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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/15/2024 has/have been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 18 is dependent upon claim 15. However, both claims have the same claim limitations. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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) 11, 13, 19, 21, 25-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orecher (US 20140072176 A1) in view of Kurosawa (US 20220002978 A1).
-Regarding claim 11, Orecher discloses computer-implemented method for determining a probability of occurrence of wildlife in a predetermined region in a main direction of travel of an automated motor vehicle (Abstract; FIGS. 1-4
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[0003], “predefined area in the forward direction of the vehicle”; [0008]; [0015], “types of animals, pedestrians and/or bicyclists who are situated and/or are moving in the edge area of a road and/or on a road, on which the motor vehicle is driving”; [0027]; ), the method comprising: receiving image data from a sensor arrangement installed in and/or on the motor vehicle (FIG 1, image unit 3; FIGS. 2-4; [0014]: “The imaging unit … living beings … detected … animals and/or persons …”; [0031]), and determining the probability of occurrence based on the received image data ([0008], “a probability of a presence of the object in the respective image detail can be determined … detecting the object …”; [0027]; [0030]-[0031]).
Orecher does not disclose using neural networks or machine learning to determine the probability of occurrence.
In the same field of endeavor, Kurosawa teaches a method to obtain useful information on a work area in an area surrounding the construction machine (Kurosaw: Abstract; FIGS. 1-9). Kurosawa further teaches using neural networks or machine learning to determine the probability of occurrence (Kurosaw: FIG. 7; [0082], “environmental information (for example, a captured image) … input to the neural network DNN … output the probability (predicted probability) of the presence of an object with respect to each of types of objects corresponding to a predefined monitoring target … predicted probability of the presence of a “person” in an area”; [0086])
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Orecher with the teaching of Kurosaw by using neural network to determine the probability of occurrence in order to more accurate detect wildlife to avoid collision.
-Regarding claim 13, Orecher in view of Kurosawa teaches the method of claim 11. The combination further comprising outputting a warning signal to a user of the motor vehicle and/or into an environment of the motor vehicle based on the determined probability of occurrence (Orecher: FIG. 1; [0011], “a signal is generated for signaling a predefined warning …to draw the vehicle user's … attention to a danger in sufficient time, so that the driver and/or the vehicle can appropriately react for avoiding a collision. The warning may … be signaled by means of an acoustic signal, a visual signal, and/or a haptic signal”; [0027]; [0035]).
-Regarding claims 19 and 21, Orecher in view of Kurosawa teaches the method of claims 11 and 13. The combination further teaches training the artificially intelligent system before and/or during use of the method during operation of the motor vehicle (Kurosawa: FIG. 4; [0080], “learned model”; [0084], [0089]).
-Regarding 25, Orecher in view of Kurosawa teaches the method of claim 11. The combination further teaches a control apparatus configured to carry out a method according to claim 11 (Orecher: FIG. 1).
-Regarding 26, Orecher in view of Kurosawa teaches the method of claim 25. The combination further teaches that an automated motor vehicle including the control apparatus and a camera which is installed in and/or on the motor vehicle and is configured to output image data to the control apparatus (Orecher: FIG. 1).
-Regarding 27, Orecher in view of Kurosawa teaches the method of claim 11. The combination further teaches a non-transitory computer-readable storage medium comprising instructions which, when executed by a control apparatus, cause the control apparatus to carry out a method according to claim 11 (Orecher: FIG. 1).
Claim(s) 12, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orecher (US 20140072176 A1) in view of Kurosawa (US 20220002978 A1), and further in view of Lin et al (US 20160350930 A1), hereinafter Lin.
-Regarding claim 12, Orecher in view of Kurosawa teaches the method of claim 11.
Orecher in view of Kurosawa teaches determining vegetation and/or a terrain in a motor vehicle surrounding area determining the probability of occurrence based on the determined vegetation and/or the determined terrain (Orecher: FIG. 1; FIGS. 2-4 (see vegetation in the FIGS.); [0003], ‘predefined area”; [0015], “edge area”; [0037], “urban areas”). Orecher in view of Kurosawa has no limitation on the areas or regions to determine probability of occurrence. A person for ordinary skills in the art would understand that it can be performed in any areas such as near a forest (for example, see Axel (DE102004050597A1): [0025]; [0028]; [0072]).
Orecher in view of Kurosawa does not teach determining a semantic segmentation map based on the image data; determining a depth map based on the image data and/or 3D sensor data; fusing the semantic segmentation map and the depth map in order to thus obtain a semantic segmentation map with depth information ;
However, Lin is an analogous art pertinent to the problem to be solved in this application and teaches joint depth estimation and semantic labeling techniques usable for processing of a single image (Lin: Abstract; FIGS. 1-17). Lin further teaches determining a semantic segmentation map based on the image data; determining a depth map based on the image data and/or 3D sensor data; fusing the semantic segmentation map and the depth map in order to thus obtain a semantic segmentation map with depth information to provide probabilities of semantic tags (Lin: FIG. 4; FIG. 16, step 1608; [0054], [0055]; [0069]; [0070]; [0086]);
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Orecher in view of Kurosawa with the teaching of Lin by fusing semantic segmentation map and the depth map in order to improve accuracy of describing semantic layout of the image and depth of objects in the image (Lin: [0054]).
-Regarding claim 14, Orecher in view of Kurosawa, and further in view of Lin teaches the method of claim 1. The modification further teaches comprising outputting a warning signal to a user of the motor vehicle and/or into an environment of the motor vehicle based on the determined probability of occurrence (Orecher: FIG. 1; [0011], “a signal is generated for signaling a predefined warning …to draw the vehicle user's … attention to a danger in sufficient time, so that the driver and/or the vehicle can appropriately react for avoiding a collision. The warning may … be signaled by means of an acoustic signal, a visual signal, and/or a haptic signal”; [0027]; [0035]).
-Regarding claim 20, Orecher in view of Kurosawa, and further in view of Lin teaches the method of claim 1. The modification further teaches training the artificially intelligent system before and/or during use of the method during operation of the motor vehicle (Kurosawa: FIG. 4; [0080], “learned model”; [0084], [0089]).
Claim(s) 15, 17, 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orecher (US 20140072176 A1) in view of Kurosawa (US 20220002978 A1), and further in view of Markus (DE 102014207399 A1).
-Regarding claims 15 and 17, Orecher in view of Kurosawa teaches the methods of claim 11 and 13.
Orecher in view of Kurosawa does not teach outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle based on the determined probability of occurrence.
However, Markus is an analogous art pertinent to the problem to be solved in this application and teaches a method for managing risk situations with living beings (Markus: FIGS. 1-3). Markus further teaches outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle based on the determined probability of occurrence (Markus: FIG.1; [0018], “a driver assistance system takes over the lateral and longitudinal guidance at least for a certain period of time and/or in a specific situation”; [0019]; [0024], “ a second actuator … driver assistance system …”; [0046], “the first control device 8 generates, on the one hand, signals which cause further devices to propose specific measures to the driver and/or to carry out specific technical interventions in the driving operation of the vehicle 5”; [0047], “sends signals to a first actuator 11, which influences the steering wheel impacts and/or to a second actuator 12, which serves for braking assistance”)
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Orecher in view of Kurosawa with the teaching of Markus by outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle in order to provide the user or driver with sufficient time reserve the is necessary for driving task and help to managing accident risk situations with living beings (Markus: [0019]; [0013]; [0024]).
-Regarding claim 22, Orecher in view of Kurosawa, and further in view of Markus teaches the method of claim 15. The modification further teaches training the artificially intelligent system before and/or during use of the method during operation of the motor vehicle (Kurosawa: FIG. 4; [0080], “learned model”; [0084], [0089]).
-Regarding claim 23, Orecher in view of Kurosawa, and further in view of Markus teaches the method of claim 15. The modification further teaches wherein the artificially intelligent system is trained before use of the method during operation of the motor vehicle based on first training data comprising image data which were recorded during a test drive by a camera installed in and/or on a motor vehicle and are optionally linked to a probability of occurrence (Orecher: FIG. 1; Kurosawa: FIGS. 1, 4; [0080], “learned model”; [0084], [0089]; [0115], “predicted probability of the presence of a “person””).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orecher (US 20140072176 A1) in view of Kurosawa (US 20220002978 A1), and further in view of Lin et al (US 20160350930 A1), hereinafter Lin, in view of Markus (DE 102014207399 A1).
-Regarding claim 16, Orecher in view of Kurosawa, and further in view of Lin teaches the method of claim 11.
Orecher in view of Kurosawa, and further in view of Lin does not teach outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle based on the determined probability of occurrence.
However, Markus is an analogous art pertinent to the problem to be solved in this application and teaches a method for managing risk situations with living beings (Markus: FIGS. 1-3). Markus further teaches outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle based on the determined probability of occurrence (Markus: FIG.1; [0018], “a driver assistance system takes over the lateral and longitudinal guidance at least for a certain period of time and/or in a specific situation”; [0019]; [0024], “ a second actuator … driver assistance system …”; [0046], “the first control device 8 generates, on the one hand, signals which cause further devices to propose specific measures to the driver and/or to carry out specific technical interventions in the driving operation of the vehicle 5”; [0047], “sends signals to a first actuator 11, which influences the steering wheel impacts and/or to a second actuator 12, which serves for braking assistance”)
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Orecher in view of Kurosawa, and further in view of Lin with the teaching of Markus by outputting a control signal for influencing lateral and/or longitudinal guidance of the motor vehicle in order to provide the user or driver with sufficient time reserve the is necessary for driving task and help to managing accident risk situations with living beings (Markus: [0019]; [0013]; [0024]).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orecher (US 20140072176 A1) in view of Kurosawa (US 20220002978 A1), and further in view of Markus (DE 102014207399 A1), in view of Huval (US 20180373980 A1).
-Regarding claim 24, Orecher in view of Kurosawa, and further in view of Markus teaches the method of claim 23.
Orecher in view of Kurosawa, and further in view of Markus does not teach wherein the artificially intelligent system is trained during use of the method during operation of the motor vehicle based on second training data comprising image data which were recorded in a situation by the camera installed in and/or on the motor vehicle.
However, Huval is an analogous art pertinent to the problem to be solved in this application and teaches a method for training and refining an artificial intelligence (Huval: Abstract; FIGS. 1-5 ). Huval further teaches wherein the artificially intelligent system is trained during use of the method during operation of the motor vehicle based on second training data comprising image data which were recorded in a situation by the camera installed in and/or on the motor vehicle (Huval: Abstract; FIGS. 1, 3-4; [0009], “recorded during operation of the road vehicle; passing … through the neural network … retraining …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Kurosawa, and further in view of Markus with the teaching of Huval by updating artificially intelligent system during the driving in order to more accurate detect objects in different situations.
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664