DETAILED ACTION
Claims 1, 3, 6-9, 11, & 14 are currently pending and have been examined.
This action is in response to the amendment filed on 4/7/2026.
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 .
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, 3, 6, 11, & 14 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1, 3, & 6-8 are directed to a method (i.e., a process); Claims 7-8 are directed to a computer-readable medium (i.e. manufacture); and Claims 9, 11, & 14 are directed to a system (i.e., a machine). Accordingly, claims 1-14 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. (Currently amended): A method for providing obesity prediction and solution for each growth stage using artificial intelligence, the method comprising:
receiving, by a processor, time series physical information on an evaluation subject, the time series physical information comprising at least one of body weight, body fat, skeletal muscle mass, body mass index (BMI), or basal metabolic rate of the evaluation subject measured over time;
classifying a plurality of growth stages comprising a normal growth stage, a rapid growth stage, a decelerated growth stage, and anon-growth stage based on the input physical information on the evaluation subject, and then extracting physical information corresponding to the rapid growth stage among the plurality of growth stages;
predicting obesity by inputting the extracted physical information to a trained neural network implemented with a Long Short Term Memory (LSTM) architecture, wherein the trained neural network trains physical information corresponding to the rapid growth stage among the plurality of growth stages as training data based on time series physical information on a plurality of sample subjects; and
providing an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity, wherein when the evaluation subject is a man, the obesity management solution is provided to increase a growth prediction value of the evaluation subject in the rapid growth stage, and when the evaluation subject is a woman, the obesity management solution is provided to increase a period of the rapid growth stage.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because receiving user health data, classifying growth stages based on the input data and extracting physical information, predicting obesity based on the extracted physical information, and providing a obesity management solution are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 1 and analogous independent claims 7-9 recite at least one abstract idea.
Furthermore, dependent claims 3, 6, 11, & 14 further narrow the abstract idea described in the independent claims. Claims 3 & 11 recites classifying a gender and using gender data for classification. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 1 and analogous independent claims 7-9, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
1. (Currently amended): A method for providing obesity prediction and solution for each growth stage using artificial intelligence, the method comprising:
receiving, by a processor, time series physical information on an evaluation subject, the time series physical information comprising at least one of body weight, body fat, skeletal muscle mass, body mass index (BMI), or basal metabolic rate of the evaluation subject measured over time;
classifying a plurality of growth stages comprising a normal growth stage, a rapid growth stage, a decelerated growth stage, and anon-growth stage based on the input physical information on the evaluation subject, and then extracting physical information corresponding to the rapid growth stage among the plurality of growth stages;
predicting obesity by inputting the extracted physical information to a trained neural network implemented with a Long Short Term Memory (LSTM) architecture, wherein the trained neural network trains physical information corresponding to the rapid growth stage among the plurality of growth stages as training data based on time series physical information on a plurality of sample subjects; and
providing an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity, wherein when the evaluation subject is a man, the obesity management solution is provided to increase a growth prediction value of the evaluation subject in the rapid growth stage, and when the evaluation subject is a woman, the obesity management solution is provided to increase a period of the rapid growth stage.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the use of a trained neural network implemented with a Long Short Term Memory (LSTM) architecture trained on physical information data from a plurality of sample subjects, this limitation amounts to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claims 7-9 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recites at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 6 & 14: These claims recite the use of the neural network and training the neural network model with growth stage data, which amounts to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)). (see MPEP § 2106.05(f)).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the use of a trained neural network implemented with a Long Short Term Memory (LSTM) architecture trained on physical information data from a plurality of sample subjects, this limitation amounts to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP § 2106.05(f)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, Claims 1, 3, 6-9, 11, & 14 are ineligible under 35 USC §101.
Response to Arguments
Applicant’s arguments on pages 6-8 regarding Claims 1, 3, 6-9, 11, & 14 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The claims achieve specific technical improvements in the field of obesity technology via using a LSTM neural network to enhance the accuracy of the obesity model similar to Enfish and McRO.
The Examiner asserts that the claimed invention is unlike McRo and the holding of McRo actually supports the Examiner's position. McRo held that an improvement may be one that “that improve[s] computer-related technology by allowing computer performance of a function not previously performable by a computer.” There is nothing in the Applicant’s invention that allows a computer to perform functions not previously performable by a computer. Furthermore, even assuming, arguendo, that the current invention achieves the improvement of, for example, enhance the accuracy of the obesity model, these improvements represent improvements to the abstract idea and not technological improvements in the same vein as those recited by McRO (i.e. automating a process that previously could not be automated through specific rules, and/or no longer requiring a manual process that relies on subjective determinations). With regards to Enfish, the Examiner asserts that the inventive concept of the present invention is not drawn towards the creation of a specific data structure that enables a particular function. The specification in Enfish described how invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. The Examiner argues that the Applicant’s specification fails to describe how the invention provides an improvement to computer functionality.
Similar to Vanda the claims are directly applied to the prevention of the specific medical condition of obesity.
The Examiner asserts that the present claims are not similar to Vanda because the present claims are not directed to treating a specific disease using a specific medication and dosage. The patent discussed in Vanda covered a method of using a drug iloperidone to treat patients diagnosed with schizophrenia by analyzing the patient’s genotype and determining the dosage based on the genotype. The present claims broadly claim a method and apparatus for providing obesity prediction and solution for each growth stage using artificial intelligence. The Examiner asserts that the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it” – similarly, the current invention merely recites classifying and predicting obesity and providing an obesity solution without any description for accomplishing a specific result. The claims do not include any limitations indicating that a treatment is to be administered, nor provide any information as to how the patient is to be treated or what the treatment is, but instead covers any possible treatment that a doctor decides to administer to the patient – see Example 43 Claim 1 in the Appendix 1 to the October 2019 Update: Subject Matter Eligibility.
Applicant’s arguments on pages 8-10 regarding Claims 1, 3, 6-9, 11, & 14 being rejected under 35 USC § 103 have been fully considered and are persuasive. The 103 rejection has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krans (US20170354351) – teaches to an apparatus (42) that provides advice on nutritional and caloric intake requirements for a child based on the child's current growth phase activity behavior and status corresponding to the child's current body mass index, the nutritional requirements determined in terms of a ratio of nutrient components that are tailored to the growth phase of the child.
Anand (WO2018078653A1) teaches to a method for assessing early stage development delays of a child and providing recommendations thereof. According to one embodiment, a cluster managing module receives the child profile features and growth check database of a given child. The cluster managing module analyzes both the child profile features and information in the growth check database and provides the result to a recommendation engine.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/Primary Examiner, Art Unit 3619
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