CTNF 18/405,514 CTNF 100458 DETAILED ACTION This action is in response to the application filed 01/05/2026. Claims 1-20 are pending and have been examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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. 07-30-06 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: “ wherein the target modality is operable to detect several objects within an input frame of the target data ” in claim 8. Regarding the target modality, in paragraph 0039, the specification states “a vision and/or camera may be used as a source modality and a radar sensor may be used as a target modality.” 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 07-30-02 AIA 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. 07-34-01 Claims 1-20 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. Regarding claim 1, “the source neural network of the foundation model having at least one source encoder having one more source weights which have been pre-trained to detect a source feature” is indefinite. Specifically, it is unclear as to whether the at least one source encoder has been pre-trained or if the one or more source weights have been pre-trained. For purposes of examination, Examiner has interpreted this to be that the at least one source encoder has been pre-trained. Regarding claims 2-18, claims 2-18 are rejected for at least the same reasons as claim 1 since claims 2-18 depend on claim 1. Regarding claim 19, “wherein the source neural network includes at least one source encoder having one more source weights which have been pre-trained to detect a source feature” is indefinite. Specifically, it is unclear as to whether the at least one source encoder has been pre-trained or if the one or more source weights have been pre-trained. For purposes of examination, Examiner has interpreted this to be that the at least one source encoder has been pre-trained. Regarding claim 20, “the source neural network of the foundation model having at least one source encoder having one more source weights which have been pre-trained to detect one or more source features” is indefinite. Specifically, it is unclear as to whether the at least one source encoder has been pre-trained or if the one or more source weights have been pre-trained. For purposes of examination, Examiner has interpreted this to be that the at least one source encoder has been pre-trained. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Claim 1: Subject Matter Eligibility Analysis Step 1: Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 1 recites detect a source feature that is detectable within the source data of the source modality; (This limitation is a mental process as it encompasses a human mentally detecting a source feature and is thus an observation.) computing one or more target features within the target data of the target modality; (This limitation is a mental process as it encompasses a human mentally computing a feature and is thus an evaluation.) training the one or more target weights by pairing the target data with the source data and freezing the one or more source weights of the source neural network for a predetermined epoch; (This limitation is a mental process as it encompasses a human mentally pairing data and freezing weights and is thus an evaluation.) operating on the source modality using the source data to detect one or more objects (This limitation is a mental process as it encompasses a human mentally operating using data to detect objects and is thus an evaluation.) Therefore, claim 1 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 1 further recites additional elements of A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) inputting source data to the foundation model, (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) the source neural network of the foundation model having at least one source encoder having one or more source weights which have been pre-trained to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) the target neural network including at least one target encoder having one or more target weights for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) wherein the source neural network is pre-trained for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, claim 1 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). inputting source data to the foundation model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). the source neural network of the foundation model having at least one source encoder having one or more source weights which have been pre-trained to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). the target neural network including at least one target encoder having one or more target weights for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). wherein the source neural network is pre-trained for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). Therefore, claim 1 is subject-matter ineligible. Regarding Claim 2: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 2 recites the same abstract ideas as claim 1. Therefore, claim 2 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 2 further recites additional elements of wherein the target data comprises radar data received from a radar sensor. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 2 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the target data comprises radar data received from a radar sensor specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 2 is subject-matter ineligible. Regarding Claim 3: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 3 recites wherein the step of pairing the target data with the source data further comprising: pairing the image data and the radar data. (This limitation is a mental process as it encompasses a human mentally pairing data and is thus an evaluation.) Therefore, claim 3 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 3 is subject-matter ineligible. Regarding Claim 4: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 4 recites the same abstract ideas as claim 3. Therefore, claim 4 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 4 further recites additional elements of wherein the radar data is spectral radar data. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 4 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the radar data is spectral radar data specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 4 is subject-matter ineligible. Regarding Claim 5: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 5 recites the same abstract ideas as claim 1. Therefore, claim 5 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 5 further recites additional elements of wherein the target data comprises Lidar data from a Lidar sensor. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 5 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the target data comprises Lidar data from a Lidar sensor specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 5 is subject-matter ineligible. Regarding Claim 6: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 6 recites the same abstract ideas as claim 4. Therefore, claim 6 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 6 further recites additional elements of wherein the target neural network is a transformer based neural network. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 6 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the target neural network is a transformer based neural network specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 6 is subject-matter ineligible. Regarding Claim 7: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 7 recites the same abstract ideas as claim 6. Therefore, claim 7 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 7 further recites additional elements of wherein the target neural network is a convolutional neural network. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 7 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the target neural network is a convolutional neural network specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 7 is subject-matter ineligible. Regarding Claim 8: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 8 recites the same abstract ideas as claim 2. Therefore, claim 8 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 8 further recites additional elements of wherein the radar data is radar point cloud data. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).) Therefore, claim 8 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the radar data is radar point cloud data specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)). Therefore, claim 8 is subject-matter ineligible. Regarding Claim 9: Subject Matter Eligibility Analysis Step 1: Claim 9 recites a method and is thus a process, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 9 recites detect several objects within an input frame of the target data. (This limitation is a mental process as it encompasses a human mentally detecting objects and is thus an observation.) Therefore, claim 9 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 9 further recites additional elements of wherein the target modality is operable to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, claim 9 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the target modality is operable to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). Therefore, claim 9 is subject-matter ineligible. Regarding Claim 10: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 10 recites the same abstract ideas as claim 2. Therefore, claim 10 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 10 further recites additional elements of wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a radar encoder, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) and training the target weight further comprises: training only the target weight of the radar encoder during the pre-determined epoch, training a first source weight of the image encoder and the target weight of the radar encoder after the pre-determined epoch. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training weights (see MPEP 2106.05(g)).) Therefore, claim 10 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 10 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a radar encoder uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). and training the target weight further comprises: training only the target weight of the radar encoder during the pre-determined epoch, training a first source weight of the image encoder and the target weight of the radar encoder after the pre-determined epoch is the well understood, routine, and conventional activity of training weights (Rafanavicius et al. (US 2024/0129329 A1), page 6, paragraph 0021, “As is well known in the art, training adjusts the biases and weights of nodes within a neural network such that the trained network (model) is capable of recognizing malware and classifying the detected malware as to its maliciousness”). Therefore, claim 10 is subject-matter ineligible. Regarding Claim 11: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 11 recites freezing a second source weight of the text encoder after the pre-determined epoch. (This limitation is a mental process as it encompasses a human mentally freezing a weight and is thus an evaluation.) Therefore, claim 11 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 11 does not further recite any additional elements. Therefore, claim 11 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 11 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 11 is subject-matter ineligible. Regarding Claim 12: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 12 recites the same abstract ideas as claim 2. Therefore, claim 12 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 12 further recites additional elements of wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a radar encoder, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) and training the target weight further comprises: training only the target weight of the radar encoder during the pre-determined epoch, training a first source weight of the image encoder, a second source weight of the text encoder, and the target weight of the image encoder after the pre-determined epoch. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training weights (see MPEP 2106.05(g)).) Therefore, claim 12 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a radar encoder uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). and training the target weight further comprises: training only the target weight of the radar encoder during the pre-determined epoch, training a first source weight of the image encoder, a second source weight of the text encoder, and the target weight of the image encoder after the pre-determined epoch is the well understood, routine, and conventional activity of training weights (Rafanavicius et al. (US 2024/0129329 A1), page 6, paragraph 0021, “As is well known in the art, training adjusts the biases and weights of nodes within a neural network such that the trained network (model) is capable of recognizing malware and classifying the detected malware as to its maliciousness”). Therefore, claim 12 is subject-matter ineligible. Regarding Claim 13: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 13 recites learning a feature embedding of the foundation model (This limitation is a mental process as it encompasses a human mentally learning an embedding and is thus an evaluation.) and classifying the object using the target data, wherein the object is classified according to a multiclass classification. (This limitation is a mental process as it encompasses a human mentally classifying an object and is thus a judgement.) Therefore, claim 13 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 13 does not further recite any additional elements. Therefore, claim 13 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 13 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 13 is subject-matter ineligible. Regarding Claim 14: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 14 recites generating a new classification for the object within the multiclass classification using a text embedding corresponding to one or more hierarchy levels within the multiclass classification. (This limitation is a mental process as it encompasses a human mentally generating a new classification and is thus an evaluation.) Therefore, claim 14 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 14 does not further recite any additional elements. Therefore, claim 14 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 14 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 14 is subject-matter ineligible. Regarding Claim 15: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 15 recites generating one or more feature embeddings from the source encoder and the target encoder for one or more objects detected within the source data and the target data; (This limitation is a mental process as it encompasses a human mentally generating an embedding and is thus an evaluation.) and generating one or more regression parameters for a predicted bounding boxes for the one or more objects detected. (This limitation is a mental process as it encompasses a human mentally generating regression parameters and is thus an evaluation.) Therefore, claim 15 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 15 does not further recite any additional elements. Therefore, claim 15 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 15 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 15 is subject-matter ineligible. Regarding Claim 16: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 16 recites wherein a loss value for the one or more feature embeddings and the one or more regression parameters is combined using a bipartite matching loss function. (This limitation is a mental process as it encompasses a human mentally combining regression parameters and is thus an evaluation.) Therefore, claim 16 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 16 does not further recite any additional elements. Therefore, claim 16 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 16 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 16 is subject-matter ineligible. Regarding Claim 17: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 17 recites wherein a loss value for the one or more regression parameters of the predicted bounding boxes is computed as an Lp norm. (This limitation is a mental process as it encompasses a human mentally computing a loss value and is thus an evaluation.) Therefore, claim 17 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 17 does not further recite any additional elements. Therefore, claim 17 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 17 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 17 is subject-matter ineligible. Regarding Claim 18: Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 18 recites wherein a loss value is computed using a mean square error loss function or a cosine-similarity loss function. (This limitation is a mental process as it encompasses a human mentally computing a loss value and is thus an evaluation.) Therefore, claim 18 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 18 does not further recite any additional elements. Therefore, claim 18 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: Since there are no additional elements, claim 18 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 18 is subject-matter ineligible. Regarding Claim 19: Subject Matter Eligibility Analysis Step 1: Claim 19 recites a method and is thus a process, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 19 recites generates target data relating to a target modality; (This limitation is a mental process as it encompasses a human mentally generating data and is thus an evaluation.) detect one or more source features that is computable within the source data of the source modality; (This limitation is a mental process as it encompasses a human mentally detecting features and is thus an observation.) train the one or more target weights by pairing the target data with the source data and freezing the one or more source weights of the source neural network for a predetermined epoch. (This limitation is a mental process as it encompasses a human mentally pair data and freeze weights and is thus an evaluation.) Therefore, claim 19 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 19 further recites additional elements of A system for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) a target sensor system that (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) memory operable to store the source neural network associated with the foundation model; (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).) a processor configured to: (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) receive source data for the source neural network of the foundation model, (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) wherein the source neural network includes at least one source encoder having one or more source weights which have been pre-trained to (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) receive the target data for a target neural network, (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) wherein the target neural network includes at least one target encoder having one or more target weights for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, claim 19 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because A system for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). a target sensor system that uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). memory operable to store the source neural network associated with the foundation model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). a processor configured to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). receive source data for the source neural network of the foundation model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). wherein the source neural network includes at least one source encoder having one or more source weights which have been pre-trained to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). receive the target data for a target neural network, is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). wherein the target neural network includes at least one target encoder having one or more target weights for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). Therefore, claim 19 is subject-matter ineligible. Regarding Claim 20: Subject Matter Eligibility Analysis Step 1: Claim 20 recites a method and is thus a process, one of the four statutory categories of patentable subject matter. Subject Matter Eligibility Analysis Step 2A Prong 1: Claim 20 recites detect one or more source features that is detectable within the source data of the source modality; (This limitation is a mental process as it encompasses a human mentally detecting features and is thus an observation.) detecting a target feature within the target data of the target modality; (This limitation is a mental process as it encompasses a human mentally detecting a feature and is thus an observation.) training the target weight by pairing the target data with the source data and freezing the source weight of the source neural network for a pre-determined epoch; (This limitation is a mental process as it encompasses a human mentally pairing data and freezing weights and is thus an evaluation.) generating one or more feature embeddings …for one or more objects detected within the source data and the target data; (This limitation is a mental process as it encompasses a human mentally generating embeddings and is thus an evaluation.) generating one or more regression parameters for a predicted bounding boxes for the object detected. (This limitation is a mental process as it encompasses a human mentally generating regression parameters and is thus an evaluation.) Therefore, claim 20 recites an abstract idea. Subject Matter Eligibility Analysis Step 2A Prong 2: Claim 20 further recites additional elements of A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, comprising (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) inputting source data to the foundation model, (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) the source neural network of the foundation model having at least one source encoder having one or more source weights which has been pre-trained to, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) inputting target data to a target neural network operating on a target modality, (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).) the target neural network including at least one target encoder having a target weight for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) from the source encoder and the target encoder (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).) Therefore, claim 20 is not integrated into a practical application. Subject Matter Eligibility Analysis Step 2B: The additional elements of claim 20 do not provide significantly more than the abstract idea itself, taken alone and in combination because A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). inputting source data to the foundation model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). the source neural network of the foundation model having at least one source encoder having one or more source weights which has been pre-trained to uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). inputting target data to a target neural network operating on a target modality is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). the target neural network including at least one target encoder having a target weight for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). from the source encoder and the target encoder uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)). Therefore, claim 20 is subject-matter ineligible. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1, 5, 13-15, and 18-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hess et al. (“LidarCLIP or: How I Learned to Talk to Point Clouds”) (hereafter referred to as Hess) . Regarding claim 1, Hess teaches A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, comprising (Hess, page 3, Figure 2 caption, “Overview of LidarCLIP. We use existing CLIP image and text encoders (top left), and learn to embed point clouds into the same feature space (bottom left). To that end, we train a lidar encoder to match the features of the froze image encoder on a large automotive dataset with image-lidar pairs. This enables a wide range of applications, such as scenario retrieval (top right), zero-shot classification, as well as lidar-to-text and lidar-to-image generation (bottom right).” Examiner notes that the existing encoders are the pretrained source neural network use to train the target neural network or lidar encoder.) : inputting source data to the foundation model, the source neural network of the foundation model having at least one source encoder having one or more source weights which have been pre-trained to detect a source feature that is detectable within the source data of the source modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the source encoder is the image encoder and the source feature is the features of the frozen image encoder. Examiner further notes that the image encoder was previously trained to detect features and that the hyperparameters of the original implementation are the weights.) inputting target data to a target neural network operating on a target modality, the target neural network including at least one target encoder having one or more target weights for computing one or more target features within the target data of the target modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the target neural network is the lidar encoder and the target feature is the features of the lidar encoder. Examiner further notes that the that the hyperparameters of the SST encoder are the target weights.) ; training the one or more target weights by pairing the target data with the source data and freezing the one or more source weights of the source neural network for a pre- determined epoch (Hess, page 2, 1 st column, 2 nd paragraph, “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy.” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that by freezing the CLIP image encoder, the weights associated with the encoder are also frozen. Examiner further notes that the predetermined epoch is one of the 3 epochs of training.) ; and wherein the source neural network is pre-trained for operating on the source modality using the source data to detect one or more objects (Hess, page 3, Figure 2 caption, “Overview of LidarCLIP. We use existing CLIP image and text encoders (top left), and learn to embed point clouds into the same feature space (bottom left). To that end, we train a lidar encoder to match the features of the froze image encoder on a large automotive dataset with image-lidar pairs. This enables a wide range of applications, such as scenario retrieval (top right), zero-shot classification, as well as lidar-to-text and lidar-to-image generation (bottom right).” Examiner notes that the existing encoders are the pretrained source neural network use to detect objects.) . Regarding claim 5, Hess teaches The method of claim 1, wherein the target data comprises Lidar data from a Lidar sensor (Hess, page 7, 1 st column, 2 nd paragraph, “Besides its usefulness for retrieval, LidarCLIP can offer more understanding of what concepts can be captured with a lidar sensor. While lidar data is often used in tasks such as object detection [44], panoptic/semantic segmentation [1, 21], and localization [9], research into capturing more abstract concepts with lidar data is limited and focused mainly on weather classification [17, 40]. However, we show that LidarCLIP can indeed capture complex scene concepts, as already demonstrated in Tab. 3.” Examiner notes that the target data is the lidar data.) . Regarding claim 13 , Hess teaches The method of claim 1, further comprising: learning a feature embedding of the foundation model (Hess, page 2, 1 st column, 2 nd paragraph, “At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, or both, enabling various applications.” Examiner notes that LidarCLIP is the foundation model.) ; and classifying the object using the target data, wherein the object is classified according to a multiclass classification (Hess, page 12, 2 nd column, last paragraph, “In many cases, LidarCLIP and image-based CLIP give similar results. Highlighting the transfer of knowledge to the lidar domain. In some cases, however, the two models give contradictory classifications. For instance, LidarCLIP misclassifies the cyclist as a pedestrian, potentially due to the upright position and fewer points for the bike than the person. While their disagreement influences the joint method, ‘cyclist’ remains the dominating class.” Examiner notes that the multiclass classification is the cyclist versus the pedestrian.) . Regarding claim 14, Hess teaches The method of claim 13, further comprising: generating a new classification for the object within the multiclass classification using a text embedding corresponding to one or more hierarchy levels within the multiclass classification (Hess, page 4, 2 nd column, last paragraph, “Instead, automotive datasets typically have fine-grained annotations for each scene, such as object bounding boxes. Segmentation masks, etc. This is also true for ONCE, which contains annotations in terms of 2D and 3D bounding boxes for 5 classes, and metadata for the time of day and weather. We leverage these detailed annotations and available metadata, to create as many retrieval categories as possible. For object retrieval, we consider a scene positive if it contains one or more instances of that object. To probe understanding of the model, we also propose a ‘nearby’ category, searching specifically for objects closer than 15m. We verify that the conclusions hold for thresholds between 10m and 25 m. Finally to minimize the effect of prompt engineering, we follow [15] and average multiple text embeddings to improve results and reduce variability.” Examiner notes that the new classification for the object is the retrieval category and the text embedding corresponds to the scene level which is a hierarchy level within the multiclass classification.) . Regarding claim 15 , Hess teaches The method of claim 1 further comprising: generating one or more feature embeddings from the source encoder and the target encoder for one or more objects detected within the source data and the target data (Hess, page 5, 1 st column, 3 rd paragraph, “Typically, LidarCLIP outputs a set of voxel features that are pooled into a single, global, CLIP feature. We construct the object embeddings by only pooling features for voxels inside the corresponding bounding box, without any object-specific training/fine-tuning” where “In this case, one can search for scenes where the image embedding matches ‘an image with extreme sun glare’ and re-rank the top-K results by their lidar embeddings similarity to ‘a scene containing a large truck’. This kind of scene would be almost impossible to retrieve using a single modality” (Hess, page 4, 2 nd column, 2 nd paragraph). Examiner notes that the feature embeddings from the source and target encoder are the image and lidar embeddings.) ; and generating one or more regression parameters for a predicted bounding boxes for the one or more objects detected (Hess, page 12, 1 st column, Table 8, PNG media_image2.png 182 325 media_image2.png Greyscale Examiner notes the window shape is the regression parameters since according to paragraph 0024, “Regression parameters, e.g., size of a box (length, width, height) and orientation angle of the detected object may also be learned from the foundation model”.) . Regarding claim 18, Hess teaches The method of claim 15, wherein a loss value is computed using a mean square error loss function or a cosine-similarity loss function (Hess, page 4, 1 st column, 1 st paragraph, “To this end, we adopt either the mean squared error (MSE) or the cosine similarity loss.”) . Regarding claim 19, Hess teaches A system for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, comprising (Hess, page 3, Figure 2 caption, “Overview of LidarCLIP. We use existing CLIP image and text encoders (top left), and learn to embed point clouds into the same feature space (bottom left). To that end, we train a lidar encoder to match the features of the froze image encoder on a large automotive dataset with image-lidar pairs. This enables a wide range of applications, such as scenario retrieval (top right), zero-shot classification, as well as lidar-to-text and lidar-to-image generation (bottom right).” Examiner notes that the existing encoders are the pretrained source neural network use to train the target neural network or lidar encoder.) : A target sensor system that generates target data relating to a target modality (Hess, page 7, 1 st column, 2 nd paragraph, “Besides its usefulness for retrieval, LidarCLIP can offer more understanding of what concepts can be captured with a lidar sensor. While lidar data is often used in tasks such as object detection [44], panoptic/semantic segmentation [1, 21], and localization [9], research into capturing more abstract concepts with lidar data is limited and focused mainly on weather classification [17, 40]. However, we show that LidarCLIP can indeed capture complex scene concepts, as already demonstrated in Tab. 3.” Examiner notes that the target data is the lidar data.) . Memory operable to store the source neural network associated with the foundation model (Hess, page 12, 1 st column, C. Training details, “The training was done on four NVIDIA A100s with a batch size of 128, requiring about 27 hours for 3 epochs.”) ; A processor configured to (Hess, page 12, 1 st column, C. Training details, “The training was done on four NVIDIA A100s with a batch size of 128, requiring about 27 hours for 3 epochs.”) Receive source data for the source neural network of the foundation model, wherein the source neural network includes at least one source encoder having one or more source weights which have been pre-trained to detect one or more source features that is computable within the source data of the source modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the source encoder is the image encoder and the source feature is the features of the frozen image encoder. Examiner further notes that the image encoder was previously trained to detect features and that the hyperparameters of the original implementation are the weights.) Receive the target data for a target neural network, wherein the target neural network includes at least one target encoder having one or more target weights for detecting a target feature within the target data of the target modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the target neural network is the lidar encoder and the target feature is the features of the lidar encoder. Examiner further notes that the that the hyperparameters of the SST encoder are the target weights.) ; train the one or more target weights by pairing the target data with the source data and freezing the one or more source weights of the source neural network for a pre- determined epoch (Hess, page 2, 1 st column, 2 nd paragraph, “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy.” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that by freezing the CLIP image encoder, the weights associated with the encoder are also frozen. Examiner further notes that the predetermined epoch is one of the 3 epochs of training.) . Regarding claim 20 , Hess teaches A method for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality, comprising (Hess, page 3, Figure 2 caption, “Overview of LidarCLIP. We use existing CLIP image and text encoders (top left), and learn to embed point clouds into the same feature space (bottom left). To that end, we train a lidar encoder to match the features of the froze image encoder on a large automotive dataset with image-lidar pairs. This enables a wide range of applications, such as scenario retrieval (top right), zero-shot classification, as well as lidar-to-text and lidar-to-image generation (bottom right).” Examiner notes that the existing encoders are the pretrained source neural network use to train the target neural network or lidar encoder.) : inputting source data to the foundation model, the source neural network of the foundation model having at least one source encoder having one or more source weights which have been pre-trained to detect a source feature that is detectable within the source data of the source modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the source encoder is the image encoder and the source feature is the features of the frozen image encoder. Examiner further notes that the image encoder was previously trained to detect features and that the hyperparameters of the original implementation are the weights.) inputting target data to a target neural network operating on a target modality, the target neural network including at least one target encoder having a target weight for detecting a target feature within the target data of the target modality (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “by combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios” (Hess, page 1, Abstract) where “additionally, LidarCLIP learns meaningful features for overall scene lighting conditions” (Hess, page 7, 1 st column, last paragraph) and where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph). Examiner notes that the target neural network is the lidar encoder and the target feature is the features of the lidar encoder. Examiner further notes that the that the hyperparameters of the SST encoder are the target weights.) ; training the target weight by pairing the target data with the source data and freezing the source weight of the source neural network for a pre- determined epoch (Hess, page 2, 1 st column, 2 nd paragraph, “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy.” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that by freezing the CLIP image encoder, the weights associated with the encoder are also frozen. Examiner further notes that the predetermined epoch is one of the 3 epochs of training.) ; generating one or more feature embeddings from the source encoder and the target encoder for one or more objects detected within the source data and the target data (Hess, page 5, 1 st column, 3 rd paragraph, “Typically, LidarCLIP outputs a set of voxel features that are pooled into a single, global, CLIP feature. We construct the object embeddings by only pooling features for voxels inside the corresponding bounding box, without any object-specific training/fine-tuning” where “In this case, one can search for scenes where the image embedding matches ‘an image with extreme sun glare’ and re-rank the top-K results by their lidar embeddings similarity to ‘a scene containing a large truck’. This kind of scene would be almost impossible to retrieve using a single modality” (Hess, page 4, 2 nd column, 2 nd paragraph). Examiner notes that the feature embeddings from the source and target encoder are the image and lidar embeddings.) ; and generating one or more regression parameters for predicted bounding boxes for the one or more objects detected (Hess, page 12, 1 st column, Table 8, PNG media_image2.png 182 325 media_image2.png Greyscale Examiner notes the window shape is the regression parameters since according to paragraph 0024, “Regression parameters, e.g., size of a box (length, width, height) and orientation angle of the detected object may also be learned from the foundation model”.) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim (s) 2-4, 6-12, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hess in view of Rebut et al. (“Raw High-Definition Radar for Multi-Task Learning”) (hereafter referred to as Rebut) . Regarding claim 2 , Hess teaches the method of claim 1. Hess does not teach, but Rebut does teach wherein the target data comprises radar data received from a radar sensor (Rebut, page 1, Figure 1, “Overview of our RADIal dataset. RADIal includes a set of 3 sensors (camera, laser scanner, high-definition radar) and comes with GPS and vehicle’s CAN traces;” Examiner notes that the target data is the dataset.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 3 , Hess in view of Rebut teaches the method of claim 2. Hess further teaches wherein the step of pairing the target data with the source data further comprising: pairing the image data and the …data (Hess, page 2, 1 st column, 2 nd paragraph, “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications”. Examiner notes that the images are the image data and the point clouds are the other data.) ; Hess therefore teaches, pairing data. Hess does not explicitly teach that the other data is radar data, but Rebut does teach radar data (Rebut, page 1, Figure 1, “Overview of our RADIal dataset. RADIal includes a set of 3 sensors (camera, laser scanner, high-definition radar) and comes with GPS and vehicle’s CAN traces;” Examiner notes that the target data is the dataset.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 4 , Hess in view of Rebut teaches the method of claim 3. Hess does not teach, but Rebut does teach wherein the radar data is spectral radar data (Rebut, page 5, Figure 3, PNG media_image3.png 570 1150 media_image3.png Greyscale Examiner notes that the FPN encoder is the encoder encoding radar data and the range-Doppler spectrum is the spectral radar data.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 6 , Hess in view of Rebut teaches the method of claim 4. Hess further teaches wherein the target neural network is a transformer based neural network (Hess, page 4, 2 nd column, 4 th paragraph, “As out lidar encoder, we use the Single-stride Sparse Transformer (SST) [10] (randomly initialized).)”) . Regarding claim 7 , Hess in view of Rebut teaches the method of claim 6. Hess does not teach, but Rebut does teach wherein the target neural network is a convolutional neural network (Rebut, page 4, 2 nd column, 4.2. FPN encoder, “To prevent losing the signature of small objects (typically few pixels in the RD spectrum), the FPN encoder performs a 2x2 down-sampling per block, leading to a total reduction of the tensor size by a factor of 16 in height and width. For similar reasons and to avoid overlaps between adjacent Tx’s, it uses 3x3 convolution kernels.” Examiner notes that the FPN encoder is the target neural network and that it is a convolutional neural network because it uses convolutions.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to use a convolutional neural network. Doing so is advantageous because it “prevent[s] losing the signature of small objects….and to avoid overlaps between adjacent Tx’s” (Rebut, page 4, 2 nd column, 4.2. FPN encoder). Regarding claim 8 , Hess in view of Rebut teaches the method of claim 2. Hess does not teach, but Rebut does teach wherein the radar data is radar point cloud data (Rebut, page 1, Figure 1, “Overview of our RADIal dataset. RADIal includes a set of 3 sensors (camera, laser scanner, high-definition radar) and comes with GPS and vehicle’s CAN traces; 25k synchronized samples are recorded is raw format. (a) Camera image with projected laser point cloud in red and radar point cloud in indigo, vehicle annotation in orange and free-driving-space annotation in green” Examiner notes that the camera image with the radar point cloud is the radar data.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 9 , Hess in view of Rebut teaches the method of claim 8. Hess further teaches wherein the target modality is operable to detect several objects within an input frame of the target data (Hess, page 4, 2 nd column, last paragraph, “Instead, automotive datasets typically have fine-grained annotations for each scene, such as object bounding boxes. Segmentation masks, etc. This is also true for ONCE, which contains annotations in terms of 2D and 3D bounding boxes for 5 classes, and metadata for the time of day and weather. We leverage these detailed annotations and available metadata, to create as many retrieval categories as possible. For object retrieval, we consider a scene positive if it contains one or more instances of that object.” Examiner notes that the one or more instances of the objects are the several objects within an input frame.) . Regarding claim 10 , Hess in view of Rebut teaches the method of claim 2. Hess further teaches wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a … encoder, (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and training the target weight further comprises: training only the target weight … during the pre-determined epoch, training a first source weight of the image encoder and the target weight … after the pre-determined epoch (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” (Hess, page 2, 1 st column, 2 nd paragraph) where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that by freezing the CLIP image encoder, the weights associated with the source encoder are also frozen. Thus, the lidar encoder is the only encoder that trains during the epoch. Examiner further notes that the predetermined epoch is one of the 3 epochs of training. Examiner additionally notes that the top right of Figure 2 shows training using all of the encoders after the 3 epochs.)) . Hess does not explicitly teach a radar encoder, but Rebut does teach a radar encoder (Rebut, page 5, Figure 3, PNG media_image3.png 570 1150 media_image3.png Greyscale Examiner notes that the FPN encoder is the encoder encoding radar data and the range-Dopppler spectrum is the spectral radar data.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 11 , Hess in view of Rebut teaches the method of claim 10. Hess further teaches freezing a second source weight of the text encoder after the pre-determined epoch (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” (Hess, page 2, 1 st column, 2 nd paragraph) where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that the text encoder is frozen during the training epochs and thus, the weights of the text encoder are also frozen after the predetermined epoch which are the 3 epochs.) . Regarding claim 12 , Hess in view of Rebut teaches the method of claim 2. Hess further teaches wherein the at least one source encoder includes an image encoder and a text encoder, the at least one target encoder includes a … encoder, (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and training the target weight further comprises: training only the target weight of the radar encoder during the pre-determined epoch, training a first source weight of the image encoder, a second source weight of the text encoder, and the target weight of the image encoder after the pre-determined epoch (Hess, page 3, Figure 2, PNG media_image1.png 724 1233 media_image1.png Greyscale and “We propose LidarCLIP, a method to connect the CLIP embedding space to the lidar point cloud domain. While combined text and point cloud datasets are not easily accessible, many robotics applications capture images and point clouds simultaneously. One example is autonomous driving, where data is both openly available and large scale. To this end, we supervise a lidar encoder with a frozen CLIP image encoder using pairs of images and point clouds form the large-scale automotive dataset ONCE [28]. This way, the image encoder’s rich and diverse semantic understanding is transferred to the point cloud domain. At inference, we can compare LidarCLIP’s embedding of a point cloud with the embeddings from either CLIP’s text encoder, image encoder, or both, enabling various applications” (Hess, page 2, 1 st column, 2 nd paragraph) where “Tab. 8 shows the hyperparameters for the SST encoder [11] used for embedding point clouds in the CLIP space. Window shape refers to the number of voxels in each window. Other hyperparameters are used as is from the original implementation” (Hess, page 12, 1 st column, 1 st paragraph) and where “SST is trained for 3 epochs corresponding to ~20 million training examples, using the Adam optimizer and the one-cycle learning rate policy” (Hess, page 4, 2 nd column, Implementation details). Examiner notes that by freezing the CLIP image encoder, the weights associated with the source encoder are also frozen. Thus, the lidar encoder is the only encoder that trains during the epoch. Examiner further notes that the predetermined epoch is one of the 3 epochs of training. Examiner additionally notes that the top right of Figure 2 shows training using all of the encoders after the 3 epochs.)) . Hess does not explicitly teach a radar encoder, but Rebut does teach a radar encoder (Rebut, page 5, Figure 3, PNG media_image3.png 570 1150 media_image3.png Greyscale Examiner notes that the FPN encoder is the encoder encoding radar data and the range-Dopppler spectrum is the spectral radar data.) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to have used radar data and a radar sensor. Doing so is advantageous because “Radars are more robust to adverse weather conditions, provid accurate distance estimates together with the velocity of the objects, and are especially well suited to the cost and size constraints of automotive applications” (Rebut, page 11, 2 nd column, 1 st paragraph). Regarding claim 17 , Hess teaches the method of claim 15. Hess does not teach, but Rebut does teach wherein a loss value for the one or more regression parameters of the predicted bounding boxes is computed as an Lp norm (Rebut, page 5, 2 nd column, 2 nd paragraph, “This two-fold detection head is trained with a multi-task loss composed of a focal loss applied to all the locations for the classification and of a ‘smooth L1’ loss for the regression applied only on positive detection.”) . Hess and Rebut are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to use an Lp norm. Doing so is advantageous because “the regression part finely predicts the range and azimuth values corresponding to the detected object” (Rebut, page 5, 2 nd column, 1 st paragraph) . 07-21-aia AIA Claim (s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hess in view of Carion et al. (“End-to-End Object Detection with Transformers”) (hereafter referred to as Carion) . Regarding claim 16 , Hess teaches the method of claim 15. Hess does not teach, but Carion does teach wherein a loss value for the one or more feature embeddings and the one or more regression parameters is combined using a bipartite matching loss function (Carion, page 5, 3.1 Object detection set prediction loss, “Our loss produces an optimal bipartite matching between predicted and ground truth objects, and then optimize object-specific (bounding box) losses” and Carion, page 2, Fig. 1, PNG media_image4.png 468 1359 media_image4.png Greyscale ) . Hess and Carion are considered analogous to the claimed invention because they use object detection and encoders. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Hess to use bipartite matching. Doing so is advantageous because “this enforces permutation-invariance, and guarantees that each target element has a unique match” (Carion, page 3, 2.1 Set Prediction) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Radford et al. (“Learning Transferable Visual Models From Natural Language Supervision”) also describes text and image encoders within the same model . Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 7:15 am - 5:15 pm EST. 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, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148 Application/Control Number: 18/405,514 Page 2 Art Unit: 2148 Application/Control Number: 18/405,514 Page 3 Art Unit: 2148 Application/Control Number: 18/405,514 Page 4 Art Unit: 2148 Application/Control Number: 18/405,514 Page 5 Art Unit: 2148 Application/Control Number: 18/405,514 Page 6 Art Unit: 2148 Application/Control Number: 18/405,514 Page 7 Art Unit: 2148 Application/Control Number: 18/405,514 Page 8 Art Unit: 2148 Application/Control Number: 18/405,514 Page 9 Art Unit: 2148 Application/Control Number: 18/405,514 Page 10 Art Unit: 2148 Application/Control Number: 18/405,514 Page 11 Art Unit: 2148 Application/Control Number: 18/405,514 Page 12 Art Unit: 2148 Application/Control Number: 18/405,514 Page 13 Art Unit: 2148 Application/Control Number: 18/405,514 Page 14 Art Unit: 2148 Application/Control Number: 18/405,514 Page 15 Art Unit: 2148 Application/Control Number: 18/405,514 Page 16 Art Unit: 2148 Application/Control Number: 18/405,514 Page 17 Art Unit: 2148 Application/Control Number: 18/405,514 Page 18 Art Unit: 2148 Application/Control Number: 18/405,514 Page 19 Art Unit: 2148 Application/Control Number: 18/405,514 Page 20 Art Unit: 2148 Application/Control Number: 18/405,514 Page 21 Art Unit: 2148 Application/Control Number: 18/405,514 Page 22 Art Unit: 2148 Application/Control Number: 18/405,514 Page 23 Art Unit: 2148 Application/Control Number: 18/405,514 Page 24 Art Unit: 2148 Application/Control Number: 18/405,514 Page 25 Art Unit: 2148 Application/Control Number: 18/405,514 Page 26 Art Unit: 2148 Application/Control Number: 18/405,514 Page 27 Art Unit: 2148 Application/Control Number: 18/405,514 Page 28 Art Unit: 2148 Application/Control Number: 18/405,514 Page 29 Art Unit: 2148 Application/Control Number: 18/405,514 Page 30 Art Unit: 2148 Application/Control Number: 18/405,514 Page 31 Art Unit: 2148 Application/Control Number: 18/405,514 Page 32 Art Unit: 2148 Application/Control Number: 18/405,514 Page 33 Art Unit: 2148 Application/Control Number: 18/405,514 Page 34 Art Unit: 2148 Application/Control Number: 18/405,514 Page 35 Art Unit: 2148 Application/Control Number: 18/405,514 Page 36 Art Unit: 2148 Application/Control Number: 18/405,514 Page 37 Art Unit: 2148 Application/Control Number: 18/405,514 Page 38 Art Unit: 2148 Application/Control Number: 18/405,514 Page 39 Art Unit: 2148 Application/Control Number: 18/405,514 Page 40 Art Unit: 2148 Application/Control Number: 18/405,514 Page 41 Art Unit: 2148 Application/Control Number: 18/405,514 Page 42 Art Unit: 2148 Application/Control Number: 18/405,514 Page 43 Art Unit: 2148 Application/Control Number: 18/405,514 Page 44 Art Unit: 2148 Application/Control Number: 18/405,514 Page 45 Art Unit: 2148 Application/Control Number: 18/405,514 Page 46 Art Unit: 2148 Application/Control Number: 18/405,514 Page 47 Art Unit: 2148 Application/Control Number: 18/405,514 Page 48 Art Unit: 2148 Application/Control Number: 18/405,514 Page 49 Art Unit: 2148 Application/Control Number: 18/405,514 Page 50 Art Unit: 2148 Application/Control Number: 18/405,514 Page 51 Art Unit: 2148 Application/Control Number: 18/405,514 Page 52 Art Unit: 2148 Application/Control Number: 18/405,514 Page 53 Art Unit: 2148 Application/Control Number: 18/405,514 Page 54 Art Unit: 2148 Application/Control Number: 18/405,514 Page 55 Art Unit: 2148 Application/Control Number: 18/405,514 Page 56 Art Unit: 2148 Application/Control Number: 18/405,514 Page 57 Art Unit: 2148 Application/Control Number: 18/405,514 Page 58 Art Unit: 2148 Application/Control Number: 18/405,514 Page 59 Art Unit: 2148 Application/Control Number: 18/405,514 Page 60 Art Unit: 2148
Prosecution Insights