize imperfections/defects) (e.g. ¶40-41, 52 and 58).
Regarding claim 10, Baidya discloses the method of claim 1, further comprising generating, using the at least one simulation model example data for at least one condition, with defined feature information, correlate (e.g. ¶40-41, 52 and 58); correlating, using the at least one simulation model, the input data to the at least one condition (e.g. ¶40-41, 52 and 58); and generalizing and enhancing (e.g. improving machine learning model), using the at least one simulation model, the decision tree analytics model (e.g. ¶40-41, 52 and 58).
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) 2 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baidya as applied to the claims above, and further in view of Colwell et al. U.S. Patent 10,628,546 (hereinafter “Colwell”).
Baidya discloses using a decision tree to improve yield in a semiconductor manufacturing environment, but does not explicitly disclose using a Gradient Boosted Decision Tree.
Colwell discloses alternatively using a Gradient Boosted Decision Tree in a semiconductor manufacturing environment (e.g. col. 17, lines 44-53).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to use a Gradient Boosted Decision Tree in a semiconductor manufacturing environment. One of ordinary skill in the art would have been motivated to do this since it is known for having high predictive accuracy.
Therefore, it would have been obvious to modify Baidya with Colwell to obtain the invention as specified in claims 2 and 13.
Allowable Subject Matter
Claims objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES R KASENGE whose telephone number is (571)272-3743. The examiner can normally be reached Monday - Friday 7:30am to 4pm EST.
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, Kamini Shah can be reached at (571) 272-2279. 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.
CK
September 30, 2025
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116