Prosecution Insights
Last updated: October 02, 2026
Application No. 18/432,627

TORQUE-BASED ARTIFICIAL ROAD FRICTION LEARNING

Final Rejection §103
Filed
Feb 05, 2024
Examiner
LEWIS, TISHA D
Art Unit
3619
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Ford Global Technologies LLC
OA Round
2 (Final)
88%
Grant Probability
Favorable
3-4
OA Rounds
0m
Est. Remaining
97%
With Interview

Examiner Intelligence

Grants 88% — above average
88%
Career Allowance Rate
1092 granted / 1246 resolved
+35.6% vs TC avg
Moderate +10% lift
Without
With
+9.6%
Interview Lift
resolved cases with interview
Typical timeline
2y 2m
Avg Prosecution
36 currently pending
Career history
1280
Total Applications
across all art units

Statute-Specific Performance

§101
0.9%
-39.1% vs TC avg
§103
35.1%
-4.9% vs TC avg
§102
26.5%
-13.5% vs TC avg
§112
30.7%
-9.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1246 resolved cases

Office Action

§103
DETAILED ACTION The following is a response to the amendment filed 7/10/2026 which has been entered. Response to Amendment Claims 1-15 and 21-25 are pending in the application. Claims 16-20 are cancelled and claims 21-25 are new. -The 112(b) rejection has been withdrawn due to applicant cancelling claim 16. Response to Arguments Applicant's arguments have been fully considered but they are not persuasive. -As to applicant’s argument that, “Claim 1 requires more than controlling torque based on a stored coefficient of friction. Claim 1 requires that, responsive to cessation of slip due to the driven wheel transitioning from the first surface to the second surface, the controller commands torque such that the torque increases at a rate that depends on the last learned value of the coefficient of friction of the first surface at the transitioning. Thus, the claimed rate of torque increase is tied to a particular value, namely, the last learned value for the first surface at the time of the transition. Beever does not disclose this. Beever describes use of a stored coefficient of friction for controlling drive torque. A stored coefficient of friction is not the same as the claimed "last learned value of the coefficient of friction of the first surface at the transitioning." Beever also does not disclose increasing torque at a rate that depends on that last learned value. The Office has not identified any disclosure in Beever in which a post-slip torque-increase rate is selected or modified based on a last learned coefficient of friction value for the surface from which the vehicle transitioned. The rejection therefore relies on an improper broadening of the claim language. The claimed "last learned value" is not merely any stored coefficient of friction value. It is a learned value associated with the first surface at the transition, and that value controls the rate at which torque is increased after slip ceases. Because Beever does not disclose this relationship, the combination of Park and Beever does not disclose or render obvious all limitations of claim 1. Therefore, claim 1 is patentable.” has been acknowledged. However, KR (as the primary prior art used in rejection) discloses a controller programmed to increase torque at a rate (via level of target engine torque value) depending on value of coefficient of first surface at the transitioning (via engine torque control value inputted during low friction coefficient, page 3, line 27 to page 4, line 6) wherein WO (as secondary prior art used in rejection) was used to show that it is well known in the art to command torque from a powerplant depending on a previously stored value of coefficient of friction (page 4, lines 22-25). It would have been obvious to provide the KR controller that already increases torque at a rate depending on a coefficient of friction with a “previously stored value of coefficient of friction” dependence in view of WO. Further, a “stored value” coefficient of friction can be considered a “learned value” if the value is determined based on wheel slip measurement and then stored to adjust wheel behavior (value doesn’t remain a “static value”). WO discloses updating of currently stored value (or changing of stored value) based on measured wheel slip being inconsistent with the stored coefficient of friction value (page 4, lines 22-25). WO discloses the currently stored value is updated using the measured wheel slip and further control is based on that updated value (unless another inconsistency is determined, page 11, lines 1-15). Therefore, WO does show that it is well known in the art to have a controller as taught by KR use “last learned value (or previously stored value)” of coefficient of friction for commanding powerplant torque output. -As to applicant’s argument that, “it would not have been obvious to combine Park and Beever. The Office states that it would have been obvious to provide Park with a last learned value of a coefficient of friction in view of Beever "to reduce redundancy in determining coefficient for the same consistently traveled surfaces which increases operating efficiency of machine learning system." This rationale does not support the rejection. Claim 1 does not merely recite storing a coefficient of friction or reusing a coefficient of friction from a previously traveled surface. Claim 1 requires the controller to command torque such that "the torque increases at a rate that depends on a last learned value of a coefficient of friction of the first surface at the transitioning." Thus, the limitation at issue is not directed to avoiding recalculation of a coefficient of friction. The limitation is directed to how quickly torque is increased after cessation of slip, and specifically requires the rate of that increase to depend on the last learned value of the coefficient of friction of the first surface at the transitioning. The Examiner's redundancy rationale, even if accepted, would only explain a benefit to reusing a coefficient of friction value to avoid re-determining that value. The rationale does not explain why a person of ordinary skill in the art would use that value as a basis for setting a rate at which torque is increased after wheel slip ceases. Reducing redundancy in determining a coefficient of friction is unrelated to selecting a torque-increase rate following a transition from one surface to another. The rejection therefore fails to provide an articulated reason why the alleged stored value of Beever would have been used to control the rate limitation required by claim 1.The Office has not shown that Park, Beever, or their combination teaches or suggests increasing torque at a rate that depends on the last learned coefficient of friction value of the first surface at the transitioning. Nor has the Office provided a reason why one of ordinary skill in the art would have modified Park to include such functionality. Accordingly, the rejection of claim 1 is improper.”, has been acknowledged. However, KR (as the primary prior art used in rejection) discloses a controller programmed to increase torque at a rate (via level of target engine torque value) depending on value of coefficient of first surface at the transitioning (via engine torque control value inputted during low friction coefficient, page 3, line 27 to page 4, line 6) wherein WO (as secondary prior art used in rejection) was used to show that it is well known in the art to command torque from a powerplant depending on a previously stored value of coefficient of friction (page 4, lines 22-25). It would have been obvious to provide the KR controller that already increases torque at a rate depending on a coefficient of friction with a “previously stored value of coefficient of friction” dependence in view of WO to reduce redundancy in determining coefficient for the same consistently traveled surfaces which increases operating efficiency of machine learning system. In other words, KR using a “last learned value” as taught by WO would eliminate the need for the KR controller to have to consistently re-measure wheel slip during every transition if the determined traveled surface is based on the “last learned value”. Therefore, the examiner has provided a motivation (rationale) to combine that does support the rejection. 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. Claim(s) 1, 3, 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over KR 1020190015855 (with machine translation, note: references pertaining to translation in claims below have been updated to reflect current translation as filed versus paragraphs from EPO translation previously used) in view of WO 2013186208. As to claim 1, KR discloses a vehicle comprising: a powerplant (engine as described in abstract); an accelerator pedal (page 3, lines 5-7 in translation describes acceleration performance); a wheel (page 5, lines 6-7) driven by the powerplant; and a controller (100) programmed to, while a position of the accelerator pedal is constant (page 4, lines 5-6 describes acceleration increase which suggest that constant pedal depression is occurring) and responsive to cessation of slip of the driven wheel due to the driven wheel transitioning from a first surface to a second surface (page 3, lines 17-19 and page 6, lines 21-24 describe no reaction to spin when it is determined that transition from low to high friction has occurred), command torque from the powerplant such that the torque increases at a rate (via level of target engine torque value) that depends on a value of a coefficient of friction of the first surface at the transitioning (via engine torque control value inputted during low friction coefficient, page 3, line 27 to page 4, line 6). However, KR doesn't disclose the torque increasing at a rate that depends on a last learned value of a coefficient of friction of the first surface at the transitioning. WO discloses a vehicle and shows that it is well known in the art to have a previously stored value of coefficient of friction wherein a powerplant drive torque is controlled based on the stored value (page 4, lines 22-25). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide KR with a last learned value of a coefficient of friction in view of WO to reduce redundancy in determining coefficient for the same consistently traveled surfaces which increases operating efficiency of machine learning system. As to claim 3, KR in view of WO discloses wherein the torque is capped at a limit value that is based on the last learned value (page 4, lines 28-31 in WO). As to claim 7, wherein the powerplant is an electric machine (page 6, lines 7-11 in WO). As to claim 8, wherein the powerplant is an engine (as described in abstract in KR). Allowable Subject Matter Claims 9-15 and 21-25 are allowed. See reasons for allowance in previous office action filed 5/19/26 (claim 21 would be allowed for same reasons indicated for claim 9). Claims 2 and 4-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. See reasons for allowance in previous office action filed 5/19/26. Conclusion There is no additional prior art made of record and relied upon as the examiner considers the prior art used in the rejections and previously cited as the most pertinent to applicant’s disclosure. 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 TISHA D LEWIS whose telephone number is (571)272-7093. The examiner can normally be reached Mon-Fri: 8:30am to 5:00pm. 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, Anna M Momper can be reached at 571-270-5788. 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. Tdl /TISHA D LEWIS/Primary Examiner, Art Unit 3619 September 9, 2026
Read full office action

Prosecution Timeline

Feb 05, 2024
Application Filed
May 19, 2026
Non-Final Rejection mailed — §103
Jul 10, 2026
Response Filed
Sep 11, 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

3-4
Expected OA Rounds
88%
Grant Probability
97%
With Interview (+9.6%)
2y 2m (~0m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 1246 resolved cases by this examiner. Grant probability derived from career allowance rate.

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