DETAILED ACTION
The Applicant’s filing, received 21 August 2023, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
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 .
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
There are no domestic applications for which benefit is claimed.
Benefit is claimed for foreign application:
TAIWAN 112115448, filed 26 April 2023.
Information Disclosure Statement
The information disclosure statements (IDS) received 21 August 2023, 17 October 2023, and 11 November 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, these information disclosure statements have been considered by the examiner.
Drawings
The drawings received 21 August 2023 are objected to, as noted below.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
The specification mentions a ‘diagram M’ shown in Fig. 6 (at least at paragraph [0050]), however, Fig. 6 does not show a reference character ‘M’.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because:
The abstract contains reference characters, mathematical expressions, and mathematical equations. The purpose of the abstract is to enable the Office and the public generally to determine quickly from a cursory inspection the nature and gist of the technical disclosure (37 C.F.R. 1.72).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claim 1 recites the limitation “a coordinate-formation step, coordinating a signal S,” and claim 7 recites the limitation “a coordinate-formation step, coordinating the photoplethysmography signal SPPG.” The specification does not provide proper antecedent basis for the limitation ‘a coordinate-formation step, coordinating …” at least because the specification does not provide the term ‘a coordinate-formation step,’ nor does the specification provide the terms ‘form’ or ‘formation,’ nor does the specification provide the term ‘coordinating a signal S.’
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 limitations are:
“a photoplethysmography signal input device, configured to obtain …” in claim 14;
“a memory device, configured to store …” in claim 14;
“a processing device, configured to perform …” in claim 14;
“a sampling section, configured to sample …” in claim 14;
“a drawing section, configured to generate …” in claim 14;
“a calculation section, configured to calculate …” in claims 14-16;
“a determination section, configured to determine …” in claim 14;
“an output section, configured to output …” in claim 14; and
“a feature adjustment section, configured to determine …” in claim 20.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The specification discloses a corresponding structure for the non-structural generic placeholder:
“a photoplethysmography signal input device, configured to obtain …” in claim 14 at para. [0063] (the biomedical signal measurement device may include light-emitting device (a light emitting diode) and light-sensing device (a photodiode));
“a memory device, configured to store …” in claim 14 at para. [0044] (a non-volatile memory device);
“a processing device, configured to perform …” in claim 14 at para. [0032] (an electronic circuit) and at para. [0033] (the electronic circuit may be a central processing unit (CPU)).
“a sampling section, configured to sample …” in claim 14 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU));
“a drawing section, configured to generate …” in claim 14 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU));
“a calculation section, configured to calculate …” in claims 14-16 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU));
“a determination section, configured to determine …” in claim 14 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU));
“an output section, configured to output …” in claim 14 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU)); and
“a feature adjustment section, configured to determine …” in claim 20 at para. [0062] (a program module (i.e., software) performed by a processing device (i.e., a CPU)).
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid 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 limitations recite sufficient structure to perform the claimed function so as to avoid 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 1-20 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.
Claims 1 and 2 are indefinite for reciting the limitation “signal S” because the metes and bounds of the limitation are unclear with respect to the meaning that one of skill in the art would attribute to the limitation, e.g., the single letter ‘S’ standing alone could be interpreted to simply be an abbreviation (e.g., of ‘signal’), or a symbol (e.g., of a particular signal), or a single-letter variable (e.g., representing all signals).
Claims 2-6 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claims 3-6 are indefinite for depending from claim 2 and failing to remedy the indefiniteness of claim 2.
Claims 7, 8, and 14, are indefinite for reciting “signal SPPG” because the metes and bounds of the limitation are unclear with respect to the meaning that one of skill in the art would attribute to the limitation, e.g., ‘SPPG’ standing alone could be interpreted to simply be an abbreviation (e.g., of ‘photoplethysmography signal’), or a symbol (e.g., of a particular photoplethysmography signal), or a single-letter variable (e.g., representing all photoplethysmography signals).
Claims 8-13 are indefinite for depending from claim 7 and failing to remedy the indefiniteness of claim 7.
Claims 9 and 10 are indefinite for depending from claim 8 and failing to remedy the indefiniteness of claim 8.
Claims 15-20 are indefinite for depending from claim 14 and failing to remedy the indefiniteness of claim 14.
Claim 1 is indefinite for reciting “calculating a degree of dispersion of a coordinate of the signal S” because it is not clear as to how dispersion of a single plotted point is able to be calculated.
Claims 2-6 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 7 recites the limitation "the method for determining photoplethysmography signal" in lines 14-15. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “A method for identifying photoplethysmography signal” in line one.
Claims 8-13 are indefinite for depending from claim 7 and failing to remedy the indefiniteness of claim 7.
Claim 9 recites the limitation "the distances" in line two. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the maximum integer" in lines one and two. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the distances" in line two. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the score" in line two. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the memory section" in line three. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the maximum integer" in lines one and two. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the heart rate" in line two. There is insufficient antecedent basis for this limitation in the claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-6 recite a method (i.e., a process); claims 7-13 recite a method (i.e., a process); and claims 14-20 recite a device (i.e., a machine and/or a manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a coordinate-formation step, coordinating a signal S (i.e., mental processes and mathematical concepts, e.g., drawing a 2D plot according to the sampled plurality of signal values);
a degree-of-dispersion calculation step, calculating a degree of dispersion of a coordinate of the signal S (i.e., mental processes and mathematical concepts, e.g., a linear regression and least square method to calculate the average error between each point on the 2D dot plot and regression line); and
a determination step, determining a relative relationship between the degree of dispersion and a feature of a periodic signal and generating a determination result (i.e., mental processes and mathematical concepts, e.g., calculating the differences in magnitudes between the degree of dispersion and a threshold).
Independent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a coordinate-formation step, coordinating the photoplethysmography signal SPPG (i.e., mental processes and mathematical concepts);
a degree-of-dispersion calculation step, calculating a degree of dispersion of coordinates of the photoplethysmography signal SPPG (i.e., mental processes and mathematical concepts);
a determination step, determining a relative relationship between the degree of dispersion and a feature of a periodic signal and generating a determination result (i.e., mental processes and mathematical concepts); and
determining that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard, and redoing the method for determining photoplethysmography signal (i.e., mental processes and mathematical concepts).
Independent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
sample the photoplethysmography signal SPPG at a time interval t to obtain (k+1) signal values SPPG(t0+i·t), wherein i=0~k (i.e., mental processes and mathematical concepts);
generate a two-dimensional dot plot on an XY coordinate plane, wherein the coordinates (x, y) of each point in the two-dimensional dot plot are (SPPG (t0+i·t), SPPG (t0+(i+1)·t)), wherein i=0~(k-1) (i.e., mental processes and mathematical concepts);
calculate a degree of dispersion of the two-dimensional dot plot (i.e., mental processes and mathematical concepts); and
determine a relative relationship between the degree of dispersion and a feature of a periodic signal and to generate a determination result (i.e., mental processes and mathematical concepts).
Dependent claims 2-6, 8-13, 15-20 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
the coordinate-formation step comprises:
sampling the signal S at a time interval t to obtain (k+1) signal values S(t0+i·t), wherein i=0~k (i.e., mental processes and mathematical concepts); and
generating a two-dimensional dot plot on an XY coordinate plane, wherein coordinates (x, y) of each point in the two-dimensional dot plot are (S(t0+i·t), S(t0+(i+1)·t)), wherein i=0~(k-1) (i.e., mental processes and mathematical concepts);
the degree-of-dispersion calculation step comprises:
calculating the degree of dispersion of the two-dimensional dot plot (i.e., mental processes and mathematical concepts).
Dependent claim 3 further recites:
the degree of dispersion is calculated according to distances between each point in the two-dimensional dot plot and a linear regression line of each point in the two-dimensional dot plot (i.e., mental processes and mathematical concepts).
Dependent claim 4 further recites:
the degree of dispersion is calculated by calculating a score for each point in the two-dimensional dot plot in a template diagram (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
the feature of the periodic signal is determined using cluster analysis (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
the feature of the periodic signal is determined using a k-means algorithm (i.e., mental processes and mathematical concepts).
Dependent claim 8 further recites:
the coordinate-formation step comprises:
sampling the photoplethysmography signal SPPG at a time interval t to obtain (k+1) signal values SPPG(t0+i·t), wherein i=0~k (i.e., mental processes and mathematical concepts); and
generating a two-dimensional dot plot on an XY coordinate plane, wherein the coordinates (x, y) of each point in the two-dimensional dot plot are (SPPG (t0+i·t), SPPG (t0+(i+1)·t)), wherein i=0~(k-1) (i.e., mental processes and mathematical concepts);
the degree-of-dispersion calculation step comprises:
calculating the degree of dispersion of the two-dimensional dot plot (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
the degree of dispersion is calculated according to the distances between each point in the two-dimensional dot plot and a linear regression line of each point in the two-dimensional dot plot (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
the degree of dispersion is calculated by calculating the score of each point in the two-dimensional dot plot in a template diagram (i.e., mental processes and mathematical concepts).
Dependent claim 11 further recites:
the feature of the periodic signal is determined using a k-means algorithm (i.e., mental processes and mathematical concepts).
Dependent claim 12 further recites:
the time interval t is the maximum integer less than one-tenth of a heart rate (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
the feature of the periodic signal is determined again using the k-means algorithm according to the degree of dispersion and the determination result (i.e., mental processes and mathematical concepts).
Dependent claim 15 further recites:
calculates the degree of dispersion according to the distances between each point in the two-dimensional dot plot and a linear regression line of each point in the two-dimensional dot plot (i.e., mental processes and mathematical concepts).
Dependent claim 16 further recites:
calculates the degree of dispersion by calculating the score of each point in the two-dimensional dot plot in a template diagram (i.e., mental processes and mathematical concepts).
Dependent claim 17 further recites:
the feature of the periodic signal is determined using cluster analysis (i.e., mental processes and mathematical concepts).
Dependent claim 18 further recites:
the feature of the periodic signal is determined using a k-means algorithm (i.e., mental processes and mathematical concepts).
Dependent claim 19 further recites:
the time interval t is the maximum integer less than one-tenth of the heart rate (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
determine the feature of the periodic signal again using the cluster analysis according to the degree of dispersion and the determination result stored in the memory section (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., determining that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., calculating a degree of dispersion of a coordinate of the signal S) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the independent claims provide additional elements to obtain data of a ‘photoplethysmography signal’ (e.g., claim 7) and a processing device (claims 1, 7, and 14) to process the signal data, however, once the data is obtained, the subsequent steps only perform analysis and/or calculations using the data to generate a ‘determination result’ (e.g., claim 1), comparing the ‘determination result’ to a predefined standard (claim 7) and then outputting a message (claim 7). Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘determination result’ (i.e., the result of the data analysis) is practically applied.
Dependent claims 2-6, 8-13, 18, and 19 do not further recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
a processing device (e.g., a CPU).
The additional elements in independent claim 7 include:
a processing device (e.g., a CPU);
obtaining a photoplethysmography signal SPPG from a photoplethysmography signal input device (i.e., obtaining data); and
outputting an alert message (i.e., outputting data).
The additional elements in independent claim 14 include:
a photoplethysmography signal input device;
a memory device;
a processing device (e.g., a CPU); and
output an alert message in response to the determination result indicating that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard (i.e., output data).
The additional elements in dependent claims 17 and 20 include:
the feature of the periodic signal is stored in the memory (i.e., store data) (claim 17);
stores the degree of dispersion and the determination result in the memory (i.e., store data) (claim 20).
The additional elements of a processing device (e.g., a CPU) (claims 1, 7, and 14); and a memory device (claim 14); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of obtaining a photoplethysmography signal SPPG from a photoplethysmography signal input device (i.e., obtaining data) (claim 7) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of a photoplethysmography signal input device (claim 14) is merely a part of the pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of outputting an alert message (claim 7) and output an alert message in response to the determination result indicating that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard (claim 14) are merely a post-solution activity of outputting data – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of the feature of the periodic signal is stored in the memory (claim 17) and stores the degree of dispersion and the determination result in the memory (claim 20) are merely a post-solution activity of storing data – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools, and/or amount to insignificant extra-solution activity, and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-6, 8-13, 18, and 19 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 7, and 14 and dependent claims 17 and 20 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a processing device (e.g., a CPU) (claims 1, 7, and 14); a memory device (claim 14); obtaining data (claim 7); outputting data (claims 7 and 14); and storing data (claims 17 and 20); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of a photoplethysmography signal input device (claim 14) and obtaining a photoplethysmography signal SPPG from a photoplethysmography signal input device (claim 7) are conventional. Evidence of conventionality is shown by:
Castaneda et al. (“A review on wearable photoplethysmography sensors and their potential future applications in health care.” International Journal of Biosensors & Bioelectronics, 2018, vol. 4(4): pp. 195-202).
Castaneda et al. reviews the current developments and challenges of wearable PPG-based monitoring technologies and discusses some of the potential applications of this technology in clinical settings (Abstract). Castaneda et al. shows that photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes, and is a non-invasive technology that uses a light source and a photodetector at the surface of the skin to measure volumetric variations of blood circulation (Abstract). Castaneda et al. further shows that a PPG signal’s second derivative wave contains important health-related information, thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness, and assist in early detection and diagnosis of various cardiovascular illnesses that may appear later in life (Abstract).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. (“A method for analyzing temporal patterns of variability of a time series from Poincare plots.” Journal of Applied Physiology, 2012, vol. 113, pp. 297-306).
Independent claim 1 recites a method that encompasses steps of coordinating a signal (e.g., plotting at least one value of a signal on a graph), calculating a degree of dispersion of a coordinate of the signal, and determining a relative relationship between the degree of dispersion and a feature of a periodic signal and generating a determination result.
Dependent claims 2 and 4 further define the step of coordinating a signal (claim 2) and further define the steps of calculating the degree of dispersion (claims 2 and 4).
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
Regarding independent claim 1, Fishman et al. shows a method for analyzing temporal patterns of variability of a time series (i.e., of heart beats (R-R intervals)) from Poincare plots (Title; and Abstract) that retains information about individual points such that the method ensures that temporal correlations are preserved in the analysis (page 297, col. 2, para. 3); an illustrative example of plotting points from a time series on a coordinate plane (Fig. 1); a description of a traditional method of plot creation and variability (e.g., dispersion) calculation (page 298, col. 1, paras. 1-3); a description of the TPV analysis method including a measure of variability (e.g., dispersion) of plotted points of an interval in terms of the distance from the line of identify (as demonstrated in Fig. 2A) and summarized through the standard deviation (i.e., a measure of how far individual data values scatter away from the average (mean) (page 298, col. 2, paras. 4-5 through page 299, col. 1, paras. 1-5); and shows an R-R analysis of plotted data points for normal and heart failure (HF) subjects that shows the Poincare plots of the normal subjects were symmetrical and long (i.e. features of the plotted signal values), whereas by contrast, the HF subjects had Poincare plots that were shorter, wider, and more irregular in shape (i.e. features of the plotted signal values) resulting in a determination that a greater percentage of the HF overall variance was beat-to-beat variance, thus the shorter/wider characteristics of these plots compared with those of normal subjects (page 302, col. 1, para. 5; and Fig. 5(1)).
Regarding independent claim 1, Fishman et al. does not show using the plotted signal values for determining whether a signal is periodic, however Fishman et al. does show that a Poincare plot is used to quantify recurrence, self-similarity, or periodicity in a time series (page 297, col. 1, para. 2).
Regarding dependent claim 2, Fishman et al. further shows illustrations of a time series of heart beats (R-R intervals) (Fig. 3(A)) and generating Poincare plots for heart beats from sections of that time series (Fig. 3(B)). Fishman et al. further describes a coefficient of variation (i.e., a statistical measure that compares the relative dispersion or spread of data points) (Fig. 3, caption).
Regarding dependent claim 4, Fishman et al. further shows that distances from the origin (X) for every centroid (i.e., group of points) is calculated and then a standard deviation (i.e., score) is calculated for those distances (Fig. 2, caption; and Fig. 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. to incorporate alternative methods to sample a time series signal to generate a 2D coordinate plot of signal values, and using the 2D coordinate plot for determining periodicity. One of ordinary skill in the art would have been motivated to modify the methods of Fishman et al., because Fishman et al. shows a method for analyzing temporal patterns of variability of a time series of heart rate intervals from Poincare plots, as discussed above. This modification would have had a reasonable expectation of success given that Fishman et al. further shows that Poincare plots can be used to quantify periodicity.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. as applied to claims 1, 2, and 4 above, and further in view of Yang et al. (“Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation.” Bioinformatics, 2010, vol. 26, issue 12, pp. i168-i174).
Dependent claim 3 further defines how the degree of dispersion is calculated.
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
Yang et al. teaches a method for analyzing circadian expression data, and that circadian rhythms are prevalent in most organisms, and further teaches that identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Yang et al. further teaches that such genes are largely detected by searching periodic patterns in microarray data, however, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise, which makes circadian rhythmic analysis of temporal microarray data very challenging.
Fishman et al. as applied to claims 1, 2, and 4 above, does not show the degree of dispersion is calculated according to distances between each point in the two-dimensional dot plot and a linear regression line of each point in the two-dimensional dot plot (claim 3).
Regarding dependent claim 3, Yang et al. shows an algorithm for identification of sinusoidal and non-sinusoidal periodic patterns in short, noisy and non-stationary time-series (Abstract: Results); and further shows modelling the rhythmic patterns in the time-series of gene expression using a harmonic regression model to represent the cycle trends, which involves fitting sinusoidal models to a time-series using Equation (3) which can be reduced to an even simpler multiple linear regression model Equation (4) (page i170, col. 1, Section 2.3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. as applied to claims 1, 2, and 4 above, by incorporating a linear regression technique for use in modeling time-series data, as shown by Yang et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Fishman et al. as applied to claims 1, 2, and 4 above, with the methods of Yang et al., because Yang et al. shows methods for modeling the rhythmic patterns in time-series data of gene expression using a regression model that can be simplified to a multiple linear regression model. This modification would have had a reasonable expectation of success given that both Fishman et al. as applied to claims 1, 2, and 4 above, and Yang et al. disclose methods for analyzing temporal patterns of variability of time series data.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. as applied to claims 1, 2, and 4 above, and further in view of Miniak-Gorecka et al. (“Using K-Means Clustering in Python with Periodic Boundary Conditions.” Symmetry, 2022, vol. 14, no. 1237, pp. 1-14).
Dependent claims 5 and 6 further define the step of determining the feature of a periodic signal.
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
Miniak-Gorecka et al. teaches a method for using K-means clustering with periodic boundary conditions, and further teaches that periodic boundary conditions are natural in many scientific problems and often lead to particular symmetries. Miniak-Gorecka et al. further teaches that working with datasets that express periodicity properties requires special approaches when analyzing these phenomena.
Fishman et al. as applied to claims 1, 2, and 4 above, does not show the feature of the periodic signal is determined using cluster analysis (claim 5) nor the feature of the periodic signal is determined using a k-means algorithm (claim 6).
Regarding dependent claims 5 and 6, Miniak-Gorecka et al. shows applying the k-means method (of clustering) to datasets presenting periodic (cyclic) patterns with the expectation that dividing the data into clusters will express the symmetry related to periodicity in the data (page 1, para. 2; and Figure 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. as applied to claims 1, 2, and 4 above, by incorporating the k-means clustering method, as shown by Miniak-Gorecka et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Fishman et al. as applied to claims 1, 2, and 4 above with the methods of Miniak-Gorecka et al., because Miniak-Gorecka et al. shows using the k-means clustering method to account for periodicity in a dataset. This modification would have had a reasonable expectation of success given that both Fishman et al. as applied to claims 1, 2, and 4 above, and Miniak-Gorecka et al. disclose methods for analyzing datasets presenting periodic (cyclic patterns).
Claims 7, 8, 10, 12, 14, 16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. (as cited above) in view of de Pedro-Carracedo et al. (“Dynamical Analysis of Biological Signals with the 0-1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal.” Applied Sciences, 2021, vol. 11, no. 6508, pp. 1-19).
Independent claims 7 and 14 encompass steps for obtaining a photoplethysmography signal from a photoplethysmography signal input device; coordinating the signal (e.g., plotting values of the signal on a graph) (claims 7 and 14); calculating a degree of dispersion of coordinates of the signal (claims 7 and 14); determining a relative relationship between the degree of dispersion and a feature of a periodic signal and generating a determination result (claims 7 and 14); determining that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard (claims 7 and 14); outputting an alert message in response to the determination result (claims 7 and 14); and redoing the method (claim 7).
Dependent claims 8, 10, 12, 16, 19, and 20 further define the coordinate-formation and degree-of-dispersion calculation steps (claim 8); the calculation of the degree of dispersion (claims 10 and 16); the time interval t (claims 12 and 19); and storing data of steps in the method and determining the feature of the periodic signal again (claim 20).
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
de Pedro-Carracedo et al. teaches a method for the dynamical analysis of photoplethysmographic (PPG) signals (biological signals) and further teaches that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales, however, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor).
Regarding independent claims 7 and 14, Fishman et al. shows a method for analyzing temporal patterns of variability of a time series (i.e., of heart beats (R-R intervals)) from Poincare plots (Title; and Abstract) that retains information about individual points such that the method ensures that temporal correlations are preserved in the analysis (page 297, col. 2, para. 3); an illustrative example of plotting points from a time series on a coordinate plane (Fig. 1); a description of a traditional method of plot creation and variability (e.g., dispersion) calculation (page 298, col. 1, paras. 1-3); a description of the TPV analysis method including a measure of variability (e.g., dispersion) of plotted points of an interval in terms of the distance from the line of identify (as demonstrated in Fig. 2A) and summarized through the standard deviation (i.e., a measure of how far individual data values scatter away from the average (mean) (page 298, col. 2, paras. 4-5 through page 299, col. 1, paras. 1-5); and shows an R-R analysis of plotted data points for normal and heart failure (HF) subjects that shows the Poincare plots of the normal subjects were symmetrical and long (i.e. features of the plotted signal values), whereas by contrast, the HF subjects had Poincare plots that were shorter, wider, and more irregular in shape (i.e. features of the plotted signal values) resulting in a determination that a greater percentage of the HF overall variance was beat-to-beat variance, thus the shorter/wider characteristics of these plots compared with those of normal subjects (page 302, col. 1, para. 5; and Fig. 5(1)).
Regarding independent claims 7 and 14, Fishman et al. does not show obtaining a photoplethysmography signal SPPG from a photoplethysmography signal input device (claims 7 and 14).
Regarding independent claims 7 and 14, de Pedro-Carracedo et al. shows applying a data analysis method to the PPG signals from 40 subjects to identify various dynamic behaviors present in this biological signal (i.e., PPG signal) (page 2, para. 3).
Further regarding independent claims 7 and 14, Fishman et al. does not show determining that the relative relationship between the degree of dispersion and the feature of the periodic signal does not comply with a predefined standard (claims 7 and 14); outputting an alert message (claims 7 and 14); nor redoing the method for determining photoplethysmography signal (claim 7). However, Fishman et al. does show an output of an R-R (i.e., heart rate interval) analysis of plotted data points for normal and heart failure (HF) subjects that shows the Poincare plots of the normal subjects were symmetrical and long (i.e. features of the plotted signal values), whereas by contrast, the HF subjects had Poincare plots that were shorter, wider, and more irregular in shape (i.e. features of the plotted signal values) resulting in a determination that a greater percentage of the HF overall variance was beat-to-beat variance, thus the shorter/wider characteristics of these plots compared with those of normal subjects (page 302, col. 1, para. 5; and Fig. 5(1)). Thus, in Fig. 5(1) the normal plot (A1) could be considered as a predefined standard for comparison with the heart failure plot (B1) thereby providing a basis for outputting an alert message and a basis for redoing the method (e.g., validating the result).
Regarding dependent claim 8, Fishman et al. further shows illustrations of a time series of heart beats (R-R intervals) (Fig. 3(A)) and generating Poincare plots for heart beats from sections of that time series (Fig. 3(B)). Fishman et al. further describes a coefficient of variation (i.e., a statistical measure that compares the relative dispersion or spread of data points) (Fig. 3, caption).
Regarding dependent claims 10 and 16, Fishman et al. further shows that distances from the origin (X) for every centroid (i.e., group of points) is calculated and then a standard deviation (i.e., score) is calculated for those distances (Fig. 2, caption; and Fig. 3).
Regarding dependent claims 12 and 19, Fishman et al. further shows R-R (heart beats) intervals for normal and heart failure subjects with inter-beat intervals (Fig. 5).
Regarding dependent claim 20, Fishman et al. further shows an evaluation of elliptical (A) and nonelliptical (B) Poincare plots with multiple clusters of points forming the nonelliptical distribution (Fig. 7); and Fishman et al. further shows an output of an R-R (i.e., heart rate interval) analysis of plotted data points for normal and heart failure (HF) subjects that shows the Poincare plots of the normal subjects were symmetrical and long (i.e. features of the plotted signal values), whereas by contrast, the HF subjects had Poincare plots that were shorter, wider, and more irregular in shape (i.e. features of the plotted signal values) resulting in a determination that a greater percentage of the HF overall variance was beat-to-beat variance, thus the shorter/wider characteristics of these plots compared with those of normal subjects (page 302, col. 1, para. 5; and Fig. 5(1)). Thus, in Fig. 5(1) the normal plot (A1) could be considered as a predefined standard for comparison with the heart failure plot (B1) thereby providing a basis for determining the feature of the periodic signal again (i.e., redoing the method, e.g., for validating the result).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. by incorporating methods for obtaining biological signals that are a photoplethysmography (PPG) signal, as shown by de Pedro-Carracedo et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Fishman et al. with the methods of de Pedro-Carracedo et al., because de Pedro-Carracedo et al. shows that while mainly used for heart rate and blood oxygen saturation monitoring, the PPG signal contains extensive physiological dynamics information. This modification would have had a reasonable expectation of success given that both Fishman et al. and de Pedro-Carracedo et al. disclose methods for analyzing variability of time-series data.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, and further in view of Yang et al. (as cited above).
Dependent claims 9 and 15 further define how the degree of dispersion is calculated.
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
de Pedro-Carracedo et al. teaches a method for the dynamical analysis of photoplethysmographic (PPG) signals (biological signals) and further teaches that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales, however, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor).
Yang et al. teaches a method for analyzing circadian expression data, and that circadian rhythms are prevalent in most organisms, and further teaches that identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Yang et al. further teaches that such genes are largely detected by searching periodic patterns in microarray data, however, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise, which makes circadian rhythmic analysis of temporal microarray data very challenging.
Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, does not show that the degree of dispersion is calculated according to the distances between each point in the two-dimensional dot plot and a linear regression line of each point in the two-dimensional dot plot (claims 9 and 15).
Regarding dependent claims 9 and 15, Yang et al. shows an algorithm for identification of sinusoidal and non-sinusoidal periodic patterns in short, noisy and non-stationary time-series (Abstract: Results); and further shows modelling the rhythmic patterns in the time-series of gene expression using a harmonic regression model to represent the cycle trends, which involves fitting sinusoidal models to a time-series using Equation (3) which can be reduced to an even simpler multiple linear regression model Equation (4) (page i170, col. 1, Section 2.3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, by incorporating a linear regression technique for use in modeling time-series data, as shown by Yang et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, with the methods of Yang et al., because Yang et al. shows methods for modeling the rhythmic patterns in time-series data of gene expression using a regression model that can be simplified to a multiple linear regression model. This modification would have had a reasonable expectation of success given that both Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, and Yang et al. disclose methods for analyzing temporal patterns of variability of time series data.
Claims 11, 13, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, and further in view of Miniak-Gorecka et al. (as cited above).
Dependent claims 11, 13, 17, and 18 further define the step of determining the feature of a periodic signal.
Fishman et al. teaches an analysis method called temporal Poincare variability (TPV) that uses standard techniques to quantify the temporal distribution of points of a time series of heart beats (R-R interval) and to detect nonlinear sources responsible for physiological variability. (Note: a Poincare plot is a scatter graph that shows the relationship between consecutive data points in a time series to spot patterns, trends, or random behavior, wherein each point on the graph represents a pair of values from a sequence (xn) and the next value (xn+1), wherein the horizontal axis (x-axis) show the current data point (xn) and the vertical axis (y-axis) shows the next data point (xn+1), and wherein the shape and spread of the resulting dots reveal if the system (e.g., heart rate or respiratory pattern variabilities) is stable, periodic, or chaotic).
de Pedro-Carracedo et al. teaches a method for the dynamical analysis of photoplethysmographic (PPG) signals (biological signals) and further teaches that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales, however, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor).
Miniak-Gorecka et al. teaches a method for using K-means clustering with periodic boundary conditions, and further teaches that periodic boundary conditions are natural in many scientific problems and often lead to particular symmetries. Miniak-Gorecka et al. further teaches that working with datasets that express periodicity properties requires special approaches when analyzing these phenomena.
Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, does not show the feature of the periodic signal is determined using a k-means algorithm (claims 11 and 18) nor the feature of the periodic signal is determined again using the k-means algorithm according to the degree of dispersion and the determination result (claim 13) nor the feature of the periodic signal is determined using cluster analysis (claim 17).
Regarding dependent claims 11, 13, 17, and 18, Miniak-Gorecka et al. shows applying the k-means method (of clustering) to datasets presenting periodic (cyclic) patterns with the expectation that dividing the data into clusters will express the symmetry related to periodicity in the data (page 1, para. 2; and Figure 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, by incorporating the k-means clustering method, as shown by Miniak-Gorecka et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, with the methods of Miniak-Gorecka et al., because Miniak-Gorecka et al. shows using the k-means clustering method to account for periodicity in a dataset. This modification would have had a reasonable expectation of success given that both Fishman et al. in view of de Pedro-Carracedo et al. as applied to claims 7, 8, 10, 12, 14, 16, 19, and 20 above, and Miniak-Gorecka et al. disclose methods for analyzing datasets presenting periodic (cyclic patterns).
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/STEVEN W. BAILEY/Examiner, Art Unit 1687
