DETAILED 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 .
Claims 1-20 are presented for examination (filed on 07 March 2024).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07 March 2024, 17 July 2025 and 09 December 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites, “A method for selecting data to be stored at an edge device, the method comprising:
training a machine learning model at the edge device using learning data obtained at the edge device, each of the learning data comprising a data type that is part of a feature space;
processing the machine learning model to derive a scoring vector for the machine learning model, the scoring vector comprising a series of elements specifying a relevance of each data type for training the machine learning model;
deriving a similarity matrix from the learning data and the scoring vector, the similarity matrix comprising a plurality of cells indicative of a similarity between data of different data types and the series of elements in the scoring vector; and
generating a list of selected data of the learning data to be stored at the edge device based at least on the similarity matrix.”
(Step 1) The claim recites “A method for selecting data to be stored at an edge device, the method comprising…” as drafted, the claimed method is a process, which is a statutory category of invention.
(Step 2A-Prong One) The limitations of “training a machine learning model at the edge device using learning data obtained at the edge device, each of the learning data comprising a data type that is part of a feature space;
processing the machine learning model to derive a scoring vector for the machine learning model, the scoring vector comprising a series of elements specifying a relevance of each data type for training the machine learning model;
deriving a similarity matrix from the learning data and the scoring vector, the similarity matrix comprising a plurality of cells indicative of a similarity between data of different data types and the series of elements in the scoring vector; and
generating a list of selected data of the learning data to be stored at the edge device based at least on the similarity matrix,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “edge device” and “machine learning model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “edge device” and “machine learning model,” language, “training,” “processing…derive,” “derive” and “generating” in the context of this claim encompasses the user manually
training…using learning data obtained, each of the learning data comprising a data type that is part of a feature space;
processing to derive a scoring vector, the scoring vector comprising a series of elements specifying a relevance of each data type for training the machine learning model;
deriving a similarity matrix from the learning data and the scoring vector, the similarity matrix comprising a plurality of cells indicative of a similarity between data of different data types and the series of elements in the scoring vector; and
generating a list of selected data of the learning data to be stored at the edge device based at least on the similarity matrix in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Additionally, it also fells in the grouping of “Mathematical Concepts” (e.g. Vector spaces and distance metrics are mathematical relationships, formulas, and calculations). Accordingly, the claim recites an abstract idea.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “edge device” and “machine learning model” to perform the “training,” “processing…derive,” “derive” and “generating” steps. The “edge device” and “machine learning model” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “edge device” and “machine learning model” to perform “training,” “processing…derive,” “derive” and “generating” steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
For claim 2, it recites “The method of claim 1, wherein the edge device is part of a cloud computing infrastructure configured to implement the machine learning model at the edge device.”
(Step 2A-Prong Two) and (Step 2B)
No additional elements are provided in the claim, therefore there is still no practical application and the claim does not provide significantly more as per claim 1 analysis.
For claim 3, it recites, “The method of claim 1, wherein the learning data comprises input data obtained from one or more sensors connected to the edge device.”
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “edge device” and “sensor” to perform the “obtained” step. The “edge device” and “sensor” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, the additional element – “…input data obtained from one or more sensors connected to the edge device” which is mere data gathering and is in form of insignificant extra-solution activity (MPEP: 2106.05(g), “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc.,”).
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “edge device” and “sensor” to perform “obtained” steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
The limitation is not sufficient to amount to significantly more than the judicial exception because “obtained” only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, MPEP 2106.05(d)(II), “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec…,”
“iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.…,”
“v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank.…,”
“iii. Detecting DNA or enzymes in a sample, Sequenom, 788.…,”
Thus, limitation does not amount to significantly more. Even when considered in combination, this additional element represent mere instructions to apply an exception and insignificant extra-solution activity, which does not provide an inventive concept. The claim is not patent eligible.
For claim 4, it recites, “The method of claim 1, wherein each of the elements in the scoring vector provides a value ranking the relevance of each data type for the machine learning model using data specific to the machine learning model.
(Step 2A-Prong One) The limitations of “provides a value ranking the relevance of each data type for the machine learning model using data specific to the machine learning model,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “machine learning model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “machine learning model,” language, “provides” in the context of this claim encompasses the user manually provides a value ranking the relevance of each data type for the machine learning model using data specific to the machine learning model in his mind. If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
(Step 2A-Prong Two) and (Step 2B)
No additional elements are provided in the claim, therefore there is still no practical application and the claim does not provide significantly more as per claim 1 analysis.
For claim 5, it recites, “The method of claim 1, wherein the scoring vector is derived using a feature importance model.
(Step 2A-Prong One) The limitations of “the scoring vector is derived using a feature importance model,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “feature importance model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “feature importance model,” language, “derived” in the context of this claim encompasses the user manually the scoring vector is derived using a feature importance model in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, it also fells in the grouping of “Mathematical Concepts.” Accordingly, the claim recites an abstract idea.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “feature importance model” to perform the “derived” steps. The “feature importance model” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “feature importance model” to perform “derived” step amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
For claim 6, it recites, “The method of claim 1, wherein the plurality of cells in the similarity matrix include a value specifying a commonality between data instances in the learning data. ” which is merely data (e.g. contents) and does not meet any of the categories (MPEP: 2106.03, “Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product).”).
For the above reason, the limitation does not change the result of the analysis from the independent claim 1. Therefore, claim 6 is also rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
For claim 7, it recites, “The method of claim 1, wherein the similarity matrix is derived using a gaussian kernel function.
(Step 2A-Prong One) The limitations of “the similarity matrix is derived using a gaussian kernel function,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “gaussian kernel function,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “gaussian kernel function,” language, “derived” in the context of this claim encompasses the user manually the similarity matrix is derived using a gaussian kernel function in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, it also fells in the grouping of “Mathematical Concepts.” Accordingly, the claim recites an abstract idea.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “gaussian kernel function to perform the “derived” steps. The “gaussian kernel function” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “gaussian kernel function” to perform “derived” step amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
For claim 8, it recites, “The method of claim 1, wherein the list of selected data is generated based on at least one constraint to the edge device, the at least one constraint comprising any of: a specified data storage capacity of the edge device, a specified processing power of the edge device, a specified bandwidth of the edge device, and a specified power consumption of the edge device.
(Step 2A-Prong One) The limitations of “wherein the list of selected data is generated based on at least one constraint to the edge device…,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “edge device,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “edge device,” language, “generated” in the context of this claim encompasses wherein the list of selected data is manually generated by the user based on at least one constraint to the edge device in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
(Step 2A-Prong Two) and (Step 2B)
No additional elements are provided in the claim, therefore there is still no practical application and the claim does not provide significantly more as per claim 1 analysis.
For claim 9, it recites, “The method of claim 1, wherein the list of selected data is generated using a maximize variance model.
(Step 2A-Prong One) The limitations of “the list of selected data is generated using a maximize variance model,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “maximize variance model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “maximize variance model,” language, “generated” in the context of this claim encompasses the list of selected data is manually generated by the user using a maximize variance model in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, it also fells in the grouping of “Mathematical Concepts.” Accordingly, the claim recites an abstract idea.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “maximize variance model” to perform the “generated” steps. The “maximize variance model” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “maximize variance model” to perform “generated” step amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
For claim 10, it recites, “The method of claim 1, further comprising: further training the machine learning model using data stored at the edge device according to the list of selected data.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “edge device” and “machine learning model” to perform the “using” step. The “edge device” and “machine learning model” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, the additional element – “…using data stored at the edge device according to the list of selected data…” which is mere data gathering and is in form of insignificant extra-solution activity (MPEP: 2106.05(g), “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc.,”).
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “edge device” and “machine learning model” to perform “using” steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
The limitation is not sufficient to amount to significantly more than the judicial exception because “using” only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, MPEP 2106.05(d)(II), “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec…,”
“iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.…,”
“v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank.…,”
“iii. Detecting DNA or enzymes in a sample, Sequenom, 788.…,”
Thus, limitation does not amount to significantly more. Even when considered in combination, this additional element represent mere instructions to apply an exception and insignificant extra-solution activity, which does not provide an inventive concept. The claim is not patent eligible.
For claim 11, it is a system claim having similar limitations as recited in claim 1. Thus, claim 11 is also rejected under the same analysis as explained in the rejection of rejected claim 1.
Claim 11 recites additional claim limitations,
one or more computing nodes;
one or more sensors; and
an edge device in electrical communication with the one or more computing nodes and the one or more sensors, where the edge device is operative to:
obtain a machine learning model from the one or more cloud computing nodes;
obtain learning data from the one or more sensors;”
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “computing nodes,” “cloud computing nodes,” “edge device” and “sensor” to perform the “obtain” and “obtain” steps. The “computing nodes,” “cloud computing nodes,” “edge device” and “sensor” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, the additional element – “…obtain a machine learning model from the one or more cloud computing nodes;” and “obtain learning data from the one or more sensors” which is mere data gathering and is in form of insignificant extra-solution activity (MPEP: 2106.05(g), “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc.,”).
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “computing nodes,” “cloud computing nodes,” “edge device” and “sensor” to perform “obtain” and “obtain” steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
The limitation is not sufficient to amount to significantly more than the judicial exception because “obtain” and “obtain” only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, MPEP 2106.05(d)(II), “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec…,”
“iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.…,”
“v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank.…,”
“iii. Detecting DNA or enzymes in a sample, Sequenom, 788.…,”
Thus, limitation does not amount to significantly more. Even when considered in combination, this additional element represent mere instructions to apply an exception and insignificant extra-solution activity, which does not provide an inventive concept. The claim is not patent eligible.
For claim 12, it is a system claim having similar limitations as recited in claim 1 and 4. Thus, claim 12 is also rejected under the same analysis as explained in the rejection of rejected claim 1 and 4.
For claim 13, it is a system claim having similar limitations as recited in claim 5. Thus, claim 13 is also rejected under the same analysis as explained in the rejection of rejected claim 5.
For claim 14, it is a system claim having similar limitations as recited in claim 6. Thus, claim 14 is also rejected under the same analysis as explained in the rejection of rejected claim 6.
For claim 15, it is a system claim having similar limitations as recited in claim 8. Thus, claim 15 is also rejected under the same analysis as explained in the rejection of rejected claim 8.
For claim 16, it is a system claim having similar limitations as recited in claim 10. Thus, claim 16 is also rejected under the same analysis as explained in the rejection of rejected claim 10.
For claim 17, it is a computer-readable storage medium claim having similar limitations as recited in claim 1. Thus, claim 17 is also rejected under the same analysis as explained in the rejection of rejected claim 1.
Claim 17 recites additional claim limitations,
storing a subset of the learning data according to the list of selected data; and
further training the machine learning model using the subset of the learning data stored at the edge device.
(Step 2A-Prong One) The limitation of “further training the machine learning model using the subset of the learning data stored at the edge device” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “edge device” and “machine learning model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “edge device” and “machine learning model,” language, “training” in the context of this claim encompasses the user manually training using the subset of the learning data stored at the edge device in his mind.
If claim limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
(Step 2A-Prong Two) This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – using “machine learning model” and “edge device” to perform the “storing” and “training” steps. The “machine learning model” and “edge device” in these steps are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, the additional element – “…storing a subset of the learning data according to the list of selected data” which is mere data gathering and is in form of insignificant extra-solution activity (MPEP: 2106.05(g), “v. Consulting and updating an activity log, Ultramercial…”
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “computing nodes,” “cloud computing nodes,” “edge device” and “sensor” to perform “storing” and “training” steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
The limitation is not sufficient to amount to significantly more than the judicial exception because “storing” only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, MPEP 2106.05(d)(II), “iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log)” “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs.,”
Thus, limitation does not amount to significantly more. Even when considered in combination, this additional element represent mere instructions to apply an exception and insignificant extra-solution activity, which does not provide an inventive concept. The claim is not patent eligible.
For claim 18, it is a computer-readable storage medium claim having similar limitations as recited in claim 4. Thus, claim 18 is also rejected under the same analysis as explained in the rejection of rejected claim 4.
For claim 19, it is a computer-readable storage medium claim having similar limitations as recited in claim 6. Thus, claim 19 is also rejected under the same analysis as explained in the rejection of rejected claim 6.
For claim 20, it is a computer-readable storage medium claim having similar limitations as recited in claim 8. Thus, claim 20 is also rejected under the same analysis as explained in the rejection of rejected claim 8.
Claims 17-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
For claim 17, it recites “A computer-readable storage medium containing program instructions for a method being executed by an application…” Specification does not clearly define which forms the above medium may take. Therefore, the claimed “computer-readable storage medium” could include signals and waves. Therefore, claims 17 is rejected under 35 USC 101 for being signal per se.
Dependent claims 18-20 are rejected for fully incorporating the deficiencies of their respective base claims by dependency.
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.
Claims 11-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation " the one or more cloud computing nodes " in line 6. There is insufficient antecedent basis for this limitation in the claim.
Dependent claims 12-16 are rejected for fully incorporating the deficiencies of their respective base claims by dependency.
Allowable Subject Matter
Claims 1, 11 and 17 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Desreumaux et al. (U.S. Pub. No.: US 20220366315), Abstract, “Systems and methods include determination of a first plurality of sets of data, each including values associated with respective ones of a first plurality of features, partial training of a first machine-learning model based on the first plurality of sets of data, determination of one or more of the first plurality of features to remove based on the partially-trained first machine-learning model, removal of the one or more of the first plurality of features to generate a second plurality of sets of data, partial training of a second machine-learning model based on the second plurality of sets of data, determination that a performance of the partially-trained second machine-learning model is less than a threshold, addition, in response to the determination, of the one or more of the first plurality of features to the second plurality of sets of data, and training of the partially-trained first machine-learning model based on the first plurality of sets of data.”
Conclusion
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/YU ZHAO/Primary Examiner, Art Unit 2169
Prosecution Insights