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 .
This communication is in response to application 18/075,702 filed 12/06/2022. Claims 1, 3, 5, 7-9, 11 are amended and hereby entered. Claims 4 and 10 are canceled. No claims are allowed.
Response to Arguments
Applicant’s arguments, see page 6, filed 10/16/2025 , with respect to the rejection(s) of claim(s) 1 and 7 under 112a have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments, see page 6, filed 10/16/2025 , with respect to the rejection(s) of claim(s) 3 and 9 under 112d have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 112a for claims 3 and 9, see 112a rejection below.
Applicant's arguments, regarding 35 USC 101 and 102/103 filed 10/16/2025 have been fully considered but they are not persuasive.
Regarding 35 USC 101:
The applicant submits the claims are eligible because they do not recite abstract ideas similar to example 23 of “Subject matter eligibility examples: Abstract ideas”. However, the analysis for identifying recited abstract ideas is based off the MPEP 2106.04. Drawing conclusory statements based on examples does not determine whether a claim recites an abstract idea. Therefore, the examiner respectfully disagrees and the rejection is maintained.
Further, the applicant submits the claims integrate the abstract idea into a practical application because there is an improvement to a computer or technological field. However, there is no improvement to the technology of computers or learning algorithms. The invention is a data management method and system. An algorithm is being retrained with new data (i.e. the new driving data simply retrains an algorithm resulting in an updated output, not actually improving the technology of machine learning algorithms). The applicant is simply using computing to perform the abstract idea of processing data and making a determination which instead falls under 2106.05(f). Therefore, the examiner respectfully disagrees and the rejection is maintained.
Further, the applicant submits that the claims recite significantly more than the abstract idea because there is a specific and complex approach to improving safety by improving an algorithm based on data. However, the only additional elements in the claims recite general purpose computer components. Improving safety does not qualify as an additional element. Improving safety instead recites intended use or results of the data management method, see MPEP 2111.04). Therefore, the examiner respectfully disagrees and the rejection is maintained.
Regarding 35 USC 102/103
The applicant submits that Chen does not teach “adjusting the value of NFT data”. However, paragraph 0031, 0030, and figures 4-6 were cited in the previous office action dated 7/22/2025 under USC 101 rejections, claim 4. The applicant submits figure 6 does not show contribution of data from multiple vehicles and NFTs attached to different vehicles. However, paragraphs 0031, 0030, and figures 4-6 as a whole show this limitation, with figure 6 emphasizing the presence of multiple vehicles and values for the vehicles. Further the claim amendments no longer include “adjusting the value of NFT data”, and instead recite “a value of the NFT data is determined based on a contribution of the first driver data and the second driver data to improvement of the autonomous driving algorithm, the contribution is used to determine a dividend for a respective autonomous vehicle.” This newly amended limitation differs from the previous limitation of “adjusting the value of NFT data” and is taught by Yu and Chen in combination, (see rejection below 35 USC 103). Therefore, the examiner respectfully disagrees and the rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 3 and 9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
There is no description in the specification that supports the scope of the following claimed step of:
“Determining whether there is no illegality and no accident in a driving situation according to the driver data” (Claims 3 and 9).
The specification discusses that data is received and collected when there is no accident or illegality, but there is no discussion on how the computer or algorithm determines there is no accident occurring or illegality occurring. Further, in paragraph 0062 the specification shows data is also collected when data matches the information in the autonomous driving algorithm. For the purposes of compact prosecution, “determining whether there is no illegality and no accident in a driving situation according to the driver data” is interpreted as any situation where the driving data matches the information in the current database or autonomous driving algorithm.
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.
Claim 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
Claims 1-6 are methods, and Claims 7-12 are systems. Thus, each claim on its face is directed to one of the statutory categories of 35 USC 101. However, claims 1-12 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claims (1 and 7) recite a method and systems to receive and process driver data for autonomous driving algorithms, and generate NFT data associated with the autonomous vehicles. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using driver data to adjust an autonomous driving algorithm and update the stored data can equivalently be achieved by human evaluation. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of receiving data, processing data, and storing results (i.e. receiving and processing driver data to generate NFT data) on a generically claimed computer structure. The claims do not include additional elements that amount to significantly more than the judicial exception. The independent claims recite the additional elements “a server” and “a processor”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional element is simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims
Claims 2-6 and 8-12 do not introduce any new additional abstract ideas or new additional elements and do not impact analysis under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
Claims 1-2 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Yu (US 20200004249 A1) in view of Chen (US 20230132804 A1)
Regarding Claim 1 and 7 (substantially similar in scope and language), Yu teaches:
A method of managing NFT-based driver data for an autonomous vehicle, the method comprising: receiving driver data including first driver data including driver's driving information, traveling information and environment information transmitted from the autonomous vehicle; [(Para 0006) “acquiring sensor data of the autonomous driving vehicle and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle encounters an interference during driving”, (Para 0047) “The vehicle data may include data acquired by various types of sensors installed on the autonomous driving vehicles 101, 102, 103, such as distance data between the autonomous driving vehicles 101, 102, 103 and surrounding objects acquired by distance sensors; angle data of tire deflection directions acquired by angle sensors; driving speed data of the autonomous driving vehicles 101, 102, and 103 acquired by speed sensors…”]
determining whether an autonomous driving algorithm applied to the autonomous vehicle is improvable based on the driver data; [(Para 0008) “in response to determining that the autonomous driving vehicle switches from the manual driving mode to the autonomous driving mode; and marking vehicle data acquired between the first switching moment and the second switching moment as the correction sample”, (Para 0016) “In some embodiments, the vehicle data includes expected driving data and actual driving data; and the driving mode determining subunit includes: a third judging module, configured to determine whether a difference between the expected driving data and the actual driving data is greater than a preset threshold”]
in response to a determination that the autonomous driving algorithm applied to the autonomous vehicle is improvable, improving the autonomous driving algorithm based on the driver data based on at least one condition, [(Para 0008-0010), (Para 0020) “According to the data training method and apparatus for an autonomous driving vehicle provided by the embodiments of the present disclosure, first sensor data of the autonomous driving vehicle and a correction sample are acquired, then an end-to-end model is built using the sensor data and the correction sample, thereby improving the driving safety of the autonomous driving vehicle.”]
the at least one condition including whether the driver data is applicable to the autonomous driving algorithm, whether a safety level is increased when applicable, and whether the driver data is different from the previously learning data; [(Para 0008-0010), (Para 0020)]
wherein the improved autonomous driving algorithm is distributed to the autonomous vehicle in real time and applied to vehicle control to improve safety in exceptional situations not predefined in the autonomous driving algorithm, [(Figure 1), (Para 0042) “In some alternative implementations of the present embodiment, the method further includes: pushing the end-to-end model to the autonomous driving vehicle and correcting the end-to-end model using measured feedback data”, (Para 0043) “After obtaining the end-to-end model, the end-to-end model is introduced into the autonomous driving vehicles 101, 102, and 103, so that the autonomous driving vehicles 101, 102, and 103 can output corresponding control instructions in time through the end-to-end model to avoid emergencies, when encountering similar emergencies… The end-to-end model is corrected by the feedback data to improve the accuracy and robustness for outputting a control instruction by the end-to-end model”]
and wherein the driver data includes second driver data including driver's driving information, traveling information and environment information of another autonomous vehicle,
[(Figure 1) (Para 0006) “acquiring sensor data of the autonomous driving vehicle and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle encounters an interference during driving”, (Para 0047) “The vehicle data may include data acquired by various types of sensors installed on the autonomous driving vehicles 101, 102, 103, such as distance data between the autonomous driving vehicles 101, 102, 103 and surrounding objects acquired by distance sensors; angle data of tire deflection directions acquired by angle sensors; driving speed data of the autonomous driving vehicles 101, 102, and 103 acquired by speed sensors…”]
…. based on a contribution of the first driver data and the second driver data to improvement of the autonomous driving algorithm… [(Para 0050) “Acquiring the vehicle data in this process may help to improve the driving safety of the autonomous driving vehicles 101, 102, 103.”]
However, Yu does not teach but Chen does teach:
generating NFT data corresponding to the driver data as the autonomous driving algorithm is improved; [(Figure 1 and 4-6)]
and registering the NFT data in an NFT market for the autonomous vehicle. [(Figure 2) “(Para 0008) “a method for establishing an association between a virtual vehicle and a real vehicle in a blockchain network is provided, which includes: … wherein the virtual vehicle corresponds to a non-fungible token in the blockchain network”]
a value of the NFT data is determined … the contribution is used to determine a dividend for a respective autonomous vehicle. [(Abstract) “and determining a value reward based on a blockchain network gain of the associated virtual vehicle and the driving data of the real vehicle”, “Furthermore, in the embodiments according to the present disclosure, the props and outfits may have a “service life” and their values are subject to a depreciation method, thereby controlling the circulation and rarity of the props and outfits. Moreover, users can rent, sell, auction or exchange NFTs (props, outfits, virtual vehicles, or other goods such as skins, stickers, etc.) on the decentralized trading market platform”, (Para 0040) “ the client can include tools for users to hold and manage cryptocurrency (i.e., value rewards mentioned above) and digital assets (i.e., NFTs of virtual vehicles, props, outfits, etc. mentioned above, and other goods such as skins, stickers, etc.), that is, encrypted digital wallets and garages.”]
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date to combine the method of collecting and processing data for improve autonomous driving algorithms taught by Yu, with the generation and registration of NFT data taught by Chen. Receiving, processing, and storing the data as an NFT is merely a combination of old elements, and each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized the benefits of storing data in an NFT as real time updates to a driving algorithm occur. Further one of ordinary skill would have recognized that valuing the NFT (Chen) based on driving data contribution to autonomous driving algorithms (Yu) would have incentivized data collection and led to better algorithms.
Regarding Claims 2 and 8, Yu in view of Chen teach the limitations set forth above, Yu further teaches:
receive the first the driver data when the first driver data generated in a state in which an autonomous driving function of the autonomous vehicle is released is different from a determination result of the autonomous driving algorithm in the environment information. [(Para 0010) “and the determining whether the autonomous driving vehicle is switched from an autonomous driving mode to a manual driving mode, includes: determining whether a difference between the expected driving data and the actual driving data is greater than a preset threshold; and determining that the autonomous driving vehicle is switched from the autonomous driving mode to the manual driving mode, if the difference is greater than the preset threshold.”]
Claims 3, 5-6, 9, and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Yu (US 20200004249 A1) in view of Chen (US 20230132804 A1) in further view of Kanevsky (US 10902521 B1).
Regarding Claims 3 and 9, the combination of Yu and Chen teach all of claim 2, Yu further teaches:
receiving the first driver data when there is no illegality and no accident in the driving situation according to the driver data. [(Para 0033) “The server 105 may be a server that provides various services, such as a server performing data processing on sensor data and correction samples acquired by the autonomous driving vehicles 101, 102, 103 to obtain an end-to-end model. The server may analyze the sensor data and the correction samples acquired by the autonomous driving vehicles 101, 102, and 103, determine the switching between an autonomous driving mode and a manual driving mode of the autonomous driving vehicles 101, 102, and 103, and further obtain the end-to-end model.”]
However, the combination of Yu and Chen do not teach but Kanevsky does teach:
determining whether there is no illegality and no accident in a driving situation according to driver data; [See USC 112a rejection, the limitation recites any situation where the driving data matches the information in the current database or autonomous driving algorithm; (Column 7 Lines 20-25) “may be configured to receive and analyze the driving pattern data from the movement data analysis application 222 on the mobile device 220, and to identify a driver for the driving trip by determining and matching an observed driving pattern to previously-stored driving pattern in the driving pattern database 212” (Column 18 Lines 33-40) “According to a third set of algorithms that may be used in steps 303 and 304, a mapping into multidimensional space may be performed in order to test the hypothesis that the observed driving data, represented by ECPDF F.sup.n.sub.−(u), matches the stored driving pattern”]
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting information from autonomous vehicles and NFT storage taught by Yu in view of Chen, with the method of matching driver data to existing data. Collecting data and retrieving NFT data to then determining if data sets match other autonomous vehicles is merely a combination of old elements, and each element merely would have performed the same function as it did separately. One of ordinary skill would have recognized that matching autonomous vehicle data would be beneficial to apply new data to an algorithm.
Regarding Claims 5 and 11, the combination of Yu and Chen teach the limitations of claim 1, Yu further teaches:
collecting traveling information and environment information of the another autonomous vehicle during autonomous driving of the another autonomous vehicle based on the distributed autonomous driving algorithm; [(Figure 1), (Para 0033) “The server 105 may be a server that provides various services, such as a server performing data processing on sensor data and correction samples acquired by the autonomous driving vehicles 101, 102, 103 to obtain an end-to-end model”, (Para 0035) “ It should be understood that the number of autonomous driving vehicles, networks and servers in FIG. 1 is merely illustrative. Depending on the implementation needs, there may be any number of autonomous driving vehicles, networks and servers.”]
However, Yu does not teach but Chen does teach:
retrieving NFT data corresponding to the driver data of the another autonomous vehicle; [(Para 0030) “Users may own multiple real vehicles and multiple virtual vehicles, and may establish a mapping relationship between the virtual vehicles and the real vehicles. This mapping relationship may not be fixed, but may be defined and modified by the users themselves. In the embodiments according to the present disclosure, such a mapping relationship may be one-to-one, i.e., one real vehicle may be associated with one virtual vehicle. In the embodiments according to the present disclosure, such a mapping relationship may also be one-to-more, e.g., one real vehicle may be associated with a plurality of virtual vehicles.”]
and increasing the contribution of the retrieved NFT data. [(Para 0065) “With the data structure as shown in FIG. 6, real-time driving data of real vehicles can be recorded in the blockchain network, and the real-time driving data can include mileage information and other driving-related data.”]
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date to combine the method of collecting information from autonomous vehicles taught by Yu, with retrieving NFT data and increasing contribution taught by Chen. Collecting data from other autonomous vehicles and retrieving NFT data from other vehicles is merely a combination of old elements, and each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, the combination of Yu and Chen do not teach but Kanevsky does teach:
determining whether driver data of the another autonomous vehicle matching the traveling information and environment information of the another autonomous vehicle exists in the autonomous driving algorithm; [(Column 7 Lines 20-25) “may be configured to receive and analyze the driving pattern data from the movement data analysis application 222 on the mobile device 220, and to identify a driver for the driving trip by determining and matching an observed driving pattern to previously-stored driving pattern in the driving pattern database 212” (Column 18 Lines 33-40) “According to a third set of algorithms that may be used in steps 303 and 304, a mapping into multidimensional space may be performed in order to test the hypothesis that the observed driving data, represented by ECPDF F.sup.n.sub.−(u), matches the stored driving pattern”]
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting information from autonomous vehicles and retrieving NFT data taught by Yu in view of Chen, with the method of matching driver data to existing data. Collecting data and retrieving NFT data to then determining if data sets match other autonomous vehicles is merely a combination of old elements, and each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 6 and 12, the combination of Yu, Chen and Kanevsky teach the limitations of claim 5, Yu further teaches:
increasing the contribution based on a number of other autonomous vehicles to which the distributed autonomous driving algorithm is applied [(Para 0020) “then an end-to-end model is built using the sensor data and the correction sample, thereby improving the driving safety of the autonomous driving vehicle”, (Para 0035) “It should be understood that the number of autonomous driving vehicles, networks and servers in FIG. 1 is merely illustrative. Depending on the implementation needs, there may be any number of autonomous driving vehicles, networks and servers.”]
However, the combination of Yu and Chen do not teach but Kanevsky does teach:
and a number of times of application of driver data of the other autonomous vehicles matching traveling information and environment information of the other autonomous vehicles. [(Column 7 Lines 20-25) “may be configured to receive and analyze the driving pattern data from the movement data analysis application 222 on the mobile device 220, and to identify a driver for the driving trip by determining and matching an observed driving pattern to previously-stored driving pattern in the driving pattern database 212” (Column 15 Lines 53-58) “The driving pattern comparisons in step 304 may be used to identify a driver, a driver-vehicle combination, and/or additional driving variables or conditions (e.g., weather, time of day, driving route, etc.), by matching the observed (i.e., current) driving pattern to one or more predetermined driving patterns.” ]
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of contributing based on other autonomous vehicles (Yu and Chen), with the method of contributing based on times the autonomous vehicles information matches (Kanevsky). These factors are merely a combination of old elements, and each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized the benefits of increasing contribution based on adoption and accuracy, yielding predictable results
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST)
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber SPE can be reached on 571-270-3923. 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.
/B.L.T./
Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626