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 response to the reply filed 7/07/2026.
Claims 1, 6, 8, 10 and 12 were amended 7/07/2026.
Claims 1, 3, 6, 8-10 and 12 are currently pending 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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 12 is drawn to a non-transitory computer readable storage medium which is a statutory category of invention (Step 1: YES).
Independent claim 12 recites an a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured HbA1c measured value of a subject; a step of generating an HbA1c risk estimation model by providing labels indicating existence of HbA1C risk to the training data set based on the blood-measured HbA1c measured value, when a difference between the number of pieces of data with the HbA1C risk and the number of pieces of data without the HbA1c risk 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 and a step of acquiring attribute information and non- invasive biological information of a predetermined user, wherein the attribute information comprises an age and a sex of the predetermined user, and the non-invasive biological information comprises only information obtained non-invasively including: a weight, a body mass index (BMI), a circulating blood amount, a blood pressure including a diastolic blood pressure, a pulse pressure, and an average arterial blood pressure, pulse wave data including an elasticity index, electrocardiogram data including a heart rate and a heart rate variation indicator LF/HF, and biological impedance including values related to a muscle mass, moisture, a cardiac output, a conductance and R (omega); a step of storing the attribute information and the non-invasive biological information of the predetermined user configured to store an HbA1c risk estimation model; and a step of inputting, only the attribute information and the non-invasive biological information of the predetermined user into the HbA1c risk estimation model, and output, from anHbA1c risk estimation model, an HbA1c risk estimated value.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a user and a subject, as reflected in the specification, which states that “A "user" is a person who uses the HbAlc risk estimation system to non-invasively obtain an estimated value of the HbAlc risk. A "subject" is a person who provides, upon a predetermined procedure and an agreement, attribute information such as age or sex, noninvasive biological information, and a blood-measured HbAlc measured value as a training data set to be used in the HbAlc risk estimation system.” (see: specification paragraph 24). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “risk estimation device”, “terminal device”, “estimation model storage unit”, “estimation processing unit”, and “machine learning”, “biological information measurement device configure to measure the non-invasive biological information”, “display device”, “non-transitory computer readable storage medium”, “computer”, “machine learning based on the training data set”, “user data storage unit”, are recited at a high level of generality (e.g., that the storing and estimating is performed using generic computer components and the machine learning is generically recited using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 3 and
Paragraph 39, where “The user data storage unit 33 stores the attribute information and the non-invasive biological information of the user, which are acquired from the first acquisition unit 31 and the second acquisition unit 32.”
Paragraph 41, where “The learning processing unit 35 acquires the training data sets stored in the training data storage unit 34 and produces an HbAlc risk estimation model by using the training data sets. Specifically, when the HbAlc risk is estimated, the relation among the attribute information, the non-invasive biological information, and the HbAlc risk is learned by machine learning with each acquired training data set by gradient boosting such as XGBoost, a neural network, logistic regression, or ensemble learning with results of the learning.”
Paragraph 43, where “The display device 39 can display the HbAlc risk estimated value together with the attribute information and the non-invasive biological information of the user. Note that these pieces of data may be displayed on the terminal device 10 possessed by the user.”
Paragraph 44, where “As illustrated in Figure 3, the HbAlc risk estimation device 30 is configured as a computer 300 including one or a plurality of processors 301, a memory 302, a storage 303, an input-output port 304, and a communication port 305. Each processor 301 performs processing related to HbAlc estimation according to the present embodiment by executing a computer program. The memory 302 temporarily stores a computer program and a calculation result of the computer program. The storage 303 stores a computer program configured to execute processing by the HbAlc risk estimation device 30. The storage 303 may be any computer-readable storage and may be, for example, various kinds of recording media such as a magnetic disk, an optical disk, a random-access memory, a flash memory, and a read-only memory. The input-output port 304 performs inputting of information from the terminal device 10 and the biological information measurement device 20 and outputting of a HbAlc estimated value to the display device 39. The communication port 305 transmits and receives data to and from a non-illustrated information terminal such as another computer. Communication may be performed by wireless and wired communication methods. Note that the first acquisition unit 31, the second acquisition unit 32, the learning processing unit 35, the estimation processing unit 37, and the like described above function at the processors 301 of the HbAlc risk estimation device 30 when operating.”
Paragraph 46, “At step ST102, the learning processing unit 35 performs machine learning by logistic regression, a neural network, and a gradient boosting decision tree, and at step STl 03, ensemble learning (voting) is further performed with results of the machine learning by the logistic regression, the neural network, and the gradient boosting decision tree.”
Paragraph 26, where “The biological information measurement device 20 measures the non-invasive biological information of the user. The non-invasive biological information is biological information acquired by a method that does not require insertion of an instrument into the skin or an opening part of the body. The non-invasive biological information may be measured by using, for example, a commercially available height meter, weight meter, blood pressure meter, pulse oximeter, pulse wave meter, electrocardiogram meter, impedance measurement machine, or galvanic skin measurement machine”
Paragraph 39, “The user data storage unit 33 stores the attribute information and the non-invasive biological information of the user, which are acquired from the first acquisition unit 31 and the second acquisition unit 32.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Allowable Subject Matter
Claims 1, 3, 6, 8-10 are allowed. Claims 1, 3, 6, 8-10 provide a practical application to overcome the 101 rejection. Specifically, the claim amendments to independent claims 1 and 10 provide a specific improvement of the learning processing unit that generates the HbA1c risk estimation model by machine learning. By providing a specific improvement of improving the learning processing unit by increasing the number of pieces of sample data to reduce the difference between the number of pieces of data that improves the accuracy of the machine learning model, provides a practical application that overcomes the abstract idea. The 101 rejection has been withdrawn and the claims are allowed.
Response to Arguments
The arguments filed 7/7/2026 have been fully considered.
Regarding the arguments pertaining to the 101 rejection, these arguments are partially persuasive. Independent claims 1 and 10 provide a specific improvement of the learning processing unit that generates the HbA1c risk estimation model by machine learning. By providing a specific improvement of improving the learning processing unit by increasing the number of pieces of sample data to reduce the difference between the number of pieces of data that improves the accuracy of the machine learning model, provides a practical application that overcomes the abstract idea. The 101 rejection has been withdrawn and the claims 1, 3, 6, 8-10 are allowed.
Regarding the arguments pertaining to claim 12, these arguments are not persuasive. Applicant argues that the claimed invention is similar to CardioNet as the claimed invention provides an improvement of accuracy in detecting certain cardiac conditions. However, CadrioNet, as recited in the arguments, provided an “improved medical device”, however the claimed invention in claim 12 does not provide an improvement to a medical device, it provides a generic computing device with a generic machine learning model that relies on the input/output of data through the generic computing components to create an output of data. It is dissimilar to CardioNet as there is no recitation of a medical device itself, nor an improvement upon a medical device. The accuracy of the data of CardioNet was achieved through an improvement of the medical device itself, and not through the input/output of data on a generic computing system, and therefore is dissimilar from the current claimed invention.
Applicant further argues that the claimed invention is similar to DesJardins, however Examiner respectfully disagrees as the claimed invention does not recite an improvement to the machine learning model, but rather provides calculations of differences between data in a training data set. The claim limitations focus on the data that is input/output rather than the technology of the machine learning model and the system.
Claim 12 does not recite specific improvements to technology and merely recites the directions of the input/output of data using generic computing components and a generic machine learning model as evidenced in the specification above. Claim 12 does not recite a “learning processing unit” and relies on the generic computing device to implement the claim limitations. Therefore, the 101 rejection on Claim 12 remains.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lopes-Virella (US 20110124119 A1) teaches using risk estimation calculations to determine coronary artery disease, however it does not teach using labels of blood-measured values to predict the risk calculations.
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.
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/KIMBERLY A. SASS/ Examiner, Art Unit 3686
