Prosecution Insights
Last updated: May 29, 2026
Application No. 18/526,803

SPEECH-BASED VEHICULAR CONTROL

Final Rejection §103
Filed
Dec 01, 2023
Examiner
KWON, JOHN
Art Unit
3747
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
Qualcomm Incorporated
OA Round
2 (Final)
86%
Grant Probability
Favorable
3-4
OA Rounds
0m
Est. Remaining
82%
With Interview

Examiner Intelligence

Grants 86% — above average
86%
Career Allowance Rate
680 granted / 793 resolved
+15.8% vs TC avg
Minimal -4% lift
Without
With
+-4.2%
Interview Lift
resolved cases with interview
Typical timeline
2y 2m
Avg Prosecution
7 currently pending
Career history
806
Total Applications
across all art units

Statute-Specific Performance

§101
0.8%
-39.2% vs TC avg
§103
67.9%
+27.9% vs TC avg
§102
7.8%
-32.2% vs TC avg
§112
6.9%
-33.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 793 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 . Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhary (US 2021/0012770) in view of Yu (US 2021/0261141). Choudhary discloses a device comprising: memory (110)configured to store scene data from one or more scene sensors associated with a vehicle; and one or more processors configured to (See [0032]): obtain, via a first machine-learning model (112) of a contextual encoder system, a first embedding based on data representing speech (See [0045]) that includes one or more commands for operation of the vehicle; obtain, via a second machine-learning model of the contextual encoder system, a second embedding based on the scene data and based on state data of the first machine-learning model; and generate one or more vehicle control signals for the vehicle based on the first embedding and the second embedding (See [0032][0039][0045]). However, Choudhary does not show the scene data includes an image data and the position data. Yu discloses that the provision of a scene data includes an image data as well as a position data is old and well known in the art (See [0021] [0046] [0096]). Since the prior art references art from the same field of endeavor, the purpose disclosed by Yu would have been recognized in the pertinent art of Choudhary. Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to provide the device of Choudhary with the scene data includes the image data and the position data as taught by Yu. Regarding claim 2, 20, and 27, Choudhary discloses at least one command of the one or more commands relates an action to be performed to a feature of a local context in which the vehicle is operating (see [0045] [0064]) Regarding claim 3, Choudhary discloses the first embedding corresponds to a semantic embedding and the second embedding corresponds to a text-grounded scene embedding (114, [0083] [0032] [0106]). Regarding claim 4, 21, and 28, Choudhary discloses the one or more processors are configured to generate a navigation feature embedding based on the first embedding and the second embedding, and wherein the one or more vehicle control signals are based on the navigation feature embedding (See [0033] [0045] [0049] [0064]). Regarding claim 5, claimed particular generating /aligning/sharing the navigation feature embedding as claimed is consider a matter of design choice since one of ordinary skilled in the art has the ability to allocate the components depending upon the usage. Regarding claim 6, claimed particular projection models as claimed is consider a matter of design choice since one of ordinary skilled in the art has the ability to dimension the components depending upon the usage. Regarding claims 7, and 8, claimed particular processors to generate a masked navigation feature embedding based on the navigation feature embedding and a contextual safety mask is considered a matter of design choice since one of ordinary skilled in the art has the ability to choose the components depending upon the usage. Regarding claim 9, 24, and 29, Choudhary discloses the speech commend feature, thus obtain audio data captured by one or more microphones associated with the vehicle, wherein at least a portion of the audio data represents the speech; obtain, from one or more speech-to-text models, text representing the speech would be implied. Regarding language models, text feature data based on the text; and provide the text feature data as input to the first machine-learning model to generate the first embedding, it would be considered a matter of design choice since one of ordinary skilled in the art has the ability to choose the components depending upon the usage (See [0033]). Regarding claim 10, and 25, Choudhary discloses the contextual encoder system includes the first machine-learning model interconnected with the second machine-learning model for two-way exchange of shared intermediate state data, and wherein the shared intermediate state data includes: the state data of the first machine-learning model which is shared with the second machine-learning model during generation of the second embedding; and second state data of the second machine-learning model which is shared with the first machine-learning model during generation of the first embedding (See [0033] [0045] [0049] [0064]). Regarding claim 11, Choudhary discloses the first machine-learning model includes an encoder of a language transformer model, and the second machine-learning model includes an encoder of an image transformer model (See [3303][0034]). Regarding claim 12, Choudhary discloses the one or more processors are configured to: determine scene feature data based on the scene data; and provide the scene feature data as input to the second machine-learning model to generate the second embedding (See [3303][0034]). Regarding claim 13, Choudhary discloses the one or more vehicle control signals include maneuvering signals including steering control signals, brake control signals, transmission control signals, acceleration control signals, or a combination thereof (See [3303][0034]). Regarding claim 14, Choudhary discloses the one or more vehicle control signals include controls signals for vehicle alert and communication systems(See [3303][0034]). Regarding claim 15, Choudhary discloses the scene sensors include one or more image sensors, one or more lidar sensors, one or more sonar sensors, one or more radar sensors, or a combination thereof (See [3303][0034]). Regarding claim 16, Choudhary discloses one or more processors are integrated in the vehicle (See [3303][0034]). Regarding claim 17, Choudhary discloses a modem configured to send a signal representing the vehicle control signals to the vehicle (See [3303][0034]). Regarding claim 18, Choudhary discloses a modem configured to receive a signal representing the scene data, audio data representing the speech, or both, from one or more remote devices (See [3303][0034]). Regarding claims 22 and 23, claimed particular determining a path plan for the vehicle and obtaining commands based on the first embedding and the second embedding, it would be considered a matter of design choice since one of ordinary skilled in the art has the ability to choose the components depending upon the usage. Response to Arguments Applicant’s arguments with respect to claims 1-30 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN KWON whose telephone number is (571)272-4846. The examiner can normally be reached M-F; 9A-5P. 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, Phuttiwat Wongwian can be reached at 571-270-5426. 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. /JOHN KWON/Primary Examiner, Art Unit 3747 April 4, 2026
Read full office action

Prosecution Timeline

Dec 01, 2023
Application Filed
Nov 03, 2025
Non-Final Rejection mailed — §103
Jan 09, 2026
Applicant Interview (Telephonic)
Jan 09, 2026
Examiner Interview Summary
Feb 03, 2026
Response Filed
Apr 08, 2026
Final Rejection mailed — §103
May 04, 2026
Applicant Interview (Telephonic)
May 16, 2026
Examiner Interview Summary

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

3-4
Expected OA Rounds
86%
Grant Probability
82%
With Interview (-4.2%)
2y 2m (~0m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 793 resolved cases by this examiner. Grant probability derived from career allowance rate.

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