DETAILED ACTION
Applicant canceled claim 12. Claims 1-11 and 13-21 are pending and hereby under examination.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“apparatus” first recited in claim 1; and
“analyzer apparatus” first recited in claim 1;
“spectrum generating apparatus” first recited in claim 2;
“detection apparatus” first recited in claim 6.
The identified structure for the corresponding claim limitations are as follows:
“apparatus” is identified as “The apparatus that acquires the data may include an apparatus that acquires the data from the cloud or the like, or may be a detection apparatus that acquires the data onsite from a living organism. The detection apparatus may include a Raman spectrometer that acquires a Raman spectrum and may be a device that non-invasively acquires the data from a living organism” (Paragraph 0011) and “This Raman spectrometry apparatus 10 is suitably configured for use in experiments, and further includes a visible light source 15 that enables the position of the laser irradiated onto the sample 5 via the probe 13 to be optically confirmed” (Paragraph 0019).
“analyzer apparatus” is identified as “The blood glucose monitor 33 includes an analyzer apparatus 20 that is configured to analyze the analysis target spectrum 51 included in the data 52 that has been acquired via interface 32. The analyzer apparatus 20 includes a first analysis unit 21 that analyzes the analysis target spectrum 51, which reflects the components in a body fluid and is obtained by irradiating the body fluid with lasers, based on an analysis reference spectrum 53 which, out of a plurality of reference spectra (second spectra) 54, each of which mainly reflects one of a plurality of principal components of the body fluid, is highly similar to the analysis target spectrum 51, and determines the concentration of a target component, for example, a substance such as glucose, included in the body fluid …
The analyzer apparatus 20 may further include a reference spectrum generating apparatus 22 that is configured to determine the analysis reference spectrum 53 based on a group of similar spectra 55 including highly similar spectra that repeatedly appear in the plurality of spectra 51 included in the acquired data 52. The generating apparatus 22 may include a device (AI(2)) 22a that self-learns a plurality of reference spectra (second spectra) 52 from groups 55 of a plurality of spectra, out of the plurality of analysis target spectra (first spectra) 51, in which some spectral components exhibit high correlation or similarity.
The analyzer apparatus 20 uses the learning model 21a to obtain the concentration of glucose (target) component included in the blood 5t from the plurality of analysis target spectra 51 that were obtained in a time series (that is, intermittently over time) by irradiating laser light onto a body fluid (here, blood) 5t in a flowing state using the Raman spectrometry apparatus 10. The learning model 21a may be trained to obtain the concentration of a target component, such as glucose, based on a plurality of reference spectra (second spectra) 54 that are provided in advance and have been stored in a library 25. The self-learning device 22a may refer to a standard (normal) spectrum 56 of each of the principal components of blood, such as red blood cells and plasma, that has been obtained in advance, and generate (self- learn) reference spectra or spectrum 54, which include the analysis reference spectrum 53 that serves as a standard for determining concentrations, from groups 55 of a plurality of spectra, out of the plurality of analysis target spectra 51 obtained from the Raman spectrometry apparatus 10, that have high similarity or correlation with the standard spectrum 56.
The analyzer apparatus 20 may include a function which, or be configured to, through cooperation between the learning models 21a and 22a, analyze an analysis target spectrum 51, which is included in a group of similar spectra 55 that include highly similar spectra that repeatedly appear in the plurality of spectra 51 included in the acquired data 52, based on an analysis reference spectrum 53 including spectral components that are common to the group of similar spectra, and determine the concentration of a target component in the body fluid (Paragraphs 0021-0025).
“spectrum generating apparatus” is identified as “The analyzer apparatus 20 may further include a reference spectrum generating apparatus 22 that is configured to determine the analysis reference spectrum 53 based on a group of similar spectra 55 including highly similar spectra that repeatedly appear in the plurality of spectra 51 included in the acquired data 52. The generating apparatus 22 may include a device (AI(2)) 22a that self-learns a plurality of reference spectra (second spectra) 52 from groups 55 of a plurality of spectra, out of the plurality of analysis target spectra (first spectra) 51, in which some spectral components exhibit high correlation or similarity” (Paragraph 0022).
“detection apparatus” is identified as “The biological monitoring system 30 includes a detection apparatus 31 including a Raman spectrometry apparatus (optical system) 10 that acquires CARS spectra 51 from the blood 5t flowing through the blood vessel 5a… The Raman spectrometry apparatus 10 includes a probe (probe end or sampler) 13 for irradiating the blood 5t in the blood vessel being monitored with laser lights to obtain CARS light, a laser source 11 for irradiating the blood 5t with pump light (with a wavelength of 1030 nm, for example) 59p and Stokes light (with a wavelength of 1100 to 1300 nm, for example) 59s via the probe 13, and a spectrometer 12 that obtains a spectrum (spectra) 51 of the CARS light 50 emitted from the blood 5t” (Paragraph 0017)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-11, 13-18, and 21 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.
Regarding claim 5, it is unclear how a learning model “obtains” the concentration of the target component based on the analysis reference spectrum. What input is provided to the learning model, and what algorithm or calculations are made to determine the concentration of the target component based on the analysis reference spectrum? Is there a comparison being made with the target spectrum, or does the learning model not use the target spectrum? For examination purposes, the claim limitation will be interpreted such that a machine learning model or equivalent compares the target and reference spectra to determine a concentration of the target component.
Regarding claims 6 and 21, it is unclear what the difference is between the “apparatus” of claims 1 and 3 and the “detection apparatus” of claims 6 and 21. Claims 1 and 3 recite that the apparatus is configured to acquire data by irradiating a part of the body fluid with laser light. Per the claim interpretation above of the “apparatus” and the “detection apparatus”, the “apparatus” and the “detection apparatus” are limited to a Raman spectrometer or an equivalent structure. Thus, it becomes unclear how the apparatus includes a detection apparatus if they are interpreted to be the same or equivalent structure. For examination purposes, the “apparatus” and the “detection apparatus” will be interpreted to be the same structure. However, Applicant should clarify the differences, if any, between the two. Claims 7-9 are also rejected due to their dependence on claim 6.
Regarding claim 10, it is unclear how the concentration of a target component in the body fluid is determined. Per line 8 of claim 10, the determination step does not rely on any input or calculation step. It appears Applicant intends to claim that the determination is made based on the analysis of the target spectrum and reference spectrum, and, for examination purposes, that is how the claim will be interpreted. Claims 11 and 13-18 are also rejected due to their dependence on claim 10.
Regarding claims 10-11 and 13-18, the claims are directed towards a method of detecting a component in a body fluid. However, there is no structure recited for performing the method. For example, in claim 10, what acquires that data by irradiating a body fluid with laser light? What analyzes the spectra? For examination purposes, the claims will be interpreted such that any structure capable of performing the method will read on the claims.
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 10-11 and 13-18 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.
Analysis of independent claim 10:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 10 is directed to a computer implemented method, which describes one of the four statutory categories of patentable subject matter, i.e., a method. Therefore, further consideration is necessary regarding claims.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claim 10 recites an abstract idea. In particular, the claims generally recite
the following:
analyzing an analysis target spectrum, which is included in the acquired data, based on an analysis reference spectrum, which is highly similar to the analysis target spectrum and is selected out of a plurality of reference spectra that primarily reflect one out of a plurality of principal components of the body fluid respectively; and
determining a concentration of a target component in the body fluid.
These elements recited in claim 10 are drawn to an abstract idea since mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
“analyzing an analysis target spectrum, which is included in the acquired data, based on an analysis reference spectrum, which is highly similar to the analysis target spectrum and is selected out of a plurality of reference spectra that primarily reflect one out of a plurality of principal components of the body fluid respectively” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably review a target spectrum and compare that to a reference spectrum. There is nothing to suggest an undue level of complexity in “analyzing an analysis target spectrum, which is included in the acquired data, based on an analysis reference spectrum, which is highly similar to the analysis target spectrum and is selected out of a plurality of reference spectra that primarily reflect one out of a plurality of principal components of the body fluid respectively”.
“determining a concentration of a target component in the body fluid” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably compare reference and obtained spectrums to determine a concentration of a target component. There is nothing to suggest an undue level of complexity in “determining a concentration of a target component in the body fluid”.
Prong Two: Claim 10 does not recite additional elements that integrate the exception into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional elements merely:
Add insignificant extra-solution activity (the pre-solution activity of: using generic data gathering components (e.g., "acquiring data including a plurality of spectra in a time series obtained by irradiating a body fluid in a flowing state with laser light" (claim 10))).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claim 10 does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on generic processing components, the pre-solution activity of using generic data-gathering components, and generic post-solution activities, which merely facilitate the abstract idea.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. However, claim 10 does not recite any specific structure for performing the method. Claim 10 does recite acquiring data by irradiating a body fluid with laser light, which can be understood to be associated with the “apparatus” as described above. The “apparatus” as disclosed in the Applicant’s specification paragraph 0011, “The apparatus that acquires the data may include an apparatus that acquires the data from the cloud or the like, or may be a detection apparatus that acquires the data onsite from a living organism. The detection apparatus may include a Raman spectrometer that acquires a Raman spectrum and may be a device that non-invasively acquires the data from a living organism”.
The apparatus does not qualify as significantly more because this limitation is simply appending well understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements include a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Analysis of the dependent claims:
Claims 11 and 13-18 depend from the independent claims. Dependent claims 11 and 13-18 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“wherein the determining includes determining the concentration of the target component, which is included in any one of the plurality of principal components of the body fluid” (claim 13), “wherein the acquiring includes acquiring the data from a living organism” (claim 14), “wherein the acquiring includes acquiring a Raman spectrum” (claim 15), “wherein the body fluid is blood and the principal components include plasma and blood cells” (claim 16), “herein the body fluid is blood, and the principal components include at least one component out of red blood cells, white blood cells, and platelets, and a plasma component” (claim 17), and “wherein the target component includes at least one of glucose, hemoglobin Al c, creatinine, and albumin” (claim 18)), and
Further describe the pre-solution activity (“selecting the analysis reference spectrum based on a group of highly similar spectra that repeatedly appear among the plurality of spectra included in the acquired data” (claim 11)).
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. The result of the abstract idea does not cause the apparatus to perform differently.
Therefore, claims 10-11 and 13-18 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-8, 10-11, and 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (US 20170079565 – cited by Applicant) and Kawamura (US 20200408789 – cited by Applicant).
Regarding claims 1-2 and 5, Choi discloses a system for measuring a concentration of a component included in a body fluid, the system comprising:
an apparatus that is configured to acquire data including a plurality of spectra in a time series (Paragraph 0075, “a plurality of the in vivo spectra of the analyte in the human body is obtained continually (S201). In such an embodiment, a plurality of the in vivo spectra may be obtained continually at the predetermined time interval, e.g., a one-minute interval”) obtained by irradiating at least a part of the body fluid in a flowing state with laser light (Paragraph 0063, “the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves) are transmitted or diffusely reflected to an organism, e.g., a human body, and then absorbed or dispersed by the analyte therein, and may be obtained continually at a predetermined time interval … the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”); and
an analyzer apparatus that is configured to analyze an analysis target spectrum, which is included in the acquired data, and determine the concentration of a target component in the body fluid (Paragraph 0063, “concentration predicting apparatus 100 may be configured to predict the concentration of the analyte by analyzing an in vivo spectrum thereof using a concentration predicting algorithm for the analyte”).
While Choi discloses determining concentration based on a similarity between spectra (Paragraph 0076), Choi fails to explicitly disclose analyzing the target spectrum based on a highly similar reference spectrum selected out of a plurality of reference spectra. With regard to the limitation of claim 2, Choi fails to disclose determining the analysis reference spectrum. Regarding the limitation of claim 5, while Choi discloses a learning unit (Fig. 1, learning unit 120), Choi fails to disclose wherein the learning unit is trained to obtain the concentration of the target component based on the analysis of the reference spectrum.
Kawamura teaches an analogous method of using spectral analysis to determine concentrations of constituents in a sample (Paragraph 0002). The spectral information selection unit acquires the result of analysis of samples and selects two pieces of spectral information from a plurality of samples from a database, the spectral information of the plurality of samples being stored in advance (Paragraph 0040). An estimation unit causes the learning model to estimate quantitative information on the test substance contained in the sample by inputting the connected spectral information into the learning model (Paragraphs 0041-0045), the quantitative information being defined as concentration (Paragraph 0007).
As Choi is concerned with determining concentrations of substances in the blood such as glucose, Kawamura introduces a method of comparing obtained spectral information to reference spectral information to determine a concentration. Kawamura discloses this is useful to do even if the sample contains other foreign substances having overlapping peaks. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the analysis of Choi to incorporate the method of comparing obtained data to reference data as taught by Kawamura to distinguish measure one of many components in the blood.
Regarding claims 3 and 20, Choi discloses a system that measures a concentration of a component included in a body fluid, the system comprising:
an apparatus that is configured to acquire data including a plurality of spectra in a time series (Paragraph 0075, “a plurality of the in vivo spectra of the analyte in the human body is obtained continually (S201). In such an embodiment, a plurality of the in vivo spectra may be obtained continually at the predetermined time interval, e.g., a one-minute interval”) obtained by irradiating at least a part of the body fluid in a flowing state with laser light (Paragraph 0063, “the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves) are transmitted or diffusely reflected to an organism, e.g., a human body, and then absorbed or dispersed by the analyte therein, and may be obtained continually at a predetermined time interval … the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”); and
an analyzer apparatus that is configured to analyze an analysis target spectrum, which is included in a group of similar spectra including highly similar spectra that repeatedly appear in the plurality of spectra included in the acquired data, and determine the concentration of a target component in the body fluid (Paragraph 0063, “concentration predicting apparatus 100 may be configured to predict the concentration of the analyte by analyzing an in vivo spectrum thereof using a concentration predicting algorithm for the analyte”, wherein the in vivo spectrum is a part of the plurality of in vivo spectra (see Fig. 4)).
While Choi discloses determining concentration based on a similarity between spectra (Paragraph 0076), Choi fails to explicitly disclose analyzing the target spectrum based on a reference spectrum with spectral components common to the group of similar spectra. Regarding the limitation of claim 20, while Choi discloses a learning unit (Fig. 1, learning unit 120), Choi fails to disclose wherein the learning unit is trained to obtain the concentration of the target component based on the analysis of the reference spectrum.
Kawamura teaches an analogous method of using spectral analysis to determine concentrations of constituents in a sample (Paragraph 0002). The spectral information selection unit acquires the result of analysis of samples and selects two pieces of spectral information from a plurality of samples from a database, the spectral information of the plurality of samples being stored in advance (Paragraph 0040). An estimation unit causes the learning model to estimate quantitative information on the test substance contained in the sample by inputting the connected spectral information into the learning model (Paragraphs 0041-0045), the quantitative information being defined as concentration (Paragraph 0007).
As Choi is concerned with determining concentrations of substances in the blood such as glucose, Kawamura introduces a method of comparing obtained spectral information to reference spectral information to determine a concentration. Kawamura discloses this is useful to do even if the sample contains other foreign substances having overlapping peaks. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the analysis of Choi to incorporate the method of comparing obtained data to reference data as taught by Kawamura to distinguish measure one of many components in the blood.
Regarding claim 4, Choi as modified further discloses wherein the analyzer apparatus is configured to determine the concentration of the target component (Paragraph 0063, “the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”), which is included in any one of the plurality of principal components of the body fluid (Applicant defines principal components as blood cells and plasma in paragraphs 0004 and 0011, and specifically mentions the target component may include glucose in paragraph 0011. Choi discloses in paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”, thus, the analyte (glucose) is included in the principal components (blood cells and plasma) of the body).
Regarding claim 6, Choi as modified further discloses wherein the apparatus that acquires the data includes a detection apparatus that is configured to acquire the data from a living organism (Paragraph 0070, “ the concentration predicting apparatus 100 may further include the communicator 140 configured to obtain the in vivo spectrum from the optical sensor through a wired or wireless network”).
Regarding claim 7, Choi as modified further discloses wherein the detection apparatus includes a Raman spectrometer that is configured to acquire a Raman spectrum (Paragraph 0063, “The in vivo spectrum may be applied to the concentration predicting algorithm after being obtained by an infra-red spectroscopy or a Raman spectroscopy”).
Regarding claim 8, Choi as modified further discloses wherein the detection apparatus includes a probe that is configured to acquire the data on blood flowing through a blood vessel as the body fluid (Paragraph 0063, “the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves)”; Applicant defines the probe as an element that irradiates the blood with laser light (Paragraphs 0018-0019); thus, Chois laser/infrared rays read on the limitation of a probe).
Regarding claims 10-11, Choi discloses a method of detecting a component in a body fluid, comprising:
acquiring data including a plurality of spectra in a time series (Paragraph 0075, “a plurality of the in vivo spectra of the analyte in the human body is obtained continually (S201). In such an embodiment, a plurality of the in vivo spectra may be obtained continually at the predetermined time interval, e.g., a one-minute interval”) obtained by irradiating a body fluid in a flowing state with laser light (Paragraph 0063, “the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves) are transmitted or diffusely reflected to an organism, e.g., a human body, and then absorbed or dispersed by the analyte therein, and may be obtained continually at a predetermined time interval … the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”); and
analyzing an analysis target spectrum, which is included in the acquired data, and determine the concentration of a target component in the body fluid (Paragraph 0063, “concentration predicting apparatus 100 may be configured to predict the concentration of the analyte by analyzing an in vivo spectrum thereof using a concentration predicting algorithm for the analyte”).
Choi discloses determining concentration based on a similarity between spectra (Paragraph 0076). With regards to the limitations of claims 10 and 11, Choi fails to explicitly disclose selecting and analyzing the target spectrum based on a highly similar reference spectrum selected out of a plurality of reference spectra.
Kawamura teaches an analogous method of using spectral analysis to determine concentrations of constituents in a sample (Paragraph 0002). The spectral information selection unit acquires the result of analysis of samples and selects two pieces of spectral information from a plurality of samples from a database, the spectral information of the plurality of samples being stored in advance (Paragraph 0040). An estimation unit causes the learning model to estimate quantitative information on the test substance contained in the sample by inputting the connected spectral information into the learning model (Paragraphs 0041-0045), the quantitative information being defined as concentration (Paragraph 0007).
As Choi is concerned with determining concentrations of substances in the blood such as glucose, Kawamura introduces a method of comparing obtained spectral information to reference spectral information to determine a concentration. Kawamura discloses this is useful to do even if the sample contains other foreign substances having overlapping peaks. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the analysis of Choi to incorporate the method of comparing obtained data to reference data as taught by Kawamura to distinguish measure one of many components in the blood.
Regarding claim 13, Choi as modified further discloses wherein the determining includes determining the concentration of the target component (Paragraph 0063, “the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”), which is included in any one of the plurality of principal components of the body fluid (Applicant defines principal components as blood cells and plasma in paragraphs 0004 and 0011, and specifically mentions the target component may include glucose in paragraph 0011. Choi discloses in paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”, thus, the analyte (glucose) is included in the principal components (blood cells and plasma) of the body).
Regarding claim 14, Choi as modified further discloses wherein the acquiring includes acquiring the data from a living organism (Paragraph 0063, “Herein, the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves) are transmitted or diffusely reflected to an organism, e.g., a human body”).
Regarding claim 15, Choi as modified further discloses wherein the acquiring includes acquiring a Raman spectrum (Paragraph 0063, “The in vivo spectrum may be applied to the concentration predicting algorithm after being obtained by an infra-red spectroscopy or a Raman spectroscopy”).
Regarding claim 16, Choi as modified further discloses wherein the body fluid is blood and the principal components include plasma and blood cells (Applicant defines principal components as blood cells and plasma in paragraphs 0004 and 0011, and specifically mentions the target component may include glucose in paragraph 0011. Choi discloses in paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”, thus, the analyte (glucose) is included in the principal components (blood cells and plasma) of the body).
Regarding claim 17, Choi as modified further discloses wherein the body fluid is blood, and the principal components include at least one component out of red blood cells, white blood cells, and platelets, and a plasma component (Applicant defines principal components as blood cells and plasma in paragraphs 0004 and 0011, and specifically mentions the target component may include glucose in paragraph 0011. Choi discloses in paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”, thus, the analyte (glucose) is included in the principal components (blood cells and plasma) of the body).
Regarding claim 18, Choi as modified further discloses wherein the target component includes at least one of glucose (Paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”).
Regarding claim 19, Choi as modified further discloses wherein the analyzer apparatus is configured to determine the concentration of the target component (Paragraph 0063, “the in vivo analyte may be at least one of glucose, urea, lactate, triglyceride, protein, cholesterol and ethanol”), which is included in any one of the plurality of principal components of the body fluid (Applicant defines principal components as blood cells and plasma in paragraphs 0004 and 0011, and specifically mentions the target component may include glucose in paragraph 0011. Choi discloses in paragraph 0064, “where the in vivo analyte is glucose, the concentration of the analyte may represent a blood sugar level”, thus, the analyte (glucose) is included in the principal components (blood cells and plasma) of the body).
Regarding claim 21, Choi as modified further discloses wherein the apparatus that acquires the data includes a detection apparatus that is configured to acquire the data from a living organism (Paragraph 0063, “The in vivo spectrum may be applied to the concentration predicting algorithm after being obtained by an infra-red spectroscopy or a Raman spectroscopy”; Paragraph 0063, “the in vivo spectrum may be generated when infer-red rays or laser beams (that is, single wavelength electromagnetic waves)”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Choi (US 20170079565 – cited by Applicant) and Kawamura (US 20200408789 – cited by Applicant) as applied to claim 8 above, and further in view of Yang (US 6167290).
Regarding claim 9, Choi in combination with Kawamura disclose the probe as described above. The combination of Choi and Kawamura fails to disclose wherein the probe includes a compressor for compressing the blood vessel to control a flow rate of the blood.
Yang teaches an analogous method and apparatus for non-invasively measuring blood glucose using a laser and Raman spectrometer (Abstract), wherein the system additionally includes a vacuum system. The vacuum system creates a negative air pressure so that a blood is sucked into a small area of the human finger for an enhanced Raman signal measurement (Col 4, lines 46-52). As Choi in combination with Kawamura is concerned with measuring the blood glucose levels in human blood, Yang introduces an enhanced Raman signal measurement by creating negative air pressure during measurement. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Choi and Kawamura to incorporate the vacuum system of Yang to enhance Raman signal measurement.
Conclusion
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/NOAH M HEALY/Examiner, Art Unit 3791 /JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
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