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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/12/ 26 has been entered.
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)), further in view of Cope (US 2014/0214391) and Wolf et al. (US 2020/0237452).
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.
Ruchti does not disclose deep learning. However, he discloses artificial neural network which is a machine learning model. Deep learning model is just subset or a type of machine learning model. It only requires routine skill in the art to use different type of machine learning model. In the same field of endeavor, Wolf discloses deep learning model (see [0079], [0083], [0238] and [0244]; Wolf also explicitly discloses using machine learning (deep learning) to train to identify patient characteristic such as estimated age of the patient (see [0720]); he also explicitly discloses supervised learning/training (see [0356], [0447], [0635] and [0639]); supervised learning use a completely labeled training dataset is the definition of supervised learning; deep learning performing automatic model building is the definition of deep learning (see the prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the conclusion section)). 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 modify Ruchti to use deep learning because using any machine learning method only require routine skill in the art and deep learning provide automatic feature learning and superior performance on complex tasks.
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), Wolf et al. (US 2020/0237452) 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), Wolf et al. (US 2020/0237452) 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 with respect to claim(s) 2-14 and 16-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 2016/0027046 (see [0090] and [0093]; estimate age with machine learning train model such as regression model); 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); US 2016/0004794 (see claim 18; automatic creation of model with machine deep learning) and US 2012/0051629 (see abstract and [0052-0054]; estimate age with supervise learning and semi-supervise learning; supervised learning is learning using the labeled training data only; semi-supervised learning is in which the unlabeled training data is utilized in addition to the aforementioned respective labeled training data is performed). 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.
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/HIEN N NGUYEN/
Primary Examiner
Art Unit 3797
Prosecution Insights