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 .
Information Disclosure Statement
Acknowledgment is made of the Information Disclosure Statement dated 4/18/2023. All of the cited references have been considered.
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Claim Rejections - 35 USC § 103
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, 2, 3, 4, 10, 11, 12, 13, 14, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20220070260 A1); hereinafter Chang in view of Kesavan et al. (US 20230060461 A1); hereinafter Kesavan
Claim 1 is rejected over Chang and Kesavan.
Regarding claim 1, Chang teaches a computing platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: (Chang [0040]: “FIG. 4 shows an illustrative utilities-based network management system 110. As shown, system 110 may include, without limitation, a memory 402 and a processor 404 selectively and communicatively coupled to one another. Memory 402 and processor 404 may each include or be implemented by computer hardware that is configured to store and/or execute computer software.”)
train, using a plurality of historical thermal images, a thermal image classification model, wherein training the thermal image classification model configures the thermal image classification model to classify thermal images based on performance of systems represented by the thermal images; (Chang [0055]: “System 110 may be configured to analyze the maps in any suitable way, including by providing maps as input to an artificial intelligence algorithm such as a machine learning algorithm (e.g., a trained neural network) that is configured to process the maps and output potential utilizations of the network facility 106 that are determined based on the maps.”; Note: The heat map is the thermal image.)
collect current system performance information for a first computing system; (Chang [0026]: “In certain examples, at least some utilities data 112 may indicate real-time information about utilities at network facilities 106. Such real-time information may represent real-time conditions of utilities at network facilities 106 as the conditions exist approximately at a moment in time that utilities data 112 indicating the conditions is obtained by system 110. In these examples, system 110 may obtain utilities data 112 in real-time, which may mean that utilities data 112 is obtained immediately and without undue delay, even if it is not possible for there to be absolutely zero delay.”)
generate, using the current system performance information, a new thermal image, representative of the current system performance information; (Chang [0036]: “Configuration 300 further includes thermal imaging devices 306 deployed in network facility 106 and configured to capture thermal images that may be used to generate one or more maps of temperatures (e.g., a heat map) at network facility 106.”; [0026]: “In certain examples, at least some utilities data 112 may indicate real-time information about utilities at network facilities 106. Such real-time information may represent real-time conditions of utilities at network facilities 106 as the conditions exist approximately at a moment in time that utilities data 112 indicating the conditions is obtained by system 110. In these examples, system 110 may obtain utilities data 112 in real-time, which may mean that utilities data 112 is obtained immediately and without undue delay, even if it is not possible for there to be absolutely zero delay.”)
classify, using the thermal image classification model, the new thermal image; and (Chang [0055]: “System 110 may be configured to analyze the maps in any suitable way, including by providing maps as input to an artificial intelligence algorithm such as a machine learning algorithm (e.g., a trained neural network) that is configured to process the maps and output potential utilizations of the network facility 106 that are determined based on the maps.”; Note: The heat map is the thermal image.)
based on the classification of the new thermal image, send one or more network action commands, [wherein sending the one or more network action commands causes a network traffic manager to redirect traffic from the first computing system to a second computing system.] (Chang [0067]: “Based on utilization of a network facility 106, system 110 may generate and output data representing a network management operation to be performed. System 110 may provide the data to any entity that may perform or facilitate performance of the network management operation to be performed. As an example, system 110 may provide the data to a computing system that is configured to present the data to a network operator. As another example, system 110 may provide the data to a computing system (e.g., network equipment) that is configured to perform the network management operation. The providing of the data to such a computing system may in effect direct the computing system to perform the network management operation. For example, system 110 may direct a component of network equipment 104 to adjust an operation of network equipment 104, such as by changing a state of operation network equipment 104 or a component of network equipment 104. The change in the state of operation may include, for example, powering off, powering on, turning up (e.g., increasing operation intensity, frequency, etc. to consume more power), turning down (e.g., decreasing operation intensity, frequency, etc. to consume less power), transitioning between an active mode and a standby mode of operation, etc. of network equipment 104 or a component of network equipment 104.”)
Chang does not appear to explicitly teach wherein sending the one or more network action commands causes a network traffic manager to redirect traffic from the first computing system to a second computing system.
However, Kesavan teaches wherein sending the one or more network action commands causes a network traffic manager to redirect traffic from the first computing system to a second computing system. (Kesavan [0108]: “If the confirmation 3-32 is unable to establish that the server is healthy or indicates the server has additional indications of unreliability, action 3-33 may occur either automatically or at the direction of the telco person 3-40 (shown generally as input 3-42).”; [0109]: The action 3-33 may cause a shift 4-60 in the cloud of servers 1-5 as shown in FIG. 4C.”; [0125]: “FIG. 4C illustrates exemplary details of a shift 4-60 to move a load 4-61 to a low-risk server 4-62 (for example to server K and/or server L).”; Note: Server 1-8 is the first computing system being shifted to server K and/or L which is the second computing system.)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Claim 2 is rejected over Chang and Kesavan with the incorporation of claim 1.
Regarding claim 2, Chang teaches generate, using the historical system performance information, the plurality of historical thermal images. (Chang [0055]: “System 110 may be configured to analyze the maps in any suitable way, including by providing maps as input to an artificial intelligence algorithm such as a machine learning algorithm (e.g., a trained neural network) that is configured to process the maps and output potential utilizations of the network facility 106 that are determined based on the maps.”; Note: The heat map is the thermal image.)
Chang does not appear to explicitly teach monitor a plurality of computing systems including the first computing system and the second computing system to detect historical system performance information; and
However, Kesavan teaches monitor a plurality of computing systems including the first computing system and the second computing system to detect historical system performance information; and (Kesavan [0017]: “A server is also referred to as a “node.” In some embodiments, the model training is performed by: 1) loading historical data for servers (may be, for example, approximately 6,000 servers)”; and [0114]: “Example servers K, L, and 1-8 are shown in FIG. 4A. The number of servers in FIG. 4A is 1,000 or more (up to 6,000). VM11 and VM12 (virtual machines) are example virtual machines running on server K. VM21 and VM22 are example virtual machines running on server L. VM31 and VM32 are example virtual machines running on server 1-8.”; and [0103]: “The health scores 1-3 of the servers 1-4 and the heat map data 3-39 is provided to an operating console computer 3-30 for inspection by a telco person 3-40 (a human being).”)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Claim 3 is rejected over Chang and Kesavan with the incorporation of claim 1.
Regarding claim 3, Chang teaches wherein the historical system performance information and the current system performance information comprise one or more of: application performance information, system performance information, or test case results. (Chang [0047]: “A utilization of a network facility 106 may represent a past, current (e.g., real-time), and/or predicted utilization of the network facility 106. For example, system 110 may be configured to determine a past utilization of the network facility 106 based on historical utilities data 112, a current utilization of the network facility 106 based on current utilities data 112, and/or a predicted future utilization of the network facility 106 based on historical utilities data 112 (e.g., patterns of utilizations identified in historical utilities data 112) or a combination of historical and current utilities data 112” and [0057]: “Network data may indicate information about the use and/or performance of one or more resources of communication network 102. For example, network data may indicate utilization of network equipment 104, processors (e.g., CPUs), memory, etc. Network data may additionally or alternatively indicate network performance parameters such as network traffic, throughput, and latency. System 110 may be configured to use such network data together with other data (e.g., utilities data 112, subscription data, application data, etc.) to determine utilization of a network facility 106 and/or perform network management operations.”)
Claim 4 is rejected over Chang and Kesavan with the incorporation of claim 1.
Regarding claim 4, Chang does not appear to explicitly teach wherein training the thermal image classification model comprises labelling the plurality of historical thermal images based on corresponding system performance, and
training the thermal image classification model to identify correlations between the plurality of historical thermal images and the corresponding system performances.
However, Kesavan teaches wherein training the thermal image classification model comprises labelling the plurality of historical thermal images based on corresponding system performance, and (Kesavan [0150]: “In a first process, health scores for each server of the servers 1-4 are obtained. In a second process, a list of at-risk servers is maintained, and a heat map for the at-risk servers is obtained every ten minutes. There may be, in this example, six heat maps 3-41 per hour. In this example, there is an at-risk heat map and a system-wide heat map. “; [0017]: “the model training is performed by: 1) loading historical data for servers (may be, for example, approximately 6,000 servers); 2) setting targets based on if and when a server failed (obtain labels by labelling nodes by failure time, using the data), 3) computing statistical features of the data, and adding the statistical features to the data object, 4) identifying leading indicators for failures, this identification is based on the data and the labels, 5) training an AI model with the newly found leading indicators, this training is based on the data, the leading indicators and the labels, and 6) optimizing the AI model by performing hyperparameter tuning and model validation. The output of the above approach is the AI model.”)
training the thermal image classification model to identify correlations between the plurality of historical thermal images and the corresponding system performances. (Kesavan [0016]: “The heat map can also indicate commonalities between at-risk servers, such as if the at-risk servers are correlated in terms of protocols in use, geographic location, server manufacturer, server OS (operating system) load, or the particular hardware failure mechanism predicted for the at-risk servers. The heat map allows a telco person to find out in real time or near-real time, the health of their overall network.”
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Claim 10 is rejected over Chang and Kesavan with the incorporation of claim 1.
Regarding claim 10, Chang teaches wherein classifying the new thermal image comprises assigning a performance score to the first computing system and classifying the first computing system based on the performance score. (Chang [0057]: “Network data may indicate information about the use and/or performance of one or more resources of communication network 102. For example, network data may indicate utilization of network equipment 104, processors (e.g., CPUs), memory, etc. Network data may additionally or alternatively indicate network performance parameters such as network traffic, throughput, and latency. System 110 may be configured to use such network data together with other data (e.g., utilities data 112, subscription data, application data, etc.) to determine utilization of a network facility 106 and/or perform network management operations.”; and [0055]: “Confidence scores for the potential utilizations may also be provided. From the potential utilizations, system 110 may determine a utilization of the network facility 106, which may be represented in any of the illustrative ways described herein and/or in any other suitable way.)
Claim 11 is rejected over Chang and Kesavan with the incorporation of claim 1.
Regarding claim 11, Chang does not appear to explicitly teach identify whether or not the classification of the first computing system corresponds to a network action, wherein sending the one or more network action commands is based on identifying that the classification of the first computing system corresponds to the network action.
However, Kesavan teaches identify whether or not the classification of the first computing system corresponds to a network action, wherein sending the one or more network action commands is based on identifying that the classification of the first computing system corresponds to the network action. (Kesavan [0108]: “If the confirmation 3-32 is unable to establish that the server is healthy or indicates the server has additional indications of unreliability, action 3-33 may occur either automatically or at the direction of the telco person 3-40 (shown generally as input 3-42).”; [0109]: The action 3-33 may cause a shift 4-60 in the cloud of servers 1-5 as shown in FIG. 4C.”; [0125]: “FIG. 4C illustrates exemplary details of a shift 4-60 to move a load 4-61 to a low-risk server 4-62 (for example to server K and/or server L).”; Note: Server 1-8 is the first computing system being shifted to server K and/or L which is the second computing system.)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Claim 12 is rejected over Chang and Kesavan.
Regarding claim 12, Chang teaches a method comprising:
at a computing platform comprising at least one processor, a communication interface, and memory: (Chang [0040]: “FIG. 4 shows an illustrative utilities-based network management system 110. As shown, system 110 may include, without limitation, a memory 402 and a processor 404 selectively and communicatively coupled to one another. Memory 402 and processor 404 may each include or be implemented by computer hardware that is configured to store and/or execute computer software.”)
The remainder of claim 12 is claim 1 in the form of a method and is rejected for the same reasons as claim 1 stated above.
Dependent claim 13 is claim 2 in the form of a method and is rejected for the same reasons as claim 2 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Dependent claim 14 is claim 3 in the form of a method and is rejected for the same reasons as claim 3 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Dependent claim 15 is claim 4 in the form of a method and is rejected for the same reasons as claim 4 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Claim 20 is rejected over Chang and Kesavan.
Regarding claim 20, Chang teaches one or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: (Chang [0041]: “Memory 402 may be implemented by one or more memory or storage devices, including any memory or storage devices described herein, that are configured to store data in a transitory or non-transitory manner.”)
The remainder of claim 20 is claim 1 in the form of a non-transitory computer-readable media and is rejected for the same reasons as claim 1 stated above.
Claims 5, 6, 7, 16, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chang and Kesavan in further view of Rosier et al. (US 20160057034 A1); hereinafter Rosier
Claim 5 is rejected over Chang, Kesavan and Rosier with the incorporation of claim 1.
Regarding claim 5, Chang does not appear to explicitly teach comparing a plurality of image features of the new thermal image to the corresponding image features of the plurality of historical thermal images to identify a highest image matching score, and
selecting a classification corresponding to the highest image matching score.
However, Rosier teaches comparing a plurality of image features of the new thermal image to the corresponding image features of the plurality of historical thermal images to identify a highest image matching score, and (Rosier [0024]: “the administrator can create an analyzer that takes current high memory usage jobs and compares them to historical usage of processes as described in logs. This can allow an administrator to decide whether to kill the process or transfer the process to a higher-cost server more capable of running the process without slowing down other users.”; [0048]: “Monitor system 212 can provide information to an administrator regarding system metrics. In some embodiments, a dashboard is presented to the administrator. During normal operation, a dashboard can show a set of metrics to the administrator. Some metrics can be displayed as graphs and can be color coded to identify low, medium and high resource usage (or more granularity of colors depending on system settings, e.g., a heat map) and/or low, medium and high importance (as related to one or more thresholds).”
selecting a classification corresponding to the highest image matching score. (Rosier [0023]: “As each analyzer is processed, the outputs (or sometimes intermediate internal values) can be compared against one or more thresholds. Based on the comparison, each analyzer can be scored. Analyzers that take inputs from outputs of analyzers lower in the analyzer hierarchy can inherit scores from analyzers from which inputs are received (e.g., sum, average, weighted average, decreasing historic weighted average, etc.). In some embodiments, correlation can be based on hierarchical relationships. In other embodiments, correlation can be based on scores.”; [0032]: “there are multiple “high score” alerts that relate to the same problem. A combined alert message can be shown that is similar to the single alert. A primary emphasis can be placed on the combined high-level analysis of the various systems experiencing the same issue, and the supporting graphs can focus on each individual system. This is in contrast to the single-alert scenario where the supporting graphs can allow a user to drill down to the various symptoms.”)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the method of correlating derived metrics of system activity of Rosier to efficiently fix system issues (Rosier, [0040]). Chang and Rosier are analogous art because they both concern analyzing system performance using heat maps.
Claim 6 is rejected over Chang, Kesavan and Rosier with the incorporation of claim 1.
Regarding claim 6, Chang does not appear to explicitly teach wherein the plurality of image features comprise: image peaks and troughs, center of gravity, moment, and spatial frequency.
However, Kesavan teaches wherein the plurality of image features comprise:
image peaks and troughs, (See Figure 6 of Kesavan to see that a high heath score is the peak and a low health score is the trough. Also see Figure 12 to see that the peak and trough of the time series data.)
center of gravity, (See Figure 12 of Kesavan to see that the center of gravity of the values are around 0.)
moment, and (Kesavan [0187]: “The AI inference engine of note 4, wherein the trained AI model represents a plurality of decision trees, wherein a first decision tree of the plurality of decision trees includes a plurality of decision nodes, a corresponding plurality of decision thresholds are associated with the plurality of decision nodes (see FIG. 10 ), and the trained AI model is configured to cause the plurality of decision trees to detect anomaly patterns of the at least one leading indicator over a first time interval (see FIG. 13 ).”; Note: The anomaly pattern from the peak and the center of gravity is the moment. )
spatial frequency. (Kesavan [0187]: “The AI inference engine of note 4, wherein the trained AI model represents a plurality of decision trees, wherein a first decision tree of the plurality of decision trees includes a plurality of decision nodes, a corresponding plurality of decision thresholds are associated with the plurality of decision nodes (see FIG. 10 ), and the trained AI model is configured to cause the plurality of decision trees to detect anomaly patterns of the at least one leading indicator over a first time interval (see FIG. 13 ).”; Note: The anomaly pattern from the peak and the center of gravity is the moment. )
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers using heat maps and time series data of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Claim 7 is rejected over Chang, Kesavan and Rosier with the incorporation of claim 1.
Regarding claim 7, Chang does not appear to explicitly teach wherein comparing the image peaks and troughs comprises comparing one or more of: a number of the image peaks and troughs, or total areas of the image peaks and troughs.
However, Kesavan teaches wherein comparing the image peaks and troughs comprises comparing one or more of: a number of the image peaks and troughs, or total areas of the image peaks and troughs. (Kesavan [0158]: “FIG. 13 illustrates, for at-risk server 1-8, exemplary time series data of different statistics types applied to server parameters 3-50, according to some embodiments. Also see Table 2 for exemplary at-risk server data. The data is from an operational server cloud. The peak of the IOWait Rolling ZScore at a time of approximately 10:32 indicates the sever is at-risk. This server is an actual server and did eventually fail. By using the logic of FIGS. 7A, 7B, 8 and/or 9 , the at-risk server can be predicted as at-risk before failure, and virtual machines supporting services used by UEs 4-11 can be shifted to low-risk servers from the at-risk server without loss or delay of data to the UEs 4-11. This improves performance of the system 4-9.”)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the shifting to low-risk servers of Kesavan to improve system performance (Kesavan [0159]). Chang and Kesavan are analogous art because they both concern redirecting network traffic based on heat maps used to analyze the performance of computer systems.
Dependent claim 16 is claim 5 in the form of a method and is rejected for the same reasons as claim 5 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Dependent claim 17 is claim 6 in the form of a method and is rejected for the same reasons as claim 6 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Dependent claim 18 is claim 7 in the form of a method and is rejected for the same reasons as claim 7 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Claims 8, 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang, Kesavan and Rosier and in further view of Saraee et al. (US 20210264161 A1); hereinafter Saraee
Claim 8 is rejected over Chang, Kesavan, Rosier and Saraee with the incorporation of claim 1.
Regarding claim 8, Chang does not appear to explicitly teach wherein training the thermal image classification model comprises initially applying equal weighting values to the plurality of image features.
However, Saraee teaches wherein training the thermal image classification model comprises initially applying equal weighting values to the plurality of image features. (Saraee [0046]: “For example, the training data manager can apply a weight to the engagement metrics of images in the dataset based on engagement rates of each of the images. In one example, the training data manager 108 can divide an engagement metric for an image by a number of followers of its respective web-based property to determine a normalized engagement metric for the image.”)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the weighting values of Saraee to improve the accuracy of the model’s performance (Saraee, [0026]). Chang and Saraee are analogous art because they both concern heat map generation.
Claim 9 is rejected over Chang, Kesavan, Rosier and Saraee with the incorporation of claim 1.
Regarding claim 9, Chang does not appear to explicitly teach update, using a dynamic feedback loop and based on the classification of the new thermal image, the thermal image classification model,
wherein updating the thermal image classification model includes modifying the weighting values to weight at least one of the plurality of image features higher than at least one other feature of the plurality of image features.
However, Saraee teaches update, using a dynamic feedback loop and based on the classification of the new thermal image, the thermal image classification model, (Saraee [0118]: “The data processing system may train a machine learning model with training data generated by a particular target audience by feeding images from the training data through the machine learning model, determining a difference between the engagement metric for the image and the output performance score, and backpropagating the output through the machine learning model according to a loss function based on the determined difference.”)
wherein updating the thermal image classification model includes modifying the weighting values to weight at least one of the plurality of image features higher than at least one other feature of the plurality of image features. (Saraee [0117]: “The data processing system may extract values (e.g., weights) from the machine learning model that were generated by the machine learning model when generating the performance score and feed the values back through the model to obtain indications of how different features of the image impacted the performance score. The data processing system may then use the indications to generate a heat map overlay that corresponds to different portions of the image to indicate whether the respective portions positively or negatively impacted the image's performance score.”)
It would have been obvious before the effective filing date to combine heat map generation of network conditions of Chang with the weighting values of Saraee to improve the accuracy of the model’s performance (Saraee, [0026]). Chang and Saraee are analogous art because they both concern heat map generation.
Dependent claim 19 is claim 8 in the form of a method and is rejected for the same reasons as claim 8 stated above. For the rejection of the limitations specifically pertaining to the method of claim 12, see the rejection of claim 12 above.
Conclusion
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/DAVID H TRAN/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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