Prosecution Insights
Last updated: May 29, 2026
Application No. 18/098,224

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND PROGRAM FOR DEFECT DATA EXAMINATION

Final Rejection §103
Filed
Jan 18, 2023
Priority
Jan 27, 2022 — JP 2022-011027
Examiner
MUHEBBULLAH, SAJEDA
Art Unit
2174
Tech Center
2100 — Computer Architecture & Software
Assignee
Toyota Jidosha Kabushiki Kaisha
OA Round
4 (Final)
30%
Grant Probability
At Risk
5-6
OA Rounds
1y 4m
Est. Remaining
64%
With Interview

Examiner Intelligence

Grants only 30% of cases
30%
Career Allowance Rate
76 granted / 256 resolved
-25.3% vs TC avg
Strong +35% interview lift
Without
With
+34.8%
Interview Lift
resolved cases with interview
Typical timeline
4y 9m
Avg Prosecution
17 currently pending
Career history
287
Total Applications
across all art units

Statute-Specific Performance

§101
0.4%
-39.6% vs TC avg
§103
93.2%
+53.2% vs TC avg
§102
6.3%
-33.7% vs TC avg
§112
0.1%
-39.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 256 resolved cases

Office Action

§103
DETAILED ACTION This communication is responsive to Amendment filed 02/19/2026. Claims 1, 3 and 6-7 are pending in this application. In the Amendment, claims 1 and 6-7 are amended and claims 2, 4 and 5 are cancelled. This action is made Final. 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 . Response to Arguments Applicant's arguments filed 02/19/2026 have been fully considered but they are not persuasive. Applicant argued Tarnowski does not teach to present a result of the classification obtained and highlight the plurality of defect data pieces with a large difference from the representative data for each group; and arrange the plurality of defect data pieces within each group in descending order of an ambient error, as further recited in amended independent claims 1, 6, and 7. The Examiner respectfully disagrees in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The system of Bagschik, Kim, Sun and Nister teaches to arrange the plurality of defect data pieces within each group in descending order of an ambient error (Bagschik, para.55, ordered list according to success; Sun, para.63, 75, rank unsuccessful simulation scenarios based on class) while as recited in the previously rejected claim 4, Tarnowski teaches visualizing defects that presents a result of the classification and highlights the defect data piece with a large difference from the representative data for each group (Tarnowski, para.65-68, Fig.6, , cluster view displayed with representative image 92A-H). 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. Claims 1, 3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Bagschik et al. (“Bagschik”, US 2021/0094540) in view of Kim et al. (“Kim”, US 2019/0189013) in view of Sun et al. (“Sun”, US 2023/0229826) in view of Nister et al. (“Nister”, US 2019/0250622) and further in view of Tarnowski et al. (“Tarnowski”, US 2013/0322733). As per claim 1, Bagschik teaches an information processing system comprising: a processor (Bagschik, para.105; Fig.7, processor 730 ) configured to: acquire a plurality of simulation data pieces corresponding to a plurality of simulation conditions, respectively, or a plurality of experimental data pieces corresponding to a plurality of experimental conditions, respectively (Bagschik, para.12, 24, 28-29, 35, 51, Fig.1, input data 102, sensor data, parameter data are used to generate simulation conditions), and at least one simulation condition of the plurality of simulation conditions is user-defined and includes a departure point and time of each of a plurality of mobile bodies (Bagschik, para.34, scenarios may be user-defined), the plurality of mobile bodies being configured to pass each other in a T-shaped passage curve (Bagschik, para.45, 48, region data 118 includes segments: T junction; scenarios include lane change, etc); extract a plurality of defect data pieces from the plurality of simulation data pieces or the plurality of experimental data pieces (Bagschik, para.14, 20, 50-51, 54, 75, unsuccessful/successful performance based on simulation data); and classify the plurality of defect data pieces into a plurality of groups based on feature data calculated from each of the plurality of defect data pieces (Bagschik, para.15, 55, 60, 71, 76, clustering scenarios, analysis component determine degree of failure). acquire the simulation data pieces or the experimental data pieces related to movements of the plurality of mobile bodies (Bagschik, para.12, 24, 28-29, 35, 51, Fig.1, input data 102, sensor data, parameter data include object positions/velocities); extract the plurality of defect data pieces originating from a degree of freedom of the movement of each of the plurality of mobile bodies (Bagschik, para.14, 20, 50-51, 54, 75, object errors); acquire the plurality of simulation data pieces by conducting a search-based test for a purpose of searching for data that violates a system requirement (Bagschik, para.61-62, filter data), the system requirement including an error of position estimation for each autonomous mobile body of the plurality of mobile bodies to be less than or equal to a threshold (Bagschik, para.21, 55, parameter threshold); and extract the plurality of defect data pieces that violate the system requirement (Bagschik, para.53-54, 60, 79, performance data indicates rules violated). Although Bagschik teaches the visualization of data (Bagschik, para.73-77, error model), Bagschik does not teach to visualize representative data extracted from each group and present the visualized representative data to a user including displaying: i) an identifier corresponding to identification of the plurality of simulation data pieces, and a number that is displayed adjacent to the shape of the T-shaped passage curve; and ii) a graph showing a trajectory of each of the plurality of mobile bodies relative to the T-shaped passage curve, the graph including a shape of the T-shaped passage curve. Kim teaches a system of simulation configured to visualize data extracted from each path group and present the visualized representative data to a user (Kim, Figs.8,12, visualization paths; p.102, 158-159) including displaying: an identifier corresponding to identification of the plurality of simulation data pieces, and a number that is displayed adjacent to the shape of the passage curve (Kim, Figs.8,12, Path 1-6 numbers adjacent to curve) and a graph showing a trajectory (Kim, Fig.12, Path 1-6 visualization shows trajectories). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include Kim‘s teaching with Bagschik’s system in order to visually distinguish errors. Additionally, the system of Bagschik and Kim does not teach a graph showing a trajectory of each of the plurality of mobile bodies relative to the T-shaped passage curve, the graph including a shape of the T-shaped passage curve. Sun teaches a system of visualizing vehicle simulations wherein a graph showing a trajectory of each of the plurality of mobile bodies relative to the T-shaped passage curve, the graph including a shape of the T-shaped passage curve (Sun, Fig.5-6, 8; para.55, 58-59, 70-72, graphic illustrations of simulation scenarios including T-shaped intersections). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include Sun‘s teaching with the system of Bagschik and Kim in order to visualize the vehicles within the environment. Furthermore, the system of Bagschik, Kim and Sun does not teach to regard, as success data, the simulation data pieces in which a predetermined mobile body from the plurality of mobile bodies reaches its destination point by a target time. Nister teaches a vehicle simulation system to regard, as success data, the simulation data pieces in which a predetermined mobile body from the plurality of mobile bodies reaches its destination point by a target time (Nister 105, 106-107, 112, 115, test failed if arrival time not safe). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include Nister‘s teaching with the system of Bagschik, Kim and Sun in order to distinguish the success parameters. Although the system of Bagschik, Kim, Sun and Nister teaches to arrange the plurality of defect data pieces within each group in descending order of an ambient error (Bagschik, para.55, ordered list according to success; Sun, para.63, 75, rank unsuccessful simulation scenarios based on class), the system of Bagschik, Kim, Sun and Nister does not teach to present a result of the classification obtained and highlight the plurality of defect data pieces with a large difference from the representative data for each group. Tarnowski teaches a system of visualizing defects that presents a result of the classification and highlights the defect data piece with a large difference from the representative data for each group (Tarnowski, para.65-68, Fig.6, , cluster view displayed with representative image 92A-H). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include Tarnowski‘s teaching with the system of Bagschik, Kim, Sun and Nister in order to condense the groups displayed. As per claim 3, the system of Bagschik, Kim, Sun, Nister and Tarnowski teaches the information processing system according to claim 1, wherein the processor is further configured to: extract position coordinates of each of the plurality of mobile bodies at each of a plurality of time points from each of the defect data pieces and calculate the feature data by arranging the extracted position coordinates (Bagschik, para.27, 67, coordinate data). Claims 6-7 are similar in scope to claim 1, and are therefore rejected under similar rationale. Conclusion 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. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gao et al. (US 7,359,544) teaches a method of grouping defects with a representative set. Iwayama et al. (US 2007/0288442) teaches a method of clustering documents and ranking in descending order within each cluster. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAJEDA MUHEBBULLAH whose telephone number is (571)272-4065. The examiner can normally be reached Mon-Tue/Thur-Fri 10am-8pm. 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, William L Bashore can be reached on 571-272-4088. 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.M./ Sajeda MuhebbullahExaminer, Art Unit 2174 /WILLIAM L BASHORE/ Supervisory Patent Examiner, Art Unit 2174
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Prosecution Timeline

Show 3 earlier events
May 06, 2025
Examiner Interview Summary
May 12, 2025
Response Filed
Aug 28, 2025
Final Rejection mailed — §103
Nov 26, 2025
Request for Continued Examination
Nov 28, 2025
Response after Non-Final Action
Dec 03, 2025
Non-Final Rejection mailed — §103
Feb 19, 2026
Response Filed
Apr 01, 2026
Final Rejection mailed — §103 (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

5-6
Expected OA Rounds
30%
Grant Probability
64%
With Interview (+34.8%)
4y 9m (~1y 4m remaining)
Median Time to Grant
High
PTA Risk
Based on 256 resolved cases by this examiner. Grant probability derived from career allowance rate.

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