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 .
DETAILED ACTION
1. The amendment filed 07/16/2026 has been received and considered. Claims 21-22, 24-31 and 33-42 are presented for examination.
Information Disclosure Statement
2. The information disclosure statement filed 07/16/2026 IDS fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. The copy of Lee reference has not been located.
Specification
3. The amendment filed 07/16/2026 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: “adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.” (claims 21, 30, and 37). Applicant is required to cancel the new matter in the reply to this Office Action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
4. Claim 21-22, 24-31 and 33-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. Patent No. 11,8988,62. Although the claims at issue are not identical, they are not patentably distinct from each other because the Claims in the instant invention are anticipated by Claims of U.S. Patent No. 11,8988,62 and constitutes an obvious variation. Also it would be obvious to claim a media off the system and/or vice versa.
5. Claim 21-22, 24-27, 29-31, and 33-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-3, 7-9, 11-13, and 15-18 of U.S. Patent No. 10,802,477. Although the claims at issue are not identical, they are not patentably distinct from each other because the Claims in the instant invention are anticipated by Claims of U.S. Patent No. 10,802,477 and constitutes an obvious variation. Also it would be obvious to claim a media off the system and/or vice versa.
6. Claim 21-22, 24-31 and 33-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-15 of U.S. Patent No. 11,656,975. Although the claims at issue are not identical, they are not patentably distinct from each other because the Claims in the instant invention are anticipated by Claims of U.S. Patent No. 11,656,978 and constitutes an obvious variation. Also it would be obvious to claim a media/system off the system/media.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
7. Claims 21, 22, 24-31, and 33-42 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 21, 30, and 37 each recite “adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.” Applicant identifies paragraphs [0246], [0249], [0251], and [0260] and FIG. 10 of the specification as support (Remarks, p. 9).
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However, the cited disclosure describes adjusting simulated input data based upon the output data generated by the software routines ([0260]; FIG. 10, block 1020). The quality metric, by contrast, is calculated at the conclusion of the disclosed method (FIG. 10, terminal block 1024). Further the specification discloses that “Once the quality metrics have been generated, the method 1000 may terminate” ([0263]).
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FIG. 10 depicts no feedback path from block 1024 to block 1008, 1014, or 1020, and neither the cited paragraphs nor any other identified portion of the original disclosure describe adjusting the simulated sensor data based upon the quality metric, or providing simulated sensor data so adjusted to the emulator program in one or more further implementations. Accordingly, written description support for adjusting “based upon the recorded output and the quality metric” has not been located.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. Claims 21-22, 24-31 and 33-42 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 20160314224 A1), in view of Medium (“The View from the Front Seat of the Google Self-Driving Car, Chapter 4”) and Espindle et al. (“Safety Analysis of Upgrading to TCAS Version 7.1 Using the 2008 U.S. Correlated Encounter Model”), further in view of Lee (“Adaptive Stress Testing of Airborne Collision Avoidance Systems”).
As per Claim 21, 30, and 37, Wei et al. teaches a computer-implemented method/system/ non-transitory computer-readable storage media for evaluating environmental control software (Abstract, Fig. 1, [0017]), comprising:
storing, in a memory, a plurality of combinations of test conditions for a simulated environment (Fig. 1-2 [0019], [0031], [0033], [0039], “the memory system 18 includes model data 22 and simulation behavioral data 24. The model data 22 can include data associated with simulated renderings of the simulated virtual environment in which the simulated version of the autonomous vehicle interacts in a given simulated mission.”);
generating, by one or more processors, simulated sensor data for evaluating an autonomous operation feature of a plurality of autonomous operation features of an environmental operating system within the simulated environment under the plurality of combinations of test conditions ([0003] “a simulation controller configured to generate simulated sensor data based on model data and behavioral data associated with each of the simulated virtual environment and the spontaneous simulated events.”; [0016] “the autonomous vehicle simulation system 10 is configured to test the autonomous operation of the autonomous vehicle in a simulated manner,” [0020] “The simulation driver 20 is configured to integrate the simulation inputs SIM provided via the user interface 14 with the model data 22 and the simulation behavioral data 24 to provide the simulation commands SIM_CMD to the autonomous vehicle control system 12. Additionally, the simulation driver 20 is configured to receive the feedback signals SIM_CMD from the autonomous vehicle control system 12”, [0032], “the sensor models 60 can include models associated with a navigation sensor (e.g., modeled as a global navigation satellite system (GNSS) and/or inertial navigation sensor(s) (INS)), a radar system, a lidar system, a video system, electro-optical sensors, and/or a variety of other types of sensors.”; [0044]: simulated sensor data is generated from the modeled conditions for testing the operational capabilities of the control system);
…
recording, in the memory, output generated by the emulator program, the recorded output including control decisions generated during each instance of the emulator program for the control of one or more settings of the autonomous operation feature in response to a corresponding input sequence of simulated sensor data ([0003-[0004], [0017]-[0018] “can store the results of a given simulated mission in an event log associated with the simulated mission”, [0020], [0038]-[0043], [0045] “the signals SIM CMD can include output signals provided from the autonomous vehicle control system 12 corresponding to the control of autonomous vehicle and the reactive behavior of the autonomous vehicle control system 12 in response to the simulated sensor data”: the recorded event-log output includes the control outputs, i.e., the “control decisions” as claimed, generated in response to the simulated sensor input sequence of each simulated mission run); and ….
Wei et al. fails to teach explicitly simultaneously implement a plurality of instances of an emulator program using the simulated sensor data to mimic the environmental operating system and control one or more settings of the autonomous operation feature under at least one combination of test conditions within the simulated environment based upon the simulated sensor data corresponding to the at least one combination of test conditions, each instance of the emulator program corresponding to a respective emulated control of the autonomous operation feature under a respective one of the combinations of test conditions within the simulated environment;
generating, by the one or more processors, a quality metric for the autonomous operation feature based upon the recorded output generated in response to the simulated sensor data, wherein the quality metric indicates one or more risk levels associated with use of the autonomous operation feature of the plurality of autonomous operation features; and
adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.
Medium teaches simultaneously implement a plurality of instances of an emulator program using the simulated sensor data to mimic the environmental operating system and control one or more settings of the autonomous operation feature under at least one combination of test conditions within the simulated environment based upon the simulated sensor data corresponding to the at least one combination of test conditions, each instance of the emulator program corresponding to a respective emulated control of the autonomous operation feature under a respective one of the combinations of test conditions within the simulated environment (“We do this on our test track, in the real world (more than 1.3 million miles to date), and in our simulator (more than 3 million miles a day).” on pg 3, “ we replayed a real-world situation in our simulator,” on pg 4, “powerful simulator generates thousands of virtual testing scenarios for us; it executes dozens of variations on situations we’ve encountered in the real world by adjusting parameters such as the position and speed of our vehicle and of other road users around us” on pg 5, “our powerful simulator generates thousands of virtual testing scenarios for us; it executes dozens of variations on situations we’ve encountered in the real world by adjusting parameters such as the position and speed of our vehicle and of other road users around us.” on pg 9). In particular, Medium teaches that Google’s simulator generates thousands of virtual testing scenarios, executing dozens of variations on real-world situations by adjusting parameters such as the position and speed of vehicle and other road users achieving more than three million simulated miles per day. The scale of three million simulated miles per day across thousands of scenario variations with different parameter sets necessarily implies a massively parallel server infrastructure as this throughput is physically impossible on a single server which require simultaneously running a plurality of instances of the simulation program, each instance corresponding to a respective emulated control of an autonomous driving feature under a respective one of the different combinations of test conditions.
Furthermore, Espindle et al. teaches generating, by the one or more processors, a quality metric for the autonomous operation feature based upon the recorded output generated in response to the simulated sensor data, wherein the quality metric indicates one or more risk levels associated with use of the autonomous operation feature of the plurality of autonomous operation features (“We created 500,000 sample encounters from the U.S. correlated encounter model in order to test the safety of V7.1. We then used our simulation environment, CASSATT, to run the encounters in simulation under various equipage and pilot response combinations, and computed metrics such as risk ratios and Near Mid Aid Collision (NMAC) rates.”, “The risk ratio for V7.1 when both aircraft respond to their RAs is 1.59%, compared to 1.61% with V7.0.” On pg iii; section 2.3.1.; section 2.2.2, pg. 5 “Each of the 500,000 sampled encounters is simulated under various TCAS and transponder equipage and pilot response model combinations”; “A common metric that incorporates P(nmac | enc) is the risk ratio, which compares the P(nmac | enc) resulting from equipping one or more aircraft during the encounter with TCAS versus the nominal encounter condition where neither plane is equipped with TCAS” on pg 6-7; “The Collision Avoidance System Safety Assessment Tool (CASSATT) performs fast-time Monte Carlo analysis of aircraft encounters. CASSATT takes either real radar tracks or encounter model data,” on pg 41). In particular, Espindle et al. teaches computing a risk ration metric defined as the probability of a near mid-air collision (NMAC) with the collision avoidance system equipped divided by the probability of NMAC without the system equipped, there by generating a quality metric from simulation output that indicates risk levels associated with use of an autonomous collision avoidance feature (section 2.3.1. pg 6-7) after running 500,000 simulated encounters through the CASSAT fast time Monte Carlo simulation environment and computing the rick ratio for each equipage and pilot response combination, reporting, for example, that the risk ration when both aircraft respond to their resolution advisories is 1.59%, compared to 1.61% for V7.0 (pg iii, pg 5-6 & 41).
Wei et al., Medium, and Espindle et al. are analogous art because they are all from the same field of endeavor evaluating autonomous vehicle control software.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Medium and Espindle et al. into Wei et al.’s invention to help test how a car would have performed under slightly different circumstances such as valuable preparation for a public road environment in which fractions of seconds can be of critical importance. (Medium: pg 9) and in order to determine the improved overall safety version of an autonomous operation feature (Espindle et al.: pg 13).
However, Wei as modified by Medium and Espindle fails to teach explicitly adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.
adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.
On the other hand, Lee teaches adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations (pg. 2 “
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”; pg. 4 “
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”; pg. 6 Algorithm 1; pg. 7: the recorded simulation outputs are converted into a risk-based reward metric, and the reward adjusts the seed and control inputs that generate the simulated sensor measurements supplied to the system under test in the subsequent simulation runs). In particular, Lee teaches closed-loop adaptive stress testing of an aircraft collision avoidance system in which each simulation rollout’s recorded output is scored by a reward function based on the miss distance to a near mid-air collision, and the reward steers the selection of the seeds and control inputs that drive the sensor, pilot, and dynamics models of the further simulation runs.
Wei, Medium, Espindle, and Lee are analogous art because they are all from the same field of endeavor, simulation-based evaluation of autonomous vehicle control software.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Lee into Wei as modified by Medium and Espindle’s invention for the purpose of testing the autonomous operation of an autonomous vehicle control system in a simulated virtual environment to provide a method that performs significantly better both in finding events and in maximizing their likelihood compared with direct Monte Carlo search (Lee: pg. 1).
As per Claim 22, 31, and 38, Wei et al. teaches wherein the simulated sensor data comprises one or more sequences of simulated sensor signals associated with one or more time durations of simulated environmental operation ([0039], [0043]-[0045]).
As per Claim 24, 33, and 39, Wei et al. fails to teach explicitly wherein generating the quality metric includes: comparing, by one or more processors, the recorded output with one or more baseline output values associated with a set of test data.
Espindle et al. teaches wherein generating the quality metric includes: comparing, by one or more processors, the recorded output with one or more baseline output values associated with a set of test data (“We created 500,000 sample encounters from the U.S. correlated encounter model in order to test the safety of V7.1. We then used our simulation environment, CASSATT, to run the encounters in simulation under various equipage and pilot response combinations, and computed metrics such as risk ratios and Near Mid Aid Collision (NMAC) rates.”, “The risk ratio for V7.1 when both aircraft respond to their RAs is 1.59%, compared to 1.61% with V7.0.” On pg iii: same 500,000 encounter set run through both V7.0 (corresponding to “baseline”) and V7.1, results directly compared ).
As per Claim 25, 33, and 39, Wei et al. fails to teach explicitly wherein generating the quality metric further includes: determining, by one or more processors, one or more differences between the recorded output and the one or more baseline output values; and determining, by one or more processors, the quality metric based upon the one or more determined differences.
Espindle et al. teaches wherein generating the quality metric further includes: determining, by one or more processors, one or more differences between the recorded output and the one or more baseline output values (“V7.1 lowered the risk of NMAC over V7.0, in some cases substantially.”, “The risk ratio for V7.1 when both aircraft respond to their RAs is 1.59%, compared to 1.61% with V7.0.”, “The risk ratio for V7.1 when one aircraft does not respond to their RAs is 9.61%, compared to 9.85% with V7.0.” on pg iii-iv, Table 1 o pg 7; Table (9) and (10) on pg 23); and determining, by one or more processors, the quality metric based upon the one or more determined differences (“V7.1 lowered the risk of NMAC over V7.0, in some cases substantially.”, “The risk ratio for V7.1 when both aircraft respond to their RAs is 1.59%, compared to 1.61% with V7.0.”, “The risk ratio for V7.1 when one aircraft does not respond to their RAs is 9.61%, compared to 9.85% with V7.0.” on pg iii-iv, Tables on pg 7-8; Table (9) and (10) on pg 23).
As per Claim 26 and 34, Wei et al. fails to teach explicitly wherein the emulator program is associated with a first version of environmental control software, wherein the one or more baseline output values are associated with a second version of environmental control software for the autonomous operation feature, wherein the second version is different from the first version.
Espindle et al. teaches wherein the emulator program is associated with a first version of environmental control software, wherein the one or more baseline output values are associated with a second version of environmental control software for the autonomous operation feature, wherein the second version is different from the first version (section 2.2 “Each of the 500,000 sampled encounters is simulated under various TCAS”, “V7.0: The aircraft is equipped with a TCAS unit with the Version 7.0 software, and a Mode S transponder with 25ft altitude quantization”, “V7.1: The aircraft is equipped with a TCAS unit with the Version 7.1 software, and a Mode S transponder with 25ft altitude quantization.” on pg 5-6: two explicitly different software versions, V7.0 (baseline) and V7.1, tested against same encounter set).
As per Claim 27 and 35, Wei et al. fails to teach explicitly wherein the one or more determined differences include at least one indication of an improvement of the recorded output over the one or more baseline output values.
Espindle et al. teaches wherein the one or more determined differences include at least one indication of an improvement of the recorded output over the one or more baseline output values (“WhenV7.1 changes the vertical miss distance (VMD) compared to V7.0, VMD increases 91% of the time.”, “All supporting metrics support the same conclusion, that more risk lies in remaining with the status-quo over upgrading to V7.1.” on pg iv).
As Claim 28, Wei et al. teaches wherein a set of test data includes a plurality of subsets of test data, each subset associated with a respective combination of the plurality of combinations of test conditions ([0031], [0033], [0039], [0042], Fig. 7: the stored model and behavioral data are partitioned into per-scenario subsets, each associated with a respective combination of conditions of the simulated virtual environment).
As per Claim 29, 36, and 40, Wei et al. teaches wherein the environmental operating system is one of a smart home operating system and an autonomous vehicle operating system (Title, Abstract, [0016]-[0017]).
As per Claim 41, Wei teaches wherein generating the simulated sensor data comprises: receiving a selection of the plurality of test conditions, each test condition comprising a respective indication of sensors available for use by the autonomous operation feature ([0039] “The event control interface 256 can be implemented to provide the event inputs SIM_EVT before a simulated mission or during a simulated mission, such as to control the conditions of the simulated virtual environment”; [0032] “each sensor device associated with the autonomous vehicle can include a variety of detailed specifications, such as frame rate, resolution, field of view, dynamic range, mounting positions, and data formats”: the received event-script test conditions are defined over the modeled suite of sensors available to the control system, including their operating specifications and configuration within the simulated environment); and
generating the simulated sensor data based upon the selection ([0044] “the simulation integrator 314 can access the sensor models 60 to translate the event entity 304 into sensor data”: the simulated sensor data is generated from the selected event and sensor-model conditions).
As per Claim 42, Wei teaches wherein the simulated sensor data further includes simulated control data from one or more other autonomous operation features ([0020] “the simulation driver 20 is configured to receive the feedback signals SIM_CMD from the autonomous vehicle control system 12 to update the conditions and status of the simulated mission”; [0045]: control outputs of the vehicle’s operational features are integrated back into the simulation data stream provided to the system under test).
Response to Arguments
9. Applicant's arguments filed on 07/16/2026 have been fully considered but they are not persuasive.
Applicants asserted that:
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However, as set forth in the rejection under 35 U.S.C. 112(a) above, the cited paragraphs and FIG. 10 do not describe adjusting the simulated sensor data based upon the quality metric; paragraph [0260] describes adjustment based upon the software-routine output data only, and the quality metric is generated at the terminal step of the disclosed method ([0246], [0262]-[0263]; FIG. 10, blocks 1020 and 1024).
As per the double patenting rejections: Applicant traverses “based at least in part upon the scope of the pending claims” (Remarks, pg. 9) without specifically pointing out how the pending claims are patentably distinct from the reference claims. A general allegation that the claims define a patentable invention, without specifically pointing out the supposed errors in the rejection, does not comply with 37 CFR 1.111(b). The amendment does not alter the anticipated-by or obvious-variation relationship between the pending claims and the reference claims. Thus the double patenting maintains until a properly filed terminal disclaimer in compliance with 37 CFR 1.321(c) is filed and approved.
As per 103 rejection, applicants have argued that:
Specifically, none of these references discloses or even suggests recording output generated from multiple parallel instances of an emulator program, [1] the recorded output including control decisions generated during each instance of the emulator program for the control of one or more settings of the autonomous operation feature in response to a corresponding input sequence of simulated sensor data, generating a quality metric based on the recorded output, and adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations.[2]
Rather, Wei merely describes "feedback" in the sense of providing displayed or audio outcomes of a simulated vehicle operation;[3] Medium merely describes using simulation of "rare or wacky" scenarios to test a "stability" of an autonomous driving system;[4] and Espindle merely describes comparing changes in responses based upon a software upgrade.[5]
As per [1], in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Examiner relies on the teaching in Wei for recording output generated in response to the simulated sensor inputs while Medium is relied upon for the simultaneous plurality of emulator instances, and Espindle for the quality metric indicating risk levels, and Lee for the metric-driven adjustment of the simulated data. Further regarding the limitation “recording output generated from multiple parallel instances of an emulator program”, the argument is unpersuasive as Wei supplies the recording of control-decision output in an event log ([0018], [0045]), and Medium supplies the simultaneous plurality of instances (pg. 3 & 5) as rejected above.
As per [2], applicant’s arguments with respect to the amended limitation “adjusting the simulated sensor data based upon the recorded output and the quality metric for providing to the emulator program in one or more further implementations” 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 – Lee et al.
As per [3], applicant’s characterization about Wei is not consistent with Wei’s disclosure. Wei’s feedback is consumed by the simulation machinery itself, not merely displayed. In particular, see paragraphs [0018], [0020], [0045] of Wei where the simulation driver receives the feedback signals SIM CMD from the autonomous vehicle control system to update the conditions and status of the simulated mission and the recorded signals include output signals corresponding to the control of the autonomous vehicle and the reactive behavior of the control system in response to the simulated sensor data.
As per [4], applicant’s characterization about Medium omits the simulator disclosure of Medium actually relied upon. As explained above in the rejection, Medium is relied upon for the simultaneous plurality of emulator instances
As per [5], applicant’s characterization about Espindle concedes the teaching for which Espindle is applied. As explained above in the rejection, Espindle computes, from recorded simulation output, a risk ratio that expressly quantifies risk levels (§ 2.3.1, pg. 6), and the version-to-version comparison what dependent claims 26 and 34 recite, while the determined differences and indications of improvement meet dependent claims 25, 27, 33, 35, and 39 (pg. 7; Table 1). Thus, 103 rejection maintains.
Conclusion
10. 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.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188
Prosecution Insights