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 .
This is a non-final office action. Claims 1-20 were considered.
Continued Examination Under 37 CFR 1.114
A 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, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/28/2025 has been entered.
Response to Amendment
3. This action is in response to communication filed on 07/28/2025.
a. Claims 1-20 are pending in this application.
b. Claims 1, 3-4, 6-7, 9, 11-12, 14-15, 17 and 19 are amended.
Response to Arguments Regarding Claim Rejections – 35 USC § 103
4. Applicant's arguments, see page 11-16 of Remarks, filed on 07/28/2025, with respect to Claim Rejections - 35 USC § 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 3, 5, 8-9, 13, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2019/0196893 A1, hereinafter Lee) in view of Pi-Sunyer (US 2015/0370615 A1, hereinafter Sunyer) further in view of Jain et al. (US 2019/0215236 A1, hereinafter Jain) further in view of Picardi et al. (US 2021/0071889 A1, hereafter Picardi).
Regarding claim 1, Lee teaches an apparatus for using varied device activity data from a dynamic home communications network to select a predicted operational support data object from a device operational support management repository (Fig. 1(100, 120) and [146]), the apparatus comprising at least one processor and at least one memory, the at least one memory having computer-coded instructions stored thereon that, in execution with the at least one processor, causes the apparatus to:
identify a device identification data set associated with a networked device set communicable with the dynamic home communications network ([106-107]: In operation 710, the managing server receives abnormal data containing at least one or more data items and appliance identifier information and time of production information from the appliance. In operation 715, the managing server compares the received abnormal data with the information about the data pattern detection routine transmitted in operation 705 (i.e. server identifies device identifier and data associated with the device in the home network 100)),
retrieve a device activity data set associated with the networked device set ([106-107]: In operation 705, the managing server transmits information about a data pattern detection routine that targets appliance operation data, to the appliance. In operation 710, the managing server receives abnormal data containing at least one or more data items and appliance identifier information and time of production information from the appliance (i.e. retrieve data associated with an appliance in home network));
apply a malfunction classification data model to the device activity data set ([02, 91]: The disclosure relates to methods and apparatuses for managing operation data of appliances for failure prediction and, particularly, to artificial intelligence (AI) systems that may mimic the human brain's capabilities of perception or determination using machine learning algorithms and their applications;
output the predicted operational support data object to a client device in communication with the dynamic home communications network ([153]: In operation 1235, the managing server transmits the customized diagnosis treatment solution generated in operation 1225 or the global diagnosis treatment solution determined in operation 1230 to the appliance.).
Lee however does not teach identify, in real-time, a device identification data set associated with a network device, wherein the networked device set comprises a plurality of networked devices, each networked device associated with a device type and at least two of the plurality of networked devices comprising different device types; identify, based at least in part on the device identification data set, the device type for each of the plurality of networked devices in the networked device set, including at least the different device types for the at least two of the plurality of networked devices; determine a malfunction classification identifier associated with the plurality of networked devices of the networked device set; wherein the malfunction classification data model is trained based on training data from the dynamic home communications network, external aggregated device activity data from one or more external dynamic home communications networks, device type of networked devices, and malfunction device history data from the device operational support management repository, and wherein the malfunction classification identifier is determined at least in part using the identification of the different device types for the at least two of the plurality of networked devices; select the predicted operational support data object from the device operational support management repository based on the malfunction classification identifier.
Sunyer teaches wherein the networked device set comprises a plurality of networked devices, each networked device associated with a device type and at least two of the plurality of networked devices comprising different device types ([270, 274]: The 3P (third-party) Device type entity 704 represents a class of devices belonging to a single vendor that have the same information payload. Device types are immutable. Accordingly, once the device type metadata is obtained, it can be cached indefinitely. Device types can be versioned. For example, differing data pathways may be provided for device types with different version. Thus, in one embodiment, versioning may be handled, for example, by appending a version to common prefix, for example washer_v1 may relate to a first version of a dishwasher device type and washer_v2 may relate to a second version the dishwasher device type);
identify, based at least in part on the device identification data set, the device type for each of the plurality of networked devices in the networked device set, including at least the different device types for the at least two of the plurality of networked devices (274-275]: Versioning may be handled, for example, by appending a version to common prefix, for example washer_v1 may relate to a first version of a dishwasher device type and washer_v2 may relate to a second version the dishwasher device type (i.e. based on device version like v1 or v2 included in the device identification data, different device type of network device is determined)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee to incorporate the teachings of Sunyer and the networked device set comprises a plurality of networked devices, each networked device associated with a device type and at least two of the plurality of networked devices comprising different device types, and identify, based at least in part on the device identification data set, the device type for each of the plurality of networked devices in the networked device set, including at least the different device types for the at least two of the plurality of networked devices. One of ordinary skilled in the art would have been motivated to combine the teachings in order to represent a class of devices (Sunyer, [274]).
Lee in view of Sunyer however does not teach identify, in real-time, a device identification data set associated with a network device; determine a malfunction classification identifier associated with the plurality of networked devices of the networked device set; wherein the malfunction classification data model is trained based on training data from the dynamic home communications network, external aggregated device activity data from one or more external dynamic home communications networks, device type of networked devices, and malfunction device history data from the device operational support management repository, and wherein the malfunction classification identifier is determined at least in part using the identification of the different device types for the at least two of the plurality of networked devices; select the predicted operational support data object from the device operational support management repository based on the malfunction classification identifier.
Jain teaches determine a malfunction classification identifier associated with the plurality of networked devices of the networked device set (Fig. 4(304) and ([41, 43]: The resolution identifier component 304, based upon the alarm (and optionally other received alarms), can determine that the alarm is indicative of an actionable network failure. the resolution identifier component 304 can determine that a network alarm generated by the switch 116 indicates that the third computing device 108 in the data center 100 is not responding to heartbeat requests, which can be mapped to, for example, the following previously observed failure symptoms for the third computing device 108 (or other devices in the data center 100 or in another data center): 1) “link flapping”; and 2) “device down”.);
select the predicted operational support data object from the device operational support management repository based on the malfunction classification identifier ([43-45]: The resolution identifier component 304 can determine that a network alarm generated by the switch 116 indicates that the third computing device 108 in the data center 100 is not responding to heartbeat requests, which can be mapped to, for example, the following previously observed failure symptoms for the third computing device 108 (or other devices in the data center 100 or in another data center): 1) “link flapping”; and 2) “device down”. For each of such symptoms identified by the resolution identifier component 304, the resolution identifier component 304 can identify troubleshooting options and corresponding debugging steps in the historical data 308 indicated as previously being performed to resolve network failures that have such a symptom).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer to incorporate the teachings of Jain and determine a malfunction classification identifier associated with a plurality of networked devices of the networked device set; wherein the malfunction classification identifier indicates a malfunction associated with each of the plurality of networked devices; select the predicted operational support data object from the device operational support management repository based on the malfunction classification identifier. One of ordinary skilled in the art would have been motivated to combine the teachings in order to identify troubleshooting options and corresponding debugging steps (Jain, [43]).
Lee in view of Sunyer and Jain however does not explicitly teach identify, in real-time, a device identification data set associated with a network device; wherein the malfunction classification data model is trained based on training data from the dynamic home communications network, external aggregated device activity data from one or more external dynamic home communications networks, device types of networked devices, and malfunction device history data from the device operational support management repository, and wherein the malfunction classification identifier is determined at least in part using the identification of the different device types for the at least two of the plurality of networked devices.
Picardi teaches identify, in real-time, a device identification data set associated with a network device ([38, 71]: The security system 134 stores the model from the trained machine learning algorithm in the HVAC database 126 for further analyzing in real time (i.e. malfunction identification in HVAC devices are done in real time. Here, HVAC devices have device information));
wherein the malfunction classification data model is trained based on training data from the dynamic home communications network (Fig. 3 and [38-39]: The security system 128 may train a machine-learning algorithm stored on the HVAC database 126 to detect and predict issues associated with the HVAC system 146. Thermostat, sensor data, and outdoor weather information can be fed into the machine learning algorithm during training),
external aggregated device activity data from one or more external dynamic home communications networks ([39]: The security system 128 may train a machine-learning algorithm stored on the HVAC database 126 to detect and predict issues associated with the HVAC system 146. The training is additionally based on HVAC systems that have similar performance. This makes the machine-learning algorithm detect issues for various HVAC systems, that may be agnostic, that include similar performances (i.e. machine-learning algorithm is trained with the data from other HVAC systems)),
device types of networked devices ([14-15, 81-82]: During 506, the control unit server 104 applies the thermostat data and the sensor data to an HVAC model 101 that is trained using past sensor data, past thermostat data, past errors of the HVAC system 146, and past actions that corrected the errors of the HVAC system 146. The past actions that corrected the errors of the HVAC system 146 include actions taken by the HVAC technician 132 to fix the HVAC system 146. The HVAC technician 132 can replace the burner for an issue in the heating vs. cooling category (i.e. machine-learning algorithm is trained with the data relating to devices in different categories)),
malfunction device history data from the device operational support management repository ([39]: The security system 128 may train a machine-learning algorithm stored on the HVAC database 126 to detect and predict issues associated with the HVAC system 146. The security system 128 may provide each of the failure data obtained from broken HVAC systems to the machine learning algorithm for training);
wherein the malfunction classification identifier is determined at least in part using the identification of the different device types for the at least two of the plurality of networked devices ([60-61]: The control unit server 104 obtains thermostat information from the monitored property 102. During 204, the control unit server 104 determines an HVAC system issued based on an analysis of the thermostat information using a trained model. During 206, the control unit server 104 sorts the HVAC system issue into a category that specifies a type of the HVAC system issue. In some implementations, the categories may include heating vs. cooling category, acute vs inefficient category, a filter change category, and a long cycling category (i.e. identify HVAC system issue category, here HVAC system have set of devices)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer and Jain to incorporate the teachings of Picardi and the malfunction classification data model is trained based on training data from the dynamic home communications network, external aggregated device activity data from one or more external dynamic home communications networks, device type of networked devices, and malfunction device history data from the device operational support management repository, and wherein the malfunction classification identifier is determined at least in part using the identification of the different device types for the at least two of the plurality of networked devices. One of ordinary skilled in the art would have been motivated to combine the teachings in order to detect and predict issues associated with the system (Picardi, [39]).
Regarding claim 3, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Jain teaches the apparatus further caused to determine malfunction classification data associated with the networked device set ([54]: The resolution identifier component 304 receives the alarm 400 and, in an exemplary embodiment, can determine whether the alarm is indicative of an actionable network failure. With more specificity, the resolution identifier component 304 includes a failure identifier component 402 that analyzes the alarm 400 and can identify that the alarm 400 represents an actionable network failure, and can further identify a failing device or link (e.g., based upon the device ID and/or the network graph 310)), wherein to determine the malfunction classification data, the apparatus is caused to: identify first device identification data from the device identification data set ([53]: The resolution identifier component 304 receives an alarm 400 generated by a device in the data center 100. For example, the device may be one of the computing devices 104-110 or one of the network infrastructure devices 114-120. In the example shown in FIG. 4, the alarm 400 includes a plurality of failure conditions: 1) a timestamp that indicates when the alarm was generated by the device; 2) an alarm ID that identifies a unique alarm generated from a device; 3) a device II) that identifies the device that generated the alarm), the first device identification data associated with a first device of the networked device set ([54]: The resolution identifier component 304 includes a failure identifier component 402 that analyzes the alarm 400 and can identify that the alarm 400 represents an actionable network failure, and can further identify a failing device or link (e.g., based upon the device ID and/or the network graph 310) (i.e. failing device is the first device)); and determine the malfunction classification data corresponding to the first device identification data and the device activity data set ([54]: The resolution identifier component 304 includes a failure identifier component 402 that analyzes the alarm 400 and can identify that the alarm 400 represents an actionable network failure, and can further identify a failing device or link (e.g., based upon the device ID and/or the network graph 310). For example, the device that generated the alarm 400 (the generating device) may be operating properly; however, a network infrastructure device (the failing device) connected to the device that generated the alarm (e.g., by way of the interface link identified in the alarm 400) may be failing. In an example, the event description in the alarm 400 can indicate that the device identified by the device ID is not responding to heartbeat requests over a particular network link).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Jain and identify first device identification data from the device identification set, the first device identification data associated with a first device of the networked device set, and determine the malfunction classification data corresponding to the first identification data and the device activity data set. One of ordinary skilled in the art would have been motivated to combine the teachings in order to output the troubleshooting options, debugging steps (Jain, [64]).
Regarding claim 5, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Lee further teaches the apparatus further caused to determine malfunction classification data associated with the networked device set ([106]: In operation 705, the managing server transmits information about a data pattern detection routine that targets appliance operation data, to the appliance. The data pattern detection routine is intended for detecting abnormal data needed to be reported to the managing server among various pieces of operation data generated in the appliance, and the data pattern detection routine may include methods for determining abnormal data and information about at least one pre-defined normal data pattern and/or at least one pre-defined abnormal data pattern (i.e. determine normal or abnormal appliance data)), wherein to determine the malfunction classification data, the apparatus is caused to: determine the device activity data set indicates a first malfunction classification represented by the malfunction classification data ([107]: In operation 710, the managing server receives abnormal data containing at least one or more data items and appliance identifier information and time of production information from the appliance. In operation 715, the managing server compares the received abnormal data with the information about the data pattern detection routine transmitted in operation 705 and determines whether the received abnormal data matches the existing abnormal data pattern contained in the data pattern detection routine unit 126 (i.e. determine appliance data indicating abnormal pattern, here abnormal data pattern is first malfunction classification)).
Regarding claim 8, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Lee teaches the apparatus further caused to: identify historical activity data ([108]: The managing server monitors whether the failure history and failure repair history about the appliance is received during a predetermined period after the abnormal data is received (i.e. determine historical data)).
determine the historical activity data indicates a first malfunction ([108]: The managing server monitors whether the failure history and failure repair history about the appliance is received during a predetermined period after the abnormal data is received (i.e. determine historical data having failure history)); and
store a first malfunction classification identifier representing the first malfunction associated with the historical activity data ([109]: Where the failure related to the abnormal data occurs in the appliance, the managing server generates a new abnormal data pattern that indicates the abnormal data and updates the data pattern detection routine for the appliance to include the new abnormal data pattern in operation 730 (i.e. storing abnormal data pattern associated with the appliance data)).
Regarding Claims 9, 11, 13 and 16, they do not teach or further define over claims 1, 3, 5 and 8 respectively. Therefore, claims 9, 11, 13 and 16 are rejected for the same reason as set forth above in claims 1, 3, 5 and 8 respectively.
Regarding Claims 17 and 20, they do not teach or further define over claim 1 and 8 respectively. Therefore, claims 17 and 20 are rejected for the same reason as set forth above in claims 1 and 8 respectively.
Claims 2, 4, 6-7, 10-12, 14-15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Sunyer, Jain and Picardi further in view of Brown et al. (US 2021/0301985 A1, hereafter Brown).
Regarding claim 2, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Lee in view of Sunyer, Jain and Picardi however does not teach wherein the predicted operational support data object comprises a data link to a solution page associated with remediating malfunction classification data associated with the networked device set.
Brown teaches wherein the predicted operational support data object comprises a data link to a solution page associated with remediating malfunction classification data associated with the networked device set ([134-138]: At 1014, the system can determine whether the collected data indicates the water device has a problem. For example, if the reported sensor values deviate a predetermined amount from the normal operation signature, or if they are similar to a problem signature, there may be a problem. At 1030 the system can automatically provide to the user any available electronic support resources. For example, the system can send a notification containing links to product troubleshooting guides and/or videos).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Brown and the predicted operational support data object comprises a data link to a solution page associated with remediating malfunction classification data associated with the networked device set. One of ordinary skilled in the art would have been motivated to combine the teachings in order to provide the electronic support resources (Brown, [135]).
Regarding claim 4, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Lee in view of Sunyer, Jain and Picardi however does not teach the apparatus further caused to determine malfunction classification data associated with the networked device set, wherein to determine the malfunction classification data, the apparatus is caused to: identify a plurality of device identification data from the device identification data set, the plurality of device identification data associated with the plurality of networked devices of the networked device set; and determine the malfunction classification data corresponding to the plurality of device identification data and the device activity data set.
Brown teaches the apparatus further caused to determine malfunction classification data associated with the networked device set ([131]: The system can generate and use different types of signatures, such as a signature indicating the water device is operating normally (i.e., a “normal operation” or “normal use” signature), and a signature indicating the device's operation is not normal, or more specifically indicating the device is having or may soon have a particular problem (i.e., a “problem” or “predictive” signature)), wherein to determine the malfunction classification data, the apparatus is caused to:
identify a plurality of device identification data from the device identification data set, the plurality of device identification data associated with the plurality of networked devices of the networked device set ([132-134]: FIG. 10 illustrates an example method 1000 performed by the system to generate and use a health profile for a water device, such as a pump, a filter, a heater, etc., installed in the water system. The system can query a device health profile data store associated with the user, using the water device's identifier as a primary key (i.e. identify water device from plurality of devices based on an identifier)); and
determine the malfunction classification data corresponding to the plurality of device identification data and the device activity data set ([134-138]: The system can presume that the water device is operating normally, and can use the initially-reported values from the controller and sensors as baseline values for normal operation. If the system does identify an existing device health profile for the water device, at 1012 the system can compare the real-time data to the device health profile to determine whether the device is operating normally, or is exhibiting potentially problematic behavior. At 1014, the system can determine whether the collected data indicates the water device has a problem. For example, if the reported sensor values deviate a predetermined amount from the normal operation signature, or if they are similar to a problem signature, there may be a problem (i.e. determine problem signature for water device based on collected data))
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Brown and identify a plurality of device identification data from the device identification data set, the plurality of device identification data associated with the plurality of networked devices of the networked device set; and determine the malfunction classification data corresponding to the plurality of device identification data and the device activity data set. One of ordinary skilled in the art would have been motivated to combine the teachings in order to provide the electronic support resources (Brown, [135]).
Regarding claim 6, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Lee in view of Sunyer, Jain and Picardi however does not teach wherein the device operational support management repository comprises at least the predicted operational support data object associated with a set of device types and at least one malfunction classification identifier in malfunction classification data associated with the networked device set, and wherein the device identification data set indicates the networked device set comprises one or more networked devices of the set of device types.
Brown teaches wherein the device operational support management repository comprises at least the predicted operational support data object associated with a set of device types ([117]: Static support can include enabling the installer/customer to access existing audible and/or visual electronic materials, such as device-specific installation guides, user manuals, walkthrough/troubleshooting videos, etc. The system can use the identifier, product name, etc., to identify and obtain files comprising the materials and can send the files to the on-site user devices, or to an email address of the customer or installer, and/or can send hyperlinks to the online storage location(s) of the materials.) and at least one malfunction classification identifier in malfunction classification data associated with the networked device set ([135]: If the data points do match a problem signature, at 1024 the system can generate an alert and deliver the alert to the user, notifying the user of the existing or emergent problem condition(s). At 1030 the system can automatically provide to the user any available electronic support resources. For example, the system can send a notification containing links to product troubleshooting guides and/or videos (i.e. identifying and providing troubleshooting guide from storage based on problem signature associated with device)), and
wherein the device identification data set indicates the networked device set comprises one or more networked devices of the set of device types ([117]: The system can use the identifier, product name, etc., to identify and obtain files comprising the materials and can send the files to the on-site user devices, or to an email address of the customer or installer, and/or can send hyperlinks to the online storage location(s) of the materials (i.e. device identification including identifier for specific product type, here product name can indicate set of devices)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Brown and the device operational management repository comprises at least the predicted operational support data object associated with a set of device types and at least one malfunction classification identifier in malfunction classification data associated with the networked device set, and wherein the device identification data set indicates the networked device set comprises one or more networked devices of the set of device types. One of ordinary skilled in the art would have been motivated to combine the teachings in order to provide the electronic support resources (Brown, [135]).
Regarding claim 7, Lee in view of Sunyer, Jain and Picardi teaches the apparatus according to claim 1.
Picardi further teaches the apparatus further caused to: identify a first malfunction classification associated with a set of device types ([60-61]: The control unit server 104 obtains thermostat information from the monitored property 102. During 204, the control unit server 104 determines an HVAC system issued based on an analysis of the thermostat information using a trained model. During 206, the control unit server 104 sorts the HVAC system issue into a category that specifies a type of the HVAC system issue. In some implementations, the categories may include heating vs. cooling category, acute vs inefficient category, a filter change category, and a long cycling category (i.e. identify HVAC system issue category, here HVAC system have set of devices)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Picardi and the apparatus identifies a first malfunction classification associated with a set of device types. One of ordinary skilled in the art would have been motivated to combine the teachings in order to detect and predict issues associated with the system (Picardi, [39]).
Lee in view of Sunyer, Jain and Picardi however does not teach store first malfunction classification data representing the first malfunction classification associated with the set of device types.
Brown teaches store first malfunction classification data representing the first malfunction classification associated with the set of device types ([133]: The system can generate (e.g., using the method 1000) one or more libraries of device behavior signatures. In various embodiments, signatures in the libraries can account for, and can be categorized by, various characteristics pertaining to the water device, the water system or subsystem, the geographic and/or environmental details of the water system location, etc. In one embodiment, a first set of libraries can store normal operation and problem signatures for water devices, where each library is dedicated to a particular manufacturer's products (i.e. problem signatures associated with water devices for a particular manufacturer is stored in the library)),
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee in view of Sunyer, Jain and Picardi to incorporate the teachings of Brown and store first malfunction classification data representing the first malfunction classification associated with the set of device types. One of ordinary skilled in the art would have been motivated to combine the teachings in order to provide the electronic support resources (Brown, [135]).
Regarding Claims 10, 12 and 14-15, they do not teach or further define over claims 2, 4 and 6-7 respectively. Therefore, claims 10, 12 and 14-15 are rejected for the same reason as set forth above in claims 2, 4 and 6-7 respectively.
Regarding Claims 18 and 19, they do not teach or further define over claims 2 and 7 respectively. Therefore, claims 18 and 19 are rejected for the same reason as set forth above in claims 2 and 7 respectively.
Additional References
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. Ketharaju et al., US 20230360146 A1: CUSTOMER SUPPORT COMPLAINT MANAGEMENT.
b. Ghatage et al., US 20220019935 A1: UTILIZING MACHINE LEARNING MODELS WITH A CENTRALIZED REPOSITORY OF LOG DATA TO PREDICT EVENTS AND GENERATE ALERTS AND RECOMMENDATIONS.
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJANA KHAKURAL whose telephone number is (571)272-3704. The examiner can normally be reached on M-F: 7:30AM - 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal B Divecha can be reached on 571-272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJANA KHAKURAL/Examiner, Art Unit 2453
/KAMAL B DIVECHA/Supervisory Patent Examiner, Art Unit 2453
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