DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/12/2026 has been entered.
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 1/12/2026, the following has occurred: Claims 1, 34, and 35 have been amended.
Claims 7 and 8 have been previously canceled.
Claims 1 – 6 and 9 – 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 and 9 – 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 - 33), 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 personal care condition or the predicted changes in the personal care condition
These steps of claims 1 – 6 and 9 – 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 and 9 – 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 – 33, 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 – 33, 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 and 9 – 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 – 33, 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 (paragraph 223 therapeutic and paragraph 128 cosmetic); and
a user interface coupled to the one or more processors, the user interface configured to provide the personal care recommendation to the user (paragraph 255 display).
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 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 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 personal care 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 personal care 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 a 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 a 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 a 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.
As per claim 33, 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 user interface includes at least one display for providing the user with an augmented reality (AR) simulation of potential impacts of at least one product recommended in the personal care recommendation over time based on the genetic profile (paragraph 452, .
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 1/12/2026 have been fully considered but they are not persuasive.
New Matter
The Applicant states, “Revised claim 1 now recites " a diagnostic device ... configured to detect skin conditions using spectral analysis,." This limitation is supported at least at paragraph [0078] of the originally-filed specification…” The Examiner appreciates the citation to the Specification. However, the support only proves that something existed and was used. However, the Specification does not show how to make a device to “detect skin conditions using spectral analysis.” The Examiner understands this feature as well-known and therefore does not provide a written description rejection.
Patent Eligibility Under 35 U.S.C. § 101
The Applicant states, “The claims recite specific technical limitations including: a "diagnostic device configured to detect a personal care condition using spectral analysis," "a trained machine learning model configured to analyze specific genetic markers that indicate skin health conditions in combination with…” The Ex Parte Desjardins decision was about a technological improvement. The Examiner believes, in light of the Specification, that there is no technological improvement but rather a use of technology. The Applicant’s opinion that Desjardins applies is presented without proof that a technological improvement exists.
Claim Rejections - 35 U.S.C. § 101
Step 2A, Prong One
Elements Cannot Be Performed in the Human Mind
The Applicant states, “The integration of these technological elements creates a system that fundamentally cannot be performed in the human mind.” The Examiner agrees that the technology was applied to the abstract idea to obtain all the benefits of applying the technology to the abstract idea. However, the Examiner believes that the Abstract idea can be performed in the human mind.
Not Methods of Organizing Human Activity
The Applicant states, “First, the claims are not directed to "a person following a set of instructions." Rather, claim 1 recites " use a trained machine learning model configured to 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 " and "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".” The Examiner’s rejection distinguishes between the abstract idea itself and the application of technology to the abstract idea.
The Applicant states, “None of these activities are similar to the examples given for managing personal behavior, including following rules or instructions.” The Examiner notes that the Applicant states “are similar to the examples given…” as proof that the examples are complete. Regardless, the Examiner understands the invention understood as a whole in light of the Specification. The Examiner stands by his interpretation of the claims in light of the Specification, as shown by this example.
DETAILED DESCRIPTION
Overview
[0015] The present disclosure provides a personalized personal care regimen (in some examples directed to skincare and beauty, although embodiments are not limited thereto) that can leverage artificial intelligence (Al), machine learning (ML), genetic data, diagnostic device data, lifestyle factors, social media inputs, geographical information, and other relevant inputs. The system collects and processes these data to create the personal care regimen. Systems according to embodiments may identify trends (using e.g., social media, product sales databases, print media and other traditional media, etc.) and may adjust the personal care regimen or provide further insights or features regarding the personal care regimen based on the trends. Systems can also predict future skin conditions using diagnostic devices and predictors based on lifestyle and genetic factors. Systems according to some embodiments can ensure product safety and product efficacy using feedback provided through machine learning or other mechanisms.
The Examiner finds no proof that the Applicant’s assertion is correct.
The Applicant states, “Characterizing all predictive systems that output recommendations as abstract would constitute the type of oversimplification cautioned against in Desjardins and McRO.” Yet, both cited cases regarded technological improvements that are not disclosed.
The Applicant states, “Third, the Office Action's reliance on the Specification's background and summary sections takes those sections out of context.” Oddly, the Applicant does not provide Specification paragraphs that the Examiner should focus upon. Rather, the Examiner believes that the Applicant’s response is more opinion. The Examiner cites paragraph 15 above and additional paragraphs here.
[0017] The system may include a user interface, which allows the user to interact with the system, view their personalized regimen, track their progress over time, and adjust their skincare goals as needed. The system may also include a personalized notification system, which sends reminders to the user to apply products, make lifestyle changes, etc., based on knowledge of user schedules and routines. User privacy is protected using a blockchain-based data storage module for secure, transparent, and immutable storage of genetic data, diagnostic device data, and the personalized beauty regimen.
[0042] The server 106 can use the machine learning models 1 30 or other software programs or modules to identify correlations between genetic markers and skin health. The machine learning models 130 use these correlations to predict how a user's skin may respond to different beauty products and treatments, or other software programs/modules can retrieve expected responses from a database or other data storage. The machine learning models 130 can output or update product recommendations, product application schedules, and the like based on the genetic information. Inputs can be additionally provided from known or detected family members and predictions made regarding likely effects on a user based on product effects on a family member. Predictions can include predictions of potential allergic or adverse reactions based on the user's genetic data or based on user knowledge of same or similar products to which the user has had an adverse reaction in the past. Outputs of the models 130 or other software programs or modules therefore can include adjustments to recommendations and personalized regimens based on problematic skin care ingredients.
The invention provides suggestions to users and therefore does not have a practical application.
Step 2A, Prong Two
The Claims Recite a Practical Application
"Apply It" consideration
The Applicant states, “As amended, claim 1 recites "a diagnostic device ...configured to detect skin conditions using spectral analysis," and "a trained machine learning model.. to predict future skin condition changes" which goes beyond merely "providing a report with information" as alleged by the Office Action.” The Examiner believes that the Applicant is arguing that applying more technology to the abstract idea is not applying technology to the abstract idea. The Examiner is not persuaded.
The Applicant states, “Regarding the first consideration, amended claim 1 does not recite "only the idea of a solution or outcome" but rather "covers a particular solution to a problem or a particular way to achieve a desired outcome.” However, the Applicant does not include that this is related towards technological improvements that are absent here. The invention has a claimed way of providing a result.
The Applicant states, “This is not automating an existing manual process-claim 1 recites a new technological capability enabled by the specific integration of "specific genetic markers that indicate skin health conditions in combination with information about the skin conditions detected by the diagnostic device using spectral analysis" as recited in claim 1.” The Applicant’s opinions are noted. The Specification does not describe a technological improvement. The Examiner is not aware of a “technological capability” standard unless that Applicant mean that the Applicant is arguing field of use.
The Applicant states, “Regarding the third consideration, claim 1 recites a particular application of any judicial exception, not a general application.” The Examiner believes that the “particular application” argument is misplaced as this also relates to technological improvements that are absent.
In the end, the Applicant provides no proof that there is a practical application. Rather, the Applicant argues that the claimed invention, as a whole, should not be interpreted in light of the Specification. The Applicant does not provide proof that the Examiner’s understanding is incorrect.
"Particular Machine" Consideration
The Applicant states, “Here, claim 1 applies apply any alleged exception with "a diagnostic device ... configured to detect skin conditions using spectral analysis." The "diagnostic device" is integral to the claims as a whole because the "trained machine learning model is configured to analyze specific genetic markers . . . in combination with information about the skin conditions detected by the diagnostic device using spectral analysis to predict future skin condition changes . . . based on . . . information provided by the diagnostic device."” The diagnostic device is providing input into the invention and therefore is not “integral.” Replacing the diagnostic device with a database produces the same output.
The Precedential Desjardins Decision Confirms Patent Eligibility
The Applicant states, “This is not merely "providing user recommendations" as characterized by the Office Action, but rather a technological system that integrates analysis of a "diagnostic device" and "a trained machine learning model."” As noted above, Desjardins does not apply here because the invention is not a technological improvement. Any further arguments directed towards Desjardins are considered moot.
The Applicant states, “The Specification describes "machine learning models ... trained to analyze genetic data, diagnostic device 102 data, lifestyle factors, social media inputs, geographical information, and other relevant input data." Specification, paragraph [0040]. The Specification further describes "the server ... can use the machine learning models ... to identify correlations between genetic markers and skin health." Specification, paragraph [0042].” However, the Examiner can’t find where the Specification describes a technological improvement. Paragraphs 40 – 42 describe the use of technology. These paragraphs describe the invention at a high level using functional terms.
Claim Rejections - 35 U.S.C. § 102
The Applicant states, “First, Tran does not disclose a "trained machine learning model…” The Examiner notes that the Applicant is arguing the previous rejection with the updated claim language. Please see the updated rejection above.
The Applicant states, “These paragraphs describe product labeling information, not labeling in the sense of machine learning as recited in claim 1.” The interesting thing here is how little claim 1 requires. The Claim does not limit the inputs, the outputs, or the particular machine learning algorithm. For example, the claim includes
a diagnostic device coupled to the one or more processors, the diagnostic device configured to detect skin conditions using spectral analysis;
provided by the diagnostic device
How the spectral analysis is used is not claimed. The kinds of skin conditions detected are not claimed. The particular output is also not claimed.
The broad claim language is included here.
use a trained machine learning model configured to 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 to predict changes in the personal care condition future skin condition changes based on the genetic profile and on information,
As mentioned above, the particular machine learning model is not claimed. What is meant by “indicate” is not claimed. The relationship between the unknown diagnostic device results and the non-specific genetic markers is further not claimed. As mentioned above, the Specification provides the Examiner little guidance.
The Applicant states, “Second, Tran does not disclose, at least, "genetic markers that indicate skin health conditions," as recited in amended claim 1.” However, neither the claim nor the Specification provide specific details as to what is required. “Genetic markers” appears 6 times within the detailed description and none list what those could be. Please see the updated rejection above.
The Applicant states, “Third, claim 14 is allowable on its own merits as well as by virtue of its dependency from claim 1. Claim 14 recites "the diagnostic device uses spectral analysis to detect the personal care condition." The Office Action at pages 10-11, point 27, cites Tran paragraph [0094] as allegedly teaching this limitation. Applicant respectfully disagrees.” The Applicant’s opinion is strongly stated but the Examiner is left with the Specification. The Specification does not describe how this is performed or the required results. It is the Examiner’s opinion based upon the facts as provide that this is applicable.
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
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626
Prosecution Insights