DETAILED ACTION
Claims 1-19 and 21 have been examined.
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 U.S.C. § 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.
The claimed invention is directed to “mental steps” without significantly more.
The claims recite:
• training data that tracks metrics associated with one or more hardware resources over a first window of time (i.e., mathematical data)
• detecting a transition point in additional training data (i.e., mental steps)
• the additional training data tracks the metrics of the one or more hardware resources (i.e., mathematical data)
• switching, by a monitoring process, from using the first baseline model (i.e., a graph; See, FIG. 4) to the second baseline model (i.e., a graph; See, FIG. 4) to monitor one or more streams of the metrics of the one or more hardware resources (i.e., mental steps)
Claims 1-19 and 21 are rejected.
Claim 1
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “A method comprising …” Therefore, it is a “method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 1 that recite abstract ideas?
YES. The following limitations in Claim 1 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps”:
• training data that tracks metrics associated with one or more hardware resources over a first window of time
• detecting a transition point in additional training data
• the additional training data tracks the metrics of the one or more hardware resources
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) “An unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “at least one machine learning process, unsupervised training of a first model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
This “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) “An unsupervised training of a second model”/“ generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “unsupervised training of a second model”/“ generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 1 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 2
Claim 2 recites:
2. (Currently Amended) The method of Claim 1, wherein the first baseline model is a non-seasonal model to detect non-seasonal anomalous system behavior and the second baseline model is a seasonal model to detect anomalous seasonal system behavior.
Applicant’s Claim 2 merely teaches two unspecified models. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 2 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 3
Claim 3 recites:
3. (Currently Amended) The method of Claim 1, wherein the first baseline model is for a first season having a first seasonal period and the at least one machine learning process fits the first set of training data to the first baseline model, wherein the second baseline model is for a second season having a second seasonal period that is different than the first seasonal period and the at least one machine learning process fits the first set of training data and the additional training data to the second baseline model.
Applicant’s Claim 3 merely teaches partitioning data to different models and mathematical machine learning models. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 3 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 4
Claim 4 recites:
4. (Currently Amended) The method of Claim 1, further comprising: pausing training by the at least one machine learning process responsive to detecting the transition point; and resuming training by the at least one machine learning process responsive to receiving a second set of additional training data, wherein the second model is trained after resuming training.
Applicant’s Claim 4 merely teaches pausing and resuming training. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 4 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 5
Claim 5 recites:
5. (Currently Amended) The method of Claim 1, wherein the first baseline model is associated with a first set of one or more intervals for a first set of one or more patterns and the second baseline model is associated with a second set of one or more intervals for a second set of one or more patterns, wherein the first set of one or more intervals are different than the second set of one or more intervals.
Applicant’s Claim 5 merely teaches “associating” models with intervals. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 5 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 6
Claim 6 recites:
6. The method of claim 5, wherein the second set of one or more intervals are uncertainty intervals that indicate a greater or lesser amount of uncertainty than the first set of one or more intervals.
Applicant’s Claim 6 merely teaches uncertainty intervals. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 6 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 7
Claim 7 recites:
7. (Currently Amended) The method of Claim 1, further comprising: monitoring at least one time-series signal using the second baseline model.
Applicant’s Claim 7 merely teaches signal monitoring. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 7 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 8
Claim 8 recites:
8. (Currently Amended) The method of Claim 7, further comprising:
detecting anomalous behavior in the at least one time-series signal based on said monitoring the at least one time- series signal using the second baseline model; and
generating an alert responsive to detecting the anomalous behavior in the at least one time-series signal.
Applicant’s Claim 8 merely teaches detecting anomalous data and generating alert data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 8 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 9
Claim 9 recites:
9. (Currently Amended) The method of Claim 7, further comprising: monitoring the at least one time-series signal using the first baseline model before detecting the transition point.
Applicant’s Claim 9 merely teaches signal monitoring. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 9 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 10
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “10. One or more non-transitory computer-readable media storing instructions, which, when executed by one or more hardware processors, cause performance of operations comprising…” Therefore, it is a “non-transitory computer-readable medium” (or “product of manufacture”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 10 that recite abstract ideas?
YES. The following limitations in Claim 10 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps”:
• training data that tracks metrics associated with one or more hardware resources over a first window of time
• detecting a transition point in additional training data
• the additional training data tracks the metrics of the one or more hardware resources
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) processors
(4) non-transitory computer-readable media
(5) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “at least one machine learning process, unsupervised training of a first model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “unsupervised training of a second model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “unsupervised training of a second model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “processors” is a broad term which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
This “processors” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “non-transitory computer-readable media” is a broad term which is described at a high level. Applicant’s Specification recites:
[0129] The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1110. Volatile media includes dynamic memory, such as main memory 1106. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
This “non-transitory computer-readable media” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
This “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) An “unsupervised training of a second model”/“ generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) processors
(4) non-transitory computer-readable media
(5) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “unsupervised training of a second model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “processors” is a broad term which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “non-transitory computer-readable media” is a broad term which is described at a high level. Applicant’s Specification recites:
[0129] The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1110. Volatile media includes dynamic memory, such as main memory 1106. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 10 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 11
Claim 11 recites:
11. (Currently Amended) The one or more non-transitory computer-readable media of Claim 10, wherein the first baseline model is a non-seasonal model to detect non-seasonal anomalous system behavior and the second baseline model is a seasonal model to detect anomalous seasonal system behavior.
Applicant’s Claim 11 merely teaches two unspecified models. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 11 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 12
Claim 12 recites:
12. (Currently Amended) The one or more non-transitory computer-readable media of Claim 10, wherein the first baseline model is for a first season having a first seasonal period and the at least one machine learning process fits the first set of training data to the first baseline model, wherein the second baseline model is for a second season having a second seasonal period that is different than the first seasonal period and the at least one machine learning process fits the first set of training data and the additional training data to the second baseline model.
Applicant’s Claim 12 merely teaches partitioning data to different models and unspecified mathematical machine learning models. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 12 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 13
Claim 13 recites:
13. (Currently Amended) The one or more non-transitory computer-readable media of Claim 10, wherein the instructions further cause:
pausing training by the at least one machine learning process responsive to detecting the transition point; and
resuming training by the at least one machine learning process responsive to receiving a second set of additional training data, wherein the second model is trained after resuming training.
Applicant’s Claim 13 merely teaches pausing and resuming training. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 13 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 14
Claim 14 recites:
14. (Currently Amended) The one or more non-transitory computer-readable media of Claim 10, wherein the first baseline model is associated with a first set of one or more intervals for a first set of one or more patterns and the second baseline model is associated with a second set of one or more intervals for a second set of one or more patterns, wherein the first set of one or more intervals are different than the second set of one or more intervals.
Applicant’s Claim 14 merely teaches “associating” models with intervals. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 14 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 15
Claim 15 recites:
15. The one or more non-transitory computer-readable media of claim 14, wherein the second set of one or more intervals are uncertainty intervals that indicate a greater or lesser amount of uncertainty than the first set of one or more intervals.
Applicant’s Claim 15 merely teaches uncertainty intervals. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 15 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 16
Claim 16 recites:
16. (Currently Amended) The one or more non-transitory computer-readable media of Claim 10, wherein the instructions further cause: monitoring at least one time-series signal using the second baseline model.
Applicant’s Claim 16 merely teaches signal monitoring. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 16 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 17
Claim 17 recites:
17. (Currently Amended) The one or more non-transitory computer-readable media of Claim 16, wherein the instructions further cause:
detecting anomalous behavior in the at least one time- series signal based on said monitoring the at least one time-series signal using the second baseline model; and
generating an alert responsive to detecting the anomalous behavior in the at least one time-series signal.
Applicant’s Claim 17 merely teaches detecting anomalous data and generating alert data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 17 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 18
Claim 18 recites:
18. (Currently Amended) The one or more non-transitory computer-readable media of Claim 16, wherein the instructions further cause: monitoring the at least one time-series signal using the first baseline model before detecting the transition point.
Applicant’s Claim 18 merely teaches signal monitoring. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 18 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 19
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “19. A system comprising…” Therefore, it is a “system” (or “apparatus”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 19 that recite abstract ideas?
YES. The following limitations in Claim 19 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps”:
• training data that tracks metrics associated with one or more hardware resources over a first window of time
• detecting a transition point in additional training data
• the additional training data tracks the metrics of the one or more hardware resources
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) An unsupervised training of a second model/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) processors
(4) non-transitory computer-readable media
(5) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “at least one machine learning process, unsupervised training of a first model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “unsupervised training of a second model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
This “unsupervised training of a second model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “processors” is a broad term which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
This “processors” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “non-transitory computer-readable media” is a broad term which is described at a high level. Applicant’s Specification recites:
[0129] The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1110. Volatile media includes dynamic memory, such as main memory 1106. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
This “non-transitory computer-readable media” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
This “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A at least one machine learning process, unsupervised training of a first model
(2) An unsupervised training of a second model/“generating, by the second baseline model, an output that identifies at least one anomaly in the behavior of a computing resource, the behavior associated with performance issues associated with one or more hardware resources”
(3) processors
(4) non-transitory computer-readable media
(5) “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources”
An “at least one machine learning process, unsupervised training of a first model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “unsupervised training of a second model” is a broad term which is described at a high level. Applicant’s Specification recites:
[0041] In one or more embodiments, baselining and anomaly detection services 130 models system behavior from an input set of historical time-series data. Training the model may be performed without user input through unsupervised machine learning techniques. The unsupervised techniques may include automatically detecting seasonal patterns, approximating the behavior of each seasonal pattern, and determining a normal or other representative distribution for each seasonal pattern.
Note that the model may be any unsupervised learning model. Further, it may be used for numerous purposes to include “determin[ing]” any “representative distribution”.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “processors” is a broad term which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “non-transitory computer-readable media” is a broad term which is described at a high level. Applicant’s Specification recites:
[0129] The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1110. Volatile media includes dynamic memory, such as main memory 1106. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
An “executing, based on the output, at least one operation to address the performance issues associated with the one or more hardware resources” is a broad term for execution of unspecified functions on unspecified data, which is described at a high level. Applicant’s Specification recites:
[0124] For example, FIG. 11 is a block diagram that illustrates computer system 1100 upon which one or more embodiments may be implemented. Computer system 1100 includes bus 1102 or other communication mechanism for communicating information, and hardware processor 1104 coupled with bus 1102 for processing information. Hardware processor 1104 may be, for example, a general purpose microprocessor.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 19 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 21
Claim 21 recites:
21. (New) The method of Claim 1, wherein after detecting the transition point, the at least one machine learning process changes which baseline model is used to detect the anomalous system behavior, wherein the at least one machine learning process changes from the first baseline model to the second baseline model to monitor for the anomalous system behavior.
Applicant’s Claim 21 merely teaches switching between two unspecified models after a detection of data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 21 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 16 DEC 2025 have been fully considered but they are not persuasive. Specifically, Applicant argues:
Argument 1
Applicant respectfully submits that the claims presently recite limitations that are directed to (a) dynamically adapting unsupervised machine learning models to evolving system behavior, thereby improving the training and/or operation of the machine learning models and (b) improving the functioning of hardware resources by applying improved unsupervised machine learning models to efficiently detect and address performance issues associated with said hardware resources. As such, Applicant respectfully submits that the claims recite a practical application and/or an inventive concept.
Part (a) is merely training an unspecified, generic “unsupervised machine learning model,” which is merely the application of an unspecified generic model to an unspecified set of data/application. Further, it is well-understood, routine, and conventional.
Part (b) is merely using an unspecified model to detect unspecified “performance issue” data. There is no actual application of the result.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 2
A. The claim is not directed to an abstract idea under Alice Step 2A Prong One and/or are integrated into a practical application under Prong Two
As noted above, the claim is directed to a technical method for dynamically adapting unsupervised machine learning models to evolving system behavior by detecting a transition point in data and transitioning to a second trained model to address anomalous behavior. The steps recited in the claims recite more than mere mental processes or mathematical abstractions - the steps recite concrete machine learning training operations and system control actions that improve how anomalous behavior is detected and addressed in a computing environment.
***
Applicant respectfully submits that, similar to the claims at issue in Desjardins, the claims in the present application recite specific limitations that are directed to improving the training and operation of machine learning models themselves. Also similar to Desjardins, these improvements are emphasized in the specification and captured in the recited claims. For example, paragraphs 0031 and 0091 of the specification highlight that transitioning the baseline model allows for a model that evolves and "becomes more accurate over time", thereby improving training and operation of the model. Paragraph 0094 highlights that the techniques help to prevent "false flags." The specific steps of training the different baseline models, transitioning between the two, a monitoring process applying the different model before and after the transition is explicitly recited, capturing these technical improvements. Thus, as in Desjardins, the claimed features should be viewed as integrated into a practical application rather than characterized as an abstract idea divorced from its implementation.
Applicant does not even specify the type of unsupervised model, much less improvements on how to train it. Possible types of unsupervised models are: K-Means Clustering, Hierarchical Clustering, Autoencoders, Generative Adversarial Networks, Self-Organizing Maps, Competitive Neural Networks, etc. Each has a different structure and different training method. Applicant has addressed nothing in these matters and Applicant's references to unsupervised learning and training in the claims remain generic.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 3
B. The claims recite an inventive concept under Alice Step 2B
The Office Action, at p. 50, asserts that "Examiner did not assert that the claimed use is well understood, routine, and conventional. There is not practical application." However, Applicant respectfully submits, that, even assuming arguendo, there is no practical application, a claim may still recite an inventive concept if the claim recites additional elements other than what are well-understood, routine, and conventional activities previously known in the industry. (See MPEP 2106.05(d)), "Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well- understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis."). Thus, it is respectfully maintained that the claims also recite an inventive concept for at least this reason.
Regardless of whether Applicant has an “inventive concept” Applicant has not improved a “technology.”
Applicant's argument is unpersuasive.
The rejections stand.
Argument 4
Additionally, an analysis under step 2B for "computer-related technologies" requires a determination of "whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool." The claim presently purports to improve computer functionality by using unsupervised machine learning models to efficiently monitor, detect, and execute, based on the output of said models, operations addressing the performance issues associated with the one or more hardware resources. Thus, the claim does not invoke computers merely as a tool but includes express limitations for improving their performance.
Applicant does not specify what a “performance issue” is or whether that issue resides in the hardware, itself, or is merely an issue regarding algorithmic (mental step) or mathematical issues inside the computer. Applicant does not specify the claimed “association.”
Applicant's argument is unpersuasive.
The rejections stand.
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 inquiries concerning this communication or earlier communications from the examiner should be directed to Wilbert L. Starks, Jr., who may be reached Monday through Friday, between 8:00 a.m. and 5:00 p.m. EST. or via telephone at (571) 272-3691 or email: Wilbert.Starks@uspto.gov.
If you need to send an Official facsimile transmission, please send it to (571) 273-8300.
If attempts to reach the examiner are unsuccessful the Examiner’s Supervisor (SPE), Kakali Chaki, may be reached at (571) 272-3719.
Hand-delivered responses should be delivered to the Receptionist @ (Customer Service Window Randolph Building 401 Dulany Street, Alexandria, VA 22313), located on the first floor of the south side of the Randolph Building.
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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
28 MAR 2026