DETAILED ACTION
Claims 1-14 are currently pending and have been examined.
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 1-14 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 1-8 are directed to a method (i.e., a process); Claims 9-14 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. A method for providing precocious obesity prediction and solution for each growth stage using artificial intelligence, the method comprising:
receiving time series physical information on an evaluation subject;
classifying a plurality of growth stages based on the input physical information on the evaluation subject, and then extracting physical information corresponding to a rapid growth stage among the plurality of growth stages;
predicting obesity by inputting the extracted physical information to a trained neural network; and
providing an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because classifying growth stages and extracting physical information based on physical information from a subject, predicting obesity using a model and physical information, and providing a solution are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind. As an example, a user could practically in their mind predicting obesity for each grow stage of growing children and adolescents.
Accordingly, independent claim 1 and analogous independent claim 9 recite at least one abstract idea.
Furthermore, dependent claims 2-8 & 10-14 further narrow the abstract idea described in the independent claims. Claims 2-3 & 10-11 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 claim 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. A method for providing precocious obesity prediction and solution for each growth stage using artificial intelligence, the method comprising:
receiving time series physical information on an evaluation subject;
classifying a plurality of growth stages based on the input physical information on the evaluation subject, and then extracting physical information corresponding to a rapid growth stage among the plurality of growth stages;
predicting obesity by inputting the extracted physical information to a trained neural network; and
providing an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity.
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 use of a trained neural network, 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 claim 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 4-6 & 12-14: These claims recite the use of the neural network, training, and using a second model 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)).
Claims 7-8: These claims recite a program and computer-readable recording medium; these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (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 10 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 use of a trained neural network, 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 1-14 are ineligible under 35 USC §101.
Independent claim 9 is directed to a system as described in the preamble. However, the claim does not positively recite any elements that necessarily constitute a system or apparatus, such as computer hardware. It is not clear what structure is included or excluded by the claim language. Software per se is not patentable under § 101; therefore, the claimed invention does not fall within a statutory class of patentable subject matter. See MPEP 2106.01. Examiner recommends amending the claim to clearly include hardware in order to overcome this rejection. Dependent claims 10-14 are also rejected due to their dependency from claim 9.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 & 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (KR10-2014-0045759) in view of Sung (KR102304563).
As per claim 1, Choi teaches a method for providing precocious obesity prediction and solution for each growth stage using artificial intelligence, the method comprising:
receiving time series physical information on an evaluation subject (para. 32: subject data obtained);
classifying a plurality of growth stages based on the input physical information on the evaluation subject (para. 59: growth phases classified), and then extracting physical information corresponding to a rapid growth stage among the plurality of growth stages (para. 59: various growth stages determined including rapid growth).
Choi does not expressly teach predicting obesity by inputting the extracted physical information to a trained neural network; and providing an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity.
Sung, however, teaches to inputting patient parameter data into a trained machine learning model and outputting obesity prediction and recommendations for treatment (pg. 14).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Sung with Choi based on the motivation of measure and collect individual data, construct health big data by connecting various kinds of data existing between the government and the civiliary, reveal a correlation between the data through artificial intelligence analysis, and manage or prevent an obesity state in a daily life. (Sung - pg. 5).
As per claim 2, Choi and Sung teach the method of claim 1. Choi teaches further comprising:
after the receiving of the physical information on the evaluation subject, classifying a gender of the evaluation subject (para. 52: gender data obtained).
As per claim 7, Choi and Sung teach a program stored in a computer-readable recording medium including a program code for executing the method for providing obesity prediction and solution for each growth stage using artificial intelligence of claim 1 (Choi – para. 84: computer performing operations).
As per claim 8, Choi and Sung teach a computer-readable recording medium in which a program for executing the method for providing obesity prediction and solution for each growth stage using artificial intelligence of claim 1 is recorded (Choi – para. 84: computer performing operations).
As per claim 9, Choi teaches an apparatus for providing obesity prediction and solution for each growth stage using artificial intelligence, the apparatus comprising:
an input unit configured to receive time series physical information on an evaluation subject (para. 32: subject data obtained);
a growth stage determination unit configured to classify a plurality of growth stages based on physical information on the evaluation subject input to the input unit, and then extract physical information corresponding to a rapid growth stage among the plurality of growth stages (para. 59: growth phases classified various growth stages determined including rapid growth);
Choi does not expressly teach an obesity prediction unit configured to predict obesity by inputting the extracted physical information to a trained neural network;
a solution generation unit configured to generate an obesity management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the obesity; and
a display unit configured to display the generated obesity management solution.
Sung, however, teaches to inputting patient parameter data into a trained machine learning model and outputting obesity prediction and recommendations for treatment (pg. 14). Sung also teaches to a user terminal comprising a computer and a screen for input and output of data (pg. 8).
As per claim 10, Choi and Sung teach the apparatus of claim 9. Choi teaches further comprising:
a gender determination unit configured to classify the gender of the evaluation subject input to the input unit (para. 18: growth state data obtained based on age and gender of the child compared to average health history of similar population using age and gender; practice information output for improving problems of the child),
wherein the solution generation unit generates an obesity management solution differently depending on the gender of the evaluation subject (para. 18: growth state data obtained based on age and gender of the child compared to average health history of similar population using age and gender; practice information output for improving problems of the child).
Claims 3-5 & 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (KR10-2014-0045759) in view of Sung (KR102304563) as applied to claims 2 & 9 above, and in further view of Tsuru (JP2020038573).
As per claim 3, Choi and Sung teach the method of claim 2, but do not expressly teach wherein a time of the rapid growth stage is set differently based on the gender of the evaluation subject to extract physical information.
Tsuru, however, teaches to managing the state of development of children where a reference growth rate curve and identifying peak values of an age growth rate curve are used corresponding to gender type (para. 103, 105).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Tsuru with Choi and Sung based on the motivation of provide an evaluation system and an evaluation program capable of rapidly and objectively evaluating the state of a subject.. (Tsuru - para. 6).
As per claim 4, Choi, Sung, and Tsuru teach the method of claim 3. Choi teaches wherein when the evaluation subject is a man, a solution is provided to increase a growth prediction value of the evaluation subject in the rapid growth stage (para. 18: growth state data obtained based on age and gender of the child compared to average health history of similar population using age and gender; practice information output for improving problems of the child).
As per claim 5, Choi, Sung, and Tsuru teach the method of claim 3. Choi teaches wherein when the evaluation subject is a woman, a solution for increasing a period of the rapid growth stage is provided (para. 18: growth state data obtained based on age and gender of the child compared to average health history of similar population using age and gender; practice information output for improving problems of the child).
Claims 11-13 recite substantially similar limitations as those already addressed in claim 3-5, and, are such, is rejected for similar reasons as given above.
Claims 6 & 14 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (KR10-2014-0045759) in view of Sung (KR102304563) as applied to claims 1 & 9 above, and in further view of Jang (KR10-2019-0072292).
As per claim 6, Choi and Sung teach the method of claim 1, but do not expressly teach wherein the 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.
Jang, however, teaches to modeling a growth prediction model on the basis of input physical growth data of a user and historical physical growth data for a plurality of subjects (para. 39).
Claim 14 recites substantially similar limitations as those already addressed in claim 6, and, as such, is rejected for similar reasons as given above.
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.
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