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 performance metrics of one or more hardware resources over a first window of time (i.e., mathematical expressions)
• detecting a transition point in additional training data (i.e., mental steps)
• the additional training data tracks the performance metrics of the one or more hardware resources (i.e., mathematical expressions)
• 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 performance metrics of the one or more hardware resources (i.e., mental steps)
• generating, …, an output that identifies at least one anomaly in the system behavior that causes performance degradation of the one or more hardware resources (i.e., mental steps)
• the at least one operation comprises one or more of: …, or updating a configuration of the one or more hardware resources (i.e., mathematical expressions because it could be a parameter update)
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 performance metrics of one or more hardware resources over a first window of time (i.e., mathematical expressions)
• detecting a transition point in additional training data (i.e., mental steps)
• the additional training data tracks the performance metrics of the one or more hardware resources (i.e., mathematical expressions)
• 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 performance metrics of the one or more hardware resources (i.e., mental steps)
• generating, …, an output that identifies at least one anomaly in the system behavior that causes performance degradation of the one or more hardware resources (i.e., mental steps)
• the at least one operation comprises one or more of: …, or updating a configuration of the one or more hardware resources (i.e., mathematical expressions because it could be a parameter update)
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 system behavior”
(3) “executing, based on the output, at least one operation to address the the performance degradation of the one or more hardware resources”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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 system behavior” 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)).
(3) An “executing, based on the output, at least one operation to address the the performance degradation of 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 the performance degradation of 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 system behavior”
(3) “executing, based on the output, at least one operation to address the the performance degradation of the one or more hardware resources”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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)).
(3) An “executing, based on the output, at least one operation to address the the performance degradation of 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 seasonal anomalous 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 baseline 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 performance metrics of one or more hardware resources over a first window of time (i.e., mathematical expressions)
• detecting a transition point in additional training data (i.e., mental steps)
• the additional training data tracks the performance metrics of the one or more hardware resources (i.e., mathematical expressions)
• 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 performance metrics of the one or more hardware resources (i.e., mental steps)
• generating, …, an output that identifies at least one anomaly in the system behavior that causes performance degradation of the one or more hardware resources (i.e., mental steps)
• the at least one operation comprises one or more of: …, or updating a configuration of the one or more hardware resources (i.e., mathematical expressions because it could be a parameter update)
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 system behavior”
(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”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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 system behavior” 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)).
(3) A “processor” 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)).
(4) 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)).
(5) 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 system behavior”
(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”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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)).
(3) A “processor” 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)).
(4) A “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)).
(5) 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 seasonal anomalous 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 baseline 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 performance metrics of one or more hardware resources over a first window of time (i.e., mathematical expressions)
• detecting a transition point in additional training data (i.e., mental steps)
• the additional training data tracks the performance metrics of the one or more hardware resources (i.e., mathematical expressions)
• 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 performance metrics of the one or more hardware resources (i.e., mental steps)
• generating, …, an output that identifies at least one anomaly in the system behavior that causes performance degradation of the one or more hardware resources (i.e., mental steps)
• the at least one operation comprises one or more of: …, or updating a configuration of the one or more hardware resources (i.e., mathematical expressions because it could be a parameter update)
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 system behavior”
(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”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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 system behavior” 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)).
(3) A “processor” 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)).
(4) A “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)).
(5) 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 system behavior”
(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”
(1) 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)).
(2) An “unsupervised training of a second model”/“generating, by the second baseline model, an output that identifies at least one anomaly in the system behavior” 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)).
(3) A “processor” 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)).
(4) A “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)).
(5) 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. 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 anomalies in the system behavior, wherein the at least one machine learning process changes from the first baseline model to the second baseline model to monitor the 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 01 JUL 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues:
Argument 1
In response to Applicant's previous arguments relating to the rejection under 35 U.S.C. 101, the Office Action asserts: "Part (b) is merely using an unspecified model to detect unspecified 'performance issue' data. There is no actual application of the result." The Office Action further asserts: "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 respectfully submits that the claim amendments address the specific issues highlighted by the Office Action above. Specifically, the claims presently recite that "the at least one anomaly in the system behavior causes performance degradation of the one or more hardware resources." Thus, the claims now recite that the performance issue resides in the hardware itself as the anomalous system behavior causes the hardware performance to degrade, and the baseline models operate on "performance metrics of the one or more hardware resources."
In the broadest reasonable interpretation of the term “system behavior,” the claimed/argued “system behavior” may be calculative system behavior. It may be pure mathematics or mental steps.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 2
Additionally, the claims presently recite "executing, based on the output, at least one operation to address the performance degradation of the one or more hardware resources, wherein the at least one operation comprises one or more of: deploying one or more additional hardware resources to satisfy an increase in resource demand, bringing one or more hardware resources offline that are associated with the at least one anomaly in the behavior, or updating a configuration of the one or more hardware resources." As such, the claims recite an "actual application of the result." In particular, the output of the machine learning model is applied to improve the hardware resource performance by deploying, bringing offline, or otherwise configuring the hardware resources based on the detected anomaly.
In view of the above, Applicant respectfully submits that the claims are directed to specific ML-based anomaly detection techniques that improve the performance/functioning of hardware resources being monitored. Therefore, the claims recite a technical improvement pursuant to MPEP 2106.05(a) and are patentable under 35 U.S.C. § 101.
In the broadest reasonable interpretation of the term “otherwise configuring,” the claimed/argued “otherwise configuring” may be simply changing mathematical parameters (i.e., mathematical expressions.)
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 3
For the foregoing reasons, Applicant respectfully requests, reconsideration and withdrawal of the rejection of claims 1-19 and 21 under 35 U.S.C. § 101.
Regarding independent claims 10 and 19, similar arguments for similar claims are similarly unpersuasive. Regarding the claims that depend on independent claims one, 10, and 19, since there is no eligible matter in the independent claims, there is no eligible matter that may be incorporated by reference to the dependent claims to cure them.
Applicant’s argument is unpersuasive.
The rejections stand.
Conclusion
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
18 SEP 2026
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