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 1-13 are currently pending and under exam herein.
Claims 1-13 are rejected.
Priority
The instant application claims priority from foreign application JP2021-023760 filed on 2/17/2021. Thus, the effective filing date of the instant application is 2/17/2021.
Drawings
The Drawings filed on 07/13/2023 were considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/04/2023, 09/06/2024, 10/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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.
The terms “superiority” and “inferiority” in claims 1-4, 6-8, 11-13 are relative terms which renders the claim indefinite. The terms “superiority” and “inferiority” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 1-13 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: Claims 1-19 are directed to an information processing apparatus, information processing method, program, and drug evaluation method
[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:
performs a determination of the superiority or inferiority for each of the plurality of unknown waveform data based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked, and (mental process, mathematical concept)
outputs the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (mental process, mathematical concept)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor performs clustering on a set including the plurality of teacher waveform data and the plurality of unknown waveform data, and (mathematical concept, mental process)
performs the determination of the superiority or inferiority by obtaining, for each of clusters including at least one of the plurality of unknown waveform data, a probability that the unknown waveform data has a superior determination, as a result of the clustering. (mathematical concept, mental process)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor obtains the probability based on the number of the teacher waveform data with a superior determination and the number of the teacher waveform data with a superior determination and an inferior determination, for each of the clusters (mathematical concept)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the probability is represented by a value obtained by dividing the number of the teacher waveform data with the superior determination by the number of the teacher waveform data with the superior determination and the inferior determination, and (mathematical concept)
the processor ranks and outputs the superiority or inferiority of the plurality of unknown waveform data based on the probability in a comparable manner. (mathematical concept, mental process)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor performs the clustering by a k-medoids method or a k-means method. (mathematical concept)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor performs a filtering process of excluding unknown waveform data that does not satisfy an evaluation criterion from the set or lowering a rank in terms of the superiority or inferiority. (mathematical concept, mental process)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor inputs the unknown waveform data to a neural network that has been trained through machine learning based on the teacher waveform data, and performs the determination of the superiority or inferiority based on a result output from the neural network. (mathematical concept)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the processor inputs the unknown waveform data to an encoder of an auto-encoder that has been trained through machine learning based on the teacher waveform data with a superior determination, and then performs the determination of the superiority or inferiority based on a difference between waveform data restored by a decoder and the unknown waveform data input to the encoder. (mathematical concept)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the unknown waveform data with a high rank of superiority or inferiority is used for drug evaluation. (mental process)
Independent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and (mental process, mathematical concept)
outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (mental process, mathematical concept)
Independent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a determination process of performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and (mental process, mathematical concept)
an output process of outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (mental process, mathematical concept)
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 pencil and paper, 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 are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-13 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[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.
The additional element in independent claim 1 includes:
An information processing apparatus comprising: a processor, wherein the processor
acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown,
The additional element in dependent claim 9 includes:
wherein the unknown waveform data is a pulse signal output by a cell (Claim 9)
The additional element in dependent claim 10 includes:
wherein the cell is a myocardial cell.
The additional element in independent claim 12 includes:
An information processing method comprising:
acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown;
The additional element in independent claim 13 includes:
A non-transitory computer-readable storage medium storing a program causing a computer to execute:
an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown;
The additional elements of acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, (Claim 1), wherein the unknown waveform data is a pulse signal output by a cell (Claim 9), wherein the cell is a myocardial cell (Claim 10), acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown (Claim 12), an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; (Claim 13) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of an information processing apparatus comprising: a processor, wherein the processor (Claim 1), an information processing method comprising (Claim 12), a non-transitory computer-readable storage medium storing a program causing a computer to execute (Claim 13) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-13 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-13 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.
The additional elements recited in claims 1-13 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 acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, (Claim 1), wherein the unknown waveform data is a pulse signal output by a cell (Claim 9), wherein the cell is a myocardial cell (Claim 10), acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown (Claim 12), an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; (Claim 13) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by Hwang et al. (Hwang, H.; Liu, R.; Maxwell, J. T.; Yang, J.; Xu, C. Machine Learning Identifies Abnormal Ca2+ Transients in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. Scientific Reports 2020, 10 (1)) which builds machine learning methods to analyze the same type of data and Sugimoto et al. which builds machine learning algorithms using the same type of data (Sugimoto, K.; Kon, Y.; Lee, S.; Okada, Y. Detection and Localization of Myocardial Infarction Based on a Convolutional Autoencoder. Knowledge-Based Systems 2019, 178, 123–131.)
The additional elements of an information processing apparatus comprising: a processor, wherein the processor (Claim 1), an information processing method comprising (Claim 12), a non-transitory computer-readable storage medium storing a program causing a computer to execute (Claim 13) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
When taken alone, all additional elements in claims 1-13 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-13 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 § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 7, 12, 13 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US5630019A
US5630019A teaches disclosed is a waveform evaluating apparatus for evaluating and adjusting a waveform measured by a measurement apparatus such as a synchroscope and, more particularly, a waveform evaluating apparatus having a plurality of neural network modules formed independently for each object of judgment, in which the neural weight ratio of each neural network module is determined by causing the module to learn with a first ideal waveform module as an ideal signal and the like. Such an arrangement is also made, in which a signal in phase with the learned teacher signal extracted from the signal from an object of judgment signal is input to the input layer, and in which phasic information is detected in a phase detecting portion and a waveform is sliced in a waveform slicing portion on the basis of the phasic information. Further, such an arrangement is made, in which signal waveform data as an object of evaluation is input to the input layer and an analog output is output from the output layer. Therefore, waveform adjustments can also be achieved by causing the module to learn with a waveform to be adjusted (abstract) A second variation of the first embodiment of the present invention will be described with reference to FIG. 10. The second variation is such that is provided additionally with an integrated reference judgment portion 30 for performing judgment by reference and the integrated reference judgment portion 30 has plural sets of neural network modules 231(a), 231(b), . . . connected in parallel. More specifically, there are plural sets of neural network modules 231(a), 231(b), . . . independently provided for waveform data of each position of the object of measurement 10 (waveform 1, waveform 2, . . . , waveform n) and output results from all of them are connected to the integrated reference judgment portion 30. In the second variation, defective positions, overall performance, etc. of the object of measurement 10 can be judged by reference to results of identification, measurement values of the waveform, and the like obtained by the neural network modules 231(a), 231(b), . . . for each position as shown in FIG. 11. The integrated reference judgment portion 30 is made up of logical processes, other neural networks, fuzzy reference processes, and the like. By providing such additional function, a high level and intelligent waveform evaluating apparatus 1 can be provided. (Second Variation of the First Embodiment) First, in step 1 (hereinafter briefly referred to as S1, etc.) the learning is started and the control/computing portion 27 switches so that the input waveform is led to the training conducting system A. In the following S2, the waveform data to be collected from the object of measurement 10 is input to the waveform statistically processing portion 21 so that the waveform statistically processing portion 21 performs statistical processing of the waveform to generate an ideal waveform. In S3, the waveform statistically processing portion 21 determines the waveform which it collects, and performs the collection of the waveform. (First embodiment, An information processing apparatus comprising: a processor, wherein the processor acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, (Claim 1) Teacher signals are formed by the teacher waveform determining portion 22. As to the teacher signals for each of the neural network modules 231, 231, for example representative waveforms considered best for each of the above described elements of characteristics are selected as first ideal waveform modules, while representative waveforms to be identified as unacceptable ones are established as second ideal waveform modules. The learning with the first ideal waveform module is made such that for example 1 is output from one output neuron, while the learning with the second ideal waveform module is made such that the output neuron responds with 0. As another method, one more output neuron responding with 1 or 0 may be prepared. The response is not limited to 1 or 0, but any other values can be used provided that they can be distinguished by a threshold value (First embodiment, performs a determination of the superiority or inferiority for each of the plurality of unknown waveform data based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked, and outputs the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 1), wherein the processor inputs the unknown waveform data to a neural network that has been trained through machine learning based on the teacher waveform data, and performs the determination of the superiority or inferiority based on a result output from the neural network (Claim 7), An information processing method comprising: acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown;
performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 12). A non-transitory computer-readable storage medium storing a program causing a computer to execute: an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; a determination process of performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and an output process of outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 13)
Claim(s) 1, 9, 10, 11, 12, 13 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Hwang et al. (Hwang, H.; Liu, R.; Maxwell, J. T.; Yang, J.; Xu, C. Machine Learning Identifies Abnormal Ca2+ Transients in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. Scientific Reports 2020, 10 (1))
Hwang et al. teaches human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+ transient signals. By applying this pipeline to our Ca2+ transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+ analysis. (abstract) Further, we provide an R library to implement this pipeline, which includes SVM classifiers trained using our Ca2+ transient data as well as functions for peak detection, peak variable quantification, training peak-level SVM classifier, cell variable quantification, training signal-level SVM classifier, and predicting signal abnormality. Our R library is freely available through GitHub and is expected to serve as a convenient tool for people in need of a Ca2+ transient analysis software with high speed and accuracy. (Introduction, last paragraph) and Live cell imaging of intracellular Ca2+ transient was performed using Fluo-4 AM (Thermo Fisher Scientific, F14202). At differentiation day 18, cells were seeded in a 96-well plate at a low density to acquire single-cell Ca2+ transients. At differentiation days 20 to 22, cells were treated with or without arrhythmogenic drugs including TNF-α, ethanol, and melphalan for 3 to 5 days. At differentiation days 23 to 25, cells were acquired for Ca2+ transient signals. Beating hiPSC-CMs were incubated with 10 µM Fluo-4 AM for 25 min at 37℃ followed by a 5 min wash with warm 1 × Normal Tyrode solution (148 mM NaCl, 4 mM KCl, 0.5 mM MgCl2·6H2O, 0.3 mM NaPH2O4·H2O, 5 mM HEPES, 10 mM d-Glucose, 1.8 mM CaCl2·H2O, pH adjusted to 7.4 with NaOH). Fluorescence images were acquired in 1 × Normal Tyrode’s solution immediately after the wash using ImageXpress Micro XLS System (Molecular Devices) with excitation at 488 nm and emission at 515–600 nm at a frequency of 5 frames/sec and 20× magnification for 12 or 32 s. Fluorescence intensity plots from spontaneously beating cells were obtained using MetaXpress software (Molecular Devices) by region of interest measurements (Ca2+ transient assay, An information processing apparatus comprising: a processor, wherein the processor acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, performs a determination of the superiority or inferiority for each of the plurality of unknown waveform data based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked, and outputs the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (Claim 1), wherein the unknown waveform data is a pulse signal output by a cell. (Claim 9), wherein the cell is a myocardial cell. (Claim 10), A drug evaluation method of evaluating a drug based on information output from the information processing apparatus according to claim 9, wherein the unknown waveform data with a high rank of superiority or inferiority is used for drug evaluation. (Claim 11), An information processing method comprising: acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 12), an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; a determination process of performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and an output process of outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (claim 13)
Claims 1, 2, 3, 12, 13 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US5090418A.
US5090418A teaches a time saving automatic screening system for detection, measurement, analysis and plotting of electrocardiographic (ECG) signals employing arrhythmia analysis programs on long term ambulatory (Holter) recordings to assess the ECG signals and categorize the recorded data as either artifact, ventricular ectopic, superventricular ectopic, unknown or normal and to calculate a level of confidence that the category as chosen is a correct assessment. A system is disclosed that thresholds the occurrences of each category as well as the level of confidence to determine if significant abnormalities have occurred in the recording process, the hearts arrhythmia, or the heartbeat morphology. The method and apparatus disclosed make it possible to identify and screen out entire long term (Holter) ECG recordings containing no significant abnormalities in the hearts arrhythmia or the beat morphology. Thus, the cost of Holter scanning is greatly reduced by reserving for manual scanning only those recordings that contain significant abnormalities in the hearts arrhythmia or the beat morphology. (abstract, artifact, ventricular ectopic, superventricular ectopic, unknown or normal examiner considers superior or inferior) Disclosed is a system that consists of a high-speed ECG analyzer which detects heart beats on one, two or three channels using digital techniques for feature vector extraction and noise estimation. Beats having similar feature vectors are grouped into clusters which are then classified to provide for rhythm determination. Confidence testing is performed on all beat and cluster classifications by determining the degree of difficulty in making the respective classifications. (SUMMARY OF THE INVENTION) Cluster Creation 240 is responsible for the generation of clusters in accordance with beat morphology and confidence levels, which is a form of probability (DESCRIPTION OF THE PREFERRED EMBODIMENT) (An information processing apparatus comprising: a processor, wherein the processor acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, performs a determination of the superiority or inferiority for each of the plurality of unknown waveform data based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked, and outputs the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 1), wherein the processor performs clustering on a set including the plurality of teacher waveform data and the plurality of unknown waveform data, and performs the determination of the superiority or inferiority by obtaining, for each of clusters including at least one of the plurality of unknown waveform data, a probability that the unknown waveform data has a superior determination, as a result of the clustering (Claim 2), (wherein the processor obtains the probability based on the number of the teacher waveform data with a superior determination and the number of the teacher waveform data with a superior determination and an inferior determination, for each of the clusters. (Claim 3) An information processing method comprising: acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (claim 12), A non-transitory computer-readable storage medium storing a program causing a computer to execute:
an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown;
a determination process of performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and
an output process of outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (claim 13)
Claims 1, 2, 5, 6, 7, 8, 12, 13 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Sugimoto et al. (Sugimoto, K.; Kon, Y.; Lee, S.; Okada, Y. Detection and Localization of Myocardial Infarction Based on a Convolutional Autoencoder. Knowledge-Based Systems 2019, 178, 123–131.)
Sugimoto et al teaches twelve-lead electrocardiograms (ECG) are widely used for the diagnosis of myocardial infarction (MI). For MI detection and localization, 12 ECG signals should be comprehensively checked through visual observation. This process is time-consuming, requires significant effort, and is prone to inducing errors. Hence, computer-aided automatic detection technology is required. Many existing methods perform MI detection and localization using features extracted from normal and abnormal ECG data. However, abnormal ECG signals show various waveforms for the same heart disease; therefore, it is difficult to extract the waveform features common to all the waveforms. In addition, ECG data is extremely imbalanced, and the minority class, including abnormal ECG data, may not be adequately learned. Because of the difficulty of feature extraction in the imbalanced data, in this study, we propose a new method for MI detection and localization that learns only normal ECG data in the public ECG database. This method is based on a convolutional autoencoder (CAE) model for normal ECG waveforms. The CAE model is constructed for each lead and outputs reconstructed input ECG data if normal ECG data is inputted. Otherwise, the waveform is distorted and outputted. MI detection and localization is performed by a k-nearest neighbor (k-NN) classifier using an error vector whose dimension corresponds to each lead and whose element is a degree of deviation between the normal ECG data and the output waveform. In the experiments, the classification performance of the proposed method was evaluated using 353640 beats obtained from the ECG data of MI patients (10 class infarct sites) and healthy subjects. Consequently, the proposed scheme demonstrated a classification performance higher than or comparable to that of existing methods, and the false positive and false negative rates could be reduced compared to existing methods. In this study, the following beats were excluded: the first and last beat of the ECG data, the beats showing arrhythmia , and the beats that were impossible to detect owing to noise. Finally, the amplitude value of each beat was normalized with the z-score. (abstract, an information processing apparatus comprising: a processor, wherein the processor acquires a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown, performs a determination of the superiority or inferiority for each of the plurality of unknown waveform data based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked, and outputs the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (Claim 1), wherein the processor performs clustering on a set including the plurality of teacher waveform data and the plurality of unknown waveform data, and performs the determination of the superiority or inferiority by obtaining, for each of clusters including at least one of the plurality of unknown waveform data, a probability that the unknown waveform data has a superior determination, as a result of the clustering (Claim 2), wherein the processor performs the clustering by a k-medoids method or a k-means method (Claim 5), wherein the processor performs a filtering process of excluding unknown waveform data that does not satisfy an evaluation criterion from the set or lowering a rank in terms of the superiority or inferiority. (Claim 6), wherein the processor inputs the unknown waveform data to a neural network that has been trained through machine learning based on the teacher waveform data, and performs the determination of the superiority or inferiority based on a result output from the neural network.(Claim 7), wherein the processor inputs the unknown waveform data to an encoder of an auto-encoder that has been trained through machine learning based on the teacher waveform data with a superior determination, and then performs the determination of the superiority or inferiority based on a difference between waveform data restored by a decoder and the unknown waveform data input to the encoder (Claim 8) An information processing method comprising: acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown; performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner. (claim 12), A non-transitory computer-readable storage medium storing a program causing a computer to execute:
an acquisition process of acquiring a plurality of unknown waveform data of which a determination result of superiority or inferiority based on similarity to an ideal waveform is unknown;
a determination process of performing a determination of the superiority or inferiority for each of the plurality of unknown waveform data through machine learning based on a plurality of teacher waveform data to which the determination result of the superiority or inferiority is linked; and
an output process of outputting the superiority or inferiority of the plurality of unknown waveform data in a comparable manner (claim 13)
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US5630019A in further view of US5090418A in further view of Hwang et al. in view of Sugimoto et al. as applied to claims 1-3, 5-13 above. The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claim 4, Hwang et al. teaches the cell normality labels based on peak normality assessments obtained by our improved Analytical Algorithm and SVM-LOOCV approach are considered as cell variables. We consider additional cell variables as follows: proportion of abnormal peaks per signal (prop_abnormal), variance of peak amplitude per signal (var_A), variance of peak distances per signal (var_delta), and variance of peak areas per signal (var_R). These cell variables are centered and standardized and then used as predictive features to train a cell-level SVM classifier to predict cell abnormality, where outcomes are taken as human-expert assessments about cell normality. This trained cell-level SVM classifier can then be used to predict cell normality for additional independent signals. (Train cell-level SVM classifier (wherein the probability is represented by a value obtained by dividing the number of the teacher waveform data with the superior determination by the number of the teacher waveform data with the superior determination and the inferior determination, (Claim 4)
With respect to the limitations of Claim 4, US5630019A teaches by causing the neural network 231 to make the learning as described above, the neural network 231 comes to have waveform interpolating function. When, as with the present embodiment, the a waveform coming between 0.4 and 0.6 is taken as acceptable, the waveform may be adjusted to 0.5. If it is desired to make a finer adjustment, the ranges may be set more finely. Although the range 1 was set to 0.5, the range 3 to 0.4, the range 2 to 0.6, the range 4 to 0.7, the range 5 to 0.3, and so on, the set values are not limited to them but they may be changed only they are sequential in order of sandwiching. Further, by establishing the teacher signals such that the waveform whose amplitude is within the range 1 is set to 1.0, the waveforms whose amplitudes are within the range 2 and the range 3 are set to 0.9, and the waveforms whose amplitudes are within the range 4 and the range 5 are set to 0.8, and by causing the neural network to learn the same, it may be arranged, for example by setting the range of adjustment to be from 0.9 to 1.0, to take the evaluation and adjustment is better the closer to 1.0 it is ([Third Embodiment] the processor ranks and outputs the superiority or inferiority of the plurality of unknown waveform data based on the probability in a comparable manner (Claim 4)
A person of ordinary skill in the art would be motivated to combine US5630019A in further view of US5090418A in further view of Hwang et al. in view of Sugimoto et al. as all works are in the same field of endeavor of signal analysis. Applicant is just putting together different methods of analysis previously used. There is a reasonable expectation of success because each part works individually therefore, they are expected to work together. Applicant just combined several well known signal analysis methods.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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