DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-14 are pending for examination.
Claim Objections
Regarding claims 1 and 7, recite the initials “AI”. Pease consider to spell out the words of the initials in the first appearance to reduce confusion.
Regarding claims 2-6 and 8-14, are also objected because they depend on claims 1 and 7 respectively.
Regarding claim 9, recites the limitation “wherein the predicted alarm information is provided when a likelihood that the alarm threshold will be exceeded exceeds a predefined likelihood threshold.” with emphasis underlined. It appears to be a typographical error. Please review and amend accordingly.
Appropriate correction is required.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 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.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Rohrkemper (Pub. No.: US 2023/0061280 A1) in view of Figura (Pat. No.: US 9,959,734 B1).
Regarding claim 1, Rohrkemper teaches a method for managing alarms in a distributed control system (Abstract, Fig. 1, root cause analysis system and method for a distributed system), the method comprising:
monitoring at least one process parameter (Fig. 1, para [0045], “The deterministic ML model 114 receives a set of time-series data 113 and generates operational results 117 based on the set of time-series data 113. For example, the deterministic ML model 114 may monitor, in real-time, signals from sensors monitoring a system.”. The analysis platform 110 receives and monitors the time-series data set from sensors); and
determining by an AI based alarm control system before the alarm is activated at least one predicted alarm information, which comprises information about whether the alarm threshold will be exceeded (para [0072], “For example, the system may generate a text output based on an early detection of a fault in a cooling component of a server facility by the deterministic ML model as follows: “A data outage fault is predicted for [server A] due to a [failure] of the [cooling component B]. [Sensors X, Y, and Z] indicate [cooling component B] is operating [near operational parameters] [and is likely to fail]. Recommend replacement of [cooling component B].” In such an example, information contained in the brackets may be generated based on the abductive model identifying the root cause of the predicted fault.”).
Rohrkemper teaches the predictive model outputs a warning before the sensors or server fails or exceeds a threshold (para [0061]) but fails to expressly teach activating an alarm when the process parameter exceeds an alarm threshold; and deactivating an alarm when the process parameter no longer exceeds the alarm threshold;
However, in the same field of predictive system, Figura teaches the activation of an alarm if the sensor parameter exceeds a threshold and deactivates the alarm if the parameter falls below the threshold. Abstract “A predictive shock alert warning system (SA) installed adjacent a body of water and monitoring a voltage level present in the water to alert persons in or about the water the water when the level is approaching, or reaches or exceeds a threshold that will produce a dangerous shock to a person. The system comprises a plurality of probes (1-3) placed in the water, each probe monitoring the voltage level in a zone (Z1-Z3) of water surrounding the probe and producing an output representing the voltage level for the respective zone in which the sensor is placed. An apparatus (100) processes the respective signals from each probe and triggers an alarm (13) when a voltage approaches, reaches or exceeds the danger voltage level.” and Col. 6 line 67 – Col. 7 line 5, “Once the signals processed by microcontroller 10 indicate that the sensed voltage level in a zone has fallen below the established threshold and that a dangerous condition no longer exists, microcontroller 10 will deactivate the various a006udio and visual alarms.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed inventio to modify Rohrkemper’s system to activate an alarm if the sensor or server fails and to deactivates the alarm if the sensor or server is working normally to provide accurate notification.
Regarding claim 2, Rohrkemper in the combination teaches the method according to claim 1, further comprising providing the predicted alarm information to a user before the alarm is activated (para [0061], “In one or more embodiments, the deterministic ML model generates operational results indicating a future fault state of one or more components in a target system even before time-series signals cross threshold operating values.” and para [0072]. The prediction is generated before the sensor cross threshold values or fails.).
Regarding claim 3, Rohrkemper in the combination teaches the method according to claim 1, further comprising:
determining by the AI based alarm control system at least one control parameter of the distributed control system that is adaptable to reduce a likelihood that the alarm threshold will be exceeded, and
providing at least one control parameter information to the user;
wherein the control parameter information comprises information about the control parameter; and/or
wherein the control parameter information comprises information about at least one suitable adaption of the control parameter to reduce the likelihood that the alarm threshold will be exceeded (Fig. 3, step 341 and para [0072], “For example, the system may generate a text output based on an early detection of a fault in a cooling component of a server facility by the deterministic ML model as follows: “A data outage fault is predicted for [server A] due to a [failure] of the [cooling component B]. [Sensors X, Y, and Z] indicate [cooling component B] is operating [near operational parameters] [and is likely to fail]. Recommend replacement of [cooling component B].” In such an example, information contained in the brackets may be generated based on the abductive model identifying the root cause of the predicted fault.”. The prediction determines the cooling component will cause failure of the server because the operating temperature is near threshold. The predication also provides a recommendation to replace the cooling component to reduce server failure in the future.).
Regarding claim 4, Rohrkemper in view of Figura teaches the method according to claim 3, further comprising providing at least one alarm information to the user when the alarm is activated, wherein the alarm information and the predicted alarm information are provided together or separately to a user (Rohrkemper teaches indication of possible failure of a sensor or server before the actual occurrence of the failure and Figura teaches the activation of alarm after the occurrence. Therefore, the predication alarm and the actual alarm occur at separate time.).
Regarding claim 5, Rohrkemper in the combination teaches the method according to claim 3, further comprising automatically adapting the control parameter to reduce the likelihood that the alarm threshold will be exceeded (para [0086], “The system may further generate one or more notifications to a system administrator or host device user with a human-understandable explanation of the root cause and the proposed solution. In one or more embodiments, the system may initiate a solution without intervening user input. The system stores solutions, including recommendations provided and solutions initiated, in a solutions database 450.”. The system initiates a solution automatically.).
Regarding claim 6, recites a limitation that is similar to claim 4. Therefore,
it is rejected for the same reasons.
Regarding claim 7, Rohrkemper teaches a system for managing alarms in a distributed control system (Abstract, Fig. 1, root cause analysis system and method for a distributed system), wherein the system comprises:
a process parameter monitor configured to monitor at least one process parameter (Fig. 1, para [0045], “The deterministic ML model 114 receives a set of time-series data 113 and generates operational results 117 based on the set of time-series data 113. For example, the deterministic ML model 114 may monitor, in real-time, signals from sensors monitoring a system.”. The analysis platform 110 receives and monitors the time-series data set from sensors);
an AI based alarm control system configured to determine at least one predicted alarm information before the alarm is activated, the AI based alarm control system comprising information about whether the alarm threshold will be exceeded (para [0072], “For example, the system may generate a text output based on an early detection of a fault in a cooling component of a server facility by the deterministic ML model as follows: “A data outage fault is predicted for [server A] due to a [failure] of the [cooling component B]. [Sensors X, Y, and Z] indicate [cooling component B] is operating [near operational parameters] [and is likely to fail]. Recommend replacement of [cooling component B].” In such an example, information contained in the brackets may be generated based on the abductive model identifying the root cause of the predicted fault.”).
Rohrkemper teaches the predictive model outputs a warning before the sensors or server fail or exceeds a threshold (para [0061]) but fails to expressly teach an alarm control configured to activate an alarm when the process parameter exceeds an alarm threshold and to deactivate the alarm when the process parameter no longer exceeds the alarm threshold;
However, in the same field of predictive system, Figura teaches the activation of an alarm if the sensor parameter exceeds a threshold and deactivates the alarm if the parameter falls below the threshold. Abstract “A predictive shock alert warning system (SA) installed adjacent a body of water and monitoring a voltage level present in the water to alert persons in or about the water the water when the level is approaching, or reaches or exceeds a threshold that will produce a dangerous shock to a person. The system comprises a plurality of probes (1-3) placed in the water, each probe monitoring the voltage level in a zone (Z1-Z3) of water surrounding the probe and producing an output representing the voltage level for the respective zone in which the sensor is placed. An apparatus (100) processes the respective signals from each probe and triggers an alarm (13) when a voltage approaches, reaches or exceeds the danger voltage level.” and Col. 6 line 67 – Col. 7 line 5, “Once the signals processed by microcontroller 10 indicate that the sensed voltage level in a zone has fallen below the established threshold and that a dangerous condition no longer exists, microcontroller 10 will deactivate the various a006udio and visual alarms.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed inventio to modify Rohrkemper’s system to activate an alarm if the sensor or server fails and to deactivates the alarm if the sensor or server is working normally to provide accurate notification.
Regarding claim 8, Rohrkemper in the combination teaches the system according to claim 7, wherein the system further comprises an information device configured to provide the predicted alarm information to a user before the alarm is activated (para [0061], “In one or more embodiments, the deterministic ML model generates operational results indicating a future fault state of one or more components in a target system even before time-series signals cross threshold operating values.” and para [0072]. The prediction is generated before the sensor cross threshold values or fails.).
Regarding claim 9, Rohrkemper in the combination teaches the system according to claim 8, wherein the predicted alarm information is provided when a likelihood that the alarm threshold will be exceeded exceeds a predefined likelihood threshold (para [0072], “For example, the system may generate a text output based on an early detection of a fault in a cooling component of a server facility by the deterministic ML model as follows: “A data outage fault is predicted for [server A] due to a [failure] of the [cooling component B]. [Sensors X, Y, and Z] indicate [cooling component B] is operating [near operational parameters] [and is likely to fail]. Recommend replacement of [cooling component B].” In such an example, information contained in the brackets may be generated based on the abductive model identifying the root cause of the predicted fault.”. The system predicts the server will likely to fail because a cooling component has failed and the sensors indicate the server is operating near failure temperature.).
Regarding claim 10, Rohrkemper in the combination teaches the system according to claim 7, wherein the system is further configured to:
determine by the AI based alarm control system at least one control parameter of the distributed control system that is adaptable to reduce a likelihood that the alarm threshold will be exceeded; and
provide at least one control parameter information to the user;
wherein the control parameter information comprises information about the control parameter; and/or
wherein the control parameter information comprises information about at least one suitable adaption of the control parameter to reduce the likelihood that the alarm threshold will be exceeded (Fig. 3, step 341 and para [0072], “For example, the system may generate a text output based on an early detection of a fault in a cooling component of a server facility by the deterministic ML model as follows: “A data outage fault is predicted for [server A] due to a [failure] of the [cooling component B]. [Sensors X, Y, and Z] indicate [cooling component B] is operating [near operational parameters] [and is likely to fail]. Recommend replacement of [cooling component B].” In such an example, information contained in the brackets may be generated based on the abductive model identifying the root cause of the predicted fault.”. The prediction determines the cooling component will cause failure of the server because the operating temperature is near threshold. The predication also provides a recommendation to replace the cooling component to reduce server failure in the future.).
Regarding claim 11, Rohrkemper in view of Figura teaches the system according to claim 7, wherein the system is further configured to provide at least one alarm information to the user when the alarm is activated, wherein the alarm information and the predicted alarm information are provided together or separately to the user (Rohrkemper teaches indication of possible failure of a sensor or server before the actual occurrence of the failure and Figura teaches the activation of alarm after the occurrence. Therefore, the predication alarm and the actual alarm occur at separate time.).
Regarding claim 12, Rohrkemper in the combination teaches the system according to claim 7, wherein the system further comprises a plurality of control sub-systems, wherein each of the plurality of control sub-systems is addable to and/or removable from the distributed control system (Fig. 3, the system 310 includes various sensors 316a-h and components 311-315), and wherein the AI based alarm control system is an AI based alarm control sub-system (Fig. 3 and para [0076], “FIG. 3 illustrates an example of a system 300 according to one embodiment. The system 300 includes a vehicle 310 that is being monitored by a deterministic ML model. The deterministic ML model includes an MSET model 320 and an SPRT model 330. The system includes an abductive model 340 for identifying a root cause of an operational result output by the deterministic ML model. An explanation generator 350 generates a human-readable explanation for the root cause.”. The root cause system is part of the system 310 that performs predication by using various models.).
Regarding claim 13, Rohrkemper in the combination teaches the system according to claim 12, wherein the system further comprises a non-AI based alarm control sub-system configured to add or remove one or more first alarm rules for managing alarms of the distributed control system (para [0086], “Based on the identified root cause 461, the recommendations 441, and the identified anomalies and risks, the system may initiate or propose one or more solutions 444. For example, the system may recommend, based on the risk to the host device, increasing a security setting of a firewall, changing an anti-malware service, or updating an anti-malware application. The system may further generate one or more notifications to a system administrator or host device user with a human-understandable explanation of the root cause and the proposed solution.”. The anti-malware application is considered as a non-AI based alarm control sub-system because a human needs to perform the update of the anti-malware application. The update removes previous alarm rules and adds new alarm rules.), wherein the system is further configured to allow an addition or removal of one or more second alarm rules for managing alarms of the distributed control system defined by the AI based alarm control sub-system (para [0086], “In one or more embodiments, the system may initiate a solution without intervening user input. The system stores solutions, including recommendations provided and solutions initiated, in a solutions database 450.”. The root cause system provides solution, such as to firewall update, automatically without user intervention.).
Regarding claim 14, Rohrkemper in the combination teaches the system according to claim 13, wherein the system is further configured to allow a modification of the first alarm rules by the AI based alarm control sub-system to create one or more modified first alarm rules for managing alarms of the distributed control system (para [0086], “In one or more embodiments, the system may initiate a solution without intervening user input. The system stores solutions, including recommendations provided and solutions initiated, in a solutions database 450.”. The root cause system updates anti-malware application automatically without user intervention).
Conclusion
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/ZHEN Y WU/Primary Examiner, Art Unit 2685