Office Action Predictor
Last updated: April 16, 2026
Application No. 18/613,245

COMPUTER-IMPLEMENTED METHOD AND DEVICE FOR EVALUATING A DATASET BASED ON A RANGE OF UNCERTAINTY

Non-Final OA §112
Filed
Mar 22, 2024
Examiner
SHARPLESS, SAMUEL
Art Unit
2165
Tech Center
2100 — Computer Architecture & Software
Assignee
Siemens Mobility GMBH
OA Round
3 (Non-Final)
80%
Grant Probability
Favorable
3-4
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 80% — above average
80%
Career Allow Rate
99 granted / 123 resolved
+25.5% vs TC avg
Strong +25% interview lift
Without
With
+24.6%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
29 currently pending
Career history
152
Total Applications
across all art units

Statute-Specific Performance

§101
14.0%
-26.0% vs TC avg
§103
52.0%
+12.0% vs TC avg
§102
21.0%
-19.0% vs TC avg
§112
7.1%
-32.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 123 resolved cases

Office Action

§112
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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/2025 has been entered. Response to Amendment The amendment filed 11/11/2025 has been entered. Applicant has amended claim 1. No claims have been cancelled or added. Claims 1-20 are currently pending. Response to Arguments Applicant’s arguments, see pages, filed 11/11/2025, with respect to claims 1-20 have been fully considered and are persuasive. The 35 U.S.C 102 and 103 rejection of claims 1-20 has been withdrawn. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites multiple limitations with the word “can”. A computer-implemented method comprising: performing a physical process and acquiring data from the physical process; creating a dataset based on the physical process; evaluating a probability of a misclassification of the dataset by the following steps: providing the dataset, which includes :a plurality of first data points, which can be assigned to a first label, wherein the first data points at least partially form at least one first local cluster area; and a plurality of second data points, which can be assigned to a second label, wherein the second data points at least partially form at least one second local cluster area; and wherein the first data points and the second data points are separable; establishing a range of uncertainty between the first local cluster area of the first data points and the second local cluster area of the second data points; and evaluating the dataset based on the range of uncertainty by determining a limit range of the range of uncertainty based on a stochastic distribution; and executing the step of evaluating a probability of a misclassification of the dataset when the following assumptions hold true for the dataset or a transformation of the dataset was successful such that the following assumptions hold true: the data points are separated in the m-dimensional space by (m-1) dimensional sets of hyperareas or hyperplanes, the hyperarea and/or the limit area of the hyperarea can be defined or measured and is finite, the data points that can be assigned to a label cluster or form a local cluster area, and in the vicinity of the limit range of the hyperarea, the distribution of the data points is locally homogeneous. These limitations recite a capability and do not positively define the scope of the invention. The claim defines some of the limitations holding true, but it is unclear how a function that “may” happen to always to occur. For the above reasons, Examiner recommends to amend claim 1 to positively recite the claim limitations to overcome the current rejection. Claims 2-20 inherit the deficiency, therefore are rejected as well. Allowable Subject Matter Claim 1-20 are allowable once the above 35 U.S.C. 112(b) rejection has been overcome. The following is a statement of reasons for the indication of allowable subject matter: Braband generally teaches many models of artificial intelligence, in particular machine learning, are statistical models. Safety assessment would then have to concentrate on the model that is used in AI, besides the normal assessment procedure. Part of the budget of dangerous random failures for the relevant safety integrity level needs to be used for the probabilistic faulty behavior of the Al system. Tan generally teaches acquiring a multidimensional information data set to be detected of urban rail transit, and matching the multidimensional information data set to be detected with a plurality of preset passenger flow modes to obtain a passenger flow mode corresponding to the multidimensional information data set to be detected; inputting the multidimensional information data set to be detected into an urban rail transit passenger flow early warning model corresponding to a passenger flow mode, and outputting predicted urban rail transit passenger flow corresponding to the multidimensional information data set to be detected through the urban rail transit passenger flow early warning model; the urban rail transit passenger flow early warning model adopts an optimized least square support vector machine based on an immune genetic algorithm; and determining the early warning level of the urban rail according to the predicted urban rail traffic passenger flow. The method is based on the immune genetic algorithm to optimize the least square support vector machine algorithm to generate the model, and can accurately predict the urban rail transit passenger flow by applying the model, thereby avoiding the generation of the peak passenger flow treading risk. The cited prior art when considered individually or in combination does not disclose the claimed invention. An updated prior art search was conducted and no prior anticipates or obviously teaches the claimed invention as recited in the dependent claims. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL SHARPLESS whose telephone number is (571)272-1521. The examiner can normally be reached M-F 7:30 AM- 3:30 PM (ET). 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, ALEKSANDR KERZHNER can be reached at 571-270-1760. 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. /S.C.S./Examiner, Art Unit 2165 /ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
Read full office action

Prosecution Timeline

Mar 22, 2024
Application Filed
Feb 22, 2025
Non-Final Rejection — §112
Jul 16, 2025
Response Filed
Oct 24, 2025
Final Rejection — §112
Nov 11, 2025
Response after Non-Final Action
Dec 12, 2025
Request for Continued Examination
Dec 21, 2025
Response after Non-Final Action
Jan 24, 2026
Non-Final Rejection — §112
Mar 13, 2026
Response Filed

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12585614
PREDICTING OUTAGE CONDITIONS AND HANDLING ARCHIVING
2y 5m to grant Granted Mar 24, 2026
Patent 12561321
MATERIALIZED VIEW GENERATION AND PROVISION BASED ON QUERIES HAVING A SEMANTICALLY EQUIVALENT OR CONTAINMENT RELATIONSHIP
2y 5m to grant Granted Feb 24, 2026
Patent 12554717
DYNAMICALLY SUBSTITUTING A MODIFIED QUERY BASED ON PERFORMANCE ANALYSIS
2y 5m to grant Granted Feb 17, 2026
Patent 12547609
SYSTEMS AND METHODS FOR STREAMING DATA PIPELINES
2y 5m to grant Granted Feb 10, 2026
Patent 12536140
ADAPTIVE AGGREGATION AND COMPRESSION OF METADATA
2y 5m to grant Granted Jan 27, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

3-4
Expected OA Rounds
80%
Grant Probability
99%
With Interview (+24.6%)
2y 11m
Median Time to Grant
High
PTA Risk
Based on 123 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in for Full Analysis

Enter your email to receive a magic link. No password needed.

Free tier: 3 strategy analyses per month