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 .
DETAILED ACTION
2. The request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for Continued Examination under 37 CFR 1.114, the fee set forth in 37 CFR 1.17(e) has been paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed 10/10/2025 has been entered. An action on the RCE follows.
Summary of claims
3. Claims 1-10, 128, 10-20 are pending,
Claims 1, 8, 12 are amended,
Claims 11, 13 are previously cancelled,
Claims 1, 8, 12 are independent claims,
Claims 1-10, 12 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 7/3/2025, with respect to the rejection(s) of claim(s) 1-10, 12 under 103 have been fully considered and are persuasive.
Applicant argued on pages 8-9 that the cited references including Davios and Jain, Brahmajosyula did not teach the amended features cited in claim 1, such as, “analyzing the original data to obtain problem description information including obtaining a task document from a cloud task management database according to the device identification and time information.” Examiner respectfully disagrees and submits that at least Davios discloses a cloud-based platform ([0005]), and Davios discloses a troubleshooting session can comprise: a serial number, a problem statement, a status, a unique user ID, symptoms, a diagnoses, and actions pending, etc. ([0101]), timestamps information ([0122]), a problem statement will be searched by AIDE ([0101]), that is, information regarding a problem may be obtained based on a unique ID and timestamp data. Accordingly, the cited references including Davios and Jain, Brahmajosyula disclose the amended features in claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 1-4, 8, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Efstratios Davlos et al (US Publication 20140310222 A1, hereinafter Davlos), and in view of Navendu Jain et al (US Publication 20140006861 A1, hereinafter Jain), and Jagadeesh Brahmajosyula et al (US Publication 20150276433 A1, hereinafter Brahmajosyula).
As for independent claim 1, Davlos discloses: A method for fault diagnosis and solution recommendation (Davlos: Abstract, Troubleshooting a technical problem on a user device using a network-based remediation platform. Receiving problem statements relating to technical problems associated with a user device, activating a domain of cases, assigning a score for the cases based on a scoring algorithm, and determining one or more remediation actions to suggest based on the score; [0007], remediating technical problems with an electronic device using a network-based diagnostic platform containing a knowledge base, a diagnostic engine for processing optimization algorithms, a machine learning component, and a network interface configured for accepting input from users), the method comprising: obtaining original data including fault problem of a target device (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session), … analyzing the original data to obtain problem description information including obtaining a task document (Davlos: [0101], searching a problem statement) from a cloud task management database (Davlos: [0005], a cloud-based platform) according to the device identification and time information (Davlos: [0011], The platform can receive problem statements relating to technical problems associated with a user device and determine one or more remediation actions to suggest. The platform can analyze the details of the problem statement, activate a domain of cases, and assign a score for the cases based on a scoring algorithm; [0101], a unique ID; [0122], timestamp information); analyzing the problem description information to obtain a diagnosis report (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request); according to the diagnosis report, obtaining a video and/or document solution for the fault based on a cloud knowledge map, and recommending the solution to a user (Davlos: [0052], recommend instructions for remediation of the problem. In some embodiments of the present technology, the diagnostic platform 110 includes an analytics database 124 containing remediation objects such as text-based instructions, graphical instructions, instructional videos, links to external web pages providing instructions, forms used to send parts away for repair, warranty forms, software patches, software applications, etc);
Davlos discloses a troubleshooting and remediation system using a network-based diagnostic platform containing a knowledge base including rule tree hierarchies (both preprogrammed and generated by machine learning) (Davlos: [0008]) and end nodes (Davlos: [0116], cases are generated from decision trees with end nodes comprising an instruction or inference), Davlos does not clearly disclose nodes in the knowledge map, in an analogous art of automatic fault diagnosis and repair system, Jain discloses: wherein the knowledge map comprises (Jain: [0003], mapping individual phrases from the subset of phrases to classes of an ontology model and storing the individual phrases in a knowledge base): nodes representing the fault, video solution and/or document solution (Jain: [0073], Each class is represented by a corresponding node in FIG. 8, e.g., [Action node 801, Negation node 802, Sentiment node 803, Quantity node 804, Entity node 805, Incident node 806, Condition node 807]), and multiple edges representing the relationship between nodes (Jain: [0074], the arrows show relationships between individual classes consistent with the ontology model. These relationships represent valid interactions between the ontology classes, an action "taken on" an entity is a valid interaction according to ontology model 800, as shown by the arrow connecting action node 801 to entity node 805).
Davlos and Jain are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Jain to expressly include knowledge map including nodes representing objects and arrows representing relationships between individual classes. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
Further, Davlos does not clearly disclose obtaining an error signal generated by the target device, in another analogous art of automatic fault diagnosis and repair system, Brahmajosyula discloses: wherein the original data includes an error signal generated by the target device (Brahmajosyula: [0039], [0041], using alarm codes as addition information to diagnose the root cause);
Davlos and Brahmajosyula are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Brahmajosyula to expressly include using alarm codes as addition information to diagnose the root cause. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
As for claim 2, Davlos-Jain-Brahmajosyula discloses: the original data comprises a data stream with device identification and time information (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session); the cloud task management database stores task documents comprising the standard processes and steps of each task of each device (Davlos: [0134], The matching case will execute a set of general diagnostics to determine the state of the system and remediation steps; [0163], list potential root causes to the symptom and can provide the user with a roadmap of steps for addressing the problem based on the probabilities, as explained above. Also, as shown in FIG. 19, the graphical user interface can display suggestions for addressing the identified problem. FIG. 20 illustrates a resource page displaying resources to provide the user with information about the identified problem. In some embodiments, the resources can include documents, videos, etc. stored in an analytic database); dividing the data stream into corresponding data segments according to the standard processes and steps in the task document (Davlos: [0111], recommended disassembly steps; Jain: [0093], Activities can identify steps performed on the network entity during troubleshooting of a problem, e.g., pinging a network device, checking and cleaning cables, verifying device configuration; please note the task may divide as step by step); determining a target step with a fault by locating an error signal in a data segment (Jain: [0078], The incident class can also include an Errorincident subclass); comparing the part corresponding to the target step in the task document with that in the data stream, analyzing a fault reason based on the comparison, and generating the problem description information (Davlos: [0008], indicate the most probable cause of the reported problem; [0047], the diagnostic platform utilizes a case-based reasoning (CBR) and machine introspection techniques to identify the root cause and resolve problems, a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0072], The CBR engine will compare the attributes of the session to the existing case objects to find likely matches).
As for claim 3, Davlos-Jain-Brahmajosyula discloses: obtaining original data including fault problem of a target device comprises collecting data stream with device identification and time information (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session) at a set frequency (Jain: [0111], during the specified time period; [0047], updating output responsive to various user selections, e.g., different types of devices, date ranges, etc.; [0109], generate an analysis for support tickets dated within the specified time frame); and in response to receiving a fault report from a user, extracted corresponding data stream of the target device from the obtained data stream according to the device identification and time information provided by the fault report (Jain: [0111], during the specified time period; [0047], updating output responsive to various user selections, e.g., different types of devices, date ranges, etc.; [0109], generate an analysis for support tickets dated within the specified time frame).
As for claim 4, Davlos-Jain-Brahmajosyula discloses: analyzing the problem description information to obtain a diagnosis report comprises inputting the problem description information into a pre trained fault diagnosis model based on convolutional neural network (Davlos: [0005], combines artificial intelligence, machine learning, human feedback, and intelligent optimization algorithms into a cloud-based platform serving and learning from a distributed userbase; [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0147], Some embodiments involve supervised learning models that accept input from statisticians, technicians, and domain experts familiar with the scientific characteristics of the raw data. The supervisors extract feature vectors that can generalize raw data from a training data set to predict either class membership (classification) or quantitative values (regression) from unseen new raw data), and obtaining a diagnosis report output by the fault diagnosis model (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request); the fault diagnosis model is trained by taking a large number of historical problem description information as the input samples, and taking the historical diagnosis report corresponding to each piece of historical problem description information as the output samples (Davlos: [0009], Some embodiments involve storing records or reference codes of past diagnostic repairs (historical data) on a device itself that can be later used as an input; [0047], a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0050], the diagnostic platform 110 involves case-based reasoning (CBR) engine 112 for diagnosing the devices 150.sub.1, 150.sub.2, 150.sub.3, 150.sub.4, 150.sub.5, . . . , 150.sub.n using input received from a device interface module 114 and knowledge from a diagnostic database 116 in the form of past cases, previously discovered solutions, etc. The CBR engine 112 also includes a generative rule engine 118 for gathering data from new cases, analyzing the effectiveness of the solutions derived from past cases, and applying machine learning techniques for developing new solutions; Jain: [0112], The outputs can be used to enhance the ability of network engineers to readily diagnose network problems for new or incoming support tickets by leveraging an ontology model that classifies phrases from previous support tickets).
As for independent claim 8, Davlos discloses: A device for fault diagnosis and solution recommendation (Davlos: Abstract, Troubleshooting a technical problem on a user device using a network-based remediation platform. Receiving problem statements relating to technical problems associated with a user device, activating a domain of cases, assigning a score for the cases based on a scoring algorithm, and determining one or more remediation actions to suggest based on the score; [0007], remediating technical problems with an electronic device using a network-based diagnostic platform containing a knowledge base, a diagnostic engine for processing optimization algorithms, a machine learning component, and a network interface configured for accepting input from users), the device comprising: a data obtaining module, to obtain original data including fault problem of a target device (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session); a fault analysis module, to analyze the original data to obtain problem description information including obtaining a task document (Davlos: [0101], searching a problem statement) from a cloud task management database (Davlos: [0005], a cloud-based platform) according to the device identification and time information (Davlos: [0011], The platform can receive problem statements relating to technical problems associated with a user device and determine one or more remediation actions to suggest. The platform can analyze the details of the problem statement, activate a domain of cases, and assign a score for the cases based on a scoring algorithm; [0101], a unique ID; [0122], timestamp information); a fault diagnosis module, to analyze the problem description information to obtain a diagnosis report (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request); and a solution recommendation module, to obtain obtaining a video and/or document solution for the fault based on a cloud knowledge map according to the diagnosis report, and to recommend the video and/or document solution to a user (Davlos: [0052], recommend instructions for remediation of the problem. In some embodiments of the present technology, the diagnostic platform 110 includes an analytics database 124 containing remediation objects such as text-based instructions, graphical instructions, instructional videos, links to external web pages providing instructions, forms used to send parts away for repair, warranty forms, software patches, software applications, etc);
Davlos discloses a troubleshooting and remediation system using a network-based diagnostic platform containing a knowledge base including rule tree hierarchies (both preprogrammed and generated by machine learning) (Davlos: [0008]) and end nodes (Davlos: [0116], cases are generated from decision trees with end nodes comprising an instruction or inference), Davlos does not clearly disclose the knowledge map, in an analogous art of automatic fault diagnosis and repair system, Jain discloses: a solution recommendation module, to obtain obtaining a video and/or document solution for the fault based on a cloud knowledge map (Jain: [0003], mapping individual phrases from the subset of phrases to classes of an ontology model and storing the individual phrases in a knowledge base; [0073], Each class is represented by a corresponding node in FIG. 8, e.g., [Action node 801, Negation node 802, Sentiment node 803, Quantity node 804, Entity node 805, Incident node 806, Condition node 807]; [0074], the arrows show relationships between individual classes consistent with the ontology model. These relationships represent valid interactions between the ontology classes, an action "taken on" an entity is a valid interaction according to ontology model 800, as shown by the arrow connecting action node 801 to entity node 805).
Davlos and Jain are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Jain to expressly include knowledge map including nodes representing objects and arrows representing relationships between individual classes. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
Further, Davlos does not clearly disclose obtaining an error signal generated by the target device, in another analogous art of automatic fault diagnosis and repair system, Brahmajosyula discloses: wherein the original data includes an error signal generated by the target device (Brahmajosyula: [0039], [0041], using alarm codes as addition information to diagnose the root cause);
Davlos and Brahmajosyula are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Brahmajosyula to expressly include using alarm codes as addition information to diagnose the root cause. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
As for independent claim 12, Davlos discloses: A system for fault diagnosis and solution recommendation (Davlos: Abstract, Troubleshooting a technical problem on a user device using a network-based remediation platform. Receiving problem statements relating to technical problems associated with a user device, activating a domain of cases, assigning a score for the cases based on a scoring algorithm, and determining one or more remediation actions to suggest based on the score; [0007], remediating technical problems with an electronic device using a network-based diagnostic platform containing a knowledge base, a diagnostic engine for processing optimization algorithms, a machine learning component, and a network interface configured for accepting input from users), system comprising: a device comprising: a data obtaining module, to obtain original data including fault problem of a target device (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session), a fault analysis module, to analyze the original data to obtain problem description information including obtaining a task document (Davlos: [0101], searching a problem statement) from a cloud task management database (Davlos: [0005], a cloud-based platform) according to the device identification and time information (Davlos: [0011], The platform can receive problem statements relating to technical problems associated with a user device and determine one or more remediation actions to suggest. The platform can analyze the details of the problem statement, activate a domain of cases, and assign a score for the cases based on a scoring algorithm; [0101], a unique ID; [0122], timestamp information), a fault diagnosis module, to analyze the problem description information to obtain a diagnosis report (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request), and a solution recommendation module, to obtain obtaining a video and/or document solution for the fault based on a cloud knowledge map according to the diagnosis report, and to recommend the video and/or document solution to a user (Davlos: [0052], recommend instructions for remediation of the problem. In some embodiments of the present technology, the diagnostic platform 110 includes an analytics database 124 containing remediation objects such as text-based instructions, graphical instructions, instructional videos, links to external web pages providing instructions, forms used to send parts away for repair, warranty forms, software patches, software applications, etc); a cloud task management database storing task documents including the standard processes and steps of each task of each device (Davlos: [0111], recommended disassembly steps); a cloud image database storing historical fault multimedia content and corresponding historical problem description information of fault multimedia content (Davlos: [0009], Some embodiments involve storing records or reference codes of past diagnostic repairs (historical data) on a device itself that can be later used as an input; [0047], a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0050], the diagnostic platform 110 involves case-based reasoning (CBR) engine 112 for diagnosing the devices 150.sub.1, 150.sub.2, 150.sub.3, 150.sub.4, 150.sub.5, . . . , 150.sub.n using input received from a device interface module 114 and knowledge from a diagnostic database 116 in the form of past cases, previously discovered solutions, etc. The CBR engine 112 also includes a generative rule engine 118 for gathering data from new cases, analyzing the effectiveness of the solutions derived from past cases, and applying machine learning techniques for developing new solutions;
Davlos discloses a troubleshooting and remediation system using a network-based diagnostic platform containing a knowledge base including rule tree hierarchies (both preprogrammed and generated by machine learning) (Davlos: [0008]) and end nodes (Davlos: [0116], cases are generated from decision trees with end nodes comprising an instruction or inference), Davlos does not clearly disclose nodes in the knowledge map, in an analogous art of automatic fault diagnosis and repair system, Jain discloses: and an cloud instructional resources knowledge map (Jain: [0003], mapping individual phrases from the subset of phrases to classes of an ontology model and storing the individual phrases in a knowledge base) showing nodes representing a fault, a video solution and/or document solution (Jain: [0073], Each class is represented by a corresponding node in FIG. 8, e.g., [Action node 801, Negation node 802, Sentiment node 803, Quantity node 804, Entity node 805, Incident node 806, Condition node 807]), and multiple edges representing the relationship between nodes (Jain: [0074], the arrows show relationships between individual classes consistent with the ontology model. These relationships represent valid interactions between the ontology classes, an action "taken on" an entity is a valid interaction according to ontology model 800, as shown by the arrow connecting action node 801 to entity node 805).
Davlos and Jain are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Jain to expressly include knowledge map including nodes representing objects and arrows representing relationships between individual classes. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
Further, Davlos does not clearly disclose obtaining an error signal generated by the target device, in another analogous art of automatic fault diagnosis and repair system, Brahmajosyula discloses: wherein the original data includes an error signal generated by the target device (Brahmajosyula: [0039], [0041], using alarm codes as addition information to diagnose the root cause);
Davlos and Brahmajosyula are analogous arts because they are in the same field of endeavor, automatic fault diagnosis and repair system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Brahmajosyula to expressly include using alarm codes as addition information to diagnose the root cause. It would provide Davlos’s method with enhanced capabilities of improving the efficiency of the troubleshooting system.
6. Claims 5-7, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Davlos and Jain and Brahmajosyula as applied on claims 1 and 8, and further in view of Paul Schmirler et al (US Publication 20180131907 A1, hereinafter Schmirler).
As for claim 5, Davlos-Jain-Brahmajosyula discloses: … analyzing the original data including fault problem to obtain problem description information comprises inputting the fault multimedia content into a fault analysis model trained in advance based on convolutional neural network, and obtaining the problem description information output by the fault analysis model (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request); the fault analysis model is trained by taking a large number of historical fault multimedia content as input samples, and taking corresponding historical problem description information of fault multimedia content as output samples (Davlos: [0009], Some embodiments involve storing records or reference codes of past diagnostic repairs (historical data) on a device itself that can be later used as an input; [0047], a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0050], the diagnostic platform 110 involves case-based reasoning (CBR) engine 112 for diagnosing the devices 150.sub.1, 150.sub.2, 150.sub.3, 150.sub.4, 150.sub.5, . . . , 150.sub.n using input received from a device interface module 114 and knowledge from a diagnostic database 116 in the form of past cases, previously discovered solutions, etc. The CBR engine 112 also includes a generative rule engine 118 for gathering data from new cases, analyzing the effectiveness of the solutions derived from past cases, and applying machine learning techniques for developing new solutions; Jain: [0112], The outputs can be used to enhance the ability of network engineers to readily diagnose network problems for new or incoming support tickets by leveraging an ontology model that classifies phrases from previous support tickets); and the historical fault multimedia content and corresponding historical problem description information are stored in a cloud image database (Davlos: [0050], the diagnostic platform 110 involves case-based reasoning (CBR) engine 112 for diagnosing the devices 150.sub.1, 150.sub.2, 150.sub.3, 150.sub.4, 150.sub.5, . . . , 150.sub.n using input received from a device interface module 114 and knowledge from a diagnostic database 116 in the form of past cases, previously discovered solutions, etc.); and Davlos-Jain discloses a video being shown (Davlos: Fig. 23), but Davlos-Jain does not clearly disclose a video containing fault process or a photo containing fault area, in another analogous art of automate fault diagnosis system, Schmirler discloses: original data including fault problem comprises: fault multimedia content, and the fault multimedia content comprises: a video containing fault process or a photo containing fault area (Schmirler: [0056], if the user's current view encompasses a real or virtualized motor-driven conveyor and a motor drive that controls the motor, the presentation system may superimpose a current operating status of the motor drive (e.g., a current speed, a fault condition, an operating mode, etc.) near the image or view of the motor drive as perceived by the user);
Davlos and Jain and Brahmajosyula and Schmirler are analogous arts because they are in the same field of endeavor, automatic fault troubleshooting system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Schmirler to expressly include obtaining a video or photo of the fault area. It would provide Davlos’ method with enhanced capabilities of providing live data of the fault area so as to improve the efficiency of the troubleshooting system.
As for claim 6, Davlos-Jain-Brahmajosyula-Schmirler discloses: the fault analysis model comprises a multimedia content classification model and multiple fault analysis sub models (Davlos: [0147], The supervisors extract feature vectors that can generalize raw data from a training data set to predict either class membership (classification) or quantitative values (regression) from unseen new raw data; Jain: [0077], A condition can further be classified as a ProblemCondition subclass or a MaintenanceCondition subclass); the multimedia content classification model is to classify the multimedia content, and input the multimedia content to a corresponding fault analysis sub model according to a classification result (Davlos: [0147], The supervisors extract feature vectors that can generalize raw data from a training data set to predict either class membership (classification) or quantitative values (regression) from unseen new raw data; Jain: [0077], A condition can further be classified as a ProblemCondition subclass or a MaintenanceCondition subclass); and each fault analysis sub model is to output corresponding problem description information according to the input multimedia content (Davlos: [0147], The supervisors extract feature vectors that can generalize raw data from a training data set to predict either class membership (classification) or quantitative values (regression) from unseen new raw data; Jain: [0077], A condition can further be classified as a ProblemCondition subclass or a MaintenanceCondition subclass).
As for claim 7, Davlos-Jain-Brahmajosyula-Schmirler discloses: feeding back the problem description information to the user for checking, and receiving problem description information confirmed by the user (Davlos: [0163], the troubleshooting homepage can include tools for a user to provide feedback about a problem (e.g. "Are the Fans Noisy?"); Fig. 24, request for feedback and the user may confirm by selecting “Yes”); taking the problem description information confirmed by the user as finally determined problem description information (Davlos: [0118], user activities such as: `read a document`, performed an instruction, accept/reject of an instruction; Claim 1, a feedback module configured to accept feedback from a user device describing the effectiveness of the recommended diagnostic solutions, and wherein the feedback is provided to the generative rule engine to use in future analysis); and storing the finally determined problem description information and the fault multimedia content in the cloud image database as a new historical sample to optimize the fault analysis model (Davlos: [0163], the resources can include documents, videos, etc. stored in an analytic database).
As for claim 9, Davlos-Jain-Brahmajosyula discloses: the original data comprises data stream with a device identification and time information, and/or fault multimedia content (Davlos: [0118]-[0122], a session can hold: information about the device such as the serial number or board ID; information about the user such as: user ID, name, profile, location, IP address; the raw problem statement as typed by the operator; timestamps marking the start and end of a session); and Davlos-Jain discloses a video being shown (Davlos: Fig. 23), but Davlos-Jain does not clearly disclose a video containing fault process or a photo containing fault area, in another analogous art of automate fault diagnosis system, Schmirler discloses: and the fault multimedia content comprises: a video containing fault process or a photo containing fault area (Schmirler: [0056], if the user's current view encompasses a real or virtualized motor-driven conveyor and a motor drive that controls the motor, the presentation system may superimpose a current operating status of the motor drive (e.g., a current speed, a fault condition, an operating mode, etc.) near the image or view of the motor drive as perceived by the user);
Davlos and Jain and Brahmajosyula and Schmirler are analogous arts because they are in the same field of endeavor, automatic fault troubleshooting system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Davlos using the teachings of Schmirler to expressly include obtaining a video or photo of the fault area. It would provide Davlos’ method with enhanced capabilities of providing live data of the fault area so as to improve the efficiency of the troubleshooting system.
As for claim 10, Davlos-Jain-Brahmajosyula-Schmirler discloses: the fault analysis module comprises a data stream analysis module and/or a multimedia content analysis module (Davlos: [0010], The system can identify remedial actions by scoring items in a database containing a plurality of diagnostic cases describing possible diagnostic solutions for a plurality of technical problems); the data stream analysis module is further programmed to obtain a task document from a cloud task management database according to the device identification and time information (Davlos: [0010], The system can identify remedial actions by scoring items in a database containing a plurality of diagnostic cases describing possible diagnostic solutions for a plurality of technical problems); the cloud task management database stores task documents comprising the standard processes and steps of each task of each device (Davlos: [0134], The matching case will execute a set of general diagnostics to determine the state of the system and remediation steps; [0163], list potential root causes to the symptom and can provide the user with a roadmap of steps for addressing the problem based on the probabilities, as explained above. Also, as shown in FIG. 19, the graphical user interface can display suggestions for addressing the identified problem. FIG. 20 illustrates a resource page displaying resources to provide the user with information about the identified problem. In some embodiments, the resources can include documents, videos, etc. stored in an analytic database); divide the data stream into corresponding data segments according to the standard processes and steps in the task document (Davlos: [0111], recommended disassembly steps; Jain: [0093], Activities can identify steps performed on the network entity during troubleshooting of a problem, e.g., pinging a network device, checking and cleaning cables, verifying device configuration; please note the task may divide as step by step); determine a target step with a fault by locating an error signal in a data segment (Jain: [0078], The incident class can also include an Errorincident subclass); and compare the part corresponding to the target step in the task document with that in the data stream, analyze a fault reason based on the comparison, and generate the problem description information (Davlos: [0008], indicate the most probable cause of the reported problem; [0047], the diagnostic platform utilizes a case-based reasoning (CBR) and machine introspection techniques to identify the root cause and resolve problems, a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0072], The CBR engine will compare the attributes of the session to the existing case objects to find likely matches); the multimedia content analysis module is further programmed to input the fault multimedia content into a fault analysis model trained in advance based on convolutional neural network (Davlos: [0005], combines artificial intelligence, machine learning, human feedback, and intelligent optimization algorithms into a cloud-based platform serving and learning from a distributed userbase; [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0147], Some embodiments involve supervised learning models that accept input from statisticians, technicians, and domain experts familiar with the scientific characteristics of the raw data. The supervisors extract feature vectors that can generalize raw data from a training data set to predict either class membership (classification) or quantitative values (regression) from unseen new raw data), and obtain the problem description information output by the fault analysis model (Davlos: [0008], The diagnostic engine accepts a plurality of inputs relating to the reported problems, device properties (derived from serial number), diagnostic decision rules (scripts) and rule tree hierarchies (both preprogrammed and generated by machine learning), etc. Next, the diagnostic engine creates a vector of all of the relevant inputs and applies optimization algorithms to create a plurality of inferences about how to solve the problem and associated weights for the inferences to indicate the most probable cause of the reported problem; [0164], the device running the troubleshooting session can be in-session with more than one UUT. FIG. 18 illustrates a diagnostic homepage listing a plurality of diagnostics, tests, suggestions, requests for feedback, etc. In some embodiments, the diagnosis are available by sending the AIDE system a request); the fault analysis model is trained by taking a large number of historical fault multimedia content as input samples, and taking corresponding historical problem description information of fault multimedia content as output samples (Davlos: [0009], Some embodiments involve storing records or reference codes of past diagnostic repairs (historical data) on a device itself that can be later used as an input; [0047], a CBR engine compares facts about the unit under test (UUT) to a database of cases representing previously discovered solutions to problems; [0050], the diagnostic platform 110 involves case-based reasoning (CBR) engine 112 for diagnosing the devices 150.sub.1, 150.sub.2, 150.sub.3, 150.sub.4, 150.sub.5, . . . , 150.sub.n using input received from a device interface module 114 and knowledge from a diagnostic database 116 in the form of past cases, previously discovered solutions, etc. The CBR engine 112 also includes a generative rule engine 118 for gathering data from new cases, analyzing the effectiveness of the solutions derived from past cases, and applying machine learning techniques for developing new solutions; Jain: [0112], The outputs can be used to enhance the ability of network engineers to readily diagnose network problems for new or incoming support tickets by leveraging an ontology model that classifies phrases from previous support tickets); and the historical fault multimedia content and corresponding historical problem description information are stored in an cloud image database (Davlos: [0163], the resources can include documents, videos, etc. stored in an analytic database).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118