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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 3/7/24. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were received on 5/15/24. These drawings are Figures 1A-1E and 4A-4F.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the abstract is copy of the Claim 1. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 13 is objected to because of the following informalities: Insert comma after “a processor” in line 3. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it is directed to products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") claimed as a product without any structural recitations; and because a computer readable storage medium can be comprised of transitory matter (ie. Carrier waves).
Non-limiting examples of claims that are not directed to any of the statutory categories include:
• Products that do not have a physical or tangible form, such as information (often referred to as “data per se”) or a computer program per se (often referred to as “software per se”) when claimed as a product without any structural recitations;
• Transitory forms of signal transmission (often referred to as “signals per se”), such as a propagating electrical or electromagnetic signal or carrier wave; and
• Subject matter that the statute expressly prohibits from being patented, such as humans per se, which are excluded under The Leahy-Smith America Invents Act (AIA ), Public Law 112-29, sec. 33, 125 Stat. 284 (September 16, 2011).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 7-10, 12-13 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Rotker et al (US 20220366176 A1).
Regarding Claim 1, Rotker et al teaches a method for generating a representation of the surroundings, comprising the following steps: providing at least one image that results from a recording by an image detection device (see Paragraph [0030] … FIG. 1 is a block diagram of a vehicle 100 that obtains a sensor fusion-based top-view 3D stixel representation 150 for general obstacle detection. The exemplary vehicle 100 shown in FIG. 1 is an automobile 101. The vehicle 100 is shown to include a camera 110, a lidar system 120, and a radar system 130….”) and that represents objects and/or surfaces in the surroundings of the image detection device wherein the provided image is subdivided into multiple image columns (Fig. 1), and
generating the representation of the surroundings, wherein for this purpose multiple three-dimensional stixels (3D stixel representation 150 in Fig. 1) for each image column of the provided image are parameterized for representing the objects and/or surfaces in three-dimensional space, characterized in that the generation of the representation of the surroundings takes place using a model which uses the provided image as input. (See Figs. 1-2; Paragraph [0031] According to one or more embodiments, the controller 140 obtains a sensor fusion-based top-view 3D stixel representation 150 to perform general obstacle detection. Aspects of an exemplary top-view 3D stixel representation 150 are shown for explanatory purposes. The exemplary top-view 3D stixel representation 150 facilitates detection of objects 170a, 170b, and 170c (generally referred to as 170) as detailed herein. In FIG. 1, some of the stixels 155 that make up the top-view stixel representation 150 are indicated. As detailed, the top-view 3D stixel representation 150 is in the polar coordinate system. As previously noted, stixel representation refers to information being encoded in each stixel 155. For example, distance D to and height of an object 170 (a closest point of the object 170 from the vehicle 100) encountered by the stixel 155 may be attributes that are encoded. The length of an exemplary stixel 155 to the exemplary object 170b is indicated. As indicated, the length of the stixel 155 from the center of the polar coordinate system (e.g., center of the front grill of the vehicle 100) indicates the distance D, which is the range to the object 170 from the vehicle 100. Each angular slice 160 of the top-view stixel representation 150 is indicated. Each angular slice 160 is associated with a vector of information based on the stixels 155 that make up the angular slice 160.).
Regarding Claim 2, Rotker et al teaches the method characterized in that the model is designed as an end-to-end machine learning model, preferably as a neural network, preferably as a convolutional neural network (See Paragraph [0037] for “Then, at block 250, general obstacle detection is performed using a neural network acting on the top-view 3D stixel representation 150 obtained at block 240. A deep convolutional neural network may be used to identify the positions of objects 170, the range to each object 170, the height of each object 170, and other attributes (e.g., type, color, appearance) based on the stixels 155.…”); See Paragraph 0037; Fig. 2).
Regarding Claims 4, 9, Rotker et al teaches the method characterized in that the parameterization of the stixels takes place by defining the particular stixel by a bottom point and a top point, to which a piece of depth information concerning a distance of the object represented by the stixel and/or of the surface represented by the stixel is assigned in each case. (See Paragraph [0031] “…The exemplary top-view 3D stixel representation 150 facilitates detection of objects 170a, 170b, and 170c (generally referred to as 170) as detailed herein. In FIG. 1, some of the stixels 155 that make up the top-view stixel representation 150 are indicated. As detailed, the top-view 3D stixel representation 150 is in the polar coordinate system. As previously noted, stixel representation refers to information being encoded in each stixel 155. For example, distance D to and height of an object 170 (a closest point of the object 170 from the vehicle 100) encountered by the stixel 155 may be attributes that are encoded. The length of an exemplary stixel 155 to the exemplary object 170b is indicated. As indicated, the length of the stixel 155 from the center of the polar coordinate system (e.g., center of the front grill of the vehicle 100) indicates the distance D, which is the range to the object 170 from the vehicle 100. Each angular slice 160 of the top-view stixel representation 150 is indicated. Each angular slice 160 is associated with a vector of information based on the stixels 155 that make up the angular slice 160.”).
Regarding Claim 5, Rotker et al teaches the method characterized in that the steps are carried out for further provided images that result from a recording of further regions of the surroundings by further image detection devices in order to expand the representation of the surroundings to the further regions. (See Paragraph [0032] “….. As also detailed, another feature of the top-view 3D stixel representation 150 is fusion of the data provided by two or more sensors. For example, the camera 110 provides relatively high spatial resolution but poor depth perception as compared with a lidar system 120 or a radar system 130, but the lidar system 120 or radar system 130 provide three-dimensional accurate measurements. The fusion of the camera 110 with the lidar system 120 or radar system 130, for example, provides the spatial resolution of the camera 110 with the accuracy and additional information (e.g., height of and range to an object 170) from the lidar system 120 or the radar system 130.”).
Regarding Claim 7, Rotker et al teaches the method characterized in that based on an at least semiautomated evaluation of the generated representation of the surroundings, an at least semiautonomous robot, in particular a vehicle, is controlled, preferably in an at least semiautomated manner and preferably autonomously, the image detection device preferably being designed as a camera (The vehicle 100 is shown to include a camera 110 in Fig.1) (See Fig.1; Paragraph [0028] As previously noted, sensors provide information about the environment around a vehicle and facilitate autonomous or semi-autonomous operation of the vehicle. The information may alternately or additionally be used to provide warnings to a driver. “).
Regarding Claim 8, Rotker et al teaches a method for training a machine learning model for generating a representation of the surroundings, comprising the following steps: providing an image which represents objects and/or surfaces in the surroundings, carrying out the training of the machine learning model, in which the provided image is used as input for the machine learning model in order to train the machine learning model for an output of multiple three-dimensional stixels (3D stixel representation 150 in Fig. 1) for each image column of the provided image, the stixels representing the objects and/or surfaces in three-dimensional space, wherein the machine learning model is trained end-to-end (See Paragraph [0037] for “Then, at block 250, general obstacle detection is performed using a neural network acting on the top-view 3D stixel representation 150 obtained at block 240. A deep convolutional neural network may be used to identify the positions of objects 170, the range to each object 170, the height of each object 170, and other attributes (e.g., type, color, appearance) based on the stixels 155.…”); See Paragraph 0037; Fig. 2).
Regarding Claim 10, Rotker et al teaches the method characterized in that the trained machine learning model for generating the representation of the surroundings is used as the model. (See Fig. 2; Paragraph [0037] for “Then, at block 250, general obstacle detection is performed using a neural network acting on the top-view 3D stixel representation 150 obtained at block 240. A deep convolutional neural network may be used to identify the positions of objects 170, the range to each object 170, the height of each object 170, and other attributes (e.g., type, color, appearance) based on the stixels 155.…”).
Regarding Claim 12, the apparatus Claim 12 is rejected for same reason as the method Claim 1, since claim limitations are same in both claims.
Regarding Claim 13, the apparatus Claim 13 is rejected for same reason as the method Claim 1, since claim limitations are same in both claims.
Allowable Subject Matter
Claim 3, 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY SHANKAR whose telephone number is (571)272-7682. The examiner can normally be reached M-F 9 am- 6 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Eason can be reached at 571-270-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VIJAY SHANKAR
Primary Examiner
Art Unit 2624
/VIJAY SHANKAR/Primary Examiner, Art Unit 2624