DETAILED ACTION
This non-final office action is responsive to application 18/667,357 as submitted 17 May 2024.
Claim status is currently pending and under examination for claims 1-23 of which independent claims are 1 and 22-23.
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 .
Information Disclosure Statement
As required by MPEP 609(c), the applicant’s submissions of the Information Disclosure Statement dated 06/15/26 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by MPEP 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Claim Objections
Claims 10 and 17 are objected to because the lines are crowded too closely together, making reading difficult. Substitute claims with lines one and one-half or double spaced on good quality paper are required. See 37 CFR 1.52(b).
Claim Rejections - 35 USC § 112
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.
Claim 12 is rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Particularly, the terms “short-term” and “long-term” are relative terms which renders the claim indefinite. The terms “short” and “long” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification at [0072] repeats claim language without defining or otherwise providing a measure for establishing the metes and bounds afforded by the terms short and long. Therefore, the claim is indefinite and rejected under 35 U.S.C. 112(b).
Claim Rejection – 35 USC § 101
8. 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.
9. Claims 1-21 are rejected under 35 U.S.C. 101 for being directed to non-statutory subject matter. Claim 1 is drawn to a computer-readable medium which encompasses transitory signals per-se. The specification fails to disavow the computer-readable medium as non-transitory noted at [0032] that memory may optionally comprise non-transitory embodiments, however memory is not claimed and even if it were, the open-ended description is not binding of the recited computer-readable medium. A computer readable medium without tangible form merely represents instructions without structural relation to permit the function of the descriptive material to be realized. Thus, the computer-readable medium constitutes non-statutory functional descriptive material. This deficiency applies to claims 2-21 which depend from claim 1 without further mediating the issue. The rejection may easily be addressed by amending as “non-transitory computer-readable medium.” See MPEP 2106.01.
Claims 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within one of the four statutory categories: claim 22 is a method/process, and claim 23 is a system/machine. Therefore, the claims recite statutory subject matter identified under MPEP 2106.03 and the analysis should proceed.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the grouping of abstract idea being “Mental Processes” enumerated under MPEP 2106.04(a). More particularly, claims recite:
Claim 22
“analyzing skin characteristics of the user based on the image” (Mental Process being evaluation based on observation)
“providing a skin care regimen based on the skin characteristics” (Mental Process being an opinion or judgment)
Claim 23
“analyze skin characteristics of the user based on the image” (Mental Process being evaluation based on observation)
Focus of the claims analyzing skin characteristics to provide skin care regimen. These functions may be performed in the mind by a human such as a dermatologist or cosmetologist, whom may assess skin in need of treatment. As such, the claims recite at least mental processes as the abstract idea.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
Claim 22
“accessing an image of a user” MPEP 2106.05(g) adding insignificant extra-solution activity to the abstract idea, e.g. mere data gathering
“generating a visualization of the image as the image would appear, after a time lapse, with implementation of the skin care regimen” MPEP 2106.05(g) adding insignificant extra-solution activity to the abstract idea, e.g. necessary data outputting
“providing the visualization to a display” for preamble “providing a skin care augmented reality visualization” MPEP 2106.05(h) generally linking the use of the abstract idea to a particular technological environment or field of use
Claim 23 recites limitations generate and provide similar to claim 22, and further comprising
“an image system configured to provide an image of a user; a display for displaying an image; and one or more processors coupled to the image system and to the display”
MPEP 2106.05(f) mere use of commodity computer as a tool to perform an abstract idea e.g. ubiquitous smartphone with camera
“train a machine learning model to generate a skin care regimen based on the skin characteristics and on product information for skin care products” MPEP 2106.05(h) generally linking the use of the abstract idea to a particular technological environment or field of use
Balance of the claims concern image access for generating visualization provided to display using common computer elements to train a machine learning model. The augmented reality is not positively recited being only at the level of preamble, and the trained machine learning model is recited at a high level of generality without technical solution for meaningful limitation. The remaining elements provide image input and output visualization using general computer elements. As set forth under MPEP 2106.04(a)(2)(III) “A claim that requires a computer may still recite a mental process.” Accordingly, claims remain drawn to mental processes as the abstract idea and the additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and do not demonstrate inventive concept. Particularly, the additional elements are as follows:
Claim 22
“accessing an image of a user” MPEP 2106.05(g) adding insignificant extra-solution activity to the abstract idea, e.g. mere data gathering. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(i) receiving data over a network
“generating a visualization of the image as the image would appear, after a time lapse, with implementation of the skin care regimen” MPEP 2106.05(g) adding insignificant extra-solution activity to the abstract idea, e.g. necessary data outputting. Particularly, said extra- solution activity is a well-understood, routine and conventional activity as evidence by An “ARCosmetics” at [P.4 Sect.3 ¶1] “typical augmented reality” emphasis typical/conventional. The limitation does not meaningfully limit the claim under MPEP 2106.05(e) since a visualization may be generated by hand of artist to render preview of desired effect.
“providing the visualization to a display” for preamble “providing a skin care augmented reality visualization” MPEP 2106.05(h) generally linking the use of the abstract idea to a particular technological environment or field of use. Particularly, the technological environment or field of use pertaining to augmented reality which is noted above with respect to evidentiary reference An. The AR does not detail a new sensor or specific device and may employ commercially available products like Hololens for example.
Claim 23 recites limitations generate and provide similar to claim 22, and further comprising
“an image system configured to provide an image of a user; a display for displaying an image; and one or more processors coupled to the image system and to the display”
MPEP 2106.05(f) mere use of commodity computer as a tool to perform an abstract idea e.g. ubiquitous smartphone with camera. Particularly, these elements do not qualify as a particular machine under MPEP 2106.05(b)
“train a machine learning model to generate a skin care regimen based on the skin characteristics and on product information for skin care products” MPEP 2106.05(h) generally linking the use of the abstract idea to a particular technological environment or field of use. Particularly, the technological environment or field of use is machine learning for apply-it AI using established models like known CNNs. The model training does not provide technical detail of a particular transformation under MPEP 2106.05(c) such as through specified weighting or parameterization. Further, evidence of trained models establishing relevant state of art is evidenced by Zhou et al., “SkinGPT-4” arXiv: 2304.10691 at Fig 1.
Significantly more is not satisfied for at least the reasons above looking at the additional elements individually. Considering the claim as a whole with limitations as an ordered combination fails to elevate the claim beyond that which is already established in the art. A combination of AI+AR for skin considerations is established in view of numerous evidences such as An noted above, alternatively Suresh US12,315,090B1 or Rajegowda “An AI-Assisted Skincare Routine Recommendation System in XR” Therefore, the additional elements do not breathe life into the claim in a manner that distinguishes from established practices in the technological environment or field of use. If the claim language provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it, then the claims do contain an inventive concept. As such, the balance of evidence favors a finding of ineligibility for claims 22-23 which are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 6 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by:
Dissanayake, US PG Pub No 2025/0166040A1 hereinafter Dissanayake (Procter & Gamble).
With respect to claim 1, Dissanayake teaches:
A computer-readable medium including instructions that, when executed on a processor, cause the processor to perform operations for providing a visualization of results of application of a skin care product {Dissanayake [0016] “computer-readable medium storing instructions for providing personalized skin product recommendation” Fig 6 shows a visualization on device which includes processors e.g. CPU/GPU [0045], [0058]}, the operations including:
obtaining training data including skin characteristics for a population of users, an indication of the respective skin care products used on the population of users, and a respective treatment outcome for each user {Dissanayake Fig 2A Training, e.g. [0070] “receive the training data” comprising [0107] “skin features, which may be used to train a given AI model… skin features (e.g., spots) of the user’s skin” and Fig 3 indicating ingredients of skin care products described [0068-69] “trained to determine efficacy of one or more skin products… AI model trained to determine effects of one or more product attributes (e.g., active ingredients, pigmentation, etc.) corresponding to respective products” where efficacy/effects convey outcome/result of using the product for “treatment” again at Fig 3 e.g. [0103] “improved skin features depicting the skin following treatment”, [0097] “trained on images such as image 202_dt1 in order to determine how the skin issue would be effected based on treatment”};
training a machine learning model, using the training data, to predict a treatment outcome for a new user based on skin characteristics of the new user and on an indication of the skin care product used on the new user {Dissanayake Fig 2A Model Training, [0061] “model is trained… such as GPT” for [0010] “predictive effect and/or efficacy of the recommended skin product” at [0088] “predicted change after treatment of the at least one feature within the pixel data with the manufactured product” e.g. [0116-17] “what the user’s skin is predicted to appear like after using a recommended product” as a “user-specific prediction” e.g. [0125] “predicting how the user would successfully use night face cream to reduce melanin and/or hemoglobin” shown Figs 6, 3};
generating a visualization of the treatment outcome based on applying the trained machine learning model to an image of the new user {Dissanayake [0088] “generate a modified image (e.g., a simulated or enhanced image)… the modified image may depict a rendering of how the user’s skin or skin regions are predicted to appear after treating… image to represent a possible or predicted change after treatment” predicted by trained model such as vision transformer [0066,69]. See similar [0124-25], [0043] synthetic image generation with predicted appearance after applying recommended product, further shown Figs 2B:274, 5B and 6}; and
providing the visualization to a display {Dissanayake [0058] “visualizations …for display” e.g. at Fig 2B:274 display simulated image, and/or [0088] “modified image may then be rendered on the display screen”}.
With respect to claim 2, Dissanayake teaches the computer-readable medium of claim 1, wherein:
the training data further includes at least one of historical skin data, lifestyle data, or genetic factors and corresponding treatment outcomes for each user the population of users {Dissanayake [0108,112] “trained on pixel data… pixels previously associated with melanin” previous is historical thus trained on historical data regarding skin melanin, corresponding treatment outcome is e.g. [0125] “predicting how the user would successfully use night face cream to reduce melanin” such that the prediction is by training on said training data (i.e. features), [0074]. See also Figs 2A and 3 “Lifestyle… can also help improve the appearance of your skin”}, and
the machine learning model is trained to predict the treatment outcome for the new user further based on at least one of historical skin data, lifestyle data, or genetic factors of the user {Dissanayake [0125] “retraining or reanalyzing by AI models… predicting how the user would successfully use night face cream to reduce melanin” melanin associate with previous/historical pixels for training [0112,108]. See also [0071] “previously trained”}.
With respect to claim 3, Dissanayake teaches the computer-readable medium of claim 1, wherein:
the training data further includes at least one of geographic location, weather conditions, and local environmental pollutant concentrations and corresponding treatment outcomes for each user of the population of users {Dissanayake [0070-71] “AI models may be trained with user demographic data (e.g., data indicating race, skin color etc.) and environment data (e.g., amount of sunshine, geography, weather conditions, etc.) of respective users… geographic locations associated with such demographics” e.g. [0040] “environmental factors such as sun exposure, pollutants”, corresponding treatment outcome may comprise reduced or eliminated hyper melanin production upon LLM recommendation for avoiding sun exposure e.g. [0086], [0120]}; and
wherein the machine learning model is trained to predict the treatment outcome for the new user further based on at least one of geographic location, weather conditions, and local environmental pollutant concentrations for the new user {Dissanayake [0071] “training a machine learning based model… geographic locations associated with such demographics and/or ethnicities. Synthetic image generation model and/or image simulation model may generate output…based on the ethnicity and/or demographic value of the respective individual” trained model outputs are predictions, e.g. [0086,84] “avoid direct sun exposure) in order to reduce or eliminate hyper melanin production” as output predicted by LLM, similar at [0120] “avoiding excessive sun exposure, and the like to address user-specific issue as detected within the pixel data by the AI model”}.
With respect to claim 6, Dissanayake teaches the computer-readable medium of claim 1, wherein
the training data further includes at least one of user age, user gender, or user ethnicity, and corresponding treatment outcomes for each user of the population of users {Dissanayake [0063-64] “Demographic attributes of a user may comprise… gender, age, ethnicity” describes training based on demographics, similar at [0078-79] and [0091,94] further includes demographic classifications and discloses user-specific treatments [0097], [0103,117]}, and
wherein the machine learning model is trained, using the training data, to predict the treatment outcome for the new user based on at least one of user age, user gender, and user ethnicity {Dissanayake [0062] “model may further be trained or otherwise configured to output one or more demographic classifications corresponding to one or more demographic attributes… (a) a gender type classification, (b) an age type classification, (c) an ethnicity type classification” again at [0097] with treatments therefore described e.g. [0097], [0103,117]}.
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Sinclar et al., US PG Pub No 2025/0037871A1 hereinafter Sinclair (L’Oreal).
With respect to claim 4, Dissanayake teaches the computer-readable medium of claim 3. Dissanayake teaches wherein the operations further comprise:
detecting change in user geographic location to a new geographic location {Sinclair [0032] “track geolocations of a subject and estimate exposure of the subject to environmental pollutants to provide actionable guidance to the subject about healthcare, skincare” e.g. [0057] “first geolocation-specific pollutant level and a second geolocation-specific pollutant level” the change is from first to second/new geolocation, detecting is by tracking}; and
wherein the machine learning model is trained to predict the treatment outcome for the new user further based on the new geographic location {Sinclair Fig 11:1102 SkinConsult AI, see [0037-38] “user provides geolocation and receives consultation or advice 1102 from an artificial intelligence (AI), e.g., ‘SkinConsult AI’” & “tracking individual’s geolocations and environmental exposure information and correlating that information with healthcare conditions and/or treatments… predicting or correlating with the onset or progression of skin conditions” the AI regarding model trained is by weighting factors for the regression model described [0026, 0049-50] and shown Figs 3:34, 4:46}.
Sinclair is directed to skincare, visualization and AI modeling thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include geolocation tracking for prediction with AI in combination with Dissanayake’s trained model in combination to arrive at the invention as claimed for a motivation of addressing [0021] “there is a need for a geolocation-specific exposome level… used as an input into beauty or skincare guidance or recommendations” cont’d [0022-23] “For example, given a smartphone’s collected geolocations, corresponding mean values are collected for each environmental variable… tailored to a specific individual with a specific set of geolocation data” with [0057] “actionable guidance or recommended action is based on the result of comparison between two or more geolocation-specific pollutant levels.”
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Sinclar and further in view of Rami et al., US PG Pub No 2025/0037333A1 hereinafter Rami.
With respect to claim 5, Dissanayake teaches the computer-readable medium of claim 4, wherein the operations further comprise:
detecting whether the user is permanently or temporarily in the new geographic location {Sinclair Fig 1 timestamped latitude/longitude geographic locations, Fig 12:1203-04 questioning where do you live (permanent) & whether time is commonly spent at other places (temporary) similar at Fig 6:62, described e.g. [0022] “series of time intervals, which can range from hourly to monthly temporal resolution” and/or [0057] “spend time outside at location ‘A’ or spend time outside at location ‘B’”}; and
Sinclair includes time in the model Fig 4:45. However, Sinclair does not appear to disclose refraining from training which is disclosed by Rami:
refraining from training the machine learning model to predict the treatment outcome for the new user based on the new geographic location if the user is temporarily in the new geographic location {Rami [0026] “training component 902 may freeze some weights” similarly at [0150,160] the frozen, held fixed or locked weights by training component is refraining from training Figs 9-10:1013, predicted outcome is generated try-on image 1014, and further discloses [0052] “geolocation for a predefined amount of time”. See also [0027] training includes skin tone}.
Rami is directed to trained models with generative imaging for virtual try-on thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to freeze weight as refraining from training per Rami in combination with Sinclair to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or a motivation of [0024] “fine-tuning training preserves details” e.g. in order [0026] “to preserve what has already been learned” and/or [0160] “some fixed weights 1013 can be preserved after the initial training.”
Claim 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Seyfaie et al., US PG Pub No 2025/0335963A1 hereinafter Seyfaie (L’Oreal).
With respect to claim 7, Dissanayake teaches the computer-readable medium of claim 7. Seyfaie teaches wherein the operations further include:
providing a skin care regimen for the new user, based on predicted treatment outcomes for the new user of using indicated skin care products {Seyfaie Figs 4A-4B “skin care routine” provided by [0065] “LLM 130 responds by agreeing to help with a new skin care routine” routine is regimen and the LLM is predictive e.g. GPT [0019], outcomes comprise answers [0020], effect achieved [0053] and/or results [0067], with treatment being application of skin care products per Fig 4C moisturizer, cleanser. See also [0065-71], [0051-54]}.
Seyfaie is directed to trained models for skin care thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ the LLM-based skincare routine per Seyfaie in combination with the predicted effect after treatment of Dissanayake’s LLM to arrive at the invention as claimed for a motivation that the LLM helps to recommend a skincare routine [0065] and answers from the perspective a skilled beauty consultant for personalized care routines [0019-20].
With respect to claim 10, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7, wherein
the training data further includes an indication of the skin care regimen used on the population of users and corresponding treatment outcomes {Seyfaie [0069-72] “pre-training or prompting the LLM 130 to respond to provide product and/or routine recommendations once information about the user’s skin concerns, skin type, and skin condition have been obtained” and “update product and/or routine recommendations” where [0020] “the system uses skin treatment information in order to provide the answer” discloses treatment, outcome thereof corresponds to user’s skin condition used for updated recommendation of skincare routine} the operations further comprise:
However, the following limitations are disclosed by Dissanayake:
training the machine learning model, using the training data, to predict a treatment outcome for the new user, based on skin characteristics of the new user and on an indication of the skin care regimen used by the new user {Dissanayake Fig 2A Model Training with image data, so as for [0088] “predicted change after treatment” where “user’s skin or skin regions are predicted to appear after treating the at least one feature with the manufactured product” e.g. [0116,18] “user’s skin is predicted to appear like after using a recommended product… user-specific prediction”}; and
generating a visualization of the treatment outcome {Dissanayake discloses [0088] “generate a modified image (e.g., a simulated or enhanced image) …rendering of how the user’s skin or skin regions are depicted to appear… modified image may then be rendered on the display screen” similar [0116,18], shown Fig 5B}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to generate modified images after treatment based on training for prediction in combination for a motivation being [0010] “graphically enhanced simulated image may depict a predictive effect” e.g. [0097] “in order to determine how the skin issue would be effected based on treatment with a given product (e.g., a skin creme), and to generate a user-specific simulated image”
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake and Seyfaie in view of Yadin, Lior, US PG Pub No 2022/0301692A1 hereinafter Yadin.
With respect to claim 8, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7. Yadin teaches wherein the operations further include
comparing predicted treatment outcomes with actual treatment outcomes obtained from images of the user and providing feedback to the trained machine learning model based on the comparing {Yadin discloses [0073,75] “comparison of the actual post-treatment image and the predicted post-treatment image may cause the machine learning engine 144 to update parameters… minimize the difference between the before and after image” see Fig 2 pre- and post-treatment images for trained model}.
Yadin is directed to visualization of image aesthetics with trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to compare as per Yadin in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or for a motivation [0075] “Due to the multiple comparisons… the machine learning engine 144 may learn the effect and range of tissue behaviors post-injection per specific area” and [0073] “improve the accuracy of the machine learning engine.”
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake and Seyfaie in view of Ebert et al., PCT WO2024/023815A1 hereinafter Ebert.
With respect to claim 9, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7, wherein the operations further include:
accessing at least one of product formulation updates or product availability updates from a product database, and wherein the training data further includes at least one of product formulation updates or product availability updates {Seyfaie [0028] “database 120 ingests product information from the data sources such as the product data store 122 (e.g., product descriptions, product ingredients)” similar at [0069,71] “retrieves product information from the vector database… pre-training or prompting the LLM 130 to update product” and/or provide [0053] “custom product formulations” [0046]}; and
However, Seyfaie does not appear to disclose the following limitation which is disclosed by Ebert:
wherein the machine learning model is trained to predict the treatment outcome for the new user further based on at least one of product formulation updates or product availability updates {Ebert Fig 4 Predicting a Product Effect, predicted by Fig 3 deep Bayesian network for skin that inputs skincare product ingredients and output personalized regimen recommendations, described [P.18 Last¶] “AI-model (here Bayesian network) is trained… model may output a personalized recommendation for a subject’s cosmetic/skincare/haircare regimen”, [P.13 ¶3,2] “output a formulation and/or ingredient list that addresses the needs of a specific bio-individuality cluster” that “regroups and analyzes data coming from both the user and the ingredients of the cosmetic/skincare product” such that regrouping and/or user feedback [P.20 ¶2] provide for update of product ingredients/formulation, hence [P.16 ¶1] “ingredients may be retrieved automatically”, also [P.21 ¶2-3] “easily identifies the good products among all of the available options in each category”}.
Ebert is directed to skincare regimen recommendation with trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify predicted product effect/outcome per Ebert in combination with the updated products and formulations of Seyfaie to arrive at the invention as claimed for a motivation of [P.2 ¶1-3] “personalized matching of cosmetics and skincare products… enabling recommending the right skin product and regimen to a specific customer” with “continual learning… to maximize the system’s predictive power.”
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake and Seyfaie in view of James et al., US PG Pub No 2023/0165358A1 hereinafter James (L’Oreal).
With respect to claim 11, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7. James teaches wherein the operations further comprise
providing a notification to a product manufacturer to adjust formulation of a skin care product upon receiving user input that at least one user is exhibiting less than perfect compliance with the skin care regimen {James [0209] “notifications are sent to the app user to adjust the recipe… shift the master skintone formula for the user that is sent when the tanning level of the consumer is different from the initial diagnosis” such that [0274] “manufacturing facility can receive updates directly to change ingredients dispensed” teaches notifications received by manufacturer as update to adjust recipe/formulation for dispensing products that are subject to consumer adherence to a diagnosis, similar at [0192], [0157]. Figs 20A and 22A show dispensing for a skincare product, similarly Fig 18A as a lipstick color-mix dispenser}.
James is directed to skincare product manufacturing with display images and trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to provide notifications to adjust recipe or formulation per James in combination to arrive at the invention as claimed for a motivation of a [0058] “specialized dispensing device that allows ingredients for a cosmetic product to be instantly blended into a user’s preferred end result” e.g. [0198] “recommended skincare formulation (‘blend’) which addresses the user’s priority skincare concerns while taking into account the current environmental conditions… the recipe is transmitted to the dispensing device.”
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake and Seyfaie in view of Vodrahalli et al., US PG Pub No 2023/0092766A1 hereinafter Vodrahalli (Seebe).
With respect to claim 12, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7, wherein the operations further include providing the visualization comprises
providing a time-lapse feature to illustrate potential outcomes that reflect compliance and non-compliance with the skin care regimen by (i) providing a first image depicting a short-term outcome of complying with the skin care regimen and a second image depicting a short-term outcome of not complying with the skin care regimen, and (ii) providing at least a third image depicting a long-term outcome of complying with the skin care regimen and at least a fourth image depicting a long-term outcome of not complying with the skin care regimen, and (iii) providing at least the first image, second image, third image and fourth image to a display, wherein the at least first image, second image third image and fourth image are generated based on extrapolation of features of the user according to predicted treatment outcomes {Vodrahalli Figs 11-15 illustrate progression over time in weeks and months for [0003-04] “prognostic visualization… predicted outcome of treatments over a future course of treatment” where non-compliance is [0103] “missing a treatment… simulated result of missing a treatment and may allow the user visualize the impact of the behavior on future treatment progress… timeline GUI to visualize missing a treatment” and compliance is [0104] “getting back on track of the treatment progress”. A trained model for temporal sequence is LSTM long-short term memory network [0088], such that images of 2nd, 3rd, 4th or Nth images are [0006] “temporal sequence of images” again at [0008,11], e.g. video [0039,69]. The features utilize [0086] “feature extraction” corresponding to claimed ‘extrapolation of features’}
Vodrahalli is directed to visualization of predicted treatment outcomes for skin conditions using trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to provide temporal feature according to the teachings of Vodrahalli in combination to arrive at the invention as claimed for a motivation [0075] “beneficially increase engagement from the user” with [0104] “suggested action items for remaining or getting back on track of the treatment progress. A user may be provided with actionable feedback (e.g., intervention) to help the user”.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake and Seyfaie in view of Ueda et al., “A Cosmetic Differences Visualization System for Beauty Recommendation using the Scores of Various Evaluation Items” hereinafter Ueda.
With respect to claim 13, the combination of Dissanayake and Seyfaie teaches the computer-readable medium of claim 7, wherein providing the visualization comprises
receiving user input to enable or display a different visualization, the different visualization reflecting use of a different skin care regimen {Seyfaie Figs 4A-D user inputs to LLM interface display, e.g. [0065] “new skin care routine… routines with a follow up question” new or personal/customized [0020,53] is a different skin care regimen/routine}, and
However, Seyfaie does not disclose the following limitation which is disclosed by Ueda:
wherein the operations comprise providing a simultaneous comparison of the visualization and the different visualization {Ueda Fig 3 “Visualizing system of differences” side-by-side comparison for visualization of cosmetics owned and of-interest with scored features, used for recommender Fig 4}.
Ueda is directed to visualization display for beauty/cosmetics product recommendation thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to provide visualization per Ueda in combination to arrive at the invention as claimed for a motivation [Sect.3.3 ¶3] “help a user visualize a more accurate picture of the differences.”
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of An et al., “ARCosmetics: a real-time augmented reality cosmetics try-on system” hereainfter An.
With respect to claim 14, Dissanayake teaches the computer-readable medium of claim 1, wherein the operations further comprise
updating the machine learning model based on user feedback pertaining to perceived accuracy of the machine learning model {Dissanayake discloses [0076,75] “updating models” over “training iterations …train multiple generations of models until a satisfactory model, e.g., a model that provides sufficient prediction accuracy when given test level or production level inputs, is generated” emphasis accuracy, feedback as ‘until satisfactory’ and/or deemed sufficient, thus [0105] “training yields improved accuracy”}.
However, Dissanayake does not appear to explicitly state the that the user is to perceive which is disclosed by An. Particularly, An discloses [P.7 Last¶] “perceptual preference of users” as “participants select the satisfaction score… Are you satisfied with the accuracy of the overlaid try-on cosmetic” where cosmetic try-on is overlaid using model to render images Fig 1, implemented [P.4-5].
An is directed to trained models to rendering images for cosmetics try-on thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include the user’s perception of accuracy as feedback per An in combination with Dissanayake’s model update based on accuracy to arrive at the invention as claimed for a motivation [P.10 ¶2,4] “evaluate the perceptual quality” e.g. “rated the satisfaction score” which may further help to [P.4 ¶3] “improve the accuracy” and ultimately [P.1 ¶1] “help users efficiently choose suitable cosmetics.”
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Li et al., “Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation” hereinafter Li (arXiv: 2302.11950v1).
With respect to claim 15, Dissanayake teaches the computer-readable medium of claim 1. Li teaches wherein the operations further comprise
updating the machine learning model based on user feedback pertaining to perceived user satisfaction with the visualization {Li [P.5 ¶4] “Are you satisfied with the simulation of facial pore changes over time” questioning respondent, see Fig 4. The simulation by model is UNet [P.3 ¶1-2] which is updated by loss function penalty Eq.1 that uses F1 Score (an accuracy measure of machine learning model performance based on precision and recall)}.
Li is directed to generative image simulation for skincare products with trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ user satisfaction with simulated facial images for model updates per Li in combination with Dissanayake’s updated models to arrive at the invention as claimed for a motivation [P.1 ¶1] “simulation of these changes are helpful for the consumers to dynamically understand their skin and evaluate the potential benefit of skincare products” and which considers the subjective satisfaction of a user.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Cao et al., “BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis” hereinafter Cao (arXiv: 2311.06752v1).
With respect to claim 16, Dissanayake teaches the computer-readable medium of claim 1. Cao teaches wherein
the machine learning model is trained using a reinforcement learning algorithm {Cao [Sect.4] “BeautifulPrompt training scheme… Reward model training and Reinforcement Learning) as shown in Figure 3” further detailed [Sect. 4.2-4.3] comprises “algorithm to directly optimize the expected reward” and aesthetic score, see Fig 6 face portrait images which may be input Fig 8 e.g. celebrity face portrait}.
Cao is directed to beauty and aesthetics image synthesis with generative trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ trained reinforcement learning per Cao in combination to arrive at the invention as claimed for a motivation [P.2 ¶1] “Reinforcement Learning with Visual AI Feedback (RLVAIF) …RVLAIF makes the generated prompts more compatible with human preferences without any manual labeling” thereby [P.5 ¶1] “improve the model performance” and with further benefit being to [P.3 ¶2] “give users a good source of inspiration and generate more beautiful images.”
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Despois et al., US PG Pub No 2021/0407153A1 hereinafter Despois (L’Oreal).
With respect to claim 17, Dissanayake teaches the computer-readable medium of claim 1. Despois teaches wherein
the training data further includes images of the population of users and an indication of skin age progression, and at least one of currently-used skin care products, current skin condition, genetics, lifestyle, or environmental factors {Despois [107-08] “training using respective training images and associated aging maps and wherein the associated aging maps provide weak spatial supervision to guide the aging transformations of the respective skin signs” similar at [0117] “training environment… (GANs-based) age simulation generator” shown Fig 14:1402 Training environment comprising 1420,1422 respectively image and age mapa, and the skin signs suggest current skin condition e.g. wrinkles [0107], see also [0004] “genetic factors…lifestyle choices”, the progression is “continuous” [0006], [0117]}; and
the operations further include training the machine learning model, using the training data, to predict skin age progression of the new user based on an image of the new user and at least one of the currently-used skin care products, current skin condition, genetics, lifestyle, or environmental factors for the new user {Despois Fig 14:1414 Model Trainer with generator and discriminator of GAN (Fig 5, generative adversarial network) with respective training data in the training environment 1402, [0038-39] “training process is based on the StarGAN …training a generator…training a discriminator” equations detail expectation Eq.1 of loss such that combined loss Eq.4 decomposes with age-loss Eq.2, the generator and discriminator respectively G and D process the training image data, and further prediction is estimation e.g. [0037] “estimation of the aging”}.
Despois is directed to trained models for image synthesis thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to train a GAN model on training data for age estimation per Despois in combination with Dissanayake to arrive at the invention as claimed for a motivation to [0006] “controllably transform an image of a face, including a high resolution image, to simulate continuous aging” e.g. [0030] “to generate realistic high-definition aged faces, with continuous control over the fine-grained aging signs to create smooth transformations between the original and transformed images.”
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Zhang et al., US PG Pub No 2025/0278779A1 hereinafter Zhang (L’Oreal).
With respect to claim 18, Dissanayake teaches the computer-readable medium of claim 1. Zhang teaches wherein
the image comprises a video or a frame of a video {Zhang Figs 9-12 video image for preview makeup try-on, frames are processed Fig 3, e.g. [0097] “selfie video is augmented to present an augmented reality experience” and/or [0008] “stream video frames… effects applied to a sequence of preview frames”}.
Zhang is directed to rendering images for makeup virtual try-on using trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify video per Zhang to support Dissanayake’s nominal recitation of video data in arriving the claimed invention for a motivation that [0094] “images are continuously received as a component of a live stream (e.g., a selfie video)” and [0097-98] allows for augmented reality thereof for video conferencing and teleconsultation Fig 6. This may serve to [0002] “aid in purchasing decisions …minimize imperfections in the VTO so as not to break the realism of the effect.”
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Lotti et al,. US Patent No 12,198,289B1 hereinafter Lotti (Lashify).
With respect to claim 19, Dissanayake teaches the computer-readable medium of claim 1. Lotti teaches wherein
the visualization includes a three-dimensional model of a user face or a user facial feature {Lotti Fig 9 [Col5 Lines11-12] “3D model of a face” with facial features Fig 8:804A-N, see e.g. Fig 3:310-312 “3D model of the eye area… 3D model of false eyelashes” and further discloses dimensionality techniques [Col12 Lines51-65] e.g. PCA principal comp. analysis Figs 10B, 11B. Note [Col6 Lines29-51] “Skincare previews through AR can simulate the potential effects of various treatments and serums on the user’s skin”}.
Lotti is directed to augmented reality for image preview in the space of beauty products using trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify 3D model of face per Lotti to support Dissanayake’s three-dimensional space of color channels to arrive the invention as claimed for a motivation being [Col12 Lines61-65] “Each element (e.g., dimension) of the vector can correspond to a feature or property of the original data… size of the embedding vector (e.g., embedding dimension) can be adjust during training” e.g. [Col2 Lines28-30] “scaling dimensions of the first 3D model” and more generally to [Col20 Lines29-39] “add dimension by incorporating longer and shorter lashes… multidimensional look.”
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Suresh et al., US Patent No 12,315,090B1 hereinafter Suresh (Amazon).
With respect to claim 20, Dissanayake teaches the computer-readable medium of claim 1. Suresh teaches wherein providing the visualization comprises:
providing the visualization on an augmented reality (AR) device {Suresh [Col7 Lines35-65] “augmented reality virtual makeup try-on on a computing device… AR glasses” disclosing “visualization” illustrated e.g. Figs 8A-B, see e.g. [Col2 Line19] “augmented reality skin”}.
Suresh is directed to augmented reality virtual makeup try-on to using trained models to render images thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ AR glasses and devices per Suresh in combination with Dissanayake to arrive at the invention as claimed for a motivation [Col2 Line66 – Col3 Line2] “goal is to provide an immersive augmented reality experience by allowing users to try-on beauty products virtually through augmented reality” and [Col 19 Lines9-10] “improve the overall client experience.”
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over: Dissanayake in view of Sun et al., “DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection” hereinafter Sun (arXiv: 2405.09882v1).
With respect to claim 21, Dissanayake teaches the computer-readable medium of claim 1. Sun teaches wherein providing the visualization comprises
providing a revision feature to visualize effects of reversing or terminating a skin care action {Sun Fig 2 shows “Reverse” for Makeup Removal, images visualize face with & without makeup, described [Sect. 3.2.1 ¶2,4] “reverse diffusion process, the diffusion model єθ1 is fine-tuned for makeup removal… effects of makeup removal” visualization again at Fig 10 by example}.
Sun is directed to generative imaging for makeup with trained models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to reverse in accordance with makeup removal per Sun in combination to arrive at the invention as claimed for a motivation [P.2 ¶3,2] “we aim to remove the makeup of reference images, gaining the corresponding non-makeup reference images… simplifies the exploration of the makeup and non-makeup domains’ relationship” noting “Diffusion models have shown better performance than GANs in image generation tasks thanks to more stable training process and better coverage of image distribution.”
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over:
Seyfaie et al., US PG Pub No 2025/0335963A1 hereinafter Seyfaie (L’Oreal) in view of
Dissanayake, US PG Pub No 2025/0166040A1 hereinafter Dissanayake (Procter & Gamble).
With respect to claim 22, Seyfaie teaches:
A method of providing a skin care augmented reality visualization {Seyfaie discloses [0051,53] “augmented reality techniques, to modify an image of the consumer’s face, hair, skin, etc.” cont’d “Visual elements of the user interface 276 are presented on a display…skin care routines, may be obtained”, [0065] “LLM 130 responds by agreeing to help with a new skin care routine”}, the method comprising:
accessing an image of a user {Seyfaie [0049] “user’s self-portrait photo to obtain a skin analysis” i.e. selfie Fig 4B, and/or Fig 3:270 image capture, [0051-54], [0022]};
analyzing skin characteristics of the user based on the image {Seyfaie [0051] “skin analysis engine 112 obtains one or more digital images… skin characteristics” e.g. “skin features including spots, texture, and wrinkles, and the like” and/or [0054] “detect blemishes, areas of hyper-pigmentation, visible pores, etc.” similar [0018] “analyzes the photo to determine characteristics (e.g., …skin tone, etc.)”, see also [0049,66] and Figs 2:170 & 4A-4B:420};
providing a skin care regimen based on the skin characteristics {Seyfaie Figs 4A-4B illustrating “skin care routine” a regimen is routine, provided by [0065] “LLM 130 responds by agreeing to help with a new skin care routine” cont’d [0065-71], similar [0051-54] considers skin features or characteristics, and [0020] “uses skin treatment information in order to provide the answer”};
However, Seyfaie does not disclose visualization after a time lapse which is disclosed by Dissanayake:
generating a visualization of the image as the image would appear, after a time lapse, with implementation of the skin care regimen {Dissanayake [0088] “generate a modified image (e.g., a simulated or enhanced image)… the modified image may depict a rendering of how the user’s skin or skin regions are predicted to appear after treating… image to represent a possible or predicted change after treatment” where ‘after’ conveys lapse of some time, treatment regards a regimen for “skin care” e.g. [0068], as would/predicted-to appear. See similar [0124-25], [0043] synthetic image generation with predicted appearance after applying recommended product, Fig 1-6}; and
providing the visualization to a display {Dissanayake [0058] “visualizations …for display” e.g. at Fig 2B:274 display simulated image, and/or [0088] “modified image may then be rendered on the display screen”}.
Dissanayake is directed to skin care visualization with modified images thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to generate modified images after treatment for subsequent display for a motivation being [0010] “graphically enhanced simulated image may depict a predictive effect” e.g. [0097] “in order to determine how the skin issue would be effected based on treatment with a given product (e.g., a skin creme), and to generate a user-specific simulated image”
With respect to claim 23, Seyfaie teaches:
A system for providing a skin care augmented reality visualization {Seyfaie discloses [0017] “system for providing personalized beauty recommendations” with [0020] “augmented reality virtual try-on applications” e.g. [0048] “try a cosmetic product in an augmented reality” and/or [0051] “augmented reality techniques, to modify an image of the consumer’s face, hair, skin” see Figs 1, 3 and 6}, the system comprising:
an image system configured to provide an image of a user {Seyfaie Fig 3:270 “image capture” in computing device 104, image described [0051,54] and/or [0054,66] photo/selfie Fig 4B};
a display for displaying the image {Seyfaie Fig 3:240 “display” described e.g. [0051,53] “images or scans from the client computing device 104, such as a smart phone… visual elements”}; and
one or more processors coupled to the image system and to the display, the one or more processors {Seyfaie Fig 6:602 “Processor” coupled via bus 606 to network interface 610, described e.g. [0078,82], see also Figs 1 and 3} configured to:
analyze skin characteristics of the user based on the image {Seyfaie discloses [0051] “skin analysis engine 112 obtains one or more digital images… skin characteristics” e.g. “skin features including spots, texture, and wrinkles, and the like” and/or per [0054] “detect blemishes, areas of hyper-pigmentation, visible pores, etc.” similar [0018] “analyzes the photo to determine characteristics (e.g., …skin tone, etc.)”, see also [0049,66] and Figs 2:170 & 4A-4B:420};
train a machine learning model to generate a skin care regimen based on the skin characteristics and on product information for skin care products {Seyfaie discloses [0065,67] “pre-training or prompting the LLM 130 to respond initially to queries about skin care routines” pre-training type of training for large language model, the routines correspond to regimens for skin care, describing Figs 4A-B detailing skin characteristics like dryness or redness, and products like moisturizer or cleanser, cont’d Fig 4C};
However, Seyfaie does not disclose visualization after a time lapse which is disclosed by
Dissanayake:
generate a visualization of the image as the image would appear, after a time lapse, with implementation of the skin care regimen {Dissanayake [0088] “generate a modified image (e.g., a simulated or enhanced image)… the modified image may depict a rendering of how the user’s skin or skin regions are predicted to appear after treating… image to represent a possible or predicted change after treatment” where ‘after’ conveys lapse of some time, treatment regards a regimen for “skin care” e.g. [0068], as would/predicted-to appear. See similar [0124-25], [0043] synthetic image generation with predicted appearance after applying recommended product, Fig 1-6}; and
provide the visualization to the display {Dissanayake discloses [0058] “visualizations… for display” e.g. at Fig 2B:274 display simulated image, and/or [0088] “modified image may then be rendered on the display screen”}.
Dissanayake is directed to skin care visualization with modified images thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to generate modified images after treatment for subsequent display for a motivation being [0010] “graphically enhanced simulated image may depict a predictive effect” e.g. [0097] “in order to determine how the skin issue would be effected based on treatment with a given product (e.g., a skin creme), and to generate a user-specific simulated image”
Conclusion
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124
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