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 .
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.
Response to Amendment
In the amendment dated 7/7/2026, the following has occurred: Claims 1, 3, 4, 26, 29, 30, 31, 34, and 35 have been amended; Claim 33 has been canceled.
Claims 7 and 8 have been previously canceled.
Claims 1 – 6, 9 – 32, 34, and 35 are pending.
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 – 6, 9 – 32, 34, and 35 and rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) subject matter within a statutory category as a process (claim 34), machine (claims 1 – 6 and 9 - 32), and manufacture (claims 35) which recite the abstract idea steps of
assess a genetic profile of a user;
identify that the user has at least one characteristic in common with a population or is a member of the population;
detect a skin condition
predict changes in the personal care condition based on the genetic profile and on information provided; and
generate the personal care recommendation based on at least one of the skin condition or the future skin condition changes
These steps of claims 1 – 6, 9 – 32, 34, and 35, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the system language, accessing in the context of this claim encompasses a mental process of the user. Similarly, the limitation of generate, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the non-transitory language, predicting in the context of this claim encompasses a mental process of the user. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding the “trained machine learning model,” the Specification, paragraph 40, includes, “These machine learning models 130 may include, for instance, a machine learning model trained to analyze genetic data, diagnostic device 102 data, lifestyle factors, social media inputs, geographical information, and other relevant input data to generate a personal care (e.g., skin care or beauty care) regimen for a user of the system 100.” Paragraph 41 includes, “The machine learning models 130 can include models such as decision trees, support vector machines, neural networks, and the like.” There are two comments found by the Specification’s descriptions. First, the “trained machine learning model” is disclosed at a high level and therefore is understood to be a generic computer component. Second, the “trained machine learning model” includes non-specific decisions trees which can be performed mentally.
These steps of claims 1 – 6, 9 – 32, 34, and 35, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. The invention, as a whole, provides user recommendations. Using exemplary claim 1
a user interface coupled to the one or more processors, the interface configured to provide the personal care recommendation to the user.
The claim language is mirrored within the Specification.
PERSONALIZED CARE RECOMMENDATION USING GENETICS ANALYSIS
FIELD OF THE INVENTION
[0001] The present invention relates generally to the field of personal care and, more specifically, to systems capable of providing skin care recommendations utilizing machine learning, artificial intelligence, augmented reality, and other technologies.
Other places the Specification mirrors the claimed invention include (Emphasis added)
[0006] In still another aspect, a non-transitory computer-readable storage medium storing instructions for providing a personal care recommendation is provided. The computer-readable instructions, when executed by one or more processors, may cause the one or more processors to perform a method. The method may include assessing a genetic profile of a user: predicting changes in a personal care condition based on at least one of the genetic profile and an output of a diagnostic device; generating the personal care recommendation based on at least one of the personal care condition or the predicted personal care condition; and providing the generated personal care recommendation to the user. The instructions may direct additional, fewer, or alternative functionality, including that discussed elsewhere herein.
[0039] Furthermore, the memories 128 may store instructions that, when executed by the processors 126, cause the processors 126 to receive data from various databases such as the databases 134 and 136, and/or data from the diagnostic device 102 and/or the user device 104 (e.g., via the network 108). The data from the diagnostic device 102 and/ or the user device 104 may include, for instance, data captured by the sensors 112 or imaging system 110 of the diagnostic device 102 and/or data captured by the sensors 120 of the user device 104, data input by a user via a user interface 119 of the user device 104, etc. The instructions stored on the memories 128, when executed by the processors 126, may cause the processors 126 to analyze data received from the database, and/or the diagnostic device 102 and/or the user device 104 to make a recommendation or prediction based on the received data, and subsequently send the recommendation and/or prediction to the diagnostic device 102 and/or the user device 104. For instance, this analysis and recommendation and/or prediction may be based upon applying a trained machine learning model 130 to the data received from the databases and/or the diagnostic device 102 and/or the user device 104.
The invention as a whole is directed towards creating information. That information is inputted into the system via manual user input, via a database, or via sensors. This combined information is then processed using algorithms. The result of the inputted and processed data is then output. The resultant data has only a potential usage and therefore there is no practical application. The invention is not directed towards a technical or technological solution to overcome a technological problem. Rather, the instant invention applies technology to the abstract idea to achieve all the benefits of applying the technology to the abstract idea.
It should be emphasized that outputting information is not a practical application. Further, the Applicant did not invent the machine learning but rather uses machine learning.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2 - 6 and 9 – 32, reciting particular aspects of how recommendations or predictions may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of executing by a processor amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of accessing, detect a person care condition including by a sensor amounts to mere data gathering, recitation of provide the personal are recommendation amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 - 6 and 9 – 32, additional limitations which amount to invoking computers as a tool to perform the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as claims 1 - 6, 9 – 32, 34, and 35; accessing, predicting, generating, and presenting, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i))
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 - 6 and 9 – 32, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, assessing, collecting, sensing, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
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 – 4, 6, 9, 12 – 16, 18, 20 – 22, 25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tran, U.S. Pre-Grant Publication 2020/ 0098444.
As per claim 1,
Tran teaches a system for providing a personal care recommendation, the system comprising:
one or more processors (#202 server);
a diagnostic device coupled to the one or more processors (figure 2 #214 phone or #220 camera and #202 server),
the diagnostic device configured to detect a personal care condition (paragraph 45);
using spectral analysis (paragraph 94 spectrophotometer face skin tone. –It should be emphasized that this is only described in paragraph 78 as, “The method 300 may further include using an imaging system configured to detect skin conditions. Spectral analysis or other analysis can be utilized to detect skin conditions.” There is no further detail provided as to what is captured or the potential results)
one or more non-transitory memory devices storing computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to (paragraph 259):
assess a genetic profile of a user of the system (paragraph 212);
identify that the user has at least one characteristic in common with a population or is a member of the population (paragraph 180 for example genotyped to BRCA);
use a trained machine learning model to (figure 3)
analyze specific genetic markers that indicate skin health conditions in combination with information about the skin conditions detected by the diagnostic device using spectral analysis (paragraph 37 SNPs or other genetic markers and figure 1B and paragraph 20)
to predict future skin condition changes based on the genetic profile and on information provided by the diagnostic device (paragraph 40 the system can predict a predisposition of a user toward developing a specific attribute - The Specification provides no guidance as to how this result is obtained other than as an output of a machine learning model),
the trained machine learning model being trained to identify at least one skin care product recommended for the user using training data including genetic profiles of the population labeled with skin care products used by members of the population (paragraphs 189 – 195); and
generate the personal care recommendation based on at least one of the personal care condition or the predicted changes in the personal care condition; and
generate the personal care recommendation based on at least one of
the skin condition (paragraph 128 skin tone) or
the future skin condition changes (paragraph 223 therapeutic and paragraph 128 cosmetic); and
generate a visual simulation of the future skin condition changes of the user (paragraph 73 simulate application of one or more cosmetic products – Please see below regarding the Specification and simulation) ,
including a predictive image based on use of at least one product recommended in the personal care recommendation (paragraph 73 simulate what the generic image or the photograph of the user's face would look like with one or more cosmetic products applied thereto): and
a user interface coupled to the one or more processors, the user interface configured to provide the personal care recommendation and the visual simulation to the user (paragraph 255 display).
The only place that “simulation” appears within the specification is paragraph 31 copied here for convenience
[0031] In some examples, the user interface 119 may further include an augmented reality (AR) component operable to generate and display an AR rendering of three-dimensional map of the user's face. In some cases, the AR rendering may be overlaid upon an image or video of the user's face as captured in real-time by any of the sensors 112 provided in the diagnostic device 102. The AR technology can also be used to provide users with a visual simulation of potential future skin conditions based on their personalized beauty regimen.
Note how the Specification does not disclose what the simulation must include or even should include. There are no examples provided.
As per claim 2, Tran teaches the system of claim 1 as described above.
Tran further teaches the system wherein the one or more processors are configured to assess the genetic profile of the user by:
collecting genetic data from the user (Abstract); and
identifying genetic markers of the user (Abstract genetic information).
As per claim 3, Tran teaches the system of claim 2 as described above.
Tran further teaches the system wherein collecting the genetic data comprises receiving data associated with deoxyribonucleic acid (DNA) sample of the user (Abstract).
As per claim 4, Tran teaches the system of claim 2 as described above.
Tran further teaches the system wherein collecting the genetic data comprises accessing a genetic testing service (paragraph 183, genetic sequencers - Although Specification paragraph 68 describes a “testing service,” paragraph 68 does not limit what a testing service is.).
As per claim 6, Tran teaches the system of claim 2 as described above.
Tran further teaches the system wherein the genetic markers indicate conditions that affect one or more of skin health, skin care product efficacy, or potential allergic reactions to skin care products (paragraph 98 acne).
As per claim 9, Tran teaches the system of claim 8 as described above.
Tran further teaches the system
wherein the one or more processors are further configured to update the trained machine learning model using expanded training data of a second population that is a superset of the population (paragraph 182 larger population vs clinical trial)
wherein the one or more processors are configured to identify that the user has at least one characteristic in common with the second population or is a member of the second population (paragraph 182 using personal genomes to qualify the effectiveness and need for that specific cosmetic material).
As per claim 12, Tran teaches the system of claim 7 as described above.
Tran further teaches the system wherein the one or more processors are configured to store diagnostic device measurement data in a user database (paragraph 42).
As per claim 13, Tran teaches the system of claim 1 as described above.
Tran further teaches the system wherein the diagnostic device comprises an imaging system configured to detect skin conditions (paragraphs 45, 94 camera 220) .
As per claim 14, Tran teaches the system of claim 13 as described above.
Tran further teaches the system wherein the diagnostic device uses spectral analysis to detect the skin condition (paragraph 94 spectrophotometer).
As per claim 15, Tran teaches the system of claim 13 as described above.
Tran further teaches the system wherein the one or more processors are configured to provide information regarding detected skin conditions as updated data and to update the personal care recommendation based on the updated data (figure 2).
As per claim 16, Tran teaches the system of claim 13 as described above.
Tran further teaches the system wherein the diagnostic device is integrated into a user interface device that includes the user interface (figure 2, #214 smart phone).
As per claim 18, Tran teaches the system of claim 1 as described above.
Tran further teaches the system further comprising a user database to store the genetic profile (paragraph 48).
As per claim 20, Tran teaches the system of claim 1 as described above.
Tran further teaches the system as described above in claim 8.
As per claim 21, Tran teaches the system of claim 20 as described above.
Tran further teaches the system as described above in claim 8.
As per claim 22, Tran teaches the system of claim 20 as described above.
Tran further teaches the system wherein the one or more processors are configured to provide access to an online purchasing system to purchase a product associated with the product recommendation (paragraph 75).
As per claim 25, Tran teaches the system of claim 1 as described above.
Tran further teaches the system wherein the one or more processors are configured to provide access to further information, including at least one of science information, chemistry information, or expert advice, pertaining to the personal care recommendation (paragraph 197).
As per claim 34,
Tran teaches a computer-implemented method of providing a personal care recommendation as described above in claim 1.
As per claim 35,
Tran teaches a non-transitory computer-readable medium storing instructions for providing a personal care recommendation that, when executed on a processor, cause the processor to perform operations as described above in claim 1.
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 5, 10, 11, 17, 19, 23, 26 – 33 are rejected under 35 U.S.C. 103 as being unpatentable over Tran, U.S. Pre-Grant Publication 2020/ 0098444 in view of Tran et al., U.S. Pre-Grant Publication 2018/ 0001184. The Examiner believes that both applications are the same Tran.
As per claim 5, Tran teaches the system of claim 2 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein collecting the genetic data includes providing an encryption system to protect privacy of the genetic data (paragraph 561 are encrypted to protect patient identifiable information and other private details of the person).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran. One of ordinary skill in the art before the effective filing date would have added these features into Tran with the motivation to recommend lifestyle modification to mitigate the disease risks (Tran ‘184 Paragraph 3).
As per claim 10, Tran teaches the system of claim 7 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the one or more processors are further configured to update the trained machine learning model with global datasets to predict personal care conditions that are prevalent among one or more of an ethnic group, a cultural group, or a national group (paragraph 536).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 11, Tran teaches the system of claim 7 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the one or more processors are further configured to update the trained machine learning model based on feedback input by the user (paragraph 559).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 17, Tran teaches the system of claim 13 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the diagnostic device includes one or more of a moisture sensor and a thermal sensor (paragraph 555).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 19, Tran teaches the system of claim 18 as described above.
Tran ‘444 in view of Tran ‘184 the system as described above in claim 5.
As per claim 23, Tran teaches the system of claim 20 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the product recommendation is generated to account for a user preference (paragraph 299 ).
Tran ‘444 in view of Tran ‘184 do not explicitly teach that the user preferences are for one or more of cruelty-free products, organic products, or vegan beauty products.
However, the specification, paragraph 73 does not limit what these listed preferences actually incorporate. These listed preferences functionally represent data labels attached to a preference. Sorting on one preference item is functionally equivalent to sorting on another preference item except that the labels differ. Therefore, it would have been prima facie obvious to substitute one data label with a second data label to achieve a predictable result.
As per claim 26, Tran teaches the system of claim 1 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein predicting the skin condition further includes requesting or receiving input pertaining to lifestyle information, diet information, environmental information pertaining to the user or a location of the user (paragraph 558 watch).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 27, Tran ‘444 in view of Tran ‘184 teaches the system of claim 26 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the input is received from a wearable device or an Internet of Things (IoT) device proximate the user (paragraph 558 and Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 28, Tran teaches the system of claim 1 as described above.
Tran further teaches the system comprising
at least one wired or wireless interface to a network (paragraph 256), and
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system
wherein the one or more processors are configured to provide access to a social media network (paragraphs 308, 438, 451) and
wherein the user interface implements functionality to share the personal care recommendation with the social media network (paragraph 308).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 29, Tran ‘444 in view of Tran ‘184 teaches the system of claim 28 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the one or more processors are configured to retrieve data from the social media network pertaining to the personal care recommendation or products similar to products, to update the trained machine learning model (paragraph 308 coordinate care).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 30, Tran ‘444 in view of Tran ‘184 teaches the system of claim 28 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the trained machine learning model implements natural language processing (NLP) to detect patterns and correlations in data posted on the social media network (paragraphs 491, 514, and 518).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
As per claim 31, Tran ‘444 in view of Tran ‘184 teaches the system of claim 28 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the trained machine learning model receives emotional health as an input from the social media network and generates an updated product recommendation based on the emotional health (paragraph 438).
As per claim 32, Tran teaches the system of claim 1 as described above.
Tran ‘444 does not explicitly teach however, Tran ‘184 further teaches the system wherein the one or more processors are configured to provide an alert system to alert the user to product updates of products related to the personal care recommendation (paragraph 299).
It would have been obvious to one of ordinary skill in the art before the effective filing date to add these features into Tran for the reasons as described above.
Claims 24 are rejected under 35 U.S.C. 103 as being unpatentable over Tran, U.S. Pre-Grant Publication 2020/ 0098444.
As per claim 24, Tran teaches the system of claim 1 as described above.
Tran does not explicitly teach the system wherein the personal care recommendation includes a recommendation for at least one of timing or sequence for application of personal care operations. Paragraph 75 includes instructional information.
However a recommendation is a statement with an intended function. That statement may never be read or may never be correctly acted upon. Therefore, the functional step of the claim is to create a recommendation that has words. Substituting one set of words for another set of words produces the same functional result. Therefore, it would have been prima facie obvious of one of ordinary skill in the art at the time of the filing to substitute one recommendation for another recommendation. The process of substituting words was known and the result are predictable.
Response to Arguments
Applicant's arguments filed 7/7/2026 have been fully considered but they are not persuasive.
II. REJECTIONS UNDER 35 U.S.C. § 101
Step 2A, Prong One
Elements Cannot Be Performed in the Human Mind
The Applicant states, “In Example 37 the underlying analysis step (determining the amount of use of each icon) of claim 1 was deemed a mental process, yet in Example 37, claim 1 was found to be eligible because "[t]he claim as a whole integrates the mental process into a practical application. Specifically, the additional elements recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices." The Examiner believes that the Applicant missed a section of Example 37. The entire Example 37 response
2A - Prong 2: Integrated into a Practical Application?
Yes. The claim recites the combination of additional elements of receiving, via a GUI, a user selection to organize each icon based on the amount of use of each icon, a processor for performing the determining step, and automatically moving the most used icons to a position on the GUI closest to the start icon of the computer system based on the determined amount of use. The claim as a whole integrates the mental process into a practical application. Specifically, the additional elements recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices. Thus, the claim is eligible because it is not directed to the recited judicial exception.
The Applicant appears to focus upon the first half of the response and misses the second. That is, Example 37 states that “the additional elements recite a specific manner of automatically displaying icons to the user.” However, the remainder “based on usage which provides a specific improvement over prior systems” is not discussed. There are no technological improvements disclosed in the instant invention. This example does not apply.
The Applicant states, “Amended claim 1 similarly recites a specific manner of displaying information that provides a specific improvement over prior systems.” Please see above.
The Applicant states, “Second, the additional elements of amended claim 1 impose meaningful limits on any alleged abstract idea, limiting the claim to a specific practical application.” The Examiner disagrees with the Applicant’s opinion.
The Applicant states, “Amended claim 1 parallels Diehr rather than Alice. Like Diehr, the elements of amended claim 1 constrain the input, the processing, and the output.” This is an interesting opinion. However, the Applicant provides no proof that manipulating an algorithm or a machine learning model is considered significantly more. Therefore, the Examiner disagrees that a non-physical manipulation is considered significantly more.
Claim Rejections - 35 U.S.C. § 102
The Applicant states, “However, while Tran '444 discloses "simulat[ing] what . . . the photograph of the user's face would look like with one or more cosmetic products applied thereto," Tran '444 fails to disclose generating a visual simulation of future skin conditions of the user, including a predictive image based on use of a product recommended in a personal care recommendation.” Please see the updated rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lefkofsky Pub. No.: US 2021/0118559 There is a need for systems and methods that make use of a personalized medicine approach to analyze the results of laboratory, imaging, and other testing in medicine that make use of fixed reference values.
Nova et al Pub. No.: US 2018/0328945 This present application generally relates to methods and systems that allow for the establishment of personalized skin care regimen for an individual based upon the individual's genetic profile
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571)272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626
Prosecution Insights