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 .
DETAILED ACTION
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “unit”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 2-12, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ruchti et al. (US 6,501,982 (provided in the IDS)), in view of Park et al. (WO 2019/225870 (provided in the IDS)) and further in view of Cope (US 2014/0214391).
Addressing claim 7, Ruchti discloses a computer-implemented method, the method comprising (see col. 3, lines 43-52):
i. using at least one sample reflection spectrum of at least one portion of the skin of the living being over a spectral measurement range, the spectral measurement range comprising at least one portion of the wavelength range of from 1 µm to 2.5 µm (see col. 4, line 53 – col. 5, line 31);
ii. determining the estimated age of the skin of the living being by applying, to the sample reflection spectrum, at least one trained model (see col. 3, lines 53-62),
wherein the trainable model is trained on a training dataset comprising a plurality of labeled reference reflection spectra, each of the reference reflection spectra being acquired over a spectral range at least partially overlapping with the spectral measurement range of the sample reflection spectrum of step i. (see col. 3, lines 53-62, col. 6, line 64 – col. 7, line 32 and Fig. 3; the spectrum measurement range and reference reflection spectra are both in infrared and NIR so they overlap; Ruchti explicitly discloses using artificial neural networks, nonlinear partial-least squares regression, linear regression in calibration model to estimate age of skin; an artificial neural network (ANN) is fundamentally a trainable model),
wherein each of the reference reflection spectra is a reflection spectrum of at least one portion of a skin of a living test being having a known age (see col. 3, lines 53-62; actual chronological age),
wherein the reference reflection spectra are at least partially labeled with at least the known age of the corresponding living test being (see col. 3, lines 53-62; obvious to one of ordinary skill in the art that actual chronological age is label on the reference reflection spectra in order to known the age and use it to determine age of new sample),
the method further comprising at least one training step for training the trainable model for use in step ii., the training step comprising providing the labeled reference reflection spectra as defined in step ii., the method further comprising using at least one of a supervised and a semi-supervised learning architecture (see col. 3, lines 53-62 and col. 6, line 64 – col. 7, line 32; use of regression model implies that the learning has to be supervised; train the model from a set of exemplary samples consisting of NIR tissue measurement; artificial neural network is a trainable machine learning model).
Ruchti does not disclose automatically selecting the at least one recommendation for the human being based on the estimated age of the human being, wherein the automatically selecting of the at least one recommendation is performed by using at least one relation relating the estimated age to the at least one recommendation. In the same field of endeavor, Park discloses automatically selecting the at least one recommendation for the human being based on the estimated age of the human being, wherein the automatically selecting of the at least one recommendation is performed by using at least one relation relating the estimated age to the at least one recommendation (see [0068-0069]/page 40; recommend moisturizing product; sunscreen etc.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruchti to have automatically selecting the at least one recommendation for the human being based on the estimated age of the human being, wherein the automatically selecting of the at least one recommendation is performed by using at least one relation relating the estimated age to the at least one recommendation as taught by Park because this help provide treatment to slow down aging (see [0069]).
Ruchti does not explicitly use principal component regression model. However, Ruchti use many types linear regression model. Principal component regression model is just one type of linear regression model. Cope explicitly disclose using principal component regression model (see [0012]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruchti to use principal component regression model as taught by Cope because it is a technique used to handle multicollinearity in multiple regression by reducing the dimensionality of the data. Instead of regressing directly on the original (potentially correlated) variables, PCR first projects the data onto a lower-dimensional subspace defined by the principal components and then performs a least-squares regression on these components. Ruchti and Cope disclose using many types of regression model. Use any type of regression model only require routine skill in the art.
Addressing claims 2-3, 5-6, 11-12 and 14, Ruchti discloses:
Addressing claim 2, wherein the spectral measurement range and the spectral ranges of the reference reflection spectra are identical (see abstract; col. 4, line 53 – col. 5, line 31 and col. 6, line 3 – col. 7, line 32; transmit and measure near infrared for spectral measurement range and the reference reflection spectra therefore they are identical).
Addressing claim 3, replacing the sample reflection spectrum by a preprocessed sample reflection spectrum being derived from the sample reflection spectrum, the preprocessed sample reflection spectrum specifically comprising at least one of a first or higher order derivative of the sample reflection spectrum; a robust normal variate transform of the sample reflection spectrum; a filtered spectrum determined by filtering the sample reflection spectrum; or a scaling of the sample reflection spectrum, wherein the scaling comprises at least one of a unit scaling, a standard normal variate or a range scaling (see col. 6, lines 38-62; preprocessed sample spectrum involve scaling).
Addressing claim 5, wherein the portion of the skin of the living being and the portions of the skin of the living test beings are portions, selected from the group consisting of a skin portion at the temple of the living being or the living test beings, respectively; a skin portion at the forehead of the living being or the living test beings, respectively; a skin portion at the face of the living being or the living test beings, respectively; a skin portion at the neck of the living being or the living test beings, respectively; a skin portion at the forearm of the living being or the living test beings, respectively; and a skin portion essentially not covered by hair and/or a hairless skin portion of the living being or the living test beings, respectively, with at least up to a tolerance of at most 20% of the skin portion being covered by hair (see col. 10, line 64 – col. 11, 8, skin portion of forearm; forearm has less than 20% skin portion cover by hair).
Addressing claim 6, providing the at least one sample reflection spectrum of the at least one portion of the skin of the living being over a spectral measurement range, the spectral measurement range comprising the at least one portion of the wavelength range of from 1 µm to 2.5 µm (see col. 4, line 53 – col. 5, line 31).
Addressing claim 11, a computer-implemented training method of training the trainable model for use in step ii. of the method according to the method comprising training the trainable model on the training dataset as defined in step ii (see col. 6, line 64 – col. 7, line 32 and claim 8; calibration model receive training dataset (a set of exemplary samples consisting of NIR tissue measurements)).
Addressing claim 12, obvious to one of ordinary skill in the art that there is a system that perform the method therefore the system is being rejected for the same reason as the method (see col. 5, lines 23-31; spectrometer to measure NIR; Figs. 1, 3 and col. 5, lines 55-60; 18 is the processing unit
Addressing claim 14, wherein the living being is a human being (see abstract).
Addressing claims 4, 8-10 and 18, Park discloses:
Addressing claim 4, wherein the portion of the skin of the living being and the portions of the skin of the living test beings are portions in the same region of the body (see [0068]/page 40; database of facial and back skin which is the living test being of facial and back skin; user’s facial and back skin is the living being facial and back skin; user’s facial and back skin is compare with the database of the facial and back skin to determine user age).
Addressing claim 8, wherein the at least one recommendation refers to at least one of a cosmetic treatment recommendation, a nutritional recommendation; a recommendation regarding at least one of a use of drugs and a use of medication; a recommendation regarding an exposition to at least one of sunlight and ultraviolet radiation; a recommendation regarding a use of sun screen; a recommendation to seek for medical consultation; a recommendation regarding sleeping habits; a recommendation regarding exercise; a recommendation regarding exposure to stress; or a recommendation regarding the need for at least one of rest and vacation (see [0069]/page 40; recommend moisturizing product; sunscreen etc.).
Addressing claim 9, wherein, in step ii., the recommendation is selected on the basis of a discrepancy between the estimated age of the human being and an actual age of the human being (see [0068-0070]; high skin age therefore recommend product that slow down skin aging).
Addressing claim 10, wherein the automatically selecting of the at least one recommendation is performed by using at least one relation further relating at least one of a discrepancy between the estimated age of the human being and an actual age of the human being to the at least one recommendation (see [0068-0070]; recommend moisturizing product; sunscreen etc. to slow down aging when estimate age is higher than actual age).
Addressing claim 18, wherein the cosmetic treatment recommendation is a treatment with at least one of a moisturizer and a skin cream with oil (see [0068-0070]; moisturizer and sunscreen (cream with oil)).
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ruchti et al. (US 6,501,982 (provided in the IDS)), in view of Park et al. (WO 2019/225870 (provided in the IDS)), further in view of Cope (US 2014/0214391) and Li et al. (US 2022/0206007).
Addressing claims 16-17, Ruchti does not explicitly disclose the artificial neural network is selected from the group consisting of a deep neural network, a convolutional neural network, a recurrent neural network, and a long-short-term neural network; the decision tree classificatory is at least one of a Random Forrest Classifier or a Boosted Decision Tree Classifier. However, these are well-known modelling technique. These are well-known regression model use in the computer imaging field. Examiner only relies on Li to explicitly discloses a convolutional neural network and Boosted Decision Tree Classifier (see [0060-0061]) to provide evidence that these are well-known in the field of imaging. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruchti to use a convolutional neural network and Boosted Decision Tree Classifier as taught by Li because this improves accuracy, extract features and image recognition.
Claims 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ruchti et al. (US 6,501,982 (provided in the IDS)), in view of Park et al. (WO 2019/225870 (provided in the IDS)), further in view of Cope (US 2014/0214391) and Knuebel et al. (US 2019/0307392).
Addressing claims 13 and 19, Ruchti does not disclose a spectroscopy module being part of at least one wearable device, and wherein the at least one wearable device selected from the group consisting of a smartwatch and a smartphone. Knuebel discloses a spectroscopy module being part of at least one wearable device, and wherein the at least one wearable device selected from the group consisting of a smartwatch and a smartphone (see [0050]; NIR spectrometer integrated into the smartphone). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ruchti to have a spectroscopy module being part of at least one wearable device, and wherein the at least one wearable device selected from the group consisting of a smartwatch and a smartphone as taught by Knuebel because this allow the system to be miniaturized and perform treatment/diagnostic method by the patient themselves (see [0042]).
Response to Arguments
Applicant's arguments filed 02/03/2026 have been fully considered but they are not persuasive. Applicant argues a model and corresponding benefits are not taught or suggested in the cited references. Applicant’s argument is not persuasive because Cope disclose trainable model comprise principal component regression model (see Cope’s paragraph [0012]; “The sequence-activity model may be produced from the training set by many different techniques”; the sequence-activity model is a trainable model being produced/train from a training set; the sequence-activity trainable model comprise principal component regression model; the sequence-activity model is a trainable model use to sequency protein and being train by a training set of protein) and the benefits are not in the claim. Applicant argues the Office further based the rejections in part on the prior art made of record and not relied upon is considered pertinent to applicant's disclosure section. Examiner explained the technology in the field to further explain the rejection. Examiner now move this section to conclusion paragraph to avoid confusion. Applicant request a clear citation to a reference that teaches using machine learning and artificial neural network to estimate age. Examiner clearly cited artificial neural network to estimate age in col. 6, line 64 – col. 7, line 32 and claim 14 (“the estimation procedure employs a calibration model 31 that maps the preprocessed spectrum through a linear or nonlinear mapping to an estimate of the age,” “One skilled in the art will appreciate that a nonlinear mapping from x to y can also be easily specified through artificial neurral networks”; mapping to estimate age and using artificial neural network to map; using artificial neural network to estimate age by mapping). Examiner further provided prior arts with cited paragraphs in the conclusion, but not cited in the rejection to further explain that machine learning model/train artificial intelligent/neural network was use and well-known in the field to estimate age (US 2023/0054197 (see [0096]; machine learning train model to estimate age; machine learning model/algorithm is trainable model); US 2021/0289070 (see [0069]; machine learning train model to estimate age); US 2020/0237452 (see [0720]) and US 2016/0027046 (see [0090] and [0093]; estimate age with machine learning train model such as regression model)). Applicant argues Ruchti does not disclose a usage of a loss function nor an optimization procedure, such as a decreasing gradient method. Applicant’s argument is not persuasive because these are not in the claims. Furthermore, if one ordinary skill in the art googles neural network and read about neural network one ordinary skill in the art know that neural network is a machine learning trainable model with optimization procedure. Applicant argues Ruchti does not disclose trainable network. Applicant’s argument is not persuasive because neural network is trainable model. Furthermore, examiner had provided many prior arts made of record and not relies in the rejection in the conclusion that disclose train machine learning model use for estimate age. Examiner had stated that using trainable model to estimate age is well-known in the field and provided many prior arts made of record and not relies in the rejection in the conclusion section to disclose this. Estimate age using trainable model is not an allowable feature since examiner had provided many prior arts that teach this limitation in the record. Applicant argues using a regression model does not imply “supervised learning”. Applicant’s argument is not persuasive because applicant made a conclusory statement without providing fact. If one ordinary skill in the art googles supervise learning, regression model, neural network one ordinary skill in the art would learn that regression models are a fundamental type of supervised learning.
Applicant argues Cope is non-analogous art therefore it would not be obvious to combine Ruchti with Cope. Applicant’s argument is not persuasive because in response to applicant's argument that Cope is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the prior art reference reasonably pertinent to the particular problem with which the inventor was concerned. Principal component regression is used to handle highly/multicollinear predictor variables in training dataset to predict a target variable. The reasonably pertinent to the particular problem here is the to address highly/multicollinear predictor variables in training dataset to predict a target variable. Use principal component regression to estimate age or select molecule is the intended use in different fields. Even though the art in different fields, principal component regression addresses the same problem of highly/multicollinear predictor variables in training dataset used to predict a target variable.
Applicant argues the cited references do not teach trainable model comprises at least one principal component regression (PCR) model. Applicant’s argument is not persuasive because Ruchti in view of Cope disclose this limitation. Applicant argues Ruchti published in 2002, trained models only existed in theory or as purely academic models. Applicant’s argument is not persuasive because principal component regression https://www.google.com/search?q=is+principal+component+regression+a+training+model&safe=active&rlz=1C1GCEA_enUS1152US1152&biw=1097&bih=608&sca_esv=0066cc4b3f6e172b&sxsrf=ANbL-n6548Hx8uVcjNhfPnpOuIa4_JW0dw%3A1771871879155&ei=h56caeeJCZ-l5NoP34ezoQ8&ved=0ahUKEwin6caPofCSAxWfElkFHd_DLPQQ4dUDCBE&uact=5&oq=is+principal+component+regression+a+training+model&gs_lp=Egxnd3Mtd2l6LXNlcnAiMmlzIHByaW5jaXBhbCBjb21wb25lbnQgcmVncmVzc2lvbiBhIHRyYWluaW5nIG1vZGVsMgUQIRigATIFECEYoAEyBRAhGKABMgUQIRigATIFECEYoAEyBRAhGJ8FSMsrUABYmilwAHgBkAEAmAHMAaABmQ-qAQU4LjkuMbgBA8gBAPgBAZgCEqAChhDCAgYQABgWGB7CAgsQABiABBiGAxiKBcICBRAAGO8FwgIIEAAYgAQYogTCAgUQIRirApgDAJIHBjQuMTMuMaAHwHqyBwY0LjEzLjG4B4YQwgcGMC41LjEzyAdGgAgA&sclient=gws-wiz-serp) (PCR) is a supervised learning training model used to predict a target variable, combining unsupervised dimensionality reduction with linear regression. It trains by first finding principal components (PCs) that maximize variance in predictors (X), then running ordinary least squares (OLS) regression on those selected components to predict target (Y) has been disclose by Ruchti in US 2003/0060693 filed 2002. Train model comprised PCR to use in the field of estimating age is known by 2002 long before applicant’s invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0054197 (see [0096]; machine learning train model to estimate age; machine learning model/algorithm is trainable model); US 2021/0289070 (see [0069]; machine learning train model to estimate age); US 2020/0237452 (see [0720]); US 2016/0027046 (see [0090] and [0093]; estimate age with machine learning train model such as regression model) and US 2003/0060693 (see [0028] and [0069]; estimation using train model comprise PCR; estimation using train model comprise PCR could use in the field to estimate chronological age). Using machine learning and artificial neural network to estimate age is well-known in the field. Machine learning and artificial neural network are trainable model. Machine learning and artificial neural network often variety type of regression model.
THIS ACTION IS MADE FINAL. 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.
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/HIEN N NGUYEN/
Primary Examiner
Art Unit 3797
Prosecution Insights