DETAILED ACTION
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 .
Response to Amendment
This Office Action is in response to communications filed January 02, 2026. No claims have been amended/added. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
All 35 USC § 112 rejections have been overcome by Applicant.
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.
Claim(s) 1-8, 14-16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallagher et al. (Gallagher; US Pub No. 2019/0332902 A1) in view of Gupta et al. (Gupta; US Pub No. 2024/0326873 A1).
As per claim 1, Gallagher teaches a method of determining a driver score for a driver of a vehicle, the method comprising:
receiving at least one human influencing factor (paragraph [0032]);
receiving at least one vehicle influencing factor (paragraph [0083]: environmental factors);
receiving at least one context influencing factor (paragraph [0083]: environmental factors);
concatenating… to generate a concatenated vector (paragraph [0084]: amalgamation – concatenated); and
determining the driver score for the driver based on the concatenated vector (paragraph [0084]: amalgamation – concatenated) utilizing a machine learning algorithm (paragraph [0117]).
Gallagher does not expressly teach and embedding the at least one human influencing factor into a human vector… and embedding the at least one vehicle influencing factor into a vehicle vector… and embedding the at least one context influencing factor into a context vector… the human vector, the vehicle vector, and the context vector.
Gupta teaches a plurality of different vectors -- and embedding the at least one human influencing factor into a human vector… and embedding the at least one vehicle influencing factor into a vehicle vector… and embedding the at least one context influencing factor into a context vector… the human vector, the vehicle vector, and the context vector (paragraph [0060]: human vector, paragraph [0055]: state vector).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the vectors as taught by Gupta, since Gupta states that such a modification would result in utilizing multiple data strings in order to increase accuracy with respect to identifying a vehicle behavior.
As per claim 2, Gallagher in view of Gupta further teaches the method of claim 1, wherein the at least one human influencing factor includes a driving characteristic of the driver (Gallagher, paragraph [0072]: brain function, body chemistry, biometric signals).
As per claim 3, Gallagher in view of Gupta further teaches the method of claim 2, wherein the driving characteristic includes at least one of a duration of vehicle operation for the driver or a health and emotional status of the driver (Gallagher, paragraph [0072], lines 12-14).
As per claim 4, Gallagher in view Gupta further teaches the method of claim 1, wherein the at least one vehicle influencing factor includes a mechanical status of the vehicle (Gallagher, paragraph [0083], lines 4-5).
As per claim 5, Gallagher in view of Gupta further teaches the method of claim 1, wherein the at least one vehicle influencing factor includes at least one of a load type carried by the vehicle, a load status carried by the vehicle, or a service history of the vehicle (Gallagher, paragraph [0083], lines 4-5: check engine indicator – service history).
As per claim 6, Gallagher in view of Gupta further teaches the method of claim 1, wherein the at least one context influencing factor includes at least one of a temporal context, a spatial context, a spatiotemporal context, or a social context for the driver and the vehicle (Gallagher, paragraph [0083]).
As per claim 7, Gallagher in view of Gupta further teaches the method of claim 1, including determining an adaptive response by utilizing an adaptive response engine to evaluate the driver score and a major influencer to the driver score to determine the adaptive response (Gallagher, paragraph [0072], lines 55-70).
As per claim 8, Gallagher in view of Gupta further teaches the method of claim 7, wherein the major influencer is determined based on selecting which of the human vector, the vehicle vector, or the context vector provided a greatest contribution to the driver score (Gallagher, paragraph [0058], lines 1-3).
As per claim 14, (see rejection of claim 1 above) a non-transitory computer-readable storage medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising:
receiving at least one human influencing factor and embedding the at least one human
influencing factor into a human vector;
receiving at least one vehicle influencing factor and embedding the at least one vehicle
influencing factor into a vehicle vector;
receiving at least one context influencing factor and embedding the at least one context
influencing factor into a context vector;
concatenating the human vector, the vehicle vector, and the context vector to generate a
concatenated vector; and
determining a driver score for a driver based on the concatenated vector utilizing a neural
network.
As per claim 15, (see rejection of claim 7 above) the non-transitory computer-readable storage medium of claim 14, wherein the method includes determining an adaptive response by utilizing an adaptive response engine to evaluate the driver score and a major influencer to the driver score to determine an adaptive response.
As per claim 16, (see rejection of claim 8 above) the non-transitory computer-readable storage medium of claim 15, wherein the major influencer is determined based on selecting which of the human vector, the vehicle vector, or the context vector provided a greatest contribution to the driver score.
As per claim 19, (see rejection of claim 1 above) a vehicle comprising:
a body defining a passenger compartment (Gallagher, Fig. 1);
a plurality of wheels supporting the body (Gallagher, Fig. 1);
a plurality of sensors fixed relative to the body (Gallagher, Fig. 1, Sensors 150, 155, 156); and
a controller in communication with the plurality of sensors (Gallagher, Fig. 1, Controller 102), the controller being programmed to:
receive at least one human influencing factor and embedding the at least one human influencing factor into a human vector;
receive at least one vehicle influencing factor and embedding the at least one vehicle influencing factor into a vehicle vector;
receive at least one context influencing factor and embedding the at least one context influencing factor into a context vector;
concatenate the human vector, the vehicle vector, and the context vector to generate a concatenated vector; and
determine a driver score for a driver based on the concatenated vector utilizing a machine learning algorithm.
As per claim 20, (see rejection of claim 7 above) the vehicle of claim 19, wherein the controller is programmed to determine an adaptive response by utilizing an adaptive response engine to evaluate the driver score and a major influencer on the driver score.
Claim(s) 9-13, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallagher in view of Gupta as applied above, and further in view of Hu et al. (Hu; US Patent No. 9,574,888 B1).
As per claim 9, Gallagher in view of Gupta teaches the method of claim 8, wherein the adaptive response includes updating a route for the vehicle (Gallagher, paragraph [0096]).
Gallagher in view of Gupta does not expressly teach wherein the adaptive response includes updating a route for the vehicle when the context vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value.
Hu teaches wherein the adaptive response includes updating a route for the vehicle when the context vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value (col. 8, lines 52-67; col. 9, line 55-col. 11, line 26: weighting different factors and determining a driver safety level).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the weighting technique as taught by Hu, since Hu states in column 8, lines 52-67 that such a modification would result in controlling a vehicle output based on data which is determined to be of the most importance.
As per claim 10, Gallagher in view of Gupta teaches the method of claim 8.
Gallagher in view of Gupta does not expressly teach wherein the adaptive response includes providing the driver a driver score explanation when the human vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value.
Hu teaches wherein the adaptive response includes providing the driver a driver score explanation (col. 12, lines 8-10) when the human vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value (col. 8, lines 52-67; col. 9, line 55-col. 11, line 26: weighting different factors and determining a driver safety level).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the weighting technique as taught by Hu, since Hu states in column 8, lines 52-67 that such a modification would result in controlling a vehicle output based on data which is determined to be of the most importance.
As per claim 11, Gallagher in view of Gupta teaches the method of claim 8, wherein the adaptive response includes providing a vehicle maintenance alert (Gallagher, paragraph [0083]: check engine indicator).
Gallagher in view of Gupta does not expressly teach when the vehicle vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value.
Hu teaches teach when the vehicle vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value (col. 8, lines 52-67; col. 9, line 55-col. 11, line 26: weighting different factors and determining a driver safety level).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the weighting technique as taught by Hu, since Hu states in column 8, lines 52-67 that such a modification would result in controlling a vehicle output based on data which is determined to be of the most importance.
As per claim 12, Gallagher in view of Gupta teaches the method of claim 8.
Gallagher in view of Gupta does not expressly teach wherein the adaptive response includes generating a driver score explanation for the driver when the driver score is below a predetermined threshold value.
Hu teaches wherein the adaptive response includes generating a driver score explanation for the driver (col. 12, lines 8-10) when the driver score is below a predetermined threshold value (col. 8, lines 52-67; col. 9, line 55-col. 11, line 26: weighting different factors and determining a driver safety level).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the weighting technique as taught by Hu, since Hu states in column 8, lines 52-67 that such a modification would result in controlling a vehicle output based on data which is determined to be of the most importance.
As per claim 13, Gallagher in view of Gupta, and further in view of Hu, further teaches the method of claim 12, wherein the driver score explanation (Hu, col. 12, lines 8-10) is generated from a domain-specific large language model receiving at least the driver score and the major influencer (Hu, col. 8, lines 52-67; col. 9, line 55-col. 11, line 26).
As per claim 17, (see rejection of claim 9 above) the non-transitory computer-readable storage medium of claim 16, wherein the adaptive response includes providing the driver with a driver score explanation when the human vector provided the greatest contribution to the driver score and the driver score was below a predetermined threshold value.
As per claim 18, (see rejection of claim 13 above) the non-transitory computer-readable storage medium of claim 17, wherein the driver score explanation for the driver is generated from a large language model receiving at least the driver score and the major influencer.
Response to Arguments
Applicant’s arguments with respect to the above claim(s) 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAOMI J SMALL whose telephone number is (571)270-5184. The examiner can normally be reached Monday-Friday 8:30AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Quan-Zhen Wang can be reached at 571-272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAOMI J SMALL/Primary Examiner, Art Unit 2685
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