CTNF 18/940,431 CTNF 100773 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. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted on 07 November 2024 and 12 December 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 07-29-01 AIA Claim 13 is objected to because of the following informalities: “wherein the machine learning model and is configured to…” . Appropriate correction is required. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-2, 5-6, 10-13 and 16-17 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 5-10 and 16-17 of co-pending application 18/940,486 in view of Yang. Claim Current application 18/940,431 Claim Co-pending application 18/940,486 1 A processing system for machine learning, comprising: 1 A processing system for machine learning, comprising: At least one memory having executable instructions stored thereon; and At least one memory having executable instructions stored thereon; and One or more processors configured to execute the executable instructions in order to cause the processing system to: One or more processors configured to execute the executable instructions in order to cause the processing system to: Receive a request to generate an output of a machine learning model, the request specifying a plurality of attributes of the output of the machine learning model; Receive a request to generate an output of a machine learning model, the request specifying a plurality of attributes of the output of the machine learning model; Generate a set of intermediate outputs via a plurality of adapters of the machine learning model, each respective adapter of the plurality of adapters being associated with a respective attribute of the specified plurality of attributes and including a respective mask in a low-rank dimension associated with the respective adapter; Generate a set of intermediate outputs via a plurality of adapters of the machine learning model, each respective adapter of the plurality of adapters being associated with a respective attribute of the specified plurality of attributes and being trained based on a cycle-consistency loss between different attributes of the specified plurality of attributes; Merge the set of intermediate outputs into a combined output of the plurality of adapters of the machine learning model; and Merge the set of intermediate outputs into a combined output of the plurality of adapters of the machine learning model; and Generate the output of the machine learning model based on the combined output of the plurality of adapters. Generate the output of the machine learning model based on the combined output of the plurality of adapters. 2 The processing system of claim 1, 2 The processing system of claim 1, Wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: Wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: An object to be depicted in the image output generated by the machine learning model; and An object to be depicted in the image output generated by the machine learning model; and A style of the image output generated by the machine learning model. A style of the image output generated by the machine learning model. 5 The processing system of claim 1, 5 The processing system of claim 1, Wherein the set of intermediate outputs comprises a plurality of images, each image of the plurality of images corresponding to an image conforming to an attribute from the specified plurality of attributes. Wherein the set of intermediate outputs comprises a plurality of images, each image of the plurality of images corresponding to an image conforming to an attribute from the specified plurality of attributes. 6 The processing system of claim 1, 6 The processing system of claim 1, Wherein a first adapter of the plurality of adapters is biased to operating on earlier layers over later layers in the machine learning model and a second adapter of the plurality of adapters is biased to operating on later layers over earlier layers of the machine learning model. Wherein a first adapter of the plurality of adapters is biased to operating on earlier in the machine learning model and a second adapter of the plurality of adapters is biased to operating on later layers in the machine learning model over the first adapter. 10 The processing system of claim 9, 7 The processing system of claim 1, Wherein at least one of the adapters has frozen weights and an output mask with learnable weights associated with the cycle-consistency loss. Wherein at least one of the adapters has frozen weights and an output mask with learnable weights associated with the cycle-consistency loss. 11 The processing system of claim 9, 8 The processing system of claim 1, Wherein at least one of the adapters has learnable weights associated with the cycle-consistency loss in the low-rank dimension of the at least one of the adapters. Wherein at least one of the adapters has learnable weights associated with the cycle-consistency loss in a rank dimension of the at least one of the adapters. 12 A processing system for machine learning, comprising: 9 A processing system for machine learning, comprising: At least one memory having executable instructions stored thereon; and At least one memory having executable instructions stored thereon; and One or more processors configured to execute the executable instructions in order to cause the processing system to: One or more processors configured to execute the executable instructions in order to cause the processing system to: Receive a first data set associated with a first attribute for which a machine learning model is to be trained; Receive a first data set associated with a first attribute for which a machine learning model is to be trained; Receive a second data set associated with a second attribute for which the machine learning model is to be trained; Receive a second data set associated with a second attribute for which the machine learning model is to be trained; Train a first adapter of the machine learning model to finetune outputs in accordance with the first attribute based on the first data set, the first adapter including a first mask in a low-rank dimension associated with the first adapter; Train a first adapter of the machine learning model to finetune outputs in accordance with the first attribute based on the first data set; Train a second adapter of the machine learning model to finetune outputs in accordance with the second attribute based on the second data set, the second adapter including a second mask in a low-rank dimension associated with the second adapter, such that an output of the first adapter is orthogonal to an output of the second adapter; Train a second adapter of the machine learning model to finetune outputs in accordance with the second attribute based on the second data set; Trained a merged adapter comprising the trained first adapter and the trained second adapter; and Trained a merged adapter based on a cycle-consistency loss between the first attribute and the second attribute, the merged adapter comprising the first adapter and the second adapter; and Deploy the machine learning model with the trained merged adapter. Deploy the machine learning model with the trained merged adapter. 13 The processing system of claim 12, 10 The processing system of claim 9, Wherein the machine learning model and is configured to generate an image output and wherein the plurality of attributes includes: Wherein the first attribute comprises an object to be depicted in an image output generated by the machine learning model and wherein the second attribute comprises a style for the image output generated by the machine learning model. An object to be depicted in the image output generated by the machine learning model; and A style of the image output generated by the machine learning model. 16 A processor-implemented method for machine learning, comprising: 16 A processor-implemented method for machine learning, comprising: Receiving a request to generate an output of a machine learning mode, the request specifying a plurality of attributes of the output of the machine learning model; Receiving a request to generate an output of a machine learning mode, the request specifying a plurality of attributes of the output of the machine learning model; Generating a set of intermediate outputs via a plurality of adapters of the machine learning model, each respective adapter of the plurality of adapters being associated with a respective attribute of the specified plurality of attributes and including a respective mask in a low-rank dimension associated with the respective adapter; Generating a set of intermediate outputs via a plurality of adapters of the machine learning model, each respective adapter of the plurality of adapters being associated with a respective attribute of the specified plurality of attributes and being trained on a cycle-consistency loss between different attributes of the specified plurality of attributes; Merging the set of intermediate outputs into a combined output of the plurality of adapters of the machine learning model; and Merging the set of intermediate outputs into a combined output of the plurality of adapters of the machine learning model; and Generating the output of the machine learning model based on the combined output of the plurality of adapters. Generating the output of the machine learning model based on the combined output of the plurality of adapters. 17 The method of claim 16, 17 The method of claim 16, Wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: Wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: An object to be depicted in the image output generated by the machine learning model; and An object to be depicted in the image output generated by the machine learning model; and A style of the image output generated by the machine learning model. A style of the image output generated by the machine learning model. Regarding current claim 1, claim 1 of the co-pending application teaches all of the limitations of current claim 1 except for the following, which are taught by Yang: Including a respective mask in a low-rank dimension associated with the respective adapter (Figure 1, Concepts V1, V2, and V3 are processed by their own respective LoRA; Figure 3, Concept Region Mask describes outputting different concepts in different areas of the final image). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Yang in the same field of AI image generation and include a respective mask in a low-rank dimension associated with the respective adapter. Doing so would have allowed using a known way to customize different areas of AI generated images. Regarding current claims 10 and 11, claims 7 and 8 of the co-pending application have differing independent claims, but recite the same limitations of the current claims, respectively. Claim 8 of the co-pending application teaches a rank dimension which can be interpreted as a low-rank dimension. Regarding current claim 12, claim 9 of the co-pending application teaches all of the limitations of current claim 12 except for the following, which are taught by Yang: The first adapter including a first mask in a low-rank dimension associated with the first adapter (Figure 1, Concepts V1, V2, and V3 are processed by their own respective LoRA; Figure 3, Concept Region Mask describes outputting different concepts in different areas of the final image); The second adapter including a second mask in a low-rank dimension associated with the second adapter, such that an output of the first adapter is orthogonal to an output of the second adapter (Figure 1, Concepts V1, V2, and V3 are processed by their own respective LoRA; Figure 3, Concept Region Mask describes outputting different concepts in different areas of the final image; Yang, Figure 3, Concept Region Mask describes outputting different concepts in different areas of the final image); It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Yang in the same field of AI image generation and include a respective mask in a low-rank dimension associated with the respective adapter. Doing so would have allowed using a known way to customize different areas of AI generated images. 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. 07-21-aia AIA Claim s 1-2, 4-6, 8, 12-13, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models) in view of Menges (US 20250086865) . Regarding claim 1, Yang teaches a processing system for machine learning, comprising: Generate a set of intermediate outputs via a plurality of adapters of the machine learning model, each respective adapter of the plurality of adapters being associated with a respective attribute of the specified plurality of attributes and including a respective mask in a low-rank dimension associated with the respective adapter (Figure 1, Concepts V1, V2, and V3 are processed by their own respective LoRA); Merge the set of intermediate outputs into a combined output of the plurality of adapters of the machine learning model (Section 3.2, LoRA-Composer efficiently combines these acquired LoRAs into a unified, coherent image); Generate the output of the machine learning model based on the combined output of the plurality of adapters (Figure 1, Concepts V1, V2, and V3 are processed by their own respective LoRA and combined into a final output image). While Yang fails to disclose the following, Menges teaches: At least one memory having executable instructions stored thereon (Paragraph 40, a non-transitory computer-readable storage medium storing instructions); One or more processors configured to execute the executable instructions in order to cause the processing system (Paragraph 40, a non-transitory computer-readable storage medium storing instructions that, when executed, cause the one or more processors to carry out the functions attributed to the respective devices) to: Receive a request to generate an output of a machine learning model, the request specifying a plurality of attributes of the output of the machine learning model (Paragraph 30, Users may provide instructions to the generative design system to modify attributes of media content displayed at an infinite canvas); Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and including a memory storing executable instructions and a processor to execute the instructions to receive a request to generate an output of a machine learning model, where the request specifies a plurality of attributes of the output. Doing so would have allowed for using a known way to perform AI image generation. Method claim 16 corresponds to system claim 1. Therefore, claim 16 is rejected for the same reasons as used above. Regarding claim 2, the combination of Yang and Menges teaches the processing system of claim 1. While the combination as presented previously fails to disclose the following, Menges further teaches wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: An object to be depicted in the image output generated by the machine learning model (Paragraph 35, The generative design system may subsequently generate the second set 110 of images based on the first set 106 in response to the user selecting the image 108 and providing instructions using a natural language prompt of “add dogs to the park” in the input 102); A style of the image output generated by the machine learning model (Paragraph 57, the generative design system 300 can modify a subject matter of the first image to have a style of the second image). Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and specifying an object to be included in the output image of the machine learning model and a style of the image output generated by the machine learning model. Doing so would have allowed for customizing the image output by AI image generation. Method claim 17 corresponds to system claim 2. Therefore, claim 17 is rejected for the same reasons as used above. Regarding claim 4, the combination of Yang and Menges teaches the processing system of claim 1, wherein the respective mask associated with the respective adapter comprises a weighting vector with learnable weight values in the low-rank dimension (Yang, Section 2.2, collects Low-Rank Adaptation (LoRA) [10] weights of massive custom concepts, then trains the model to predict the LoRA weights from input concept image) and wherein a masked output associated with a first adapter of the plurality of adapters is orthogonal to a masked output associated with a second adapter of the plurality of adapters (Yang, Figure 3, Concept Region Mask describes outputting different concepts in different areas of the final image). Method claim 19 corresponds to system claim 4. Therefore, claim 19 is rejected for the same reasons as used above. Regarding claim 5, the combination of Yang and Menges teaches the processing system of claim 1, wherein the set of intermediate outputs comprises a plurality of images, each image of the plurality of images corresponding to an image conforming to an attribute from the specified plurality of attributes (Yang, Figure 3 shows the concept region masks where each concept (object) is placed after being generated separately. Then the concepts are combined into the final output image). Regarding claim 6, the combination of Yang and Menges teaches the processing system of claim 1. While the combination as presented previously fails to disclose the following, Menges further teaches: Wherein a first adapter of the plurality of adapters is biased to operating on earlier layers over later layers in the machine learning model and a second adapter of the plurality of adapters is biased to operating on later layers over earlier layers of the machine learning model (Paragraph 59, …applying different generative models or layers of generative models to an image (e.g., an image to be merged). In response to the user selecting two or more images to be merged into a target image, the generative design system 300 may select a highest priority attribute from the two or more images based on one or more of an order in which the user has selected the two or more images, the distance from the two or more images to the target image on the canvas, the times at which the two or more images were generated, the frequency at which the user has previously generated images having certain image attributes, etc.). Note: Menges teaches the ability to prioritize specific layers of the image. Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and prioritizing different layers in the image generation process. Doing so would have allowed for customizing the image output by AI image generation. Regarding claim 8, the combination of Yang and Menges teaches the processing system of claim 1. While the combination as presented previously fails to disclose the following, Menges further teaches: A rank associated with a first attribute of the specified plurality of attributes exceeds a rank associated with a second attribute of the specified plurality of attributes in a first plurality of layers of the machine learning model (Paragraph 59, The generative design system 300 may determine an attribute priority, where the attribute priority represents an order in which image attributes are modified by the generative design system 300 in the merged image); A rank associated with the second attribute of the specified plurality of attributes exceeds a rank associated with the first attribute of the specified plurality of attributes in a second plurality of layers of the machine learning model, the second plurality of layers being layers subsequent to the first plurality of layers (Paragraph 59, The generative design system 300 may determine an attribute priority, where the attribute priority represents an order in which image attributes are modified by the generative design system 300 in the merged image. For example, a background style may have the highest attribute priority and a foreground subject matter may have the lowest attribute priority). Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and determining a ranking of different specified attributes of the output image. Doing so would have allowed for further customizing the image output by AI image generation by determining priority of specified attributes. Method claim 20 corresponds to system claim 8. Therefore, claim 20 is rejected for the same reasons as used above. Regarding claim 12, Yang teaches a processing system for machine learning, comprising: Receive a first data set associated with a first attribute for which a machine learning model is to be trained (Figure 3, V1 with associated LoRA); Receive a second data set associated with a second attribute for which the machine learning model is to be trained (Figure 3, V2 with associated LoRA); Train a first adapter of the machine learning model to finetune outputs in accordance with the first attribute based on the first data set, the first adapter including a first mask in a low-rank dimension associated with the first adapter (Figure 3, V1 with associated LoRA and mask M1); Train a second adapter of the machine learning model to finetune outputs in accordance with the second attribute based on the second data set, the second adapter including a second mask in a low-rank dimension associated with the second adapter, such that an output of the first adapter is orthogonal to an output of the second adapter (Figure 3, V2 with associated LoRA and mask M2); Deploy the machine learning model with the trained merged adapter (Figure 3, Layout condition combines the first and second concepts). While Yang fails to disclose the following, Menges teaches: At least one memory having executable instructions stored thereon (Paragraph 40, a non-transitory computer-readable storage medium storing instructions); One or more processors configured to execute the executable instructions in order to cause the processing system (Paragraph 40, a non-transitory computer-readable storage medium storing instructions that, when executed, cause the one or more processors to carry out the functions attributed to the respective devices) to: Trained a merged adapter comprising the trained first adapter and the trained second adapter (Paragraph 59, The generative design system 300 may determine, from a set of image attributes of the image(s) requested to be merged); Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and including a memory storing executable instructions and merge two adapters to create the final output image. Doing so would have allowed for using a known way to perform AI image generation. Regarding claim 13, the combination of Yang and Menges teaches the processing system of claim 12. While the combination as presented previously fails to disclose the following, Menges further teaches wherein the output of the machine learning model is an image output and wherein the plurality of attributes includes: An object to be depicted in the image output generated by the machine learning model (Paragraph 35, The generative design system may subsequently generate the second set 110 of images based on the first set 106 in response to the user selecting the image 108 and providing instructions using a natural language prompt of “add dogs to the park” in the input 102); A style of the image output generated by the machine learning model (Paragraph 57, the generative design system 300 can modify a subject matter of the first image to have a style of the second image). Menges and Yang are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yang by using Menges and specifying an object to be included in the output image of the machine learning model and a style of the image output generated by the machine learning model. Doing so would have allowed for customizing the image output by AI image generation . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Menges as applied to claims 1-2, 4-6, 8, 12-13, 16-17 and 19-20 and further in view of Bhansali (US 20260010799 Note: Bhansali claims the benefit of the provisional application 63/668,592, which will be referenced for further prosecution) Regarding claim 7, the combination of Yang and Menges teaches the processing system of claim 1. While the combination fails to disclose the following, Bhansali teaches: Wherein each respective mask is based on a loss based on a rank constraint and a sparsity constraint (Paragraph 47, Aggregation may be done by accessing each of the selected client’s LoRA adapters and creating a mask based on its sparsity). Bhansali and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Bhansali and creating each mask based on rank and sparsity. Doing so would have allowed for customizing the final generated image . 07-21-aia AIA Claim s 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Menges as applied to claims 1-2, 4-6, 8, 12-13, 16-17 and 19-20 and further in view of Huang (Multimodal unsupervised image-to-image translation) . Regarding claim 9, the combination of Yang and Menges teaches the processing system of claim 1. While the combination fails to disclose the following, Huang teaches: The plurality of adapters of the machine learning model comprise adapters configured based on a cycle-consistency loss between a first attribute of the plurality of attributes and a second attribute of the plurality of attributes (Page 3, Paragraph 1, Another popular constraint is the cycle consistency loss [7-9]. It enforces that if we translate an image to the target domain and back, we should obtain the original image); A cycle associated with the cycle-consistency loss comprises an application and a removal of an attribute from the specified plurality of attributes to data complying with another attribute from the specified plurality of attributes (Page 3, Paragraph 1, Another popular constraint is the cycle consistency loss [7-9]. It enforces that if we translate an image to the target domain and back, we should obtain the original image). Huang and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Huang and using cycle-consistency loss in the machine learning model. Doing so would have allowed for using a known way of performing AI image generation. Regarding claim 10, the combination of Yang, Menges, and Huang teaches the processing system of claim 9, wherein at least one of the adapters has frozen weights (Yang, Section 2.2, fixes the pre-trained weights) and an output mask with learnable weights associated with the cycle-consistency loss (Yang, Section 2.2, first collects Low-Rank Adaptation (LoRA) [10] weights of massive custom concepts, then trains the model to predict the LoRA weights from input concept image). Note: Huang teaches cycle-consistency loss. Huang and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Huang and using cycle-consistency loss associated with the weights of the adapters. Doing so would have allowed for using a known way of determining weights associated with LoRA. Regarding claim 11, the combination of Yang, Menges, and Huang teaches the processing system of claim 9, wherein at least one or the adapters has learnable weights associated with the cycle-consistency loss in the low-rank dimension of the at least one of the adapters (Yang, Section 2.2, first collects Low-Rank Adaptation (LoRA) [10] weights of massive custom concepts, then trains the model to predict the LoRA weights from input concept image). Note: Huang teaches cycle-consistency loss. Huang and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Huang and using cycle-consistency loss associated with low rank dimension of the adapters. Doing so would have allowed for using a known way of using cycle-consistency loss in AI image generation . 07-21-aia AIA Claim s 3, 14-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Menges as applied to claims 1-2, 4-6, 8, 12-13, 16-17 and 19-20 and further in view of Vlassis (US 20260017839) . Regarding claim 3, the combination of Yang and Menges teaches the processing system of claim 1. While the combination fails to disclose the following, Vlassis teaches: Wherein the machine learning model comprises a model trained based on one or more of a content loss, a style loss, or a scaling factor associated with a similarity term (Paragraph 215, the training component computes the content loss based on the comparison (e.g., according to a similarity of the annotation and the target token)). Vlassis and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Vlassis and training a machine learning model based on content loss associated with similarity. Doing so would have allowed for using a known way training a machine learning model. Method claim 18 corresponds to system claim 3. Therefore claim 18 is rejected for the same reasons as above. Regarding claim 14, the combination of Yang and Menges teaches the processing system of claim 12. While the combination fails to disclose the following, Vlassis teaches: Wherein the merged adapter is trained based on a loss associated with the first adapter, a loss associated with the second adapter (Paragraph 215, the training component computes the content loss). Vlassis and the combination of Yang and Menges are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Menges by using Vlassis and training a machine learning model based on content loss. Doing so would have allowed for using a known way training a machine learning model. While the combination as presented previously fails to disclose the following, Menges further teaches: A scaling factor associated with a similarity term (Paragraph 64, The generative design system 300 may determine the set of personalization media content by determining media content having different image attributes (e.g., multiple images each having different styles, subject matter, sizes… The generative design system 300 may determine attribute similarity based on a history of user selections of image attributes; Paragraph 107, a resizing operation is performed on the remaining portion of the second image to increase the size of the woman and the balloon). Menges and the combination of Yang and Vlassis are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Vlassis by using Menges and scaling based on similarity. Doing so would have allowed for generating a desired output image. Regarding claim 15, the combination of Yang, Menges, and Vlassis teaches the processing system of claim 14. While the combination as presented previously fails to disclose the following, Menges further teaches: Wherein the scaling factor is applied to a combination of the first mask and the second mask (Paragraph 72, Although not depicted as identical, the crown used in the image 808 may be identical to sketched crown or substantially identical (i.e., rotated, resized, recolored, etc. to appear more naturally integrated in the resulting image 808)). Menges and the combination of Yang and Vlassis are both considered to be analogous to the claimed invention because they are in the same field of AI image generation. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yang and Vlassis by using Menges and scaling a combination of the first mask and the second mask. Doing so would have allowed for generating a desired output image. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SNIGDHA SINHA whose telephone number is (571)272-6618. The examiner can normally be reached Mon-Fri. 12pm-8pm. 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, Jason Chan can be reached at 571-272-3022. 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. /SNIGDHA SINHA/Examiner, Art Unit 2619 /JASON CHAN/Supervisory Patent Examiner, Art Unit 2619 Application/Control Number: 18/940,431 Page 2 Art Unit: 2619 Application/Control Number: 18/940,431 Page 3 Art Unit: 2619 Application/Control Number: 18/940,431 Page 4 Art Unit: 2619 Application/Control Number: 18/940,431 Page 5 Art Unit: 2619 Application/Control Number: 18/940,431 Page 6 Art Unit: 2619 Application/Control Number: 18/940,431 Page 7 Art Unit: 2619 Application/Control Number: 18/940,431 Page 8 Art Unit: 2619 Application/Control Number: 18/940,431 Page 9 Art Unit: 2619 Application/Control Number: 18/940,431 Page 10 Art Unit: 2619 Application/Control Number: 18/940,431 Page 11 Art Unit: 2619 Application/Control Number: 18/940,431 Page 12 Art Unit: 2619 Application/Control Number: 18/940,431 Page 13 Art Unit: 2619 Application/Control Number: 18/940,431 Page 14 Art Unit: 2619 Application/Control Number: 18/940,431 Page 15 Art Unit: 2619 Application/Control Number: 18/940,431 Page 16 Art Unit: 2619 Application/Control Number: 18/940,431 Page 17 Art Unit: 2619 Application/Control Number: 18/940,431 Page 18 Art Unit: 2619 Application/Control Number: 18/940,431 Page 19 Art Unit: 2619 Application/Control Number: 18/940,431 Page 20 Art Unit: 2619
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