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) and further in view of qi et al (U.S. 2022/0292974).
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, Reichardt did not disclose uploading the historical driving data to a cloud server to provide data support configured for subsequent accident cause analysis.
In the same field of endeavor qi disclosed, “In some embodiments, the vehicle crash prediction is performed by a vehicle crash prediction system, such as vehicle crash prediction system 300 as described above. Some or all of the blocks of process 400 can be performed by a mobile device (e.g., mobile device 104, mobile device 304, etc.), by a server in a cloud infrastructure (e.g., electronic device 204), or by a combination of both” (Paragraph. 0083).
It would have been obvious to one having ordinary skill in the art before the effective filing date was made to have incorporated in some embodiments, the vehicle crash prediction is performed by a vehicle crash prediction system, such as vehicle crash prediction system 300 as described above. Some or all of the blocks of process 400 can be performed by a mobile device (e.g., mobile device 104, mobile device 304, etc.), by a server in a cloud infrastructure (e.g., electronic device 204), or by a combination of both as taught by qi in the method and system of Reichardt to improve the vehicle crash prediction.
4. As per claims 2,10,18 Kim-Qi disclosed wherein predicting a vehicle accident occurrence probability of the target vehicle according to the vehicle driving data and a preset analysis model 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 (Kim, Paragraph. 0056).
5. As per claims 3,11,19 Kim-Qi 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 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 Reichardt-Qi disclosed further comprising: selecting data from the vehicle driving data;
predicting the vehicle accident occurrence probability according to the preset analysis model and selected data (Kim, Paragraph. 0056).
8. As per claims 6,14 Reichardt-Qi disclosed wherein uploading the historical driving data to a cloud server 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 Reichardt-Qi 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 Reichardt-Qi disclosed wherein obtaining vehicle driving data of a 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 with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
12. THIS ACTION IS MADE FINAL. 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.
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