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 .
Response to Arguments
Applicant’s arguments filed on 04/08/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed on April 08, 2026 with regard to the 35 USC 101 rejections have been fully considered and they are persuasive. The 35 USC 101 rejections for claims 1-20 are now withdrawn.
Response to Amendment
The amendment to the claims received on 04/23/2008 has been entered.
The amendment of claims 1, 6-9, 11, 13-16 and 18-20 is acknowledged.
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.
Claims 1-3, 5-10, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and further in view of Mayuri’769 (AU 2021/101769), Frosch’933 (US 2021/0158933).
With respect to claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches a method for determining a cutaneous lesion score of a companion animal suspected to have an atopic dermatitis condition, using an a machine learning trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of the images having cutaneous lesion(s) [determining the human skin diseases is considered as determining the animal skin diseases (abstract section and introduction section). In addition, biologically and scientifically, humans are animals], the method comprising at least the steps of:
a) receiving companion animal data corresponding to said companion animal, wherein the companion animal data include at least one global lesions image of said companion animal [Our data originates from the Softened Water Eczema Trial (SWET), which is a randomised controlled trial of 12 weeks duration followed by a 4-week crossover period, for 310 AD children aged from 6 months to 16 years. The original
data contains 1393 photos of representative AD regions taken during their clinic
visits, along with the corresponding severity of each disease sign. During each
visit, a disease assessment was made for SASSAD and TISS, using the 7 disease
signs labelled for each image. The severity of each sign was determined on an
ordinal scale: none (0), mild (1), moderate (2), or severe (3) (Section 2, Data).],
b) operating said trained machine learning model on the companion animal data [During each visit, a disease assessment was made for SASSAD and TISS, using the 7 disease
signs labelled for each image (Section 2, Data and Fig.2)], and
c) based on said machine learning model, generating a cutaneous lesion score indicative of at least one cutaneous state of said companion animal [Finally, the predictions for the disease signs are combined to produce a probability distribution of the regional severity scores (SASSAD,TISS and EASI) per image (section 3.2 Severity Prediction and Fig.2).].
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis does not teach a machine learning model trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of the previously acquired these images having cutaneous lesion(s) due to atopic dermatosis, some of the previously acquired images depicting animals which are not affected with cutaneous lesions, and some of the previously acquired images having cutaneous lesions due to conditions other than atopic dermatosis, wherein the machine learning model is trained based on an iterative process comprising: in a current iteration: extracting at least one lesion feature from each previously acquired image, associating each previously acquired image with its at least one lesion feature to an animal cutaneous state, updating weights of the machine learning model according to said association, training the machine learning model to learn said association, and modifying said at least one lesion feature for a subsequent iteration; the companion animal data includes at least one metadata relative to said companion animal.
Mayuri’769 teaches a machine learning model (Fig.2) trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of the previously acquired these images having cutaneous lesion(s) due to atopic dermatosis, some of the previously acquired images depicting animals which are not affected with cutaneous lesions, and some of the previously acquired images having cutaneous lesions due to conditions other than atopic dermatosis [biologically and scientifically, infants of human are animals (paragraph 12)],
wherein the machine learning model is trained based on an iterative process (Fig.2) comprising: in a current iteration:
extracting at least one lesion feature from each previously acquired image (Fig.2),
associating each previously acquired image with its at least one lesion feature to an animal cutaneous state (Fig.2),
updating weights of the machine learning model according to said association (Fig.2),
training the machine learning model to learn said association (Fig.2), and
modifying said at least one lesion feature for a subsequent iteration [the system shown in Fig. 2 including at least one lesion feature for a subsequent iteration is being updated according a set of new images for training]
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis according to the teaching of Mayuri’769 to train a model with both of the images of infants having normal skin (healthy infants) and images of infants suffering from Atopic Dermatitis because this will allow a trained model to learn the critical differences between normal skin and pathological markers, essential for high-accuracy classification.
The combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 does not teach the companion animal data includes at least one metadata relative to said companion animal.
Frosch’933 teaches that teach the companion animal data includes at least one metadata relative to said companion animal (paragraph 39).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 according to the teaching of Frosch’933 to include metadata in original data for identifying the age the humans associated with the original data because this will allow the severity prediction network to be trained more effectively.
With respect to claim 2, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said at least one global lesions image comprise at least one image of the underside body surface of said companion animal [as shown in Fig.2, an image of a foot of a human is being received for obtaining the probabilistic predictions for each disease].
With respect to claim 3, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said companion animal data further comprise at least one lesion specific image of a cutaneous lesion of said companion animal [as shown in Fig.2, an image of a foot of a human is being received for obtaining the probabilistic predictions for each disease].
With respect to claim 5, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis does not teach wherein said at least one meta data relative to said companion animal includes animal data such as breed, species, activity level, medical history, reproductive status, age, gender, weight, spayed or neutered status, a biological value from a biological sample, body condition, health status, lifestyle, habitat, coat information, or risk factor, and/or medical data, such as the age of disease onset, the existence of previous episodes of hotspots, urticaria or angioedema, the presence of cortico-response pruritus, the excess of hair loss, scaling or dryness, gastrointestinal signs, an indication on whether or not symptoms worsen after walking in grass, or medical history of chronic and/or recurrent dermatoses or otitis.
Frosch’933 teaches wherein said at least one meta data relative to said companion animal includes animal data such as breed, species, activity level, medical history, reproductive status, age (paragraph 39), gender, weight, spayed or neutered status, a biological value from a biological sample, body condition, health status, lifestyle, habitat, coat information, or risk factor, and/or medical data, such as the age of disease onset, the existence of previous episodes of hotspots, urticaria or angioedema, the presence of cortico-response pruritus, the excess of hair loss, scaling or dryness, gastrointestinal signs, an indication on whether or not symptoms worsen after walking in grass, or medical history of chronic and/or recurrent dermatoses or otitis.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 according to the teaching of Frosch’933 to generate metadata from original data for identifying the age the humans associated with the original data because this will allow the severity prediction network to be trained more effectively.
With respect to claim 6, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis does not teach providing a pathological profile of said companion animal based on a plurality of metadata relative to said companion animal.
Frosch’933 teaches providing a pathological profile of said companion animal based on a plurality of metadata relative to said companion animal (paragraph 39).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 according to the teaching of Frosch’933 to generate metadata from original data for identifying the age the humans associated with the original data because this will allow the severity prediction network to be trained more effectively.
With respect to claim 7, which claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein the machine learning model comprise one or more of an object detection model, a neural network, a convolutional neural network, or a metadata encoding module (section, Introduction).
With respect to claim 8, which further limits claim 7, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said object detection model performs a recognition analysis on said at least one lesion image to reference said at least one cutaneous lesion (section, Introduction), one or more lesion features being referencedas the origin coordinates, the height, and/or the width.
With respect to claim 9, which further limits claim 7, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said neural network is trained beforehand to recognize cutaneous states of companion animals based at least on one lesion feature extracted from said plurality of previously acquired images of companion animal body surfaces (Fig. 2).
With respect to claim 10, which further limits claim 9, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said generated score depends at least on an assessed correlation between said lesion features and said lesion image (Fig.2).
With respect to claim 13, which further limits claim1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said areas of said companion animal body surface are chosen among a head, the ear, perioral and/or periocular areas, one or more legs comprising the front feet (Fig.2) and/or the interdigital areas, the flexor surface of the tarsal joint and/or the extensor surface of the carpal joint, and the a trunk comprising the groin, armpit, ventral and/or perineal areas.
With respect to claim 14, it is a method claim which is being rejected for the same manner as described in the rejected claim 1.
With respect to claim 15, which further limits claim 14, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein, said machine learning model comprising at least one neural network, the method comprising the step of updating weights of the neural network according to said association between said at least one lesion feature and said animal cutaneous state [when the severity network is being trained, the severity network is considered being updated weights of the severity network (Section 2, Data)].
With respect to claim 16, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches a device for determining a cutaneous lesion score of a companion animal suspected to have an atopic dermatitis condition [regarding to the system shown in Fig.2, In addition, biologically and scientifically, humans are animals],
the device comprising a machine learning model trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of these images having cutaneous lesion(s) (Section 2, Data),
said trained machine learning model being configured to operate on the companion animal data wherein the companion animal data includes at least one lesions global image of said companion animal body surface [Our data originates from the Softened Water Eczema Trial (SWET), which is a randomised controlled trial of 12 weeks duration followed by a 4-week crossover period, for 310 AD children aged from 6 months to 16 years. The original data contains 1393 photos of representative AD regions taken during their clinic visits, along with the corresponding severity of each disease sign. During each visit, a disease assessment was made for SASSAD and TISS, using the 7 disease signs labelled for each image. The severity of each sign was determined on an ordinal scale: none (0), mild (1), moderate (2), or severe (3) (Section 2, Data).],
to generate a cutaneous lesion score indicative of at least one cutaneous state of said companion animal [Finally, the predictions for the disease signs are combined to produce a probability distribution of the regional severity scores (SASSAD,TISS and EASI) per image (section 3.2 Severity Prediction and Fig.2).].
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis does not teach a machine learning model trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of the previously acquired these images having cutaneous lesion(s) due to atopic dermatosis, some of the previously acquired images depicting animals which are not affected with cutaneous lesions, and some of the previously acquired images having cutaneous lesions due to conditions other than atopic dermatosis, wherein the machine learning model is trained based on an iterative process comprising: in a current iteration: extracting at least one lesion feature from each previously acquired image, associating each previously acquired image with its at least one lesion feature to an animal cutaneous state, updating weights of the machine learning model according to said association, training the machine learning model to learn said association, and modifying said at least one lesion feature for a subsequent iteration; the companion animal data includes at least one metadata relative to said companion animal.
Mayuri’769 teaches a machine learning model (Fig.2) trained beforehand to learn features indicative of an atopic dermatitis condition in a companion animal, based at least on a plurality of previously acquired images of companion animal body surfaces, some of the previously acquired these images having cutaneous lesion(s) due to atopic dermatosis, some of the previously acquired images depicting animals which are not affected with cutaneous lesions, and some of the previously acquired images having cutaneous lesions due to conditions other than atopic dermatosis [biologically and scientifically, infants of human are animals (paragraph 12)],
wherein the machine learning model is trained based on an iterative process (Fig.2) comprising: in a current iteration:
extracting at least one lesion feature from each previously acquired image (Fig.2),
associating each previously acquired image with its at least one lesion feature to an animal cutaneous state (Fig.2),
updating weights of the machine learning model according to said association (Fig.2),
training the machine learning model to learn said association (Fig.2), and
modifying said at least one lesion feature for a subsequent iteration [the system shown in Fig. 2 including at least one lesion feature for a subsequent iteration is being updated according a set of new image for training]
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis according to the teaching of Mayuri’769 to train a model with both of the images of infants having normal skin (healthy infants) and images of infants suffering from Atopic Dermatitis because this will allow a trained model to learn the critical differences between normal skin and pathological markers, essential for high-accuracy classification.
The combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 does not teach the companion animal data includes at least one metadata relative to said companion animal.
Frosch’933 teaches that teach the companion animal data includes at least one metadata relative to said companion animal (paragraph 39).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis and Mayuri’769 according to the teaching of Frosch’933 to include metadata in original data for identifying the age the humans associated with the original data because this will allow the severity prediction network to be trained more effectively.
With respect to claim 17, which further limits claim 16, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches an acquisition module for acquiring said at least one lesion image of said companion animal body surface (Fig.2).
With respect to claim 18, it is a program claim which is being rejected for the same manner as described in the rejected claim 16.
With respect to claim 19, which further limits claim 18, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said at least one global lesions image comprise at least one image of the underside body surface of said companion animal (Fig.2).
With respect to claim 20, which further limits claim 18, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis teaches wherein said companion animal data further comprise at least one lesion specific image of a cutaneous lesion of said companion animal (Fig.2).
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 (AU 2021/101769), Frosch’933 (US 2021/0158933) and further in view of Negishi’912 (US 2006/0229912).
With respect to claim 4, which further limits claim 3, the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 and Frosch’933 does not teach wherein said at least one lesion specific image comprises at least an enlarged image of at least one area of interest including said at least one cutaneous lesion.
Negishi’912 teaches wherein said at least one lesion specific image comprises at least an enlarged image of at least one area of interest including said at least one cutaneous lesion (abstract).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 and Frosch’933 according to the teaching of Negishi’912 to enlarge the cropped skin image because this will allow the diseases of the skin to be analyzed more effectively.
With respect to claim 11, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis does not teach wherein an additional lesion image is used, wherein the additional lesion image comprises an optical zoomed image of said lesion.
Negishi’912 teaches wherein an additional lesion image is used, wherein the additional lesion image comprises an optical zoomed image of said lesion (abstract).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 and Frosch’933 according to the teaching of Negishi’912 to enlarge the cropped skin image because this will allow the diseases of the skin to be analyzed more effectively.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 (AU 2021/101769), Frosch’933 (US 2021/0158933) and further in view of Kimura’089 (US 10,682,089).
With respect to claim 12, which further limits claim 1, EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 and Frosch’933 does not teach comprising the step of providing to a user interface said score relative to said companion animal and generated at step b).
Kimura’089 teaches comprising the step of providing to a user interface said score relative to said companion animal and generated at step b) (Fig.5).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis, Mayuri’769 and Frosch’933 according to the teaching of Kimura’089 to include a user interface to display the severity scores for the skin image because this will allow the severity scores for the skin image to be provided to a user more effectively.
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 extension fee 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 date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUO LONG CHEN whose telephone number is (571)270-3759. The examiner can normally be reached on M-F 9am - 5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tieu, Benny can be reached on (571) 272-7490. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HUO LONG CHEN/Primary Examiner, Art Unit 2682
Prosecution Insights