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 .
Summary of claims
Claims 1, 3-6, 8, and 9 are pending
Claims 1, 3, 4, 6, 8, and 9 are amended
Claims 2 and 7 are cancelled
Response to Arguments
Rejection Under 35 U.S.C. §112
Applicant’s arguments with respect to claims 2 have been fully considered and are persuasive. The rejection for claim 2 under 35 USC § 112(a) and 112(b) has been withdrawn.
However, the shifting of the “model generation unit” from claim 2 to claim 1 render claim 1 to be rejected under 35 U.S.C. 112(a) and 112(b). The underlying 35 U.S.C. 112(f) issues with regards to the lack of structure for the “model generation unit” remains unresolved. There is no description on how the “model generation unit” functions within the specification or amended claims. There is still no structure given to the unit as to being hardware or software. The applicant’s amendments merely flesh out the function of the determinator rather than defining its fundamental structure. The office assumes the applicant intended the “model generation unit” to function software paired to a processor in line with ¶ 0046 (“Each component may perform an operation through an arithmetic processing means such as an electronic control unit (ECU)”) of the specification for the “Image input unit”, “Image analysis unit”, and “Control generation unit”. Amending the claims to reflect similar structure onto the “model generation unit” would overcome the 35 U.S.C. 112(f).
Regarding the applicant’s arguments with respect to claim 4 for the rejection 35 U.S.C. 112(a) and 112(b), the examiner respectfully disagrees. The underlying issues regarding 35 U.S.C. 112(f) for the “initial condition determinator” have not been resolved. There is still no structure given to the “initial condition determinator” as to the hardware or software that does the determination. The use of the word “determinator” makes it analogous to “unit” and implies the vagueness of the “initial condition determinator” having an either hardware or software structure. The applicant’s amendments merely flesh out the function of the determinator rather than defining its fundamental structure. The office assumes the applicant intended the “initial condition determinator” to function software paired to a processor in line with ¶ 0046 (“Each component may perform an operation through an arithmetic processing means such as an electronic control unit (ECU)”) of the specification for the “Image input unit”, “Image analysis unit”, and “Control generation unit”. Amending the claims to reflect similar structure onto the “initial condition determinator” would overcome the 35 U.S.C. 112(f).
Please see 35 U.S.C. §112 rejections below.
Rejection Under 35 U.S.C. §102
Applicant’s arguments with respect to claims 1, 5, and 6 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.
Please see 35 U.S.C. §103 rejection below.
Rejection Under 35 U.S.C. §103
Applicant’s arguments with respect to claims 2 and 7 have been fully considered and are persuasive. The 35 USC § 103 has been withdrawn.
Applicant’s arguments with respect to claim(s) 3, 4, 8, and 9 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.
Please see 35 U.S.C. §103 rejection below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“Image input unit” in claim 1
This limitation is given structure as software paired to a processor based on ¶ 0046.
“Image analysis unit” in claim 1
This limitation is given structure as software paired to a processor based on ¶ 0046.
“Control generation unit” in claim 1, 5
This limitation is given structure as software paired to a processor based on ¶ 0046.
“Model generation unit” in claim 1
“Initial condition determinator” in claim 4
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 3-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “Model generation unit” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification is devoid of adequate structure to perform the claimed function. In particular, the specification merely states the claimed function of a unit that “generate the learning model that analyzes the input front image data and outputs the pitch rotation angle (or the vertical angle of the driving direction) of the vehicle” (¶ 0056). There is no disclosure of any particular structure, either explicitly or inherently for generating learning models. The use of “learning model generated by the model generation unit 400 may be stored in the image analysis unit 200 to perform an operation” (¶ 0057) is not adequate structure for performing the generation of the model and merely dictates where its eventual result goes.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “Initial condition determinator” in claim 4 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification is devoid of adequate structure to perform the claimed function. In particular, the specification merely states the claimed function of a unit that “analyze the front image data acquired by the image collector 410 to determine whether the driving condition of the vehicle and that of the opposing vehicle each meet predetermined conditions” (¶ 0078). There is no disclosure of any particular structure, either explicitly or inherently for determining vehicle conditions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 3-5 are rejected under 35 U.S.C. 112(b) due to their dependency on claim 1.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 3-5 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim limitation “Model generation unit” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification is devoid of adequate structure to perform the claimed function. In particular, the specification merely states the claimed function of a unit that “generate the learning model that analyzes the input front image data and outputs the pitch rotation angle (or the vertical angle of the driving direction) of the vehicle” (¶ 0056). There is no disclosure of any particular structure, either explicitly or inherently for generating learning models. The use of “learning model generated by the model generation unit 400 may be stored in the image analysis unit 200 to perform an operation” (¶ 0057) is not adequate structure for performing the generation of the model and merely dictates where its eventual result goes.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “Initial condition determinator” in claim 4 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification is devoid of adequate structure to perform the claimed function. In particular, the specification merely states the claimed function of a unit that “analyze the front image data acquired by the image collector 410 to determine whether the driving condition of the vehicle and that of the opposing vehicle each meet predetermined conditions” (¶ 0078). There is no disclosure of any particular structure, either explicitly or inherently for determining vehicle conditions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 3-5 are rejected under 35 U.S.C. 112(a) due to their dependency on claim 1.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 5, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over MURAMATSU (WO2017163414) in view of NISHIDA (WO2021049062A1).
Regarding claim 1:
MURAMATSU discloses:
(Currently Amended) A front light irradiation angle adjustment system comprising: (see at least MURAMATSU, ¶ 0009, “A light distribution control device according to the present invention, A light distribution control device that is mounted on a vehicle and controls light distribution of a headlight of the vehicle, The present invention is provided with: a glare region setting unit that sets a glare region in which a driver of afront vehicle ahead of the vehicle may be confused when light distribution is performed in a space region in which light distribution of the headlight is possible; on the basis of at least one of the relative angle of the vehicle and the front vehicle in the pitch direction and the relative angle in the yaw direction between the vehicle and the front vehicle, and the glare region, sets a light distribution prohibition region in which the light distribution of the headlight is prohibited.”)
an image input unit receiving front image data of a host vehicle; (see at least MURAMATSU, ¶ 0018, “The on-vehicle camera 410 is installed at a specific position in the vehicle 100, for example, in the vicinity of a room mirror in the cabin. The on-vehicle camera 410 captures an image of a space in front of the vehicle 100 and performs image analysis on the image data obtained by photographing, thereby detecting information on a relative position of the front vehicle with respect to the vehicle 100. On the basis of the image data, the on-vehicle camera410 detects information on the position of the headlight or the tail lamp of the front vehicle (hereinafter, the position of the light).”)
an image analysis unit analyzing the front image data using the learning model generated by the model generation unit to estimate a pitch rotation angle of an opposing vehicle included in the front image data; and (see at least MURAMATSU, ¶ 0018; ¶ 0035, “In the light distribution control device 1, the front vehicle information collection unit 101 acquires, from the communication unit 107, information relating to the relative position of the front vehicle with respect to the vehicle100, the position of the light of the front vehicle, the angle of the front vehicle in the pitch direction, the angle of the front vehicle in the yaw direction, and the information relating to the vehicle division of the front vehicle. Then, the front vehicle information collection unit 101 transmits, to the glare region setting unit 103, information on the relative position of the front vehicle with respect to the vehicle 100, the position of the light of the front vehicle, the angle of the front vehicle in the pitch direction, the information on the angle of the front vehicle in the yaw direction, and the information on the vehicle classification of the front vehicle. Further, the vehicle information collection unit 102 acquires information on the angle of the vehicle 100 in the pitch direction and the angle of the vehicle 100 in the yaw direction from the communication unit 107. Then, the vehicle information collection unit 102transmits information on the angle of the vehicle 100 in the pitch direction and the angle of the vehicle 100 in the yaw direction to the glare region setting unit 103. The glare region setting unit 103 sets the glare region.”)
a control generation unit generating a control signal for adjusting an irradiation angle of front lights of the host vehicle by comparing a set front light irradiation angle of the host vehicle with the estimated pitch rotation angle of the opposing vehicle. (see at least MURAMATSU, ¶ 0027, “The light distribution prohibition region setting unit 108 sets the light distribution prohibition region on the basis of the relative angle in the pitch direction between the vehicle 100 and the front vehicle, the relative angle in the yaw direction between the vehicle 100 and the front vehicle, and the glare region. The light distribution prohibition region is a space region in which light distribution of the headlights”; ¶ 0035; ¶ 0036, “FIG. 4 illustrates an example of a glare region. In the present embodiment, with reference to the front of the vehicle, the depth of the glare region is defined as x, the lateral width of the glare region is defined as y, and the height of the glare region is defined as z. The glare region setting unit 103 sets the length of x, y, z of the glare region, the height from the ground of the glare region, and the position of the glare region based on the relative position of the front vehicle with respect to the vehicle 100 and the position of the light of the front vehicle or the vehicle section of the front vehicle. FIG. 5 shows the glare region from the side direction of the vehicle. FIG. 5 shows a glare region when the relative angle theta = 0° in the pitch direction is z, and the width of the glare region in the vertical direction is z. The glare region setting unit 103 transmits, to the vertical direction setting unit 104, information about the length of x, y, z of the glare region, the information on the height from the ground of the glare region, the information on the position of the glare region, the angle of the vehicle 100 in the pitch direction, the angle of the vehicle 100 in the yaw direction, the information on the angle of the front vehicle in the pitch direction, and the angle of the front vehicle in the yaw direction.”; ¶ 0037, “The vertical direction setting unit 104 sets the length of the light distribution prohibited area in the vertical direction according to the relative angle theta in the pitch direction of the front vehicle with respect to the vehicle100. The vertical direction setting unit 104 calculates the relative angle theta in the pitch direction by the difference between the absolute value of the angle in the pitch direction of the front vehicle and the value of the angle of the vehicle 100 in the pitch direction. FIG. 6 illustrates an outline of the operation of the vertical direction setting unit 104, and indicates a glare region when the relative angle theta ≠ 0° in the pitch direction is 0°. Since the glare region is inclined in the pitch direction by the relative angle theta, it is necessary to expand in the vertical direction with the light distribution prohibition region. The vertical direction setting unit 104 obtains the length in the vertical direction of the light distribution prohibition region by the following equation (1). xcos (90°-theta) + z sin (90°-theta) (1)”; ¶ 0039, “In step S 100, the vertical direction setting unit 104 determines the presence or absence of the front vehicle. If information on the length of x, y, and z in the glare region is received from the glare region setting unit 103, the vertical direction setting unit 104 determines that the front vehicle is present. When it is determined that the front vehicle is present, the vertical direction setting unit 104 calculates, in step S101, the relative angle theta in the pitch direction of the forward vehicle with respect to the vehicle 100 from the information on the angle of the front vehicle in the pitch direction and the information on the angle of the vehicle100 in the pitch direction. Next, in step S 102, the vertical direction setting unit 104 sets the length of the light distribution prohibition area in the vertical direction according to the relative angle theta according to the equation (1) described above. In a case where it is determined in step S 100 that there is no front vehicle, the vertical direction setting unit 104ends the processing as the absence of the light distribution prohibition region. When the vertical length of the light distribution prohibition area is set in step S 102, the vertical direction setting unit 104 transmits, to the horizontal direction setting unit 105, information about the length in the vertical direction of the light distribution prohibition area, information about the length of x, y, z of the glare area, information about the height from the ground of the glare area, information on the angle of the vehicle 100 in the yaw direction, and information on the angle of the front vehicle in the yaw direction.”)
EXAMINERS NOTE: MURAMATSU does not disclose using the learning model generated by the model generation unit, given this, it is not bolded in the limitations above. However, NISHIDA still teaches the limitation below.
MURAMATSU does not disclose, but NISHIDA teaches:
a model generation unit generating a learning model that analyzes front image data, (see at least NISHIDA, ¶ 0033, “Next, returning to FIG. 1, the image generating device 1 will be further described. The parameter generating unit 12 generates parameters for generating training data for generating a plurality (a large number)of traffic scenes similar to the three-dimensional computer graphics image reconstructed by the data analyzing unit 11 . The parameters generated here include, for example, parameters for the area in which the traffic flow simulation is performed, parameters for adjusting vehicle speed distribution and occurrence probability, parameters for adjusting pedestrian speed distribution and occurrence probability, as well as parameters for adjusting the occurrence probability of events such as pedestrians running out onto the road. Specifically, in the parameter generation unit 12 of this embodiment, when a pedestrian cannot be detected as jumping out in front of a leading vehicle and the preceding vehicle comes to a sudden halt, the probability of a vehicle occurring and the probability of an event occurring due to a person jumping out can be increased, making it easier to generate similar scenes.”; ¶ 0099, “The result comparison unit 15 judges whether there has been an error in recognition in the verification unit 222 in the image recognition unit 22 of the recognition model generation device 2, i.e., whether there is a discrepancy between the correct data of the object to be recognized and the recognition result for each frame of the three-dimensional computer graphic image output from the three-dimensional object generation unit 13.”; ¶ 0102, “With the above configuration, according to Example 2, multiple (many) traffic scene images similar to the traffic scene in the image that failed to be recognized during verification of the learning model are generated as training data, so that the recognition accuracy of the recognition model can be improved before the recognition model is distributed to the vehicle 3 that learns these.”)
the learning model generated by performing a supervised learning process using a pre-stored three- dimensional (3D) object recognition network: (see at least NISHIDA, ¶ 0003, “In image recognition technology for images captured by on-board cameras installed in vehicles, machine learning techniques such as deep learning may be applied. When applying machine learning technology, samples of vehicles actually traveling on roads are required, but it has been difficult to extract samples needed for learning that include harsh weather conditions such as rain, backlight, and fog.”; ¶ 0016, “The data analysis unit 11 receives the vehicle data transmitted from the vehicle 3 . Here, the data acquired from the vehicle 3 includes sensor data from the sensors of the external environment recognition unit 32equipped in the vehicle, i.e., sensors such as cameras, LIDAR (Light Detection and Ranging or Laser Imaging Detection and Ranging), sonar, etc., as well as data related to operations related to the vehicle's driving control, i.e., data regarding the amount of steering, acceleration, and braking operations, and also data regarding the position and orientation of the vehicle.”; ¶ 0023, “The image reconstructing unit 113 generates a three-dimensional static traffic object from map information of a portion of the vicinity of the vehicle 3 provided from the map database 112, and places a three-dimensional vehicle object within the object in accordance with the orientation of the vehicle 3. Furthermore, the image reproduction unit 113 arranges three-dimensional traffic objects such as other vehicles and pedestrians so that the image obtained from the onboard camera of the vehicle 3 produces the same recognition result as the result recognized by the recognition unit 111, and reproduces the image using a camera in a three-dimensional simulation space that simulates the vehicle's onboard camera. This image is input to the recognition unit 111, and the placement of the three-dimensional traffic objects and the weather and sunlight in the three-dimensional simulation space are adjusted until the recognition result becomes close to the recognition result of the vehicle 3.”; ¶ 0070, “For example, in the case of a model that performs semantic segmentation to categorize each pixel of each frame of video, in addition to regular 3D computer graphics images, it generates 3D computer graphics images in which 3D traffic objects are filled in according to their type, and semantic information according to the filled color.”; ¶ 0102)
using the learning model generated by the model generation unit (see at least NISHIDA, ¶ 0023)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the camera-based light distribution control device for determining vehicle information of the front vehicle for angling headlights within MURAMATSU to include image recognition technology with deep learning for determining orientation of external objects and building 3d objects in NISHIDA to generate an effective vehicle headlight angle adjuster that can permit more targeted headlight targeting maneuvers based on 3d representations of the surrounding environment generated via machine learning.
Regarding claim 5:
MURAMATSU in view of NISHIDA discloses the limitations with claim 1 and MURAMATSU further discloses:
compares the set front light irradiation angle of the host vehicle with the estimated pitch rotation angle of the opposing vehicle, (see at least MURAMATSU, ¶ 0037, "The vertical direction setting unit 104 sets the length of the light distribution prohibited area in the vertical direction according to the relative angle theta in the pitch direction of the front vehicle with respect to the vehicle100. The vertical direction setting unit 104 calculates the relative angle theta in the pitch direction by the difference between the absolute value of the angle in the pitch direction of the front vehicle and the value of the angle of the vehicle 100 in the pitch direction. FIG. 6 illustrates an outline of the operation of the vertical direction setting unit 104, and indicates a glare region when the relative angle theta ≠ 0° in the pitch direction is 0°. Since the glare region is inclined in the pitch direction by the relative angle theta, it is necessary to expand in the vertical direction with the light distribution prohibition region. The vertical direction setting unit 104 obtains the length in the vertical direction of the light distribution prohibition region by the following equation (1). xcos (90°-theta) + z sin (90°-theta) (1)"; ¶ 0046, "According to the present embodiment, it is possible to set a light distribution prohibition area according to the relative angle in the pitch direction between the vehicle 100 and the front vehicle, the relative angle in the pitch direction between the vehicle 100 and the front vehicle, and the relative angle between the vehicle 100 and the front vehicle in the yaw direction. Therefore, when traveling on a curve or a slope, the possibility of dazzling the driver of the front vehicle by light distribution by the headlight of the vehicle 100 can be reduced.")
generates the control signal for adjusting the front light irradiation angle of the host vehicle based on (see at least MURAMATSU, ¶ 0045, "The headlamp driving devices 600 and 610 receive information specifying the light distribution region transmitted from the light distribution region setting unit 106 through the network 20. Then, the headlamp driving devices 600and 610 turn on the light source in the headlights 700 and 710 on the basis of the light distribution area specified by the received information/Power for turning off is generated, and power is individually or simultaneously supplied to each light source to drive each light source. In addition, the headlight driving devices 600 and 610change the brightness at the time of lighting of each light source by changing, for example, a supply current value or performing PWM control in power supply in driving of each light source.")
a value acquired by subtracting the estimated pitch rotation angle of the opposing vehicle from the front light irradiation angle of the host vehicle when the estimated pitch rotation angle of the opposing vehicle is larger, and (see at least MURAMATSU, ¶ 0037; ¶ 0046)
transmits the generated control signal to a linked control. (see at least MURAMATSU, ¶ 0045)
Regarding claim 6:
With regards to claim 6, this claim the method claim for claim 1 is substantially similar to claim 1 with claim 5 appended and is therefore rejected using the same references and rationale.
Claims 3, 4, 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over MURAMATSU (WO2017163414) in view of NISHIDA (WO2021049062A1) in further view of LIN (US 20200124422 A1).
Regarding claim 3:
MURAMATSU in view of NISHIDA discloses the limitations with claim 1 and MURAMATSU further discloses:
an image collector acquiring front image data in multiple driving states from a test vehicle; (see at least MURAMATSU, ¶ 0006, “In the technology of Patent Literature 1, it is determined whether the vehicle is in a transient state or not by the angle in the pitch direction and the yaw direction, or the amount of change in the angle. In the technology of Patent Literature 1, in order to prevent dazzling to the driver of the front vehicle when the vehicle is in a transient state, the light distribution region is controlled from the first predetermined light distribution region (for example, the light distribution region of the high beam or the light distribution region between the high beam and the low beam) to the second predetermined light distribution region (for example, the light distribution region of the low beam).”; ¶ 0009, “A light distribution control device according to the present invention, A light distribution control device that is mounted on a vehicle and controls light distribution of a headlight of the vehicle, The present invention is provided with: a glare region setting unit that sets a glare region in which a driver of afront vehicle ahead of the vehicle may be confused when light distribution is performed in a space region in which light distribution of the headlight is possible; on the basis of at least one of the relative angle of the vehicle and the front vehicle in the pitch direction and the relative angle in the yaw direction between the vehicle and the front vehicle, and the glare region, sets a light distribution prohibition region in which the light distribution of the headlight is prohibited.”; ¶ 0018, “The on-vehicle camera 410 is installed at a specific position in the vehicle 100, for example, in the vicinity of a room mirror in the cabin. The on-vehicle camera 410 captures an image of a space in front of the vehicle 100 and performs image analysis on the image data obtained by photographing, thereby detecting information on a relative position of the front vehicle with respect to the vehicle 100. On the basis of the image data, the on-vehicle camera410 detects information on the position of the headlight or the tail lamp of the front vehicle (hereinafter, the position of the light).”)
a difference analyzer setting a pitch rotation angle of the opposing vehicle positioned in front of the test vehicle as a calculated difference between the first inclination angle value and the second inclination angle value; and (see at least MURAMATSU, ¶ 0018; ¶ 0037, “The vertical direction setting unit 104 sets the length of the light distribution prohibited area in the vertical direction according to the relative angle theta in the pitch direction of the front vehicle with respect to the vehicle100. The vertical direction setting unit 104 calculates the relative angle theta in the pitch direction by the difference between the absolute value of the angle in the pitch direction of the front vehicle and the value of the angle of the vehicle 100 in the pitch direction. FIG. 6 illustrates an outline of the operation of the vertical direction setting unit 104, and indicates a glare region when the relative angle theta ≠ 0° in the pitch direction is 0°. Since the glare region is inclined in the pitch direction by the relative angle theta, it is necessary to expand in the vertical direction with the light distribution prohibition region. The vertical direction setting unit 104 obtains the length in the vertical direction of the light distribution prohibition region by the following equation (1). xcos (90°-theta) + z sin (90°-theta) (1)”)
the image collector and (see at least MURAMATSU, ¶ 0018)
the pitch rotation angle set by the difference analyzer, and (see at least MURAMATSU, ¶ 0037)
MURAMATSU does not disclose, but NISHIDA teaches:
a learning processor performing the supervised learning process of the pre-stored 3D object recognition network by (see at least NISHIDA, ¶ 0016, "The data analysis unit 11 receives the vehicle data transmitted from the vehicle 3 . Here, the data acquired from the vehicle 3 includes sensor data from the sensors of the external environment recognition unit 32equipped in the vehicle, i.e., sensors such as cameras, LIDAR (Light Detection and Ranging or Laser Imaging Detection and Ranging), sonar, etc., as well as data related to operations related to the vehicle's driving control, i.e., data regarding the amount of steering, acceleration, and braking operations, and also data regarding the position and orientation of the vehicle. ")
generating a learning data set including the front image data acquired by (see at least NISHIDA, ¶ 0033, “Next, returning to FIG. 1, the image generating device 1 will be further described. The parameter generating unit 12 generates parameters for generating training data for generating a plurality (a large number)of traffic scenes similar to the three-dimensional computer graphics image reconstructed by the data analyzing unit 11 . The parameters generated here include, for example, parameters for the area in which the traffic flow simulation is performed, parameters for adjusting vehicle speed distribution and occurrence probability, parameters for adjusting pedestrian speed distribution and occurrence probability, as well as parameters for adjusting the occurrence probability of events such as pedestrians running out onto the road. Specifically, in the parameter generation unit 12 of this embodiment, when a pedestrian cannot be detected as jumping out in front of a leading vehicle and the preceding vehicle comes to a sudden halt, the probability of a vehicle occurring and the probability of an event occurring due to a person jumping out can be increased, making it easier to generate similar scenes.”; ¶ 0099, "The result comparison unit 15 judges whether there has been an error in recognition in the verification unit 222 in the image recognition unit 22 of the recognition model generation device 2, i.e., whether there is a discrepancy between the correct data of the object to be recognized and the recognition result for each frame of the three-dimensional computer graphic image output from the three-dimensional object generation unit 13.")
generating the learning model based on a learning result. (see at least NISHIDA, ¶ 0033; ¶ 0099; ¶ 0102, “With the above configuration, according to Example 2, multiple (many) traffic scene images similar to the traffic scene in the image that failed to be recognized during verification of the learning model are generated as training data, so that the recognition accuracy of the recognition model can be improved before the recognition model is distributed to the vehicle 3 that learns these”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the camera-based light distribution control device for determining vehicle information of the front vehicle for angling headlights within MURAMATSU to include image recognition technology with deep learning for determining orientation of external objects, 3d object generation, and training data generation/correction within NISHIDA to generate an effective vehicle headlight angle adjuster that can permit more targeted headlight targeting maneuvers based on 3d representations of the surrounding environment generated via machine learning.
MURAMATSU in view of NISHIDA does not disclose, but LIN teaches:
a host vehicle position analyzer recognizing a position of the test vehicle by using a differential global positioning system (DGPS) applied to the test vehicle, and (see at least LIN, ¶ 0022, “In accordance with an exemplary embodiment of the present invention, the method further comprises determining the set of geographical coordinates of the ego-vehicle by means of a Global Navigation Satellite System, GNSS. As a prerequisite, the position in the map (geographical position) of the ego-vehicle may be determined by a satellite-based system, such as e.g. GPS, GLONASS, or any other regional GNSS.”; ¶ 0023, “Moreover, additionally, or alternatively the determination of the set of geographical coordinates of the ego-vehicle may further comprise measuring the ego-vehicle's specific force and angular rate and comparing the measured specific force and angular rate with the road geometry in order to determine the set of geographical coordinates of the ego-vehicle. In other words, an internal IMU unit of the ego-vehicle may be used to determine the initial map position of the ego-vehicle. This may be advantageously used in combination with the above-mentioned GNSS system in order to increase the accuracy of the map positioning of the ego-vehicle.”)
applying the recognized position to a pre-stored 3D3-high definition (HID) map to analyze a first inclination angle value of a road surface where the test vehicle is positioned; (see at least LIN, ¶ 0004, “An important requirement on autonomous and semi-autonomous vehicles is that they have the ability to accurately estimate the road geometry ahead, and there are two generally different ways to do this: either using forward-looking sensors that directly perceive the road geometry, or using a map that contains the road geometry (often referred to as an HD-map) together with a module that estimates the vehicle's position in the map.”; ¶ 0034, “Yet further, in accordance with a second aspect of the present invention, there is provided a vehicle control system for determining a map position of an ego-vehicle, the system comprising a localization system configured to determine a set of geographical coordinates of the ego-vehicle and an orientation of the ego-vehicle. The system further comprises a vehicle perception system comprising at least one sensor for detecting objects that are external to the ego-vehicle. The system further comprises a vehicle control unit connected to the localization system and the vehicle perception system, wherein the vehicle control unit is configured to acquire map data comprising a road geometry of a surrounding environment of the ego-vehicle. The vehicle control unit is further configured to estimate a geographical position of the ego-vehicle by means of the localization system. The vehicle control unit is further configured to initialize at least one dynamic landmark by measuring, by means of the vehicle perception system, a position and velocity, relative to the ego-vehicle, of a surrounding vehicle located in the surrounding environment, and determining a first map position of the surrounding vehicle based on the geographical position of the ego-vehicle and the measured position of the surrounding vehicle. The system further comprises predict a second map position of the surrounding vehicle based on the determined first map position of the surrounding vehicle, the measured velocity of the surrounding vehicle, and the road geometry. The system further comprises measure, by means of the localization system, a location of the ego-vehicle relative to the surrounding vehicle, when the surrounding vehicle is estimated to be at the second map position and update the estimated geographical position of the ego-vehicle based on the predicted second map position of the surrounding vehicle and the measured location of the surrounding vehicle.”; ¶ 0057, “Moving on, the method 200 includes a step of acquiring 101 map data, and forming 110 a state vector for the ego-vehicle. The state vector comprises a set of ego-vehicle elements representative of the geographical coordinates of the ego-vehicle and an orientation of the ego-vehicle, in other words the state vector can be said to contain a pose of the ego-vehicle (position and orientation). In more detail, the method 200 can be construed as a positioning filter having a state vector x, that contains a 2D Cartesian position (ξ.sub.x, ξ.sub.y) and an orientation θ of the ego-vehicle. Naturally, as already realized by the skilled reader the state vector may also contain a 3D Cartesian position and three different orientations/angles (between each axis) Nevertheless, continuing the former 2D example, the state vector can be expressed as:”)
a relative position analyzer using a light detection and ranging (LiDAR) sensor mounted on the test vehicle to estimate a position of an opposing vehicle positioned in front of the test vehicle, and (see at least LIN, ¶ 0060, “Further, the ego-vehicle's geographical position and orientation is estimated together with the map position of the surrounding vehicle(s) in the same filter. Thus, the state vector parametrization is modified to include surrounding traffic in the positioning filter, starting with that one or more dynamic landmarks are initialized 102. The initialization 102 includes measuring 104 a position and a velocity of one or more surrounding vehicles relative to the ego-vehicle (e.g. by means of a radar, LIDAR or any other suitable sensory arrangement of the ego-vehicle). Thus, the relative position of N surrounding vehicles are stored in the measurement vector:”)
analyzing a second inclination angle value of a road surface where the opposing vehicle positioned in front of the test vehicle is positioned as estimated using the pre-stored 3D-HD map; (see at least LIN, ¶ 0062, “In a further step of the initialization, the surrounding vehicles positions relative to the ego-vehicle are transformed to a geographical/map position. In more detail, a transformation from the ego-vehicle's coordinate system to a global (map) coordinate system is made. In other words, the map position of each surrounding vehicle is determined 105 based on the measured position (x.sub.i.sup.m, y.sub.i.sup.m) of each surrounding vehicle and the geographical position of the ego-vehicle. Here, the state vector is extended 111 so to include elements representative of the map positions (X.sub.i, Y.sub.i) of each surrounding vehicle:”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the camera-based light distribution control device for determining vehicle information of the front vehicle for angling headlights with machine learning within MURAMATSU in view of NISHIDA to include a GPS, lidar, and HD map for determining the location and orientation of external objects in LIN to generate an effective machine-learning-driven headlight control system that is capable of verifying the output result based on alternative sensors.
Regarding claim 4:
MURAMATSU in view of NISHIDA discloses the limitations with claim 3 and MURAMATSU further discloses:
initial condition determinator analyzing the front image data acquired by the image collector to determine (see at least MURAMATSU, ¶ 0018, "The on-vehicle camera 410 is installed at a specific position in the vehicle 100, for example, in the vicinity of a room mirror in the cabin. The on-vehicle camera 410 captures an image of a space in front of the vehicle 100 and performs image analysis on the image data obtained by photographing, thereby detecting information on a relative position of the front vehicle with respect to the vehicle 100. On the basis of the image data, the on-vehicle camera410 detects information on the position of the headlight or the tail lamp of the front vehicle (hereinafter, the position of the light).")
MURAMATSU does not disclose, but NISHIDA teaches:
whether a driving condition of the test vehicle and that of the opposing vehicle positioned in front of the test vehicle each meet predetermined conditions, and (see at least NISHIDA, ¶ 0033, "Next, returning to FIG. 1, the image generating device 1 will be further described. The parameter generating unit 12 generates parameters for generating training data for generating a plurality (a large number)of traffic scenes similar to the three-dimensional computer graphics image reconstructed by the data analyzing unit 11 . The parameters generated here include, for example, parameters for the area in which the traffic flow simulation is performed, parameters for adjusting vehicle speed distribution and occurrence probability, parameters for adjusting pedestrian speed distribution and occurrence probability, as well as parameters for adjusting the occurrence probability of events such as pedestrians running out onto the road. Specifically, in the parameter generation unit 12 of this embodiment, when a pedestrian cannot be detected as jumping out in front of a leading vehicle and the preceding vehicle comes to a sudden halt, the probability of a vehicle occurring and the probability of an event occurring due to a person jumping out can be increased, making it easier to generate similar scenes."; ¶ 0093, "When predetermined conditions are met, the vehicle data collection unit 34 collects operation information of the vehicle's driving control by the vehicle control unit 33 and sensor information of the external environment recognition unit by the external environment recognition unit 32, i.e., image data from the on-board camera, distance information from LIDAR, received signals from sonar, etc., as well as information regarding the vehicle's position and orientation, and information for identifying the vehicle, such as the vehicle identification number. Here, the predetermined conditions are, for example, when the relative speed, relative acceleration, or relative distance to the preceding vehicle falls below a specific value, or when the steering angular velocity exceeds a specific value, in order to detect when the distance to the preceding vehicle suddenly decreases, or when a pedestrian or obstacle on the road is avoided by making a sudden turn.")
performs the learning process of the 3D object recognition network only when the driving conditions of the two vehicles each meet the predetermined conditions based on a determination result by the initial condition determinator. (see at least NISHIDA, ¶ 0003, "In image recognition technology for images captured by on-board cameras installed in vehicles, machine learning techniques such as deep learning may be applied. When applying machine learning technology, samples of vehicles actually traveling on roads are required, but it has been difficult to extract samples needed for learning that include harsh weather conditions such as rain, backlight, and fog."; ¶ 0064, "In FIG. 5, the drawing area management unit 135 determines a point at which to install a camera within the three-dimensional simulation space in which the three-dimensional traffic objects managed by the three-dimensional object management unit 134are placed. In this embodiment, since it is assumed that the present invention is applied to autonomous driving using an on-board camera of an automobile, a vehicle is selected from three-dimensional vehicle objects managed by the three-dimensional object management unit 134. This selection may be made arbitrarily by the user or randomly. Furthermore, when the selected vehicle object is deleted due to reaching the destination, etc., a different vehicle may be automatically selected. Furthermore, a three-dimensional vehicle object that satisfies a specific condition may be selected. The specific condition is, for example, when the relative speed with respect to the vehicle ahead is below a predetermined value, or when proceeding straight into an intersection where pedestrians are crossing the road."; ¶ 0093)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the camera-based light distribution control device for determining vehicle information of the front vehicle for angling headlights within MURAMATSU to include the condition determination for determining when to initialize the image recognition technology with deep learning for determining orientation of external objects and building 3d objects within NISHIDA to generate an efficient vehicle headlight adjuster that executes demanding calculations for generating the 3d representations of the surrounding environment only when driving conditions make analysis reliable.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KOBAYASHI (US 20140175978 A1)
¶ 0160, “FIG. 14A is a diagram indicating that the matrix LEDs are lit when forming a basic beam (BL). FIG. 14B shows a light distribution pattern formed by the matrix LEDs that are in the lighting status shown in FIG. 14A. FIG. 14C is a diagram indicating that the matrix LEDs are lit when forming a town beam (TL). FIG. 14D shows a light distribution pattern formed by the matrix LEDs that are in the lighting status shown in FIG. 14C. FIG. 14E is a diagram indicating that the matrix LEDs are lit when forming a motorway beam (ML). FIG. 14F shows a light distribution pattern formed by the matrix LEDs that are in the lighting status shown in FIG. 14E. FIG. 14G is a diagram indicating that the matrix LEDs are lit when forming a wet beam (WL). FIG. 14H shows a light distribution pattern formed by the matrix LEDs that are in the lighting status shown in FIG. 14G. FIG. 14I is a diagram indicating that the matrix LEDs are lit when the motorway beam (ML) is swiveled. FIG. 14J shows a light distribution pattern formed by the matrix LEDs that are in the lighting status shown in FIG. 14I.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAFAEL VELASQUEZ VANEGAS whose telephone number is (571)272-6999. The examiner can normally be reached M-F 8 - 4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VIVEK KOPPIKAR can be reached at (571) 272-5109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAFAEL VELASQUEZ VANEGAS/Patent Examiner, Art Unit 3667
/JOAN T GOODBODY/Examiner, Art Unit 3667
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