DETAILED ACTION
Applicant’s arguments, filed 05/22/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant has amended their claims, filed 05/22/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant has canceled claims 2, 5, and 11. Claims 1, 3-4, and 6-10 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 10 is objected to because of the following informalities:
Claim 10, line 2, “wavelength” should read “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:
“light emitting units” first recited in claim 1;
“light detection unit” first recited in claim 1;
“computing module” first recited in claim 1;
“processing sub-module” first recited in claim 1;
“fitting sub-module” first recited in claim 1;
“evaluation sub-module” first recited in claim 1;
“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:
“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).
“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).
“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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-4, and 6-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As noted above, the identified limitations invoke a 35 USC 112(f) means plus function interpretation. As a result, the examiner looks to the specification to determine the corresponding structure for performing the claimed functions. However, specification does not reasonably convey to one skilled in the art how the computing module, the processing sub-module, the evaluation sub-module, the storage module, and the post processing module performs their corresponding algorithms. There is no corresponding hardware/structure to perform the function. (i.e. Is the storage module a disk drive, or some other storage device? Are the algorithmic modules identified above performed on a non-specific central processing unit, a specialized processor, a computer, or a mobile device?) Per specification paragraphs 0024-0031, it appears the modules are merely algorithmic, without any specific structure or hardware to perform the algorithm on. Thus, the subject matter is not described in the specification to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time the application was filed, because an indefinite (see 112(b) rejections below), unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See MPEP 2163.03, VI. And MPEP 2181 IV.
Examiner notes that the “fitting module” appears to be directed towards a machine learning / artificial intelligence model, which is sufficiently disclosed in the specification and would be understood by one of ordinary skill in the art (see paragraphs 0024-0026).
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, 3-4, and 6-10 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, 3-4, and 6-10, 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. That is, there does not appear to be any hardware component for executing the algorithms.
Claim limitations “computing module”, “processing sub-module”, “fitting module”, “evaluation sub-module”, “post processing module”, and “storage module” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the functions in the claim. Applicant does not disclose any kind of hardware capable of performing the functions of the modules and sub-modules identified above. Applicant does not disclose any structure capable of storing data aside from a “storage module”. For examination purposes, the claim limitations will be interpreted as any structure capable of performing the claimed functions. 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, 3-4, and 6-10 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:
establishing a physiological normal model having multiple physiological sample values corresponding to a data format comprising parameters of wavelength, distance, and light intensity of the multi-band light (claims 1 and 9);
normalizing the optical signals based on a mean value and a standard deviation of the physiological normal model to convert the interference signals and the physiological target signal into converted signals compatible with the data format of the physiological normal model (claims 1 and 9);
fit the converted interference signals and the converted physiological target signal with the physiological normal model using multi-variable fitting algorithm to eliminate the interference signals based on the physiological sample values (claims 1 and 9); and
generate a physiological target value corresponding to the physiological target signal and a feature importance evaluation corresponding to a contribution of each parameter of the data format to the physiological target value (claims 1 and 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) and mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I).
“establishing a physiological normal model having multiple physiological sample values corresponding to a data format comprising parameters of wavelength, distance, and light intensity of the multi-band light” 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 and generate a standard curve therefrom. There is nothing to suggest an undue level of complexity in “establishing a physiological normal model having multiple physiological sample values corresponding to a data format comprising parameters of wavelength, distance, and light intensity of the multi-band light”.
“normalizing the optical signals based on a mean value and a standard deviation of the physiological normal model to convert the interference signals and the physiological target signal into converted signals compatible with the data format of the physiological normal model” is drawn to a mathematical concept. Calculating the mean and standard deviation of a signal are standard mathematical calculations. Converting signals into a compatible data format is merely changing a signal from one data format to another.
“fit the converted interference signals and the converted physiological target signal with the physiological normal model using multi-variable fitting algorithm to eliminate the interference signals based on the physiological sample values” is drawn to a mathematical concept. Applying a fitting algorithm to eliminate outlier data is a common mathematical algorithm.
“generate a physiological target value corresponding to the physiological target signal and a feature importance evaluation corresponding to a contribution of each parameter of the data format to the physiological target value” 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 based on the parameters measured. There is nothing to suggest an undue level of complexity in “generate a physiological target value corresponding to the physiological target signal and a feature importance evaluation corresponding to a contribution of each parameter of the data format to the physiological target value”.
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:
Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “computing module” (claims 1 and 9), “processing sub-module” (claims 1 and 9), “a fitting sub-module” (claims 1 and 9), and an “evaluation sub-module” (claims 1 and 9)) and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data gathering components (e.g., “an input module comprising a plurality of light emitting units and at least one light detection unit, the input module for providing a multi-band light via the light emitting units to illuminate a testee to produce reflected light, and receiving the reflected light via the at least one light detection unit to generate a plurality of optical signals wherein the optical signals include a plurality of interference signals and a physiological target signal” (claim 1) and “providing the multi-band light via a plurality of light emitting units of an input module to illuminate a testee to produce reflected light; receiving the reflected light via at least one light detection unit of the input module to generate a plurality of optical signals wherein the optical signals include a plurality of interference signals and a physiological target signal” (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 dependent claims 3-4, 6-8, and 10:
Claim 7 recites mental steps that may be performed in the human mind with the aid of pen and paper or a generic computer, which add to the abstract idea. The mental steps are identified as:
“a post processing module for creating a long-term trend report corresponding to the optical signals” (claim 7).
Claims 3-4, 6, 8, and 10 recite limitation in addition to the abstract idea: they merely
Further describe the pre-solution activity (“wherein the light emitting units are capable of providing the multi-band light with wavelengths 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), “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), etc.), 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) and “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 differently.
Therefore, claims 1, 3-4, and 6-10 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, 4, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara (US 20150216457), Kurasawa (US 20160103063), Bynam (US 10980484), and Prendin (“The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP”), further evidenced by Clark (“The Beer-Lambert Law”).
Regarding claims 1, Kasahara discloses a physiological value sensing device, comprising:
an input module comprising a plurality of light emitting units and at least one light detection unit, the input module providing a multi-band light via the light emitting units to illuminate a testee to produce reflected light, and receiving the reflected light via the at least one light detection unit to generate a plurality of optical signals wherein the optical signals include 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; Figs. 15-17, measuring blood glucose); and
a computing module electronically coupled to the input module, comprising a processing sub-module and a fitting sub-module,
wherein the computing module is configured to establish a physiological normal model having multiple physiological sample values to a data format comprising parameters of wavelength, distance, and light intensity (Paragraph 0069-0073, wherein a determined optimum distance between the light emitter and receiver W is used, and the depth D between the emitter/receiver and the measured vessel is determined based on the pre-determined distance W; Paragraphs 0075-0078, wherein a wavelength of light is emitted by the light emitting element and is changed such that the wavelength changes when it is reflected back. The light intensity is obtained, and the transmittance for each wavelength is measured; Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured); While Kasahara may not explicitly recite the calibration curve/glucose measurement includes all of the parameters for comparison, measuring the absorbance/transmittance of a sample takes into account the recited parameters to determine concentration, as further evidenced by Clark. See page 2, “The Beer Lambert Law” wherein the path length of the light and the light intensity are considered to determine concentration and page 1, “The Absorbance of a Solution”, wherein the intensity is measured for a certain wavelength to determine maximum absorbance at specific wavelengths.
the processing sub-module processes the plurality of optical signals by normalizing the optical signals based on a mean value of the physiological normal model to convert the physiological target signal into converted signals compatible with the data format of the physiological normal model (Paragraph 0105, “In addition, in a case where there are a plurality of measurement target blood vessel parts 6, respective absorption spectra of the plurality of measurement target blood vessel parts 6 are averaged, and thus an average absorption spectrum is calculated; Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured. Examiner interprets the comparing step to the calibration curve to mean that the signal is “compatible” with the model),
the fitting sub-module fits the converted physiological target signal with the physiological normal model using multi-variable fitting algorithm (Paragraph 0079, “Next, on the basis of the absorption spectrum, a blood glucose level is estimated and calculated by using a calibration curve indicating a relationship between a predefined blood glucose level (a glucose concentration in blood) and an absorbance. In addition, a technique for calculating a concentration of a predetermined component (glucose in the present embodiment) on the basis of the absorption spectrum is well known, and the well-known technique may be employed in the present embodiment”), and
the evaluation sub-module generates a physiological target value corresponding to the physiological target signal (Paragraph 0079, determining a glucose concentration).
Kasahara fails to explicitly disclose wherein the optical signals include a plurality of interference signals and, when fitting the signals to the model, eliminating the interference signals based on the physiological sample values, and the evaluation sub-module generating a feature importance evaluation corresponding to a contribution of each parameter of the data format to the physiological target value. Lastly, Kasahara fails to disclose normalizing the signals based on a standard deviation.
Kasahara and Kurasawa are in the same field of glucose measurement. Kurasawa teaches a calibration curve generation device (Abstract) that normalizes the data based on averaging the data and taking a standard deviation (Paragraph 0137). As Kasahara discloses averaging the glucose data, Kurasawa teaches normalizing the data based on an average and a standard deviation. One of ordinary skill in the art could have applied normalizing the data based on a standard deviation to the device of Kasahara, and the result of normalizing the data would have been predictable to one of ordinary skill in the art. 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 with the method of taking a standard deviation as taught by Kurasawa, and the result of normalizing the data would have been obvious to one of ordinary skill in the art.
Kasahara, Kurasawa, and Bynam are in the same field of glucose measurement. Bynam teaches a method of measuring glucose using near-infrared spectroscopy wherein different constituents are measured from the signal (Fig. 3) and the glucose signal is extracted by removing the interferents (Fig. 2; Col 5, lines 30-55). The glucose signal is removed by separating the spectrums of the other components and extracting only the glucose signal (Col 7, lines 24-34). As Kasahara discusses measuring the reflected light from an artery to determine blood glucose levels, Bynam introduces a similar method that explicitly teaches the signal including other constituents being measured and extracting the glucose signal out from the other constituents. Kasahara fits the data to the calibration curve and, in combination with Bynam, the signal contains multiple component spectrums. When fitting the signal with interferent components to the calibration curve, the spectrums of the other constituents would necessarily be removed as the calibration curve of Kasahara is only calibrated for glucose. As such, the combination of Kasahara and Bynam teach the interference signal elimination step. 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 and Kurasawa to incorporate the removal of other constituents of the body of Bynam to extract the glucose measurement.
Lastly, Prendin is pertinent art to the combination of Kasahara, Kurasawa, and Bynam. Prendin teaches a machine learning model for predicting blood glucose using SHAP (Shapley Additive exPlanation). The SHAP plot details the SHAP value of each individual feature for every sample in the dataset. The SHAP value measures the contribution of a feature on the model’s prediction for a certain instance of the dataset (Page 3, “Interpretation” paragraph 2). As the combination of Kasahara, Kurasawa, and Bynam are concerned with measuring blood glucose based off of the parameters on a calibration curve, Prendin teaches the method of further using the parameters in a machine learning model to learn the contributions of the parameters to the prediction. Prendin discusses this is useful to determine learning biases in glucose prediction (Page 2, paragraph 2). 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, Kurasawa, and Bynam with the SHAP machine learning prediction model, the benefit being determining the individual contributions of the parameters to the glucose prediction to learn their biases.
Regarding claim 4, the combination of Kasahara, Kurasawa, Bynam, and Prendin discloses the device of claim 1. Kasahara 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, the combination of Kasahara, Kurasawa, Bynam, and Prendin discloses the device of claim 1. 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 claim 9, Kasahara discloses a physiological value sensing method, comprising:
establishing, by a computing module, a physiological normal model wherein the physiological normal model has multiple physiological sample values corresponding to a data format comprising parameters of wavelength, distance, and light intensity of a multi-band light (Paragraph 0069-0073, wherein a determined optimum distance between the light emitter and receiver W is used, and the depth D between the emitter/receiver and the measured vessel is determined based on the pre-determined distance W; Paragraphs 0075-0078, wherein a wavelength of light is emitted by the light emitting element and is changed such that the wavelength changes when it is reflected back. The light intensity is obtained, and the transmittance for each wavelength is measured; Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured); While Kasahara may not explicitly recite the calibration curve/glucose measurement includes all of the parameters for comparison, measuring the absorbance/transmittance of a sample takes into account the recited parameters to determine concentration, as further evidenced by Clark. See page 2, “The Beer Lambert Law” wherein the path length of the light and the light intensity are considered to determine concentration and page 1, “The Absorbance of a Solution”, wherein the intensity is measured for a certain wavelength to determine maximum absorbance at specific wavelengths.
providing the multi-band light via a plurality of light emitting units of an input module to illuminate a testee to produce reflected light; receiving the reflected light via at least one light detection unit of the input module 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; Figs. 15-17, measuring blood glucose); and
processing, by the computing module, the plurality of optical signals by normalizing the optical signals based on a mean value of the physiological normal model to convert the physiological target signal into converted signal compatible with the data format of the physiological normal model for fitting (Paragraph 0105, “In addition, in a case where there are a plurality of measurement target blood vessel parts 6, respective absorption spectra of the plurality of measurement target blood vessel parts 6 are averaged, and thus an average absorption spectrum is calculated; Paragraph 0079, comparing calibration curve of blood glucose to absorbance as measured. Examiner interprets the comparing step to the calibration curve to mean that the signal is “compatible” with the model),
fitting, by the computing module, the converted interference signals and the converted physiological target signal with the physiological normal model using a multi-variable fitting algorithm (Paragraph 0079, “Next, on the basis of the absorption spectrum, a blood glucose level is estimated and calculated by using a calibration curve indicating a relationship between a predefined blood glucose level (a glucose concentration in blood) and an absorbance. In addition, a technique for calculating a concentration of a predetermined component (glucose in the present embodiment) on the basis of the absorption spectrum is well known, and the well-known technique may be employed in the present embodiment”), and
generating, by the computing module, a physiological target value corresponding to the physiological target signal (Paragraph 0079, determining a glucose concentration).
Kasahara fails to explicitly disclose wherein the optical signals include a plurality of interference signals and, when fitting the signals to the model, eliminating the interference signals based on the physiological sample values, and generating a feature importance evaluation corresponding to a contribution of each parameter of the data format to the physiological target value. Lastly, Kasahara fails to disclose normalizing the signals based on a standard deviation.
Kasahara and Kurasawa are in the same field of glucose measurement. Kurasawa teaches a calibration curve generation device (Abstract) that normalizes the data based on averaging the data and taking a standard deviation (Paragraph 0137). As Kasahara discloses averaging the glucose data, Kurasawa teaches normalizing the data based on an average and a standard deviation. One of ordinary skill in the art could have applied normalizing the data based on a standard deviation to the device of Kasahara, and the result of normalizing the data would have been predictable to one of ordinary skill in the art. 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 with the method of taking a standard deviation as taught by Kurasawa, and the result of normalizing the data would have been obvious to one of ordinary skill in the art.
Kasahara, Kurasawa, and Bynam are in the same field of glucose measurement. Bynam teaches a method of measuring glucose using near-infrared spectroscopy wherein different constituents are measured from the signal (Fig. 3) and the glucose signal is extracted by removing the interferents (Fig. 2; Col 5, lines 30-55). The glucose signal is removed by separating the spectrums of the other components and extracting only the glucose signal (Col 7, lines 24-34). As Kasahara discusses measuring the reflected light from an artery to determine blood glucose levels, Bynam introduces a similar method that explicitly teaches the signal including other constituents being measured and extracting the glucose signal out from the other constituents. Kasahara fits the data to the calibration curve and, in combination with Bynam, the signal contains multiple component spectrums. When fitting the signal with interferent components to the calibration curve, the spectrums of the other constituents would necessarily be removed as the calibration curve of Kasahara is only calibrated for glucose. As such, the combination of Kasahara and Bynam teach the interference signal elimination step. 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 and Kurasawa to incorporate the removal of other constituents of the body of Bynam to extract the glucose measurement.
Lastly, Prendin is pertinent art to the combination of Kasahara, Kurasawa, and Bynam. Prendin teaches a machine learning model for predicting blood glucose using SHAP (Shapley Additive exPlanation). The SHAP plot details the SHAP value of each individual feature for every sample in the dataset. The SHAP value measures the contribution of a feature on the model’s prediction for a certain instance of the dataset (Page 3, “Interpretation” paragraph 2). As the combination of Kasahara, Kurasawa, and Bynam are concerned with measuring blood glucose based off of the parameters on a calibration curve, Prendin teaches the method of further using the parameters in a machine learning model to learn the contributions of the parameters to the prediction. Prendin discusses this is useful to determine learning biases in glucose prediction (Page 2, paragraph 2). 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, Kurasawa, and Bynam with the SHAP machine learning prediction model, the benefit being determining the individual contributions of the parameters to the glucose prediction to learn their biases.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kasahara (US 20150216457), Kurasawa (US 20160103063), Bynam (US 10980484), and Prendin (“The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP”), further evidenced by Clark (“The Beer-Lambert Law”) as applied to claims 1 and 9 above, and further in view of Kasahara (US 20150216482), hereinafter Kasahara ‘482.
Regarding claims 3 and 10, the combination of Kasahara, Kurasawa, Bynam, and Prendin discloses the device according to claims 1 and 9, above. Kasahara 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; See also col 7, lines 1-2 of Bynam, wherein the NIR spectrum comprises at least the range of 1530 – 1830nm).
The combination of Kasahara, Kurasawa, Bynam, and Prendin fails to explicitly disclose a wavelength interval of not less than 100 nanometers.
Kasahara, Kurasawa, Bynam, Prendin, and Kasahara ‘482 are in the same field of glucose measurement. 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. As Kasahara and Bynam are concerned with measuring glucose in the blood from emitted wavelengths, Kasahara ‘482 discloses a controlled method of 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, Kurasawa, Bynam, and Prendin 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 (US 20150216457), Kurasawa (US 20160103063), Bynam (US 10980484), and Prendin (“The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP”), further evidenced by Clark (“The Beer-Lambert Law”) as applied to claim 6 above, and further in view of Ko (US 20220015669).
Regarding claim 7, the combination of Kasahara, Kurasawa, Bynam, and Prendin discloses the device according to claim 6, above. The combination fails to explicitly disclose creating a trend report.
Kasahara, Kurasawa, Bynam, Prendin, and Ko are in the same field of glucose measurement. 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, the combination of Kasahara, Kurasawa, Bynam, Prendin, and Ko discloses the device of claim 7 above. Kasahara 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).
Response to Arguments
Applicant’s arguments, see page 5, filed 05/22/2026, with respect to the claim objections have been fully considered and are persuasive. Applicant has amended claim 3 per the suggestion of the Examiner. The objection of the claim has been withdrawn.
Per the claim interpretation above, the “input module” is no longer interpreted under 112(f) as sufficient structure of a plurality of light emitting units and at least one light detection unit. Applicant’s amendment to the claims has not changed the claim interpretation of the other elements, and they are still interpreted as described above.
Applicant’s arguments, see pages 5-6, filed 05/22/2026, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are partially persuasive.
Applicant has amended claims 1, 3-4, and 6-8 to recite elements that are “configured to” perform certain steps rather than method steps. Applicant has further amended claims 9-10 to recite method steps. Applicant argues that the computing module is not a generic functional block, but is structurally defined by its specific sub-modules and is supported by specific, algorithmic structure. Examiner respectfully disagrees.
The claim interpretation under §112(f) of a computing module and its corresponding sub-modules refer to algorithmic steps. However, the corresponding structure, material, or act must be disclosed in the specification itself in a way the one skilled in the art will understand what structure, material, or acts will perform the recited function. The structure of the modules is not identified in the claim or the specification; therefore, it remains unclear what structure is performing the method of the computing module and its corresponding sub-modules. As such, the claims remain rejected.
Applicant has provided no arguments against the other 112(b) rejections.
It remains unclear how the computing module establishes a physiological normal model having multiple sample values corresponding to the parameters of multi-band light.
Applicant has amended claim 1 to further clarify the “converting” step, wherein the processing submodule processes the signals through normalization to convert the interference/physiological target signals into a compatible format for the model. Thus, this rejection is withdrawn.
Applicant has not clarified the language surrounding the physiological target value or the amended limitation of a feature importance evaluation. Thus, this rejection remains.
The written description rejection of the “storage module” remains as Applicant has provided no arguments against the rejection.
Applicant’s arguments, see page 6, filed 05/22/2026, with respect to the 35 U.S.C. §101 rejection have been fully considered but are not persuasive.
Applicant asserts that the specialized functional units of the computing module are a specific, non-conventional algorithmic method rather than a mathematical concept. Examiner disagrees. Examiner points out that the structure that is claimed refers to light emitting and light detecting units, which are routine, well-known, and conventional elements. The claimed modules merely point to algorithmic steps rather than specific structure.
Applicant further argues that by normalizing the parameters using a fitting algorithm to output a “feature importance evaluation”, the invention is integrated into a practical application. Examiner disagrees.
The claim must include more than mere instructions to perform the method on generic components/processing components to qualify as an improvement to the technology. As discussed above, there is no structure of a physical device claimed that amounts to more than mere instructions to apply the abstract ideas on a generic computer.
While it may be true that the claimed method may provide an improvement to the generation of a feature importance evaluation, the improvement cannot rely solely within the judicial exception. That is, the judicial exception alone cannot provide the improvement.
The judicial exception, as described above, is identified as the abstract ideas of establishing a physiological normal model, normalizing the signals to convert the signals to the same format as the model, fitting the signals to the model to eliminate the interference signals, and generating a physiological target value and feature importance evaluation. Applicant asserts that the improvement of the method is generating the feature importance evaluation; however, this improvement is the abstract idea, and the abstract idea itself is not an improvement in the technology (see MPEP 2106.05(a)).
Therefore, Applicant’s argument is found not persuasive. The rejection above has been updated to reflect the amendments made to the claims.
Applicant’s arguments, see pages 6-8, filed 05/22/2026, with respect to the rejections of claims 1, 3-4, and 6-10 under 35 U.S.C. §103 have been fully considered and are persuasive.
Applicant argues that the combination of Kasahara ‘457 and Bynam do not teach or disclose mapping optical signals comprising the data format comprising parameters of wavelength, distance, and light intensity. Examiner disagrees. As described above, and as evidenced by Clark, measuring the absorbance and transmittance of blood glucose uses the parameters of the wavelength emitted and received, the light intensity, and the depth (i.e., distance) from the artery the measurement is performed.
Applicant further argues that Bynam teaches eliminating interference signals that is incompatible with the claim invention. Examiner respectfully disagrees. Kasahara fits the data to the calibration curve and, in combination with Bynam, the signal contains multiple component spectra. When fitting the signal with interferent components to the calibration curve, the spectrums of the other constituents would necessarily be removed as the calibration curve of Kasahara is only calibrated for glucose. As such, the combination of Kasahara and Bynam teach the interference signal elimination step.
Lastly, Applicant argues that the combination of Kasahara and Bynam do not teach a feature importance evaluation that corresponds to a contribution of each parameter of the data format to the physiological target value. Examiner agrees that Kasahara and/or Bynam do not teach this limitation. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Kasahara, Kurasawa, Bynam, and Prendin, as described above.
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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/NOAH M HEALY/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
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