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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: IMAGE PROCESSING METHOD AND APPARATUS, AND MODEL TRAINING METHOD AND APPARATUS COMPRISING OBTAINING A FORWARD VIEW OF A VEHICLE AND EXTRINSIC CAMERA PARAMETERS TO INPUT INTO A NEURAL NETWORK MODEL TO OBTAIN A SEMANTICALLY SEGMENTED TOP-DOWN VIEW.
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 limitation(s) is/are: transformer, semantic segmenter, obtaining unit, processing unit and output unit in claims 2, 5 and 14-17.
Because this/these claim limitation(s) is/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 § 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 18 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 the computer-readable medium can be considered as transitory or non-transitory. Since this can be considered as non-transitory, this claim limitation is considered as non-statutory.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6 and 14-17 is/are rejected under 35 U.S.C. 102(a1 and/or a2) as being anticipated by Zhou (Document titled: Cross-view for real-time Map-view Semantic Segmentation, Pub Date: 5/5/2022).
Re claim 1: (Original) Zhou discloses an image processing method, comprising:
obtaining a forward view of a vehicle, wherein the forward view at least shows an area in front of the vehicle (e.g. in the section 3 titled cross-view transformers on page 3, the camera views of the front of the vehicle are used as an input into an encoder-decoder framework.);
obtaining parameters of a camera used to capture the forward view, wherein the parameters include extrinsic parameters of the camera (e.g. in the same section on page 3, the system obtains both camera intrinsics and extrinsic rotations and translations that impact the camera monocular views, which is on page 3. On page 4, the different views of the front and back views are taken into consideration when determining positional embeddings, which is in the cross-view attention section.); and
inputting the forward view and the camera parameters into a neural network model to obtain a semantically segmented top-down view (e.g. an encoder decoder framework is used to take the different views and camera extrinsic parameters of the rotation and translations to output a semantic segmentation in the map-view, which is seen in figure 2 and explained in section 3 titled cross-view transformers on page 3.).
Re claim 2: (Original) Zhou discloses the image processing method according to claim 1, wherein the neural network model comprises a transformer and a semantic segmenter, the transformer (interpretation: The neural network model can be pre-stored in the image processing device. the neural network model comprises a transformer and a semantic segmenter, the transformer being configured to transform features in the forward view into features in the top-down view of the vehicle based on the camera parameters, which was taught on page 13. This term and its equivalents are utilized for this claim term hereinafter in the Office Action.) being configured to transform features in the forward view into features in the top-down view of the vehicle based on the camera parameters (e.g. the system discloses transforming the inputs of the forward and backward views into features of a top-down map using the camera extrinsic parameters, which is taught on page 3 in the section 3 titled Cross-view transformers.), and
the semantic segmenter (interpretation: The neural network model can be pre-stored in the image processing device. the neural network model comprises a transformer and a semantic segmenter, the semantic segmenter being configured to perform semantic segmentation on the target based on the features in the top-down view, which is taught on page 13. This term and its equivalents are utilized for this claim term hereinafter in the Office Action.) being configured to perform semantic segmentation on a target based on the features in the top-down view (e.g. the convolutional decoder is used to produce a segmentation output in order to create a semantic segmentation, which is taught on page 3 in the section 3 titled Cross-view transformers.).
Re claim 3: (Original) Zhou discloses the image processing method according to claim 1, further comprising outputting the semantically segmented top-down view of the vehicle (e.g. as seen in figure 2 and discussed in section 3 on page 3 titled Cross-view transformers, the system outputs a semantic segmentation in the map-view from the various camera views.).
Re claim 4: (Original) Zhou discloses the image processing method according to claim 1, wherein the camera parameters further comprise intrinsic parameters of the camera (e.g. the camera parameters input are also intrinsic parameters, which is taught in page 3, section 3 titled Cross-view transformers.).
Re claim 5: (Original) The image processing method according to claim 1, wherein:
the neural network model further comprises a multilayer perceptron configured to transform the camera parameters into a camera parameter vector (e.g. in the Camera-aware positional encoding section on pages 3 and 4, a multilayer perception (MLP) to transform camera extrinsic parameters into a vector.), and
the transformer is configured to transform features in the forward view into features in the top-down view of the vehicle based on the camera parameter vector (e.g. the neural network transforms the forward and back views into the top-down view using the camera extrinsic parameter vector, which is taught on page 4 in the Camera-aware positional embedding, map-view latent embedding and Cross-view attention sections.).
Re claim 6: (Original) Zhou discloses the image processing method according to claim 1, wherein the neural network model is trained as follows:
obtaining variation data of the extrinsic parameters of the camera (e.g. in the training, the system discloses acquiring the different camera extrinsic parameters, which is taught on page 3 in section titled Cross-view transformers.);
obtaining training forward views, training parameters of the camera corresponding to the training forward views (e.g. the system contains the views monocular views as well as camera intrinsic parameters associated with the monocular views, which is taught on page 3 in section titled Cross-view transformers), and semantically segmented top-down training ground truth views of the target corresponding to the training forward views (e.g. the system builds up multiple semantically segmented top-down views over various iterations of the transformer for the different views and camera parameters, which is explained in Sections Map-view latent embedding, Cross-view attention and Section 3.2 titled A cross-view transformer architecture on page 4.);
adjusting the training forward views, the training parameters of the camera, and the training ground truth views based on the variation data (e.g. the system acquires various views from the front views as well as camera parameters associated with the views and ground truth views based on the frontal views and camera parameters, which is taught in Sections Map-view latent embedding, Cross-view attention and Section 3.2 titled A cross-view transformer architecture on page 4. Also see figure 5.); and
training the neural network model using the adjusted training forward views, the training parameters of the camera, and the training ground truth views (e.g. the neural network is refined using the training views, camera parameters with the ground truth views that create a trained neural network, which is taught in Sections Map-view latent embedding, Cross-view attention and Section 3.2 titled A cross-view transformer architecture on page 4.).
14. (Original) An image processing apparatus, comprising:
an obtaining unit (interpretation: The obtaining unit 21 obtains training forward views, training parameters of the camera corresponding to the training forward views, and semantically segmented top-down training ground truth views of the target corresponding to the training forward views, which is taught on page 13. This interpretation and its equivalents are utilized for this claim term hereinafter in the Office Action.) configured to obtain a forward view of a vehicle and parameters of a camera used to capture the forward view, wherein the forward view at least shows an area in front of the vehicle (e.g. in the section 3 titled cross-view transformers on page 3, the camera views of the front of the vehicle are used as an input into an encoder-decoder framework with camera parameters.), and
the parameters include extrinsic parameters of the camera (e.g. in the same section on page 3, the system obtains both camera intrinsics and extrinsic rotations and translations that impact the camera monocular views, which is on page 3. On page 4, the different views of the front and back views are taken into consideration when determining positional embeddings, which is in the cross-view attention section.); and
a processing unit (interpretation: The processing unit 12 inputs the forward view and the camera's parameters into a neural network model to obtain a semantically segmented top-down view, which is taught on page 13. This interpretation and its equivalents are utilized for this claim term hereinafter in the Office Action.) configured to input the forward view and the camera parameters into a neural network model to obtain a semantically segmented top-down view (e.g. an encoder decoder framework is used to take the different views and camera extrinsic parameters of the rotation and translations to output a semantic segmentation in the map-view, which is seen in figure 2 and explained in section 3 titled cross-view transformers on page 3.).
Re claim 15: (Original) Zhou discloses the image processing apparatus according to claim 14, wherein:
the neural network model comprises a transformer and a semantic segmenter, the transformer (interpretation: The neural network model can be pre-stored in the image processing device. the neural network model comprises a transformer and a semantic segmenter, the transformer being configured to transform features in the forward view into features in the top-down view of the vehicle based on the camera parameters, which was taught on page 13. This term and its equivalents are utilized for this claim term hereinafter in the Office Action.) being configured to transform features in the forward view into features in the top- down view of the vehicle based on the camera parameters (e.g. the system discloses transforming the inputs of the forward and backward views into features of a top-down map using the camera extrinsic parameters, which is taught on page 3 in the section 3 titled Cross-view transformers.), and
the semantic segmenter (interpretation: The neural network model can be pre-stored in the image processing device. the neural network model comprises a transformer and a semantic segmenter, the semantic segmenter being configured to perform semantic segmentation on the target based on the features in the top-down view, which is taught on page 13. This term and its equivalents are utilized for this claim term hereinafter in the Office Action.) being configured to perform semantic segmentation on the target based on the features in the top-down view (e.g. the convolutional decoder is used to produce a segmentation output in order to create a semantic segmentation, which is taught on page 3 in the section 3 titled Cross-view transformers.).
Re claim 16: (Original) Zhou discloses the image processing apparatus according to claim 14, further comprising an output unit (interpretation: The output unit 13 outputs the semantically segmented top-down view of the vehicle, which is taught on page 13. This phrase and its equivalents are utilized for this claim term hereinafter in the Office Action.) configured to output the semantically segmented top-down view of the vehicle (e.g. as seen in figure 2 and discussed in section 3 on page 3 titled Cross-view transformers, the system outputs a semantic segmentation in the map-view from the various camera views.).
Re claim 17: (Original) Zhou discloses the image processing apparatus according to Claim 14, wherein the neural network model further comprises a multilayer perceptron configured to transform the camera parameters into a camera parameter vector (e.g. in the Camera-aware positional encoding section on pages 3 and 4, a multilayer perception (MLP) to transform camera extrinsic parameters into a vector.),
wherein the transformer is configured to transform features in the forward view into features in the top-down view of the vehicle based on the camera parameter vector (e.g. the neural network transforms the forward and back views into the top-down view using the camera extrinsic parameter vector, which is taught on page 4 in the Camera-aware positional embedding, map-view latent embedding and Cross-view attention sections.).
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Goel (US Pub 2023/0029900).
Re claim 18: (Original) However, Zhou fails to specifically teach the features of a computer-readable medium storing computer program units, wherein the computer program units, when executed by a processor or computer, cause the processor or computer to execute the image processing method according to claim 1.
However, this is well known in the art as evidenced by Goel. Similar to the primary reference, Goel discloses input of front views to output top-down views (same field of endeavor or reasonably pertinent to the problem).
Goel discloses a computer-readable medium storing computer program units, wherein the computer program units, when executed by a processor or computer, cause the processor or computer to execute the image processing method according to claim 1 (e.g. the system discloses computing devices that utilize a memory and processors storing models to perform the feature of input image data to create a top-down view, which is taught in ¶ [82]-[84].).
[0082] The computing device(s) 1038 can include processor(s) 1040 and a memory 1042 storing one or more estimated depth data models 1044 and/or object detection models 1046. As described above, the estimated depth data models 1044 may include one or more trained neural networks, machine-learned models, and/or other heuristics-based algorithms configured to determine estimated depth data for individual regions (e.g., pixels) in an image. The object detection models 1046 may include one or more trained neural networks, machine-learned models, and/or other heuristics-based algorithms configured to perform object detection and object categorization/classification (e.g., instance segmentation and/or semantic segmentation), based on 2D and/or 3D data representing individual regions of interest or an environment as a whole. In various examples, the computing devices 1038 may implement one or more machine learning systems or heuristics-based systems to train, test, and optimize the estimated depth data models 1044 and/or object detection models 1046, based on log data received from vehicle 1002 and/or additional vehicles operating within environments. Additionally, any of the features or functionalities described in connection with the image-based object detector 1024 (e.g., generating point clouds based on 2D image data and associated depth data, generating 3D grid representations using predetermined depth quanta, performing object detection, etc.) also may be performed by computing devices 1038 using heuristics-based techniques and/or neural network models and algorithms. In this example, neural networks are algorithms which pass input data through a series of connected layers to produce an output. Each layer in a neural network can also comprise another neural network, or can comprise any number of layers (whether convolutional or not). As can be understood in the context of this disclosure, a neural network can utilize machine learning, which can refer to a broad class of such algorithms in which an output is generated based on learned parameters. Any type of machine learning can be used consistent with this disclosure.
[0083] The processor(s) 1016 of the vehicle 1002 and the processor(s) 1040 of the computing device(s) 1038 can be any suitable processor capable of executing instructions to process data and perform operations as described herein. By way of example and not limitation, the processor(s) 1016 and 1040 can comprise one or more Central Processing Units (CPUs), Graphics Processing Units (GPUs), or any other device or portion of a device that processes electronic data to transform that electronic data into other electronic data that can be stored in registers and/or memory. In some examples, integrated circuits (e.g., ASICs, etc.), gate arrays (e.g., FPGAs, etc.), and other hardware devices can also be considered processors in so far as they are configured to implement encoded instructions.
[0084] Memory 1018 and 1042 are examples of non-transitory computer-readable media. The memory 1018 and 1042 can store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems. In various implementations, the memory can be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory capable of storing information. The architectures, systems, and individual elements described herein can include many other logical, programmatic, and physical components, of which those shown in the accompanying figures are merely examples that are related to the discussion herein.
Therefore, in view of Goel, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of a computer-readable medium storing computer program units, wherein the computer program units, when executed by a processor or computer, cause the processor or computer to execute the image processing method according to claim 1, incorporated in the device of Zhou, in order to use 2D image inputs to create 3D and top-down outputs, which can improve efficiency and quality of image-based detection (as stated in Goel ¶ [15]).
Allowable Subject Matter
Claim 7 is 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.
The following is a statement of reasons for the indication of allowable subject matter: The underlined features of claim 7 were not found.
Re claim 7: (Original) The image processing method according to claim 6, wherein the variation data of the extrinsic parameters of the camera is obtained as follows:
obtaining vibration distribution data by fitting distribution to vibration data representing vibrations of the camera;
selecting specific vibration data of the camera based on the vibration distribution data; and
determining the variation data of the extrinsic parameters of the camera based on the specific vibration data of the camera.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ko discloses turning 2D inputs into 3D output.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD S DICKERSON whose telephone number is (571)270-1351. The examiner can normally be reached Monday-Friday 10AM-6PM EST..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abderrahim Merouan can be reached at 571-270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD DICKERSON/ Primary Examiner, Art Unit 2683
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