DETAILED ACTION
This is the initial Office action based on the application filed on June 28, 2024. Claims 1-20 are currently pending and have been considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 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 – 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).
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 2-10 and 12-19 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.
Claim Rejections - 35 USC § 103
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.
Claims 1-7, 10-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Spisic et al (US Patent Application Publication 2018/0322130) in view of Cheng et al (US Patent Application Publication 2023/0153311).
Claims 1 and 11: Spisic discloses a method and a system comprising:
identifying a target dataset within a plurality of datasets, wherein each of the plurality of datasets comprises a plurality of similar attribute types [0021]. [See at least establishing a set of subgroups.]
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 [0021-0022, 0036]. [See at least creating subgroups.]
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 [0021-0022, 0036]. [See at least using a tightness factor.]
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 [0044-0046]. [See at least using an information criterion.]
Spisic alone does not explicitly disclose 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; and returning the benchmark attribute type and the outlier value.
However, Spisic [0044-0046] discloses comparing parameters in subclusters and Cheng [0029] discloses detecting anomalies in datasets based at least on parameters and reporting the anomaly to a user.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing data to modify Spisic with Cheng. One would have been motivated to do so in order to have appropriate data in data clusters.
Claims 2 and 12: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Cheng, for the same reasons as above, further discloses wherein returning the benchmark attribute type and the outlier value comprises: adjusting a parameter of a server controller according to at least one of the benchmark attribute type and the outlier value [0029]. [See at least configuring an anomaly detector.]
Claims 3 and 13: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Spisic further discloses wherein returning the benchmark attribute type and the outlier value further comprises: predicting an action to change a target value of the target attribute type; and presenting the action [0020]. [See at least using predictive analysis.]
Claim 4: Spisic as modified discloses the method of Claim 1 above, and Cheng, for the same reasons as above, further discloses returning an identity of the outlier dataset [0029].
Claims 5 and 14: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Spisic further discloses 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; and identifying the cluster of datasets as ones of the remaining datasets for which the plurality of distances satisfy a threshold distance [0057-0058]. [See at least calculating distances between sub-clusters.]
Claims 6 and 15: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Spisic further discloses wherein applying the second clustering model comprises clustering the cluster of datasets according to selected attribute values of a selected attribute type among the plurality of similar attribute types [0039].
Claims 7 and 16: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Spisic in view of Cheng, for the same reasons as above, further disclose wherein identifying the outlier value comprises one of: identifying a highest benchmark value of the benchmark attribute type for a selected dataset in the at least one of the first subcluster and the second subcluster, wherein the outlier value comprises the highest benchmark value; and identifying a plurality of benchmark values of the benchmark attribute type for datasets in the at least one of the first subcluster and the second subcluster, wherein the plurality of benchmark values satisfy a threshold value, and wherein the plurality of benchmark values comprise the outlier value [See at least Cheng [0025] where at outlier is greater than a threshold value (i.e. highest value).]
Claims 10 and 19: Spisic as modified discloses the method and the system of Claims 1 and 11 above, and Spisic in view of Cheng, for the same reasons as above, further disclose wherein identifying the benchmark attribute type comprises: identifying a selected dataset in the first subcluster or the second subcluster, wherein the selected dataset comprises a selected attribute type of the plurality of similar attribute types that has a selected attribute value above a threshold value, and specifying the selected attribute type as the benchmark attribute type [See at least Spisic [0044-0045].]
Claims 8-9 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Spisic et al (US Patent Application Publication 2018/0322130) in view of Cheng et al (US Patent Application Publication 2023/0153311) and further in view of Matei et al (US Patent 11,983,209).
Claims 8 and 17: Spisic as modified discloses the method and the system of Claims 1 and 11 above, but Spisic alone does not explicitly disclose wherein identifying the target dataset comprises: receiving a query requesting the target dataset be compared to the plurality of datasets.
However, Matei (Col 2 ln 17-32, Col 5 ln 55-67) discloses receiving a query for a target dataset and then compares vectors to identify a proper dataset.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing data to modify Spisic with Matei. One would have been motivated to do so in order to identify appropriate datasets.
Claims 9 and 18: Spisic as modified discloses the method and the system of Claims 1 and 11 above, but Spisic alone does not explicitly disclose wherein identifying the benchmark attribute type comprises: receiving the benchmark attribute type from a query requesting the target dataset be compared to the plurality of datasets.
However, Matei (Col 2 ln 17-32, Col 5 ln 55-67) discloses receiving a query including a particular vector (i.e. attribute type) and then compares vectors to identify a proper dataset.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing data to modify Spisic with Matei. One would have been motivated to do so in order to identify appropriate datasets.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ranjan (2023/0350915) describes at least identifying anomalous parameters.
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/ALEX GOFMAN/Primary Examiner, Art Unit 2163