DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/25/2024 and 08/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is: “a device for performing…” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112a
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.
Claim 8 is 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. Paragraph 0033 in the disclosure mentions a conventional data processing device to perform the method is not properly described in the application filed. It is not clear what or how the device performs the claimed method. For examination purposes, the device performing the methods is a generic processor.
Claim Rejections - 35 USC § 112b
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.
Claim limitation “device for performing…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. the disclosure is devoid of any structure that performs the function in the claim. 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.
For examination purposes, the device performing the claimed method will be interpreted as a generic processor.
Claim Rejections - 35 USC § 101 CRM
Claim(s) 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim is directed to a signal per se.
In reference to claim 9, a “A computer program product comprising instructions” is not defined in the specification. Using BRI, “A computer program product comprising instructions” is directed to a signal per se.
Claim Rejections - 35 USC § 101 Abstract Idea
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“executing a method for clustering data points in the input data sets in order to obtain data point clusters;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could perform a method for clustering data points in the input data set in order to obtain data point clusters.
“determining a cluster model quality for each cluster, wherein the cluster model quality indicates an accuracy of the trained system model at the cluster data points with regard to the label assigned to the data points;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a cluster model quality for each cluster.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer-implemented method for training a data-based system model, comprising: providing training data sets for training the system model, wherein the training data sets each comprise an input data set and a label;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“training the system model using at least some of the training data sets;” is 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 (MPEP 2106.05(f)).
“depending on the cluster model quality of each cluster, providing additional training data for the relevant cluster;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and further training the system model using the additional training data.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer-implemented method for training a data-based system model, comprising: providing training data sets for training the system model, wherein the training data sets each comprise an input data set and a label;” (well-understood, routine, conventional MPEP 2106.05(d))
“training the system model using at least some of the training data sets;” is 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 (MPEP 2106.05(f)).
“depending on the cluster model quality of each cluster, providing additional training data for the relevant cluster;” (well-understood, routine, conventional MPEP 2106.05(d))
“and further training the system model using the additional training data.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method according to claim 1, wherein the system model is provided as a neural network or as a data-based probabilistic regression model.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method according to claim 1, wherein the system model is provided as a neural network or as a data-based probabilistic regression model.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method according to claim 1, wherein the input data sets comprise at least one signal time series, wherein the clustering method is performed on data points of the input data sets which are determined at least by the at least one signal time series of the input data sets.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method according to claim 1, wherein the input data sets comprise at least one signal time series, wherein the clustering method is performed on data points of the input data sets which are determined at least by the at least one signal time series of the input data sets.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method according to claim 1,wherein the clustering method is configured to detect only clusters having a predetermined minimum number of data points as clusters, and wherein the clustering method considers a distribution density of the data points.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 5:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method according to claim 1,wherein the clustering method comprises: creating a neighborhood graph having an edge weighting determined from a predetermined distance metric;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
“executing a renormalization of the edge weighting in the neighborhood graph according to a local distribution density of the data points;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
“creating a minimum span tree from the neighborhood graph;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
“and extracting clusters from the spanning tree to aggregate the data points into clusters.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method according to claim 1, wherein the training data sets are partitioned,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“wherein the clustering method is performed on the partitioned training data sets separately,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“wherein partitioning is performed depending on at least one state variable in the input data sets,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“and wherein partitioning is performed with respect to predetermined value ranges of the at least one state variable and/or the label.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method according to claim 1,wherein determining the cluster model quality for each cluster comprises determining an average value or a median value of differences between the model output of the system model at the data points of the relevant cluster and the labels respectively associated with the data points.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 8 incorporates abstract idea of claim 1
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A device for performing the method according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A device for performing the method according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 9 incorporates abstract idea of claim 1
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer program product comprising instructions that, when the program is executed by a computer, prompt said computer to carry out the steps of the method according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer program product comprising instructions that, when the program is executed by a computer, prompt said computer to carry out the steps of the method according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 10 incorporates abstract idea of claim 1.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A machine-readable storage medium comprising instructions which, when performed by a computer, prompt the computer to perform the method steps according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A machine-readable storage medium comprising instructions which, when performed by a computer, prompt the computer to perform the method steps according to claim 1.” is 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 (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claim(s) 1-4, and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Matthew Hagen et al; US 20200387755 A1 filed on Jun 5, 2020 (hereinafter “Hagen”) in view of Estepan Meliksetian et al; US 20230094000 A1 filed Sep 22, 2021 (hereinafter “Meliksetian”) in further view of Kenneth Liam Kiemele et al; US 20190220697 A1 filed on Jan 12, 2018 (hereinafter “Kiemele”)
Regarding claim 1, Hagen teaches A computer-implemented method for training a data-based system model, comprising: providing training data sets for training the system model, wherein the training data sets each comprise an input data set and a label; (Hagen Paragraph 0021; “The method 200 may include a step 202 that includes obtaining training data and testing data. Both the training data and the testing data may include a plurality of data points and may be obtained from a labeled set stored in a database or other data store. Each data point in the training data may include a paired example (e.g., an image) and classification or category for that example.” Examiner notes that training data sets for training the system model (Both the training data and the testing data) is provided/obtained, wherein the training data sets each comprise input data set and a label (Each data point in the training data may include a paired example (e.g., an image) and classification or category for that example))
training the system model using at least some of the training data sets; (Hagen Paragraph 0022; “The method 200 may further include a step 204 that includes training a machine learning algorithm with the training data (e.g., the full training data set) to create embeddings.” Examiner notes that the system model (machine learning algorithm) is trained using at least some of the training data sets (the training data))
executing a method for clustering data points in the input data sets in order to obtain data point clusters; (Hagen Paragraph 0023; “The method 200 may further include a step 206 that includes applying a clustering algorithm to the training data... The output of the clustering algorithm may be a data set comprising one or more classification clusters, based on the classification(s) that the machine learning tool is intended to output.” Examiner notes that a method for clustering data points (clustering algorithm) is executed/applied in the input data sets (training data) in order to obtain data point clusters (output of the clustering algorithm may be a data set comprising one or more classification clusters))
Hagen does not teach determining a cluster model quality for each cluster, wherein the cluster model quality indicates an accuracy of the trained system model at the cluster data points with regard to the label assigned to the data points;
However, Meliksetian does teach determining a cluster model quality for each cluster, wherein the cluster model quality indicates an accuracy of the trained system model at the cluster data points with regard to the label assigned to the data points; (Meliksetian Paragraph 0031; “the ML computer model instances are then executed on the group of datasets to generate predictions/classifications. The performance of the ML computer model instances is then evaluated by generating various evaluation metrics, e.g., mean absolute percentage error, false alarm rate, hit rate, root mean square error, etc., thereby evaluating each ML computer model instance’s predictions/classifications against actual or ground truth data… This process is performed for each grouping of datasets such that a set of ML computer model instances meeting predetermined levels of performance are generated for each grouping.” Examiner notes that for each cluster (each grouping of datasets) a cluster model quality (performance of the ML computer model) is determined, wherein the cluster model quality indicates an accuracy (various evaluation metrics) of the trained model at the cluster data points (each grouping of datasets) with regard to the label assigned to the data points (actual or ground truth data))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen and Meliksetian. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. One of ordinary skill would have motivation to combine Hagen and Meliksetian to provide an improved computing tool and improved computing tool process that operates to automate ML computer model generation, training, and testing “The illustrative embodiments provide an improved computing tool and improved computing tool process that operates to automate ML computer model generation, training, testing, and final model selection while taking into account thresholding of individual features of the ML computer models and sparsity of input datasets.” (Meliksetian Paragraph 0024).
Hagen in view of Meliksetian does not teach depending on the cluster model quality of each cluster, providing additional training data for the relevant cluster;
and further training the system model using the additional training data.
However, Kiemele does teach depending on the cluster model quality of each cluster, providing additional training data for the relevant cluster; (Kiemele Paragraph 0063; “In the event that the new ML model 334 is not determined in the testing mode to be effective for the environment 302, the sensor device 310 and the training device 312 may return to the retraining mode to generate additional location-specific training data 370” Examiner notes that depending on the cluster model quality of each cluster (not determined in the testing mode to be effective for the environment), providing additional training data for relevant cluster (generate additional location-specific training data ))
and further training the system model using the additional training data. (Examiner refers to previous mapping to show that system model (ML model) is trained using the additional training data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen, Meliksetian, and Kiemele. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. Kiemele teaches automated localized machine learning training. One of ordinary skill would have motivation to combine Hagen, Meliksetian, and Kiemele to establish a more reliable ML model “The development of an improved ML model training process is necessary to establish more reliable ML models in these and other types of situations.” (Kiemele Paragraph 0002).
Regarding claim 2, Hagen teaches The method according to claim 1, wherein the system model is provided as a neural network or as a data-based probabilistic regression model. (Hagen Paragraph 0050; “Each of the two experimental data sets was subjected to the same iterative process utilizing a ResNet-50 neural network with 4 separate experiments.” Examiner notes that the system model is provided as a neural network (ResNet-50 neural network))
Regarding claim 3, Hagen teaches wherein the clustering method is performed on data points of the input data sets [which are determined at least by the at least one signal time series of the input data sets]. (Hagen Paragraph 0023; “The method 200 may further include a step 206 that includes applying a clustering algorithm to the training data... The output of the clustering algorithm may be a data set comprising one or more classification clusters, based on the classification(s) that the machine learning tool is intended to output.” Examiner notes that clustering method is performed on data points of the input data sets (training data))
Hagen does not teach The method according to claim 1, wherein the input data sets comprise at least one signal time series,
However, Kiemele does teach The method according to claim 1, wherein the input data sets comprise at least one signal time series, (Kiemele Paragraph 0024; “for generating a machine learning (ML) model 170 effective for detecting events from a first type of physical sensor data 122 from a first physical sensor 120 with the assistance of a second physical sensor 130 for generating location-specific training data 152.” Examiner notes that the input data sets (training data) comprise at least one signal time series (sensor data is data collected over time meaning time series data))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen, Meliksetian, and Kiemele. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. Kiemele teaches automated localized machine learning training. One of ordinary skill would have motivation to combine Hagen, Meliksetian, and Kiemele to establish a more reliable ML model “The development of an improved ML model training process is necessary to establish more reliable ML models in these and other types of situations.” (Kiemele Paragraph 0002).
Regarding claim 4, Hagen teaches The method according to claim 1, wherein the clustering method is configured to detect only clusters having a predetermined minimum number of data points as clusters, (Hagen Paragraph 0040; “The clustering algorithm may utilize a distance parameter, Epslion (Eps), a minimum number of points per cluster parameter (MinPts).” Examiner notes that the clustering method (clustering algorithm) is configured to detect only clusters having a predetermined minimum number of data points as clusters (a minimum number of points per cluster parameter))
and wherein the clustering method considers a distribution density of the data points. (Hagen Paragraph 0039; “a density-based clustering algorithm in which the number of clusters are automatically determined” Examiner notes that the clustering method considers a distribution density of the data points (density-based clustering algorithm))
Regarding claim 7, Hagen does not teach The method according to claim 1,wherein determining the cluster model quality for each cluster comprises determining an average value or a median value of differences between the model output of the system model at the data points of the relevant cluster and the labels respectively associated with the data points.
However, Meliksetian does teach The method according to claim 1,wherein determining the cluster model quality for each cluster comprises determining an average value or a median value of differences between the model output of the system model at the data points of the relevant cluster and the labels respectively associated with the data points. (Meliksetian Paragraph 0031; “the ML computer model instances are then executed on the group of datasets to generate predictions/classifications. The performance of the ML computer model instances is then evaluated by generating various evaluation metrics, e.g., mean absolute percentage error, false alarm rate, hit rate, root mean square error, etc., thereby evaluating each ML computer model instance’s predictions/classifications against actual or ground truth data… This process is performed for each grouping of datasets such that a set of ML computer model instances meeting predetermined levels of performance are generated for each grouping.” Examiner notes that an average value of differences (root mean square error) between the model output (predictions/classifications) of the system model at the data points of the relevant cluster (performed for each grouping of datasets) and the labels respectively associated with the data points (actual or ground truth data))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen and Meliksetian. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. One of ordinary skill would have motivation to combine Hagen and Meliksetian to provide an improved computing tool and improved computing tool process that operates to automate ML computer model generation, training, and testing “The illustrative embodiments provide an improved computing tool and improved computing tool process that operates to automate ML computer model generation, training, testing, and final model selection while taking into account thresholding of individual features of the ML computer models and sparsity of input datasets.” (Meliksetian Paragraph 0024).
Regarding claim 8, Hagen teaches A device for performing the method according to claim 1. (Hagen Paragraph 0013; “the functional modules 106, 108, 110, 112 of the machine learning system 104 may be embodied in a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform the functionality of one or more of the functional modules and/or other functionality of this disclosure.”)
Regarding claim 9, Hagen teaches A computer program product comprising instructions that, when the program is executed by a computer, prompt said computer to carry out the steps of the method according to claim 1. ((Hagen Paragraph 0013; “the functional modules 106, 108, 110, 112 of the machine learning system 104 may be embodied in a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform the functionality of one or more of the functional modules and/or other functionality of this disclosure.”))
Regarding claim 10, Hagen teaches A machine-readable storage medium comprising instructions which, when performed by a computer, prompt the computer to perform the method steps according to claim 1. ((Hagen Paragraph 0013; “the functional modules 106, 108, 110, 112 of the machine learning system 104 may be embodied in a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform the functionality of one or more of the functional modules and/or other functionality of this disclosure.”))
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Matthew Hagen et al; US 20200387755 A1 filed on Jun 5, 2020 (hereinafter “Hagen”) in view of Estepan Meliksetian et al; US 20230094000 A1 filed Sep 22, 2021 (hereinafter “Meliksetian”) in further view of Kenneth Liam Kiemele et al; US 20190220697 A1 filed on Jan 12, 2018 (hereinafter “Kiemele”) in further view of Gaurav Mishra et al; “RDMN:ARelative Density Measure Based on MST Neighborhood for Clustering Multi-Scale Datasets” published January 2022 (hereinafter “Mishra”)
Regarding claim 5, Hagen does not teach The method according to claim 1,wherein the clustering method comprises: creating a neighborhood graph having an edge weighting determined from a predetermined distance metric;
executing a renormalization of the edge weighting in the neighborhood graph according to a local distribution density of the data points;
creating a minimum span tree from the neighborhood graph;
and extracting clusters from the spanning tree to aggregate the data points into clusters.
However, Mishra does teach The method according to claim 1,wherein the clustering method comprises: creating a neighborhood graph having an edge weighting determined from a predetermined distance metric; (Mishra Page 421 Paragraph 5; “Let t be the number of rounds of MSTs. The t-rounds of MST is constructed as follows [13]: Let G... be the complete weighted undirected graph of the dataset where vertex set V denotes the points and edge set E is the weighted edges between every pair of points. The weight of an edge … is the euclidean distance between u and v. The first round of MST of G, say T1 is computed. Then the consecutive MSTs are computed by removing the edges of the MSTs computed in the previous rounds, i.e., MST Ti of the graph … is constructed where 2 i t and Ej denotes the edges of jth round MST Tj. Then t-round of MST neighborhood graph is defined as Gt.” Examiner notes that a neighborhood graph (MST neighborhood graph) is created having an edge weighting (weighted edges) determined from a predetermined distance metric (euclidean distance between u and v))
executing a renormalization of the edge weighting in the neighborhood graph according to a local distribution density of the data points; (Mishra Page 421 Paragraph 6; “At certain iteration, union of MST edges may not affect the diameter of the neighborhood graph which indicates that the two graphs in the consecutive iterations are structurally similar. With this intuition, we use diameter of two subsequent rounds as the stopping criterion. We empirically observed that, t ¼ 3 is sufficient to capture the neighborhood information of data points. Nearest neighbor of a point u is the vertices which are adjacent to u. It is defined as follows:… Density of a data point should be positive and also it should be inversely proportional to the total edge weights with its neighbors. With this observations, we define a den sity measure based on NN as follows:” Examiner notes that a renormalization of the edge weighting (total edge weights) in the neighborhood graph according to a local distribution density of the data points (Density of a data point should be positive and also it should be inversely proportional to the total edge weights))
creating a minimum span tree from the neighborhood graph; (Mishra Page 423 Paragraph 1; “Then the dense regions (sub-clusters) are identified based on RDMN values. In the third step, a MST of the complete graph is constructed considering the centroid of all the dense regions as vertices.” Examiner notes that a minimum span tree (MST is constructed) from the neighborhood graph (complete graph))
and extracting clusters from the spanning tree to aggregate the data points into clusters. (Mishra Page 424 Paragraph 1; “Then we compute MST, Tm, of the graph Gm. Removing an MST edge can partition the tree into two disjoint trees where each tree may contain several sub-clusters.” Examiner notes that clusters (sub-clusters) are extracted from the spanning tree (MST) to aggregate the data points into clusters)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen, Meliksetian, Kiemele, and Mishra. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. Kiemele teaches automated localized machine learning training. Mishra teaches a new Relative Density measure based on MST Neighborhood graph (RDMN) to compute the density of data points. One of ordinary skill would have motivation to combine Hagen, Meliksetian, and Kiemele to outperform other popular clustering techniques “Experimental results on synthetic and real datasets demonstrate that the proposed algorithm outperforms other popular clustering techniques in terms of cluster quality, accuracy, and robustness against noise and detecting the outliers.” (Mishra Abstract).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Matthew Hagen et al; US 20200387755 A1 filed on Jun 5, 2020 (hereinafter “Hagen”) in view of Estepan Meliksetian et al; US 20230094000 A1 filed Sep 22, 2021 (hereinafter “Meliksetian”) in further view of Kenneth Liam Kiemele et al; US 20190220697 A1 filed on Jan 12, 2018 (hereinafter “Kiemele”) in further view of Sandeep Bhatia et al; US 10409648 B1 filed on Mar 1, 2017 (hereinafter “Bhatia”)
Regarding claim 6, Hagen teaches wherein the clustering method is performed on the partitioned training data sets separately, (Hagen Paragraph 0023; “The method 200 may further include a step 206 that includes applying a clustering algorithm to the training data... The output of the clustering algorithm may be a data set comprising one or more classification clusters, based on the classification(s) that the machine learning tool is intended to output.” Examiner notes that a method for clustering data points (clustering algorithm) is executed/applied to separate input data sets (training data))
Hagen does not teach The method according to claim 1, wherein the training data sets are partitioned,
wherein partitioning is performed depending on at least one state variable in the input data sets,
and wherein partitioning is performed with respect to predetermined value ranges of the at least one state variable and/or the label.
However, Bhatia does teach The method according to claim 1, wherein the training data sets are partitioned, (Bhatia Column 2 Line 60; “In one embodiment, partitions of a data set may be defined prior to the occurrence of a scale out operation (instead of being split after a split event occurs) so that the data of the partitions may be stored in separate storage locations. For example, in one embodiment, a shard of a database may include multiple logical groupings of data. One or more of these groupings of data (e.g., a set of items in the database with a field or attribute within a value range) may be stored in a single unit of storage (e.g., a volume, drive, page, block, etc.) as a partition.” Examiner notes that the training dataset (data set) is partitioned)
wherein partitioning is performed depending on at least one state variable in the input data sets, (Bhatia Column 2 Line 60; “In one embodiment, partitions of a data set may be defined prior to the occurrence of a scale out operation (instead of being split after a split event occurs) so that the data of the partitions may be stored in separate storage locations. For example, in one embodiment, a shard of a database may include multiple logical groupings of data. One or more of these groupings of data (e.g., a set of items in the database with a field or attribute within a value range) may be stored in a single unit of storage (e.g., a volume, drive, page, block, etc.) as a partition.” Examiner notes that partitioning is performed depending on at least one state variable in the input datasets (One or more of these groupings of data (e.g., a set of items in the database with a field or attribute within a value range))
and wherein partitioning is performed with respect to predetermined value ranges of the at least one state variable and/or the label. (Examiner refers to previous mapping to show that the partitioning is performed with respect to predetermined value ranges (attribute within a value range) of the at least one state variable (attribute))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hagen, Meliksetian, Kiemele, and Bhatia. Hagen teaches a method for machine learning-based classification. Meliksetian teaches automated artificial intelligence model generation, training and testing. Kiemele teaches automated localized machine learning training. Bhatia teaches processing responsibility for a data set may be split according to separately stored partitions of the data set. One of ordinary skill would have motivation to combine Hagen, Meliksetian, Kiemele, and Bhatia to reduce the burden caused by split operations for improved computational efficiency “splitting processing responsibility for separately stored partitions may be implemented to reduce the burden caused by split operations for processing access requests to data.” (Bhatia Column 2 Line 57).
Conclusion
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/D.D.T./Examiner, Art Unit 2147 /ERIC NILSSON/Primary Examiner, Art Unit 2151
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