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 .
Priority
1. Acknowledgment is made of applicant’s claim for foreign priority based on application filed in the People’s Republic of China on 11/14/2023.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 07/26/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
3. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (U.S.2020/0191950), qi et al (U.S. 2022/0292974) and further in view of Kloeppel (U.S. 2026/0141459).
As per claims 1,9,17 Kim disclosed a method for uploading vehicle driving data, the method comprising: obtaining vehicle driving data of a target vehicle [a sensor unit configured to acquire vehicle information of the vehicle; and a control unit configured to determine an accident occurrence probability of the vehicle on the basis of at least one of the vehicle surrounding image and the vehicle information] (Paragraph. 0009);
predicting a vehicle accident occurrence probability of the target vehicle according to the vehicle driving data and a preset analysis model [in response to determining that a collision has occurred to the vehicle on the basis of the vehicle information, determine a storage form of the vehicle surrounding image on the basis of the accident occurrence probability and store the vehicle surrounding image in the storage unit] (Paragraph. 0009); comprising monitoring blind spots and obstacles by a blind spot detection (BSD) system model of the preset analysis model and calculating the vehicle accident occurrence probability of the target vehicle according to the blind spot and the obstacles. [In addition, the control unit 130 may also be configured to determine the accident occurrence probability on the basis of information derived from the functions of forward collision-avoidance assist (FCA) and blindspot collision-avoidance Assist (BCA) that perform collision avoidance through emergency automatic braking and steering according to the environment of the neighboring vehicle.] (Paragraph. 0061).
in response that the vehicle accident occurrence probability meets preset conditions, obtaining historical driving data of the target vehicle within a preset time period before current time, [the control unit 130 may determine that a collision has occurred to the vehicle 1 when the acceleration of the vehicle 1 obtained by the acceleration sensor described above exceeds a predetermined value. That is, since when the accident occurrence probability is high, the vehicle surrounding image having a high quality is required, the control unit 130 may store the vehicle surrounding image with a high quality in response to the accident occurrence probability being high and may store the vehicle surrounding image with a low quality in response to the accident occurrence probability being low.] (Paragraph. 0051),
and
However, KIm did not disclose uploading the historical driving data to a cloud server instructing the cloud server to supply the historical driving data as data supports configured for subsequent accident cause analysis.
In the same field of endeavor qi disclosed, “FIG. 7A illustrates an example of a vehicle crash prediction system 700 used to perform a vehicle crash prediction based on data from multiple mobile devices according to an embodiment of the present invention. Vehicle crash prediction system 700 can be performed by mobile device 104 of FIG. 1 and FIG. 2, and/or one or more servers in a cloud infrastructure. As shown in FIG. 7A, vehicle crash prediction system 700 can receive data 702 from a mobile device 704, such as mobile device 104 as described above in relation to FIG. 1 and FIG. 2. Mobile device 704 can be operated by a driver of a vehicle 706. In some examples, mobile device 704 can also be associated with a first entity (e.g., a first insurance carrier that insures vehicle 706).” (Paragraph. 0105).
It would have been obvious to one having ordinary skill in the art before the effective filing date was made to have incorporated FIG. 7A illustrates an example of a vehicle crash prediction system 700 used to perform a vehicle crash prediction based on data from multiple mobile devices according to an embodiment of the present invention. Vehicle crash prediction system 700 can be performed by mobile device 104 of FIG. 1 and FIG. 2, and/or one or more servers in a cloud infrastructure. As shown in FIG. 7A, vehicle crash prediction system 700 can receive data 702 from a mobile device 704, such as mobile device 104 as described above in relation to FIG. 1 and FIG. 2. Mobile device 704 can be operated by a driver of a vehicle 706. In some examples, mobile device 704 can also be associated with a first entity (e.g., a first insurance carrier that insures vehicle 706) as taught by qi in the method and system of Reichardt to improve the vehicle crash prediction.
However kim-Qi, did not disclose in detail, “and to upload the historical driving data to a blockchain to avoid data loss or data tampering”.
In the same field of endeavor kloeppel disclosed, “A blockchain (also referred to herein as a distributed ledger or a shared ledger) is a way of achieving a distributed consensus on the validity or invalidity of information in the chain (such as a vehicle build sheet, vehicle records, home records usage data associated with a home and/or vehicle, etc.). (Paragraph. 0030).
It would have been obvious to one having ordinary skill in the art before the effective filing was made to have incorporated A blockchain (also referred to herein as a distributed ledger or a shared ledger) is a way of achieving a distributed consensus on the validity or invalidity of information in the chain (such as a vehicle build sheet, vehicle records, home records usage data associated with a home and/or vehicle, etc.) as taught by Kloeppel in the method and system Kim-Qi to improve vehicle crash prediction.
4. As per claims 2,10,18 Kim-Qi-Kloeppel disclosed wherein predicting a vehicle accident occurrence probability of the target vehicle according to the vehicle driving data and the preset analysis model further comprises:
detecting a driving state of the target vehicle by using the preset analysis model, and obtaining driving state data (Kim, Paragraph. 0011);
predicting the vehicle accident occurrence probability according to the driving state data and the vehicle driving data of the target vehicle (Kim, Paragraph. 0056).
5. As per claims 3,11,19 Kim-Qi-Kloeppel disclosed wherein predicting the vehicle accident occurrence probability according to the driving state data and the vehicle driving data comprises:
inputting the driving state data and the vehicle driving data into a preset neural network model (Kim, Paragraph. 0011);
encoding the driving state data and the vehicle driving data by using the preset neural network model, and obtaining a target feature vector (Kim, Paragraph. 0016);
calculating a similarity value between the target feature vectors and each of preset feature vectors (Kim, Paragraph. 0017);
determining a preset probability of one preset feature vector corresponding to a similarity value that is greater than a preset threshold, as the vehicle accident occurrence probability (Kim, Paragraph. 0056).
6. As per claims 4,12,20 Kim-Qi-Kloeppel disclosed further comprising:
in response that the vehicle accident occurrence probability is within a preset probability range, determining that the vehicle accident occurrence probability meets the preset conditions (Kim, Paragraph. 0080).
7. As per claims 5,13 Kim-Qi-Kloeppel disclosed further comprising: selecting data from the vehicle driving data;
predicting the vehicle accident occurrence probability according to the preset analysis model and the data selected form the vehicle driving data (Kim, Paragraph. 0056).
8. As per claims 6,14 Kim-Qi-Kloeppel disclosed wherein in response that the vehicle accident occurrence probability meets preset conditions (Kim, Paragraph. 0068-0069), obtaining historical driving data of the target vehicle within a preset time period before current time uploading the historical driving data to the cloud server (Qi, Paragraph. 0105) comprises:
obtaining marking time of the historical driving data, and sorting sub-data in the historical driving data according to the marking time, and determining a data upload sequence (Kim, Paragraph. 0085);
uploading the historical driving data to the cloud server according to the data upload sequence and a preset priority (Qi, Paragraph. 0083). The claim 6 has the same motivation as to claim 1.
9. As per claims 7,15 Kim-Qi-Kloeppel disclosed further comprising:
monitoring an upload progress of the historical driving data;
in responses that the upload progress does not meet preset requirements,
adjusting an upload speed of the historical driving data (Qi, Paragraph. 0118). The claim 7 has the same motivation as to claim 1.
10. As per claims 8,16 Kim-Qi-Kloeppel disclosed wherein obtaining vehicle driving data of the target vehicle comprises:
detecting and recording self-state data and environmental state data of the target vehicle; using the self-state data and the environmental state data as the vehicle driving data (Qi, Paragraph. 0067). The claim 8 has the same motivation as to claim 1.
Response to Arguments
11. Applicant's arguments filed 03/07/2026 have been fully considered but they are not persuasive. Response to applicant’s argument is as follows.
A. Applicant argued that prior art did not disclose, “uploading the historical driving data to a cloud server instructing the cloud server to supply the historical driving data as data supports configured for subsequent accident cause analysis”.
As to applicant’s argument Qi disclosed, “FIG. 7A illustrates an example of a vehicle crash prediction system 700 used to perform a vehicle crash prediction based on data from multiple mobile devices according to an embodiment of the present invention. Vehicle crash prediction system 700 can be performed by mobile device 104 of FIG. 1 and FIG. 2, and/or one or more servers in a cloud infrastructure. As shown in FIG. 7A, vehicle crash prediction system 700 can receive data 702 from a mobile device 704, such as mobile device 104 as described above in relation to FIG. 1 and FIG. 2. Mobile device 704 can be operated by a driver of a vehicle 706. In some examples, mobile device 704 can also be associated with a first entity (e.g., a first insurance carrier that insures vehicle 706).” (Paragraph. 0105)”.
B. Applicant argued that prior art did not disclose, “and to upload the historical driving data to a blockchain to avoid data loss or data tampering”.
As to applicant’s argument Kloeppel disclosed, ““A blockchain (also referred to herein as a distributed ledger or a shared ledger) is a way of achieving a distributed consensus on the validity or invalidity of information in the chain (such as a vehicle build sheet, vehicle records, home records usage data associated with a home and/or vehicle, etc.). (Paragraph. 0030).
Conclusion
13. Any inquiry concerning this communication or earlier communication from the
examiner should be directed to Adnan Mirza whose telephone number is (571)-272-3885.
14. The examiner can normally be reached on Monday to Friday during normal
business hours. If attempts to reach the examiner by telephone are unsuccessful, the
examiner’s supervisor, Faris Almatrahi can be reached on (313)-446-4821.
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667
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