Detailed Action
1. Claims1-22 are pending in this Application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to amendment
3. Applicant’s response to the last Office Action filed on 05/13/2025 has been entered and made of record.
4. Claims1,8 and 18-20 have been amended. New claims 21-22 have been added
.
Response to Argument
5. The Applicant’s argument filed on 11/13/2025 is fully consider. For Examiner response see discussion below.
a). Based on the Applicant’s argument the objection of claims 1 and 2 under 35 U.S.C 12(f) is expressly withdrawn
b) Applicant’s has substantially amended claims 1,18-20 , and substantially argue the Applied prior art LABICHE does not teach the claims as amended. The Applicant’s argument is persuasive. Thus, the U.S,C 102(a) based on LABICHE expressly withdrawn. After further search and consideration a new prior arts that teach the amended claims are found.
Duplicate claims Objection
6. Claim 22 is objected to under 37 CFR 1.75 as being a substantial duplicate of
claim 21. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing
one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 706.03(k).
Applicant is advised that should claim 1 be found allowable, claim 22 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 706.03(k)
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 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.
7. Claims 1-2, 9, 15, 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Bandic et al. , (hereafter Bandic), US20100185064 A1, pub. 10/07/2010, in view of Daijiro Wadaz et al.,(hereafter Daijiro), “Region extraction method for skin grafts via image analysis”, pub. Published online: 05 Sep 2021.
.
As to claim 1, Bandic teaches A system for identifying material components on skin to be used as a graft product (Figs.9 and 7 [0116], , for example FIG. 7 depicts a recommendations page of a skin care system, where the system configured to select, analyze, and prepare specifics skin parts of a human face) the system comprising:
an image capture device configured to obtain image data of a skin (Figs. 6 and 7, the figures illustrates the image data associated the skin); and
a processor configured to process the image data using an artificial neural network, the processor, with the artificial neural networks being configured to classify materials on the skin and determine a location of classified materials of the skin from the image data (Figs. 6 and 7, [0303], [0312], For example, the algorithm 150 (includes artificial neural networks)may be used in skin lesion diagnosis based on a probabilistic framework for classification.
The classification include classifying a biophysical skin state 158 and locations. The A skin state 158 may be determined from the aggregate biophysical data obtained from one or more skin structures as well as a visual analysis of the captured images and any additional data obtained from the user anecdotally. For example, the skin state 158 may encompass data on moisture, wrinkles, pores, elasticity, luminosity, and any of a number of measures, as described herein, that include skin burn), wherein, in a case that the skin comprises unwanted materials thereon, including at least fascia or flesh, the processor classifies the unwanted materials as fascia or flesh on the skin and determines a location of the fascia or flesh on the skin from the image data ([0303] the classifier classifies biophysical
skin state 158, wherein the skin state 158 may encompass data on moisture, wrinkles, pores, elasticity, luminosity, and any of a number of measures, as described herein, that include skin burn scars, keloids, hypertrophic scars).
However it is noted that Bandic does not specifically teaches the underline section of the limitation “ the material components on skin to be used as a graft product”.
On the other hand Daijiro teaches the material components on skin to be used as a graft product (Abstract, Fig.3).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method of segmenting an image of transplanted skin and an image burned skin from the skin of a patient , and analyze the extracted images to determine an optimal dermal grafts operation taught by Daijiro into Bandic The suggestion/motivation for doing so would have been to provide user of Bandic
a proper matrix for skin growth that minimize the formation of skin damage (such as hypertrophic or keloid scars).
As to claim 2, Bandic teaches the image capture device comprises an ultraviolet light source, an optical filter, and an image sensor ( [0035], [0790]-[0791], The images of skin are captured with the imaging sensing unit including the digital imaging device 10004. The images may be captured under white light or blue light or ultra violet light source. The method and system may further comprise filtering the reflected or re-emitted light to obtain light of a wavelength defined by the filter output.).
As to claim 9, Bandic teaches the artificial neural network comprises a convolutional neural network ( [0035], a method and system for determining a skin state done using an artificial neural networks, non-linear regression genetic algorithms…. . Further It is known that an artificial neural network (ANN) comprises a convolutional neural network (CNN)).
As to claim 15, Bandic teaches the convolutional neural network is configured to output an image defining an area of each material feature of the skin( [0035], [0102], a method and system for determining a skin state. The method and system, determining may be done using an algorithm. The algorithm may involve artificial neural networks. The method include determining a predisposition of sebaceous pores and skin structures around a sebaceous gland, a level of acne, and a predisposition of a portion of skin to improve and worsen the acne is disclosed).
Claim 18 is rejected the same as claim 1 except claim 18 is directed to a method claim.
As to claim 19, Bandic teaches said capturing the image data of the skin further comprises: irradiating the skin with an ultraviolet light source ([0095], the illuminator for illuminating a portion of a surface on the skin may include a white light source, a blue light source, an ultraviolet light source and the like.);
filtering light emitted and reflected by the skin with an optical filter of the image capture device; and capturing the filtered light using an image sensor of the image capture device ([0067], the system and method may further include filtering the reflected and/or re-emitted light to obtain light of a wavelength defined by the filter output. Algorithmic analysis is performed on the filtered image).
As to claim 20, Bandic teaches A non-transitory hardware storage device having stored thereon computer executable instructions (see [0802]);
regarding the remaining limitations of claim 20, all the remaining limitations are similar to the limitations of rejected claim 1.
As to claim 21, Bandic teaches in a case that the skin comprises unwanted materials thereon, including at least fascia, the processor classifies the unwanted materials as fascia on the skin and determines a location of the fascia on the skin from the image data ([0303] the classifier classifies biophysical skin state 158, wherein the skin state 158 may encompass data on moisture, wrinkles, pores, elasticity, luminosity, and any of a number of measures, as described herein, that include skin burn scars, keloids, hypertrophic scars).
8. Claims 3-5 and 16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bandic, US20100185064 A1, in view of Daijiro, “Region extraction method for skin grafts via image analysis”, further in view of SCHÜTTINGER ALFRED (hereafter SCHÜTTINGER), WO 2020148182, pub. 07/23/2020
.
As to claim 3, Bandic teaches ultraviolet light source (see claim 2 above) , but fails to teach “the ultraviolet light source is configured to emit light having a wavelength of 365 nm to 395 nm”;
On the other hand SCHÜTTINGER teaches ultraviolet light source is configured to emit light having a wavelength of 365 nm to 395 nm(claim 3, 3. Optical element according to claim 1 or 2, characterized in that the first and the second base section (4a, 4b) each have a transmission of less than 10% transmission, in particular less than 5% transmission, preferably 0, in a wavelength range below 400 nm % transmission, where light with wavelength range below 400 nm is ultraviolet light).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a well-known an optical long-pass filter device taught by SCHÜTTINGER(see page 4 last par., -page 5 1st par., ) into modified Bandic
The suggestion/motivation for doing so would have been allows user of modified Bandic to protect light detectors from unwanted short-wavelength radiation, and to isolate fluorescence signals from excitation light.
As to claim 4, SCHÜTTINGER teaches the optical filter comprises a long-pass filter configured with a cut-on wavelength of 435 nm( page 4 last par., -page 5 1st par., The optical properties of the first base area section can therefore have the characteristics of an optical long-pass filter device or an optical long-pass filter. The cut-on The wavelength of the optical long-pass filter device can be in a range between 400 and 435 nm.).
As to claim 5, SCHÜTTINGER teaches the optical filter has a transmittance of 85% for wavelengths greater than 435 nm(Claim 9, Optical element according to claim 8, characterized in that the second base section (4b) in a wavelength range between 650 nm and 750 nm has a transmission in a range between 60 and 85%.)
As to claim 16, SCHÜTTINGER teaches optical filter is configured with a cut-on wavelength of 400 nm to 600 nm ( page 5 1st par., The cut-on wavelength of the optical long-pass filter device can be in a range between 435 nm and 450 nm or
9. Claims 6-7, 10,14 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bandic, US20100185064 A1, in view of Daijiro, “Region extraction method for skin grafts via image analysis”, further in view of LABICHE; Clément (hereafter LABICHE),US20200219249 A1, pub. 07/09/2020
Regarding claim 6, while modified Bandic, teaches the limitation of claim 1, but fails to teach the limitation of claim 6.
On the other hand in the same filed of endeavor ,LABICHE teaches the processor is configured to divide the image data into a plurality of image tiles (Fig.11 unit 117, [0135] , [0152], a step 117 of segmentation of the image into image subparts . FIG. 14 shows examples of segmentation results).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a well-known method of segmenting an image into a plurality of image subparts(tiles) taught by LABICHE into modified Bandic.
The suggestion/motivation for doing so would have been allows user of modified Bandic to detect subtle skin disease such as textures like pores or pigment changes that might be lost when analyzing a full-scale image.
As to claim 7, LABICHE teaches each of the plurality of image tiles has an identical size (Fig.14, [0109], FIG. 14 shows examples of segmentation results, Fig.14 shows six image tiles with identical size. The whole image was then divided in non-overlapping patches of size 100×100 pixels starting from the top-left image corner)
As to claim 10, LABICHE teaches the convolutional neural network comprises a stepped contracting path, each step of the stepped contracting path comprising: a first contracting convolutional layer; a second contracting convolutional layer; a first contracting rectifier layer following the first contracting convolutional layer; a second contracting rectifier layer following the second contracting convolutional layer; a storage operation that stores an output following the second contracting rectifier layer; and a pooling layer following the storage operation (Fig.12, [0146]-[148], FIG. 12 illustrates a schematic of a fully convolutional neural network (“FCNN”),
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As to claim 14, LABICHE teaches an output step comprises: a first output convolutional layer; a second output convolutional layer; a first output rectifier layer following the first output convolutional layer; a second output rectifier layer following the second output convolutional layer; and a sigmoid layer following the second output rectifier layer(Fig.12, [0146]-[148], FIG. 12 illustrates a schematic of a fully convolutional neural network (“FCNN”),).
As to claim 17, LABICHE teaches the processor is configured to resize the image data prior to dividing the image data into the plurality of image tiles([0109], [0156] In this section, the masks and images have been cropped to reduce their dimensions to 1224×1224 to make them square. Subsequently, the images were resized successively in 512×512, to reduce the computing power and memory required, the proportions were maintained and the images were not distorted. The whole image was then divided in non-overlapping patches of size 100×100 pixels starting from the top-left image corner. We chose such patch size as image-patch size is usually of the order of magnitude of 102×102 pixels. The rightest part of the image, for which it was not possible to select full patches, was discarded)
10. Claims 11-13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bandic, US20100185064 A1, in view of Daijiro, “Region extraction method for skin grafts via image analysis”, further in view of LIU, LING et al., (hereafter LIU), CN 111814608 A, pub. 10/23/2020.
Regarding claim 11 , while Bandic teaches the limitation of claim 10, but fails to teach the limitation claim 11.
On the other hand, LIU teaches the convolutional neural network comprises a stepped expanding path, each step of the stepped expanding path comprising: a first expanding convolutional layer; a second expanding convolutional layer; a first expanding rectifier layer following the first expanding convolutional layer; a second expanding rectifier layer following the second expanding convolutional layer; an up-sampling layer following the second expanding rectifier layer; and a concatenation operation that stacks an output of the up-sampling layer with the stored output of the stepped contracting path (Claim 1 AR target classification based on fast full convolutional neural network comprises (i) expanding MASTAR data set to expand existing MASTAR data set to meet training needs by comprising image flip, zoom, noise, cropping and movement, (ii) constructing special convolution layer with step ≥ 2 and using convolution layer with step ≥ 2 for down sampling, and expanding size of special convolutional layer kernel, (iii) constructing fully convolutional neural
network with step of 2 for MASTAR data set obtained in step (i) and special convolution layer established in step (ii), (iv) constructing fully convolutional neural network with step of 3 for fully convolutional neural network built in step (iii), (v) constructing fully convolutional neural network with step of 4 and continuing to expand stepping of special convolution layer and size of convolution kernel on basis of step (iv), and (vi) constructing fast fully convolutional neural network according to step (iii), step (iv) and step (v)).
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to incorporate a method of expanding size of special convolutional layer kernel taught by LIU into modified Bandic.
The suggestion/motivation for doing so would have been allows user of modified Bandic, to improve the training efficiency of convolutional neural networks by introducing a special convolutional layer for down sampling and also improves the efficiency and accuracy of image recognition.
As to claim 12, LIU teaches the stepped contracting path and the stepped expanding path comprise a same number of steps (claims 1-3, . : building step is 4 of the full convolutional neural network on the basis of the step S4 continuously expanding the size of the step and convolution kernel of special convolutional layer, using stepping is 4, the convolution kernel size is 4 * 4; S6: building fast full convolution neural network according to step S3, step S4 and step S5, using 3 layer convolution layer for feature extraction, 3 layer special convolution layer for down-sampling and 2 layer full connection layer classification; the convolution layer extracted by the characteristic is step 1, the size is 3 * 3; the special convolutional layer of the lower sampling adopts mixed progressive mode, respectively is stepping is 4, the size is 4 * 4; the stepping is 3, the size is 3 * 3; the step is 2, the size is 2 * 2.).
As to claim 13, LIU teaches the stepped contracting path and the stepped expanding path each comprise six steps(claim 1, building step is 4 of the full convolutional
neural network on the basis of the step S4 continuously expanding the size of the step and convolution kernel of special convolutional layer, using stepping is 4, the convolution kernel size is 4 * 4,….)
11. Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable Bandic, US20100185064 A1, in view of Daijiro, “Region extraction method for skin grafts via image analysis”, further in view of in view of TAJDARAN et al. ( hereafter TAJDARAN), us-provisional-application US 63294083, filed on 12/28/ 2021. For examination purpose US 20230204601A is utilized.
Regarding claim 8 , while modified Bandic teaches “the skin” but fails to teach “the skin comprises piscine skin”
On the other hand, TAJDARAN teaches the graft product comprises piscine skin ([0021], selected from piscine, amphibian, or insect tissue. The tissue may be a synthetic tissue, such as, but not limited to, laboratory-grown tissue or 3D-printed tissue; A suitable tissue graft may be a nerve graft, for example, a peripheral nerve graft.)
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to incorporate piscine , amphibian, or insect tissue taught by TAJDARAN into modified Bandic The suggestion/motivation for doing so would have been allow user of modified Bandic to accelerate wound healing and tissue regeneration, since Piscine skin, is increasingly being studied and used in human medicine, particularly for wound healing and tissue regeneration.
Conclusion
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.
Contact Information
Any inquiry concerning this communication or earlier communication from the examiner should be directed to Mekonen Bekele whose telephone number is (469) 295-9077.The examiner can normally be reached on Monday -Friday from 9:00AM to 6:50 PM Eastern Time.
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/MEKONEN T BEKELE/Primary Examiner, Art Unit 2699