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 .
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-20 recites at least one step or instruction for determine skin type based on surveys, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claims 1, 11 and 20 recites using processors and memories and an artificial neural network to determine a first survey score group for a target user’s skin survey, inputs the user’s skin image into the neural network to determine a second survey score group based on the network’s output, and determines a score for one or more indexes indicated the user’s skin type/condition based on the first and second survey score groups (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Further, dependent Claims 2-10 and 12-19 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1, 11 and 20 (and their respective dependent Claims 2-10 and 12-19) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims1, 11 and 20), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: processors and memories are generically recited computer elements in independent Claims 1, 11 and 20 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims1, 11 and 20 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., processors and memories as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1, 11 and 20 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1, 11 and 20 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-20 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: processors and memories.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Accordingly, in light of Applicant’s specification, the claimed term processors and memories is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the processors and memories. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-20 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatus, method and computer implemented process of Claims 1-20 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-20 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1, 11 and 20 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-20 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1, 11 and 20, the claims first recite “one or more processors” and then go on to recite a single “processor”. Therefore, it is unclear how many processors are actually claimed. For purposes of examination the indefinite limitation has been deemed to claim that the instructions are executed by “processors”.
Regarding Claims 1, 11 and 20, the claim first recites a “plurality of survey questions” including both of “a survey about skin provided to the plurality of users” and “a survey about skin of a target user”. Since two surveys are recited, it is unclear what entails the plurality of surveys and the first and second survey score groups. For purposes of examination the indefinite limitation has been deemed to claim that there are two surveys, one from the group, and one from the user. This issue continues in any dependent claim that claims a survey.
Regarding all claims that recite “score” including Claims 1, 3-11, 13-20, multiple scores are recited including “scores for a plurality of survey questions”, “a first survey score group”, “a second survey score group”, “a score”, “the score”, “the score for each of the one or more indexes”. There is great ambiguating in what score comprises what, and the differences or similarities between each of the recited scores in different claimed contexts. For purposes of examination the indefinite limitation has been deemed to claim that there is a score from a first survey score group and a second survey score group that are converted into first and second scores.
The term “accuracy” in claim 6 and 15 is a relative term which renders the claim indefinite. The term “accuracy” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding Claims 7 and 16, the limitation “each question group” lacks antecedent basis, because question groups have not been previously established. Claims 7 and 16 have been treated as being dependent from Claims 2 and 12, respectively.
The term “expert” in claims 8-10 and 17-19 is a relative term which renders the claim indefinite. The term “expert” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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.
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.
Claim(s) 1-5, 7-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over KR 20200028571 A to Yoo et al. (hereinafter, Yoo) in view of US 20230123037 A1 to Jiang et al. (hereinafter, Jiang).
Regarding Claims 1, 11 and 20, Yoo discloses an apparatus, method and computer implemented computer instructions comprising inter alia:
one or more processors (“… can be implemented by one or more servers for generating analysis information…” p. 4, ll. 8-9) (sever “calculate[es] the result value by substituting the variable value… to the learned AI module” p. 4, ll. 42-44); and
one or more memories configured to store instructions that, when executed by the processor, cause the one or more processors to perform calculations (“140: management D / B” p. 12, ll. 19; server stores and runs the AI module and units), and an artificial intelligence model constructed through modeling of a correlation between a plurality of images for skin of a plurality of users and scores for a plurality of survey questions included in a survey about skin provided to the plurality of users (“a plurality of learning data including variable values for each information and variable values for each output information to a designated artificial intelligence module” p. 13, ll. 10-12) (“the artificial intelligence module may include a module that implements machine learning based artificial intelligence algorithm.” p. 6, ll. 38-40),
wherein the one or more processors are configured to, according to the instructions:
determine a first survey score group for a survey about skin of a target user (“variable value for each i 'input information and the user's wireless terminal app based on the i' (1≤i '<i) survey information displayed on the survey interface” p. 2, ll. 13-14) (“the input information is a variable called 'skin pulling after washing', and the variable value for each input information may be a number '2' selected from 1.very pulling, a little pulling, and 3.not pulling.” p. 5, ll. 30-32);
input derived variable values from an image of the skin of the target user into the artificial neural network (“A fourth step of determining variable values for each (i-i ') input information based on the user's face photo using the received k face photo data” p. 13, ll. 18-19) (“A fourth step of determining variable values for each (i-i ') input information based on the user's face photo using the received k face photo data” p. 9, ll. 12-13);
determine a second score group comprising variable values determined from the facial image for a subset of input attributes (p. 11, ll. 30-38 performed by “variable value determination unit 120” p. 4, ll. 40-42);
determine a score of each of one or more indexes indicating a skin type of the target user or a skin condition of the target user, based on the first survey score group and the second survey score group (“The designated j outputs by substituting the i variable values for each input information including the received i 'input variable values and the determined (i-i') variable values for each input information into the learned AI module A fifth step of calculating a result value corresponding to the information” p. 13, ll. 20-22) (outputs are “wrinkle-based… elasticity-based… moisture based… pore-based… pigment based… skin tone-based… and sensitivity-based skin condition categories… freckles” p. 6, ll. 17-22 and p. 10, ll. 1-5).
(Claims 2 and 12) Yoo discloses wherein the survey comprises question groups for at least one target to be determined, and wherein the question groups for at least one target to be determined corresponds to the one or more indexes, respectively (“The designated j outputs by substituting the i variable values for each input information including the received i 'input variable values and the determined (i-i') variable values for each input information into the learned AI module A fifth step of calculating a result value corresponding to the information” p. 13, ll. 20-22) (outputs are “wrinkle-based… elasticity-based… moisture based… pore-based… pigment based… skin tone-based… and sensitivity-based skin condition categories… freckles” p. 6, ll. 17-22 and p. 10, ll. 1-5);
(Claims 3 and 13) Yoo discloses wherein the one or more processors are configured to acquire a survey result from the target user, the survey result comprising an answer to each of the plurality of survey questions included in the survey; and determine the first survey score group by converting the answer to each of the plurality of survey questions included in the survey into a score (“the input information is a variable called 'skin pulling after washing', and the variable value for each input information may be a number '2' selected from 1.very pulling, a little pulling, and 3.not pulling.” p. 5, ll. 30-32) (“Variable values per i 'input information based on i' (1≤i '<i) survey information displayed on the questionnaire interface output through the app of the user's wireless terminal” p. 13, ll. 13-14);
(Claim 4 – partial) Yoo discloses the second score group is determined from the image, after the facial phone is received (“variable values for each (i-i ') input information based on the user's face photo using the received k face photo data” p. 13, ll. 18-19);
(Claims 5 and 14 – partial) Yoo discloses a first tyhpe score from the first (user) group (“input variable value and the (i-i ') variable value. I variable values of input information” p. 40-41), a second type score from the second (image group) (“the determined (i-i') variable values for each input information” p. 13, ll. 21) and a final group from both followed by indexing and determination of skin type (“The i value including the variable value and the determined (i-i ') variable value for each input information is substituted into the learned AI module to calculate a result value corresponding to the designated j output information. p. 7, ll. 28-30”);
(Claims 8 and 17 – partial) Yoo discloses determining an expert score group for the image for the skin of the target user (“A fourth step of determining variable values for each (i-i ') input information based on the user's face photo using the received k face photo data” p. 13, ll. 18-19) (“A fourth step of determining variable values for each (i-i ') input information based on the user's face photo using the received k face photo data” p. 9, ll. 12-13) and determining the score from the skin type of each user (“The designated j outputs by substituting the i variable values for each input information including the received i 'input variable values and the determined (i-i') variable values for each input information into the learned AI module A fifth step of calculating a result value corresponding to the information” p. 13, ll. 20-22); and
(Claims 9 and 18 – partial) wherein the expert score group comprises scores for at least one target to be determined among flush, pigment, pore, wrinkle, and acne determined based on the image for the skin of the target user (outputs are “wrinkle-based… elasticity-based… moisture based… pore-based… pigment based… skin tone-based… and sensitivity-based skin condition categories… freckles” p. 6, ll. 17-22 and p. 10, ll. 1-5); and
(Claims 10 and 19) Yoo in view of Jiang teach determining the score from the combined survey-based and expert (image) score groups. While Yoo in view of Jiang do not expressly recite combining them “by calculating a weighted sum”, it is noted that a weight sum is a well-known, conventional way to combine two sets of scores. It would have been notoriously obvious to do so to yield the predictable result of a single score reflecting both assessments.
Yoo discloses the claimed invention except for expressly disclosing (Claims 1, 11 and 20) where the artificial intelligence model is an artificial neural network, that an image of the target user’s skin input into the artificial neural network, and that the second score group is output by / based on an output of the artificial neural network, (Claim 4) where the network is an artificial neural network, where the image is input into the network and the scores being output by the network, (Claims 5 and 14) that the second survey group, from which the second type question score is acquired, is “based on an output of the artificial neural network” nor “inputting an image for skin of the target user into the artificial neural network”, (Claims 8 and 17) determining the scores by adding up scores of questions included in the final survey score group according to each question group and determine the skin type based on the score for each of the one or more indexes, (Claims 9 and 18) where the expert score group is based on the output of the artificial neural network.
However, Jiang teaches:
(Claims 1, 11 and 20) a device for skin diagnostics using images and deep learning [0002]. Jiang teaches where an artificial intelligence model being artificial neural network (“a convolutional neural network (CNN)… wherein the CNN comprises a deep neural network for image classification” [0006]), where an image of the target user’s skin input into the artificial neural network (“receiving the image; and processing the image using the CNN to generate the N respective skin sign diagnoses.” [0018]), and that the second score group is output by / based on an output of the artificial neural network (the CNN is “configured to generate the N respective skin sign diagnoses” i.e., the per-attribute image scores are the network’s output);
(Claim 4) where the artificial intelligence module is an artificial neural network (“a convolutional neural network (CNN)… wherein the CNN comprises a deep neural network for image classification” [0006]), the image being input into the network and the scores being output by the network ([0018] “…receiving the image; and processing the image using the CNN to generate the N respective skin sign diagnoses”); and
(Claims 5, 8, 9, 14, 17, 18) an artificial neural network into which the image is input and from which the scores are output and the expert score group being based on the output of the artificial neural network ([0018] “…receiving the image; and processing the image using the CNN to generate the N respective skin sign diagnoses”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify Yoo to include the artificial neural network that has input into it an image of the target user’s skin and providing the second score group that is output based on the artificial neural network of Jiang as Jiang teaches their artificial neural network and image analysis would have obtained accurate and automatic attribute skin scoring and the system would have outperformed human experts and predicted each skin-sign score within an error of 1 in over 90% while making skin analysis fast and cheaper (Abstract, [0003], [0006]).
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view Jiang, and further in view of US 20060265244 A1 to Baumann.
Yoo in view of Jiang teach one or more processors are configured to determine a final survey score group, based on the first survey score group and the second survey score group (Yoo: “substituting the i variable values for each input information including the 20 received i 'input variable values and the determined (i-i') variable values for each input information into the learned AI module” p. 13, ll. 20-22). Yoo in view of Jiang do not expressly teach one or more processors are configured to determine the score for each of the one or more indexes by adding up scores of questions included in the final survey score group according to each question group included in the survey and determine the skin type, based on the score for each of the one or more indexes.
However, Baumann teaches one or more processors are configured to determine the score for each of the one or more indexes by adding up scores of questions included in the final survey score group according to each question group included in the survey ([0025] “the questionnaire comprises questions designed to determine four factors, such as whether oily (O) or dry (D) skin is present, whether sensitive (S) or resistant (R) skin is present, whether pigmented (P) or non-pigmented (N) skin is present”) ([0027] “ on the oily vs dry portion of the questionnaire, a score of 11 to 44 is possible, with a score between 34-44 representing very oily skin, a score between 27-33 representing slightly oily skin, a score between 17-26 representing slightly dry skin, and a score between 11-16 representing dry skin”) and determine the skin type, based on the score for each of the one or more indexes ([0009] “Thus, a particular user can be defined as having oily, sensitive, pigmented, and tight skin (OSPT) or any other of the sixteen possible combinations using these particular factors.”)
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify Yoo in view of Jiang with the adding of scores and determining skin type based on the scores of Baumann, as Baumann teaches this would have prevent undesirable effects and would have improved patient’s outcome with their skin or hair ([0022]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN PATRICK DOUGHERTY whose telephone number is (571)270-5044. The examiner can normally be reached 8am-5pm (Pacific Time).
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/SEAN P DOUGHERTY/ Primary Examiner, Art Unit 3791
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