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 .
Claims 1-20 are pending for examination.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1, Statutory Category: Yes, the claim 1 is a method that recites a series of steps and therefore falls in the statutory category of a process.
Step 2A- Prong 1: Judicial Exception Recited: Yes, the claim recites: “after storing the records, determining, based on an efficiency criterion and for a time range of the records, inefficiencies related to the usage of the computing resources;” As drafted, the claim as a whole recites a method including steps that could be performed in the human mind, but for the recitation of generic computing components. The human mind can easily judging/evaluating/determining based on an efficiency criterion and for a time range of the records, inefficiencies related to the usage of the computing resources (i.e., based on comparing the efficiency criterion for a time range of the stored records). Therefore, but for the recitation of generic computing components, these steps may be a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion).
Therefore, yes, the claims do recite judicial exceptions.
Step 2A- Prong 2: Integrated into a practical Application: No, this judicial exception is not integrated into a practical application. In particular, the claim recites an additional limitations that “receiving, from a computing system, usage data including entries specifying usage of computing resources of the computing system” which is insignificant pre-solution data gathering (see MPEP § 2106.05(g)). In addition, “storing, as structured data, records that include representations of the entries” which is insignificant extra-solution activity and merely data storing (see MPEP § 2106.05(g)). Further, “providing a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies” which is insignificant extra solution activity (i.e., transmitting data) See MPEP 2106.05(g). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept: No. The additional element that “receiving, from a computing system, usage data including entries specifying usage of computing resources of the computing system” which is insignificant pre-solution data gathering (see MPEP § 2106.05(g)). In addition, “storing, as structured data, records that include representations of the entries” which is insignificant extra-solution activity and merely data storing (see MPEP § 2106.05(g)). Further, “providing a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies” which is insignificant extra solution activity (i.e., transmitting data) See MPEP 2106.05(g). And they are well understood, routine, conventional activity (see MPEP § 2106.05(d)). Courts have identified “receiving and transmitting data, storing and retrieving information”, et cetera as well understood, routine, conventional and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f))). These additional elements and combination of the elements does not amount to significant more than the exception itself or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the “receiving”, “storing” and “providing” steps were considered to be extra-solution activity in Step 2A as insignificant data gathering and communication and are well understood, routine, conventional activity in the field. The “receiving” and “providing” steps are for the purpose of “communication” and “transmitting the data” and these can be reached on one of court case (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) see MPEP § 2106.05(d) II). Accordingly, a conclusion that “receiving” and “providing”” are well understood, routine, conventional activity is supported under Berkheimer options 2. In addition, The “storing” step is for purpose of merely data storing, and this can be reached on one of court case (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP §2106.05(d)(II) iv.). Accordingly, a conclusion that the storing step is well understood, routine, conventional activity is supported under Berkheimer options 2
For these reasons, there is no inventive concept in the claim, and thus the claim is ineligible.
Independent claims 13 and 20 are rejected for the same reason as claim 1 above. Claim 13 further recites “A non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by one or more processors, cause the one or more processors to perform operations”. Claim 20 further recites “A system comprising: one or more processors; and memory, containing program instructions that, upon execution by the one or more processors, cause the system to perform operations”. These additional elements are directed to generic computing components/functions merely applying the abstract idea (MPEP § 2106.05(f)).
With respect to the dependent claim 2, the claim elaborates that modifying, by way of remote access to the computing system, future use of the subset of the computing resources so that the inefficiencies are reduced (“modifying…future use” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further, the claim as a whole is a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion)).
With respect to the dependent claims 3-4, the claim 3 elaborates that wherein the structured data includes a distributed, cryptographically immutable sequence of blocks containing the records. The claim 4 elaborates that wherein the structured data includes a time series database containing the records (these limitations are directed to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
With respect to the dependent claim 5, the claim elaborates that wherein determining the inefficiencies related to the usage of the computing resources comprises an alerting system detecting abnormal patterns of the usage in the time range of the records, and wherein providing the notification indicating the inefficiencies and the subset of the computing resources that are producing the inefficiencies comprises the alerting system providing an alert relating to the abnormal patterns of the usage (“detecting abnormal patterns” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further, “an alerting system” and “the alerting system providing an alert” are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
With respect to the dependent claims 6-8, the claim 6 elaborates that wherein the abnormal patterns of the usage in the time range of the records include the usage of computing resources associated with a service other than a pre-defined set of allowed services, and wherein the subset of the computing resources includes the computing resources associated with the service. The claim 7 elaborates that wherein the abnormal patterns of the usage in the time range of the records include usage of computing resources outside of a pre-defined set of hours, and wherein the subset of the computing resources includes the computing resources used outside of the pre-defined set of hours. The claim 8 elaborates that wherein the abnormal patterns of the usage in the time range of the records include under-utilization or overutilization of computing resources in comparison to one or more pre-defined threshold levels of utilization, and wherein the subset of the computing resources includes the computing resources that are under-utilized or over-utilized (these limitations are directed to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
With respect to the dependent claim 9, the claim elaborates that wherein determining the inefficiencies related to the usage of the computing resources comprises a recommendation system detecting the inefficiencies, and wherein providing the notification indicating the inefficiencies and the subset of the computing resources that are producing the inefficiencies comprises the recommendation system providing a recommendation to modify an allocation of the computing resources (“detecting the inefficiencies” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. In addition, “a recommendation system” are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f). Further, “providing a recommendation to modify an allocation…” which is insignificant extra solution activity (i.e., transmitting data) See MPEP 2106.05(g) and they are well understood, routine, conventional activity (see MPEP § 2106.05(d)). Courts have identified “receiving and transmitting data, storing and retrieving information”, et cetera as well understood, routine, conventional and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f))).
With respect to the dependent claim 10, the claim elaborates that wherein the inefficiencies are detected based on a trend analysis or volume analysis of the usage of the computing resources, and wherein the recommendation to modify the allocation of the computing resources comprises a suggestion to change a type or quantity of the computing resources used (“the inefficiencies are detected based on a trend analysis or volume analysis” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further “recommendation to modify the allocation” which is insignificant extra solution activity (i.e., transmitting data) See MPEP 2106.05(g) and they are well understood, routine, conventional activity (see MPEP § 2106.05(d)). Courts have identified “receiving and transmitting data, storing and retrieving information”, et cetera as well understood, routine, conventional and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f))).
With respect to the dependent claim 11, the claim elaborates that wherein the inefficiencies are detected based on a carbon footprint analysis of the usage of the computing resources, and wherein the recommendation to modify the allocation of the computing resources comprises a suggestion to replace at least some of the computing resources with more power efficient computing resources (“the inefficiencies are detected based on a carbon footprint analysis of the usage of the computing resources” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further “recommendation to modify the allocation of the computing resources comprises a suggestion to replace” which is insignificant extra solution activity (i.e., transmitting data) See MPEP 2106.05(g) and they are well understood, routine, conventional activity (see MPEP § 2106.05(d)). Courts have identified “receiving and transmitting data, storing and retrieving information”, et cetera as well understood, routine, conventional and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f))).
With respect to the dependent claim 12, the claim elaborates that wherein determining the inefficiencies related to the usage of the computing resources is caused by addition of a pre-determined number of the records to the structured data (these limitations are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind).
Dependent claims 14-17, 18 and 19 recite the same features as applied to claims 2-5, 9 and 12 respectively above, therefore they are also rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA et al. (US Pub. 2021/0311791 A1) in view of Xu et al. (US Pub. 2011/0119115 A1).
As per claim 1, ROSANOVA teaches the invention substantially as claimed including A method comprising:
providing, usage data including entries specifying usage of computing resources of the computing system (ROSANOVA, [0035] lines 1-17, The resource usage monitoring server 160 provides time-based resource tracking data associated with one or more software development projects. The resource tracking data for a project includes, at least, project identifying data and project time data identifying one or more time periods reflecting use of a computing resource in association with the project…The time-tracking application may support, for each of the one or more projects, time logging, time-based data analysis, and insights reporting. The time-based resource tracking data may be stored in a database 161 (shown in FIG. 1) that is coupled to the resource usage monitoring server 160; also see [0032] lines 5-8, the client device 110 may be a computing device that is used by a developer for working on one or more software development project);
storing, as structured data, records that include representations of the entries (ROSANOVA, [0035] lines 1-17, The resource usage monitoring server 160 provides time-based resource tracking data associated with one or more software development projects. The resource tracking data for a project includes, at least, project identifying data and project time data identifying one or more time periods reflecting use of a computing resource in association with the project…The time-tracking application may support, for each of the one or more projects, time logging, time-based data analysis, and insights reporting. The time-based resource tracking data may be stored in a database 161 (shown in FIG. 1) that is coupled to the resource usage monitoring server 160; [0059] lines 15-19, The resource tracking data may, for example, include a listing of time entries. Each time entry may be associated with a specific project (e.g. via a project identifier, such as a project number) and a date);
after storing the records, determining, based on an efficiency criterion and for a time range of the records, inefficiencies related to the usage of the computing resources (ROSANOVA, [0036] lines 1-16, The SDPM server 180 is configured to provide functionalities relating to project management for one or more software development projects…the SDPM server 180 may obtain data from one or both of the activity logging server 150 and the resource usage monitoring server 160 to determine costs associated with tasks of a software development project, identify inefficiencies in a current allocation of computing resources, and determine a more efficient re-allocation of the computing resources; [0059] lines 1-20, the server obtains, from a resource usage monitoring system, time-based resource tracking data associated with at least one of the projects. The resource tracking data includes identifying data associated with the at least one project, such as a project number or name. The resource tracking data additionally includes logged time data associated with the projects. More specifically, the resource tracking data includes project time data identifying one or more time periods reflecting use of a computing resource in association with the at least one project. In at least some embodiments, the project time data may include data information associated with the at least one project. The date information may specify dates on which the computing resource was assigned and/or used for development tasks on the at least one project; [0060] lines 13-15, determine that a particular task for a specific project was being worked on using the computing resource during a range of date; [0061] lines 1-15, the server determines, based on the mappings, that at least one task-based resource usage criterion is satisfied. In some embodiments, a total cost associated with usage of the computing resource may be considered when assessing for a resource usage criterion. Specifically, the server may determine that a resource usage criterion is satisfied if the total cost associated with usage of the computing resource for one or more of the defined computing tasks exceeds a predefined threshold value. A “cost” associated with resource usage may be expressed in terms of monetary value of the resource use (e.g. expenses associated with the use, opportunity cost, etc.). The cost of the resource usage may be compared to a monetary threshold); and
providing a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies (ROSANOVA, [0030] lines 11-25, The system may compare the values of the metrics (e.g. overall cost) to predefined threshold values, in order to assess the optimality of a current allocation of computing resources to the development project. If the metric values satisfy certain criteria with respect to the predefined threshold values, the system may determine that the computing resources should be re-allocated. For example, if an overall cost of the project is determined to be greater than a threshold value, the system may generate a notification indicating a re-allocation of the computing resources currently associated with (or committed) to the development project (as a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies). Additionally, or alternatively, the system may control access to computing resources, such as shared software assets, associated with the project based on the comparison of the metric values to thresholds).
ROSANOVA fails to specifically teach the provided usage data, it is by receiving, from a computing system.
However, Xu teaches the provided usage data, it is by receiving, from a computing system (Xu, Fig. 1, 114, 128 and 124; [0017] lines 1-14, The client provides information for each organizational unit relevant to environmental impact. Some of the information may be related to resource consumption of an organizational unit…Server 112 may save the individual resource consumption entries as resource consumption items for the organizational unit in database 124 (as receiving from a computing system (see Fig. 1, 114 client computer)).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA with Xu because Xu’s teaching of providing/receiving the recourse usage from the client computer would have provided ROSANOVA’s system with the advantage and capability to allow the system to easily obtaining, organizing and analyzing the resource usages from computer systems in order to improving the system performance and efficiency.
As per claim 2, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA further teaches modifying, by way of remote access to the computing system, future use of the subset of the computing resources so that the inefficiencies are reduced (ROSANOVA, Fig. 1, 180, network 120, 110, 160, 150; [0030] if an overall cost of the project is determined to be greater than a threshold value, the system may generate a notification indicating a re-allocation of the computing resources currently associated with (or committed) to the development project. Additionally, or alternatively, the system may control access to computing resources, such as shared software assets, associated with the project based on the comparison of the metric values to thresholds; [0036] lines 1-13, The SDPM server 180 is configured to provide functionalities relating to project management for one or more software development projects. The SDPM server 180 may serve as a centralized tool for aggregating projects-related data, controlling assignment (or allocation) of computing resources to tasks, project planning, requirements and change management, and controlling software release schedules; also see [0078] lines 3-7, The notification may, for example, indicate that the re-allocation of computing resources may result in lower overall cost for the project compared to the current allocation).
As per claims 13-14, they are non-transitory computer-readable medium claims of claims 1-2 respectively above. Therefore, they are rejected for the same reasons as claims 1-2 respectively above.
As per claim 20, it is a system claim of claim 1 above. Therefore, it is rejected for the same reason as claim 1 above.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA and Xu, as applied to claims 1 and 13 respectively above, and further in view of CELLA et al. (US Pub. 2023/0206329 A1).
As per claim 3, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA and Xu fail to specifically teach wherein the structured data includes a distributed, cryptographically immutable sequence of blocks containing the records.
However, CELLA teaches wherein the structured data includes a distributed, cryptographically immutable sequence of blocks containing the records (CELLA, [3150] the chain of storage blocks may function as a time sequence, which may be cryptographically secured to form an immutable time sequence. This structure may be advantageous because someone who has access to the storage system may be able to determine a history of data storage transactions with relative ease).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA and Xu with CELLA because CELLA’s teaching of chain of storage blocks may function as a time sequence, which may be cryptographically secured to form an immutable time sequence would have provided ROSANOVA and Xu’s system with the advantage and capability to allow the system to determine a history of data storage transactions with relative ease in order to improving the system performance and efficiency.
As per claim 15, it is a non-transitory computer-readable medium claim of claim 3 above. Therefore, it is rejected for the same reason as claim 3 above.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA and Xu, as applied to claims 1 and 13 respectively above, and further in view of Seth et al. (US Pub. 2023/0004447 A1).
As per claim 4, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA and Xu fail to specifically teach wherein the structured data includes a time series database containing the records.
However, Seth teaches wherein the structured data includes a time series database containing the records (Seth, [0047] lines 1-10, To compute the excess capacity, the cluster agent 355 of the VPC controller cluster 300 in some embodiments estimates the peak CPU/memory usage of legacy workloads 335 by analyzing the data sample records stored in the time series database 360, and sets the request of the occupancy Pod 405 to the peak usage of legacy workloads 33).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA and Xu with Seth because Seth’s teaching of estimates the peak CPU/memory usage of legacy workloads by analyzing the data sample records stored in the time series database would have provided ROSANOVA and Xu’s system with the advantage and capability to allow the system to easily determining the resource usages based on the time series database which improving the system efficiency and performance.
As per claim 16, it is a non-transitory computer-readable medium claim of claim 4 above. Therefore, it is rejected for the same reason as claim 4 above.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA and Xu, as applied to claims 1 and 13 respectively above, and further in view of Mathews et al. (US Pub. 2020/0288221 A1).
As per claim 5, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA teaches wherein determining the inefficiencies related to the usage of the computing resources, and wherein providing the notification indicating the inefficiencies and the subset of the computing resources that are producing the inefficiencies comprises (ROSANOVA , [0030] lines 11-25, The system may compare the values of the metrics (e.g. overall cost) to predefined threshold values, in order to assess the optimality of a current allocation of computing resources to the development project. If the metric values satisfy certain criteria with respect to the predefined threshold values, the system may determine that the computing resources should be re-allocated. For example, if an overall cost of the project is determined to be greater than a threshold value, the system may generate a notification indicating a re-allocation of the computing resources currently associated with (or committed) to the development project (as a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies). Additionally, or alternatively, the system may control access to computing resources, such as shared software assets, associated with the project based on the comparison of the metric values to thresholds); [0036] lines 1-16, The SDPM server 180 is configured to provide functionalities relating to project management for one or more software development projects…the SDPM server 180 may obtain data from one or both of the activity logging server 150 and the resource usage monitoring server 160 to determine costs associated with tasks of a software development project, identify inefficiencies in a current allocation of computing resources, and determine a more efficient re-allocation of the computing resources).
ROSANOVA and Xu fail to specifically teach an alerting system detecting abnormal patterns of the usage in the time range of the records, and the alerting system providing an alert relating to the abnormal patterns of the usage.
However, Mathews teaches an alerting system detecting abnormal patterns of the usage in the time range of the records, and the alerting system providing an alert relating to the abnormal patterns of the usage (Mathews, [0018] a resource server, coupled to the internet cloud, that monitors use of a resource, and that engages resource users when unusual patterns of resource consumption are detected, the resource server including: a meter reading processor, configured to communicate with a plurality of resource monitors that are each disposed within radio range of a corresponding plurality of resource meters, where the resource meters transmit corresponding radio signals indicative of corresponding meter identifiers and current readings, and where the each of the plurality of resource monitors receives and decodes one or more of the corresponding radio signals to obtain one or more of the corresponding plurality of meter identifiers and current readings, and where the each of the plurality of resource monitors transmits the one or more of the corresponding plurality of meter identifiers and current readings over the internet cloud, and where the meter reading processor creates and updates a corresponding plurality of records in a resource database that indicate resource consumption of corresponding facilities; a disaggregation processor, configured to analyze the plurality of records to distinguish between normal and abnormal usage patterns by the corresponding facilities, and to detect the unusual patterns of resource consumption; and an engagement processor, that causes alerts to be transmitted to one or more client devices based on analyses of corresponding resource consumption, where the alerts notify corresponding users of the unusual patterns of resource consumption (as including alert system); [0065] 60-minute time windows are employed by the disaggregation processor 154 to analyze resource consumption. The disaggregation processor 154 then employs an averaged shifted histogram method with data points in bins representing subranges to analyze consumption during each of the windows).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA and Xu with Mathews because Mathews’s teaching of analyze the plurality of records to distinguish between normal and abnormal usage patterns by the corresponding facilities, and to detect the unusual patterns of resource consumption; and an engagement processor, that causes alerts to be transmitted to one or more client devices based on analyses of corresponding resource consumption would have provided ROSANOVA and Xu’s system with the advantage and capability to allow the system to easily identifying the unusual resource usage patterns based on the specific time windows in order to improving the resource utilization and system performance.
As per claim 17, it is a non-transitory computer-readable medium claim of claim 5 above. Therefore, it is rejected for the same reason as claim 5 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA, Xu and Mathews, as applied to claim 5 above, and further in view of Lim (US Pub. 2007/0156696 A1).
As per claim 6, ROSANOVA, Xu and Mathews teach the invention according to claim 5 above. Mathews teaches wherein the abnormal patterns of the usage in the time range of the records include the usage of computing resources (Mathews, [0018] a resource server, coupled to the internet cloud, that monitors use of a resource, and that engages resource users when unusual patterns of resource consumption are detected, the resource server including: a meter reading processor, configured to communicate with a plurality of resource monitors that are each disposed within radio range of a corresponding plurality of resource meters, where the resource meters transmit corresponding radio signals indicative of corresponding meter identifiers and current readings, and where the each of the plurality of resource monitors receives and decodes one or more of the corresponding radio signals to obtain one or more of the corresponding plurality of meter identifiers and current readings, and where the each of the plurality of resource monitors transmits the one or more of the corresponding plurality of meter identifiers and current readings over the internet cloud, and where the meter reading processor creates and updates a corresponding plurality of records in a resource database that indicate resource consumption of corresponding facilities; a disaggregation processor, configured to analyze the plurality of records to distinguish between normal and abnormal usage patterns by the corresponding facilities, and to detect the unusual patterns of resource consumption; and an engagement processor, that causes alerts to be transmitted to one or more client devices based on analyses of corresponding resource consumption, where the alerts notify corresponding users of the unusual patterns of resource consumption (as including alert system); [0065] 60-minute time windows are employed by the disaggregation processor 154 to analyze resource consumption. The disaggregation processor 154 then employs an averaged shifted histogram method with data points in bins representing subranges to analyze consumption during each of the windows).
ROSANOVA, Xu and Mathews fail to specifically teach the usage of computing resources associated with a service other than a pre-defined set of allowed services, and wherein the subset of the computing resources includes the computing resources associated with the service.
However, Lim teaches the usage of computing resources associated with a service other than a pre-defined set of allowed services, and wherein the subset of the computing resources includes the computing resources associated with the service (Lim, [0068] A policy enforcer can be installed on a workstation 403 to provide document access and information usage control at a point-of-use. The policies can be stored locally on the workstation. Objectives of implementing point-of-use control include preventing unauthorized access to documents anywhere on the network and preventing unauthorized information usage and operation on application data or usage of application functions; [0303] an access policy on a file server controls whether users are allowed to create, read, update, or delete documents that are stored on that file server. An access policy on a client computer can control whether users on that client computer are allowed to create, read, update, or delete documents in specified storage locations; [0120] Compliance officers can use event forensics to investigate specific incidents of information misuse. Information security officers can use event forensics to detect information fraud, hacking attempts, unauthorized access to information; Claim 68, wherein the condition includes at least one of information fraud, information misuse, operational inefficiency, potential improvement in workforce productivity, potential improvement in resource utilization, a change in user behavior, a change in group behavior, a change in application program behavior, change in resource utilization, or abnormal or suspicious activity pattern (as the usage of computing resources associated with a service other than a pre-defined set of allowed services, and wherein the subset of the computing resources includes the computing resources associated with the service (i.e., suspicious service (i.e., access) with change resource utilization) other than a pre-defined set of allowed services (i.e., authorized accesses)).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA, Xu and Mathews with Lim because Lim’s teaching of detecting a change in application program behavior, change in resource utilization, or abnormal or suspicious activity pattern with unauthorized access other than the previous allowed accesses would have provided ROSANOVA, Xu and Mathews’s system with the advantage and capability to allow the system to prevent unauthorized access service in order to improving the system security and performance.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA, Xu and Mathews, as applied to claim 5 above, and further in view of Smith et al. (US Pub. 2021/0064431 A1).
As per claim 7, ROSANOVA, Xu and Mathews teach the invention according to claim 5 above. ROSANOVA, Xu and Mathews fail to specifically teach wherein the abnormal patterns of the usage in the time range of the records include usage of computing resources outside of a pre-defined set of hours, and wherein the subset of the computing resources includes the computing resources used outside of the pre-defined set of hours.
However, Smith teaches wherein the abnormal patterns of the usage in the time range of the records include usage of computing resources outside of a pre-defined set of hours, and wherein the subset of the computing resources includes the computing resources used outside of the pre-defined set of hours (Smith, [0015] When the system determines that the current or recent usage by a user or environment (or group of users and environments) is outside the typical or expected range, as determined from historical activity for the user or environment, the system can send a notification of the anomaly to an administrator or to the user involved in the anomalous condition…the system can detect a usage anomaly based on determining that a cloud computing environment is using more resources than is typical or expected, is using less resources than is typical or expected, has an unexpected number of active users, has a resource utilization that is currently above a threshold, has a resource utilization that is currently below a threshold, etc. In response to detecting an anomaly, the system can generate one or more anomaly notifications and send them to an administrator of a cloud computing environment corresponding to the detected anomaly; [0038] setting a default maximum duration for cloud computing environments to run; [0043] the baseline usage characteristics indicate a range or typical level for at least one of: an amount of instances, a frequency of execution, an amount of computing resources used, a duration of execution; [0174] For example, the usage data 606 provided by the cloud computing system 108 can include recent usage data 610. The recent usage data 610 can include, for example, the most recent recording of CPU utilization by the cloud computing system 108 (e.g., 85%), the average CPU utilization by the cloud computing system 108 over a period of time (e.g., 62% average utilization since 12:00 am), the cumulative CPU hours over a period of time (e.g., 9.4 CPU hours spent since 12:00 am); also see [0225] A usage anomaly can include for example an abnormal pattern of usage, an abnormal allocation of resources (e.g., an unusually high amount of cloud computing resources have been allocated to one or more of the cloud computing environments 602, an unusually low amount of cloud computing resources have been allocated to one or more of the cloud computing environments 602), a rate of usage meeting a threshold rate (e.g., indicating a sharp increase in usage, or sharp decrease in usage), an abnormal pattern in spending for cloud computing resources).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA, Xu and Mathews with Smith because Smith’s teaching of detecting abnormal patterns of the usage based on the outside of a pre-defined set of hours (i.e., outside of set CPU hours to spent) would have provided ROSANOVA, Xu and Mathews’s system with the advantage and capability to allow the system to easily determine the abnormal pattern based on the resource usage exceeding the expected runtime hours in order to improving the resource efficiency and system performance.
As per claim 8, ROSANOVA, Xu and Mathews teach the invention according to claim 5 above. ROSANOVA, Xu and Mathews fail to specifically teach wherein the abnormal patterns of the usage in the time range of the records include under-utilization or overutilization of computing resources in comparison to one or more pre-defined threshold levels of utilization, and wherein the subset of the computing resources includes the computing resources that are under-utilized or over-utilized.
However, Smith teaches wherein the abnormal patterns of the usage in the time range of the records include under-utilization or overutilization of computing resources in comparison to one or more pre-defined threshold levels of utilization, and wherein the subset of the computing resources includes the computing resources that are under-utilized or over-utilized (Smith, [0006] enable more efficient and effective use of cloud computing services with information and tools to plan usage, monitor usage, and enforce usage plans. The system can be configured to track cloud computing resource usage for a group of multiple users or systems, e.g., for an organization, a department, etc. This may include tracking aggregate usage for the group as well as more fine-grained usage tracking, such as for projects, users, or computing environments. A usage plan or budget can be set for the group as a whole and/or for elements within the group (e.g., projects, users, computing environments, etc.). The system may track the usage of cloud computing resources in an ongoing or real-time manner, so the system can rapidly initiate management actions and send notifications in response to changing conditions. In addition, or as an alternative, the system can track usage for defined time periods, e.g., a day, a week, a month, etc., and periodically send notifications and initiate management actions based on usage over those periods. The system can compare the tracked usage of cloud computing resources to one or more thresholds to determine if any of the thresholds are met or exceeded (as including subset of the computing resources includes the computing resources that are under-utilized or over-utilized). The thresholds may represent, for example, planned or budgeted levels of usage or milestones along the way to reaching a planned level of usage; [0225] A usage anomaly can include for example an abnormal pattern of usage, an abnormal allocation of resources (e.g., an unusually high amount of cloud computing resources have been allocated to one or more of the cloud computing environments 602, an unusually low amount of cloud computing resources have been allocated to one or more of the cloud computing environments 602), a rate of usage meeting a threshold rate (e.g., indicating a sharp increase in usage, or sharp decrease in usage), an abnormal pattern in spending for cloud computing resources).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA, Xu and Mathews with Smith because Smith’s teaching of detecting abnormal patterns of the usage based on the comparing with threshold would have provided ROSANOVA, Xu and Mathews’s system with the advantage and capability to allow the system to easily determine the abnormal pattern based on the resource usage exceeding the threshold in order to improving the resource efficiency and system performance.
Claims 9-10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA and Xu, as applied to claims 1 and 13 respectively above, and further in view of Chen et al. (US Pub. 2024/0311718 A1).
As per claim 9, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA further teaches wherein determining the inefficiencies related to the usage of the computing resources comprises a system detecting the inefficiencies, and wherein providing the notification indicating the inefficiencies and the subset of the computing resources that are producing the inefficiencies comprises the system providing a recommendation to modify an allocation of the computing resources (ROSANOVA, Fig. 1, 180, network 120, 110, 160, 150; [0030] if an overall cost of the project is determined to be greater than a threshold value, the system may generate a notification indicating a re-allocation of the computing resources currently associated with (or committed) to the development project. Additionally, or alternatively, the system may control access to computing resources, such as shared software assets, associated with the project based on the comparison of the metric values to thresholds; [0036] lines 1-13, The SDPM server 180 is configured to provide functionalities relating to project management for one or more software development projects. The SDPM server 180 may serve as a centralized tool for aggregating projects-related data, controlling assignment (or allocation) of computing resources to tasks, project planning, requirements and change management, and controlling software release schedules; also see [0078] lines 3-7, The notification may, for example, indicate that the re-allocation of computing resources may result in lower overall cost for the project compared to the current allocation).
ROSANOVA and Xu fail to specifically teach the system is recommendation system.
However, Chen teaches the system is recommendation system (Chen, [0011] providing adaptive prescriptive analytics using multi-layer correlation and data analytics, comprising the steps of: a) collecting and analyzing data from various sources within a system, including workload data, resource utilization data, and performance data; b) using multi-layer correlation and prescriptive analytics to obtain data dynamics and identify bottlenecks and inefficiencies in the system; c) generating recommendations by a recommendation engine for resource allocation based on the analyzed data, including scaling or resizing resources to meet demand; d) implementing the recommendations in real-time, and adapting the recommendation engine as needed to optimize resource allocation and minimize waste; e) continuously monitoring and analyzing data to continually optimize resource allocation and improve efficiency of the system; and f) providing a user interface for visualizing and managing processes of resource allocation).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA and Xu with Chen because Chen’s teaching of providing a recommendation engine for resource allocation based on the analyzed data, including scaling or resizing resources to meet demand would have provided ROSANOVA and Xu’s system with the advantage and capability to allow the system to recommend the resource scaling or resizing in order to improving the system stability and resource efficiency.
As per claim 10, ROSANOVA, Xu and Chen teach the invention according to claim 9 above. Chen further teaches wherein the inefficiencies are detected based on a trend analysis or volume analysis of the usage of the computing resources, and wherein the recommendation to modify the allocation of the computing resources comprises a suggestion to change a type or quantity of the computing resources used (Chen, Fig. 5A, (as trend); Abstract, uses multi-layer correlation and causality analytics to identify patterns and trends in workload operation metadata, and generates resource orchestration based on this analysis, thereby achieving efficient operations. In addition, the system also includes an adaptive scaling module, which can scale specific modules of the recommendation engine based on the volume of data being processed. This allows the recommendation engine to maintain high levels of efficiency and accuracy without wasting resources; [0011] providing adaptive prescriptive analytics using multi-layer correlation and data analytics, comprising the steps of: a) collecting and analyzing data from various sources within a system, including workload data, resource utilization data, and performance data; b) using multi-layer correlation and prescriptive analytics to obtain data dynamics and identify bottlenecks and inefficiencies in the system; c) generating recommendations by a recommendation engine for resource allocation based on the analyzed data, including scaling or resizing resources to meet demand; d) implementing the recommendations in real-time, and adapting the recommendation engine as needed to optimize resource allocation and minimize waste; e) continuously monitoring and analyzing data to continually optimize resource allocation and improve efficiency of the system; and f) providing a user interface for visualizing and managing processes of resource allocation; [0034] The adaptive scaling module is able to scale certain, but not all, modules of the recommendation engine based on the volume of data being processed. This allows the recommendation engine to maintain high levels of efficiency and accuracy without wasting resources. When the operation data volume is high, the adaptive scaling module may scale up specific modules of the recommendation engine to handle the increased volume of data. On the contrary, if the data volume decreases, the adaptive scaling module may scale down these modules of the recommendation engine to avoid resource waste. In this way, the present invention provides an adaptive prescriptive analytics system, which can make recommendations more efficient in resource usage without affecting its performance).
As per claim 18, it is a non-transitory computer-readable medium claim of claim 9 above. Therefore, it is rejected for the same reason as claim 9 above.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA, Xu and Chen, as applied to claim 9 above, and further in view of Kumar et al. (US Pub. 2022/0327538 A1).
As per claim 11, ROSANOVA, Xu and Chen teach the invention according to claim 9 above. Chen further teaches wherein the recommendation to modify the allocation of the computing resources comprises a suggestion (Chen, [0011] providing adaptive prescriptive analytics using multi-layer correlation and data analytics, comprising the steps of: a) collecting and analyzing data from various sources within a system, including workload data, resource utilization data, and performance data; b) using multi-layer correlation and prescriptive analytics to obtain data dynamics and identify bottlenecks and inefficiencies in the system; c) generating recommendations by a recommendation engine for resource allocation based on the analyzed data, including scaling or resizing resources to meet demand; d) implementing the recommendations in real-time, and adapting the recommendation engine as needed to optimize resource allocation and minimize waste; e) continuously monitoring and analyzing data to continually optimize resource allocation and improve efficiency of the system; and f) providing a user interface for visualizing and managing processes of resource allocation).
ROSANOVA, Xu and Chen fail to specifically teach wherein the inefficiencies are detected based on a carbon footprint analysis of the usage of the computing resources, and the suggestion to replace at least some of the computing resources with more power efficient computing resources.
However, Kumar teaches wherein the inefficiencies are detected based on a carbon footprint analysis of the usage of the computing resources, and the suggestion to replace at least some of the computing resources with more power efficient computing resources (Kumar, [0064] determine a portion of or the entire carbon chain or footprint of the enterprise from source to recycle or reuse. The entire carbon chain or footprint includes a number of activities, including for example tracking, monitoring and assessing the carbon usage or generation associated with, for example, the raw materials that are sourced for making a device or a building or equipment, the activities associated with transporting the raw materials to a processing or production location, the processing of the raw materials, the activities associated with assembling or manufacturing the designed product, the activities associated with the storing, distributing, and selling the product to customers including the enterprise, and customers using the product. The carbon chain also includes activities associated with the enterprise (e.g., customer), such as operating their facilities, the reuse or recycling of emissions or materials, and the like. The climate related data (e.g., emissions data) can used to determine the appropriate climate actions, the impact of selected climate actions on the enterprise, and to help determine the allocation of resources, including financial resources; [0069 The climate actions can also include for example initiatives related to carbon reduction (e.g., energy efficiency); [0071] The emissions reporting unit 84 can employ pre-defined techniques to track and analyze the impact of the different climate actions undertaken by the enterprise to reduce their overall emissions and leverage the insights generated by the cognitive intelligence unit 34 for subsequent emissions planning; [0115] Examples of suitable climate actions include reducing the consumption of natural resources, deploying renewable energy, retrofitting one or more systems or devices to consume less energy, deploying or replacing existing devices with more energy efficient devices, reducing the scale or scope of operations).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA, Xu and Chen with Kumar because Kumar’s teaching of detecting the carbon footprint and replacing the resources with more power efficient would have provided ROSANOVA, Xu and Chen’s system with the advantage and capability to allow the system to build an emissions resilience in the enterprise and to sustain or improve its financial performance, while concomitantly maintaining its market relevance (See Kumar [0090]).
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over ROSANOVA and Xu, as applied to claims 1 and 13 respectively above, and further in view of KIM (US Pub. 2019/0026189 A1).
As per claim 12, ROSANOVA and Xu teach the invention according to claim 1 above. ROSANOVA teaches determining the inefficiencies related to the usage of the computing resources and records to the structured data (ROSANOVA, [0036] lines 1-16, The SDPM server 180 is configured to provide functionalities relating to project management for one or more software development projects…the SDPM server 180 may obtain data from one or both of the activity logging server 150 and the resource usage monitoring server 160 to determine costs associated with tasks of a software development project, identify inefficiencies in a current allocation of computing resources, and determine a more efficient re-allocation of the computing resources).
ROSANOVA and Xu fail to specifically teach the determination of the inefficiencies is caused by addition of a pre-determined number of the records to the structured data.
However, KIM teaches the determination of the inefficiencies is caused by addition of a pre-determined number of the records to the structured data (KIM, [0046] lines 1-6, a threshold value which is the number of records which can be included in one record group may be set to 100000. If the selected record group includes an exceed record group having the number of records which exceeds the threshold value, this may lead overload of the computer or inefficiency, which may be problematic).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of ROSANOVA and Xu with KIM because KIM’s teaching of determination of the selected record group includes an exceed record group having the number of records which exceeds the threshold value, this may lead overload of the computer or inefficiency would have provided ROSANOVA and Xu’s system with the advantage and capability to allow the system to identifying the computing resource inefficiency based on the number of records exceeding the threshold in order to take appropriate action to improving the resource efficiency and system performance.
As per claim 19, it is a non-transitory computer-readable medium claim of claim 12 above. Therefore, it is rejected for the same reason as claim 12 above.
Conclusion
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/ZUJIA XU/Examiner, Art Unit 2195
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