DETAILED ACTION
This is a response to Application # 18/761,542 filed on July 2, 2024 in which claims 1-10 were presented for examination.
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 1-10 are pending, of which claims 1-10 are rejected under 35 U.S.C. § 101, claims 2-4 and 6-9 are rejected under 35 U.S.C. § 112(b), claims 1-9 are rejected under 35 U.S.C. § 102(a)(1), and claim 10 is rejected under 35 U.S.C. § 103.
Priority
Receipt is acknowledged of certified copies of papers required by 37 C.F.R. § 1.55.
Claim Interpretation
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
Claims 1 and 10 are objected to because of the following informalities: These claims 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. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 1, this claim is directed to an abstract idea without significantly more. The claims recite, when considered individually or as a whole, a method for predicting a sensor value.
The limitations “calculating virtual object data of a virtual vehicle surroundings model that includes a virtual second vehicle sensor, wherein a sensor acquisition range of the first vehicle sensor and a sensor acquisition range of the virtual second vehicle sensor overlap spatially and/or temporally in the vehicle surroundings model; calculating at least second modeled sensor data of the virtual second vehicle sensor using a trained first sensor data algorithm depending on the virtual object data, wherein the first sensor data algorithm is based on a training process using training data that include selective first sensor measurement data of the first vehicle sensor” under the broadest reasonable interpretation, cover performance of these limitations in the mind and/or “by a human using a pen and paper.” See MPEP § 2106.04(a)(2)(III). For example, a human mind is capable of reading a vehicle sensor, such as speedometer, and using previous experiences (i.e., a trained sensor data algorithm) determine likely speeds in imagined alternate (i.e., virtual) environments. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, these claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim first merely recite the additional element “providing first sensor measurement data of at least a first vehicle sensor.” This merely describes data gathering. See MPEP § 2106.05(g). Additionally, this claim merely recites “outputting a sensor data set that at least includes the first sensor measurement data and the second modeled sensor data.” This merely describes insignificant extra-solution activities. See MPEP § 2106.05(g).
Therefore, this is not a “practical application.” Additionally, this is not “something more” because it is a well-understood, routine, and conventional activity that cannot provide an inventive concept. See MPEP § 2106.05(d) and Bruns et al., US Publication 2020/0384989, as described below.
Therefore, this claim is not patent eligible.
Regarding claims 2-9, this claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually or as a whole. For example, these claims merely require additional mental steps, which are part of the abstract idea.
Therefore, these claims are not patent eligible.
Regarding claim 10, this claim is directed to an abstract idea without significantly more. The claims recite, when considered individually or as a whole, a method for predicting a sensor value.
The limitations “providing a vehicle model for simulating the vehicle dynamics of a vehicle; sensor data processing including processing a sensor data set generated by: … calculating virtual object data of a virtual vehicle surroundings model that includes a virtual second vehicle sensor, wherein a sensor acquisition range of the first vehicle sensor and a sensor acquisition range of the virtual second vehicle sensor overlap spatially and/or temporally in the vehicle surroundings model, calculating at least second modeled sensor data of the virtual second vehicle sensor using a trained first sensor data algorithm depending on the virtual object data, wherein the first sensor data algorithm is based on a training process using training data that include selective first sensor measurement data of the first vehicle sensor” under the broadest reasonable interpretation, cover performance of these limitations in the mind and/or “by a human using a pen and paper.” See MPEP § 2106.04(a)(2)(III). For example, a human mind is capable of reading a vehicle sensor, such as speedometer, and using previous experiences (i.e., a trained sensor data algorithm) determine likely speeds in imagined alternate (i.e., virtual) environments. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, these claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim merely recite the additional element “providing first sensor measurement data of at least a first vehicle sensor.” This merely describes data gathering. See MPEP § 2106.05(g). Next, this claim merely recites “outputting the sensor data set that at least includes the first sensor measurement data and the second modeled sensor data; and actuating at least one simulated vehicle actuator assigned to the vehicle model depending on the sensor data processing.” This merely describes insignificant extra-solution activities. See MPEP § 2106.05(g).
Therefore, this is not a “practical application.” Additionally, this is not “something more” because it is a well-understood, routine, and conventional activity that cannot provide an inventive concept. See MPEP § 2106.05(d), Bruns et al., US Publication 2020/0384989, and Lee et al., US Publication 2024/0233396, as described below.
Therefore, this claim is not patent eligible.
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 2-4 and 6-9 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 2, this claim includes the limitation “wherein the second modeled sensor data are calculated as input data of the first sensor data algorithm depending on first modeled sensor data of a virtual first vehicle sensor that maps the first vehicle sensor in the vehicle surroundings model calculated from the virtual object data.” (Emphasis added).
This limitation is subject to multiple, mutually exclusive interpretations. First, this may be interpreted as the calculation of the second modeled sensor data is dependent on the first modeled sensor data. In other words, under this interpretation, the calculation of the second modeled sensor data is conditional upon the first modeled sensor data being mapped. Thus, under this interpretation, this limitation may not receive any patentable weight.1
Second, this may be interpreted as the first sensor data algorithm being what depends on the first model sensor data mapping the first vehicle sensor in the model.
Third, this may be interpreted as the first sensor data algorithm receiving as input both the second modeled sensor data and the first modeled sensor data.
“[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 claim 4, this claim includes the limitation “wherein the second modeled sensor data are calculated directly as input data of the first sensor data algorithm depending on the virtual object data.” (Emphasis added). This limitation is subject to two, mutually exclusive interpretations.
First, this limitation may be interpreted as the calculation of the second modeled sensor data being conditional upon the virtual object data. Thus, under this interpretation, this limitation may not receive any patentable weight.2
Second, this limitation may be interpreted as the first sensor data algorithm receiving as input both the second modeled sensor data and the virtual object data.
“[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 4, 6, 7, and 9, these claims use the terms “directly” or “indirectly,” respectively. These terms are relative terms that render the claims indefinite. The terms “directly” and “indirectly” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, a person of ordinary skill in the art would not be able to determine how close of a relationship the components must have to be deemed “directly” or “indirectly” used as claimed.
Regarding claims 3, 8, and 9, these claims depend from one of the above claims and, therefore, inherit the rejection of that claim.
Claim Rejections - 35 U.S.C. § 102
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 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 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.
Claims 1-9 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Bruns et al., US Publication 2020/0384989 (hereinafter Bruns).
Regarding claim 1, Bruns discloses a method for generating sensor data, comprising the following steps “providing first sensor measurement data of at least a first vehicle sensor” (Bruns ¶ 10) by “detecting a first stat set of sensor data which is measured by a sensor device of the driver assistance system, i.e. real sensor data.” Additionally, Bruns discloses “calculating virtual object data of a virtual vehicle surroundings model that includes a virtual second vehicle sensor, wherein a sensor acquisition range of the first vehicle sensor and a sensor acquisition range of the virtual second vehicle sensor overlap spatially and/or temporally in the vehicle surroundings model” (Bruns ¶¶ 11-12) by generating a “representation” of the sensor data set using a classification algorithm. Bruns gives examples of the real and virtual sensor data being the same “downward-pointing triangle,” meaning that overlap spatially and/or temporally. Further, Bruns discloses “calculating at least second modeled sensor data of the virtual second vehicle sensor using a trained first sensor data algorithm depending on the virtual object data, wherein the first sensor data algorithm is based on a training process using training data that include selective first sensor measurement data of the first vehicle sensor” (Bruns ¶ 12) by “generating a second data set of simulated, i.e. virtual sensor data.” Finally, Bruns discloses “outputting a sensor data set that at least includes the first sensor measurement data and the second modeled sensor data” (Bruns ¶ 23, see also ¶ 28) where the first data set is “expanded” with the simulated data.
Regarding claim 2, Bruns discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Bruns discloses “wherein the second modeled sensor data are calculated as input data of the first sensor data algorithm depending on first modeled sensor data of a virtual first vehicle sensor that maps the first vehicle sensor in the vehicle surroundings model calculated from the virtual object data” (Bruns ¶ 18) where Bruns indicates that this process takes place repeatedly, meaning that the virtual sensor data is used as input data of the first sensor data algorithm when the first data set is expanded with that simulation data.
Regarding claim 3, Bruns discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, Bruns discloses “wherein the first sensor measurement data are enriched with the first modeled sensor data, and the sensor data set includes the enriched first sensor measurement data” (Bruns ¶¶ 18, 23) by expanding the first data set with the first modeled sensor data, as explained in the rejection to claim 2 above.
Regarding claim 4, Bruns discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Bruns discloses “wherein the second modeled sensor data are calculated directly as input data of the first sensor data algorithm depending on the virtual object data” (Bruns ¶ 18) where Bruns indicates that this process takes place repeatedly, meaning that the virtual sensor data is used as input data of the first sensor data algorithm when the first data set is expanded with that simulation data.
Regarding claim 5, Bruns discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Bruns discloses “wherein the selective first sensor measurement data are selectively 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” (Bruns ¶ 11) where an example is given of an entire image but only the “yield sign in the upper right quadrant of the image” is selected as the sensor measurement data.
Regarding claim 6, Bruns discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Bruns discloses “wherein the training process involves training the first sensor data algorithm directly with the training data including the selective first sensor measurement data.” (Bruns ¶ 11).
Regarding claim 7, Bruns discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Bruns discloses “wherein the training process involves indirectly training the first sensor data algorithm with the training data including the selective first sensor measurement data” ” (Bruns ¶ 18) where Bruns indicates that this process takes place repeatedly, meaning that the virtual sensor data is used as input data of the first sensor data algorithm when the first data set is expanded with that simulation data. This further means that after some number of iterations, the training will become “indirect” training. Further, Bruns discloses “wherein the selective first sensor measurement data form training data of an upstream sensor data algorithm which, when used in the training process, in turn generates further virtual sensor data depending on the virtual object data of the virtual vehicle surroundings model that then form the training data of the first sensor data algorithm” (Bruns ¶ 25) by disclosing that the neural network may us a gradient descent method, which a person of ordinary skill in the art would understand to learn its parameters through an iterative process to reduce the error of the parameters, making it an “upstream sensor data algorithm” within Applicant’s stated interpretation. This process generates further virtual sensor data depending on the virtual vehicle surrounding model by repeating the processes and enriching the real sensor data with the virtual data.
Regarding claim 8, Bruns discloses the limitations contained in parent claim 7 for the reasons discussed above. In addition, Bruns discloses “ wherein, when used in the training process, the input data of the upstream sensor data algorithm include first modeled sensor data of a virtual first vehicle sensor that maps the first vehicle sensor in the vehicle surroundings model calculated from the virtual object data” (Bruns ¶ 18) by repeating the processes and enriching the real sensor data with the virtual data.
Regarding claim 9, Bruns discloses the limitations contained in parent claim 7 for the reasons discussed above. In addition, Bruns discloses “ wherein the training data of the first sensor data algorithm directly includes the virtual object data” (Bruns ¶ 18) where Bruns indicates that this process takes place repeatedly, meaning that the virtual object data is used as input data of the first sensor data algorithm when the first data set is expanded with that simulation data.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
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. Applicants are advised of the obligation under 37 C.F.R. § 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.
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Bruns in view of Lee et al., US Publication 2024/0233396 (hereinafter Lee).
Regarding Claim 10, Bruns discloses a method for vehicle simulation, comprising the following steps “providing a vehicle model for simulating the vehicle dynamics of a vehicle” (Bruns ¶ 16) by creating a classification algorithm, which is a model because it represents the world observable by the vehicle’s sensors. Additionally, Bruns discloses “sensor data processing including processing a sensor data set generated by: providing first sensor measurement data of at least a first vehicle sensor” (Bruns ¶ 10) by “detecting a first stat set of sensor data which is measured by a sensor device of the driver assistance system, i.e. real sensor data.” Further, Bruns discloses “calculating virtual object data of a virtual vehicle surroundings model that includes a virtual second vehicle sensor, wherein a sensor acquisition range of the first vehicle sensor and a sensor acquisition range of the virtual second vehicle sensor overlap spatially and/or temporally in the vehicle surroundings model” (Bruns ¶ 11) by generating a “representation” of the sensor data set using a classification algorithm. Bruns gives examples of the real and virtual sensor data being the same “downward-pointing triangle,” meaning that overlap spatially and/or temporally. Moreover, Bruns discloses “calculating at least second modeled sensor data of the virtual second vehicle sensor using a trained first sensor data algorithm depending on the virtual object data, wherein the first sensor data algorithm is based on a training process using training data that include selective first sensor measurement data of the first vehicle sensor” (Bruns ¶ 12) by “generating a second data set of simulated, i.e. virtual sensor data.” Finally, Bruns discloses “outputting the sensor data set that at least includes the first sensor measurement data and the second modeled sensor data” (Bruns ¶ 23, see also ¶ 28) where the first data set is “expanded” with the simulated data.
Although Bruns discloses that this method is used in a vehicle, it does not appear to explicitly disclose “actuating at least one simulated vehicle actuator assigned to the vehicle model depending on the sensor data processing.”
However, Lee discloses a method for simulating a vehicle including the step of “actuating at least one simulated vehicle actuator assigned to the vehicle model depending on the sensor data processing” (Lee ¶ 39) by issuing a “vehicle control instruction [that] includes an initial acceleration of the virtual vehicle.”
Bruns and Lee are analogous art because they are from the “same field of endeavor,” namely that of simulations for autonomous vehicles.
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 Bruns and Lee before him or her to modify the vehicle simulation of Bruns to include the actual control of the vehicle of Lee.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Bruns teaches the “base device” for simulating the sensor data of a vehicle. Further, Lee teaches the “known technique” for using simulated sensor data to control a simulated vehicle that is applicable to the base device of Bruns. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system because using the simulated data is the natural result of simulating the data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Gonzalez Aguirre et al., US Publication 2019/0138848, System and method for simulating sensor data.
Van der Velden et al., US Publication 2020/0342152, System and method for simulating sensor data.
Peake et al., US Publication 2020/0074266, System and method for simulating sensor data.
Nasle, US Publication 2021/0326731, System and method for simulating sensor data.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662
1 Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential).
2 Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential).