Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
1. A computer-implemented method comprising:
predicting one or more resource support parameters for the at least one resource and the one or more users associated therewith by processing at least a portion of the input data using one or more artificial intelligence techniques (predicting one or more resource support parameters in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
determining one or more resource support-related data allocations, across one or more systems, for the at least one resource and the one or more users associated therewith based at least in part on the one or more predicted resource support parameters (determining one or more resource support-related data allocations in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
The claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the following additional elements:
obtaining input data comprising data pertaining to at least one resource and data pertaining to one or more users associated with the at least one resource (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d));
performing one or more automated actions based at least in part on the one or more resource support-related data allocations (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of applying it under MPEP 2106.05(d));
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (amounts to a generic computer component to perform a computer function as discussed in MPEP 2106.05(f)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 2-6: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
2. The computer-implemented method of claim 1, wherein processing at least a portion of the input data using one or more artificial intelligence techniques comprises implementing at least one multi-output regression technique using one or more ensemble learning techniques.
3. The computer-implemented method of claim 2, wherein implementing at least one multi-output regression technique comprises using one or more of at least one ensemble boosting technique and at least one ensemble bagging technique.
4. The computer-implemented method of claim 3, wherein using at least one ensemble boosting technique comprises using at least one gradient boosting regression technique.
5. The computer-implemented method of claim 3, wherein using at least one ensemble bagging technique comprises using at least one random forest regression technique.
6. The computer-implemented method of claim 1, wherein processing at least a portion of the input data using one or more artificial intelligence techniques comprises processing the at least a portion of the input data using at least one deep neural network regressor model.
The use of various techniques amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 7-8: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
7. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically initiating at least a portion of the one or more resource support-related data allocations in connection with the one or more systems.
8. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part feedback related to the one or more resource support- related data allocations.
Automatically initiating/training action amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of applying it under MPEP 2106.05(d));
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 9-13: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
9. The computer-implemented method of claim 1, wherein data pertaining to at least one resource comprises one or more of resource-related lifespan information, one or more error logs, one or more system alerts, on/off statistics, install, move, add, change (IMAC) data, and resource component information.
10. The computer-implemented method of claim 1, wherein data pertaining to one or more users associated with the at least one resource comprises one or more of resource-related user communication data, data related to one or more resource-related remedy actions requested by the one or more users, user identifying information, and user location information.
11. The computer-implemented method of claim 1, wherein predicting one or more resource support parameters comprises predicting one or more costs associated with providing resource support to the one or more users in connection with the at least one resource by processing at least a portion of the input data using one or more artificial intelligence techniques.
12. The computer-implemented method of claim 1, wherein predicting one or more resource support parameters comprises predicting at least one expected margin associated with providing resource support to the one or more users in connection with the at least one resource by processing at least a portion of the input data using one or more artificial intelligence techniques.
13. The computer-implemented method of claim 1, wherein determining the one or more resource support-related data allocations comprises determining, based at least in part on the one or more predicted resource support parameters, at least one customized price associated with providing resource support to the one or more users in connection with the at least one resource.
The use of various data and prediction parameters amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 14-17 are non-transitory computer readable storage medium claims having similar limitation as claims 1-3, 6 and are rejected under the same rationale. The additional elements in claim 14 is A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claims 18-20 are apparatus claims having similar limitation as claims 1-3 and are rejected under the same rationale. The additional elements in claim 18 is An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 7, 9-14, 18 is/are rejected under 35 U.S.C. 102a1 as being anticipated by O’Brien et al (US 10885135 B1)
1. A computer-implemented method comprising:
obtaining input data comprising data pertaining to at least one resource and data pertaining to one or more users associated with the at least one resource (See Fig. 5-500, C1L45-C2L15 5) In one embodiment, an apparatus comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of at least a first cloud-based system. The processing platform further comprises one or more interfaces configured to enable interaction between multiple types of actors and the processing platform, wherein the multiple types of actors comprise one or more cloud-based vendors seeking to add one or more individual resource offerings to the processing platform, and one or more customers seeking to procure one or more resource offerings via the processing platform. Also, the processing platform further comprises a resource offering repository configured to maintain the individual resource offerings added to the processing platform via one or more of the interfaces and data pertaining thereto, wherein the data comprise one or more customer attributes required for procuring the individual resource offerings. The processing platform additionally comprises a matchmaking module configured to match two or more of the individual resource offerings based on one or more of the customer attributes associated therewith, and a resource offering bundling module configured to generate at least one resource bundle offering comprising two or more of the individual resource offerings based on a particular set of one or more customer attributes and the matching of the individual resource offerings via the matchmaking module. Further, the processing platform comprises a resource offering display module configured to output to a customer, via one or more of the interfaces, at least one of the at least one resource bundle offering and one or more of the individual resource offerings based on the customer attributes of the customer. C11L25-50 61) FIG. 7 shows a system view of dynamic offering catalog components in an illustrative embodiment. By way of illustration, FIG. 7 depicts a hypertext markup language (HTML) client 700, which receives static content from a dynamic catalog web application 702 and also interacts with a dynamic catalog API 714 (which is hosted by the dynamic catalog web application 702). The dynamic catalog web application 702 also hosts one or more vendor APIs 712, and interacts with dynamic catalog application components 704. The dynamic catalog application components 704 interact with a dynamic catalog repository 706 as well as a CRM integration component 710, which provides input to a CRM solution component 708. As further detailed herein, a dynamic catalog can include three major layers: a repository, application interfaces, and a web-based or mobile-native user interface. By establishing these layers, at least one embodiment of the invention can include linking systems (via the application layer) and interactive components (such as APIs) into the catalog without risking compromising the repository.
predicting one or more resource support parameters for the at least one resource and the one or more users associated therewith by processing at least a portion of the input data using one or more artificial intelligence techniques (See C1L45-C2L15 5) In one embodiment, an apparatus comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of at least a first cloud-based system. The processing platform further comprises one or more interfaces configured to enable interaction between multiple types of actors and the processing platform, wherein the multiple types of actors comprise one or more cloud-based vendors seeking to add one or more individual resource offerings to the processing platform, and one or more customers seeking to procure one or more resource offerings via the processing platform. Also, the processing platform further comprises a resource offering repository configured to maintain the individual resource offerings added to the processing platform via one or more of the interfaces and data pertaining thereto, wherein the data comprise one or more customer attributes required for procuring the individual resource offerings. The processing platform additionally comprises a matchmaking module configured to match two or more of the individual resource offerings based on one or more of the customer attributes associated therewith, and a resource offering bundling module configured to generate at least one resource bundle offering comprising two or more of the individual resource offerings based on a particular set of one or more customer attributes and the matching of the individual resource offerings via the matchmaking module. Further, the processing platform comprises a resource offering display module configured to output to a customer, via one or more of the interfaces, at least one of the at least one resource bundle offering and one or more of the individual resource offerings based on the customer attributes of the customer. C6L60-C7L15 (41) Further, in one or more embodiments of the invention, design recommendations and landscape optimizations can be provided by CRP agents based on machine learning techniques (for example, via a recommendations system with smart AI-based agents). As additionally detailed herein, CRP provides frictionless cloud service processes across service providers (open to partners via shared application programming interfaces (APIs) and processes), as well as provides a single management console for technical and business units across an orchestrated cloud landscape (42) Enterprise trends can be used to define required features for an implementation by CRP. For example, at least one embodiment of the invention can include implementation of AI and/or machine learning techniques on big data (landscape deployment and operations experience, for example) with forecasting capabilities. Also, one or more embodiments of the invention can include facilitating both an external and an internal perspective on operations.).
determining one or more resource support-related data allocations, across one or more systems, for the at least one resource and the one or more users associated therewith based at least in part on the one or more predicted resource support parameters (See C1L45-C2L15 5) In one embodiment, an apparatus comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of at least a first cloud-based system. The processing platform further comprises one or more interfaces configured to enable interaction between multiple types of actors and the processing platform, wherein the multiple types of actors comprise one or more cloud-based vendors seeking to add one or more individual resource offerings to the processing platform, and one or more customers seeking to procure one or more resource offerings via the processing platform. Also, the processing platform further comprises a resource offering repository configured to maintain the individual resource offerings added to the processing platform via one or more of the interfaces and data pertaining thereto, wherein the data comprise one or more customer attributes required for procuring the individual resource offerings. The processing platform additionally comprises a matchmaking module configured to match two or more of the individual resource offerings based on one or more of the customer attributes associated therewith, and a resource offering bundling module configured to generate at least one resource bundle offering comprising two or more of the individual resource offerings based on a particular set of one or more customer attributes and the matching of the individual resource offerings via the matchmaking module. Further, the processing platform comprises a resource offering display module configured to output to a customer, via one or more of the interfaces, at least one of the at least one resource bundle offering and one or more of the individual resource offerings based on the customer attributes of the customer. C6L60-C7L15 (41) Further, in one or more embodiments of the invention, design recommendations and landscape optimizations can be provided by CRP agents based on machine learning techniques (for example, via a recommendations system with smart AI-based agents). As additionally detailed herein, CRP provides frictionless cloud service processes across service providers (open to partners via shared application programming interfaces (APIs) and processes), as well as provides a single management console for technical and business units across an orchestrated cloud landscape (42) Enterprise trends can be used to define required features for an implementation by CRP. For example, at least one embodiment of the invention can include implementation of AI and/or machine learning techniques on big data (landscape deployment and operations experience, for example) with forecasting capabilities. Also, one or more embodiments of the invention can include facilitating both an external and an internal perspective on operations; and
performing one or more automated actions based at least in part on the one or more resource support-related data allocations (C7L30-50 (44) Accordingly, and as further described herein, at least one embodiment of the invention can include automate ad hoc planning and execution of an end-user IT landscape via a CRP platform, wherein such an IT landscape can include one or more cloud services, one or more business processes, and one or more technical processes, in conjunction with available cloud resources. Additionally, as used herein, “ad hoc” planning and execution of an end-user IT landscape refers to a specific end-user selected or designed IT landscape, wherein the end-user is enabled (via the CRP platform) to deploy and/or implement particular cloud services, business process, technical processes and/or cloud resources with the single CRP platform based on the offerings and capabilities of the CRP platform.);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (C4L1-10 (23) The processing platform 106 is assumed to include a plurality of processing devices each having a processor coupled to a memory, and is configured to implement the virtual resources 110 of the cloud-based system 112 for use by client applications.).
7. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically initiating at least a portion of the one or more resource support-related data allocations in connection with the one or more systems (See Fig. 5-522 on process automation and C7L30-50 (44) Accordingly, and as further described herein, at least one embodiment of the invention can include automate ad hoc planning and execution of an end-user IT landscape via a CRP platform, wherein such an IT landscape can include one or more cloud services, one or more business processes, and one or more technical processes, in conjunction with available cloud resources. Additionally, as used herein, “ad hoc” planning and execution of an end-user IT landscape refers to a specific end-user selected or designed IT landscape, wherein the end-user is enabled (via the CRP platform) to deploy and/or implement particular cloud services, business process, technical processes and/or cloud resources with the single CRP platform based on the offerings and capabilities of the CRP platform. Examiner Note: execution of end-user landscape indicates initializing the execution).
9. The computer-implemented method of claim 1, wherein data pertaining to at least one resource comprises one or more of resource-related lifespan information (See Fig. 5-500 and 508), one or more error logs, one or more system alerts, on/off statistics, install, move, add, change (IMAC) data, and resource component information (See Fig. 5-500).
10. The computer-implemented method of claim 1, wherein data pertaining to one or more users associated with the at least one resource comprises one or more of resource-related user communication data, data related to one or more resource-related remedy actions requested by the one or more users, user identifying information, and user location information (C4L15-30 (25) Examples of different types of clouds that may be utilized in illustrative embodiments include private, public and hybrid clouds. Private clouds illustratively include on-premises clouds and off-premises clouds, where “premises” refers generally to a particular site or other physical location of the business, enterprise, organization or other entity that utilizes the private cloud. Public clouds are assumed to be off-premises clouds. Hybrid clouds comprise combinations of public and private cloud aspects and thus may include various combinations of on-premises and off-premises portions, such as on-premises placement of managed appliances that can be consumed on-demand like off-premises cloud capacity, but at the same time offer the benefits of on-premises deployments such as security, compliance and physical control.).
11. The computer-implemented method of claim 1, wherein predicting one or more resource support parameters comprises predicting one or more costs associated with providing resource support to the one or more users in connection with the at least one resource by processing at least a portion of the input data using one or more artificial intelligence techniques (C4L30-65 (26) The interfaces 114 are configured to enable interaction between multiple types of actors and the processing platform 106, wherein the multiple types of actors comprise one or more cloud-based vendors seeking to add one or more individual resource offerings to the processing platform 106, and one or more customers seeking to procure one or more resource offerings via the processing platform 106. The resource offering repository 116 is configured to maintain the individual resource offerings added to the processing platform via one or more of the interfaces 114 and data pertaining thereto, wherein the data comprise one or more customer attributes required for procuring the individual resource offerings. The matchmaking module 118 is configured to match two or more of the individual resource offerings based on one or more of the customer attributes associated therewith. For example, the matchmaking module 118 can use proximity in a tree structure for bundling. Forming an offering out of single resources can also be carried out, for example, by exploratory data mining as used in machine learning. Forming a customer-relevant offering or a catalog cluster in this context can include a multi-objective optimization problem involving factors such as placement, price and general preference. The resource offering bundling module 120 is configured to generate at least one resource bundle offering comprising two or more of the individual resource offerings based on a particular set of one or more customer attributes and the matching of the individual resource offerings via the matchmaking module 118. The resource offering display module 122 is configured to output to a customer, via one or more of the interfaces 114, at least one of the at least one resource bundle offering and one or more of the individual resource offerings based on the customer attributes of the customer.).
12. The computer-implemented method of claim 1, wherein predicting one or more resource support parameters comprises predicting at least one expected margin associated with providing resource support to the one or more users in connection with the at least one resource by processing at least a portion of the input data using one or more artificial intelligence techniques (C6L35-50 (39) In order to comply with geographic and/or intercorporate regulations, a cloud provider or an enterprise may need to offer different configurations of resources and services. For example, during a sales cycle, sales actors within such providers may need to be able to craft unique offerings to fit a particular need for a particular prospect or entity. An offering catalog with the ability to selectively offer services, resources and bundles of these services based on attributes of the viewing parties can enable the ability to dynamically meet such complex demands. At least one embodiment of the invention, in conjunction with a cloud resource planning platform, includes generating and providing a design and implementation of such a dynamic offering catalog. Examiner Note: any sales inherently has expect profit margin involved).
13. The computer-implemented method of claim 1, wherein determining the one or more resource support-related data allocations comprises determining, based at least in part on the one or more predicted resource support parameters, at least one customized price associated with providing resource support to the one or more users in connection with the at least one resource (C2L110-25 (6) Illustrative embodiments can provide significant advantages relative to conventional enterprise cloud computing platforms. For example, challenges associated with the limitations of existing catalog systems containing a single vendor and a static offering are overcome through generating a cloud resources planning platform that provides cloud service providers and enterprises the ability to selectively offer services, resources and bundles of services based on attributes of the viewing parties and/or customers. Such a platform can fulfill the needs of enterprises to leverage the benefits of cloud computing across multiple vendors while providing a dynamic and flexible offerings catalog via a single point of entry. Examiner Note: selective offering is customized offering).
Claim 14 are non-transitory computer readable storage medium claims having similar limitation as claim 1 and is rejected under the same rationale. The additional elements in claim 14 is A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device (C16L50-60 (87) Functionality such as that described in conjunction with the flow diagram of FIG. 16 can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as a computer or server. As will be described below, a memory or other storage device having executable program code of one or more software programs embodied therein is an example of what is more generally referred to herein as a “processor-readable storage medium.”)
Claim 18 is apparatus claims having similar limitation as claim 1 and are rejected under the same rationale. The additional elements in claim 18 is An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured (C1L45-50 (5) In one embodiment, an apparatus comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-6, 8, 15-17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over O’Brien et al (US 10885135 B1) in view of Wickramanayake et al (“Fuel Consumption Prediction of Fleet Vehicles Using Machine Leaning: A Comparative Study” 2015)
2. O’Brien disclose machine learning techniques for predictive analytics, but fails to explicitly disclose multi-output regression technique using one or more ensemble learning techniques.
However, Wickramanayake disclose using machine learning techniques for predictive analytics (thereby in the same field of endeavor) and further disclose multi-output regression technique using one or more ensemble learning techniques (See section III and IV).
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning techniques of O’Brien to incorporate ensemble learning of Wickramanayake.
Given the advantage of ensemble learning (see section III), one having ordinary skill in the art would have been motivated to make this obvious modification with predictable result of wherein processing at least a portion of the input data using one or more artificial intelligence techniques comprises implementing at least one multi-output regression technique using one or more ensemble learning techniques.
3. Wickramanayake disclose the computer-implemented method of claim 2, wherein implementing at least one multi-output regression technique comprises using one or more of at least one ensemble boosting technique and at least one ensemble bagging technique (See section III on two well-known ensemble learning method: boosting and bagging).
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4. Wickramanayake disclose the computer-implemented method of claim 3, wherein using at least one ensemble boosting technique comprises using at least one gradient boosting regression technique (See section III on Gradient Boosting).
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5. Wickramanayake disclose the computer-implemented method of claim 3, wherein using at least one ensemble bagging technique comprises using at least one random forest regression technique (See section III on random forest).
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6. Wickramanayake disclose The computer-implemented method of claim 1, wherein processing at least a portion of the input data using one or more artificial intelligence techniques comprises processing the at least a portion of the input data using at least one deep neural network regressor model (See section III-C on ANN)
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8. Wickramanayake disclose training with feedback (incorrect instances)(See section III). O’Brien disclose resource support related data allocation (See C1L45-C2L15). The combined teaching disclose the computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques based at least in part feedback related to the one or more resource support- related data allocations.
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Claims 15-17 are non-transitory computer readable storage medium claims having similar limitation as claims 2-3, 6 and are rejected under the same rationale.
Claims 19-20 are apparatus claims having similar limitation as claims 2-3 and are rejected under the same rationale.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chapin et al (US 20190025810 A1) disclose component maintenance cost prediction using ML. See abstract and Fig. 1.
Puthenveetil et al (US 20220405706 A1) disclose automated procurement using AI. See abstract. See [0005] that profit margins are typical for any business.
Yueng et al (US 20190278529 A1) disclose using ML for device failure prediction and repair visit scheduling. See abstract.
Kim et al (“Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management” 2022) disclose predictive resource management using multi-class regression. See abstract.
Meng et al (“Price forecasting using an ACO-based support vector regression ensemble in cloud manufacturing” 2018) disclose regression ensemble for price forecasting. See abstract.
Taiwo et al (“PFAI: A Predictive Financial Planning and Analysis Intelligence Framework for Transforming Enterprise Decision-Making” 2022) disclose predictive planning using ensemble ML such as random forest and gradient boosting. See abstract and pg. 473 and 475.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUT WONG whose telephone number is (571)270-1123. The examiner can normally be reached M-F 10am-6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUT WONG/Primary Examiner, Art Unit 2127
Prosecution Insights