DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
As detailed on the Filing Receipt filed 11/25/2024, the instant application is a National Phase entry of PCT Application No. PCT/JP2022/015104 (filed 3/28/2022) and claims the benefit of foreign patent applications JP2021-130216 (filed 8/6/2021) and JP2022-040639 (filed 3/15/2022). Applicant has supplied certified copies of the JP foreign priority documents, but has not supplied English translations of these documents. All references applied herein stand as prior art with respect to the claimed foreign priority dates. However, please note that intervening references may be applied during future prosecution because Applicant has not provided English translations. Applicant may be required to provide English translations if intervening references are applied. See 37 CFR 1.55(g)(3); MPEP 213.04 and 216.
Information Disclosure Statement
The Information Disclosure Statements filed on 2/16/2023, 6/12/2025 and 12/2/2025 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. Signed copies] of the IDS are included with this Office Action.
Claim Status
Claims 1-12 are pending, and under examination.
Objection to the Specification (Abstract)
Applicant is reminded of the proper content of, language and format for an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because of the following informalities:
The abstract refers to purported merits of the invention, and compares the invention with the prior art (“Heretofore, for the measurement of creatinine, it is required to collect blood from a subject… in an invasive manner, which places psychological and physical burdens on the subject. [Solution] The present invention… makes it possible to estimate a creatinine risk in a non-invasive manner”)
the abstract is not entirely in narrative form (“[Solution]”).
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Interpretation
This section documents the examiner’s interpretation of certain claim elements under USPTO standards.
Broadest Reasonable Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art (MPEP 2111-2111.01).
The claims recite the term “creatinine risk” (e.g., claim 1). This is not a term of record in the art. The specification does not explicitly define the term, but does discuss associations between creatinine and health. The Background section discusses the association between circulating creatinine levels and kidney function, stating: “Typically, creatinine is discharged from muscle tissue to blood, filtrated through the glomerulus of the kidney, and then discharged into urine. Thus, the amount of creatinine in blood is utilized as an indicator for the evaluation of kidney function” (paras. 0002-3).
The Summary section characterizes the purpose of the invention thus: “The present invention… is intended to provide a creatinine risk estimation device… method, and… computer program that are capable of extremely accurately and swiftly estimating creatinine risk on health based on non-invasive biological information without blood or urine collection” (paras. 0003-4 and 0008).
In view of these portions of the specification, the term “creatinine risk” is interpreted as a health risk associated with creatinine levels, e.g., risk of impaired kidney function.
35 USC § 112(f)
One or more claim elements are being interpreted under 35 USC § 112(f), which reads as follows:
(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 broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 USC § 112(f) is invoked. As explained in MPEP 2181 § I, claim limitations that meet the following three-prong test will be interpreted under 35 USC § 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 USC § 112(f). The presumption that the claim limitation is not interpreted under 35 USC § 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 USC § 112(f), because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited functions and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
an “information acquisition unit” which can:
acquire attribute information and non-invasive biological information of a predetermined user (claim 1), and
acquire, as biological information of a predetermined user, biological information estimated by a biological information estimation unit (claim 9);
an “estimation model storage unit” which can:
store a creatinine risk estimation model (claim 1);
an “estimation processing unit” which can:
calculate a creatinine risk 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 creatinine risk estimation model (claim 1), and
calculate the creatinine risk estimated value of a predetermined user by using a first creatinine risk estimation model and a second creatinine risk estimation model (claim 8);
a “training data storage unit” which can:
store a training data set (claim 4);
a “learning processing unit” which can:
generate the creatinine risk estimation model by machine learning based on the training data set (claim 4),
provide labels indicating existence of the creatinine risk to the training data set based on a blood-measured creatinine measured value (claim 7),
increase the number of pieces of sample data in the training data set, when a difference between the number of pieces of data with the creatinine risk and the number of pieces of data without the creatinine risk among the labels is equal to or larger than a predetermined value, to reduce said difference (claim 7), and
generate a first creatinine risk estimation model and a second creatinine risk estimation model by machine learning based on each of training data sets of different kinds (claim 8);
a “biological information estimation unit” which can:
estimate at least one piece or more of biological information among BMI, blood pressure, pulse wave data, electrocardiogram data, biological impedance, and oxygen saturation included in the biological information (claim 9); and
a “biological information measurement device” which can:
measure non-invasive biological information (claim 10).
Because these claim limitation are being interpreted under 35 USC § 112(f), they are being interpreted to cover the corresponding structures described in the specification as performing the claimed functions, and equivalents thereof.
Regarding the various ‘units’, the specification states: “the first acquisition unit, the second acquisition unit, the learning processing unit, the estimation processing unit, and the like described above function at the processors of the creatinine risk estimation device when operating” (pg. 10, paras. 1-2).
The specification thus indicates that the ‘units’ effect their respective functions via hardware processors during device operation. The claimed ‘units’ are interpreted, in light of the specification, as encompassing computer programs and the recited functions of each ‘unit’ are accordingly interpreted as computer-implemented functions. See ‘Claim Rejections – 35 USC 112’ section.
Regarding the ‘biological information measurement device’, the specification states: “The biological information measurement device 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” (para. 0026). The specification goes on to list particular embodiments of the BIMD, including: a commercially-available height meter, weight meter, blood pressure meter, pulse oximeter, pulse wave meter, electrocardiogram meter, impedance measurement machine, galvanic skin measurement machine, and “ES-TECK BC-3 (Ryobi Systems Co., Ltd.) that can simultaneously measure pulse wave data, electrocardiogram data, biological impedance, and oxygen saturation (SpO2)” (para. 0026).
Particular embodiments appearing in the written description may not be read into a claim, as further limitations, when the claim language is broader than the embodiments (MPEP 2111.01 § II). The broadest reasonable, and specification-consistent, interpretation of the claimed biological information measurement device is any device capable of measuring biological information of a user by a method that does not require insertion of an instrument into the skin or body. The claimed biological information measurement device is interpreted accordingly, and is considered to encompass, but not be limited to, the described particular embodiments.
If applicant does not intend to have these limitations interpreted under 35 USC § 112(f), applicant may:
(1) amend the claim limitations to avoid their being interpreted under 35 USC § 112(f) (e.g., by reciting sufficient structures to perform the claimed functions); or
(2) present a sufficient showing that the claim limitations recite sufficient structures to perform the claimed functions so as to avoid them being interpreted under 35 USC § 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 USC § 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-12 are rejected under 35 USC § 112(a) for failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that a joint inventor, at the time the application was filed, had possession of the claimed invention.
With respect to claims 1-9 and dependents thereof, the following recited computer-implemented (see ‘Claim Interpretation’ section) limitations invoke 112(f) and lack adequate written description support:
an “information acquisition unit” (claims 1 and 9);
an “estimation model storage unit” (claim 1);
an “estimation processing unit” (claims 1 and 8);
a “training data storage unit” (claim 4);
a “learning processing unit” (claims 4 and 7-8); and
a “biological information estimation unit” (claim 9).
In cases involving computer-implemented means-plus-function limitations, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer, or microprocessor, and that the specification must disclose an algorithm for performing the claimed function. Thus, to adequately support computer-implemented ‘means-plus-function’ limitations, the specification must disclose an implementing computer and algorithms in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at time of filing. See MPEP 2161.01 and 2181 § IV. See also Aristocrat Techs. Australia PTY Ltd. v. Int’l Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008, hereafter “Aristocrat”); Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008); Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312 (Fed. Cir. 2012).
The specification states that the claimed creatinine risk estimation device may be implemented by a commercially available PC (para. 0043). The specification does not describe any particular algorithms that implement the functions ascribed to each claimed ‘unit’, but merely states that “the computer may be a computer capable of executing various functions with various computer programs installed thereon, such as a… personal computer” (para. 0065). The specification thus fails provide a disclosure of a computer and algorithms in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at time of filing.
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as limitations under 35 USC § 112(f);
(b) Amend the written description of the specification such that it expressly recites what structures, materials, or acts perform the entire claimed functions, without introducing any new matter (35 USC § 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structures, materials, or acts disclosed therein to the functions recited in the claims, without introducing any new matter (35 USC § 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 functions so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed functions, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structures, materials, or acts for performing the claimed functions and clearly links or associates the structures, materials, or acts to the claimed functions, without introducing any new matter (35 USC § 132(a)); or
(b) Stating on the record what the corresponding structures, materials, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed functions. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of 35 USC § 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.
Claims 1-12 are rejected under 35 USC § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, or a joint inventor, regards as the invention. The claims include numerous ‘means-plus-function’ limitations invoking 35 USC § 112(f), but the written description fails to disclose the corresponding structure(s), material(s), or act(s) for performing the entire claimed functions and to clearly link the structure(s), material(s), or act(s) to the functions. The limitations at issue include the following, which are directed to various ‘units’:
an “information acquisition unit” (claims 1 and 9);
an “estimation model storage unit” (claim 1);
an “estimation processing unit” (claims 1 and 8);
a “training data storage unit” (claim 4);
a “learning processing unit” (claims 4 and 7-8) and
a “biological information estimation unit” (claim 9).
The specification indicates that the claimed device encompasses a general purpose computer, wherein the claimed ‘units’ effect their respective functions via device processors during operation of the device. However, the specification discloses no corresponding algorithm(s) associated with a computer or processor that implements said functions.
Mere reference to a general purpose computer with appropriate programming (e.g., a “device” comprising functionally-configured “units”), without providing an explanation of the appropriate programming, is not an adequate disclosure of the corresponding structure. See, e.g., Aristocrat, 521 F.3d at 1333-34 and 1337-38; Finisar Corp. v. DirecTV Group, Inc., 523 F.3d 1323, 1340-41 (Fed. Cir. 2008); see also MPEP 2181 § II(B). Therefore, the claims are indefinite and are rejected under 35 USC § 112(b).
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as limitations under 35 USC § 112(f);
(b) Amend the written description of the specification such that it expressly recites what structure(s), material(s), or act(s) perform the entire claimed functions, without introducing any new matter (35 USC § 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure(s), material(s), or act(s) disclosed therein to the functions recited in the claims, without introducing any new matter (35 USC § 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure(s), material(s), or act(s) and clearly links them to the functions so that one of ordinary skill in the art would recognize what structure(s), material(s), or act(s) perform the claimed functions, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure(s), material(s), or act(s) for performing the claimed functions and clearly links or associates the structure(s), material(s), or act(s) to the claimed functions, without introducing any new matter (35 USC § 132(a)); or
(b) Stating on the record what corresponding structure(s), material(s), or act(s), which are implicitly or inherently set forth in the written description of the specification, perform the claimed functions. For more information, see 37 CFR 1.75(d) and MPEP 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 USC § 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 1-12 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
"Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
Natural phenomena and laws of nature and include principles, relations, and products that are naturally occurring or do not have markedly different characteristics compared to what occurs in nature (MPEP 2106.04(b)).
The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea and a natural phenomenon.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
Claims 1-11 are directed to a device (claims 1-9), system (claim 10), and method (claim 11), which fall under categories of statutory subject matter.
Claim 12 is directed to “A computer program”, i.e., software per se. The claimed subject matter encompasses transitory embodiments (e.g., propagating signals) which do not fall under any category of statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007); Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294 (Fed. Cir. 2017).
The Examiner suggests amendment to, e.g., “A non-transitory computer readable storage medium storing a computer program”. Direction of the claim to non-transitory embodiments would cause the claim to fall under a category of statutory subject matter and overcome this portion of the rejection.
However, this amendment alone would likely not overcome rejection for recitation of
judicial exceptions without significantly more. In the interest of compact prosecution, the recited subject matter of claim 12 has been interpreted according to the Examiner’s suggestion for further analysis below regarding recitation of judicial exceptions without significantly more.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements which encompass mathematical concepts, at least under their broadest reasonable interpretation, including:
calculat[ing] a creatinine risk estimated value of [a] predetermined user based on the attribute information and/or the non-invasive biological information of the predetermined user by using [a] creatinine risk estimation model (claims 1 and 11-12), i.e., calculating an output value from particular input values via an algorithm, wherein:
estimation accuracy of the creatinine risk estimated value is accuracy at which risk existence can be classified with ROC_AUC of 0.7 or larger (claim 3), i.e., the calculation exhibits accuracy reflected by an AUC performance metric in the recited range, and
the creatine risk estimated value of the predetermined user [is calculated] by using [a] first creatinine risk estimation model and [a] second creatinine risk estimation model (claim 7), i.e., via two algorithms;
generat[ing] the creatinine risk estimation model by machine learning based on [a] training data set (claims 4 and 11-12);
generat[ing] a first creatinine risk estimation model and a second creatinine risk estimation model by machine learning based on each of training data sets of different kinds (claim 8); and
estimat[ing] at least one piece or more of biological information… included in the biological information (claim 9), i.e., calculating at least one value, thereby:
acquir[ing], as biological information of the predetermined user, the biological information estimated (claim 9).
The recited acts of algorithmic calculation constitute mathematical concepts.
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The claims recite elements that encompass processes that are practicably performable in the human mind, at least under their broadest reasonable interpretation, including:
provid[ing] labels indicating existence of the creatinine risk to the training data set based on a blood-measured creatinine measured value (claim 7), i.e., labeling data based on a particular constituent variable; and
when a difference between the number of pieces of data with the creatinine risk and the number of pieces of data without the creatinine risk among the labels is equal to or larger than a predetermined value… increas[ing] the number of pieces of sample data in the training data set to reduce the difference (claim 7), i.e., adding data to a data set in response to a set membership threshold condition.
These recited steps of augmenting information, which are practicably performable in the human mind at least with use of a physical aid, constitute mental processes.
Mathematical concepts and mental processes constitute enumerated groupings of abstract ideas (MPEP 2106.04(a)(2) §§ I and III). Hence, the claims recite elements that, individually and in combination, constitute an abstract idea.
The claims further recite the following claim elements, which require that analyzed data embodies particular natural phenomena and/or laws of nature:
the attribute information includes any one or a combination of age and sex (claim 2);
the non-invasive biological information includes any one or a combination of BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance (claim 2);
the training data set includes attribute information, non-invasive biological information, and a blood-measured creatinine measured value of a subject (claims 5 and 11-12);
the noninvasive biological information further includes oxygen saturation (SpO2) (claim 6); and
estimated biological information includes one or more of BMI, blood pressure, pulse wave data, electrocardiogram data, biological impedance, and oxygen saturation (claim 9).
The above elements specify that analyzed data represents naturally occurring user attributes, i.e., natural phenomena, having naturally occurring relationships with user creatinine levels and health risk, i.e., laws of nature, that the claimed invention allows a user of the claimed device, system, method and/or storage medium to observe.
The claims must therefore be examined further to determine whether they integrate these judicial exceptions into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite the following additional elements directed to data gathering activity necessary for performance of claimed method steps:
acquir[ing] attribute information and non-invasive biological information of a predetermined user (claim 1); and
a biological information measurement device configured to measure non-invasive biological information (claim 10).
Necessary data gathering is considered to be insignificant pre-solution activity, and as such insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). Express inclusion of machines (e.g., the biological information measurement device) which merely perform necessary data gathering is likewise considered insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(b) § III).
The claims further recite additional elements encompassing computer hardware and software that implement recited functions:
a creatinine risk estimation device (claims 1-10), which the specification describes as a general-purpose computer (see ‘Claim Interpretation’);
configured computer programs (see ‘Claim Interpretation’), including:
an “information acquisition unit” (claims 1 and 9),
an “estimation model storage unit” (claim 1),
an “estimation processing unit” (claims 1 and 8),
a “training data storage unit” (claim 4),
a “learning processing unit” (claims 4 and 7-8), and
a “biological information estimation unit” (claim 9); and
a non-transitory computer readable storage medium (see ‘Claim Interpretation’) storing a computer program configured to cause a computer to execute recited functions (claim 12).
Additionally, certain of the recited functions implicitly require performance in a computer environment, including the following:
stor[ing] a creatinine risk estimation model (claim 1); and
stor[ing] a training data set (claims 4 and 11-12).
The claims do not describe any specific computational steps by which a computer performs or carries out functions drawn to the judicial exceptions, nor do they provide any details of how specific structures of a computer are used to implement these functions. The claims state nothing more than that a generic computer performs functions drawn to the judicial exceptions, and are therefore mere instructions to apply the judicial exceptions using a computer. As such, the claims do not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d) § I and 2106.05(f)).
No further additional elements are recited.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the judicial exceptions to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the judicial exceptions with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the judicial exceptions in some other meaningful way beyond linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., non-invasive creatinine risk assessment; MPEP 2106.05(e) and 2106.05(h)).
Hence, the recited judicial exceptions are not integrated into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea and a natural phenomenon, and do not integrate those judicial exceptions into a practical application, the claims are directed to those judicial exceptions. Claims that are directed to judicial exceptions must be examined further to determine whether the additional elements besides the judicial exceptions render the claims significantly more than the judicial exceptions. Additional elements besides the judicial exceptions may constitute inventive concepts that are sufficient to render the claims significantly more (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, several recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited judicial exceptions, particularly when the activities are well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
Recited additional elements amounting to insignificant extra-solution activity encompass the following structures and processes, which are indicated as commercially-available products or activity that may be performed with commercially-available products by the instant specification (see MPEP 2106.07(a) § III):
acquiring non-invasive biological information, including via a biological information measurement device (para. 0026: “The biological information measurement device measures the non-invasive biological information of the user… The non-invasive biological information may be measured by using, for example, a commercially available height meter, …or galvanic skin measurement machine”).
Additionally, recited additional elements encompass the following computer-implemented functions, which the courts have held as coextensive with a general-purpose computer and/or well-understood, routine and conventional:
Receiving, storing, and processing data (In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011); EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 622 (Fed. Cir. 2015)); and
Storing and retrieving information in memory (OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Versata Dev. Group, Inc. v. SAP America, Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Hence, the encompassed extra-solution activity is considered well-understood, routine and conventional. Well-understood, routine and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than judicial exceptions (MPEP 2106.05(d)).
Mere instructions to implement judicial exceptions using a computer are, when considered individually, similarly insufficient to constitute an inventive concept that would render the claims significantly more than said judicial exceptions (see MPEP 2106.05(f)).
When the claims are considered as a whole, they do not integrate the judicial exceptions into a practical application; they do not confine the use of the judicial exceptions to a particular technology; they do not solve a problem rooted in or arising from the use of a
particular technology; they do not improve a technology by allowing the technology to
perform a function that it previously was not capable of performing; and they do not
provide any limitations beyond generally linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., non-invasive creatinine risk assessment; MPEP 2106.05(e) and 2106.05(h)).
Hence, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. See MPEP 2106.05.
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to judicial exceptions and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the judicial exceptions, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 USC §§ 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 USC § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6 and 9-12 are rejected under 35 USC §§ 102(a)(1) and 102(a)(2) as being anticipated by Newberry (WO 2019/161411; effectively filed 2/17/2018; on IDS filed 6/12/2025).
Claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to claim 1, Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (label 110 in Fig. 1; paras. 0048-49), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to disclose a creatinine risk estimation device as claimed.
With respect to claim 2, Newberry exemplifies input data including age, body mass index, blood pressure, and photoplethysmography (PPG) signals, i.e., biological impedance (paras. 00197 and 00203). Newberry also states that health data can be obtained using known methods such as ECG (para. 00243).
With respect to claim 4, Newberry discloses an embodiment of the memory device that stores a learning vector and a training set (para. 00237). Newberry also discloses pre-configuration of the neural network processing device with weights, parameters or learning vectors derived from a training set (para. 00203), and describes a method of generating a learning vector (i.e., identifying neural network parameters), via a learning algorithm (e.g., gradient descent), based on the training set (para. 00209; Fig. 23).
With respect to claim 5, Newberry exemplifies types of data that may be included in a training set, such as: age, pre-existing conditions, and medical history, i.e., attribute information; and PPG signals, weight and BMI, i.e., non-invasive biological information (para. 00203). Newberry also states that the training set may be updated with data obtained from clinical studies, or health care facility, such as blood levels of creatinine obtained using blood tests (para. 00243).
With respect to claim 6, Newberry discloses that the PPG circuit may be configured to detect oxygen saturation levels in blood flow (paras. 0049 and 00113).
With respect to claim 9, Newberry discloses embodiments wherein the processing device determines health data including one or more of blood pressure and oxygen saturation level based on obtained PPG signals (paras. 00199-200).
With respect to claim 10, Newberry discloses a biosensor device comprising an optical sensor or photoplethysmography (PPG) circuit configured to transmit a plurality of wavelengths of light at skin tissue of a user, detect reflected light, and generate spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., a biological information measurement device configured to measure non-invasive biological data as claimed.
Newberry describes an aspect in which a biosensor may detect spectral responses and provide an indicator of a concentration of creatinine in blood flow and the neural network processing device may obtain creatinine levels in blood flow, e.g., the input vector includes a corresponding PPG signal or parameters derived therefrom (para. 00222). Newberry additionally discusses embodiments wherein biosensors are communicatively coupled to (e.g., integrated with) the neural network processing device, and data obtained by the former are processed by the latter to generate output (para. 0239). Newberry is thus considered to disclose a system comprising a creatinine risk estimation device and a biological information measurement device as claimed.
With respect to claim 11, Newberry discloses methods for health monitoring (para. 0001). The disclosure of Newberry is considered to read on the method limitations of the claim in the same manner as detailed above with respect to the functional limitations of claims 1 and 4-5.
With respect to claim 12, Newberry discloses embodiments wherein the memory device is a non-transitory processor readable medium that stores instructions which when executed by a processing circuit, cause the processing circuits to perform one or more functions described therein (para. 0050). The disclosure of Newberry is considered to read on the functional limitations of the claim in the same manner as detailed above with respect to the functional limitations of claims 1 and 4-5.
In this way, the disclosure of Newberry anticipates the limitations of claims 1-2, 4-6 and 9-12. Thus, the claimed invention is anticipated.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 USC § 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 USC § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 3 and 7-8 are rejected under 35 USC § 103 as being unpatentable over Newberry, as applied to claim 1 above, and further in view of Patterson (Deep learning: A practitioner’s approach. O’Reilly Media, Inc.; published July 2017).
With respect to claim 3, Newberry discloses training of the neural network via a gradient descent algorithm, wherein the difference between actual output and target output is determined and network weights are adjusted to minimize this error (para. 00196). Newberry discloses embodiments of a processing device that determine health data including creatinine level, wherein obtained health data may be compared to expected ranges or thresholds (paras. 00200-201).
However, Newberry does not discuss specific model performance metrics such as AUC. Newberry does not disclose embodiments wherein classification accuracy is accuracy at which risk existence can be classified with ROC_AUC of 0.7 or larger.
Patterson is a textbook that covers machine learning fundamentals, and discusses application of machine learning to clinical prediction (pg. 33, pg. 73, final para. – pg. 74). Patterson discusses use of significantly class-imbalanced training sets in applying machine learning to domains in which an event of predictive interest occurs far less often than not, and teaches that a model trained on unbalanced data (e.g., 99% negative and 1% positive) will, under the majority of learning methods, just learn to always predict the dominant class (pg. 509). As a remedy, Patterson suggests evaluating model performance according to metrics such as Area Under the Curve (pg. 509).
Patterson does not specifically teach construction of a model having classification accuracy with ROC_AUC of 0.7 or larger. However, the claimed AUC threshold value of 0.7 is a result-effective variable that one of ordinary skill in the art could arrive at through routine optimization. As such, it is not considered to patentably distinguish the claims from the disclosure of Newberry, in view of Patterson.
With respect to claim 7, Patterson teaches that a training set must be a representative sample of a population of interest, i.e., a subset having a distribution that represents the accurate distribution of the population, otherwise the model may overfit (pg. 51; see also discussion of samples at pg. 40). Patterson thus teaches that a trained model may overfit when a training set includes data with a distribution that is skewed relative to a known distribution for a population of interest, e.g., when a difference between the number of pieces of data with a creatinine risk and the number of pieces of data without a creatinine risk among the labeled training data is equal to or larger than a predetermined value.
Patterson also teaches that increasing the size of the training set is an effective method of controlling overfitting (pp. 153 and 515). Patterson thus teaches that a conventional remedy to overfitting, caused by skewed training data, is to increase the number of pieces of sample data in the training data set to reduce the difference.
With respect to claim 8, Patterson discusses a parallel training strategy wherein each of multiple ‘workers’ is given a split of the total training set and trains over just the records in the split, then sends its parameter vector (i.e., model) back to a ‘master’ node which performs parameter averaging (pg. 485, Fig. 6-3; pp. 487-488). As applied to estimation of creatinine risk, this would involve generating a first creatinine risk model and a second creatinine risk model based on each of training data sets of different kinds, and calculating an estimated creatinine risk value by using the first and second models. Patterson teaches that the involved parameter averaging acts as a regularizing function, and in some cases precludes the need for prior regularization of the data (pp. 489 and 507).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have evaluated performance of the neural network model of Newberry according to AUC, because Patterson teaches that utilizes AUC as a performance metric avoids model performance issues that arise from training on class-unbalanced, real-world) data (pg. 509). The claimed AUC threshold value of 0.7 is a result-effective variable that one of ordinary skill in the art could arrive at through routine optimization.
Additionally, said practitioner would have implemented a function of, when a difference between the number of pieces of training data with label of interest and the number of pieces of training data without the label of interest is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set, because Patterson teaches that a training set with a label distribution that is skewed relative to the population it samples (i.e., wherein the pieces of data respectively with and without the label meets or exceeds a skewness threshold) will produce an overfit model, while increasing the training set remedies this issue (pp. 51, 153 and 515).
Additionally, said practitioner would have generated a first and a second models and estimated the risk value using both, as Patterson presents this as a strategy that regularizes the data without need for pre-processing (pg. 485, Fig. 6-3; pp. 487-489 and 507).
Said practitioner would have had a reasonable expectation of success because Newberry and Patterson both concern application of machine learning techniques to predictive modeling of data.
In this way the disclosure of Newberry, in view of Patterson, makes obvious the limitations of claims 3 and 7-8. Thus, the claimed invention is prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Instant claims 1-2 and 4-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 and 8-12 of co-pending U.S. Application No. 18/041,928 (hereafter, “‘928”) in view of Newberry. ‘928 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other, in view of Newberry, for the following reasons:
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘928 claims a blood neutral fat estimation device comprising: an information acquisition unit configured to acquire attribute information and noninvasive biological information of a user; an estimation model storage unit configured to store [a] blood neutral fat estimation model; and an estimation processing unit configured to calculate a blood neutral fat risk estimated value of the user based on the attribute information and/or the non-invasive biological information of the user by using the blood neutral fat estimation model (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘928.
With respect to instant claim 2, ‘928 claims the blood neutral fat estimation device wherein the attribute information includes any one or a combination of age and sex, 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 (claim 2).
With respect to instant claim 4, ‘928 claims the blood neutral fat estimation device further comprising: a training data storage unit configured to store the training data set; and a learning processing unit configured to generate the blood neutral fat estimation model by machine learning based on the training data set (claim 3).
With respect to instant claim 5, ‘928 claims the blood neutral fat estimation device wherein the training data set includes attribute information, noninvasive biological information, and blood-measured blood neutral fat measured values of a plurality of subjects (claim 1).
With respect to instant claim 6, Newberry teaches detection of oxygen saturation levels in blood flow (paras. 0049 and 00113).
With respect to instant claim 7, ‘928 claims the blood neutral fat estimation device 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, and wherein, 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 (claim 1).
With respect to instant claim 8, ‘928 claims the blood neutral fat estimation device wherein 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 the blood neutral fat risk estimated value of the user by using the first blood neutral fat risk estimation model and the second blood neutral fat risk estimation model (claim 8).
With respect to instant claim 9, ‘928 claims the blood neutral fat estimation device further comprising 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 user, the biological information estimated by the biological information estimation unit (claim 9).
With respect to instant claim 10, ‘928 claims a blood neutral fat estimation system comprising: the blood neutral fat estimation device; and a biological information measurement device configured to measure non-invasive biological information (claim 10).
With respect to instant claim 11, ‘928 claims a blood neutral fat estimation method comprising: a step of storing a training data set including attribute information, non-invasive biological information, and blood-measured blood neutral fat measured values of a plurality of subjects; a step of generating a blood neutral fat estimation model by machine learning based on the training data set; and a step of calculating a blood neutral fat risk estimated value of a user based on attribute information and/or non-invasive biological information of the user by using the blood neutral fat estimation model (claim 11).
With respect to instant claim 12, ‘928 claims a non-transitory computer readable medium storing a computer program configured to cause a computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and blood-measured blood neutral fat measured values of a plurality of subjects; a step of generating a blood neutral fat estimation model by machine learning based on the training data set; and a step of calculating a blood neutral fat risk estimated value of a user based on attribute information and/or non-invasive biological information of the user by using the blood neutral fat estimation model (claim 12).
The instant claims are similar in form to those of ‘928. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘928 specifies that the recited device is a “blood neutral fat estimation device” that utilizes a stored “blood neutral fat estimation model” to calculate a “blood neutral fat risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and blood neutral fat-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., age and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘928.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘928 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘928.
In this way, instant claims 1-2 and 4-12 are not patentably distinct from claims of ‘928, in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5 and 9-13 of co-pending U.S. Application No. 18/041,929 (hereafter, “‘929”) in view of Newberry. ‘929 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other, in view of Newberry, for the following reasons:
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘929 claims a uric acid level estimation device comprising: an information acquisition unit to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit to store [a] uric acid level estimation model; and an estimation processing unit to: input… the attribute information and the non-invasive biological information of the predetermined user into the uric acid level estimation model, and output, from the uric acid level estimation model, the uric acid level risk estimated value (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘929.
With respect to instant claim 2, ‘929 claims the uric acid level estimation device wherein the attribute information includes sex, and the non-invasive biological information includes: BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance (claim 1).
With respect to instant claim 3, ‘929 claims the uric acid level estimation device wherein the trained machine learning model has an area under the receiver operating characteristic curve of 0.75 or greater (claim 1).
With respect to instant claim 4, ‘929 claims the uric acid level estimation device comprising a training data storage unit to store a training data set; and a learning processing unit to generate a uric acid level estimation model by machine learning based on the training data set (claim 1).
With respect to instant claim 5, ‘929 claims the uric acid level estimation device wherein the training data set includes attribute information, noninvasive biological information, and blood-measured uric acid values of a plurality of subjects (claim 1).
With respect to instant claim 6, ‘929 claims the uric acid level estimation device wherein the non-invasive biological information further includes oxygen saturation (SpO2) (claim 5).
With respect to instant claim 7, ‘929 claims the uric acid level estimation device comprising a learning processing unit to: provide labels indicating existence of a uric acid level risk to the training data set based on the blood-measured uric acid measured values, and wherein, when a difference between a number of pieces of data with the uric acid level risk and a number of pieces of data without the uric acid level 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 (claim 1).
With respect to instant claim 8, ‘929 claims the uric acid level estimation device wherein the at least one processor is further configured to execute the computer program to cause: the learning processing unit to generate a first uric acid level risk estimation model and a second uric acid level risk estimation model by machine learning based on each of training data sets of different kinds, and the estimation processing unit to calculate the uric acid level risk estimated value of the predetermined user by using the first uric acid level risk estimation model and the second uric acid level risk estimation model (claim 9).
With respect to instant claim 9, ‘929 claims the uric acid level estimation device wherein the at least one processor is further configured to execute the computer program to cause: a biological information estimation unit to estimate at least one piece or more of biological information among BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance, and oxygen saturation included in the biological information, and the information acquisition unit to acquire, as biological information of the predetermined user, the biological information estimated by the biological information estimation unit (claim 10).
With respect to instant claim 10, ‘929 claims a uric acid level estimation system comprising: the uric acid level estimation device; and a biological information measurement device configured to measure non-invasive biological information (claim 11).
With respect to instant claim 11, ‘929 claims a uric acid level estimation method comprising: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured uric acid measured value of a plurality of subjects; a step of generating a uric acid level estimation model by: machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the uric acid level estimation model, and outputting, from the uric acid level estimation model, the uric acid level risk estimated value (claim 12).
With respect to instant claim 12, ‘929 claims a non-transitory computer readable medium storing a computer program configured to cause a computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured uric acid measured value of a plurality of subjects; a step of generating a uric acid level estimation model by: machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the uric acid level estimation model, and outputting, from the uric acid level estimation model, the uric acid level risk estimated value (claim 13).
The instant claims are similar in form to those of ‘929. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘929 specifies that the recited device is a “uric acid level estimation device” that utilizes a stored “uric acid level estimation model” to calculate a “uric acid level risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and uric acid level-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., sex and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘929.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘929 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘929.
In this way, instant claims 1-12 are not patentably distinct from claims of ‘929 in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-10 and 12 of co-pending U.S. Application No. 18/041,939 (hereafter, “‘939”) in view of Newberry. ‘939 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other in view of Newberry.
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘939 claims an HbA1c risk estimation device comprising: an information acquisition unit configured to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit configured to store an HbA1c risk estimation model; and an estimation processing unit configured to: input… 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 (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘939.
With respect to instant claim 2, ‘939 claims the HbA1c risk estimation device wherein the attribute information includes an age and a sex of the predetermined user, and the non-invasive biological information… includ[es]: BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance (claim 1).
With respect to instant claim 3, ‘939 claims the HbA1c risk estimation device wherein estimation accuracy of the HbA1c risk estimated value is accuracy at which risk existence can be classified with area under the receiver operating characteristic curve of 0.7 or greater (claim 3).
With respect to instant claim 4, ‘939 claims the HbA1c risk estimation device comprising a training data storage unit configured to store a training data set; and a learning processing unit configured to generate the HbA1c risk estimation model by machine learning based on the training data set (claim 4).
With respect to instant claim 5, ‘939 claims the HbA1c risk estimation device wherein the training data set includes attribute information, noninvasive biological information, and a blood-measured HbA1c measured value of a subject (claim 5).
With respect to instant claim 6, ‘939 claims the HbA1c risk estimation device wherein the non-invasive biological information further includes oxygen saturation (SpO2) (claim 6).
With respect to instant claim 7, ‘939 claims the HbA1c risk estimation device wherein the learning processing unit provides labels indicating existence of the HbA1c risk to the training data set based on a blood-measured HbA1c measured value, and wherein, when a difference between a number of pieces of data with the HbA1c risk and a number of pieces of data without the HbA1c 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 (claim 7).
With respect to instant claim 8, ‘939 claims the HbA1c risk estimation device wherein the learning processing unit generates a first HbA1c risk estimation model and a second HbA1c risk estimation model by machine learning based on each of training data sets of different kinds, and the estimation processing unit calculates the HbA1c risk estimated value of the predetermined user by using the first HbA1c risk estimation model and the second HbA1c risk estimation model (claim 8).
With respect to instant claim 9, ‘939 claims the HbA1c risk estimation device further comprising a biological information estimation unit configured to estimate at least one piece or more of biological information among the BMI, the blood pressure, the pulse wave data, the electrocardiogram data, the biological impedance, and oxygen saturation 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 (claim 9).
With respect to instant claim 10, ‘939 claims a HbA1c risk estimation system comprising: an HbA1c risk estimation device; and a biological information measurement device configured to measure the non-invasive biological information (claim 10).
With respect to instant claim 11, the claims of ‘939 are considered to read on the method limitations of the instant claim in the same manner as detailed above with respect to the functional limitations of instant claims 1 and 4-5.
With respect to instant claim 12, ‘939 claims a non-transitory computer readable storage medium storing a computer program which, when executed, causes a computer to execute: 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 machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the HbA1c risk estimation model, and outputting, from the HbA1c risk estimation model, the HbA1c risk estimated value (claim 12).
The instant claims are similar in form to those of ‘939. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘939 specifies that the recited device is a “HbA1c risk estimation device” that utilizes a stored “HbA1c risk estimation model” to calculate a “HbA1c risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and HbA1c-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., age and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘939.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘939 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘939.
In this way, instant claims 1-12 are not patentably distinct from claims of ‘939 in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-2 and 4-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4-12 of co-pending U.S. Application No. 18/041,960 (hereafter, “‘960”) in view of Newberry. ‘960 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other in view of Newberry.
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘960 claims an blood sugar level estimation device comprising: an information acquisition unit configured to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit configured to store a trained blood sugar level estimation model; and an estimation processing unit configured to calculate a blood sugar 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 level estimation model, and outputting, from the blood sugar level estimation model, the blood sugar level estimated value (claim 1). ‘960 further claims embodiments wherein the estimation processing unit calculates a blood sugar level risk in place of the blood sugar level estimated value (claim 6).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘960.
With respect to instant claim 2, ‘960 claims the blood sugar level estimation device wherein the 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), electrocardiogram data, and a biological impedance of the predetermined user (claim 1).
With respect to instant claim 4, ‘960 claims the blood sugar level risk estimation device further comprising: a training data storage unit configured to store a training data set; and a learning processing unit configured to generate the blood sugar level estimation model by machine learning based on the training data set (claim 4).
With respect to instant claim 5, ‘960 claims the blood sugar level estimation device wherein the training data set includes attribute information, noninvasive biological information, and a blood-measured blood sugar level of a subject (claim 5).
With respect to instant claim 6, ‘960 claims the blood sugar level risk estimation device wherein the non-invasive biological information further includes an oxygen saturation (SpO2) of the predetermined user (claim 1).
With respect to instant claim 7, ‘960 claims the blood sugar level estimation device wherein the learning processing unit provides labels indicating existence of the blood sugar level risk to the training data set based on the blood-measured blood sugar level, and wherein, when a difference between a number of pieces of data with the blood sugar level risk and a number of pieces of data without the blood sugar level 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 (claim 7).
With respect to instant claim 8, ‘960 claims the blood sugar level estimation device wherein the learning processing unit generates a first blood sugar level estimation model and a second blood sugar level estimation model by machine learning based on each of training data sets of different kinds, and wherein the estimation processing unit calculates the blood sugar level estimated value of the predetermined user by using the first blood sugar level estimation model and the second blood sugar level estimation model (claim 8).
With respect to instant claim 9, ‘960 claims the blood sugar level estimation device further comprising 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, biological impedance, and oxygen saturation 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 (claim 9).
With respect to instant claim 10, ‘960 claims a non-invasive blood sugar level estimation system comprising: the blood sugar level estimation device; and a biological information measurement device configured to measure non-invasive biological information (claim 10).
With respect to instant claim 11, ‘960 claims a blood sugar level estimation method 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 subject; a step of generating a blood sugar level estimation model by: machine learning… on the training data set; and a step of calculating a blood sugar 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 level estimation model, and outputting, from the blood sugar level estimation model, the blood sugar level estimated value (claim 11).
With respect to instant claim 12, ‘960 claims a computer program configured to cause a computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured blood sugar level of a subject; a step of generating an blood sugar level estimation model by: machine learning… on the training data set; and a step of calculating a blood sugar 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 level estimation model, and outputting, from the blood sugar level estimation model, the blood sugar level estimated value (claim 12).
The instant claims are similar in form to those of ‘960. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claims 1 and 6 of ‘960 specify that the recited device is a “blood sugar level estimation device” that utilizes a stored “blood sugar level estimation model” to calculate a “blood sugar level risk”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and blood sugar level-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., sex and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘960.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘960 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘960.
In this way, instant claims 1-2 and 4-12 are not patentably distinct from claims of ‘960 in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims1-2, 5-6 and 8-12 of co-pending U.S. Application No. 18/041,971 (hereafter, “‘971”) in view of Newberry. ‘971 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other in view of Newberry.
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘971 claims a cholesterol risk estimation device comprising: an information acquisition unit to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit to store the cholesterol risk estimation model; and an estimation processing unit to: input… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and output, from the cholesterol risk estimation model, an cholesterol risk estimated value of the predetermined user (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘971.
With respect to instant claim 2, ‘971 claims the cholesterol risk estimation device wherein the attribute information includ[es] age of the predetermined user, and the non-invasive biological information… includ[es]: blood pressure, pulse wave data, electrocardiogram data, and biological impedance of the predetermined user (claim 1).
With respect to instant claim 3, ‘971 claims the cholesterol risk estimation device wherein estimation accuracy of the cholesterol risk estimated value is accuracy at which risk presence can be classified with ROC_AUC of 0.7 or greater (claim 3).
With respect to instant claim 4, ‘971 claims the cholesterol risk estimation device comprising a training data storage unit to store a training data set; and a learning processing unit to: generate a cholesterol risk estimation model by machine learning based on the training data set (claim 1).
With respect to instant claim 5, ‘971 claims the cholesterol risk estimation device wherein the training data set includes attribute information, noninvasive biological information, and a blood-measured cholesterol measured value of a subject (claim 5).
With respect to instant claim 6, ‘971 claims the cholesterol risk estimation device wherein the non-invasive biological information further includes oxygen saturation (SpO2) (claim 6).
With respect to instant claim 7, ‘971 claims the cholesterol risk estimation device comprising a learning processing unit to: provide labels indicating existence of the cholesterol risk to the training data set based on a blood-measured cholesterol value, and when a difference between a number of pieces of data with the cholesterol risk and a number of pieces of data without the cholesterol risk among the labels is equal to or larger than a predetermined value, the learning processing unit increases a number of pieces of sample data in the training data set to reduce the difference (claim 1).
With respect to instant claim 8, ‘971 claims the cholesterol risk estimation device wherein the at least one processor is further configured to control: the learning processing unit to generate a first cholesterol risk estimation model and a second cholesterol risk estimation model by machine learning based on each of training data sets of different kinds, and the estimation processing unit to calculate the cholesterol risk estimated value of the predetermined user by using the first cholesterol risk estimation model and the second cholesterol risk estimation model (claim 8).
With respect to instant claim 9, ‘971 claims the cholesterol risk estimation device wherein the at least one processor is further configured to control: a biological information estimation unit to estimate at least one piece or more of biological information among BMI, blood pressure, pulse wave data, electrocardiogram data, biological impedance, and oxygen saturation included in the biological information, and the information acquisition unit to acquire, as biological information of the predetermined user, the biological information estimated by the biological information estimation unit (claim 9).
With respect to instant claim 10, ‘971 claims a non-invasive cholesterol risk estimation system comprising: the cholesterol risk estimation device; and a biological information measurement device configured to measure the non-invasive biological information (claim 10).
With respect to instant claim 11, ‘971 claims a cholesterol risk estimation method comprising: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured cholesterol measured value of a subject; a step of generating a cholesterol risk estimation model by: machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and outputting, from the cholesterol risk estimation model, a cholesterol risk estimated value (claim 11).
With respect to instant claim 12, ‘971 claims a non-transitory computer readable storage medium storing a computer program which, when executed, cause[s] the computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured cholesterol measured value of a subject; a step of generating an cholesterol risk estimation model by machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and outputting, from the cholesterol risk estimation model, a cholesterol risk estimated value of the predetermined user (claim 12).
The instant claims are similar in form to those of ‘971. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘971 specifies that the recited device is a “cholesterol risk estimation device” that utilizes a stored “cholesterol risk estimation model” to calculate a “cholesterol risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and cholesterol-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., age and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘971.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘971 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘971.
In this way, instant claims 1-12 are not patentably distinct from claims of ‘971 in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-2 and 4-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims1-2, 4-5 and 9-13 of co-pending U.S. Application No. 18/042,118 (hereafter, “‘118”) in view of Newberry. ‘118 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other in view of Newberry.
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘118 claims a gamma-glutamyl transpeptidase (γGT) estimation device comprising: an information acquisition unit configured to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit configured to store [a] γGT estimation model; and an estimation processing unit configured to calculate a γGT risk estimated value of the predetermined user by inputting the attribute information and/or the non-invasive biological information of the predetermined user into the γGT estimation model and outputting, from the γGT estimation model, the γGT risk estimated value of the predetermined user (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘118.
With respect to instant claim 2, ‘118 claims the γGT estimation device wherein the attribute information includes any one or a combination of age and sex, 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 (claim 2).
With respect to instant claim 4, ‘118 claims the γGT estimation device comprising: a training data storage unit configured to store a training data set; and a learning processing unit configured to generate a γGT estimation model by machine learning based on the training data set (claim 1).
With respect to instant claim 5, ‘118 claims the γGT estimation device wherein the training data set includes attribute information, noninvasive biological information, and blood-measured γGT measured value of the subject (claim 4).
With respect to instant claim 6, ‘118 claims the γGT estimation device wherein the non-invasive biological information further includes oxygen saturation (SpO2) (claim 5).
With respect to instant claim 7, ‘118 claims the γGT estimation device wherein the learning processing unit provides labels indicating existence of a γGT risk to the training data set based on the blood-measured γGT measured value, and wherein, when a difference between a number of pieces of data with the γGT risk and a number of pieces of data without the γGT 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 (claim 1).
With respect to instant claim 8, ‘118 claims the γGT estimation device wherein the learning processing unit generates a first γGT risk estimation model and a second γGT risk estimation model by machine learning based on each of training data sets of different kinds, and wherein the estimation processing unit calculates the γGT risk estimated value of the predetermined user by using the first γGT risk estimation model and the second γGT risk estimation model (claim 9).
With respect to instant claim 9, ‘118 claims the γGT estimation device further comprising 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, biological impedance and oxygen saturation included in the biological information, wherein the information acquisition unit acquires, as biological information of the user, the biological information estimated by the biological information estimation unit (claim 10).
With respect to instant claim 10, ‘118 claims a γGT estimation system comprising: the γGT estimation device; and a biological information measurement device configured to measure the non-invasive biological information (claim 11).
With respect to instant claim 11, ‘118 claims a γGT estimation method comprising: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured γGT measured value of a subject; a step of generating a γGT estimation model by: machine learning based on the training data set; and a step of calculating a γGT risk estimated value of a predetermined user by inputting the attribute information and/or the non-invasive biological information of the predetermined user into the γGT estimation model and outputting, from the γGT estimation model, the γGT risk estimated value of the predetermined user (claim 12).
With respect to instant claim 12, ‘118 claims a computer program configured to cause a computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured γGT measured value of a subject; a step of generating a γGT estimation model by: machine learning based on the training data set; and a step of calculating a γGT risk estimated value of a predetermined user by inputting the attribute information and/or the non-invasive biological information of the predetermined user into the γGT estimation model and outputting, from the γGT estimation model, the γGT risk estimated value of the predetermined user (claim 13).
The instant claims are similar in form to those of ‘118. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘118 specifies that the recited device is a “γGT estimation device” that utilizes a stored “γGT estimation model” to calculate a “γGT risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and γGT-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., age and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘118.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘118 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘118.
In this way, instant claims 1-2 and 4-12 are not patentably distinct from claims of ‘118, in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claims 1-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of co-pending U.S. Application No. 19/573,725 (hereafter, “‘725”) in view of Newberry. ‘725 shares joint inventors (ANDO, Noritaka; SHOBAKO, Naohisa) and a common assignee (NISSIN FOODS HOLDINGS CO., LTD.) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other in view of Newberry.
Instant claim 1 is directed to a creatinine risk estimation device comprising: an information acquisition unit, configured to acquire attribute information and non-invasive biological information of a predetermined user; an estimation model storage unit, configured to store a creatinine risk estimation model; and an estimation processing unit, configured to calculate a creatinine risk 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 creatinine risk estimation model.
With respect to instant claim 1, ‘725 claims a cholesterol risk estimation device comprising: an information acquisition unit to acquire attribute information and noninvasive biological information of a predetermined user; an estimation model storage unit to store the cholesterol risk estimation model; and an estimation processing unit to: input… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and output, from the cholesterol risk estimation model, an cholesterol risk estimated value of the predetermined user (claim 1).
Newberry discloses systems for health monitoring (para. 0001), including a neural network processing device (para. 00215; label 2100 in Fig. 24) comprising: a photoplethysmography (PPG) circuit that obtains PPG signals, a signal processing circuit that processes the PPG signals to generate PPG input data, an input vector generation module that generates an input vector including the PPG input data and/or patient data (para. 00216); and a memory device that stores a learning vector, i.e., a model storage unit (para. 00215).
Newberry further describes obtaining PPG signals by means of transmitting a plurality of wavelengths of light at skin tissue of a user, detecting reflected light, and generating spectral responses (paras. 0048-49; label 110 in Fig. 1), i.e., acquiring non-invasive biological data. Newberry also states that patient data may include age, weight and BMI (para. 00197), i.e., attribute information.
Newberry describes processing of the input vector, which includes the PPG input data such as the PPG signals, by the neural network processing device to determine health data including creatinine level (para. 0200). Newberry also states that the PPG input data may include parameters generated from the PPG signals, and describes exemplary aspects in which indicators of a substance concentration level are included in the input vector and the neural network processing device determines indicators of associated health conditions, e.g., kidney function, as output (para. 00223). In this way, Newberry is considered to demonstrate estimation of creatinine risk based on the same types of data (acquired attribute information and non-invasive biological information), and via the same technique (by a processing unit using an estimation model), as utilized by ‘725.
With respect to instant claim 2, ‘725 claims the cholesterol risk estimation device wherein the attribute information includ[es] sex of the predetermined user, and the non-invasive biological information… includ[es]: a body mass index (BMI), electrocardiogram data, and biological impedance (claim 1).
With respect to instant claim 3, ‘725 claims the cholesterol risk estimation device wherein estimation accuracy of the cholesterol risk estimated value is accuracy at which risk presence can be classified with ROC_AUC of 0.7 or greater (claim 2).
With respect to instant claim 4, ‘725 claims the cholesterol risk estimation device comprising a training data storage unit to store a training data set; and a learning processing unit to: generate a cholesterol risk estimation model by machine learning based on the processed training data set (claim 1).
With respect to instant claim 5, ‘725 claims the cholesterol risk estimation device wherein the training data set includes attribute information, noninvasive biological information, and a blood-measured cholesterol measured value of a subject (claim 3).
With respect to instant claim 6, ‘725 claims the cholesterol risk estimation device wherein the non-invasive biological information further includes oxygen saturation (SpO2) (claim 4).
With respect to instant claim 7, ‘725 claims the cholesterol risk estimation device comprising a learning processing unit to: provide labels indicating existence of the cholesterol risk to the training data set based on a blood-measured cholesterol value, and when a difference between a number of pieces of data with the cholesterol risk and a number of pieces of data without the cholesterol risk among the labels is equal to or larger than a predetermined value, the learning processing unit increases a number of pieces of sample data in the training data set to reduce the difference (claim 1).
With respect to instant claim 8, ‘725 claims the cholesterol risk estimation device wherein the at least one processor is further configured to control: the learning processing unit to generate a first cholesterol risk estimation model and a second cholesterol risk estimation model by machine learning based on each of training data sets of different kinds, and the estimation processing unit to calculate the cholesterol risk estimated value of the predetermined user by using the first cholesterol risk estimation model and the second cholesterol risk estimation model (claim 5).
With respect to instant claim 9, ‘725 claims the cholesterol risk estimation device wherein the at least one processor is further configured to control: a biological information estimation unit to estimate at least one piece or more of biological information among the BMI, the electrocardiogram data, or biological impedance included in the biological information, and the information acquisition unit to acquire, as biological information of the predetermined user, the biological information estimated by the biological information estimation unit (claim 6).
With respect to instant claim 10, ‘725 claims a non-invasive cholesterol risk estimation system comprising: the cholesterol risk estimation device; and a biological information measurement device (claim 7).
With respect to instant claim 11, ‘725 claims a non-invasive cholesterol risk estimation method comprising: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured cholesterol measured value of a subject; a step of generating a cholesterol risk estimation model by machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and outputting, from the cholesterol risk estimation model, a cholesterol risk estimated value (claim 8).
With respect to instant claim 12, ‘725 claims a non-transitory computer readable storage medium storing a computer program which, when executed, cause[s] the computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured cholesterol measured value of a subject; a step of generating an cholesterol risk estimation model by machine learning based on the training data set; and a step of inputting… the attribute information and the non-invasive biological information of the predetermined user into the cholesterol risk estimation model, and outputting, from the cholesterol risk estimation model, a cholesterol risk estimated value of the predetermined user (claim 9).
The instant claims are similar in form to those of ‘725. The significant differences between the two sets of claims regard the direction of each application to estimation of particular health risks. For example, instant claim 1 specifies that the recited device is a “creatinine risk estimation device” that utilizes a stored “creatinine risk estimation model” to calculate a “creatinine risk estimated value”, while claim 1 of ‘725 specifies that the recited device is a “cholesterol risk estimation device” that utilizes a stored “cholesterol risk estimation model” to calculate a “cholesterol risk estimated value”. Such language differences are present throughout the claims at issue and require that various elements specifically pertain to estimation of, respectively, creatinine-associated risk and cholesterol-associated risk.
However, Newberry demonstrates estimation of creatinine risk based on the same types of user data (e.g., sex and blood pressure), and via the same technique (e.g., by a processing unit using a trained machine learning model), as utilized by ‘725.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. Newberry indicates that the device and data processing functions of ‘725 are applicable to estimation of creatinine risk. Thus, the instant claims are directed to a predictable variation of the claims of ‘725.
In this way, instant claims 1-12 are not patentably distinct from claims of ‘725 in view of Newberry. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Theodore C. Striegel whose telephone number is (571)272-1860. The examiner can normally be reached Mon-Fri 12pm-8pm ET.
The following prior art, made of record and not relied upon, is considered pertinent to applicant's disclosure:
An (US 2017/0290551; effectively filed 4/6/2016) discloses systems and methods for assessing a patient’s risk of renal dysfunction based on physiological signal processing (Abstract);
Kodama (WO 2020/203728; effectively filed 3/29/2019; on IDS filed 2/16/2023) discloses a health information provision system comprising: a biometric information acquisition unit, that acquires input biometric information including age, gender, bioelectrical impedance and BMI of a user including via a measurement device (paras. 044 and 0065-70; labels 51-53 in Fig. 4); a storage unit that stores data, measurement programs, and learning models obtained via machine learning based on training data and adjusted based on actual measured values (paras. 0026, 0030 and 0063; label 504 in Fig. 3); a biochemical test value estimation unit, that estimates biochemical test values based on the body composition information as input by using a learning model (paras. 0025 and 0071; label 55 in Fig. 4), and a health risk evaluation unit, that calculates health risks based on estimated biochemical test values (para. 0081; label 56 in Fig. 4). Kodama also discloses implementation of the system as a health information provision program that causes a computer to perform described system functions (para. 0047), and exemplifies estimation of values for biochemical test items including creatinine (para. 0072).
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 May 29, 2026
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