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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/31/2025 has been entered.
Status of Claims
This action is in response to the RCE filed 12/31/2025.
Claims 1, 8-10, and 12 were amended 12/31/2025.
Claims 1, 3-10, 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.
Claims 1, 3-10, 12 are 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.
Claims 1, 3-10, 12 are drawn to a system, device and a non-transitory computer readable storage medium which are statutory categories of invention (Step 1: YES).
Independent claims 1, 10, and 12 recite an information acquisition unit configured to acquire 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); store the attribute information and the non-invasive biological information of the predetermined user configured to store an HbA1c risk estimation model; and configured to input, only the attribute information and the non-invasive biological information of the predetermined user into the HbA1c risk estimation model, and output, from the HbA1c risk estimation model, an HbA1c risk estimated value.
Independent claim 12 further recites: 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, a step of acquiring attribute information and non-invasive biological information of a predetermined user, wherein the attribute information comprises an age and a sec of the predetermined user, and the non-invasive biological information comprises only information obtained non-invasively…
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).
Dependent claims 3-9 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 4 further recites “a training data storage unit” and “a learning processing unit” and “machine learning” which are generically recited in the specification paragraphs 10, 46 and do not provide significantly more than the abstract idea. Claim 7 further recites “learning processing unit” which is generically recited in the specification paragraphs 10, 46 and do not provide significantly more than the abstract idea. Claim 8 further recites “learning processing unit” and “machine learning based on each of training data sets of different kinds” which are generically recited in the specification paragraphs 10, 46 and do not provide significantly more than the abstract idea. These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 1, 3-10 and 12 are allowable over the prior art. Specifically, the prior art does not teach non-invasively gathering data including pulse wave data including an elasticity index, biological impedance including values related to a muscle mass, moisture, a cardiac output, a conductance and R (omega) to output from a risk estimation model. In combination with the other claim limitations, the independent claims overcome the prior art of Kim (US 2022/0415507 A1), Razavian (US 2017/0308981 A1), and Wexler (US 2020/0375549 A1). A new prior art search was conducted and found the prior art of Lu (US 20210217485 A1) that teaches monitoring heart events using statistical models and non-invasive biological data from sensors, however it did not teach using non-invasive data types as recited in the amendments being fed into an Hb1Ac prediction model. The 103 rejection has been withdrawn.
Response to Arguments
The arguments filed 12/31/2025 have been fully considered.
Regarding the arguments pertaining to the 103 rejection, these arguments are persuasive. The amendments overcome the prior art of record and the rejection has been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the claimed invention recites a technical improvement that provides a practical application to overcome the abstract idea. Applicant further argues that the claimed invention implements a novel process that enables a computer to accurately determine an HbA1c risk estimated value with only biological information obtained non-invasively. Examiner respectfully disagrees. Implementing a novel process on a generic computing device that does not improve the functioning of the computer does not provide a practical application to overcome the abstract idea. Further, accurately determining an HbA1c risk estimated value with only biological information obtained non-invasively is directed related to the grouping “Certain Methods of Organizing Human Activity”. Improving a patient’s risk score by obtaining data as a user from them non-invasively is an improvement on the medical field by improving risk evaluations between people. The “medical field” is not necessarily a “technical field”, nor is a treatment effected. Classen is an example of adding a meaningful limitation to the claims that create a practical application, however Classen integrated the results of the analysis into a specific and tangible method that resulted in the method “moving from abstract scientific principle to specific application” (Classen Immunotherapies Inc. v. Biogen IDEC).
Applicant further argues that the claimed invention is similar to CardioNet. Examiner respectfully disagrees as CardioNet was directed towards an improvement of cardiac monitoring systems. However, the current claimed invention is not directed towards an improvement towards a system nor an improvement towards a computer but is directed towards the argued improvement of “accurately determining an HbA1c risk estimated value with only biological information obtained non-invasively”. Improving data using generic computer components and generic machine learning models and generic input/output devices (i.e., sensors that gather the non-invasive biological data as shown in paragraph 26) does not improve the system itself and does not provide a practical application to overcome the abstract idea.
The dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lu (US 20210217485 A1) teaches monitoring heart events using statistical models and non-invasive biological data from sensors.
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/KIMBERLY A. SASS/ Examiner, Art Unit 3686
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