Detailed Action
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The IDS (Information Disclosure Statements) filed 4/26/23 have been entered.
3. Claims 1-20 are pending.
Claim Objections
4. Claim 11 is objected to because of the following informalities: Claim 11 recites “the method of claim 1” but should recite “the method of claim 10”. Claim 11 refers to the hardware registers of claim 10, and it appears to depend off claim 10. For purposes of examination, it will be treated as depending off claim 10, but appropriate correction is required.
Claim Rejections - 35 USC § 103
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-9, and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al “Wang” (US 20210306201 A1) and Vaughn et al “Vaughn” (US 20190347117 A1).
6. Regarding claim 1, Wang shows responsive to an issue occurring with a computer system, receiving, by a troubleshooting analysis engine, data from the computer system representing information about the computer system (para 28, 30, 41, 44 show when a problem or fault occurs in a computer network system, receiving diagnostic data information about the computer network system from an automated diagnostic engine of the network management system that monitors and diagnoses the problem to determine a root cause, para 63 show physical circuitry of the network management system and therefore of the diagnostic engine); searching, by the troubleshooting analysis engine, a database to identify an infrastructure of the computer system causally linked to the issue (para 40-41 show searching a database to find information related to the problem, para 41 shows this may be done by the automated diagnostic engine, and para 28, 50, 56, 65 show the diagnostic data received [and thus searched for comparison] includes infrastructure and hardware details of the computer system and affected components that are related to possible causes of the problem); processing, by the troubleshooting analysis engine, the data to identify a parameter of the computer system having an unexpected value (para 27, 41, 66, 146 show the diagnostic engine/network management system identifies a parameter that goes beyond the default range or threshold of values); and responsive to the identification of the parameter and the identification of the infrastructure, analyzing, by the troubleshooting analysis engine, the infrastructure using machine learning to identify a candidate cause of the issue (para 112, 124, 134, 145 show the parameter and infrastructure/hardware information is used by a machine learning model to identify candidate causes of the problem). Wang does not explicitly show the database stores infrastructure designs per se such that it is searched to identify a design infrastructure related to the problem. Vaughn however does show searching a design database to identify a design infrastructure related to an infrastructure/hardware problem (Figure 5, para 30, 31, 40, 44 show searching the content database for images which include diagrams of hardware and infrastructure design, para 23 shows this could include computer system infrastructure, para 31-32, 36, 42 show the searched image information relates to the extracted information about the troubleshooted problem). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have the database in Wang include the hardware/infrastructure designs like the design database in Vaughn, because it would provide an efficient way to analyse and compare infrastructure information related to a problem (Vaughn para 32).
7. Regarding claim 2, the analyzing comprises applying a machine learning classifier to, based on input data representing the infrastructure and the parameter, classify the input data as belonging to a class corresponding to the candidate cause (Wang para 46, 48, 50, 69, 100 show using the input data based on infrastructure/hardware information and parameter information, to classify the input data to a class corresponding to a most likely cause of the problem, para 47, 57, 100, 105 show the classifying is accomplished using a machine learning classifier model).
8. Regarding claim 3, Wang shows training the machine learning classifier based on historical data associated with other computer systems (para 56, 61, 80, 82 show the machine learning model for the classifier is based on historical data with related computer systems within the network).
9. Regarding claim 5, the analyzing further comprises providing a confidence that the candidate cause is a root cause of the issue (Wang para 66-68 and 105 show a confidence is provided that the possible cause is the root cause of the problem).
10. Regarding claim 6, Wang shows processing the data to identify the parameter comprises applying machine learning to determine an expected value or an expected range of values for the parameter (para 27, 40, 66, 146 show the information is processed using a machine learning model to identify a parameter that goes beyond the default range or threshold of values).
11. Regarding claim 7, processing the data to identify the parameter
comprises applying machine learning to identify a subset of the data correlated to the issue (Wang para 81, 90 show the machine learning model is used to identify particular data that is correlated with the problematic system performance).
12. Regarding claim 8, processing the data comprises extracting a
subset of data corresponding to a session and processing the subset of data to
identify the parameter (Wang para 41, 52, 66, 76 show identifying the parameter based on particular sets of data obtained from sessions from different devices).
13. Regarding claim 9, note the alternative recitation. Wang para 56, 57, 97 shows receiving data from the computer system comprises receiving data representing an identifier for a hardware component of the computer system.
14. Regarding claim 12, note the alternative recitation. Wang shows receiving data from the computer system comprises receiving data representing an operation history of the computer system (para 27, 41, 43, 47 show the data obtained represents records of various operational parameters).
15. Regarding claim 13, the design infrastructure comprises a hardware infrastructure or a software infrastructure (Wang para 28, 50, 56, 65 show the diagnostic data received includes infrastructure and hardware details, Wang para 28, 50, 51, 56 show the diagnostic data received includes software details; furthermore Vaughn para 30, 31, 40, 44 show the design infrastructure includes diagrams of hardware design – motivation to combine this design database with Wang is the same as that given for claim 1.
16. Regarding claim 14, Wang shows providing, by the troubleshooting
analysis engine, data representing a resolution for the candidate cause (para 28, 30, 52, 77 show providing rectifying action information
17. Regarding claim 15, Wang shows an apparatus comprising: a processor; and a memory to store instructions (para 148-149 show the processor and hardware memory storing instructions executed by the processor to carry out method steps) that, when executed by the processor, cause the processor to: responsive to an issue associated with a computer system, receive data from the computer system representing information about an issue associated with the computer system (para 28, 30, 41, 44 show when a problem or fault occurs in a computer network system, receiving diagnostic data information about the computer network system from an automated diagnostic engine of the network management system that monitors and diagnoses the problem to determine a root cause, para 63 show physical circuitry of the network management system and therefore of the diagnostic engine); process the data to identify a parameter of the computer system associated with the issue (para 27, 41, 66, 146 show the diagnostic engine/network management system identifies a parameter that goes beyond the default range or threshold of values); access a database associated with the computer system to receive data representing infrastructure of the computer system associated with the issue (para 40-41 show searching a database to find information related to the problem, para 41 shows this may be done by the automated diagnostic engine, and para 28, 50, 56, 65 show the diagnostic data received [and thus searched for comparison] includes infrastructure and hardware details of the computer system and affected components that are related to possible causes of the problem); and use machine learning to analyze the infrastructure to identify a candidate cause of the issue (para 112, 124, 134, 145 show the parameter and infrastructure/hardware information is used by a machine learning model to identify candidate causes of the problem). Wang does not explicitly show the database stores infrastructure designs per se such that it is searched to identify a design infrastructure related to the problem. Vaughn however does show searching a design database to identify a design infrastructure related to an infrastructure/hardware problem (Figure 5, para 30, 31, 40, 44 show searching the content database for images which include diagrams of hardware and infrastructure design, para 23 shows this could include computer system infrastructure, para 31-32, 36, 42 show the searched image information relates to the extracted information about the troubleshooted problem). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have the database in Wang include the hardware/infrastructure designs like the design database in Vaughn, because it would provide an efficient way to analyse and compare infrastructure information related to a problem (Vaughn para 32).
18. Regarding claim 16, Wang shows determining, based on the information, whether an application of rules identifies a root cause of a set of potential root causes for the issue (para 120, 144 show applying a rule to identify a root cause having a higher confidence compared to other possible root causes), wherein each rule of rules is associated with a root cause of the potential root cause and provides an indication of whether the information corresponds to the associated root cause (para 144, 146 show a rule is associated with a possible root cause and a confidence of how well it corresponds is provided); and determine to proceed with the processing, searching and analyzing based on the determination that the application of the rules does not identify the root cause (para 107, 116-117, 120, 147 show when the confidence of the identification based on the rules does not meet a threshold, then further diagnostic information is obtained and analyzed).
19. Regarding claim 17, a given rule of the rules provides an indication of whether the information violates a configuration rule (Wang para 120-121 show that a confidence is provided by the rule to indicate whether the obtained diagnostic information shows the hardware component violates a component configuration standard).
20. Regarding claim 18, Wang shows a non-transitory machine-readable storage medium to store machine-readable instructions (para 148-149 show a hardware memory storing instructions executed by a processor to carry out method steps) that, when executed by a machine, cause the machine to:
identify an infrastructure of a computer system associated with an issue of the computer system (Wang para 28, 30, 41, 44 show when a problem or fault occurs in a computer network system, receiving diagnostic data information about the computer network system, para 28, 50, 56, 65 show the diagnostic data received includes infrastructure and hardware details); receive data representing the identified infrastructure (para 40-41 show searching a database to find information related to the problem, and para 28, 50, 56, 65 show the diagnostic data received [and thus searched for comparison] includes infrastructure and hardware details of the computer system and affected components that are related to possible causes of the problem, para 56, 57, 97 shows receiving data from the computer system comprises receiving data representing identifiers for related hardware components of the computer system); receive data from the computer system representing information about the computer system (para 56, 57, 97 shows receiving data from the computer system comprises receiving data representing identifiers for hardware components of the computer system); and apply a machine learning classifier to, based on the data representing the identified infrastructure and the data representing the information about the computer system, identify a component of the identified infrastructure as being a candidate cause of the issue (para 112, 124, 134, 145 show the parameter and infrastructure/hardware information is used by a machine learning model to identify a candidate cause of the problem, para 124 in particular shows identifying a component as a candidate cause of the problem, para 46, 48, 50, 69, 100 show the identification is accomplished in part by classifying the input data to a class corresponding to a most likely cause of the problem, and para 47, 57, 100, 105 show the classifying is accomplished using a machine learning classifier model). Wang does not explicitly show the infrastructure information is design information per se such that design infrastructure is searched/analyzed to identify a design infrastructure related to the problem. Vaughn however does show searching a design database to identify a design infrastructure related to an infrastructure/hardware problem (Figure 5, para 30, 31, 40, 44 show searching the content database for images which include diagrams of hardware and infrastructure design, para 23 shows this could include computer system infrastructure, para 31-32, 36, 42 show the searched image information relates to the extracted information about the troubleshooted problem). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to analyse/search/identify design infrastructure information in Wang as is done in Vaughn, because it would provide an efficient way to analyse and compare infrastructure information related to a problem (Vaughn para 32).
21. Regarding claim 19, Wang shows identifying a plurality of candidate causes of the issue (para 44, 66, 91, 114 show the infrastructure/hardware information is used by the machine learning model to identify candidate causes of the problem).
22. Regarding claim 20, Wang shows applying a correlation rule based on the issue and the data from the computer system to identify the infrastructure (para 66, 81, 90, 101 show time correlated network data or confidence probability based on the problem and obtained data on the computer system, to identify specific hardware components). Wang does not explicitly show the infrastructure information is design information per se such that design infrastructure is searched/analyzed to identify a design infrastructure related to the problem. Vaughn however does show searching a design database to identify a design infrastructure related to an infrastructure/hardware problem (Figure 5, para 30, 31, 40, 44 show searching the content database for images which include diagrams of hardware and infrastructure design, para 23 shows this could include computer system infrastructure, para 31-32, 36, 42 show the searched image information relates to the extracted information about the troubleshooted problem). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to analyse/search/identify design infrastructure information in Wang as is done in Vaughn, because it would provide an efficient way to analyse and compare infrastructure information related to a problem (Vaughn para 32).
23. Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Vaughn and Sharma et al “Sharma” (US 2019/0005018 A1).
24. Regarding claim 4, note the alternative recitation. In addition to that mentioned for claim 3, Wang and Vaughn do not explicitly show the historical data comprises data representing at least one of issue tickets, logs, engineering advisories, or customer advisories, but Wang para 56, 61, 80, 82 for example show obtaining historical data relating to diagnosing a problem on the computer system network. Furthermore, Sharma para 50, 57, 58 show obtaining log data relating to diagnosing a problem on the computer system network. Log data is itself historical data on the system. It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to use log data as is done in Sharma in the diagnostic method of Wang, especially as modified by Vaughn, because it would provide an efficient way to use historical data to diagnose a problem.
25. Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Vaughn and Chen et al “Chen” (WO 2015117458 A1).
(Please see the attached copy of Chen that numbers paragraphs in the same manner as that used in the Action).
26. Regarding claim 10, in addition to that mentioned for claim 1, Wang and Vaughn do not explicitly show receiving data from the computer system
comprises receiving data representing contents of hardware registers per se of the computer system, such that a given hardware register of the hardware registers contains the value, but Wang para 28, 50, 56, 65 show the diagnostic data received includes infrastructure and hardware details, and para 112, 124, 134, 145 show the infrastructure/hardware information is used by a machine learning model to identify a candidate cause of the problem. Furthermore, Chen para 82 shows a diagnostic function receiving a value (which represents content) from a CPU hardware register to help determine a fault. It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to receive a CPU hardware register value in the diagnostic method of Wang, especially as modified by Vaughn, because it would provide useful diagnostic hardware information with which to diagnose the problem and help determine a candidate cause.
27. Regarding claim 11, note the alternative recitation. In addition to that mentioned for claim 10, Chen para 82 shows the given hardware register comprises a register of a central processing unit (CPU). Motivation to combine Chen with the diagnostic method of Wang as modified by Vaughn is the same as that mentioned for claim 10.
28. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Fan (US 2022/0391917 A1) shows using a machine learning model and diagnostic information to identify a root cause of a problem and provide a confidence level with a root cause recommendation.
b) Xiao (CN 103116538 A) shows a computer performance diagnostic system which identifies parameters associated with the a computer system being diagnosed and determines whether they exceed certain thresholds.
c) Singh (US 2021/0158146 A1) diagnoses a fault on a computer system.
29. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 Est.
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/STEVEN P SAX/ Primary Examiner, Art Unit 2146