DETAILED ACTION
Amendment submitted May 13, 2026 has been considered by examiner. Claims 1, 3-11 and 13-20 are pending.
Response to Arguments
Applicant's arguments filed May 13, 2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The Applicant states that “the claims represent a practical application because the claimed subject matter shows an improvement in computer functionality. MPEP § 2106.05(a).” The Examiner respectfully disagrees.
The Applicant specifically believes that a limitation “returning the benchmark attribute type and the outlier value by adjusting a parameter of a server controller according to at least one of the benchmark attribute type and the outlier value.” However, the feature of adjusting a parameter of a server controller does not discuss how that is accomplished technologically. As such, it is not clear how such adjusting would improve functionality of a computing system. Furthermore, there is no sufficient explanation as to why a result of adjusting the parameter indicates improvement in the functionality of the computing system.
Moreover, an example provided in para [0021] discusses that “a server controller may be reprogrammed to impose tighter controls on expenses that correspond to the benchmark attributes.” That would imply that changing a parameter of expenses (such as expenses for a business) and another attribute would result in improvement of a computing system. However, there was no explanation provided as to why or how changing at least an expanses parameter would improve functionality of a computing system.
As such, the 35 USC 101 rejection is maintained.
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, 3-1 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The Claims recite abstract subject matter directed towards comparing datasets.
Specifically, Independent Claims 1 and 11 recite:
identifying a target dataset within a plurality of datasets, wherein each of the plurality of datasets comprises a plurality of similar attribute types – Identifying a particular dataset is something that a person can perform in the mind.
applying a first clustering model to the plurality of datasets and the target dataset to generate a cluster of datasets comprising fewer datasets than the plurality of datasets, wherein the first clustering model clusters according to a similarity attribute type – Grouping similar datasets is something that a person can perform in the mind.
applying a second clustering model to the cluster of datasets to generate a first subcluster of the cluster of datasets and a second subcluster of the cluster of datasets, wherein the second clustering model clusters according to a performance attribute type, different than the similarity attribute type – Grouping datasets based on a particular attribute is something that a person can perform in the mind.
identifying a benchmark attribute type, comparable to a target attribute type of the target dataset, in at least one of the first subcluster and the second subcluster, wherein the similarity attribute type, the performance attribute type, the benchmark attribute type, and the target attribute type are members of the plurality of similar attribute types – Identifying data types based on particular attributes is something that a person can perform in the mind.
identifying an outlier value for the benchmark attribute type of an outlier dataset in the at least one of the first subcluster and the second subcluster – Identifying a particular value that is abnormal is something that a person can perform in the mind.
returning the benchmark attribute type and the outlier value by adjusting a parameter of a server controller according to at least one of the benchmark attribute type and the outlier value – Returning a value is a generic function of a computer as discussed at least at MPEP 2106.05. Furthermore, returning a value is extra-solution activity as discussed at least at MPEP 2106.05(g). As to the adjusting feature, adjusting how a user determines a particular value is something that a person can perform in the mind. Also, adjusting a particular variable in a software may be considered a well-understood, routine and conventional operation of a computing system.
Independent Claim 20 recites:
identifying a target dataset within a plurality of datasets, wherein each of the plurality of datasets comprises a plurality of similar attribute types - Identifying a particular dataset is something that a person can perform in the mind.
applying a first clustering model to the plurality of datasets and the target dataset to generate a cluster of datasets comprising fewer datasets than the plurality of datasets - Grouping similar datasets is something that a person can perform in the mind.
wherein applying the first clustering model further comprises: comparing, to determine a plurality of distances, i) target values of the plurality of similar attribute types for the target dataset to ii) corresponding values of the plurality of similar attribute types for remaining datasets in the plurality of datasets – Determining distances between datasets is something that a person can perform in the mind. Also, calculating distances could be considered a mathematical operation, and is thus abstract.
identifying the cluster of datasets as ones of the remaining datasets for which the plurality of distances satisfy a threshold distance – Identifying datasets based on particular values is something that a person can perform in the mind.
applying a second clustering model to the cluster of datasets to generate a first subcluster of the cluster of datasets and a second subcluster of the cluster of datasets by clustering according to a performance attribute type different than the similarity attribute type - Grouping datasets based on a particular attribute is something that a person can perform in the mind.
wherein applying the second clustering model further comprises: clustering the cluster of datasets according to selected attribute values of a first selected attribute type among the plurality of similar attribute types - Grouping datasets based on a particular attribute is something that a person can perform in the mind.
identifying a benchmark attribute type, comparable to a target attribute type of the target dataset, in at least one of the first subcluster and the second subcluster - Identifying data types based on particular attributes is something that a person can perform in the mind.
wherein the similarity attribute type, the performance attribute type, the benchmark attribute type, and the target attribute type are members of the plurality of similar attribute types - Identifying data types based on particular attributes is something that a person can perform in the mind.
wherein identifying the benchmark attribute type comprises identifying a first selected dataset in the first subcluster or the second subcluster, wherein the first selected dataset comprises a second selected attribute type of the plurality of similar attribute types that has a selected attribute value above a threshold value – Grouping data based on particular attribute values is something that a person can perform in the mind.
specifying the second selected attribute type as the benchmark attribute type – Identifying a particular attribute type is something that a person can perform in the mind.
identifying an outlier value for the benchmark attribute type of an outlier dataset in the at least one of the first subcluster and the second subcluster, wherein identifying the outlier value comprises identifying a highest benchmark value of the benchmark attribute type for a second selected dataset in the at least one of the first subcluster and the second subcluster, wherein the outlier value comprises the highest benchmark value - Identifying a particular value that is abnormal is something that a person can perform in the mind.
adjusting a parameter of a server controller according to at least one of the benchmark attribute type and the outlier value – Adjusting how a user determines a particular value is something that a person can perform in the mind.
This judicial exception is not integrated into a practical application. Other, the abstract idea, the claims recite additional elements of hardware such as a processor, memory, etc executing the abstract idea. The additional elements are recited at a high level of generality, i.e. as generic computer components performing generic computer functions of information processing. 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.
Dependent Claims 3-10 and 13-19 further describe more details of the above identified mental processes and thus do not provide additional elements that would make them statutory under 35 USC 101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX GOFMAN whose telephone number is (571)270-1072. The examiner can normally be reached Monday-Friday 8-5.
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/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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