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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/1/2026 has been entered.
Status of Claims
Claims 1, 5, 8-9, 11-12 are amended.
Claims 2-4, 6-7, and 13-21 are cancelled
Response to Arguments
Applicant’s arguments, see pages 9-13, filed 6/1/2026, with respect to the rejection(s) of
claim(s) 1, 5, and 8-12 under 35 U.S.C. 103 rejection have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
35 U.S.C. 103:
Regarding claim 1, applicant argues that Lee, alone or in combination with the prior art, does
not teach “a learning processing unit configured to generate a blood sugar risk level estimation model by machine learning based on the training data set, wherein the learning processing unit provides labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, and wherein, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, the learning processing unit increases the number of pieces of sample data in the training data set to reduce the difference.” After further search and consideration, the examiner found no art to teach this limitation in combination with the rest of the claim. Therefore, the 35 U.S.C. 103 rejection is withdrawn.
Applicant's arguments, see pages 14-16, filed 6/1/2026, have been fully considered but they
are not persuasive.
35 U.S.C. 101:
Regarding claim 1, applicant argues that the claim recites limitations from claim 21, of claim set
filed 11/12/2025, which was not rejected under 35 U.S.C. 101. The examiner argues that claim 21 is a dependent claim of independent claim 1, which was rejected under 35 U.S.C. 101. Furthermore, the claim limitations are only directed towards the specificity of attribute information and non-invasive biological information. No significant additional elements or form of practical application is recited. Therefore, the 35 U.S.C. 101 rejection is maintained.
Regarding Applicant’s arguments with respect to Cardionet, the examiner respectfully disagrees that Claims 1, 5, and 8-12 should be found patent eligible in view of Cardionet.
The Claims at issue in Cardionet were focused on a specific asserted improvement in cardiac monitoring using rules of a particular type. According to the court, the claimed process used a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: cardiac monitoring accuracy for distinguishing atrial fibrillation. By incorporating the specific features of the rules as claim limitations, these claims were found by the court to be limited to a specific process for cardiac monitoring using particular information and techniques.
However, none of Claims 1, 5, and 8-12 is directed to a specific process for automatically animating characters using particular information and techniques as discussed in Cardionet. Further, unlike the claims at issue in Cardionet, Claims 1, 5, and 8-12 merely apply an abstract idea to a computer and do not either improve the performance of the computer itself or distinguishing atrial fibrillation in any way.
The applicant argues that the amended claim 1 provides a technological improvement to the
functioning of the computer and the technological field. Specifically, the improvement towards the accuracy of blood sugar level measurement by yielding a correct-answer percentage of 97%, which exceeds the standard of the ISO 15197. Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). See Genetic Technologies Limited v. Merial LLC (Fed Cir 2016). Here, the additional elements of claims 1, 11, and 12 are merely generically recited computer elements used as tools for executing the abstract ideas or insignificant extra-solution activity. The blood sugar level estimation model, in combination with linear regression model, the neural network model, and the gradient boosting decision tree mode are recited as computer implementation to an abstract calculation step. Therefore, the 35 U.S.C. 101 rejection is maintained.
Claim Rejections - 35 USC § 101
Claims 1, 5, and 8-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to an abstract idea without significantly more. Claims 1, 11, and 12 recite an apparatus, method, and a non-transitory computer with instructions for performing operations of the device comprising:
a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured blood sugar level of a plurality of patient
a step of generating a blood sugar risk level estimation model by: providing labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set to reduce the difference;
a step of calculating a blood sugar risk level estimated value of the predetermined user by inputting the attribute information and the non-invasive biological information of the predetermined user into the blood sugar risk level estimation mode
outputting the blood sugar risk level estimated value;
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is
evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to an apparatus, claim 11 is directed to a method and claim 12 is directed to
a computer program implemented on a computer and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claims 1, 11, and 12 recite an apparatus, method, and program implemented on a computer for performing operations of the device comprising:
a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured blood sugar level of a plurality of patient
a step of generating a blood sugar risk level estimation model by: providing labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set to reduce the difference;
a step of calculating a blood sugar risk level estimated value of the predetermined user by inputting the attribute information and the non-invasive biological information of the predetermined user into the blood sugar risk level estimation mode
outputting the blood sugar risk level estimated value;
If a claim limitation, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1, 11, and 12 recite an abstract idea of a mental process.
Claims 1, 11, and 12 recite the abstract idea of a mental process. The limitations as drafted in
the claims, under its broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting a generic processing system and memory, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example:
“A step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured blood sugar level of a plurality of patient;” A physician may obtain/record data using appropriate sensors and store it manually.
“A step of generating a blood sugar risk level estimation model by: providing labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set to reduce the difference;” A physician may build formulas and diagrams based on a correlation from the training data set and use that as a model for blood sugar level estimation. Mathematical formulas and equation may be used to determine when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value.
“a step of calculating a blood sugar risk level estimated value of the predetermined user by inputting the attribute information and the non-invasive biological information of the predetermined user into the blood sugar risk level estimation mode.” A physician may use equations to calculate a blood sugar level estimated value based on collected information on a user.
“Outputting the blood sugar risk level estimated value;” A physician may give a diagnosis based on blood sugar level information calculated and acquired.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claims 1, 11, and 12 recite the additional elements of a “information acquisition unit”,
“estimation model storage unit”, “a blood sugar level estimation model”, “linear regression model”, “neural network model”, “gradient boosting decision tree model”, “data storage unit” and a “estimation processing unit” which are being interpreted as a processor of a data gathering device.
The information acquisition unit to gather “attribute information includes the sex of the predetermined user, and the non-invasive biological information further includes: a blood pressure, a body mass index (BMI),an oxygen saturation (SpO2),electrocardiogram data, and a biological impedance of the predetermined user;” is an additional element that recite pre-solution activity to the step of data gathering.
The blood sugar level estimation model, in combination with a linear regression model, a neural network model, and a gradient boosting decision tree model in parallel, on the training data set, and ensemble learning based on outputs of the machine learning by the linear regression model, the neural network model, and the gradient boosting decision tree model are recited as computer implementation to an abstract calculation step.
Data storage unit is a generic computer component used to store data.
However, these elements are recited at a high level of generality performing the function of
generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
Accordingly, the additional elements do not integrate the abstract idea into a practical
application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
The additional elements when considered individually and in combination are not enough to
qualify as significantly more than the abstract idea.
The information acquisition unit to gather “attribute information includes the sex of the predetermined user, and the non-invasive biological information further includes: a blood pressure, a body mass index (BMI),an oxygen saturation (SpO2),electrocardiogram data, and a biological impedance of the predetermined user;” is an additional element that recite pre-solution activity to the step of data gathering.
The blood sugar level estimation model, in combination with a linear regression model, a neural network model, and a gradient boosting decision tree model in parallel, on the training data set, and ensemble learning based on outputs of the machine learning by the linear regression model, the neural network model, and the gradient boosting decision tree model are recited as computer implementation to an abstract calculation step.
Data storage unit is a generic computer component used to store data.
As discussed above with respect to integration of the abstract idea into a practical application,
“information acquisition unit”, “estimation model storage unit”, “a blood sugar level estimation model”, “linear regression model”, “neural network model”, “gradient boosting decision tree model”, “data storage unit” and a “estimation processing unit” which are being interpreted as a processor of a data gathering device as recited to perform the steps of:
a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured blood sugar level of a plurality of patient
a step of generating a blood sugar risk level estimation model by: providing labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set to reduce the difference;
a step of calculating a blood sugar risk level estimated value of the predetermined user by inputting the attribute information and the non-invasive biological information of the predetermined user into the blood sugar risk level estimation mode
outputting the blood sugar risk level estimated value;
amount to no more than mere instructions to apply the exception using generic computer
components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (TRAN et al. US Pub.: US 20210212606 A1, hereinafter Tran) teaches a data gathering device with a processor and memory, and conventional limitations that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
The following is an examiner’s statement of reasons for not providing a prior art rejection:
The closest prior art: US 20210212606 A1 teaches a learning processing unit configured to generate a blood sugar risk level estimation model by machine learning and storage of training data set (paragraph 4, 48-49 and 214-215). However, the examiner found no prior art to teach every limitation of claim 1, specifically, “wherein the learning processing unit provides labels indicating existence of a blood sugar risk level to the training data set based on the blood-measured blood sugar level, and wherein, when a difference between the numbers of pieces of data with the blood sugar risk level and data without the blood sugar risk level among the labels is equal to or larger than a predetermined value, the learning processing unit increases the number of pieces of sample data in the training data set to reduce the difference, “ in combination with the rest of the claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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