Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/23/2024 is being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 2/23/2024.
Claims 1-18 are rejected under 35 U.S.C. 101 and 35 U.S.C. 103
Claim 13 is rejected under 35 U.S.C. 112(b)
Specification is objected to
Drawings
The drawings filed on 2/23/2024 are acceptable for examination purposes.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 4, “The one of the conventional methods” should read “One of the conventional methods”
In paragraph 23, “input/output interface 117” should read “input/output interface 107”
In paragraph 74, “to generation of the score” should read “generation of the score”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 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 13 recites the limitation "the penalized value" in line 1. There is insufficient antecedent basis for this limitation in the claim. Examiner’s Note: For the purpose of examination, this claim will be interpreted as dependent on claim 12.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Is the claim to a process, machines, manufacture, or composition of matter?
Claim 1 is a method type claim. Therefore, Claims 1-9 are directed to a process.
2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
generating a set of model clusters via the unsupervised clustering ML model (mental process – generating a set of model clusters via the unsupervised clusters ML model may be performed manually by a user with the aid of pen and paper by observing/analyzing a dataset and grouping together datapoints with similar characteristics)
comparing a set of test set clusters and the set of model clusters (mental process – comparing a set of test set clusters and the set of model clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing the sets of test set clusters and model clusters by determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters)
categorizing each of the set of model clusters into an assessment group based on the comparison, wherein the categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group (mental process – categorizing each of the set of model clusters into an assessment group based on the comparison, wherein the categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group may be performed manually by a user with the aid of a pen and paper by observing/analyzing the received comparison input and grouping together by the amount of overlap between the set of test set clusters and the set of model clusters (spec. [0044] “categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
assigning a similarity value to each of the set of model clusters based on the categorized assessment group (mental process – assigning a similarity value to each of the set of model clusters based on the categorized assessment group may be performed mentally by a user with the aid of a pen and paper by observing/analyzing the received categorized assessment group for the set of model clusters, and, for example, assigning a similarity score of 1 to the set of model clusters (spec. [0039] “For example, the processor 101 may assign the similarity value indicative of a non-zero integer to each of the set of model clusters categorized into the match group”))
and determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model (mental process – determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters may be performed by manually by a user with the aid of a pen and paper by observing/analyzing the received similarity value of each of the set of model clusters, and, for example, adding together the similarity value of each of the set of model clusters to determine a total similarity value)
2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Additional elements:
There are no additional elements.
2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Additional elements:
There are no additional elements.
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-9. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Claim 2 further clarifies the categorization step of claim 1, which is a mental process, by performing the following steps when the assessment group is the match group
2A Prong 1:
determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
assigning the similarity value indicative of a non-zero integer to the match group, such that the match group indicates that each of the one or more model data points completely matches the one or more test data points (mental process – assigning the similarity value indicative of a non-zero integer to the match group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 2.0 to each of the set of model clusters categorized in the match group (spec. [0039] “may assign the similarity value indicative of a non-zero integer to each of the set of model clusters categorized into the match group”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
Additional elements:
There are no additional elements.
Regarding Claim 3:
Claim 3 further clarifies the categorization step of claim 1, which is a mental process, by performing the following steps when the assessment group is the correct group
2A Prong 1:
determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assigning the similarity value indicative of a penalized value to the correct group, such that the correct group indicates that the one or more model data points completely match the one or more test data points and the one or more model data points include at least one additional model data point different from the one or more test data points (mental process – assigning the similarity value indicative of a penalized value to the correct group may be performed manually by a user with the aid of pen and paper by, for example, assigning a value of 0.9998766054240138 to the set of model clusters categorized in the correct group (spec. [0052] and Fig. 3 “For example, for the cluster of 4 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.7310585786300049”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
Additional elements:
There are no additional elements.
Regarding Claim 4:
Claim 4 further clarifies the assigning step of claim 3, which is a mental process, by performing the following steps
2A Prong 1:
The method as claimed in claim 3, wherein the penalized value indicates a sigmoid function which is scaled based on a number of one or more test data points (mental process –the penalized value indicating a sigmoid function which is scaled based on the number of one or more test data points may be performed manually by a user with the aid of pen and paper by, for example, assigning a value of 0.9998766054240138 to the set of model clusters categorized in the correct group (spec. [0052] “scales the penalty with the size of the set of test set clusters using the sigmoid function. For example, for the cluster of 4 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.7310585786300049. Further, for the cluster of 12 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.9998766054240138”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 5:
Claim 5 further clarifies the categorization step of claim 1, which is a mental process, by performing the following steps when the assessment group is the partial group
2A Prong 1:
determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assigning the similarity value indicative of a non-zero integer to the partial group, such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points (mental process – assigning the similarity value indicative of a non-zero integer to the partial group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 1.0 to each of the set of model clusters categorized in the partial group (spec. [0060] “the processor 101 assigns the similarity value indicative of the non-zero integer to the partial group”))
2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 6:
Claim 6 further clarifies the categorization step of claim 1, which is a mental process, by performing the following steps when the assessment group is the incorrect group
2A Prong 1:
determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assigning the similarity value indicative of a zero value to the incorrect group, such that the incorrect group indicates that the one or more model data points are different from the one or more test data points, wherein a number of different one or more model data points is more than a number of matching one or more model data points (mental process – assigning the similarity value indicative of a zero value to the incorrect group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 0.0 to each of the set of model clusters categorized in the incorrect group (spec. [0039] “the processor 101 may assign the similarity value indicative of a zero value to the set of model clusters categorized into the incorrect group”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 7:
Claim 7 further clarifies the generating the set of model clusters step of claim 1, which is a mental process, by performing the following steps
2A Prong 1:
The method as claimed in claim 1, wherein the set of model clusters generated includes an unlabeled dataset (mental process – the set of model clusters generated including an unlabeled dataset may be performed manually by a user with the aid of a pen and paper by observing/analyzing an unlabeled dataset and grouping together datapoints with similar characteristics)
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 8:
2A Prong 1:
See the rejection of Claim 1 above, which Claim 8 depends on.
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
The method as claimed in claim 1, wherein the unsupervised clustering ML model includes at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), or Mean Shift (adding the words "apply it" (or an equivalent) with the judicial exception, or 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) - Examiner's note: high level recitation of applying a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
2A Prong 1:
assigning a set of hyperparameters corresponding to the unsupervised clustering ML model for generating the set of model clusters such that the total similarity value corresponds to the assigned set of hyperparameters (mental process - assigning a set of hyperparameters corresponding to the unsupervised clustering ML model for generating the set of model clusters may be performed manually with the aid of a pen and paper by observing/analyzing the received input, for example, of a set of hyperparameters specifying cluster size rangers, and assigning it to the ML model for generating the set of model clusters (spec. [0070] “For example, cluster size ranges from 10 to 100 are assigned as the set of hyperparameters”))
selecting the set of hyperparameters based on comparing the total similarity value and a pre-defined threshold (mental process - selecting the set of hyperparameters based on comparing the total similarity value and a pre-defined threshold may be performed manually with the aid of a pen and paper by observing/analyzing the received input of a pre-defined threshold values, and selecting the set of hyperparameters with the highest total similarity value above the pre-defined threshold (spec. [0076] “the processor may select a set of optimal hyperparameters by comparing the determined similarity value for each set of hyperparameters and a pre-defined threshold”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 10:
Step 1: Is the claim to a process, machines, manufacture, or composition of matter?
Claim 10 is a system type claim. Therefore, Claims 10-18 are directed to a machine.
2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
generate a set of model clusters via the unsupervised clustering ML model (mental process – generating a set of model clusters via the unsupervised clusters ML model may be performed manually by a user with the aid of pen and paper by observing/analyzing a dataset and grouping together datapoints with similar characteristics)
compare a set of test set clusters and the set of model clusters (mental process – comparing a set of test set clusters and the set of model clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing the sets of test set clusters and model clusters by determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters)
categorize each of the set of model clusters into an assessment group based on the comparison, wherein the categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group (mental process – categorizing each of the set of model clusters into an assessment group based on the comparison, wherein the categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group may be performed manually by a user with the aid of a pen and paper by observing/analyzing the received comparison input and grouping together by the amount of overlap between the set of test set clusters and the set of model clusters (spec. [0044] “categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
assign a similarity value to each of the set of model clusters based on the categorized assessment group (mental process – assigning a similarity value to each of the set of model clusters based on the categorized assessment group may be performed mentally by a user with the aid of a pen and paper by observing/analyzing the received categorized assessment group for the set of model clusters, and, for example, assigning a similarity score of 1 to the set of model clusters (spec. [0039] “For example, the processor 101 may assign the similarity value indicative of a non-zero integer to each of the set of model clusters categorized into the match group”))
and determine a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model (mental process – determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters may be performed by manually by a user with the aid of a pen and paper by observing/analyzing the received similarity value of each of the set of model clusters, and, for example, adding together the similarity value of each of the set of model clusters to determine a total similarity value)
2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Additional elements:
a memory (recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components)
at least one processor in communication with the memory, wherein the at least one processor is configured to: generate a set of model clusters via the unsupervised clustering ML model (recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components)
2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Additional elements:
a memory (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
at least one processor in communication with the memory, wherein the at least one processor is configured to: generate a set of model clusters via the unsupervised clustering ML model (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
For the reasons above, Claim 10 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 11-18. The additional limitations of the dependent claims are addressed below.
Regarding Claim 11:
Claim 11 further clarifies the categorization step of claim 10, which is a mental process, by performing the following steps when the assessment group is the match group
2A Prong 1:
determine similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assign the similarity value indicative of a non-zero integer to the match group, such that the match group indicates that each of the one or more model data points completely matches the one or more test data points (mental process – assigning the similarity value indicative of a non-zero integer to the match group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 2.0 to each of the set of model clusters categorized in the match group (spec. [0039] “may assign the similarity value indicative of a non-zero integer to each of the set of model clusters categorized into the match group”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
Additional elements:
There are no additional elements.
Regarding Claim 12:
Claim 12 further clarifies the categorization step of claim 10, which is a mental process, by performing the following steps when the assessment group is the correct group
2A Prong 1:
determine similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assign the similarity value indicative of a penalized value to the correct group, such that the correct group indicates that the one or more model data points completely match the one or more test data points and the one or more model data points include at least one additional model data point different from the one or more test data points (mental process – assigning the similarity value indicative of a penalized value to the correct group may be performed manually by a user with the aid of pen and paper by, for example, assigning a value of 0.9998766054240138 to the set of model clusters categorized in the correct group (spec. [0052] and Fig. 3 “For example, for the cluster of 4 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.7310585786300049”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
Additional elements:
There are no additional elements.
Regarding Claim 13:
Claim 13 further clarifies the assigning step of claim 12, which is a mental process, by performing the following steps
2A Prong 1:
The system as claimed in claim 10, wherein the penalized value indicates a sigmoid function which is scaled based on a number of one or more test data points (mental process –the penalized value indicating a sigmoid function which is scaled based on the number of one or more test data points may be performed manually by a user with the aid of pen and paper by, for example, assigning a value of 0.9998766054240138 to the set of model clusters categorized in the correct group (spec. [0052] “scales the penalty with the size of the set of test set clusters using the sigmoid function. For example, for the cluster of 4 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.7310585786300049. Further, for the cluster of 12 test data points with 3 incorrect model data points, the penalized value is calculated as (1-exp(3-4)) = 0.9998766054240138”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 14:
Claim 14 further clarifies the categorization step of claim 10, which is a mental process, by performing the following steps when the assessment group is the partial group
2A Prong 1:
determine similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assign the similarity value indicative of a non-zero integer to the partial group, such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points (mental process – assigning the similarity value indicative of a non-zero integer to the partial group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 1.0 to each of the set of model clusters categorized in the partial group (spec. [0060] “the processor 101 assigns the similarity value indicative of the non-zero integer to the partial group”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 15:
Claim 15 further clarifies the categorization step of claim 10, which is a mental process, by performing the following steps when the assessment group is the incorrect group
2A Prong 1:
determine similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters (mental process – determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters may be performed manually by a user with the aid of pen and paper by observing/analyzing if one or more model data points completely matches one or more test data points (spec. [0044] “The processor 101 may categorize the first set of the generated model clusters into the match group if each of the one or more model data points in the first set of the generated model clusters completely matches the one or more test data points”))
and assign the similarity value indicative of a zero value to the incorrect group, such that the incorrect group indicates that the one or more model data points are different from the one or more test data points, wherein a number of different one or more model data points is more than a number of matching one or more model data points (mental process – assigning the similarity value indicative of a zero value to the incorrect group may be performed manually by a user with the aid of a pen and paper, for example, by assigning a value of 0.0 to each of the set of model clusters categorized in the incorrect group (spec. [0039] “the processor 101 may assign the similarity value indicative of a zero value to the set of model clusters categorized into the incorrect group”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 16:
Claim 16 further clarifies the generating the set of model clusters step of claim 10, which is a mental process, by performing the following steps
2A Prong 1:
The system as claimed in claim 10, wherein the set of model clusters generated includes an unlabeled dataset (mental process – the set of model clusters generated including an unlabeled dataset may be performed manually by a user with the aid of a pen and paper by observing/analyzing an unlabeled dataset and grouping together datapoints with similar characteristics)
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Regarding Claim 17:
2A Prong 1:
See the rejection of Claim 10 above, which Claim 17 depends on.
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
The system as claimed in claim 10, wherein the unsupervised clustering ML model includes at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), or Mean Shift (adding the words "apply it" (or an equivalent) with the judicial exception, or 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) - Examiner's note: high level recitation of applying a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 10.
Regarding Claim 18:
2A Prong 1:
assign a set of hyperparameters corresponding to the unsupervised clustering ML model for generating the set of model clusters such that the total similarity value corresponds to the assigned set of hyperparameters (mental process - assigning a set of hyperparameters corresponding to the unsupervised clustering ML model for generating the set of model clusters may be performed manually with the aid of a pen and paper by observing/analyzing the received input, for example, of a set of hyperparameters specifying cluster size rangers, and assigning it to the ML model for generating the set of model clusters (spec. [0070] “For example, cluster size ranges from 10 to 100 are assigned as the set of hyperparameters”))
select the set of hyperparameters based on comparing the total similarity value and a pre-defined threshold (mental process - selecting the set of hyperparameters based on comparing the total similarity value and a pre-defined threshold may be performed manually with the aid of a pen and paper by observing/analyzing the received input of a pre-defined threshold values, and selecting the set of hyperparameters with the highest total similarity value above the pre-defined threshold (spec. [0076] “the processor may select a set of optimal hyperparameters by comparing the determined similarity value for each set of hyperparameters and a pre-defined threshold”))
2A Prong 2 & Step 2B: This judicial exception is not integrated into a practical application.
There are no additional elements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 8-12, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Nanayakkara et al. (“Evaluation measure for group-based record linkage”, 29 Nov 2019, herein Nanayakkara) in view of Hackett-Jones et al. (US 20170255688 A1, herein Hackett-Jones).
Regarding Claim 1:
Nanayakkara teaches:
“A method for evaluating an unsupervised clustering machine learning (ML) model, the method comprising” (preamble)
“generating a set of model clusters via the unsupervised clustering ML model”
(Nanayakkara, Methods, “Assuming an automated computer-based linkage of large datasets, our proposed clustering quality evaluation method considers how individual records have been allocated into predicted groups/clusters (the result of a clustering algorithm)”; “To demonstrate our record-based cluster evaluation measure, we applied three clustering algorithms… Connected component clustering”; Examiner’s Note: generating a set of model clusters (i.e. predicted groups/clusters) via the unsupervised clustering ML model (i.e. Connected component clustering is an unsupervised clustering algorithm) is taught)
“comparing a set of test set clusters and the set of model clusters”
(Nanayakkara, Abstract, “The proposed linkage evaluation method assesses how well individual records have been allocated into predicted groups/clusters with respect to ground-truth data. We first identify the best representative predicted cluster for each ground-truth cluster and, based on the resulting mapping, each record in a ground-truth cluster is assigned to one of seven categories”; Examiner’s Note: comparing a set of test set clusters (i.e. ground-truth clusters) and the set of model clusters (i.e. predicted clusters) is taught)
“categorizing each of the set of model clusters into an assessment group based on the comparison, wherein the categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group”
(Nanayakkara, Methods and Table 3: Classification of records for evaluation measures, “We first identify the best representative predicted cluster for each ground-truth cluster and, based on the resulting mapping, each record in a ground-truth cluster is assigned to one of seven categories”; “
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”; Examiner’s Note: categorizing each of the set of model clusters into an assessment group based on the comparison (i.e. each predicted cluster is mapped to a ground-truth cluster, and each ground-truth cluster is assigned to one of seven categories, therefore each predicted cluster is assigned to one of seven categories based on the mapping), wherein the categorized assessment group is at least one of a match group (i.e. exact group match), a correct group, a partial group, and an incorrect group is taught)
“assigning a similarity value to each of the set of model clusters based on the categorized assessment group”
(Nanayakkara, Methods, “We use the Jaccard similarity [1] and the true link similarity for this purpose, defined as:
Jaccard similarity: simJacc =
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The ratio between the records common to both the ground-truth and predicted cluster, and the total number of records in the union of the two clusters. The Jaccard similarity always returns a similarity between 0 and 1.
True link similarity: simtl =|𝐠𝐭∩𝐩𝑖|
The number of records common to both the ground-truth and predicted cluster. This gives a positive integer similarity”;
Examiner’s Note: assigning a similarity value to each of the set of model clusters (i.e. Jaccard similarity) based on the categorized assessment group is taught)
Nanayakkara fails to teach determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model.
Hackett-Jones teaches (“PARAMETER SET DETERMINATION FOR CLUSTERING OF DATASETS” (title)” comprising:
“determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model”
(Hackett-Jones, Paragraph 19, “A plurality of cluster solutions are generated from each of the initial parameter sets. The goodness of each cluster solution is measured using a respective total score. The total score may be calculated as the sum of a business value score, a solution diversity score and an average centroid distance score for each cluster”; Examiner’s Note: determining a total similarity value (i.e. the total score) based on combining the assigned similarity value of each of the set of model clusters (i.e. the sum of a business value score, a solution diversity score and an average centroid distance score for each cluster), such that the total similarity value indicates evaluation (i.e. the goodness of each cluster solution) of the unsupervised clustering ML model is taught)
It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention was made to modify the invention in Nanayakkara by applying the determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model as taught in Hackett-Jones as “a given parameter set can produce thousands of cluster solutions. Each cluster solution can be evaluated based on its total score … average of the total scores of the upper quartile of cluster solutions accepted by the cluster module 130 can be used as the Fitness score of a given parameter set” and the fitness score can be used to select the optimized set of hyperparameters for the clustering machine learning model (Hackett-Jones, Paragraphs 30 and 33).
Regarding Claim 2:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein when the assessment group is the match group, the method comprises determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters”
(Nanayakkara, Contribution, “To address the problem of the lack of suitable linkage evaluation measures for group-based record linkage, we propose a novel method for evaluating the quality of the clusters generated in a record linkage process, which classifies records (rather than links) according to how correctly they have been generated when compared to the ground-truth clusters”; Examiner’s Note: determining similarity (i.e. compared) between one or more model data points within the set of model clusters (i.e. records in the clusters generated in a record linking process) and one or more test data points within the set of test set clusters (i.e. records in the ground-truth clusters) is taught)
“assigning the similarity value indicative of a non-zero integer to the match group, such that the match group indicates that each of the one or more model data points completely matches the one or more test data points”
(Nanayakkara, Methods, “These attribute similarities are then aggregated for each record pair and normalised into 0 to 1, where a similarity of 1 reflects a perfect matching record pair (all compared attribute values are the same) while a similarity of 0 reflects a complete non-match (all compared attribute values are different)”; Examiner’s Note: assigning the similarity value indicative of a non-zero integer (i.e. similarity of 1) to the match group, such that the match group indicates that each of the one or more model data points completely matches the one or more test data points (i.e. of 1 reflects a perfect matching record pair) is taught)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 3:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein when the assessment group is the correct group, the method comprises determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters”
(Nanayakkara, Contribution, “To address the problem of the lack of suitable linkage evaluation measures for group-based record linkage, we propose a novel method for evaluating the quality of the clusters generated in a record linkage process, which classifies records (rather than links) according to how correctly they have been generated when compared to the ground-truth clusters”; Examiner’s Note: determining similarity (i.e. compared) between one or more model data points within the set of model clusters (i.e. records in the clusters generated in a record linking process) and one or more test data points within the set of test set clusters (i.e. records in the ground-truth clusters) is taught)
“and assigning the similarity value indicative of a penalized value to the correct group, such that the correct group indicates that the one or more model data points completely match the one or more test data points and the one or more model data points include at least one additional model data point different from the one or more test data points”
(Nanayakkara, Table 3 and Methods, “A majority group match occurs when at least 50% of the records in a predicted cluster (containing at least two records) come from a single ground-truth cluster. For this classification, the best representative predicted cluster of a ground-truth cluster (which contains at least two records from the ground-truth cluster) must be identified”; “
Jaccard similarity: simJacc =
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The ratio between the records common to both the ground-truth and predicted cluster, and the total number of records in the union of the two clusters. The Jaccard similarity always returns a similarity between 0 and 1.
”; Examiner’s Note: and assigning the similarity value indicative of a penalized value to the correct group (i.e. similarity between 0 and 1), such that the correct group indicates that the one or more model data points completely match the one or more test data points and the one or more model data points include at least one additional model data point different from the one or more test data points (i.e. majority group (at least 50%, but less than 100% which would be exact match group)) is taught)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 6:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein when the assessment group is the incorrect group, the method comprises determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters”
(Nanayakkara, Contribution, “To address the problem of the lack of suitable linkage evaluation measures for group-based record linkage, we propose a novel method for evaluating the quality of the clusters generated in a record linkage process, which classifies records (rather than links) according to how correctly they have been generated when compared to the ground-truth clusters”; Examiner’s Note: determining similarity (i.e. compared) between one or more model data points within the set of model clusters (i.e. records in the clusters generated in a record linking process) and one or more test data points within the set of test set clusters (i.e. records in the ground-truth clusters) is taught)
“and assigning the similarity value indicative of a zero value to the incorrect group, such that the incorrect group indicates that the one or more model data points are different from the one or more test data points, wherein a number of different one or more model data points is more than a number of matching one or more model data points”
(Nanayakkara, Methods, “These attribute similarities are then aggregated for each record pair and normalised into 0 to 1, where a similarity of 1 reflects a perfect matching record pair (all compared attribute values are the same) while a similarity of 0 reflects a complete non-match (all compared attribute values are different)”; “
Jaccard similarity: simJacc =
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∪
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The ratio between the records common to both the ground-truth and predicted cluster, and the total number of records in the union of the two clusters. The Jaccard similarity always returns a similarity between 0 and 1.
”; Examiner’s Note: and assigning the similarity value indicative of a zero value (i.e. similarity of 0) to the incorrect group, such that the incorrect group indicates that the one or more model data points are different from the one or more test data points, wherein a number of different one or more model data points is more than a number of matching one or more model data points (i.e. 0 reflects a complete non-match) is taught)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 8:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein the unsupervised clustering ML model includes at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), or Mean Shift”
(Hackett-Jones, Paragraph 16, “As a particular example, a clustering algorithm known as the k-means algorithm takes as input a data set, as well as a selection of variables and a target number of clusters, and returns a grouping of the data set based on the characteristics of those variables”; Examiner’s Note: The method as claimed in claim 1, wherein the unsupervised clustering ML model includes at least one of K-Means Clustering (i.e. k-means algorithm), Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), or Mean Shift is taught)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 9:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein prior to generating the set of model clusters via the unsupervised clustering ML model, the method comprises” (preamble)
“assigning a set of hyperparameters corresponding to the unsupervised clustering ML model for generating the set of model clusters such that the total similarity value corresponds to the assigned set of hyperparameters”
(Hackett-Jones, Paragraph 22-23, “Parameter sets 104, including an initial pool of parameter sets, P={P1, P2, P3,…} are transmitted to the cluster module 130. For each parameter set Pi in the pool, the cluster generation module 132 generates a plurality of cluster solutions so that each cluster solution corresponds to a sequence of clusters or a clustered dataset”; “A single fitness score is further obtained for each parameter set Pi respectively (shown as fitness scores 105) based on the total scores of the clustered datasets generated from the parameter set Pi”; Examiner’s Note: assigning a set of hyperparameters (i.e. P transmitted to the cluster module) corresponding to the unsupervised clustering ML model (i.e. the cluster generation module) for generating the set of model clusters (i.e. generates a plurality of cluster solutions) such that the total similarity value (i.e. fitness score) corresponds to the assigned set of hyperparameters (i.e. fitness score is obtained for each parameter set Pi) is taught)
“and selecting the set of hyperparameters based on comparing the total similarity value and a pre-defined threshold”
(Hackett-Jones, Paragraph 24 and 35, “A hyper optimized parameter set 106 is selected that has a high, stable fitness score”; “Stabilization may be determined based on the difference between the Fitness score of the current parameter set and a previous parameter set. If the difference is less than a threshold then the Fitness score may be considered to be stable, or if the Fitness score difference is determined to be less than a threshold for a predetermined number of iterations, then the Fitness score may be considered to be stable”; Examiner’s Note: and selecting the set of hyperparameters (i.e. parameter set) based on comparing the total similarity value (i.e. fitness score) and a pre-defined threshold (i.e. the difference is less than a threshold) is taught)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 10:
Claim 10 recites substantially the same limitations as Claim 1, in the form of a system, therefore, it is rejected under the same rationale.
Hackett-Jones teaches the additional elements of:
“a memory”
(Hackett-Jones, Paragraph 58, “For example, the computer readable medium 1006 may be non-transitory or non-volatile medium, such as a magnetic disk or solid-state non-volatile memory or volatile medium such as RAM.”; Examiner’s Note: a memory is taught)
“at least one processor in communication with the memory, wherein the at least one processor is configured to:”
(Hackett-Jones, Paragraph 58, “The computer readable medium 1006 may be any suitable medium which participates in providing instructions to the processor(s) 1002 for execution. For example, the computer readable medium 1006 may be non-transitory or non-volatile medium, such as a magnetic disk or solid-state non-volatile memory or volatile medium such as RAM”; Examiner’s Note: at least one processor in communication with the memory (i.e. providing instructions to the processor(s)) is taught)
Regarding Claim 11:
Claim 11 is a system to perform the method of Claim 2, therefore, it is rejected under the same rationale.
Regarding Claim 12:
Claim 12 is a system to perform the method of Claim 3, therefore, it is rejected under the same rationale.
Regarding Claim 15:
Claim 15 is a system to perform the method of Claim 6, therefore, it is rejected under the same rationale.
Regarding Claim 17:
Claim 17 is a system to perform the method of Claim 8, therefore, it is rejected under the same rationale.
Regarding Claim 18:
Claim 18 is a system to perform the method of Claim 9, therefore, it is rejected under the same rationale.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Nanayakkara in view of Hackett-Jones as applied in claims 1 and 10, in view Tsai et la. (US 20230025641 A1, herein Tsai).
Regarding Claim 4:
The combination of Nanayakkara and Hackett-Jones teaches assigning the similarity value indicative of a penalized value to the correct group, such that the correct group indicates that the one or more model data points completely match the one or more test data points and the one or more model data points include at least one additional model data point different from the one or more test data points (claims 3 and 12). The combination of Nanayakkara and Hackett-Jones fails to teach wherein the penalized value indicates a sigmoid function which is scaled based on a number of one or more test data points.
Tsai teaches “Training A Machine Learning Model To Determine A Predicted Time Distribution Related To Electronic Communications (title)” comprising:
“The method as claimed in claim 3, wherein the penalized value indicates a sigmoid function which is scaled based on a number of one or more test data points”
(Tsai, Paragraph 112, “In a second approach, the probability of open estimated by the freshness score may be used as a scaling factor for the duration (e.g., inflection point) of a sigmoid penalty function”; Examiner’s Note: The method as claimed in claim 3, wherein the penalized value indicates a sigmoid function (i.e. sigmoid penalty function) which is scaled based on a number of one or more test data points (i.e. scaling factor) is taught)
It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention was made to modify the invention in Nanayakkara and Hackett-Jones by applying that the penalized value indicates a sigmoid function which is scaled based on a number of one or more test data points as taught in Tsai as a sigmoid function and a scaling factor to calculate the similarity value can adjust the applied penalty (Tsai, Paragraph 112).
Regarding Claim 13:
Claim 13 is a system to perform the method of Claim 4, therefore, it is rejected under the same rationale.
Claims 5, 7, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nanayakkara in view of Hackett-Jones as applied in claims 1 and 10, in view Pandit et al. (US 20260253715 A1, herein Pandit).
Regarding Claim 5:
The combination of Nanayakkara and Hackett-Jones teaches:
“The method as claimed in claim 1, wherein when the assessment group is the partial group, the method comprises determining similarity between one or more model data points within the set of model clusters and one or more test data points within the set of test set clusters”
(Nanayakkara, Contribution, “To address the problem of the lack of suitable linkage evaluation measures for group-based record linkage, we propose a novel method for evaluating the quality of the clusters generated in a record linkage process, which classifies records (rather than links) according to how correctly they have been generated when compared to the ground-truth clusters”; Examiner’s Note: determining similarity (i.e. compared) between one or more model data points within the set of model clusters (i.e. records in the clusters generated in a record linking process) and one or more test data points within the set of test set clusters (i.e. records in the ground-truth clusters) is taught)
Nanayakkara and Hackett-Jones fail to teach assigning the similarity value indicative of a non-zero integer to the partial group, such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points.
Pandit teaches “LEVERAGING LARGE LANGUAGE MODELS FOR STANDARDIZING CLINICAL DATA (title)” comprising:
“and assigning the similarity value indicative of a non-zero integer to the partial group, such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points”
(Pandit, Paragraph 19, “During testing, a predicted structure is categorized as an ‘Absolute Match’ if all of its blocks align with those in the actual structure (e.g., the groundtruth). If the resource aligns but some or all other metadata elements do not, this leads to a ‘Partial Match’. Alternatively, if none of the blocks match, or if the resource does not match even when other elements do, the predicted structure is considered a ‘Mismatch’. Partial Score and Match Score are then defined as below:
PNG
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”;
Examiner’s Note: and assigning the similarity value indicative of a non-zero integer to the partial group (i.e. partialscore equation), such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points (i.e. resource aligns but some or all other metadata elements do not) is taught)
It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention was made to modify the invention in Nanayakkara and Hackett-Jones by assigning the similarity value indicative of a non-zero integer to the partial group, such that the partial group indicates that a portion of the one or more model data points completely matches the one or more test data points and has at least one additional model data point different from the one or more test data points as taught in Pandit as a way to show if the set of clusters match but some elements do not using an integer value (Pandit, Paragraph 19).
Regarding Claim 7:
The combination of Nanayakkara, Hackett-Jones, and Pandit teaches:
“The method as claimed in claim 1, wherein the set of model clusters generated includes an unlabeled dataset”
(Pandit, Paragraph 16, “The LLM 106 can represent any language model that includes a neural network with many parameters (tens of thousands, millions, or sometimes even billions or more) that is trained on large quantities of unlabeled text”; Examiner’s Note: The method as claimed in claim 1, wherein the set of model clusters generated includes an unlabeled dataset (i.e. unlabeled text) is taught)
The reasons of obviousness have been noted in the rejection of Claim 5 above and applicable herein.
Regarding Claim 14:
Claim 14 is a system to perform the method of Claim 5, therefore, it is rejected under the same rationale.
Regarding Claim 16:
Claim 16 is a system to perform the method of Claim 7, therefore, it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Saphal et al. (US 2024/0045929) teaches systems and methods for auto-thresholding using pairwise feature cross-correlation for hyperparameter value selection.
Das et al. (US 2022/0114490) teaches methods and systems for processing unstructured and unlabeled data.
Ben-Itzhak et al. (US 2022/0012625) teaches techniques for implementing unsupervised anomaly detection via supervised methods.
Amrani et al. (US 2021/0133602) teaches classifier training using noisy samples.
Bridges et al. (US 2022/0374515) teaches intrusion detection on automotive controller area networks.
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/P.M./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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