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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “the output layer 621” in Para. [0044]; and “services 1232, 1234, and 1236 stored in storage device 1230” in Para. [0076], and similarly, “storage device 1230” in Para. [0078-0079]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Para. [0044], line 12, “the output layer 621” should be changed to “the output layer 1021” (see corresponding objection to the drawings, as set forth above).
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1-4, 6, 8, 11-15, and 20 are objected to because of the following informalities:
Claim 1, line 4, the limitation “determining a body surface area of a patient” should be changed to “determining a body surface area of the patient”;
Claim 1, line 9, the limitation “determining the treatment based on the diagnosis” should be changed to “determining the appropriate treatment based on the diagnosis”;
Claim 1, line 10, the limitation “wherein the treatment is determined” should be changed to “wherein the appropriate treatment is determined”;
Claim 2, line 1, the limitation “wherein determining” should be changed to “wherein the determining”;
Claim 3, lines 1-4, the limitation “wherein the mild threshold is greater than 0% of the body surface area of the patient is affected, wherein the moderate threshold is greater than 3% of the body surface area of the patient is affected, and wherein the severe threshold is greater than 10% of the body surface area of the patient is affected” should be changed to “wherein the mild threshold is when the calculated percentage of the affected areas is greater than 0% of the body surface area of the patient being affected, wherein the moderate threshold is when the calculated percentage of the affected areas is greater than 3% of the body surface area of the patient being affected, and wherein the severe threshold is when the calculated percentage of the affected areas is greater than 10% of the body surface area of the patient being affected”;
Claim 4, line 1, the limitation “wherein determining” should be changed to “wherein the determining”;
Claim 6, lines 1-2, the limitation “wherein determining the body surface area of the patient, segmenting the one or more images, and determining the diagnosis” should be changed to “wherein the determining the body surface area of the patient, the segmenting the one or more images, and the determining the diagnosis”;
Claim 8, lines 1-2, the limitation “administering the treatment to the patient” should be changed to “administering the appropriate treatment to the patient”;
Claim 11, line 2, the limitation “the treatment” should be changed to “the appropriate treatment”;
Claim 12, line 11, the limitation “determining the treatment based on the diagnosis” should be changed to “determining the appropriate treatment based on the diagnosis”;
Claim 12, line 12, the limitation “wherein the treatment is determined” should be changed to “wherein the appropriate treatment is determined”;
Claim 13, line 1, the limitation “wherein determining” should be changed to “wherein the determining”;
Claim 14, lines 1-5, the limitation “wherein the mild threshold is greater than 0% of the body surface area of the patient is affected, wherein the moderate threshold is greater than 3% of the body surface area of the patient is affected, and wherein the severe threshold is greater than 10% of the body surface area of the patient is affected” should be changed to “wherein the mild threshold is when the calculated percentage of the affected areas is greater than 0% of the body surface area of the patient being affected, wherein the moderate threshold is when the calculated percentage of the affected areas is greater than 3% of the body surface area of the patient being affected, and wherein the severe threshold is when the calculated percentage of the affected areas is greater than 10% of the body surface area of the patient being affected”;
Claim 15, line 1, the limitation “wherein determining” should be changed to “wherein the determining”; and
Claim 20, lines 2-3, the limitation “determining an efficacy of the treatment based on an improvement in the diagnosis after the treatment has been administered for the period of time” should be changed to “determining an efficacy of the appropriate treatment based on an improvement in the diagnosis after the appropriate treatment has been administered for the period of time”.
Appropriate correction is required.
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 10-11 and 19-20 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.
The term “about” in claims 10 and 19 is a relative term which renders the claim indefinite. The term “about” 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. Claims 10 and 19 each recite the limitation “wherein the period of time is about 1 month to about 12 months”, in which the claimed “period of time” is rendered indefinite by the use of the relative term “about”. For examination purposes, the Examiner is interpreting the claimed “period of time” to be a specified time that is within a range between 1 month and 12 months. Clarification is required.
Claims 11 and 20 are also rejected under 35 U.S.C. 112(b) due to the dependency on claims 10 and 19, respectively.
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 1-7 and 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims are directed to a process/method or a machine/system, and therefore satisfy step 1 of the subject matter eligibility test.
Step 2A, Prong 1: The claims recite the following limitations that are directed to judicial exceptions (abstract ideas): “determining a body surface area of a patient” in claim 1 and similarly in claim 12; “segmenting the one or more images into affected areas of the body surface area of the patient and unaffected areas of the body surface area of the patient by identifying the affected areas and applying one or more masks to the affected areas” in claim 1 and similarly in claim 12; “determining a diagnosis from the affected areas of the body surface area” in claim 1 and similarly in claim 12; “determining the treatment based on the diagnosis, wherein the treatment is determined based on a mild threshold, a moderate threshold, and a severe threshold of the diagnosis” in claim 1 and similarly in claim 12; “wherein determining the diagnosis comprises calculating a percentage of the affected areas in relation to the body surface area of the patient” in claim 2 and similarly in claims 3 and 13-14; “wherein determining the body surface area of the patient comprises determining an image scale of the one or more images, a height of the patient, and a weight of the patient” in claim 4 and similarly in claim 15; “wherein determining the body surface area of the patient, segmenting the one or more images, and determining the diagnosis are conducted” in claim 6; “segments the one or more images and determines the diagnosis from the affected areas” in claim 7 and similarly in claim 17; “repeating the operations after a period of time” in claim 18; and “wherein the period of time is about 1 month to about 12 months” in claim 19; etc., which recite either mathematical concepts and/or mental processes that can be performed in the human mind or with the aid of pen and paper.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea (i.e., the mental processes and/or mathematical concepts) as the generically recited computer elements only amount to simply implementing the abstract idea on the machine. Additional elements include the “computing system” in claim 6, the “convolutional neural network” in claims 7 and 17, the “computing device” in independent claim 12, and other elements/components capable of performing the mere data gathering steps as claimed (i.e., “capturing and/or receiving the one or more images of a patient” in claim 1 and similarly in claim 12; and “wherein the skin disease is plaque psoriasis” in claim 5 and similarly claim 16), etc., which are components recited at a high level of generality that merely links the judicial exception to a particular technological environment and/or a computer as a tool to perform the abstract idea.
Step 2B: For similar reasons set forth above, the additional limitations also do not provide an inventive concept that would be substantially more than the judicial exception. Adding insignificant extra-solutionary activity to the judicial exception, e.g., the mere data gathering steps of the claims in conjunction with an abstract idea, does not qualify as “significantly more” when recited in a claim with a judicial exception.
Conclusion: Claims 1-7 and 12-19 are not patent-eligible.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-2, 5-7, 12-13, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (US 2021/0174512 A1, with publication date 06/10/2021, hereinafter Chen).
Regarding claims 1 and 12, Chen discloses a method for diagnosing a skin disease and determining an appropriate treatment by labeling one or more images (and a corresponding non-transitory computer-readable medium, encoded with instructions for diagnosing and determining an appropriate treatment for a skin disease that, when executed by a computing device, cause the computing device to perform operations for diagnosing the skin disease) (see, e.g., Abstract, and Para. [0005], “An image processing method is provided that automatically calculates BSA score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented (“over segmented” or “over-segmented”), and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores. Also included in the present invention is a device (or computer system) driven by computer instructions that is used in connection with the method, e.g., computer-related device or medium for performing the method as known in the relevant art”, and Para. [0017], “FIG. 6 is a flowchart of a computerized method for determining severity of skin disease (e.g., psoriasis) based on percentage of BSA that is covered by lesions”), the method comprising:
capturing and/or receiving the one or more images of a patient (see, e.g., Para. [0023], “Perform image segmentation on a test image of a body surface area that includes skin disease”, and Para. [0031], “To gather the dataset, about 300 images of guttate psoriasis and 100 images of chronic plaque psoriasis were scraped from Google Images. Inaccurate and/or misleading image data were filtered out, leaving a final dataset of 86 guttate psoriasis images and 31 chronic plaque psoriasis images”);
determining a body surface area of a patient (see, e.g., Para. [0023], “Perform image segmentation on a test image of a body surface area that includes skin disease using the Felzenszwalb segmentation algorithm, and output regions in the test image of the body surface area”);
segmenting the one or more images into affected areas of the body surface area of the patient and unaffected areas of the body surface area of the patient by identifying the affected areas and applying one or more masks to the affected areas (see, e.g., Para. [0023], “Perform image segmentation on a test image of a body surface area that includes skin disease using the Felzenszwalb segmentation algorithm, and output regions in the test image of the body surface area”, and Para. [0024], “Oversegment the test image”, and Para. [0025], “Input the regions of the oversegmented test image into the trained neural network”, and Para. [0026], “Use the trained neural network 704′ to identify and filter out non-lesion regions from the oversegmented test image, wherein the remaining regions of the oversegmented test image are classified as lesion regions”);
determining a diagnosis from the affected areas of the body surface area (see, e.g., Para. [0002], “Disease severity evaluations of skin diseases such as Psoriasis involves calculating the percentage of Body Surface Area that is covered by lesions and inflammation (i.e., BSA score). Hereafter, lesions and inflammation are collectively referred to as “lesions.” BSA is the measured or calculated surface area of a human body”, and Para. [0017], “FIG. 6 is a flowchart of a computerized method for determining severity of skin disease (e.g., psoriasis) based on percentage of BSA that is covered by lesions”, and Para. [0027], “Calculate a percentage of BSA in the test image that is covered by lesions using areas of the classified lesion regions of the oversegmented test image, and areas of the identified non-lesion regions of the oversegmented test image”, and Para. [0070], “The BSA calculation methods described above may be used to generate a digitized Psoriasis disease score calculation system. For example, by training similar convolutional neural networks with 5 input images of front body, back, front leg, back leg, and head regions to automating the full PASI scoring system may output a severity index PASI score between 0-72. Such computer systems, […], can assist doctors in creating better, faster, and more informed decisions in diagnosis and monitoring of skin diseases such as Psoriasis”); and
determining the treatment based on the diagnosis, wherein the treatment is determined based on a mild threshold, a moderate threshold, and a severe threshold of the diagnosis (see, e.g., Para. [0002], “Disease severity evaluations of skin diseases such as Psoriasis involves calculating the percentage of Body Surface Area that is covered by lesions and inflammation (i.e., BSA score)”, and Para. [0003], “Psoriasis is an autoimmune skin disease manifested as red and inflammatory areas that is distinct from healthy normal skin. An important part of disease severity measurements for Psoriasis is to monitor what percentage of Body Surface Area is covered by inflamed areas called “lesions.” For Plaque Psoriasis, two major disease measurements are BSA and PASI (Psoriasis Area and Severity Index), both of which involve calculating a percentage score that is used to monitor the disease progression and treatment effect”, and Para. [0017], “FIG. 6 is a flowchart of a computerized method for determining severity of skin disease (e.g., psoriasis) based on percentage of BSA that is covered by lesions”, and Para. [0070], “The BSA calculation methods described above may be used to generate a digitized Psoriasis disease score calculation system. For example, by training similar convolutional neural networks with 5 input images of front body, back, front leg, back leg, and head regions to automating the full PASI scoring system may output a severity index PASI score between 0-72. Such computer systems, […], can assist doctors in creating better, faster, and more informed decisions in diagnosis and monitoring of skin diseases such as Psoriasis”).
Regarding claims 2 and 13, Chen discloses the method of claim 1 and the non-transitory computer-readable medium of claim 12, respectively, as set forth above. Chen further discloses wherein determining the diagnosis comprises calculating a percentage of the affected areas in relation to the body surface area of the patient (see, e.g., Para. [0002], “Disease severity evaluations of skin diseases such as Psoriasis involves calculating the percentage of Body Surface Area that is covered by lesions and inflammation (i.e., BSA score). Hereafter, lesions and inflammation are collectively referred to as “lesions.” BSA is the measured or calculated surface area of a human body”, and Para. [0017], “FIG. 6 is a flowchart of a computerized method for determining severity of skin disease (e.g., psoriasis) based on percentage of BSA that is covered by lesions”, and Para. [0027], “Calculate a percentage of BSA in the test image that is covered by lesions using areas of the classified lesion regions of the oversegmented test image, and areas of the identified non-lesion regions of the oversegmented test image”, and Para. [0070], “The BSA calculation methods described above may be used to generate a digitized Psoriasis disease score calculation system. For example, by training similar convolutional neural networks with 5 input images of front body, back, front leg, back leg, and head regions to automating the full PASI scoring system may output a severity index PASI score between 0-72. Such computer systems, […], can assist doctors in creating better, faster, and more informed decisions in diagnosis and monitoring of skin diseases such as Psoriasis”).
Regarding claims 5 and 16, Chen discloses the method of claim 1 and the non-transitory computer-readable medium of claim 12, respectively, as set forth above. Chen further discloses wherein the skin disease is plaque psoriasis (see, e.g., Para. [0002-0003], and Para. [0031], “To gather the dataset, about 300 images of guttate psoriasis and 100 images of chronic plaque psoriasis were scraped from Google Images. Inaccurate and/or misleading image data were filtered out, leaving a final dataset of 86 guttate psoriasis images and 31 chronic plaque psoriasis images”, and Disclosed Claim 5, “wherein the skin disease is psoriasis”).
Regarding claim 6, Chen discloses the method of claim 1, as set forth above. Chen further discloses wherein determining the body surface area of the patient, segmenting the one or more images, and determining the diagnosis are conducted by a computing system (see, e.g., Para. [0017], “FIG. 6 is a flowchart of a computerized method for determining severity of skin disease (e.g., psoriasis) based on percentage of BSA that is covered by lesions. FIGS. 7A and 7B are schematic diagrams of system software and hardware for implementing FIG. 6”, and Para. [0023-0027], and Disclosed Claim 6, “A computer system for performing the method of claim 1”).
Regarding claims 7 and 17, Chen discloses the method of claim 6 and the non-transitory computer-readable medium of claim 12, respectively, as set forth above. Chen further discloses wherein a convolutional neural network segments the one or more images and determines the diagnosis from the affected areas (see, e.g., Abstract, and Para. [0005], “An image processing method is provided that automatically calculates BSA score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented (“over segmented” or “over-segmented”), and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores. Also included in the present invention is a device (or computer system) driven by computer instructions that is used in connection with the method, e.g., computer-related device or medium for performing the method as known in the relevant art”, and Para. [0023-0027], and Para. [0029], “To improve the segmentation results, a Convolutional Neural Network (CNN) was implemented”, and Disclosed Claim 3, “wherein the neural network is a convolutional neural network”).
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.
Claims 3, 8-11, 14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 2021/0174512 A1), as applied to claims 1-2 and 12-13 above, in view of Baker et al. (US 2022/0001201 A1, with publication date 01/06/2022, hereinafter Baker).
Regarding claims 3 and 14, Chen discloses the method of claim 2 and the non-transitory computer-readable medium of claim 13, respectively, as set forth above. Chen does not specifically disclose wherein the mild threshold is greater than 0% of the body surface area of the patient is affected, wherein the moderate threshold is greater than 3% of the body surface area of the patient is affected, and wherein the severe threshold is greater than 10% of the body surface area of the patient is affected.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses wherein the mild threshold is greater than 0% of the body surface area of the patient is affected, wherein the moderate threshold is greater than 3% of the body surface area of the patient is affected, and wherein the severe threshold is greater than 10% of the body surface area of the patient is affected (see, e.g., Para. [0056-0066], and Para. [0067], “assessment (i) involves assessing the skin field as including no, mild, moderate, or severe keratoses. Specifically, this assessment involves categorization of the skin field into one of four categories consisting of no, mild, moderate, and severe keratoses”, and Para. [0078], “assessment (ii) involves touching the skin. Alternatively, assessment (ii) does not involve touching the skin. For example, the assessment occurs by way of photographic or video review”, and Para. [0079], “assessment (iii) involves assessing whether no area, 1-5% of the area, less than ⅓ of the area, ⅓-⅔ of the area, greater than ⅔ of the area to 95% of the area, or 96-100% of the area in the skin field is affected by clinical or pre-clinical skin damage. Optionally, a score of 0-5 is allocated for the skin field assessed in assessment (iii) for no area of affected, 1-5% of the area affected, less than ⅓ of the area affected, ⅓-⅔ of the area affected, greater than ⅔ of the area to 95% of the area affected, or 96-100% of the area affected, respectively”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the non-transitory computer-readable medium of Chen by including wherein the mild threshold is greater than 0% of the body surface area of the patient is affected, wherein the moderate threshold is greater than 3% of the body surface area of the patient is affected, and wherein the severe threshold is greater than 10% of the body surface area of the patient is affected, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Regarding claim 8, Chen discloses the method of claim 1, as set forth above. Chen does not specifically disclose the method further comprising administering the treatment to the patient.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses the method further comprising administering the treatment to the patient (see, e.g., Para. [0056], “the present application provides a method for monitoring the response of a subject to treatment for clinical or pre-clinical skin damage”, and Para. [0061], “after the subject receiving treatment, obtaining one or more image of the skin field, and repeating assessments (i), (ii) and (iii)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Chen by including the method further comprising administering the treatment to the patient, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Regarding claims 9 and 18, Chen modified by Baker discloses the method of claim 8 and the non-transitory computer-readable medium of claim 12, respectively, as set forth above. Chen does not specifically disclose the method further comprising repeating the method after a period of time.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses the method further comprising repeating the method after a period of time (see, e.g., Para. [0061], “after the subject receiving treatment, obtaining one or more image of the skin field, and repeating assessments (i), (ii) and (iii)”, and Para. [0200], “The efficacy of the treatment is optionally evaluated about 2 weeks to 2 months after treatment is ceased, for example, 2 weeks, 4 weeks, 1 month, 6 weeks or 2 months after treatment is ceased, rather than at the end of treatment so that any potential confounding skin irritation is allowed to resolve before evaluation. The efficacy of the treatment is optionally evaluated at least 2 weeks, 4 weeks or 6 weeks after treatment is ceased”, and Para. [0201], “In embodiments where the subject is treated, the treatment improved the clinical and/or subclinical skin damage and optionally the improvement is maintained for 3, 6, 9, 12, 18 or 24 months”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method and the non-transitory computer-readable medium of Chen modified by Baker by including the method further comprising repeating the method after a period of time, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Regarding claims 10 and 19, Chen modified by Baker discloses the method of claim 9 and the non-transitory computer-readable medium of claim 18, respectively, as set forth above. Chen does not specifically disclose wherein the period of time is about 1 month to about 12 months.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses wherein the period of time is about 1 month to about 12 months (see, e.g., Para. [0200], “The efficacy of the treatment is optionally evaluated about 2 weeks to 2 months after treatment is ceased, for example, 2 weeks, 4 weeks, 1 month, 6 weeks or 2 months after treatment is ceased, rather than at the end of treatment so that any potential confounding skin irritation is allowed to resolve before evaluation. The efficacy of the treatment is optionally evaluated at least 2 weeks, 4 weeks or 6 weeks after treatment is ceased”, and Para. [0201], “In embodiments where the subject is treated, the treatment improved the clinical and/or subclinical skin damage and optionally the improvement is maintained for 3, 6, 9, 12, 18 or 24 months”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method and the non-transitory computer-readable medium of Chen modified by Baker by including wherein the period of time is about 1 month to about 12 months, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Regarding claim 11, Chen modified by Baker discloses the method of claim 10, as set forth above. Chen does not specifically disclose the method further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the period of time.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses the method further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the period of time (see, e.g., Para. [0200], “The efficacy of the treatment is optionally evaluated about 2 weeks to 2 months after treatment is ceased, for example, 2 weeks, 4 weeks, 1 month, 6 weeks or 2 months after treatment is ceased, rather than at the end of treatment so that any potential confounding skin irritation is allowed to resolve before evaluation. The efficacy of the treatment is optionally evaluated at least 2 weeks, 4 weeks or 6 weeks after treatment is ceased”, and Para. [0201], “In embodiments where the subject is treated, the treatment improved the clinical and/or subclinical skin damage and optionally the improvement is maintained for 3, 6, 9, 12, 18 or 24 months”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method of Chen modified by Baker by including the method further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the period of time, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Regarding claim 20, Chen modified by Baker discloses the non-transitory computer-readable medium of claim 19, as set forth above. Chen does not specifically disclose the operations further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the treatment has been administered for the period of time.
However, in the same field of endeavor of assessing severity of skin damage in a skin field, Baker discloses the operations further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the treatment has been administered for the period of time (see, e.g., Para. [0056], “the present application provides a method for monitoring the response of a subject to treatment for clinical or pre-clinical skin damage”, and Para. [0061], “after the subject receiving treatment, obtaining one or more image of the skin field, and repeating assessments (i), (ii) and (iii)”, and Para. [0200], “The efficacy of the treatment is optionally evaluated about 2 weeks to 2 months after treatment is ceased, for example, 2 weeks, 4 weeks, 1 month, 6 weeks or 2 months after treatment is ceased, rather than at the end of treatment so that any potential confounding skin irritation is allowed to resolve before evaluation. The efficacy of the treatment is optionally evaluated at least 2 weeks, 4 weeks or 6 weeks after treatment is ceased”, and Para. [0201], “In embodiments where the subject is treated, the treatment improved the clinical and/or subclinical skin damage and optionally the improvement is maintained for 3, 6, 9, 12, 18 or 24 months”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the non-transitory computer-readable medium of Chen modified by Baker by including the operations further comprising determining an efficacy of the treatment based on an improvement in the diagnosis after the treatment has been administered for the period of time, as disclosed by Baker. One of ordinary skill in the art would have been motivated to make this modification in order to desirably monitor the subject's response to the treatment, as recognized by Baker (see, e.g., Para. [0056-0066]).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 2021/0174512 A1), as applied to claims 1 and 12 above, in view of Kislal (US 2011/0286644 A1, with publication date 11/24/2011, hereinafter Kislal).
Regarding claims 4 and 15, Chen discloses the method of claim 1 and the non-transitory computer-readable medium of claim 12, respectively, as set forth above. Chen does not specifically disclose wherein determining the body surface area of the patient comprises determining an image scale of the one or more images, a height of the patient, and a weight of the patient.
However, in the same field of endeavor of monitoring the condition of skin, Kislal discloses wherein determining the body surface area of the patient comprises determining an image scale of the one or more images, a height of the patient, and a weight of the patient (see, e.g., Para. [0037], “Referring to FIG. 4, an embodiment provides a method for quantifying and monitoring a dermatological condition captured by an image, such as psoriasis”, and Para. [0038], “Scaling may be performed, as described herein, to identify a psoriasis area 450. To determine scale, as in the case of vitiligo, the user may drag a line through the colored blocks 220 of the calibration device 200 in the image. An embodiment then is able to determine the dominant spatial frequency along the line, and from that, derive a multiplier to convert pixels to square millimeters. For example, the number of pixels in the psoriasis category may be multiplied by the number of square mm/pixel determined from the calibration device 200 within the image to get an overall area of coverage. In order to convert square millimeters to a percentage of body covered, an embodiment may use the Mosteller formula based upon input from the user about patient height and weight to approximate the body surface area. The ratio of the area of psoriasis to the total surface area gives the percentage covered”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and the non-transitory computer-readable medium of Chen by including wherein determining the body surface area of the patient comprises determining an image scale of the one or more images, a height of the patient, and a weight of the patient, as disclosed by Kislal. One of ordinary skill in the art would have been motivated to make this modification in order to quantify and monitor a dermatological condition captured by an image, such as psoriasis, as recognized by Kislal (see, e.g., Para. [0037-0038]).
Conclusion
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/T.D./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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