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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of the USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. §101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), which is a two-prong inquiry. In prong 1, it must be determined whether the claim recites an abstract idea, a law of nature, or a natural phenomenon, and if so, in prong 2, it must be determined whether the claim recites additional elements that integrate the judicial exception into a practical application. If the claim is determined to be directed to an abstract idea in step 2a, it must additionally be determined in step 2b whether the claim amounts to significantly more than the abstract idea. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. MPEP §2106.04.
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method of product recommendation, as in independent claim 1 and in the claims that depend therefrom. Such methods fall under the statutory category of “process”. Therefore, the claims are directed to a statutory eligibility category.
Step 2A, prong 1. The invention is directed to a method of product recommendation, which is a sales method and, hence, a Certain Method of Organizing Human Activities. MPEP § 2106.04(a). As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“A … method for generating personalized cosmetic and/or skincare product recommendations and usage guidelines, the method comprising”:
“receiving user data at one or more …, the user data being indicative of at least a plurality of unique face and/or skin characteristics associated with an individual user”;
“inputting, via the one or more …, at least a portion of the user data into a …, the … being … using historical data inputs that are associated with known face and/or skin characteristics”;
“identifying, via execution of the… by the one or more …, the plurality of unique face and/or skin characteristics from the user data input by the one or more …”;
“generating, via the one or more …, at least one personalized recommendation based on the plurality of unique face and/or skin characteristics identified by the … from the user data and cosmetic and/or skincare products contained in a …, the at least one personalized recommendation being for one of the cosmetic and/or skincare products contained in the … and including associated usage guidelines”; and
“transmitting a notification of the at least one personalized recommendation for display on a …”.
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly, the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. MPEP §2106.04. Thus, under Step 2A, prong 2 of the Mayo framework, the examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" in the related arts.
The instant application includes in claim 1 additional limitations to those deemed to be abstract ideas. When taken individually, these limitations are
“computer implemented”;
“processors”;
“trained artificial intelligence module”;
“user device”;
“trained”; and
“database”.
In the instant case, claim 1 is directed to above mentioned abstract idea. Technical functions such as sending, receiving, displaying and processing data are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
Looking to MPEP §2106.05(d), based on court decisions well understood, routine and conventional computer functions or mere instruction and/or insignificant activity have been identified to include: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321,120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TU Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); O/P Techs., /no., v. Amazon.com, Inc., 788 F,3d 1359, 1363, 115 USPQ2d 1090,1093 (Fed. Cir, 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPG2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink," (emphasis added)}; Insignificant intermediate or post solution activity -See Bilski v. Kappos, 581 U.S. 593, 611 -12, 95 USPQ2d 1001,1010 (2010) (well-known random analysis techniques to establish the inputs of an equation were token extra-solution activity); In Bilski referring to Flook, where Flook determined that an insignificant post-solution activity does not makes an otherwise patent ineligible claim patent eligible. In Bilski, the court added to Flook that pre-solution (such as data gathering) and insignificant step in the middle of a process (such as receiving user input) to be equally ineffective. The specification and Claim does not provide any specific process with respect to the display output that would transform the function beyond what is well understood. Like as found in Electric Power Group, Bilski, the technical process to implement the input and display functions are conventional and well understood.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well-understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for authorizing the timing of a payment and to activate a display screen based on a trigger or camera functions that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device and require no more than a generic computing devices to perform generic functions.
CONCLUSION. It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
Dependent claims 2-12, which impose additional limitations, also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-12, these dependent claims have also been reviewed with the same analysis as independent claim 1. The dependent claims have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, applicant is invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
Claim 13 recites a “computing system” and “memories”. These additional elements are generic elements. Claim 13 is otherwise similar to claim 1 and is rejected for the same reasons. Claims 14-17 depend from claim 13, are similar to claims 2-12, and are rejected for the same reasons. Claim 18 recites a “computer-readable medium”. This additional element is a generic element. Claim 18 is otherwise similar to claim 1 and is rejected for the same reasons. Claims 19 and 20 depend from claim 18, are similar to claims 2-12, and are rejected for the same reasons.
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.
Claims 1, 2, 5-7, 13, 14, 17, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application 2023/0187055 A1 (hereinafter “Dissanayake”).
With respect to claims 1, 13, and 18, Dissanayake discloses
“A computer implemented method for generating personalized cosmetic and/or skincare product recommendations and usage guidelines, the method comprising”: Dissanayake, abstract;
“receiving user data at one or more processors, the user data being indicative of at least a plurality of unique face and/or skin characteristics associated with an individual user”; Dissanayake ¶ 0041 (user submits responses to questionnaires and images of user’s skin);
“inputting, via the one or more processors, at least a portion of the user data into a trained artificial intelligence module, the trained artificial intelligence module being trained using historical data inputs that are associated with known face and/or skin characteristics”; Dissanayake ¶¶ 0028, 0030, 0033, 0046 (artificial intelligence module is trained using large collection of historical data as well as user specific data);
“identifying, via execution of the trained artificial intelligence module by the one or more processors, the plurality of unique face and/or skin characteristics from the user data input by the one or more processors”; Dissanayake ¶ 0046 (specific skin characteristics are identified and correlated to skin conditions);
“generating, via the one or more processors, at least one personalized recommendation based on the plurality of unique face and/or skin characteristics identified by the trained artificial intelligence module from the user data and cosmetic and/or skincare products contained in a database, the at least one personalized recommendation being for one of the cosmetic and/or skincare products contained in the database and including associated usage guidelines”; Dissanayake ¶ 0046 (products and usage are recommended based on data in database); and
“transmitting a notification of the at least one personalized recommendation for display on a user device”. Dissanayake ¶ 0046, fig. 5 (recommendation is provided to user).
With respect to claims 2, 14, and 19, Dissanayake discloses
“wherein the trained artificial intelligence module is a first trained artificial intelligence module, wherein the historical data inputs are first historical data inputs, and wherein generating the at least one personalized recommendation based on the plurality of unique face and/or skin characteristics identified by the trained artificial intelligence module from the user data and the cosmetic and/or skincare products contained in the database includes:
receiving, via the one or more processors, the plurality of unique face and/or skin characteristics as an output from the first trained artificial intelligence module;
retrieving, via the one or more processors, an initial set of cosmetic and/or skincare products from the database based at least in part on the plurality of unique face and/or skin characteristics output from the first trained artificial intelligence module;
inputting, via the one or more processors, the output from the first trained artificial intelligence module and the initial set of cosmetic and/or skincare products retrieved from the database into a second trained artificial intelligence module, the second trained artificial intelligence module being trained using second historical data inputs that are associated with known product recommendations and known usage guidelines; and
receiving, via the one or more processors, the at least one personalized recommendation as an output from the second trained artificial intelligence module”. Dissanayake ¶¶ 0028, 0030, 0033, 0046, 0051 (system can perform multiple training iterations; each training iteration is an additional artificial intelligence module).
With respect to claim 5, Dissanayake discloses
“further comprising: receiving, at the one or more processors, user feedback regarding the at least one personalized recommendation; and updating the second trained artificial intelligence module based on the feedback”. Dissanayake ¶¶ 0062, 0063 (feedback is used to update artificial intelligence module).
With respect to claims 6, 17, and 20, Dissanayake discloses
“wherein the trained artificial intelligence module is further trained using additional historical data inputs that are associated with known product recommendations and known usage guidelines, and wherein generating the at least one personalized recommendation based on the plurality of unique face and/or skin characteristics identified by the trained artificial intelligence module from the user data and the cosmetic and/or skincare products contained in the database includes: querying the database via the trained artificial intelligence module; and receiving, via the one or more processors, the at least one personalized recommendation as an output from the trained artificial intelligence module”. Dissanayake ¶¶ 0028, 0030, 0033, 0046, 0051 (historical data and known usage guidelines are utilized in formulating recommendations).
With respect to claim 7, Dissanayake discloses
“further comprising: receiving, at the one or more processors, user feedback regarding the at least one personalized recommendation; and updating the second trained artificial intelligence module based on the feedback”. Dissanayake ¶¶ 0062, 0063 (feedback is used to update artificial intelligence module).
Claim Rejections - 35 USC § 103
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.
Claims 3, 4, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake in view of U.S. Patent Application Publication 2025/0259053 A1 (hereinafter “Phatak”).
With respect to claims 3 and 15, Dissanayake discloses
“wherein training the first trained artificial intelligence module comprises:
iteratively inputting, via the one or more processors, the first historical data inputs into an initial artificial intelligence module, the first historical data inputs including one or more images of skin and faces having the known face and/or skin characteristics;
iteratively receiving, via the one or more processors, training outputs from the initial artificial intelligence module, the training outputs including face and/or skin characteristics extracted by the initial artificial intelligence module from the first historical data inputs;
iteratively updating, via the one or more processors, the initial artificial intelligence module based on comparisons between the training outputs and the known face and/or skin characteristics through multiple iterations; and
storing, via the one or more processors, the initial artificial intelligence module in a memory as the first trained artificial intelligence module when the training outputs match the known face and/or skin characteristic … that match the known face and/or skin characteristics”.
Dissanayake ¶¶ 0028, 0030, 0033, 0046, 0051 (system can perform multiple training iterations; each training iteration is an additional artificial intelligence module).
Dissanayake does not explicitly disclose a reliability threshold. Phatak discloses
“… in accordance with a reliability threshold, the reliability threshold being a threshold percentage number of the training outputs …”. Phatak ¶ 0027 (iterative training is halted when reliability threshold is met).
Both Dissanayake and Phatak relate to training machine learning models. Dissanayake, abstract; Phatak, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the reliability threshold feature as taught by Phatak in the method of Dissanayake with the motivation of improving the accuracy of the artificial intelligence module. Phatak ¶¶ 0001-0005.
With respect to claims 4 and 16, Dissanayake discloses
“wherein training the second trained artificial intelligence module comprises:
iteratively inputting, via the one or more processors, the second historical data inputs into an initial artificial intelligence module, the second historical data inputs including pluralities of historical face and/or skin characteristics, historical sets of cosmetic and/or skincare products, and the known product recommendations and known usage guidelines;
iteratively receiving, via the one or more processors, training outputs from the initial artificial intelligence module, the training outputs including product recommendations and associated usage guidelines identified by the initial artificial intelligence module from the second historical data inputs;
iteratively updating, via the one or more processors, the initial artificial intelligence module based on comparisons between the training outputs and the known product recommendations and known usage guidelines; and
storing, via the one or more processors, the initial artificial intelligence module in a memory as the second trained artificial intelligence module when the training outputs match the known product recommendations and known usage guidelines … that match the known product recommendations and known usage guidelines”.
Dissanayake ¶¶ 0028, 0030, 0033, 0046, 0051 (system can perform multiple training iterations; each training iteration is an additional artificial intelligence module).
Phatak discloses
“… in accordance with a reliability threshold, the reliability threshold being a threshold percentage number of the training outputs….”. Phatak ¶ 0027 (iterative training is halted when reliability threshold is met).
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake in view of U.S. Patent Application Publication 2019/0095853 A1 (hereinafter “Kaplan”).
With respect to claim 8, Dissanayake discloses
“wherein the user data includes one or more of data indicative of a user's current face and/or skin characteristics, face and/or skin preferences received as user input, a user's location, currently used cosmetic and/or skincare products, and usage trends for the currently used cosmetic and/or skincare products, and …”. Dissanayake ¶¶ 0028, 0030, 0033, 0046 (data relating to user’s current skin characteristics and skin preferences are received).
Dissanayake does not explicitly disclose smart packaging devices. Kaplan discloses
“… wherein portions of the user data are received from smart packaging devices, smart mirrors, fitness trackers, and environmental sensors”. Kaplan, abstract, fig. 2 (sensor is integrated into packaging of product, such as a skincare product).
Both Dissanayake and Kaplan relate to skincare products. Dissanayake, abstract; Kaplan, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the smart packaging device as taught by Kaplan in the method of Dissanayake with the motivation of facilitating reordering of recommended products. Kaplan ¶¶ 0001-0005.
With respect to claim 9, Kaplan discloses
“wherein the data indicative of the usage trends for the currently used cosmetic and/or skincare products includes data received from the smart packaging devices for the currently used cosmetic and/or skincare products, the data received from the smart packaging devices monitoring use of the currently used cosmetic and/or skincare products over time, expiration dates of the currently used cosmetic and/or skincare products, and storage conditions of the currently used cosmetic and/or skincare products”. Kaplan ¶¶ 0026, 0027 (data such as usage pattern data, estimated run out date of product, and expiration of product are sent).
With respect to claim 10, Dissanayake discloses
“wherein the data indicative of the user's current face and/or skin characteristics includes one or multiple images of the user's face and/or skin, wherein the portion of the user data input into the trained artificial intelligence module includes the one or multiple images, wherein the trained artificial intelligence module identifies the plurality of unique face and/or skin characteristics from the one or multiple images of the user's face and/or skin, and wherein the historical data inputs include one or more images of skin and faces having the known face and/or skin characteristics”. Dissanayake ¶¶ 0028, 0030, 0033, 0046 (artificial intelligence module uses images of user’s skin to identify skin characteristics).
With respect to claim 11, Dissanayake discloses
“wherein the historical data inputs are first historical data inputs, the trained artificial intelligence module is a first trained artificial intelligence module, and wherein generating the at least one personalized recommendation based on the plurality of unique face and/or skin characteristics identified by the trained artificial intelligence module from the user data and the cosmetic and/or skincare products contained in the database includes:
receiving, via the one or more processors, the plurality of unique face and/or skin characteristics as an output from the first trained artificial intelligence module based upon the one or more images;
retrieving, via the one or more processors, an initial set of cosmetic and/or skincare products from the database based at least in part on the plurality of unique face and/or skin characteristics output from the first trained artificial intelligence module based upon the one or more images and the face and/or skin preferences received as user input;
inputting, via the one or more processors, the output from the first trained artificial intelligence module, the initial set of cosmetic and/or skincare products retrieved from the database, the face and/or skin preferences received as user input, the user's location, the currently used cosmetic and/or skincare products, and the usage trends for the currently used cosmetic and/or skincare products into a second trained artificial intelligence module, the second trained artificial intelligence module being trained using second historical data inputs that are associated with known product recommendations and known usage guidelines; and
receiving, via the one or more processors, the at least one personalized recommendation as an output from the second trained artificial intelligence module”.
Dissanayake ¶¶ 0028, 0030, 0033, 0046, 0051 (system can perform multiple training iterations; each training iteration is an additional artificial intelligence module).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake in view of U.S. Patent Application Publication 2020/0387942 A1 (hereinafter “Kreuzer”).
With respect to claim 12, Kreuzer discloses
“wherein the user device is a first user device, and further comprising”: Kreuzer ¶ 0024 (first user device is skin care computing device);
“receiving, at the one or more processors, a plurality of images of the user's face and/or skin during a time period when the recommended cosmetic and/or skincare product was being used”; Kreuzer ¶¶ 0021, 0025 (images of product in use are sent);
“inputting, via the one or more processors, the plurality of images into the trained artificial intelligence module”; Kreuzer ¶ 0038 (images are inputted into artificial intelligence module);
“receiving, via the one or more processors, a plurality of additional face and/or skin characteristics extracted from the plurality of images as an output from the trained artificial intelligence module based upon the one or more images”; Kreuzer ¶¶ 0038, 0041, 0048, 0053-0055 (images of product use at different times are received);
“comparing changes in the plurality of additional face and/or skin characteristics output from the trained artificial intelligence module over time to expected changes over time from use of the recommended cosmetic and/or skincare product”; Kreuzer ¶¶ 0038, 0041, 0048, 0053-0055 (images of product use are utilized to make usage recommendations); and
“transmitting a second notification documenting deviations from the expected changes over time for display on a second user device different from the first user device”. Kreuzer ¶¶ 0025, 0038, 0041, 0048, 0053-0055 (deviations from recommended usages are identified; second device can be user mobile device).
Both Dissanayake and Kreuzer relate to skin care products. Dissanayake, abstract; Kreuzer, abstract. t would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the usage recommendation feature as taught by Kreuzer in the method of Dissanayake with the motivation of enhancing user usage of skin care products. Kreuzer ¶¶ 0001-0003.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 2019/0213227 A1 (hereinafter “Ludwinski”) discloses a machine learning method for recommending skin care products. Ludwinski, abstract.
Akshya, J., "EfficientNet-based Expert System for Personalized Facial Skincare Recommendations", 2023 7th Int'l Conf. on Intelligent Computing and Control Systems 979-8-3503-9725-3/23, pp. 1151-56, March 2023 (hereinafter “Akshya”) discloses a machine learning method for recommending skin care products. Akshya, abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN D CIVAN whose telephone number is (571)270-3402. The examiner can normally be reached Monday-Thursday 8-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A Smith can be reached at (571) 272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ETHAN D. CIVAN
Primary Examiner
Art Unit 3688
/ETHAN D CIVAN/Primary Examiner, Art Unit 3688