Prosecution Insights
Last updated: April 19, 2026
Application No. 18/665,123

SYSTEM AND METHOD FOR IMPLEMENTING DIFFERENTIALLY PRIVATE PRINCIPAL COMPONENT ANALYSIS (PCA) FOR VERTICALLY PARTITIONED DATA

Non-Final OA §101§102
Filed
May 15, 2024
Examiner
PERUNGAVOOR, VENKATANARAY
Art Unit
2492
Tech Center
2400 — Computer Networks
Assignee
Shopee Ip Singpore Private Limited
OA Round
1 (Non-Final)
88%
Grant Probability
Favorable
1-2
OA Rounds
2y 10m
To Grant
91%
With Interview

Examiner Intelligence

Grants 88% — above average
88%
Career Allow Rate
877 granted / 999 resolved
+29.8% vs TC avg
Minimal +4% lift
Without
With
+3.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
43 currently pending
Career history
1042
Total Applications
across all art units

Statute-Specific Performance

§101
13.6%
-26.4% vs TC avg
§103
43.7%
+3.7% vs TC avg
§102
16.7%
-23.3% vs TC avg
§112
11.9%
-28.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 999 resolved cases

Office Action

§101 §102
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 This office action is in response to the application filed on or reply to the remarks of 5/15/2024. The instant application has claims 1-20 pending. The system, method and medium for differentially private principal component analysis. There a total of 20 claims. Allowable Subject Matter Claims 4-6, 13-15 are 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. The specific protocol BGW in combination with rest of limitations is not found in prior art. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Drawings The drawing filed on 5/15/2024 has been accepted and in compliance of 37 CFR 1.83 & 37 CFR 1.84. Specification The disclosure filed on 5/15/2024 is accepted. 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 an abstract idea without significantly more. The steps can be performed by an human on an generic computer. The claim recites column vector being discretized and adding noise that is gotten from two clients. Further the mathematical calculations and steps shown in Specifications Par. 0061-0114. That is, the recited steps are merely an mathematical algorithm. The limitation of “wherein the first client is configured to discretize the first column vector to obtain a first discretized column vector and the second client is configured to discretize the second column vector to obtain a second discretized column vector”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “wherein the first client is configured to discretize the first column vector to obtain a first discretized column vector and the second client is configured to discretize the second column vector to obtain a second discretized column vector” in the context of this claim encompasses the user manually discretizing the column vector. Similarly, the limitation of “wherein the first client is configured to introduce a first noise to the first discretized column vector and the second client is configured to introduce a second noise to the second discretized column vector to obtain a PCA result” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation done in the mind but for the recitation of generic computer components the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) Federal Register January 7, 2019. For example, but for the “by a processor” language, “wherein the first client is configured to introduce a first noise to the first discretized column vector and the second client is configured to introduce a second noise to the second discretized column vector to obtain a PCA result.” in the context of this claim encompasses the user adding manually noise to the vector. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” & “Mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform both the “wherein the first client is configured to discretize the first column vector to obtain a first discretized column vector and the second client is configured to discretize the second column vector to obtain a second discretized column vector” and “wherein the first client is configured to introduce a first noise to the first discretized column vector and the second client is configured to introduce a second noise to the second discretized column vector to obtain a PCA result.” steps. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of discretizing the column data and adding noise data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform discretizing the column data and adding noise data steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. 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) 1-3,7-12, 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy to Bao. Regarding claim 1, 10, 19, Bao discloses A computer system for implementing differentially private principal component analysis (PCA) for vertically partitioned data comprising: a first client that has a first column vector(Algorithm 1 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism, data is discretized and noise is added) ; and a second client that has a second column vector; wherein the first client is configured to discretize the first column vector to obtain a first discretized column vector and the second client is configured to discretize the second column vector to obtain a second discretized column vector(Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism); and PNG media_image1.png 14 4 media_image1.png Greyscale wherein the first client is configured to introduce a first noise to the first discretized column vector and the second client is configured to introduce a second noise to the second discretized column vector to obtain a PCA result(Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). Regarding claim 2,11, 20, Bao discloses The computer system of claim 1, wherein the PCA result is generated based on a combination of the first discretized column vector with the first noise and the second discretized column vector with the second noise(Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). . Regarding claim 3, 12, Bao discloses The computer system of claim 1, wherein at least one of the first client and the second client is configured to send the PCA result to a server for further processing using a secret sharing protocol(Algorithm 1 & Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). Regarding claim 7, Bao discloses The computer system of claim 1, wherein the first client is configured to introduce the first noise using a predetermined noise parameter and the second client is configured to introduce the second noise using the predetermined noise parameter(Algorithm 1 & Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). . Regarding claim 8, 17, Bao discloses The computer system of claim 1, wherein the first noise and the second noise are Skellam noises(Algorithm 1 & Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). . Regarding claim 9, 18, Bao discloses The computer system of claim 1, wherein the first client is configured to discretize the first column vector and the second client is configured to discretize the second column vector using a predetermined discretization algorithm to reduce a size of the first column vector and the second column vector(Algorithm 1 & Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). . Regarding claim 16. Bao discloses The computer implemented method of claim 10, wherein the first client is configured to introduce the first noise and the second client is configured to introduce the second noise using a predetermined noise parameter. (Algorithm 1 & Algorithm 2 & Abstract & 3.3 Federate Learning with Distributed Skellam Mechanism). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Venkat Perungavoor whose telephone number is (571)272-7213. The examiner can normally be reached 9-5. 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, Rupal Dharia can be reached on 571-272-3880. 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. /VENKAT PERUNGAVOOR/Primary Examiner, Art Unit 2492 Email: venkatanarayan.perungavoor@uspto.gov
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Prosecution Timeline

May 15, 2024
Application Filed
Jan 12, 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
88%
Grant Probability
91%
With Interview (+3.5%)
2y 10m
Median Time to Grant
Low
PTA Risk
Based on 999 resolved cases by this examiner. Grant probability derived from career allow rate.

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