DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 6/3/2024 Claims 1-20 are pending in this Office Action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 4-5,13-14 are objected to because of the following informalities: Claims 4-5,13-14 recite the value “M”, “O” and “P” which are undefined (i.e greater than zero or positive only value. These claims are indefinite when M,O,P are negative) . Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10 and 19:
Step 1: Statutory Category ?: Yes. claim 1 recites a system (i.e., a “machine”) , claim 10 recites a method (i.e., a “process”) and claim 19 recites a computer readable medium (i.e., an article of manufacture) which are statutory categories.
Claim 1:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 1 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“generate a first reduced dimension feature set by applying a linear dimension reduction process to the set of features; generate a second reduced dimension feature set by applying a non-linear dimension reduction process to the first reduced dimension feature set; cluster the set of records based on the second reduced dimension feature set; generate a training dataset by labeling each record in the plurality of records based on a cluster associated with each record”
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 recites additional elements of “receive a plurality of data records, wherein each record in the plurality of records includes a set of features” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 1 recites additional elements of “train a machine learning model by applying a supervised training process based on the training dataset” which amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
The additional elements of “memory and processor” amount to no more than mere instructions to apply the exception using generic computer components.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The memory, processor and machine learning model are at best equivalent of adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible.
Claim 2 recites additional element of “wherein the linear dimension reduction process comprises a feature projection process” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 2 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 2 is not patent eligible.
Claim 3 recites additional element of “wherein the non-linear dimension reduction process comprises applying a fuzzy topological structure to generate the second reduced dimension feature set” which is mathematical calculations that falls within the mathematical concepts grouping of abstract ideas. Claim 3 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 3 is not patent eligible.
Claim 4 recites additional element of “wherein the set of features includes M dimensions and the first reduced dimension feature set includes O dimensions, and wherein the linear dimension reduction process is configured to project the M dimensions of the set of features to the O dimensions of the first reduced dimensions feature set” ” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 4 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 4 is not patent eligible.
Claim 5 recites additional element of “wherein the second reduced dimension featured set includes P dimensions, and wherein P is less than O, and wherein the second reduced dimension featured set has a similar topology as the first reduced dimension featured set” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 5 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 5 is not patent eligible.
Claim 6 recites additional element of “wherein the processor is configured to determine a purity score for each cluster of the set of records” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 6 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 6 is not patent eligible.
Claim 7 recites additional element of “revise at least one hyperparameter of the non-linear dimension reduction process based on the purity score of at least one cluster; generate an updated second reduced dimension feature set by applying the non-linear dimension reduction process including the revised at least one hyperparameter, wherein the updated second reduced dimension feature set is utilized to generate the training dataset” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 7 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 7 is not patent eligible.
Claim 8 recites additional element of “wherein the processor is configured to label each record in a cluster as a first class record when the purity score exceeds a predetermined threshold” ” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 8 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 8 is not patent eligible.
Claim 9 recites additional element of “wherein clusters of the set of records are generated by a dense clustering process” which is mathematical calculations that falls within the mathematical concepts grouping of abstract ideas. Claim 9 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 9 is not patent eligible.
Claim 10:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 10 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“generating a first reduced dimension feature set by applying a linear dimension reduction process to the set of features; generating a second reduced dimension feature set by applying a non-linear dimension reduction process to the first reduced dimension feature set; cluster the set of records based on the second reduced dimension feature set; generating a training dataset by labeling each record in the plurality of records based on a cluster associated with each record”
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 10 recites additional elements of “receiving a plurality of data records, wherein each record in the plurality of records includes a set of features” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 10 recites additional elements of “train a machine learning model by applying a supervised training process based on the training dataset” which amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 10 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The machine learning model is at best equivalent of adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 10 therefore is ineligible.
Claims 11-18 recite similar features of claims 2-9 and therefore being rejected for the same rationale as indicates in the above rejection of claims 2-9.
Claim 19:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 19 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“generating a first reduced dimension feature set by applying a linear dimension reduction process to the set of features; generating a second reduced dimension feature set by applying a non-linear dimension reduction process to the first reduced dimension feature set; cluster the set of records based on the second reduced dimension feature set; generating a training dataset by labeling each record in the plurality of records based on a cluster associated with each record”
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 19 recites additional elements of “receiving a plurality of data records, wherein each record in the plurality of records includes a set of features” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 19 recites additional elements of “training a machine learning model by applying a supervised training process based on the training dataset” which amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
The additional elements of “non-transitory computer readable medium” amounts to no more than mere instructions to apply the exception using generic computer component.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 19 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The machine learning model and non-transitory computer readable medium are at best equivalent of adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 19 therefore is ineligible.
Claim 20 recites additional element of “wherein clusters of the set of records are generated by a dense clustering process” which is mathematical calculations that falls within the mathematical concepts grouping of abstract ideas. Claim 20 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 20 is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6,8-11, 13-15, 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vold et al.,(US Patent Application Publication 2024/0266009 A1, hereinafter “Vold”)
As to claim 1, Vold teaches a system, comprising: a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
receive a plurality of data records, wherein each record in the plurality of records includes a set of features;(Vold par [0139] teaches obtaining a plurality of text spans for the health record)
generate a first reduced dimension feature set by applying a linear dimension reduction process to the set of features; ( Vold par [0198] teaches one or more of a variety of dimensionality reduction technique is used)
generate a second reduced dimension feature set by applying a non-linear
dimension reduction process to the first reduced dimension feature set;(Vold par [0202] teaches the dimension reduction includes discriminant analysis. Generally, force-directed layouts are useful in various particular embodiments because of their ability to identify new, lower dimensions that encode non-linear aspects of the underlying data which arise from underlying relationships between data elements)
cluster the set of records based on the second reduced dimension feature set; (Vold par [0176] teaches clustering the set of text spans using a clustering algorithm)
generate a training dataset by labeling each record in the plurality of records based on a cluster associated with each record (Vold par [0176] teaches For each respective cluster in the subset of clusters, for each respective text span in the respective cluster, a corresponding label is assigned to the respective text span that indicates an association between the respective text span and the first health entity. In some embodiments, the respective text span is further used to train a model); and
train a machine learning model by applying a supervised training process based on the training dataset.(Vold par [0235] teaches model training)
As to claim 2, Vold teaches the system of claim 1, wherein the linear dimension reduction process comprises a feature projection process.(Vold par [0199] teaches the dimension reduction is a principal components algorithm, a random projection algorithm…etc. .)
As to claim 4, Vold teaches the system of claim 1, wherein the set of features includes M dimensions and the first reduced dimension feature set includes O dimensions, and wherein the linear dimension reduction process is configured to project the M dimensions of the set of features to the O dimensions of the first reduced dimensions feature set.( Vold par [0199] teaches the dimension reduction is a random projection algorithm )
As to claim 5, Vold teaches the system of claim 4, wherein the second reduced dimension featured set includes P dimensions, and wherein P is less than O, and wherein the second reduced dimension featured
set has a similar topology as the first reduced dimension featured set.( Vold par [0202] teaches the dimension reduction includes discriminant analysis. Generally, force-directed layouts are useful in various particular embodiments because of their ability to identify new, lower dimensions that encode non-linear aspects of the underlying data which arise from underlying relationships between data elements)
As to claim 6, Vold teaches the system of claim 1, wherein the processor is configured to determine a purity score for each cluster of the set of records. .(Vold par [0176] teaches For each respective cluster in the plurality of clusters, a corresponding cluster score is determined that reflects a signal purity of the respective cluster relative to every other cluster in the plurality of clusters”)
As to claim 8, Vold teaches the system of claim 6, wherein the processor is configured to label each record in a cluster as a first class record when the purity score exceeds a predetermined threshold.(Vold par [0176] teaches a subset of clusters is selected from the plurality of clusters, each respective cluster in the subset of clusters having a corresponding cluster score that exceeds a threshold purity score)
As to claim 9, Vold teaches the system of claim 1, wherein clusters of the set of records are generated by a dense clustering process. (Vold par [0177] teaches density based clustering)
Claims 10-11,13-15 and 17-18 merely recite a method performed by the system of claims 1-2,4-6 and 8-9 respectively. Accordingly, Vold teaches every limitation of claims 10-11,13-15 and 17-18 as indicates in the above rejection of claims 1-2,4-6 and 8-9 respectively..
Claims 19 and 20 merely recite a non-transitory computer readable storage medium storing instructions when executed by one or more processor of claim 1 and 9 respectively. Accordingly, Vold teaches every limitation of claims 19 and 20 as indicates in the above rejection of claims 1 and 9 respectively.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Vold and further in view Zhang et al.(US Patent Application Publication 2011/0167014 A1, hereinafter “Zhang”)
As to claim 3, Vold teaches the system of claim 1 but fails to teach wherein the non-linear dimension reduction process comprises applying a fuzzy topological structure to generate the second reduced dimension feature set.
However, Zhang teaches wherein the non-linear dimension reduction process comprises applying a fuzzy topological structure to generate the second reduced dimension feature set.(Zhang par [0028] teaches the discovered similarities and differences are quantified into measurable distances in a so-called feature space. With the help of this quantifying process, the original fuzzy relationship among different services can be gauged and further extracted to specify the topological structure (pattern) embedded among a large amount of services. The creation of this type of topological structure can be used to cluster services into different groups, each of which is composed of a set of services, which are similar to each other based on the measurable distance associated with the feature space)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Vold and Zhang to achieve the claimed invention. One would have been motivated to make such combination to help pinpoint the similarities and differences between features.(Zhang par [0028])
As to claim 12, see the above rejection of claim 3.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vold and further in view of Njie et al.,(US Patent Application Publication 2019/0347567 A1, hereinafter “Njie”)
As to claim 7, Vold teaches the system of claim 6, but fails to teach wherein the processor is configured to: revise at least one hyperparameter of the non-linear dimension reduction process based on the purity score of at least one cluster; and generate an updated second reduced dimension feature set by applying the non-linear dimension reduction process including the revised at least one hyperparameter, wherein the updated second reduced dimension feature set is utilized to generate the training dataset
However, Njie teaches wherein the processor is configured to: revise at least one hyperparameter of the non-linear dimension reduction process [based on the purity score of at least one cluster]; and generate an updated second reduced dimension feature set by applying the non-linear dimension reduction process including the revised at least one hyperparameter, wherein the updated second reduced dimension feature set is utilized to generate the training dataset (Njie par [0058] teaches the cluster or classification scores from the dimension reduction algorithm are connected to one or more AI algorithm to automatically identify the parameters and/or hyperparameters to achieve the best clustering results and thus best new data object identification results by the final AI. In this aspect, one set of algorithms performs a clustering task and the other tells it whether its performance is good; if the performance is sub-par, the set of algorithms changes parameters and hyperparameters until their performance in comparing and identifying the data as determined by the other set of algorithms is considered acceptable for the desired use)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Vold and Njie to achieve the claimed invention. One would have been motivated to make such combination to further improve the accuracy of the neural net. (Njue par [0085])
As to claim 16, see the above rejection of claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jha., US Patent Application Publication 2024/0346086 A1, par [0082] discloses “the hyperparameter refiner may utilize a generalized reduced gradient non-linear method in adjusting parameter values”. Karasaridis et al., US Patent Application Publication 2020/0195669 A1, par [0017] discloses dimensions reductions including non-linear and par [0022] teaches clustering process.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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