DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2022-11-30, 2024-12-20, and 2025-11-18 are being considered by the examiner.
Claim Status
Claims 1-13 are pending in the application
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: “Estimating Subjective Pain Based On Machine Learning Classification Of EEG Signals.”
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 as shown below, for the following limitations.
Claim 1: “a reception means that receives feature data”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 1: “a storage means that stores a plurality of feature templates”; this is being interpreted as a hardware memory or its equivalents, as per Specification [0053-0054]
Claim 1: “an estimation means that estimates”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 2: “wherein the estimation means is configured to perform: obtaining … and estimating…”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 5: “wherein the estimation means is configured to perform: obtaining … and estimating ....”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 7: “the estimation means is configured to further perform generating a plurality of pieces of standardized feature data …”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 9: “the reception means receives no-load feature data”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 9: “the estimation means is configured to perform: selecting ... and estimating…”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 11: “the estimation means estimates a pain”; there is no structure given to this in the Specification, 112(b) rejection below
Claim 12: “a storage means, the storage means for storing” ; this is being interpreted as a hardware memory or its equivalents, as per Specification [0053-0054]
Claim 13: “a storage means, the storage means for storing”; this is being interpreted as a hardware memory or its equivalents, as per Specification [0053-0054]
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 1-13 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.
Rejections based on Claim Interpretation under 35 USC 112(f)
The following claim limitations 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.
Claim 1: “a reception means that receives feature data”; there is no structure recited in the Specification to apprise one of ordinary skill in the art of what is a “reception means”. Claims 2-11 inherit this deficiency.
Claims 1, 2, 5, 7, 9, 11: “an estimation means”; there is no structure recited in the Specification to apprise one of ordinary skill in the art of what is an “estimation means”. Claim 10 inherits this deficiency.
Therefore, claims 1-11 are indefinite and are 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.
Rejections NOT based on Claim Interpretation under 35 USC 112(f)
Claims 1, 12, and 13 recite “a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object, or a plurality of models that have learned the plurality of feature templates …”
The word “or” presents a problem in this limitation, as the first option introduces “a plurality of feature templates”, but the second part posits an alternative (“or”) but then still assumes the antecedent existence of “the plurality of feature templates” (a model that has learned them). Then, further in the claim, which again has posited two alternatives (“a plurality of feature templates” or “a plurality of models”) also assumes the antecedent basis of “each of the plurality of models.”
Thus, in summary, the claim presents two alternatives distinguished by “or”, but then assumes that both of these alternatives exist. Clarification is required.
Claims 2-11 are rejected because they inherit the deficiencies of their parent claims.
Claim 4 has multiple issues. The claim recites “indicating the top plurality of the subjective assessments”, but gives no indication of how one determines what is the “top” plurality, especially since the assessments are “subjective”. If the assessments are pain scores, are the “top” scores the most pain? The least pain? It is also unclear why a concept like correlation, which determines how factors influence one another, why taking an average of such correlation coefficients would “indicate” the “top” plurality of “subjective” assessments. Furthermore, the claim states to “obtaining an ensemble average correlation coefficient” by performing “an ensemble average of the values” – in which the “values” are “assessments” which do not appear to be “coefficients” themselves. It is unclear how an “average” of “subjective values” can result in an “average correlation coefficient”. The true meaning of this claim cannot be determined by Examiner. Claims 9-11 are also rejected due to their dependence on Claim 4.
Claim 9 recites the limitation “wherein the reception means receives no-load feature data of a biosignal when a load is not given to the estimation target object.” The word “load” is not defined anywhere in the Specification, and one of ordinary skill in the art would not be apprised of what it means to give a “load”. Clarification is required. Claims 10 and 11 are rejected due to their dependence on Claim 9.
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they may be directed to “software per se”. The claims is directed to a “program”, which is executable by a computer comprising a processing unit and a storage means, however the hardware processing unit and storage means themselves are not positively recited. Therefore, the claim may be directed to a software program itself, and not any hardware system or machine.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-11 are directed to a system comprising a hardware memory and Claim 12 is directed to a method, and therefore Claims 1-12 are directed to one of the four statutory categories of patent eligible subject matter. As explained above, Claim 13 is not directed to one of the four statutory categories of patent eligible subject matter.
Step 2A Prong 1:
Claims 1, 12, and 13 recite:
“an estimation means that estimates the subjective assessment made by the estimation target object, based on the feature data and the plurality of feature templates or the plurality of models”; making an estimation is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process
Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements are as follows:
(Claims 12 and 13): “the method being executable by a computer”; “A program for estimating a subjective assessment made by an estimation target object, the program being executable by a computer comprising a processor unit, the program being executable by a computer system”; these limitations amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
“a reception means that receives feature data of a biosignal acquired from the estimation target object”; this amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g)
“a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object … each of the plurality of feature templates associating pieces of feature data of a plurality of samples sampled from a biosignal with values indicating subjective assessments, the plurality of feature templates including a first feature template extracted from a first biosignal acquired from the first modeling target object and a second feature template extracted from a second biosignal acquired from the second modeling target object”; this amounts to insignificant extra solution activity, mere data gathering and “selecting a particular data source or type of data to be manipulated”, as per MPEP 2106.05(g)
“or a plurality of models that have learned the plurality of feature templates … each of the plurality of models being configured to output a value indicating a subjective assessment in response to an input of feature data, and the plurality of models including a first model that has learned the first feature template and a second model that has learned the second feature template”; the use of a machine learning model, broadly recited at a high level of generality, amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
(Claims 12 and 13): “the method being executable by a computer”; “A program for estimating a subjective assessment made by an estimation target object, the program being executable by a computer comprising a processor unit, the program being executable by a computer system”; these limitations amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
“a reception means that receives feature data of a biosignal acquired from the estimation target object”; this amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g); furthermore, this is well-understood, routine, and conventional activity (“i. Receiving or transmitting data over a network”) as per MEP 2106.05(d)
“a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object … each of the plurality of feature templates associating pieces of feature data of a plurality of samples sampled from a biosignal with values indicating subjective assessments, the plurality of feature templates including a first feature template extracted from a first biosignal acquired from the first modeling target object and a second feature template extracted from a second biosignal acquired from the second modeling target object”; this amounts to insignificant extra solution activity, mere data gathering and “selecting a particular data source or type of data to be manipulated”, as per MPEP 2106.05(g); furthermore, this is well-understood, routine, and conventional activity (“i. Receiving or transmitting data over a network”, “iii. Electronic recordkeeping”, “iv. Storing and retrieving information in memory”) as per MEP 2106.05(d)
“or a plurality of models that have learned the plurality of feature templates … each of the plurality of models being configured to output a value indicating a subjective assessment in response to an input of feature data, and the plurality of models including a first model that has learned the first feature template and a second model that has learned the second feature template”; the use of a machine learning model, broadly recited at a high level of generality, amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Dependent Claims
Claims 2-11 are also rejected under 35 USC 101 for the following reasons:
Claim 2 recites: “wherein the estimation means is configured to perform: obtaining a plurality of correlation coefficient sets by correlating each of the plurality of feature templates with the piece of feature data; and estimating the subjective assessment made by the estimation target object, based on the plurality of correlation coefficient sets”; correlating and estimating are evaluations that can be performed by a human in the mind or with pen and paper, and are thus a mental process
Claim 3 recites: “wherein the estimating of the subjective assessment by the estimation target object based on the plurality of correlation coefficient sets comprises: identifying, for each of the plurality of correlation coefficient sets, a value indicating a subjective assessment that is associated with a sample corresponding to a highest correlation coefficient; taking an ensemble average of the values indicating the subjective assessments of the plurality of correlation coefficient sets; and determining a score indicating the subjective assessment based on the ensemble average”; identifying, taking an average, and determining a score are evaluations that can be performed by a human in the mind or with pen and paper, and are thus a mental process
Claim 4 recites: “wherein the estimating of the subjective assessment by the estimation target object based on the plurality of correlation coefficient sets comprises: identifying, for each of the plurality of correlation coefficient sets, values indicating subjective assessments that are associated with samples corresponding to a top plurality of correlation coefficients; obtaining an ensemble average correlation coefficient by, for each of the correlation coefficient sets, taking an ensemble average of the values indicating the top plurality of the subjective assessments; taking an ensemble average of the ensemble average correlation coefficients of the plurality of correlation coefficient sets; and determining a score indicating the subjective assessment based on the ensemble average”; identifying, taking an average, and determining a score are evaluations that can be performed by a human in the mind or with pen and paper, and are thus a mental process
Claim 5 recites: “wherein the estimation means is configured to perform: obtaining a plurality of outputs by inputting the feature data to each of the plurality of models, the plurality of outputs including a first output outputted from the first model and a second output outputted from the second model; and estimating the subjective assessment made by the estimation target object, based on the plurality of outputs”; evaluating data to produce an output based on the evaluation, and then performing an estimation is a mental process under Step 2A Prong 1; using a broadly recited machine learning “model” at a high level of generality to do so, amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f) under Steps 2A Prong 2 and 2B.
Claim 6 recites: “wherein the estimating of the subjective assessment by the estimation target object based on the plurality of outputs comprises: taking an ensemble average of the plurality of outputs; and determining a score indicating the subjective assessment based on the ensemble average”; taking an average and determining a score are evaluations that can be performed by a human in the mind or with pen and paper, and are thus a mental process
Claim 7 recites: “wherein the storage means further stores a plurality of standardization parameters extracted from the plurality of biosignals, the plurality of standardization parameters including a plurality of first standardization parameters extracted from the first biosignal acquired from the first modeling target object and a plurality of second standardization parameters extracted from a plurality of second biosignals acquired from the second modeling target object”; this amounts to insignificant extra solution activity, mere data gathering and “selecting a particular data source or type of data to be manipulated”, as per MPEP 2106.05(g); furthermore, this is well-understood, routine, and conventional activity (“i. Receiving or transmitting data over a network”, “iii. Electronic recordkeeping”, “iv. Storing and retrieving information in memory”) as per MEP 2106.05(d)
the estimation means Is configured to further perform generating a plurality of pieces of standardized feature data by standardizing the feature data by the plurality of standardization parameters, the plurality of pieces of standardized feature data including a plurality of pieces of first standardized feature data obtained by standardizing the feature data by the plurality of first standardization parameters, and a plurality of pieces of second standardized feature data obtained by standardizing the feature data by the plurality of second standardization parameters; manipulating data in order to standardize it is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process
“and the obtaining of the plurality of outputs by inputting the feature data to each of the plurality of models comprises obtaining a plurality of outputs of the plurality of models by inputting the plurality of pieces of standardized feature data to the plurality of models, the plurality of outputs of the plurality of models including a plurality of first outputs obtained by inputting the plurality of pieces of first standardized feature data to the first model and a plurality of second outputs obtained by inputting the plurality of pieces of second standardized feature data to the second model”; evaluating data to produce an output based on the evaluation, and then performing an estimation is a mental process under Step 2A Prong 1; using a broadly recited machine learning “model” at a high level of generality to do so, amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f) under Steps 2A Prong 2 and 2B.
Claim 8 recites: “wherein the estimating of the subjective assessment by the estimation target object based on the plurality of outputs comprises: obtaining a plurality of ensemble average outputs by taking an ensemble average of the plurality of outputs of each of the plurality of models, the plurality of ensemble average outputs including a first ensemble average output obtained by taking an ensemble average of the plurality of first outputs and a second ensemble average output obtained by taking an ensemble average of the plurality of second outputs; taking an ensemble average of the plurality of ensemble average outputs; and determining a score indicating the subjective assessment based on the ensemble average”; taking an average and determining a score are evaluations that can be performed by a human in the mind or with pen and paper, and are thus a mental process
Claim 9 recites: “wherein the reception means receives no-load feature data of a biosignal when a load is not given to the estimation target object”; this amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g); furthermore, this is well-understood, routine, and conventional activity (“i. Receiving or transmitting data over a network”) as per MEP 2106.05(d)
and the estimation means is configured to perform: selecting at least one of the plurality of feature templates to be used to estimate the subjective assessment by the estimation target object or at least one of the plurality of models to be used to estimate the subjective assessment by the estimation target object, based on the no-load feature data; and estimating the subjective assessment made by the estimation target object, based on the feature data and the at least one of the plurality of the feature templates or the at least one of the plurality of models that has been selected”; selecting data and estimating data are evaluations that can be performed by a human with pen and paper, and are thus a mental process
Claim 10 recites: “wherein the plurality of modeling target objects are n modeling target objects, the plurality of feature templates are n feature templates, the plurality of models are n models, and n is an integer equal to or larger than two”; this merely quantifies elements of the analysis of the preceding claims, and thus the claim is still directed to an abstract idea
Claim 11 recites: “wherein the biosignal acquired from the estimation target object is a biosignal at a time when a stimulus is given to the estimation target object, the plurality of biosignals are a plurality of biosignals at a time when a stimulus is given to the plurality of modeling target objects, the first biosignal is a first biosignal at the time when the stimulus is given to the first modeling target object, and the second biosignal is a second biosignal at the time when the stimulus is given to the second modeling target object,”; this amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g) under Step 2A Prong 2; furthermore, this is well-understood, routine, and conventional activity (“i. Receiving or transmitting data over a network”) as per MEP 2106.05(d) under Step 2B; this may also be considered merely indicating a field of use or technological environment in which to apply a judicial exception under 2106.05(h) under both Steps 2A Prong 2 and 2B
and the estimation means estimates a pain experienced by the estimation target object; performing an estimation is an evaluation that can be performed by a human in the mind or with pen and paper, and is thus a mental process
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.
Claims 1, 5-6, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Doidge et al. (US 2022/0192578 A1; hereinafter “Doidge”) in view of Elsayed et al. (“A Novel Approach to Objectively Quantify the Subjective Perception of Pain Through Electroencephalogram Signal Analysis”; hereinafter “Elsayed”)
As per Claim 1, Doidge teaches a system for estimating a subjective assessment made by an estimation target object, the system comprising: (Doidge, Abstract: “Systems and methods are provided for evaluating a pain disorder.”)
a reception means (Doidge [0006]: “In another example, a system includes an electrogram interface.”)
that receives feature data of a biosignal acquired from the estimation target object (Doidge [0006]: “In another example, a system includes an electrogram interface that receives a recorded evoked potential from an electrogram of a subject.”)
a storage means (Doidge [0053]: “The additional memory devices 508 and 510, such as a hard disk drive, server, standalone database, or other non-volatile memory, can also be in communication with the system bus 502”)
that stores [a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object, or] a plurality of models that have learned [the plurality of feature templates]; (Doidge [0018]: “In the illustrated implementation, the machine learning model 106 includes one or both of a support vector machine and a random forest classifier.” Here, Doidge discloses the possibility of a plurality of machine learning models (both an SVM and RF)). Although one of ordinary skill in the art might assume a plurality of feature templates with labels in order to train the SVM and RF< Doidge does not recite such a plurality of feature templates explicitly, and thus another reference will be added.)
an estimation means that estimates the subjective assessment made by the estimation target object, based on the feature data [and the plurality of feature templates or] the plurality of models (Doidge [0018]: “A machine learning model 106 uses the plurality of extracted features to assign a clinical parameter to the subject. The machine learning model 106 can utilize one or more classification or regression algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter relating to a pain disorder and provide this information to a user via an appropriate output device, such as a display. The clinical parameter can represent any of … a severity of pain”)
However, Doidge does not explicitly teach a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object
Elsayed teaches a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object (Elsayed, Figure 2 and Figure 5, discloses:
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As shown above, Elsayed discloses in Figure 2 a plurality of feature templates (an EEG with a pain score) from multiple target objects (participants). This is then used to train a classifier to estimate the pain of a target object (participant)).
Elsayed is analogous art because it is in the field of endeavor of EEG classification to estimate pain. It would have been obvious before the effective filing date of the claimed invention to combine the EEG pain assessment of Doidge with the training data collection of Elsayed. One of ordinary skill in the art would have been motivated to do so in order to achieve better accuracy and achieve a better understanding of the subjective experience of pain (Elsayed, Page 199929 Conclusion: “The developed classifier was trained on a 5820 data sample and achieved an accuracy of 94.83 %. This study contributes to building a deeper understanding
of the brain’s activities inflicted by the physical pain and helps in establishing an internationally recognized objective measure of the subjective perception of pain, which will be of great importance in various clinical applications.”)
As per Claim 5, the combination of Doidge and Elsayed teaches the system according to claim 1. Doidge teaches wherein the estimation means is configured to perform: obtaining a plurality of outputs by inputting the feature data to each of the plurality of models, the plurality of outputs including a first output outputted from the first model and a second output outputted from the second model; and estimating the subjective assessment made by the estimation target object, based on the plurality of outputs. (Doidge [0038]: “The pattern recognition classifier 238 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to classify the subjects into one of the plurality of classes and provide this information to a display 240. Where multiple classification or regression models are used, an arbitration element can be utilized to provide a coherent result from the plurality of models.”)
As per Claim 6, the combination of Doidge and Elsayed teaches the system according to claim 5. Doidge teaches wherein the estimating of the subjective assessment by the estimation target object based on the plurality of outputs comprises: taking an ensemble average of the plurality of outputs; and determining a score indicating the subjective assessment based on the ensemble average. (Doidge [0042]: “A random forest classifier is a modification of the decision tree algorithm using a bootstrap aggregating, or “bagging” approach. In this approach, multiple decision trees are trained on random samples of the training set, and an average (e.g., mean, median, or mode) result across the plurality of decision trees is returned.”)
As per Claim 12, this is a method claim corresponding to system Claim 1, and is rejected for similar reasons.
As per Claim 13, this is a program claim corresponding to system Claim 1 that recites by a computer comprising a processor unit. Doidge [0057] discloses, “Computer executable logic for implementing the pain evaluation system resides on one or more of the system memory 506, and the memory devices 508 and 510 in accordance with certain examples. The processing unit 504 executes one or more computer executable instructions originating from the system memory 506 and the memory devices 508 and 510,” and therefore the claim is also rejected for similar reasons.
Claims 2-3 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Doidge and Elsayed, further in view of Hasan et al. (“A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation”; hereinafter “Hasan”)
As per Claim 2, the combination of Doidge and Elsayed teaches the system according to claim 1 as well as estimating the subjective assessment made by the estimation target object (see Doidge in Rejection to Claim 1). However, the combination does not teach wherein the estimation means is configured to perform: obtaining a plurality of correlation coefficient sets by correlating each of the plurality of feature templates with the piece of feature data; and estimating the subjective assessment made by the estimation target object, based on the plurality of correlation coefficient sets.
Hasan teaches wherein the estimation means is configured to perform: obtaining a plurality of correlation coefficient sets by correlating each of the plurality of feature templates with the piece of feature data (Hasan, Page 2 Bottom Left: “In this paper, we present a novel approach for a hybrid EEG-fNIRS BCI channel selection using the Pearson product-moment correlation coefficient (PPMCC).”)
estimating the subjective assessment made by the estimation target object, based on the plurality of correlation coefficient sets (Recall above that Doidge teaches estimating the subjective assessment made by the estimation target object. Hasan teaches based on the plurality of correlation coefficient sets in Page 7: “The highest correlation coefficient helps to separate the optimized channels from the rest. Once the channels are selected, the next task is to train the classifiers on the given dataset from different sources in order to obtain accurate results.” Here, Hasan indicates that the multiple feature templates correspond to different feature data, which in this case is different channels of the EEG, and then the best channel can be used to train and execute the classifier, which is the channel with the highest correlation coefficient. Therefore, the final estimation is made based on the model based on the feature template corresponding with the highest correlation coefficient.)
Hasan is analogous art because it is in the field of endeavor of machine learning analysis of EEG. It would have been obvious before the effective filing date of the claimed invention to combine the EEG classification for pain of Doidge and Elsayed with the feature template with multiple channels and selecting the channel with the highest correlation coefficient of Hasan. One of ordinary skill in the art would have been motivated to do so in order to make an estimation based on the most relevant data to produce the best results (Hasan, Page 2 Bottom Left: “The determination of the correlation coefficient is a statistical approach that allows us to quantify the strength of association. The calculation yields the linear association between two channels which is then used to select the most dominant ones … Therefore, this study is expected to introduce a completely new perspective for channel selection in hybrid BCI systems. Through results, it is demonstrated that the proposed approach can play its role effectively, when used in conjunction with two different classifiers, to attain reduced computational time and high classification accuracy.”)
As per Claim 3, the combination of Doidge and Elsayed teaches the system according to claim 2 as well as estimating of the subjective assessment by the estimation target object based on the plurality of correlation coefficient sets (see Doidge and Hasan in Rejection to Claim 2). Doidge teaches wherein the estimating of the subjective assessment by the estimation target object based on the plurality of correlation coefficient sets comprises: taking an ensemble average of the values indicating the subjective assessments of the plurality of [correlation coefficient] sets; and determining a score indicating the subjective assessment based on the ensemble average. (Doidge [0038]: “The pattern recognition classifier 238 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to classify the subjects into one of the plurality of classes and provide this information to a display 240. Where multiple classification or regression models are used, an arbitration element can be utilized to provide a coherent result from the plurality of models.” Doidge [0042]: “A random forest classifier is a modification of the decision tree algorithm using a bootstrap aggregating, or “bagging” approach. In this approach, multiple decision trees are trained on random samples of the training set, and an average (e.g., mean, median, or mode) result across the plurality of decision trees is returned.”)
However, the combination does not teach identifying, for each of the plurality of correlation coefficient sets, a value indicating a subjective assessment that is associated with a sample corresponding to a highest correlation coefficient
Hasan teaches identifying, for each of the plurality of correlation coefficient sets, a value indicating a subjective assessment that is associated with a sample corresponding to a highest correlation coefficient (Hasan Page 7: “The highest correlation coefficient helps to separate the optimized channels from the rest. Once the channels are selected, the next task is to train the classifiers on the given dataset from different sources in order to obtain accurate results.” Here, Hasan indicates that the multiple feature templates correspond to different feature data, which in this case is different channels of the EEG, and then the best channel can be used to train and execute the classifier, which is the channel with the highest correlation coefficient. Therefore, the final estimation is made based on the model based on the feature template corresponding with the highest correlation coefficient.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hasan with Doidge and Elsayed for at least the reasons recited in the rejection to Claim 2.
As per Claim 7, the combination of Doidge and Elsayed teaches the system according to claim 5 or 6. However, the combination does not teach wherein the storage means further stores a plurality of standardization parameters extracted from the plurality of biosignals, the plurality of standardization parameters including a plurality of first standardization parameters extracted from the first biosignal acquired from the first modeling target object and a plurality of second standardization parameters extracted from a plurality of second biosignals acquired from the second modeling target object, the estimation means Is configured to further perform generating a plurality of pieces of standardized feature data by standardizing the feature data by the plurality of standardization parameters, the plurality of pieces of standardized feature data including a plurality of pieces of first standardized feature data obtained by standardizing the feature data by the plurality of first standardization parameters, and a plurality of pieces of second standardized feature data obtained by standardizing the feature data by the plurality of second standardization parameters, and the obtaining of the plurality of outputs by inputting the feature data to each of the plurality of models comprises obtaining a plurality of outputs of the plurality of models by inputting the plurality of pieces of standardized feature data to the plurality of models, the plurality of outputs of the plurality of models including a plurality of first outputs obtained by inputting the plurality of pieces of first standardized feature data to the first model and a plurality of second outputs obtained by inputting the plurality of pieces of second standardized feature data to the second model.
Hasan teaches wherein the storage means further stores a plurality of standardization parameters extracted from the plurality of biosignals, the plurality of standardization parameters including a plurality of first standardization parameters extracted from the first biosignal acquired from the first modeling target object and a plurality of second standardization parameters extracted from a plurality of second biosignals acquired from the second modeling target object, the estimation means Is configured to further perform generating a plurality of pieces of standardized feature data by standardizing the feature data by the plurality of standardization parameters, the plurality of pieces of standardized feature data including a plurality of pieces of first standardized feature data obtained by standardizing the feature data by the plurality of first standardization parameters, and a plurality of pieces of second standardized feature data obtained by standardizing the feature data by the plurality of second standardization parameters, and the obtaining of the plurality of outputs by inputting the feature data to each of the plurality of models comprises obtaining a plurality of outputs of the plurality of models by inputting the plurality of pieces of standardized feature data to the plurality of models, the plurality of outputs of the plurality of models including a plurality of first outputs obtained by inputting the plurality of pieces of first standardized feature data to the first model and a plurality of second outputs obtained by inputting the plurality of pieces of second standardized feature data to the second model. (Hasan, Pages 3-4: “2.3. Feature Extraction. Once the channels are selected, the next task is to prepare a feature set for classification. Four different statistical features are extracted using spatial averaging of the selected channels for both EEG and fNIRS features … 2.3.1. Signal Mean (M) … 2.3.2. Signal Skewness (SK) … 2.3.3. Signal Kurtosis (KR) … 2.3.4. Signal Peak (P) … Once the feature set is defined, the next process is to normalize the feature set of both EEG-fNIRS between 0 and 1 … 2.4. Classification. Prior to classification, three different sets of features are constructed.” Here, Hasan discloses first and second pluralities of standardization parameters (“Signal Mean” and “Signal Skewness”, “Signal Kurtosis” and “Signal Peak”) which were used to standardize the feature data (“normalize”), and the standardized feature data was then put into a model for each patient (“prepare … for classification”)).
Hasan is analogous art because it is in the field of endeavor of machine learning analysis of EEG. It would have been obvious before the effective filing date of the claimed invention to combine the EEG classification for pain of Doidge and Elsayed with the feature template with the data preprocessing of Hasan. One of ordinary skill in the art would have been motivated to do so in order to make an estimation based on the most relevant data to properly prepare the data for classification so that it is aligned among all participants (Hasan, Page 4: “From here on, it is assumed that all the features are normalized.”)
As per Claim 8, the combination of Doidge, Elsayed, and Hasan teaches the system according to claim 7 as well as estimating of the subjective assessment by the estimation target object based on the plurality of outputs comprises (see Doidge in Rejection to Claim 1). Doidge teaches wherein the estimating of the subjective assessment by the estimation target object based on the plurality of outputs comprises: obtaining a plurality of ensemble average outputs by taking an ensemble average of the plurality of outputs of each of the plurality of models (Doidge [0038]: “The pattern recognition classifier 238 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to classify the subjects into one of the plurality of classes and provide this information to a display 240. Where multiple classification or regression models are used, an arbitration element can be utilized to provide a coherent result from the plurality of models.” Doidge [0042]: “A random forest classifier is a modification of the decision tree algorithm using a bootstrap aggregating, or “bagging” approach. In this approach, multiple decision trees are trained on random samples of the training set, and an average (e.g., mean, median, or mode) result across the plurality of decision trees is returned.”)
the plurality of ensemble average outputs including a first ensemble average output obtained by taking an ensemble average of the plurality of first outputs and a second ensemble average output obtained by taking an ensemble average of the plurality of second outputs; taking an ensemble average of the plurality of ensemble average outputs; (Examiner notes that Doidge discloses a multitude of ways to configure the machine learning, including “multiple classification models”, and Doidge also discloses a “random forest classifier”, which is a composite type of model which, in itself, is an ensemble. Therefore, one of ordinary skill in the art will appreciate that Doidge indeed suggests an ensemble-of-ensembles, i.e., two random forests, which would achieve this purpose. One of ordinary skill in the art will appreciate that one can take an average of averages.)
and determining a score indicating the subjective assessment based on the ensemble average. (Doidge [0038]: Where multiple classification or regression models are used, an arbitration element can be utilized to provide a coherent result from the plurality of models.”)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Doidge, Elsayed, and Hasan, further in view of Deebani et al. (“Ensemble Correlation Coefficient”; hereinafter “Deebani”)
As per Claim 4, the combination of Doidge and Elsayed teaches the system according to claim 2 as well as estimating of the subjective assessment by the estimation target