DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement filed 8/20/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. The missing document, “CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion” seems to have been mistaken for the submitted NPL document of “CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow”. Neither document has been considered. Further, the document, “Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers” does not appear on the IDS and was similarly submitted.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 65, “the those patches” should be “the patches” or “those patches”.
Paragraph 67, “(self attention)” should be “(self-attention)”.
Paragraph 73, “it’s” should be “its”
Paragraph 82, this paragraph states, “may perform self-attention on both images separately in a siamese manner”. Now, Siamese capitalized can mean, “exhibiting great resemblance or being very like each other”. If that is the intended meaning, “Siamese” in this incidence needs to be capitalized, and please provide a note on if that is the intended meaning for clarity of the record.
Appropriate correction is required.
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: “a transformer module having the transformer architecture and configured to perform a vision task”; “a training module configured to: receive a training image”; “a training module configured to: … determine N windows of tokens”; “a training module configured to: … input the N windows of tokens to the transformer module”; “a training module configured to: … train the transformer module”; “a training module configured to: … test the transformer module using a test image”; “the training module is configured to determine locations”; “wherein the training module is further configured to: receive a second training image”; “wherein the training module is further configured to: … determine N second windows of tokens of pixels”; “wherein the training module is further configured to: … input the second N windows of tokens to the transformer module”; “the training module is configured to input the N windows to the transformer module with positional embeddings”; “and a training module configured to: receive first and second training images having a predetermined resolution”; “and a training module configured to: … determine M windows of tokens of pixels”; “and a training module configured to: … input the N windows and the M windows to the transformer module”; “wherein the training module is configured to determine locations for the M windows based on locations of the N windows”; “wherein the training module is configured to select the second locations of the M second windows”; and “wherein the training module is configured to displace the second locations of the M second windows” in claims 1, 9-10, 12, 15, 17, 20-22, and 25.
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 § 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 8, 19, and 22-23 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.
The term “wherein a first total number of pixels within the N windows is 5-50 percent of a second total number of pixels of the training image” in claim 8 is a relative term which renders the claim indefinite. The term “wherein a first total number of pixels within the N windows is 5-50 percent of a second total number of pixels of the training image” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The phrasing of the expression makes it unclear as to what percentages are truly included. Does this phrasing include both five percent and fifty percent with all of the percentages in between or does it merely include all the percentages between five percent and fifty percent? Secondly, the total number of pixels within the N windows would this count pixels in overlapping windows twice, would it count them as part of only one window, or would this solely count pixels that were within the overlapping portions of the windows? Ordinary people of skill in the art could reasonably disagree on what the scope and meaning of the claim may be, so the claim is considered indefinite.
The term “approximately the same time” in claim 19 is a relative term which renders the claim indefinite. The term “approximately the same time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The phrasing of “approximately the same time” is indefinite as it would be relative to the person of ordinary skill in the art. One person of ordinary skill in the art may find the time of 10 seconds to be approximately the same time, another may find the time of five minutes, and yet another may find the time of thirty minutes to be “approximately the same time”. Similarly, the scale of time has to be considered. Five years is a long time for the average person to wait in between shots of a photo, but it would be “approximately the same time” when considered from a broader perspective such as when compared to the history of all life on Earth. So, the phrasing of “approximately the same time” would be considered indefinite to a person of ordinary skill in the art.
Claim 22 recites the limitation "displace the second locations of the M second windows" in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim 15 does not establish that any displacement occurs in the process. As such, it lacks antecedent basis.
Claim 23 recites the limitation "wherein M is greater than N " in line 1. There is insufficient antecedent basis for this limitation in the claim. Claim 15 does not establish that M is greater than N, but claim 16 does establish that. As this further modifies claim 16, it should be dependent upon that claim, and it otherwise lacks antecedent basis.
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.
Claims 1-2, 4-7, 9-11, 14-15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gani et al. (US 20240212330 A1).
Claims 1-2, 4-7, 9-11, 14-15, 17, and 24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gani et al. (US 20240212330 A1), hereinafter referred to as Gani.
In regards to claim 1, Gani discloses a training system, comprising: a transformer module having the transformer architecture and configured to perform a vision task (Abstract, Discloses that it uses a vision transformer that performs vision tasks); and a training module configured to: receive a training image having a predetermined resolution (Abstract and paragraph 35, The abstract discloses that it receives images from a training dataset with paragraph 35 disclosing that these training images are 64x64 in size which is a predetermined resolution); determine N windows of tokens of pixels in the training image and mask the tokens of all of the other pixels of the training image that are outside of the N windows (Paragraphs 58-60 and Figure 7, Discloses that the image is separated into windows and that all of the other pixels outside of those windows are masked), where N is an integer greater than or equal to 2 (Paragraphs 58-60, Discloses multiple windows which would mean that they are greater than or equal to two); input the N windows of tokens to the transformer module (Paragraph 50, Discloses that the tokenized inputs are fed into the transformer); train the transformer module based on an output of the transformer module generated based on the N windows of tokens (Abstract, Discloses that the transformer is trained via this method); and test the transformer module using a test image having the predetermined resolution (Paragraph 85, Discloses that the images in the corresponding figures were used to test the system after training).
In regards to claim 2, Gani discloses wherein the N windows are each a rectangle of pixels (Paragraph 59, Describes that the windows can be of size 4x4 which would be equal to a square and all squares are rectangles).
In regards to claim 4, Gani discloses wherein the N windows are each a square of pixels (Paragraph 59, Describes that the windows can be of size 4x4 which would be equal to a square).
In regards to claim 5, Gani discloses wherein the N windows are each the same size (Paragraph 59, Describes that the windows can be of size 4x4 which would be equal to a square which would all be the same size).
In regards to claim 6, Gani discloses wherein the N windows do not overlap (Paragraphs 14-15, Discloses that the image patches do not overlap).
In regards to claim 7, Gani discloses wherein at least a part of a first edge of a first one of the N windows abuts at least a part of a second edge of a second one of the N windows (Figure 7 and paragraph 61, The figure shows the various patches as referenced by paragraph 61 where several of the square images share an edge or it abuts at least part of an edge with one of the other windows).
In regards to claim 9, Gani discloses wherein the training module is configured to determine locations for the N windows randomly (Abstract, Discloses that the sections are selected randomly).
In regards to claim 10, Gani discloses wherein the training module is further configured to: receive a second training image having the predetermined resolution (Abstract and paragraph 35, The abstract discloses that it receives images from a training dataset with paragraph 35 disclosing that these training images are 64x64 in size which is a predetermined resolution); determine N second windows of tokens of pixels in the second training image and mask the tokens of all of the other pixels of the second training image that are outside of the N second windows (Paragraphs 58-60 and Figure 7, Discloses that the image is separated into windows and that all of the other pixels outside of those windows are masked); input the second N windows of tokens to the transformer module (Paragraph 50, Discloses that the tokenized inputs are fed into the transformer); and train the transformer module further based on a second output of the transformer module generated based on the N second windows of tokens (Abstract, Discloses that the transformer is trained via this method).
In regards to claim 11, Gani discloses wherein first locations of the N windows are different than second locations of the N second windows (Paragraph 59, Discloses that the windows shift position in between the layers).
In regards to claim 14, Gani discloses wherein the vision task is one of a monocular vision task and a multiple-view vision task (Paragraphs 26-27 and figures 9A - 10D, Paragraphs 26-27 disclose the types of images used in this process, and figures 9A-10D depict these images which are all clearly monocular as being from a single camera’s viewpoint).
In regards to claim 15, Gani discloses a training system, comprising: a transformer module having the transformer architecture and configured to perform a vision task (Abstract, Discloses that it uses a vision transformer that performs vision tasks); and a training module configured to: receive first and second training images having a predetermined resolution (Abstract and paragraph 35, The abstract discloses that it receives images from a training dataset with paragraph 35 disclosing that these training images are 64x64 in size which is a predetermined resolution); determine N windows of tokens of pixels in the first training image and mask tokens of all of the other pixels of the first training image that are outside of the N windows (Paragraphs 58-60 and Figure 7, Discloses that the image is separated into windows and that all of the other pixels outside of those windows are masked), where N is an integer greater than or equal to 2 (Paragraphs 58-60, Discloses multiple windows which would mean that they are greater than or equal to two); determine M windows of tokens of pixels in the second training image and mask tokens of all of the other pixels of the second training image that are outside of the M windows (Abstract, Paragraphs 58-60 and Figure 7, Discloses that the image is separated into windows and that all of the other pixels outside of those windows are masked and as Gani works with large datasets in the abstract, they perform the same method on multiple images), where M is an integer greater than or equal to 2 (Paragraphs 58-60, Discloses multiple windows which would mean that they are greater than or equal to two); input the N windows and the M windows to the transformer module (Paragraph 50, Discloses that the tokenized inputs are fed into the transformer); train the transformer module based on an output of the transformer module generated based on the N windows and the M windows (Abstract, Discloses that the transformer is trained via this method); and test the transformer module using a pair of test images having the predetermined resolution (Paragraph 85, Discloses that the images in the corresponding figures were used to test the system after training).
In regards to claim 17, Gani discloses wherein the training module is configured to determine locations for the M windows based on locations of the N windows (Paragraph 59, Discloses that the windows shift position in between the layers).
In regards to claim 24, Gani discloses wherein the N windows are configured to reduce an effective overall resolution of the training image during training without compromising actual resolution of the training image (Paragraph 57, Discloses that during the process the tokens representing the pixels get merged which reduces the overall effective resolution of the image without actually changing the image’s resolution).
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 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3, 8, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gani et al. (US 20240212330 A1), hereinafter referred to as Gani, in view of Roy et al. (US 20240362885 A1), hereinafter referred to as Roy.
In regards to claim 3, Gani does not explicitly disclose wherein a first one of the N windows is oriented in a landscape orientation and a second one of the N windows is oriented in a portrait orientation.
However, Roy does disclose wherein a first one of the N windows is oriented in a landscape orientation and a second one of the N windows is oriented in a portrait orientation (Figs. 3 and 11 and paragraphs 12 and 20, The figures depict particularly in Figure 3 that the various items are in a portrait mode while figure 11 has some depicted in a landscape mode).
It would be prima facie obvious to combine the teachings of Gani and Roy as it would be obvious to try. Gani uses squares which are a form of rectangle. A rectangle can be presented on a 2d image at a variety of angles in a full 360 degree rotation around the center of rotation. Landscape orientation and portrait orientation would just be a subset of those angles. As there are only so many ways one can rotate a 2d shape on a 2d image, this would be obvious to try.
In regards to claim 8, Gani does not disclose wherein a first total number of pixels within the N windows is 5-50 percent of a second total number of pixels of the training image.
Roy does disclose wherein a first total number of pixels within the N windows is 5-50 percent of a second total number of pixels of the training image (Figure 12 and paragraph 21, Roy discloses this as indicative of an obtained region and this region includes more than five percent of pixels in the image but less than fifty which would cover the claimed amount).
It would be prima facie obvious to combine these references as focusing on specific aspects in Roy would lead to a predictable increase in accuracy for target identification. If the system focus on only one aspect of an image that is important, then it allows the system to ignore superfluous or unnecessary information. As such, it would be prima facie obvious to combine these two arts.
In regards to claim 16, Gani does not explicitly disclose wherein M is greater than N.
Roy does disclose wherein M is greater than N (Paragraphs 67-68, Discloses values for the number of windows in paragraph 67 with m and n being greater than 1 where n is seemingly greater than n, but as these are generic placeholder values, the two can be just as easily set differently where one is greater than the other).
It would be prima facie obvious to combine these two arts as it would be obvious to try. This claim only requires that M is greater than N. From the prior claim, N and M could both be 2, and thus, they could both be equal or less than one another. So, M could be less than N, and N could be less than M. As such, there are only a few options for the relationship with M and N as numbers such as M being less than N; M being equal to N; or M being greater than N. As there are only so many permutations possible, it would be obvious to try and be prima facie obvious.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Gani et al. (US 20240212330 A1), hereinafter referred to as Gani, in view of Yuan et al. (US 12518512 B2), hereinafter referred to as Yuan.
Gani does not explicitly disclose wherein the training module is configured to input the N windows to the transformer module with positional embeddings.
Yuan does disclose wherein the training module is configured to input the N windows to the transformer module with positional embeddings (Column 9 Lines 1-5, Describes that it uses positional embeddings).
It would be prima facie obvious to combine these two arts as the inclusion of positional embeddings would increase the accuracy of the window placement. The inclusion of positional embeddings allows for the tokens to be more accurately placed to correspond with their respective position. This allows for an increased ability to handle sequential data as such, this would allow for the various tokens to be more properly handle in their sequential positions.
In regards to claim 13, Gani does not explicitly disclose wherein the positional embeddings are relative positional embeddings.
Yuan does disclose wherein the positional embeddings are relative positional embeddings (Column 9 Lines 1-5, Describes that it uses positional embeddings, more specifically, relative positional embeddings).
Claims 18-19 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Gani et al. (US 20240212330 A1), hereinafter referred to as Gani, in view of Xu et al. (US 20250217446 A1), hereinafter referred to as Xu.
In regards to claim 18, Gani does not explicitly disclose wherein the first and second training images each include at least a portion of a same item.
Xu does disclose wherein the first and second training images each include at least a portion of a same item (Figure 1 and paragraphs 61-62, Figure 1 shows the training data comprising of the same box object at different angles which shows that the training data can depict the same object with the associated paragraphs calling it exemplary training data).
It would be prima facie obvious to combine these two arts as repeated training on the same item would lead to a predictable increase in accuracy for identification. Repeatedly exposing the machine and training it on specific objects will allow the system to more accurately identify those objects at a variety of angles when tested out in the future. As such, it would be prima facie obvious to combine these two arts.
In regards to claim 19, Gani does not explicitly disclose wherein the first and second training images are one of: captured by first and second cameras, respectively, at approximately the same time; and two frames of video captured by one camera at different times.
However, Yuan does disclose wherein the first and second training images are one of: captured by first and second cameras, respectively, at approximately the same time; and two frames of video captured by one camera at different times (Paragraph 63, This paragraph of Xu discloses that video can be used in the training data, and as such two frames of video captured by the same camera at different times is included implicitly).
In regards to claim 25, Gani discloses a semantic segmentation module configured to segment objects in the images recorded by the camera (Paragraph 38, Discloses that the images are segmented semantically); wherein the semantic segmentation module includes a transformer module having the transformer architecture and configured to perform a vision task (Abstract, Discloses that it uses a vision transformer that performs vision tasks); and wherein the transformer module is trained by a training module configured to: receive a training image having a predetermined resolution (Abstract and paragraph 35, The abstract discloses that it receives images from a training dataset with paragraph 35 disclosing that these training images are 64x64 in size which is a predetermined resolution); determine N windows of tokens of pixels in the training image and mask the tokens of all of the other pixels of the training image that are outside of the N windows (Paragraphs 58-60 and Figure 7, Discloses that the image is separated into windows and that all of the other pixels outside of those windows are masked), where N is an integer greater than or equal to 2 (Paragraphs 58-60, Discloses multiple windows which would mean that they are greater than or equal to two); input the N windows of tokens to the transformer module; train the transformer module based on an output of the transformer module generated based on the N windows of tokens (Paragraph 50, Discloses that the tokenized inputs are fed into the transformer); and test the transformer module using a test image having the predetermined resolution (Abstract, Discloses that the transformer is trained via this method).
Gani does not explicitly disclose a system, comprising: a camera configured to record images; at least one of (a) a propulsion device configured to move an object and (b) an actuator configured to actuate an object; and a control module configured to, based on one or more of images recorded by the camera including the objects segmented in the one or more images segmented by the semantic segmentation module, control the at least one of the (a) propulsion device and (b) the actuator.
However, Xu does disclose a system, comprising: a camera configured to record images (Paragraph 123, Xu discloses that they use cameras made to record things as part of their sensor array on the controlled vehicle); at least one of (a) a propulsion device configured to move an object and (b) an actuator configured to actuate an object (Paragraphs 121-122, Xu discloses the use of actuator and to control a vehicle into a desired path where the vehicle would include a propulsion device); and a control module configured to, based on one or more of images recorded by the camera including the objects segmented in the one or more images segmented by the semantic segmentation module, control the at least one of the (a) propulsion device and (b) the actuator (Paragraphs 121-122, Xu discloses the use of actuator and to control a vehicle into a desired path where the vehicle would include a propulsion device).
It would be prima facie obvious to combine the teachings of Xu and Gani as it would be combining prior art methods to predictable results. Xu details the use of a neural network system to be utilized in the direction and control of a car via actuator, cameras, and propulsion devices. Gani discloses a separate ViT system that follows the claimed procedure. As such, the claim acts as combining prior art elements to predictable results. Further, it also acts as simply substituting the image system of Xu with the image system of Gani.
Claims 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Gani et al. (US 20240212330 A1), hereinafter referred to as Gani, in view of Dekel et al. (US 20240419382 A1), hereinafter referred to as Dekel.
In regards to claim 20, Gani does not explicitly disclose wherein the training module is configured to select the second locations of the M second windows based on noisy optical flow.
However, Dekel does disclose wherein the training module is configured to select the second locations of the M second windows based on noisy optical flow (Paragraph 99, 344, and 399, Discloses the use of a greedy algorithm which deals with noisy optical flow in positioning in a ViT system).
It would be prima facie obvious to combine Dekel and Gani as it would be using known techniques for a predictable outcome. Dekel details the usage of a greedy algorithm in a ViT system. As such, one simply would have to combine this known technique with the method presented in Gani which would lead to predictable results, and it would further lead to a predictable increase in accuracy as the utilization of a greedy algorithm will allow for better handling of noisy optical flow. As such, it is prima facie obvious.
In regards to claim 21, Dekel discloses wherein the training module is configured to select the second locations of the M second windows using a greedy algorithm (Paragraph 99, 344, and 399, Discloses the use of a greedy algorithm which deals with noisy optical flow in positioning in a ViT system).
In regards to claim 22, Dekel discloses wherein the training module is configured to displace the second locations of the M second windows in the second training image based on a noisy flow value (Paragraph 99, 344, and 399, Discloses the use of a greedy algorithm which deals with noisy optical flow in positioning in a ViT system).
Allowable Subject Matter
Claim 23 is not rejected under 35 U.S.C. 102 or 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu et al. (US 20240289550 A1) is a decent reference that is more focused on the tokenization of words and sentences although it briefly covers applying the same concepts to images and image processing.
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CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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