DETAILED ACTION
This office action is in response to the application filed on 09/27/2024. Claims 1-20 are pending and are examined.
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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 8, 12, 14, 16-17, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hanwell et al., US Patent Application Publication No.: 2025/0173829 A1 (please note the foreign application priority data EP 23213162.3, filed 11/29/2023), hereby Hanwell.
Regarding Claims 1 and 16, Hanwell discloses a computer (and a method) comprising a processor and a memory, the memory storing instructions executable by the processor to (Figs. 2 and 7):
“in response to a driving context for a vehicle being a first driving context, execute a machine-learning model on board the vehicle with a first portion of the machine-learning model enabled and a second portion of the machine-learning model disabled (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]); and
in response to the driving context being a second driving context, execute the machine-learning model with the first portion disabled and the second portion enabled (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 2, Hanwell discloses:
“wherein the instructions further include instructions to actuate a component of the vehicle based on an output of the machine-learning model (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 3, Hanwell discloses:
“wherein the output of the machine-learning model includes detections of objects in an environment surrounding the vehicle (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 8, Hanwell discloses:
“wherein the first portion is trained to perform object detection, and the second portion is trained to perform object detection (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 12, Hanwell discloses:
“wherein the driving context is an environmental condition experienced by the vehicle (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 14, Hanwell discloses:
“wherein the driving context is a weather condition (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 17, Hanwell discloses:
“actuating a component of the vehicle based on an output of the machine-learning model (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
Regarding Claim 20, Hanwell discloses:
“wherein the driving context is one of an operational mode of the vehicle or an environmental condition experienced by the vehicle (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
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.
Claims 4-7, 9-11, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hanwell, in view of Chen et al., US Patent Application Publication No.: 2026/0079258 A1, hereby Chen.
Hanwell discloses the invention substantially as claimed. Regarding Claim 4, Hanwell discloses:
“the first portion includes at least one first . . . ; and the second portion includes at least one second . . . (Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
However, although Hanwell generally discloses the claimed first head and second head (i.e., micro-neural networks), Chen does expressly disclose the following:
“the first portion includes at least one first head; and the second portion includes at least one second head (Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
Accordingly, before the effective filing date, it would have been obvious to one of ordinary skill in the art, having the teachings of Hanwell and Chen (hereby Hanwell-Chen) to modify the computer and method of Hanwell to use the claimed first and second head as in Chen. The motivation for doing so would have been to create the advantage of providing a variety of controlled output heads that process multiple types of features efficiently, thereby providing safe planning and control of the vehicle (see Chen, Figs. 1-4, [0004], [0007], and [0022]; see also Fig. 6).
Regarding Claim 5, Hanwell-Chen discloses:
“the machine-learning model (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) includes a common portion (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6); and the at least one first head and the at least one second head (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6) are arranged in the machine-learning model (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) to receive input from the common portion (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Regarding Claim 6, Hanwell-Chen discloses:
“wherein the common portion is trained to perform feature extraction on sensor data (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Regarding Claim 7, Hanwell-Chen discloses:
“wherein the at least one first head and the at least one second head are trained to perform object detection based on features from the feature extraction (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Regarding Claim 9, Hanwell-Chen discloses:
“wherein the machine-learning model (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) is a deep neural network (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Regarding Claim 10, Hanwell-Chen discloses:
“wherein the driving context (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) is an operational mode of the vehicle (Chen, [0100] and [0160]; see also Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
Accordingly, before the effective filing date, it would have been obvious to one of ordinary skill in the art, having the teachings of Hanwell-Chen to modify the computer and method of Hanwell to use the claimed driving context as an operational mode of the vehicle as in Chen. The motivation for doing so would have been to create the advantage of facilitating use of the vehicle, providing security against theft and/or carjacking, and authorizing the driver/owner of the vehicle (see Chen, Figs. 1-4, [0004], [0007], and [0022]; [0100] and [0160]; see also Fig. 6).
Regarding Claim 11, Hanwell-Chen discloses:
“wherein the operational mode indicates whether a component of the vehicle is controlled by the computer or by an operator of the vehicle (Chen, [0100] and [0160]; see also Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 10 applies equally as well here.
Regarding Claim 15, Hanwell-Chen discloses:
“wherein the driving context (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) is a location of the vehicle (Chen, [0077] and [0167]; [0100] and [0160]; see also Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
Accordingly, before the effective filing date, it would have been obvious to one of ordinary skill in the art, having the teachings of Hanwell-Chen to modify the computer and method of Hanwell to use the claimed driving context is a location of the vehicle as in Chen. The motivation for doing so would have been to create the advantage of accurately assisting in mapping, perception, and path planning functions (see Chen, Figs. 1-4, [0004], [0007], and [0022]; [0077] and [0167]; [0100] and [0160]; see also Fig. 6).
Regarding Claim 18, Hanwell-Chen discloses:
“wherein: the first portion (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) includes at least one first head (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6); the second portion (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) includes at least one second head (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6); the machine-learning model (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) includes a common portion (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6); and the at least one first head and the at least one second head (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6) are arranged in the machine-learning model (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]) to receive input from the common portion (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Regarding Claim 19, Hanwell-Chen discloses:
“wherein: the common portion is trained to perform feature extraction on sensor data (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6); and the at least one first head and the at least one second head are trained to perform object detection based on features from the feature extraction (Chen, Figs. 1-4, [0004] and [0022]; see also Fig. 6).”
The motivation that was utilized in Claim 4 applies equally as well here.
Claim Rejections - 35 USC § 103
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Hanwell, in view of Kitani et al., WO 2018/198823 A1, hereby Kitani.
Regarding Claim 13, Hanwell discloses:
“wherein the first driving context is daytime, and the second driving context is . . . (Hanwell, Figs. 2 and 7, [0060], [0069], and [0074]-[0076]).”
However, although Hanwell generally discloses the claimed second driving context is nighttime, Kitani does expressly disclose the following:
“wherein the first driving context is daytime, and the second driving context is nighttime (Fig. 3D, page 7, first two paragraphs).”
Accordingly, before the effective filing date, it would have been obvious to one of ordinary skill in the art, having the teachings of Hanwell and Kitani to modify the computer and method of Hanwell to use the claimed second driving context is nighttime as in Kirani. The motivation for doing so would have been to create the advantage of providing flexibility and accuracy in processing of the AI model (see Kitani, Fig. 3D, page 7, first two paragraphs).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Examiner notes that multiple references cited disclose use of neural networks in vehicular systems. For example, the following references show similar features in the claims, although not relied upon: Bangalore Ravi (US 20250095329 A1), Figs. 1-5.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN M WALSH whose telephone number is (571)270-0423. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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/KATHLEEN M WALSH/Primary Examiner, Art Unit 2482
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