DETAILED ACTION
This office action is responsive to the above identified application filed 09/07/2022. The application contains claims 1-20, all examined and rejected.
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 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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7, are method claims. Claims 8-14, are system claims. Claim 15-20 are computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, claims. Therefore, claims 1-20 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
Claim 1, 8 and 15 recites the following limitation(s):
automatically detecting a data analysis request made (“detecting” step encompasses observing that a request was made)
and identifying one or more subject datasets (“identifying” step encompasses making an observation or judgment about a set of data)
automatically conducting shallow term assignments on each row and column of data in the subject datasets, the shallow term assignments comprising a domain classification; (“conducting shallow term assignments” step encompasses making an observation or judgment about the topic related to the first few items in the dataset)
automatically matching the shallow term assignments for each row and column with a stored set of ranked terms; (“matching” step encompasses making an observation or judgment about the topic and popular topics that are frequently used from a person’s knowledge or memory)
automatically flagging rows or columns matching with ranked terms in the stored set of ranked terms that are above a predetermined threshold ranking for performing further analysis, (nothing in the claims element precludes the “flagging” step from being performed in the mind with the aid of pen and paper, for example, through observation and judgement rows and columns are marked for further analysis)
automatically and continuously monitoring and detecting irrelevant metadata types within the flagged rows or columns by continuously utilizing historical usage data to prevent subsequent analysis and storage of data including the irrelevant metadata types, (nothing in the claims element precludes the “continuously monitoring and detecting” step from being performed in the mind with the aid of pen and paper, for example, through observation and judgement one can identify types of data in the dataset that previously resulted in irrelevant results)
in response to detecting a stored analysis dataset, automatically generating a criticality ranking for the stored analysis dataset, the criticality ranking comprising a numerical probability of usability for the stored analysis dataset, (“detecting” step encompasses making an observation that the dataset has been annotated with relevant classification terms, and flagged and marked for subsequent analysis; (“detecting” step encompasses making an observation or judgement of the annotation that the dataset has critical data)
and in response to detecting low priority analysis datasets having the criticality ranking below a criticality threshold,
Claim 2, 9 and 16 recites the following limitation(s):
in response to detecting the low priority analysis datasets having the criticality ranking below the criticality threshold further comprises; automatically purging the low priority datasets. (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim 3, 10 and 17 recites the following limitation(s):
wherein the criticality ranking for the stored analysis datasets is generated by considering historical usage data, matches with the ranked terms, associated governance policies or rules, and past user interactions, (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim 4, 11 and 18 recites the following limitation(s):
wherein automatically conducting the shallow term assignments on each row and column of data in the subject datasets further comprises: automatically considering metadata and naming for each row and column (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim 5, 12 and 19 recites the following limitation(s):
wherein the stored set of ranked terms are domain-specific and manually marked and configured by one or more users, (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim 6, 13 and 20 recites the following limitation(s):
wherein the stored set of ranked terms are automatically generated using one or more of usage data, a number of policies related to the term, a number of rules related to the term, a number of reports related to the term, and relationships of the term to one or more other high-ranking terms, (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim 7 and 14 recites the following limitation(s):
further comprising: continuously monitoring requests for analysis data that was previously deleted or moved to the cold storage, and in response to detecting a request for analysis data that was previously deleted or moved
Accordingly, under its broadest reasonable interpretation, covers performance of the highlighted limitation(s) in the mind but for the recitation of generic computer components. That is, other than reciting “computer-based method” “computer system,” “processors,” “computer-readable memories,” “computer-readable tangible storage medium,” nothing in the claim element(s) precludes the step(s) from practically being performed in the human mind using observation, evaluation, judgment, and opinion. As such, the claim(s) falls within the “Mental Processes” grouping of abstract ideas. Therefore, the claim(s) recites an abstract idea.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application. The claim(s) recites the following additional elements:
Claim 1, recites: computer-based method; Claim 8, recites: computer system, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories; Claim 15 recites: computer program product, the computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor; (all of which are recited at high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer)
Claims 1, 12 and 20 further recites:
within a system (having the request be made within a system, is equivalent to 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)))
cold storage, (the use of a cold storage, is equivalent to 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)))
Claim 7 and 14 recites the following limitation(s):
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)))
Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim(s) are directed to an abstract idea.
Step 2B: The claim(s) does not include additional element(s) 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 element(s) amounts to no more than mere instructions to apply the exception using a generic computer and thus are mere instructions to apply an exception using a generic computer component-see MPEP 2106.05(f). Also, the additional element(s) amounts to no more than mere data gathering and output recited at a high level of generality and thus are insignificant extra-solution activity - see MPEP 2106.05(g). Also, receiving or transmitting data over a network, performing repetitive calculations, storing information in memory simply is appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 8-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kabra et al. (US 20210081435 A1) in view of Cherubini et al. (US 20160062689 A1).
Regarding claim 1, Kabra discloses:
a computer-based method, the method comprising: automatically detecting a data analysis request made within a system and identifying one or more subject datasets, at least by (paragraph [0024] “a computer system 102 responsible for data classification receives data from a data source 104… receipt of data triggers a data analysis workflow…data analysis workflow is to classify the received data”)
automatically conducting shallow term assignments on each row and column of data in the subject datasets, the shallow term assignments comprising a domain classification, at least by (paragraph [0031] “assign terms/tags to a target column to be classified; paragraph [0035] “terms directed to a specific business domain”)
automatically matching the shallow term assignments for each row and column with a stored set of ranked terms, at least by (paragraph [0032] “Terms of the glossary could then potentially be mapped to data classes in the collection of candidate data class so that data classes in the available collection can be correlated to target dataset columns.” Paragraph [0038] “this can determine confidence levels of associations between term(s) and the candidate data classes in the collection of candidate data classes that are available for classifying target columns of data, and can further assign the terms to the candidate data classes. Data class(es) selected for comparison to value(s) of the target column can include those classes that have a confidence level of related terms above a set threshold value of confidence.” Where the confidence level describes the ranking)
automatically flagging rows or columns matching with ranked terms in the stored set of ranked terms that are above a predetermined threshold ranking for performing further analysis, Paragraph [0038] “this can determine confidence levels of associations between term(s) and the candidate data classes in the collection of candidate data classes that are available for classifying target columns of data, and can further assign the terms to the candidate data classes. Data class(es) selected for comparison to value(s) of the target column can include those classes that have a confidence level of related terms above a set threshold value of confidence.”)
automatically and continuously monitoring and detecting irrelevant metadata types within the flagged rows or columns by continuously utilizing historical usage data to prevent subsequent analysis and storage of data including the irrelevant metadata types, at least by (paragraph [0037] “the historical classification characteristics identify classifications that are statistically likely to coexist. Historical outcomes of classification results can be tracked for each of several columns of data. The classification of each such column into a respective data class of a collection of candidate data classes that are available for classifying the column of data are tracked to produce characteristics of the classifications. The characteristics are knowledge—confidence levels, clusters, trends, related terms, etc.—that can be applied to the classification of other target columns. Scanning of the historical classifications can occur continuously, periodically or aperiodically, and clusters can be updated accordingly to add or remove data classes from them.)
But Kabra fails to specifically recite:
in response to detecting a stored analysis dataset, automatically generating a criticality ranking for the stored analysis dataset, the criticality ranking comprising a numerical probability of usability for the stored analysis dataset;
and in response to detecting low priority analysis datasets having the criticality ranking below a criticality threshold, automatically placing the low priority analysis datasets into cold storage.
However, Cherubini describes the above limitations at least by (paragraph [0030] which describes ranking data segments based on descriptors in order of relevance class (e.g. criticality ranking comprising a numerical probability of usability for the stored analysis dataset) and further paragraph [0094] describing a particular threshold for the relevance class associated with different tier of storage where “data segments assigned to the lowest relevance class are removed” and other tier related to lower relevance class includes (para. 0057) “other storage tiers of the storage unit, also referred to as target storage tiers. For example, when the popularity of a data segment increases it can be desirable to move it from a present slow storage tier to a faster storage tier to enable quicker access. When the popularity of a data segment decreases it can be desirable to move it from a fast present storage tier to a slower storage tier to free up space for other popular data segments”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Kabra with the ability to place less popular data in slower storage or for removal described by Cherubini to improve storage efficiency (Cherubini, para. 0077).
As per claim 2, claim 1 is incorporated and Kabra further fails to describe:
wherein, in response to detecting the low priority analysis datasets having the criticality ranking below the criticality threshold further comprises; automatically purging the low priority datasets.
However, Cherubini describes the above limitations at least by (paragraph [0030] which describes ranking data segments based on descriptors in order of relevance class (e.g. criticality ranking comprising a numerical probability of usability for the stored analysis dataset) and further paragraph [0094] describing a particular threshold for the relevance class associated with different tier of storage where “data segments assigned to the lowest relevance class are removed”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Kabra with the ability to place less popular data in slower storage or for removal described by Cherubini to improve storage efficiency (Cherubini, para. 0077).
As per claim 3, claim 1 is incorporated and Kabra further fails to describe:
wherein the criticality ranking for the stored analysis datasets is generated by considering historical usage data, matches with the ranked terms, associated governance policies or rules, and past user interactions.
However, Cherubini describes the above limitations at least by (paragraph [0030] which describes ranking data segments based on descriptors in order of relevance class, where the descriptors and class are related to: paragraph [0082] which describe context and classification of the data related to particular types associated with certain level of importance (e.g. matches with the ranked terms); paragraph [0040] “monitoring of data segment accesses by the access pattern evaluator it can be determined which relevance classes or which corresponding data segments are more popular than others” (e.g. historical usage data, past user interactions) and paragraph [0064] “e policies, according to which data is moved across different storage tiers and hence different types of storage devices, depend on access pattern characteristics and in addition preferably on the assigned relevance class.” (e.g. governance policies or rules))
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Kabra with the ability to place less popular data in slower storage or for removal described by Cherubini to improve storage efficiency (Cherubini, para. 0077).
As per claim 4, claim 1 is incorporated and Kabra further describes:
wherein automatically conducting the shallow term assignments on each row and column of data in the subject datasets further comprises: automatically considering metadata and naming for each row and column, at least by (paragraph [0032] “used in column names, metadata, term assignments”, see also paragraph [0035, 0038, 0040, 0047])
As per claim 5, claim 1 is incorporated and Kabra further describes:
wherein the stored set of ranked terms are domain-specific and manually marked and configured by one or more users, at least by (paragraph [0031] “terms could be manually assigned by a user/admin and/or automatically assigned” paragraph [0032] “Terms of the glossary could then potentially be mapped to data classes in the collection of candidate data class so that data classes in the available collection can be correlated to target dataset columns.” Paragraph [0038] “this can determine confidence levels of associations between term(s) and the candidate data classes in the collection of candidate data classes that are available for classifying target columns of data, and can further assign the terms to the candidate data classes. Data class(es) selected for comparison to value(s) of the target column can include those classes that have a confidence level of related terms above a set threshold value of confidence.” Where the confidence level describes the ranking. Paragraph [0072] “candidate data classes related by common terms of a glossary of terms directed to a specific business domain.”)
As per claim 6, claim 1 is incorporated and Kabra further describes:
wherein the stored set of ranked terms are automatically generated using one or more of usage data, a number of policies related to the term, a number of rules related to the term, a number of reports related to the term, and relationships of the term to one or more other high-ranking terms, at least by (paragraph [0032] “Terms of the glossary could then potentially be mapped to data classes in the collection of candidate data class so that data classes in the available collection can be correlated to target dataset columns.” Paragraph [0035] “a cluster that clusters candidate data classes related by common terms of a glossary of terms” Paragraph [0038] “a term assignment to columns using data governance tools, to associate, based on, e.g., a column name of a target column of data, one or more terms with the column of data, thereby tagging the column with tag(s)/term(s). Data governance tools refers to programs/rules governing the overall management of data availability, relevancy, usability, integrity and security in the enterprise. Different dataset classes can be associated with each of those terms. Through machine learning, a confidence in such association between terms and the dataset classes can be established. Therefore, in the tracking of the classification of columns of data, this can determine confidence levels of associations between term(s) and the candidate data classes in the collection of candidate data classes that are available for classifying target columns of data, and can further assign the terms to the candidate data classes. Data class(es) selected for comparison to value(s) of the target column can include those classes that have a confidence level of related terms above a set threshold value of confidence.” Where the confidence level describes the ranking. Paragraph [0053] “0055] Create an association between term(s) derived from the column name using governance tools and different dataset classes, and create confidence for the association based on the number/percentage of times the term is associated to the data class”)
Claims 8-13 recite equivalent claim limitations as claims 1-6 above, except that they set forth the claimed invention as a system; Claims 15-20 recite equivalent claim limitations as claims 1-6 above, except that they set forth the claimed invention as a computer program product, the computer program product comprising: one or more computer-readable tangible storage medium, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kabra and Cherubini further in view of Kumarasamy (US 20160292040 A1).
As per claim 7, claim 1 is incorporated and Kabra further fails to describe:
further comprising: continuously monitoring requests for analysis data that was previously deleted or moved to the cold storage, and in response to detecting a request for analysis data that was previously deleted or moved to cold storage, automatically flagging the corresponding requests and analysis data.
However, Kumarasamy (US 20160292040 A1) describes the above limitations at least by (paragraph [0289] “when the data set satisfies archiving criteria… cause the data set to be archived to the secondary storage device, including causing the archived data set to be removed from NAS 204-1… generate… stubs for the archived data set … to be stored to NAS 204-1”; paragraph [0290, 0291, 0295,0296, 0299] describes monitoring data access request for achieved data (e.g. continuously monitoring requests for analysis data that was previously deleted or moved to the cold storage), then, “intercepting access calls to NAS 204-1 and reporting on them to open-archive module 242” where the described report (i.e. programmatically flags) each intercepted request to the server for further analysis/decision making therefore teaches flagging the corresponding requests and analysis data)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Kabra with Kumarasamy to provide access to archived data without user intervention (Kumarasamy, Abstract).
Claim(s) 14 recite equivalent claim limitations as claim(s) 7 above, except that they set forth the claimed invention as a system, as such they are rejected for the same reasons as applied hereinabove.
Conclusion
Related reference(s) not relied upon:
US 20200410116 A1: set of classification rules to a data portion to obtain an output of whether the data is sensitive data (abstact).
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 09/17/2025