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 .
Election/Restrictions
Applicant’s response to the restriction requirement filed May 11th, 2026, has been entered and made of record. Applicant has elected claims 1-14 without traverse. Claims 15-20 are accordingly withdrawn from consideration.
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.
Claims 1-12 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by USPN 2023/0154566 to Steyaert et al.
With regard to claim 1, Steyaert discloses a method of predicting facial biological age of an individual, comprising:
a computer receiving methylation data describing an individual (Fig. 15 and paragraphs [0069]-[0081] and [0098]-[0108], methylation data is determined for an individual as apart an age epigenetic age prediction);
the computer receiving facial image data describing the individual (paragraphs [0088], [0094] and [0096], Facial image data is used as a type of phenotypic data in the calculation);
the computer receiving survey data provided by the individual (paragraph [0090], phenotypic data such as cardiovascular health data like heart rate, BMI, body fat percentage, etc. are also input for calculation. All such data are considered survey data that would need to be entered into the system for individuals); and
the computer applying an age predictor clock model to at least the received data to predict a facial biological age of the individual (paragraphs [0100]-[0107], A model for determining an epigenetic age prediction is presented in Fig. 11. Fig. 12 shows that the model is used to estimate an epigenetic age prediction using methylation values, facial image phenotypic values and survey data such as cardiovascular or biometric data).
With regard to claim 2, Steyaert discloses the method of claim 1, further comprising the computer identifying causal methylation markers in the methylation data (paragraph [0019]: “Models that can predict molecular phenotypes directly from biological sequences can be used as in silico perturbation tools to probe the associations between genetic variation and phenotypic variation and have emerged as new methods for quantitative trait loci identification and variant prioritization. These approaches are of major importance given that the majority of variants identified by genome-wide association studies of complex phenotypes are non-coding, which makes it challenging to estimate their effects and contribution to phenotypes. Moreover, linkage disequilibrium results in blocks of variants being co-inherited, which creates difficulties in pinpointing individual causal variants. Thus, sequence-based deep learning models that can be used as interrogation tools for assessing the impact of such variants offer a promising approach to find potential drivers of complex phenotypes. One example includes predicting the effect of noncoding single-nucleotide variants and short insertions or deletions (indels) indirectly from the difference between two variants in terms of transcription factor binding, chromatin accessibility or gene expression predictions. Another example includes predicting novel splice site creation from sequence or quantitative effects of genetic variants on splicing.” CpG markers are determined to be used in age prediction as shown in Figs. 8 and 9).
With regard to claim 3, Steyaert discloses the method claim 1, further comprising the computer generating a personalized report for the individual, the report describing the predicted facial biological age based on methylation markers, and the report further describing methylation markers causal to facial aging, the markers identified at least in the data (paragraphs [0110]-[0111], An epigenetic age prediction is output along with a confidence score and the methylation markers are listed with CpG sites are in order of their feature importance to the model).
With regard to claim 4, Steyaert discloses the method of claim 3, wherein the personalized report further contains facial ageotypes from face image data (paragraphs [0088], [0094] and [0096], Facial image data is used as a type of phenotypic data in the calculation. Ageotype information is determined in the form of wrinkles, blemishes, growths, marks, color, etc.).
With regard to claim 5, Steyaert discloses the method of claim 1, further comprising the computer copying the received data and the predicted facial biological age to a reference population database (paragraphs [0100]-[0107], A model for determining an epigenetic age prediction is presented in Fig. 11. Phenotypic data and methylation profiles from a plurality of individuals are sued to generate the model that is used to perform epigenetic age prediction. The set of individual profiles is considered to be a population database, and individual users are added to the database as the model is used to evaluate their phenotype skin profiles).
With regard to claim 6, Steyaert discloses the method of claim 1, wherein the age predictor clock model is trained and validated on reference population data stored in the reference population database (paragraphs [0100]-[0107], A model for determining an epigenetic age prediction is presented in Fig. 11. Phenotypic data and methylation profiles from a plurality of individuals are used to generate the model that is used to perform epigenetic age prediction. The set of individual profiles is considered to be a population database).
With regard to claim 7, Steyaert discloses the method of claim 1, wherein face image data of the individual is one of a selfie image taken by a smartphone camera and an image captured by a professional imaging device (paragraph [0088], facial images can be captured by several kinds of cameras).
With regard to claim 8, Steyaert discloses the method of claim 7, wherein facial age-related phenotypes (ageotypes) are extracted, via a machine learning (AI) classifier, from the facial image data, wherein the classifier is one of a proprietary, an open-source, and a third-party algorithm utilized via an application programming interface (API) (paragraphs [0019], and [0121]-[0122] Fig. 11 and Fig. 14, and Claim 1, Multiple learning classifiers are disclosed to be used in the age prediction model).
With regard to claim 9, Steyaert discloses a system for continual improvement of age prediction based at least on methylation data, comprising:
a computer and application executing thereon (Fig. 15) that:
receives epigenetics data containing at least DNA methylation markers describing an individual (Fig. 15 and paragraphs [0069]-[0081] and [0098]-[0108], methylation data is determined for an individual as a part an age epigenetic age prediction),
receives facial image data describing the individual (paragraphs [0088], [0094] and [0096], Facial image data is used as a type of phenotypic data in the calculation),
receives feedback data and survey data comprising at least chronological age (Fig. 11, 1212 known age is entered as an input into the system) and gender (Figs. 4A-4L show genomic diagrams of chromosomes indicative of the sex of a person) of the individual (paragraph [0090], phenotypic data such as cardiovascular health data like heart rate, BMI, body fat percentage, etc. are also input for calculation. All such data are considered survey data that would need to be entered into the system for individuals),
predicts a facial biological age of the individual based on the data, and propagates the received data and the predicted age to a reference population storage (paragraphs [0100]-[0107], A model for determining an epigenetic age prediction is presented in Fig. 11. Fig. 12 shows that the model is used to estimate an epigenetic age prediction using methylation values, facial image phenotypic values and survey data such as cardiovascular or biometric data).
With regard to claim 10, Steyaert discloses the system of claim 9, wherein the system uses the received data and previously stored data to improve a facial biological age prediction algorithm (paragraphs [0019], and [0121]-[0122] Fig. 11 and Fig. 14, and Claim 1, Multiple learning classifiers are disclosed to be used in the age prediction model. Data from multiple individuals is used to create the age prediction model as shown in Fig. 11. Paragraphs [0004]-[0011] describe how learning models are used to improve recognition overtime of biological systems including the DNA methylation markers).
With regard to claim 11, Steyaert discloses the system of claim 9, wherein the feedback data is further propagated to an age predictor engine and a reporter engine to improve a facial biological age prediction algorithm and identify methylation markers that are one of causal drivers of facial aging and causal anti-aging methylation markers (paragraphs [0019], and [0121]-[0122] Fig. 11 and Fig. 14, and Claim 1, Multiple learning classifiers are disclosed to be used in the age prediction model. Data from multiple individuals is used to create the age prediction model as shown in Fig. 11. Paragraphs [0004]-[0011] describe how learning models are used to improve recognition overtime of biological systems including the DNA methylation markers. Paragraphs [0036], [0040], [0069]-[0087] and Figs. 4A-N describe how specific methylation markers relate to aging and which specific markers are considered in the learning model for age prediction).
With regard to claim 12, Steyaert discloses the system of claim 9, wherein DNA methylation markers (CpGs) are pre-processed using bioinformatics methods directed to obtaining quantifiable results to enable further assessments (paragraphs [0039]-[0056] and Figs. 3 and 4A-4N describe the methylation markers CpGs).
With regard to claim 14, Steyaert discloses the system of claim 9, wherein the system builds predictive models for facial age-related phenotypes comprising at least one of wrinkles and pigmented spots, for skin age-related conditions comprising at least seborrheic keratosis, and for skin diseases comprising at least basal cell carcinoma (paragraphs [0036], [0087]-[0088], [0094] and [0096], Facial image data is used as a type of phenotypic data in the calculation. A phenotypic profile information is determined and includes the detection skin age-related conditions such as wrinkles, blemishes, growths, marks, color, etc.).
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.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of USPN 2023/0154566 to Steyaert et al. and 2021/0038729 to Zonari et al.
With regard to claim 13, Steyaert discloses the system of claim 9, but does not explicitly disclose wherein the system enables input of methylation data to compare facial biological ages of individuals before and after a recommended treatment provided by at least a third party.
Zonari discloses a skin age determination system using methylation markers (paragraph [0169]) and further teaches that before and after skin comparison is performed for use of a polypeptide treatment regimen (paragraphs [0192] and [0226]). Therefore, it would have been obvious to one of ordinary skill in the art before time of filing to use the methylation data of Steyaert in order to evaluate a skincare treatment as taught by Zonari in order to determine any change in skin ageing measurements/improvements.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY J TUCKER whose telephone number is (571)272-7427. The examiner can normally be reached 9AM-5PM Monday-Friday.
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/WESLEY J TUCKER/Primary Examiner, Art Unit 2661
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