DETAILED ACTION
Notices to Applicant
This communication is a First Action Non-Final on the merits. Claims 1-13 as filed 01/23/2025, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application is a National Stage Entry of International Application No. PCT/KR2023/012039 filed August 14, 2023, which claims priority from Korean Application No. 10-2022-0109506 filed August 30, 2022.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a feature extraction unit” and “a clustering management unit,” in claim 8.
Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 8-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 8 recites the elements “a feature extraction unit” and “a clustering management unit,” The present Application Specification fails to describe each of these elements with sufficient written description as to their corresponding structure for performing their respectively claimed functions. See MPEP 2161.01(I). That is, the present Application Specification, under broadest reasonable interpretation, at most provides at [0048] that each of the above units “[a]according to one embodiment of the invention … may be program modules to communicate with an external system (not shown). The program modules may be included in the biosignal analysis system 200 in the form of operating systems, application program modules, and other program modules, while they may be physically stored in a variety of commonly known storage devices.” As a result, claim 8 and dependent claims 9-13 are redetected as failing to comply with the written description requirement.
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 8-13 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.
Claim limitations “a feature extraction unit” and “a clustering management unit,” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The present Application Specification, under broadest reasonable interpretation, at most provides at [0048] that each of the above units “[a]according to one embodiment of the invention … may be program modules to communicate with an external system (not shown). The program modules may be included in the biosignal analysis system 200 in the form of operating systems, application program modules, and other program modules, while they may be physically stored in a variety of commonly known storage devices.” Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-7 are drawn to a method for assisting in biosignal analysis using clustering, which is within the four statutory categories (i.e. method).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
1. (Original) A method for assisting in biosignal analysis using clustering, the method comprising the steps of:
extracting features from a plurality of pieces of biosignal data for a biosignal, and
generating a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features; and
performing clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions. That is, nothing in the claim limitations precludes the steps from the rules or instructions for managing personal behavior or interactions between people for biosignal analysis using clustering. For example, extracting features from a plurality of pieces of biosignal data for a biosignal, and generating a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features; and performing clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors in the context of this claim encompasses the rules or instructions for biosignal analysis through rules or instructions for data analysis through extracting data features, generating vectors, and performing clustering. If a claim limitation, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claim limitations, such as “performing clustering…” also covers steps may practically be performed in the mind through observation, evaluation, judgment and/or opinion of biosignal data. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim does not recite any additional elements for performing the claim limitations. The claim is directed to an abstract idea.
The claim does not any additional elements such that the claim recites significantly more than the judicial exception. Accordingly, the claim is not patent eligible.
Dependent claims 2-7 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what biosignal data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the identified abstract idea(s). Dependent claim 7 recites the additional element of “a non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of claim 1,” however, this additional element amounts to a high-level recitation of a generic computer component such that the claim limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Furthermore, the additional element does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claims 8-13 are drawn to a system for assisting in biosignal analysis using clustering, which is within the four statutory categories (i.e. machine).
Independent Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 8 recites:
8. (Original) A system for assisting in biosignal analysis using clustering, the system comprising:
a feature extraction unit configured to extract features from a plurality of pieces of biosignal data for a biosignal, and
generate a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features; and
a clustering management unit configured to perform clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors.
The claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic components. That is, but for the recitation of the above bolded elements such as “a feature extraction unit” and “a clustering management unit,” nothing in the claim limitations precludes the steps from the rules or instructions for managing personal behavior or interactions between people for biosignal analysis using clustering. For example, extract features from a plurality of pieces of biosignal data for a biosignal, and generate a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features; and perform clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors in the context of this claim encompasses the rules or instructions for biosignal analysis through rules or instructions for data analysis through extracting data features, generating vectors, and performing clustering. If a claim limitation, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claim limitations, such as “performing clustering…” also covers steps may practically be performed in the mind through observation, evaluation, judgment and/or opinion of biosignal data. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using “a feature extraction unit” and “a clustering management unit,” to perform the claim limitations. The additional elements in each of the steps are recited at a high-level of generality (i.e., a feature extraction unit and a clustering management unit that may be in the form of operating systems, application program modules, and other program modules, while they may be physically stored in a variety of commonly known storage devices (Application Specification [0048])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using “a feature extraction unit” and “a clustering management unit,” to perform the claim limitations amounts to no more than mere instructions to apply the judicial exception using a generic component. (i.e., a feature extraction unit and a clustering management unit that may be in the form of operating systems, application program modules, and other program modules, while they may be physically stored in a variety of commonly known storage devices (Application Specification [0048])). Mere instructions to apply an exception using on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 8-13 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the biosignal data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the identified abstract idea(s). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
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)(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.
Claims 1, 3, 8, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 2021/0298626 A1 (hereinafter “Yu et al.”).
RE: Claim 1 Yu et al. teaches the claimed:
1. (Original) A method for assisting in biosignal analysis using clustering, the method comprising the steps of: extracting features from a plurality of pieces of biosignal data for a biosignal ((Yu et al., [0014]) (The pre-trained neural network is trained on large amounts of available ECG data to generate a first feature set to extract features from the ECG data that are lead-independent and are generalized across the available ECG data utilized for training)), and
generating a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features ((Yu et al., [0030]) (Once a user input is provided that is to be learned, a feature vector is defined based on the user input. The feature vector is then applied across at least a portion of the patient's ECG data to generate revised clustered ECG data)); and
performing clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors ((Yu et al., [0030]) (Depending on the feature vector and/or user instruction, application of the feature vector to the patient's ECG data may be different. Generating the revised clustered ECG data may include, for example, labeling all clusters based on a labeling input provided by a user. As another example, generating the revised clusters may include re-clustering some or all of the raw ECG data based on the feature vector generated based on user input providing revised clustering criteria)).
RE: Claim 3 Yu et al. teaches the claimed:
3. (Currently Amended) The method of claim 1, wherein in the step of performing the clustering, clustering on a plurality of pieces of biosignal data for a biosignal corresponding to a first classification is performed separately from clustering on a plurality of pieces of biosignal data for a biosignal corresponding to a second classification ((Yu et al., [0004], [0016], [0024]) (The patient's ECG data is then clustered into a number of clusters based on the first feature set and the second feature set to generate clustered ECG data. The clustered ECG data is presented to a user via a user interface, and user input is received from the user via the user interface regarding the clustered ECG data. A feature vector is defined based on the user input and the feature vector is applied to at least a portion of the patient's ECG data to generate revised clustered ECG data; The user provides user input to edit the clustered ECG data 38 or other classification information generated based thereon)).
RE: Claim 8 Yu et al. teaches the claimed:
8. (Original) A system for assisting in biosignal analysis using clustering, the system comprising:
a feature extraction unit configured to extract features from a plurality of pieces of biosignal data for a biosignal ((Yu et al., [0014]) (The pre-trained neural network is trained on large amounts of available ECG data to generate a first feature set to extract features from the ECG data that are lead-independent and are generalized across the available ECG data utilized for training)), and
generate a plurality of feature vectors for the plurality of pieces of biosignal data, respectively, on the basis of the extracted features ((Yu et al., [0030]) (Once a user input is provided that is to be learned, a feature vector is defined based on the user input. The feature vector is then applied across at least a portion of the patient's ECG data to generate revised clustered ECG data)); and
a clustering management unit configured to perform clustering on the plurality of pieces of biosignal data with reference to the plurality of feature vectors ((Yu et al., [0030]) (Depending on the feature vector and/or user instruction, application of the feature vector to the patient's ECG data may be different. Generating the revised clustered ECG data may include, for example, labeling all clusters based on a labeling input provided by a user. As another example, generating the revised clusters may include re-clustering some or all of the raw ECG data based on the feature vector generated based on user input providing revised clustering criteria)).
RE: Claim 10 Yu et al. teaches the claimed:
10. (Currently Amended) The system of claim 8, wherein the clustering management unit is configured to perform clustering on a plurality of pieces of biosignal data for a biosignal corresponding to a first classification separately from clustering on a plurality of pieces of biosignal data for a biosignal corresponding to a second classification ((Yu et al., [0004], [0016], [0024]) (The patient's ECG data is then clustered into a number of clusters based on the first feature set and the second feature set to generate clustered ECG data. The clustered ECG data is presented to a user via a user interface, and user input is received from the user via the user interface regarding the clustered ECG data. A feature vector is defined based on the user input and the feature vector is applied to at least a portion of the patient's ECG data to generate revised clustered ECG data; The user provides user input to edit the clustered ECG data 38 or other classification information generated based thereon)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 4-5, 7, 9, and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0298626 A1 (hereinafter “Yu et al.”) in view of U.S. 2021/0295207 A1 (hereinafter “Neumann”).
RE: Claim 2 Yu et al. teaches the claimed:
2. (Currently Amended) The method of claim 1.
Yu et al. fails to explicitly teach, but Neumann teaches the claimed:
wherein in the step of extracting the features, the plurality of pieces of biosignal data are normalized and the features are extracted from the plurality of pieces of normalized biosignal data ((Neumann, [0077]) (Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be "normalized," or divided by a "length" attribute, such as a length attribute 1 as derived using a Pythagorean norm: 1~, where a, is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the normalizing vectors of patient attribute data along an equivalent scale of values as taught by Neumann within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of classifying known biological extraction data for generating efficient response to inquiries regarding education requests pertaining to a user of the biological extraction (Neumann at [0001]-[0003], [0077]-[0078]).
RE: Claim 4 Yu et al. teaches the claimed:
4. (Original) The method of claim 1,
Yu et al. fails to explicitly teach, but Neumann teaches the claimed:
wherein in the step of performing the clustering, the clustering on the plurality of pieces of biosignal data is performed with reference to a distance map between the feature vectors ((Neumann, [0077[) (generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the distance calculation of vectors between data entry clusters as taught by Neumann within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of classifying known biological extraction data for generating efficient response to inquiries regarding education requests pertaining to a user of the biological extraction (Neumann at [0001]-[0003], [0077]-[0078]).
RE: Claim 5 Yu et al. and Neumann teach the claimed:
5. (Original) The method of claim 4, wherein in the step of performing the clustering, the clustering on the plurality of pieces of biosignal data is performed with further reference to a distance map between raw data of the plurality of pieces of biosignal data ((Yu et al., [0024]) (The user-guided clustering module implements a clustering algorithm to cluster the raw Holter ECG data into a number of clusters based on the first feature set and the second feature set. For example, the clustering algorithm may beak-means clustering algorithm for vector quantization of the patient's raw ECG data into "k" number of clusters based on "n" number of features, where each feature belongs to the cluster with the nearest mean)).
RE: Claim 7 Yu et al. teaches the claimed:
7. (Original) … a computer program for executing the method of claim 1 ((Yu et al., [0023]) (The computing system includes a processing system and a storage system storing the user-guided clustering module executable by the processing system in order to perform the functions described herein)).
Yu et al. fails to explicitly teach, but Neumann teaches the claimed:
A non-transitory computer-readable recording medium having stored thereon ((Neumann, [0098]) (A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the non-transitory computer-readable storage medium as taught by Neumann within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of classifying known biological extraction data for generating efficient response to inquiries regarding education requests pertaining to a user of the biological extraction (Neumann at [0001]-[0003], [0077]-[0078], [0098]).
RE: Claim 9 Yu et al. teaches the claimed:
9. (Original) The system of claim 8.
Yu et al. fails to explicitly teach, but Neumann teaches the claimed:
wherein the feature extraction unit is configured to normalize the plurality of pieces of biosignal data and extract the features from the plurality of pieces of normalized biosignal data ((Neumann, [0077]) (Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be "normalized," or divided by a "length" attribute, such as a length attribute 1 as derived using a Pythagorean norm: 1~, where a, is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the normalizing vectors of patient attribute data along an equivalent scale of values as taught by Neumann within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of classifying known biological extraction data for generating efficient response to inquiries regarding education requests pertaining to a user of the biological extraction (Neumann at [0001]-[0003], [0077]-[0078]).
RE: Claim 11 Yu et al. teaches the claimed:
11. (Original) The system of claim 8.
Yu et al. fails to explicitly teach, but Neumann teaches the claimed:
wherein the clustering management unit is configured to perform the clustering on the plurality of pieces of biosignal data with reference to a distance map between the feature vectors ((Neumann, [0077[) (generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the distance calculation of vectors between data entry clusters as taught by Neumann within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of classifying known biological extraction data for generating efficient response to inquiries regarding education requests pertaining to a user of the biological extraction (Neumann at [0001]-[0003], [0077]-[0078]).
RE: Claim 12 Yu et al. and Neumann teach the claimed:
12. (Original) The system of claim 11, wherein the clustering management unit is configured to perform the clustering on the plurality of pieces of biosignal data with further reference to a distance map between raw data of the plurality of pieces of biosignal data ((Yu et al., [0024]) (The user-guided clustering module implements a clustering algorithm to cluster the raw Holter ECG data into a number of clusters based on the first feature set and the second feature set. For example, the clustering algorithm may beak-means clustering algorithm for vector quantization of the patient's raw ECG data into "k" number of clusters based on "n" number of features, where each feature belongs to the cluster with the nearest mean)).
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0298626 A1 (hereinafter “Yu et al.”) in view of U.S. 2021/0295207 A1 (PRO App. No. 63/291,528) (hereinafter “Samadani et al.”).
RE: Claim 6 Yu et al. teaches the claimed:
6. (Original) The method of claim 1.
Yu et al. fails to explicitly teach, but Samadani et al. teaches the claimed:
wherein in the step of performing the clustering, hierarchical clustering is performed on the plurality of pieces of biosignal data, and a number of clusters to be outputted as a result of the clustering is determined on the basis of inspection efficiency for the biosignal data ((Samadani et al., [0081]) (the hierarchy level and the number of clusters can be determined using goodness of clustering metrics such as silhouette coefficients and/or elbow method on the sum of squared errors (the square of the Euclidean distance of the point to its cluster head or cluster centroid). In this case, clusters are formed based on either a preset level on these metrics or by users inspecting the metrics and choosing a clustering results with a favorable goodness metric as it fits their needs. The level of hierarchy and the number of clusters may also be determined using goodness of clustering metrics))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine a clustering and hierarchy formation system for hospitals to guide their clinical decision support system with inspection metrics as taught by Samadani et al. within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of effectively providing clinical decision support tools with increased accuracy and performance (Samadani et al. at [0001]-[0003], [0077]-[0078]).
RE: Claim 13 Yu et al. teaches the claimed:
13. (Original) The system of claim 8.
Yu et al. fails to explicitly teach, but Samadani et al. teaches the claimed:
wherein the clustering management unit is configured to perform hierarchical clustering on the plurality of pieces of biosignal data, and determine a number of clusters to be outputted as a result of the clustering on the basis of inspection efficiency for the biosignal data ((Samadani et al., [0081]) (the hierarchy level and the number of clusters can be determined using goodness of clustering metrics such as silhouette coefficients and/or elbow method on the sum of squared errors (the square of the Euclidean distance of the point to its cluster head or cluster centroid). In this case, clusters are formed based on either a preset level on these metrics or by users inspecting the metrics and choosing a clustering results with a favorable goodness metric as it fits their needs. The level of hierarchy and the number of clusters may also be determined using goodness of clustering metrics))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine a clustering and hierarchy formation system for hospitals to guide their clinical decision support system with inspection metrics as taught by Samadani et al. within the method and system for processing and clustering patient ECG data as taught by Yu et al. with the motivation of effectively providing clinical decision support tools with increased accuracy and performance (Samadani et al. at [0001]-[0003], [0077]-[0078]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. 12,248,383 B1 teaches a monitoring program may detect a particular condition, and the system may evaluate the monitoring data and determine that the particular condition justifies enhanced monitoring, e.g., to investigate the causes, effects, or rate of occurrence of the particular condition (Col 1, Lines 58-62);
U.S. 2020/0152320 A1 teaches characteristics data and the administration data of each patient can be used to compute a patient features vector for the patient ([0005]); and
U.S. 2020/0058403 A1 teaches in which sufficient primary patient data has been collected, the clustering module generates an aggregate dataset comprised solely of primary patient data, specifically feature vectors of data from primary patients ([0106]).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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