DETAILED ACTION
This action is in reference to the communication filed on 4 AUG 2025.
Claims 1-4 are present and have been examined.
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 1-4 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-4 the independent claim(s) 1, 3, recite(s) a method and an apparatus, each of which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-4, the independent claim(s) (claims 1, 3) is/are directed, in part, to:
A method for providing a machine-learning-based diet recommendation service by using a food intolerance test, the method comprising:
(a) generating,
(b) generating,
These claim elements are considered to be abstract ideas because they are directed to mental processes, i.e. concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Generating information based on previously generated values, and using that information to create a diet information for a user based on that information are examples of evaluation, and judgement.
These claims are also directed to certain methods of organizing human activity, including managing behaviors such as social activities, or following rules/instructions. Making a dietary plan for a user based on information about that use (i.e. the previously generated food sensitivity test values), is a method of following instructions to do so.
If a claim limitation, under its broadest reasonable interpretation covers concepts performed in the human mind, and/or managing behaviors, then it falls within the “mental processes” and/or “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: claim 1, 3 includes a service provision server, as well as wherein the recommendation is “machine learning based,” in claim 1. The server common to both claims is recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the server(s) as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). This conclusion is also reached for the machine learning in claim 1 – it is at best applied to or implementing the abstract idea(s) identified above. Examiner also notes that the nominal sending and receiving of the data involving the server(s) is generally found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: claim 1, 3 includes a service provision server, as well as wherein the recommendation is “machine learning based,” in claim 1. When considered individually, the server and nominal machine learning claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0027] The service provision server 200 may be a server installed and operated by a business operator who provides a customized diet management service on the basis of a delayed allergy test according to one embodiment of the present invention, and the service provision server 200 may generate the food-specific- intolerance information of the user on the basis of a reaction value of an antigen-antibody reaction for each food antigen in the blood of the user, and generate customized diet information and recommended nutritional supplement information for the user on the basis of the food-specific-intolerance information of the user and the questionnaire answer information of the user.
[0028] FIG. 2 is a functional block view showing a structure of a service provision server 200 which executes a method for 15 providing a machine-learning-based diet recommendation service by using a food intolerance test according to one embodiment of the present invention. Referring to FIG. 2, the service provision server 200 which executes a method for providing a machine-learning-based diet recommendation service by using a 20 food intolerance test according to one embodiment of the present invention may include a receiving part 210, a storing part 230, an operation part 250, and a transmission part 270.”
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. Examiner notes no disclosure of anything technical pertaining to the machine learning model beyond attaining the desired outcome. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The specification discloses the server in functional terms only – i.e. any server is suitable to execute the claimed invention. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2, 4:
Dependent claims 2, 4, are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as using the sensitivity information to predict a disease associate with a given insensitivity. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
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.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Neumann (US 20210074403 A1).
In reference to claim 1:
Neumann teaches: A method for providing a machine-learning-based diet recommendation service by using a food intolerance test, the method comprising:
generating, by a service provision server, food- specific-intolerance information of a user on a basis of a reaction value of an antigen-antibody reaction for each food antigen in a blood of the user (at least [fig 1 and related text] “With continued reference to FIG. 1, server 102 is configured to receive at least a dietary request. At least a dietary request as used in this disclosure includes a request for a particular diet, food, ingredient, food group, nutrition plan, style of eating, lifestyle, and/or nutrition… At least a dietary request may include elimination of certain foods or food groups because of a dislike for such foods, an allergy to a food, and/or a sensitivity. For example, at least a dietary request may include a request for an egg free diet based on a user's aversion to eggs. In yet another non-limiting example, at least a dietary request may include a request for a diet free of bell peppers because of a user's previous IgG food sensitivity testing. At least a dietary request may include a request for a diet free of shellfish because of a user's IgE allergic response to shellfish that was diagnosed when a user was a little child.”); and
generating, by the service provision server, customized diet information and recommended nutritional supplement information for the user on a basis of the food- specific-intolerance information of the user and a questionnaire answer information of the user (at least [048] “ For example, machine-learning algorithms may relate a dietary request such as a grain free diet to a user's future propensity to require an alimentary instruction set containing a recommendation to consume high fiber foods. Machine-learning algorithms may examine precursor dietary requests and future propensity to report a subsequent dietary request. For example, machine-learning algorithms may examine a user dietary request for a gluten free diet with a future propensity to report a subsequent dairy free diet.” At [0112] “One or more database tables in alimentary instruction label classification database 628 may include, without limitation, a supplement table 1008, which may describe a supplement that relates to a dietary request, such as a grain free diet with a recommendation for fiber supplementation or a vegetarian diet with a recommendation for B vitamin supplementation.” At [030] “At least an element of user data may include at least a user preference. At least a user preference may include for example religious preferences such as forbidden foods, medical interventions, exercise routines and the like. For example, a user who is of Catholic faith may report a religious preference to not consume animal products on Fridays during lent. At least a user preference may include a user's dislike such as for example a user aversion to certain foods or nutrient groups, such as for example an aversion to eggs or an aversion to beets. At least a user preference may include for example a user's likes such as a user's preference to consume animal products or full fat dairy and the like.” At [061-062] user questionnaire)
In reference to claim 2:
Neumann further teaches: The method of claim 1, further comprising: after above (a) and before above (b), generating, by the service provision server, a predicted disease information of the user on a basis of the food-specific- intolerance information of the user and the questionnaire answer information of the user (at least [0048] “Machine-learning algorithms may examine precursor dietary requests and future propensity to report a subsequent dietary request. For example, machine-learning algorithms may examine a user dietary request for a gluten free diet with a future propensity to report a subsequent dairy free diet.” – i.e. based on the gluten intolerance, a user might be more likely to be lactose intolerant. At [0056] “ With continued reference to FIG. 1, receiving by the at least a server 102 the at least a dietary request from a user device may include receiving at least a biological extraction from a user. At least a biological extraction may include any element of physiological data. Physiological data may include any data indicative of a person's physiological state; physiological state may be evaluated with regard to one or more measures of health of a person's body, one or more systems within a person's body such as a circulatory system, a digestive system, a nervous system, or the like, one or more organs within a person's body, and/or any other subdivision of a person's body useful for diagnostic or prognostic purposes. For instance, and without limitation, a particular set of biomarkers, test results, and/or biochemical information may be recognized in a given medical field as useful for identifying various disease conditions or prognoses within a relevant field. As a non-limiting example, and without limitation, physiological data describing red blood cells, such as red blood cell count, hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, and/or mean corpuscular hemoglobin concentration may be recognized as useful for identifying various conditions such as dehydration, high testosterone, nutrient deficiencies, kidney dysfunction, chronic inflammation, anemia, and/or blood loss. As an additional example, hemoglobin levels may be useful for identifying elevated testosterone, poor oxygen deliverability, thiamin deficiency, insulin resistance, anemia, liver disease, hypothyroidism, arginine deficiency, protein deficiency, inflammation, and/or nutrient deficiencies. In a further non-limiting example, hematocrit may be useful for identifying dehydration, elevated testosterone, poor oxygen deliverability, thiamin deficiency, insulin resistance, anemia, liver disease, hypothyroidism, arginine deficiency, protein deficiency, inflammation, and/or nutrient deficiencies. Similarly, measures of lipid levels in blood, such as total cholesterol, HDL, LDL, VLDL, triglycerides, LDL-C and/or HDL-C may be recognized as useful in identifying conditions such as poor thyroid function, insulin resistance, blood glucose dysregulation, magnesium deficiency, dehydration, kidney disease, familial hypercholesterolemia, liver dysfunction, oxidative stress, inflammation, malabsorption, anemia, alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease, atherosclerosis, or the like. [091] “Dietary data database 200 may include moderately compatible food table 304 which may be a table relating dietary request to foods that are moderately compatible with a particular dietary request; for instance where a dietary request contains a request for a gluten free diet from a user with a self-reported gluten intolerance, foods such as certified gluten free oats may be moderately compatible with such a user, while certified gluten free oats may not be compatible for a user following a gluten free diet because of a previous diagnosis of Celiac Disease. “)
In reference to claim 3:
Neumann teaches: A service provision server comprising:
an operation part configured to generate food-specific- intolerance information of a user on a basis of a reaction value of an antigen-antibody reaction for each food antigen in a blood of the user (at least [fig 1 and related text] “With continued reference to FIG. 1, server 102 is configured to receive at least a dietary request. At least a dietary request as used in this disclosure includes a request for a particular diet, food, ingredient, food group, nutrition plan, style of eating, lifestyle, and/or nutrition… At least a dietary request may include elimination of certain foods or food groups because of a dislike for such foods, an allergy to a food, and/or a sensitivity. For example, at least a dietary request may include a request for an egg free diet based on a user's aversion to eggs. In yet another non-limiting example, at least a dietary request may include a request for a diet free of bell peppers because of a user's previous IgG food sensitivity testing. At least a dietary request may include a request for a diet free of shellfish because of a user's IgE allergic response to shellfish that was diagnosed when a user was a little child.”), and generate customized diet information and recommended nutritional supplement information for the user on a basis of the food-specific-intolerance information of the user and a questionnaire answer information of the user (at least [048] “ For example, machine-learning algorithms may relate a dietary request such as a grain free diet to a user's future propensity to require an alimentary instruction set containing a recommendation to consume high fiber foods. Machine-learning algorithms may examine precursor dietary requests and future propensity to report a subsequent dietary request. For example, machine-learning algorithms may examine a user dietary request for a gluten free diet with a future propensity to report a subsequent dairy free diet.” At [0112] “One or more database tables in alimentary instruction label classification database 628 may include, without limitation, a supplement table 1008, which may describe a supplement that relates to a dietary request, such as a grain free diet with a recommendation for fiber supplementation or a vegetarian diet with a recommendation for B vitamin supplementation.” At [030] “At least an element of user data may include at least a user preference. At least a user preference may include for example religious preferences such as forbidden foods, medical interventions, exercise routines and the like. For example, a user who is of Catholic faith may report a religious preference to not consume animal products on Fridays during lent. At least a user preference may include a user's dislike such as for example a user aversion to certain foods or nutrient groups, such as for example an aversion to eggs or an aversion to beets. At least a user preference may include for example a user's likes such as a user's preference to consume animal products or full fat dairy and the like.” At [061-062] user questionnaire).
In reference to claim 4:
Neumann further teaches: The service provision server of claim 3, wherein the operation part generates a predicted disease information of the user on a basis of the food-specific-intolerance information of the user and the questionnaire answer information of the user (at least [0048] “Machine-learning algorithms may examine precursor dietary requests and future propensity to report a subsequent dietary request. For example, machine-learning algorithms may examine a user dietary request for a gluten free diet with a future propensity to report a subsequent dairy free diet.” – i.e. based on the gluten intolerance, a user might be more likely to be lactose intolerant. At [0056] “ With continued reference to FIG. 1, receiving by the at least a server 102 the at least a dietary request from a user device may include receiving at least a biological extraction from a user. At least a biological extraction may include any element of physiological data. Physiological data may include any data indicative of a person's physiological state; physiological state may be evaluated with regard to one or more measures of health of a person's body, one or more systems within a person's body such as a circulatory system, a digestive system, a nervous system, or the like, one or more organs within a person's body, and/or any other subdivision of a person's body useful for diagnostic or prognostic purposes. For instance, and without limitation, a particular set of biomarkers, test results, and/or biochemical information may be recognized in a given medical field as useful for identifying various disease conditions or prognoses within a relevant field. As a non-limiting example, and without limitation, physiological data describing red blood cells, such as red blood cell count, hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, and/or mean corpuscular hemoglobin concentration may be recognized as useful for identifying various conditions such as dehydration, high testosterone, nutrient deficiencies, kidney dysfunction, chronic inflammation, anemia, and/or blood loss. As an additional example, hemoglobin levels may be useful for identifying elevated testosterone, poor oxygen deliverability, thiamin deficiency, insulin resistance, anemia, liver disease, hypothyroidism, arginine deficiency, protein deficiency, inflammation, and/or nutrient deficiencies. In a further non-limiting example, hematocrit may be useful for identifying dehydration, elevated testosterone, poor oxygen deliverability, thiamin deficiency, insulin resistance, anemia, liver disease, hypothyroidism, arginine deficiency, protein deficiency, inflammation, and/or nutrient deficiencies. Similarly, measures of lipid levels in blood, such as total cholesterol, HDL, LDL, VLDL, triglycerides, LDL-C and/or HDL-C may be recognized as useful in identifying conditions such as poor thyroid function, insulin resistance, blood glucose dysregulation, magnesium deficiency, dehydration, kidney disease, familial hypercholesterolemia, liver dysfunction, oxidative stress, inflammation, malabsorption, anemia, alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease, atherosclerosis, or the like. [091] “Dietary data database 200 may include moderately compatible food table 304 which may be a table relating dietary request to foods that are moderately compatible with a particular dietary request; for instance where a dietary request contains a request for a gluten free diet from a user with a self-reported gluten intolerance, foods such as certified gluten free oats may be moderately compatible with such a user, while certified gluten free oats may not be compatible for a user following a gluten free diet because of a previous diagnosis of Celiac Disease. “)
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2018014482 to Irani-Cohen teaches food sensitivity based dietary recommendations
US 20090132284 A1 to Fey discloses a preventative health plan based on dietary and nutritional recommendations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
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