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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: machine learning module in claims 25,32,33,40,41,48.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 25-31,33-39,41-47,49-51 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20140336791 A1 (Asenjo), in view of “Security Risk Assessment of Cloud Computing Services in a Networked Environment”(Weintraub et. al. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 11, 2016 79, herein referred to as Weintraub), and US 20200104200 A1 (Kocberber).
Regarding claim 25, Asenjo teaches,
A method, comprising:
providing input on a plurality of attributes of a computing environment comprising one or more devices(fig 1:314 par 56 “Using an architecture similar to that depicted in FIG. 1, predictive maintenance system 314 can collect industrial data from the individual devices during operation and classify the data in a customer data store 302 according to the aforementioned classifications.”) to a machine learning module to produce an output value(fig 8:210; par 67 “Predictive analysis component 210 can analyze the resulting multi-industry, multi-customer data store to learn industry-specific, device-specific, and/or application- specific trends, patterns, thresholds, etc.”) that comprises a risk score that indicates a likelihood of a potential malfunctioning occurring within the computing environment(par 85 “Once these critical variations have been identified, predictive analysis component 210 can analyze customer-specific data to anticipate when the customer's particular system is at risk of exceeding these critical variations ( e.g., determine whether a pressure at an analogous station of the customer's system is trending toward a determined critical threshold).”) caused by at least firmware levels of a first set of devices of the one or more devices in the computing environment not being at a recommended level, wherein the plurality of attributes include device level predictors including firmware level, (par 10 “These services can include providing notifications when a newer firmware version is available for a given device, detection and notification of system trends indicative of an impending device or system failure, detection and notification of device or system performance degradation, recommending device upgrades or system reconfigurations that will improve system performance or device interaction, … .”; fig 10; par 77 “The system response to a determination that the on-premises device is running an out-of-date firmware version can depend on the service contract information maintained in the customer model. … .”) and device, asset, process, and system level predictors,(fig 15:1502; par 106 “Initially, at 1502, device, asset, process, and system data is collected from multiple industrial enterprises in a cloud platform. At 1504, big data analysis is performed on the collected data to identify application-specific and/or industry- specific operational behaviors and correlations. For example, subsets of the collected data relating to the use of certain industrial assets to carry out a particular industrial application are analyzed, and correlations between system performance metrics and system configuration aspects (e.g., device settings, hardware types, firmware versions, etc.) are identified based on the analysis.”) and wherein each attribute has an associated weight that is dynamically altered via learning mechanisms; (par 67 “Predictive analysis component 210 can analyze the resulting multi-industry, multi-customer data store to learn industry-specific, device-specific, and/or application-specific trends, patterns, thresholds, etc. In general, predictive analysis component 210 can perform big data analysis on the multi-enterprise data maintained BDFM data storage to learn and characterize operational trends or patterns as a function of industry type, application type, equipment in use, asset configurations, device configuration settings, or other such variables.”; par 68, 69,78,82)
in response to determining that the risk score exceeds a predetermined threshold,(fig 10; par 77 “The system response to a determination that the on-premises device is running an out-of-date firmware version can depend on the service contract information maintained in the customer model. … .”)
transmitting an indication to indicate that the potential malfunctioning is likely to occur within the computing environment by electronically sending an alert,(par 87 “For example, customer model 304 may specify that notifications relating to an impending device failure should be delivered to one or more client devices associated with selected maintenance personnel, while notifications relating to firmware upgrades or recommended device reconfigurations should be delivered to a client device associated with a plant engineer.”) wherein the indication additionally indicates customized details about the potential malfunctioning(par 87 “Notification preferences defined in the customer model may also be a function of a particular plant facility, area, or work-cell to which the notification relates.”; par 90 “Depending on the type of problem identified and the nature of the service agreement between the customer and the technical support entity, notification component 212 may initiate contact with customer support personnel in response to detection of an impending maintenance issue.”), wherein the machine learning module is trained to calculate a margin of error based on comparing a generated risk score to an expected risk score,(par 67 “Predictive analysis component 210 can analyze the resulting multi-industry, multi-customer data store to learn industry-specific, device-specific, and/or application-specific trends, patterns, thresholds, etc. In general, predictive analysis component 210 can perform big data analysis on the multi-enterprise data maintained BDFM data storage to learn and characterize operational trends or patterns as a function of industry type, application type, equipment in use, asset configurations, device configuration settings, or other such variables.”; par 68, 69,78,82 ) wherein the response to the problem is dependent on the type of the problem;(par 90 “Depending on the type of problem identified and the nature of the service agreement between the customer and the technical support entity, notification component 212 may initiate contact with customer support personnel in response to detection of an impending maintenance issue.”) and
modifying the computing environment by updating the firmware levels of the first set of devices to the recommended level to prevent the potential malfunctioning from occurring, and causing a preemptive reduction of a risk of failure in the storage controller of the computing environment.(fig 10; par 77 “If the customer's service plan does not include automated firmware upgrades, … . Alternatively, the firmware may be provided automatically to the user in accordance with the pre-existing service plan. In another scenario, for cloud-aware industrial devices that include bi-directional cloud gateways, device management component 208 may remotely deliver and install the most recent firmware version to the device automatically from the cloud platform (e.g., via device interface component 204).”)
However, Asenjo does not specifically teach a level of severity of the potential malfunctioning or wherein the expected risk score is higher in response to a data loss in comparison to a loss of access to data for a limited period of time.
On the other hand, Weintraub teaches,
A method, of risk modeling in a cloud computing environment,
wherein the indication additionally indicates a level of severity of the potential malfunctioning,(pg 82 “[24] published a list of CC risk factors. … Following the list of risks, categorized to the three cloud layers, each risk includes an indication for either risk increasing (RI) or risk decreasing (RD). Additionally the description includes the types of risks and their severity level. From figure IV it is clear that the risk level increases from Unavailability to Loss, from Loss to Theft, and from Theft to Disclosure. While assigning values to severity levels may be an open issue, the following example is an arbitrary scheme where these values are increasing as mentioned above.”; pg 81 “CSA's experts identified nine critical threats, ranked in descending order of severity: Data Breaches, Data Loss, ….”; pg 83 “Table I not only differentiate between the RI/RD factors, it also depicts the probability and damage of each factor to the four risk types (Unavailability, Loss, …). The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization.”)
, and wherein the expected risk score is higher in response to a data loss in comparison to a loss of access to data for a limited period of time, (pg 81“Starting our analysis we note that the damage of loss is greater than the damage of unavailability.”; fig 3”Temporary Unavailability”; pg 82 “disclosure of regular data has low risk, while the disclosure of critical data has higher risk. Also, temporary unavailability of regular data may be tolerated due to low damage.”; pg 82 “Following the list of risks, categorized to the three cloud layers, each risk includes an indication for either risk increasing (RI) or risk decreasing (RD). Additionally the description includes the types of risks and their severity level. From figure IV it is clear that the risk level increases from Unavailability to Loss, from Loss to Theft, ….”; pg 83 “Table I not only differentiate between the RI/RD factors, it also depicts the probability and damage of each factor to the four risk types (Unavailability, Loss, …). The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization.”) and wherein the expected risk score is higher in response to a loss of access to data for a limited period of time in comparison to an event that causes a performance impact in a storage controller of the computing environment; (pg 83 “The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization. In the example, the probability and the damage are ranked on a 5 point scale (1 to 5) and the risk is the multiplication of the probability rank and the damage rank. For each factor, each risk type is evaluated through such a multiplication yielding a scale of 1 through 25.” It would be obvious to rank loss of access based on how long the loss of access is, with longer periods being more impactful than shorter periods of loss of access. An event with a performance impact with no loss of access would be treated as a period of loss of access of zero, compared to an event with a larger time period of loss of access.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further modify Asenjo’s preventative maintenance system to incorporate the level of severity of the potential malfunctioning and wherein the expected risk score is higher in response to a data loss in comparison to a loss of access to data for a limited period of time of Weintraub. One of ordinary skill in the art would have been motivated to remedy the shortcomings of Asenjo -- a need for how to prioritize maintenance issues(Weintraub pg 83 “Table I not only differentiate between the RI/RD factors, it also depicts the probability and damage of each factor to the four risk types (Unavailability, Loss, Theft, and Disclosure). The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization.”) -- with Weintraub providing a known method to solve a similar problem. Weintraub provides a risk modeling framework to analyze different types of risks(Weintraub pg 81-89; Weintraub pg 89 “This paper proposes a technique for evaluating and comparing risks between different service providers in the three CC layers.”)
However, Asenjo and Weintraub do not specifically teach a ratio of a ratio of faulty drives and an age of drives, or wherein each attribute has an associated weight that is dynamically altered via back propagation mechanisms.
On the other hand, Kocberber teaches
A machine learning system for learning important attributes and identifying failures ahead of time(par 1 “The present invention relates to a framework for training a specific machine learning system trained with disk drive sensor data to learn important attributes and identify disk drive failures ahead of time.”)
wherein the plurality of attributes include device level predictors including firmware level, and component level predictors including a ratio of faulty drives(par 51 "Healthy/failed disk ratio defines the number of healthy disks added to the output matrix for every failed disk."; par 82 “Each disk failure causes a window of vulnerability within the system, where multiple failures in the same volume may result in permanent data loss. Embodiments presented herein enable predictions of disk failures so that informed volume allocation decisions may be employed to spread the risk evenly on different volumes.”) and an age of drives,(fig 2:220; par 44 “It shows that P sensor attributes 210 are received for each disk, the sensor attributes are received over N days 220, and the sensor attributes are received for Q disks in total 230.” N days is equivalent to the age of the device. Par 84 “Understanding the remaining useful life of a disk is a significant factor in helping businesses with capacity planning, allocation, and forecasting.” Age is directly correlated with useful life of a disk.) and wherein each attribute has an associated weight that is dynamically altered via back propagation mechanisms;(par 108 “Backpropagation entails distributing the error backward through the layers of the ANN in varying amounts to all of the connection edges within the ANN. Propagation of error causes adjustments to edge weights, which depends on the gradient of the error at each edge.”; par 109,117; fig 4:4101,412; par 66,67 “In step 412, it is determined whether the hyperparameter value may be varied to, for example, train a RNN LSTM with greater predicative accuracy. If the hypermeters are varied, then execution returns to step 410.” )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further modify Asenjo and Weintraub to incorporate the usage of the ratio of faulty replaced drives to total number of drives of Kocberber. One of ordinary skill in the art would have been motivated to remedy the shortcomings of Asenjo and Weintraub -- a need for a solution for the issue of when drives unexpectedly fail -- with Kocberber providing a known method to solve a similar problem. Kocberber provides “a framework for applying machine learning on time-series disk drive sensor data in order to automatically identify disk drive attributes that are indicative of disk drive failure without being limited by disk drive vendor, disk drive model, or disk drive attribute sensor monitoring.”(Kocberber par 6).
Regarding claim 26, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 25,
Asenjo further teaches,
wherein the computing environment is modified to prevent the potential malfunctioning from occurring. (fig 10; par 77 “If the customer's service plan does not include automated firmware upgrades, … . Alternatively, the firmware may be provided automatically to the user in accordance with the pre-existing service plan. In another scenario, for cloud-aware industrial devices that include bi-directional cloud gateways, device management component 208 may remotely deliver and install the most recent firmware version to the device automatically from the cloud platform (e.g., via device interface component 204).”)
Regarding claim 27, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 25,
Asenjo further teaches,
wherein the computing environment comprises one or more devices comprising one or more storage controllers, one or more storage drives, and one or more host computing systems,(par 32 “For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical or magnetic storage medium) including affixed (e.g., screwed or bolted) or removably affixed solid-state storage drives;”; par 51 “Each system 404 is made up of a number of assets 406 representing the machines and equipment that make up the system ( e.g., the various stages of a production line). In general, each asset 406 is made up of multiple devices 408, which can include, for example, the programmable controllers, motor drives, human-machine interfaces (HMis), sensors, meters, etc. comprising the asset 406. The various data classes depicted in FIGS. 3 and 4 are only intended to be exemplary, and it is to be appreciated that any organization of industrial data classes maintained by predictive maintenance system 314 is within the scope of one or more embodiments of this disclosure.”) wherein the one or more storage controllers manage the storage drives to allow input/output (I/O) access to the one or more host computing systems.(par 32 “As further yet another example, interface(s) can include input/ output (I/O) components as well as associated processor, application, or Application Programming Interface (API) components.”)
Regarding claim 28, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 25,
Asenjo further teaches,
wherein an attribute of the plurality of attributes is a measure of a firmware or software level of a device in comparison to a minimum or recommended firmware or software level for the device. (fig 10; par 77 “The system response to a determination that the on-premises device is running an out-of-date firmware version can depend on the service contract information maintained in the customer model. … .”)
Regarding claim 29, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 27,
Asenjo further teaches,
wherein the plurality of attributes are based on:
whether a device has reached an end of life cycle;(par 47 “For example, analysis can be performed on large sets of device, asset, process, and system data collected from multiple industrial enterprises to identify operational patterns, optimal hardware and software configurations for particular industrial applications, device lifecycle trends that can be used to predict future device or system failures, or other analysis goals.”) and
However, Asenjo and Weintraub do not specifically teach a ratio of faulty replaced drives to total number of drives over a period of time.
On the other hand, Kocberber further teaches
wherein the plurality of attributes are based on:
whether a device has reached an end of life cycle(fig 2:220; par 44 “It shows that P sensor attributes 210 are received for each disk, the sensor attributes are received over N days 220, and the sensor attributes are received for Q disks in total 230.” N days is equivalent to the age of the device. Par 84 “Understanding the remaining useful life of a disk is a significant factor in helping businesses with capacity planning, allocation, and forecasting.” Age is directly correlated with useful life of a disk.); and
a ratio of faulty replaced drives to total number of drives over a period of time(par 51 "Healthy/failed disk ratio defines the number of healthy disks added to the output matrix for every failed disk."; par 82 “Each disk failure causes a window of vulnerability within the system, where multiple failures in the same volume may result in permanent data loss. Embodiments presented herein enable predictions of disk failures so that informed volume allocation decisions may be employed to spread the risk evenly on different volumes.”).
Regarding claim 30, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 27,
However, Asenjo and Weintraub do not specifically teach RAID.
On the other hand, Kocberber further teaches
A machine learning system for learning important attributes and identifying failures ahead of time(par 1 “The present invention relates to a framework for training a specific machine learning system trained with disk drive sensor data to learn important attributes and identify disk drive failures ahead of time.”)
wherein an attribute is based on a level of redundancy in the computing environment indicated by Redundant Array of Independent Disks (RAID) configurations.(par 21 "A disk drive is said to have stopped working when the disk appears physically dead-i.e., does not respond to commands from the operating systems (e.g., generated via console commands), or the RAID system instructs that the drive cannot be read or written. "; par 82 “Avoid permanent data loss: Each disk failure causes a window of vulnerability within the system, where multiple failures in the same volume may result in permanent data loss. Embodiments presented herein enable predictions of disk failures so that informed volume allocation decisions may be employed to spread the risk evenly on different volumes.”)
Regarding claim 31, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 27,
Asenjo further teaches,
wherein the plurality of attributes indicate:
whether critical policy failures have occurred in the computing environment;(par 80 “As noted above, customer-specific data 1102 can include device and/or asset level faults and alarms, process variable values ( e.g., temperatures, pressures, product counts, cycle times, etc.), calculated or anticipated key performance indicators for the customer's various assets, indicators of system behavior over time, and other such information.”)
whether one or more devices have missed heartbeats;(par 75 “At periodic intervals ( or in response to detection of a new device being deployed at the customer premises), device management component 208 can retrieve a subset of the device data 306 relating to a particular device from the customer's data store (e.g., customer data store 302 of FIG. 3).”)
historical data of a device;(par 68 “By leveraging a large amount of historical data gathered from many different industrial systems, predictive analysis component 210 can learn common operating characteristics of many diverse configurations of industrial assets at a high degree of granularity and under many different operating contexts.”) and
problems identified with a device.(par 89 “In addition to providing automated maintenance notification services, one or more embodiments of the cloud based predictive maintenance system can also facilitate proactive involvement of technical support personnel in response to impending device failures or other system problems detected via the predictive analysis techniques described above.”)
However, Asenjo and Weintraub do not specifically teach wherein the plurality of attributes indicate: an age of a device;
On the other hand, Kocberber further teaches
A machine learning system for learning important attributes and identifying failures ahead of time(par 1 “The present invention relates to a framework for training a specific machine learning system trained with disk drive sensor data to learn important attributes and identify disk drive failures ahead of time.”)
wherein the plurality of attributes indicate: an age of a device(fig 2:220; par 44 “It shows that P sensor attributes 210 are received for each disk, the sensor attributes are received over N days 220, and the sensor attributes are received for Q disks in total 230.” N days is equivalent to the age of the device. Par 84 “Understanding the remaining useful life of a disk is a significant factor in helping businesses with capacity planning, allocation, and forecasting.” Age is directly correlated with useful life of a disk.)
Regarding claim 49, Asenjo, Weintraub, and Kocberber teaches,
The method of claim 25,
Weintraub further teaches,
wherein the expected risk score indicates an expected likelihood of the potential malfunctioning(table 1:”P=Probability”; pg 83 “Table I not only differentiate between the RI/RD factors, it also depicts the probability and damage of each factor to the four risk types (Unavailability, Loss, Theft, and Disclosure). The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization.”) and is proportional to the limited period of time for which there is the loss of access to data.(pg 82 “In terms of policy, in some cases the damage of temporary unavailability (of process or application) is so minor as to ignore it altogether.”; pg 82 “… temporary unavailability of regular data may be tolerated due to low damage.”; fig 4; pg 82 “Additionally the description includes the types of risks and their severity level. From figure IV it is clear that the risk level increases from Unavailability to Loss, from Loss to Theft, and from Theft to Disclosure. While assigning values to severity levels may be an open issue, the following example is an arbitrary scheme where these values are increasing as mentioned above.”)
Regarding claims 33-39,50 they are the system claims that implement the method of claims 25-31,49 and are rejected for the same reasons. The additional memory and processor components are taught in (Asenjo claim 1” a memory that stores computer-executable components; a processor, operatively coupled to the memory, that executes computer-executable components, the computer-executable components comprising”).
Regarding claims 41-47,51 they are the computer program product containing instructions to perform the method of claims 25-31,49 and are rejected for the same reasons.
Response to Arguments
Applicant’s arguments, see Remarks, filed 03/13/2026, with respect to the rejections under 35 U.S.C. 101 have been fully considered and are persuasive. The rejections under 35 U.S.C. 101 of 12/12/2025 has been withdrawn.
Applicant’s arguments, see remarks, filed 03/13/2026, with respect to the rejection(s) of claim(s) 25-28,33-36,41-44,49-51 under 35 U.S.C. 103 as being unpatentable over US 20140336791 A1 (Asenjo) in view of “Security Risk Assessment of Cloud Computing Services in a Networked Environment”(Weintraub et. al. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 11, 2016 79, herein referred to as Weintraub) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 as being unpatentable over Asenjo, Weintraub, and US 20200104200 A1 (Kocberber).
With respect to the independent claims, the applicant has argued that Assenjo and Weintraub does not teach limitations 1) "and wherein the expected risk scored is higher in response to a loss of access to data for a limited period of time in comparison to an event that causes a performance impact in a storage controller of the computing environment". 2) "by electronically sending an alert". 3) "wherein the plurality of attributed include device level predictors including firmware level, and component level predictors including ratio of faulty drives and age of drives;". 4) "wherein each attribute has an associated weight that is dynamically altered via back propagation mechanisms",
Regarding limitation 1) "and wherein the expected risk scored is higher in response to a loss of access to data for a limited period of time in comparison to an event that causes a performance impact in a storage controller of the computing environment". The examiner respectfully disagrees. Weintraub teaches in the cited (pg 81“Starting our analysis we note that the damage of loss is greater than the damage of unavailability.”; fig 3”Temporary Unavailability”; pg 82 “disclosure of regular data has low risk, while the disclosure of critical data has higher risk. Also, temporary unavailability of regular data may be tolerated due to low damage.”; pg 82 “Following the list of risks, categorized to the three cloud layers, each risk includes an indication for either risk increasing (RI) or risk decreasing (RD). Additionally the description includes the types of risks and their severity level.” pg 83 “The risk level of each risk type is a measure of both the probability of occurrence and the expected damage of the risk realization. In the example, the probability and the damage are ranked on a 5 point scale (1 to 5) and the risk is the multiplication of the probability rank and the damage rank. For each factor, each risk type is evaluated through such a multiplication yielding a scale of 1 through 25.” It would be obvious to rank loss of access based on how long the loss of access is, with longer periods being more impactful than shorter periods of loss of access. Weintraub would treat an event with a performance impact with no loss of access as a period of loss of access of zero time, compared to an event with a larger time period of loss of access.). The examiner interprets this as limitations “and wherein the expected risk scored is higher in response to a loss of access to data for a limited period of time in comparison to an event that causes a performance impact in a storage controller of the computing environment”.
Regarding limitation 2) "by electronically sending an alert". Asenjo teaches in the cited (par 87 “For example, customer model 304 may specify that notifications relating to an impending device failure should be delivered to one or more client devices associated with selected maintenance personnel, while notifications relating to firmware upgrades or recommended device reconfigurations should be delivered to a client device associated with a plant engineer.”) Examiner interprets this as limitation transmitting an indication to indicate that the potential malfunctioning is likely to occur within the computing environment by electronically sending an alert”.
Regarding limitation 3) "wherein the plurality of attributed include device level predictors including firmware level, and component level predictors including ratio of faulty drives and age of drives;" Asenjo teaches in the cited (par 10 “These services can include providing notifications when a newer firmware version is available for a given device, detection and notification of system trends indicative of an impending device or system failure, detection and notification of device or system performance degradation, recommending device upgrades or system reconfigurations that will improve system performance or device interaction, … .”; fig 10; par 77 “The system response to a determination that the on-premises device is running an out-of-date firmware version can depend on the service contract information maintained in the customer model. … .”) Examiner interprets this as limitation “wherein the plurality of attributed include device level predictors including firmware level,”. The newly cited Kocberber teaches a ratio of faulty drives in the cited (par 51 "Healthy/failed disk ratio defines the number of healthy disks added to the output matrix for every failed disk."; par 82 “Each disk failure causes a window of vulnerability within the system, where multiple failures in the same volume may result in permanent data loss. Embodiments presented herein enable predictions of disk failures so that informed volume allocation decisions may be employed to spread the risk evenly on different volumes.”), and an age of drives in the cited (fig 2:220; par 44 “It shows that P sensor attributes 210 are received for each disk, the sensor attributes are received over N days 220, and the sensor attributes are received for Q disks in total 230.” N days is equivalent to the age of the device. Par 84 “Understanding the remaining useful life of a disk is a significant factor in helping businesses with capacity planning, allocation, and forecasting.” Age is directly correlated with useful life of a disk.). Examiner interprets this limitation “and component level predictors including ratio of faulty drives and age of drives;”. Combined, Asenjo and Kocberber teaches limitation wherein the plurality of attributed include device level predictors including firmware level, and component level predictors including ratio of faulty drives and age of drives;".
Regarding limitation 4) "wherein each attribute has an associated weight that is dynamically altered via back propagation mechanisms", the newly cited Kocberber teaches (par 108 “Backpropagation entails distributing the error backward through the layers of the ANN in varying amounts to all of the connection edges within the ANN. Propagation of error causes adjustments to edge weights, which depends on the gradient of the error at each edge.”; par 109,117; fig 4:4101,412; par 66,67 “In step 412, it is determined whether the hyperparameter value may be varied to, for example, train a RNN LSTM with greater predicative accuracy. If the hypermeters are varied, then execution returns to step 410.” ). Examiner interprets this as "wherein each attribute has an associated weight that is dynamically altered via back propagation mechanisms".
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20200058034-A1 – Sloane - has different priority based on severity. Date is a little close, but it is different company.
US 20190124099 A1 - Matselyukh - talks about severity, but is not as good a fit as Fei.
US 20170115899 A1 - Franke - raid reference, shifts data around to prevent wearout of storage devices.
US 10824145 B1 - Konrardy - fig 7:706 determine severity of anomaly. Automatically learns what parts of road cause problems to the vehicle and tries to avoid those problem road areas.
US 20190377625 A1 - Chintalapati - predicts node failures and uses version information.
US 20200092319 A1 - Spisak - considers spares lead time when ranking failures.
US 20180114175 A1 - Fei - severity rating and machine learning to predict severity.
US 20170048109 A1 - Kant - predicts when incidents will happen and triggers actions to prevent incidents.
US 20200112489 A1 - Scherger - predicts network equipment failures
US 20180287856 A1 - Whitner - gathering version information
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.X./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
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