DETAILED ACTION
Claims 24-43 are presented for consideration.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/25/2025 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 24-43 are rejected under 35 U.S.C. 103 as being unpatentable over Hodge et al. [ US Patent Application No 2020/0349666 ], in view of Gaur et al. [ US Patent Application No 2022/0311611 ].
4. As per claim 24, Hodge discloses the invention as claimed including a driving record authentication method, applied to an electronic device serving as a node of a blockchain, the driving record authentication method comprising:
receiving real-time driving record of a driver of a vehicle, associating the real-time driving record with a current time, and storing the real-time driving record associated with the current time according to personal information of the driver [ i.e. during or at the end of a ride, passenger may request to give video review using client device, review mode may be initiated which allows the video or audio recording of a passenger’s review of a driver, the rating will be stored in the driver’s profile ] [ paragraphs 0029, 0104, 0138, and 0157 ];
periodically acquiring historical driving records of the driver and acquiring associated records of the driver, the historical driving records comprising driving records of the driver within a preset time period [ i.e. scanned in by passenger to display information such as other driver information, driving record, score, etc. ] [ paragraphs 0096, 0099 and 0102 ];
determining a level of the driver based on the historical driving records and the associated records [ i.e. generate an independent safety rating of each driver and stores the rating for each testing period in the driver profile, and driver ratings and reviews ] [ paragraphs 0036, and 0123-0132 ]; and
in response to a query request associated with the driver, transmitting the image to a user terminal that sent the query request [ i.e. upon scanning the QR code, the use’s smartphone may provide driver information such as an image of the driver’s license and/or a driver profile ] [ Figure 11; and paragraphs 0102, and 0103 ].
Hodge does not specifically disclose
minting a non-fungible token (NFT) image corresponding to the level of the driver; and transmitting the NFT image.
Gaur discloses
minting a non-fungible token (NFT) image corresponding to the level of the driver [ i.e. score validator may determine a reputation score for the driver and append the driver’s NFT ID with a metadata tag indicating his updated reputation score ] [ paragraphs 0122, 0135, and 0149 ]; and transmitting the NFT image [ i.e. retrieve information from the ID repository for a driver ] [ paragraphs 0056, 0058, and 0134 ].
It would have been obvious to a person skill in the art before the effective filing date of the claimed invention to combine the teaching of Hodge and Gaur because the teaching of Gaur would enable to provide reputation profile propagation on blockchain networks [ Gaur, paragraph 0023 ].
5. As per claim 25, Gaur discloses minting a NFT image template corresponding each of a plurality of levels; invoking the NFT image template corresponding to the level of the driver in response that the level of the driver is determined; and minting the NFT image corresponding to the level of the driver based on a current time and personal information of the driver using the invoked NFT image template [ i.e. periodically submit a review for the driver the reviews may be electronically logged with the blockchain network, and used when the reputation modulator smart contract is invoked ] [ paragraphs 0046-0049, and 0135 ].
6. As per claim 26, Hodge discloses wherein determining the level of the driver based on the historical driving records and the associated records comprises: invoking a level recognition model; and obtaining the level of the driver by inputting the historical driving records and the associated records in the level recognition model [ i.e. event detection algorithms ] [ paragraphs 0077-0079, 0140 and 0159 ].
7. As per claim 27, Hodge discloses training the level recognition model by: obtaining a preset number of sample data corresponding to different levels, each sample data comprising driving records and associated records; labeling each sample data corresponding to each level with a category, making the sample data corresponding to each level comprising a category label [ i.e. tagging events ] [ paragraphs 0077, and 0078 ]; determining the preset number of sample data comprising the category labels as training samples; randomly dividing the training samples into a training set and a verification set, the training set comprising a first preset ratio of the preset number of sample data [ i.e. training function ] [ paragraphs 0079, and 0140 ], and the verification set comprising a second preset ratio of the preset number of sample data [ i.e. accurate assessment ] [ paragraphs 0112, and 0119 ]; obtaining the level recognition model by training a deep neural network using the training set, and verifying an accuracy rate of the level recognition model by using the verification set; and ending a training in response that the accuracy rate is greater than or equal to a preset accuracy rate; increasing, in response that the accuracy rate is less than the preset accuracy rate, a number of the training samples to retrain the deep neural network until the accuracy rate of the level recognition model is greater than or equal to the preset accuracy rate [ i.e. neural network may be trained with the set of inputs used by the system to recognize the set of possible tagging events ] [ paragraph 0079 ].
8. As per claim 28, Hodge discloses wherein determining the level of the driver based on the historical driving records and the associated records comprises: obtaining an analysis result of each record in the historical driving records and the associated records; obtaining quantitative data by quantifying the analysis result of each record; and determining the level of the driver based on the quantitative data [ i.e. passenger rates/reviews the driver, and getting 5 star rating ] [ paragraphs 0038, 0103, 0117, and 0138 ].
9. As per claim 29, Hodge discloses wherein quantifying the analysis result of each record comprises: assigning different scores to different analysis results; wherein determining the level of the driver based on the quantitative data comprises: pre-determining different scores corresponding to different levels; and calculating an average value of the quantified data, and determining the level of the driver based on the average value [ i.e. rating of each driver, and other statistics and information of the driver ] [ paragraphs 0036, 0102, 0117 and 0132 ].
10. As per claim 30, Gaur discloses storing the NFT image and obtaining a link of the NFT image, the link of the NFT image indicating a storing position of the NFT image in the blockchain; and transmitting the link of the NFT image to the user terminal [ i.e. block storage location ] [ paragraphs 0097, 0100, and 0109 ].
11. As per claims 31-37, they are rejected for similar reasons as stated above in claims 24-30.
12. As per claims 38-43, they are rejected for similar reasons as stated above in claims 24-29.
Response to Arguments
Applicant’s arguments with respect to claim(s) 24-43 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li [ US Patent Application No 2018/0338241 ] discloses allow the user to record and view remotely the driving habit of other drivers, such as driving speed
Friesen et al. [ US Patent Application No 2025/0156506 ] discloses mint at least one non-fungible token that is assigned to a vehicle and includes at least one proof of ownership, usage rights, and/or properties of the vehicle
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/DUSTIN NGUYEN/Primary Examiner, Art Unit 2446