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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 07/28/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
This Office Action is in response to the claims filed on 06/30/2026.
Claims 1-20 have been presented for examination.
Claims 1-20 are currently rejected.
Claims 1, 3-5, and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891).
Claims 2, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891), further in view of Luo et al. (U.S. Patent Publication Number 2019/0147372).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891) and Luo et al. (U.S. Patent Publication Number 2019/0147372), further in view of Ahmed et al. (U.S. Patent Publication Number 2016/0328272).
Response to Argument
35 U.S.C. 101
Applicant’s arguments, see Applicant Remarks filed on 06/30/2026, with respect to 35 U.S.C. 101, have been fully considered and are persuasive. The 35 U.S.C. 101 rejection has been withdrawn.
35 U.S.C. 102
The Applicant’s arguments, see Applicant Remarks filed on 06/30/2026, appear to be primarily directed to the amended claim language. The Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because amendments shift the scope of claims and necessitate a new ground of rejection, which is made in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891).
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.
Claims 1, 3-5, and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891).
Regarding claim 1, Akotkar discloses a vehicle-based cloud computing system (VCCS) for autonomous driving, comprising:
an onboard unit (OBU): and (Akotkar in at least ¶ 18 “onboard computer 218”)
a cloud subsystem, (Akotkar in at least ¶ 20 “decision unit 220 may receive data packets from other CA/AD driving systems 101s included in other SA/AD vehicles 102s (not shown), data packets and/or data streams from the cloud”)
wherein an autonomous vehicle (AV) comprises said OBU; and (Akotkar ¶ 18 discloses an “onboard computer 218 ... located in a forward portion of vehicle 102”)
wherein the OBU comprises: a communication module configured to communicate with one or more of- (a) a roadside unit (RSU) network, (b) another OBU, (c) a cloud platform, (d) a traffic control center/traffic control unit (TCC/TCU), or (e) a traffic operations center (TOC); (Akotkar ¶ 50 discloses that the onboard computer operates using communication circuitry of the vehicle, wherein the “communications circuitry of the vehicle 702 may communicate with the cloud 705 via wireless access node 703,” the wireless access node being an RSU, see ¶ 51)
a vehicle sensing module configured to collect and/or provide information describing the driving environment; (Akotkar ¶ 49 discloses that “vehicle 702 may include, for example, a LIDAR sensor 725 (e.g., to locate itself and other objects, in an environment),” also see Fig. 2)
a computing subsystem configured to perform computation methods; (Akotkar ¶ 34 discloses computational logic 522, see Fig. 5, which provides capability for implementing the instructions supported by the processors 502)
a data storage subsystem (Akotkar ¶ 31 discloses that system 500 includes a memory 504, wherein system 500 is the onboard computer, see ¶ 30. Also see Fig. 5.)
a control module configured to execute control instructions for driving tasks; (Akotkar ¶ 21 discloses controlling “a steering control module 228 [i.e., a control module] to assist in controlling particular driving elements 209 to guide SA/AD vehicle 102,” also see Fig. 2)
However, Moghe discloses:
wherein the computation methods comprise performing a control algorithm, training a general model, and inferring from a general model; and (Moghe ¶ 103 discloses “training a machine learning model to analyze the data (i.e., the monitored scheduled periods and scheduling characteristics over time),” wherein the “machine learning may be used to detect and predict patterns of users and vehicles, such as by monitoring scheduled periods of time and scheduling characteristics over time [i.e., inferring from a general model]” and based on the scheduled period of time, determining a first set of instructions to control the vehicle to move autonomously [i.e., a control algorithm], see ¶ 104)
wherein the OBU (Moghe ¶ 54) is configured to perform prediction methods, comprising predicting vehicle behaviors based on data collected by said OBU and modifying a prediction according to environmental data collected and/or predicted by an RSU; (Moghe ¶ 103 discloses “machine learning may be used to detect and predict patterns of users and vehicles, such as by monitoring scheduled periods of time and scheduling characteristics over time, and training a machine learning model to analyze the data,” wherein the data includes sensor information, see at least ¶ 58, and wherein the data is derived based on changes in the location and changes in the velocity of the vehicle 160 over time, see ¶ 34. One having ordinary skill in the art would recognize that predictions made using data that is derived based on changes over time indicate that the predictions would be modified as the data input changes over time.)
wherein the cloud subsystem comprises an OBU-vehicle end subsystem configured to provide navigation, guidance, and control. (Moghe ¶ 62 discloses that the communication infrastructure comprises an RSU 150 that communicates with cloud 550, and uses the machine learning model to predict patterns of vehicles over time, see ¶ 103, and generating “a first set of instructions to control the vehicle to autonomously move from the current parking spot to the particular charging spot 515 for the scheduled period of time in step 825,” see ¶ 104.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the control algorithm of Akotkar with training a general model, and inferring from a general model, and wherein the OBU is configured to perform prediction methods, comprising predicting vehicle behaviors based on data collected by said OBU and modifying a prediction according to environmental data collected and/or predicted by an RSU, as disclosed by Yang, with reasonable expectation of success, to support point-to-point and point-to-multipoint or multipoint-to-point traffic (Moghe ¶ 19).
Regarding claim 3, Akotkar in combination with Moghe discloses the VCCS of claim 1, wherein:
the OBU is configured to provide a function selected from the group consisting of sensing; (Akotkar ¶ 49 discloses that “vehicle 702 may include, for example, a LIDAR sensor 725 (e.g., to locate itself and other objects, in an environment),” also see Fig. 2)
prediction; (Akotkar ¶ 20 discloses that a decision unit 220 may “assist in determining a next action for SA/AD vehicle 102 in response to the alarm signal,” and “decision unit 220 may work with navigation control system 226 as well as information from a cloud (e.g., cloud 705 of FIG. 7) to determine a location from which the alarm signal and thus emergency vehicle may be approaching [i.e., a prediction]”)
planning; (Akotkar ¶ 22 discloses “decision unit 220 may further work with navigation control system 226 to determine how and when [i.e., plan] SA/AD vehicle 102 should respond to the alarm signal, e.g., whether to slow down or pull over to the side of a road or take other action”)
decision-making; and (Akotkar ¶ 22 discloses that “decision unit 220 may determine that communications with an emergency vehicle should be initiated [i.e., a decision]”)
control. (Akotkar in at least ¶ 17 discloses subsequently controlling driving elements of the SA/AD vehicle 102 to respond to a situation associated with audio signal 104)
Regarding claim 4, Akotkar in combination with Moghe discloses the VCCS of claim 1, wherein:
the OBU is configured to receive an intelligence allocation. (Akotkar ¶ 19 discloses “plurality of microphones 201 may be semi-intelligent and may provide e.g., intermediate classification outputs from the classifier (in connection with FIG. 4) to onboard computer 218”)
Regarding claim 5, Akotkar in combination with Moghe does not expressly disclose the VCCS of claim 1, wherein:
the computing subsystem identifies and divides sequential works and parallel works based on the properties of the sequential works and parallel works. (Akotkar Fig. 1 depicts a block diagram 100 of a process divided into steps that includes sequential works in block diagram elements 108, 110, 112, and 114, which contain parallel works such as extracting sensor features. Also see corresponding ¶ 16 and Fig. 3 with corresponding ¶ 23.)
Regarding claim 7, Akotkar in combination with Moghe discloses the VCCS of claim 1, wherein:
the data storage subsystem is configured to manage data, verify data, and provide efficient data storage and access. (Akotkar in at least ¶ 20 “decision unit 220 may receive data packets from other CA/AD driving systems 101s included in other SA/AD vehicles 102s (not shown), data packets and/or data streams from the cloud and/or network infrastructure (e.g., core network elements of a cellular communications network, etc.),” wherein the data processing further includes “weight decay [i.e., verifying data], L1/L2 regularization, mini-batch learning, dropout, and pre-training,” see ¶ 29)
Regarding claim 8, Akotkar in combination with Moghe discloses the VCCS of claim 1, wherein:
the vehicle sensing module is configured to perform a sensing method comprising sensing the environment and detecting objects at a microscopic level, a mesoscopic level, and/or a macroscopic level. (Akotkar ¶ 48 discloses “data obtained by the onboard computer may include sensor data from one or more microphones embedded in, on, or around the vehicle 702 [i.e., microscopic], data packets from other onboard computers included in other vehicles 702 [i.e., macroscopic]”)
Claims 2, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891), further in view of Luo et al. (U.S. Patent Publication Number 2019/0147372).
Regarding claim 2, Akotkar in combination with Moghe does not expressly disclose the computing subsystem of claim 1, wherein:
the computation methods comprise training a tensor-centered model and/or inferring from a tensor- centered model.
However, Luo discloses:
the computation methods comprise training a tensor-centered model and/or inferring from a tensor- centered model. (Luo ¶ 95 discloses “an example model 200 for object detection, tracking, and prediction that includes a tensor 202 (e.g., a four dimensional tensor),” wherein the tensor 202 includes “one or more spatial dimensions (e.g., spatial dimensions corresponding to the dimensions of a three-dimensional space) and a temporal dimension (e.g., a temporal dimension associated with one or more time intervals),” see ¶ 96)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the models for training of Akotkar to specifically incorporate a tensor-centered model, as disclosed by Luo, with reasonable expectation of success, because it allows for an improvement in operational safety through faster, more accurate, and precise object detection, tracking, and motion prediction that more efficiently utilizes computing resources (Luo ¶ 25), rendering the limitation to be an obvious modification.
Regarding claim 9, Akotkar in combination with Moghe does not expressly disclose the VCCS of claim 1, wherein:
the OBU is configured to perform data fusion at a microscopic level.
However, Luo discloses:
the OBU is configured to perform data fusion at a microscopic level. (Luo ¶ 134 discloses “the machine-learning computing system 1270 can determine, based at least in part on one or more fusion criteria, whether to aggregate the temporal data including the temporal information at the first layer of the machine-learned model or gradually over multiple layers of the machine-learned model” implemented on one or more computing device of a vehicle [i.e., microscopic])
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the OBU of Akotkar with performing data fusion at a microscopic level, as disclosed by Luo, with reasonable expectation of success, for faster, more accurate, and precise object detection, tracking, and motion prediction that more efficiently utilizes computing resources (Luo ¶ 25), rendering the limitation to be an obvious modification.
Regarding claim 10, Akotkar in combination with Moghe does not expressly disclose the VCCS of claim 1, wherein:
the OBU is configured to provide vehicles with individually customized information and real-time control instructions for vehicles to fulfill driving tasks.
However, Luo discloses:
the OBU is configured to provide vehicles with individually customized information and real-time control instructions for vehicles to fulfill driving tasks. (Luo ¶ 86 discloses that the computing system of the vehicle includes a perception system 124 that “can obtain state data 130 descriptive of a current and/or past state of an object that is proximate to the vehicle 108. The state data 130 for each object [i.e., individually customized information] can describe, for example, an estimate of the object's current and/or past: location and/or position; speed; velocity; acceleration; heading; orientation; size/footprint (e.g., as represented by a bounding shape); class (e.g., pedestrian class vs. vehicle class, building class vs. bicycle class), and/or other state information.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the onboard computer of Akotkar to expressly disclose that the onboard computer is configured to provide vehicles with individually customized information and real-time control instructions for vehicles to fulfill driving tasks, as disclosed by Luo, with reasonable expectation of success, to enhance vehicle safety through improved object detection, tracking, and prediction and a reduction in wear and tear on vehicle components through smoother vehicle navigation based on more effective object detection, tracking, and prediction (Luo ¶ 66), rendering the limitation to be an obvious modification.
Regarding claim 11, Akotkar in combination with Moghe discloses the parallel limitations contained in parent claim 1 for the reasons discussed above. In addition, Akotkar discloses:
wherein an RSU of the RSU network comprises: a sensing module configured to measure characteristics of the driving environment (Akotkar ¶ 50 discloses that “the wireless access node 703 may obtain data intended for the onboard computer from the cloud 705 over link 709, and may provide that data, e.g., additional data to supplement information about location of an emergency vehicle (or additional information to assist decision unit 220 with determining a next action,” wherein the data packets include sensor data, see ¶ 48); a communication module configured to communicate with vehicles, the TCC/TCU, and the cloud platform (Akotkar in at least Fig. 7); and
Akotkar in combination with Moghe does not expressly disclose:
a data processing module configured to process, fuse, and compute data from the sensing module and/or the communication module.
However, Luo discloses:
a data processing module configured to process, fuse, and compute data from the sensing module and/or the communication module. (Luo ¶ 134 discloses “the machine-learning computing system 1270 can determine, based at least in part on one or more fusion criteria, whether to aggregate the temporal data including the temporal information at the first layer of the machine-learned model or gradually over multiple layers of the machine-learned model” implemented on one or more computing device of a vehicle)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified the combination of data disclosed by Akotkar in at least ¶ 15 with expressly disclosing a data processing module configured to process, fuse, and compute data from the sensing module and/or the communication module, with reasonable expectation of success, to allow for an improvement in operational safety through faster, more accurate, and precise object detection, tracking, and motion prediction that more efficiently utilizes computing resources (Luo ¶ 25), rendering the limitation to be an obvious modification.
Regarding claim 12, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 2 for the reasons discussed above.
Regarding claim 13, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 3 for the reasons discussed above.
Regarding claim 14, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 4 for the reasons discussed above.
Regarding claim 15, Akotkar in combination with Moghe discloses the parallel limitations contained in parent claim 5 for the reasons discussed above.
Regarding claim 17, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 7 for the reasons discussed above.
Regarding claim 18, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 8 for the reasons discussed above.
Regarding claim 19, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 9 for the reasons discussed above.
Regarding claim 20, Akotkar in combination with Moghe and Luo discloses the parallel limitations contained in parent claim 10 for the reasons discussed above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. (U.S. Patent Publication Number 2019/0049989) in view of Moghe et al. (U.S. Patent Publication Number 2019/0381891), further in view of Ahmed et al. (U.S. Patent Publication Number 2016/0328272).
Regarding claim 6, Akotkar in combination with Moghe does not expressly disclose the VCCS of claim 1, wherein:
the computing subsystem assigns sequential tasks to a central processing unit as a general-purpose processor and assigns parallel tasks to a graphics processing unit as a special-purpose processor.
However, Ahmed discloses:
the computing subsystem assigns sequential tasks to a central processing unit as a general-purpose processor and assigns parallel tasks to a graphics processing unit as a special-purpose processor. (Ahmed ¶ 143 discloses that in process 1100, wherein CPU 1102 has a plurality of generated tasks, “CPU 1102 is shown first generating the low priority task and passing a portion of weather display 1110 to GPU 1104 via a GPU driver,” wherein “CPU 1102 and/or GPU 1104 may divide each task into a plurality of tiles such that GPU 1104 may process and render each tile individually,” such that “While GPU 1312 is busy processing task 1302 [i.e., parallel], CPU 1310 may continue to generate tasks 1303 [i.e., sequential], 1304 for future processing,” see ¶ 149)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the sequential tasks and parallel tasks of Akotkar with assigning sequential tasks to a central processing unit as a general-purpose processor and assigning parallel tasks to a graphics processing unit as a special-purpose processor, with reasonable expectation of success to enable efficient communications between the different virtual machines (Ahmed ¶ 96) and manage the prioritization of each tile of a display for each received task (Ahmed ¶ 145), which may be advantageous for implementations in which graphics commands for OpenGL drivers may not be compatible with other graphics commands (Ahmed ¶ 152), rendering the limitation to be an obvious modification.
Regarding claim 16, Akotkar in combination with Moghe and Ahmed discloses the parallel limitations contained in parent claim 6 for the reasons discussed above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE T SU whose telephone number is (571)272-5326. The examiner can normally be reached Monday to Friday, 9:30AM - 5:00PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANISS CHAD can be reached at (571)270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEPHANIE T SU/Primary Examiner, Art Unit 3662
Prosecution Insights