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 .
Status of Claims
This action is in reply to an application filed on 09/11/2025. Claims 1-13 are currently pending and have been examined.
Drawings
Figures 2, 3, 6, and 7 are objected to because they are of insufficient quality that would not allow for infinite reproduction. The Examiner suggests the Applicant change the drawings to simply black and white and eliminate the greyscale to make the drawings clearer. 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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.
Claim Objections
Claims 1, 11 and 13 are objected to for reciting "periorbitar" at item (vii) of the derm area list; correction to "periorbital" is required. No § 112(b) rejection is made, the specification using the same spelling consistently.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-13 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
The specification discloses a single worked example. At p. 28 the user supplies breed from a list of predisposed breeds, time since onset, indoor or outdoor life, and whether the dermatitis is chronic, recurrent or a permanent background, the specification stating that "Four animal data are thus used in this example." At p. 29 the reported performance is "with four animal data and fifteen derm area data" — sensitivity 0.95, specificity 0.89, PPV 0.95, NPV 0.88 — and training used "at least 100 companion animals with cutaneous lesions."
Claims 1, 11 and 13 embrace any two or more of sixteen animal data items combined with any two or more of thirteen derm area items. No embodiment describes obtaining or using "(xiv) a biological value from a biological sample," "(xii) coat information," "(xiii) activity level," "(iv) weight" or "(v) spayed or neutered status" as inputs to the predictive model. A single performance result from one four-item combination does not demonstrate possession of, or enable, the claimed genus. See Amgen Inc. v. Sanofi, 598 U.S. 594, 610-13 (2023); Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc); MPEP §§ 2163, 2164.
"Risk factor" (claim 4) is recited at pp. 11 and 19 only within the same lists and is never described; the limitation is not adequately described.
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.
Claims 1, 4, 5, 8, 9, 10, 11 and 13 are rejected under 35 U.S.C. 112(b) as indefinite.
Claims 1, 11 and 13 — duplicate item in data group (A)
Data group (A) recites "age at onset" twice, at items (vii) and (xv). Because each claim requires selection of "two or more" items, it cannot be determined whether selection of age at onset alone satisfies the requirement. The corresponding disclosure at specification p. 11 recites the animal data without duplication, confirming the repetition is a defect in the claims.
Claims 1, 11 and 13 — "two or more of at least"
It cannot be determined whether "at least" modifies the number of items selected or renders the enumerated list non-exhaustive. The specification does not resolve the ambiguity.
Claim 4 — undefined and relative terms; antecedent basis
(1) "risk factor" is recited without qualification. The specification recites the term only within the same lists, at p. 11 and p. 19, and nowhere defines it or gives an example distinct from the other enumerated items.
(2) "the excess of hair loss, scaling or dryness" — "excess" is a relative term for which neither the claim nor the specification supplies a standard of comparison. MPEP § 2173.05(b).
(3) It cannot be determined whether the items following "and/or an indication about at least one affected area" are alternatives within the group or objects of "an indication about."
(4) "the existence of previous episodes," "the presence of cortico-response pruritus," "the excess of hair loss" and "symptoms" lack antecedent basis.
Claim 5 — "said plurality of metadata … of said companion animal"
Claim 1 recites two distinct bodies of data: "a plurality of previously acquired metadata relative to companion animals" (the training corpus) and, at step (a), "metadata relative to said companion animal." The only antecedent for "said plurality of metadata" is the training corpus, which is not data "of said companion animal." The specification at p. 19 repeats the same conflated phrase — "the pathologic profile may comprise a readily retrievable, centrally located information record that contains said plurality of metadata of said companion animal" — and therefore does not cure the defect.
Claim 8 — "each lesion" and "the atopic dermatitis condition"
(1) "each lesion of said companion animal" lacks antecedent basis; claim 1 recites only an animal "suspected to have" the condition and derm area data that may correspond to unaffected areas.
(2) "a severity of the atopic dermatitis condition" presupposes a condition whose existence claim 1 step (e) merely assesses. The specification confirms the defect at p. 22, contemplating that the score may assess "that said at least one cutaneous lesion is not indicative of an atopic dermatitis condition," in which case "said score assesses other dermatitis conditions such as sarcoptic mange, demodicosis, bacterial overgrowth syndrome, Malassezia dermatitis, bacterial folliculitis, contact dermatitis."
Claim 9 — nested method recitation; conflict with "trained beforehand"
(1) "the step of training the assessment module, … the method comprising: - extracting …, - associating …, and - training …" — a single step cannot itself be "the method comprising" further steps.
(2) Claim 1 requires a module "trained beforehand" while claim 9 adds the step of training it; it cannot be determined whether the same or a further training operation is intended, nor when it occurs relative to steps (a)-(e).
Claim 10 — grammatical construction
"[W]herein, said assessment module comprising at least one supervised classifier machine learning model, the method comprising the step of updating weights …" is grammatically incomplete. No inconsistency with claim 6 is asserted: specification p. 19 discloses that the assessment module uses a predictive model which includes the supervised classifier, so both recitations are consistent with the disclosed hierarchy.
Claim 11 — "the companion animal data"
The recitation lacks antecedent basis. The corresponding disclosure at specification p. 24 recites the module "configured to operate on metadata relative to said companion animal," indicating the gap was introduced in the claims.
Claim 13 — "configured to: a) receiving"; "a support"
(1) "these instructions being configured to: a) receiving …, b) encoding …" is grammatically inconsistent; it cannot be determined whether the recitations are functional limitations or required steps.
(2) "a support" is not defined anywhere in the specification. This is in addition to the rejection of claim 13 under § 101, the broadest reasonable interpretation of "a support" encompassing transitory signals. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007).
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
"Assessment module" (claims 1, 9, 10, 11, 13) and "acquisition module" (claim 12) invoke § 112(f): each is a generic placeholder coupled with functional language. See Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1348-51 (Fed. Cir. 2015); MPEP § 2181(I).
Corresponding structure is disclosed for the assessment module. At specification p. 19, under the heading "Assessment module," the disclosure states that "said assessment module can use a predictive model," that "said predictive model can include a supervised classifier machine learning model, and a metadata encoding module," and that "[i]n a preferred embodiment, said model is a Random Forest Classification algorithm," comprising "Step 1: Select random samples from a given data or training set, Step 2: Construction of a decision tree for every training data, Step 3: Voting take place by averaging the decision tree or by majority voting decision tree, and Step 4: Validation of the model performance with Test Set." At p. 20 the metadata encoding module "uses a binarization function to attribute a 1 or a 0 to each possible answer leading to a metadata vector."
This satisfies Aristocrat Techs. Austl. Pty Ltd. v. Int'l Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008), and MPEP § 2181(II)(B). No rejection under § 112(b) is made on this basis. Claims 11 and 13, and the "assessment module" recitations of claims 1, 9 and 10, are accordingly construed as limited to the disclosed structure and equivalents thereof.
Acquisition module (claim 12). The only disclosure is at specification p. 24: "Said device can include an acquisition module for acquiring said metadata," which restates the claimed function. Acquiring or receiving data is nonetheless a basic function a general-purpose computer performs without special programming, for which no algorithm need be disclosed. See In re Katz Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir. 2011). No rejection is made.
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.
Claim 13 — Non-Statutory Subject Matter
Claim 13 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Claim 13 recites "the computer program product comprising a support and stored on this support instructions that can be read by a processor." The term "support" is not defined in the claim, and the specification does not limit "support" to non-transitory media. Accordingly, under the broadest reasonable interpretation, "a support" encompasses transitory forms of signal transmission, including a carrier wave or propagating electrical signal.
A transitory, propagating signal is not a process, machine, manufacture, or composition of matter, and therefore falls outside the four statutory categories. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007); MPEP § 2106.03(I). Amendment of claim 13 to recite "a non-transitory computer-readable storage medium" would overcome this ground of rejection.
Claims 1-13 — Judicial Exception Without Significantly More
Claims 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 — Statutory Category
Claims 1-10 recite a series of steps and are directed to a process. Claims 11-12 recite a device and are directed to a machine. Claim 13 is addressed in Part I above; to the extent amended to recite a non-transitory medium, it is directed to an article of manufacture. The analysis proceeds to Step 2A.
Step 2A, Prong One — The Claims Recite an Abstract Idea
Independent claim 1 recites, as its operative steps:
"a) receiving metadata relative to said companion animal, including two or more animal data and two or more derm area data listed in the data group (A), b) encoding said metadata into a metadata vector, c) operating said trained assessment module on said metadata vector, wherein the operating comprises inputting said metadata vector into a predictive model, d) based on an analysis of said metadata vector by said predictive model associated with said assessment module, generating a cutaneous lesion score indicative of at least one cutaneous state of said companion animal, and e) based on the cutaneous lesion score indicative of the at least one cutaneous state of said companion animal, assessing whether said companion animal has an atopic dermatitis condition."
Limitations (a), (b), (d) and (e), under their broadest reasonable interpretation, recite a mental process. The recited "animal data" are items of signalment and history — breed, species, gender, weight, spayed or neutered status, age, age at onset, body condition, health status, lifestyle, habitat, coat information, activity level, and dermatitis history — that a veterinary practitioner obtains by observation and by questioning the owner. The recited "derm area data corresponding to affected or unaffected derm area" — inguinal, axillar, ventral chest, perineal/genital, ventral neck, pinnae, periorbital, perioral, flexoral elbow, fore feet, flexoral tarsal, hind feet, and nonspecific atopic area — are observations of which anatomical regions do and do not bear lesions. Recording those observations in an ordered form (step (b)), deriving a lesion score from them (step (d)), and concluding from that score "whether said companion animal has an atopic dermatitis condition" (step (e)) are evaluations, judgments, and opinions. See MPEP § 2106.04(a)(2)(III).
Step (e) is the terminal limitation of the claim and is a diagnostic conclusion. A claim that begins with observation of a subject and ends with a practitioner’s assessment of whether the subject has a condition recites the mental processes grouping in its most direct form.
That these steps are practically performable in the human mind, or by a human using pen and paper, is established by the art of record. Olivry et al., "Validation of CADESI-03, a severity scale for clinical trials enrolling dogs with atopic dermatitis," Veterinary Dermatology 18:78-86 (2007), discloses a validated instrument in which a clinician manually scores four lesion types at 62 body sites and derives a numerical severity score for canine atopic dermatitis. Favrot et al., "A prospective study on the clinical features of chronic canine atopic dermatitis and its diagnosis," Veterinary Dermatology 21(1):23-31 (2010), discloses a set of clinical criteria comprising age at onset under three years, predominantly indoor life, corticosteroid-responsive pruritus, affected ear pinnae, affected front feet, non-affected ear margins, and non-affected dorso-lumbar area, from which the practitioner assesses whether the dog has atopic dermatitis. Favrot in particular discloses the very structure recited in claim 1 — a combination of animal data with derm area data expressed
as affected and unaffected areas, evaluated to reach an atopic dermatitis assessment.
The recitation of "an assessment module trained beforehand" and of "a predictive model" does not remove these limitations from the grouping. It is acknowledged that claim limitations encompassing artificial intelligence in a way that cannot practically be performed in the human mind do not fall within the mental processes grouping. See Memorandum, "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (Aug. 4, 2025), § II.A. In claim 1, however, the module and model are recited functionally and at a high level of generality. Step (c) recites only that operating the module "comprises inputting said metadata vector into a predictive model." No architecture, training operation, weight adjustment, feature transformation, or other computational step is recited in claim 1. The claim recites that a model is applied and that a score results.
Mathematical concepts — not relied upon as to claim 1. Consistent with the contrast drawn between Example 39 and Example 47 of the July 2024 Subject Matter Eligibility Examples, the recitation of "encoding said metadata into a metadata vector" and of a module "trained beforehand" does not set forth or describe any mathematical relationship, calculation, formula, or equation using words or mathematical symbols. The mathematical concepts grouping is therefore not relied upon as to claim 1. It is addressed separately as to claims 3 and 8 below.
Step 2A, Prong Two — No Integration Into a Practical Application
The additional elements beyond the abstract idea are: the "assessment module trained beforehand"; the "predictive model" of step (c); the "supervised classifier machine learning model" of claims 6 and 10; the "user interface" of claim 7; the "acquisition module" of claim 12; and the support and processor of claim 13. Considered individually and as an ordered combination, these do not integrate the exception into a practical application.
(a) Mere instructions to apply the exception. Step (c) recites that "the operating comprises inputting said metadata vector into a predictive model." This is a recitation of applying the exception using a generic computational tool. See MPEP § 2106.05(f). The governing inquiry is "the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome." Aug. 4, 2025 Memorandum, § II.B. Claim 1 recites the outcome — generation of a cutaneous lesion score and an atopic dermatitis assessment — without reciting any particular way in which the predictive model produces it. Claim 6 adds only that the model "comprises at least a supervised classifier machine learning model," which names a broad and conventional category rather than a particular technique.
(b) No improvement to a computer or other technology. Neither the claims nor the specification establishes an improvement to the functioning of a computer or to any other technology or technical field. See MPEP § 2106.05(a). The asserted advance is improved accuracy or accessibility of a veterinary clinical assessment, which is an improvement in medical practice rather than in computer capability. Applying a generically recited trained model to a new body of data does not confer eligibility. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea are not sufficient); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016).
(c) Insignificant extra-solution activity. Step (a), "receiving metadata relative to said companion animal," is mere data gathering. Step (b), "encoding said metadata into a metadata vector," is mere data formatting: the claim recites the existence of a vector but no particular encoding scheme, ordering, or transformation. Both are insignificant extra-solution activity. See MPEP § 2106.05(g); Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014) (combining data into a data structure held abstract).
(d) No particular treatment or prophylaxis. The claims do not recite a particular treatment or prophylaxis for a disease or medical condition. See MPEP § 2106.04(d)(2). Claim 1 terminates at step (e), "assessing whether said companion animal has an atopic dermatitis condition." No step of administering a therapeutic agent to the companion animal, or of otherwise acting upon the animal in response to the assessment, is recited in any claim. Compare Vanda Pharms. Inc. v. West-Ward Pharms. Int’l Ltd., 887 F.3d 1117, 1135-36 (Fed. Cir. 2018) (claims reciting administration of a specific dose held eligible), with Cleveland Clinic Found. v. True Health Diagnostics LLC, 859 F.3d 1352, 1362 (Fed. Cir. 2017) (claims terminating in a diagnostic correlation held ineligible).
Step 2B — No Inventive Concept
The additional elements, considered individually and in combination, do not amount to significantly more than the judicial exception. The generically recited assessment module, predictive model, supervised classifier, user interface, acquisition module, support, and processor amount to generic computer implementation. See MPEP § 2106.05(f).
The additional elements are further shown to be well-understood, routine, and conventional in the field, as evidenced by the following, each published before the March 7, 2024 effective filing date: (1) KR 10-2022-0018813 A (AI for Pet Co., Ltd., published Feb. 15, 2022; English family members WO 2022/030685 A1 and US 12,558,024 B2) discloses a health management server that repeatedly learns and verifies training data, extracts an analysis image for each body part, analyzes erythema, abrasion, and lichenification per part, and quantifies and evaluates the severity of the skin disease of a companion animal, the result being delivered to a portable user terminal, with the program stored on a computer-readable recording medium. It further discloses evaluating severity using the area of the wound region relative to total area. (2) KR 10-2419567 B1 (AI2U Co., Ltd., granted July 12, 2022; English family member WO 2022/031067 A1) discloses receiving questionnaire data from a user terminal — including the subject’s gender, age, underlying disease, family history, living environment, occupation, and medications taken, together with image data indicating the site of occurrence of a symptom — and inputting that questionnaire data to a neural network model to determine and transmit diagnostic data for a skin disease.
These references establish that inputting subject metadata and body-site data to a trained model to output a dermatological assessment, and quantifying cutaneous lesion severity per body area for a companion animal, were each conventional before the effective filing date. See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018); MPEP § 2106.05(d)(II).
III. Dependent Claims 2-10 and 12
Claim 2 recites receiving "a description of a pattern associated with a lesion" and inputting it into the predictive model. Observing and describing a lesion pattern is an observation within the mental processes grouping; inputting the description is additional data gathering under MPEP § 2106.05(g).
Claim 3 recites "calculating second-or-more order cross-features based on cross feature interactions between said metadata." Unlike claim 1, this limitation recites a calculation performed on the metadata and is therefore additionally addressed under the mathematical concepts grouping. See MPEP § 2106.04(a)(2)(I)(C). The claim recites that cross-features are calculated but not how, and therefore adds no particular technique that would integrate the exception.
Claim 4 enumerates further data items — reproductive status, risk factor, affected area indications, previous episodes of hotspots, urticaria or angioedema, cortico-response pruritus, excess hair loss, scaling or dryness, gastrointestinal signs, whether symptoms worsen after walking in grass, and medical history of chronic or recurrent dermatoses or otitis. These narrow the abstract idea by specifying which observations are made; narrowing an abstract idea does not render it less abstract. Each recited item is a clinical history observation, and corticosteroid-responsive pruritus in particular is a criterion disclosed by Favrot (2010).
Claim 5 recites providing "a pathological profile" comprising "an information record comprising said plurality of metadata." This recites the assembly of collected data into a record, which is data organization rather than an additional element imposing a meaningful limit. See Digitech, 758 F.3d at 1350-51.
Claim 6 recites that the predictive model "comprises at least a supervised classifier machine learning model." Naming a broad, conventional class of model is generic computer implementation under MPEP § 2106.05(f).
Claim 7 recites providing the cutaneous lesion score to a user interface. Presenting a result on a generic interface is insignificant post-solution activity. See MPEP § 2106.05(g); Electric Power Grp., 830 F.3d at 1354.
Claim 8 recites determining a respective size associated with each lesion, "normalizing the respective size associated with each lesion by a size of said companion animal," and assessing severity from the score and the normalized sizes. Measuring lesion size and comparing it to the animal’s size is an observation and evaluation within the mental processes grouping; the normalization step additionally recites a mathematical calculation under MPEP § 2106.04(a)(2)(I)(C). Assessment of lesion extent relative to body size is disclosed in the CADESI literature of record, and evaluation of severity using wound area relative to total area is disclosed by KR 10-2022-0018813 A.
Claims 9 and 10 recite training the assessment module by extracting at least one feature from each previously acquired metadata, associating the feature to an animal cutaneous state, and training the module to learn the association (claim 9), and updating weights of the supervised classifier machine learning model according to that association (claim 10). The feature-extraction and association limitations of claim 9 describe correlating observed characteristics with a clinical state, which a practitioner performs mentally. The weight-updating limitation of claim 10 is an additional element rather than part of the exception; however, it is recited without any particular training algorithm or update rule, and amounts to generic training of a conventional classifier. It therefore neither integrates the exception into a practical application nor supplies an inventive concept. See Recentive, 134 F.4th 1205.
Claim 12 recites "an acquisition module for acquiring said companion animal data," which is a generic component performing the data gathering already addressed under MPEP § 2106.05(g).
IV. Claims 11-12 — Device Claims
Claims 11 and 12 recite the same abstract idea as claim 1, framed as a device. The only structure recited in claim 11 is "an assessment module trained beforehand … configured to operate on the companion animal data … and to generate a cutaneous lesion score." Reciting a generic module configured to perform the abstract idea does not confer eligibility; a claim to a machine that merely implements an abstract idea on generic components remains directed to that idea. See MPEP § 2106.05(f). Claim 12 adds only a generic acquisition module.
V. Conclusion
Claims 1-13 are directed to an abstract idea without significantly more and are rejected under 35 U.S.C. § 101. Claim 13 is separately rejected as directed to non-statutory subject matter.
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 and 3-13 are rejected under 35 U.S.C. 103 as being unpatentable over Heo, et al. (US 2023/0301582 A1) in view of Wang, R., Fu, B., Fu, G., & Wang, M. (2017). Deep & Cross Network for Ad Click Predictions (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1708.05123.
With regards to claim 1, Heo teaches a method for determining a cutaneous lesion score of a companion animal (see at least ¶ 0007, quantifying severity of the skin disease of the test subject using the analyzed characteristic distribution diagram; ¶ 0020, when an abnormality occurs in the skin of a test target, particularly, a companion animal, a current state of the companion animal can be quickly and accurately determined; ¶ 0041, the skin disease measurement system 1000 is used for imaging the skin of a companion animal, particularly, a puppy, and determining severity of a skin disease) indicative of an atopic dermatitis condition in a companion animal (see at least ¶ 0131, quantifying severity of atopic dermatitis), using an assessment module trained beforehand to learn features suspected to have an atopic dermatitis condition, based at least on a plurality of previously acquired metadata relative to companion animals, selected in a data group (A) comprising at least animal data including two or more of at least (i) breed, (ii) species, (iii) gender, (iv) weight, (v) spayed or neutered status, (vi) age, (vii) age at onset, (viii) body condition, (ix) health status, (x) lifestyle, (xi) habitat, (xii) coat information, (xiii) activity level, (xiv) a biological value from a biological sample, (xv) age at onset and (xvi) dermatitis history, and derm area data corresponding to affected or unaffected derm area including two or more of at least (i) inguinal, (ii) axillar, (iii) ventral chest, (iv) perineal/genital, (v) ventral neck, (vi) pinnae, (vii) periorbitar, (viii) perioral, (ix) flexoral elbow, (x) fore feet, (xi) flexoral tarsal, (xii) hind feet and (xiii) nonspecific atopic area (see at least ¶ 0010, ¶ 0090, the system is repeatedly learning using a convolutional neural network (CNN) the basic skin result data corresponding to the basic skin state information to verify the basic skin result data; ¶ 0049, basic information may include a dog breed, a sex, an age, a weight, a neutered state, etc. [animal data]; ¶ 0076, skin imaging information of various skin parts such as faces, heads, abdomens, feet, chests, etc. of the companion animals [derm area data]; ¶ 0113, the healthcare server 20 may acquire basic information from a plurality of companion animals [model trained beforehand based on metadata previously acquired from a plurality of companion animals]; ¶ 0131, for quantifying severity of atopic dermatitis), the method comprising at least the steps of: a) receiving metadata relative to said companion animal, including two or more animal data and two or more derm area data listed in the data group (A) (see at least ¶ 0049, basic information may include a dog breed, a sex, an age, a weight, a neutered state, etc. [animal data]; ¶ 0076, skin imaging information of various skin parts such as faces, heads, abdomens, feet, chests, etc. of the companion animals [derm area data]), b) encoding said metadata (see at least ¶ 0046, 0049, 0076, skin state information, including basic information such as dog breed, a sex, an age, a weight, a neutered state, etc., and basic skin imaging information such as faces, heads, abdomens, feet, chests, etc., is transmitted to healthcare server [encoded]…, c) operating said trained assessment module on said metadata …, wherein the operating comprises inputting said metadata into a predictive model (see at least ¶ 0090, 0131, skin state information is input into model), d) based on an analysis of said metadata vector by said predictive model associated with said assessment module, generating a cutaneous lesion score indicative of at least one cutaneous state of said companion animal (see at least ¶ 0007, quantifying severity of the skin disease of the test subject using the analyzed characteristic distribution diagram; ¶ 0011, quantifying and assessing severity of the skin disease of the test subject to generate the skin health result data; figure 9, ¶ 0092, 0131, management control unit extracts part-specific analysis of actual images by preprocessing a photograph and/or a video included in the skin state measurement information, calculates a characteristic distribution diagram for the actual analysis images using an MRC algorithm by analyzing the actual analysis images, and generate skin health result data by comparing and analyzing the characteristic distribution diagram with the skin health standard data obtained by quantifying severity of atopic dermatitis on a scale of 0-3), and e) based on the cutaneous lesion score indicative of the at least one cutaneous state of said companion animal, assessing whether said companion animal has an atopic dermatitis condition (see at least ¶ 0078, 0081, quantify and severity of atopic dermatitis, thereby generating the basic skin result data for the basic skin imaging information).
Heo does not explicitly teach …into a metadata vector; …vector. Wang teaches …into a metadata vector; …vector (see at least page 2, “2.1 Embedding and Stacking Layer”, the inputs are mostly categorical features, e.g. "country=usa". Such features are often encoded as one-hot vectors e.g. "[0,1,0]". In the end, we stack the embedding vectors, along with the normalized dense features xdense, into one vector:
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Heo discloses acquiring "basic information from a plurality of companion animals," that information comprising "unique identification numbers, dog breeds, sexes, ages, weights, neutered statuses" (¶ 0113), and further discloses that this basic information forms part of the "basic skin state information" (¶ 0049) on which the healthcare server performs "repeatedly learning … to verify the basic skin result data" (¶ 0010). Heo therefore trains a model on data that is, in substantial part, categorical — breed, sex, and neutered state are nominal fields, not measurements.
Heo does not state how those categorical fields are presented to the model. That omission creates the very problem Wang addresses. Wang § 2.1 opens by identifying it: "We consider input data with sparse and dense features … the inputs are mostly categorical features, e.g. 'country=usa'." Wang then states the conventional solution and its known drawback — "Such features are often encoded as one-hot vectors e.g. '[0,1,0]'; however, this often leads to excessively high-dimensional feature spaces for large vocabularies" — and specifies the assembly step: "In the end, we stack the embedding vectors, along with the normalized dense features xdense, into one vector" (Eq. (2)).
One of ordinary skill implementing Heo's trained model on the categorical metadata Heo itself collects would necessarily have had to render those fields in a numerical form the classifier accepts. Wang supplies that step, and Wang's own word "often" establishes that one-hot encoding was the routine practice rather than Wang's contribution — Wang is relied upon as documentary evidence of the ordinary level of skill. See MPEP § 2141.03. The result is entirely predictable: encoding a finite set of enumerated answers into a binary vector is a deterministic operation with no unpredictability, so a reasonable expectation of success is established. This is the use of a known technique to improve a similar known method in the same way. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007); MPEP § 2143(C), (D).
Wang is analogous art. The field of endeavor is predictive modeling — Heo ¶ 0010 trains a model, claim 1 recites a "predictive model," and claim 6 a "supervised classifier machine learning model." Wang is independently reasonably pertinent to the particular problem, which is how to present categorical subject attributes to a predictive model; nothing in that problem is specific to advertising. Wang confirms the point, reporting results "on the CTR prediction dataset and dense classification dataset" (p. 1, Abstract) and framing its premise generally: "Feature engineering has been the key to the success of many prediction models." In re Klein, 647 F.3d 1343, 1348 (Fed. Cir. 2011); MPEP § 2141.01(a).
Heo further discloses the corresponding device: a healthcare server that generates the skin health standard data and the skin health result data (¶¶ 0007, 0011), together with a manager terminal configured to repeatedly learn the skin health standard data and generate the skin health result data corresponding to the skin state measurement information" (¶ 0017). Claim 11 is otherwise coextensive with claim 1 and is rejected for those reasons.
Heo additionally discloses ¶ 0018, another aspect of the present invention provides a program stored in a computer-readable recording medium to perform the skin disease measurement method using a portable terminal and the skin disease management method using a portable terminal in combination with a computer which is hardware. Claim 13 otherwise recites the operations of claim 1 and is rejected for those reasons.
With regards to claim 3, Wang teaches the method of claim 1, further comprising the steps of: calculating second-or-more order cross-features based on cross feature interactions between said metadata; and inputting the second-or-more order cross-features into the predictive model; wherein generating the cutaneous lesion score is further based on an analysis of the second- or-more order cross-features by the predictive model (see at least p. 1, Abstract, identifies that deep networks "generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features," and proposes "a novel cross network that is more efficient in learning certain bounded-degree feature interactions," which "explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model."; p. 2, § 2.2 ("Cross Network"), discloses that "[t]he key idea of our novel cross network is to apply explicit feature crossing in an efficient way. The cross network is composed of cross layers, with each layer having the following formula:
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The same page, under "High-degree Interaction Across Features," states that "[t]he special structure of the cross network causes the degree of cross features to grow with layer depth." Wang p. 6, Conclusion, confirms that the model "learns explicit cross features of bounded degree" and that "[t]he degree of cross features increases by one at each cross layer." Wang therefore discloses calculating second-order and higher-order cross features from interactions between input features and supplying them to the predictive model, whose output is based in part upon them. The motivation to combine Wang with Heo is the same as stated in claim 1 rejection above.
With regards to claim 4, Heo teaches the method of claim 1, wherein said data group (A) further includes reproductive status, risk factor, and/or an indication about at least one affected area of the companion animal body surface, 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 (see at least ¶ 0049, neutered status [reproductive status]).
With regards to claim 5, Heo teaches the method of claim 1, further comprising the step of providing a pathological profile of said companion animal based on said plurality of metadata, wherein the pathological profile comprises an information record comprising said plurality of metadata of said companion animal (see at least ¶ 0049, maintaining a record including caregiver information, abandonment information, hospital record information, a unique identification number, a dog breed, a sex, an age, a weight, a neutered state, etc. The caregiver information includes a contact number and the like, and the hospital record information may include vaccination information, medical treatment information, allergies, etc. According to the embodiment, the hospital record information may include beauty information [pathological profile]).
With regards to claim 6, Heo teaches the method of claim 1, wherein said predictive model comprises at least a supervised classifier machine learning model (see at least ¶ 0010, generation of the skin health standard data may include repeatedly learning, by the healthcare server, the basic skin result data corresponding to the basic skin state information to verify the basic skin result data [labeled pairs = supervised classifier machine learning model]).
With regards to claim 7, Heo teaches the method of claim 1, further comprising the step of providing to a user interface said cutaneous lesion score relative to said companion animal (see at least ¶ 0042, The user terminal 10 is a portable terminal carried by a caregiver of a companion animal 1 and may operate using an application program being a smartphone, a personal digital assistant (PDA), a tablet, a wearable device; ¶ 0043, the user terminal includes a display unit 130; ¶ 0011, the server transmits treatment management data generated in accordance with the skin health result data to the user terminal).
With regards to claim 8, Heo teaches the method of claim 1, further comprising the step of: determining a respective size associated with each lesion of said companion animal, normalizing the respective size associated with each lesion by a size of said companion animal, and assessing a severity of the atopic dermatitis condition based on the cutaneous lesion score and the respective normalized size associated with each lesion (see at least ¶ 0009: the server may assess severity of excoriation using a distribution diagram of ratios of an area of wounded parts to a total area calculated from the part-specific analysis images. Expressing lesion area as a proportion of the total area of the analyzed region rather than as an absolute measurement is the normalization claim 8 recites, and for the same purpose. ¶ 0078 assesses severity of atopic dermatitis in four grades from the resulting analysis).
With regards to claim 9, Heo teaches the method of claim 1, further comprising the step of training the assessment module, using at least the plurality of previously acquired metadata relative to companion animals, selected in the data group (A), the method comprising: …- associating at least said at least one feature to an animal cutaneous state, and- training the assessment module to learn said association (see at least ¶ 0113 acquires the metadata from a plurality of companion animals; ¶ 0010 performs repeatedly learning the basic skin result data corresponding to the basic skin state information, which is the association of features with a cutaneous state; ¶ 0017 discloses a manager terminal that repeatedly learns the skin health standard data).
Furthermore, Wang teaches …- extracting at least one feature from each previously acquired metadata (see at least § 2.1 which supplies the feature extraction, each categorical field being embedded via
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the corresponding embedding matrix). The motivation to combine Wang with Heo is the same as stated in claim 1 rejection above.
With regards to claim 10, Heo teaches the method of claim 9, wherein, said assessment module comprising at least one supervised classifier machine learning model (see at least ¶ 0010, generation of the skin health standard data may include repeatedly learning, by the healthcare server, the basic skin result data corresponding to the basic skin state information to verify the basic skin result data [labeled pairs = supervised classifier machine learning model]).
Furthermore, Wang teaches …the method comprising the step of updating weights of the supervised classifier machine learning model according to said association between said at least one feature and said animal cutaneous state See at least § 2.2 identifies
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as the weight and bias parameters of the l-th layer; § 2.1 states that the embedding matrix will be optimized together with other parameters in the network, the optimization being against the labelled log loss of Eq. (6) [updating the weights of the supervised classifier according to the association]). The motivation to combine Wang with Heo is the same as stated in claim 1 rejection above.
With regards to claim 12, Heo teaches the device of claim 11, comprising an acquisition module for acquiring said companion animal data (see at least ¶ 0043, the user terminal may include an imaging unit 100, a transceiver unit 110, a memory unit 120, a display unit 130, and a terminal control unit 140; ¶ 0044, The imaging unit 100 may recognize the skin of the companion animal 1 using a camera to acquire actual skin imaging information; ¶ 0045, it may select a skin part of the companion animal 1 to be diagnosed and acquire actual skin imaging information of the selected skin part [acquisition module for acquiring the companion animal data]).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Heo, et al. (US 2023/0301582 A1) in view of Wang, R., Fu, B., Fu, G., & Wang, M. (2017). Deep & Cross Network for Ad Click Predictions (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1708.05123 in further view of Son, et al. (WO 2022/031067 A1)
With regards to claim 2, Heo teaches the method of claim 1, further comprising the step of: …wherein generating the cutaneous lesion score is further based on an analysis of the description by the predictive model (see at least ¶ 0010, 0049, 0076, 0090, 0131, the system uses a model for quantifying severity of atopic dermatitis using descriptive inputs such as a dog breed, a sex, an age, a weight, a neutered state, etc., skin imaging information of various skin parts such as faces, heads, abdomens, feet, chests, etc. of the companion animals).
Heo does not explicitly teach …receiving a description of a pattern associated with a lesion of said companion animal; and inputting the description into the predictive model. Son teaches receiving a description of a pattern associated with a lesion of said companion animal (see at least top of page 4, pattern of skin disease symptoms); and inputting the description into the predictive model (see at least middle of page 2 “Tech-Solution”, inputting selection and questionnaire items).
Heo determines cutaneous severity from images. Its analysis extracts "part-specific analysis images" and grades erythema, excoriation and lichenification from them (¶¶ 0007-0009). Although Heo separately collects non-image subject attributes — "a dog breed, a sex, an age, a weight, [a] neutered state" and hospital-record information (¶ 0049) — it does not state that those attributes are supplied to the model as features. That is the deficiency AI2U addresses.
Son teaches, expressly, that visual examination alone is insufficient and that structured history is what drives diagnostic accuracy: "In diagnosing skin diseases, the examination by looking at the lesion with the eyes is also important, but an accurate and effective questionnaire is the basis of diagnosis and is a very important process in increasing the accuracy of diagnosis results." Son further identifies the practical obstacle — "it is difficult for medical staff to spend a lot of time on questionnaires within a limited time, and even if they do, there are many questions that are omitted or missed in busy medical conditions" — and its solution is to collect that history automatically and feed it to the model, its claim 1 "determining a skin disease diagnosis result for the skin lesion area based on the skin image and the questionnaire data collected through the chatbot."
Son also identifies the content: diagnosis "based on the various questionnaire data entered by the patient (underlying disease, drug taking history, occupation, living environment, products in use, diet, lifestyle, etc.) and the location and pattern of skin disease symptoms," and discloses "identifying a body part in which the skin lesion region appears through a user input."
One of ordinary skill would therefore have been motivated to supply Heo's already-collected subject metadata to Son's model, rather than relying on images alone, because Son teaches that doing so is "the basis of diagnosis" and increases accuracy. The result is predictable: Son demonstrates a model that accepts both an image and structured metadata and returns a diagnosis. KSR, 550 U.S. at 417; MPEP § 2143(C), (D).
Son is analogous art, and Heo itself supplies the bridge. Heo ¶ 0041 states that its system is not limited to companion animals: "it is possible to measure not only skin diseases of various animals including vertebrates … but also skin diseases of humans." A reference directed to human dermatological diagnosis is therefore within the field of endeavor the primary reference expressly claims for itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kao, et al. (US 2017/0290516 A1) which discloses systems and methods for screening for, indicating, diagnosing, treating, and identifying dermatologic and/or respiratory disease states in domestic cats. Systems and methods for screening for, indicating, diagnosing, treating, and identifying dermatologic and/or respiratory disease states in domestic cats. Systems and methods for screening for, indicating, diagnosing, treating, and identifying dermatologic and/or respiratory disease states in domestic cats.
Olivry T, Marsella R, Iwasaki T, Mueller R; International Task Force On Canine Atopic Dermatitis. Validation of CADESI-03, a severity scale for clinical trials enrolling dogs with atopic dermatitis. Vet Dermatol. 2007 Apr;18(2):78-86. doi: 10.1111/j.1365-3164.2007.00569.x. PMID: 17355421 which discloses in dogs, atopic dermatitis (AD) is a common and chronic allergic skin disease that often necessitates treatment with pharmacological interventions. In the last 30 years, numerous clinical trials testing the efficacy of anti-inflammatory drugs have been reported, but there has been a lack of consistency in the assessment of outcome measures. Several clinical scales have been employed over time, but none of these scoring systems were ever tested for validity and reliability. A committee of the International Task Force on Canine Atopic Dermatitis evaluated the currently available scales used to assess disease morbidity in humans and dogs with AD, and a third version of the Canine Atopic Dermatitis Extent and Severity Index (CADESI-03) was designed. This version was expanded from previous ones by redistribution and increase in body sites tested, the use of an additional lesion reflecting underlying pruritus (e.g. self-induced alopecia) and an increase in the numerical range of severity for each lesion. The CADESI-03 scale was tested for validity and reliability in a cohort of 38 dogs with AD. Overall, this revised version of the CADESI was found to exhibit acceptable content, construct, criterion, and inter- and intra-observer reliability and sensitivity to change. As a result, this scale is recommended as a validated tool for assessment of disease severity in clinical trials testing the efficacy of interventions in dogs with AD
Favrot C, Steffan J, Seewald W, Picco F. A prospective study on the clinical features of chronic canine atopic dermatitis and its diagnosis. Vet Dermatol. 2010 Feb;21(1):23-31. doi: 10.1111/j.1365-3164.2009.00758.x. PMID: 20187911 which discloses canine atopic dermatitis (CAD) is a multifaceted disease associated with exposure to various offending agents such as environmental and food allergens. The diagnosis of this condition is difficult because none of the typical signs are pathognomonic. Sets of criteria have been proposed but are mainly used to include dogs in clinical studies. The goals of the present study were to characterize the clinical features and signs of a large population of dogs with CAD, to identify which of these characteristics could be different in food-induced atopic dermatitis (FIAD) and non-food-induced atopic dermatitis (NFIAD) and to develop criteria for the diagnosis of this condition. Using simulated annealing, selected criteria were tested on a large and geographically widespread population of pruritic dogs. The study first described the signalment, history and clinical features of a large population of CAD dogs, compared FIAD and NFIAD dogs and confirmed that both conditions are clinically indistinguishable. Correlations of numerous clinical features with the diagnosis of CAD are subsequently calculated, and two sets of criteria associated with sensitivity and specificity ranging from 80% to 85% and from 79% to 85%, respectively, are proposed. It is finally demonstrated that these new sets of criteria provide better sensitivity and specificity, when compared to Willemse and Prélaud criteria. These criteria can be applied to both FIAD and NFIAD dogs.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
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