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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/8/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 4/8/2026, have been fully considered and the examiner’s responses are given below.
The 35 U.S.C. 101 rejections are withdrawn.
Applicant’s amendments to the independent claims of controlling movement of the vehicle based on the planned driving path is a practical application.
The 35 U.S.C. 103 rejections are withdrawn, however new grounds are presented below.
Applicant’s amendments to the independent claims alters the scope of the claims, therefore new prior art has been applied and applicant’s arguments are moot.
Applicant argues that Motoyama does not teach identifying category labels of the environment images by using a deep neural network model. Examiner respectfully disagrees. It appears the applicant has cited to paragraphs in Niendorf rather than paragraphs in Motoyama for arguments, therefore applicant’s arguments are moot. Determining road images and traffic flow images from the environment images according to category labels is taught by Niendorf. The road images and traffic flow images are formed by fusing data from different types of sensors. The road and traffic flow images are also given labels based on “visual information (e.g., bounding boxes, color-coded shapes, etc.) that represent various data of interest (e.g., vehicles, pedestrians, lanes, buildings, etc.)” (Niendorf – Paragraph 0055).
Applicant argues that Barrera fails to teach identifying whether a vehicle has freight or engineering functions. Examiner respectfully disagrees. The claim limitation recites “or” which means only one of the items in the list must be taught, in this case. Barrera teaches a vehicle with an automatic driving function (Barrera - Paragraphs 0217, 0229).
Claim Rejections - 35 USC § 112
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 1-2, 4-9, 11-16, and 18-20 are 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.
Regarding claims 1, 8, and 15, the applicant has not pointed out where the amended claim is supported, nor does there appear to be a written description of the claim limitation “controlling movement of the vehicle based on the planned driving path” in the application as filed. On the other hand, the specification details a path planning system.
Regarding claims 2, 4-7, 9, 11-14, 16, and 18-20, these claims depend from one of claims 1, 8, and 15 and are therefore rejected for the same reason as one of claims 1, 8, and 15 above, as they do not cure the deficiencies of one of claims 1, 8, and 15 noted above.
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 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.
Claims 1-2, 4, 7-9, 11, 14-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Niendorf (US 20220048535 A1, cited in a previous office action) in view of Schlacter (US 20250026376 A1, cited in a previous office action), Motoyama (US 20220342427 A1, cited in a previous office action), Lingg (US 20190163193 A1), and Barrera (US 20240416907 A1, cited in a previous office action).
Regarding claim 1, Niendorf discloses a path planning method applied to a vehicle, the path planning method comprising (Abstract, Paragraph 0036);
obtaining a current location of the vehicle in real time (Paragraphs 0048, 0050, 0054);
determining road data and traffic flow data matched with the current location of the vehicle, comprising (Paragraphs 0048-0050);
determining road images and traffic flow images from the environment images according to the category labels (Paragraphs 0052-0055; “the contextual environment may be represented by a raster image that visually depicts the agent, semantic information, etc. For example, the raster image may be a birds-eye view of the vehicle and its surrounding, up to a predetermined distance. The raster image may include visual information (e.g., bounding boxes, color-coded shapes, etc.) that represent various data of interest (e.g., vehicles, pedestrians, lanes, buildings, etc.)”);
extracting the road data from the road images and extracting the traffic flow data from the traffic flow images (Paragraphs 0052-0055; “information about the contextual environment may be represented by a raster image that visually depicts the agent, semantic information, etc. For example, the raster image may be a birds-eye view of the vehicle and its surrounding, up to a predetermined distance. The raster image may include visual information (e.g., bounding boxes, color-coded shapes, etc.) that represent various data of interest (e.g., vehicles, pedestrians, lanes, buildings, etc.)”);
the
the
the
determining an objective vehicle according to the vehicle identification information (Paragraph 0054-0055);
obtaining a vehicle location of the objective vehicle (Paragraph 0054);
obtaining an objective driving location of the vehicle (Paragraph 0046, 0050, 0057);
determining a planned driving path of the vehicle based on the vehicle location of the objective vehicle, the current location and the objective driving location of the vehicle (Paragraph 0057).
controlling movement of the vehicle based on the planned driving path (Niendorf - Paragraphs 0057-0058) “control module 1025 may transmit commands to a steering actuator to maintain a particular steering angle for a particular amount of time to move a vehicle on a particular trajectory”
Niendorf does not specifically state obtaining environment images in a circle centered on the current location of the vehicle and with a radius of preset distance.
However, Schlacter teaches obtaining environment images in a circle centered on the current location of the vehicle and with a radius of preset distance (Paragraph 0019, Fig. 1; “the components of perception engine aboard the automated vehicle enable the truck 102 to perceive the environment 100 within a perception radius 130”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with obtaining environment images center on the current location of the vehicle and within a radius of the vehicle of Schlacter with a reasonable expectation of success. One of ordinary skill in the art would understand that an ego autonomous vehicle may use cameras to image surrounding vehicles within a radius. A close vehicle within the camera radius poses the most danger to the vehicle as opposed to one outside the sensor radius. The ego vehicle can use these hazardous other vehicles within a radius to perform an avoidance action. One would have been motivated to combine Niendorf with Schlacter as this achieves increased driving safety. As stated in Schlacter, “What is further needed is a means for taking action to avoid the traffic vehicle or provide a safety buffer” (Paragraphs 0004-0005).
Niendorf does not specifically state identifying category labels of the environment images by using a deep neural network model.
However, Motoyama teaches identifying category labels of the environment images by using a deep neural network model (Paragraphs 0113-0120; “The semantic segmentation processing unit 72 reads and uses a neural network learned in advance by machine learning such as deep learning and stored in the storage unit 73, classifies the types of subjects in the image”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with identifying category labels of environment images by using a deep neural network model of Motoyama with a reasonable expectation of success. One of ordinary skill in the art would understand that a deep neural network model is able to label and extract obstacles within an environment. This allows the vehicle to plan trajectories to avoid the obstacle. One would have been motivated to combine Niendorf with Motoyama as this improves object detection and recognition. As stated in Motoyama, “because only the feature points labeled with the semantic label that can be an obstacle when the moving body 11 moves are extracted, it is possible to form a Delaunay mesh in a region where an object to be recognized as an obstacle on the basis of the extracted feature points exists. This makes it possible to plan a trajectory to avoid the obstacle” (Paragraph 0143).
Niendorf does not specifically state performing a data cleaning on the road data and the traffic flow data, and removing redundant data or useless data from the road data and the traffic flow data; and converting a format of cleaned traffic flow data and cleaned road data; Cleaning/cleaned road data and traffic flow data.
However, Lingg teaches performing a data cleaning on the road data and the traffic flow data, and removing redundant data or useless data from the road data and the traffic flow data (Lingg - Paragraphs 0043, 0047) “filters the resulting regions by region size and/or aspect ratio to remove image regions that do not fit expectations of objects of interest”
converting a format of cleaned traffic flow data and cleaned road data (Lingg - Paragraph 0043, 0052) “produce merged results 126. The results mapping module 108 stores the merged results 126 as a composite map including a representation of the traffic related object superimposed upon a defined map”
Cleaning/cleaned road data and traffic flow data (Lingg - Paragraphs 0043, 0047) “filters the resulting regions by region size and/or aspect ratio to remove image regions that do not fit expectations of objects of interest”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with cleaning the road and traffic flow data, and converting a format of the cleaned traffic flow and road data of Lingg with a reasonable expectation of success. One of ordinary skill in the art would understand that that both Niendorf and Lingg discuss sensing and processing vehicle sensor data. One would have been motivated to combine as this assists the vehicle in path planning and future destination planning (Lingg – Paragraph 0003).
Niendorf does not specifically state the objective vehicle comprises a first function, which illustrates the vehicle with an automatic driving function, a cargo function, or an engineering function.
However, Barrera teaches the objective vehicle comprises a first function, which illustrates the vehicle with an automatic driving function, a cargo function, or an engineering function (Barrera - Paragraphs 0217, 0229) “If V detects a nearby vehicle O, it will detect if O is autonomous or non-autonomous by performing license plate detection”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with an objective vehicle with an automatic driving function of Barrera with a reasonable expectation of success. One of ordinary skill in the art would understand that an ego autonomous vehicle can determine whether a neighboring vehicle is also an autonomous vehicle or not based on their license plate. In a lane merge situation, the ego autonomous vehicle can communicate through V2V and coordinate maneuvers if the other vehicle is also an autonomous vehicle. The ego autonomous vehicle can also predict the other vehicle’s behavior, based on whether the other vehicle is an autonomous vehicle, which ensures a safer merge. One would have been motivated to combine Niendorf with Barrera as this improves safety and navigation efficiency. As stated in Barrera, “When a lane ends or is reduced, it can create dangerous situations for human drivers, as they may not be able to react quickly enough to avoid collisions. This is especially true in high-speed or high-traffic areas. Therefore, there is a need for a method and system for autonomous vehicles to navigate the roads safely and efficiently, reducing the risk of accidents and improving the overall experience for passengers and other drivers on the road” (Paragraph 0003).
Regarding claim 2, Niendorf discloses determining the road data and the traffic flow data matched with the current location of the vehicle comprises (Paragraphs 0048-0050).
Niendorf does not specifically state obtaining the road data and the traffic flow data in a circle centered on the current location of the vehicle and radiused by a preset distance.
However, Schlacter teaches obtaining the road data and the traffic flow data in a circle centered on the current location of the vehicle and radiused by a preset distance (Paragraph 0019, Fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with obtaining road data and traffic flow data within a radius of the vehicle of Schlacter with a reasonable expectation of success. One of ordinary skill in the art would understand that an ego autonomous vehicle may detect surrounding vehicles within a radius to determine what is most relevant. A close vehicle within a sensor radius poses the most danger to the vehicle as opposed to one outside the sensor radius. The ego vehicle can use these hazardous other vehicles within a radius to perform an avoidance action. One would have been motivated to combine Niendorf with Schlacter as this achieves increased driving safety. As stated in Schlacter, “What is further needed is a means for taking action to avoid the traffic vehicle or provide a safety buffer” (Paragraphs 0004-0005).
Regarding claim 4, Niendorf discloses extracting the traffic flow data from the traffic flow images comprises (Paragraphs 0053-0055);
determining the vehicle identification information matching with the current location of the vehicle based on the traffic flow images (Paragraphs 0052-0055).
Niendorf does not specifically state the vehicle identification information comprises first license plate information; determining objective license plate information matching with second license plate information from the first license plate information; a vehicle corresponding to the second license plate information comprises a first function.
However, Barrera teaches the vehicle identification information comprises first license plate information (Paragraphs 0200, 0204);
determining objective license plate information matching with second license plate information from the first license plate information (Paragraphs 0217, 0229);
a vehicle corresponding to the second license plate information comprises a first function (Paragraphs 0217, 0229).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with obtaining first license plate information and determining objective license plate information by matching of Barrera with a reasonable expectation of success. One of ordinary skill in the art would understand that an ego autonomous vehicle can determine whether a neighboring vehicle is also an autonomous vehicle or not based on their license plate. In a lane merge situation, the ego autonomous vehicle can communicate through V2V and coordinate maneuvers if the other vehicle is also an autonomous vehicle. The ego autonomous vehicle can also predict the other vehicle’s behavior, based on whether the other vehicle is an autonomous vehicle, which ensures a safer merge. One would have been motivated to combine Niendorf with Barrera as this improves safety and navigation efficiency. As stated in Barrera, “When a lane ends or is reduced, it can create dangerous situations for human drivers, as they may not be able to react quickly enough to avoid collisions. This is especially true in high-speed or high-traffic areas. Therefore, there is a need for a method and system for autonomous vehicles to navigate the roads safely and efficiently, reducing the risk of accidents and improving the overall experience for passengers and other drivers on the road” (Paragraph 0003).
Regarding claim 7, Niendorf discloses determining the planned driving path of the vehicle based on the vehicle location of the objective vehicle, and the current location and the objective driving location of the vehicle comprises (Paragraph 0057);
planning an initial driving path of the vehicle based on the current location and the objective driving location of the vehicle (Paragraph 0050);
adjusting the initial driving path in real time to obtain the planned driving path of the vehicle based on the vehicle location of the objective vehicle (Paragraphs 0056-0058).
Regarding claim 8, Niendorf discloses an electronic device, comprising (Paragraph 0059-0064);
at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to (Paragraph 0059-0064).
all other limitations have been examined with respect to the method in claim 1. The electronic device taught/disclosed in claim 8 can be clearly performed with the method of claim 1. Therefore, claim 8 is rejected under the same rationale.
Regarding claim 9, Niendorf discloses the at least one processor (Paragraph 0059-0064).
All other limitations have been examined with respect to the method in claim 2. The electronic device taught/disclosed in claim 9 can be clearly performed with the method of claim 2. Therefore, claim 9 is rejected under the same rationale.
Regarding claim 11, Niendorf discloses the at least one processor (Paragraph 0059-0064).
All other limitations have been examined with respect to the method in claim 4. The electronic device taught/disclosed in claim 11 can be clearly performed with the method of claim 4. Therefore, claim 11 is rejected under the same rationale.
Regarding claim 14, Niendorf discloses the at least one processor (Paragraph 0059-0064).
all other limitations have been examined with respect to the method in claim 7. The electronic device taught/disclosed in claim 14 can be clearly performed with the method of claim 7. Therefore, claim 14 is rejected under the same rationale.
Regarding claim 15, Niendorf discloses a non-transitory storage medium having stored thereon instructions that, when executed by a processor of an electronic device, causes the electronic device to perform a path planning method, the path planning method comprising (Paragraph 0059-0064).
all other limitations have been examined with respect to the method in claim 1. The non-transitory storage medium taught/disclosed in claim 15 can be clearly performed with the method of claim 1. Therefore, claim 15 is rejected under the same rationale.
Regarding claim 16, all limitations have been examined with respect to the method in claim 2. The non-transitory storage medium taught/disclosed in claim 16 can be clearly performed with the method of claim 2. Therefore, claim 16 is rejected under the same rationale.
Regarding claim 18, all limitations have been examined with respect to the method in claim 4. The non-transitory storage medium taught/disclosed in claim 18 can be clearly performed with the method of claim 4. Therefore, claim 18 is rejected under the same rationale.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Niendorf, Schlacter, Motoyama, Lingg, and Barrera, as applied to claims 1, 8, and 15 above, and further in view of Cui (US 20210389780 A1, cited in a previous office action).
Regarding claim 5, Niendorf discloses determining the objective vehicle according to the vehicle identification information comprises (Paragraph 0054-0055).
Niendorf does not specifically state marking a vehicle matching with the objective license plate information as the objective vehicle.
However, Cui teaches marking a vehicle matching with the objective license plate information as the objective vehicle (Paragraphs 0035, Claims 4-7, Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with marking a vehicle with the objective license plate information as the objective vehicle of Cui with a reasonable expectation of success. One of ordinary skill in the art would understand that marking nearby autonomous vehicles enhances a vehicle and driver’s understanding of their environment. Understanding nearby autonomous vehicles allows the ego vehicle to take proper evasive actions which improve road traffic safety. One would have been motivated to combine Niendorf with Cui as this achieves improved safety. As stated in Cui, “an augment reality display of the vehicles can be used to further facilitate communication between non-autonomous and autonomous vehicles. For example, a predicted direction or an imperative direction associated with an autonomous vehicle can be communicated to a non-autonomous vehicle such that an augmented reality display of the non-autonomous vehicle can alert the driver of the non-autonomous vehicle of a of such to increase safety measures” (Abstract).
Regarding claim 12, Niendorf discloses the at least one processor (Paragraph 0059-0064).
All other limitations have been examined with respect to the method in claim 5. The electronic device taught/disclosed in claim 12 can be clearly performed with the method of claim 5. Therefore, claim 12 is rejected under the same rationale.
Regarding claim 19, all limitations have been examined with respect to the method in claim 5. The non-transitory storage medium taught/disclosed in claim 19 can be clearly performed with the method of claim 5. Therefore, claim 19 is rejected under the same rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Niendorf, Schlacter, Motoyama, Lingg, and Barrera, as applied to claims 1, 8, and 15 above, and further in view of Sasaki (US 20190094881 A1, cited in a previous office action).
Regarding claim 6, Niendorf discloses after obtaining the vehicle location of the objective vehicle, the method further comprises (Paragraph 0054).
Niendorf does not specifically state storing the current location of the vehicle; a road date; the vehicle location of the objective vehicle to a database of the vehicle.
However, Sasaki teaches storing the current location of the vehicle (Paragraphs 0065-0066);
a road date (Paragraphs 0065-0066);
the vehicle location of the objective vehicle to a database of the vehicle (Paragraphs 0065-0066).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with storing the current location of the vehicle, road date, and the objective vehicle location to a database of the vehicle of Sasaki with a reasonable expectation of success. One of ordinary skill in the art would understand that a vehicle can store and process its current location, date, and vehicle location of the objective vehicle in order to perform navigation while avoiding collisions. The date allows the ego vehicle to determine traffic patterns, while understanding the current location and the location of the objective vehicle allows the ego vehicle to avoid colliding with the objective vehicle. One would have been motivated to combine Niendorf with Sasaki as this achieves successfully navigating the vehicle to a destination. As stated in Niendorf, “the autonomous vehicle 940 may be equipped with a processing unit (e.g., one or more CPUs and GPUs), memory, and storage. The vehicle 940 may thus be equipped to perform a variety of computational and processing tasks, including processing the sensor data, extracting useful information, and operating accordingly… the navigation system 946 may use its determinations to control the vehicle 940 to operate in prescribed manners and to guide the autonomous vehicle 940 to its destinations without colliding into other objects” (Paragraphs 0049-0050).
Regarding claim 13, Niendorf discloses after the at least one processor (Paragraph 0059-0064);
obtains the vehicle location of the objective vehicle (Paragraph 0054);
the at least one processor is further caused to (Paragraph 0059-0064).
Niendorf does not specifically state store the current location of the vehicle; the road data; the vehicle location of the objective vehicle to a database of the vehicle.
However, Sasaki teaches store the current location of the vehicle (Paragraphs 0065-0066);
the road data (Paragraphs 0062, 0065-0066; Road data is mapped to site information);
the vehicle location of the objective vehicle to a database of the vehicle (Paragraphs 0065-0066).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Niendorf with storing the current location of the vehicle, road data, and the objective vehicle location to a database of the vehicle of Sasaki with a reasonable expectation of success. One of ordinary skill in the art would understand that a vehicle can store and process its current location, road data, and vehicle location of the objective vehicle in order to perform navigation while avoiding collisions. One would have been motivated to combine Niendorf with Sasaki as this achieves successfully navigating the vehicle to a destination. As stated in Niendorf, “the autonomous vehicle 940 may be equipped with a processing unit (e.g., one or more CPUs and GPUs), memory, and storage. The vehicle 940 may thus be equipped to perform a variety of computational and processing tasks, including processing the sensor data, extracting useful information, and operating accordingly… the navigation system 946 may use its determinations to control the vehicle 940 to operate in prescribed manners and to guide the autonomous vehicle 940 to its destinations without colliding into other objects” (Paragraphs 0049-0050).
Regarding claim 20, all limitations have been examined with respect to the electronic device in claim 13. The non-transitory storage medium taught/disclosed in claim 20 can be clearly performed with the electronic device of claim 13. Therefore, claim 20 is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to Matthew Ho whose telephone number is (571) 272-1388. The examiner can
normally be reached on Mon-Thurs 9:00-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached on (571)-272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW HO/ Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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