DETAILED ACTION
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
This action is in response to the Applicant’s arguments and amendments filed on 4/07/2026. Applicant amended claims 1, 3, 7-8, 10, 14-15, 17 and 20. Claims 1-20 are pending and are examined below.
RESPONSE TO REMARKS AND ARGUMENTS
In regards to the objections to the specification, Applicant’s amendments filed on 4/07/2026 obviate said objections — accordingly, the objections to the specification are withdrawn.
In regards to the claim objections, Applicant’s amendments filed on 4/07/2026 obviate said claim objections — accordingly, the claim objections are withdrawn.
In regards to the claim rejections under § 112(b), Applicant’s amendments filed on 4/07/2026 obviate said claim rejections — accordingly, the claim rejections under § 112(b) are withdrawn.
In regards to the claim rejections under § 101, Applicant’s arguments and amendments filed on 4/07/2026 have been fully considered but are unpersuasive.
As to amended independent claims 1, 8 and 15, Applicant presents the following arguments:
Step 2A, Prong One
Applicant argues that claim 1 does not recite an abstract idea. Namely, Applicant submits that the alleged abstract ideas, especially “exchanging the extracted data with one or more second vehicles through a vehicle to everything (V2X) network” and “executing a specified action” do not analogize to abstract ideas.
Step 2A, Prong Two
Applicant argues that claim 1 integrates the recited mental processes into a practical application of a computer-implemented system for autonomously managing motor vehicles over a network which provides improvements to autonomous vehicle management system technology. Applicant cites paragraphs [0015] and [0016] of the specification as disclosure pertaining to improvements in how a computer-implemented system for autonomously managing motor vehicles over a network improves existing autonomous vehicle management systems. Namely, the specification discloses how embodiments of the present invention enable vehicles to collectively analyze shared V2V sensor data and learned driving patterns to autonomously make safer, context-aware control decisions such as adjusting speed or activating fog lights beyond what isolated, single-vehicle sensor systems can achieve.
Examiner respectfully disagrees.
Addressing the Step 2A, Prong One argument, this portion of the Alice/Mayo test asks whether a judicial exception is recited at all. The “categorizing,” “analyzing” and “determining” steps have been determined as mental processes — reciting additional elements does not negate that the core steps are mental processes. Therefore, the claim triggers further analysis proceeding from Step 2A, Prong One.
Addressing the Step 2B, Prong Two argument, Examiner respectfully disagrees that the claim limitation at issue puts forward an improvement in technology. The claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. (MPEP 2106.05(a) II.) As the claim limitation at issue analogizes to mere instructions to perform an abstract idea through a generic computing component, it cannot be considered as an improvement in technology. It is also critical to note that “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” (MPEP 2106.05(a).)
Of note, the claim limitation of “executing the specified action on the first vehicle and on the one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles” does not render the claim limitation as patent eligible because the BRI of this claim encompasses to merely “turning on headlights,” which analogizes to an insignificant post-solution activity; i.e., a well-understood/conventional activity which is tangential to the claim. (See Specification, ¶ 64.)
Also, independent claim 15 still constitutes software per se because the claimed computer program product provides no structure (e.g. a computer or processor) to execute the program instructions.
Accordingly, the claim rejections under § 101 are maintained.
In regards to the claim rejections under §§ 102,103, Applicant’s arguments and amendments filed on 4/07/2026 have been fully 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 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. § 101 because the claims fail to pass the Alice/Mayo test for determining patent eligibility.
The patent eligibility test is performed below for independent claims 1, 8 and 15.
Step 1—Does the claim fall within a statutory category?
Claim 1: Yes, the claim recites a process.
Claim 8: Yes, the claim recites a machine or manufacture.
Claim 15: No, the claims recites software per se; see below.
Step 2A, Prong One—Is a judicial exception recited?
Claim 1 is provided below with the abstract idea indicated in bold and additional elements without bold. Examiner notes that claim 8 recites similar subject matter but for minor differences; hence, the analysis of claim 1 will pertain to claim 8 as well.
1. A computer-implemented method comprising:
extracting and selecting collected data, associated with a first vehicle, according to different needs of an event and one or more travel-related features;
exchanging the extracted data with one or more second vehicles through a vehicle to everything (V2X) network, wherein the exchanged extracted data comprises vehicle status, actions taken by the one or more second vehicles and environmental data;
categorizing received inputs, the collected data and the exchanged data into different categories associated with different events;
analyzing the categorized inputs to identify a control event;
determining a specific action is to be performed based on the analysis of the categorized inputs and identified control event according to predefined rules;
transmitting an action request associated with the specific action to a vehicle management system associated with at least the first vehicle;
executing the determined specified action on the first vehicle and on the one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles; and
responsive to executing the specified action, transmitting updated vehicle status information from at least the first vehicle to the one or more second vehicles through the V2X network.
The above shows: yes, a judicial exception is recited. But for the additional elements, the claim limitations pertaining to categorizing, analyzing and determining are process which can practically be performed in the human mind with or without the use of a physical aid. Specifically, the broadest reasonable interpretation (BRI) of the claim encompasses evaluating obtained data and performing judgments. The courts have held such forms of observation, evaluation, judgment, or opinion to represent the abstract idea of a mental process. As a result, the bolded limitations represent a mental process. Hence, the claim recites an abstract idea. (See MPEP § 2106.04(a)(2)(C)(III).)
Step 2A, Prong Two—Is the abstract idea integrated into a practical application?
No. The claims as a whole merely use generic computer components — i.e., a computer system, one or more computer processors and one or more computer readable storage devices — that are recited at a high level of generality such that they cannot be considered more than mere instructions to apply the judicial exception using generic computer components. Therefore, the abstract idea is not integrated into a practical application.
Step 2B—Does the claim provide an inventive concept?
No. The additional elements of the claims amount to either:
Insignificant pre-solution activity in the form of mere data gathering
extracting and selecting collected data
Insignificant extra-solution activity in the form of well-understood and conventional activity
exchanging extracted data with one or more second vehicles through a vehicle to everything (V2X) network
executing the specified action on the first vehicle and on the one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles
the BRI of this claim encompasses to merely “turning on headlights,” which analogizes to an insignificant post-solution activity; i.e., a well-understood/conventional activity which is tangential to the claim. (See Specification, ¶ 64.)
transmitting updated vehicle status information from at least the first vehicle to the one or more second vehicles through the V2X network.
Claims 2-7 depend from claim 1 but do not render the claimed invention patent eligible because they are directed to additional mental steps:
learning statistical relationships between events and associated sensor data,
determining a control event,
conducting statistical analysis, descriptive statistics, correlation analysis, regression analysis, clustering, and time series analysis,
clustering one or more second vehicles and grouping data,
defining general vehicle management rules, and
adjusting future action determinations based on monitored responses
or insignificant extra-solution activity (e.g., gathering data):
collecting real time data from a plurality of on-vehicle sensors,
training a decision module based on learned statistical relationships,
In particular, this limitation does not integrate the abstract idea into a practical application because the claim is merely training a decision module (i.e., model) in a generic fashion to apply an abstract idea without placing any limits on how the model functions.
enabling users to configure vehicle management settings, and
monitoring subsequent vehicle responses
Claims 9-14 depend from claim 8 claim 1 but do not render the claimed invention patent eligible for at least the same reasons as claims 2-7.
Claims 1-20 do not pass the patent eligibility test. Accordingly, claims 1-20 are rejected under § 101.
Software per se
As to claims 15-20, the claimed invention is directed to nonstatutory subject matter. The claims are considered software per se because the claimed computer program product provides no structure (e.g. a computer or processor) to execute the program instructions. As a result, the claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because software per se is an explicit example of a claim that is not directed to any of the statutory categories given a lack of physical or tangible form. (See MPEP 2106.03 I.)
Further note that the claims appear to mirror claims 1-14; hence, even if the statutory category issue was to be resolved, the claims would likely be rejected through the same analysis as penned above.
Patent-eligibility suggestions
Following the following suggestions may render the claims as patent eligible pending further consideration:
Amend the “vehicle control” to a more specific form of vehicle control which necessarily actuates structure. Support appears to be present in at least PGPUB [0053] — e.g., change lanes or control driving speed via one or more actuators; and
Amend claim 15 to recite a non-transitory form of media, such as a processor, for executing the claimed program instructions — support appears to be present in at least PGPUB [0022].
CLAIM REJECTIONS—35 U.S.C. § 103
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 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.
Claim(s) 1, 2, 8, 9, 15 and 16 is/are rejected under § 103 as being unpatentable over Oh (US20210107487A1; “Oh”) in view of Kataoka et al. (US20210339777A1; “Kataoka”) and in view of Ito et al. (US20190077399A1; “Ito”).
As to independent claim 1, Oh discloses a computer-implemented method comprising:
extracting and selecting collected data, associated with a first vehicle, according to different needs of an event and one or more travel-related features (“The controller 40 may perform deep learning by subdividing the various situation information into groups to be considered for safety at a time of the lane change of the autonomous vehicle, and perform various controls required in the operation of determining whether the lane change of the autonomous vehicle is possible based on the learning result.” ¶ 48. “The seventh data extractor 237 extracts behavior data of the autonomous vehicle, such as a speed, an acceleration, a driving direction, a steering wheel angle, a yaw rate, a trouble code, and the like, through the infrastructure information and the vehicle network 217 as the seventh group data.” ¶ 88 and FIG. 2.);
exchanging the extracted data with one or more second vehicles through a vehicle to everything (V2X) network, wherein the exchanged extracted data comprises vehicle status, actions taken by the one or more second vehicles and environmental data (“The V2X module 214 may include a vehicle-to-vehicle (V2V) module and a vehicle-to-infrastructure (V2I) module. The V2V module may communicate with a nearby vehicle to obtain the location, speed, acceleration, yaw rate, traveling direction, and the like of another nearby vehicle.” ¶ 61. “The first data extractor 231 may extract the first group data for preventing a collision with surrounding vehicles 320, 330 and 340 at the time of the lane change of an autonomous vehicle 310 from the object information and the infrastructure information. In this case, the first group data may include the locations, speeds, accelerations, yaw rates, traveling directions, and the like of the surrounding vehicles 320, 330 and 340.” ¶ 72; see also FIGS. 2 and 3A-3C.);
categorizing received inputs, the collected data and the exchanged data into different categories associated with different events (“The controller 40 may perform deep learning by subdividing the various situation information into groups to be considered for safety at a time of the lane change of the autonomous vehicle, and perform various controls required in the operation of determining whether the lane change of the autonomous vehicle is possible based on the learning result.” ¶ 48. “The learning device 30 learns a lane changeable situation of the autonomous vehicle by using data extracted by the first to third data extractors 231 to 233 and the sixth and seventh data extractors 236 and 237 based on the deep learning.” ¶ 92.);
analyzing the categorized inputs to identify a control event (“The learning device 30 learns a lane changeable situation of the autonomous vehicle by using data extracted by the first to third data extractors 231 to 233 and the sixth and seventh data extractors 236 and 237 based on the deep learning.” ¶ 92.);
determining a specific action is to be performed based on the analysis of the categorized inputs and identified control event according to predefine rules (“The controller 40 may perform deep learning by subdividing the various situation information into groups to be considered for safety at a time of the lane change of the autonomous vehicle, and perform various controls required in the operation of determining whether the lane change of the autonomous vehicle is possible based on the learning result.” ¶ 48. “In operation 1002, the controller 40 controls the lane change of the autonomous vehicle based on the learning result of the learning device 30.” ¶ 106.);
transmitting an action request associated with the specific action to a vehicle management system associated with at least the first vehicle (“In operation 1002, the controller 40 controls the lane change of the autonomous vehicle based on the learning result of the learning device 30.” ¶ 106.)
executing the specified action on the first vehicle, wherein executing the specified action comprises executing a vehicle control on the first vehicle (“In operation 1002, the controller 40 controls the lane change of the autonomous vehicle based on the learning result of the learning device 30.” ¶ 106.).
Oh fails to explicitly disclose: executing the specified action on the first vehicle and on the one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles.
Nevertheless, Kataoka teaches: executing a specified action on a first vehicle and on one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles (“A system and method for controlling one or more vehicles with one or more controlled vehicles may include one or more processors and a memory in communication with the one or more processors. The memory may include one or more modules that cause the one or more modules to … select one more actions to control a plurality of controlled vehicles to control the operation of one or more anomaly vehicles and direct the plurality of controlled vehicles execute the one or more actions.” Abstract.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Oh to include the feature of: executing a specified action on a first vehicle and on one or more second vehicles, wherein executing the specified action comprises executing a vehicle control on the first vehicle and on the one or more second vehicles, as taught by Kataoka, with a reasonable expectation of success because this feature is useful for operating vehicles in concert, thereby enhancing traffic management. (See Kataoka, ¶ 3.)
The combination of Oh and Kataoka fails to explicitly disclose: responsive to executing the specified action, transmitting updated vehicle status information from at least the first vehicle to the one or more second vehicles through the V2X network.
Nevertheless, Ito teaches: responsive to executing a specified action, transmitting updated vehicle status information from at least a first vehicle to one or more second vehicles through a V2X network. (“The inter-vehicle communication may also be referred to as vehicle to vehicle (V2V) communication.” ¶ 23. “[W]hen a sensor detects occurrence of a lane change or merging of the second vehicle, in the first vehicle, a behavior of the first vehicle is recorded until the lane change or the merging process is completed. … Further, the first vehicle performs broadcast transmission of a message including the recorded vehicle behavior through the inter-vehicle communication …. Vehicles that are traveling in vicinity of the first vehicle receive the message.” ¶ 25. Note: V2V constitutes a V2X network as V2V is simply a more specific form of V2X.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh and Kataoka to include the feature of: responsive to executing a specified action, transmitting updated vehicle status information from at least a first vehicle to one or more second vehicles through a V2X network, as taught by Ito, to yield the claim limitation at issue with a reasonable expectation of success because this feature is useful for alerting surrounding vehicles of an action that a first vehicle is performing, thereby enabling concert movement and enhancing safety. (See Ito, ¶¶ 24-25.)
Independent claims 8 and 15 are rejected for at least the same reasons as claim 1 as the claims recite similar subject matter but for minor differences.
As to claims 2, 9 and 16, Oh discloses: collecting real time data from a plurality of on-vehicle sensors of the first vehicle (“The input device 20 may include a light detection and ranging (LiDAR) sensor 211, a camera 212, a radio detecting and ranging (Radar) sensor 213.” ¶ 57 and FIG. 2.).
Claims 3, 7, 10, 14 and 17 are rejected under § 103 as being unpatentable over Oh in view of Kataoka and in view of Ito as applied to claim 1 — further in view of Jiang et al. (US20210116915A1; “Jiang”).
As to claims 3, 10 and 17, Oh discloses:
learning relationships between events and associated sensor data from the first vehicle and the one or more second vehicles connected by the V2X network (“The learning device 30 learns a lane changeable situation of the autonomous vehicle by using data extracted by the first to third data extractors 231 to 233 and the sixth and seventh data extractors 236 and 237 based on the deep learning.” ¶ 92.); and
training a decision module based on learned relationships for determining a control event from sensor data input and V2X data inputs (“The learning device 30 learns a lane changeable situation of the autonomous vehicle by using data extracted by the first to third data extractors 231 to 233 and the sixth and seventh data extractors 236 and 237 based on the deep learning.” ¶ 92. Note: The process of learning analogizes to training as the device is ultimately trained to determine a control event associated with the extracted data.).
The combination of Oh, Kataoka and Ito fails to explicitly disclose performing the above in regards to statistical relationships.
Nevertheless, Jiang teaches:
learning statistical relationships between events (action requests) and associated sensor data from vehicles (“The historical driving statistics 322 includes real-world data collected by sensors (e.g., IMU and GPS) to record real-time dynamics (e.g., states) of vehicles in various scenarios.” ¶ 33.); and
training a decision module based on learned statistical relationships for determining a control event from sensor data input (“FIG. 3 illustrates an example system 300 for training a dynamic model shown in FIG. 2 in accordance with an embodiment. As shown in FIG. 3, the example dynamic training system 300 includes the automated dynamic training module 124, a feature extraction module 307, a training controller 309, historical driving statistics 322, and driving statistics 303 for controlled testing scenarios.” ¶ 32 and FIG. 3. “Process 600 may be performed as a part of dynamic model 500. Referring to FIG. 6, at block 601, processing logic receives a set of parameters representing a first state of an ADV to be simulated and a set of control commands to be issued at a first point in time.” ¶ 61 and FIG. 6.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito to include the features of: learning statistical relationships between events (action requests) and associated sensor data from vehicles; and training a decision module based on learned statistical relationships for determining a control event from sensor data input, as taught by Jiang, with a reasonable expectation of success because these features are useful for successfully training a model to learn control events in the context of autonomous driving. Indeed, one of ordinary skill in the art would have recognized that learning models (e.g., machine learning models) by nature utilize statistical relationships — Jiang provides the explicit teaching that such was known in the art.
As to claims 7 and 14, the combination of Oh, Kataoka and Ito fails to explicitly disclose: monitoring subsequent vehicle responses following execution of the specified action and adjusting future action determinations based on the monitored responses.
Nevertheless, Jiang teaches: monitoring subsequent vehicle responses following execution of a specified action and adjusting future action determinations based on the monitored responses (“During the training of the dynamic model, the dynamic model changes connection weights after each piece of data is processed based on the amount of error in the output compared to the expected result.” ¶ 29. “[T]he input data 201 for the neural network model 200 includes states of an ADV (e.g., a speed, an acceleration, and an angular velocity), and control commands (e.g., a throttle command, a brake command, and a steering command) for a first driving cycle. The input data 201 can be processed by one or more hidden layers and transformed to the output data 204, which are expected states of the ADV for a second driving cycle. Based on the acceleration and angular velocity over time, the speed of the ADV for the second driving cycle can be computed. The input data represents a number of feature scenarios (i.e. states) of the ADV.” ¶ 31. See also ¶ 42. Note: Summarizing, Jiang’s training of a neural network, including updating weights, based on input data of vehicle states and control commands (specified actions) analogizes to the BRI of adjusting future action determinations based on monitored responses because training is designed to refine (i.e., adjust) how the neural network performs future action determinations.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito to include the feature of: monitoring subsequent vehicle responses following execution of a specified action and adjusting future action determinations based on the monitored responses, as taught by Jiang, with a reasonable expectation of success because this feature is useful for successfully refining a model to determine optimal control events in the context of autonomous driving. Indeed, one of ordinary skill in the art would have recognized that learning models (e.g., machine learning models) operate in the claimed manner through conventional training methods; hence, it would have been obvious to incorporate Jiang’s training into Oh’s neural network.
Claims 4, 11 and 18 are rejected under § 103 as being unpatentable over Oh in view of Kataoka and in view of Ito as applied to claim 1 — further in view of Jiang, in view of Singh (US20210064046A1; “Singh”), in view of Isele et al. (US20200150654A1; “Isele”) and in view of Ucar et al. (US20220080977A1; “Ucar”).
As to claims 4, 11 and 18, Oh discloses: wherein analyzing the categorized inputs comprises:
conducting regression analysis (“The deep learning is used to solve various problems such as classification, regression, localization, detection, segmentation, and the like.” Emphasis added; ¶ 4.), and
clustering (“The deep learning is used to solve various problems such as classification, regression, localization, detection, segmentation, and the like.” Emphasis added; ¶ 4. “The controller 40 may perform deep learning by subdividing the various situation information into groups to be considered for safety at a time of the lane change of the autonomous vehicle, and perform various controls required in the operation of determining whether the lane change of the autonomous vehicle is possible based on the learning result.” ¶ 48. Note: While the explicit word “clustering” is not used, the foregoing describes the process of clustering.).
The combination of Oh, Kataoka and Ito fails to explicitly disclose: wherein analyzing the categorized inputs comprises statistical analysis.
Nevertheless, Jiang teaches: wherein analyzing categorized inputs comprises statistical analysis (“The historical driving statistics 322 includes real-world data collected by sensors (e.g., IMU and GPS) to record real-time dynamics (e.g., states) of vehicles in various scenarios.” ¶ 33.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito to include the feature of: wherein analyzing categorized inputs comprises statistical analysis, as taught by Jiang, with a reasonable expectation of success because this feature is useful for analyzing categorized inputs, especially in the context of autonomous driving.
The combination of Oh, Kataoka, Ito and Jiang fails to explicitly disclose: wherein analyzing the categorized inputs comprises conducting descriptive statistics.
Nevertheless, Singh teaches: analyzing categorized inputs comprises conducting descriptive statistics (“The computer 110 can generate a distribution using the predictions. The distribution may represent the value of the predictions corresponding to the sensor data input to the DNN 200. In an example implementation, the computer 110 calculates the standard deviation and the average values, e.g., the mean, the mode, and the median, of the predictions. Based on the standard deviation, the computer 110 can determine a confidence parameter …. the computer 110 determines an output based on the standard deviations.” Emphasis added; ¶ 51. NOTE: The emphasized elements are by definition forms of descriptive statistics.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka, Ito and Jiang to include the feature of: analyzing categorized inputs comprises conducting descriptive statistics, as taught by Sing, with a reasonable expectation of success because this feature is useful for analyzing categorized inputs, especially in the context of autonomous driving.
The combination of Oh, Kataoka, Ito, Jiang and Singh fails to explicitly disclose: wherein analyzing the categorized inputs comprises conducting correlation analysis.
Nevertheless, Isele teaches: analyzing categorized inputs comprises conducting correlation analysis (“Another reward format may include rewards between the virtual ego agent 102a and the virtual target agent 104a to be correlated.” ¶ 83.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka, Ito, Jiang and Singh to include the feature of: analyzing categorized inputs comprises conducting correlation analysis, as taught by Isele, with a reasonable expectation of success because this feature is useful for analyzing categorized inputs, especially in the context of autonomous driving.
The combination of Oh, Kataoka, Ito, Jiang, Singh and Isele fails to explicitly disclose: wherein analyzing the categorized inputs comprises conducting time series analysis.
Nevertheless, Ucar teaches: analyzing categorized inputs comprises conducting time series analysis (“The anomaly management system includes a time series analysis algorithm and the behavior data for each vehicle is determined based on a processor of the ego vehicle executing the time series analysis algorithm using the ego sensor data and the remote sensor data as inputs in order to output the behavior data.” ¶ 390.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka, Ito, Jiang, Singh and Isele to include the feature of: analyzing categorized inputs comprises conducting time series analysis, as taught by Ucar, with a reasonable expectation of success because this feature is useful for analyzing categorized inputs, especially in the context of autonomous driving.
Claims 5, 12 and 19 are rejected under § 103 as being unpatentable over Oh in view of Kataoka and in view of Ito as applied to claim 1 — further in view of Wang et al. (US20210096566A1; “Wang”) and in view of Mishra et al. (US20230133248A1; “Mishra”).
As to claims 5, 12 and 19, Oh discloses: clustering the one or more second vehicles and grouping data associated with the one or more second vehicles based on analyzed insights, wherein the analyzed insights comprise position, speed and direction of the one or more second vehicles (“The first data extractor 231 may extract the first group data for preventing a collision with surrounding vehicles 320, 330 and 340 at the time of the lane change of an autonomous vehicle 310 from the object information and the infrastructure information. In this case, the first group data may include the locations, speeds, accelerations, yaw rates, traveling directions, and the like of the surrounding vehicles 320, 330 and 340.” ¶ 72.).
Oh fails to explicitly disclose: wherein the analyzed insights comprise displacement.
Nevertheless, Wang teaches: clustering the one or more second vehicles and grouping data associated with the one or more second vehicles based on analyzed insights, wherein the analyzed insights comprise displacement (“Wherein classifying the first subset of the sensor data as the ground classification comprises at least one of: … determining, as the first subset, first points that have a variance in spacing or variance in angular displacement less than or equal to a variance threshold.” ¶ 98.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito with the feature of: clustering the one or more second vehicles and grouping data associated with the one or more second vehicles based on analyzed insights, wherein the analyzed insights comprise displacement, as taught by Wang, with a reasonable expectation of success because this feature is useful for increasing the accuracy of trajectory generation. (See Wang, ¶ 18.)
The combination of Oh, Kataoka, Ito and Wang fails to explicitly disclose: wherein the analyzed insights comprise route and destination.
Nevertheless, Mishra teaches: clustering the one or more second vehicles and grouping data associated with the one or more second vehicles based on analyzed insights, wherein the analyzed insights comprise route and displacement (“Some embodiments may obtain vehicle sensor data or vehicle event data associated with the set of vehicle categories, as indicated by block 312. Some embodiments may obtain the odometer measurement or other vehicle sensor measurements of a vehicle directly from the vehicle. For example, some embodiments may receive an odometer measurement from a vehicle via a wireless or wired signal when the vehicle is stationed at a designated fueling or charging location …. For example, some embodiments may receive … vehicle odometer measurements for a trip from a starting location to a destination location of a navigation path.” ¶ 36.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka, Ito and Wang with the feature of: clustering the one or more second vehicles and grouping data associated with the one or more second vehicles based on analyzed insights, wherein the analyzed insights comprise route and displacement, as taught by Mishra, with a reasonable expectation of success because this feature is useful for improving the accuracy of the classification of vehicle data.
Claims 6 and 13 are rejected under § 103 as being unpatentable over Oh in view of Kataoka and in view of Ito as applied to claim 1 — further in view of Philosof et al. (US20180204456A1; “Philosof”).
As to claims 6 and 13, the combination of Oh, Kataoka and Ito fails to explicitly disclose: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters.
Nevertheless, Philosof teaches: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters (“A method of a vehicle obtaining sensor data originating at one or more of other vehicles includes receiving input from an operator …, and generating a request for the sensor data based on the input. The request specifies one or more parameters and the one or more parameters include a location at which the sensor data is obtained.” Abstract.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito with the feature of: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters, as taught by Philosof, with a reasonable expectation of success because this feature is useful for enabling a user to configure Oh’s invention to their own preference, thereby enhancing the operability of the invention.
Claim 20 is rejected under § 103 as being unpatentable over Oh in view of Kataoka and in view of Ito as applied to claim 1 — further in view of Jiang and in view of Philosof.
As to claim 20, the combination of Oh, Kataoka and Ito fails to explicitly disclose: monitoring subsequent vehicle responses following execution of the specified action and adjusting future action determinations based on the monitored responses.
Nevertheless, Jiang teaches: monitoring subsequent vehicle responses following execution of a specified action and adjusting future action determinations based on the monitored responses (“During the training of the dynamic model, the dynamic model changes connection weights after each piece of data is processed based on the amount of error in the output compared to the expected result.” ¶ 29. “[T]he input data 201 for the neural network model 200 includes states of an ADV (e.g., a speed, an acceleration, and an angular velocity), and control commands (e.g., a throttle command, a brake command, and a steering command) for a first driving cycle. The input data 201 can be processed by one or more hidden layers and transformed to the output data 204, which are expected states of the ADV for a second driving cycle. Based on the acceleration and angular velocity over time, the speed of the ADV for the second driving cycle can be computed. The input data represents a number of feature scenarios (i.e. states) of the ADV.” ¶ 31. See also ¶ 42. Note: Summarizing, Jiang’s training of a neural network, including updating weights, based on input data of vehicle states and control commands (specified actions) analogizes to the BRI of adjusting future action determinations based on monitored responses because training is designed to refine (i.e., adjust) how the neural network performs future action determinations.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka and Ito to include the feature of: monitoring subsequent vehicle responses following execution of a specified action and adjusting future action determinations based on the monitored responses, as taught by Jiang, with a reasonable expectation of success because this feature is useful for successfully refining a model to determine optimal control events in the context of autonomous driving. Indeed, one of ordinary skill in the art would have recognized that learning models (e.g., machine learning models) operate in the claimed manner through conventional training methods; hence, it would have been obvious to incorporate Jiang’s training into Oh’s neural network.
The combination of Oh, Kataoka, Ito and Jiang fails to explicitly disclose: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters.
Nevertheless, Philosof teaches: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters (“A method of a vehicle obtaining sensor data originating at one or more of other vehicles includes receiving input from an operator …, and generating a request for the sensor data based on the input. The request specifies one or more parameters and the one or more parameters include a location at which the sensor data is obtained.” Abstract.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Oh, Kataoka, Ito and Jiang with the feature of: enabling users to configure vehicle management settings and defining general vehicle management rules related to sensor data and current vehicle parameters, as taught by Philosof, with a reasonable expectation of success because this feature is useful for enabling a user to configure Oh’s invention to their own preference, thereby enhancing the operability of the invention.
CONCLUSION
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, this action is 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mario C. Gonzalez whose telephone number is (571) 272-5633. The Examiner can normally be reached M–F, 10:00–6:00 ET.
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/M.C.G./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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