DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/27/2026 has been entered. Claims 1-13, 15-21 are currently pending and have been examined. Claims 1, 12, and 21 have been amended. Claim 14 is cancelled.
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.
Step 1: The claims 1-11 are a system, claims 12-13, 15-20 are a method, and claims 21 is a computer readable medium. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-13 and 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 12 and 21, taking claim 1 as a representative claim) recite:
An information processing apparatus for facial feature-based makeup recommendation, the apparatus comprising: one or more processors, coupled with memory, to:
receive, via a client device, face information on a user's face, the face information comprising image data, and needs information on the user's needs regarding makeup;
specify, via analysis of the image data, a feature quantity of the user's face based on the face information, the feature quantity representing at least one of a feature regarding a form of each of a plurality of parts of the face and a feature regarding a balance of forms between the plurality of parts of the face, the plurality of parts of the face selected from contour, eyebrow, eyelash, eye, nose, lip, cheek, forehead, and chin, and the form including at least one of size, shape, placement, color, angle, light and shade, unevenness, or hair mass;
store, in a database, the face information, the needs information, and the specified feature quantity in association with a user identifier that identifies the user;
determine, using a recommendation model, recommendation makeup information on the makeup recommended for the user based on a correlation between the specified feature quantity, the needs information, and makeup information on the makeup;
present, via the client device, the determined recommendation makeup information to the user; and
receive, via the client device, to update the recommendation model, evaluation information on evaluation of the recommendation makeup information from the user who has applied the makeup after the recommendation makeup information is presented.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving information about a user’s facial features and needs, making a recommendation of makeup to the user, and receiving evaluation information from the user about the recommended makeup. The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
An information processing apparatus for facial feature-based makeup recommendation, the apparatus comprising: one or more processors, coupled with memory, to: (claim 1)
An information processing method causing a computer executing the steps of: (claim 12)
A non-transitory computer readable medium including one or more instructions stored thereon and executable by a processor to: (claim 21)
Via a client device
in a database
The additional elements of emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Dependent claims 2-11, 13, and 15-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 12 and 21 without significantly more.
Claims 2 and 13 recite wherein the one or more processors are configured to determine determines the recommendation makeup information based on a history of the evaluation information associated with user identification information that identifies the user. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 3 and 15 recite further comprising: the memory configured to store the recommendation model, wherein the
Claim 4 and 16 recite wherein the recommendation makeup information includes at least one of information on a makeup product recommended to the user, information on a makeup method recommended to the user, and information on why the makeup product and the makeup method is recommended to the user. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 5 and 17 recite wherein the needs information includes at least one of information on direction of makeup, information on usage scenes of makeup, information on makeup tools possessed by the user, and information on skin troubles of the user. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 6 and 18 recite wherein the needs information includes at least one of information on the user's makeup preferences, information on the user's attributes, information on the user's makeup skill level, and information on the user's facial features concerns. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 7 and 19 recite wherein the evaluation information includes at least one of information on an evaluation of preference, an evaluation of a color tone of the recommended makeup product, an evaluation of impression given by makeup, and an evaluation of makeup technique. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 8 and 20 recite further comprising the one or more processors to receive, from the user, information that the user wants to obtain the recommended makeup product, wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user obtaining the recommended makeup product. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 9 recites the apparatus of claim 1,further comprising the one or more processors configured to receive, from the user, a makeup face image of the user to which the makeup applied based on the recommendation makeup information, wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user whose makeup face image is received. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
Claim 10 recites the apparatus of claim 3, further comprising:
Claim 11 recites the apparatus of claim 1, further comprising a the one or more processors configured to present a reward to the user inputting the evaluation information. The claim merely further limits the abstract idea and does not integrate the abstract idea into a practical application.
For these reasons claims 1-13 and 15-21 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
Claim 1-13 and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20220211163) in view of Satori (US 20180075523) in further view Peyrelevade (US 20030065578).
Regarding claims 1, 12, and 21, Yang discloses:
An information processing apparatus for facial feature-based makeup recommendation, the apparatus comprising: one or more processors, (processor [0025]) coupled with memory, to: (claim 1) (Figure 6- makeup assistance apparatus)
An information processing method for facial feature-based makeup recommendation, the method comprising (claim 12) (Figure 6- makeup assistance apparatus)
A non-transitory computer readable medium including one or more instructions stored thereon and executable by a processor to: (claim 21) (Figure 6- makeup assistance apparatus)
receive, via a client device [0042] The smart mirror and such terminal devices each may be equipped with a display for displaying images and an imaging device for capturing images of users, such as a camera., face information on a user's face (facial features), the face information comprising image data, [0063] For example, the face super-resolution algorithm may comprise facial image de-noising, facial image magnification selection, facial feature point extraction, make-up information extraction, and facial skin quality assessment. The facial image de-noising may provide two parameters which are 0 and 1. The facial image magnification selection may provide four parameters which are 2 times, 4 times, 6 times and 8 times. In some embodiments, the smart mirror or the smart terminal may provide different magnifications of the facial image for selection by the user, for example, 2 times, 4 times, 8 times, etc., and the higher the magnification, the larger the amount of calculation required, and the longer the calculation time required. In some embodiments, in order to ensure a fast speed of a response to the user, a magnification of 2 times may be set by default, and the user may select a desired magnification according to his/her own needs. The facial feature point extraction may comprise the extraction of information about the facial features. The make-up information extraction may comprise determining a positional relationship of the facial features by performing matching. The facial skin quality assessment may comprise generation of a make-up plan based on skin quality. The information pulled from the facial image in processing is interpreted as image data and needs information on the user's needs regarding makeup (skin quality);
specify, via analysis of the image data, [0063] For example, the face super-resolution algorithm may comprise facial image de-noising, facial image magnification selection, facial feature point extraction, make-up information extraction, and facial skin quality assessment. The facial image de-noising may provide two parameters which are 0 and 1. a feature quantity (a positional relationship of the facial features) of the user's face based on the face information, ([0063] The facial feature point extraction may comprise the extraction of information about the facial features. The make-up information extraction may comprise determining a positional relationship of the facial features by performing matching. The facial skin quality assessment may comprise generation of a make-up plan based on skin quality.)
the feature quantity representing at least one of a feature regarding a form of each of a plurality of parts of the face and a feature regarding a balance of forms between the plurality of parts of the face, [0087] Specifically, based on extraction of facial features, corresponding classifiers may be established for different parts of the human face according to the extracted facial features, and face shapes, mouth shapes, eye shapes, eyebrow types, eye shadows, lip gloss, beards, etc. common in natural faces are respectively classified and identified according to different parts, and identification results can directly guide subsequent smart face pinching operations. Therefore, the face pinching system mainly produces the characteristics of various face models by adjusting different facial organ shapes and positional relationships, and changing different makeup. Based on summary of common categories of various parts of natural faces, combined with the extracted facial features, the shapes or color categories of more face regions related to smart face pinching are identified by configuring classifiers. the plurality of parts of the face selected from contour [0061], eyebrow ([0087] eyebrow types), eyelash, eye ([0087]eye shapes), nose [0108] the nose-to-mouth distance and the nose-to-eye distance, lip, cheek, forehead, and chin, and the form including at least one of size, shape ([0087] eye and eyebrow type), placement ([0108] the nose-to-mouth distance and the nose-to-eye distance), color ( [0087] the shapes or color categories of more face regions), angle [0108], light and shade, unevenness, or hair mass; [0061] As shown in FIG. 2, 68 feature points included in a face are detected and located. The 68 feature points may include 17 feature points of a face contour, 10 feature points of eyebrows, 12 feature points of eyes, 9 feature points of the nose, and 20 feature points of the mouth. Based on the feature point detection results, from the aspects of geometric and image visions, multiple facial features that may reflect face shape, nose shape, mouth shape, eye shape, eyebrow shape, facial organ distribution, eye shadow, lip gloss, beard, etc.
present, via the client device [0042], the determined recommendation makeup information to the user; and ([0139] The presentation unit 47 is configured to present at least one make-up plan determined by the makeup matching server by performing matching according to the face make-up region.)
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference does not explicitly disclose:
store, in a database, the face information, the needs information, and the specified feature quantity in association with a user identifier that identifies the user;
determine, using a recommendation model, recommendation makeup information on the makeup recommended for the user based on a correlation between the specified feature quantity, the needs information, and makeup information on the makeup;
receive, via the client device, to update the recommendation model, evaluation information on evaluation of the recommendation makeup information from the user who has applied the makeup after the recommendation makeup information is presented.
However Satori teaches:
determine, using a recommendation model, recommendation makeup information on the makeup recommended for the user based on a correlation between the specified feature quantity, the needs information, and makeup information on the makeup; [0095] In some embodiments, the looks displayed via menu 916i may include user-specific recommendations for combinations of makeup products. For example, a user may load an image of their face (e.g., via option 902). The loaded image may be analyzed by platform 120 (e.g., by using the previously described feature detection processes and/or any other computer vision processes) to determine certain characteristics of the face depicted in the loaded image. For example, analysis performed on the image may detect a general shape of the face, shapes and arrangement of certain key features (e.g., eyes, nose, mouth, etc.), hair color and texture, and skin tone. Based on the detected characteristics, platform 120 may generate one or more recommended looks that are specifically tailored to the characteristics of the face in the loaded image. In some embodiments, product recommendations may be based on a user-specific product browsing or selection history. For example, the system may recommend a look that includes a makeup product previously selected by the user. [0096] Product recommendations may be based on objective and or subjective “rules” such as general color combination rules (e.g., primary vs. secondary color combinations), specific application rules (e.g., product combinations for daytime vs. nighttime usage), makeup effects and predetermined goals (e.g., applying bronzer to define or sculpt certain facial features), etc.. In some embodiments, these rules may be generated by one or more expert makeup artists, manufacturers/designers, retailers, etc.. For example, in an embodiment, retailer (such as a department store) may individually define certain makeup recommendation rules to generate looks that are selectable via menu 916i. Alternatively or in addition, individual makeup artists or designers may define their own recommendation rules to automatically generate user selectable looks via menu 916i. In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
receive, via the client device, to update the recommendation model, In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. evaluation information on evaluation of the recommendation makeup information from the user who has applied the makeup after the recommendation makeup information is presented. [0096] For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, and Satori teaches the application of recommended product to a user image and receiving feedback, the references do not explicitly disclose:
store, in a database, the face information, the needs information, and the specified feature quantity in association with a user identifier that identifies the user;
However Peyrelevade teaches:
store, in a database (memory for storing user profile(s) in [0090]), the face information, the needs information, and the specified feature quantity in association with a user identifier that identifies the user; (shown in Figure 16- the user profile with an identifying name, information about the facial shape, eye color, special conditions and preferences (needs information), and a plurality of other facial and user related pieces of information)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang in view of Satori to include store, in a database, the face information, the needs information, and the specified feature quantity in association with a user identifier that identifies the user, as taught in Peyrelevade, in order to increase sales through cross selling based on improved recommendations based on user specific data (paragraph 008).
Regarding claims 2 and 13, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above. While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference Yang does not explicitly disclose:
one or more processors are configured to determine the recommendation makeup information based on a history of the evaluation information associated with user identification information that identifies the user.
However Satori teaches:
one or more processors are configured to determine the recommendation makeup information based on a history of the evaluation information associated with user identification information that identifies the user. [0095-0096] In some embodiments, product recommendations may be based on a user-specific product browsing or selection history. For example, the system may recommend a look that includes a makeup product previously selected by the user. In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
Regarding claims 3 and 15, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above. While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference Yang does not explicitly disclose:
further comprising: the memory configured to store the recommendation model, wherein the one or more processors are configured to determine that the recommendation makeup information refers to the recommendation model and determine that the recommendation makeup information corresponds to a combination of the specified feature quantity and the received needs information as the recommendation makeup information; and the one or more processors are configured to modify the recommendation model based on the received evaluation information.
However Satori teaches:
further comprising: the memory configured to store the recommendation model, wherein the one or more processors are configured to determine that the recommendation makeup information refers to the recommendation model and determine that the recommendation makeup information corresponds to a combination of the specified feature quantity and the received needs information as the recommendation makeup information; and the one or more processors are configured to modify the recommendation model based on the received evaluation information. [0095] In some embodiments, the looks displayed via menu 916i may include user-specific recommendations for combinations of makeup products. For example, a user may load an image of their face (e.g., via option 902). The loaded image may be analyzed by platform 120 (e.g., by using the previously described feature detection processes and/or any other computer vision processes) to determine certain characteristics of the face depicted in the loaded image. For example, analysis performed on the image may detect a general shape of the face, shapes and arrangement of certain key features (e.g., eyes, nose, mouth, etc.), hair color and texture, and skin tone. Based on the detected characteristics, platform 120 may generate one or more recommended looks that are specifically tailored to the characteristics of the face in the loaded image. In some embodiments, product recommendations may be based on a user-specific product browsing or selection history. For example, the system may recommend a look that includes a makeup product previously selected by the user. [0096] Product recommendations may be based on objective and or subjective “rules” such as general color combination rules (e.g., primary vs. secondary color combinations), specific application rules (e.g., product combinations for daytime vs. nighttime usage), makeup effects and predetermined goals (e.g., applying bronzer to define or sculpt certain facial features), etc.. In some embodiments, these rules may be generated by one or more expert makeup artists, manufacturers/designers, retailers, etc.. For example, in an embodiment, retailer (such as a department store) may individually define certain makeup recommendation rules to generate looks that are selectable via menu 916i. Alternatively or in addition, individual makeup artists or designers may define their own recommendation rules to automatically generate user selectable looks via menu 916i. In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
Regarding claims 4 and 16, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above. Yang further discloses:
wherein the recommendation makeup information includes at least one of information on a makeup product recommended to the user, information on a makeup method recommended to the user ([0108 …determine at least one makeup plan conforming to the user requirement for the user to select. For example, the makeup plan may include cosmetics and see [0049]), and information on why the makeup product and the makeup method is recommended to the user.
Regarding claims 5 and 17, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above. Yang further discloses:
wherein the needs information includes at least one of information on direction of makeup ([0049] the make-up plan may comprise a description of the make-up steps and a corresponding makeup effect image.), information on usage scenes of makeup, information on makeup tools possessed by the user, and information on skin troubles of the user.
Regarding claims 6 and 18, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above. Yang further discloses:
wherein the needs information includes at least one of information on the user's makeup preferences, ([0151] providing the user with a make-up plan according to the user's personalized needs, so that the user may further modify the make-up effect image according to his/her preference,) information on the user's attributes, information on the user's makeup skill level, and information on the user's facial features concerns.
Regarding claims 7 and 19, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above.
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference Yang does not explicitly disclose:
wherein the evaluation information includes at least one of information on an evaluation of preference, an evaluation of a color tone of the recommended makeup product, an evaluation of impression given by makeup, and an evaluation of makeup technique.
However Satori teaches:
wherein the evaluation information includes at least one of information on an evaluation of preference, an evaluation of a color tone of the recommended makeup product, an evaluation of impression given by makeup, and an evaluation of makeup technique. [0096] Alternatively or in addition, individual makeup artists or designers may define their own recommendation rules to automatically generate user selectable looks via menu 916i. In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
Regarding claim 9, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above.
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference does not explicitly disclose:
further comprising the one or more processors configured to receive, from the user, a makeup face image of the user to which the makeup applied based on the recommendation makeup information,
wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user whose makeup face image is received.
However Satori teaches:
further comprising the one or more processors configured to receive, from the user, a makeup face image of the user to which the makeup applied based on the recommendation makeup information, [0095] In some embodiments, the looks displayed via menu 916i may include user-specific recommendations for combinations of makeup products. For example, a user may load an image of their face (e.g., via option 902). The loaded image may be analyzed by platform 120 (e.g., by using the previously described feature detection processes and/or any other computer vision processes) to determine certain characteristics of the face depicted in the loaded image.
wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user whose makeup face image is received. [0096] In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
Regarding claim 10, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above.
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, the reference Yang does not explicitly disclose:
the one or more processors configured to refer to the modified recommendation model and redetermine recommendation makeup information corresponding to the combination of the specified feature quantity and the received needs information as the recommendation makeup information;
the one or more processors configured to present the re-determined redetermined recommendation makeup information to the user.
However Satori teaches:
the one or more processors configured to refer to the modified recommendation model and redetermine recommendation makeup information corresponding to the combination of the specified feature quantity and the received needs information as the recommendation makeup information; [0095] In some embodiments, the looks displayed via menu 916i may include user-specific recommendations for combinations of makeup products. For example, a user may load an image of their face (e.g., via option 902). The loaded image may be analyzed by platform 120 (e.g., by using the previously described feature detection processes and/or any other computer vision processes) to determine certain characteristics of the face depicted in the loaded image. For example, analysis performed on the image may detect a general shape of the face, shapes and arrangement of certain key features (e.g., eyes, nose, mouth, etc.), hair color and texture, and skin tone. Based on the detected characteristics, platform 120 may generate one or more recommended looks that are specifically tailored to the characteristics of the face in the loaded image. In some embodiments, product recommendations may be based on a user-specific product browsing or selection history. For example, the system may recommend a look that includes a makeup product previously selected by the user. [0096] Product recommendations may be based on objective and or subjective “rules” such as general color combination rules (e.g., primary vs. secondary color combinations), specific application rules (e.g., product combinations for daytime vs. nighttime usage), makeup effects and predetermined goals (e.g., applying bronzer to define or sculpt certain facial features), etc.. In some embodiments, these rules may be generated by one or more expert makeup artists, manufacturers/designers, retailers, etc.. For example, in an embodiment, retailer (such as a department store) may individually define certain makeup recommendation rules to generate looks that are selectable via menu 916i. Alternatively or in addition, individual makeup artists or designers may define their own recommendation rules to automatically generate user selectable looks via menu 916i.
the one or more processors configured to present the re-determined redetermined recommendation makeup information to the user. In some embodiments, look recommendations may be automatically generated using machine-learning based models, for example based on historical aesthetic feedback data. For example, although not shown in screen 900i, in some embodiments, users may be presented with options to rate makeup products and looks, both in general and as applied to their specific facial features.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang to include the limitations above, as taught in Satori, in order to produce a composite image that simulate the application of a corresponding real-world makeup product (paragraph 0025).
Regarding claims 8 and 20, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above.
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, and Satori teaches the application of recommended product to a user image and receiving feedback, the references do not explicitly disclose:
further comprising the one or more processors configured to receive, from the user, information that the user wants to obtain the recommended makeup product,
wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user obtaining the makeup product.
However Peyrelevade teaches:
further comprising the one or more processors configured to receive, from the user, information that the user wants to obtain the recommended makeup product, ("Buy" icon in Figure 10)
wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user obtaining the makeup product. (Step 150 "enable the subject to indicated whether the first product is acceptable" and Step 160 "Select at least one second beauty product complementary with the first beauty product" and see Figure 20 "Buy")
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang in view of Satori to include further comprising the one or more processors configured to receive, from the user, information that the user wants to obtain the recommended makeup product, wherein the one or more processors configured to receive the evaluation information receives the evaluation information from the user obtaining the makeup product, as taught in Peyrelevade, in order to increase sales through cross selling based on improved recommendations (paragraph 008).
Regarding claim 11, Yang in view of Satori in further view of Peyrelevade teaches the limitations set for the above.
While Yang discloses a makeup apparatus for making recommendations about products using extracted information about the facial features of the user, skin information of the user, and other user data inputs, and Satori teaches the application of recommended product to a user image and receiving feedback, the references do not explicitly disclose:
further comprising a module configured to present a reward to the user inputting the evaluation information.
However Peyrelevade teaches:
further comprising a module configured to present a reward to the user inputting the evaluation information. ([0094] Recommended products 720 may be an individual product and/or a package of products. In one embodiment, the package option may include a special discount. The discount may be applied if the user purchases an individual recommended product and/or package of recommended products with the selected product. The discount may be offered at time of payment. The discount may be used by the AI engine 540 as a sales tool to encourage sales that may not otherwise occur.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the makeup recommendation process of Yang in view of Satori to include further comprising a module configured to present a reward to the user inputting the evaluation information, as taught in Peyrelevade, in order to increase sales through cross selling based on improved recommendations (paragraph 008).
Relevant Art Not Cited
Tamura (US 20150366328) discloses an application for applying makeup based on the similarity of a received user image and a target face image.
Response to Argument
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to 35 USC 101, the examiner has updated the rejection above in view of the claim amendments.
First, the examiner has withdrawn the categorization of mental process to the claims as amended and therefore the remarks directed to mental process are considered moot. However, the claims still recite an abstract idea under “method of organizing human activity”. As shown, the steps culminate into provided a recommendation to the user. Providing recommendations to a user is a form of marketing, advertising, sales techniques. The examiner notes the analysis of the image, the storage of the data, and the recommendation model are recited at a very high level. While the claim now recites a database, which is considered an additional element, the remainder of the claim recites analyzing the image and recommending items using a model that are not tied to any level of technical detail. For at least these reasons the claims still recite an abstract idea.
Second, to the remarks directed to the claims reciting technical solution to a technical problem, the image analysis is not recited in a manner tied to a technical implementation beyond being performed by a processor. Applicant specifically states “Such a coordinated sequence of image analysis, database storage in association with a user identifier, and recommendation model modification based on evaluation information improves the functioning of a computer-implemented recommendation system by supporting scalable, accurate, and personalized results through computer-based processing of image data and iterative adaptation of the recommendation model impossible to achieve through manual observation or judgment. The present application advances computing-based facial feature analysis and recommendation model adaptation, not merely the abstract idea of makeup recommendation.”, however this is not reflected in the claimed invention. As claimed the processor and client device are recited at a high level as a tool to implement the abstract idea. Basic iteration and adaptation of recommendations falls into the abstract idea and not a technical solution to a technical problem. The processor performing the recommendation model and updating the recommendation model does not provide technical solution to a technical problem. This also applies to the image analysis as the analysis is simply performed by the processor and not improved in some manner by the processor nor does the image analysis improve the processor itself.
For at least these reasons, the claims remain rejected under 35 USC 101.
With respect to the remarks directed to 35 USC 103, the claims remain rejected. The rejection has been updated above in light of the claim amendments.
As to maintaining the teachings of Yang in light of the claim amendments, as shown Yang does teach the image analysis now claimed. Yang states at least in [0063] For example, the face super-resolution algorithm may comprise facial image de-noising, facial image magnification selection, facial feature point extraction, make-up information extraction, and facial skin quality assessment. The facial image de-noising may provide two parameters which are 0 and 1. The limitation of the image analysis is not limited in the manner in which the analysis is implemented, only what information is collected. Therefore, Yang is still applied. As to the information collected now required by the claim, Yang, further still applies. Yang teaches in [0061] and [0087] the extraction of facial features with respect to the facial features and their positional relationship is determined in [0063] (note the elements are specifically mapped in the rejection). The examiner interprets this process set forth in Yang that extracts the particular features of the face, identifies their shapes, and determines their positional relationship to teach the amended limitation of the claims.
The teachings of Satori are maintained, but not used to teach newly amended claim language.
With respect to the now added step of storing in a database the facial information, needs information, and user identifier, the examiner has used the teachings of Peyrelevade to teach this limitation. As shown in Figure 16 (detailed in the mapping above), the reference teaches a user profile storing in a memory a plurality of data points for the specific user. These include facial features, needs information, shapes of facial features, and user identifying information.
For at least these reasons the claims remain rejected under 35 USC 103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/4/2026
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