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 .
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The information disclosure statement (IDS) submitted on 05/17/2024 has been considered by the examiner.
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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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 10-11, 13, 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over A. Hani et al, “Determination of melanin types and relative concentrations: an observational study using a non-invasive inverse skin reflectance analysis”, International Journal of Cosmetic Science, vol. 36, pp. 451-458, April 2014 in view of R. Saager et al, “In vivo measurements of cutaneous melanin across spatial scales: using multiphoton microscopy and spatial frequency domain spectroscopy”, Journal of Biomedical Optics, vol. 20, no. 6, pp. 1-10, June 2015 and W. Huang et al, “High correlation between skin color based on CIELAB color space, epidermal melanocyte ratio, and melanocyte melanin content”, PeerJ, pp. 1-17, Jan. 2018, hereinafter referred to as Hani, Saager, and Huang, respectively.
Regarding claim 1, and similarly for claims 10 and 15, Hani teaches an apparatus for determining anthropological type of a user, the apparatus comprising:
a spectral optical sensor configured to emit radiation on a user's skin and obtain scattering intensity values for the radiation on the user's skin (see pg. 453, col. 2, para. 3 – “The spectrophotometer [spectral optical sensor] is used to provide the spectral reflectance of pigmented lesion and the surrounding skin surface.”);
a thickness sensor configured to obtain a thickness value of the user's skin (Fig. 4, spectrophotometer (also as thickness sensor) measuring spectral reflectance, where spectral reflectance is used to measure parameters including epidermal (skin) thickness);
memory storing one or more computer programs; and one or more processors communicatively coupled to the spectral optical sensor, the thickness sensor, and the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors (see pg. 453, col. 2, para. 3 – “The system comprises of a hand-held spectrophotometer and Graphic Processing Unit (GPU) workstation. The spectrophotometer is used to provide the spectral reflectance of pigmented lesion and the sur rounding skin surface. The data are then analysed using an inverse Monte Carlo (MC) simulation, running on a GPU workstation based on works developed by Alerstam [17].”), cause the apparatus to:
compare:
the obtained scattering intensity values with values of scattering intensity corresponding to a special skin tone from a database of simulated data, to select a set of skin sample corresponding to the special skin tone from the database of simulated data (Fig. 4, comparing simulated spectral reflectance and measured spectral reflectance; see pg. 453, col. 1, para. 2 – “The acquired reflectance data of participants were stored in a database.”; see pg. 453, col. 2, para. 3 – “The data are then analysed using an inverse Monte Carlo (MC) simulation, running on a GPU workstation based on works developed by Alerstam [17]…Error is then calculated between the simulated reflectance and measured reflectance. The values of the variable parameters are changed, and spectral reflectance is simulated again using the changed parameters; this process is repeated until the error is minimized that is, reduced to a given threshold. To decide the change of parameters, an optimization technique is used.”),
the obtained thickness value with data of skin thickness from the database of simulated data, to select skin samples having the closest values with the obtained thickness value from the selected set of skin sample (Fig. 4, comparing parameters of simulated epidermal (skin) thickness and measured skin thickness), and
the calculated overall melanin concentration value and the calculated melanin concentration ratio value of the user's skin with data of an overall melanin concentration and a melanin concentration ratio from the database of simulated data, to select a skin sample having the closest value with the calculated overall melanin concentration value and the melanin concentration ratio value from the selected skin samples (Fig. 4, comparing parameters of simulated eumelanin concentration and pheomelanin concentration (overall and ratio) and measured eumelanin concentration and pheomelanin concentration).
Hani teaches calculating melanin concentration in skin based spectral reflectance, but does not explicitly teach calculating melanin concentration in skin based on spectral reflectance and skin thickness.
Whereas, Saager, in an analogous field of endeavor, teaches calculating an overall melanin concentration value and a melanin concentration ratio value of the user's skin based on the obtained scattering intensity values and the obtained thickness value (see pg. 3, col. 2, para. 2 – “We used the TPEF/SHG cross-sectional images acquired at 790-nm excitation wavelength [scattering intensity values] (Fig. 2) in order to estimate epidermal thickness [skin thickness] in order to provide reference points for calculating the melanin volume fraction [melanin concentration] in the epidermis.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified calculating melanin concentration in skin based spectral reflectance, as disclosed in Hani, by calculating melanin concentration in skin based on spectral reflectance and skin thickness, as disclosed in Saager. One of ordinary skill in the art would have been motivated to make this modification in order to calculate the distribution of melanin content as a function of depth in the epidermis, as taught in Saager (see pg. 3, col. 2, para. 1).
Hani in view of Saager teaches estimating skin parameters based on spectral reflectance, but does not explicitly teach assign an anthropological type to the user in accordance with the selected skin sample.
Whereas, Huang, in an analogous field of endeavor, teaches assigning an anthropological type to the user in accordance with the selected skin sample (see pg. 7, para. 1 – “Given these results, an equation describing skin color relationship was generated: as L∗ = a×(M ×R)+b (M: melanin concentration per 106 melanocytes; R: epidermal melanocyte ratio; a=−0.095; and b=55.872).”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified estimating skin parameters based on spectral reflectance, as disclosed in Hani in view of Saager, by also assigning an anthropological type to the user in accordance with the selected skin sample, as disclosed in Huang. One of ordinary skill in the art would have been motivated to make this modification in order to achieve precise skin color match in treating vitiligo or burn patients, as taught in Huang (see Abstract).
Furthermore, regarding claims 3, 13, and 18 Hani further teaches wherein comparison is based on a machine learning algorithm (see pg. 453, col. 2, para. 3 – “The spectrophotometer is used to provide the spectral reflectance of pigmented lesion and the surrounding skin surface. The data are then analysed using an inverse Monte Carlo (MC) simulation, running on a GPU workstation based on works developed by Alerstam [17].” where Monte Carlo simulation is a machine learning algorithm).
Furthermore, regarding claims 4, 11, and 16, Hani further teaches wherein the melanin concentration ratio is a concentration ratio of eumelanin and phaeomelanin (see pg. 456, col. 2, para. 3 – “Table IV shows the statistics of eumelanin and pheomelanin [ratio] of all participants determined by the pigmentation analysis system.”).
Furthermore, regarding claim 9, Hani further teaches wherein the thickness sensor includes an optical sensor (Fig. 4, spectrophotometer (also as thickness sensor) measuring spectral reflectance, where spectral reflectance is used to measure parameters including epidermal (skin) thickness).
Claims 2 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Hani in view of Saager and Huang, as applied to claim 1 above, and in further view of Saager and Cuccia et al, “A Light Emitting Diode (LED) Based Spatial Frequency Domain Imaging System for Optimization of Photodynamic Therapy of Nonmelanoma Skin Cancer: Quantitative Reflectance Imaging”, Lasers Surg Med, vol. 45, no. 4, pp. 207-215, 2013, hereinafter referred to as Cuccia.
Regarding claim 2, Hani in view of Saager and Huang teaches all of the elements disclosed in claim 1 above.
Hani in view of Saager and Huang teaches a spectral optical sensor transmitting and receiving light, but does not explicitly teach where the sensor includes LEDs.
Whereas, Cuccia, in an analogous field of endeavor, teaches wherein the spectral optical sensor comprises light emitting diodes (LEDs) and photodetectors, wherein the light emitting diodes are configured to emit the radiation on the user's skin (Fig. 1; see pg. 208, col. 2, para. 4 – “In the imaging/treatment head, a digital micromirror device (DMD; DLP developers kit, Texas Instruments) system is used to project structured patterns of light from each of the LEDs onto the tissue.”), and
wherein the photodetectors are configured to obtain the scattering intensity values of the radiation from each of the light emitting diodes on the user's skin (see pg. 208, col. 2, para. 5 – “Conjugate to this image-based projection system, a CCD camera (Lm135, 12bit Monochrome, Lumenera) is located within the imaging/treatment head (Fig. 1). This mono chromatic CCD is employed as an imaging detector with a coincident image plane and field of view to that of the projected spatially modulated illumination.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a spectral optical sensor transmitting and receiving light, as disclosed in Hani in view of Saager and Huang teaches, by having the sensor include LEDs, as disclosed in Cuccia. One of ordinary skill in the art would have been motivated to make this modification in order to be designed with clinical deployment and accessibility in mind and has therefore incorporated consumer grade components, managing the overall instrument costs, as taught in Cuccia (see pg. 214, col. 2, para. 2).
Furthermore, regarding claim 5, Cuccia further teaches wherein the light emitting diodes are configured such that only one of the light emitting diodes is emitted at a time (see pg. 209, col. 1, para. 3 – “In reflectance mode, each LED is serially triggered and directed to the DMD via a fiber bundle. A sinusoidal pattern is projected at three spatially off-set phases (0°, 120°, and 240°) for each spatial frequency. This cycle is repeated for each LED.”).
Furthermore, regarding claim 6, Cuccia further teaches wherein the light emitting diodes comprises a red light emitting diode, a green light emitting diode, and an infrared light emitting diode (see pg. 208, col. 2, para. 3 – “This instrument uses five different LEDs, centered at 460 [blue], 525 [green], 630 [red], 730, and 850 [infrared] nm to provide interrogation of tissue at discrete wavelength bands…”).
The motivation for claims 5-6 was shown previously in claim 2.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hani in view of Saager and Huang, as applied to claim 1 above, and in further view of Youngquist et al. (US 20150230863 A1, published August 20, 2015), hereinafter referred to as Youngquist.
Regarding claim 7, Hani in view of Saager and Huang teaches all of the elements disclosed in claim 1 above.
Hani in view of Saager and Huang teaches measuring skin thickness, but does not explicitly teach measuring skin thickness via a bioimpedance sensor.
Whereas, Youngquist, in an analogous field of endeavor, teaches wherein the thickness sensor is a bioimpedance sensor (see oara. 0177 – “…skin thickness using electrical impedance spectroscopy [bioimpedance]…”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified measuring skin thickness, as disclosed in Hani in view of Saager and Huang, by measuring skin thickness via a bioimpedance sensor, as disclosed in Youngquist. One of ordinary skill in the art would have been motivated to make this modification in order to calculate skin thickness non-invasively.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hani in view of Saager and Huang, as applied to claim 1 above, and in further view of Sayegh et al. (US 20150216477 A1, published August 6, 2015), hereinafter referred to as Sayegh.
Regarding claim 8, Hani in view of Saager and Huang teaches all of the elements disclosed in claim 1 above.
Hani in view of Saager and Huang teaches measuring skin thickness, but does not explicitly teach measuring skin thickness via an ultrasound sensor.
Whereas, Sayegh, in an analogous field of endeavor, teaches wherein the thickness sensor is an ultrasonic sensor (see para. 0058 – “Skin thickness of select portion(s) of the lower extremity of an individual can be determined by ultrasound scanning using real-time 20-MHz high-resolution ultrasound imaging.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified measuring skin thickness, as disclosed in Hani in view of Saager and Huang, by measuring skin thickness via an ultrasound sensor, as disclosed in Sayegh. One of ordinary skill in the art would have been motivated to make this modification in order to calculate skin thickness non-invasively.
Claims 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hani in view of Saager and Huang, as applied to claim 10 and 15 above, and in further view of Hosoi (JP 2003339699 A, published December 2, 2003), hereinafter referred to as Hosoi.
Regarding claims 12 and 17, Hani in view of Saager and Huang teaches all of the elements disclosed in claim 10 and 15 above.
Hani in view of Saager and Huang teaches measuring skin thickness, but does not explicitly teach measuring skin thickness based on height, weight, age, and sex of the user.
Whereas, Hosoi, in an analogous field of endeavor, teaches wherein the thickness value is obtained by using data of height, weight, age, and sex of the user (see pg. 4, para. 8-9 – “Outside the case unit 1, input means 4 for inputting personal parameters (sex, age, height, weight), Display means 5 for displaying various output information such as input items and results (thickness of skin layer of body, amount of change in thickness of skin layer of body, body water content, body water deficiency rate, evaluation information) .”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified measuring skin thickness, as disclosed in Hani in view of Saager and Huang, by measuring skin thickness based on height, weight, age, and sex of the user, as disclosed in Hosoi. One of ordinary skill in the art would have been motivated to make this modification in order to improve estimation of body water content, as taught in Hosoi (see pg. 5, para. 3).
Claims 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hani in view of Saager and Huang, as applied to claim 10 and 15 above, and in further view of Stadler et al. (US 20220280047 A1, published September 8, 2022 with a priority date of March 8, 2021), hereinafter referred to as Stadler.
Regarding claims 14 and 19, Hani in view of Saager and Huang teaches all of the elements disclosed in claim 10 and 15 above.
Hani in view of Saager and Huang teaches determining the anthropological type, but does not explicitly teach determining physiological parameters based on anthropological type.
Whereas, Stadler, in an analogous field of endeavor, teaches wherein the anthropological type is taken into account while determining user's physiological parameters (see para. 0055 – “The skin color (e.g., face color) may vary based on blood flow, and other physiological parameters.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining the anthropological type, as disclosed in Hani in view of Saager and Huang, by also determining physiological parameters based on anthropological type, as disclosed in Stadler. One of ordinary skill in the art would have been motivated to make this modification in order to determine a change in skin color, such as face color, to determine whether there is a change in physiological parameters, and based on a determination that there is a change in physiological parameters, determine whether the change is sufficient to indicate that patient is experiencing the acute health event or the change is caused by patient experiencing the acute health event, as taught in Stadler (see para. 0055-0056).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gibson et al. (US 20160019283 A1, published January 21, 2016) discloses the wearable device may detect one or more physiological parameters, such as heart rate, blood pressure, respiration rate, blood oxygen saturation (SpO), skin temperature, skin color, etc.
Choi et al. (US 20090024041 A1, published January 22, 2009) discloses the intensity of a detection light detected by the photo detector varies depending on the color and the thickness of the skin, and in addition to this, optical characteristics, such as sex, age, height, weight, and the like, that affect the intensity of a detection light.
Kim et al. (US 20220257971 A1, published August 18, 2022 with a priority date of May 15, 2020) discloses measuring skin thickness via ultrasound sensor and optical sensor.
Lam et al. (US 20160131574 A1, published May 12, 2016) discloses skin color was measured by a Spectrophotometer.
Vilenskii (US 20190269363 A1, published September 5, 2019) discloses the portable electronic device may analyze a skin state based on the detected light, and process a captured image by analyzing the intensity in the image that is executed.
Vilenskii (US 20160166150 A1, published June 16, 2016) discloses quantitative (brightness) and quality (color) color characteristics of the skin are determined using colorimetric systems for measuring skin reflectance.
De Bock et al. (US 20230240591 A1, published August 3, 2023 with a priority date of October 7, 2020) discloses the spectral sensors associated with the mobile device first sample a received light spectrum from a user's skin, the spectral response being used to classify the skin type of the user before sampling the received light spectrum; the classified skin type can be used to determine melanin levels of the skin and/or skin color to aid in determination of the predetermined threshold(s) for safe ultraviolet (UV) radiation instant and accumulated exposure level; the skin classification for melanin can include a determination of eumelanin level and pheomelanin level, and in a related example, the ratio between eumelanin and pheomelanin is determined.
Mestha et al. (US 20180303351 A1, published October 25, 2018) discloses the RGB data of a skin and camera model that includes the skin characteristics (e.g., melanin concentration, thickness of the epidermis layer, blood volume concentration, oxygen saturation, spectral scattering, etc.) of the estimated vector, p0, are compared to the averaged RGB image data from the camera device.
Bandic et al. (US 20100185064 A1, published July 22, 2010) discloses the photoype of skin of the acquired digital image is determined according to the corrected Fitzpatrick classification using a decision tree.
Yudovsky (US 20160042513 A1, published February 11, 2016) discloses applying the hyperspectral imaging data set against a classifier comprising a two layered media model, the two layered media model comprising a first layer of a modeled human tissue overlying a second layer of the modeled human tissue, wherein the two layered media model has been trained by application of simulated data from a set of photons across a set of optical and geometric properties associated with the presence or the absence of a skin indication.
Mohamad Hani et al. (US 20150057552 A1, published February 26, 2015) discloses determining the concentration of the types of melanin, either eumelanin or pheomelanin, in the skin wherein the process for such determination is non-invasive and based on digital signal and image analysis of hyperspectral sensing and multi spectral data.
Miyamae et al. (US 20090134331 A1, published May 28, 2009) discloses estimating at least one of an epidermal skin thickness and a dermal skin thickness by analysis of the near infrared absorption spectrum of skin.
R. Chen et al, “Monte Carlo simulation of cutaneous reflectance and fluorescence measurements – The effect of melanin contents and localization”, Journal of Photochemistry and Photobiology, vol. 86, pp. 219-226, March 2006 discloses based on the skin optical model and MC (Monte Carlo) simulation, the content and distribution of melanin in skin, or other component of skin could be simulated and predicted.
T. Wissel et al, “Estimating soft tissue thickness from light-tissue interactions––a simulation study”, Biomedical Optics Express, vol. 4, no. 7, pp. 1176-1187, May 2013 discloses training a regression model with a simulated data set, from Monte Carlo simulations for multi-layered tissue, identifies patterns that allow for predicting skin thickness.
U. Birgersson et al, “Estimating electrical properties and the thickness of skin with electrical impedance spectroscopy: Mathematical analysis and measurements”, Journal of Electrical Bioimpedance, vol. 3, pp. 51-60, Sept. 2012 discloses the potential application of EIS to estimate the thickness of the stratum corneum is explored in the form of a mathematical model for EIS.
D. Yudovsky et al, “Rapid and accurate estimation of blood saturation, melanin content, and epidermis thickness from spectral diffuse reflectance”, Applied Optics, vol. 49, no. 10, pp. 1707-1719, April 2010 discloses an inverse method was developed to retrieve these physiologically meaningful parameters (melanosome volume fraction, epidermal thick ness, blood volume, and oxygen saturation) from the simulated diffuse reflectance spectra of skin.
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/N.C./Examiner, Art Unit 3798
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