DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12 May 2026 has been entered.
Response to Amendment
3. According to the Preliminary Amendment, filed 12 May 2026, the status of the claims is as follows:
Claims 1, 7, 11, and 12 are currently amended;
Claims 3-6, 8-10, and 16-18 are previously presented;
Claims 19-24 are new; and
Claims 2 and 13-15 are cancelled.
Response to Arguments
4. Applicant’s arguments, see Remarks, pp. 1-2, filed 12 May 2026, with respect to the rejection of claims 1-12 and 16-18 under 35 U.S.C. 102(a)(1) as being anticipated by Yamanashi et al., U.S. Patent Application Publication No. 2015/0356344 A1 (“Yamanashi”), have been fully considered, but they are not persuasive.
Applicant contends, see Remarks, pp. 1-2, the following:
The Office Action on page 3 equates a facial component detection unit 130 of Yamanashi (FIG. 2) with the subject matters related to a nasal feature recited in claim 1. In rejecting claim 1, the Office Action on pages 4-5 refers to Yamanashi in FIGS. 2-4 and paragraphs [0040], [0044], [0064], [0095]-[0096] of Yamanashi to assert that Yamanashi discloses identifying the nasal feature of the user and estimating a skin state of the user based on the nasal feature of the user. Specifically, the Office Action on page 4 alleges that para. [0040] of Yamanashi discloses the nasal feature including a nose shape of the user.
In Yamanashi, "Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi." See, Yamanashi, para. [0040].
However, regarding the nose, para. [0096] of Yamanashi states that the "area estimation unit 210 estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions." Here, the estimation of the gloss level of the nose is based on the nose skin reflectivity or shininess, and not based on the nose shape regarding a facial skeleton estimated from the user. The Applicant respectfully submits that the gloss level of skin is an indicator of how much light is reflected from the skin surface, and refers to the degree of skin brightness or shininess. Therefore, the estimation of the gloss level in Yamanashi may be related to the skin brightness or reflectivity of the nose or cheek skin, but not related to the nose shape regarding the facial skeleton estimated from the user. In fact, Yamanashi is completely silent about the facial skeleton estimated from the user.
Accordingly, the noted feature of amended claim 1, namely "identifying a nasal feature of a user, the nasal feature including a nose shape regarding a facial skeleton estimated from the user," is a distinction over Yamanashi.
Anticipation requires the presence in a single prior art reference disclosure of each and every element of the claimed invention, arranged as in the claim. In view of the distinction of claim 1 noted above, at least one claimed element is not present in Yamanashi. Hence, Yamanashi does not anticipate amended claim 1.
Independent claims 11 and 12 are amended to include the similar features of claim 1.
However, respectfully, this argument is not persuasive. Based on broadest reasonable interpretation, Yamanashi teaches the claimed limitation “identifying a nasal feature of a user, the nasal feature including a nose shape regarding a facial skeleton estimated from the user”. Yamanashi discloses (see para. [0040]):
Facial component detection unit 130 detects, from the photographed image, positions of facial components in the photographed image. Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi. Facial component detection unit 130 detects the positions of the facial components by extracting the feature of the face from the photographed image, for example, by using a known image feature detection method such as pattern matching. Facial component detection unit 130 then outputs the photographed image and facial component positional information that indicates the detected positions of the respective facial components to skin state detection unit 140.
Yamanashi identifies different facial components (features) of the face, which includes a nose, and detects the positions of the facial components (see para. [0040]). Yamanashi identifies the nose shape by using an “area estimation unit 210”, which “… estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions” (see para. [0096]). “Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” (see para. [0095]). Detecting the positions of the facial components, like the nose, and estimating the gloss level to identify the shape of the nose is in regards to the facial skeleton estimated from the user being tested as the user’s facial skeleton determines the positions of the facial components and how the nose’s structure would cause a certain gross level to be estimated by the “area estimation unit 210”. Thus, the phrase “regarding a facial skeleton estimated from the user” does not distinguish over Yamanashi.
For this reason, the rejection is maintained below.
The rejection of independent claims 11 and 12 are maintained below for the same reason as claim 1.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1, 3-12, and 16-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more.
Step 1 of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.03):
Claims 1, 3-10, 16, 19, and 20 are directed to a “method”, which describes one of the four statutory categories of patentable subject matter, i.e. a process.
Claims 11, 17, 21, and 22 are directed to a “device”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Claims 12, 18, 23, and 24 are directed to a “non-transitory computer-readable storage medium storing a program”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Step 2A of the Revised Patent Subject Matter Eligibility Guidance (see MPEP 2106.04):
Claim(s) 1, 3-12, and 16-24, recite the following mental process:
identifying a nasal feature of a user, the nasal feature including a nose shape regarding a facial skeleton estimated from the user; and
estimating a skin state of the user based on the nose shape of the nasal feature of the user.
Based on broadest reasonable interpretation, these limitations are directed to obtaining information and performing a mathematical operation based on the information, which can be done mentally or using pen and paper.
This judicial exception is not integrated into a practical application because the additional limitations of “executed by a computer including a memory and a processor” in claim 1, “an identifier configured to” and “an estimator configured to” in claim 11, and “A non-transitory computer-readable recording medium storing a program that cause a computer to execute a process” in claim 12, are merely parts or functions of a computer to be used as a tool to perform the mental process.
Step 2B of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.05):
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered separately and in combination.
Analyzing the additional claim limitations individually, the additional limitations that are not directed to the mental process are “executed by a computer including a memory and a processor” in claim 1, “an identifier configured to” and “an estimator configured to” in claim 11, and “A non-transitory computer-readable recording medium storing a program that cause a computer to execute a process” in claim 12. These additional limitations are merely parts of a computer to be used as a tool to perform the mental process, and amounts to computer implementation of the abstract idea.
The additional limitations of dependent claims 3-10 and 16-24 are merely directed to and further narrow the scope of the mental process or further narrow the scope of the additional limitations that do not integrate the mental process into a practical application or are not significantly more than the mental process.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide computer implementation of the abstract idea using collected data without: improvement to the functioning of a computer or to any other technology or technical field; applying the mental process with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; applying or using the mental process in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment; or adding a specific limitation other than what is well-understood, routine, conventional activity in the field.
Claim Rejections - 35 USC § 102
7. 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.
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
9. Claims 1, 3-12, and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yamanashi et al., U.S. Patent Application Publication No. 2015/0356344 A1 (“Yamanashi”).
As to Claim 1, Yamanashi teaches the following:
A skin state estimation method (see “The present disclosure relates to a wrinkle detection apparatus and a wrinkle detection method for detecting a wrinkle area of skin included in an image.” in para. [0002]) executed by a computer including a memory (“computer-readable recording medium”, not labeled) and a processor (“integrated circuit”, not labeled) (see “These comprehensive or specific aspects may be implemented by a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium, and may be implemented by an arbitrary combination of a system, a method, an integrated circuit, a computer program, and a computer-readable recording medium. Examples of the computer-readable recording medium include a nonvolatile recording medium, such as a CD-ROM (Compact Disc-Read Only Memory).” in para. [0014]), comprising:
identifying a nasal feature (“nose”) of a user (see “Facial component detection unit 130 detects, from the photographed image, positions of facial components in the photographed image. Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi.” in para. [0040]), the nasal feature including a nose shape regarding a facial skeleton estimated from the user (see “Facial component detection unit 130 detects, from the photographed image, positions of facial components in the photographed image. Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi. Facial component detection unit 130 detects the positions of the facial components by extracting the feature of the face from the photographed image, for example, by using a known image feature detection method such as pattern matching. Facial component detection unit 130 then outputs the photographed image and facial component positional information that indicates the detected positions of the respective facial components to skin state detection unit 140.” in para. [0040]. Yamanashi identifies the nose shape by using an “area estimation unit 210”, which “… estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions” (see para. [0096]). “Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” (see para. [0095]). Detecting the positions of the facial components, like the nose, and estimating the gloss level to identify the shape of the nose is in regards to the facial skeleton estimated from the user being tested as the user’s facial skeleton determines the positions of the facial components and how the nose’s structure would cause a certain gross level to be estimated by the “area estimation unit 210”.); and
estimating a skin state (“wrinkle”) of the user based on the nose shape of the nasal feature of the user (see “Area estimation unit 210 estimates image areas that are positions of the plurality of areas in the image, each of the areas having a different gloss level of skin, based on the facial component positional information that is input from facial component detection unit 130. Area estimation unit 210 then outputs the photographed image and area positional information that indicates the estimated respective image areas to wrinkle detection unit 220 and chloasma detection unit 230.” in para. [0044]); and see “With reference to parameter value table 310 (see FIG. 4), parameter determination unit 222 of FIG. 3 determines the one or more parameter values used for wrinkle area detection for each of the image areas indicated by the area positional information that is input from area estimation unit 210.” in para. [0064]).
As to Claim 3, Yamanashi teaches the following:
wherein the skin state of the user is a future skin state of the user (see “or example, chloasma detection unit 230 performs processing for extracting the pixel having the pixel value equal to or less than a threshold, for at least a detection area indicated by detection area information that is input, among the photographed image, by using signals of RGB channels, thereby performing such chloasma area detection. Chloasma detection unit 230 then outputs chloasma area information that indicates the detected chloasma area to image generation unit 150.” in para. [0066]).
As to Claim 4, Yamanashi teaches the following:
wherein the skin state is a wrinkle (“wrinkle”), a spot, facial sagging, dark circles, a nasolabial fold, dullness of skin, elasticity, moisture, sebum, melanin, blood circulation, a blood vessel, blood properties, texture of skin, pore of skin, a skin color, or any combination thereof (see para. [0041]).
As to Claim 5, Yamanashi teaches the following:
estimating a comprehensive indicator of skin from the skin state (see “Based on the area positional information and parameter information that are input from parameter determination unit 222, wrinkle detection processing unit 223 detects the wrinkle area from the photographed image that is input from parameter determination unit 222, through use of the one or more parameter values determined for each area. In the present exemplary embodiment, wrinkle detection processing unit 223 calculates the gradient value for each portion of the photographed image through use of the Gabor filter processing. Wrinkle detection processing unit 223 then detects the wrinkle area from the photographed image through comparison of the calculated gradient value with a threshold. That is, wrinkle detection processing unit 223 performs known edge detection processing. When the gradient value becomes higher as the degree of change in the pixel value becomes higher, an area where the gradient value is equal to or greater than a threshold is detected as the wrinkle area. Wrinkle detection processing unit 223 then outputs the wrinkle area information that indicates the detected wrinkle area to image generation unit 150 (see FIG. 2).” in para. [0065]).
As to Claim 6, Yamanashi teaches the following:
wherein the skin state is a skin state in a part of a face (“an area from a lower eyelid of a left eye to a left cheek, and an area from a lower eyelid of a right eye to a right cheek”), a whole face, or a plurality of sites in a face (see “In the present exemplary embodiment, the plurality of areas, each of the areas having a different gloss level of skin, refer to an area of from a lower eyelid of a left eye to above a left cheek and an area of from a lower eyelid of a right eye to above a right cheek (hereinafter referred to as “areas below both eyes”), and facial areas other than these areas (hereinafter referred to as “an overall area”). In the following description, the image areas corresponding to the areas below both eyes are referred to as “image areas below both eyes.” The image area corresponding to the overall area is referred to as “an overall image area.” The overall area does not necessarily need to be an entire face, and may be, for example, an area portion that is a target of detection of a wrinkle, such as cheeks or a forehead.” in para. [0045]).
As to Claim 7, Yamanashi teaches the following:
estimating a shape regarding the facial skeleton of the user based on the nasal feature of the user, wherein the estimation of the skin state of the user is based on the shape regarding the facial skeleton of the user (see “For example, area estimation unit 210 estimates the gloss distribution based on the positions of the facial components, and divides the photographed image into the plurality of image areas in accordance with the gloss level. Area estimation unit 210 then determines the parameter values for each image area with reference to the previously stored table that associates the gloss level with the parameter values. When wrinkle detection apparatus 100 includes a three-dimensional shape obtaining unit for obtaining a three-dimensional shape of skin from the photographed image, area estimation unit 210 may estimate the gloss distribution based on the three-dimensional shape obtained by the three-dimensional shape obtaining unit. Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” in para. [0095]; and see “For example, area estimation unit 210 estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions.” in para. [0096]).
As to Claim 8, Yamanashi teaches the following:
wherein the skin state of the user is attributed to the shape regarding the facial skeleton of the user (see “For example, area estimation unit 210 estimates the gloss distribution based on the positions of the facial components, and divides the photographed image into the plurality of image areas in accordance with the gloss level. Area estimation unit 210 then determines the parameter values for each image area with reference to the previously stored table that associates the gloss level with the parameter values. When wrinkle detection apparatus 100 includes a three-dimensional shape obtaining unit for obtaining a three-dimensional shape of skin from the photographed image, area estimation unit 210 may estimate the gloss distribution based on the three-dimensional shape obtained by the three-dimensional shape obtaining unit. Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” in para. [0095]).
As to Claim 9, Yamanashi teaches the following:
wherein the nasal feature (“position of a feature of the face, such as inner canthi”) is a nasal root, a nasal bridge, a nasal tip, nasal wings, or any combination thereof (these features are within the scope of Yamanashi’s teaching, see “Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi.” in para. [0040], where “inner canthi” is merely an example).
As to Claim 10, Yamanashi teaches the following:
wherein the skin state of the user is estimated using a trained model that outputs the skin state in response to an input of the nasal feature (see the determination method of the wrinkle area in para. [0044]-[0071], which operates as a trained model).
As to Claim 11, Yamanashi teaches the following:
A skin state estimation device (see “The present disclosure relates to a wrinkle detection apparatus and a wrinkle detection method for detecting a wrinkle area of skin included in an image.” in para. [0002]), comprising:
an identifier (“Facial component detection unit”) 130 configured to identify a nasal feature (“nose”) of a user, the nasal feature including a nose shape regarding a facial skeleton estimated from the user (see “Facial component detection unit 130 detects, from the photographed image, positions of facial components in the photographed image. Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi. Facial component detection unit 130 detects the positions of the facial components by extracting the feature of the face from the photographed image, for example, by using a known image feature detection method such as pattern matching. Facial component detection unit 130 then outputs the photographed image and facial component positional information that indicates the detected positions of the respective facial components to skin state detection unit 140.” in para. [0040]. Yamanashi identifies the nose shape by using an “area estimation unit 210”, which “… estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions” (see para. [0096]). “Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” (see para. [0095]). Detecting the positions of the facial components, like the nose, and estimating the gloss level to identify the shape of the nose is in regards to the facial skeleton estimated from the user being tested as the user’s facial skeleton determines the positions of the facial components and how the nose’s structure would cause a certain gross level to be estimated by the “area estimation unit 210”.); and
an estimator (“Wrinkle detection unit”) 220 configured to estimate a skin state (“wrinkle”) of the user based on the nose shape of the nasal feature of the user (see “Area estimation unit 210 estimates image areas that are positions of the plurality of areas in the image, each of the areas having a different gloss level of skin, based on the facial component positional information that is input from facial component detection unit 130. Area estimation unit 210 then outputs the photographed image and area positional information that indicates the estimated respective image areas to wrinkle detection unit 220 and chloasma detection unit 230.” in para. [0044]); and see “With reference to parameter value table 310 (see FIG. 4), parameter determination unit 222 of FIG. 3 determines the one or more parameter values used for wrinkle area detection for each of the image areas indicated by the area positional information that is input from area estimation unit 210.” in para. [0064]).
As to Claim 12, Yamanashi teaches the following:
A non-transitory computer-readable recording medium storing a program that causes a computer to execute a process (see “The present disclosure relates to a wrinkle detection apparatus and a wrinkle detection method for detecting a wrinkle area of skin included in an image.” in para. [0002]) comprising:
identifying a nasal feature (“nose”) of a user, the nasal feature including a nose shape regarding a facial skeleton estimated from the user (see “Facial component detection unit 130 detects, from the photographed image, positions of facial components in the photographed image. Each of the facial components refers to a section that constitutes a face, such as eyes, a nose, and cheeks, and can be defined, for example, by a position of a feature of the face, such as inner canthi. Facial component detection unit 130 detects the positions of the facial components by extracting the feature of the face from the photographed image, for example, by using a known image feature detection method such as pattern matching. Facial component detection unit 130 then outputs the photographed image and facial component positional information that indicates the detected positions of the respective facial components to skin state detection unit 140.” in para. [0040]. Yamanashi identifies the nose shape by using an “area estimation unit 210”, which “… estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions” (see para. [0096]). “Area estimation unit 210 may previously store a three-dimensional shape model of a typical face, and may estimate the gloss distribution based on such a model.” (see para. [0095]). Detecting the positions of the facial components, like the nose, and estimating the gloss level to identify the shape of the nose is in regards to the facial skeleton estimated from the user being tested as the user’s facial skeleton determines the positions of the facial components and how the nose’s structure would cause a certain gross level to be estimated by the “area estimation unit 210”.); and
estimating a skin state (“wrinkle”) of the user based on the nose shape of the nasal feature of the user (see “Area estimation unit 210 estimates image areas that are positions of the plurality of areas in the image, each of the areas having a different gloss level of skin, based on the facial component positional information that is input from facial component detection unit 130. Area estimation unit 210 then outputs the photographed image and area positional information that indicates the estimated respective image areas to wrinkle detection unit 220 and chloasma detection unit 230.” in para. [0044]); and see “With reference to parameter value table 310 (see FIG. 4), parameter determination unit 222 of FIG. 3 determines the one or more parameter values used for wrinkle area detection for each of the image areas indicated by the area positional information that is input from area estimation unit 210.” in para. [0064]).
As to Claims 16-18, Yamanashi teaches the following:
wherein the nose shape of the nasal feature of the user includes at least one of a nasal root, a nasal bridge, a nasal tip, or a nasal wing (see “For example, area estimation unit 210 estimates that the gloss level is higher in a portion of a protruding shape, such as a tip of a nose or a cheek, than in other portions.” in para. [0096]).
Allowable Subject Matter
10. Claims 19-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office Action.
11. The following is a statement of reasons for the indication of allowable subject matter:
As to Claims 19-24, neither Yamanashi nor the prior art of record teaches the skin state estimation method of base claim 1, the skin state estimation device of base claim 11, and the non-transitory computer-readable recording medium according to claim 12, including the following, in combination with all other limitations of the base claim(s):
obtaining an image including the nose shape of the user;
extracting a nose region from the image of the user based on the nasal feature;
calculating a nasal feature value based on image information of the nose region of the user; and
estimating the skin state of the user based on the nose shape of the nasal feature of the user and the nasal feature value relating to the image information.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVIN NATNITHITHADHA whose telephone number is (571)272-4732. The examiner can normally be reached Monday - Friday 8:00 am - 8:00 am - 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason M Sims can be reached at 571-272-7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 05/29/2026
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