DETAILED ACTION
This action is in reply to the Amendments filed on 04/28/2026.
Claims 2 and 5 are cancelled.
Claims 1, 3-4, and 6-19 are rejected.
Claims 1, 3-4, and 6-19 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 04/28/2026, has been entered. Claims 1, 6, 13, 16, and 18-19 have been amended.
Claim Objections
The claim objections from the prior Office Action have been withdrawn pursuant Applicant’s amendments.
Claim Rejections under 35 USC § 112(b)
The claim rejections under 35 USC § 112(b) from the prior Office Action have been withdrawn pursuant Applicant’s amendments, however, a new 112(b) has been added in light of the amendments.
Priority
The current Application claims priority from International Application No. PCT/EP2022/058717 filed 03/31/2022. Therefore, the instant claims receive the effective filing date of 03/31/2022.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112(b)
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.
Claim 16 is 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.
Claim 16 recites “wherein the compression garment fit information includes at least one of the circumferences to be measured according to RALGZ387/l or RAL-GZ387/2, the at least one circumferences including a waist circumference, an upper hip circumference, a calfB 1 circumference and a foot Y circumference.” It is unclear to one of ordinary skill in the art how the at least one circumferences includes all of a waist circumference, an upper hip circumference, a calfB 1 circumference and a foot Y circumference. For the purpose of this examination, Examiner interprets “wherein the compression garment fit information includes at least one of the circumferences to be measured according to RALGZ387/l or RAL-GZ387/2, the at least one circumferences including a waist circumference, an upper hip circumference, a calfB 1 circumference and a foot Y circumference” as “wherein the compression garment fit information includes at least one of the circumferences to be measured according to RALGZ387/l or RAL-GZ387/2, the at least one circumferences including at least one of a waist circumference, an upper hip circumference, a calfB 1 circumference and a foot Y circumference.”
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 4 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The content of dependent claim 4 is recited in independent claim 1 and, therefore, claim 4 does not further limit independent claim 1.
Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, and 6-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-A computer implemented method for generating compression garment fit information, the method comprising:
-acquiring a video or images of a person;
-inputting the acquired video or images to an artificial intelligence module;
-determining the compression garment fit information by the artificial intelligence module; and
-outputting the compression garment fit information,
-wherein the artificial intelligence module comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person,
-wherein the compression garment fit information corresponds to a body part of the person,
-wherein the artificial intelligence module comprises a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person based on the determined dimension information
The above limitations recite the concept of determining and providing compression garment fit information. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of acquiring a video or images of a person; outputting the compression garment fit information, and wherein the compression garment fit information corresponds to a body part of the person are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “acquiring” and “outputting” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of a computer implemented method for generating compression garment fit information, the method comprising: inputting the acquired video or images to an artificial intelligence module; determining the compression garment fit information by the artificial intelligence module; and wherein the artificial intelligence module comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person, wherein the artificial intelligence module comprises a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person based on the determined dimension information are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the method is computer implemented, that the inputting is to an artificial intelligence module, that the determining is by the artificial intelligence module, that the first model is a first artificial intelligence model comprised in the artificial intelligence module, that the first model is configured to be pretrained, that the second model is a second artificial intelligence model comprised in the artificial intelligence module, and that the second model is configured to be pretrained, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer implemented,” “artificial intelligence module,” “first artificial intelligence model,” “pretrained,” and “second artificial intelligence model” language, “inputting,” “determining,” “determine,” and “determine” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-A computer implemented method for generating compression garment fit information, the method comprising:
-acquiring a video or images of a person;
-inputting the acquired video or images to an artificial intelligence module;
-determining the compression garment fit information by the artificial intelligence module; and
-outputting the compression garment fit information,
-wherein the artificial intelligence module comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person,
-wherein the compression garment fit information corresponds to a body part of the person,
-wherein the artificial intelligence module comprises a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person based on the determined dimension information
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0058] of Applicant’s specification – “The device/apparatus may be a general computing device, e.g., a laptop, a tablet, a mobile phone, a smart TV, a dedicated compression garment measuring device, etc.” Specifically, the additional elements of a computer implemented method, an artificial intelligence module, a first artificial intelligence model, models being pretrained, and a second artificial intelligence model are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of acquiring data, inputting data, determining data, and outputting data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 18 is a computing device reciting similar functions as claim 1. Examiner notes that claim 18 recites the additional elements of a computing device, an artificial intelligence module, a first artificial intelligence model, models being pretrained, and a second artificial intelligence model, however, claim 18 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 19 is a storage medium reciting similar functions as claim 1. Examiner notes that claim 19 recites the additional elements of a computing device an artificial intelligence module, a first artificial intelligence model, models being pretrained, and a second artificial intelligence model, however, claim 19 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 18, and 19 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 3-4, 6-17, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 3-4, 6-17 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 3, 9-10, and 14-17 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 4, 6-8, and 11-13 recite the additional elements of the artificial intelligence module, a pretrained artificial intelligence model, a neural network model, a machine learning model, a first artificial intelligence model configured to be pretrained, a second artificial intelligence model configured to be pretrained, second artificial intelligence model comprising different versions, each version being separately trained, a third artificial intelligence model, and third artificial intelligence model comprises different versions, each version being separately trained, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 3-4, 6-17 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 3-4, 6-17 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 18, and 19, dependent claims 3-4, 6-17 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining and providing compression garment fit information) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 6-11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (US 2021/0287274 A1), hereinafter Nguyen, in view of Thelemann et al. (US 2021/0298401 A1), hereinafter Thelemann.
Regarding claim 1, Nguyen discloses a computer implemented method for generating compression garment fit information, the method comprising:
-acquiring a video or images of a person (Nguyen, see at least: “Continuing with FIG. 7, in the second method, the entire body measurement process as described in method 200 in FIG. 2 is performed in virtual try-on module 720. Specifically, body measurement module 400 provides the 3D body model and measurements of end-users 311. From user image/video input unit 722 [i.e. acquiring a video or images], user body part parsing unit 723 detects a particular end-user 311 [i.e. of a person]. A user image background segmentation unit 726 cuts and removes the particular end-user 311 from the background and/or other peoples in the photo and video” [0085]);
-inputting the acquired video or images to an artificial intelligence module (Nguyen, see at least: “Continuing with FIG. 7, in the second method, the entire body measurement process as described in method 200 in FIG. 2 is performed in virtual try-on module [i.e. to an artificial intelligence module] 720. Specifically, body measurement module 400 provides the 3D body model and measurements of end-users 311. From user image/video input unit 722 [i.e. inputting the acquired video or images], user body part parsing unit 723 detects a particular end-user 311. A user image background segmentation unit 726 cuts and removes the particular end-user 311 from the background and/or other peoples in the photo and video” [0085] and “virtual try-on module 700 provides clothes fitting services using machine learning algorithms [i.e. to an artificial intelligence module] and presents the fitting results on the communication devices of end-users 311. Virtual try-on module 700 includes communication links 701, a fashion and apparel (F&A) database 710, a user measurement predictor unit 721, a user posture in images or videos 722, a user body parts segmentation unit 723, an intermediate body measurement result unit 724, a F&A size selector 725, a body shape removal unit 726, a sparse keypoint detector 727, a dense keypoint detector 728, a virtual try-on machine learning unit 729, a retailer/seller server 730, and user communication devices 731” [0084]);
-determining the garment fit information by the artificial intelligence module (Nguyen, see at least: “virtual try-on module 700 provides clothes fitting services using machine learning algorithms [i.e. by the artificial intelligence module] and presents the fitting results on the communication devices of end-users 311 … The results of user measurement predictor 721 may include different sizes and measurements. F&A size selector 725 is operative to select the proper size and measurements of end-users 311. Then, Virtual try-on machine learning unit 729 [i.e. by the artificial intelligence module] tailors and mounts the selected F&A items onto the parametric body model of end-users 311. The result can be tracked and rotated 360° so that end-users 311 can perceive the fitting of the selected F&A items [i.e. determining the garment fit information]. Finally, the try-on results are sent and stored in store server 730 for future uses and commercial purposes” [0084]); and
-outputting the garment fit information (Nguyen, see at least: “virtual try-on module 700 provides clothes fitting services using machine learning algorithms and presents the fitting results on the communication devices of end-users 311 [i.e. outputting the garment fit information] … The results of user measurement predictor 721 may include different sizes and measurements. F&A size selector 725 is operative to select the proper size and measurements of end-users 311. Then, Virtual try-on machine learning unit 729 tailors and mounts the selected F&A items onto the parametric body model of end-users 311. The result can be tracked and rotated 360° so that end-users 311 can perceive the fitting of the selected F&A items. Finally, the try-on results are sent [i.e. outputting the garment fit information] and stored in store server 730 for future uses and commercial purposes” [0084]),
-wherein the artificial intelligence module comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person (Nguyen, see at least: “Continuing with FIG. 7, in the second method, the entire body measurement process as described in method 200 in FIG. 2 is performed in virtual try-on module 720 [i.e. wherein the artificial intelligence module]. Specifically, body measurement module 400 provides the 3D body model and measurements of end-users 311” [0085] and “a body measurement service is provided by using the fashion and apparel (F&A) image files obtained in step 101 processed by different convolutional neural network (CNN) algorithms [i.e. comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person]” [0046]),
-wherein the garment fit information corresponds to a body part of the person (Nguyen, see at least: “The virtual try-on service also includes fitting modules similar to body measurements service but applicable to fashion and apparel (F&A) items, not on end-users. First, F&A items worn by an end-user are extracted from either photos or videos. Second, end-user parameters including tailor measurements of height, weight, age, neck size, chest size, arm length, waist, legs, buttstock, etc. [i.e. wherein the garment fit information corresponds to a body part of the person] are obtained” [0048]),
-wherein the artificial intelligence module comprises a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the garment fit information corresponding to the body part of the person based on the determined dimension information (Nguyen, see at least: “The virtual try-on service also includes fitting modules [i.e. wherein the artificial intelligence module comprises a second artificial intelligence model] similar to body measurements service but applicable to fashion and apparel (F&A) items, not on end-users. First, F&A items worn by an end-user are extracted from either photos or videos. Second, end-user parameters including tailor measurements of height, weight, age, neck size, chest size, arm length, waist, legs, buttstock, etc. are obtained. Third, using a CNN algorithm [i.e. wherein the artificial intelligence module comprises a second artificial intelligence model], dense keypoints are placed on those extracted F&A items, e.g., clothing units. Dense keypoints are placed to describe feature of each clothing units. Dense keypoints can be derived from picture pixels of a photo or video image of the clothing units. Dense keypoints depict the feature of each clothing unit. Each dense keypoint includes a locator and a discriminative descriptor. Thus, dense keypoints of a pant will be different from those of a shirt [i.e. the second artificial intelligence model being configured to be pretrained to determine the garment fit information corresponding to the body part of the person]. Fourth, sparse keypoints are obtained from dense keypoints to describe the border or outline of a clothing unit. For example, sparse keypoints describe the outlined shape of a skirt. Either this is a dress, a short skirt, or a long skirt. Fifth, three-dimension (3D) pivot keypoints are derived to reconstruct a 3D body model (3D reconstruction) and measurements of each clothing unit. The 3D reconstructions of the F&A items are obtained from a convolutional neural network algorithm such as histogram of local gradients known as SIFT. Other algorithms that can provide both repeatable and reliable local descriptors are also used in step 104. Finally, a mapping unit is used to map the 3D reconstruction model of a clothing item onto the 3D body model and measurements of an end-user [i.e. based on the determined dimension information]” [0048] and “the F&A items are mapped on the most current body model of the end-user to see if these F&A items fit the end-user [i.e. the second artificial intelligence model being configured to be pretrained to determine the garment fit information]” [0058] and “Virtual try-on machine learning unit 729 tailors and mounts the selected F&A items onto the parametric body model of end-users 311. The result can be tracked and rotated 360° so that end-users 311 can perceive the fitting of the selected F&A items [i.e. the second artificial intelligence model being configured to be pretrained to determine the garment fit information corresponding to the body part of the person based on the determined dimension information]” [0084]).
Nguyen does not explicitly disclose the garment fit information being compression garment fit information.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of compression garment fit information (Thelemann, see at least: “additional to the measurement assistance function, a product properties assistance function to determine further product specification values of a non-measurement group is used. The product specification values of the non-measurement group may, for example, comprise a type of the medical equipment and/or a compression profile of the compression garment and/or at least one material of the medical equipment and/or a weaving and/or knitting method for the medical equipment, in particular the compression garment [i.e. compression garment fit information], and/or a configuration of the medical equipment, as well as further properties” [0040] and “The measurement assistance function 2 and the product properties assistance function 3 of the group 5 are both used at a time-point 9 corresponding to the action of compiling a complete set of product specification values, which allow, at a time-point 10, the production/manufacturing of a custom-tailored medical equipment 6 or a selection of a pre-manufactured medical equipment 6 from among standard sizes [i.e. compression garment fit information]. The medical equipment 6 is then provided to a future wearer, who starts using the medical equipment 6 at a time-point 11” [0086]). This known technique is applicable to the method of Nguyen as they both share characteristics and capabilities, namely, they are directed to fitting garments.
It would have been recognized that applying the known technique of garment fit information being compression garment fit information, as taught by Thelemann, to the teachings of Nguyen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of garment fit information being compression garment fit information, as taught by Thelemann, into the method of Nguyen would have been recognized by those of ordinary skill in the art as resulting in an improved method that would detect patterns of product specification values that often lead to complaint (Thelemann, [0034]).
Regarding claim 3, Nguyen in view of Thelemann teaches the method of claim 1. Nguyen further discloses:
-wherein the video or images are 2D video and 2D images, respectively (Nguyen, see at least: “Input section 410 receives input images and videos from end-users 311 via different means. Such means include an online social media 411, an online/offline storage devices 412, a user camera(s) 413, user parameters 414, user images or videos 415 [i.e. wherein the video or images are 2D video and 2D images, respectively]” [0077]).
Regarding claim 4, Nguyen in view of Thelemann teaches the method of claim 1. Nguyen further discloses:
-wherein the artificial intelligence module comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person (Nguyen, see at least: “Continuing with FIG. 7, in the second method, the entire body measurement process as described in method 200 in FIG. 2 is performed in virtual try-on module 720 [i.e. wherein the artificial intelligence module]. Specifically, body measurement module 400 provides the 3D body model and measurements of end-users 311” [0085] and “a body measurement service is provided by using the fashion and apparel (F&A) image files obtained in step 101 processed by different convolutional neural network (CNN) algorithms [i.e. comprises a first artificial intelligence model, wherein the first artificial intelligence model is configured to be pretrained to determine dimension information of the person]” [0046]).
Regarding claim 6, Nguyen in view of Thelemann teaches the method of claim 1. Nguyen further discloses:
-wherein the first artificial intelligence model is configured to be pretrained to determine the garment fit information corresponding to the body part of the person further based on additional information of the person (Nguyen, see at least: “FIG. 12 shows the front-end display of the body measurement module [i.e. wherein the first artificial intelligence model is configured to be pretrained to determine the garment fit information corresponding to the body part of the person] that uses the artificial intelligence (AI) service of personal fashion coaching and assistance system in accordance with an exemplary embodiment of the present invention. FIG. 12 illustrates an implementation of step 102 (providing body measurement services, please refer back to FIG. 1), step 202 (receive user picture, personal parameters, and/or videos; please refer back to FIG. 2) which includes displays 1211-1218 realized by the execution of method 100 and method 200 embedded in the hardware/software of personal fashion and apparel (F&A) advising and coaching system 300. In display 1211, personal parameters such as gender, age (or birth year), height, weight, etc. are entered by end-users 311 [i.e. further based on additional information of the person] after logging in into web-based smart fashion system 1070” [0105]).
Nguyen does not explicitly disclose the second artificial intelligence model is configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person further based on additional information of the person.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the second artificial intelligence model is configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person further based on additional information of the person (Thelemann, see at least: “The artificial intelligence plausibility algorithm [i.e. the second artificial intelligence model], whose parametrisation implicitly comprises a body part information which may also have been used as training data for the artificial intelligence plausibility algorithm. This algorithm may also have been trained regarding feedback data describing shortcomings of already produced and/or selected medical equipment, thus detecting patterns of product specification values often leading to complaints [i.e. is configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person]” [0034] and “additional to the measurement assistance function, a product properties assistance function to determine further product specification values of a non-measurement group is used. The product specification values of the non-measurement group may, for example, comprise a type of the medical equipment and/or a compression profile of the compression garment and/or at least one material of the medical equipment and/or a weaving and/or knitting method for the medical equipment, in particular the compression garment [i.e. compression garment fit information], and/or a configuration of the medical equipment, as well as further properties … preferably additionally, the measurement assistance function and/or the product properties assistance function may further comprise at least one additional communication process to acquire at least one wearer-information describing at least one characteristic of the wearer. Wearer information may, for example, comprise the weight of the wearer and/or the age of the wearer and/or the body-mass-index (BMI) of the wearer. Of course, additional wearer information may also be used, for example gender, conditions to be treated and the like [i.e. based on additional information of the person]” [0040]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 7, Nguyen in view of Thelemann teaches the method of claim 6.
Nguyen does not explicitly disclose the second artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the second artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part (Thelemann, see at least: “The artificial intelligence plausibility algorithm [i.e. wherein the second artificial intelligence model], whose parametrisation implicitly comprises a body part information which may also have been used as training data for the artificial intelligence plausibility algorithm [i.e. comprises different versions, each version being separately trained with data corresponding to a different targeted body part]. This algorithm may also have been trained regarding feedback data describing shortcomings of already produced and/or selected medical equipment, thus detecting patterns of product specification values often leading to complaints” [0034] and “at least one artificial intelligence algorithm, in particular the artificial intelligence interaction algorithm and/or the artificial intelligence plausibility algorithm, may be further trained for at least one product specification value determination process according to provided outcome and/or feedback information” [0044] and “The plausibility check uses body part information and may comprise comparisons with at least one threshold and/or already measured and input product specification values” [0107]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 8, Nguyen in view of Thelemann teaches the method of claim 6.
Nguyen does not explicitly disclose the second artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the second artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information (Thelemann, see at least: “The artificial intelligence plausibility algorithm [i.e. wherein the second artificial intelligence model], whose parametrisation implicitly comprises a body part information which may also have been used as training data for the artificial intelligence plausibility algorithm [i.e. comprises different versions, each version being separately trained with data corresponding to a different combination of a targeted body part]. This algorithm may also have been trained regarding feedback data describing shortcomings of already produced and/or selected medical equipment, thus detecting patterns of product specification values often leading to complaints” [0034] and “at least one artificial intelligence algorithm, in particular the artificial intelligence interaction algorithm and/or the artificial intelligence plausibility algorithm, may be further trained for at least one product specification value determination process according to provided outcome and/or feedback information” [0044] and “especially preferred embodiment provides that, for at least a part of the received product specification values of the measurement group, a plausibility check using a body part information describing typical ranges of such product specification values, in particular relative to at least one other product specification value, is performed. Such a plausibility check allows for including knowledge regarding the physical background of the measurement and the body part, such that the product specification values of the measurement group may more reliably be determined. In particular, the plausibility check may comprise comparisons with at least one threshold value and/or at least one already measured and input product specification value. For example, tension values should be equal or less than corresponding skin values and/or the slope along a limb or other body parts may also fall within certain boundaries or follow a typical course [i.e. and value ranges of parameters in the additional information]” [0033]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 9, Nguyen in view of Thelemann teaches the method of claim 1.
Nguyen does not explicitly disclose the compression garment fit information comprises tension values.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the compression garment fit information comprises tension values (Thelemann, see at least: “for at least a part of the received product specification values of the measurement group, a plausibility check using a body part information describing typical ranges of such product specification values, in particular relative to at least one other product specification value, is performed. Such a plausibility check allows for including knowledge regarding the physical background of the measurement and the body part, such that the product specification values of the measurement group may more reliably be determined. In particular, the plausibility check may comprise comparisons with at least one threshold value and/or at least one already measured and input product specification value. For example, tension values should be equal or less than corresponding skin values [i.e. wherein the compression garment fit information comprises tension values] and/or the slope along a limb or other body parts may also fall within certain boundaries or follow a typical course” [0033]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 10, Nguyen in view of Thelemann teaches the method of claim 9. Nguyen further discloses:
Nguyen does not explicitly disclose each of the tension values being calculated based on a corresponding predetermined tension factor and corresponding skin surface dimension values.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of each of the tension values being calculated based on a corresponding predetermined tension factor and corresponding skin surface dimension values (Thelemann, see at least: “circumference values may be measured for both an uncompressed limb and a compressed limb (skin value/tension value). The skin value usually describes the circumference of the limb without any applied compression and is thus equivalent to a base circumference value. The tension value describes the circumference of the limb with the wearable medical equipment, in particular the compression garment, applying a desired compression [i.e. wherein each of the tension values is calculated based on a corresponding predetermined tension factor and corresponding skin surface dimension values]. In this context, but also generally, it may be advantageous that at least two product specification values sharing all of their at least one associated measurement position share a communication process, in particular a pair of a skin value and a tension value. In this manner, the user is only guided to each measurement positions once, where they may measure both the skin value (which may also be termed uncompressed circumference value) and the tension value (which may also be called tight measurement value). For the tension value, instruction information may also comprise instructions on setting the compression” [0024]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 11, Nguyen in view of Thelemann teaches the method of claim 9.
Nguyen does not explicitly disclose that the tension values are determined by the second artificial intelligence model based on the determined dimension information and/or the additional information of the person, or the tension values are determined by a third artificial intelligence model based on the determined dimension information and/or the additional information of the person.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the tension values are determined by the second artificial intelligence model based on the determined dimension information and/or the additional information of the person, or the tension values are determined by a third artificial intelligence model based on the determined dimension information and/or the additional information of the person (Thelemann, see at least: “the plausibility check may further comprise employing an artificial intelligence plausibility algorithm [i.e. wherein the tension values are determined by the second artificial intelligence model]. The artificial intelligence plausibility algorithm, whose parametrisation implicitly comprises a body part information which may also have been used as training data for the artificial intelligence plausibility algorithm” [0034] and “for at least a part of the received product specification values of the measurement group, a plausibility check [i.e. determined by the second artificial intelligence model] using a body part information describing typical ranges of such product specification values, in particular relative to at least one other product specification value, is performed. Such a plausibility check allows for including knowledge regarding the physical background of the measurement and the body part, such that the product specification values of the measurement group may more reliably be determined. In particular, the plausibility check may comprise comparisons with at least one threshold value and/or at least one already measured and input product specification value. For example, tension values should be equal or less than corresponding skin values [i.e. the tension values are determined by the second artificial intelligence model based on the determined dimension information and/or the additional information of the person] and/or the slope along a limb or other body parts may also fall within certain boundaries or follow a typical course” [0033]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 13, Nguyen in view of Thelemann teaches the method of claim 11.
Nguyen does not explicitly disclose that the second artificial intelligence model and/or the third artificial intelligence model comprises a version being trained to target a body shape that is asymmetric.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the second artificial intelligence model and/or the third artificial intelligence model comprising a version being trained to target a body shape that is asymmetric (Thelemann, see at least: “at least one artificial intelligence algorithm, in particular the artificial intelligence interaction algorithm and/or the artificial intelligence plausibility algorithm, may be further trained for at least one product specification value determination process [i.e. wherein the second artificial intelligence model and/or the third artificial intelligence model comprises a version being trained to target a body shape] according to provided outcome and/or feedback information” [0044] and “a body part health information may be derived from measured product specification values of the measurement group and/or wearer information, wherein at least one product specification value or a proposal value therefore is determined to yield a medical equipment, in particular a compression garment, suitable for treating a health condition of the body part [i.e. comprises a version being trained to target a body shape that is asymmetric] described by the body part health information” [0043] and "product specification values of the measurement group may, in particular, relate to the geometry of the body part. However, since this geometry may be variable according to the current condition/state of the body part [i.e. to target a body shape that is asymmetric], it is, of course, also possible to measure such length values and/or circumference values depending on such a condition/state" [0024]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 14, Nguyen in view of Thelemann teaches the method of claim 6. Nguyen further discloses:
-a step of receiving the additional information via user input (Nguyen, see at least: “FIG. 12 shows the front-end display of the body measurement module that uses the artificial intelligence (AI) service of personal fashion coaching and assistance system in accordance with an exemplary embodiment of the present invention. FIG. 12 illustrates an implementation of step 102 (providing body measurement services, please refer back to FIG. 1), step 202 (receive user picture, personal parameters, and/or videos; please refer back to FIG. 2) which includes displays 1211-1218 realized by the execution of method 100 and method 200 embedded in the hardware/software of personal fashion and apparel (F&A) advising and coaching system 300. In display 1211, personal parameters such as gender, age (or birth year), height, weight, etc. are entered by end-users 311 [i.e. a step of receiving the additional information via user input] after logging in into web-based smart fashion system 1070” [0105]).
Regarding claim 15, Nguyen in view of Thelemann teaches the method of claim 6. Nguyen further discloses:
-wherein the additional information comprises at least one of height, age, weight, body mass index (BMI), and gender of the person (Nguyen, see at least: “FIG. 12 shows the front-end display of the body measurement module that uses the artificial intelligence (AI) service of personal fashion coaching and assistance system in accordance with an exemplary embodiment of the present invention. FIG. 12 illustrates an implementation of step 102 (providing body measurement services, please refer back to FIG. 1), step 202 (receive user picture, personal parameters, and/or videos; please refer back to FIG. 2) which includes displays 1211-1218 realized by the execution of method 100 and method 200 embedded in the hardware/software of personal fashion and apparel (F&A) advising and coaching system 300. In display 1211, personal parameters such as gender, age (or birth year), height, weight, etc. are entered by end-users 311 [i.e. wherein the additional information comprises at least one of height, age, weight, body mass index (BMI), and gender of the person] after logging in into web-based smart fashion system 1070” [0105]).
Regarding claim 16, Nguyen in view of Thelemann teaches the method of claim 1.
Nguyen does not explicitly disclose the compression garment fit information including at least one of the circumferences to be measured according to RAL-GZ387/1 or RAL-GZ387/2, for example a waist circumference, an upper hip circumference, a calf B 1 circumference and a foot Y circumference.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the compression garment fit information including at least one of the circumferences to be measured according to RAL-GZ387/1 or RAL-GZ387/2, the at least one circumferences including a waist circumference, an upper hip circumference, a calf B 1 circumference and a foot Y circumference (Thelemann, see at least: “in preferred embodiments, the measurement positions may be defined according to at least one standard, in particular the RAL. Often, measurement positions which are relevant for certain treatments by wearable medical equipment are pre-defined in standards, such that those measurement positions may also form the base for specifying medical equipment such that a successful treatment is achieved by custom-tailoring the respective medical equipment or selecting the correct medical equipment, in particular from a number of pre-manufactured sets of medical equipment in different standard sizes [i.e. wherein the compression garment fit information]” [0030] and “when measuring circumference values at a certain RAL measurement position, in the beginners operating mode 43, the user may be guided step-by-step from the anatomical feature 30 to the measurement position 29. In the expert operating mode 45, the RAL measurement position may simply be named” [0120] and “the anatomical feature 30 is the ankle, from which a certain length upwards a circumference at measurement position 29 shall be measured [i.e. includes at least one of the circumferences to be measured according to RAL-GZ387/1 or RAL-GZ387/2]” [0095] Examiner notes that the calf is above the ankle and Fig. 3 indicated the RAL-GZ387/2 of the calf [i.e. includes at least one of the circumferences to be measured according to RAL-GZ387/1 or RAL-GZ387/2, the at least one circumferences including a waist circumference, an upper hip circumference, a calf B 1 circumference and a foot Y circumference]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Regarding claim 17, Nguyen in view of Thelemann teaches the method of claim 1.
Nguyen does not explicitly disclose the compression garment fit information comprising fit information for one or more prefabricated and/or preconfigured compression garments.
Thelemann, however, teaches fitting garments (i.e. [0003]), including the known technique of the compression garment fit information comprising fit information for one or more prefabricated and/or preconfigured compression garments (Thelemann, see at least: “in preferred embodiments, the measurement positions may be defined according to at least one standard, in particular the RAL. Often, measurement positions which are relevant for certain treatments by wearable medical equipment are pre-defined in standards, such that those measurement positions may also form the base for specifying medical equipment such that a successful treatment is achieved by custom-tailoring the respective medical equipment or selecting the correct medical equipment, in particular from a number of pre-manufactured sets of medical equipment in different standard sizes [i.e. wherein the compression garment fit information comprises fit information for one or more prefabricated and/or preconfigured compression garments]” [0030]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nguyen with Thelemann for the reasons identified above with respect to claim 1.
Claim 18 recites limitations directed towards a computing device. The limitations recited in claim 18 is parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Claim 19 recites limitations directed towards a non-transitory storage medium configured to store instructions configured to be executed by at least one processor. The limitations recited in claim 19 is parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen, in view of Thelemann, in further view of Aleem et al. (US 2022/0343608 A1), hereinafter Aleem.
Regarding claim 12, Nguyen in view of Thelemann teaches the method of claim 11.
Nguyen in view of Thelemann does not explicitly teach the third artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part, and/or corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information.
Aleem, however, teaches computing a wearable fit value (i.e. [0004]), including the known technique of the third artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part, and/or corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information (Aleem, see at least: “a separate ML model 404 [i.e. wherein the third artificial intelligence model] is provided for each of the contact points 406, the skin elasticity 482, the device slippage tolerance 484, the tolerable contact pressure 485, and the nose shape 466” [0066] and “the ML model 404 may be configured to predict tolerable contact pressure 485 based on at least one of the 3D model 410, the 3D model 416, or the predicted contact points 406 between the 3D model 410 and the 3D model 416. The tolerable contact pressure 485 is a parameter that indicates a level of accepted pressure (or force) on the relevant body part [i.e. comprises different versions, each version being separately trained with data corresponding to a different targeted body part, and/or corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information]” [0065]). This known technique is applicable to the method of Nguyen in view of Thelemann as they both share characteristics and capabilities, namely, they are directed to computing a wearable fit value.
It would have been recognized that applying the known technique of the third artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part, and/or corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information, as taught by Aleem, to the teachings of Nguyen in view of Thelemann would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of the third artificial intelligence model comprising different versions, each version being separately trained with data corresponding to a different targeted body part, and/or corresponding to a different combination of a targeted body part and value ranges of parameters in the additional information, as taught by Aleem, into the method of Nguyen in view of Thelemann would have been recognized by those of ordinary skill in the art as resulting in an improved method that would simulate a placement of a wearable garment (Aleem, [0008]).
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that claim 1 as amended is not an abstract idea but rather integrates any abstract into a practical application. More specifically, claim 1 recites a "computer implemented method for generating compression garment fit information" that includes "acquiring a video or images of a person," "inputting the acquired video or images to an artificial intelligence module," "determining the compression garment fit information by the artificial intelligence module," and "outputting the compression garment fit information." The claim therefore requires processing of acquired video or images of a person using an artificial intelligence module in order to generate compression garment fit information corresponding to a body part of the person. The claim is not directed to a mere mental process, but to the use of an artificial intelligence module operating on video or image data to generate compression garment fit information (Remarks, pages 5-6).
Examiner respectfully disagrees. The limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a), as they encompass advertising, and marketing or sales activities. Examiner has not stated that the claims fall under the “Mental Processes” grouping as Applicant appears to be arguing. Additionally, merely utilizing an artificial intelligence module to generate the compression garment fit information amounts to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and does no more than generally linking the use of the judicial exception to a particular technological environment or field of use (such as artificial intelligence). Accordingly, the claim are not integrated into a practical application.
Applicant further argues that claim 1 recites that "the artificial intelligence module comprises a first artificial intelligence model" configured to be pretrained to determine "dimension information of the person," and that the artificial intelligence module further comprises "a second artificial intelligence model" configured to be pretrained to determine "the compression garment fit information corresponding to the body part of the person based on the determined dimension information." Therefore, the claim defines a specific arrangement of a first artificial intelligence model and a second artificial intelligence model, where the second artificial intelligence model operates based on the determined dimension information generated by the first artificial intelligence model (Remarks, page 6).
Examiner respectfully disagrees. Merely utilizing a first artificial intelligence model to determine dimension information of the person and merely utilizing a second artificial intelligence model to determine the compression garment fit information amounts to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and does no more than generally linking the use of the judicial exception to a particular technological environment or field of use (such as artificial intelligence). Additionally, using the determined dimension information generated by the first artificial intelligence model in the second artificial intelligence model fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For instance, the machine learning technology itself is not improved regardless of what data is input into the models (i.e. improving data does not improve the machine learning technology itself). Accordingly, the claim are not integrated into a practical application.
Applicant further argues that claim 7 recites "the second artificial intelligence model comprises different versions, each version being separately trained with data corresponding to a different targeted body part." This limitation defines a particular configuration of the artificial intelligence module in which multiple versions of the second artificial intelligence model are separately trained with data corresponding to different targeted body parts. The claim therefore recites a specific artificial intelligence module structure and training configuration for determining compression garment fit information corresponding to a body part of the person (Remarks, page 6).
Examiner respectfully disagrees. Using different data to train different versions of the second artificial intelligence model doesn’t improve the machine learning technology or structure itself, it merely improves the data used to train the models. Accordingly, the claim are not integrated into a practical application.
Applicant further argues that the claims are directed to a computer implemented method that uses a defined artificial intelligence module, including a first artificial intelligence model and multiple versions of a second artificial intelligence model separately trained with data corresponding to different targeted body parts, to generate compression garment fit information based on acquired video or images. The claim therefore integrates any alleged abstract idea into a practical application and is directed to patent-eligible subject matter (Remarks, pages 6-7).
Examiner respectfully disagrees. As detailed in response to the arguments above, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, the claims are ineligible.
Rejections under 35 U.S.C. §103
Applicant argues that Nguyen does not disclose "a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the compression garment fit information corresponding to the body part of the person based on the determined dimension information." Nguyen does not disclose an artificial intelligence module that includes a first artificial intelligence model configured to be pretrained to determine dimension information of the person, and a second artificial intelligence model configured to be pretrained to determine compression garment fit information corresponding to a body part of the person based on the determined dimension information. In particular, Nguyen does not disclose a second artificial intelligence model that is separately pretrained to receive the determined dimension information as input and, based on that determined dimension information, to output compression garment fit information corresponding to a body part of the person. Contrary to Applicant's claims, Nguyen describes processing bodyrelated data within a broader garment evaluation framework, without disclosing a distinct second artificial intelligence model that performs the claimed determination. Furthermore, Nguyen does not disclose that the compression garment fit information corresponds to a body part of the person in the manner required by the claim, nor that such information is determined by a second artificial intelligence model based on dimension information determined by a first artificial intelligence model. The Examiner's interpretation of Nguyen's modules as equating to the claimed first artificial intelligence model and second artificial intelligence model improperly construes general garment evaluation processing with the specific claimed architecture requiring two pretrained artificial intelligence models having the recited functional relationship. Thelemann does not cure the above-mentioned deficiencies of Nguyen. Thelemann, for example, describes "an artificial intelligence plausibility algorithm" and/or "an artificial intelligence interaction algorithm" whose parametrization may comprise body part information (Remarks, pages 7-8).
Examiner respectfully disagrees. Nguyen discloses a virtual-try on module that includes a body measurement algorithm, such as a CNN algorithm, that determines body measurements of a user and then using a different CNN algorithm [i.e. a second artificial intelligence model, the second artificial intelligence model being configured to be pretrained to determine the compression garment fit information] that obtains the previously determined user measurements [i.e. based on the determined dimension information] to determine the fit of a particular clothing item on body parts of the user (e.g. how pants fit around the legs and waist of a user) [i.e. the compression garment fit information corresponds to a body part of the person] (see Nguyen, [0085], [0046], [0048], [0058], and [0084]). Additionally, while the claims don’t recite that the first artificial intelligence model and the second artificial intelligence model are separately pretrained, Nguyen nonetheless discloses this as the two separate CNN algorithms are used for different purposes and would therefore be trained using different data. Accordingly, Nguyen in view of Thelemann teach the amended features.
Applicant further argues that the combination the first and a second model as recited in claim 1 provides a technical effect of allowing each model to be pretrained independently using different datasets relevant to their respective output. For example, the first model is configured to be pretrained to determine dimension information of the person, using video or images as input. Accordingly, the training of the first model can be carried out entirely independently of any particular intended use and/or application of the compression garment. The second artificial intelligence model, however, model is configured to use the dimension information as input and is pretrained to determine compression garment fit information. As described in paragraphs [0044] to [0048] of the application, the compression garment fit information is application-dependent, and it may vary depending on whether the method is intended to assist in selecting a prefabricated/preconfigured garment, or in manufacturing a customized compression garment, as well as the targeted body part and/or the desired therapeutic or non-therapeutic effect. Thus, the second artificial intelligence model can be pretrained with less focus on the person. Accordingly, the accuracy of the generated compression garment fit information may be improved, thereby providing a technical effect not achieved by the combination of the cited art. Thelemann, for instance, describes two different artificial intelligence algorithms: the (1) "artificial intelligence interaction algorithm" (See Thelemann, par. [0028]); and (2) "artificial intelligence plausibility algorithm" (See Thelemann, par. [0034]). However, these algorithms serve to support the user during the measurement process and do not relate to the generation of compression garment fit information. Moreover, the algorithms are not utilized with one another to produce a target result (e.g., compression garment fit information corresponding to the body part of the person based on the determined dimension information). On the contrary, Thelemann's two algorithms utilize input data and produce output data that are independent of one another. As a result, incorporating Thelemann's algorithms into Nguyen does not achieve the technical effect provided by the claims combination of first and second artificial intelligence models presently described in independent claim 1 (Remarks, page 8).
Examiner respectfully disagrees. As detailed in response to the arguments above, Nguyen the combination the first and a second model as recited in claim 1. Thelemann is not cited to teach the first artificial intelligence model and the second artificial intelligence model recited in claim 1. Additionally, the claims don’t recite that the first artificial intelligence model and the second artificial intelligence model are separately pretrained and also don’t recite what data is utilized to train the models. Nonetheless, Nguyen discloses that the two separate CNN algorithms are used for different purposes and would therefore be trained using different data such as data to train the first artificial intelligence model to determine user dimensions and data to train the second artificial intelligence model to determine the fit of a garment. Since Nguyen discloses the combination the first and a second model, it achieves the above mentioned ‘technical effects.’ Accordingly, Nguyen in view of Thelemann teach the amended features.
Applicant further argues that, for at least the reasons discussed above, the combination of Nguyen and Thelemann fails to teach, show, suggest, or render obvious each and every feature recited in independent claim 1. In addition, Applicant submits that claims 2-11 and 13-19 are also patentable in view of their respective dependencies on claim 1. Accordingly, reconsideration and withdrawal of the rejection are respectfully requested (Remarks, page 9).
Examiner respectfully disagrees. As detailed in response to the arguments above, the cited references teach amended claim 1. Accordingly, claims 2-11 and 13-19 are not allowable.
Applicant further argues that, regarding claim 7, neither Nguyen nor Thelemann disclose "wherein the second artificial intelligence model comprises different versions, each version being separately trained with data corresponding to a different targeted body part". As described above, Thelemann describes "an artificial intelligence plausibility algorithm" and/or "an artificial intelligence interaction algorithm" whose parametrization may comprise body part information. Thelemann, however, does not disclose that the second artificial intelligence model comprises different versions, nor that each version is separately trained with data corresponding to a different targeted body part. Thelemann, at most, describes that body part information may be used as input to, or encoded within, a single artificial intelligence algorithm. However, the use of body part information as a training parameter or input feature does not disclose or suggest
multiple versions of the second artificial intelligence model, or that separate training processes are performed to generate distinct model versions, where each trained using data restricted to a different targeted body part. Indeed, Thelemann does not disclose multiple versions of an artificial intelligence model, does not disclose separate training of different model
versions, and does not disclose that each such version corresponds to a different targeted body
part. Therefore, Accordingly, Thelemann fails to disclose or suggest "wherein the second artificial intelligence model comprises different versions, each version being separately trained with data corresponding to a different targeted body part," and therefore does not remedy the deficiencies of Nguyen. For at least the reasons discussed above, the combination of Nguyen and Thelemann fails to teach, show, suggest, or render obvious each and every feature recited in independent claim 7. Accordingly, reconsideration and withdrawal of the rejection are respectfully requested (Remarks, pages 9-10).
Examiner respectfully disagrees. Thelemann teaches parametrization an artificial intelligence plausibility algorithm for a particular body part by training the artificial intelligence plausibility algorithm with body part information for that particular body part (see Thelemann, [0034], [0044], and [0107]). In other words, there are different versions of the artificial intelligence plausibility algorithm that are tailored to a particular body part based on the particular body part it is trained for. Accordingly, Thelemann teaches this argued feature.
Applicant further argues that claim 12 ultimately depends from independent claim 1, which is patentable over the cited art for at least the reasons discussed above. Aleem fails to cure the deficiencies of the cited art discussed in detail above. Therefore, the combination of Nguyen, Thelemann and Aleem fails to teach, show, suggest, or render obvious each and every feature
recited in independent claim 12. Accordingly, reconsideration and withdrawal of the rejection are respectfully requested (Remarks, page 10).
Examiner respectfully disagrees. As detailed in response to the arguments above, the cited references teach amended claim 1. Accordingly, claim 12 is not allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Kamiyama et al. (US 2020/0319015 A1) teaches using machine learning to analyze images and predict body dimensions.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
Prosecution Insights