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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/29/2026 has been entered.
Status of Claims
Claims 1-3, 5-11, 13-18 and 20 are pending of which claims 1, 9 and 16 are in independent form.
Claims 1-3, 5-11, 13-18 and 20 are rejected under 35 U.S.C. 101 (Abstract Idea).
Claims 1-3, 5-11, 13-18 and 20 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 5-11, 13-18 and 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.
Applicant’s Argument:
Applicant argues, in page 8 of the “Remarks”, that “As agreed upon during the telephone interview, the cited references do not teach or suggest at least "herein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata, and the weight is used for calculating weighted averages of parameters of a plurality of local machine learning model," as recited in amended independent claim 1”.
Examiner’s Response:
Examiner specifies that, upon further examination of the prior art, it is clear that Roth teaches, the weight is used for calculating weighted averages of parameters of a plurality of local machine learning model (a federated server 350 aggregates neural network weights, such as local model 306, 318, 330 weights or scalars, … by weighted averaging ¶ [0091], computes global parameterization value … using weighted averaging on local parameterization values ¶ [0099]. Also see ¶ [0094], [0096], [0100], [0104], teaches local models, local aggregation weights, global aggregation weights, federated averaging, and learnable aggregation weights).
Additionally, Wu (new reference) discloses, wherein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata (parallel computing power of each federated learning unit…to determine a weight of each federated learning unit ¶ [0013], [0015], [0024], [0050], [0052]).
Examiner’s Note
Examiner has reviewed claims 1 and 16; according to the specification (¶ [0022], [0034]), the “controller” has been specified as “one or more processors”. Therefore claims 1 and 16 comply with 35 USC 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-11, 13-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) contribution aware federated learning.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claim 1 directed to a vehicle comprising a controller (hardware), which are directed to one of the four statutory subject matters.
Independent claim 9 is directed to a method, which is a process.
Independent claim 16 directed to a system, comprising a server; a plurality of vehicles,... comprising a controller (hardware) which are directed to one of the four statutory subject matters.
Independent All other claims depend on claims 1, 10 and 20. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 9 and 16:
With respect to step 2A, prong one (Judicial Exception), the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claims recite:
“obtain metadata for hardware elements of the vehicle” the limitation as drafted recites a mental process involving observation and data evaluation (collecting and elevating information regarding hardware characteristics).
“wherein the metadata includes information about a computing power of a processor of the vehicle” the limitation as drafted recites a mental process involving analysis and comparison of information (evaluating capabilities of a processor of a hardware),
“wherein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata” the limitation as drafted recites a mental process involving judgement/evaluation and a mathematical concept involving numerical weighting operation (assigning weighting vales based on evaluated characteristics).
“the weight is used for calculating weighted averages of parameters of a plurality of local machine learning model” the limitation as drafted recites a mathematical algorithms/concept grouping of abstract ideas (mathematical calculations involving weighted averaging),
“wherein the aggregated machine learning model is generated based on the trained local machine learning model, the determined … another local machine learning model” the limitation as drafted recites combining weighted information from multiple models according to mathematical relationships and evaluation rules, which constitutes mathematical aggregation and evaluation processes (mathematical algorithms/concept).
These limitations correspond to concept that can be performed in the human mind (Mental Process) and mathematical algorithms therefore fall within the Mental Process/Mathematical Algorithm/Concept category of abstract idea (see MPEP 2016.04(a)(2)).
Nothing in the claim requires a new hardware structure, improved storage technique, or any specific technological improvement.
With respect to step 2A, Prong Two, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims are generic computer components preforming their routine functions. The claims include:
“a vehicle” as drafted recites generic computing environment, in which the abstract idea is performed and merely limits the use of the abstract idea to a particular technological field (vehicle-based ML environment), which odes not integrate the abstract idea into a practical application (see MPEP 2106.05(h)-field of use).
“a controller programmed to” as drafted recites generic computing components performing generic computer functions, including receiving information, processing data, transmitting information, and executing instructions (see MPEP 2106.05(f)).
“train a local machine learning model using first local data” as drafted recites a generic data processing and mathematical model training operations performed using generic ML learning techniques, which amount to mere instructions to apply the abstract idea using conventional computer technology.
“obtain metadata for hardware elements of the vehicle” as drafted recites insignificant extra solution activity including gathering and analyzing information regarding hardware characteristics (see MPEP 2106.05(g)).
“transmit the trained local machine learning model and the metadata to a server; receive an aggregated machine learning model from the server” as drafted recites a generic communication and data transmission functions performed by conventional networked computing components.
“wherein a weight for the trained local machine learning model is determined based on the computing power of the processor…, and the weight is used for calculating weighted averages of parameters” as drafted recites the abstract idea itself, namely mathematical weighted and aggregation operations based on evaluated information, and do not impose any meaningful limit on how the abstract idea is performed.
The additional elements mentioned above fail to integrate the abstract idea into a practical application because the additional elements, individually and in combination, amount to no more that:
Generic computer components performing generic computer functions;
Insignificant extra-solution activity including data gathering and transmission; and
Mathematical weight calculation and aggregation operations performed in a conventional federated learning environment.
The claims do not:
Improve the functioning of a computer, processor, or network;
Improve federated learning technology itself;
Improve ML training architecture;
Recite a specific technical mechanism for improving model convergence, latency or processor efficiency
Provide a specific technical implementation for weighted aggregation based on a processor capability.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract idea of evaluating a graph representation, making a decision, and updating information based on the decision.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the claim merely uses generic computing components as tools to evaluate computing capability information, assign weighted values, and mathematical aggregate model parameters.
The recited components perform their generic/ordinary, expected functions, which is considered insufficient. These claims are merely functional software components performing generic task. Therefore, the claims do not integrate the abstract idea into a practical application.
Therefore, the claims do not integrate the abstract idea into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims merely provide conventional techniques, architecture, federated learning or ML models (generic processor; generic ML/learning). The steps in the claims are a routine and conventional.
The claims recite only, without significantly more, listing specific signals and external signals are result oriented and functional (without technical implementation), no practical algorithm, signal generation technique, or ranking mechanism. All are routine, conventional operations business/ market place logic.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2, 3, 5, 7, 10, 11, 14, 18 and 20 (Sensor/Metadata Characteristics),
The claim recites:
Image sensor configured to capture local data (claims 2, and 10).
Metadata includes: number of sensors, quality of sensors (claims 3, 11, and 18).
Processor is a GPU (claim 5).
Metadata includes a resolution of the first local data (claims 7, 14 and 20).
This is merely refining: the type of input data collected, the characteristics of hardware resources, and the types of processors used. These are considered: Mental Process (evaluation and classification of hardware characteristics), field of use limitations involving GPU hardware (sensor quality/quantity, GPU). This does not improve: image processing technology, sensor functionality, GPU operations, federated learning architecture.
There is no technical mechanism provide for: how sensor metadata improves federated aggregation, how GPU processing is specially optimized, how image resolution is technically utilized.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 6, 8, 13, and 15 (Autonomous Vehicle Control),
The claim recites:
Operating the vehicle autonomously using the trained aggregated ML model (claims 6, and 13).
Operating actuators of the vehicle to keep the vehicle within lane boundaries using the aggregated machine learning model (claims 8, and 15).
This is merely refining: the abstract federated learning model, autonomous driving and vehicle control. These are considered: Mental Process (evaluation and decision making), field of use limitations, and result oriented functional limitations. This does not recite: a specific autonomous driving control algorithm, a lane detection improvement, an improved vehicle actuator, etc..
There is no technical mechanism provide for: how autonomous driving decisions are generated, how lane boundaries are detected or maintained, or how actuators are technically controlled using a federated model.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
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.
Claim(s) 1-3, 5-11, 13-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Roth; Holger Reinhard et al. (US 20220366220 A1) [Roth] in view of Wang; Jiajing et al. (US 10963734 B1) [Wang] in view of Bernat; Francesc Guim et al. (US 20210397999 A1) [Bernat] in view of Wu; Xin et al. (US 20230238156 A1) [Wu].
Regarding claims 1, 9 and 15, Roth discloses, a vehicle comprising: a controller programmed to: train a local machine learning model using first local data (A federated server 132 further facilitates federated training of or federated learning by local models 108, 116, 124 by distributing neural network weights values and/or updated models from a global model 134 to local models 108, 116, 124 usable by edge devices or clients 102, 110, 118 located at a plurality of locations ¶ [0056]-[0057], [0062]-[0064]);
transmit the trained local machine learning model [and the metadata to a server] (Figs. 2 and 3, Model Transfer to the Federated Server);
receive an aggregated machine learning model from the server (FIG. 1 is a block diagram illustrating an example architecture for federated learning by one or more neural networks 108, 116, 124, 134 to process medical and/or other data, according to at least one embodiment. In at least one embodiment, a federated server 132 collects neural network neural network weights comprising numerical values calculated as a result of neural network training, and/or aggregation weights from one or more edge devices or clients 102, 110, 118, such as computing systems belonging to hospitals at different locations. In at least one embodiment, a federated server 132 is a computing system comprising hardware components and memory containing software instructions that, when executed, train a global model 134 according to neural network data values collected from one or more edge devices or clients 102, 110, 118, such as computing systems belonging to hospitals at different locations. In at least one embodiment, neural network weights comprise one or more numerical values or other data values associated with one or more neural networks ¶ [0056]. FIG. 3 is a block diagram illustrating an architecture to perform federated learning using learnable aggregation weights 352, 354, 356, according to at least one embodiment ¶ [0077]-[0090]);
and train the aggregated machine learning model using second local data, wherein the aggregated machine learning model is generated based on the trained local machine learning model (FIG. 4 illustrates adjustment of learnable aggregation weights during training rounds between a federated server and a plurality of client locations, according to at least one embodiment ¶ [0006]. Also see ¶ [0056], [0086], [0092], [0095], [0099], [0100]) the determined weight for the trained local machine learning model, and another local machine learning model trained by another vehicle and another weight determined for the another local machine learning model (federated learning or federated training is neural network training using data and/or local neural network models 108, 116, 124 from edge devices or clients 102, 110, 118 at a plurality of locations. In at least one embodiment, a federated server 132 performs federated training or federated learning of a global model 134 by aggregating neural network data values received from edge devices or clients 102, 110, 118, such as hospital computing systems, where each hospital is located at different geographic locations, and training said global model 134 using said neural network data values. A federated server 132 further facilitates federated training of or federated learning by local models 108, 116, 124 by distributing neural network weights values and/or updated models from a global model 134 to local models 108, 116, 124 usable by edge devices or clients 102, 110, 118 located at a plurality of locations ¶ [0056]. Also see ¶ [0059], [0069]-[0070], [0072]-[0073], [0081]-[0082]);
and the weight is used for calculating weighted averages of parameters of a plurality of local machine learning model (a federated server 350 aggregates neural network weights, such as local model 306, 318, 330 weights or scalars, … by weighted averaging ¶ [0091], computes global parameterization value … using weighted averaging on local parameterization values ¶ [0099]. Also see ¶ [0094], [0096], [0100], [0104], teaches local models, local aggregation weights, global aggregation weights, federated averaging, and learnable aggregation weights).
However, Roth does not explicitly facilitate obtain metadata for hardware elements of the vehicle; and the metadata to a server; and the metadata.
Wang discloses, obtain metadata for hardware elements of the vehicle (For example, continuing from the earlier example where a scene captured by the LIDAR sensor 124 was analyzed by the object detection model 147 to generate the first set of labels, … Metadata associated with the LIDAR point cloud image 400A (now labeled) may appended with the manually created bounding boxes and/or labels [col. 14, ll. 22-48]. Also see [col. 15, ll. 29-35], [col. 19, ll. 32-42]);
the metadata to a server; and the metadata (the method may further comprise receiving, by the one or more processors, an user input flagging the object for further training the object detection model; and in response to receiving the user input, sending, by the one or more processors, the metadata to a remote server for further training the object detection model [col. 3, ll. 36-41]. Also see [col. 4, ll. 3-13], [col. 24, ll. 20-31], [col. 25, ll. 8-23]).
It would have been obvious, to one ordinary skilled in the art, before the effective filing date of the claimed invention to combine the teachings of the cited references because Wang's system would have allowed Roth to explicitly facilitate obtain metadata for hardware elements of the vehicle; and the metadata to a server; and the metadata. The motivation to combine is apparent in the Roth’s reference, because a need for labeling various sensors for autonomous vehicles in order to improve training the ML models.
However, neither Roth nor Wang explicitly facilitates wherein the metadata includes information about a computing power of a processor of the vehicle.
Bernat discloses, wherein the metadata includes information about a computing power of a processor of the vehicle (The example model adjustor circuitry 715 generates layer-specific metadata for each layer in the machine learning model. The layer specific metadata enables a computation of how much time and/or energy will be required to execute the layer given different resources (e.g., at a local node or at a remote node) ¶ [0054]. The example offload controller circuitry 740 of the illustrated example of FIG. 7 estimates resource requirements for local and remote execution of each layer of the machine learning model. In examples disclosed herein, the local and remote resource requirements are estimated based on compute capabilities of the local and remote nodes, respectively, as well as the layer-specific metadata associated with each layer of the model ¶ [0059], [0080]).
It would have been obvious, to one ordinary skilled in the art, before the effective filing date of the claimed invention to combine the teachings of the cited references because Bernat's system would have allowed Roth and Wang to explicitly facilitate wherein the metadata includes information about a computing power of a processor of the vehicle. The motivation to combine is apparent in the Roth and Wang’s reference, because a need for improving the availability of different compute resources including, for example, available power (e.g., computing power).
However, neither one of Roth, Wang or Bernat explicitly facilitate wherein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata.
Wu discloses, wherein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata (parallel computing power of each federated learning unit…to determine a weight of each federated learning unit ¶ [0013], [0015], [0024], [0050], [0052]).
It would have been obvious, to one ordinary skilled in the art, before the effective filing date of the claimed invention to combine the teachings of the cited references because Wu's system would have allowed Roth, Wang and Bernat to explicitly facilitate wherein a weight for the trained local machine learning model is determined based on the computing power of the processor in the metadata. The motivation to combine is apparent in the Roth, Wang, and Bernat’s reference, because a need for improving object detection efficiency, for detecting a typical object based on vehicle federated learning.
Regarding claims 2 and 10, the combination of Roth, Wang, Bernat, and Wu discloses, an imaging sensor configured to capture the first local data and the second local data (Roth: In at least one embodiment, RISC cores may interact with image sensors (e.g., image sensors of any cameras described herein), image signal processor(s), etc. ¶ [0170]. LIDAR ¶ [0207]-[0209]).
Regarding claims 3, 11 and 18, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the metadata includes information about a number of sensors of the vehicle and a quality of sensors of the vehicle (Wang: For still another example, the metadata may include other object information 726, such as number of LIDAR points, colors in the object, lighting on the object, etc. For yet another example, the metadata may include scene information 728, such as the scene number, timestamp of the scene, total number of LIDAR points in the scene, lighting and colors of the scene, etc. [col. 23, ll. 62-col. 24, ll. 2]. Also see [col. 19, ll. 54-63]).
Regarding claims 4, 12 and 19, (Canceled).
Regarding claims 5, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the processor is a graphics processing unit (Roth: FIG. 21 illustrates a multi-graphics processing unit (GPU) system, according to at least one embodiment ¶ [0034]. Also see ¶ [0114], [0115]).
Regarding claims 6 and 13, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the controller is programed to operate the vehicle to drive autonomously using the trained aggregated machine learning model (Roth: In at least one embodiment, one or more PPUs 3200 are configured to accelerate High Performance Computing (“HPC”), data center, and machine learning applications. In at least one embodiment, PPU 3200 is configured to accelerate deep learning systems and applications including following non-limiting examples: autonomous vehicle platforms, deep learning, high-accuracy speech, image, text recognition systems, intelligent video analytics, molecular simulations, drug discovery, disease diagnosis, weather forecasting, big data analytics, astronomy, molecular dynamics simulation, financial modeling, robotics, factory automation, real-time language translation, online search optimizations, and personalized user recommendations, and more ¶ [0461]).
Regarding claims 7, 14 and 20, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the metadata includes a resolution of the first local data (Roth: In at least one embodiment, organ segmentation 3810 application and/or container may read an image file from a cache, normalize or convert an image file to format suitable for inference (e.g., convert an image file to an input resolution of a machine learning model), and run inference against a normalized image ¶ [0544]).
Regarding claims 8 and 15, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the controller is programmed to operate one or more actuators of the vehicle to keep the vehicle within lane boundaries using the aggregated machine learning model (Roth: In at least one embodiment, a steering system 1054, which may include, without limitation, a steering wheel, is used to steer vehicle 1000 (e.g., along a desired path or route) when propulsion system 1050 is operating (e.g., when vehicle 1000 is in motion). In at least one embodiment, steering system 1054 may receive signals from steering actuator(s) 1056. In at least one embodiment, a steering wheel may be optional for full automation (Level 5) functionality. In at least one embodiment, a brake sensor system 1046 may be used to operate vehicle brakes in response to receiving signals from brake actuator(s) 1048 and/or brake sensors ¶ [0136], [0137]. In at least one embodiment, vehicle 1000 may include ADAS system 1038. In at least one embodiment, ADAS system 1038 may include, without limitation, an SoC, in some examples. In at least one embodiment, ADAS system 1038 may include, without limitation, any number and combination of an autonomous/adaptive/automatic cruise control (“ACC”) system, a cooperative adaptive cruise control (“CACC”) system, a forward crash warning (“FCW”) system, an automatic emergency braking (“AEB”) system, a lane departure warning (“LDW)” system, a lane keep assist (“LKA”) system, a blind spot warning (“BSW”) system, a rear cross-traffic warning (“RCTW”) system, a collision warning (“CW”) system, a lane centering (“LC”) system, and/or other systems, features, and/or functionality ¶ [0215]).
Regarding claim 17, the combination of Roth, Wang, Bernat, and Wu discloses, wherein the server determines contributions of the trained local machine learning models based on the metadata received from the plurality of vehicles (Roth: see Figs. 1-3).
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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5/11/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
