DETAIL OFFICE ACTIONS
The United States Patent & Trademark Office appreciates the response filed for the current application that is submitted on 5/18/2026. The United States Patent & Trademark Office reviewed the following documents submitted and has made the following comments below.
Applicant Arguments:
In regards to Argument 1, Applicant/s state/s Pronovost is not prior art since it was filed on June 18, 2024, therefore the rejection of 35 U.S.C. 103 should be removed.
Examiner’s Responses:
In response to Argument 1, Applicant’s arguments, see Remarks, filed 5/18/2026, with respect to the rejection(s) of claim(s) 1-11, 13-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhang et al (U.S. Patent Pub. No. 2021/0248181, hereafter referred to as Zhang) in view of Zhang et al (U.S. Patent Pub. No. 2023/0118864, hereafter referred to as Zhang2).
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.
Claims 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When reviewing independent claim 6 and 16 and based upon consideration of all of the relevant factors with respect to the claim as a whole, claim(s) 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze Claim 6, and similar rationale applies to independent Claim/s 16. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a process since the claim is directed to a method comprising.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since Claim 6 limitation of determining a plurality of feature vectors of the scene, clustering the plurality of feature vectors, into a subset, receiving an instruction to generate a dataset, and generating a sub-sample of clusters, and selecting a subset are directed to an abstract idea. The claim is related to certain methods of organizing human activity by an abstract idea . If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The Examiner interprets that Claim 6 limitation does not provide additional elements or combination of additional elements to a practical application since the claim/s are Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). See, MPEP §2106.04(d), In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing improvement. The specification need not explicitly set forth improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Here, the Examiner interprets that specification does not provide improvement and does not explicitly set forth an improvement but must make conclusory manner, (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art). Thus, the Exmainer finds there is no judicial exception.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the Claims do not amount to significantly more since the Claim/s is/state Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Furthermore, the generic computer components of the computer recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20 are depending on the independent claim/s include all the limitation of the independent claim. The Examiner finds that Claim 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20does not states significantly more since the claim only recites additional steps for processing data.
Thus, Claims 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, the Examiner interprets that the claims are rejected under 35 U.S.C. 101.
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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Claims 6 and 16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhang et al (U.S. Patent Pub. No. 2021/0248181, hereafter referred to as Zhang) in view of Zhang et al (U.S. Patent Pub. No. 2023/0118864, hereafter referred to as Zhang2).
Regarding Claim 6, Zhang teaches A method comprising: determining a plurality of feature vectors (paragraph 18, Zhang teaches feature vectors.), each representing a scene data of the plurality of scene data (paragraph 19, Zhang teaches feature vectors for the images data.); clustering, based on a distance metric in a space of the feature vectors (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data such as face data, which can be substitute with other image data, such as scene image data. ), the plurality of feature vectors into one or more clusters, each cluster representing a subset of the plurality of scene data (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data into groups of A and B for different clustering phase.), receiving an instruction to generate a dataset, the instruction including a target size of the dataset, the target size being less than a number of scene data in the plurality of scene data (paragraph 151-paragraph 157, Zhang teaches different types of clustering algorithms that are used, and then using different methods to determine the accuracy of similar images.); and generating, by sub-sampling the one or more clusters, the dataset of the target size (paragraph 157-paragraph 164, Zhang teaches the method of the using the clustering information in CNN to determine the similar image data.),
wherein the sub-sampling selects at least one scene data from the one or more clusters (paragraph 451, paragraph 452, Zhang teaches finding similar images.).
Zhang does not explicitly disclose receiving a plurality of scene data, each indicating an environment traversed by an autonomous vehicle.
Zhang2 is in the same field of art of image classification using clustering. Further, Zhang2 teaches receiving a plurality of scene data, each indicating an environment traversed by an autonomous vehicle (paragraph 33-paragraph 36, Zhang2 teaches capturing image of a scene of a vehicle).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhang by including the images of the scenes from a vehicle that is taught by Zhang2, to make the invention that using a system that finds similar images using clustering data (Zhang) and finds similar images of the scene from vehicles; thus, one of ordinary skilled in the art would be motivated to combine the references since these changes could be due to illumination and appearance changes (e.g. due to annual seasonal variation), as well as viewpoint variations between the captured image and the reference image, and in light of this, a new approach to visual place recognition is required (paragraph ).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 16, Zhang teaches one or more non-transitory computer-readable media storing processor- executable instructions that (paragraph 545, Zhang),
when executed by one or more processors, perform operations (paragraph 545, Zhang teaches using a processor) comprising:
determining a plurality of scene features representing the scene data (paragraph 18, Zhang teaches feature vectors.);
clustering, based on a similarity between the scene features (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data such as face data, which can be substitute with other image data, such as scene image data. ), the scene data into one or more clusters, each cluster representing a subset of the plurality of top-down representations (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data into groups of A and B for different clustering phase.);
receiving an instruction to generate a dataset, the instruction indicating criteria of a target dataset (paragraph 151-paragraph 157, Zhang teaches different types of clustering algorithms that are used, and then using different methods to determine the accuracy of similar images.);
sampling, based on the criteria of the target dataset, the one or more clusters(paragraph 157-paragraph 164, Zhang teaches the method of the using the clustering information in CNN to determine the similar image data.); and
generating the dataset satisfying the criteria, the dataset comprising a subset of the scene data (paragraph 451, paragraph 452, Zhang teaches finding similar images.).
Zhang does not explicitly disclose receiving scene data comprising a plurality of top-down representations of an environment.
Zhang2 is in the same field of art of image classification using clustering. Further, Zhang2 teaches receiving scene data comprising a plurality of top-down representations of an environment (paragraph 33-paragraph 36, Zhang2 teaches capturing image of a scene of a vehicle).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhang by including the images of the scenes from a vehicle that is taught by Zhang2, to make the invention that using a system that finds similar images using clustering data (Zhang) and finds similar images of the scene from vehicles; thus, one of ordinary skilled in the art would be motivated to combine the references since these changes could be due to illumination and appearance changes (e.g. due to annual seasonal variation), as well as viewpoint variations between the captured image and the reference image, and in light of this, a new approach to visual place recognition is required (paragraph ).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 6 and 16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhang et al (U.S. Patent Pub. No. 2021/0248181, hereafter referred to as Zhang) in view of Zipfl et al (NPL “Self Supervised Clustering of Traffic Scenes using Graph Representation”, arXiv:2211.15508v1 [cs.CV] 24 Nov 2022).
Regarding Claim 6, Zhang teaches A method comprising: determining a plurality of feature vectors (paragraph 18, Zhang teaches feature vectors.), each representing a scene data of the plurality of scene data (paragraph 19, Zhang teaches feature vectors for the images data.); clustering, based on a distance metric in a space of the feature vectors (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data such as face data, which can be substitute with other image data, such as scene image data. ), the plurality of feature vectors into one or more clusters, each cluster representing a subset of the plurality of scene data (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data into groups of A and B for different clustering phase.), receiving an instruction to generate a dataset, the instruction including a target size of the dataset, the target size being less than a number of scene data in the plurality of scene data (paragraph 151-paragraph 157, Zhang teaches different types of clustering algorithms that are used, and then using different methods to determine the accuracy of similar images.); and generating, by sub-sampling the one or more clusters, the dataset of the target size (paragraph 157-paragraph 164, Zhang teaches the method of the using the clustering information in CNN to determine the similar image data.),
wherein the sub-sampling selects at least one scene data from the one or more clusters (paragraph 451, paragraph 452, Zhang teaches finding similar images.).
Zhang does not explicitly disclose receiving a plurality of scene data, each indicating an environment traversed by an autonomous vehicle.
Zipfl is in the same field of art of image classification using clustering. Further, Zipfl teaches receiving a plurality of scene data, each indicating an environment traversed by an autonomous vehicle (page 1-2 I. Introduction, Zipfl teaches clustering traffic scenes using a self supervised clustering; page 3, A. Dataset, Augmentation and Graphgeneration).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhang’s face images with scene data by clustering the scene data using a self-supervised clustering that is taught by Zipfl, to make the invention that using a system that finds similar images using clustering data (Zhang) and determining different scene data with self-supervised clustering; thus, one of ordinary skilled in the art would be motivated to combine the references since the challenge of reconstructing a graph and at the same time obtain a representation to describe the similarity of traffic scenes; we could show, that the resulting clusters possess common semantic characteristics. The approach was evaluated on the INTERACTION dataset (Abstract Zipfl).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 16, Zhang teaches one or more non-transitory computer-readable media storing processor- executable instructions that (paragraph 545, Zhang),
when executed by one or more processors, perform operations (paragraph 545, Zhang teaches using a processor) comprising:
determining a plurality of scene features representing the scene data (paragraph 18, Zhang teaches feature vectors.);
clustering, based on a similarity between the scene features (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data such as face data, which can be substitute with other image data, such as scene image data. ), the scene data into one or more clusters, each cluster representing a subset of the plurality of top-down representations (paragraph 90, Zhang teaches clustering algorithm; paragraph 106, paragraph 107, Zhang also, teaches the clustering of image data into groups of A and B for different clustering phase.);
receiving an instruction to generate a dataset, the instruction indicating criteria of a target dataset (paragraph 151-paragraph 157, Zhang teaches different types of clustering algorithms that are used, and then using different methods to determine the accuracy of similar images.); sampling, based on the criteria of the target dataset, the one or more clusters(paragraph 157-paragraph 164, Zhang teaches the method of the using the clustering information in CNN to determine the similar image data.); and
generating the dataset satisfying the criteria, the dataset comprising a subset of the scene data (paragraph 451, paragraph 452, Zhang teaches finding similar images.).
Zhang does not explicitly disclose receiving scene data comprising a plurality of top-down representations of an environment.
Zipfl is in the same field of art of image classification using clustering. Further, Zipfl teaches receiving scene data comprising a plurality of top-down representations of an environment (page 1-2 I. Introduction, Zipfl teaches clustering traffic scenes using a self supervised clustering; page 3, A. Dataset, Augmentation and Graphgeneration).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhang’s face images with scene data by clustering the scene data using a self-supervised clustering that is taught by Zipfl, to make the invention that using a system that finds similar images using clustering data (Zhang) and determining different scene data with self-supervised clustering; thus, one of ordinary skilled in the art would be motivated to combine the references since the challenge of reconstructing a graph and at the same time obtain a representation to describe the similarity of traffic scenes; we could show, that the resulting clusters possess common semantic characteristics. The approach was evaluated on the INTERACTION dataset (Abstract Zipfl).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claims 1-5 allowed.
Claims 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Conclusion
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/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
