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 amendment filed June 24, 2026 has been entered. Claims 1-20 remain pending in the instant application. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed March 24, 2026.
Response to Arguments
Applicant’s arguments, filed June 24, 2026, in light of amendments regarding rejections under U.S.C 101 have been fully considered and are persuasive. The rejections of Claims 1-20 under U.S.C. 101 has been overcome.
Applicant’s arguments with respect to Claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7 and 9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (U.S. Pub. No. 2020/0384998 A1), hereinafter Yang, in view of Micks et al. (U.S. Pub. No. 2018/0203445 A1), hereinafter Micks.
Regarding Claim 1, Yang teaches A that, when executed by one or more processing units, cause the one or more processing units to perform operations (“In some implementations, methods described in the various embodiments in this patent document are embodied in a computer readable program stored on a non-transitory computer readable media.”) (e.g., paragraph [0047]).
the operations comprising: receiving road data collected from a real-world reference driving scene (“The vehicle simulation computer 102 can be used to play and analyze sensor data and/or to perform simulations based on real-world sensor data that can be retrieved from a dataset 104.”) (e.g., paragraph [0019]).
extracting, from the road data, features of the real-world reference driving scene (“The vehicle simulation computer 102 can be used to play and analyze sensor data and/or to perform simulations based on real-world sensor data that can be retrieved from a dataset 104 […] The data publisher module 106 can also separate the sensor data according to the type of sensor data. For example, the data publisher module 106 can obtain the camera 1 image data, camera 2 image data, and LiDAR sensor data from the dataset 104”) (e.g., paragraphs [0019] and [0020]).
obtaining, from the road data, real-world sensor real-world reference driving scene and responsive to the extracted features (“The sensor data related dataset 104 may be located in a hard drive or a memory device, where the sensor data is obtained from an autonomous vehicle previously operated in the real-world..”) (e.g., paragraph [0019]).
generating a simulated scenario comprising an object having one or more features and one or more simulated sensor inputs (“At the performing operation 304, the vehicle simulation computer performs, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle. As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time.”) (e.g., paragraph [0040]).
wherein generating the one or more simulated sensor inputs comprises processing, using the road data response prediction model, the one or more features to generate the one or more simulated sensor inputs (“As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time […] At the generating operation 306, the vehicle simulation computer generates, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of the autonomous vehicle.”) (e.g., paragraphs [0040] and [0041]).
training, using the simulated scenario in a simulator, one or more models for navigating an AV (“The vehicle simulation computer 102 can allow a user to debug an algorithm module and replace an old algorithm module having a known software bug with a revised algorithm module,” wherein the algorithm modules are interpreted as one or more models for navigating an AV, and revising an algorithm module is analogous to training.) (e.g., paragraph [0022]).
and providing the one or more models to the AV, wherein the AV is configured to use the one or more models to navigate the AV in a real-world environment (“The virtual vehicle engine module 116 can obtain the one or more control signal values from the one or more algorithm modules 110a-110b. The virtual vehicle engine module 116 of FIG. 1 can determine the simulated driving behavior of the autonomous vehicle to be shown in image frames based on receiving the one or more control signal values.” The virtual vehicle engine module may instead be used to navigate a vehicle in a real-world environment.) (e.g., paragraph [0029]).
However, Yang does not appear to specifically teach calculating a statistical measure of the real-world sensor real-world sensor
On the other hand, Micks, which relates similarly as a method for simulating sensor data for a vehicle, does teach calculating a statistical measure of the real-world sensor (“The statistical model may define an approach for updating a probability for a predicted location of an obstacle or occupancy of a particular position outside of a vehicle […] 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.” The output of a statistical model is interpreted as a statistical measure.) (e.g., paragraph [0030]).
generating a road data response prediction model to map the extracted features of the road data to the statistical measure of the real-world sensor (“The controller 122 may execute a collision avoidance module 130 that receives outputs from some or all of the imaging devices 124, microphones 126, and other sensors 128. The collision avoidance module 130 then analyzes the outputs to identify potential obstacles […] The collision avoidance module 130 may include an obstacle identification module 132a […] the obstacle identification module 132a may identify obstacles using a machine learning model or statistical model as described below with respect to FIGS. 3 through 11.”) (e.g., paragraphs [0041] and [0042]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant’s claimed invention to combine Yang with Micks. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Yang teaches a method for analyzing autonomous vehicle operations comprising processing sensor data using algorithm modules to control a virtual autonomous vehicle. However, Yang does not appear to specifically teach calculating a statistical measure of the sensor data, wherein the statistical measure is an indication of various aspects of a LIDAR system. On the other hand, Micks, which relates similarly as a method for simulating sensor data, does teach a statistical model to process sensor data. As both Yang and Micks relate to simulating an autonomous vehicle (e.g., Yang, paragraph [0001]; Micks, paragraph [0001]), one of ordinary skill in the art could have combined the simulation of Yang with the statistical model of Micks. In combination, each element merely performs the same function as it does separately. Furthermore, Yang discloses algorithm modules that perform various autonomous vehicle operations, such as processing sensor data to detect objects (e.g., Yang, paragraph [0021]). Micks merely provides statistical model that may be used as an algorithm module in Yang to process sensor data. Thus, one of ordinary skill would have recognized the results of the combination as predictable. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine Yang with Micks in order to provide a statistical model as one of the algorithm modules for processing the sensor data of Yang.
Regarding Claim 2, Yang in view of Micks teaches The non-transitory computer- readable media of claim 1. Yang further teaches wherein: the real-world sensor response data includes sensing information associated with the real- world reference driving scene (“The sensor data related dataset 104 may be located in a hard drive or a memory device, where the sensor data is obtained from an autonomous vehicle previously operated in the real-world […] The sensor data for each sensor can be associated with a time stamp that indicates when the sensor obtained the sensor data.”) (e.g., paragraph [0019]).
Micks further teaches and real-world sensor data. (“The controller 122 may execute a collision avoidance module 130 that receives outputs from some or all of the imaging devices 124, microphones 126, and other sensors 128. The collision avoidance module 130 then analyzes the outputs to identify potential obstacles […] The collision avoidance module 130 may include an obstacle identification module 132a […] the obstacle identification module 132a may identify obstacles using a machine learning model or statistical model as described below with respect to FIGS. 3 through 11.”) (e.g., paragraphs [0041] and [0042]).
Regarding Claim 3, Yang in view of Micks teaches The non-transitory computer- readable media of claim 1. Micks further teaches wherein the statistical measure includes an indication of a quantity of light detection and ranging (LIDAR) data points responsive to an object in the real-world reference driving scene (“For a LIDAR sensor, a point cloud from the point of view of the LIDAR 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.” A point cloud is interpreted as an indication of a quantity of data points.) (e.g., paragraph [0060]).
Regarding Claim 4, Yang in view of Micks teaches The non-transitory computer- readable media of claim 3. Micks further teaches wherein the extracted features include an indication of a dimension of the object (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value.” The 3D coordinate of a point is interpreted as indicating a dimension of an object.) (e.g., paragraph [0060]).
Regarding Claim 5, Yang in view of Micks teaches The non-transitory computer- readable media of claim 3. Micks further teaches wherein the extracted features include an indication of a distance from the object to the vehicle in the real-world reference driving scene (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value.” The 3D coordinate of a point is interpreted as indicating a distance of an object.) (e.g., paragraph [0060]).
Regarding Claim 6, Yang in view of Micks teaches The non-transitory computer- readable media of claim 3. Micks further teaches wherein the extracted features include an indication of an angle at which the object is located with respect to the vehicle in the real-world reference driving scene (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value […] In some embodiments, a sensor has a field of view and can indicate presence of an object in that field of view but does not provide a coordinate. Accordingly, a function f(R,theta) may indicate the probability of an object being present at a given radius and angle relative to the sensor when the sensor indicates the object is detected.” The 3D coordinate of a point is interpreted as indicating an angle of an object.) (e.g., paragraphs [0060] and [0132]).
Regarding Claim 7, Yang in view of Micks teaches The non-transitory computer- readable media of claim 3. Micks further teaches wherein the extracted features include an indication of an angle between a path of the object and a path of the vehicle in the real-world reference driving scene (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value […] In some embodiments, a sensor has a field of view and can indicate presence of an object in that field of view but does not provide a coordinate. Accordingly, a function f(R,theta) may indicate the probability of an object being present at a given radius and angle relative to the sensor when the sensor indicates the object is detected.” The probability is interpreted as indicating an angle between a path of an object and a path of a vehicle.) (e.g., paragraphs [0060] and [0132]).
Regarding Claim 9, Yang teaches A that, when executed by one or more processing units, cause the one or more processing units to perform operations (“In some implementations, methods described in the various embodiments in this patent document are embodied in a computer readable program stored on a non-transitory computer readable media.”) (e.g., paragraph [0047]).
the operations comprising: determining a plurality of feature values associated with a real-world driving scene;(“The vehicle simulation computer 102 can be used to play and analyze sensor data and/or to perform simulations based on real-world sensor data that can be retrieved from a dataset 104 […] The data publisher module 106 can also separate the sensor data according to the type of sensor data. For example, the data publisher module 106 can obtain the camera 1 image data, camera 2 image data, and LiDAR sensor data from the dataset 104”) (e.g., paragraphs [0019] and [0020]).
generating a simulated scenario comprising an object having one or more features and one or more simulated sensor inputs (“At the performing operation 304, the vehicle simulation computer performs, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle. As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time.”) (e.g., paragraph [0040]).
wherein generating the one or more simulated sensor inputs comprises processing, using the road data response prediction model, the plurality of feature values to generate the one or more simulated sensor inputs (“As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time […] At the generating operation 306, the vehicle simulation computer generates, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of the autonomous vehicle.”) (e.g., paragraphs [0040] and [0041]).
training, using the simulated scenario in a simulator, one or more models for navigating an AV (“The vehicle simulation computer 102 can allow a user to debug an algorithm module and replace an old algorithm module having a known software bug with a revised algorithm module,” wherein the algorithm modules are interpreted as one or more models for navigating an AV, and revising an algorithm module is analogous to training.) (e.g., paragraph [0022]).
and providing the one or more models to the AV, wherein the AV is configured to use the one or more models to navigate the AV in a real-world environment (“The virtual vehicle engine module 116 can obtain the one or more control signal values from the one or more algorithm modules 110a-110b. The virtual vehicle engine module 116 of FIG. 1 can determine the simulated driving behavior of the autonomous vehicle to be shown in image frames based on receiving the one or more control signal values.” The virtual vehicle engine module may instead be used to navigate a vehicle in a real-world environment.) (e.g., paragraph [0029]).
However, Yang does not appear to specifically teach obtaining configured to map the plurality of feature values to obtain a predicted statistical measure of a real-world sensor response for the real-world driving scene with respect to a real-world vehicle in the real-world driving scene;
On the other hand, Micks, which relates similarly as a method for simulating sensor data for a vehicle, does teach obtaining configured to map the plurality of feature values to obtain a predicted statistical measure of a real-world sensor response for the real-world driving scene with respect to a real-world vehicle in the real-world driving scene (“The statistical model may define an approach for updating a probability for a predicted location of an obstacle or occupancy of a particular position outside of a vehicle […] 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.” The output of a statistical model is interpreted as a statistical measure.) (e.g., paragraph [0030]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant’s claimed invention to combine Yang with Micks for the same reasons as in Claim 1, above.
Regarding Claim 10, Yang in view of Micks teaches The non-transitory computer- readable media of claim 9. Yang further teaches wherein the plurality of feature values are associated with an object in the real-world driving scene (“The sensor data related dataset 104 may be located in a hard drive or a memory device, where the sensor data is obtained from an autonomous vehicle previously operated in the real-world […] The sensor data for each sensor can be associated with a time stamp that indicates when the sensor obtained the sensor data […] For example, algorithm module 1 can be a perception module that can obtain sensor data from one or more sensor data nodes 108a-108n to determine whether an object is located within a detection range of the autonomous vehicle.”) (e.g., paragraphs [0019] and [0021]).
Regarding Claim 11, Yang in view of Micks teaches The non-transitory computer- readable media of claim 10. Micks further teaches wherein the predicted statistical measure of the real-world sensor response includes an indication of a predicted number of light detection and ranging (LIDAR) data points responsive to the object (“For a LIDAR sensor, a point cloud from the point of view of the LIDAR 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.” A point cloud is interpreted as an indication of a predicted number of data points.) (e.g., paragraph [0060]).
Regarding Claim 12, Yang in view of Micks teaches The non-transitory computer- readable media of claim 10. Micks further teaches wherein one or more of the plurality of feature values are associated with a three-dimensional (3D) bounding box representing a dimension of the object in a 3D space (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value […] The data objects may include a location, e.g. a coordinate of a center, of the feature, an extent of the feature, a total volume or facing area or the size or vertex locations of a bounding box or cube, or other data.”) (e.g., paragraphs [0060] and [0078]).
Regarding Claim 13, the claim recites substantially similar limitations to Claims 5, 6, and 7, and the claim is rejected under U.S.C. 103 for the same reasons.
Regarding Claim 14, Yang in view of Micks teaches The non-transitory computer- readable media of claim 9. Yang further teaches wherein the operations further comprise: executing a sensor test in a simulation that simulates the real-world driving scene (“At the performing operation 304, the vehicle simulation computer performs, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle. As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time.”) (e.g., paragraph [0040]).
and extracting, from the simulation, the plurality of feature values associated with the real- world driving scene (“The vehicle simulation computer 102 can be used to play and analyze sensor data and/or to perform simulations based on real-world sensor data that can be retrieved from a dataset 104 […] The data publisher module 106 can also separate the sensor data according to the type of sensor data. For example, the data publisher module 106 can obtain the camera 1 image data, camera 2 image data, and LiDAR sensor data from the dataset 104.”) (e.g., paragraphs [0019] and [0020]).
Regarding Claim 15, Yang in view of Micks teaches The non-transitory computer- readable media of claim 14. Mick further teaches wherein the operations further comprise: determining a fidelity of the simulation based on a comparison between a statistical measure of a reference sensor response and the predicted statistical measure of the real-world sensor response (“The validation module 114e may then characterize accuracy of the statistical model as compared to the annotations of the annotation module 114c. For example, where the statistical model produces an output indicating a high probability that an obstacle is at a given location, this location may be compared to the annotations. A distance between this location and a location indicated in the annotations may be compared. The larger the distance, the lower the accuracy of the statistical model.” The accuracy of the statistical model is interpreted as a fidelity, wherein the annotations correspond to a reference sensor and the statistical model output is a predicted statistical measure.) (e.g., paragraph [0036]).
Regarding Claim 16, Yang teaches A method comprising: determining a plurality of feature values associated with a real-world driving scene; (“The vehicle simulation computer 102 can be used to play and analyze sensor data and/or to perform simulations based on real-world sensor data that can be retrieved from a dataset 104 […] The data publisher module 106 can also separate the sensor data according to the type of sensor data. For example, the data publisher module 106 can obtain the camera 1 image data, camera 2 image data, and LiDAR sensor data from the dataset 104”) (e.g., paragraphs [0019] and [0020]).
generating a simulated scenario comprising an object having one or more features and one or more simulated sensor inputs (“At the performing operation 304, the vehicle simulation computer performs, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle. As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time.”) (e.g., paragraph [0040]).
wherein generating the one or more simulated sensor inputs comprises processing, using the road data response prediction model, the plurality of feature values to generate the one or more simulated sensor inputs (“As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time […] At the generating operation 306, the vehicle simulation computer generates, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of the autonomous vehicle.”) (e.g., paragraphs [0040] and [0041]).
training, using the simulated scenario in a simulator, one or more models for navigating an AV (“The vehicle simulation computer 102 can allow a user to debug an algorithm module and replace an old algorithm module having a known software bug with a revised algorithm module,” wherein the algorithm modules are interpreted as one or more models for navigating an AV, and revising an algorithm module is analogous to training.) (e.g., paragraph [0022]).
and; providing the one or more models to the AV, wherein the AV is configured to use the one or more models to navigate the AV in a real-world environment (“The virtual vehicle engine module 116 can obtain the one or more control signal values from the one or more algorithm modules 110a-110b. The virtual vehicle engine module 116 of FIG. 1 can determine the simulated driving behavior of the autonomous vehicle to be shown in image frames based on receiving the one or more control signal values.” The virtual vehicle engine module may instead be used to navigate a vehicle in a real-world environment.) (e.g., paragraph [0029]).
However, Yang does not appear to specifically teach querying a road data response prediction model based on the plurality of feature values to obtain a predicted statistical measure of a real-world sensor response for the real-world driving scene with respect to a real-world vehicle in the real-world driving scene, wherein the predicted statistical measure of the real-world sensor response includes a predicted number of light detection and ranging (LIDAR) data points responsive to the real-world driving scene.
On the other hand, Micks, which relates similarly as a method for simulating sensor data for a vehicle, does teach querying a road data response prediction model based on the plurality of feature values to obtain a predicted statistical measure of a real-world sensor response for the real-world driving scene with respect to a real-world vehicle in the real-world driving scene (“The statistical model may define an approach for updating a probability for a predicted location of an obstacle or occupancy of a particular position outside of a vehicle […] 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.” The output of a statistical model is interpreted as a statistical measure.) (e.g., paragraph [0030]).
wherein the predicted statistical measure of the real-world sensor response includes a predicted number of light detection and ranging (LIDAR) data points responsive to the real-world driving scene (“For a LIDAR sensor, a point cloud from the point of view of the LIDAR 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.” A point cloud is interpreted as an indication of a quantity of data points.) (e.g., paragraph [0060]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant’s claimed invention to combine Yang with Micks for the same reasons as in Claim 1, above.
Regarding Claim 17, Yang in view of Micks teaches The method of claim 16. Yang further teaches the method further comprising: executing a sensor test in a simulation that simulates the real-world driving scene (“At the performing operation 304, the vehicle simulation computer performs, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle. As an example, the one or more programs can process the stored sensor data to obtain a location of the autonomous vehicle and/or other vehicles that surround the autonomous vehicle at different points in time.”) (e.g., paragraph [0040]).
Mick further teaches and determining a fidelity of the simulation based on a comparison between a statistical measure of a reference sensor response and the predicted statistical measure of the real-world sensor response obtained from the querying (“The validation module 114e may then characterize accuracy of the statistical model as compared to the annotations of the annotation module 114c. For example, where the statistical model produces an output indicating a high probability that an obstacle is at a given location, this location may be compared to the annotations. A distance between this location and a location indicated in the annotations may be compared. The larger the distance, the lower the accuracy of the statistical model.” The accuracy of the statistical model is interpreted as a fidelity, wherein the annotations correspond to a reference sensor and the statistical model output is a predicted statistical measure.) (e.g., paragraph [0036]).
wherein the determining the plurality of feature values is based on the simulation (“The method 500 may include receiving 502 sensor data, which is simulated sensor data in the context of the methods 300a, 300b or actual sensor data when in use in an actual vehicle […] The method 500 may include identifying 504 features in the sensor data.”) (e.g., paragraphs [0075] and [0077]).
Regarding Claim 18, Yang in view of Mick teaches The method of claim 17. Mick further teaches the method further comprising: adjusting a parameter of the simulation based on a comparison between the statistical measure of the reference sensor response and a previous predicted statistical measure of a sensor response (“Where the statistical model provides a location having an above-threshold probability as being the location of an obstacle, this location may be compared to the annotations to determine whether the scenario included an obstacle at that location. The comparison of step 312 may be manual or automated. Step 312 may include tuning one or more parameters of the model either manually or automatically based on an output of the comparison.”) (e.g., paragraph [0065]).
wherein the executing the sensor test is based on the adjustment (“For example, for a particular sensor, the statistical model may include a function defining an amount by which the probability is increased in response to a detection. Parameters defining this function may be adjusted to reduce false positives and false negatives. For example, for the same set of simulated sensor outputs, the methods of FIGS. 5-6 may be executed repeatedly with the parameters of the probability update functions for the various sensors being adjusted at each iteration in order to reduce the number of false positives and false negatives.” Executing the simulation repeatedly using updated parameters is interpreted as executing a sensor test based on an adjustment.) (e.g., paragraph [0100]).
Regarding Claim 19, Yang in view of Mick teaches The method of claim 16, wherein the plurality of feature values include at least one of: a width of an object in the real-world driving scene; a length of the object; or a height of the object (“In some LIDAR systems, points measured may include both a three-dimensional coordinate and a reflectivity value […] The data objects may include a location, e.g. a coordinate of a center, of the feature, an extent of the feature, a total volume or facing area or the size or vertex locations of a bounding box or cube, or other data.”) (e.g., paragraphs [0060] and [0078]).
Regarding Claim 20, the claim recites substantially similar limitations to Claims 5, 6, and 7, and the claim is rejected under U.S.C. 103 for the same reasons.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Micks, further in view of Kronprasert et al. (Kronprasert, Nopadon, Katesirint Boontan, and Patipat Kanha. "Crash prediction models for horizontal curve segments on two-lane rural roads in Thailand." Sustainability 13, no. 16 (2021): 9011.), hereinafter Kronprasert.
Regarding Claim 8, Yang in view of Micks teaches The non-transitory computer- readable media of claim 1. However, neither Yang nor Micks teaches wherein the road data response prediction model is a generalized linear model (GLM).
On the other hand, Kronprasert, which relates similarly as a method for analyzing road traffic, does teach wherein the road data response prediction model is a generalized linear model (GLM) (“Crash prediction models by Safety Performance Functions (SPFs) are useful tools for describing the statistical associations between significant variables of roadway characteristics […] Among statistical techniques which including Discrete-outcome Models, Data mining Techniques, Soft Computing Techniques, and Generalised Linear Models [11], Generalised Linear Models (GLM) have been broadly applied for studies conducted on the associations between significant variables.”) (e.g., page 2, paragraph 3).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant’s claimed invention to combine the modified reference of Yang in view of Micks with Kronprasert. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Yang in view of Micks, teaches a statistical model to process sensor data. However, the Yang-Micks combination does not appear to specifically teach wherein the road response prediction model is a generalized linear model. On the other Hand, Kronprasert, which relates similarly as a method for analyzing road traffic, does teach a generalized linear model to associate roadway characteristics and crash prediction. The Yang-Micks combination discloses using a statistical model to associate sensor data with an obstacle (e.g., Micks, paragraph [0065]), and Kronprasert merely provides a specific implementation for said statistical model. Furthermore, as disclosed in Kronprasert, GLMs have been broadly applied to associate significant variables (e.g., page 2, paragraph 3). Thus, one of ordinary skill in the art could have combined the elements as claimed according to known methods; in combination, each element merely performs the same function as it does separately. Thus, one of ordinary skill in the art would have recognized that the results of the combination were predictable. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the modified reference of Yang in view of Micks with Kronprasert in order to provide a specific implementation for the statistical model in the Yang-Micks combination.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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