CTFR 18/499,584 CTFR 90274 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims This Office Action is in response to the Applicant’s amendments and remarks filed on November 25, 2025. Claim 10 is currently amended. Claims 1-24 are pending and have been examined. Response to Arguments Regarding the outstanding Claim Interpretation under 35 U.S.C. § 112(b) : The outstanding 35 USC 112(b) rejection of claim 10 is withdrawn in view of Applicant’s amendment deleting “the” and replacing it with “a” before “second class of vehicles” in line 3. However, there is a new 35 USC 112(b) rejection for claim 10 due to the second amendment to the claim, in line 7. It is not clear if this second amendment to the claim, “for use in a second class of vehicles” is referring to the previously introduced, “sensor system of a second class of vehicles”, in line 3 of the claim. Regarding the outstanding 35 U.S.C. § 101 Rejections : 07-37 AIA Applicant’s arguments filed on November 25, 2025 have been fully considered but they are not persuasive. Applicant argues that “training models is an established technology and that the subject matter of independent claims 1, 10, 19, and 22 provides a particular solution/improvement to federated learning. For example, at least the claimed features of ‘comparing, in the vehicle processing system of the first class of vehicle, a first output of a sensor processing model for use in a self-driving system for the second class of vehicles to a second output of a complex sensor processing model of the self-driving system of the first class of vehicle’ and ‘training the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model’ provide a solution to a ‘challenge faced by car manufacturers and fleet operator is to improve the performance and safety of the second class of vehicles in their product line without significantly increasing costs, including the costs of training self-driving AI/ML models,’ such as by enabling a first class of autonomous or semi-autonomous vehicles to train AI/ML sensor processing models, and potentially other modules, for self-driving systems suitable for the second class of vehicles.” See p. 10 of Applicant’s arguments submitted November 25, 2025. The Examiner respectfully disagrees. The limitations that have been identified as including an abstract idea, as stated in representative claim 10, are “compare a first output of a sensor processing model for use in the second class of vehicles of a low-end self-driving system to a second output of a complex sensor processing model” and “train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model”. The current claim limitations closely resemble the claim limitations in Example 47, claim 2, in the July 2024 Subject Matter Eligibility Examples, which was found to be ineligible. The claim limitation, “comparing, in the vehicle processing system of the first class of vehicle, a first output of a sensor processing model for use in a self-driving system for the second class of vehicles to a second output of a complex sensor processing model of the self-driving system of the first class of vehicle” is similar to the claim limitation in Example 47, claim 2, step (b) “discretizing, by the computer, the continuous training data to generate input data”. The current claim limitation does not limit the plain meaning of “comparing”, nor does the explanation in the background section of the specification, similar to the explanation given for claim 2 in Example 47 on page 6. Comparing data output from two different sets of calculations can practically be performed in the human mind, similar to the claim limitations in Example 47, claim 2. Further, the current claim limitations are performed on a generic computer, a processing system, similar to the computer in Example 47, claim 2. Therefore, the abstract idea of “comparing” data being performed on a generic processing system is an additional limitation that merely describes how to generally “apply” the mental process of “comparing” in a generic or general purpose navigation environment. The current claim limitation, “training the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing mode” is similar to the claim limitation in Example 47, claim 2, step (c) “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm”. When given its broadest reasonable interpretation in light of the specification, the sensor processing model is an algorithm comparing data. The current claim limitation does not limit the plain meaning of “comparing”, nor does the explanation in the background section of the specification, similar to the explanation given for claim 2 in Example 47 on page 6. Comparing data output from two different sets of calculations can practically be performed in the human mind, similar to the claim limitations in Example 47, claim 2. Further, the current claim limitations are performed on a generic computer, a processing system, similar to the computer in Example 47, claim 2. Therefore, the abstract idea of “comparing” data being performed on a generic processing system is an additional limitation that merely describes how to generally “apply” the mental process of “comparing” in a generic or general purpose navigation environment. Regarding the current claim limitation, “make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles”, it is similar to Example 47, claim 2, step (f), “outputting the anomaly data from the trained ANN.” The current claim limitation of “make the trained sensor processing model… available for deployment” can be considered to make an algorithm, which is broadly considered to be data, available for output. It does not state a positive recitation of outputting but rather making the algorithm, or data, available for output. This is similar to outputting data from an algorithm, which is outputting generic data and is considered to be mere instructions to implement an abstract idea on a generic computer, similar to the explanation given for claim 2 in Example 47 on page 8. See MPEP 2106.05 (f). Regarding the current claim limitations, “a complex sensor system”, “a sensor system of a second class of vehicles”, these systems are considered to be additional elements that are used to gather data. These additional elements are recited at a high level of generality and are a general means of gathering data for use in a processing algorithm, or model, in a well-understood, routine, and conventional manner. They function to merely gather data, which is a form of insignificant extra-solution activity. Regarding the current claim limitations, “a memory”, and “a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to”, which the “processing system” has been discussed above, are recited at a high level of generality and are considered to be generic computing components that merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose navigation environment in a well-understood, routine, and conventional manner. These components merely automate the abstract idea steps. Therefore, the outstanding 35 USC 101 rejections are maintained, but are slightly modified based on Applicant’s amendments. Regarding the outstanding 35 U.S.C. § 103 Rejections : 07-37 AIA Applicant’s arguments filed on November 25, 2025 have been fully considered but they are not persuasive. The Applicant argues that, “the cited references, alone and in combination, fail to describe or suggest, at least, ‘train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model,’ as claimed.” See p. 12 of Applicant’s Remarks filed on November 25, 2025. In response to Applicant’s arguments regarding claim 10, the Wunsch reference discloses, “ compare a first output of a sensor … for use in a second class of vehicles of a low-end self-driving system to a second output of a complex sensor … “, as disclosed in [0010] and [0020]. Wunsch further discloses, “ train the sensor processing model for use in the second class of vehicles [see at least Wunsch [0020] “… it is provided that the data conversion model is trained as target data… “] based on data from the sensor system of the second class of vehicles [see at least Wunsch [0025] “In a special embodiment of the invention, it is advantageous if the data processing model is trained with additional second measurement data from at least one sensor of the sensor class as training data in addition to the output data. The additional second measurement data can be provided by the at least one sensor that also provided the second measurement data…”] and the comparison of the first output of the sensor … for use in the second class of vehicles to the second output of the complex sensor … “ [see at least Wunsch [0020] “…it is provided that the data conversion model is trained as target data by applying at least one loss function to reduce deviations between the output data of the data conversion model calculated on the basis of the first measurement data during the training and the second measurement data linked to the first measurement data .”] Secondary reference, Tong, discloses, “… compare a first output of a … processing model…to a second output of a … processing model …” [see at least Tong [0019] “A method includes receiving, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receiving, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, comparing the output generated by the first neural network with the output generated by the second neural network, and generating an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold."] To summarize, the Wunsch reference discloses comparing a first output of a sensor to use in a second class of vehicles to a second output of a complex sensor. This comparison is then used in a training model, the data conversion model. The data conversion model applies at least one loss function that is calculated based on the comparison between the first measurement data and the second measurement data linked to the first measurement data. The statement that the second measurement data is linked to the first measurement data is at least one indication that the second data has been compared to the first measurement data. Wunsch also discloses that there is an embodiment the invention, that additional second measurement data from at least one sensor of the sensor class is used as training data. The additional second measurement data can be provided by the at least one sensor that also provided the second measurement data. While the Wunsch reference discloses training a model based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor for use in the second class of vehicles to the second output of the complex sensor, the secondary reference, Tong, discloses comparing data output from two different models. It would be obvious to one of ordinary skill in the art before the effective filing date of the invention to not only compare the data output results themselves, but to also compare the output results of two different models that have processed the input data. Therefore, the outstanding 35 USC 103 rejection is maintained but slightly modified based on Applicant’s amendments to the claim language . Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 10 has been amended, in lines 7-8, to “compare a first output of a sensor processing model for use in [[the]] a second class of vehicles of a low-end self-driving system”. It is not clear if this second class of vehicles of a low-end self-driving system is referring to the same previously introduced “sensor system of [[the]] a second class of vehicles” that was introduced in line 3. Therefore, claim 10 is rejected under 35 USC 112(b) as being indefinite. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See the 2019 Revised Patent Subject Matter Eligibility Guidance. Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application: the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Examples in which the judicial exception has not been integrated into a practical application include: the additional element(s) merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; the additional element(s) adds insignificant extra-solution activity to the judicial exception; and the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use. See the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Patent Subject Matter Eligibility Guidance Update Including on Artificial Intelligence. 101 Analysis – Step 1 Claim 1 is directed to a method (i.e., a process). Claim 10 is directed to a vehicle (i.e., an apparatus). Claim 19 is directed to a method (i.e., a process). Claim 22 is directed to a server (i.e., an apparatus). Therefore, claims 1, 10, 19, and 22 are each within at least one of the four statutory categories. 101 Analysis – Step 2A, Prong 1 Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Claims 1-18 Independent claim 10 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim (representing claim 1) for the remainder of the 101 rejection. Claim 10 recites: A vehicle of a first class, comprising: a complex sensor system; a sensor system of a second class of vehicles; a memory; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to: compare a first output of a sensor processing model for use in a second class of vehicles of a low-end self-driving system to a second output of a complex sensor processing model ; train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model ; and make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles. The Examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “ compare a first output of a sensor processing model for use in a second class of vehicles of a low-end self-driving system to a second output of a complex sensor processing model; and train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model” in the context of this claim encompasses a person performing these limitations in the human mind, or by a human using a pen and paper. The complex sensor processing model does not limit how the application is performed, and there is nothing about how any results are utilized. The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. The recitation of merely indicates a field of use or technological environment in which the judicial exception is performed, and confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the claim recites at least one abstract idea. 101 Analysis – Step 2A, Prong II Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”): A vehicle of a first class, comprising: a complex sensor system ; a sensor system of a second class of vehicles ; a memory ; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to : compare a first output of a sensor processing model for use in a second class of vehicles of a low-end self-driving system to a second output of a complex sensor processing model ; train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model ; and make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles . For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “ a memory; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to”, they merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose navigation environment. The at least one processor and communicatively connected memory are recited at a high level of generality and merely automate the abstract idea steps. Regarding the additional limitation of “ a complex sensor system” and “a sensor system of a second class of vehicles”, they are recited at a high level of generality (i.e. as a general means of gathering data for use in the “sensor processing model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitations of “ make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles”, it is recited at a high level of generality (i.e. as a general means of sending and receiving data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 101 Analysis – Step 2B Regarding Step 2B of the 2019 PEG, representative independent claim 10 does not include additional elements (considered both individually and as an ordered combination) that are sufficient o amount to significantly more than the judicial exception for the same reasons as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the “ compare a first output of a sensor processing model for use in a second class of vehicles of a low-end self-driving system to a second output of a complex sensor processing model; and train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model” amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “ a memory; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to”, “a complex sensor system”, “a sensor system of the second class of vehicles”, and “ make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles”, the Examiner submits that these limitations are insignificant extra-solution activities. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “ a memory; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to” , are well-understood, routine, and conventional activities because the specification does not provide any indication that the processor is anything other than a conventional computer within a vehicle. Regarding the additional limitation of “ a complex sensor system” and “a sensor system of the second class of vehicles”, they are recited at a high level of generality (i.e. as a general means of gathering data for use in the “sensor processing model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity, is well understood, routine, and conventional, and does not amount to significantly more than the judicial exception. Regarding the additional limitations of “ make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles”, it is recited at a high level of generality (i.e. as a general means of sending and receiving data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity, is well understood, routine, and conventional, and does not amount to significantly more than the judicial exception. See MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp . 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC , 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc. , 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible. Dependent claims 11-18 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-6 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1. Further, claims 1-9 are not patent eligible under the same rationale as provided for the rejection of claims 10-18. Therefore, claims 1-18 are ineligible under 35 USC §101. Claims 19-24 Independent claim 22 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim (representing claim 19) for the remainder of the 101 rejection. Claim 22 recites: A server, comprising: a processing system configured to: receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles; generate a consolidated sensor processing model for use in a second class of vehicles from the received sensor processing model for use in the second class of vehicles ; and provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system . The Examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “ generate a consolidated sensor processing model for use in a second class of vehicles from the received sensor processing model for use in the second class of vehicles” in the context of this claim encompasses a person performing these limitations in the human mind, or by a human using a pen and paper. The consolidated sensor processing model does not limit how the application is performed, and there is nothing about how any results are utilized. The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. The recitation of merely indicates a field of use or technological environment in which the judicial exception is performed, and confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the claim recites at least one abstract idea. 101 Analysis – Step 2A, Prong II Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”): A server, comprising: a processing system configured to : receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles ; generate a consolidated sensor processing model for use in a second class of vehicles from the received sensor processing model for use in the second class of vehicles ; and provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system. For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “ a processing system configured to :”, “ receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles ”, and “ provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system ” , they merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose navigation environment. The at least one processor and communicatively connected memory are recited at a high level of generality and merely automate the abstract idea steps. Regarding the additional limitation of “ receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles ”, they are recited at a high level of generality (i.e. as a general means of gathering data for use in the “consolidated sensor processing model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitations of “ provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system”, it is recited at a high level of generality (i.e. as a general means of sending and receiving data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 101 Analysis – Step 2B Regarding Step 2B of the 2019 PEG, representative independent claim 122 does not include additional elements (considered both individually and as an ordered combination) that are sufficient o amount to significantly more than the judicial exception for the same reasons as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the “ generate a consolidated sensor processing model for use in a second class of vehicles from the received sensor processing model for use in the second class of vehicles” amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “ a processing system configured to :”, “ receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles ”, and “ provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system ” , the Examiner submits that these limitations are insignificant extra-solution activities. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “ a processing system configured to :”, is well-understood, routine, and conventional activities because the specification does not provide any indication that the processor is anything other than a conventional computer within a vehicle. Regarding the additional limitation of “ receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles ”, it is recited at a high level of generality (i.e. as a general means of gathering data for use in the “consolidated sensor processing model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity, is well understood, routine, and conventional, and does not amount to significantly more than the judicial exception. Regarding the additional limitations of “ provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system ” , it is recited at a high level of generality (i.e. as a general means of sending and receiving data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity, is well understood, routine, and conventional, and does not amount to significantly more than the judicial exception. See MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp . 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC , 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc. , 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible. Dependent claims 23-24 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 23-24 are not patent eligible under the same rationale as provided for in the rejection of independent claim 22. Further, claims 19-21 are not patent eligible under the same rationale as provided for the rejection of claims 22-24. Therefore, claims 1-24 are ineligible under 35 USC §101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-02-aia AIA 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-4, 6, 10-13, 15, 19, 21-22, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Wunsch (WO 2024/156499 A1), in view of Tong, et al. (Publication US 2023/0139521 A1) (hereinafter referred to as “Wunsch” and “Tong”.) As per claim 10 (representative of claim 1) , Wunsch discloses a vehicle of a first class, comprising: a complex sensor system [see at least Wunsch [0007] "…The training data can be generated synthetically from existing high-resolution initial sensor data. This allows the data processing model to be trained more cost-effectively to process low-resolution sensor data."]; a sensor system of a second class of vehicles [see at least Wunsch [0010] "The first and second measurement data can each indicate the same environmental scene in perspective. The only difference between the first and second measurement data compared to each other may be the resolution of the measurement data."; [0007] "According to the present invention, a method for creating training data with the features of claim 1 is proposed. This allows the training data for the data processing model to be provided more cost-effectively. The training data can be synthetically generated from existing high-resolution additional first sensor data. This allows the data processing model to be trained more cost-effectively to process low-resolution sensor data."]; a memory [see at least Wunsch [0028] "Furthermore, a storage unit is proposed which is machine-readable and accessible by at least one computer and on which the said computer program is stored."]; and a processing system coupled to the complex sensor system, the sensor system of the second class of vehicles, and the memory, wherein the processing system is configured to [see at least Wunsch [0008] "The vehicle can be an assistance-supported, semi-autonomous or autonomous Vehicle. The control of the operation of the vehicle can involve a driver assistance system. The operation of the vehicle, in particular of the driver assistance system, can depend on input data formed from the sensor data, which are passed on to the data processing model. The data processing model can calculate output data from this, on which the operation of the vehicle, in particular of the driver assistance system, depends."]: compare a first output of a sensor … for use in the second class of vehicles of a low-end self-driving system to a second output of a complex sensor … [see at least [0010] "The first and second measurement data can each indicate the same environmental scene in perspective. The only difference between the first and second measurement data compared to each other may be the resolution of the measurement data."; [0020] “…calculated on the basis of the first measurement data during the training and the second measurement data linked to the first measurement data”]; train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles [see at least Wunsch [0025] "In a special embodiment of the invention, it is advantageous if the data processing model is trained with additional second measurement data from at least one sensor of the sensor class as training data in addition to the output data. The additional second measurement data can be provided by the at least one sensor that also provided the second measurement data..."] and the comparison of the first output of the sensor … for use in the second class of vehicles to the second output of the complex sensor … [see at least Wunsch [0020] "... it is provided that the data conversion model is trained as target data by applying at least one loss function to reduce deviations between the output data of the data conversion model calculated on the basis of the first measurement data during the training and the second measurement data linked to the first measurement data”; [0010] "The first and second measurement data can each indicate the same environmental scene in perspective. The only difference between the first and second measurement data compared to each other may be the resolution of the measurement data."]; and make the trained sensor processing model for use in the second class of vehicles available for deployment in the second class of vehicles [see at least Wunsch [0026] "According to the present invention, a method for controlling the operation of a vehicle with a data processing model learned according to a method with at least one of the previously described features is also proposed, depending on sensor data from at least one vehicle sensor of the vehicle as input data of the data processing model. The operation of the vehicle can include the operation of a driver assistance system, a partially autonomous driving system and/or an autonomous driving system of the vehicle, depending on the sensor data via the calculation with the data processing model."] Wunsch fails to disclose … compare a first output of a … processing model … to a second output of a … processing model … . However, Tong teaches this limitation [see at least Tong [0019] "A method includes receiving, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receiving, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, comparing the output generated by the first neural network with the output generated by the second neural network, and generating an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in Wunsch to use … compare a first output of a … processing model … to a second output of a … processing model … as disclosed in Tong with a reasonable expectation of success for the benefit of improved accuracy of neural network output. [See at least Tong [0032].] As per claim 11 (representative of claim 2) , Wunsch discloses … wherein the processing system is configured further to: use data from the sensor system of the second class of vehicles in the sensor processing model for use in the second class of vehicles executing in the processing system to generate the first output [see at least Wunsch [0025] "In a special embodiment of the invention, it is advantageous if the data processing model is trained with additional second measurement data from at least one sensor of the sensor class as training data in addition to the output data. The additional second measurement data can be provided by the at least one sensor that also provided the second measurement data..."]; and use data from the complex sensor system in the complex sensor processing model executing in the processing system to generate the second output [see at least Wunsch [0007] "…The training data can be generated synthetically from existing high-resolution initial sensor data. This allows the data processing model to be trained more cost-effectively to process low-resolution sensor data."] As per claim 12 (representative of claim 3) , Wunsch fails to disclose … wherein the processing system is further configured to train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model by adjusting weights in a machine learning module of the sensor processing model for use in the second class of vehicles to reduce a difference identified in the comparison of the first output to the second output . However, Tong teaches this limitation [see at least Tong [0048] "The nodes 305 are sometimes referred to as artificial neurons 305, because they are designed to emulate biological, e.g., human, neurons. A set of inputs (represented by the arrows) to each neuron 305 are each multiplied by respective weights. The weighted inputs can then be summed in an input function to provide, possibly adjusted by a bias, a net input. The net input can then be provided to activation function, which in turn provides a connected neuron 305 an output. The activation function can be a variety of suitable functions, typically selected based on empirical analysis."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in Wunsch to use disclose … wherein the processing system is further configured to train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model by adjusting weights in a machine learning module of the sensor processing model for use in the second class of vehicles to reduce a difference identified in the comparison of the first output to the second output as disclosed in Tong with a reasonable expectation of success for the benefit of improved accuracy of neural network output. [See at least Tong [0032].] As per claim 13 (representative of claim 4) , Wunsch discloses … wherein the processing system is further configured to train the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the … for use in the second class of vehicles to the second output of the complex sensor … by training a machine learning module of the sensor processing model for use in the second class of vehicles executing in the processing system to reduce a knowledge distillation loss function based on the comparison [see at least Wunsch [0005] "In order to make driver assistance systems in vehicles more cost-effective, the high-resolution vehicle sensors can be replaced by cheaper vehicle sensors with lower resolution. If the assistance system software is based on a machine learning process (e.g. deep neural networks), no fundamental change to the algorithms for replacing the vehicle sensors is required to switch to the lower-resolution sensors. However, it is crucial that there is sufficient data available for training and testing the model for the high-resolution sensors in order to retrain it and adapt it to processing data from low-resolution vehicle sensors."; [0020] "In a preferred embodiment of the invention, it is provided that the data conversion model is trained as target data by applying at least one loss function to reduce deviations between the output data of the data conversion model calculated on the basis of the first measurement data during the training and the second measurement data linked to the first measurement data."] Wunsch fails to disclose … the comparison of the first output of the sensor processing model … to the second output of the … processing model … . However, Tong teaches this limitation [see at least Tong [0019] "A method includes receiving, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receiving, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, comparing the output generated by the first neural network with the output generated by the second neural network, and generating an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold." It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in Wunsch to use … the comparison of the first output of the sensor processing model … to the second output of the … processing model … as disclosed in Tong with a reasonable expectation of success for the benefit of improved accuracy of neural network output. [See at least Tong [0032].] As per claim 15 (representative of claim 6) , Wunsch discloses … wherein the sensor processing model for use in the second class of vehicles and the complex sensor processing model are one or more of line-detection models, object detection models, object detection segmentation models, human detection models, animal detection models, vehicle detection models, or distance or depth estimation models [see at least Wunsch [0010] "The ambient scene can be a detectable ambient situation of the respective sensor. The ambient scene can be an ambient situation of an ambient area of a vehicle assigned to the sensor at a point in time or over a period of time. The first and second measurement data can each indicate the same ambient scene in perspective."; [0012] "The first measurement data can be provided by one or more sensors of the sensor class. The further first measurement data can capture additional environmental scenes that differ from the environmental scenes of the first measurement data."] As per claim 22 (representative of claim 19) , Wunsch discloses a server, comprising: a processing system configured to [see at least Wunsch [0008] "The vehicle can be an assistance-supported, semi-autonomous or autonomous Vehicle. The control of the operation of the vehicle can involve a driver assistance system. The operation of the vehicle, in particular of the driver assistance system, can depend on input data formed from the sensor data, which are passed on to the data processing model. The data processing model can calculate output data from this, on which the operation of the vehicle, in particular of the driver assistance system, depends."]: receive trained sensor processing model for use in a second class of vehicles from one or more first class of vehicles [see at least Wunsch [0005] "In order to make driver assistance systems in vehicles more cost-effective, the high-resolution vehicle sensors can be replaced by cheaper vehicle sensors with lower resolution."; [0007] "…The training data can be generated synthetically from existing high-resolution initial sensor data. This allows the data processing model to be trained more cost-effectively to process low-resolution sensor data."; [0008] "The vehicle can be ...semi-autonomous or autonomous vehicle."; [0018] "The point clouds of the second measurement data may have a smaller number of points than the point clouds of the first measurement data."]; generate a … sensor processing model for use in a second class of vehicles from the received sensor processing model for use in the second class of vehicles [see at least Wunsch [0010] "The first and second measurement data can each indicate the same environmental scene in perspective. The only difference between the first and second measurement data compared to each other may be the resolution of the measurement data."]; and provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more second class of vehicles for use in a self-driving system [see at least Wunsch [0026] "According to the present invention, a method for controlling the operation of a vehicle with a data processing model learned according to a method with at least one of the previously described features is also proposed, depending on sensor data from at least one vehicle sensor of the vehicle as input data of the data processing model. The operation of the vehicle can include the operation of a driver assistance system, a partially autonomous driving system and/or an autonomous driving system of the vehicle, depending on the sensor data via the calculation with the data processing model."] Wunsch fails to disclose … a consolidated sensor processing model … . However, Tong teaches this limitation [see at least Tong [0019] "A method includes receiving, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receiving, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, comparing the output generated by the first neural network with the output generated by the second neural network, and generating an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in Wunsch to use … a consolidated sensor processing model … as disclosed in Tong with a reasonable expectation of success for the benefit of improved accuracy of neural network output. [See at least Tong [0032].] As per claim 24 (representative of claim 21) , Wunsch discloses … wherein the processing system is further configure to: generate a low-end self-driving system based on the consolidated sensor processing model for use in the second class of vehicles [see at least Wunsch [0026] "According to the present invention, a method for controlling the operation of a vehicle with a data processing model learned according to a method with at least one of the previously described features is also proposed, depending on sensor data from at least one vehicle sensor of the vehicle as input data of the data processing model. The operation of the vehicle can include the operation of a driver assistance system, a partially autonomous driving system and/or an autonomous driving system of the vehicle, depending on the sensor data via the calculation with the data processing model."]; and provide at least the consolidated sensor processing model for use in the second class of vehicles to one or more the second class of vehicles for use in a self-driving system by providing the generated low-end self-driving system to one or more the second class of vehicles [see at least Wunsch [0007] "According to the present invention, a method for creating training data with the features of claim 1 is proposed. This allows the training data for the data processing model to be provided more cost-effectively. The training data can be synthetically generated from existing high-resolution additional first sensor data. This allows the data processing model to be trained more cost-effectively to process low-resolution sensor data."] 07-21-aia AIA Claim s 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wunsch, in view of Tong, and further in view of Moustafa, et al. (Publication US 2022/0126864 A1) (hereinafter referred to as “Moustafa”.) As per claim 14 (representative of claim 5) , Wunsch discloses … wherein the processing system is further configured to: … perform the training of the sensor processing model for use in the second class of vehicles based on data from the sensor system of the second class of vehicles and the comparison of the first output of the sensor processing model for use in the second class of vehicles to the second output of the complex sensor processing model when the consensus score fails to satisfy an acceptability threshold [see at least Wunsch [0020] "... it is provided that the data conversion model is trained as target data by applying at least one loss function to reduce deviations between the output data of the data conversion model calculated on the basis of the first measurement data during the training and the second measurement data linked to the first measurement data."; [0025] "In a special embodiment of the invention, it is advantageous if the data processing model is trained with additional second measurement data from at least one sensor of the sensor class as training data in addition to the output data. The additional second measurement data can be provided by the at least one sensor that also provided the second measurement data... ."] The combination of Wunsch and Tong fails to disclose … periodically generate a consensus score that averages comparisons of multiple complex sensor processing model outputs to corresponding multiple outputs of the sensor processing model for use in the second class of vehicles … . However, Moustafa teaches this limitation [see at least Moustafa [0792] (periodically generating a consensus score that averages comparisons of multiple complex sensor processing model outputs to corresponding multiple outputs of the sensor processing model ..."… an interpolated sample may be the average of the previous actual sample and the next actual sample. Although the example focuses on fusion at the level of sensor data, fusion may additionally or alternatively be performed at the output also."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch and Tong to use … periodically generate a consensus score that averages comparisons of multiple complex sensor processing model outputs to corresponding multiple outputs of the sensor processing model for use in the second class of vehicles … as disclosed in Moustafa with a reasonable expectation of success for the benefit of improving autonomous driving results provided through vehicles. [See at least Moustafa [0167].] 07-21-aia AIA Claim s 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wunsch, in view of Tong, and further in view of Wang, et al. (Publication US 2023/0078218 A1) (hereinafter referred to as “Wang”.) As per claim 16 (representative of claim 7) , the combination of Wunsch and Tong fails to disclose … wherein the processing system is further configured to make the trained a low-end self-driving system available for deployment in the second class of vehicles by transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to a remote server in a format that enables the remote server to deploy trained self-driving systems to the second class of vehicles . However, Wang teaches this limitation [see at least Wang [0033] "...in some instances, the updated student model may be transmitted to an edge device and/or one or more endpoint devices (e.g., a smart surveillance camera, an autonomous vehicle) over a network to be used for object detection."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch and Tong to use … wherein the processing system is further configured to make the trained a low-end self-driving system available for deployment in the second class of vehicles by transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to a remote server in a format that enables the remote server to deploy trained self-driving systems to the second class of vehicles as disclosed in Wang with a reasonable expectation of success for the benefit of improving performance of the network training process. [See at least Wang [0109].] As per claim 17 (representative of claim 8) , the combination of Wunsch and Tong fails to disclose … wherein the processing system is further configured such that transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to the remote server comprises transmitting a trained machine learning module of the trained sensor processing model for use in the second class of vehicles to the remote server . However, Wang teaches this limitation [see at least Wang [0033] "...in some instances, the updated student model may be transmitted to an edge device and/or one or more endpoint devices (e.g., a smart surveillance camera, an autonomous vehicle) over a network to be used for object detection."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch and Tong to use … wherein the processing system is further configured such that transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to the remote server comprises transmitting a trained machine learning module of the trained sensor processing model for use in the second class of vehicles to the remote server as disclosed in Wang with a reasonable expectation of success for the benefit of improving performance of the network training process. [See at least Wang [0109].] 07-21-aia AIA Claim s 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wunsch, in view of Tong, further in view of Wang, and further in view of Mondello, et al. (Publication US 2019/0205765 A1) (hereinafter referred to as “Mondello”.) As per claim 18 (representative of claim 9), the combination of Wunsch and Tong fails to disclose … wherein the processing system is further configured such that transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to the remote server comprises transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to a remote server … . However, Wang teaches this limitation [see at least Wang [0033] "...in some instances, the updated student model may be transmitted to an edge device and/or one or more endpoint devices (e.g., a smart surveillance camera, an autonomous vehicle) over a network to be used for object detection."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch and Tong to use … wherein the processing system is further configured such that transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to the remote server comprises transmitting at least a portion of the trained sensor processing model for use in the second class of vehicles to a remote server … as disclosed in Wang with a reasonable expectation of success for the benefit of improving performance of the network training process. [See at least Wang [0109].] The combination of Wunsch, Tong, and Wang fails to disclose … in a format that prevents disclosure of user privacy information . However, Mondello teaches this limitation [see at least Mondello [0096] "The communication of the sensor inputs (151) from one vehicle (111) to another vehicle (113) may be restricted in view privacy concerns and/or user preferences"] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch, Tong, and Wang to use … in a format that prevents disclosure of user privacy information as disclosed in Mondello with a reasonable expectation of success for the benefit of improved ANN models. [See at least Mondello [0030].] 07-21-aia AIA Claim s 20 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wunsch, in view of Tong, and further in view of Gee, et al. (Publication US 2022/0194400 A1) (hereinafter referred to as “Gee”.) As per claim 23 (representative of claim 20) , the combination of Wunsch and Tong fails to disclose … wherein the processing system is further configure to provide compensation to owners of the one or more first class of vehicles in return for providing the trained sensor processing model for use in the second class of vehicles . However, Gee teaches this limitation [see at least Gee [0332] "... the first vehicle's artificial intelligence algorithm (e.g., a neural network) may more accurately predict the wear on a component (e.g., a tire) if it has sensed information from the second vehicle. In aspects, the first vehicle may offer to pay the owner or driver of the second vehicle for that type of information. In another example, the first vehicle may offer its own information to the owner or driver of the second vehicle in exchange for the sensor information of the second vehicle."] It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle as disclosed in the combination of Wunsch and Tong to use … wherein the processing system is further configure to provide compensation to owners of the one or more first class of vehicles in return for providing the trained sensor processing model for use in the second class of vehicles as disclosed in Gee with a reasonable expectation of success for the benefit of improving the predictive output of the neural network. [See at least Gee [0120].] Conclusion 07-40 AIA 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 PAULA L SCHNEIDER whose telephone number is (703)756-4606. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm 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, Fadey Jabr can be reached at 571-272-1516. 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. /P.L.S/Examiner, Art Unit 3668 /Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668 Application/Control Number: 18/499,584 Page 2 Art Unit: 3668 Application/Control Number: 18/499,584 Page 3 Art Unit: 3668 Application/Control Number: 18/499,584 Page 4 Art Unit: 3668 Application/Control Number: 18/499,584 Page 5 Art Unit: 3668 Application/Control Number: 18/499,584 Page 6 Art Unit: 3668 Application/Control Number: 18/499,584 Page 7 Art Unit: 3668 Application/Control Number: 18/499,584 Page 8 Art Unit: 3668 Application/Control Number: 18/499,584 Page 9 Art Unit: 3668 Application/Control Number: 18/499,584 Page 10 Art Unit: 3668 Application/Control Number: 18/499,584 Page 11 Art Unit: 3668 Application/Control Number: 18/499,584 Page 12 Art Unit: 3668 Application/Control Number: 18/499,584 Page 13 Art Unit: 3668 Application/Control Number: 18/499,584 Page 14 Art Unit: 3668 Application/Control Number: 18/499,584 Page 15 Art Unit: 3668 Application/Control Number: 18/499,584 Page 16 Art Unit: 3668 Application/Control Number: 18/499,584 Page 17 Art Unit: 3668 Application/Control Number: 18/499,584 Page 18 Art Unit: 3668 Application/Control Number: 18/499,584 Page 19 Art Unit: 3668 Application/Control Number: 18/499,584 Page 20 Art Unit: 3668 Application/Control Number: 18/499,584 Page 21 Art Unit: 3668 Application/Control Number: 18/499,584 Page 22 Art Unit: 3668 Application/Control Number: 18/499,584 Page 23 Art Unit: 3668 Application/Control Number: 18/499,584 Page 24 Art Unit: 3668 Application/Control Number: 18/499,584 Page 25 Art Unit: 3668 Application/Control Number: 18/499,584 Page 26 Art Unit: 3668 Application/Control Number: 18/499,584 Page 27 Art Unit: 3668 Application/Control Number: 18/499,584 Page 28 Art Unit: 3668 Application/Control Number: 18/499,584 Page 29 Art Unit: 3668 Application/Control Number: 18/499,584 Page 30 Art Unit: 3668 Application/Control Number: 18/499,584 Page 31 Art Unit: 3668 Application/Control Number: 18/499,584 Page 32 Art Unit: 3668 Application/Control Number: 18/499,584 Page 33 Art Unit: 3668 Application/Control Number: 18/499,584 Page 34 Art Unit: 3668 Application/Control Number: 18/499,584 Page 35 Art Unit: 3668 Application/Control Number: 18/499,584 Page 36 Art Unit: 3668 Application/Control Number: 18/499,584 Page 37 Art Unit: 3668
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