DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendments filed 08 October 2025 have been entered. Claims 1 – 30 are pending, which includes newly-added claims 25 - 30. Applicant’s amendments have overcome each and every objection to the claims, specification, and abstract previously applied in the office action dated 08 August 2025. Applicant’s amendments to the claims have overcome each and every rejection under 35. U.S.C. 112 previously applied in the office action dated 08 August 2025.
Drawings
The drawings were received on 08 October 2025. These drawings are accepted.
Claim Interpretation
Applicant has not made an amendment regarding the 112(f) Claim Interpretations applied in the Office Action dated 08 August 2025. Applicant has not provided any reason to withdraw the 112(f) claim interpretation, so the interpretation detailed in the Office Action dated 08 August 2025 is maintained for the terms “judgment unit”, “blood sugar level acquisition unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “training data forming unit”, and “learning unit.”
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
“a blood sugar level acquisition step” in Claim 23
“a judgment step in Claim 23
“an output step in Claim 23
“a learning information storage step in Claim 26
“an NIRS acquisition step in Claim 26
“an estimation step in Claim 26
Because these claim limitations are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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. 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 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited 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 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“health support device is configured to employ information….and alert the user” in claim 1.
The claim limitation is interpreted according to paragraph [0233] “each health support device A is, for example, a smart phone, a personal computer, a tablet terminal, or the like. Each terminal device 5 is, for example, a smart watch, a smart phone, a personal computer, a table terminal, or the like.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 1 is objected to for the following informalities: The expanded form of “NIRS” is not defined in the claim set. For the first recitation of “NIRS” in the claims (Claim 1), an accompanying expanded form of the acronym, “Near-Infrared Spectroscopy”, is necessary. Appropriate correction is required.
Claim 15 is objected for the term “emitting near-infrared light to a user”. It is suggested to revise the term to “emitting near-infrared light to the user” for readability. Appropriate correction is required.
Applicant is advised that should claim 15 be found allowable, claim 25 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. The preamble of “A blood sugar level estimation device that acquires a blood sugar level that is to be acquired by the blood sugar level acquisition unit according to claim 1” of Claim 15 does not impart additional limitations to “The health support device of Claim 1” of Claim 25, particularly as the limitations of patentable weight following each preamble are identical. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 16 is objected for the term “emitting near-infrared light of two or more wavelengths to a user”. It is suggested to revise the term to “emitting near-infrared light of two or more wavelengths to the user” for readability. Appropriate correction is required.
Claim 23 is objected for the terms:
“a blood sugar level acquisition step in which the blood sugar level acquisition unit acquires a blood sugar level of a user.” It is suggested to revise the term to “acquiring, with the blood sugar level acquisition unit, a blood sugar level of the user”.
“a judgment step in which the judgment unit judges whether or not the blood sugar level satisfies a predetermined output condition.” It is suggested to revise the term to “judging, with the judgment unit, whether or not the blood sugar level satisfies a predetermined output condition.”
“an output step in which the output unit outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied.” It is suggested to revise the term to “outputting, with the output unit, blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied.”
“wherein the health support method employs information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level and alert the user.” It is suggested to revise the term to “and using information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level to alert the user” to enhance readability of the claim.
Appropriate correction is required.
Claim 26 is objected for the terms:
“a learning information storage step in which the learning information storage unit stores learning information acquired.” It is suggested to revise the term to “storing, with the learning information storage unit, learning information acquired”.
“an NIRS acquisition step in which the NIRS acquisition unit acquires one or more pieces of NIRS information.” It is suggested to revise the term to “acquiring, with the NIRS acquisition unit, one or more pieces of NIRS information.”
“an estimation step in which the estimation unit acquires an estimated blood sugar level.” It is suggested to revise the term to “acquiring, with the estimation unit, an estimated blood sugar level.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 - 30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (lines 7 – 9), Claim 23 (lines 9 – 11), and Claim 24 (lines 8 – 10) each recite the term “employ information acquired by NIRS non-invasively to provide the blood-sugar level-related information regarding the blood sugar level and alert the user”. The term “employ” is indefinite, as it is unclear what function this intends, whether it is “analyze information”, “communicate information”, or some other generic “using” function. Further, it is unclear if the “employ” and “alert” are intended to be separate functions, or if the intended meaning is “use the information to alert the user”. For the purposes of examination, the term “employ information acquired by NIRS non-invasively to provide the blood-sugar level-related information regarding the blood sugar level and alert the user” is deemed to claim “use information acquired by NIRS non-invasively to provide the blood-sugar level-related information regarding the blood sugar level to alert the user” or ”using” rather than “wherein the health support method employs” for claim 23. Claims 2 – 14 and 25 – 30 are similarly rejected due to their dependence on Claims 1, 23 and 24.
Claim 15 (line 6), Claim 25 (line 5), Claim 26 (lines 4 - 5), and Claim 27 (line 4) each recite the limitation "one or more pieces of NIRS information." It is unclear if the one or more pieces of NIRS information are intended to be the same or different than the “information acquired by NIRS non-invasively” previously recited in Claims 1, 23, and 24 from which these claims depend, respectively. For the purposes of examination, the term "acquires one or more pieces of NIRS information" is deemed to claim "acquires one or more pieces of the information acquired by NIRS non-invasively." Claim 16 – 22 are similarly rejected due to their dependence on Claim 15.
Claim 25 (line 6), Claim 26 (line 8), and Claim 27 (line 7) each recite the limitation "emitting near-infrared light to a user”. It is unclear if the one or more pieces of NIRS information are intended to be the same or different than the “information acquired by NIRS non-invasively” previously recited in Claims 1, 23, and 24 from which these claims depend, respectively. For the purposes of examination, the term "emitting near-infrared light to a user” is deemed to claim "emitting near-infrared light to the user.”
Claim 27 (line 1 - 2) and Claim 30 (line 1 - 2) each recite the term “the readable medium”. There is insufficient antecedent basis for this limitation in the claims. It is unclear if the “readable medium” is intended to be the same as the “non-transitory computer readable medium”, or if it is another medium (or type of medium) that can be “read”. For the purposes of examination, the term “the readable medium” is deemed to claim “the readable medium non-transitory computer readable medium”.
Claim 28 (lines 4 - 6), Claim 29 (lines 6 - 9), and Claim 30 (line 5 - 8) each recite the term “wherein the judgement unit that judges whether or not blood sugar level-related information…satisfies a predetermined output condition to acquire the judgment result”, which is indefinite. With “wherein” and “judgment unit that judges”, there are not positively-recited things that satisfy what happens “wherein”. For the purposes of examination, the term “wherein the judgement unit that judges whether or not blood sugar level-related information…satisfies a predetermined output condition to acquire the judgment result” is deemed to claim “wherein the judgement unit judges whether or not blood sugar level-related information…satisfies a predetermined output condition to acquire the judgment result.”
Claim 28 (lines 4 - 6), Claim 29 (lines 6 - 9), and Claim 30 (line 5 - 8) each recite the term “satisfies a predetermined output condition to acquire the judgment result”. It is unclear if the predetermined output condition is intended to be the same or different than the previously-recited predetermined output condition recited in Claims 1, 23, and 24, from which these claims depend, respectively. There is an additional condition required (the environment information) over Claims 1, 23, and 24. For the purposes of examination, the term “satisfies a predetermined output condition to acquire the judgment result” is deemed to claim “satisfies a second predetermined output condition to acquire the judgment result”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claims 1, 15, 20, and 24 the claims recite an apparatus, which is one of the statutory categories of invention (Step 1). The claims are then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Regarding Claim 23, the claims recite "an act or step, or series of acts or steps" and is therefore a process, which is a statutory category of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of claims 1 – 30 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 30 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 – 30 recites an abstract idea.
Specifically, Claims 1 – 30 recite (underlined are observations, judgments, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 1
A health support device comprising:
a blood sugar level acquisition unit that acquires a blood sugar level of a user;
a judgment unit that judges whether or not blood sugar level-related information regarding the blood sugar level satisfies a predetermined
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output condition to acquire a judgment result; and
an output unit that outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied.
wherein the health support device is configured to employ information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level and alert the user.
Claim 15
A blood sugar level estimation device that acquires a blood sugar level that is to be acquired by the blood sugar level acquisition unit according to claim 1, comprising:
a learning information storage unit that stores learning information acquired using two or more pieces of training data that each include one or more pieces of NIRS information acquired using a reflected light of near- infrared light emitted to a human body and a blood sugar level;
an NIRS acquisition unit that acquires one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user and
an estimation unit that acquires an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information.
Claim 20
A learning device that acquires learning information that is used by the blood sugar level estimation device according to claim 15, comprising:
the NIRS acquisition unit that, using reflected light of near-infrared light emitted from one or more light emitting units and received by one or more light receiving units, acquires one or more pieces of NIRS information regarding the reflected light;
a measured blood sugar level acquisition unit that acquires a blood sugar level of a user;
a training data forming unit that forms training data, using the one or more pieces of NIRS information and the blood sugar level; and
a learning unit that acquires learning information, using the training data.
Claim 23
A health support method that is realized using a blood sugar level acquisition unit, a judgment unit, and an output unit, comprising:
a blood sugar level acquisition step in which the blood sugar level acquisition unit acquires a blood sugar level of a user;
a judgment step in which the judgment unit judges whether or not the blood sugar level satisfies a predetermined output condition; and
an output step in which the output unit outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied
wherein the health support method is configured to employ information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level and alert the user.
Claim 24
A non-transitory computer readable medium having recorded thereon a program for enabling a computer to function as:
a blood sugar level acquisition unit that acquires a blood sugar level of a user;
a judgment unit that judges whether or not the blood sugar level satisfies a predetermined output condition; and
an output unit that outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied
wherein the computer is configured to employ information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level and alert the user.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claims 1, 15, 20, 23, and 24, these limitations include:
Observation and judgment of a blood sugar level of a user
Observation and judgment of whether or not blood sugar level-related information regarding the blood sugar level satisfies a predetermined
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output condition to acquire a judgment result
Observation and judgment to communicate blood sugar level information regarding the blood sugar level when the observation and judgment result indicates that the output condition is satisfied.
Observation and judgment of one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user
Observation and judgment of an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information.
Observation and judgment of one or more pieces of NIRS information regarding the reflected light, using reflected light of near-infrared light emitted from one or more light emitting units and received by one or more light receiving units
Observation and judgment of a blood sugar level of a user;
Observation and judgment to evaluate training data, using the one or more pieces of NIRS information and the blood sugar level
Observation and judgment of learning information, using the training data
observe and judge information acquired by NIRS non-invasively to provide the blood sugar level-related information regarding the blood sugar level and communicate an alert the user using a computer or health support device.
all of which are grouped as mental processes under the 2019 PEG.
Similarly, Dependent Claims 2 – 14, 16 – 19, and 21 - 22 include the following abstract limitations, in addition the aforementioned limitations in Independent Claims 1, 15, 20, 23, and 24 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
acquires a blood sugar level group constituted by two or more blood sugar levels of the user acquired in a time series,
Observation and judgment of a blood sugar level group constituted by two or more blood sugar levels of the user acquired in a time series,
acquires fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group,
Observation and judgment of fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group,
judges whether or not the fluctuation information satisfies a predetermined output condition, to acquire the judgment result
Observation and judgment whether or not the fluctuation information satisfies a predetermined output condition, to acquire the judgment result
outputs the fluctuation information when the judgment result indicates that the output condition is satisfied.
Observation and judgment to communicate the fluctuation information when the judgment result indicates that the output condition is satisfied.
uses the blood sugar level group and the reference information or the past blood sugar level group to acquire the fluctuation information.
Evaluates the blood sugar level group and the reference information and/or the past blood sugar level group to acquire the fluctuation information.
acquires fluctuation information regarding the warning when the judgment result indicates that the warning condition is satisfied.
Observation and judgment of fluctuation information regarding the warning when the judgment result indicates that the warning condition is satisfied.
judges whether or not blood sugar level- related information regarding the blood sugar level satisfies a predetermined suggestion condition, to acquire a judgment result, and
Observation and judgment whether or not blood sugar level- related information regarding the blood sugar level satisfies a predetermined suggestion condition, to acquire a judgment result, and
acquires suggestion information when the judgment result indicates that the suggestion condition is satisfied
Observation and judgment of suggestion information when the judgment result indicates that the suggestion condition is satisfied
acquires a blood sugar level group constituted by two or more blood sugar levels of one user acquired in a time series on one day
Observation and judgment of a blood sugar level group constituted by two or more blood sugar levels of one user acquired in a time series on one day
acquires fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group
Observation and judgment of fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group
judges whether or not the fluctuation information satisfies the suggestion condition
Observation and judgment whether or not the fluctuation information satisfies the suggestion condition
acquires suggestion information corresponding to the fluctuation information when the judgment result indicates that the suggestion condition is satisfied.
Observation and judgment of suggestion information corresponding to the fluctuation information when the judgment result indicates that the suggestion condition is satisfied.
determines a suggestion condition that matches acquired blood sugar level-related information from a correspondence information storage unit that stores two or more pieces of correspondence information that each indicate a correspondence between a suggestion condition regarding blood sugar level related information and suggestion information or suggestion basis information that is a basis of suggestion information
Observation and judgment of a suggestion condition that matches acquired blood sugar level-related information from a correspondence information storage unit that stores two or more pieces of correspondence information that each indicate a correspondence between a suggestion condition regarding blood sugar level related information and suggestion information or suggestion basis information that is a basis of suggestion information
acquires suggestion information corresponding to the suggestion condition or suggestion information that uses suggestion basis information corresponding to the suggestion condition.
Observation and judgment of suggestion information corresponding to the suggestion condition or suggestion information that uses suggestion basis information corresponding to the suggestion condition.
uses the blood sugar level and other information other than the fluctuation information to judge whether or not the blood sugar level or the fluctuation information satisfies the suggestion condition.
Observation and judgment of the blood sugar level and other information other than the fluctuation information to judge whether or not the blood sugar level or the fluctuation information satisfies the suggestion condition.
uses the blood sugar level and other information other than the fluctuation information to acquire the suggestion information.
Observation and judgment of the blood sugar level and other information other than the fluctuation information to judge suggestion information.
acquires environment information regarding user environment,
Observation and judgment of environment information regarding user environment,
acquires fluctuation information that is a score corresponding to fluctuations over time of a blood sugar level, using the blood sugar level group.
Observation and judgment of fluctuation information that is a score corresponding to fluctuations over time of a blood sugar level, using the blood sugar level group.
acquires one or more pieces of NIRS information that can be acquired by emitting near-infrared light of two or more wavelengths to a user.
Observation and judgment of one or more pieces of NIRS information that can be acquired by emitting near-infrared light of two or more wavelengths to a user.
that acquires one or more user static attribute values of the user
Observation and judgment of one or more user static attribute values of the user
uses the one or more pieces of NIRS information acquired by the NIRS acquisition unit, the one or more user static attribute values acquired by the user static attribute value acquisition unit, and the learning information to acquire the estimated blood sugar level.
Observation and judgment of the one or more pieces of NIRS information acquired by the NIRS acquisition unit, the one or more user static attribute values acquired by the user static attribute value acquisition unit, and the learning information to evaluate the estimated blood sugar level.
acquires one or more device characteristic values of a device that is used to acquire one or more pieces of NIRS information of the user
Observation and judgment of one or more device characteristic values of a device that is used to acquire one or more pieces of NIRS information of the user
uses the one or more pieces of NIRS information acquired by the MRS acquisition unit, the one or more device characteristic values acquired by the device characteristic values acquisition unit, and the learning information to acquire the estimated blood sugar level.
Observation and judgment of the one or more pieces of NIRS information acquired by the MRS acquisition unit, the one or more device characteristic values acquired by the device characteristic values acquisition unit, and the learning information to evaluate the estimated blood sugar level.
acquires one or more pieces of NIRS information regarding the reflected light;
Observation and judgment of one or more pieces of NIRS information regarding the reflected light;
acquires a blood sugar level of a user
Observation and judgment of a blood sugar level of a user
forms training data, using the one or more pieces of NIRS information and the blood sugar level
Observation and judgment to evaluate training data, using the one or more pieces of NIRS information and the blood sugar level
acquires learning information, using the training data
Observation and judgment of learning information, using the training data
acquires one or more user static attribute values that are static attribute values of the user,
Observation and judgment of one or more user static attribute values that are static attribute values of the user,
uses the one or more pieces of NIRS information, the one or more user static attribute values, and the blood sugar level to form the training data.
Observation and judgment of the one or more pieces of NIRS information, the one or more user static attribute values, and the blood sugar level to evaluate the training data.
acquires one or more device characteristic values of a device that includes the one or more light receiving units and the one or more light transmitting units,
Observation and judgment of one or more device characteristic values of a device that includes the one or more light receiving units and the one or more light transmitting units,
uses the one or more pieces of NIRS information, the one or more device characteristic values, and the blood sugar level to form the training data.
Observation and judgment of the one or more pieces of NIRS information, the one or more device characteristic values, and the blood sugar level to evaluate the training data.
acquires one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user
Observation and judgment of one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user
acquires an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information.
Observation and judgment of an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information
acquires environment information relating to the user's environment, including weather information,
Observation and judgment of environment information relating to the user's environment, including weather information
judges whether or not blood sugar level-related information regarding the blood sugar level and the environment information, including the weather information, satisfies a predetermined output condition to acquire the judgment result.
Observation and judgment of whether or not blood sugar level-related information regarding the blood sugar level and the environment information, including the weather information, satisfies a predetermined output condition to acquire the judgment result.
all of which are grouped as mental processes under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claims 1, 15, 20, 23, and 24 (and their respective Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in Independent Claims 1, 15, 20, 23, and 24), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
“blood sugar level acquisition unit”
“judgment unit”
“output unit”
“fluctuation information acquisition unit”
“reference information storage unit”
“past blood sugar level group storage unit”
“suggestion information acquisition unit”
“suggestion information output unit”
“environment information acquisition unit”
“learning information storage unit”
“NIRS acquisition unit”
“estimation unit”
“user static attribute value acquisition unit”
“device characteristic value acquisition unit
“measured blood sugar level acquisition unit”
“training data forming unit”
“learning unit”
“non-transitory computer readable medium”
Additional elements recited include an “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” in the Independent Claims 1, 15, 20, 23, and 24 their dependent claims. These component are recited at a high level of generality, , i.e., as a generic blood sugar level acquisition unit performing a generic function of acquiring blood sugar levels (the acquiring). These generic hardware component limitations for “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Independent Claims 1, 15, 20, 23, and 24 include pre-solution activity limitations, such as:
a learning information storage unit that stores learning information acquired using two or more pieces of training data that each include one or more pieces of NIRS information acquired using a reflected light of near- infrared light emitted to a human body and a blood sugar level;
In addition the aforementioned extra-solution activity limitations in Independent Claims 1, 15, 20, 23, and 24, additional extra-solution activity limitations recited in Dependent Claims 2 – 14, 16 – 19, and 21 - 22 include:
a reference information storage unit that stores reference information regarding a reference blood sugar level,
a past blood sugar level group storage unit that stores a past blood sugar level group constituted by two or more past blood sugar levels of the user,
wherein the output condition is a warning condition for outputting a warning,
a suggestion information output unit that outputs the suggestion information.
wherein the other information is the environment information.
a learning information storage unit that stores learning information acquired using two or more pieces of training data that each include one or more pieces of NIRS information acquired using a reflected light of near- infrared light emitted to a human body and a blood sugar level;
wherein the training data includes one or more pieces of NIRS information that can be acquired by emitting near-infrared light of two or more wavelengths to a human body,
wherein the training data includes one or more user static attribute values that are static attribute values of a person with the human body,
wherein the training data includes one or more device characteristic values that are characteristic values of a device that is used to acquire the one or more pieces of NIRS information,
wherein the learning information is a learning model acquired through machine learning processing using the two or more pieces of training data.
the NIRS acquisition unit that, using reflected light of near-infrared light emitted from one or more light emitting units and received by one or more light receiving units,
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” as recited in Independent Claims 1, 15, 20, 23, and 24 and their dependent claims are generically recited computer and hardware elements which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract ideas identified above in Independent Claims 1, 15, 20, 23, and 24 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer processor as claimed. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Independent Claims 1, 15, 20, 23, and 24 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claims 1, 15, 20, 23, and 24 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 – 30 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” as recited in Independent Claims 1, 15, 20, 23, and 24 and their dependent claims.
The additional elements of the “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” Claims 1 - 30, as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “blood sugar level acquisition unit” is described generically in [0342] as a “non-invasive blood sugar level tester” that can also be [0341] “typically realized as a processor, a memory, or the like”. The “blood sugar level acquisition unit” is shown as generic box element “blood sugar level acquisition unit 31” in figure 1.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “judgment unit” is described generically in [0341] as “typically realized as a processor, a memory, or the like”. The “judgment unit” is shown as generic box element “judgment unit 33” in figure 1.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “output unit” is described generically in [0343] as either having or not having an “output device such as a display” and “can be realized using…driver software of the output device.” The “output unit” is shown as generic box element “output unit 4” in Figure 1.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “fluctuation information acquisition unit” is described generically in [0341] as “can be realized using a processor, a memory, or the like”. The “fluctuation information acquisition unit” is shown as generic box element “fluctuation information acquisition unit 32” in figure 1.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “reference information storage unit” is described as a generic storage in [0099], [0104], and [0365], as part of generic “storage unit 11”. The “reference information storage unit” is shown in Figure 1 as generic box element “reference information storage unit 11”, part of “storage unit 11”.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “past blood sugar level group storage unit” is described generically in described as a generic storage in [0099] and [0269], as part of generic “storage unit 11”. The “past blood sugar level group storage unit” is shown in Figure 1 as generic box element “past blood sugar level group storage unit 12”, part of “storage unit 11”.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “suggestion information acquisition unit” is described generically in [0341] that it “can typically be realized using a processor, a memory, or the like”. The “suggestion information acquisition unit” is shown as generic box element “suggestion information acquisition unit C34” in Figure 9.
Per Applicant’s specification, as described above in the 112(f) interpretation, “suggestion information output unit” is described generically in [0343] as either having or not having an “output device such as a display” and “can be realized using…driver software of the output device.” The “suggestion information output unit” is shown as generic box element “suggestion information output unit C41” in Figure 9.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “environment information acquisition unit” is described generically in [0341] that it “can typically be realized using a processor, a memory, or the like”. The “environment information acquisition unit” is shown as “environment information acquisition unit C30” in Figure 9.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “learning information storage unit” is described generically in [0079] as ha== storage accessible by “program”. The “learning information storage unit” is shown as generic box element “learning information storage unit E711” that is part of generic “storage unit E71” in Figure 20.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “NIRS acquisition unit” is described generically in [0079] as having “light emitting units” and “light receiving units”, and [0169] where the “processing unit” processing procedures “may be realized using software”, or “hardware”, as a “CPU, an MPU, a GPU, or the like, and there is not limitation on the type thereof”. The “NIRS acquisition unit” is shown as generic box element “NIRS acquisition unit E63” that is part of “Processing Unit E73 in Figure 25.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “estimation unit” and “user static attribute value acquisition unit” are described generically in [0169] where the “processing unit” processing procedures “may be realized using software”, or “hardware”, as a “CPU, an MPU, a GPU, or the like, and there is not limitation on the type thereof”. The “estimation unit” is shown as generic box element “estimation unit E734” that is part of “Processing Unit E73 in Figure 25. The “user static attribute value acquisition unit” is shown as generic box element “user static attribute value acquisition unit E732” that is part of “Processing Unit E73 in Figure 25.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “device characteristic value acquisition unit” is described generically in [0483] “the device characteristic value acquisition unit E733…can typically be realized using a processor, a memory, or the like” and “may be realized using software”, or “hardware”, as a “CPU, an MPU, a GPU, or the like, and there is not limitation on the type thereof”. The “device characteristic value acquisition unit” is shown as generic box element “device characteristic value acquisition unit E733” in Figure 25.
Per Applicant’s specification, the “measured blood sugar level acquisition unit” is described generically in [0079] as “acquiring a blood sugar level of a user” and [0561] as “measured blood sugar level acquisition unit G931”. The “measured blood sugar level acquisition unit” is shown as generic box element “measured blood sugar level acquisition unit G931” as part of generic “Learning Processing Unit G93” in Figure 30.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “training data forming unit” is described generically in [0561] as “various kinds of processing” as part of ‘learning processing unit G93”, and [0571], where “there is no limitation on the module that performs machine learning processing.” The “training data forming unit” is shown as generic block element “training data forming unit G932” as part of “Learning Processing Unit” G93 in Figure 30.
Per Applicant’s specification, as described above in the 112(f) interpretation, the “learning unit” is described generically in [0561] “the learning unit G933” as “various kinds of processing” as part of ‘learning processing unit G93”, and [0571], where “there is no limitation on the module that performs machine learning processing.” The “learning unit” is shown as generic block element “learning unit G933” as part of “Learning Processing Unit” G93 in Figure 30.
Per Applicant’s specification, the “non-transitory computer readable medium” is described generically in [0575] as “learning storage unit G91 is a non-volatile non-transitory computer readable medium, but may be realized using a volatile non-transitory computer readable medium.” It is shown as “Learning Storage Unit G91” in Figure 30.
Accordingly, in light of Applicant’s specification, the claimed terms “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium” are reasonably construed as a generic computing and hardware devices. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “blood sugar level acquisition unit”, “judgment unit”, “output unit”, “fluctuation information acquisition unit”, “reference information storage unit”, “past blood sugar level group storage unit”, “suggestion information acquisition unit”, “suggestion information output unit”, “environment information acquisition unit”, “learning information storage unit”, “NIRS acquisition unit”, “estimation unit”, “user static attribute value acquisition unit”, “device characteristic value acquisition unit”, “measured blood sugar level acquisition unit”, “training data forming unit”, “learning unit”, and “non-transitory computer readable medium”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1 – 24 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatuses and method of Claims 1 - 30 are directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1 - 30 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in Independent Claims 1, 15, 20, 23, and 24 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1 - 30 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1 - 30 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 - 30 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 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 U.S.C. 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.
Claims 1 and 15 – 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Newberry, (WO 2019/161411 A1), hereinafter Newberry.
Regarding Claim 15 and Claim 1, Newberry discloses A blood sugar level estimation device that acquires a blood sugar level ([Abstract]) that is to be acquired by the blood sugar level acquisition unit according to claim 1,
Regarding Claim 1, Newberry discloses
A health support device ([Abstract]) comprising:
a blood sugar level acquisition unit ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”) that acquires a blood sugar level of a user ([0060] “detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques…blood glucose”)
a judgment unit (Fig 1, “Processing Circuit 102”) that judges whether or not blood sugar level-related information regarding the blood sugar level satisfies a predetermined output condition to acquire a judgment result (Fig 25, Box 2508 “Compare output vector to predetermined thresholds in calibration table”; Box 2510 “Display health data on user device”)(Examiner notes that there is a judgment of the data, or a “comparison”; [00202] “output vector 2104…include health data such as…glucose level”); and
an output unit (Fig 1, “display 116”) that outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied (Fig 25, “2510: Display health data on user device”).
wherein the health support device ([Abstract]) is configured to employ information acquired by NIRS non-invasively ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”; [[00125] “… noninvasive monitoring of insulin response and glucose levels. Though 390nm and 940nm were used in this embodiment, another first wavelength with a high absorption coefficient for NO and another second wavelength with a lower absorption coefficient may be used.”; [00195] “…obtain a concentration level of NO or glucose or other health data from input data derived from PPG signals.”; [00060]) to provide the blood sugar level-related information regarding the blood sugar level (Fig 25, “2510: Display health data on user device”; [00195]) and alert the user (Fig 14; “Biometrics within thresholds?” 1406 -> No, “Generate alert” 1408)
For the remainder of Claim 15, Newberry discloses comprising:
a learning information storage unit (Fig 24, “NN Memory Device” 2404) that stores learning information ([0038] “training set”) includes acquired using two or more pieces of training data ([00203] “…other learning vectors 2106 derived from a training set…includes sets with the same type of information in the input vector and known values of the health data in the output vector 2104”) that each include one or more pieces of NIRS information acquired using a reflected light of near- infrared light emitted to a human body ([00203] “includes sets with the same type of information in the input vector and known values of the health data in the output vector 2104”) and a blood sugar level ([0203] “same type of information”; [00202] “output vector 2104…include health data such as…glucose level”);
an NIRS acquisition unit (Fig 2, “PPG Circuit 110”) that acquires one or more pieces of NIRS information ([0055] “second photodetector circuit 230 may be configured to detect IR light.”; Fig 2, “Reflected Light” 240, [0056] “…detect the intensity of reflected light 240 from skin tissue of a user”) that can be acquired ([0056] “…detect…”) by emitting near-infrared light to a user ([0053] “light source 210 configured to emit a plurality of wavelengths of light across various spectrums. “, “plurality of LEDs 212a-n..including infrared (IR) light”) and
an estimation unit (Fig 21, “Neural Network Processing Device”; [00200] “a processing device”; [00202] “neural network processing device 2100”) that acquires an estimated blood sugar level ([00200] “…processing device executes the machine learning algorithm with the input vector and determines heath data at 2010…health data includes…Glucose level”), using one or more pieces of NIRS information acquired by the NIRS acquisition unit ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]) and the learning information (Fig 21, “Input Vector 2102”);
Regarding Claim 16, Newberry discloses as described above, The blood sugar level estimation device according to claim 15. For the remainder of Claim 16, Newberry discloses wherein the training data ([0038] “training set”) includes one or more pieces of NIRS information that can be acquired ([0055] “second photodetector circuit 230 may be configured to detect IR light.”; Fig 2, “Reflected Light” 240, [0056] “…detect the intensity of reflected light 240 from skin tissue of a user”) by emitting near-infrared light of two or more wavelengths to a human body ([0053] “light source 210 configured to emit a plurality of wavelengths of light across various spectrums. “, “plurality of LEDs 212a-n..including infrared (IR) light”, “emit light over one or more frequencies or ranges of frequencies or spectrums in response to driver circuit 218.”)(Examiner notes that a “range of spectrums” includes two or more wavelengths.); and
the NIRS acquisition unit ([0055] “photodetector circuits 230a-n”) acquires one or more pieces of NIRS information ([0055] “second photodetector circuit 230 may be configured to detect IR light.”; Fig 2, “Reflected Light” 240, [0056] “…detect the intensity of reflected light 240 from skin tissue of a user”) that can be acquired by emitting near-infrared light of two or more wavelengths to a user ([0053] “tunable LEDs..spectrums”, Fig 2, “emitted light” 216, [0056] “…emitted light 216…directed at the surface or epidermal layer of the skin tissue of a user”).
Regarding Claim 17, Newberry discloses as described above, The blood sugar level estimation device according to claim 15. For the remainder of Claim 17, Newberry discloses wherein the training data ([0038] “training set”) includes one or more user static attribute values ([00197] “patient data…age, weight, body mass index…”) that are static attribute values of a person with the human body [00197] “patient data…age, eight, body mass index…”)(Examiner notes that Applicant’s specification describes at [00448] that “…user static attribute values are, for example, a sex, an age, an age group, a height, a weight…”)
the blood sugar level estimation device ([Abstract]) further comprises a user static attribute value acquisition unit (Fig 1, “Biosensor 100” and “Health Data 120”) that acquires one or more user static attribute values of the user (Fig 20; [00197] “patient data is obtained at 2002. The patient data may include…age, weight, body mass index…”) , and
the estimation unit (Fig 21, “Neural Network Processing Device”) uses the one or more pieces of NIRS information acquired by the NIRS acquisition unit ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]), the one or more user static attribute values acquired by the user static attribute value acquisition unit ([00197] “patient data…age, eight, body mass index…”), and the learning information (Fig 21, “Input Vector 2102”) to acquire the estimated blood sugar level ([00202] “output vector 2104 may then include heath data, such as…glucose level”)
Regarding Claim 18, Newberry discloses as described above, The blood sugar level estimation device according to claim 15. For the remainder of Claim 18, Newberry discloses wherein the training data ([0038] “training set”) includes one or more device characteristic values (Fig 4, Data from 6 sensors listed, each at a different nm, number of sensors = 6; Fig 21, Input data with corresponding device settings at 940 nm, 660 nm, 395 nm, and 530 nm)(Examiner notes that Applicant’s specification describes device characteristic values at [0073] as “…one or more pieces of information of- distance information specifying a distance between two or more light receiving units; the number of light receiving units; and the number of light emitting units.”) that are characteristic values of a device (Fig 2, “PPG Circuit”; [0181] “PPG signals…LEDs…emit light at one or more of 390nm, 468nm, 592nm, 660 nm, 940 nm or in a range of+/- 20nm from these wavelengths.”) that is used to acquire the one or more pieces of NIRS information ([00181] “obtaining PPG signals”),
the blood sugar level estimation device ([Abstract]) further comprises a device characteristic value acquisition unit ([Fig 21, “Neural Network Processing Device” and “Spectral Data 106”) that acquires one or more device characteristic values (Fig 4, Data from 6 sensors listed, each at a different nm, number of sensors = 6; Fig 21, Input data with corresponding device settings at 940 nm, 660 nm, 395 nm, and 530 nm) of a device that is used to acquire one or more pieces of NIRS information of the user (Fig 2, “PPG Circuit”; [0181] “LEDs”, “obtaining PPG signals”), and
the estimation unit (Fig 21, “Neural Network Processing Device”; [00200] “a processing device”; [00202] “neural network processing device 2100”) uses the one or more pieces of NIRS information acquired by the NIRS acquisition unit ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]), the one or more device characteristic values acquired by the device characteristic values acquisition unit (Fig 21, “input vector” data at specific detected wavelengths), and the learning information (Fig 21, ‘input vector 2102”) to acquire the estimated blood sugar level (Fig 21 and “Input Vector 2102”);
Regarding Claim 19, Newberry discloses as described above, The blood sugar level estimation device according to claim 15. For the remainder of Claim 19, Newberry discloses wherein the learning information is a learning model ([00195] “neural network models”) acquired through machine learning processing ([00195] “artificial neural networks (a.k.a. machine learning algorithms)”, “neural networks may be used to obtain a concentration level of NO or glucose…from input data derived from PPG signals.”) using the two or more pieces of training data ([00195] “…from input data derived from PPG signals.”; [00197] “patient data…age, eight, body mass index…”)(Examiner notes that the PPG signals can be input at two or more frequencies, thereby two or more pieces on their own.)
Regarding Claim 20, Newberry discloses A learning device ([Abstract] “neural network processing device”) that acquires learning information (Fig 21, “Learning Vector 2106”) that is used by the blood sugar level estimation device according to claim 15 (See citation above), comprising:
the NIRS acquisition unit (Fig 2, “PPG Circuit 110”) that, using reflected light (Fig 2, “Reflected Light” 240) of near-infrared light emitted from one or more light emitting units ([0053] “light source 210 configured to emit a plurality of wavelengths of light across various spectrums. “, “plurality of LEDs 212a-n..including infrared (IR) light”) and received ([0055] “second photodetector circuit 230..”, [0056] “…detect the intensity of reflected light 240 from skin tissue of a user”) by one or more light receiving units ([0055] “photodetector circuits 230a-n”), acquires one or more pieces of NIRS information regarding the reflected light ([0055] “second photodetector circuit 230 may be configured to detect IR light.”; Fig 2, “Reflected Light” 240, [0056] “…detect the intensity of reflected light 240 from skin tissue of a user”)(Examiner notes that the one or more pieces of NIRS information include information from multiple wavelengths);
a measured blood sugar level acquisition unit ([00210] “an independent method… a known method such as fingerprick and a blood test.”; “Biosensor 100”) that acquires a blood sugar level of a user ([00210] “glucose level of the patient are obtained…”);
a training data forming unit (Fig 21, “Neural Network Processing Device”; [00200] “a processing device”; [00202] “neural network processing device 2100”) that forms training data ([0038] “training set”), using the one or more pieces of NIRS information ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]) and the blood sugar level ([00210] “glucose level”); and
a learning unit (Fig 21, “Neural Network Processing Device”; [00200] “a processing device”; [00202] “neural network processing device 2100”; [00195] “(machine learning algorithms)”)(Examiner notes that the iterative learning machine learning algorithms are part of the overall storage of “Neural Network Processing Device”, as is “learning unit G933” within “Learning Processing Unit” G93 in Applicant’s Fig 30.) that acquires learning information using the training data (Fig 21) “Neural Network Processing Device 2100” obtains “Learning Vector 2106” from “Input Vector 2102”; [0038] “training set”).
Regarding Claim 21, Newberry discloses a user static attribute value acquisition unit (Fig 1, “Biosensor 100” and “Health Data 120”) that acquires one or more user static attribute values that are static attribute values of the user (Fig 20; [00197] “patient data is obtained at 2002. The patient data may include…age, weight, body mass index…”) (Examiner notes that Applicant’s specification describes at [00448] that “…user static attribute values are, for example, a sex, an age, an age group, a height, a weight…”),
wherein the training data forming unit (Fig 21, “Neural Network Processing Device”) uses the one or more pieces of NIRS information ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]), the one or more user static attribute values (Fig 20; [00197] “patient data is obtained at 2002. The patient data may include…age, weight, body mass index…”), and the blood sugar level ([00210] “…health data is obtained using an independent method”, “glucose level of the patient are obtained using a known method such as fingerprick and a blood test.”, “included in a training set…obtained in a clinical setting”) to form the training data (Fig 26, “Training Set 2638”; [00210]; Fig 21, “Input Vector 2102”).
Regarding Claim 22, Newberry discloses as described above, The blood sugar level estimation device according to claim 20. For the remainder of Claim 22, Newberry discloses a device characteristic value acquisition unit ([Fig 21, “Neural Network Processing Device” and “Spectral Data 106”) that acquires one or more device characteristic values of a device (Fig 4, Data from 6 sensors listed, each at a different nm, number of sensors = 6; Fig 21, Input data with corresponding device settings at 940 nm, 660 nm, 395 nm, and 530 nm) (Examiner notes that Applicant’s specification describes device characteristic values at [0073] as “…one or more pieces of information of- distance information specifying a distance between two or more light receiving units; the number of light receiving units; and the number of light emitting units.”) that includes the one or more light receiving units ([0055] “photodetector circuits 230a-n”) and the one or more light transmitting units ([0053] “tunable LEDs..spectrums”, Fig 2, “emitted light” 216, [0056] “…emitted light 216…directed at the surface or epidermal layer of the skin tissue of a user”),
wherein the training data forming unit (Fig 21, “Neural Network Processing Device”) uses the one or more pieces of NIRS information ([00199] “input vector includes the PPG input data, such as the PPG signals at one or more wavelengths”; [00200]), the one or more device characteristic values (Fig 21, “input vector” data at specific detected wavelengths), and the blood sugar level ([00210] “…health data is obtained using an independent method”, “glucose level of the patient are obtained using a known method such as fingerprick and a blood test.”, “included in a training set…obtained in a clinical setting”) to form the training data (Fig 26, “Training Set 2638”; [00210]; Fig 21, “Input Vector 2102”).).
Regarding Claims 23 and 24, Newberry discloses,
For Claim 23: A health support method ([Abstract]) that is realized using a blood sugar level acquisition unit ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”), a judgment unit (Fig 1, “Processing Circuit 102”), and an output unit (Fig 1, “display 116”), comprising: a blood sugar acquisition step ([0060] “detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques…blood glucose”), a judgment step (Fig 25, Box 2508 “Compare output vector to predetermined thresholds in calibration table”; Box 2510 “Display health data on user device”)(Examiner notes that there is a judgment of the data, or a “comparison”; [00202] “output vector 2104…include health data such as…glucose level”); an output step (Fig 25, “2510: Display health data on user device”).
wherein the health support method ([Abstract]) employs information acquired by NIRS non-invasively ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”; [[00125] “… noninvasive monitoring of insulin response and glucose levels. Though 390nm and 940nm were used in this embodiment, another first wavelength with a high absorption coefficient for NO and another second wavelength with a lower absorption coefficient may be used.”; [00195] “…obtain a concentration level of NO or glucose or other health data from input data derived from PPG signals.”; [00060]) to provide the blood sugar level-related information regarding the blood sugar level (Fig 25, “2510: Display health data on user device”; [00195]) and alert the user (Fig 14; “Biometrics within thresholds?” 1406 -> No, “Generate alert” 1408)
For Claim 24: A non-transitory computer readable medium having recorded thereon ([0050] “may include one or more non-transitory processor readable memories”) a program for enabling a computer to function ([0050] “store instructions which when executed by the one or more processing circuits 102, causes the one or more processing circuits 102 to perform one or more functions described herein.”)
wherein the computer ([Abstract]; [00250] “microcomputer…”) is configured to employ information acquired by NIRS non-invasively ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”; [[00125] “… noninvasive monitoring of insulin response and glucose levels. Though 390nm and 940nm were used in this embodiment, another first wavelength with a high absorption coefficient for NO and another second wavelength with a lower absorption coefficient may be used.”; [00195] “…obtain a concentration level of NO or glucose or other health data from input data derived from PPG signals.”; [00060]) to provide the blood sugar level-related information regarding the blood sugar level (Fig 25, “2510: Display health data on user device”; [00195]) and alert the user (Fig 14; “Biometrics within thresholds?” 1406 -> No, “Generate alert” 1408)
For the remainder of Claims 23 and 24, Newberry discloses as described above for the Claim portion of Claim 15 in the 35 U.S.C. 102 rejection, a learning information storage unit that stores learning information acquired using two or more pieces of training data that each include one or more pieces of NIRS information acquired using a reflected light of near-infrared light emitted to a human body and a blood sugar level; an NIRS acquisition unit that acquires one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user; and an estimation unit that acquires an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information (See citation above in Claim 15).
Regarding Claim 25, 26, and 27, Newberry discloses as described above, The health support device according to claim 1 (See citation with Claim 15), The health support method according to claim 23, the method is realized using a learning information storage unit, an NIRS acquisition unit, and an estimation unit, and The non-transitory computer readable medium according to claim 24, the readable medium having recorded thereon a program for enabling a computer to function, respectively (See citations in Claims 23, 24, and 15). For the remainder of Claims 25, 26, and 27, Newberry discloses, as described above in Claim 15, a learning information storage unit that stores learning information acquired using two or more pieces of training data that each include one or more pieces of NIRS information acquired using a reflected light of near-infrared light emitted to a human body and a blood sugar level; an NIRS acquisition unit that acquires one or more pieces of NIRS information that can be acquired by emitting near-infrared light to a user; and an estimation unit that acquires an estimated blood sugar level, using one or more pieces of NIRS information acquired by the NIRS acquisition unit and the learning information (See citation above in Claim 15).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 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 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 4, 6 – 13, and 23 – 24 are rejected under 35 U.S.C. 103 as being unpatentable over Hayter et. al., (United States Patent Application Publication US 2018/0271418 A1), hereinafter Hayter in view of Newberry, (WO 2019/161411 A1), hereinafter Newberry.
Regarding Claims 1, 23, and 24 Hayter discloses
For Claim 1: A health support device ([Abstract]) comprising:
wherein the health support device ([Abstract]) is configured to employ information acquired to provide the blood sugar level-related information regarding the blood sugar level (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the output condition that is satisfied is when the judged (or determined) level is in the mg/dL range to be “impaired glucose condition”) and alert the user (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the broadest reasonable interpretation of “alert the user” includes notifying a user of negative information that would put them on “alert”, such as that they are in “impaired glucose tolerance condition.”)
For Claim 23: A health support method ([Abstract]) that is realized using a blood sugar level acquisition unit (Fig 1A, “glucose monitor” 130), a judgment unit (Fig 1A, “analysis module” 110B), and an output unit (Fig 1A, “user interface 110A” and “mobile phone 110”), comprising: a blood sugar acquisition step ([0013] “generate signals corresponding to monitored glucose level in the bodily fluid…”), a judgment step ([0004] “..second glucose measurement between 140 mg/dL and 200 mg/dL…represent impaired glucose tolerance condition”; Fig 2, [0032] “…App is configure to provide or output…diagnosis information such as pre-diabetes condition, and/or impaired glucose tolerance condition from the real time glucose level information”)(Examiner notes that the glucose levels are judged or evaluated against a metric, the “impaired glucose tolerance” condition window of [0004]), an output step (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)
wherein the health support method ([Abstract]) employs information acquired to provide the blood sugar level-related information regarding the blood sugar level (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the output condition that is satisfied is when the judged (or determined) level is in the mg/dL range to be “impaired glucose condition”) and alert the user (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the broadest reasonable interpretation of “alert the user” includes notifying a user of negative information that would put them on “alert”, such as that they are in “impaired glucose tolerance condition.”)
For Claim 24: A non-transitory computer readable medium having recorded thereon (Fig 1A, “user interface 110A” and “mobile phone 110”; [0032] “the App is installed in the mobile phone 110”) a program ([0032] “…a software application ("App") that is executable by any processor controlled device”), for enabling a computer to function as:
wherein the computer ([Abstract]; [0035] “computer terminal 170]; Fig 1C) is configured to employ information acquired to provide the blood sugar level-related information regarding the blood sugar level (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the output condition that is satisfied is when the judged (or determined) level is in the mg/dL range to be “impaired glucose condition”) and alert the user (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the broadest reasonable interpretation of “alert the user” includes notifying a user of negative information that would put them on “alert”, such as that they are in “impaired glucose tolerance condition.”)
For the remainder of Claims 1, 23, and 24, Hayter discloses,
a blood sugar level acquisition unit (Fig 1A, “glucose monitor” 130; [0034] “Glucose monitor 130…includes one or more in vivo glucose sensors”) that acquires a blood sugar level of a user ([0059] “glucose data received from glucose monitor 130”; [0057] “a single glucose level identified”);
a judgment unit (Fig 1A, “analysis module” 110B) that judges whether or not blood sugar level-related information regarding the blood sugar level satisfies a predetermined output condition to acquire a judgment result ([0038] “post prandial glucose level analysis including…corresponding diagnosis indication of “impaired glucose condition””; [0004] “..second glucose measurement between 140 mg/dL and 200 mg/dL…represent impaired glucose tolerance condition”; Fig 2 and 1C) and
an output unit (Fig 1A, “user interface 110A” and “mobile phone 110”) that outputs blood sugar level information regarding the blood sugar level when the judgment result indicates that the output condition is satisfied (Fig 1C; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the output condition that is satisfied is when the judged (or determined) level is in the mg/dL range to be “impaired glucose condition”)
Hayter does not specifically disclose information acquired by NIRS non-invasively.
Newberry teaches a PPG sensor that measures in the near-infrared spectrum non-invasively to determine blood glucose. Specifically for Claims 1, 23, and 24, Newberry teaches information acquired by NIRS non-invasively ([Abstract] “A neural network processing device”; Fig 2,”Biosensor 100”; [[00125] “… noninvasive monitoring of insulin response and glucose levels. Though 390nm and 940nm were used in this embodiment, another first wavelength with a high absorption coefficient for NO and another second wavelength with a lower absorption coefficient may be used.”; [00195] “…obtain a concentration level of NO or glucose or other health data from input data derived from PPG signals.”; [00060]).
Hayter and Newberry both describe measurement devices that obtain data from interacting with a user to determine their glucose level: Hayter with a “glucose sensor” (having a portion positioned under a skin surface), and Newberry with a PPG sensor that measures in the near-infrared spectrum non-invasively to determine blood glucose. Newberry provides a motivation to combine at [00125] with “…a non-invasive, quick 1-2 minute test produced an indicator of diabetes or diabetic risk in a person…These unexpected results have advantages in early detection of diabetic risk and easier, noninvasive monitoring of insulin response and glucose levels. Though 390nm and 940nm were used in this embodiment, another first wavelength with a high absorption coefficient for NO and another second wavelength with a lower absorption coefficient may be used.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using a quick, “1-2 minute” non-invasive test measurement to determine glucose levels would be useful in a glucose analysis system to encourage consistent use due to its speed and convenience.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “glucose sensor” that transmits information about glucose level to a glucose level analysis system disclosed in Hayter with the PPG sensor that non-invasively uses NIRS to measure blood parameters for glucose level taught by Newberry, creating a single glucose level analysis system that obtains quick, non-invasive glucose measurements for increased user convenience.
Regarding Claim 2, Hayter in view of Newberry discloses as described above, The health support device according to claim 1, wherein the blood sugar level acquisition unit. For the remainder of Claim 2, Hayter discloses wherein the blood sugar level acquisition unit acquires a blood sugar level group constituted by two or more blood sugar levels of the user acquired in a time series ([0058] “pre-meal glucose parameter…post-meal glucose parameters”),
the health support device ([Abstract]) further comprises a fluctuation information acquisition unit ([0032] “analysis module 110B”, and Fig 5B; [0058] “calculating…difference between…”)(Examiner notes that 5B depicts the steps that the “analysis module 110B” uses to determine the “post-prandial glucose metric”, which is the “peak-difference” metric. Therefore, this is the portion of the processing that would be for fluctuation information acquisition, in the same way that the “fluctuation information acquisition unit 32” is part of the overall “processing unit 3”, shown in Applicant Specification’s Figure 1) that acquires fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group (Fig 5B, “peak-difference metric…the difference between the post meal peak parameter (..maximum glucose level…) and the pre-meal glucose parameter…”)(Examiner notes that a fluctuation can be a “difference” in the time elapsed between “pre-meal” and “post-meal”),
the judgment unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”)(Examiner notes that the data processing to judge data is part of the overall processing of “analysis module”, as is “judgment unit 33” within “processing unit” 3 in Applicant’s Fig 1.) judges whether or not the fluctuation information satisfies a predetermined output condition (Fig 1C and [0004] “Impaired glucose tolerance”; Fig 5B, “post-prandial glucose metric includes…peak-difference metric”)(Examiner notes that the category of “impaired glucose condition” contingent on the post-prandial glucose being in a certain range is a predetermined output condition), to acquire the judgment result (Fig 1C “Post-Prandial Glucose” shown, with “Diagnosis: Impaired glucose tolerance”; Fig 5B), and
the output unit (Fig 1A, “user interface 110A” and “mobile phone 110”) outputs the fluctuation information ([0059] “output information…”) when the judgment result indicates that the output condition is satisfied (Fig 1C, “Post-Prandial Glucose” shown, with “Diagnosis: Impaired glucose tolerance”).
Regarding Claim 6, Hayter in view of Newberry discloses as described above, The health support device according to claim 1. For the remainder of Claim 6, Hayter discloses wherein the judgment unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”) judges whether or not blood sugar level- related information regarding the blood sugar level satisfies a predetermined suggestion condition, to acquire a judgment result (Fig 1C and [0004] “Impaired glucose tolerance”)(Examiner notes that the category of “impaired glucose condition” contingent on the post-prandial glucose being in a certain range is a categorical condition that satisfy giving the suggestion to the user that the user has impaired glucose tolerance), and
the health support device ([Abstract]) further comprising:
a suggestion information acquisition unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”; Fig 1C; [0004] “impaired glucose tolerance” condition) that acquires suggestion information (Fig 1C, Display “Impaired glucose tolerance” condition) when the judgment result indicates that the suggestion condition is satisfied (Fig 1C; [0004]; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”; [0052] “diagnosis…timely corrective action, under the guidance of a physician…”); and
a suggestion information output unit (Fig 1A, “user interface 110A” and “mobile phone 110”; [0032] “…provide or output on the user interface 110A diagnosis information…and/or impaired glucose tolerance condition…”)(Examiner notes that the action to output suggestion information occurs as part of the overall “user interface 110A” display on “mobile phone 110”, as is “suggestion information output unit C41” within “output unit” C4 in Applicant’s Fig 9.) that outputs the suggestion information (Fig 1C; [0004]; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”; [0052] “diagnosis…timely corrective action, under the guidance of a physician…”);
Regarding Claim 7, Hayter in view of Newberry discloses as described above, The health support device according to claim 6. For the remainder of Claim 7, Hayter discloses wherein the blood sugar level acquisition unit acquires a blood sugar level group constituted by two or more blood sugar levels of one user acquired in a time series on one day ([0058] “pre-meal glucose parameter…post-meal glucose parameters”; [0076] “determine a post-prandial metric for each day…”),
the health support device ([Abstract]) further comprises a fluctuation information acquisition unit ([0032] “analysis module 110B”, and Fig 5B) that acquires fluctuation information regarding blood sugar level fluctuations over time, using the blood sugar level group (Fig 5B, “peak-difference metric…the difference between the post meal peak parameter (..maximum glucose level…) and the pre-meal glucose parameter…”)(Examiner notes that a fluctuation can be a “difference” in the time elapsed between “pre-meal” and “post-meal”),
the judgment unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”) judges whether or not the fluctuation information satisfies the suggestion condition (Fig 1C and [0004] “Impaired glucose tolerance”)(Examiner notes that the category of “impaired glucose condition” contingent on the post-prandial glucose being in a certain range is a categorical condition that satisfy giving the suggestion to the user that the user has impaired glucose tolerance), and
the suggestion information acquisition unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”; Fig 1C; [0004] “impaired glucose tolerance” condition)(Examiner notes that the data processing to acquire suggestion information is part of the overall processing of “analysis module”, as is “suggestion information acquisition unit C34” within “processing unit” C3 in Applicant’s Fig 9.) acquires suggestion information (Fig 1C, Display “Impaired glucose tolerance” condition) corresponding to the fluctuation information when the judgment result indicates that the suggestion condition is satisfied (Fig 1C; [0004]; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”; [0052] “diagnosis…timely corrective action, under the guidance of a physician…”)
Regarding Claims 3 and 8, Hayter in view of Newberry discloses as described above, The health support device according to claim 2, and The health support device according to claim 7, respectively. For the remainder of Claims 3 and 8, Hayter discloses a reference information storage unit (Fig 1A, [0076] “storage unit”) (Examiner notes that the storage to store reference information used for evaluating glucose metrics is part of the overall storage of “storage unit”, as is “reference information storage unit 11” within “storage unit” 1 in Applicant’s Fig 1.) that stores reference information ([0076] “store instructions…determine a post-prandial metric…generate a post-prandial glucose level information.”) regarding a reference blood sugar level ([0004] “a second blood glucose measurement between 140 mg/L and 200 mg/dL is considered to represent impaired glucose tolerance condition”)(Examiner notes that the reference information is the threshold reference blood sugar level range of 140 mg/dL – 200 mg/dL to categorize a measured glucose level.),
wherein the fluctuation information acquisition unit ([0032] “analysis module 110B”, and Fig 5B); uses the blood sugar level group and the reference information to acquire the fluctuation information (Fig 5B, [0058] “post-prandial glucose metric…peak-difference metric…the difference between the post meal peak parameter (..maximum glucose level…) and the pre-meal glucose parameter…”; [0059] “diagnosis information from the post prandial glucose level analysis is presented…median glucose level (150 mg/dL) output with a diagnosis of “impaired glucose tolerance” )(Examiner notes that a fluctuation can be a “difference”. Examiner further notes that the reference information of the 140 mg/dL – 200 mg/dL is applied to the “post-prandial glucose metric” to categorize the numeric value with diagnostic information classifying the fluctuation as evidence of “impaired glucose tolerance.”),
Regarding Claims 4 and 9, Hayter in view of Newberry discloses as described above, The health support device according to claim 2 and The health support device according to claim 7, respectively. For the remainder of Claims 4 and 9, Hayter discloses a past blood sugar level group storage unit ([0076] “storage unit”; [0035] “storage units…store…threshold parameters associated with the…post-prandial glucose tolerance analysis”; [0041] “glucose monitor 130…sensor wear of 7 days, 10 days…)(Examiner notes that the storage to store past blood sugar level group used for evaluating glucose metrics is part of the overall storage of “storage unit”, as is “past blood sugar level group storage unit C12” within “storage unit” C11 in Applicant’s Fig 9.) that stores a past blood sugar level group constituted by two or more past blood sugar levels of the user (Figure 5A; [0076] “determine a post-prandial metric for each day…”)(Examiner notes that each “post-prandial metric” requires two blood sugar levels to calculate, a pre-meal and post-meal level, as described above.),
wherein the fluctuation information acquisition unit ([0032] “analysis module 110B”, and Fig 5B) uses the blood sugar level group and the past blood sugar level group to acquire the fluctuation information ([0076] “determine a post-prandial metric for each day…determine an overall post-prandial metric from the plurality of the post-prandial metric for each day…”)(Examiner notes that each “post-prandial metric” is for a blood sugar level group, as in Claims 2, 3, 7, and 8 above.)
Regarding Claim 10, Hayter in view of Newberry discloses as described above, The health support device according to claim 6. For the remainder of Claim 10, Hayter discloses wherein the suggestion information acquisition unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”; Fig 1C; [0004] “impaired glucose tolerance” condition) determines a suggestion condition (Fig 1C, Display “Impaired glucose tolerance” condition) that matches acquired blood sugar level-related information (Fig 1C; [0004]; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”; [0052] “diagnosis…timely corrective action, under the guidance of a physician…”) from a correspondence information storage unit (Fig 1A, [0076] “storage unit”) (Examiner notes that the storage to store correspondence information used for evaluating glucose metrics is part of the overall storage of “storage unit”, as is “correspondence information storage unit C13” within “storage unit” C1 in Applicant’s Fig 9.) that stores two or more pieces of correspondence information ([0004] “below 140 mg/dL is considered normal”, “blood glucose measurement between 140 mg/dL and 200 mg/dL is considered to represent impaired glucose tolerance condition”, “greater than 200 mg/dL is considered to indicate diabetic condition”)(Examiner notes that there are two or more ranges that correspond to categories of post-prandial glucose levels.) that each indicate a correspondence between a suggestion condition regarding blood sugar level related information and suggestion information (([0004], level ranges and either “normal”, “impaired glucose tolerance”, or “diabetic condition”) or suggestion basis information that is a basis of suggestion information, and acquires suggestion information corresponding to the suggestion condition or suggestion information that uses suggestion basis information corresponding to the suggestion condition (Fig 1C; [0004]; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”; [0052] “diagnosis…timely corrective action, under the guidance of a physician…”).
Regarding Claim 11, Hayter in view of Newberry discloses as described above, The health support device according to claim 6, For the remainder of Claim 11, Hayter discloses wherein the judgment unit (Fig 1A, “analysis module” 110B; [0008] “data processing device”) uses the blood sugar level ([0058] “pre-meal glucose parameter and the post-meal glucose parameters”) and other information other than the fluctuation information ([0051] “time information associated with a meal start tag”, “user initiated meal event tag using the user interface 110A…manually indicates the start of the meal event”)((Examiner notes that Applicant’s specification at [0271] describes that “other information is, for example, environment information…” which is “for example, time…”), to judge whether or not the blood sugar level or the fluctuation information satisfies the suggestion condition (Fig 1C and [0004] “Impaired glucose tolerance”; [0051] “…meal start tag (user initiated) can be used in the analysis…comparing time information…potential meal start time is identified as the meal start time”; Fig 5A uses the “meal start time” 510 to determine the “post-prandial metric”; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”)
Regarding Claim 12, Hayter in view of Newberry discloses as described above, The health support device according to claim 6, wherein the suggestion information acquisition unit. For the remainder of Claim 12, Hayter discloses uses the blood sugar level ([0058] “pre-meal glucose parameter and the post-meal glucose parameters”) and other information other than the fluctuation ([0051] “time information associated with a meal start tag”, “user initiated meal event tag using the user interface 110A…manually indicates the start of the meal event”) to acquire the suggestion information (Fig 1C and [0004] “Impaired glucose tolerance”; [0051] “…meal start tag (user initiated) can be used in the analysis…comparing time information…potential meal start time is identified as the meal start time”; Fig 5A uses the “meal start time” 510 to determine the “post-prandial metric”; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”)
Regarding Claim 13, Hayter in view of Newberry discloses as described above, The health support device according to claim 11. For the remainder of Claim 13, Hayter discloses an environment information acquisition unit (Fig 1A communication to “Analysis module 110B”, [0051] “a user initiated meal event tag using the user interface 110A of mobile phone 110”)(Examiner notes that the data processing to acquire environment information is part of the overall processing of “analysis module 110B”, as is “environment information acquisition unit C30” within “processing unit” C3 in Applicant’s Fig 9.) that acquires environment information regarding user environment ([0051] “time information associated with a meal start tag”, “user initiated meal event tag using the user interface 110A…manually indicates the start of the meal event”)(Examiner notes that Applicant’s specification at [0271] describes that “Environment information is, for example, time…”),
wherein the other information is the environment information (Fig 1C and [0004] “Impaired glucose tolerance”; [0051] “…meal start tag (user initiated) can be used in the analysis…comparing time information…potential meal start time is identified as the meal start time”; Fig 5A uses the “meal start time” 510 to determine the “post-prandial metric”; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”)
Claims 28 – 30 are rejected under 35 U.S.C. 103 as being unpatentable over Hayter in view of Newberry, further in view of Edwards, et al., (United States Patent Application Publication US 2018/0204636 A1), hereinafter Edwards.
Regarding Claims 28, 29, and 30, Hayter in view of Newberry discloses as described above, The health support device according to claim 1, The health support method according to claim 23, and The non-transitory computer readable medium according to claim 24, the readable medium having recorded thereon a program for enabling a computer to function, respectively.
For Claim 29: the method is realized using an environment information acquisition unit (Fig 1A communication to “Analysis module 110B”, [0051] “a user initiated meal event tag using the user interface 110A of mobile phone 110”)
For the remainder of Claims 28, 29, and 30, Hayter discloses an environment information acquisition step in which the environment information acquisition unit (Fig 1A communication to “Analysis module 110B”, [0051] “a user initiated meal event tag using the user interface 110A of mobile phone 110”)(Examiner notes that the data processing to acquire environment information is part of the overall processing of “analysis module 110B”, as with the “environment information acquisition unit C30” within “processing unit” C3 in Applicant’s Fig 9.) that acquires environment information regarding user environment ([0051] “time information associated with a meal start tag”, “user initiated meal event tag using the user interface 110A…manually indicates the start of the meal event”)(Examiner notes that Applicant’s specification at [0271] describes that “Environment information is, for example, time…”),
wherein the judgment unit (Fig 1A, “analysis module” 110B) judges whether or not blood sugar level-related information regarding the blood sugar level ([0058] “pre-meal glucose parameter and the post-meal glucose parameters”) and the environment information ([0051] “time information associated with a meal start tag”, “user initiated meal event tag using the user interface 110A…manually indicates the start of the meal event”)(Examiner notes that Applicant’s specification at [0271] describes that “other information is, for example, environment information…” which is “for example, time…”), satisfies a predetermined output condition to acquire the judgment result (Fig 1C and [0004] “Impaired glucose tolerance”; [0051] “…meal start tag (user initiated) can be used in the analysis…comparing time information…potential meal start time is identified as the meal start time”; Fig 5A uses the “meal start time” 510 to determine the “post-prandial metric”; [0038] “post-prandial glucose level analysis…corresponding diagnosis indication of “Impaired glucose tolerance”)
Hayter does not specifically disclose, including weather information, and judges whether or not blood sugar level-related information regarding the blood sugar level and the environment information, including the weather information, satisfies a predetermined output condition to acquire the judgment result.
Edwards teaches glucose level analysis that interfaces with an insulin delivery device, with capability to use environmental temperature information to alarm if the device is not within its working conditions. Specifically for Claims 28, 29, and 30, Edwards teaches including weather information ([0283] “…application associated with the local weather”, “ application interface module 7818 can receive information from a weather application…”), and judges whether or not blood sugar level-related information regarding the blood sugar level ([0378] “a patient may log test data (e.g., blood glucose measurements) via a second application (i.e., an application executed by the processor 7810…application interface module 7818 can receive the information or test data…used to calculated the next delivery data or time.”) and the environment information ([0228] “Based on the temperature input the processor 5980 can execute any of the modules and/or execute any of the methods described herein,”), including the weather information ([0283] “…to enhance the temperature alarms produced by the temperature history module 7815…”), satisfies a predetermined output condition to acquire the judgment result ([0283] “…information from a weather application…; [0283] with “the application interface module 7818 can receive information from a weather application (i.e., the second application)…enhance the temperature alarms produced by the temperature history module 7815.”; [0281] “…medicament may be outside (or nearing the limits of) an acceptable temperature threshold”)(Examiner notes that the combination of information that includes weather information like the temperature can be used to satisfy a predetermined output condition of “alarm” regarding the operating temperature of the device)
Hayter and Edwards both disclose and teach glucose level analysis devices that interface with insulin delivery devices: Hayter with identifying glucose level and communicating with the “insulin delivery device” and Edwards inputting glucose level information in order to inform dosage of insulin medicament [Edwards: 0378]. Edwards provides a motivation to combine at [0283] with “the application interface module 7818 can receive information from a weather application (i.e., the second application) to enhance the temperature alarms produced by the temperature history module 7815. For example…. notification produced by the temperature history module 7815 indicating a potentially unacceptable increase in the temperature of the medicament delivery device…can be produced more quickly…if the local outside temperature is 95° F.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that a glucose monitoring device accounting for temperature would be useful for determining if the device is operating within its proper operating temperatures increasing the confidence in its results and functionality.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Hayter’s disclosed glucose level analysis device that uses glucose level and broad environmental information to make a decision that is judged valid regarding glucose tolerance with Edwards’ taught specific environmental information regarding temperature to alarm if the device is not operating within its proper working temperature, thereby judging data as invalid. This would create a single glucose monitoring system that can use environmental data to inform whether it is producing reliable data for medicine dosage within its working temperature conditions.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hayter in view Newberry, further in view of Goodnow (United States Patent Application Publication US 20080060955 A1), hereinafter Goodnow.
Regarding Claim 5, Hayter in view of Newberry discloses as described above, The health support device according to claim 2. For the remainder of Claim 5, Hayter does not disclose wherein the output condition is a warning condition for outputting a warning, and the fluctuation information acquisition unit acquires fluctuation information regarding the warning when the judgment result indicates that the warning condition is satisfied.
Goodnow teaches a glucose meter module that analyzes glucose levels, including a display and alert system. Specifically for Claim 5, Goodnow teaches wherein the output condition is a warning condition for outputting a warning ([0040] “generate an alert signal for output when the received blood glucose level data is determined to be beyond a predetermined range.”; [0041] “hyperglycemic state and impending hypoglycemic state”), and
the fluctuation information acquisition unit (“processor” 604, Fig 6) acquires fluctuation information ([0040] “glucose level data…determined…beyond a predetermined range”) regarding the warning when the judgment result indicates that the warning condition is satisfied ([0040] “alert signal…received blood glucose level data…beyond a predetermined range”; [0041] “hyperglycemic state and impending hypoglycemic state”).
Goodnow provides a motivation to combine at [0040] – [0041] where the alert signal can be output regarding “an impending hyperglycemic state and an impending hypoglycemic state.” A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize that an output of a diagnostic category of adverse glucose results, such a “impaired glucose tolerance” as disclosed by Hayter, would be more impactful and prompt if accompanied by a corresponding “alarm” for prompt action due to adverse glucose results, as taught by Goodnow. It would have been predictable to use an accompanying alert taught by Goodnow in any device that similarly presents glucose level analysis results. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the glucose level analysis and categorical results such as “impaired glucose tolerance” disclosed in Hayter with the alert taught by Goodnow, creating a single glucose level analysis apparatus to urgently notify users of adverse glucose-associated results.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Hayter in view of Newberry, further in view of Choi et. al., (United States Patent Application Publication US 2020/0077961 A1), hereinafter Choi.
Regarding Claim 14, Hayter in view of Newberry discloses as described above, The health support device according to claim 7, wherein the fluctuation information acquisition unit. For the remainder of Claim 14, Hayter discloses acquires fluctuation information corresponding to fluctuations over time of a blood sugar level, using the blood sugar level group (Fig 5B, [0058] “peak-difference metric…the difference between the post meal peak parameter (..maximum glucose level…) and the pre-meal glucose parameter…”)(Examiner notes that a fluctuation shown by the post-prandial “peak-difference metric” occurs over the time between “before” and “after” a meal.)
Hayter does not specifically disclose that is a score. Hayter does disclose specific numeric values for the post-prandial glucose level, which is a numeric value corresponding to the change in glucose levels related to the meal, which could broadly be considered a score.
Choi teaches an apparatus for glucose measurement and analysis with a wristwatch component, phone app, and scores associated with glucose measurements. As a specific score indicator Choi teaches acquires fluctuation information that is a score (Fig 4A; [0092] “blood glucose score”, [0084]) corresponding to fluctuations over time of a blood sugar level, using the blood sugar level group (Fig 4A; [0084] “processor 120 may generate a blood glucose score or a stress score indicative of a blood glucose metabolism state. For example, hypoglycemia/hyperglycemia frequency…”, “information on changes in blood glucose…”)
Choi provides a motivation to combine at [0084] with “the health indices may include a blood glucose score, a stress score…and the like”, showing an example on Figure 4A. A person having ordinary skill in the art before the effective filing data of the claimed invention would recognize that presenting the glucose result information in a “score” form would be compatible with multi-health parameter apps and give users an easily-understandable translation of their medical glucose value result. Both Hayter and Choi process glucose measurement results into a result to be presented to users: Hayter in numeric mg/dL form with a category of “normal”, “impaired glucose tolerance”, or “diabetic”, and Choi with a summarized “score”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the post-prandial glucose level and category results disclosed in Hayter with the “score” format taught by Choi, creating a single user-friendly result format for glucose fluctuation measurement results.
Response to Arguments
Applicant's arguments filed 08 October 2025 have been fully considered but they are not persuasive.
In regard to the 35 U.S.C. 101 Rejections:
Applicant argues at [Page 19, Bottom] – [Page 20, 2nd Full Paragraph] that the amended independent claims 1, 23, and 24 improve the technical field of acquiring and outputting information regarding a blood sugar level and include elements that are significantly more than the judicial exception. Acquiring information by NIRS is a non-invasive measurement process that constitutes extra-solution activity that serves as data-gathering. Further, as claimed, the process of making a measurement with NIRS is not positively recited. Rather, the limitation as claimed of “employ information acquired by NIRS non-invasively to provide information…” merely requires observing and judging information, which constitutes the abstract idea. This information could be observed and judged by a person with ordinary skill in the art in a database of measurements categorized as “NIR information”, or by using well-understood, routine, and conventional NIRS measurement equipment. “Provide the blood sugar level-related information and alert the user” can be performed by a human to communicate a result of observation and judgment of the information. rom MPEP 2106.05(a): It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). The argument is not persuasive.
In regard to the 35 U.S.C. 102 and 35 U.S.C. 103 Rejections:
Applicant argues at [Page 21, Paragraph 4] – [Page 21, Paragraph 5] that Hayter uses an in vivo glucose sensor, which does not obtain data non-invasively using NIRS, and that it does not disclose performing blood glucose estimation based on training information. Looking to the 35 U.S.C. 102 and 35 U.S.C. 103 analysis above, Hayter is not used to disclose obtaining data non-invasively using NIRS (it is combined with Newberry, which teaches obtaining data non-invasively using NIRS). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).Further, as recited in the claims, there is nothing particular recited in Claims 1 – 4, 6 – 13, and 23 – 24 to which Hayter is applied, that positively requires “training information”. Looking to MPEP 2111.01(II), it is improper to import claim limitations from the specification. As recited in the amended claims and using broadest reasonable interpretation, there is no particular limitation that indicates that the invention is accomplishing such a task relative to limited time and many wavelengths. The argument is not persuasive.
Applicant argues at [Page 21, Paragraph 6] that Newberry does not disclose any technique for acquiring blood glucose values, and that Newberry discloses that it is possible to “investigate correlations between NO concentration levels and blood glucose levels”. As cited above, Newberry discloses [[00125] “… noninvasive monitoring of insulin response and glucose levels.” Regarding the NO levels, Newberry discloses at [0060] that “the biosensor 100 may detect nitric oxide (NO) concentration levels and correlate the NO concentration level to a blood glucose level.” As recited in the instant claims, “a blood sugar level unit acquires a blood sugar level”, which broadest reasonable interpretation includes determining a blood sugar level from NO concentration correlation. The argument is not persuasive.
Applicant argues at [Page 21, Bottom] – [Page 22, Paragraph 2] that Goodnow and Choi to not cure deficiencies of Hayter or Newberry for independent claims 1, 23, and 24. Based on the 35 U.S.C 102 and 35 U.S.C. 103 rejection analysis and discussion above, Hayter and Newberry disclose and teach the elements of independent claims 1, 23, and 24. The argument is not persuasive.
Applicant summarily argues at [Page 22, Paragraph 3] that claims 1 – 24 are patentable over the combination of cited references. Based on the 35 U.S.C 102 and 35 U.S.C. 103 rejection analysis and discussion above, the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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