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 .
Response to Amendment
Receipt is acknowledged of applicant's amendment filed on 3/17/26. Claims 4, 6 and 14-16 are cancelled.. Claims 1-3, 5, 8-13 and 17-20 are currently pending and an action on the merits is as follows.
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, 5, 8-13 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the steps of calculating a blood neutral fat estimated value of the predetermined user based on the attribute information and/or the non-invasive biological information of the predetermined user by using the blood neutral fat estimation model.
The limitation of calculating a blood neutral fat estimated value of the predetermined user based on the attribute information and/or the non-invasive biological information of the predetermined user by using the blood neutral fat estimation model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processing unit” the claims are directed to concepts relating to organizing information in a way that can be performed mentally or analogous to human mental work and nothing in the claim element precludes the steps from practically being performed in the mind. For example, but for the processor, “calculating” in the context of this claim encompasses the user manually calculating a value based on the received data and the model or algorithms. 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. Accordingly, the claim recites an abstract idea.
Similarly, the judicial exception of “estimating a rPPG signal waveform of the subject” and “calculate a blood neutral fat risk” is performed “the blood neutral fat estimation model”. The learning algorithm model is used to generally apply the abstract idea without placing any limits on how the trained model functions. Rather, these limitations do not include any details about how the “calculating” is accomplished. See MPEP 2106.05(f). The recitation of performing the calculation on a “the blood neutral fat estimation model” in limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “the blood neutral fat estimation model” limits the identified judicial exceptions of the estimating steps this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an information acquisition unit and a biological information unit. These detectors involve mere data gathering and amount to insignificant extra-solutional activity, specifically pre-solutional activity. Additionally, the processing unit is a generic computer element used in its usual way. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly the dependent claims do not include additional elements that amount to significantly more. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept and well-understood, routine and conventional activity is not sufficient to amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an information acquisition unit in claim 1 which is interpreted as a module of the processor, see ¶70. estimation model storage unit in claim 1. An estimation processing unit which is interpreted as a module of the processor, see ¶70. A training data storage unit in claims 3 and 13. A learning processing unit in claims 3 and 13 which is interpreted as a machine learning model implemented on the processor, see ¶63,70. A biological estimation unit in claims 9 and 18-20. A biological measurement device in claim 10 which is interpreted as the measurement devices listed in ¶42.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9 and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is unclear what the biological estimation unit is. It is unclear if it is a sensor or some calculator or processing device.
Claim limitations of “an information acquisition unit”, “an estimation model storage unit”, “an estimation processing unit”, “a training data storage unit”, “a learning processing unit” “a biological estimation unit” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For “an estimation model storage unit” and “a training data storage unit” no structure is recited specifically for these other than the device has some storage media but it is not clear if the listed storage units are a part of that or some separate component. The “an information acquisition unit”, “an estimation processing unit” and “a learning processing unit” which are part of the processor do not recite any structure or specific code or algorithms associated with them. They are defined simply by their function and act as a black box that receives some output and give a different output. It is unclear what “a biological estimation unit” refers to as it is just show in the block diagram. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
Claim(s) 1-3, 5, 8-13 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kodama et al. US 2023/0178249 in view of Chawla et al. “SMOTE; Synthetic Minority Over-sampling Technique”
Regarding claim 1, 11 and 12, Kodama discloses a blood neutral fat estimation device comprising:
an information acquisition unit configured to acquire attribute information and non- invasive biological information of a predetermined user ([¶44-46] unit 501 and 503 receive attribute information and non-invasive biological information);
an estimation model storage unit configured to store a blood neutral fat estimation model ([¶47] storage section 504 contains the models) generated by machine learning based on a training data set including attribute information, non- invasive biological information, and blood-measured blood neutral fat measured values of a plurality of subjects acquired in advance ([¶63,98] checkup data is used as training data); and
an estimation processing unit configured to calculate a blood neutral fat estimated value of the predetermined user based on the attribute information and/or the non-invasive biological information of the predetermined user by using the blood neutral fat estimation model ([¶63-67,98] the device uses the body information with a model to estimate cholesterol and disease risk)
wherein the non-invasive biological information includes biological impedance, blood pressure, and pulse wave data ([¶3,63] general check-up data includes blood pressure [¶114] pulse wave information is used in the model),
wherein the training data set is provided with labels indicating existence of a blood neutral fat risk based on the blood-measured blood neutral fat measured values ([¶72,73] the actual risk data is part of the labelling in the training), and
Kodama does not specifically disclose when a difference between a number of pieces of data with the blood neutral fat risk and a number of pieces of data without the blood neutral fat risk among the labels is equal to or larger than a predetermined value, a number of pieces of sample data in the training data set is increased to reduce the difference. Chawla discloses a method for improving the accuracy of machine learning by increasing a set of training data to reduce the difference between the number of piece of data with the label and without the label ([pg. 328 first paragraph of section 4.2]). Therefore, it would have been obvious to one of ordinary skill in the art prior to the time of filing to combine Kodama with the correction of Chawla in order to improve performance of the classifier ([pg. 352, section 7]).
Regarding claim 2, Kodama discloses the attribute information includes any one or a combination of age and sex ([¶45] height, age and gender are input), and wherein the non-invasive biological information includes any one or a combination of BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance ([¶46,106] impedance measurements are taken as well as pulse information).
Regarding claims 3 and 13, Kodama discloses a training data storage unit configured to store a training data set ([¶71] previous data is stored and used as training data); and a learning processing unit configured to generate the blood neutral fat estimation model by machine learning based on the training data set ([¶63,83] the training data is used to train the machine learning model).
Regarding claim 4, Kodama discloses the training data set includes attribute information, non-invasive biological information, and a blood-measured blood neutral fat measured value of a subject ([¶57,63] the input information and the actual blood test information from a medical checkup are used to train the model).
Regarding claim 5, Kodama discloses using logistic regression to determine the best fit parameters for the model but does not specifically disclose a coefficient of correlation between a logarithm of the blood neutral fat estimated value and a logarithm of the blood neutral fat measured value is equal to or larger than 0.6. However, where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation (see MPEP 2144.05 IIA and In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955)).
Regarding claims 6 and 14-16, Kodama discloses wherein the training data set includes non-invasive biological information and a blood- measured blood neutral fat measured value of a subject ([¶57,63]), and wherein the estimation processing unit calculates a blood neutral fat risk estimated value in place of the blood neutral fat estimated value ([¶67-68] disease risk is determined based on the model).
Regarding claim 7, Kodama discloses the learning processing unit provides labels indicating existence of the blood neutral fat risk to the training data set based on the blood-measured blood neutral fat measured value ([88-90] the data is labeled with three categories), and wherein, when a difference between the number of pieces of data with the blood neutral fat risk and the number of pieces of data without the blood neutral fat risk 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 ([¶88-90,92] the model is adjusted to reduce error and improve accuracy with the true value).
Regarding claims 8 and 17, Kodama discloses the learning processing unit generates a first blood neutral fat risk estimation model and a second blood neutral fat risk estimation model by machine learning based on each of training data sets of different kinds, and wherein the estimation processing unit calculates a blood neutral fat risk estimated value of the predetermined user by using the first blood neutral fat risk estimation model and the second blood neutral fat risk estimation model ([¶71] different models can be used together to determine the risk value).
Regarding claims 9 and 18-20, Kodama discloses a biological information estimation unit configured to estimate at least one piece or more of biological information among BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance included in the biological information, wherein the information acquisition unit acquires, as biological information of the predetermined user, the biological information estimated by the biological information estimation unit ([¶49] the body composition estimation section estimates biological information used in the determination of the neutral fat).
Regarding claim 10, Kodama discloses a biological information measurement device configured to measure non-invasive biological information ([¶44,106] the impedance and other physiological measurements are collected).
Response to Arguments
Applicant's arguments filed 3/17/26 have been fully considered but they are not persuasive.
Regarding Applicant’s argument that the claims provide a technical improvement, Examiner respectfully disagrees. The improvement appears to be using non-invasive measures like blood pressure and pulse wave data to risk rather than invasive measures and using the machine learning model. It is not clear how this is an improvement in the technical field when the prior art discloses non-invasive blood neutral fat risk determination and training machine learning models to provide accurate results.
Applicant’s arguments, see 12-16, with respect to the rejection(s) of claim(s) 1-3, 5, 8-13 and 17-20 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chawla.
Conclusion
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 MICHAEL ANTHONY CATINA whose telephone number is (571)270-5951. The examiner can normally be reached 10-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Chen can be reached at 5712723672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL A CATINA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791
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