Prosecution Insights
Last updated: April 19, 2026
Application No. 18/398,170

DOMAIN ADAPTATION METHOD FOR LONGITUDINAL DATA AND DEVICE USING THEREOF

Non-Final OA §101§102
Filed
Dec 28, 2023
Examiner
EVANS, GEOFFREY T
Art Unit
2852
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Ajou University Industry-Academic Cooperation Foundation
OA Round
1 (Non-Final)
85%
Grant Probability
Favorable
1-2
OA Rounds
2y 10m
To Grant
94%
With Interview

Examiner Intelligence

Grants 85% — above average
85%
Career Allow Rate
674 granted / 793 resolved
+17.0% vs TC avg
Moderate +9% lift
Without
With
+9.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
19 currently pending
Career history
812
Total Applications
across all art units

Statute-Specific Performance

§101
14.2%
-25.8% vs TC avg
§103
43.7%
+3.7% vs TC avg
§102
30.1%
-9.9% vs TC avg
§112
7.1%
-32.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 793 resolved cases

Office Action

§101 §102
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 § 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The claim(s) recite(s) abstract ideas as indicated by in-line comments below . This judicial exception is not integrated into a practical application for reasons also indicated by in-line comments below . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception for reasons also indicated by in-line comments below . 1. A domain adaptation device for longitudinal data comprising: a first module (does not integrate into a practical application because generic computer performing generic computer functions; not significantly more because generic computer performing generic computer functions) configured to generate first transformation data using a projection matrix for domain transformation and a graph matrix for data filtering (abstract; mathematical concepts; mathematical calculations) ; a second module (does not integrate into a practical application because generic computer performing generic computer functions; not significantly more because generic computer performing generic computer functions) configured to determine a domain of the first transformation data (abstract; mathematical concepts; mathematical calculations) ; and a third module (does not integrate into a practical application because generic computer performing generic computer functions; not significantly more because generic computer performing generic computer functions) configured to determine a label of the first transformation data (abstract; mental processes; observation, evaluation, judgment, or opinion) . 2. The domain adaptation device of claim 1, wherein the first module is configured to: calculate a first difference, which is a difference between manifold of the first transformation data and manifold of comparison data (abstract; mathematical concepts; mathematical calculations) , and a second difference, which is a difference between distribution of the first transformation data and distribution of the comparison data (abstract; mathematical concepts; mathematical calculations) ; and modify the projection matrix such that the first difference and the second difference decrease (abstract; mathematical concepts; mathematical calculations) . 3. The domain adaptation device of claim 2, wherein the graph matrix is a matrix in which a weight matrix is normalized, and wherein the weight matrix is set based on a graph of a covariance matrix of the comparison data (abstract; mathematical concepts; mathematical relationships) . 4. The domain adaptation device of claim 2, wherein the second difference is calculated using the Kullback-Leibler divergence function (abstract; mathematical concepts; mathematical calculations) . 5. The domain adaptation device of claim 1, wherein the first transformation data is calculated by the following equation: Z t = X t P G where Z t : first transformation data, X t : input data, and P : projection matrix, and G : graph matrix (abstract; mathematical concepts; mathematical calculations) . 6. The domain adaptation device of claim 2, wherein the second difference is calculated by the following equation: where K ( P ): second difference, KL : Kullback-Leibler divergence function, P t : probability for a mean of the first transformation data, and P T : probability for the comparison data (abstract; mathematical concepts; mathematical calculations) . 7. The domain adaptation device of claim 1, wherein the second module is configured to: determine the domain of the first transformation data based on a predetermined equation (abstract; mathematical concepts; mathematical calculations) ; and output a first value (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) when the domain of the first transformation data is determined to be a first time point (abstract; mental processes; observation, evaluation, judgment, or opinion) , and a second value (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) when the domain of the first transformation data is determined to be a second time point (abstract; mental processes; observation, evaluation, judgment, or opinion) . 8. The domain adaptation device of claim 7, wherein the predetermined equation is as follows: where Y d : domain discrimination function, Z: first transformation data set, and θ d : discrimination parameter (abstract; mathematical concepts; mathematical calculations) . 9. The domain adaptation device of claim 8, wherein the discrimination parameter is optimized by minimizing a binary cross-entropy loss function between the domain discrimination function and a domain label set (abstract; mathematical concepts; mathematical calculations) . 10. The domain adaptation device of claim 9, wherein the binary cross-entropy loss function is calculated by the following equation: Where D( P , θ d ): binary cross-entropy loss function, Y d : domain discrimination function, and Y d : domain label set (abstract; mathematical concepts; mathematical calculations) . 11. The domain adaptation device of claim 1, wherein the first module is configured to generate second transformation data to which comparison data has been transformed using the projection matrix and the graph matrix (abstract; mathematical concepts; mathematical calculations) , and wherein the third module is configured to determine a class and regression of the comparison data based on the second transformation data (abstract; mathematical concepts; mathematical calculations) . 12. The domain adaptation device of claim 11, wherein the third module is configured to determine the class and regression of the comparison data using the following equation. Y l = softmax( Z T θ l ) where Y l : label prediction function, Z T : second transformation data, and θ l : label parameter (abstract; mathematical concepts; mathematical calculations) . 13. The domain adaptation device of claim 12, wherein the label parameter is optimized by minimizing the cross-entropy loss function between the label prediction function and a set of correct labels (abstract; mathematical concepts; mathematical calculations) . 14. The domain adaptation device of claim 13, wherein the cross-entropy loss function is calculated by the following equation (abstract; mathematical concepts; mathematical calculations) . where L( P , θ l ): cross-entropy loss label prediction function, and Y l : label prediction function, and Y l : a set of correct labels (abstract; mathematical concepts; mathematical calculations) . Regarding claim 15, see the foregoing rejections of claims 1 and 2 for all limitations. 16. A computer program stored in a computer-readable recording medium for executing the domain adaptation method for longitudinal data of claim 15 (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) . Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ahmed et al. (2021/0030289) . Regarding claim 1, Ahmed et al. disclose a domain adaptation device for longitudinal data (PPG signals; see paragraphs 4 and 17) comprising: a first module (a hardware item or set of software instructions to implement this respective stage; see paragraphs 62-63 and 65) configured to generate first transformation data (matrix Y; see paragraph 31) using a projection matrix for domain transformation (W; see paragraph 31) and a graph matrix for data filtering (covariance matrix C H ; see paragraph 32) ; a second module (a hardware item or set of software instructions to implement this respective stage; see paragraphs 62-63 and 65) configured to determine a domain (subspace; see paragraphs 35-36) of the first transformation data; and a third module (a hardware item or set of software instructions to implement this respective stage; see paragraphs 62-63 and 65) configured to determine a label of the first transformation data (identifying components that are not noise; see paragraphs 37-52) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT GEOFFREY T EVANS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2369 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F, 9 AM - 5:30 PM . 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, FILLIN "SPE Name?" \* MERGEFORMAT Walter Lindsay can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-1674 . 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. /WALTER L LINDSAY JR/ Supervisory Patent Examiner, Art Unit 2852 /GEOFFREY T EVANS/ Examiner, Art Unit 2852
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Prosecution Timeline

Dec 28, 2023
Application Filed
Mar 21, 2026
Non-Final Rejection — §101, §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
85%
Grant Probability
94%
With Interview (+9.0%)
2y 10m
Median Time to Grant
Low
PTA Risk
Based on 793 resolved cases by this examiner. Grant probability derived from career allow rate.

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