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 .
Amendment Entered
This Office action is responsive to the Amendment filed on July 22nd, 2025. The examiner acknowledges the amendments to claims 1-3, 6, 7, 10-12, 15, 16, and 19, as well as the cancellation of claims 4, 9, 13, and 18. Claims 1-3, 5-8, 10-12, 14-17, and 19-20 remain pending in the application.
Response to Arguments
Applicant’s remarks and amendments with respect to the claim objections have been fully considered. The claim objections are withdrawn in view of the remarks and amendments.
Applicant’s remarks and amendments with respect to the rejections under 35 U.S.C. 112(b) have been fully considered. The rejections under 35 U.S.C. 112(b) are withdrawn in view of the remarks and amendments.
Applicant’s arguments, see pages 1-2, with respect to the rejections of claims 1, 5-8, 10, 14-17, and 19-20 under 35 U.S.C. 102(a)(1) have been fully considered. The rejection under 35 U.S.C. 102(a)(1) has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103 in view of Bandic (US 20100185064 A1), Salvi (US 20190237194 A1), and Chetham (US 20090043222 A1).
Furthermore, at page 2, Applicant argues that Bandic does not teach or suggest the following limitations “receiving … health data … mixtures thereof” obviating the rejections under 35 U.S.C. 103. Examiner respectfully disagrees.
In the Office action, mailed, April 22, 2025, Bandic was not relied upon to disclose the limitations “wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof” and “wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals”. Rather, Salvi, see pages 22-23 of the Office action mailed April 22, 2025, was relied upon to disclose the limitation “wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals”, see para. [0018, 0051, 0055] of Salvi, and Chetham, see pages 21-22 of the Office action mailed April 22, 2025, was relied upon to disclose “wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof”, see para. [0020-0021, 0227, 0238-0239] of Chetham. Furthermore, Bandic discloses inputs 112, used to augment the data submitted by the user or as the primary data to obtain a personalized assessment, provided by wearable monitors 182 (wearable hydration monitor), and further that the wearable monitors may be able to assess hydration levels (para. [0400-0401, 0722]). Therefore, Bandic does disclose receiving, by the one or more processors, a health data of the user (“data … input 112 … wearable monitor 182 … heart rate … hydration levels”, para. [0307, 0400-0401]).
Applicant’s arguments with respect to the rejections under 35 U.S.C. 101 have been fully considered but are not persuasive.
At page 3, Applicant argues that the claims now recite technical aspects for training the AI based learning model with specific data in order to train and configure the model to produce a specific output based on such training data. Examiner respectfully disagrees. The claim recites no details about particular skin analysis learning models. The skin analysis learning models are used to generally apply the abstract idea (i.e., perform the mental processes, “analyzing”) without placing any limitations on how the skin analysis learning models operate to derive the improved-user-specific skin analysis. In addition, the limitation would cover every mode of implementing the recited abstract idea using skin analysis learning models. The claim omits any details as to how the skin analysis learning models solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation “wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals” represents no more than mere instructions to implements the abstract idea.
At page 4, Applicant argues that the claims integrate the abstract idea into a practical application and provides an improvement. Examiner respectfully disagrees. The improvement cannot be found in the abstract idea itself. “[I]t is important to keep in mind that an improvement in the abstract idea itself ... is not an improvement in technology.” MPEP 2106.05(a) Il. The claims recite steps for an analysis of data. The claims do not integrate the analysis into a practical application. Rather, the alleged improvement lies solely within the processing steps performed by the processor. “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method. Furthermore, the claim confines the use of the judicial exception to the technological environment of skin analysis learning models by generally linking the use of the judicial exception to the recited skin analysis learning models. Therefore, these general skin analysis learning models do not integrate the judicial exception into a practical application.
At page 5, Applicant argues that the claims as a whole add additional elements or a combination of additional elements that imposes a meaningful limit on the judicial exception. Examiner respectfully disagrees. When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole 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 mere instructions to implement the abstract idea.
At page 6, Applicant argues that the claims as a whole recite additional elements that add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field. Examiner respectfully disagrees. The claims do not apply the obtained calculation to a particular machine. The additional elements (processor(s)/computing device; analysis application/skin analysis learning models; non-transitory computer-readable medium; display screen) are well-understood, routine, and conventional means for data-gathering and computing, evidenced by: Catiller (US 20170212739 A1) discloses in para. [0003] conventional processors; Watkins (US 6417562 B1) discloses in column 2 lines 19-20 a conventional memory such as a random access memory (RAM); Hosotani (US 5225819 A) discloses in fig. 3 a conventional screen display; The Non-Patent Literature of record; X. Liu, C. -H. Chen, M. Karvela and C. Toumazou, "A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 11, pp. 3276-3284, Nov. 2020, doi: 10.1109/JBHI.2020.2978667; Buller DB, Berwick M, Lantz K, et al. Smartphone Mobile Application Delivering Personalized, Real-Time Sun Protection Advice: A Randomized Clinical Trial. JAMA Dermatol. 2015;151(5):497–504. doi:10.1001/jamadermatol.2014.3889; and Arroyo et al., Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis, Computers in Biology and Medicine, Volume 44, 2014, Pages 144-157, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2013.11.002.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 20 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 20 recites the limitation “wherein the skin data of the user is skin image data of the user” which has been incorporated into line 5 of independent claim 19. Therefore, claim 20 fails to further limit the subject matter of independent claim 19. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are all within at least one of the four categories.
Independent claim 1 recites:
receiving, by one or more processors, a skin data of a user wherein the skin data is skin image data of the user; receiving, by the one or more processors, a health data of the user, wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof, and analyzing, by one or more skin analysis learning models, the skin data of the user and the health data of the user to generate the improved user-specific skin analysis; wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals.
Independent claim 10 recites:
receive a skin data of a user, wherein the skin data is skin image data of the user; receive a health data of the user, wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof, and analyze, by the one or more skin analysis learning models, the skin data of the user and the health data of the user to generate the user-specific skin analysis.
Independent claim 19 recites:
receive, at an analysis application (app) executing on one or more processors, a skin data of the user, wherein the skin data is skin image data of the user; receive, at the analysis app, a health data of the user, wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof; and analyze, by one or more skin analysis learning models accessible by the analysis app, the skin data of the user and the health data of the user to generate a user-specific skin analysis, wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals.
The above claim limitations (analyzing, the one or more skin analysis models trained …) constitute an abstract idea that is part of the Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
The claimed steps of analyzing, the one or more skin analysis models trained …, can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for providing a user-specific skin analysis that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Claims 2-4, 7-9, 11-13, 16-18, and 20 are directed to more abstract ideas and further limitations on abstract ideas is already recited.
Claims 5-6 and 14-15 are directed to more abstract ideas and further limitations on abstract ideas is already recited and are further directed to additional data output that is well-understood, routine and previously known to the industry.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for analyzing merely invoke a computer as a tool.
The data-gathering step (receiving) does not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for analyzing, the one or more skin analysis models trained ….
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to analyze data to generate a user-specific skin analysis.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for analyzing, the one or more skin analysis models trained …. The claims do not apply the user-specific skin analysis to a particular machine.
The additional elements are identified as follows: processor(s)/computing device; analysis application/skin analysis learning models; non-transitory computer-readable medium; display screen.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant’s specification (para. [page 9 line 31-page 11 line 13]) which discloses that the processor(s)/computing device/ non-transitory computer-readable medium comprise generic computer components that are configured to perform the generic computer functions (e.g., analyzing) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
The prior art of record;
Catiller (US 20170212739 A1) discloses in para. [0003] conventional processors.
Watkins (US 6417562 B1) discloses in column 2 lines 19-20 a conventional memory such as a random access memory (RAM);
Hosotani (US 5225819 A) discloses in fig. 3 a conventional screen display;
The Non-Patent Literature of record;
X. Liu, C. -H. Chen, M. Karvela and C. Toumazou, "A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 11, pp. 3276-3284, Nov. 2020, doi: 10.1109/JBHI.2020.2978667.
Buller DB, Berwick M, Lantz K, et al. Smartphone Mobile Application Delivering Personalized, Real-Time Sun Protection Advice: A Randomized Clinical Trial. JAMA Dermatol. 2015;151(5):497–504. doi:10.1001/jamadermatol.2014.3889
Arroyo et al., Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis, Computers in Biology and Medicine, Volume 44, 2014, Pages 144-157, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2013.11.002.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e., the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole 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 § 103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-8, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bandic (US 20100185064 A1) in view of Salvi (US 20190237194 A1), and further in view of Chetham (US 20090043222 A1).
Regarding claim 1, Bandic discloses a user-specific skin analysis method for generating an improved user-specific skin analysis (“personalized skin condition analysis system and related methods”; skin state 158, para. [0033, 0307, 0717], fig. 1), the method comprising: receiving, by one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor”, para. [0802]), a skin data of a user (“obtain biophysical skin properties … images”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]) wherein the skin data is skin image data of the user (“images”; “capture images of skin structures to obtain biophysical skin properties”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]); receiving, by the one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor”, para. [0802]), a health data of the user (“data … input 112 … wearable monitor 182 … heart rate … hydration levels”, para. [0307, 0400-0401]); and analyzing (analysis 154, para. [0311-0312]), by one or more skin analysis learning models (“algorithms 150 … to process and analyze … learning algorithms”, para. [0311-0312]), the skin data of the user and the health data of the user (“skin state 158 … based on … monitoring 164 performed by a device 108 … other inputs 112”; “data communicated to … computer for analysis 154”, para. [0307, 0381]) to generate the improved user-specific skin analysis (“personalized skin condition analysis system”; “skin state 158”, para. [0033, 0226, 0341-0342]).
Bandic further discloses inputs 112, used to augment the data submitted by the user or as the primary data to obtain a personalized assessment, provided by wearable monitors 182 (wearable hydration monitor), and further that the wearable monitors may be able to assess hydration levels (para. [0400-0401, 0722]).
Bandic does not expressly disclose wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof.
However, Chetham directed to a method of determining an indication of the hydration status relating to a subject discloses wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof (indicator is at least one of … ratio of extra- to intra-cellular fluid; “total body water can be used as an indicator for hydration status”, para. [0020-0021, 0227, 0238-0239]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic such that the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof, in view of the teachings of Chetham, as such a modification would have yielded predictable results, namely inputting, as inputs 112, the hydration status based on the indicators (ratio of extra- to intra-cellular fluid and total body water) to augment the data submitted by the user or as the primary data to obtain a personalized assessment and provide the objective skin health assessment report using the inputs.
Bandic, as modified by Chetham hereinabove, does not expressly disclose wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals.
However, Salvi discloses wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals (“multiple users, first data inputs … second data inputs … create a training data set”; “training data set”; “machine learning model that has been trained on a longitudinal dataset including diet, lifestyle habits, skin concerns, severity scores and skin health metric … personalized skin care routine is generated”, para. [0018, 0051, 0055], skin health data set of fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic, as modified by Chetham hereinabove, such that the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals, in view of the teachings of Salvi, as such a modification would have been merely a substitution of the learning algorithm of Bandic for the machine learning model of Salvi to generate personalized skin care routines.
Regarding claim 2, Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, wherein the health data of the user is the body water content (Chetham, “total body water can be used as an indicator for hydration status”, para. [0020-0021, 0227, 0238-0239]).
Regarding claim 3, Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, wherein the health data of the user is the intracellular-to-extracellular water ratio (Chetham, “indicator is at least one of … ratio of extra- to intra-cellular fluid”, para. [0020-0021, 0227, 0238-0239]).
Regarding claim 5, Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, further comprising: rendering, by the one or more processors, the user-specific skin analysis on a display screen of a computing device ( as seen in figs. 6-7, para. [0389]).
Regarding claim 6, Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, further comprising: receiving, by the one or more processors (“computer”, para. [0411], see also para. [0802]), an image depicting a skin region of the user (“acquire an initial image”, para. [0411], figs. 6-7); generating, by the one or more processors (“computer”, para. [0411], see also para. [0802]), a modified image based on the image (as seen in figs. 6-7, “projection”, para. [0411]), the modified image depicting how the skin region of the user is predicted to appear after following at least one of the recommendations (“model various skin parameters … and observe changes in the images … images … optimized”; “perform a projection of skin state 158 based on various skin care regimens 118, such as maximum care, normal care, or poor care”, para. [0389, 0411]); and rendering, by the one or more processors, the modified image on the display screen of the computing device (as seen in figs. 6-7, para. [0389, 0411]).
Regarding claim 7 Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, wherein the skin data of the user is a first skin data of the user and the health data of the user is a first health data of the user (“real time tracking … heart rate”; “image suitable for analysis has been captured … baseline skin health assessment”, para. [0401, 0418, 0420]), the method further comprising: receiving, by the one or more processors, the first skin data of the user and the first health data of the user at a first time (“real time tracking … heart rate”; “first analysis … baseline”, para. [0401, 0419-0420], date imaged in fig. 10, & fig. 15); receiving, by the one or more processors, a second skin data of the user and a second health data of the user at a second time (“real time tracking … heart rate”; “subsequent images”, para. [0401, 0420], date imaged in fig. 10); analyzing, by the one or more skin analysis learning models, the second skin data of the user and the second health data of the user (“analysis … second skin health assessment”, para. [0418-0420]); and generating, based on a comparison of the second skin data of the user and the second health data of the user to the first skin data of the user and the first health data of the user, a new user- specific skin analysis (“analysis … comparing the second assessment to the baseline assessment … optimizing the regimen 118”, para. [0418-0420], fig. 15).
Regarding claim 8, Bandic, as modified by Chetham and Salvi hereinabove, discloses the method of claim 1, wherein at least one of the one or more processors comprises at least one of a processor of a mobile device or a processor of a server (“processor may be part of a server … mobile devices”, para. [0802, 0810]).
Regarding claim 19, Bandic discloses a tangible, non-transitory computer-readable medium storing instructions (para. [0802, 0807]) for generating a user-specific skin analysis (“personalized skin condition analysis system”; “skin state 158”, para. [0033, 0226, 0341-0342]), that when executed by one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor”, para. [0802]) cause the one or more processors to: receive, at an analysis application (app) executing on one or more processors (“application”, para. [0383], see also para. [0802, 0807]), a skin data of a user (“obtain biophysical skin properties … images”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]), wherein the skin data is skin image data of the user (“images”; “capture images of skin structures to obtain biophysical skin properties”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]); receive, at the analysis app, a health data of the user (“data … input 112 … heart rate”; “data may be communicated to a computer”, para. [0307, 0381, 0400-0401], fig. 1); and analyze (analysis 154, para. [0311-0312]), by one or more skin analysis learning models accessible by the analysis app (as seen in fig. 1), the skin data of the user and the health data of the user (“skin state 158 … based on … monitoring 164 performed by a device 108 … other inputs 112”; “data communicated to … computer for analysis 154”, para. [0307, 0381]) to generate the user-specific skin analysis (“personalized skin condition analysis system”; “skin state 158”, para. [0033, 0226, 0341-0342]).
Bandic further discloses inputs 112, used to augment the data submitted by the user or as the primary data to obtain a personalized assessment, provided by wearable monitors 182 (wearable hydration monitor), and further that the wearable monitors may be able to assess hydration levels (para. [0400-0401, 0722]).
Bandic does not expressly disclose wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof.
However, Chetham directed to a method of determining an indication of the hydration status relating to a subject discloses wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof (indicator is at least one of … ratio of extra- to intra-cellular fluid; “total body water can be used as an indicator for hydration status”, para. [0020-0021, 0227, 0238-0239]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic such that the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof, in view of the teachings of Chetham, as such a modification would have yielded predictable results, namely inputting, as inputs 112, the hydration status based on the indicators (ratio of extra- to intra-cellular fluid and total body water) to augment the data submitted by the user or as the primary data to obtain a personalized assessment and provide the objective skin health assessment report using the inputs.
Bandic, as modified by Chetham hereinabove, does not expressly disclose wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals.
However, Salvi discloses wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals (“multiple users, first data inputs … second data inputs … create a training data set”; “training data set”; “machine learning model that has been trained on a longitudinal dataset including diet, lifestyle habits, skin concerns, severity scores and skin health metric … personalized skin care routine is generated”, para. [0018, 0051, 0055], skin health data set of fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic, as modified by Chetham hereinabove, such that the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals, in view of the teachings of Salvi, as such a modification would have been merely a substitution of the learning algorithm of Bandic for the machine learning model of Salvi to generate personalized skin care routines.
Regarding claim 20, Bandic discloses the tangible, non-transitory computer-readable medium of claim 19, wherein the skin data of the user is skin image data of the user (“capture images of skin structures to obtain biophysical skin properties”, para. [0227-0228]).
Claims 10-12 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bandic in view of Salvi, and further in view of Chetham.
Regarding claim 10, Bandic discloses a user-specific skin analysis system configured to generating an improved user-specific skin analysis (“personalized skin condition analysis system and related methods”; skin state 158, para. [0033, 0307, 0717], fig. 1), the system comprising: one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor”, para. [0802]); and an analysis application (app) (“application”, para. [0383], see also para. [0802, 0807]) comprising computing instructions (“applications … instructions”, para. [0807-0808]) configured to execute on the one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor … executed”, para. [0802, 0807]); and one or more skin analysis learning models (“algorithms 150 … to process and analyze … learning algorithms”, para. [0311-0312]), accessible by the analysis app (as seen in fig. 1, para. [0382-0383]), wherein the computing instructions of the analysis app when executed by the one or more processors (“processor … applications … instructions … executed”, para. [0802, 0807]), cause the one or more processors (host system 104 for processing and analyzing, “data may be communicated to a computer”, para. [0226, 0381], fig. 1; see also “processor”, para. [0802]) to: receive a skin data of a user (“obtain biophysical skin properties … images”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]) wherein the skin data is skin image data of the user (“images”; “capture images of skin structures to obtain biophysical skin properties”; “image of skin … upload it … analysis 154”, para. [0227-0228, 0344]); receive a health data of the user (“data … input 112 … wearable monitor 182 … heart rate … hydration levels”, para. [0307, 0400-0401]); and analyze (analysis 154, para. [0311-0312]), by the one or more skin analysis learning models (“algorithms 150 … to process and analyze … learning algorithms”, para. [0311-0312]), the skin data of the user and the health data of the user (“skin state 158 … based on … monitoring 164 performed by a device 108 … other inputs 112”; “data communicated to … computer for analysis 154”, para. [0307, 0381]) to generate the user-specific skin analysis (“personalized skin condition analysis system”; “skin state 158”, para. [0033, 0226, 0341-0342]).
Bandic does not expressly disclose wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals to output the improved user-specific skin analysis.
However, Salvi discloses wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals to output the improved user-specific skin analysis (“multiple users, first data inputs … second data inputs … create a training data set”; “training data set”; “machine learning model that has been trained on a longitudinal dataset including diet, lifestyle habits, skin concerns, severity scores and skin health metric … personalized skin care routine is generated”, para. [0018, 0051, 0055], skin health data set of fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic, such that wherein the one or more skin analysis learning models are trained with a plurality of skin data and a plurality of health data of a plurality of individuals to output the improved user-specific skin analysis, in view of the teachings of Salvi, as such a modification would have been merely a substitution of the learning algorithm of Bandic for the machine learning model of Salvi to generate personalized skin care routines.
Bandic further discloses inputs 112, used to augment the data submitted by the user or as the primary data to obtain a personalized assessment, provided by wearable monitors 182 (wearable hydration monitor), and further that the wearable monitors may be able to assess hydration levels (para. [0400-0401, 0722]).
Bandic, as modified by Salvi hereinabove, does not expressly disclose wherein the health data of the user is chosen from: a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof.
However, Chetham directed to a method of determining an indication of the hydration status relating to a subject discloses wherein the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof (indicator is at least one of … ratio of extra- to intra-cellular fluid; “total body water can be used as an indicator for hydration status”, para. [0020-0021, 0227, 0238-0239]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bandic, as modified by Salvi hereinabove, such that the health data of the user is chosen from a body water content or amount, an intracellular-to-extracellular water ratio, or mixtures thereof, in view of the teachings of Chetham, as such a modification would have yielded predictable results, namely inputting, as inputs 112, the hydration status based on the indicators (ratio of extra- to intra-cellular fluid and total body water) to augment the data submitted by the user or as the primary data to obtain a personalized assessment and provide the objective skin health assessment report using the inputs.
Regarding claim 11, Bandic, as modified by Salvi and Chetham hereinabove, discloses the system of claim 10, wherein the health data of the user is the body water content or amount (Chetham, “total body water can be used as an indicator for hydration status”, para. [0020-0021, 0227, 0238-0239]).
Regarding claim 12, Bandic, as modified by Salvi and Chetham hereinabove, discloses the system of claim 10, wherein the health data of the user is the intracellular-to-extracellular water ratio (Chetham, “indicator is at least one of … ratio of extra- to intra-cellular fluid”, para. [0020-0021, 0227, 0238-0239]).
Regarding claim 14, Bandic, as modified by Salvi and Chetham hereinabove, discloses the system of claim 10, wherein the computing instructions, when executed by the one or more processors (para. 0802, 0807), further cause the one or more processors to: render the user-specific skin analysis on a display screen of a computing device ( as seen in figs. 6-7, para. [0389]).
Regarding claim 15, Bandic, as modified by Salvi and Chetham hereinabove, discloses the system of claim 10, wherein the computing instructions, when executed by the one or more processors (“computer”, para. [0411], see also para. [0802, 0807]), further cause the one or more processors to: receive an image depicting a skin region of the user (“acquire an initial image”, para. [0411], figs. 6-7); generate a modified image based on the image (as seen in figs. 6-7, “projection”, para. [0411]), the modified image depicting how the skin region of the user is predicted to appear after following at least one of a plurality of recommendations (“model various skin parameters … and observe changes in the images … images … optimized”; “perform a projection of skin state 158 based on various skin care regimens 118, such as maximum care, normal care, or poor care”, para. [0389, 0411]); and render the modified image on a display screen of a computing device (as seen in figs. 6-7, para. [0389, 0411]).
Regarding claim 16, Bandic discloses, system of claim 10, wherein the skin data of the user is a first skin data of the user and the health data of the user is a first health data of the user (“real time tracking … heart rate”; “image suitable for analysis has been captured … baseline skin health assessment”, para. [0401, 0418, 0420], date imaged in fig. 10), the computing instructions, when executed by the one or more processors (para. [0802, 0807]), further cause the one or more processors to: receive the first skin data of the user and the first health data of the user at a first time (“real time tracking … heart rate”; “first analysis … baseline”, para. [0401, 0419-0420], date imaged in fig. 10, & fig. 15); receive a second skin data of the user and a second health data of the user at a second time (“real time tracking … heart rate”; “subsequent images”, para. [0401, 0420], date imaged in fig. 10); analyze, by the one or more skin analysis learning models, the second skin data of the user and the second health data of the user (“analysis … second skin health assessment”, para. [0418-0420]); and generate, based on a comparison of the second skin data of the user and the second health data of the user to the first skin data of the user and the first health data of the user, a new user- specific skin analysis (“analysis … comparing the second assessment to the baseline assessment … optimizing the regimen 118”, para. [0418-0420], fig. 15).
Regarding claim 17, Bandic, as modified by Salvi and Chetham hereinabove, discloses the system of claim 10, wherein at least one of the one or more processors comprises at least one of a processor of a mobile device or a processor of a server (“processor may be part of a server … mobile devices”, para. [0802, 0810]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Knoell (US 20050240085 A1) directed to balanced care product customization discloses, in para. [0112], obtaining objective data using body-impedance analysis (BIA) to assess body water content.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/CHARLES A MARMOR II/ Supervisory Patent Examiner, Art Unit 3791
/A.E.H./Examiner, Art Unit 3791