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. Status of the Application and Claims This action is in reply to the application filed on 2/21/2023 . IDS filed on 2/21/2023 is acknowledged and considered by the Examiner. This communication is the first action on the merits. Claims 1-20 is/are currently pending and have been examined. 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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 (similarly 11 and 13) recites, “ A … method for transferring data workloads, the … method comprising: performing, by a …, a factor comparison between each of a plurality of site profiles and a customer profile; generating, by the …, a factor matching score for each respective site profile of the plurality of site profiles based on the factor comparison; determining, by the …, whether the factor matching score corresponding to a particular site profile is greater than or equal to a predefined minimum factor matching score threshold level; selecting, by the …, the particular site profile having the factor matching score greater than or equal to the predefined minimum factor matching score threshold level as a recommended site profile for transferring a data workload of a customer to a set of target sites from a current site running the data workload in response to the … determining that the factor matching score corresponding to the particular site profile is greater than or equal to the predefined minimum factor matching score threshold level; and sending, by the …, the recommended site profile to a … corresponding to the customer via a …. ” Analyzing under Step 2A, Prong 1: The limitations regarding, … performing, by a …, a factor comparison between each of a plurality of site profiles and a customer profile; generating, by the …, a factor matching score for each respective site profile of the plurality of site profiles based on the factor comparison; determining, by the …, whether the factor matching score corresponding to a particular site profile is greater than or equal to a predefined minimum factor matching score threshold level; selecting, by the …, the particular site profile having the factor matching score greater than or equal to the predefined minimum factor matching score threshold level as a recommended site profile for transferring a data workload of a customer to a set of target sites from a current site running the data workload in response to the … determining that the factor matching score corresponding to the particular site profile is greater than or equal to the predefined minimum factor matching score threshold level; and sending, by the …, the recommended site profile to a … corresponding to the customer via a .… , under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims are directed to a mental process . Further, … performing, by a …, a factor comparison between each of a plurality of site profiles and a customer profile; generating, by the …, a factor matching score for each respective site profile of the plurality of site profiles based on the factor comparison; determining, by the …, whether the factor matching score corresponding to a particular site profile is greater than or equal to a predefined minimum factor matching score threshold level; selecting, by the …, the particular site profile having the factor matching score greater than or equal to the predefined minimum factor matching score threshold level as a recommended site profile for transferring a data workload of a customer to a set of target sites from a current site running the data workload in response to the … determining that the factor matching score corresponding to the particular site profile is greater than or equal to the predefined minimum factor matching score threshold level; and sending, by the …, the recommended site profile to a … corresponding to the customer via a …, are human predicting risk and adverse disaster events , match ing and recommend ing work sites to human customers in order to mitigate risks and disaster events , which are fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, therefore the claims, are directed to certain methods of organizing human activities . Accordingly, the claims are directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A. Analyzing under Step 2A, Prong 2: This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as: Claim 1, 11, 13: computer-implemented, computer, client device, network, A computer system for transferring data workloads, the computer system comprising: a communication fabric; a storage device connected to the communication fabric, wherein the storage device stores program instructions; and a processor connected to the communication fabric, wherein the processor executes the program, computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to , and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer. Additionally, with respect to , “… performing, by a … , a factor comparison …” , “… sending … ” , these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “… performing, by a … , a factor comparison …”, data output – “… sending … ” Analyzing under Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it). Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least : [0007]Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time. [0008]A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. [0009]With reference now to the figures, and in particular, with reference to Figures 1-2,diagrams of data processing environments are provided in which illustrative embodiments may be implemented. It should be appreciated that Figures 1-2 are only meant as examples and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made. [0010]Figure 1 shows a pictorial representation of a computing environment in which illustrative embodiments may be implemented. Computing environment 100 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as data workload management code 200. For example, data workload management code 200 effectively controls movement of data workloads between sites based on a plurality of different factors to ensure data workload availability under disaster-related conditions and non-disaster-related conditions. [0011]In addition to data workload management code 200, computing environment 100 includes, for example, computer 101, wide area network (WAN) 102, end user device (EUD) 103, remote server 104, public cloud 105, and private cloud 106. In this embodiment, computer 101 includes processor set 110 (including processing circuitry 120 and cache 121), communication fabric 111, volatile memory 112, persistent storage 113 (including operating system 122 and data workload management code 200, as identified above), peripheral device set 114 (including user interface (UI) device set 123, storage 124, and Internet of Things (IoT) sensor set 125), and network module 115. Remote server 104 includes remote database 130. Public cloud 105 includes gateway 140, cloud orchestration module 141, host physical machine set 142, virtual machine set 143, and container set 144. [0012]Computer 101 may take the form of a desktop computer, laptop computer, tablet computer, mainframe computer, quantum computer, or any other form of computer now known or to be developed in the future that is capable of, for example, running a program, accessing a network, and querying a database, such as remote database 130. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 100, detailed discussion is focused on a single computer, specifically computer 101, to keep the presentation as simple as possible. Computer 101 may be located in a cloud, even though it is not shown in a cloud in Figure 1. On the other hand, computer 101 is not required to be in a cloud except to any extent as may be affirmatively indicated. [0013]Processor set 110 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and/or multiple processor cores. Cache 121 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 110. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set 110 may be designed for working with qubits and performing quantum computing. [0021]EUD 103 is any computer system that is used and controlled by an end user (for example, a customer of an entity that operates computer 101), and may take any of the forms discussed above in connection with computer 101. EUD 103 typically receives helpful and useful data from the operations of computer 101. For example, in a hypothetical case where computer 101 is designed to provide a data workload migration recommendation to an end user, this data workload migration recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way, EUD 103 can display, or otherwise present, the recommendation to the end user. In some embodiments, EUD 103 may be a client device, such as thin client, heavy client, mainframe computer, desktop computer, laptop computer, tablet computer, smartphone, and so on. [0022]Remote server 104 is any computer system that serves at least some data and/or functionality to computer 101. Remote server 104 may be controlled and used by the same entity that operates computer 101. Remote server 104 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer 101. For example, in a hypothetical case where computer 101 is designed and programmed to provide a data workload migration recommendation based on historical data, then this data workload migration historical data may be provided to computer 101 from remote database 130 of remote server 104. [0051]In this example, data workload management system 201 includes computer 202, client device 204, current site 206, and set of target sites 208. Computer 202 and client device 204 may be, for example, computer 101 and EUD 103, respectively, in Figure 1. Current site 206 may be, for example, remote server 104 in Figure 1. Set of target sites 208 may be, for example, host physical machine set 142 in Figure 1. However, it should be noted that data workload management system 201 is intended as an example only and not as a limitation on illustrative embodiments. For example, data workload management system 201 can include any number of computers, client devices, current sites, sets of target sites, and other devices and components not shown. [0052]Customer 210 utilizes client device 204 to send a request to computer 202 for computer 202 to determine whether transfer of customer data workload 211 to set of target sites 208 from current site 206 is needed. Customer data workload 211 corresponds to customer 210. Customer 210 can be, for example, an individual or an entity, such as a business, company, enterprise, organization, institution, agency, or the like. Current site 206 represents the site that is currently running customer data workload 211 for customer 210. [0072]Thus, illustrative embodiments of the present invention provide a computer-implemented method, computer system, and computer program product for managing migration of data workloads between sites based on a plurality of different factors. The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d). Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims. Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. 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 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: Determining the scope and contents of the prior art. Ascertaining the differences between the prior art and the claims at issue. Resolving the level of ordinary skill in the pertinent art. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1-4, 9-16 is/ are rejected under 35 U.S.C. 103 as being unpatentable by US Patent to US11159402B1 to Subramanian, (hereinafter referred to as “Subramanian”) in view of US Patent Publication to US20190244268A1 to Zagorin , (hereinafter referred to as “ Zagorin ”) As per Claim 1 , Subramanian teaches: A computer-implemented method for transferring data workloads, the computer-implemented method comprising: performing, by a computer, a factor comparison between each of a plurality of site profiles and a customer profile ; (in at least [col6 ln 35-60] Referring to FIG. 4, illustrated is an example of analyzing resources in accordance with this disclosure. Referring to FIG. 4, a provider network may implement an API 420 that is configured to receive requests for resource transfers. Requests may be managed by transfer analysis service 180 that may communicate with one or more systems in the provider network to access customer data 430 and system and system data 440. API 420 may also be configured to receive inputs from users pertaining to transfer requests. User inputs may be managed by transfer analysis service 180 in additional to customer data 430 and system data 440. User feedback may be received by API 420 regarding the transfer. In some embodiments, a client agent may be installed on the user-side computing environment to facilitate analysis of the user-side computing environment and provide data based on the analysis to the API 420. For example, a plug-in may be installed on one or more computing devices on the user-side computing environment that executes within the paradigm of the user-side computing environment to provide a user interface, executable components that gather information pertaining to the user side computing environment, and so on. Transfer analysis service 180 may also interact with risk/success rating component 330 and recommendation engine 460 to analyze the customer data 430 and system data 440 and determine potential improvements. Based on the analysis, transfer analysis service 180 may identify recommendations that can be provided to the customer .) generating, by the computer, a factor matching score for each respective site profile of the plurality of site profiles based on the factor comparison ; (in at least [col6 ln30-60] Transfer analysis service 180 may interact with a risk/success rating component 330. The risk/success rating component 330 may access a risk/success recommendations generated by a risk/success advisor component to generate one or more scores or ratings. The scores or ratings may be used to determine potential changes to resources 350 .) determining, by the computer, whether the factor matching score corresponding to a particular site profile is … ; (in at least [col3 ln60-col4 ln15] the transfer analysis service 180 may identify which configurations (e.g., OS version in a specific environment) has the highest chance of import success, and associate the configurations with various levels of service. In this way, configurations with known levels of success can be identified and tiered importation services can be provided that provide guaranteed or targeted rates of success based on the known levels. The risk/success score may be expressed in a number of ways. For example, the risk/success score may be expressed as percentage probability over a sliding period of time. Different risk/success scores may be associated with different periods of time based on known information. The risk/success score may also be expressed as a real number between 0 and 1, as a ratio, or as one of a set of discrete indicators (e.g., high, medium, low). Other types of scoring methods may be incorporated .) selecting, by the computer, the particular site profile having the factor matching score … as a recommended site profile for transferring a data workload of a customer to a set of target sites from a current site running the data workload in response to the computer determining that the factor matching score corresponding to the particular site profile is … ; and (in at least [col4ln65-col5 ln20] A service, such as transfer analysis service 180, may be configured to provide real-time or accumulated and/or archived monitoring of import/export activities that may be used for assessing specific import/export requests. The monitored activities may include transfers of virtual machine images of various types. The monitored resources may also include other computing resources provided by the service provider, such as storage services and database services. As used herein, computing resources can include any computing and networking resource made available by the provider network for use by customers of the provider network, including virtual computing instances, storage capacity, virtual network topologies (e.g., IP address ranges, firewall configurations, etc.), and so on. The transfer analysis service 180 may also access metrics, such as health status, data transfers, and disk usage activity. The transfer analysis service 180 may be made accessible via an API or a user interface that may be accessed via a web browser or other input mechanisms. In some embodiments, transfer analysis service 180 may include a risk/success scoring component to provide risk/success ratings or scores. The risk/success ratings or scores may be based, for example, on a risk/success scoring model and one or more best practices of the provider network and/or external sources. The risk/success scoring component may analyze a customer's computing resources and generate risk/success assessments that may be useful to indicate risk/success predictions according to one or more categories. Transfer analysis service 180 may also include a risk/success advisor component to provide recommendations that may improve the risk/success ratings or scores provided by the risk/success scoring component. The recommendations may be based on an analysis of metrics associated with a customer as well as the service provider's aggregated operational history. The risk/success advisor component may inspect a customer's allocated computing resources and generate recommendations that may, if implemented, provide improvements to import/export performance . [col18 ln50-col19 ln 5] Resources may further be available in specific availability zones 1001A and 1001B, as described above. As discussed above, an availability zone 1001 may represent a particular physical location, such as a data center or other physical and/or logical grouping of underlying host computers 1006 and computing devices supporting the resources 1004 provided by the computing platform 1002. Providing resources 1004 in different sizes and in different availability zones 1001 may allow a deployed application to be geographically dispersed, improving end-user performance and insulating the overall application from failures in one particular location or zone. For example, a customer 1020 may choose to deploy a number of small resources 1004 across multiple availability zones 1001 for some functions of the application, such as web servers, while deploying a single large resource 1004 for other functions, such as a database server, for example. The customer 1020 may also require that resources 1004 be hosted by host computers 1006 in particular geographical locations for geopolitical reasons as well . [col20 ln55-65] The customer 1020 may submit a request that includes one or more parameters so that resource advisor module 1036 can determine potentially relevant parameters for selected resources and metrics of interest. The resource advisor module 1036 and template manager module 1046 may access the resource listings 1034, storage listings 1040, and metric data 1038 in the database 1030 to access metrics data and process the metrics data to generate recommendations and templates regarding customer 1020's resources . The application servers 1024 may execute resource advisor module 1036 and template manager module 1046. The customer 1020 may utilize a web browser application executing on the customer computer system 1022 to access a user interfaces (UI) presented by the resource advisor module 1036 and template manager module 1046 through a web service. Additionally or alternatively, the resource advisor module 1036 and resource status manager module 1046 may expose an API 1032, which may be accessed over the network(s) 1044 by stand-alone application programs executing on the customer computer system 1022 . The resource advisor module 1036 and resource status manager module 1046 may further store data records regarding submitted and fulfilled requests in the database 1030 or other data storage system. The metrics data 1038 may also be utilized by customer 1020 or the computing resource provider to record billing data regarding the requested analysis. The user can also be provided a user interface for viewing recommendations from the resource advisor module 1036 and templates generated by template manager module 1046. For example, the user may be able to access a user interface presented by the resource advisor module 1036 of FIG. 10 to review recommendations. The resource advisor module 1036 or another module in the computing platform 1002 may present a user interface to the customer 1020 in a window of a web browser or other client application executing on the customer computer system 1022 . ) sending, by the computer, the recommended site profile to a client device corresponding to the customer via a network . (in at least [col5 ln 25-65] Transfer analysis service 180 may also include a risk/success advisor component to provide recommendations that may improve the risk/success ratings or scores provided by the risk/success scoring component. The recommendations may be based on an analysis of metrics associated with a customer as well as the service provider's aggregated operational history. The risk/success advisor component may inspect a customer's allocated computing resources and generate recommendations that may, if implemented, provide improvements to import/export performance. In some embodiments, the risk/success advisor component of transfer analysis service 180 may provide recommended configurations based on priorities and requirements provided by the customer. The recommendations may be based on an analysis of the customer's priorities and requirements as well as best practices and other information accessible by the service provider. In some embodiments, an API may facilitate requests for generating risk/success scores or ratings and recommendations. For example, the API can be called with information, such as a resource identifier, resource configuration, and applications. After the API is called, in one embodiment the transfer analysis service 180 may take actions such as: Invoke a function to generate a baseline of available metrics pertaining to the risk/success analysis and customer resources. Retrieve configurations of the customer's resources. Call available APIs that can provide metrics for the customer's resources. Access applicable data, such as checklists, pertaining to relevant best practices. Using the gathered information, the transfer analysis service 180 may analyze the data, combine or aggregate the data, or extract portions of the data as appropriate to determine risks and a risk/success score. The risk/success may be reported through the API along with details about any recommendations. [col20 ln55-65] The customer 1020 may submit a request that includes one or more parameters so that resource advisor module 1036 can determine potentially relevant parameters for selected resources and metrics of interest. The resource advisor module 1036 and template manager module 1046 may access the resource listings 1034, storage listings 1040, and metric data 1038 in the database 1030 to access metrics data and process the metrics data to generate recommendations and templates regarding customer 1020's resources . The application servers 1024 may execute resource advisor module 1036 and template manager module 1046. The customer 1020 may utilize a web browser application executing on the customer computer system 1022 to access a user interfaces (UI) presented by the resource advisor module 1036 and template manager module 1046 through a web service. Additionally or alternatively, the resource advisor module 1036 and resource status manager module 1046 may expose an API 1032, which may be accessed over the network(s) 1044 by stand-alone application programs executing on the customer computer system 1022 . The resource advisor module 1036 and resource status manager module 1046 may further store data records regarding submitted and fulfilled requests in the database 1030 or other data storage system. The metrics data 1038 may also be utilized by customer 1020 or the computing resource provider to record billing data regarding the requested analysis. The user can also be provided a user interface for viewing recommendations from the resource advisor module 1036 and templates generated by template manager module 1046. For example, the user may be able to access a user interface presented by the resource advisor module 1036 of FIG. 10 to review recommendations. The resource advisor module 1036 or another module in the computing platform 1002 may present a user interface to the customer 1020 in a window of a web browser or other client application executing on the customer computer system 1022 ) Although implied, Subramanian does not expressly disclose the following limitations, which however, are taught by Zagorin , …greater than or equal to a predefined minimum factor matching score threshold level …(in at least [0040] The ranking/scoring component 128 may be configured to receive and/or access data from the buyer data component 118, the request data component 120, the seller data component 122, the third-party data component 124, and/or the cost estimate generation component 126 to rank the participating seller(s) 104 with respect to one another and/or generate a score associated with the participating seller(s) 104. For example, utilizing buyer information 130 from the buyer data component 118 including current and previous buyer 102 preferences and/or acceptance behavior, the ranking/scoring component 128 may determine that the buyer 102 prefers seller(s) 104 that have been in operation for longer than ten years. Further utilizing the seller information 132 of the seller data component 122, the ranking/scoring component 128 may rank seller(s) 104 in operation for ten years or greater higher than those who have been operating for less than ten years. Additionally, or alternatively, the ranking/scoring component 128 may generate a score associated with the seller(s) 104 indicating a likelihood that the seller 104 will win the bid and/or be selected by the buyer 102. The ranking and/or score may be provided to the buyer 102 in each bidding round along with the respect cost estimates. In addition, the ranking and/or score may affect the cost estimate generation. For example, if a seller 104 is ranked lower with respect to other seller(s) 104 and/or has a low score associated, the cost estimate generation component 126 may take this into account when generating the cost estimate for that particular seller 104. That is, if a seller 104 is ranked lower and/or has a low score associated, the cost estimate generation component 126 may generate a lower cost estimate in order to offset the low-ranking displayed to the buyer 102 and increase the likelihood that the seller 104 may be selected . [0051] if the request information 208 stipulates that the buyer 204 prefers seller(s) 206 located within a certain distance of the buyer 204, the automated negotiation system 202 may rank seller(s) 206 located within the preferred distance higher than other seller(s) 206 located outside of the preferred distance. In other examples, the ranking/scoring component 222 may generate a score associated with the seller(s) 206. For example, a seller 206 located within the preferred distance may receive a high score, indicating a high likelihood of selection . [0054] once the system 202 receives a seller response 220 from each seller 206 indicating an acceptance, modification, or declination of the personalized cost estimate 218 and/or the expiration of a period of time associated with the second phase 228, the round 200 may enter the first phase 302 of the second cost estimation round 300. In the first phase 302, the ranking/scoring component 222 of the automated negotiation system 202 may utilize the request information 208 and/or additional buyer 204 information (e.g., buyer preferences, buyer demographic information, past buy behavior, etc.) to generate the ranking/scoring 224 of the one or more seller(s) 206. For example, if the request information 208 stipulates that the buyer 204 prefers seller(s) 206 located within a certain distance of the buyer 204, the automated negotiation system 202 may rank seller(s) 206 located within the preferred distance higher than other seller(s) 206 located outside of the preferred distance .) At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Subramanian as taught by Zagorin , with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Subramanian with the motivation of , …To provide an example, a buyer may request a product, such as a good or service, via a contract negotiation or auction platform. In response, the request may be provided to one or more sellers capable of fulfilling the request. Should the seller decide to make a bid for fulfilling the request, the system may utilize data, such as buyer-provided data (e.g., buyer inputs, stored buyer data, etc.), market data, third-party data, historical data, and the like, to generate a cost estimate on behalf of the seller for fulfilling the request, referred to herein as a should-cost value (e.g., the cost that the providing the product should cost the seller). Upon receiving the cost estimate, the seller has a predetermined period of time to choose to accept, modify, or deny the estimate and submit the generated cost estimate for acceptance by the buyer. Alternatively, or in addition, the seller may provide the system authorization to accept the bid on their behalf. The process may continue for one or more bidding rounds, determined by the system and/or buyer, with the final cost estimates of each seller being provided to the buyer at the conclusion of the auction. Based on the final cost estimates, the buyer may select a seller to provide the product.…help to formulate more accurate and efficient cost estimations for fulfilling the buyer's request for products or services….help prevent inefficient resource allocations and increase the processing capacity of the system components… , as recited in Zagorin . As per Claim 2 , Subramanian teaches: The computer-implemented method of claim 1, further comprising: …, by the computer, a predefined number of site profiles from the plurality of site profiles having a … in response to the computer determining that … factor matching score corresponding to the plurality of site profiles is …. ; (in at least [col17 ln10-55] A physical host 902 may be shared by multiple virtual machine slots 904, each slot 904 being capable of hosting a virtual machine, such as a guest domain. Multiple physical hosts 902 may share a power supply 906, such as a power supply 906 provided on a server rack. A router 908 may service multiple physical hosts 902 across several power supplies 906 to route network traffic. An isolation zone 910 may service many routers 908, the isolation zone 910 being a group of computing resources that may be serviced by redundant resources, such as a backup generator. Isolation zone 910 may reside at a geographical location 912, such as a data center 900. A provisioning server 914 may include a memory and processor configured with instructions to analyze user data and rank available implementation resources using determined roles and shared resources/infrastructure in the calculation. The provisioning server 914 may also manage workflows for provisioning and de-provisioning computing resources as well as detecting health and/or failure of computing resources. A provisioning server 914 may determine a placement of the resource within the data center. In some embodiments, this placement may be based at least in part on available computing resources and/or relationships between computing resources. In one embodiment, the distance between resources may be measured by the degree of shared resources. This distance may be used in the ranking of resources according to role. For example, a first system on a host 902 that shares a router 908 with a second system may be more proximate to the second system than to a third system only sharing an isolation zone 910. Depending on an application, it may be desirable to keep the distance low to increase throughput or high to increase durability. In another embodiment, the distance may be defined in terms of unshared resources. For example, two slots 904 sharing a router 908 may have a distance of a physical host 902 and a power supply 906. Each difference in resources may be weighted differently in a distance calculation .) generating, by the computer, in accordance with a set of customer-desired factors for a target site contained in the customer profile, an … site profile using the predefined number of site profiles from the plurality of site profiles ; and (in at least [col18 ln50-col19 ln 5] a customer 1020 may choose to deploy a number of small resources 1004 across multiple availability zones 1001 for some functions of the application, such as web servers, while deploying a single large resource 1004 for other functions, such as a database server, for example. The customer 1020 may also require that resources 1004 be hosted by host computers 1006 in particular geographical locations for geopolitical reasons as well . [col20 ln40-60] In some embodiments, the resource management module 1026 may allow customers 1020 to purchase both on-demand resources and reserved resources. On-demand resources may be purchased and launched immediately, allowing for quick deployment of the components of the application. On-demand resources may further be added or removed as needed, either manually or automatically through auto scaling, as demand for, or capacity requirements of the application changes over time. The customer 1020 may incur ongoing usage costs related to their on-demand resources, based on the number of hours of operation of the resources 1004 and/or the actual resources utilized, for example. The customer 1020 may submit a request that includes one or more parameters so that resource advisor module 1036 can determine potentially relevant parameters for selected resources and metrics of interest. The resource advisor module 1036 and template manager module 1046 may access the resource listings 1034, storage listings 1040, and metric data 1038 in the database 1030 to access metrics data and process the metrics data to generate recommendations and templates regarding customer 1020's resources . [col18 ln50-col19 ln 5] Resources may further be available in specific availability zones 1001A and 1001B, as described above. As discussed above, an availability zone 1001 may represent a particular physical location, such as a data center or other physical and/or logical grouping of underlying host computers 1006 and computing devices supporting the resources 1004 provided by the computing platform 1002. Providing resources 1004 in different sizes and in different availability zones 1001 may allow a deployed application to be geographically dispersed, improving end-user performance and insulating the overall application from failures in one particular location or zone. For example, a customer 1020 may choose to deploy a number of small resources 1004 across multiple availability zones 1001 for some functions of the application, such as web servers, while deploying a single large resource 1004 for other functions, such as a database server, for example. The customer 1020 may also require that resources 1004 be hosted by host computers 1006 in particular geographical locations for geopolitical reasons as well .) utilizing, by the computer, the … site profile as the recommended site profile for transferring the data workload of the customer to the set of target sites from the current site . (in at least [col20 ln55-65] The customer 1020 may submit a request that includes one or more parameters so that resource advisor module 1036 can determine potentially relevant parameters for selected resources and metrics of interest. The resource advisor module 1036 and template manager module 1046 may access the resource listings 1034, storage listings 1040, and metric data 1038 in the database 1030 to access metrics data and process the metrics data to generate recommendations and templates regarding customer 1020's resources . [col18 ln50-col19 ln 5] Resources may further be available in specific availability zones 1001A and 1001B, as described above. As discussed above, an availability zone 1001 may represent a particular physical location, such as a data center or other physical and/or logical grouping of underlying host computers 1006 and computing devices supporting the resources 1004 provided by the computing platform 1002. Providing resources 1004 in different sizes and in different availability zones 1001 may allow a deployed application to be geographically dispersed, improving end-user performance and insulating the overall application from failures in one particular location or zone. For example, a customer 1020 may choose to deploy a number of small resources 1004 across multiple availability zones 1001 for some functions of the application, such as web servers, while deploying a single large resource 1004 for other functions, such as a database server, for example. The customer 1020 may also require that resources 1004 be hosted by host computers 1006 in particular geographical locations for geopolitical reasons as well .) Although implied, Subramanian does not expressly disclose the following limitations, which however, are taught by Zagorin , …selecting…highest factor matching score that is less than the predefined minimum factor matching score threshold level … no factor matching score … greater than or equal to the predefined minimum factor matching score threshold level …(in at least [0036] the cost estimate data 136 may be provided to the seller(s) 104 via the network 108 for review. The cost estimate data 136, including the personalized cost estimates, may be provided to each seller 104 along with an option to at least one of accept, modify, or decline the proposed, generated prenasalized cost estimate. That is, the seller(s) 104 may accept, modify, or decline the cost estimate before submitting a response or offer for acceptance by the buyer 102. Alternatively, or in addition, as described herein, the seller 104 may provide the third-party contract negotiation system 110 authorization to accept the cost estimate on their behalf, without further input. The seller response data 138, including each seller's offer, may be stored within the seller data component 122, and/or the cost estimate generation component 126, for use in generating additional cost estimates for the current negotiation, as well as future negotiations conducted by the system 110 . [0040] The ranking/scoring component 128 may be configured to receive and/or access data from the buyer data component 118, the request data component 120, the seller data component 122, the third-party data component 124, and/or the cost estimate generation component 126 to rank the participating seller(s) 104 with respect to one another and/or generate a score associated with the participating seller(s) 104. For example, utilizing buyer information 130 from the buyer data component 118 including current and previous buyer 102 preferences and/or acceptance behavior, the ranking/scoring component 128 may determine that the buyer 102 prefers seller(s) 104 that have been in operation for longer than ten years. Further utilizing the seller information 132 of the seller data component 122, the ranking/scoring component 128 may rank seller(s) 104 in operation for ten years or greater higher than those who have been operating for less than ten years. Additionally, or alternatively, the ranking/scoring component 128 may generate a score associated with the seller(s) 104 indicating a likelihood that the seller 104 will win the bid and/or be selected by the buyer 102. The ranking and/or score may be provided to the buyer 102 in each bidding round along with the respect cost estimates. In addition, the ranking and/or score may affect the cost estimate generation. For example, if a seller 104 is ranked lower with respect to other seller(s) 104 and/or has a low score associated, the cost estimate generation component 126 may take this into account when generating the cost estimate for that particular seller 104. That is, if a seller 104 is ranked lower and/or has a low score associated, the cost estimate generation component 126 may generate a lower cost estimate in order to offset the low-ranking displayed to the buyer 102 and increase the likelihood that the seller 104 may be selected . [0049] The automated third-party negotiation system 202 may then provide the personalized cost estimate(s) 218 to each corresponding