CTFR 18/627,827 CTFR 83639 DETAILED ACTION Acknowledgements 07-03-fti AIA The present application is being examined under the pre-AIA first to invent provisions. Claims 1-7 and 9-21 are pending and have been examined. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-7 and 9-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Step 1: claims 1-7 and 9-21 are directed to a method. Therefore, these claims fall within the four statutory categories of invention. Step 2A, prong 1: Claim 1, for example, recites the abstract idea of recommending a product on a commerce platform. This idea is described by the following claim elements: processing a source image of a face using a facial attribute classifying network model to produce a plurality (K) of facial attributes using a […] model to produce the plurality of facial attributes; using at least some of the facial attributes to select at least one product from a data store storing products in association with facial attributes suited to the products; and providing the at least one product as a recommendation for presentation in an […] interface to purchase products, […]. The above steps fall into the Certain Methods of Commercial or legal interactions grouping of abstract ideas as they involve advertising, marketing or sales activities or behaviors. These steps involve receiving/identifying facial attributes, selecting a product based on the facial attributes and providing a recommendation of said selected product. Additionally, these steps can be performed mentally or manually (using a pen and a paper) and do not require a machine. Step 2A, prong 2: claims 1, 8 & 9 recite additional elements that fail to integrate the abstract idea into practical application. Claim 1, for example, recite the following additional limitations: using K classifiers, one for each facial attribute, some of the K classifiers comprising trained color-based classifiers configured to use a color-based feature vector produced by the network model from the source image and the other of the K classifiers comprising trained shape-based classifiers configured to use a shape-based feature vector produced by the network model from the source image facial attribute classifying network model in an e-commerce interface the e-commerce interface presenting a simulation of the at least one product as applied to the face by processing the source image. The classification model and commerce interface may require a general processor and is merely used as a tool to implement the abstract idea. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement the abstract idea as a tool to perform an abstract idea, cannot provide an inventive concept. See MPEP 2106.05(f). The additional elements noted above are recited at a high level of generality, and comprises computer instructions to simply perform the generic computer functions of receiving/identifying facial attributes, selecting a product based on the facial attributes and providing a recommendation of said selected product on an e-commerce interface and simulating said product on the e-commerce interface. Generic computers performing generic computer functions, alone, do not integrate the claimed abstract idea into a practical application. Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination does not integrate the abstract idea into a practical application. The additional elements fail to provide a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, ( 4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04, (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). Step 2B: claim fails to recite additional elements that amount to an inventive concept. For the reasons identified with respect to Step 2A, prong 2, claim 1, for example, fail to recite additional elements that amount to an inventive concept. For example, use computer models or instructions (e.g., to send/receive data, provide recommendation for a product and simulation and presenting said product on an interface) or simply adding a general-purpose computer system or computer component after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination do not provide significantly more than the abstract idea. The additional elements fail to (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; ( 4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the fie Id; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of the MPEP 2106.0S(a-h). Dependent claims 2-7 and 9-21 further describe the abstract idea of recommending a product on a commerce platform. These claims do not include new additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, these dependent claims are also not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-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, 7, 9-16, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake et al. (US 20200342213 Al) (“Dissanayake”) in view of Mauger (US 20210219700 Al) (“Mauger”) and further in view of Iranmanesh et al (Deep Sketch-Photo Face Recognition Assisted by Facial Attributes, published by IEEE in 2018) . As per claim 1, Dissanayake discloses: processing a source image of a face using a facial attribute classifying network model to produce a plurality […] of facial attributes using [regression or classification model] (¶¶ [0064[, [0067], [0072], the at least one-color channel image 60L is analyzed in step 92 using entropy statistics to obtain an analysis output 80 wherein the analysis output 80 comprises an entropy value. In step 93, the cosmetic skin attribute of the at least one portion of skin of the person is determined based on the entropy value.), using at least some of the facial attributes to select at least one product from a data store storing products in association with facial attributes suited to the products (¶¶ [0100]- the system 10 configured as a stand-alone imaging system that is located at a retail cosmetics counter for the purpose of visualizing at least one cosmetic skin attribute and recommending cosmetic and skin care products based on the visualized at least one cosmetic skin attribute.) ; and providing the at least one product as a recommendation for presentation in an e-commerce interface to purchase products (¶¶ [0113]- Specifically, the method 300 may further comprise displaying at least one product recommendation item to treat the displayed cosmetic skin attribute.) , Dissanayake further discloses visualizing at least one cosmetic skin attribute and displaying at least one product recommendation item (¶ [0115]). Dissanayake does not explicitly teach simulation of the at least one product as applied to the face by processing the source image. Mauger, however, clearly discloses an e-commerce interface presenting a simulation of the at least one product as applied to the face by processing the source image (¶¶ [0051], [0150]-[0151]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dissanayake’ systems and methods for recommending cosmetic products based on face attributes to include simulating the recommended product, as disclosed by Mauger, in order to provide consumers with accurate representation of the colors/rendering of the recommended product (Mauger: ¶¶ [0002]-[0009]). Dissanayake further discloses processes a digital image of a face by extracting color channel images (L*, a*, b*, etc.) (¶¶ [0067], [0069], Table 1). Each color channel image is analyzed (using entropy/statistics and possibly a statistical or machine learning model) to produce one or more cosmetic skin attributes (¶¶ [0069], Table 1; [0071]–[0072]). the model for generating a skin attribute may be a regression or classification model (SVM, SVR, random forest, etc.) (¶¶ [0071]–[0072]). Dissanayake/Mauger does not disclose: produce a plurality (K) of facial attributes using K classifiers, one for each facial attribute, some of the K classifiers comprising trained color-based classifiers configured to use a color-based feature vector produced by the network model from the source image and the other of the K classifiers comprising trained shape-based classifiers configured to use a shape-based feature vector produced by the network model from the source image Iranmanesh, however, discloses produce a plurality (K) of facial attributes using K classifiers, one for each facial attribute (Sec. 2.2, Eq. 5–8; Sec. 3.1; a multi-task deep network that predicts multiple facial attributes (K), using a separate binary classifier for each attribute (e.g., hair color, skin color, bald, gender, ethnicity, eyeglasses)), some of the K classifiers comprising trained color-based classifiers configured to use a color-based feature vector produced by the network model from the source image (Abstract, Sec. 2.2, Sec. 3.1; The network predicts color-based attributes (e.g., hair color, skin color, eye color, pale skin, black hair, blond hair, brown hair, gray hair) from RGB photo input. These classifiers are trained on feature vectors produced from the color image.) and the other of the K classifiers comprising trained shape-based classifiers configured to use a shape-based feature vector produced by the network model from the source image (Sec. 3.1, Sec. 2.2; he network predicts shape-based attributes (e.g., bald, male (gender), Asian/Indian/White/Black (ethnicity), eyeglasses) from both photos and sketches. The feature vector encodes shape/geometry for these classifiers…. The network produces a feature vector (size 64) from the image, which is input to all attribute classifiers.) It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the attribute classification system of Dissanayake/Mauger to include the use of classifiers for color-based and shape-based facial attributes, as disclosed by Iranmanesh, in order to improve the accuracy and robustness of facial attribute recognition by leveraging the complementary information present in both color and shape features (Iranmanesh: Sec. 2.2, Sec. 3.1). As per claim 2, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the products comprise make-up products (¶ [0064]). As per claim 7, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses providing an e-commerce shopping service to purchase at least some of the recommended products (¶¶ [0123]). As per claim 9, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the e-commerce interface comprises one or more screens for presenting: facial attribute analysis, product recommendation, […] and product purchasing (¶¶ [0111], [0117], [0121], [0115], [0117], [0123]), the screens including: at least one image acquisition screen to obtain and display the source image (¶¶ [0116], [0120]); at least one facial analysis screen to obtain the facial attributes determined by the […] classifiers and prepare and present an annotated source image responsive to the facial attributes, the annotated source image presenting facial attribute information determined for the face (¶¶ [0111], [0117], [0121]); at least one recommendation screen to obtain the recommendation (¶¶ [0115], [0117], [0123]); and at least one transaction screen to conduct a purchase transaction (¶ [0123]) . Dissanayake does not disclose a screen for product simulation. Mauger, however, discloses an e-commerce interface comprising a product simulation screen (¶¶ ([0148]-[0151], [0177]). One of ordinary skill in the art would have been motivated to combine Dissanayake’s e-commerce facial analysis and recommendation interface with Mauger’s simulation system to allow the user to preview or simulate the application of a recommended product on their own image before purchasing, thereby increasing user confidence and engagement (see Mauger [0002]–[0013], [0177]). Dissanayake does not disclose K classifiers. Iranmanesh, however, discloses using “K classifiers” for facial attribute analysis (Sec. 2.2, Eq. 5–8; Sec. 3.1; a multi-task deep network that predicts multiple facial attributes (K), using a separate binary classifier for each attribute (e.g., hair color, skin color, bald, gender, ethnicity, eyeglasses)). One of ordinary skill in the art would have been motivated to combine the facial attribute analysis of Dissanayake with the multi-classifier (K-classifier) approach of Iranmanesh to improve accuracy and flexibility in attribute prediction, as using a separate classifier for each attribute is a well-known technique in multi-task learning. As per claim 10, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the one or more display screens are configured to present the facial attribute information contextually and in association with regions of the face related to the facial attributes (¶¶ [0110], [0124]), the facial attribute information identifying the attribute (¶¶ [0124], Table 5). and an associated value as determined by facial attribute classifying network model (¶¶ [0075], [0082], [0083], [0124]). As per claim 11, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the regions are located on the source image by performing feature detection on the face (¶¶ [0108], [0109]). As per claim 12, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the at least one facial analysis screen provides a control to select among a plurality of highlightable regions to selectively present the facial attribute information, the highlightable regions selected from a whole face region, a brow region, a lip region and an eye region (¶¶ [0109], [0110]). As per claim 13, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the at least one product comprises a plurality of make-up products grouped into make-up looks, the recommendation comprises one or more make-up looks and the at least one recommendation screen comprises a screen to selectively present the one or more looks, each look of the one or more make-up looks comprising a plurality of associated make-up products having different product types, wherein the associated make-up products have specific effects and/or are applied using specific techniques to achieve the look (¶¶ [0064], [0100], [0113, [0115], [0116]). As per claim 14, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the at least one recommendation screen includes a control to present overall make-up looks for the face and a control to present make-up looks for specific face regions (¶¶ [0064], [0100], [0113, [0115], [0116]). As per claim 15, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein the at least one recommendation screen is configured to receive a skin type comprising one of normal, oily, or dry and the method determines the at least one recommendation responsive to the skin type (¶¶ [0111]-[0115]). As per claim 16, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake does not discloses wherein the simulation is processed in accordance with one of the one or more looks and the at least one recommendation screen comprises a control to select between the one more or more looks to change the simulation. Mauger, however, discloses wherein the simulation is processed in accordance with one of the one or more looks and the at least one recommendation screen comprises a control to select between the one more or more looks to change the simulation (¶¶ [[0158], [0177]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dissanayake’ systems and methods for recommending cosmetic products based on face attributes to include simulating the recommended product, as disclosed by Mauger, in order to provide consumers with accurate representation of the colors/rendering of the recommended product (Mauger: ¶¶ [0002]-[0009]) As per claim 18, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake does not disclose wherein the network model comprises a convolutional neural network (CNN)-based backbone network model having a first sub- model trained to produce the color-based feature vector and a second sub model trained to produce the shape-based feature vector. Iranmanesh, however, discloses wherein the network model comprises a convolutional neural network (CNN)-based backbone network model having a first sub- model trained to produce the color-based feature vector and a second sub model trained to produce the shape-based feature vector (Section 2.1, Section 2.2, Section 3.1, Figure 1 (architecture diagram)). One of ordinary skill in the art would have been motivated to combine the sketch-photo matching framework of Iranmanesh with the color channel image entropy analysis of Dissanayake to improve the realism and accuracy of sketch-to-photo recognition, as incorporating color attribute information into recognition models is a well-known way to enhance discriminative power and compensate for missing information in sketches. As per claim 19, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake does not disclose wherein each respective classifier of the K classifiers comprises one or more fully connected linear layers, and without a convolutional layer to process one of the respective vectors and provide a prediction of a respective one of the facial attributes. Iranmanesh, however, discloses wherein each respective classifier of the K classifiers comprises one or more fully connected linear layers, and without a convolutional layer to process one of the respective vectors and provide a prediction of a respective one of the facial attributes (section 2.2; Attribute classifiers operate on the feature vector, standard MLP/linear layer approach. The attribute classifiers (heads) are only fully connected layers (linear layers), with no additional convolutional layers in those heads.) One of ordinary skill in the art would have been motivated to implement each attribute classifier as one or more fully connected linear layers, without convolutional layers, because after feature extraction by the convolutional backbone, attribute prediction is commonly performed on the resulting feature vector using linear layers for efficiency, simplicity, and effectiveness in multi-task learning architectures. As per claim 20, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses wherein: the using of the at least some of the facial attributes to select the at least one product (Abstract; ¶¶ [0005]-[0008]; [0036]; Claims 1, 3, 5, 14) comprises using rules to match facial attributes determined from image analysis to products grouped in respective looks (¶¶ [0113], [0115]; FIG. 15, FIG. 18); and the method comprises matching to more than one look (¶¶ [0113], [0115]; FIG. 15, FIG. 18) and ranking looks based upon a count of the facial attribute matches for a respective look and/or quality of the facial attribute match evaluating the scale of the match (¶¶ [0050], [0070], [0071], [0113]). As per claim 21, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake further discloses processing the source image using a network model or other image processing technique to produce the heat map/visualization of the at least one product as applied to the face (¶ [0115]). Dissanayake does not explicitly teach processing the source image using a network model or other image processing technique to produce the simulation of the at least one product as applied to the face. Mauger, however, clearly discloses producing the simulation of the at least one product as applied to the face (¶¶ [0051], [0150]-[0151]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dissanayake’s system for recommending cosmetic products based on face attributes to include simulating the recommended product as taught by Mauger, in order to provide consumers with an accurate and realistic representation of the colors and rendering of the recommended product (Mauger: ¶¶ [0002]-[0009]). This would have been a predictable use of known techniques to improve user experience and decision-making in cosmetic selection . 07-21-aia AIA Claim 3-5 are re jected under 35 U.S.C. 103 as being unpatentable over Di ssanayake et al. (US 20200342213 Al) (“Dissanayake”) in view of Mauger (US 20210219700 Al) (“Mauger”) and further in view of Iranmanesh et al (Deep Sketch-Photo Face Recognition Assisted by Facial Attributes, published by IEEE in 2018) (“Iranmanesh”) and further in view of Mallick et al (US 20090234716 A1) (“Mallick”). As per claim 3, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake does not expressly disclose wherein: the make-up products are variously associated in the data store to define predetermined make-up looks, each look comprising a plurality of associated products having different product types, wherein the associated products have specific effects and/or are applied using specific techniques to achieve the look; the method comprises receiving an identification of one of the predetermined make- up looks; and the step of using at least some of the facial attributes is responsive to the one of the predetermined make-up looks when selecting the at least one product. Mallick, however, clearly discloses wherein: the make-up products are variously associated in the data store to define predetermined make-up looks (¶¶ [0059], [0062]) , each look comprising a plurality of associated products having different product types (¶¶ [0044], [0059]), wherein the associated products have specific effects and/or are applied using specific techniques to achieve the look (¶¶ [0055], [0059]); the method comprises receiving an identification of one of the predetermined make- up looks (¶¶ [0059], [0066]); and the step of using at least some of the facial attributes is responsive to the one of the predetermined make-up looks when selecting the at least one product (¶¶ [0042]-[0044]). It would have been obvious to one of ordinary skill in the art to implement, in a system such as that disclosed by Dissanayake—which analyzes facial attributes and recommends cosmetic products—a feature in which make-up products are associated in a data store as predetermined make-up looks comprising a plurality of products of different types, and to select and recommend products for a look responsive to a user’s facial attributes, as taught by Mallick. The motivation for combining these references is to provide users with personalized, effective, and convenient virtual makeover experiences that are tailored to their features and preferences, thereby increasing user engagement and monetization opportunities. As per claim 4, Dissanayake/ Mauger/ Iranmanesh/Mallick discloses as shown above. Dissanayake further discloses wherein each of the make-up products is associated with one of a plurality of make-up types and the method comprises selecting at least one product, responsive to the facial attributes, for each of make-up types to define the recommendation (¶¶ [0100], [0113]). As per claim 5, Dissanayake/ Mauger/ Iranmanesh/Mallick discloses as shown above. Dissanayake further discloses wherein the make-up types comprise a face product type, an eye product type, a brow product type and a lip product type (¶¶ [0054], [0113], [0121]) . 07-21-aia AIA Claim 6 and 17 are is rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake in view of Mauger in view of Iranmanesh and further in view of examiner’s Official Notice or alternatively in view Yang et al. (US 20200305579 Al) (“Yang”) . As per claim 6, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake does not specifically disclose recommending a technique to use the recommended products. The examiner, however, takes official notice that providing instructions for how to use purchased products are provided with each product is old and well known in the art. Alternatively, Yang clearly discloses recommending a technique to use the recommended products (¶¶ [0025], [0049]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dissanayake’ systems and methods to include the function of providing use instructions for recommended products, as disclosed by Yang, to ensure that the customer uses the products as recommended therefore providing good customer service. As per claim 17, Dissanayake/ Mauger/ Iranmanesh discloses as shown above. Dissanayake discloses wherein the at least one recommendation screen presents look product information including product information and […] and one or more controls to add each product or some associated with the look to a cart for the purchase transaction (¶¶ [0113]) Dissanayake does not expressly disclose providing tutorial information for each product associated with one of the looks. The examiner, however, takes official notice that providing tutorial information for each product associated with one of the looks is old and well known in the art. Alternatively, Yang clearly discloses recommending a technique to use the recommended products (¶¶ [0025], [0049]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dissanayake’ systems and methods to include the function of providing use instructions for recommended products, as disclosed by Yang, to ensure that the customer uses the products as recommended therefore providing good customer service . Response to Arguments 101 REJECTION Applicants argue (page 8): For instance, Applicant submits that the claimed features make it explicitly clear that the claims cannot be considered a mental process by explicitly reciting "processing a source image of a face using a facial attribute classifying network model to produce a plurality (K) of facial attributes using K classifiers, one for each facial attribute, some of the K classifiers comprising trained color-based classifiers configured to use a color- based feature vector produced by the network model from the source image and the other of the K classifiers comprising trained shape-based classifiers configured to use a shape- based feature vector produced by the network model from the source image." It is not practical to perform these features as a mere method of organizing human activity. The examiner, however respectfully disagrees. While the claims recite a network model and classifiers that are not practical to perform entirely mentally, under current USPTO guidance, claims that implement abstract ideas (such as classification and recommendation) on generic computer technology remain ineligible unless they provide a specific technical improvement (see MPEP 2106.04(a) and (d)). Here, the claims use generic computer component. Applicants argue (page 8-9): Methods that use a Convolutional Neural Network (CNN) to classify facial attributes may use single-label learning based FAC methods These methods, however, predict each attribute individually, thus ignoring the correlations between attributes. The present disclosure describes the collection of a comprehensive dataset that enables a method to predict a special set of (facial) attributes that were not previously covered in any previous works or datasets. That is, separate classifier heads for each different attribute were not used. Thus, the present claims improve the model accuracy and the attribution classifiers resulting from the training are on par with human annotation. The examiner, however respectfully disagrees. Applicant’s argument regarding improvements in model accuracy and the use of a comprehensive dataset is noted. However, the claims do not recite specific details of the dataset, training process, or technical improvements to computer technology itself. The claims are directed to classification and recommendation using generic machine learning techniques, which under current USPTO guidance remains an abstract idea without a recited technical improvement. Therefore, the §101 rejection is maintained. Applicants argue (page 9-11): In this case, the claims should not be interpreted at a very high level of'generality of being "advertising, marketing, or sales activities" since the claims do not merely correspond to receiving/identifying facial attributes, selecting a product based on the facial attributes and providing a recommendation of said selected product. Instead, the claims involve the technical features noted above for processing the source image which cannot be highly generalized. Furthermore, Ex P'arte Guillaume Desjardins also showed that even if a claim recites an abstract idea, this does not mean that the claim as a whole is directed to an abstract idea, and thus cannot support the rejection. Id. at p. 10 (holding that while the independent claim may recite an abstract idea, it is not directed to an abstract idea, and that the independent claim, when considered as a whole, integrates an abstract idea into a practical application). Thus, it is believed that the claims do recite the "improvement" at a proper level of specificity, especially in view of the recent decision of Ex Parte Desjardins. The examiner, however respectfully disagrees. Applicant’s arguments and citation to Ex Parte Desjardins have been considered. However, the claims as presented do not recite specific technical details of the simulation or image processing beyond generic use of machine learning and simulation steps. The claims remain directed to classification and recommendation using conventional computer technology, which is treated as an abstract idea under current USPTO guidance. The recited simulation does not effect a transformation of an article in the sense required by the courts, as it merely generates a visual representation on a display. Therefore, the §101 rejection is maintained. 103 REJECTIONS Applicants’ arguments are moot in view of the new grounds of rejection. Prior Art Made of Record 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety . US 12148075 B2- discloses a method includes displaying a face image of a user collected by a camera; obtaining makeup contours corresponding to a target makeup effect image, recognizing makeup areas corresponding to the makeup contours from the face image, and adaptively superimposing and displaying the makeup contours on the makeup areas corresponding to the makeup contours to enable the user to perform makeup based on the makeup contours. US 20200211245 A1- discloses a make-up assistance method and apparatus and a smart mirror. The make-up assistance method includes: acquiring a facial image of a user; acquiring a make-up plan selected by the user, the make-up plan comprising a makeup effect image; determining difference between the makeup effect image and the facial image using a preset algorithm; and generating makeup modification prompt information for a region in the makeup effect image or the facial image where the difference is greater than a threshold. US 12333855 B1- A system and method for generating personalized product recommendations for a user, along with the personalized reasons for the recommendation, which can then be used as part of a product purchasing system to deliver that product to the user. A system for image analysis may perform: obtaining an image of a face or head of a person, generating face features from the image, retrieving a plurality of features of a plurality of objects from a database, generating a plurality of combined face and object features from the face features and object features of the plurality of objects, generating a ranking of the combined face and object features, selecting at least one object based on the ranking of the combined face and object features, generating an interactive display of the selected at least one object, and providing user interaction of the interactive display to provide purchase of the selected object . Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. 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 extension fee 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 MAMON OBEID whose telephone number is (571)270-1813. The examiner can normally be reached 8 AM- 5 PM. 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, Namrata Boveja can be reached at (571) 272-8105. 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. /MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687 Application/Control Number: 18/627,827 Page 2 Art Unit: 3687 Application/Control Number: 18/627,827 Page 3 Art Unit: 3687 Application/Control Number: 18/627,827 Page 4 Art Unit: 3687 Application/Control Number: 18/627,827 Page 5 Art Unit: 3687 Application/Control Number: 18/627,827 Page 6 Art Unit: 3687 Application/Control Number: 18/627,827 Page 7 Art Unit: 3687 Application/Control Number: 18/627,827 Page 8 Art Unit: 3687 Application/Control Number: 18/627,827 Page 9 Art Unit: 3687 Application/Control Number: 18/627,827 Page 10 Art Unit: 3687 Application/Control Number: 18/627,827 Page 11 Art Unit: 3687 Application/Control Number: 18/627,827 Page 12 Art Unit: 3687 Application/Control Number: 18/627,827 Page 13 Art Unit: 3687 Application/Control Number: 18/627,827 Page 14 Art Unit: 3687 Application/Control Number: 18/627,827 Page 15 Art Unit: 3687 Application/Control Number: 18/627,827 Page 16 Art Unit: 3687 Application/Control Number: 18/627,827 Page 17 Art Unit: 3687
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