DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
This communication is in response to communications received on 11/25/25. Claim(s) 1-2, 7, 12-13, and 17-18 is/are amended, claim(s) 6, 9, and 16 is/are cancelled, claim(s) none is/are new, and applicant states support can be found at instant specification [0073, 0076, 0080, 0081]. Therefore, Claims 1-5, 7-8, 10-15, and 17-20 is/are pending and have been addressed below.
Response to Arguments
Applicant’s arguments, see applicant’s remarks, filed 11/25/25, with respect to objections for claim(s) 1-20 have been fully considered and are persuasive. The Examiner respectfully withdraws objections for claim(s) 1-20.
Applicant’s arguments, see applicant’s remarks, filed 11/25/25, with respect to rejections under 35 USC 101 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 101 rejection(s) below.
Applicant’s arguments, see applicant’s remarks, filed 11/25/25, with respect to rejections under 35 USC 103 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s) below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-5, 7-8, 10-15, and 17-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 12, and 17 that, under its broadest reasonable interpretation, is directed to cost forecasting for cloud infrastructure.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 1-5, 6-8, 10-11 recites a series of steps) and system (claim(s) 12-15, 17-20 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claim 1: calculating a resource profile for at least one cloud resource;
performing a scheduled pattern detection process for each of the at least one cloud resources;
performing a consumer detection process for each of the at least one cloud resources to generate at least one similar consumer pattern;
computing an operational maturity score for the at least one cloud resources;
inputting data to a neural network, wherein the input data includes the resource profile, the operational maturity score, historical data including a historical cost occurring during a previous time period, the at least one similar consumer pattern, an output of the scheduled pattern detection process, and an output of the similar consumer detection process;
obtaining a cost forecast from the neural network, based on the input data;
performing an anomaly detection process to detect an anomaly indicating a cost increase of the cost forecast in response to the cost forecast exceeding a cost increase threshold over the previous time period; and
generating an alert in response to detecting the anomaly from the anomaly detection process.
Claim(s) 12 and 17: same analysis as claim(s) 11.
Dependent claims 2-5, 6-8, 10-11, 13-15, and 18-20 recite the same or similar abstract idea(s) as independent claim(s) 1, 12, and 17 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with cost forecasting for cloud infrastructure, and
mental process (concepts performed in the human mind including an observation, evaluation, judgment, opinion) because the invention is directed to performing an cost forecasting (that could be done in the mind) to help companies determine cloud infrastructure.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include computing, cloud resource, neural network (claim(s) 1, 12, 17), an electronic computation device comprising: a processor; a memory (claim(s) 12), a computer program product for an electronic computation device comprising a computer readable storage medium and a processor (claim(s) 17), cloud resources (claim 3-4), neural network (claim 10), neural network includes one of Long Short Term Memory Network (LSTM), Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and Gradient Boosted Network (claim(s) 11), processor, electronic computation device (claim(s) 13-15 and 18-20).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0050]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus the combination and arrangement of the above identified additional elements when analyzed under Step 2A fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0050]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 1, 3-5, 7-8, 10-11, 12, 14-15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Levi et al. (US 2016/0034835 A1) in view of Morgan et al. (US 2012/0226796 A1) and Amiri et al. (US 2021/0303969 A1).
Regarding claim 1, 12, and 17 (currently amended), Levi teaches a computer-implemented method for cloud computing infrastructure cost forecasting, comprising:
computing a resource profile for at least one cloud resource [see at least duration such as timeframe [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0027] deployment such as utilization “At block 304, a history of utilization of the plurality of resources may be accessed, for instance, by the utilization history accessing module 212. The utilization of the plurality of resources may be utilization levels of the cloud resources 132 by the entity 110 provided by a cloud computing services provider 130. In other examples, the utilization of the plurality of resources may be utilization levels of resources of the entity 110 or utilization levels of resources from another provider. The history of utilization of the plurality of resources may be a history of the percentage of the resources that the entity 110 has utilized over a particular period of time, e.g., percentage of the capacity available by a resource. … That is, the utilization history may be the percentage of the total capacity of the server machine used by the entity 110 over the particular period of time. As another example in which the resource is a hard drive, the utilization history of the resource may be amount of disk space utilized over a particular period of time. In one regard, the utilization of the resources differs from the usage of the resources in that the usage of the resources may pertain to the number or amount of resources that are used and the utilization of the resources may pertain to the percentages of the total capacities of the resources that are utilized.”;
[0029] “Data regarding current and past resource usage may pertain to, for instance, the number and type of machines used, the network bandwidth used, the number of I/O calls, the amount of storage used, etc. In addition data regarding current and past resource utilization may pertain to, for instance, utilization metrics over time, such as CPU utilization, I/O utilization, storage utilization, etc.”];
performing a pattern detection for each of the at least one cloud resources [see at least [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0030] “At block 306, a regression model that is to predict a cloud resource usage at a future time period may be developed, for instance, by the usage regression model developing module 214. The usage regression model developing module 214 may develop the usage regression model based upon the history of usage accessed at block 302. … According to an example, the usage regression model developing module 214 may train the predictors for multiple future time periods, for instance, ranging from anywhere between 1 day to one year. The usage regression model developing module 214 may thus predict cloud resource usages by the entity 110 at one or a plurality of future time periods.”];
computing an operational maturity score for the at least one cloud resources [see at least [0027] deployment such as utilization “At block 304, a history of utilization of the plurality of resources may be accessed, for instance, by the utilization history accessing module 212. The utilization of the plurality of resources may be utilization levels of the cloud resources 132 by the entity 110 provided by a cloud computing services provider 130. In other examples, the utilization of the plurality of resources may be utilization levels of resources of the entity 110 or utilization levels of resources from another provider. The history of utilization of the plurality of resources may be a history of the percentage of the resources that the entity 110 has utilized over a particular period of time, e.g., percentage of the capacity available by a resource. … That is, the utilization history may be the percentage of the total capacity of the server machine used by the entity 110 over the particular period of time. As another example in which the resource is a hard drive, the utilization history of the resource may be amount of disk space utilized over a particular period of time. In one regard, the utilization of the resources differs from the usage of the resources in that the usage of the resources may pertain to the number or amount of resources that are used and the utilization of the resources may pertain to the percentages of the total capacities of the resources that are utilized.”;
[0032] “At block 308, a regression model that predicts a resource utilization at the future time period may be developed, for instance, by the utilization regression model developing module 216. The utilization regression model developing module 216 may develop the utilization regression model based upon the history of utilization accessed at block 304.”;
[0034] “The utilization regression model developing module 216 may thus predict cloud resource utilizations by the entity 110 at one or a plurality of future time periods. … . The utilization regression model developing module 216 may use the time-related features to develop the utilization regression model and thus, the utilization regression model may create a trend based on the time-related features, such as month, day of year, etc. The usage regression model developing module 214 may also develop the usage regression model to create a trend based on time-related features for similar reasons.”;
[0035] “Thus, for instance, the utilization regression model may be used to predict that utilization of a particular resource at a particular period of time, e.g., a particular week, will likely be higher than other time periods. Similarly, the usage regression model may be used to predict that the usage of a particular resource at a particular period of time, e.g., a particular month, will likely be lower than other time periods.”;
[0036] “For instance, if the utilization of a number of resources at a future period of time is predicted to be relatively low … In contrast, if the utilization is predicted to be too high, e.g., more than the capacities of the predicted usage of cloud resources”];
inputting data to a model, wherein the input data includes the resource profile, the operational maturity score, historical data including a data occurring during a previous time period, an output of the scheduled pattern detection process,
obtaining a cost forecast from the model, based on the input data;
the cost forecast in response to the cost forecast [for the limitations above, see at least [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0027] “At block 304, a history of utilization of the plurality of resources may be accessed, for instance, by the utilization history accessing module 212.”;
[0036] “At block 310, a future cloud resource usage cost may be managed based upon the usage regression model and the utilization regression model, for instance, by the resource usage cost management module 218. As such, the resource usage cost management module 218 may manage the future cloud resource usage cost based upon the trends predicted through use of the utilization regression model and the usage regression model. For instance, if the utilization of a number of resources at a future period of time is predicted to be relatively low, the resource usage cost management module 218 may manage the future cloud resource usage by determining a minimum usage of the number of cloud resources, while maximizing utilization of the cloud resources. In this example, the cost for the predicted number of cloud resources required may be minimized by minimizing the reserved number of cloud resources. In contrast, if the utilization is predicted to be too high, e.g., more than the capacities of the predicted usage of cloud resources, then the predicted usage may be increased to provide sufficient capacity for the predicted utilization.”].
Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Morgan discloses
performing a scheduled pattern detection process for each of the at least one cloud resources [see at least [0041] “In aspects, the set of local usage data 152 can be transmitted to the entitlement engine 140 on a push basis, for instance, on a predetermined, event-triggered, and/or other basis initiated by one or more of the host clouds in set of host clouds 142, themselves. Other channels, schedules, and techniques for the collection of the set of local usage data 152 from any one or more of the set of host clouds 142 can be used.”;
[0042] “After receipt of the set of local usage data 152, any portion or component of the set of local usage data 152, and/or updates to the same, the entitlement engine 140 can collect and aggregate the set of local usage data 152 from the various host clouds and organize that data in a set of aggregate usage history data 148. The set of aggregate usage history data 148 can reflect recent and/or accumulated usage consumption by the set of users 190 user in all of the set of host clouds 142, over comparatively short-term periods or intervals such as minutes, one or more hours, one day, a number of days, a week, and/or other periods.”;
[0043] “According to aspects, the entitlement engine 140 can thereby identify comparatively short-term resource consumption by the virtual machines or other entities, sites or nodes operated by the set of users 190, and capture and track that consumption compared to the short-term limits or caps that may be contained in the set of subscription parameters 146 for that user. The entitlement engine 140 can therefore generate or determine a short-term consumption margin for each resource which the set of users 190 consume and/or subscribe to in each cloud in the set of host clouds 142, indicating whether over the course of an hour or other period the consumption rates or values are over the subscription limit for a given resource, under the subscription limit, or at or nearly at the subscription limit for that resource. Both the over and under-consumption margins for each resource can be captured and calculated, from which the entitlement engine 140 can generate a set of short-term user-aggregated margins 178 representing the collective short-term consumption of that resource across the diverse host clouds in set of host clouds 142, resulting in an offset or aggregate consumption value. Deviations from short-term consumption caps, limits, service level agreements (SLAs), and/or other criteria can therefore be combined, averaged, aggregated, and/or otherwise “smoothed out” to more accurately and/or timely reflect the consumption patterns of the set of users 190, as a whole on an aggregate basis. In aspects, the resource provider 156, the cloud operators or providers of the set of host clouds 142, and/or other entities can thereby charge, bill, or otherwise adjust the subscription costs or other factors encoded in the billing record 150 sent to the set of users 190, for instance via an administrator or other users, so that their subscription obligations more closely track the actual consumption behavior demonstrated by the set of users 190.”];
performing a consumer detection process for each of the at least one cloud resources to generate at least one similar consumer pattern;
historical data including a historical cost occurring during a previous time period [see at least [0042] “The set of aggregate usage history data 148 can reflect recent and/or accumulated usage consumption by the set of users 190 user in all of the set of host clouds 142, over comparatively short-term periods or intervals such as minutes, one or more hours, one day, a number of days, a week, and/or other periods.”;
[0054] historical data includes usage and associated cost “In 714, the entitlement engine 140 and/or other logic can generate the set of offset subscription costs 170 for each of the one or more short-term consumption periods 160 corresponding to the set of short-term user-aggregated margins 178 for each subscribed resource. For instance, if the record for a given one or more short-term consumption periods 160 reflects the over-consumption of 20 operating system instances, the assigned overage cost of that usage may be, for instance, $.50 times 20 instances, or $10.00 for that hour or other period. In 716, the entitlement engine 140 and/or other logic can generate the aggregate offset subscription cost 174 for one 24-hour or other period, representing the combination of the set of offset subscription costs 170 over a multiple number of the one or more short-term consumption periods 160, such as the combination of 24 one-hour periods, or other intervals, periods, or multiples. In 718, the entitlement engine 140 and/or other logic can generate the billing record 150 based on the aggregate offset subscription cost 174 for each resource being tracked and/or metered for the set of users 190, and/or based on other costs, adjustments, offsets, and/or factors.”], the at least one similar consumer pattern, and an output of the similar consumer detection process, [for the limitations above, see at least [0055] “In 810, the entitlement engine 140 and/or other logic or services, and/or the user of the entitlement engine 140, can identify any relationship or relationships between the users in the set of users 190, and align, relate, and/or otherwise associate the one or more short-term consumption periods 160 of the users in the set of users 190 to each other and/or to the set of combined variable consumption periods 192. … In aspects, the entitlement engine 140 and/or other logic or service can determine the set of combined variable subscription periods 192, the one or more short-term consumption periods 160, and/or other subscription-related parameters can be examined to determine periods that coincide, which overlaps, which represent multiples of each other, and/or are aligned or related in another fashion. For instance, it may be determined that engineering Team 1 and Team 2 operate on the same schedule and one or more short-term consumption periods of 15 minutes during the time period of 12:00 noon to 6:00 p.m., while engineering Team 3 operates on a 1-hour consumption period over the same interval and finance Team 1 operates on a 2-hour consumption period over the same interval of 12:00 noon to 6:00 p.m. Other types of alignment or association between the one or more short-term consumption periods 160, set of combined variable subscription periods 192, and/or other periods or other parameters can be used. In aspects, the alignment or association between the one or more short-term consumption periods 160, set of combined variable subscription periods 192, and/or other periods or other parameters can permit the determination of resource consumption rates or other behaviors on a combined or collective basis.”];
performing an anomaly detection process to detect an anomaly indicating a cost increase of the cost in response to the cost exceeding a cost increase threshold over the previous time period; and
generating an alert in response to detecting the anomaly from the anomaly detection process [for the limitations above, see at least [0008] “In addition, in cases it may be useful for the cloud user, cloud provider or other administrator or user to detect and monitor the instantaneous resource consumption of a set of machines or networks, for instance, to detect irregular or anomalous conditions or events that occur on a real-time or near real-time basis, for example to initiate workload rollovers or to perform other management actions.”;
[0049] predetermined threshold “According to aspects, the entitlement engine 140 can track an instantaneous consumption value 170 of the rate of consumption of any one or more resource by the set of users 190, and/or the aggregate consumption value such as the set of short-term user-aggregated margins 178, to more accurately, fairly, and/or timely reflect the resource consumption rate by the set of users 190. In aspects, the instantaneous consumption value 170 can be monitored by the entitlement engine 140 and/or other logic or service to perform various management or supervisory functions, such as to issue an immediate command to restrict consumption of a selected resource or resources when the instantaneous consumption value 170 exceeds a predetermined burst threshold. Other actions can be taken based on the instantaneous consumption value 170, such as to generate a graphical, email, and/or other alert to be delivered to an administrator or other user that can be acted upon to temporarily reduce the number of virtual machines and/or other entities operated in the set of host clouds 142 by the set of users 190. Other management actions can be taken, as likewise described herein.”;
[0005] predetermined threshold such as from a subscription “If the user, for instance, has a subscription limit of 300 instances of an executing application with a per-cloud limit of 100 instances, and reaches 100 instances in one host cloud at the same time that 90 instances of that application is operating in a second host cloud and 120 are operating in a third cloud, the application provider, individual host clouds, and/or other entities may not be able to timely or accurately determine that the user has reached their instance limit in one cloud, and exceeded their instance threshold in a second cloud.”;
[0054] historical data includes subscription usage and associated cost “In 714, the entitlement engine 140 and/or other logic can generate the set of offset subscription costs 170 for each of the one or more short-term consumption periods 160 corresponding to the set of short-term user-aggregated margins 178 for each subscribed resource. For instance, if the record for a given one or more short-term consumption periods 160 reflects the over-consumption of 20 operating system instances, the assigned overage cost of that usage may be, for instance, $.50 times 20 instances, or $10.00 for that hour or other period. In 716, the entitlement engine 140 and/or other logic can generate the aggregate offset subscription cost 174 for one 24-hour or other period, representing the combination of the set of offset subscription costs 170 over a multiple number of the one or more short-term consumption periods 160, such as the combination of 24 one-hour periods, or other intervals, periods, or multiples. In 718, the entitlement engine 140 and/or other logic can generate the billing record 150 based on the aggregate offset subscription cost 174 for each resource being tracked and/or metered for the set of users 190, and/or based on other costs, adjustments, offsets, and/or factors.”]].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Levi with Morgan to include the limitation(s) above as disclosed by Morgan. Doing so would improve Levi’s (Levi) cloud resource usage determination via additional inputs used in the usage determination [see at least Morgan [0002-0010, 0055] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Levi and b) Morgan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Levi in view of Morgan doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Amiri discloses
inputting data to a neural network, wherein the input data includes various data; and
obtaining a forecast from the neural network, based on the input data [for the limitations above, see at least [0009] “The present disclosure is directed to forecasting systems and methods for looking at historical information that can be obtained from a system or environment (e.g., an optical communication network) and making an accurate prediction of how the system or environment will behave in the future. In particular, the forecasting systems and methods of the present disclosure utilize Machine Learning (ML), such as Deep Neural Networks (DNNs) for accurately learning the forecasting functions. Also, the implementations described herein are configured to obtain historical data from the system/environment. The historical data includes multi-variate datasets (i.e., datasets having two or more types of variables). Forecasts of the historical multi-variate datasets are created, and these forecasts are mixed or combined in a non-linear manner to produce an output dataset. The output dataset may include the same type of parameters as the types of parameters of the one or more of the input datasets. In other words, the type of historical multi-variate data received may include information that is at least partially related to or unrelated to the output data representing a forecast of predicted future values of that particular type of data.”;
[0107] “In some case, some of the layers 122, 124, 126, 128 may include, at least partially, classical forecasting components (e.g., ARIMA) or modern forecasters (e.g., LSTM, ResNet, etc.). The ResNet forecasters are fairly new types of forecasters and may include features that are not publicly known.”;
[claim 6] “6. The method of claim 5, wherein the plurality of DNN forecasters includes forecasters selected from the group consisting of ResNet forecasters and Long Short-Term Memory (LSTM) forecasters.”;
[0067] forecasting for a cost;
[0072] “Although these use cases are described with respect to an optical communication network, it should be noted that the DNN module 26 may be used for predicting future events, conditions, datasets, etc. of any type of system or environment by obtaining multi-variate input datasets from the system or environment and processing the different variables in such a way that an output dataset representing the forecast can be created. Thus, the present disclosure can be extended beyond the scope of communication networks. Also, the multi-variate input datasets may include datasets that are unrelated to the monitored system. For example, datasets representing variables such as population growth, industry development, or other variables that may normally be considered to be unrelated, or at least not directly related to a system (e.g., network) for which predictions are to be determined. Thus, the population growth, for instance, can be used with network parameters to make a better prediction of network equipment deployment. This unrelated or partially related data can be coupled with network datasets in this example to also predict where traffic should be routed (i.e., when more people will be using the network) and where the population growth is trending to learn where additional equipment may be needed.”;
[0126] “The contents of this disclosure may represent a set of key algorithms in this software, which may be included in application servers, cloud-based systems, supply management software for sales forecasting, and other various servers, systems, networks. Network equipment providers with an NMS may incorporate the forecasting techniques described in the present disclosure for planning tools, orchestrators, or forecasting service (e.g., forecasting as a service). In some cases, the forecasters may be included in databases. The forecasters described herein may also be applicable in other industries or environments, such as demand forecasting in supply management, sales, trading stocks, health care, etc. In some”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Levi in view of Morgan with Amiri to include the limitation(s) above as disclosed by Amiri. Doing so would improve Levi in view of Morgan’s (Levi) cloud resource usage recommendation calculation based on usage determination via additional inputs used in the usage recommendation [see at least Amiri [0002-0008] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Levi in view of Morgan and b) Amiri and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 3, modified Levi teaches the method of claim 1,
and Levi teaches wherein computing the resource profile comprises:
determining a duration category for the at least one cloud resources; and
determining a deployment status for the at least one cloud resources [for the limitations above, see at least duration such as timeframe [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0027] deployment such as utilization “At block 304, a history of utilization of the plurality of resources may be accessed, for instance, by the utilization history accessing module 212. The utilization of the plurality of resources may be utilization levels of the cloud resources 132 by the entity 110 provided by a cloud computing services provider 130. In other examples, the utilization of the plurality of resources may be utilization levels of resources of the entity 110 or utilization levels of resources from another provider. The history of utilization of the plurality of resources may be a history of the percentage of the resources that the entity 110 has utilized over a particular period of time, e.g., percentage of the capacity available by a resource. … That is, the utilization history may be the percentage of the total capacity of the server machine used by the entity 110 over the particular period of time. As another example in which the resource is a hard drive, the utilization history of the resource may be amount of disk space utilized over a particular period of time. In one regard, the utilization of the resources differs from the usage of the resources in that the usage of the resources may pertain to the number or amount of resources that are used and the utilization of the resources may pertain to the percentages of the total capacities of the resources that are utilized.”;
[0029] “Data regarding current and past resource usage may pertain to, for instance, the number and type of machines used, the network bandwidth used, the number of I/O calls, the amount of storage used, etc. In addition data regarding current and past resource utilization may pertain to, for instance, utilization metrics over time, such as CPU utilization, I/O utilization, storage utilization, etc.”].
Regarding claim 4, 14, and 19, modified Levi teaches the method of claim 1, as well as performing a scheduled pattern detection process
and Levi teaches wherein performing a pattern detection process comprises:
analyzing a historical pattern for each of the at least one cloud resources; and
identifying a periodicity for the historical pattern [see at least [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0030] “At block 306, a regression model that is to predict a cloud resource usage at a future time period may be developed, for instance, by the usage regression model developing module 214. The usage regression model developing module 214 may develop the usage regression model based upon the history of usage accessed at block 302. … According to an example, the usage regression model developing module 214 may train the predictors for multiple future time periods, for instance, ranging from anywhere between 1 day to one year. The usage regression model developing module 214 may thus predict cloud resource usages by the entity 110 at one or a plurality of future time periods.”].
Regarding claim 5, 15, and 20, modified Levi teaches the method of claim 4,
and Levi teaches wherein the historical pattern includes one of, weekly, quarterly, weekend, and daily [see at least [0026] “With reference first to the method 300 depicted in FIG. 3, at block 302, a history of usage of a plurality of resources may be accessed, for instance, by the usage history accessing module 210. The plurality of resources may be the cloud resources 132 provided by a cloud computing services provider 130 and used by the entity 110. … In any regard, the history of usage by the entity 110 of a plurality of resources may be a history of the number and type of resources that the entity, 110 has used over a particular period of time. The particular period of time may be any suitable length of time over which the history of the resource usage is to be tracked, for instance, a day, multiple days, a month, a year, etc. In one example, the usage history accessing module 210 may access the usage history by directly or indirectly collecting the resource usages by the entity 110.”;
[0030] “At block 306, a regression model that is to predict a cloud resource usage at a future time period may be developed, for instance, by the usage regression model developing module 214. The usage regression model developing module 214 may develop the usage regression model based upon the history of usage accessed at block 302. … According to an example, the usage regression model developing module 214 may train the predictors for multiple future time periods, for instance, ranging from anywhere between 1 day to one year. The usage regression model developing module 214 may thus predict cloud resource usages by the entity 110 at one or a plurality of future time periods.”].
Regarding claim 7 (currently amended), modified Levi teaches the method of claim 1, .
Modified Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Morgan discloses
wherein the anomaly detection process comprises a customer-defined anomaly detection process [see at least [0008] “In addition, in cases it may be useful for the cloud user, cloud provider or other administrator or user to detect and monitor the instantaneous resource consumption of a set of machines or networks, for instance, to detect irregular or anomalous conditions or events that occur on a real-time or near real-time basis, for example to initiate workload rollovers or to perform other management actions.”;
[0021] “In embodiments, the user's instantiation request can specify a variety of parameters defining the operation of the set of virtual machines to be invoked. The instantiation request, for example, can specify a defined period of time for which the instantiated collection of machines, services, or processes is needed. The period of time can be, for example, an hour, a day, a month, or other interval of time. In embodiments, the user's instantiation request can specify the instantiation of a set of virtual machines or processes on a task basis, rather than for a predetermined amount or interval of time. For instance, a user could request a set of virtual provisioning servers and other resources until a target software update is completed on a population of corporate or other machines. The user's instantiation request can in further regards specify other parameters that define the configuration and operation of the set of virtual machines or other instantiated resources. For example, the request can specify a specific minimum or maximum amount of processing power or input/output (I/O) throughput that the user wishes to be available to each instance of the virtual machine or other resource. In embodiments, the requesting user can for instance specify a service level agreement (SLA) acceptable for their desired set of applications or services. Other parameters and settings can be used to instantiate and operate a set of virtual machines, software, and other resources in the host clouds. One skilled in the art will realize that the user's request can likewise include combinations of the foregoing exemplary parameters, and others. It may be noted that “user” herein can include a network-level user or subscriber to cloud-based networks, such as a corporation, government entity, educational institution, and/or other entity, including individual users and groups of users.”;
[0049] “According to aspects, the entitlement engine 140 can track an instantaneous consumption value 170 of the rate of consumption of any one or more resource by the set of users 190, and/or the aggregate consumption value such as the set of short-term user-aggregated margins 178, to more accurately, fairly, and/or timely reflect the resource consumption rate by the set of users 190. In aspects, the instantaneous consumption value 170 can be monitored by the entitlement engine 140 and/or other logic or service to perform various management or supervisory functions, such as to issue an immediate command to restrict consumption of a selected resource or resources when the instantaneous consumption value 170 exceeds a predetermined burst threshold. Other actions can be taken based on the instantaneous consumption value 170, such as to generate a graphical, email, and/or other alert to be delivered to an administrator or other user that can be acted upon to temporarily reduce the number of virtual machines and/or other entities operated in the set of host clouds 142 by the set of users 190. Other management actions can be taken, as likewise described herein.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Levi with Morgan to include the limitation(s) above as disclosed by Morgan. Doing so would improve modified Levi’s (Levi) cloud resource usage determination via additional inputs used in the usage determination [see at least Morgan [0002-0010, 0055] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Levi and b) Morgan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 8, modified Levi teaches the method of claim 7, .
Modified Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Morgan discloses
wherein the anomaly detection process comprises a data-driven anomaly detection process [see at least [0008] “In addition, in cases it may be useful for the cloud user, cloud provider or other administrator or user to detect and monitor the instantaneous resource consumption of a set of machines or networks, for instance, to detect irregular or anomalous conditions or events that occur on a real-time or near real-time basis, for example to initiate workload rollovers or to perform other management actions.”;
[0021] “In embodiments, the user's instantiation request can specify a variety of parameters defining the operation of the set of virtual machines to be invoked. The instantiation request, for example, can specify a defined period of time for which the instantiated collection of machines, services, or processes is needed. The period of time can be, for example, an hour, a day, a month, or other interval of time. In embodiments, the user's instantiation request can specify the instantiation of a set of virtual machines or processes on a task basis, rather than for a predetermined amount or interval of time. For instance, a user could request a set of virtual provisioning servers and other resources until a target software update is completed on a population of corporate or other machines. The user's instantiation request can in further regards specify other parameters that define the configuration and operation of the set of virtual machines or other instantiated resources. For example, the request can specify a specific minimum or maximum amount of processing power or input/output (I/O) throughput that the user wishes to be available to each instance of the virtual machine or other resource. In embodiments, the requesting user can for instance specify a service level agreement (SLA) acceptable for their desired set of applications or services. Other parameters and settings can be used to instantiate and operate a set of virtual machines, software, and other resources in the host clouds. One skilled in the art will realize that the user's request can likewise include combinations of the foregoing exemplary parameters, and others. It may be noted that “user” herein can include a network-level user or subscriber to cloud-based networks, such as a corporation, government entity, educational institution, and/or other entity, including individual users and groups of users.”;
[0049] “According to aspects, the entitlement engine 140 can track an instantaneous consumption value 170 of the rate of consumption of any one or more resource by the set of users 190, and/or the aggregate consumption value such as the set of short-term user-aggregated margins 178, to more accurately, fairly, and/or timely reflect the resource consumption rate by the set of users 190. In aspects, the instantaneous consumption value 170 can be monitored by the entitlement engine 140 and/or other logic or service to perform various management or supervisory functions, such as to issue an immediate command to restrict consumption of a selected resource or resources when the instantaneous consumption value 170 exceeds a predetermined burst threshold. Other actions can be taken based on the instantaneous consumption value 170, such as to generate a graphical, email, and/or other alert to be delivered to an administrator or other user that can be acted upon to temporarily reduce the number of virtual machines and/or other entities operated in the set of host clouds 142 by the set of users 190. Other management actions can be taken, as likewise described herein.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Levi with Morgan to include the limitation(s) above as disclosed by Morgan. Doing so would improve modified Levi’s (Levi) cloud resource usage determination via additional inputs used in the usage determination [see at least Morgan [0002-0010, 0055] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Levi and b) Morgan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 10, modified Levi teaches the method of claim 1,
and Levi teaches wherein computing the operational maturity score comprises inputting resource utilization and cloud service type into the model [see at least [0027] deployment such as utilization “At block 304, a history of utilization of the plurality of resources may be accessed, for instance, by the utilization history accessing module 212. The utilization of the plurality of resources may be utilization levels of the cloud resources 132 by the entity 110 provided by a cloud computing services provider 130. In other examples, the utilization of the plurality of resources may be utilization levels of resources of the entity 110 or utilization levels of resources from another provider. The history of utilization of the plurality of resources may be a history of the percentage of the resources that the entity 110 has utilized over a particular period of time, e.g., percentage of the capacity available by a resource. … That is, the utilization history may be the percentage of the total capacity of the server machine used by the entity 110 over the particular period of time. As another example in which the resource is a hard drive, the utilization history of the resource may be amount of disk space utilized over a particular period of time. In one regard, the utilization of the resources differs from the usage of the resources in that the usage of the resources may pertain to the number or amount of resources that are used and the utilization of the resources may pertain to the percentages of the total capacities of the resources that are utilized.”;
[0032] “At block 308, a regression model that predicts a resource utilization at the future time period may be developed, for instance, by the utilization regression model developing module 216. The utilization regression model developing module 216 may develop the utilization regression model based upon the history of utilization accessed at block 304.”;
[0034] “The utilization regression model developing module 216 may thus predict cloud resource utilizations by the entity 110 at one or a plurality of future time periods. … . The utilization regression model developing module 216 may use the time-related features to develop the utilization regression model and thus, the utilization regression model may create a trend based on the time-related features, such as month, day of year, etc. The usage regression model developing module 214 may also develop the usage regression model to create a trend based on time-related features for similar reasons.”;
[0035] “Thus, for instance, the utilization regression model may be used to predict that utilization of a particular resource at a particular period of time, e.g., a particular week, will likely be higher than other time periods. Similarly, the usage regression model may be used to predict that the usage of a particular resource at a particular period of time, e.g., a particular month, will likely be lower than other time periods.”;
[0036] “For instance, if the utilization of a number of resources at a future period of time is predicted to be relatively low … In contrast, if the utilization is predicted to be too high, e.g., more than the capacities of the predicted usage of cloud resources”].
Modified Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Amiri discloses
wherein computing the value comprises inputting various data into the neural network [see at least [0009] “The present disclosure is directed to forecasting systems and methods for looking at historical information that can be obtained from a system or environment (e.g., an optical communication network) and making an accurate prediction of how the system or environment will behave in the future. In particular, the forecasting systems and methods of the present disclosure utilize Machine Learning (ML), such as Deep Neural Networks (DNNs) for accurately learning the forecasting functions. Also, the implementations described herein are configured to obtain historical data from the system/environment. The historical data includes multi-variate datasets (i.e., datasets having two or more types of variables). Forecasts of the historical multi-variate datasets are created, and these forecasts are mixed or combined in a non-linear manner to produce an output dataset. The output dataset may include the same type of parameters as the types of parameters of the one or more of the input datasets. In other words, the type of historical multi-variate data received may include information that is at least partially related to or unrelated to the output data representing a forecast of predicted future values of that particular type of data.”;
[0107] “In some case, some of the layers 122, 124, 126, 128 may include, at least partially, classical forecasting components (e.g., ARIMA) or modern forecasters (e.g., LSTM, ResNet, etc.). The ResNet forecasters are fairly new types of forecasters and may include features that are not publicly known.”;
[claim 6] “6. The method of claim 5, wherein the plurality of DNN forecasters includes forecasters selected from the group consisting of ResNet forecasters and Long Short-Term Memory (LSTM) forecasters.”;
[0072] “Although these use cases are described with respect to an optical communication network, it should be noted that the DNN module 26 may be used for predicting future events, conditions, datasets, etc. of any type of system or environment by obtaining multi-variate input datasets from the system or environment and processing the different variables in such a way that an output dataset representing the forecast can be created. Thus, the present disclosure can be extended beyond the scope of communication networks. Also, the multi-variate input datasets may include datasets that are unrelated to the monitored system. For example, datasets representing variables such as population growth, industry development, or other variables that may normally be considered to be unrelated, or at least not directly related to a system (e.g., network) for which predictions are to be determined. Thus, the population growth, for instance, can be used with network parameters to make a better prediction of network equipment deployment. This unrelated or partially related data can be coupled with network datasets in this example to also predict where traffic should be routed (i.e., when more people will be using the network) and where the population growth is trending to learn where additional equipment may be needed.”;
[0126] “The contents of this disclosure may represent a set of key algorithms in this software, which may be included in application servers, cloud-based systems, supply management software for sales forecasting, and other various servers, systems, networks. Network equipment providers with an NMS may incorporate the forecasting techniques described in the present disclosure for planning tools, orchestrators, or forecasting service (e.g., forecasting as a service). In some cases, the forecasters may be included in databases. The forecasters described herein may also be applicable in other industries or environments, such as demand forecasting in supply management, sales, trading stocks, health care, etc. In some”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Levi with Amiri to include the limitation(s) above as disclosed by Amiri. Doing so would improve modified Levi’s (Levi) cloud resource usage recommendation calculation based on usage determination via additional inputs used in the usage recommendation [see at least Amiri [0002-0008] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Levi and b) Amiri and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 11, modified Levi teaches the method of claim 1, .
Modified Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Amiri discloses
wherein the neural network includes one of Long Short Term Memory Network (LSTM), Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and Gradient Boosted Network [see at least [0009] “The present disclosure is directed to forecasting systems and methods for looking at historical information that can be obtained from a system or environment (e.g., an optical communication network) and making an accurate prediction of how the system or environment will behave in the future. In particular, the forecasting systems and methods of the present disclosure utilize Machine Learning (ML), such as Deep Neural Networks (DNNs) for accurately learning the forecasting functions. Also, the implementations described herein are configured to obtain historical data from the system/environment. The historical data includes multi-variate datasets (i.e., datasets having two or more types of variables). Forecasts of the historical multi-variate datasets are created, and these forecasts are mixed or combined in a non-linear manner to produce an output dataset. The output dataset may include the same type of parameters as the types of parameters of the one or more of the input datasets. In other words, the type of historical multi-variate data received may include information that is at least partially related to or unrelated to the output data representing a forecast of predicted future values of that particular type of data.”;
[0107] “In some case, some of the layers 122, 124, 126, 128 may include, at least partially, classical forecasting components (e.g., ARIMA) or modern forecasters (e.g., LSTM, ResNet, etc.). The ResNet forecasters are fairly new types of forecasters and may include features that are not publicly known.”;
[claim 6] “6. The method of claim 5, wherein the plurality of DNN forecasters includes forecasters selected from the group consisting of ResNet forecasters and Long Short-Term Memory (LSTM) forecasters.”;
[0072] “Although these use cases are described with respect to an optical communication network, it should be noted that the DNN module 26 may be used for predicting future events, conditions, datasets, etc. of any type of system or environment by obtaining multi-variate input datasets from the system or environment and processing the different variables in such a way that an output dataset representing the forecast can be created. Thus, the present disclosure can be extended beyond the scope of communication networks. Also, the multi-variate input datasets may include datasets that are unrelated to the monitored system. For example, datasets representing variables such as population growth, industry development, or other variables that may normally be considered to be unrelated, or at least not directly related to a system (e.g., network) for which predictions are to be determined. Thus, the population growth, for instance, can be used with network parameters to make a better prediction of network equipment deployment. This unrelated or partially related data can be coupled with network datasets in this example to also predict where traffic should be routed (i.e., when more people will be using the network) and where the population growth is trending to learn where additional equipment may be needed.”;
[0126] “The contents of this disclosure may represent a set of key algorithms in this software, which may be included in application servers, cloud-based systems, supply management software for sales forecasting, and other various servers, systems, networks. Network equipment providers with an NMS may incorporate the forecasting techniques described in the present disclosure for planning tools, orchestrators, or forecasting service (e.g., forecasting as a service). In some cases, the forecasters may be included in databases. The forecasters described herein may also be applicable in other industries or environments, such as demand forecasting in supply management, sales, trading stocks, health care, etc. In some”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Levi with Amiri to include the limitation(s) above as disclosed by Amiri. Doing so would improve modified Levi’s (Levi) cloud resource usage recommendation calculation based on usage determination via additional inputs used in the usage recommendation [see at least Amiri [0002-0008] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Levi and b) Amiri and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 2, 13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Levi in view of Morgan and Amiri as applied to claim(s) 1, 12, and 17 above and further in view of Soon-Shiong et al. (US 2021/0255902 A1).
Regarding claim 2, 13, and 18 (currently amended), modified Levi teaches the method of claim 1, .
Modified Levi doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as cloud resource usage determination, Soon-Shiong discloses
performing a spike detection process on the historical data for the at least one cloud resources to identify a resource spike [see at least [0060] “The system 100 may be used to implement blockchain related tasks. For example, the first cloud computing resource 102 may determine other cloud computing resources that are optimized for energy requirements to perform blockchain tasks, distributed ledger related tasks, etc. Such an approach may provide multiple technical advantages. From the perspective of monitoring the system, bursting activities (e.g., defined bursts, predicted bursts, actual bursts, provisional bursts, trending bursts, etc.) can be recorded to a ledger for tracking purposes. Such a ledger provides an authoritative source of data that can be leveraged for billing practices as well as for data analytics. This can be achieved by storing the events in “notarized” fashion on a ledger (e.g., blockchain, hash graph, distributed ledger, notarized ledger, etc.).”];
obtaining metadata associated with the resource spike [see at least [0061] “For example, events can be compiled into a data block. The block can be linked to other previously recorded blocks by taking a hash (e.g., SHA256, scrypt, etc.) of the current block with the hash value of the previous block as well, as any other desirable metadata. The burst data can be recorded in a private ledger, public ledger (e.g., IOTA, TRON, Ethereum, BitCoin, Dogecoin, etc.), semi-private ledger, etc., which may be formed among for-fee participants. In various implementations, each burst may be quantified as a block of data, and then archived in a notarized ledger based on a previous burst. This enables creation of an immutable log of burst activity which can be accessed later, such as for forensic purposes.”]; and
rendering the metadata proximal to a rendering of the resource spike [see at least [0090] “At block 311, the method 300 optionally includes displaying one or more values on a user interface. For example, the user interface may display one or more expected performance values for performing the at least one portion of the cloud computing task using the first cloud computing resource, one or more expected performance values for performing the at least one portion of the cloud computing task using the second cloud computing resource, a comparison of performing the at least one portion of the cloud computing task using the first cloud computing resource and performing the at least one portion of the cloud computing task using the second cloud computing resource, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Levi with Soon-Shiong to include the limitation(s) above as disclosed by Soon-Shiong. Doing so would improve modified Levi’s (Levi) cloud resource usage determination via additional inputs used in the usage determination [see at least Soon-Shiong [0002-0010] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Levi and b) Soon-Shiong and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
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 extension fee 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 date of this final action.
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/J.W./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624