DETAILED ACTION
This is a response to the Amendment to Application # 18/761,542 filed on April 20, 2026 in which claims 1-5 and 7-10 were amended and claim 6 was cancelled.
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
Claims 1-5 and 7-10 are pending, which are rejected under 35 U.S.C. §§ 112(b) and 103.
Claim Interpretation
Claim 1 includes the limitation “wherein the trained algorithm: is configured to model sensor-specific measurement behavior of the virtual second vehicle sensor so as to replace use of a physical sensor model for the virtual second vehicle sensor.” (Emphasis added). This appears to recite the intended use of sensor modeling. “An intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often . . . appear in the claim’s preamble,” In re Stencel, 828 F.2d 751, 754 (Fed. Cir. 1987), a statement of intended use or purpose can appear elsewhere in a claim. Id; Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1468 (Fed. Cir. 1990); see also Roberts v. Ryer, 91 U.S. 150, 157 (1875) (‘The inventor of a machine is entitled to the benefit of all the uses to which it can be put, no matter whether he had conceived the idea of the use or not.’). Thus, it is usually improper to construe non-functional claim terms in system claims in a way that makes infringement or validity turn on their function. Paragon Solutions, LLC v. Timex Corp., 566 F.3d 1075, 1091 (Fed. Cir. 2009).
Therefore, the “so as to” clause is not accorded any patentable weight.
Claims 7 and 8 refer to an “upstream sensor data algorithm.” This does not appear to be a known term of art. However, the present specification states that “[t]he upstream sensor data algorithm can include a neural network [sic] the parameters of which have been learned.” (Spec. 6, ll. 16-17). While not limiting, the examiner shall interpret an upstream sensor data algorithm to at least include any neural network where the parameters have been learned.
Claim Objections
Claim 1 is objected to because of the following informalities: This claim contain “and/or” language. While definite, the preferred verbiage for such language is “at least one of A and B,” See Ex parte Gross (PTAB 2014) (App. S.N. 11/565,411), at Page 4, Footnote 1. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 and 7-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 1, these claims have been amended to include the limitation “generating second modeled sensor measurement data corresponding to the virtual second vehicle sensor by applying a trained algorithm to data derived from virtual object data of the virtual vehicle surroundings model, including first modeled sensor data corresponding to a virtual first vehicle sensor that maps the first vehicle sensor, a combined sensor data set being formed that includes (a) the second modeled sensor measurement data and (b) either (i) the first sensor measurement data or (ii) the first modeled sensor data.”
Due to the organization of the clauses in this limitation, it is subject to two, mutually exclusive interpretations. First, it may be interpreted as “generating second modeled sensor measurement data corresponding to the virtual second vehicle sensor by applying a trained algorithm to data derived from virtual object data of the virtual vehicle surroundings model, including first modeled sensor data corresponding to a virtual first vehicle sensor that maps the first vehicle sensor[[,]] and a combined sensor data set being formed that includes (a) the second modeled sensor measurement data and (b) either (i) the first sensor measurement data or (ii) the first modeled sensor data.”
Second, this may be interpreted as “generating second modeled sensor measurement data corresponding to the virtual second vehicle sensor by applying a trained algorithm to data derived from virtual object data of the virtual vehicle surroundings model a combined sensor data set being formed that includes (a) the second modeled sensor measurement data and (b) either (i) the first sensor measurement data or (ii) the first modeled sensor data, where the virtual vehicle surroundings model includes first modeled sensor data corresponding to a virtual first vehicle sensor that maps the first vehicle sensor.”
“[I]f a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex parte Miyazaki, 89 USPQ2d 1207, 1211 (BPAI 2008) (precedential). See also Ex parte McAward, Appeal 2015-006416 (PTAB 2017) (precedential) (affirming the holding in Ex parte Miyazaki).
Therefore, this claim is indefinite.
Regarding claims 2-5 and 7-10, these claims depend from claim 1 above and, therefore, inherit the rejection of that claim.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. § 103(a), the Examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicants are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the Examiner to consider the applicability of 35 U.S.C. § 103(c) and potential 35 U.S.C. §§ 102(e), (f) or (g) prior art under 35 U.S.C. § 103(a).
Claims 1-5 and 7-9 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Sokolov et al., US Publication 2022/0242450 (hereinafter Sokolov) in view of Moustafa et al., US Publication 2022/0126864 (hereinafter Moustafa).
Regarding claim 1, Sokolov discloses a method for vehicle simulation comprising “maintaining, for a scenario, a virtual vehicle surroundings model that represents objects in a simulated environment of the vehicle and includes a virtual second vehicle sensor” (Sokolov ¶ 74) where one or more virtual worlds are implemented (i.e., maintained) that represent real machines, which are participating in scenarios, and virtual objects. Additionally, Sokolov discloses “wherein a sensor acquisition range of a first vehicle sensor and a sensor acquisition range of the virtual second vehicle sensor overlap spatially and/or temporally within the virtual vehicle surroundings model” (Sokolov ¶ 90) by including real data from sensors (i.e., a first vehicle sensor) and virtual data from sensors. The use of virtual data makes those sensors “virtual sensors” in a manner consistent with the present specification at page 10, lines 9-23. These sensors overlap because Sokolov ¶ 154 explains that the virtual sensor data is appended to the real-world data sensors meaning that the real-world and virtual sensors overlap in at least temporally. Further, Sokolov discloses “providing first sensor measurement data of at least a first vehicle sensor” (Sokolov ¶ 173) by obtaining spatially-mapped sensor data. Sokolov later describes that real world data is obtained from real world data sources. (Sokolov ¶ 255). Moreover, Sokolov discloses “executing a simulation of a vehicle operating in the scenario.” (Sokolov ¶¶ 73-74). Likewise, Sokolov discloses “the executing including: generating second modeled sensor measurement data corresponding to the virtual second vehicle sensor by applying a trained algorithm to data derived from virtual object data of the virtual vehicle surroundings model” (Sokolov ¶¶ 200-208) by describing the process in which virtual data, representing various virtual sensor data, is integrated into the world model and how data about the objects, both real and virtual, are generated based on deep learning, which is known to those of ordinary skill in the art to be a trained algorithm. The generated state information is generated from object data such as conditions that characterize the ambient environment of the objects in the real and virtual world (i.e., virtual object data of the virtual vehicle surroundings model). Sokolov also discloses “including first modeled sensor data corresponding to a virtual first vehicle sensor that maps the first vehicle sensor” (Sokolov ¶ 154) because the virtual sensor data is appended to the real-world data sensors making the sensor a virtual sensor that maps to a vehicle sensor. Finally, Sokolov discloses “a combined sensor data set being formed that includes (a) the second modeled sensor measurement data and (b) either (i) the first sensor measurement data or (ii) the first modeled sensor data” (Sokolov ¶ 201) by fusing (i.e., combining) the virtual data (i.e., the second modeled sensor measurement data) and the real world sensor data (i.e., the first sensor measurement data).
Although Sokolov discloses a trained algorithm, it does not discuss the specifics of the trained algorithm. Therefore, Sokolov does not appear to explicitly disclose “wherein the trained algorithm: is configured to model sensor-specific measurement behavior of the virtual second vehicle sensor so as to replace use of a physical sensor model for the virtual second vehicle sensor; and is trained using training data that include selectively obtained real-world sensor measurement data of the first vehicle sensor; and simulating evolution of vehicle state and vehicle dynamics over time using the combined sensor data set as simulation input.”
However, Moustafa a method for vehicle simulation using a trained algorithm “wherein the trained algorithm: is configured to model sensor-specific measurement behavior of the virtual second vehicle sensor so as to replace use of a physical sensor model for the virtual second vehicle sensor” (Moustafa 782) where the ground truth data corresponds to simulated vehicle data (i.e., virtual vehicle sensors) including the type of sensors, which is “sensor-specific measurement behavior.” Additionally, Moustafa discloses wherein the trained algorithm “is trained using training data that include selectively obtained real-world sensor measurement data of the first vehicle sensor” (Moustafa ¶ 529) where raw sensor data is supplied to the training algorithm. Finally, Moustafa discloses “simulating evolution of vehicle state and vehicle dynamics over time using the combined sensor data set as simulation input” (Moustafa ¶ 799) where the sensor data is predicted at multiple time steps (i.e., an evolution).
Sokolov and Moustafa are analogous art because they are from the “same field of endeavor,” namely that of simulating autonomous vehicles and vehicle sensors.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sokolov and Moustafa before him or her to replace the trained algorithm of Sokolov with the trained algorithm of Moustafa.
The motivation/rationale for doing so would have been that of simple substitution. See KSR Int’l Co v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(B). Sokolov differs from the claimed invention by including a generic trained algorithm in place of the specifically claimed trained algorithm. Further, Moustafa teaches that claimed trained algorithm was well known in the art. One of ordinary skill in the art could have predictably substituted the trained algorithm of Moustafa for the trained algorithm of Sokolov because both are trained algorithms designed to accomplish the same results.
Regarding claim 2, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the second modeled sensor measurement data are generated by applying the trained algorithm using the first modeled sensor data corresponding to the virtual first vehicle sensor as input” (Sokolov ¶ 207) where the deep learning algorithm is applied to all the sensor data, both real and virtual, to generate the next most probably state of an object (i.e., the second modeled sensor measurement data).
Regarding claim 3, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the first sensor measurement data are enriched with the first modeled sensor data, and the combined sensor data set includes the enriched first sensor measurement data” (Sokolov ¶ 201) where the process of fusion is “enrichment” within the plain and ordinary meaning of the term.
Regarding claim 4, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the second modeled sensor measurement data are generated by applying the trained algorithm to the virtual object data of the virtual vehicle surroundings model” (Sokolov ¶¶ 200-208) where the deep learning is applied to all the objects, real and virtual, in the virtual world.
Regarding claim 5, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the selectively obtained real-world sensor measurement data are formed from components of the first sensor measurement data assigned to at least one object in the sensor acquisition range of the first vehicle sensor” (Sokolov ¶ 253) by disclosing a plurality of possible sensors for measuring data. This limitation appears to merely require that the sensor data is based detected objects within range of the sensor. Each of the provided sensor types are known to perform in this manner.
Regarding claim 7, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the selectively obtained real-world sensor measurement data form training data of an upstream algorithm that generates additional modeled sensor data based on the virtual object data of the virtual vehicle surroundings model” (Moustafa ¶ 523) where the sensor data is real-world image data that is used to derive additional sensor information via implemented AI algorithms to sense data such as driver fatigue. Further, the combination of Sokolov and Moustafa discloses “the additional modeled sensor data forming further training data of the trained algorithm” (Moustafa ¶ 522) where the sensor data is used to train the driver state model.
Regarding claim 8, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 7 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein input data of the upstream algorithm include first modeled sensor data corresponding to the virtual first vehicle sensor that maps the first vehicle sensor in the virtual vehicle surroundings model” (Moustafa ¶ 523) where the input into the AI algorithms is the sensor data. A person of ordinary skill in the art prior to the effective filing date would have recognized that when Moustafa was combined with Sokolov, the virtual sensor data of Sokolov would be used in place of the sensor data in Moustafa.
Regarding claim 9, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 7 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the training data of the trained algorithm includes virtual object data of the virtual vehicle surroundings model” (Moustafa ¶ 430) where the object data is used to train the algorithm. A person of ordinary skill in the art prior to the effective filing date would have recognized that when Moustafa was combined with Sokolov, the virtual object data of Sokolov would be used in place of the object data in Moustafa.
Regarding claim 10, the combination of Sokolov and Moustafa discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Sokolov and Moustafa discloses “wherein the simulating of the evolution of the vehicle state and vehicle dynamics includes actuating at least one simulated vehicle actuator assigned to a vehicle model depending on sensor data processing of the combined sensor data set, the actuation affecting the vehicle dynamics within the simulation” (Sokolov ¶ 159) where the planning and control subsystem controls the vehicle actuators in response to “all the data sent to it,” which includes processed sensor data. Because Sokolov discloses a hybrid real-world and virtual world (Sokolov ¶ 168) all actions of the vehicle affect the dynamics both in the simulation and in the real-world.
Response to Arguments
Applicant’s arguments filed April 20, 2026, with respect to the rejection of claims 1-5 and 7-10 under 35 U.S.C. § 112(b) and the rejection of claims 1-5 and 7-10 under 35 U.S.C. § 101 (Remarks 5, 11-14) have been fully considered and are persuasive. The rejection of claims 1-5 and 7-10 under 35 U.S.C. § 112(b) and the rejection of claims 1-5 and 7-10 under 35 U.S.C. § 101 have been withdrawn.
Applicant’s arguments filed April 20, 2026, with respect to the rejection of claims 1-5 and 7-10 under 35 U.S.C. §§ 102 and 103 (Remarks 6-11) have been considered but are moot in view of the new ground(s) of rejection.
Applicant's arguments filed April 20, 2026, with respect to the objection of 1 and 10 have been fully considered but they are not persuasive. Specifically, Applicant states “in Ex parte Gross, Appeal 2011-004811 (2014), concerning U.S. Pat. App. Ser. No. 11/565,411, the Patent Trial and Appeal Board stated that this term is not objectionable” and “the term “’and/or’ appears numerous times in the Manual of Patent Examining Procedure (MPEP) itself.” (Remarks 5). Applicant continues that the present Office Action itself acknowledges that this language is definite, but asserts that ‘at least one of’ is preferred” and “[e]ven if this were true, there is no requirement to amend the claims to include such verbiage.” (Remarks 5). The examiner disagrees.
First, Applicant materially misrepresents the holding of Ex parte Gross. The Patent Trial and Appeal Board never states that “this term is not objectionable.” In fact, the Patent Trial and Appeal Board has no authority over objections at all because objectionable subject matter is not subject to review by the Patent Trial and Appeal Board. See MPEP § 706.01. Applicant is reminded of the duty of candor and good faith in its dealing with the office. See 37 C.F.R. § 1.56.
Further, Applicant’s argument that “‘and/or’ appears numerous times in the Manual of Patent Examining Procedure (MPEP) itself” is nonsensical because the MPEP is not claim language.
Finally, as stated in the MPEP, an objection is made when “the form of the claim … is improper.” See MPEP § 706.01. the use of “and/or” is unquestionably a matter of form and is improper as indicated by the Patent Trial and Appeal Board.
Therefore, Applicant’s argument is unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Donderici, US Publication 2024/0140473, System and method for including overlapping real and virtual sensors.
Wittmeier et al., US Publication 2024/0176927, System and method for including overlapping real and virtual sensors.
Donderici, US Publication 2025/0020795, System and method for including overlapping real and virtual sensors.
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 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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 ANDREW R DYER whose telephone number is (571)270-3790. The examiner can normally be reached Monday-Thursday 7:30-4:30.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662
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