Prosecution Insights
Last updated: July 17, 2026
Application No. 18/215,385

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR OBJECTIVE ASSESSMENT OF THE PERFORMANCE OF AN ADAS/ADS SYSTEM

Final Rejection §102§103
Filed
Jun 28, 2023
Priority
Aug 05, 2022 — DE 10 2022 119 715.8
Examiner
MCPHERSON, JAMES M
Art Unit
3663
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Dr. Ing. h.c. F. Porsche Aktiengesellschaft
OA Round
2 (Final)
82%
Grant Probability
Favorable
3-4
OA Rounds
0m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allowance Rate
437 granted / 532 resolved
+30.1% vs TC avg
Strong +18% interview lift
Without
With
+17.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
14 currently pending
Career history
554
Total Applications
across all art units

Statute-Specific Performance

§101
6.0%
-34.0% vs TC avg
§103
65.6%
+25.6% vs TC avg
§102
13.4%
-26.6% vs TC avg
§112
14.6%
-25.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 532 resolved cases

Office Action

§102 §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 . Status of Claims This Office Action is in response to the Office Action Response dated January 2, 2026. Claims 1-17 are presently pending and are presented for examination. Response to Arguments Applicant presents arguments that Whiteside fails to disclose amended claim features of “identifying (S10) real-world scenarios (SZr) from data captured in real-time by sensors (220) while traveling on a test path with the vehicle (200); identifying real-world scenarios (SZri) from stored data from a scenario identification module (300) and/or generating (S20) simulated scenarios (SZsi) from a simulation module (400).” More particularly, Applicant argues that Whiteside fails to describe or suggest a scenario identification module and/or a simulation module, let alone identifying real-world scenarios and/or simulated scenarios from such modules. In response, as indicated in the revised rejection, Whiteside discloses the selection (i.e. identification) of “real-world scenarios” (e.g. see Fig. 1B and para 0104). Whiteside further discloses, at a minimum, the alternate claim feature of a simulator 202 (i.e. simulation module) that generates and test simulated scenarios (e.g. see Fig. 1B and para 0108). For the foregoing reasons, Applicant’s arguments are unpersuasive. Claim Interpretation - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a scenario identification module,” “a simulation module,” “an assessment module,” and “an evaluation module” in claim 9. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. In looking at the Written Description and Drawings, “a scenario identification module,” “a simulation module,” “an assessment module,” and “an evaluation module” comprise computer processor(s) and/or memory unit. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1-15 and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Publication No. 2021/0194004, to Whiteside et al. (hereinafter Whiteside). As per claim 1, Whiteside discloses a method for objective assessment of performance of an advanced driver assistance system (ADAS) and/or automated driving system (ADS) (210) of a vehicle (200) for a defined driving task in at least one selected scenario (SZL) for testing and training at least one driving function of the ADAS and/or the ADS, wherein the at least one selected scenario (SZL) represents a traffic event in a temporal sequence and is defined by a selection of scenario parameters (P1, P2,...,P) and associated scenario parameter values (PV1, PV2,.--,PV) (e.g. see Abstract and para 0001, wherein a method of evaluating a real or simulated trajectory scenarios for an ADS/ADAS mobile robot is provided, the method includes using test oracle to assess parameters captured during the scenarios and modifying an ADS/ADAS stack to mitigate determined issues), the method comprising: identifying (S10) real-world scenarios (SZr) from data captured in real-time by sensors (220) while traveling on a test path with the vehicle (200) (e.g. see Fig. 1B and para 0104, wherein real-world scenarios are selected (i.e. identified) based upon sensor data from a sensor system 110 of the vehicle during real-world testing (i.e. while traveling on a test path)); identifying real-world scenarios (SZri) from stored data from a scenario identification module (300) and/or generating (S20) simulated scenarios (SZsi) from a simulation module (400) (e.g. see Fig. 1B and para 0108, wherein simulator 202 (i.e. simulation module) runs simulated scenarios for the purpose of testing all or part of an AV run time stack 100, and the test oracle 252 evaluates the performance of the stack (or sub-stack) on the simulated scenarios); communicating (S30) the real-world scenarios (SZr) and/or the simulated scenarios (SZs) to an assessment module (500) (e.g. see Fig. 1B, wherein the real-world data and simulator data are transmitted to the test oracle to evaluate performance); calculating (S40) an assessment indicator (570) for at least one respective real-world scenario of the real-world scenario (SZri) and/or at least one respective simulated scenario of the simulated scenarios (SZsi) from the assessment module (500), wherein the assessment indicator (570) each represent performance of the ADAS and/or ADS (210) for the defined driving task (e.g. see para 0016, wherein assessment parameters are generated by the test oracle based upon evaluation rules); generating (S50) evaluation results (750) from an evaluation module (700), wherein at least one evaluation result (750) of the evaluation results, for the respective real-world scenario (SZri) and/or the respective simulated scenario (SZsi) includes a mapping between the respectively calculated assessment indicator (570) and a respective scenario parameter value (PVci) of a selected scenario parameter (Pci) of the respective real-world scenario (SZri) and/or the respective simulated scenario (SZsi) (e.g. see at least Fig. 9, wherein based upon the comparison (i.e. mapping) between the test results and rules indications are provided whether the system passes or fails the test for the particular rules/parameter); and calibrating the ADAS and/or ADS system on the vehicle based on the evaluation results (750) (e.g. see para 0103, wherein based upon the testing an improved stack (i.e. calibration) is formed). As per claim 2, Whiteside discloses the features of claim 1, and further discloses wherein the scenario parameter value (PVL) of the selected scenario parameter (PL) of the respective real-world scenario (SZri) and/or the respective simulated scenario (SZsi) is extracted by the evaluation module (500) from the dataset of the real-world scenario (SZri) and/or the simulated scenario (SZsi) (e.g. see Abstract, wherein the assessment parameters are extracted based upon an agent trace). As per claim 3, and similarly with respect to claim 10, Whiteside discloses the features of claims 1 and 9, respectively, and further discloses wherein the assessment indicators (570) are configured as key performance indicators (KPIs) (e.g. see Fig. 9 and para 0044, wherein the assessment parameter, as well as indicator, relates to failure of the system resulting in collision (i.e. a key performance indicator)). As per claim 4, and similarly with respect to claim 11, Whiteside discloses the features of claims 3 and 10, respectively, and further discloses wherein the evaluation results (750) are configured as KPI plots, and wherein values of a KPIs in each of the KPI plots represent a function of the parameter values (PVC) of a scenario parameter (P1) for a particular scenario (SZL), and each plot point includes a real-world scenario (SZri) or a specific simulated scenario (SZsi) (e.g. see Figs. 10 and 11). As per claim 5, and similarly with respect to claim 12, Whiteside discloses the features of claims 4 and 10, respectively, and further discloses wherein the KPI plots for variously formed ADAS and/or ADS are compared to one another (e.g. see Figs. 10 and 11). As per claim 6, Whiteside discloses the features of claim 4, and further discloses wherein the KPI plots are histograms with segments for cluster analysis (e.g. see Figs. 10 and 11). As per claim 7, Whiteside discloses the features of claim 1, and further discloses wherein the computational methods and/or algorithms of artificial intelligence are configured as mean values, minimum and maximum values, lookup tables, expected value models, linear regression methods, Gaussian processes, fast Fourier transforms, integral and differential calculations, Markov methods, probability methods, Monte Carlo methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, recurrent neural networks, and/or folded neural networks (e.g. see at least Figs. 9-11 and para 0131). As per claim 8, Whiteside discloses the features of claim 7, and further discloses wherein the computational methods and/or algorithms of artificial intelligence are configured as mean values, minimum and maximum values, lookup tables, expected value models, linear regression methods, Gaussian processes, fast Fourier transforms, integral and differential calculations, Markov methods, probability methods, Monte Carlo methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, recurrent neural networks, and/or folded neural networks (e.g. see at least para 0131). As per claim 9, Whiteside discloses a system (100) for testing and training at least one driving function of an advanced driver assistance system (ADAS) and/or an automated driving system (ADS) (e.g. see Abstract and para 0001, wherein a method of evaluating a real or simulated trajectory scenarios for an ADS/ADAS mobile robot is provided, the method includes using test oracle to assess parameters captured during the scenarios and modifying an ADS/ADAS stack to mitigate determined issues), the system comprising: sensors (220) connected to the vehicle (200), the sensors (220) being configured to capture data in real-time while traveling on a test path with the vehicle (200) (e.g. see Fig. 1B and para 0092, wherein an autonomous vehicle 101 is provided including on-board sensors 110 for measuring position, velocity acceleration etc. (i.e. capturing data in real-time while traveling)); a scenario identification module (300) configured to identify real-world scenarios (SZr) from the data of the sensors (220) captured in real-time or from stored data, each of the scenarios representing a traffic event in a temporal sequence and being defined by a selection of scenario parameters (P1, P2, …, Pn) and associated scenario parameter values (PV1, PV2, …, PVn) (e.g. see Fig. 1B and para 0104, wherein real-world scenarios are selected (i.e. identified) based upon sensor data from a sensor system 110 of the vehicle during real-world testing (i.e. while traveling on a test path); the Office further notes that the scenario identification module, though not particularly identified, would be required for selection and testing by simulator 202); a simulation module (400) configured to generate simulated scenarios (SZs) (e.g. see Fig. 1B and para 0108, wherein simulator 202 (i.e. simulation module) runs simulated scenarios for the purpose of testing all or part of an AV run time stack 100, and the test oracle 252 evaluates the performance of the stack (or sub-stack) on the simulated scenarios); an assessment module (500) configured to calculate an assessment indicator (570) for at least one real-world scenario (SZri) and/or at least one simulated scenario (SZsi), the assessment indicator (570) representing performance of the ADAS and/or ADS (210) for the defined driving task; and an evaluation module (700) is configured to generate evaluation results (750) for a real-world scenario (rSZsg) and/or for a simulated scenario (SZsi) (e.g. see Fig. 1B, wherein the real-world data and simulator data are transmitted to the test oracle (i.e. assessment/evaluation module) to evaluate performance (i.e. generate indicators and evaluation results) of the vehicle), the evaluation results including a mapping between the respectively determined assessment indicator (570) and the scenario parameter value (PVL) of a selected scenario parameter (PL) of the respective real-world scenario (SZri) and/or the respective simulated scenario (SZsi) (e.g. see at least Fig. 9, wherein based upon the comparison (i.e. mapping) between the test results and rules indications are provided whether the system passes or fails the test for the particular rules/parameter). As per claim 13, Whiteside discloses the features of claim 1, and further discloses a computer program product (1000) comprising an executable program code (1050) configured to execute the method of claim 1 (e.g. see at least para 0001). As per claim 14, Whiteside discloses the features of claim 1, and further discloses wherein a scenario parameter (PL) comprises a physical variable, a chemical variable, a torque, a speed, a voltage, a current strength, a speed, an acceleration, a lurch, a braking value, a direction, an angle, a radius, a location, a number, a movable object such as a motor vehicle, a person or a cyclist, a stationary object such as a building or tree, a road configuration such as a highway, a road sign, a traffic light, a tunnel, a roundabout, a turn-off lane, a traffic volume, a topographical structure such as an incline, a time, a temperature, a precipitation value, a weather condition and/or a time of year (e.g. see at least para 0131). As per claim 15, Whiteside discloses the features of claim 1, and further discloses wherein calculating (S40) the assessment indicator (570) for at least one respective real-world scenario of the real-world scenarios (SZri) includes: calculating an assessment indicator for at least one respective real-world scenario of the real-world scenarios (SZri) identified from data captured in real-time by sensors while traveling on the test path with the vehicle, and calculating an assessment indicator for at least one respective real world scenario of the real-world scenarios identified from stored data and/or at least one respective simulated scenario of the simulated scenarios (SZsi) from the assessment module (500), wherein at least one of the assessment indicators (570) represent performance of an external ADAS and/or ADS (210) for the defined driving task (e.g. see Abstract and para 0001, wherein a method of evaluating a real or simulated trajectory scenarios for an ADS/ADAS mobile robot is provided; also see Figs. 10 and 11, wherein a plurality of assessments are plotted for the different scenarios, which would include real and simulated trajectory scenarios), wherein generating (S50) the evaluation results (750) also includes a mapping between the calculated assessment indicator representing performance of an external ADAS and/or ADS (210) for the defined driving task and the respective scenario parameter value (PVci) of the selected scenario parameter (Pci) of the respective real-world scenarios (SZri) and/or the respective simulated scenario (SZsi) (e.g. see Figs. 10 and 11). As per claim 17, Whiteside discloses the features of claim 5, and further discloses wherein the KPI plots for various real-world scenarios (SZri) and/or simulated scenarios (SZsi) are also compared to one another (e.g. see Figs. 10 and 11, and para 209, wherein the real-world scenarios or simulated scenarios are compared through multiple runs). 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 16 is rejected under 35 U.S.C. 103 as being unpatentable over Whiteside, in view of Designer’s Choice. As per claim 16, Whiteside discloses the features of claim 3, but fails to disclose wherein the assessment indicators (570) are numeric values on a scale of 1 to 100. However, the Office notes that assessment indicators must be on a scale from worst to best. Accordingly, there would need to be some sort of minimum and maximum assessment value, such as 1 to 100. It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the system of Whiteside to includes assessment values being on a scale from 1-100, as a matter of designer’s choice for differentiating between a worst and best case indicator. 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 James M. McPherson whose telephone number is (313) 446-6543. The examiner can normally be reached on 7:30 AM - 5PM Mon-Fri Eastern Alt Fri. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on 571 272-9855. 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). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JAMES M MCPHERSON/Primary Examiner, Art Unit 3663B
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Prosecution Timeline

Jun 28, 2023
Application Filed
Mar 21, 2025
Non-Final Rejection mailed — §102, §103
Jun 03, 2025
Examiner Interview Summary
Jun 03, 2025
Applicant Interview (Telephonic)
Jun 16, 2025
Response after Non-Final Action
Jun 16, 2025
Response Filed
Jan 02, 2026
Response Filed
Jun 23, 2026
Final Rejection mailed — §102, §103 (current)

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Prosecution Projections

3-4
Expected OA Rounds
82%
Grant Probability
99%
With Interview (+17.5%)
2y 5m (~0m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 532 resolved cases by this examiner. Grant probability derived from career allowance rate.

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