Prosecution Insights
Last updated: April 19, 2026
Application No. 17/823,206

Crowd-Sourced Continuous Update Data Collection for Automotive Applications

Final Rejection §103
Filed
Aug 30, 2022
Examiner
KENDALL, CHUCK O
Art Unit
2192
Tech Center
2100 — Computer Architecture & Software
Assignee
Aptiv Technologies AG
OA Round
4 (Final)
87%
Grant Probability
Favorable
5-6
OA Rounds
3y 1m
To Grant
95%
With Interview

Examiner Intelligence

Grants 87% — above average
87%
Career Allow Rate
796 granted / 914 resolved
+32.1% vs TC avg
Moderate +8% lift
Without
With
+7.7%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
24 currently pending
Career history
938
Total Applications
across all art units

Statute-Specific Performance

§101
11.9%
-28.1% vs TC avg
§103
22.8%
-17.2% vs TC avg
§102
52.3%
+12.3% vs TC avg
§112
3.3%
-36.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 914 resolved cases

Office Action

§103
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 . This is in response to RCE Application filed 10/09/25. Claims 1 – 20 has been amended and is pending. 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. Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Fang et al. US 20230161583 A1 in view of Moustafa et al. 20220126864. Regarding claims 1, 19 and 20, Fang discloses system of a host vehicle comprising: a logging system operatively connected to one or more applications of the host vehicle and configured to log application metadata or sensor data associated with the one or more applications [0164, see metadata]; a data query daemon operatively connected to the logging system and configured to: receive a data query from a remote data query system, the data query comprising a request for training data satisfying one or more conditions and being directed to a vehicle fleet that includes the host vehicle [0203, see query vehicle]; the one or more conditions including at least one of a requested driving scenario, a requested location, or a requested source application; detect the requested responsive training data of the data query in the sensor data being logged by the logging system, the responsive training data satisfying the one or more conditions by having at least one of a driving scenario of the host vehicle that corresponds to the requested driving scenario, a location of the host vehicle that corresponds to the requested location, or a source application from the one or more applications of the host vehicle that corresponds to the requested source application [0014, see sensors of vehicle also regarding at least one of a driving scenario see 0117 shows several driving modes “saying at least one of only requires one of the limitations to be taught”]; extract and store the requested responsive training data in memory; and transmit the-requested responsive training data to the remote data query system to respond to the data query system to respond to the data query [0171, see extract relevant data]. Fang doesn’t expressly disclose the data query comprising a request for training data used to train a machine learning model. However, Moustafa in an analogous art and similar configuration discloses wherein querying in a machine learning model, images and data stored which is used to train one or more models [0451 – 0452]. Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to combine Fang and Moustafa because it would enable machine learning training and providing trained models during queries as suggested by Moustafa. Regarding claim 2, the system of claim 1, wherein the logging system comprises: a log server operatively connected to the one or more applications and configured to aggregate the application metadata or the sensor data associated with the one or more applications; a log serializer operatively connected to the log server and the data query daemon, the log serializer configured to serialize the aggregated application metadata or the aggregated sensor data; and a log memory operatively connected to the log serializer and configured to store the serialized application metadata or the serialized sensor data [0164, see metadata, and 0121 for Log information]. Regarding claim 3, the system of claim 2, wherein the data query daemon is configured to detect the requested training data of the data query in the application metadata or the sensor data being logged by the logging system by: constructing a filter based on the requested training data; monitor the aggregated application metadata or the aggregated sensor data; and extract, using the filter, the requested training data from the aggregated application metadata or the aggregated sensor data [0171, see extract also see 0121 for log information]. Regarding claim 4, the system of claim 2, wherein the log memory is further configured to store the serialized application metadata or the serialized sensor data in a circular buffer [0204, shows performed in serial and 0134, shows a rolling buffer]. Regarding claim 5, the system of claim 4, wherein the memory in which the requested training data is stored is separate from the circular buffer of the logging system [0134, see rolling buffer]. Regarding claim 6, the system of claim 4, wherein the memory in which the requested training data is stored is the circular buffer or a secondary circular buffer of the logging system [0121 and 0134]. Regarding claim 7, the system of claim 1, wherein the one or more conditions include at least one of a driving scenario, a location, or a source application [0089, see driving mode]. Regarding claim 8, the system of claim 1, wherein the data query includes a background application configured to: perform computations using the requested training data; and store the computations and the requested training data if performance conditions of the background application are not satisfied [0125 – 0126 see computing devices]. Regarding claim 9, the system of claim 8, wherein: the background application comprises a new or updated version of a first application of the one or more applications; and the performance conditions comprise a comparison between an output of the first application and an output of the new or updated version of the first application [0084, see updating]. Regarding claim 10, the system of claim 1, wherein the data query daemon is further configured to receive a second data query from the remote data query system, the second data query comprising a second request for data satisfying one or more additional conditions and being directed to a second vehicle fleet that includes the host vehicle, the second vehicle fleet being different than the vehicle fleet [0128, see Fleet]. Regarding claim 11, the system of claim 1, wherein the data query daemon is further configured to transmit the requested training data to the remote data query system upon the host vehicle connecting to a wireless local area network [0164, see hosting]. Regarding claim 12, the system of claim 1, wherein the system further comprises a user application operatively connected to the data query daemon and configured to request permission from a user of the host vehicle for the data query daemon to store and transmit the requested training data [0164]. Regarding claim 13, the system of claim 12, wherein the permission is provided in advance for multiple data queries [0203, see querying]. Regarding claim 14, the system of claim 12, wherein the user application is further configured to: display information related to the data query to the user including at least one of a location or a driving scenario associated with the data query; display a reward for completion of the data query to the user, the reward including at least one of a monetary amount, a reduced or eliminated fee for one or more features, or an update for a map database or the one or more features; and receive an input from the user providing permission to complete the data query [0164, see selection for display]. Regarding claim 15, the system of claim 14, wherein the user application is further configured to: display information related to multiple data queries; and receive an input from the user providing permission to complete one or more of the multiple data queries. Regarding claim 16, the system of claim 14, wherein the user application is further configured to pass the one or more conditions of the data query to a navigation system of the host vehicle, the navigation system being configured to select a route that satisfies the one or more conditions among one or more available routes [0164 see hosting and vehicle]. Regarding claim 17, the system of claim 1, wherein the remote data query system comprises a remote data query application executed by a remote computer system [0123, see remote control manager]. Regarding claim 18, the system of claim 1, the system further comprising a processor configured to execute the data query daemon to respond to the data query when the host vehicle is operating in a travel lane on a roadway [0164, see vehicle also see 0095 for while driving and traffic]. Response to Arguments Applicant's arguments filed 10/17/24 have been fully considered but are moot in view of new grounds for rejection. Correspondence Information 6. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chuck Kendall whose telephone number is 571-272-3698. The examiner can normally be reached on 10:00 am - 6:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung Sough can be reached on 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /CHUCK O KENDALL/ Primary Examiner, Art Unit 2192
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Prosecution Timeline

Aug 30, 2022
Application Filed
Jul 13, 2024
Non-Final Rejection — §103
Oct 17, 2024
Response Filed
Jan 25, 2025
Final Rejection — §103
May 09, 2025
Applicant Interview (Telephonic)
May 12, 2025
Examiner Interview Summary
May 30, 2025
Request for Continued Examination
Jun 02, 2025
Response after Non-Final Action
Jun 06, 2025
Non-Final Rejection — §103
Sep 24, 2025
Applicant Interview (Telephonic)
Sep 24, 2025
Examiner Interview Summary
Oct 09, 2025
Response Filed
Jan 10, 2026
Final Rejection — §103 (current)

Precedent Cases

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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
87%
Grant Probability
95%
With Interview (+7.7%)
3y 1m
Median Time to Grant
High
PTA Risk
Based on 914 resolved cases by this examiner. Grant probability derived from career allow rate.

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