Prosecution Insights
Last updated: April 19, 2026
Application No. 18/488,270

OPERATION MANAGEMENT APPARATUS

Final Rejection §103
Filed
Oct 17, 2023
Examiner
KOESTER, MICHAEL RICHARD
Art Unit
3624
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Toyota Jidosha Kabushiki Kaisha
OA Round
2 (Final)
40%
Grant Probability
Moderate
3-4
OA Rounds
3y 6m
To Grant
67%
With Interview

Examiner Intelligence

Grants 40% of resolved cases
40%
Career Allow Rate
73 granted / 181 resolved
-11.7% vs TC avg
Strong +26% interview lift
Without
With
+26.4%
Interview Lift
resolved cases with interview
Typical timeline
3y 6m
Avg Prosecution
32 currently pending
Career history
213
Total Applications
across all art units

Statute-Specific Performance

§101
39.8%
-0.2% vs TC avg
§103
42.8%
+2.8% vs TC avg
§102
8.0%
-32.0% vs TC avg
§112
9.5%
-30.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 181 resolved cases

Office Action

§103
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 . Introduction The following is a final Office action in response to Applicant’s submission filed on 10/6/2025. Currently claims 1-5 are pending and claim 1 is independent. Claims 1, 3 have been amended from the previous claim set dated 10/17/2023. No claims have been added or cancelled. Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-167973, filed on 10/19/2022. Response to Amendments Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action. In light of Applicant’s amendments, the 35 USC §112(b) rejection of claim 3 is withdrawn. Additionally, and also in light of Applicant’s amendments, the 35 U.S.C. §101 rejections are withdrawn. Specifically, the inclusion of the limitation “control the target vehicle to drive autonomously until driving is switched from automated driving to manual driving” demonstrates a controlling feature and overcomes the 101 rejection within the Step 2A (Prong 1) analysis. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Zanghi et al. (US 10423934 B1) in view of Yu et al. (KR 20230092059 A) Regarding claim 1 (Amended), Zanghi discloses an operation management apparatus (Zanghi ABS - Systems, methods, and apparatuses described herein are directed to automated vehicle diagnostics and maintenance) that operates one or more vehicles by automated driving according to a schedule ( Zanghi COL 3 ROW 4 - Vehicles to be serviced can include autonomous vehicles in a fleet of autonomous vehicles providing transportation services to users), the operation management apparatus comprising a controller configured to:; identify, as a target vehicle, a vehicle for which time until the predicted time point is less than a threshold value (Zanghi COL 4 ROW 24 - The operation 102 can include capturing braking data over time (e.g., hours, days, weeks, months, etc.) and analyzing the data to determine changes in braking performance. If a braking performance is below a threshold value (or if a braking distance is above a threshold distance for a particular set of vehicle conditions), the operation 102 can determine that a potential service issue can be addressed for the vehicle); acquire vehicle data and/or operation record data, the vehicle data being obtained by monitoring a condition of the target vehicle, the operation record data being obtained by monitoring travel of the target vehicle; determine, based on the acquired vehicle data or the acquired operation record data, one or more types of abnormality having occurred in the target vehicle (Zanghi COL 10 ROW 44 - For example, the operation 302 can include receiving raw sensor data from an autonomous vehicle or can include receiving a determination from the autonomous vehicle of a service issue. In some instances, the autonomous vehicle can automatically determine a service issue and provide the indication as an error code corresponding to the service issue); designate, from among a plurality of workers according to the determined types of abnormality, a first worker who is to perform maintenance work on the target vehicle at a site near the target vehicle (Zanghi COL 8 ROW 61 - The technician selection module 212 can include functionality to select one or more technicians to perform vehicle servicing based a variety of factors, such as the potential service issue(s), skill level of the technician, etc… Factors on which the selection of a technician can be based include, but are not limited to: technician skill level or qualifications; technician work experience, certifications, audits of the maintenance or repairs, etc.;… technician demand (e.g., other servicing jobs to be performed, time since last job, etc.); and notify the first worker of information that prompts the first worker to take care of the abnormality (Zanghi COL 6 ROW 27 - At operation 152, the process can include dispatching the technician and/or the vehicle to the location) and control the target vehicle to drive autonomously until driving is switched from automated driving to manual driving (Zanghi COL 3 ROW 59 - For example, a vehicle 110 can be an autonomous vehicle that receives instructions from a planner system of the vehicle 110 to traverse an intended path 112 to navigate to a destination). Zanghi does not clearly disclose predict, for each vehicle based on operation plan data corresponding to the schedule, a time point at which driving is switched from automated driving to manual driving based on a time that the vehicle is to return to a garage. Yu, from the same field of endeavor, teaches predict, for each vehicle based on operation plan data corresponding to the schedule, a time point at which driving is switched from automated driving to manual driving based on a time that the vehicle is to return to a garage (Yu - Specifically, the autonomous driving state may be switched to manual driving by the system in one of the following three cases. For example…if it is scheduled to approach the boundary of the ODD and finally leave the ODD (i.e. schedule to go to a garage}). It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the vehicle maintenance methodology/system of Zanghi by including the autonomous vehicle techniques of Yu because Yu discloses “According to the present invention, it is possible to increase stability in case of an emergency in the autonomous driving system and provide an optimized response method by suggesting a stable mode transition method for the autonomous driving system (Yu ABS)”. Additionally, Zanghi further details “Vehicles to be serviced can include autonomous vehicles in a fleet of autonomous vehicles (Zanghi COL 3 ROW 4)” so it would be obvious to consider including the additional autonomous vehicle techniques that Yu discloses because it would optimize the driving of the autonomous fleet within Zanghi. Regarding claim 2, Zanghi in view of Yu discloses further acquire job classification data indicating, on a worker-by- worker basis, job classifications of the plurality of workers in association with types of abnormality (Zanghi COL 14 ROW 34 - At operation 602, the process can include determining a skill level associated with a technician. In some instances, a skill level can be based at least in part on a type of servicing to be performed (e.g., to distinguish between cleaning of a vehicle and repair, etc.). In some instances, the skill level can be based in part on previous work experience, certifications, audits of maintenance or repairs, number of servicing tasks completed, etc); and designate, as the first worker based on the acquired job classification data, a worker who has job classifications capable of taking care of all the determined types of abnormality or a worker who has job classifications capable of taking care of a greatest number of types of abnormality among the determined types of abnormality (Zanghi COL 14 ROW 42 - At operation 604, the process can include assigning a servicing task to the technician based at least in part on the skill level. For example, in one implementation where servicing tasks are rated in complexity from one to ten, with ten being the most complex task, a skill level of a technician can be associated with the complexity of tasks available to be performed by the technician. Continuing with this example, if a servicing task has a complexity level of five, and a technician has a skill level of two, the servicing task may not be assigned to the technician. If the technician has a skill level of at least five, the servicing task may be assigned to the technician). Regarding claim 3, Zanghi in view of Yu discloses in a case in which the worker who has the job classifications capable of taking care of all the determined types of abnormality cannot be designated as the first worker, the controller is configured to further designate, in addition to the first worker, a second worker who has a job classification capable of taking care of a type of abnormality that is not associated with the job classifications of the first worker, among the determined types of abnormality (Zanghi COL 8 ROW 61 - The technician selection module 212 can include functionality to select one or more technicians to perform vehicle servicing based a variety of factors, such as the potential service issue(s), skill level of the technician, etc… Factors on which the selection of a technician can be based include, but are not limited to: technician skill level or qualifications; technician work experience, certifications, audits of the maintenance or repairs, etc.;… technician demand (e.g., other servicing jobs to be performed, time since last job, etc.) {i.e. unavailable/ can’t be designated}). Regarding claim 4, Zanghi in view of Yu discloses determine, based on the vehicle data, one or more items obtained using an automatic inspection algorithm, as the types of abnormality (Zanghi COL 10 ROW 44 - For example, the operation 302 can include receiving raw sensor data from an autonomous vehicle or can include receiving a determination from the autonomous vehicle of a service issue. In some instances, the autonomous vehicle can automatically determine a service issue and provide the indication as an error code corresponding to the service issue). Regarding claim 5, Zanghi in view of Yu discloses further acquire task management data indicating operation states of the plurality of workers in chronological order; and select, based on the operation states of the plurality of workers as indicated by the acquired task management data at the time point, a candidate worker to designate the first worker ((Zanghi COL 8 ROW 61 - The technician selection module 212 can include functionality to select one or more technicians to perform vehicle servicing based a variety of factors, such as... technician demand (e.g., other servicing jobs to be performed, time since last job, etc.)). Response to Arguments Applicant's arguments filed 10/6/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above. Examiner will first note that the drawings, filed 10/17/2023 are accepted. In light of Applicant’s amendments, the 35 USC §112(b) rejection of claim 3 is withdrawn. Additionally, and also in light of Applicant’s amendments, the 35 U.S.C. §101 rejections are withdrawn. Specifically, the inclusion of the limitation “control the target vehicle to drive autonomously until driving is switched from automated driving to manual driving” demonstrates a controlling feature and overcomes the 101 rejection within the Step 2A (Prong 1) analysis. Regarding the 35 USC § 102 rejections on the previous Office action, Applicant amended the independent claims to further limit the claims with respect to switching driving modes and autonomously driving a vehicle. In light of this amendment, Examiner agrees that the original references did not clearly teach all these limitations, however the amendment necessitated further search and consideration. As a result of this further search and consideration, the previously cited prior art was found to teach autonomously driving the vehicle and newly found prior art teaches switching modes (Yu as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive. Conclusion THIS ACTION IS MADE FINAL. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM 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, Jerry O'Connor can be reached at (571) 272-6787. 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. /MICHAEL R KOESTER/Examiner, Art Unit 3624 /Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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Prosecution Timeline

Oct 17, 2023
Application Filed
Jul 12, 2025
Non-Final Rejection — §103
Sep 30, 2025
Applicant Interview (Telephonic)
Sep 30, 2025
Examiner Interview Summary
Oct 06, 2025
Response Filed
Nov 15, 2025
Final Rejection — §103
Jan 22, 2026
Applicant Interview (Telephonic)
Jan 22, 2026
Examiner Interview Summary

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

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Prosecution Projections

3-4
Expected OA Rounds
40%
Grant Probability
67%
With Interview (+26.4%)
3y 6m
Median Time to Grant
Moderate
PTA Risk
Based on 181 resolved cases by this examiner. Grant probability derived from career allow rate.

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