DETAILED ACTION
Claims 1-5, 7-13, 15-18, 20, and 24-25 are pending.
Independent claims 1, 11, and 18 are amended.
Claims 1-5, 7-13, 15-20, and 24-25 are rejected.
Notice of 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/10/2025 has been entered.
Statutory Review under 35 USC § 101
Claims 1-5, 7-10, 22, and 24-25 are directed toward a system and have been reviewed.
Claims 1-5, 7-10, 22, and 24-25 initially appear to remain statutory, as the system includes hardware (one or more memories; and one or more processors) as disclosed in ¶ 0068 of the applicant’s specification, “Processor 420 is implemented in hardware, firmware, or a combination of hardware and software ... Memory 430 includes a random access memory, a read only memory, and/or another type of memory.”
However, claims 1-5, 7-10, and 22 are not patent-eligible as they perform a method directed to an abstract idea without significantly more.
Claims 11-13 and 15-17 are directed towards a method and have been reviewed.
However, claims 11-13 and 15-17 are not patent-eligible as they perform a method directed to an abstract idea without significantly more.
Claims 18-20 are directed toward an article of manufacture and have been reviewed.
Claims 18 and 20 initially appear to be statutory, as the article of manufacture excludes transitory signals (claim says non-transitory).
However, claims 18 and 20 are not patent-eligible as they perform a method directed to an abstract idea without significantly more.
Response to Arguments
Applicant's arguments filed 12/10/2025 have been fully considered but they are not persuasive.
Step 2A, Prong One (Remarks pp14-15)
Regarding claim 1, Applicant submits (p15 of Remarks 12/10/2025) that the features of at least claim 1 cannot practically be performed in the human mind.
Regarding claim 1, Applicant argues that the human mind is not capable of using a machine learning model to detect patterns, capable of transmitting information to a computing device, or capable of registering a dataset with a metadata repository.
The Examiner argues that mental process steps are still present in independent claim 1, resulted in continued subject matter eligibility determination.
The human mind is capable of detecting patterns in prefixes of file paths; the use of one or more trained machine learning models to do so is addressed in later steps of the subject matter eligibility determination.
Transmitting information to a computing device is not being analyzed as a mental process/judicial exception but is considered an additional computing element analyzed in later steps.
Registering a dataset with a metadata repository is not being analyzed as a mental process/judicial exception but is considered an additional computing element analyzed in later steps.
Step 2A, Prong Two (Remarks pp16-18)
Regarding claim 1, Applicant states that the claims integrate the alleged abstract idea into the practical application of dataset detection.
Applicant further states that claim 1 as a whole is integrated into a practical application at least because it is directed to improvements in the technical field of dataset detection (citing para. 0012-0013 of the instant specification).
Applicant concludes (pp17-18 of Remarks 12/109/2025) that claim 1 provides an improvement to a technology or technical field, the claims thus integrating the alleged abstract idea into a practical application, patent-eligible under at least Step 2A, Prong Two.
In response to Applicant’s arguments, the Examiner can see the merits in the intended improvement; however, the Examiner is not convinced at this time that the elements of the claim integrate the abstract idea into a practical application as the claims are currently structured.
The claims have been amended to indicate that a first level of information security is provided (by detecting the detecting datasets based on the file paths), further associated with preventing leakage of confidential information. Further, a second level of information security is associated with scanning the objects stored in the data storage system.
The claims are currently structured in a manner where these techniques are implemented using a computer.
An interpretation of the new language is that there are two levels of information security at play (to prevent leakage of confidential information), one level being higher than the other, the other involving scanning the objects stored in the data storage system.
The claims bear heavy similarity to traditional techniques of storing information, such as physically storing them in physical folders or cabinets or drawers. A human can perform “scanning the objects stored in the data storage system” by physically reading the file contents of a folder; this would be the required lower-level/second level of information security, as the human would have unfettered access to the entirety of the file/document.
However, a human performing the required/claimed detection associated with a first level of information security (associated with preventing leakage of confidential information) need only look at the right folder, marked in a manner akin to the requirement of the claims of the detecting being “based on the file paths.”
As a result, despite the amendments to the claims, based on the language of “detecting the detected datasets” and “scanning the objects” “stored in the data storage system,” the claims are not considered be an abstract idea integrated into a practical application (Step 2A, Prong Two). The involvement of the detection being “based on the file paths” is more akin to generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2016.04(d), Section I).
This is also reflected by the previous analysis of the claim limitation of “wherein the inventory data identifies file paths for objects stored in the data storage system” also referring to generally linking the use of a judicial exception to a particular technological environment or field of use.
The claims are not considered to be drawn to patent-eligible subject matter at this time.
Claims 1, 11, and 18 remain rejected under 35 U.S.C. 101. The dependent claims also remain rejected under 35 U.S.C. 101.
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.
(I)
Claims 1-5, 7-10, and 24-25 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One
Independent claim 1 recites detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification, which is a mental process (including an observation, evaluation, judgment, opinion).
Normalizing prefixes specifically is considered to be a mental process according to MPEP 2106.04(a)(2), Abstract Idea Groupings, III. Mental Processes, “See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed ‘conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally,’ i.e., ‘as a person would do it by head and hand.’)”
Independent claim 1 also recites counting a number of unique folders in a group, which is a mathematical concept.
Step 2A, Prong Two
This judicial exception of detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification and counting a number of unique folders in a group is not integrated into a practical application despite the following generically recited computer elements, which amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
detect patterns … using one or more trained machine learning models;
This additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
receive inventory data associated with a data storage system,
receive, from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
These additional elements are mere data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
transmit, to a computing device, information identifying the detected datasets in the data storage system and the respective registration classification determined for each detected dataset.
These additional elements are mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional elements shown below:
…the objects stored in the data storage system…
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
receive inventory data associated with a data storage system,
receive, from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
transmit, to a computing device, information identifying the detected datasets in the data storage system and the respective registration classification determined for each detected dataset.
This performs receiving or transmitting data over a network, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d), specifically MPEP § 2106.05(d)(II)(i).
wherein the inventory data identifies file paths for objects stored in the data storage system;
This is merely a nominal or token extra-solution component of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 2:
filter the file paths for the objects stored in the data storage system based on one or more filtering rules, prior to detecting the patterns in the prefixes of the file paths.
Filtering content is managing personal behavior, which falls under certain methods of organizing human activity and thus is an abstract idea (see MPEP 2106.04(a)(2), Abstract Idea Groupings, II, C, i, referring to BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016)).
Claim 3:
remove file paths for objects with non-data file extensions.
Claim 3 does not add a meaningful limitation as these are merely nominal or token extra-solution components of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 4 performs determining a probability, determining a second probability, detecting respective portions of the prefixes associated with regular expression pattern, and detecting portions of the prefixes associated with gibberish patterns, which are abstract ideas.
The use of one or more trained regular expression pattern detection machine learning models and the use of a trained gibberish detection machine learning model are additional elements that merely use a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
Claim 5:
replace the respective portions of the prefixes associated with each regular expression pattern with a label associated with that regular expression pattern; and
replace the portions of the prefixes associated with the gibberish patterns with a label associated with the gibberish patterns.
Claim 5 does not add a meaningful limitation as these are merely nominal or token extra-solution components of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 7 recites tokeniz[ing] the prefixes, resulting in vectors, and calculat[ing] similarity scores between vectors, which are mathematical concepts and thus comprise an abstract idea.
Relevantly, ¶ 0033 of the instant specification recites, “’Tokenization’ refers to splitting text into smaller units, such as individual words or phrases … For each prefix, the dataset detection system may convert the tokens (e.g., terms) in that prefix to numeric values, resulting in a vector of numeric values that represents that prefix.” This fortifies the interpretation that tokenizing is a mathematical concept.
See also relevant example 48 of the USPTO Subject Matter Eligibility guidance, specifically in 2024 AI Examples 47 through 49 (effective July 17, 2024), p19 and step (b) involving “converting the mixed speech signal x into a spectrogram … and obtaining feature representation X,” shown to be considered part of the mathematical concepts grouping of abstract ideas, and p18 and step (e) involving “applying binary masks to the clusters to obtain masked clusters,” considered in p21 to be a mathematical operation.
Claim 8 recites calculating term frequency-inverse document frequency (TF-IDF) values for terms in the prefixes, which is a mathematical concept and thus comprises an abstract idea.
Claim 9 recites calculating at least one of a cosine similarity, an edit distance, or a Jaccard distance between vectors, which are mathematical concepts and thus comprise an abstract idea.
Claim 10 recites determining a closest dataset based on similarity scores and determining a respective classification based on similarity scores, which are mental processes and thus comprise an abstract idea.
Claim 24 recites “generate a set of grouped prefixes in which valid partitions remain and invalid partitions are removed,” which does not add a meaningful limitation as these are merely nominal or token extra-solution components of the claim; see MPEP 2106.05(g) referring to mere data gathering and more notably selecting a particular data source or type of data to be manipulated.
Claim 25 provides further detail on the machine learning model(s); however claim 25 specifying that training uses observations including target variable values or uses observations that do not include target variable values does not add a meaningful limitation as these are merely nominal or token extra-solution components of the claim; see MPEP 2106.05(g) referring to mere data gathering and more notably selecting a particular data source or type of data to be manipulated.
(II)
Claims 11-13 and 15-17 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One
Independent claim 11 recites detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification, which is a mental process (including an observation, evaluation, judgment, opinion).
Normalizing prefixes specifically is considered to be a mental process according to MPEP 2106.04(a)(2), Abstract Idea Groupings, III. Mental Processes, “See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed ‘conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally,’ i.e., ‘as a person would do it by head and hand.’)”
Independent claim 11 also recites counting a number of unique folders in a group, which is a mathematical concept.
Step 2A, Prong Two
This judicial exception of detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification and counting a number of unique folders in a group is not integrated into a practical application despite the following generically recited computer elements, which amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
detecting patterns … using one or more trained machine learning models;
This additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
receiving, by a system, inventory data associated with a data storage system,
receiving, by the system and from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
These additional elements are mere data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional elements shown below:
receiving, by a system, inventory data associated with a data storage system,
receiving, by the system and from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
This performs receiving or transmitting data over a network, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d), specifically MPEP § 2106.05(d)(II)(i).
wherein the inventory data identifies file paths for objects stored in the data storage system;
This is merely a nominal or token extra-solution component of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
…the objects stored in the data storage system…
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 12 performs detecting respective portions of the prefixes associated with regular expression pattern, and detecting portions of the prefixes associated with gibberish patterns, which are abstract ideas.
The use of one or more trained regular expression pattern detection machine learning models and the use of a trained gibberish detection machine learning model are additional elements that merely use a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
Claim 13 describes replacing portions of prefixes with patterns, which does not add a meaningful limitation as these are merely nominal or token extra-solution components of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 15 recites tokeniz[ing] the prefixes, resulting in vectors, and calculat[ing] similarity scores between vectors, which are mathematical concepts and thus comprise an abstract idea.
Relevantly, ¶ 0033 of the instant specification recites, “’Tokenization’ refers to splitting text into smaller units, such as individual words or phrases … For each prefix, the dataset detection system may convert the tokens (e.g., terms) in that prefix to numeric values, resulting in a vector of numeric values that represents that prefix.” This fortifies the interpretation that tokenizing is a mathematical concept.
See also relevant example 48 of the USPTO Subject Matter Eligibility guidance, specifically in 2024 AI Examples 47 through 49 (effective July 17, 2024), p19 and step (b) involving “converting the mixed speech signal x into a spectrogram … and obtaining feature representation X,” shown to be considered part of the mathematical concepts grouping of abstract ideas, and p18 and step (e) involving “applying binary masks to the clusters to obtain masked clusters,” considered in p21 to be a mathematical operation.
Claim 16 recites determining a closest dataset based on similarity scores and determining a respective classification based on similarity scores, which are mental processes and thus comprise an abstract idea.
Claim 17 recites transmitting, to a computing device, information identifying the detected datasets in the data storage system and the respective registration classification determined for each detected dataset.
Step 2A, Prong Two
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Step 2B
This also performs receiving or transmitting data over a network, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d), specifically MPEP § 2106.05(d)(II)(i).
(III)
Claims 18 and 20 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One
Independent claim 18 recites detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification, which is a mental process (including an observation, evaluation, judgment, opinion).
Normalizing prefixes specifically is considered to be a mental process according to MPEP 2106.04(a)(2), Abstract Idea Groupings, III. Mental Processes, “See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed ‘conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally,’ i.e., ‘as a person would do it by head and hand.’)”
Independent claim 18 also recites counting a number of unique folders in a group, which is a mathematical concept.
Step 2A, Prong Two
This judicial exception of detecting patterns, normalizing prefixes, grouping objects, determining a valid partition, determining a percentage of objects, comparing a percentage to a threshold, detecting datasets, comparing prefixes, and determining a classification and counting a number of unique folders in a group is not integrated into a practical application despite the following generically recited computer elements, which amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
detect patterns … using one or more trained machine learning models;
This additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
receive inventory data associated with a data storage system,
receive, from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
These additional elements are mere data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional elements shown below:
…the objects stored in the data storage system…
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
receive inventory data associated with a data storage system,
receive, from a metadata repository that manages metadata for the objects stored in the data storage system, registered dataset information associated with a set of registered datasets that are registered with the metadata repository;
This performs receiving or transmitting data over a network, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d), specifically MPEP § 2106.05(d)(II)(i).
wherein the inventory data identifies file paths for objects stored in the data storage system;
This is merely a nominal or token extra-solution component of the claim and serves only to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 20 recites tokeniz[ing] the prefixes, resulting in vectors, and calculat[ing] similarity scores between vectors, which are mathematical concepts and thus comprise an abstract idea.
Relevantly, ¶ 0033 of the instant specification recites, “’Tokenization’ refers to splitting text into smaller units, such as individual words or phrases … For each prefix, the dataset detection system may convert the tokens (e.g., terms) in that prefix to numeric values, resulting in a vector of numeric values that represents that prefix.” This fortifies the interpretation that tokenizing is a mathematical concept.
See also relevant example 48 of the USPTO Subject Matter Eligibility guidance, specifically in 2024 AI Examples 47 through 49 (effective July 17, 2024), p19 and step (b) involving “converting the mixed speech signal x into a spectrogram … and obtaining feature representation X,” shown to be considered part of the mathematical concepts grouping of abstract ideas, and p18 and step (e) involving “applying binary masks to the clusters to obtain masked clusters,” considered in p21 to be a mathematical operation.
Allowable Subject Matter
Claims 1-5, 7-13, 15-20, and 24-25 remain otherwise allowable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patwardhan, U.S. Patent No. 10,417,180 (published September 17, 2019), "Fast Recovery Of Backup Cloud Gateway Following Crash Without Garbage Collection"; see Patwardhan col. 4, lines 12-38 describing using a prefix to rapidly identify corresponding objects and files to perform operations faster than conventional scanning, identifying, and matching, relevant to at least the independent claims newly amended to provide a first level higher than a second level associated with scanning objects stored in a data storage system.
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/J.P.F/Examiner, Art Unit 2153 December 27, 2025
/KRIS E MACKES/Primary Examiner, Art Unit 2153