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 .
Response to Amendment
The request for reconsideration filed November 19, 2025 has been received, reviewed, and considered. No claims were canceled, amended, or newly added. Claims 1-15 remain pending in the instant application.
Response to Arguments
Applicant’s arguments with respect to claims 1-15 have been considered but are found to be unpersuasive.
Regarding 35 USC 101, Applicant argues that the claim considered as a whole is directed to practical application. Applicant refers to specification [0008] for support of technological improvement.
Examiner submits that obtaining values, inputting obtained values, and generating sensor data are data gathering activities using generic computer components. Applicant further argues features and practical application aspects based on specification paragraphs which are not explicitly recited in the claims. Also, simulating with details is not positively recited as an individual step of the process. Therefore, Examiner maintains the 35 USC 101 rejections as stated below.
Regarding 35 USC 103, Applicant argues that the "steered road wheel angle" in Micks is simply a vehicle parameter indicating the orientation of the vehicle's own wheels. It is not an angle between the vehicle and a sensed "target object”.
Applicant further argues that Micks fails to disclose "obtaining values for a plurality of attributes of a target object to be sensed by a sensor simulator in the autonomous driving simulation … the plurality of attributes comprising . . . an angle between the wheel and an autonomous vehicle." Moreover, neither Kojima's disclosure regarding correcting distortions and luminance unevenness in video projections nor Pradeepth's disclosure regarding high-frequency radar detection of wheel micro motions remedies this deficiency.
Examiner submits that the claim language does not distinguish it clearly that the claimed “angle” is angle between the vehicle and a sensed "target object". Therefore, the arguments are moot as not being directed to claimed feature. The rejections are maintained as stated below.
Claim Rejections - 35 USC § 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 1-15 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 USC 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong 1 - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2A Prong 2 - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are further evaluated to determine if the elements are beyond what is well- understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-7 are directed to a method which falls within one of the four statutory categories of a process.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
the sensor simulator simulating an active sensor that outputs rays to an object and receives reflections of the rays from the object; which can be reasonably read as evaluating the sensors of the model. This is making an evaluation which can be performed mentally. As such, this limitation, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the mind with or without a physical aid (MPEP 2106.04(a)(2)(III)(B)) and is thus the recitation of the judicial exception of abstract ideas as a mental process. Therefore, the claim recites a judicial exception.
generating sensor data corresponding to the target object based on the obtained reflection rate, which can be reasonably read as evaluating the sensors of the model. This is making an evaluation which can be performed mentally. As such, this limitation, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the mind with or without a physical aid (MPEP 2106.04(a)(2)(III)(B)) and is thus the recitation of the judicial exception of abstract ideas as a mental process. Therefore, the claim recites a judicial exception.
wherein the target object is a wheel and the plurality of attributes comprise a number of wheel spokes, a wheel speed, and an angle between the wheel and an autonomous vehicle in the autonomous driving simulation. This limitation is a further elaboration of the above limitation which was found to be a mental process. Thus, this limitation is also considered a mental process and thus reciting a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
inputting the obtained values for the plurality of attributes to a predetermined reflection rate table, in order to obtain a reflection rate mapped to the obtained values; and This limitation has been identified as the insignificant extra solution activity of necessary data gathering (MPEP 2106.05(g)). The courts have ruled adding insignificant extra-solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)).
obtaining values for a plurality of attributes of a target object to be sensed by a sensor simulator in the autonomous driving simulation. This limitation has been identified as the insignificant extra solution activity of necessary data gathering (MPEP 2106.05(g)). The courts have ruled adding insignificant extra-solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)).
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities:
inputting the obtained values for the plurality of attributes to a predetermined reflection rate table, in order to obtain a reflection rate mapped to the obtained values; and - This limitation has been identified as the insignificant extra solution activity of necessary data gathering (MPEP 2106.05(g)). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the sensor data corresponding to the target object can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud.. The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal.")
obtaining values for a plurality of attributes of a target object to be sensed by a sensor simulator in the autonomous driving simulation - This limitation has been identified as the insignificant extra solution activity of necessary data gathering (MPEP 2106.05(g)). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the sensor data corresponding to the target object can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal.")
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 USC 101.
Claim 8:
Step 1: Claim 8 and its dependent claims 9-14 are directed to a machine which falls within one of the four statutory categories of a process.
Step 2A Prong 1: Claim 8 recites substantially similar abstract idea limitations to those found in the analysis of Claim 1. Therefore, Claim 8 is directed to the same judicially recognized exception of abstract ideas as a mental process, as identified for Claim 1.
Step 2A Prong 2: Claim 8 recites substantially similar additional limitations to those found in the analysis of Claim 1. Claim 8 recite additional elements noted in italics.
at least one memory configured to store computer program code; and This limitation has been identified as mere instructions to apply an exception MPEP 2106.05(f). The limitation simply defines instructions for a generic memory component. The courts have ruled that merely using a computer as a tool to perform an abstract idea does not integrate the judicial exception into a practical application (MPEP 2106.04(d)).
at least one processor configured to execute the computer program code to: This limitation has been identified as mere instructions to apply an exception MPEP 2106.05(f). The limitation simply defines instructions for a generic processor component. The courts have ruled that merely using a computer as a tool to perform an abstract idea does not integrate the judicial exception into a practical application (MPEP 2106.04(d)).
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as linking the judicial exception to Mere Instructions To Apply An Exception (MPEP 2106.05(f)), which does not amount to significantly more than the abstract idea. With the additional elements viewed as part of the ordered combination, the claim as a whole does not appear to amount to more than the judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 USC 101.
Claim 15:
Step 1: Claim 15 is directed to an article of manufacture which falls within one of the four statutory categories.
Step 2A Prong 1: Claim 15 recites substantially similar abstract idea limitations to those found in the analysis of Claim 1 and 8. Therefore, Claim 15 is directed to the same judicially recognized exception of abstract ideas as a mental process, as identified for Claim 1 and 8.
Step 2A Prong 2: Claim 8 recites substantially similar additional limitations to those found in the analysis of Claim 1. Claim 15 recite additional elements noted in italics.
A non-transitory computer-readable storage medium, storing instructions executable by at least one processor to perform a method comprising: This limitation has been identified as mere instructions to apply an exception MPEP 2106.05(f). The limitation simply defines instructions for a generic non-transitory computer-readable storage medium. The courts have ruled that merely using a computer as a tool to perform an abstract idea does not integrate the judicial exception into a practical application (MPEP 2106.04(d)).
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as linking the judicial exception to Mere Instructions To Apply An Exception (MPEP 2106.05(f)), which does not amount to significantly more than the abstract idea. With the additional elements viewed as part of the ordered combination, the claim as a whole does not appear to amount to more than the judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 USC 101.
Dependent Claims:
Regarding dependent Claim 2, the abstract idea of independent Claim 1 is further incorporated. Claim 2 additionally recites the limitation further comprising obtaining reflection information defined for the target object. This limitation has been identified as the insignificant extra solution activity of necessary data gathering (MPEP 2106.05(g)). The courts have ruled that adding insignificant extra- solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)). With the additional element viewed as part of the ordered combination, the claim as a whole does not appear to integrate the judicial exception into a practical application (Step 2A Prong 2). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the sensor data corresponding to the target object can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated.. Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud.. The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal"). The courts have recognized the computer function of receiving or transmitting data over a network as well-understood, routine, and conventional activity when claimed in a merely generic manner (MPEP 2106.05(d)(il)(i)). Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception (Step 2B). This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 3, the abstract idea of Claims 1 and 2 from which this claim depend are further incorporated. Claim 3 additionally recites the limitation wherein the reflection information comprises a texture and a material of a reflection surface of the target object. This limitation has been identified as the insignificant extra solution activity of necessary data outputting (MPEP 2106.05(g)). The courts have ruled that adding insignificant extra-solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)). With the additional element viewed as part of the ordered combination, the claim as a whole does not appear to integrate the judicial exception into a practical application (Step 2A Prong 2). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the inputted data is used to calculate ray points which can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal"). The courts have recognized the computer function of receiving or transmitting data over a network as well-understood, routine, and conventional activity when claimed in a merely generic manner (MPEP 2106.05(d)(II)(i)). Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception (Step 2B). This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 4, the abstract idea of Claims 1 and 2 from which this claim depend are further incorporated. Claim 4 additionally recites the limitation wherein the inputting the values for the plurality of attributes to the predetermined reflection rate table comprises selecting the predetermined reflection rate table, corresponding to the obtained reflection information, from among a plurality of predetermined rate tables respectively corresponding to different reflection information, which can be reasonably read to entail making a judgement call on which reflection rate table is the most suitable. This evaluation can be done within the human mind or using a pen and paper as an assistive physical aid. Thus, this limitation includes recitation of the judicial exception of abstract ideas as a mental process (Step 2A Prong 1). The claim does not include any additional elements that integrate the judicial exception into a practical application (Step 2A Prong 2), nor amount to significantly more (Step 2B) than judicial exception. This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 5, the abstract idea of independent Claim 1 is further incorporated. Claim 5 additionally recites the limitation wherein the predetermined reflection rate table is predetermined based on real world reflection rate testing to map values the plurality of attributes of the of the target object to dynamic reflection rates, which can be reasonably read to entail determining the reflection rate table based on observed test values from real sensors in order to map the received simulated values. This evaluation can be done within the human mind or using a pen and paper as an assistive physical aid. Thus, this limitation includes recitation of the judicial exception of abstract ideas as a mental process (Step 2A Prong 1). The claim does not include any additional elements that integrate the judicial exception into a practical application (Step 2A Prong 2), nor amount to significantly more (Step 2B) than judicial exception. This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 6, the abstract idea of independent Claim 1 is further incorporated. Claim 6 additionally recites the limitation wherein the generated sensor data comprises a point cloud for the target object. This limitation has been identified as the insignificant extra solution activity of necessary data outputting (MPEP 2106.05(g)). The courts have ruled that adding insignificant extra- solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)). With the additional element viewed as part of the ordered combination, the claim as a whole does not appear to integrate the judicial exception into a practical application (Step 2A Prong 2). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the inputted data is used to calculate ray points which can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud. The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal"). The courts have recognized the computer function of receiving or transmitting data over a network as well-understood, routine, and conventional activity when claimed in a merely generic manner (MPEP 2106.05(d)(il)(i)). Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception (Step 2B). This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 7, the abstract idea of independent Claim 1 is further incorporated. Claim 7 additionally recites the limitation calculating a vector and a strength of each ray point. This limitation has been identified as explicit recitation of the additional judicial exception of abstract ideas of mathematical concept as mathematical calculations MPEP 2106.04(a)(2)(1)(C) (Step 2A Prong 1). Furthermore, Claim 7 recites the limitation wherein the generating the sensor data comprises inputting the obtained reflection rate and a number of incoming reflected rays to a random point function to calculate ray points. This limitation has been identified as the insignificant extra solution activity of necessary data inputting (MPEP 2106.05(g)). The courts have ruled that adding insignificant extra- solution activity to the judicial exception does not integrate the judicial exception into a practical application (MPEP 2106.04(d)). With the additional element viewed as part of the ordered combination, the claim as a whole does not appear to integrate the judicial exception into a practical application (Step 2A Prong 2). When read in light of the specification, acquiring data from the sensors is recited at a high level of generality and encompasses receiving data over a network because the specification mentions that the inputted data is used to calculate ray points which can be uploaded to a remote server or sent on a network to another terminal. (Specification Paragraph 0041, "Regarding step S103, using the obtained reflection rate information in the predetermined reflection rate table, sensor data corresponding to the target object is generated Specifically, according to an exemplary embodiment, the reflection rate output from the reflection rate table is used to calculate a point cloud for the target object, e.g., LiDAR point cloud or a radar point cloud The generated point may be displayed to a user via a graphical user interface, stored on the memory 102, uploaded to a remote server, or sent on a network to another terminal"). The courts have recognized the computer function of receiving or transmitting data over a network as well-understood, routine, and conventional activity when claimed in a merely generic manner (MPEP 2106.05(d)(II)(i)). Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception (Step 2B). This claim is not eligible subject matter under 35 USC 101.
Regarding dependent Claim 9, the abstract idea of independent Claim 8 is further incorporated. Claim 9 additionally recites limitations that are substantially similar to those recited in Claim 2. Therefore, Claim 9 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 2.
Regarding dependent Claim 10, the abstract idea of Claims 8 and 9 from which this claim depend are further incorporated. Claim 10 additionally recites limitations that are substantially similar to those recited in Claim 3. Therefore, Claim 10 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 3.
Regarding dependent Claim 11, the abstract idea of Claims 8 and 9 from which this claim depend are further incorporated. Claim 11 additionally recites limitations that are substantially similar to those recited in Claim 4. Therefore, Claim 11 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 4.
Regarding dependent Claim 12, the abstract idea of independent Claim 8 is further incorporated. Claim 12 additionally recites limitations that are substantially similar to those recited in Claim 5. Therefore, Claim 12 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 5.
Regarding dependent Claim 13, the abstract idea of independent Claim 8 is further incorporated. Claim 13 additionally recites limitations that are substantially similar to those recited in Claim 6. Therefore, Claim 13 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 6.
Regarding dependent Claim 14, the abstract idea of Claims 8 and 13 from which this claim depend are further incorporated. Claim 14 additionally recites limitations that are substantially similar to those recited in Claim 7. Therefore, Claim 14 is rejected under 35 USC 101 with the same rationale stated previously for the analysis of Claim 7.
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 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.
Claims 1, 2, 6, 8, 9, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Micks et al. (US 2018/0203445 A1, "Micks") in view of Kojima et al. (US 20150022726 A1, "Kojima") and Pradeepth et al. (Vehicle wheel detection using micro doppler effect, "Pradeepth").
Regarding Claim 1, Micks teaches a method of simulating a sensor in an autonomous driving simulation, the method comprising: (Micks 0028, "The models may define the geometry and location of objects in a landscape and may further include other aspects such as reflectivity to laser, RADAR, sound, light, etc. in order to enable simulation of perception of the objects by a sensor.")
obtaining values for a plurality of attributes of a target object to be sensed by a sensor simulator in the autonomous driving simulation, the sensor simulator simulating an active sensor that outputs rays to an object and receives reflections of the rays from the object; (Micks 0061, "In the method 300a, step 306 may include simulating perception of the scenario by the sensor models such that noise is present in the simulated sensor outputs Propagation of RADAR signals may be modeled such that propagation of electromagnetic waves is simulated in sufficient detail to capture multiple reflections that may create "ghost objects" and other noise in the output of the RADAR sensor. Likewise, for LIDAR, raytracing may be performed in sufficient detail to capture multiple reflections and may simulate atmospheric attenuation and scattering of the laser beam of the LIDAR sensor." Where the ghost objects are the target objects of the sensor that is being simulated.)
generating sensor data corresponding to the target object based on the obtained reflection rate, (Micks 0060, "In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value. In some embodiments, the models 106a, 106b may include reflectivity values for exterior surfaces thereof. Accordingly, for points of the point cloud in the point of view of the LIDAR system, the reflectivity value of the structure including each point may be included."; Micks 0030, "As also described below, these statistical models may be evaluated and modified by processing simulated sensor outputs that simulate perception of a scenario by the sensors of the vehicle model 106b.")
wherein the target object is a wheel and the plurality of attributes comprise
What Micks doesn't teach is inputting the obtained values for the plurality of attributes to a predetermined reflection rate table, in order to obtain a reflection rate mapped to the obtained values; and
wherein the target object is a wheel and the plurality of attributes comprise a number of wheel spokes.
What Micks doesn't explicitly teach but Kojima does is inputting the obtained values for the plurality of attributes to a predetermined reflection rate table, in order to obtain a reflection rate mapped to the obtained values; and (Kojima 0049, "the method of obtaining the reflection rate is not limited to the method of obtaining the reflection rate by calculating equation (11), and may be a method of calculating the reflection rate while sequentially looking up a correspondence table of the angle .theta. and the reflection rate as a lookup table. A plurality of types of coefficients may be held to cope with a plurality of screen materials having different screen gains, and the coefficient may be switched for each material.")
Micks and Kojima are analogous art because they are from the same field of endeavor in visual processing. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and Kojima to include inputting a plurality of values to obtain a reflection rate from a lookup table. As mentioned in Kojima 0049, usage of a lookup table allows for accounting for a plurality of different variables such as reflecting materials and angle of view.
What Micks doesn't explicitly teach but Pradeepth does is wherein the target object is a wheel and the plurality of attributes comprise a number of wheel spokes. (Pradeepth Page 4 Paragraph 8, "Simulation of the system design is used to predict the behavior of the system under certain conditions. In this simulation model, the car is modeled by six scatterers.
- Rotation Center
- 5 Spokes of the wheel" Where citation informs upon simulating wheels accounting for number of spokes.)
Micks and Pradeepth are analogous art because they are from the same field of endeavor in simulation of automotive vehicles. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and Pradeepth to include the number of spokes in a wheel model. As mentioned in Pradeepth Page 5 Paragraph 2 and demonstrated in Table 2, the different spokes displayed different velocities compared to the center of the wheel and thus the inclusion of spokes would more accurately simulate the reflection of light back to the sensor due to the more real to life.
Regarding Claim 2, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 1. In addition, Micks teaches further comprising obtaining reflection information defined for the target object. (Micks 0061, "Likewise, for LIDAR, raytracing may be performed in sufficient detail to capture multiple reflections and may simulate atmospheric attenuation and scattering of the laser beam of the LIDAR sensor.")
Regarding Claim 6, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 1. In addition, Micks teaches wherein the generated sensor data comprises a point cloud for the target object. (Micks 0060, "For a LIDAR sensor, a point cloud from the point of view of the LID AR sensor may be simulated, where the points of the point cloud are points of structures of the environment or vehicles 402, 404, 406 of the scenario that are in the field of view of the LIDAR sensor.")
Regarding Claim 8, Micks teaches at least one memory configured to store computer program code; and (Micks 0047, "Processor(s) 202 include one or more processors or controllers that execute instructions stored in memory device".)
at least one processor configured to execute the computer program code to: (Micks 0047, "Processor(s) 202 include one or more processors or controllers that execute instructions stored in memory device".)
The rest of the limitations in Claim 8 are substantially similar to that of Claim 1, thus Claim 8 is rejected under the same rationale as provided for Claim 1.
Regarding Claim 9, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 8. In addition, this claim is analogous to the language of Claim 2, and thus rejected under the same rationale as Claim 2.
Regarding Claim 13, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 8.
In addition, this claim is analogous to the language of Claim 6, and thus rejected under the same rationale as Claim 6.
Regarding Claim 15, Micks teaches a non-transitory computer-readable storage medium, storing instructions executable by at least one processor to perform a method comprising: (Micks 0020, "In selected embodiments, a computer-readable medium may comprise any non-transitory medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.")
The rest of the limitations in Claim 15 are substantially similar to that of Claim 1, thus Claim 15 is rejected under the same rationale as provided for Claim 1.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Micks in view of Pradeepth, Kojima, and English (US 2018/0060459 A1, "English").
Regarding Claim 3, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 2. In addition, Micks teaches wherein the reflection information comprises [a texture and] a material of a reflection surface of the target object. (Micks 0025, "The geometry data 108a may further include material data, such as hardness, reflectivity, or material type.")
What Micks in view of Pradeepth and Kojima doesn't teach is wherein the reflection information comprises a texture of a reflection surface of the target object.
What Micks doesn't explicitly teach but English does is herein the reflection information comprises a texture of a reflection surface of the target object. (English 0167, "In some implementations, SD process 10 may fully implement a suite of example sensors, including LIDAR, Automotive Radar, Stereo Cameras and GPS... In some implementations, SD process 10 may include physics-based enhanced surface rendering using multi-texture mapping. This may improve diffuse texture mapping for geometry in the simulation, where a color image may be mapped to the vertices of the geometry mesh defined by texture coordinates.")
Micks and English are analogous art because they are from the same field of endeavor in simulation of autonomous vehicle sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and English to include the have included texture in reflection information of a reflection surface for a target model. This is because as further discussed in English 0167, the inclusion of texture mapping would improve the visual (and thus reflected light) realism of the rendered object, which would improve the accuracy of the simulation.
Regarding Claim 10, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 9.
Additionally, this claim is analogous to the language of Claim 3, and thus rejected under the same rationale as Claim 3.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Micks in view of Pradeepth, Kojima, and Valois (US 9841763 B1, "Valois").
Regarding Claim 4, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 2.
What Micks teaches is wherein the inputting the values for the plurality of attributes to the predetermined reflection rate table [comprises selecting the predetermined reflection rate table, corresponding to the obtained reflection information, from among a plurality of predetermined rate tables respectively corresponding to different reflection information]. (Micks 0025, "In particular, the database 104 may store vehicle models 106a that include geometry data 108a for the vehicle, e.g. the shape of the body, tires, and any other visible features of the vehicle. The geometry data 108a may further include material data, such as hardness, reflectivity, or material type." Where reflectivity is analogous to reflection rate, and database is read as a data table.)
What Micks in view of Pradeepth and Kojima doesn't teach is selecting the predetermined reflection rate table, corresponding to the obtained reflection information, from among a plurality of predetermined rate tables respectively corresponding to different reflection information.
What Micks doesn't explicitly teach but Valois does is selecting the predetermined reflection rate table, corresponding to the obtained reflection information, from among a plurality of predetermined rate tables respectively corresponding to different reflection information. (Valois Column 2 Lines 45-67, "In some aspects, the predictive configuration system can maintain lookup tables (LUTs) that include any number of sensor configurations for each of the passive or active sensor systems included in the sensor array. The LUTs can be constructed via external sensor characterization tests under a wide range of anticipated operation conditions (e.g., lighting or weather conditions and/or surface surroundings) in order to provide an optimal set of sensor configurations for each operation condition. Additionally or alternatively, the predictive configuration system can .ocr_line, .ocr_header { display:block; }
dynamically determine the configurations for the sensor array by performing an optimization utilizing any number of possible configurations for a particular passive or active sensor system (e.g., the stereo camera system based on the imminent lighting conditions, or the LiDAR system based on a particular reflectance anomaly). In such examples, the optimization can converge on an optimal set of configurations, which the predictive configuration system can select to preemptively anticipate the imminent lighting conditions or resolve the reflectance anomaly." Where citation informs upon lookup tables that account for reflectance/reflection rates and the selection of said tables based on various conditions.)
Micks and Valois are analogous art because they are from the same field of endeavor in utilization of autonomous vehicle sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and Valois to have included selecting a data table from a plurality of data tables. As explained in Valois Column 3 Lines 45-67, having the ability to do so allows for preemptive anticipation of lighting conditions based on a plurality of scenarios or resolve reflectance anomalies.
Regarding Claim 11, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 9.
Additionally, this claim is analogous to the language of Claim 4, and thus rejected under the same rationale as Claim 4.
Claims 5, 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Micks in view of Pradeepth, Kojima, and Kristensen (US 2021/0286923 A1, "Kristensen"). .ocr_line, .ocr_header { display:block; }
Regarding Claim 5, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 1.
Micks in view of Pradeepth and Kojima doesn't teach wherein the predetermined reflection rate table is predetermined based on real world reflection rate testing to map values the plurality of attributes of the of the target object to dynamic reflection rates.
What Micks doesn't explicitly teach but Kristensen does is wherein the predetermined reflection rate table is predetermined based on real world reflection rate testing to map values the plurality of attributes of the of the target object to dynamic reflection rates. (Kristensen Paragraph 0028, "real-world data and/or virtual data may be collected from RADAR and/or LIDAR sensor(s) and used to encode the existence of reflections and values for the reflections such as bearing, azimuth, elevation, range (e.g., time of beam flight), intensity, Doppler velocity, RADAR cross section (RCS), reflectivity, signal-to-noise ratio, some combination thereof, and/or the like.")
Micks and Kristensen are analogous art because they are from the same field of endeavor in simulation of autonomous vehicle sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and Kristensen to include basing the reflection rate data on real world reflection testing. This is because according to Kristensen Paragraph 0028, the real-world data can be used to "derive training data (e.g., input scene configurations and/or ground truth sensor data), which may be used to train the sensor model to predict virtual sensor data for a given scene configuration."
Regarding Claim 7, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 1. Micks in view of Pradeepth and Kojima doesn't teach wherein the generating the sensor data comprises inputting the obtained reflection rate and a number of incoming reflected rays to a random point function to calculate ray points, and calculating a vector and a strength of each ray point. .ocr_line, .ocr_header { display:block; }
What Micks doesn't explicitly teach but Kristensen teaches wherein the generating the sensor data comprises inputting the obtained reflection rate and a number of incoming reflected rays to a random point function to calculate ray points, and calculating a vector and a strength of each ray point. (Kristensen 0044, "the designated reflections and corresponding reflection characteristics may be encoded into corresponding vectors, and the vectors may be concatenated to form a single dimensional input vector"; Kristensen 0028, "real-world data and/or virtual data may be collected from RADAR and/or LIDAR sensor(s) and used to encode the existence of reflections and values for the reflections such as bearing, azimuth, elevation, range (e.g., time of beam flight), intensity" where intensity is read as analogous to ray strength.)
Micks and Kristensen are analogous art because they are from the same field of endeavor in simulation of autonomous vehicle sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Micks and Kristensen to include inputting the obtained reflection rate and number of incoming reflected rays to a function to calculate ray points and the associated vector and strength. As mentioned in Kristensen 0031, the ability for the modeled sensor to perform calculation of ray points (amongst other functions) being included within the simulation means that the simulator can rely on its own functionality instead of third-party dependencies.
Regarding Claim 12, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 8.
Additionally, this claim is analogous to the language of Claim 5, and thus rejected under the same rationale as Claim 5. .ocr_line, .ocr_header { display:block; }
Regarding Claim 14, Micks, Pradeepth, and Kojima teaches the prerequisite limitations of Claim 13.
Additionally, this claim is analogous to the language of Claim 7, and thus rejected under the same rationale as Claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Levinson et al. (US-20170132334-A1) - [0151] Sensor modeler 3625 is configured to generate data models representing various functions of one or more sensors of various types of sensors, based on sensor data 3635 extracted, as logged data, from fleet 3630a of autonomous vehicles. For example, sensor data 3635 may include one or more subsets of sensor data of one or more types of sensor data, such as, but not limited to, Lidar data, radar data, sonar data, image/camera data, acoustic data, ultrasonic data, IMU-related data, odometry data, wheel angle data, and any other types of sensor data. Simulator 3640 may use data generated by sensor modeler 3625 to model any number of sensors implemented in a simulated autonomous vehicle 3630. For example, consider that an autonomous vehicle controller (not shown), which may be simulated, may be configured to identify a pose 3670 of simulated autonomous vehicle 3630 or a simulated Lidar sensor configured to ray-trace laser scans, at least one of which is depicted as a laser return 3671, as reflected from surface portion 3672. Further, the autonomous vehicle controller may access 3D map data to identify an external geometry 3672 (as well as the range or locations of such geometries), and may also be configured to identify one or more of an x-coordinate, a y-coordinate, z coordinate, a roll value, a pitch value, and a yaw value to describe the pose of the simulated Lidar sensor. In some examples, a simulator controller 3656 of simulator 3640 may be configured to compare the simulated values and measurements (e.g., intensities, ranges, reflectivity, etc.) for simulated laser return 3671 against empirically-derived Lidar data (e.g., sensor data 3635) to determine the accuracy of the simulation.
Ji et al. (US-20210394789-A1) - [0095] Before entering the scene, a subject keeps hands on his/her knees in a natural posture, and looks at the main screen of the simulation with both eyes; after entering the scene, the subject continues to watch a driving image generated by the main display of the simulator; after receiving the system take-over request, the subject is immediately in contact with the steering wheel and forms effective control of the steering wheel; the simulator is set to continue to provide an autonomous driving program for 1 second after the subject touches the steering wheel, and then the autonomous driving program is closed; after touching the steering wheel, the subject is required to always control the steering wheel and make the vehicle travel along the center line of the lane; the subject controls the vehicle to travel within a lateral deviation range of 0.5 m for 2s, and then the autonomous driving system re-intervenes; the test is terminated and the subject gives a subjective evaluation result according to the requirements of the questionnaire; and the simulator is reset, the subject takes a break, and data is recorded to prepare for the next test. Every time the test stops, steering wheel angle data recorded by the simulator sensor, steering wheel speed data obtained from differential steering wheel angle data, driver hand torque data, and road curvature information collected by the Labview software. The above process is repeated to form a training data set for model parameter training.
Liu et al (US-20210221434-A1) - [0106] In some embodiments of the present disclosure, the target program may be a game program such as a sim racing game, and the target object may be a simulated object such as a simulated racing car in the game. For example, when the target program is a sim racing game, the user may trigger the sim racing game to run and operate the steering wheel. The steering wheel controller 51 reads the rotation angle and rotation speed of the steering wheel 10, and transmits the rotation angle and rotation speed of the steering wheel 10 to the communication bus. When the vehicle is in the autonomous-driving mode and the running of the target program is detected, the in- vehicle intelligent device may directly read the rotation information of the steering wheel 10 through the communication bus, process the rotation information of the steering wheel 10 to obtain rotation information (including a rotation angle, a rotation speed or a rotation angle and a rotation speed) of the target object, and then perform steering control on the target object according to the rotation information of the target object. For example, a simulated racing car in the sim racing game may rotate according to the obtained rotation angle and rotation speed of the target object.
[0114] In some embodiments of the present disclosure, the target program may be a game program such as a sim racing game, and the target object may be a simulated object such as a simulated racing car in the game. Alternatively, the target program may be an unmanned-aerial-vehicle remote control program, and the target object may be an unmanned aerial vehicle. For example, when the target program is an unmanned-aerial-vehicle remote control program, the user may trigger the unmanned-aerial-vehicle remote control program to run and operate the steering wheel. The steering wheel controller 51 reads the rotation angle and rotation speed of the steering wheel 10 and transmits the rotation angle and rotation speed of the steering wheel 10 to the steering controller 52, and the steering controller 52 transmits the rotation angle and rotation speed of the steering wheel 10 to the unmanned aerial vehicle in real time. When the vehicle is in the autonomous-driving mode and the running of the target program is detected, the unmanned aerial vehicle may process the rotation information of the steering wheel 10 to obtain rotation information (including a rotation angle, a rotation speed or a rotation angle and a rotation speed) of the target object. Steering control may then be performed on the unmanned aerial vehicle according to the rotation information of the target object, so that the unmanned aerial vehicle is remotely controlled.
[0297] In some embodiments of the present disclosure, the target program may be a game program such as a sim racing game, and the target object may be a simulated object such as a simulated racing car in the game. For example, when the target program is a sim racing game, the user may trigger the sim racing game to run and operate the steering wheel. The steering wheel controller 51 reads the rotation angle and rotation speed of the steering wheel 10, and transmits the rotation angle and rotation speed of the steering wheel 10 to the communication bus. When the vehicle is neither in the steering-wheel driving mode nor in the autonomous-driving mode and the running of the target program is detected, the in-vehicle intelligent device may directly read the rotation information of the steering wheel 10 through the communication bus, process the rotation information of the steering wheel 10 to obtain rotation information (including a rotation angle, a rotation speed or a rotation angle and a rotation speed) of the target object, and then perform steering control on the target object according to the rotation information of the target object. For example, the simulated racing car in the sim racing game may rotate according to the obtained rotation angle and rotation speed of the target object.
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/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189