DETAILED ACTION
Claims 1-11 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 Objections
Claim 3 is objected to because of the following informalities:
Claim 3, line 2, “with a wavelength …” should read “with wavelengths …”.
Appropriate correction is required.
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.
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 limitation(s) is/are:
“input module” first recited in claim 1;
“computing module” first recited in claim 1;
“light emitting units” first recited in claim 2;
“light detection unit” first recited in claim 2;
“processing sub-module” first recited in claim 5;
“fitting sub-module” first recited in claim 5;
“evaluation sub-module” first recited in claim 5;
“storage module” first recited in claim 6;
“post processing module” first recited in claim 7; and
“output module” first recited in claim 8;
The identified structure for the corresponding claim limitations are as follows:
“input module” is identified as “includes several light emitting units 12, at least one light detection unit 14, and a microstructure 16” (Paragraph 0020).
“computing module” is identified as “The computing module 20 calculates and establishes a normal model for making the physiological sample values correspond to the optical signals” (Paragraph 0023) and “the computing module 20 includes a processing sub-module 22, a fitting sub-module 24, and an evaluation sub-module 26 … “Specifically, the processing sub-module 22 is used to process the optical signals generated by the light detection unit 14 of the input module 10. In particular, it converts the various interference signals (such as feature signals from water, lipids, fats, hemoglobin, protein and the like) contained in the optical signals, as well as the physiological target signal (such as the glucose feature signal), to the physiological normal model. It should be noted that the conversion of the optical signals from the input module to the physiological normal model at this point refers to ensuring that the optical signals obtained from the testee must conform to the data format of the physiological normal model” (Paragraph 0024),
“the fitting sub-module 24 is used to fit the converted optical signals with the physiological normal model. Specifically, it fits the various interference signals and the physiological target signal on the optical signals with the physiological normal model to generate a fitting result. Based on the known sample physiological values in the physiological normal model, it eliminates interference signals other than glucose values and generates the physiological target value (such as blood glucose concentration) corresponding to the physiological target signal … These signals are then converted into multiple photoelectric feature values according to the data format of the physiological normal model. The fitting sub-module of the present invention utilizes an artificial intelligence model or machine learning model to fit the converted interference signals (including photoelectric feature values of albumin/protein, lipids/fats, etc.) and the physiological target signal (i.e., the photoelectric feature values of blood glucose) with the known physiological sample values in the physiological normal model to generate a fitting result. Then, the fitting sub-module corrects the interference signals in the optical signals (such as corresponding to photoelectric feature values of albumin/protein, lipids/fats, etc.) and generates the physiological target value (i.e., the photoelectric feature values of blood glucose) corresponding to the physiological target signal” (Paragraph 0025), and
“an evaluation sub-module 26, which is used to analyze the model based on the fitting result obtained from the fitting sub-module 24 and output a feature importance evaluation, such as, but not limited to, the SHAP (SHapley Additive exPlanations) feature importance evaluation. This evaluation considers the contribution of each feature to the prediction results and assigns a SHAP value to each feature. These values describe the impact of each feature on the model's predictions with positive values indicating an increase in the predicted values and negative values indicating a decrease in the predicted values. SHAP values can be used to explain how artificial intelligence models use various optical features to predict blood glucose concentration. These optical features may include physiological values, environmental factors, and the like. Using SHAP values, the impact of each feature on the output of the artificial intelligence model can be evaluated for providing a better understanding of the prediction process of the artificial intelligence model accordingly” (Paragraph 0029).
“light emitting units” is identified as “three or more light emitting diodes for providing infrared light in different wave bands ranging from 800 nanometers to 1700 nanometers” (Paragraph 0020).
“light detection unit” is identified as “one or multiple photodiodes … for receiving the multi-band infrared light emitted by the light emitting units 12” (Paragraph 0021).
“processing sub-module” is identified as “Specifically, the processing sub-module 22 is used to process the optical signals generated by the light detection unit 14 of the input module 10. In particular, it converts the various interference signals (such as feature signals from water, lipids, fats, hemoglobin, protein and the like) contained in the optical signals, as well as the physiological target signal (such as the glucose feature signal), to the physiological normal model. It should be noted that the conversion of the optical signals from the input module to the physiological normal model at this point refers to ensuring that the optical signals obtained from the testee must conform to the data format of the physiological normal model” (Paragraph 0024).
“fitting sub-module” is identified as “the fitting sub-module 24 is used to fit the converted optical signals with the physiological normal model. Specifically, it fits the various interference signals and the physiological target signal on the optical signals with the physiological normal model to generate a fitting result. Based on the known sample physiological values in the physiological normal model, it eliminates interference signals other than glucose values and generates the physiological target value (such as blood glucose concentration) corresponding to the physiological target signal … These signals are then converted into multiple photoelectric feature values according to the data format of the physiological normal model. The fitting sub-module of the present invention utilizes an artificial intelligence model or machine learning model to fit the converted interference signals (including photoelectric feature values of albumin/protein, lipids/fats, etc.) and the physiological target signal (i.e., the photoelectric feature values of blood glucose) with the known physiological sample values in the physiological normal model to generate a fitting result. Then, the fitting sub-module corrects the interference signals in the optical signals (such as corresponding to photoelectric feature values of albumin/protein, lipids/fats, etc.) and generates the physiological target value (i.e., the photoelectric feature values of blood glucose) corresponding to the physiological target signal” (Paragraph 0025).
“evaluation sub-module” is identified as “an evaluation sub-module 26, which is used to analyze the model based on the fitting result obtained from the fitting sub-module 24 and output a feature importance evaluation, such as, but not limited to, the SHAP (SHapley Additive exPlanations) feature importance evaluation. This evaluation considers the contribution of each feature to the prediction results and assigns a SHAP value to each feature. These values describe the impact of each feature on the model's predictions with positive values indicating an increase in the predicted values and negative values indicating a decrease in the predicted values. SHAP values can be used to explain how artificial intelligence models use various optical features to predict blood glucose concentration. These optical features may include physiological values, environmental factors, and the like. Using SHAP values, the impact of each feature on the output of the artificial intelligence model can be evaluated for providing a better understanding of the prediction process of the artificial intelligence model accordingly” (Paragraph 0029).
“storage module” is identified as “the storage module 30 of the physiological value sensing device 1 of the present invention is used to store the optical signals generated by the input module 10, the intermediate signals such as the converted interference signals and the converted physiological target signal after conversion by the computing module 20, the physiological target values, and the feature importance evaluation, as well as the final results” (Paragraph 0031).
“post processing module” is identified as “the post-processing module 40 of the physiological value sensing device 1 is used to calculate the values including the optical signals stored in the storage module 30 to establish a long-term trend report after the system has operated for a period of time” (Paragraph 0031).
“output module” is identified as “The output module 50 of the physiological value sensing device 1 of the present invention is used to output the physiological target values, the feature importance evaluation, and the long-term trend report, and can output the values of the computing module as well as image files for display on a monitor” (Paragraph 0031).
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 1-11 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 claims 1-8, the claims are directed to a sensing device; however, the claims recite method steps that the various modules perform. The claims should recite a structure of the device followed by “configured to” and the step, function, or action the module is configured to perform. Examiner acknowledges that the limitations of these claims have been interpreted under 112(f) as described above. Although the interpretations of these limitations have been interpreted in being structures, the structures (specifically the computing module, processing sub-module, fitting sub-module, evaluation sub-module, and post-processing module) are purely algorithmic and are still not tied to a structure that can execute the algorithm.
Regarding claims 9-11, the claims are directed to a method. However, the claims fail to recite any structure capable of performing the method. Thus, it is unclear how the method is performed without sufficient structure recited for performing said steps.
Regarding claim 1, it is unclear how the computing module establishes a physiological normal model having multiple samples values. Is this a step the computing module performs before every measurement with light, or is this a pre-loaded/pre-determined standard curve that is stored and the computing module compares the model to the measured optical signals? For examination purposes, the claim will be interpreted such that the physiological normal model is a pre-determined standard curve that the optical signals are compared to for obtaining a target value. Claims 2-8 are also rejected due to their dependence on claim 1.
Regarding claim 1, it is unclear how the computing module “converts” the interference signals and the physiological target signal into the physiological normal model for fitting. It appears Applicant intends to claim that the data “converts” to the same format as the physiological normal model as disclosed in paragraph 0024, and for examination purposes, that is how the claim will be interpreted. Claims 2-8 are also rejected due to their dependence on claim 1.
Regarding claim 1, it is unclear how the physiological target value is generated. Is there a calculation that is performed or is the optical signal compared to the standard curve to obtain a value? For examination purposes, the claim will be interpreted such that comparing the measured signal to a “normal model” or pre-determined curve results in the target value. Claims 2-8 are also rejected due to their dependence on claim 1.
Claim limitation “storage module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. Applicant does not disclose any structure capable of storing data aside from a “storage module”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 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 1-11 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 claims 1 and 9:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a system, which describes one of the four statutory categories of
patentable subject matter, i.e., a machine. Claim 9 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: Claims 1 and 9 recite an abstract idea. In particular, the claims generally recite
the following:
A physiological target value corresponding to the physiological target signal is generated after eliminating the interference signals based on the physiological sample values (claim 1); and
Generating a physiological target value corresponding to the physiological target signal (claim 9);
These elements recited in claims 1 and 9 are drawn to an abstract idea since they are directed towards mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
“A physiological target value corresponding to the physiological target signal is generated after eliminating the interference signals based on the physiological sample values” 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 and/or a generic computer. A person of ordinary skill in the art could reasonably receive optical signal data, compare it to a standard curve, and generate a physiological target value. There is nothing to suggest an undue level of complexity in “a physiological target value corresponding to the physiological target signal is generated after eliminating the interference signals based on the physiological sample values”.
“Generating a physiological target value corresponding to the physiological target signal” 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 and/or a generic computer. A person of ordinary skill in the art could reasonably receive optical signal data, compare it to a standard curve, and generate a physiological target value. There is nothing to suggest an undue level of complexity in “generating a physiological target value corresponding to the physiological target signal”.
Prong Two: Claims 1 and 9 do 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., “an input module for providing a multi-band light to illuminate a testee to generate a plurality of optical signals wherein the optical signals include a plurality of interference signals and a physiological target signal” (claim 1), “a computing module for establishing a physiological normal model having multiple physiological sample values corresponding to the multi-band light” (claim 1), “converting the interference signals and the physiological target signal into the physiological normal model for fitting” (claim 1), “creating a physiological normal model wherein the physiological normal model has multiple physiological sample values corresponding to a multi-band light” (claim 9), “providing the multi-band light to illuminate a testee to generate a plurality of optical signals wherein the optical signals include a plurality of interference signals and a physiological target signal” (claim 9), “converting the interference signals and the physiological target signal into the physiological normal model for fitting” (claim 9), and “eliminating the interference signals based on the physiological sample values” (claim 9))).
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).
Claims 1 and 9 do 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. For example, “an input module” as disclosed in the Applicant’s specification paragraph 0020, “includes several light emitting units 12, at least one light detection unit 14” wherein the light emitting units are “three or more light emitting diodes for providing infrared light in different wave bands ranging from 800 nanometers to 1700 nanometers” (Paragraph 0020) and the “light detection unit” is “one or multiple photodiodes … for receiving the multi-band infrared light emitted by the light emitting units 12” (Paragraph 0021). An input module 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 taken 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 2-8 and 10-11 depend from the independent claims. Dependent claims 2-8 and 10-11 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“the computing module includes a processing sub-module, a fitting sub-module and an evaluation sub-module, wherein the processing sub-module is used to process and convert the interference signals and the physiological target signal into the physiological normal model, the fitting sub-module is used to fit the converted interference signals and the converted physiological target signal with the physiological normal model to generate a fitting result, and eliminate the converted interference signals based on the physiological sample values and generate the physiological target value corresponding to the converted physiological target signal” (claim 5) and “a step of providing an artificial intelligence model for fitting the converted interference signals and the converted physiological target signal with the physiological sample values of the physiological normal model to generate a fitting result, and generating the physiological target value corresponding to the converted physiological target signal after eliminate the converted interference signals based on the fitting result” (claim 11)),
Further describe the pre-solution activity (“wherein the input module includes a plurality of light emitting units and at least one light detection unit, wherein the light emitting units are used to provide the multi-band light and are capable of adjusting the light intensity of the multi-band light, the at least one light detection unit is used to receive a reflection light reflected by the multi-band light illuminating the testee, and generate the optical signals corresponding to the reflection light” (claim 2), “wherein the light emitting units are capable of providing the multi-band light with a wavelength of 800 nanometers to 1700 nanometers and a wavelength interval of not less than 100 nanometers” (claim 3), “wherein the input module further includes a microstructure, disposed adjacent to the at least one light detection unit to prevent the multi-band light provided by the light emitting units from being received by the at least one light detection unit without being reflected by the testee” (claim 4), “the evaluation sub-module is used to output a feature importance evaluation corresponding to the fitting result” (claim 5), and “wherein the step of providing the multi-band light is to provide the multi-band light with a wavelength of 800 nanometers to 1700 nanometers and a wavelength interval of not less than 100 nanometers” (claim 10)), and
Further describe the post-solution activity (“a storage module for storing the optical signals generated by the input module, the converted interference signals and the converted physiological target signal, the physiological target value and the feature importance evaluation” (claim 6), “a post processing module for creating a long-term trend report corresponding to the optical signals” (claim 7), “an output module for outputting the physiological target value and the feature importance evaluation and the long-term trend report” (claim 8)).
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 computing device and/or application to perform
different. The result of the abstract idea does not cause output of the user-accessible output. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Examiner notes that while claim 11 claims an artificial intelligence model, the broad recitation of a generic artificial intelligence model does not integrate the judicial exception into a practical application. There is no recitation of the kind of artificial intelligence model being used and the algorithms used by the artificial intelligence model to “fit” signals to a normal model. Thus, claims 1-11 are rejected.
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 1-2, 4-6, 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara (US 20150216457) and Bynam (US 10980484).
Regarding claims 1, Kasahara discloses a physiological value sensing device, comprising:
an input module for providing a multi-band light to illuminate a testee to generate a plurality of optical signals (Fig. 2A and paragraphs 0053-0054, light emitting elements 53 are LEDs and emit light in near infrared rays and received by light receiving elements 59; Fig. 3, emitting light into skin surface of a user; While Kasahara is silent to the explicit near infrared wavelengths emitted, it is well-known within the art that “near infrared” covers 700 to 2500 nm); and
a computing module for establishing a physiological normal model having multiple physiological sample values corresponding to the multi-band light, converting the signal into the physiological normal model for fitting, wherein a physiological target value corresponding to the physiological target signal is generated (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured; Examiner is interpreting this step to mean that the values “convert” to the calibration curve).
With regards to the limitations of claims 1 and 5, Kasahara discloses the processing sub-module to process and convert the signals to the normal model (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured; Examiner is interpreting this step to include that the values are “converted” to the calibration curve values) and fit the signals to the model (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured; Examiner is interpreting this step to include that the values are “fit” to the calibration curve, i.e., compared),
Kasahara fails to disclose wherein the optical signals include a plurality of interference signals and fitting the signals to the model while eliminating the interference signals based on the physiological sample values, and the evaluation sub-module.
However, Bynam teaches a method of measuring glucose using near-infrared spectroscopy wherein different constituents (Fig. 3) are measured from the signal and a glucose meter filters and remove the interferents (Fig. 2; Col 7, lines 24-34) and a machine learning algorithm extracts features from the spectrum (Col 8, lines 54-60). Bynam discusses this process is effective for glucose monitoring (Col 2, lines 10-12). 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 method of Kasahara to incorporate the removal of other constituents of the body and extract features of Bynam to effectively monitor and measure glucose.
Regarding claim 2, Kasahara as modified further discloses wherein the input module includes a plurality of light emitting units and at least one light detection unit (Figs. 2A-B and Paragraph 0053, light emitting elements 53 and light receiving elements 59), wherein the light emitting units are used to provide the multi-band light and are capable of adjusting the light intensity of the multi-band light (Paragraph 0053, wherein light emitting elements are LEDs or OLEDs; Paragraphs 0075-0076, wherein transmittance is obtained based on light intensity applied and received), the at least one light detection unit is used to receive a reflection light reflected by the multi-band light illuminating the testee, and generate the optical signals corresponding to the reflection light (Paragraph 0054).
Regarding claim 4, Kasahara as modified further discloses wherein the input module further includes a microstructure, disposed adjacent to the at least one light detection unit to prevent the multi-band light provided by the light emitting units from being received by the at least one light detection unit without being reflected by the testee (Fig. 2B, light blocking layer 54; Paragraph 0052, “the light blocking layer 54 selectively blocks light which is not directed to the light receiving layer 58”).
Regarding claim 6, Kasahara as modified further discloses a storage module for storing the optical signals generated by the input module, the converted interference signals and the converted physiological target signal, the physiological target value and the feature importance evaluation (Paragraph 0051).
Regarding claims 9 and 11, Kasahara discloses a physiological value sensing method, comprising:
creating a physiological normal model wherein the physiological normal model has multiple physiological sample values corresponding to a multi-band light (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured);
providing the multi-band light to illuminate a testee to generate a plurality of optical signals wherein the optical signals include a plurality of interference signals and a physiological target signal (Fig. 2A and paragraphs 0053-0054, light emitting elements 53 are LEDs and emit light in near infrared rays and received by light receiving elements 59; Fig. 3, emitting light into skin surface of a user; While Kasahara is silent to the explicit near infrared wavelengths emitted, it is well-known within the art that “near infrared” covers 700 to 2500 nm);
converting the interference signals and the physiological target signal into the physiological normal model for fitting (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured; Examiner is interpreting this step to mean that the values “convert” to the calibration curve);
generating a physiological target value corresponding to the physiological target signal (Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured and calculating a blood glucose result).
With regards to the limitations of claims 9 and 11, Kasahara fails to disclose eliminating the interference signals based on the physiological sample values, and using an artificial intelligence model for fitting the signals to the model.
However, Bynam teaches a method of measuring glucose using near-infrared spectroscopy wherein different constituents (Fig. 3) are measured from the signal and a glucose meter filters and remove the interferents (Fig. 2; Col 7, lines 24-34) and a machine learning algorithm extracts features from the spectrum (Col 8, lines 54-60). Bynam discusses this process is effective for glucose monitoring (Col 2, lines 10-12). 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 method of Kasahara to incorporate the removal of other constituents of the body and extract features of Bynam to effectively monitor and measure glucose.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara and as applied to claims 2 and 9, respectively, above, and further in view of Kasahara (US 20150216482), hereinafter Kasahara ‘482.
Regarding claims 3 and 10, Kasahara as modified further discloses wherein the light emitting units are capable of providing the multi-band light with a wavelength of 800 nanometers to 1700 nanometers (Paragraph 0052, transmits near infrared rays; Paragraph 0075, transmittance is obtained for each wavelength; While Kasahara is silent to the explicit near infrared wavelengths emitted, it is well-known within the art that “near infrared” includes wavelengths between 700 to 2500 nm).
Kasahara fails to explicitly disclose a wavelength interval of not less than 100 nanometers.
However, Kasahara ‘482 teaches a blood sugar level measuring device wherein a wavelength interval is set to 100nm (Paragraph 0156) to control an amount of light emitted per one 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 device of Kasahara and Bynam to incorporate the 100nm wavelength interval taught by Kasahara ‘482 to control an amount of light emitted per one measurement.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara and Bynum as applied to claim 6 above, and further in view of Ko (US 20220015669).
Regarding claim 7, Kasahara as modified discloses the device as described above. Kasahara as modified fails to explicitly creating a trend report.
However, Ko teaches a non-invasive glucose monitoring device wherein a display provides a time history and/or trends of the patient’s glucose history, aiding in diabetes management (Paragraph 0045). 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 device of Kasahara and Bynum to incorporate the teachings of tracking patient glucose history and trends of Ko to aid in diabetes management.
Regarding claim 8, Kasahara as modified further discloses an output module for outputting the physiological target value and the feature importance evaluation and the long-term trend report (Fig. 11, display unit 120).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Prendin et. al. (“The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP”) teaches analyzing glucose levels and glucose prediction algorithms with SHAP.
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791