DETAILED ACTION 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. This application claims priority to U.S. Provisional Application No. 63/434,879, filed 12/ 22 / 2022, and U.S. Provisional Application No. 63/434,846, filed 12/22/2022. The priority claim is acknowledged by the examiner. Information Disclosure Statement The information disclosure statements (IDS) submitted on 06/14/2024 and 11/25/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Examiner’s Note The Examiner cites particular columns, paragraphs, figures, and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may also apply. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. Claim Objections Claim s 10 -17 are objected to because of the following informalities: Claim 10 recites “wherein the assessment strategy specifies a performance capability of the recommend shape ”. The phrase “recommend shape” appears to be incorrect and should read “recommended shape”. Appropriate correction is required. Any claim not explicitly mentioned above is objected to due to dependency on an objected claim. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim s 1 -6 , 18 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde et al. ( US 20240070435 A1 ) hereafter Shinde in view of Liu et al. ( US 20210365339 A1 ) hereafter Liu . Regarding claim 1, Shinde teaches: A method comprising: identifying at least one asset in a source environment ( Paragraph 33; “the server can extract the metadata of the on-premises system and identify the features and execution environment of the on-premises system”, where features and execution environment of the on-premises system correspond to the applicant’s asset. The claimed asset is represented by the features of the on-premises system identified by the server. The on-premises system corresponds to the source environment because the on-premises system provides the environment from which the asset features are extracted. ) ; adding the asset to a migration project, the migration project designating the source environment and a destination environment for replication of the asset ( Paragraph 71; “the server can produce solution blueprint for the cloud migration, such as the recommended features of the target cloud architecture”, where recommended features of the target cloud architecture corresponds to the asset added to a migration project because the solution blueprint organizes asset migration details into a structured plan. “the server can process source system details and target system details” corresponds to the source and destination environments respectively. ) ; receiving a request for generation of a recommended shape, the recommended shape designating a shape of the replicated asset in the destination environment ( Paragraphs 57, 65, 71; “ the server can receive a request to migrate an on-premises system to a cloud architecture ”. “the server can provide the option to re-assess the data in case the solution blueprint is not satisfactory”, where solution blueprint corresponds to the applicant’s recommended shape because the blueprint specifies the target cloud architecture and instance characteristics, such as CPU/GPU/memory. “virtual machine size/shape, virtual machine storage, operating system, memory allocated” corresponds to the applicant’s shape of the replicated asset in the destination environment as they define the configuration of the asset once migrated. ) ; and generating the recommended shape based on metadata characterizing attributes of the source environment (Paragraphs 70-71; “The server can extract metadata of the on-premises system and generate the set of input parameters that influences the cloud migration” and “The server can produce solution blueprint for the cloud migration, such as the recommended features of target cloud architecture”, where the server generates the solution blueprint based on input parameters and the extraction of metadata from the on-premises system, the source environment, is directly used to generate a recommended cloud architecture, the shape.); and rules designating compatibility requirements for the recommended shape ( Paragraph 39; “ the migration methods can depend on the on-premises system and target cloud architecture compatibility, target cloud database, database size, archive log mode, allowed downtime window, product license availability, etc., which can help in decision making of the appropriate migration path ”, where the compatibility to help in decision making corresponds to the rules designating compatibility requirements for the recommended shape. ); wherein the metadata characterizes at least a central processing unit (CPU) related attribute and a network related attribute of the source environment ( Paragraph 34; “number of central processing unit (CPU) cores”, “network bandwidth” both within the metadata of the on-premises system corresponds to the metadata characterizing CPU and network related attributes of the source environment. ). Shinde does not teach a replication strategy provided by a user. However, Liu teaches: a replication strategy provided by a user ( Paragraph 87; “ strategies including replication strategies that relate to fixed data protection topologies and/or changing data protection topologies. The replication strategies discussed herein can be static and/or dynamic ”, “ Changes in the topology may include, by way of example and not limitation, the addition or removal of an application, the migration of an application to a different server, changes in storage, hypervisors, domains, user input, user designated priorities, or the like or combination thereof. ”, where the prior art discloses replication strategies that can be influenced or configured by user input. ). Shinde and Liu are considered to be analogous to the claimed invention because they are in the same field of resource migration . Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shinde to incorporate the teachings of Liu and utilize a replication strategy provided by a user. A person of ordinary skill in the art would have recognized the use of known methods of generating metadata of a source environment selecting a target based on that metadata, and applying a user-provided replication strategy would yield the predictable result of generating a recommended template for a replicated asset in the destination environment that reflects the source environment attributes and conforms to user preferences. Claim 18 recites similar limitations as those of claim 1, additionally reciting a memory comprising executable instructions and one or more processors configured to execute the executable instructions. Shinde further teaches: a memory comprising executable instructions; and one or more processors configured to execute the executable instructions (Paragraph 73; “The processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504” ). Claim 18 is rejected for similar reasons as those of claim 1. Claim 20 recites similar limitations as those of claim 1, additionally reciting a non-transitory computer-readable storage medium storing a plurality of instructions. Liu further teaches: A non-transitory computer-readable storage medium storing a plurality of instructions executable by one or more processors (Paragraph 156; “ A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform the operations of any one or more of or portions of embodiments 1-11. ”). Claim 20 is rejected for similar reasons as those of claim 1. Regarding claim 2, Shinde in view of Liu teach the method of claim 1. Shinde teaches: wherein the destination environment is within a tenancy on a cloud services provider infrastructure (CSPI) (Paragraph 57; “At step 302, the server can receive a request to migrate an on-premises system to a cloud architecture” , where the on-premises system is the source environment, the cloud architecture is the destination environment, and cloud architecture corresponds to a tenancy on a cloud services provider infrastructure.). Regarding claim 3, Shinde in view of Liu teach the method of claim 1. Shinde teaches: metadata characterizing attributes of the source environment (Paragraph 70; “In the plan and analysis phase 410, the server can extract metadata of the on-premises system and generate the set of input parameters that influences the cloud migration. In step 1 412, after the server receives a request to migrate an on-premises system, the server can discover and extract metadata of the on-premises system” , where the parameters correspond to attributes of the source environment.). Liu teaches: a receiving step (Paragraph 95; “ The data protection system can monitor for these changes or receive input from a user related to the application topology ”). Regarding claim 4, Shinde in view of Liu teach the method of claim 3. Shinde teaches: wherein the metadata characterizes attributes of the asset in the source environment (Paragraph 34; “The values of the set of input parameters can provide metadata of the on-premises system”, where metadata of the on-premises system corresponds to applicant’s attributes of the asset in the source environment.). Regarding claim 5, Shinde in view of Liu teach the method of claim 4. Shinde teaches: wherein the CPU related attribute characterizes at least one of: a number of CPUs of the asset in the source environment; average CPU usage by the asset in the source environment; or maximum CPU usage in the source environment (Paragraph 34; “number of central processing unit (CPU) cores” corresponds to the applicant’s number of CPUs of the asset in the source environment element of the “at least one of...” limitation.). Regarding claim 6, Shinde in view of Liu teach the method of claim 4. Shinde teaches: wherein the network related attribute characterizes at least one of: average bandwidth usage by the asset in the source environment; or bandwidth usage by the asset in the source environment (Paragraph 34; “network bandwidth” corresponds to usage that belongs to the asset in the source environment). Shinde does not teach that the network bandwidth is necessarily a maximum. However, Liu teaches: a maximum (Paragraph 81; “A limit on the number of applications replicated to a particular replica virtual machine may be capped or limited.” ). A person of ordinary skill in the art would have recognized the use of an upper limit as equivalent to a conceptual maximum, and would have been motivated to apply the known concept of a maximum on the network related attribute of bandwidth usage yielding the predictable result of characterizing bandwidth usage of a resource by its resource limit. Regarding claim 19, Shinde in view of Liu teach the method of claim 18. Shinde teaches: metadata characterizing attributes of the source environment (Paragraph 70; “In the plan and analysis phase 410, the server can extract metadata of the on-premises system and generate the set of input parameters that influences the cloud migration. In step 1 412, after the server receives a request to migrate an on-premises system, the server can discover and extract metadata of the on-premises system” , where the parameters correspond to attributes of the source environment.); wherein the metadata characterizes attributes of the asset in the source environment (Paragraph 34; “The values of the set of input parameters can provide metadata of the on-premises system”, where metadata of the on-premises system corresponds to applicant’s attributes of the asset in the source environment.). Liu teaches: a receiving step (Paragraph 95; “ The data protection system can monitor for these changes or receive input from a user related to the application topology ”). Claims 7 -8 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Liu, further in view of Patil et al. ( US 20190340100 A1 ) hereafter Patil. Regarding claim 7, Shinde in view of Liu teach the method of claim 4. Shinde in view of Patil does not teach wherein the metadata characterizes at least: a graphics processing unit (GPU) related attribute; and a memory related attribute. However, Patil teaches: wherein the metadata characterizes at least: a graphics processing unit (GPU) related attribute; and a memory related attribute ( Paragraph 41; “As shown, the “resources []” object might store an operating system type (e.g., stored in an “ osType ” field), a number of CPUs (e.g., stored in a “ numCPU ” field), a maximum memory consumed (e.g., stored in a “ maxMem ” field), a number of GPUs (e.g., stored in a “ numGPU ” field)” , the num GPU field corresponding to the GPU related attribute, and the max Mem field corresponding to the memory related attribute. ). Shinde, Liu, and Patil are considered to be analogous to the claimed invention because they are in the same field of resource management . Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shinde in view of Liu to incorporate the teachings of Patil and also have the metadata characterize GPU and memory attributes. A person of ordinary skill in the art would recognize known modern computer systems as having many types of resources including GPU and memory and the implementation of characterizing these resource types would yield the predictable result of properly assigning resources upon replication. Regarding claim 8, Shinde in view of Liu, further in view of Patil teaches the method of claim 7. Shinde teaches: a source environment (Paragraph 33; “the server can extract the metadata of the on-premises system”, the on-premises system corresponding to the source environment.). Patil teaches: wherein the GPU related attribute characterizes at least one of: a number of GPUs of the asset; average GPU usage by the asset; or maximum GPU usage (Paragraph 41; “As shown, the “resources []” object might store an operating system type (e.g., stored in an “ osType ” field), a number of CPUs (e.g., stored in a “ numCPU ” field), a maximum memory consumed (e.g., stored in a “ maxMem ” field), a number of GPUs (e.g., stored in a “ numGPU ” field)” , the num GPU field corresponding to a number of GPUs of the asset of the “at least one of...” limitation.). Claims 9-1 3 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Liu, further in view of Moghe et al. ( US 20220021652 A1 ) hereafter Moghe . Regarding claim 9, Shinde in view of Liu teach the method of claim 4. Shinde in view of Liu does not teach receiving an assessment strategy from the user. However, Moghe teaches: receiving an assessment strategy from the user (Paragraph 54; “data lakes as provided for herein are provisioned with optimal “shapes” that deliver maximum performance, preferably within a user-defined cost threshold.” The user-defined cost threshold represents a user-defined parameter used to evaluate acceptable resource usage , corresponding to receiving an assessment strategy from the user.). Shinde, Liu, and Moghe are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shinde in view of Liu to incorporate the teachings of Moghe and receive an assessment strategy from the user. A person of ordinary skill in the art would have recognized the known method of receiving configuration input from a user would yield the predictable result of enabling the system to tailor processing to user-defined preferences. Regarding claim 10, Shinde in view of Liu, further in view of Moghe teach the method of claim 9. Moghe teaches: wherein the assessment strategy specifies a performance capability of the recommend shape (Paragraph 54; “data lakes as provided for herein are provisioned with optimal “shapes” that deliver maximum performance, preferably within a user-defined cost threshold.” Because the system delivers an optimal shape that delivers maximum performance, the user-defined parameter used for provisioning necessarily specifies the performance capability associated with the recommended shape. ). Regarding claim 11, Shinde in view of Liu, further in view of Moghe teach the method of claim 10. Moghe teaches: wherein the assessment strategy specifies at least one of: an average strategy; or a peak strategy (Paragraph 54; “data lakes as provided for herein are provisioned with optimal “shapes” that deliver maximum performance, preferably within a user-defined cost threshold.” Selecting resource shapes based on performance characteristics relative to user-defined constraints necessitates the determination of maximum performance, which corresponds to evaluating the peak performance capability of the shapes used for provisioning. Thus the performance evaluation used to determine an optimal shape corresponds to an assessment strategy specifying a peak strategy. ). Regarding claim 12, Shinde in view of Liu, further in view of Moghe teach the method of claim 10. Moghe teaches: based on the assessment strategy (Paragraph 54; “data lakes as provided for herein are provisioned with optimal “shapes” that deliver maximum performance, preferably within a user-defined cost threshold.” Selecting resource shapes based on performance characteristics relative to user-defined constraints necessitates the determination of maximum performance, which corresponds to evaluating the peak performance capability of the shapes used for provisioning. Thus the performance evaluation used to determine an optimal shape corresponds to an assessment strategy specifying a peak strategy .). Shinde teaches: generating shape requirements for the recommended shape (Paragraph 71; “ The server can produce solution blueprint for the cloud migration, such as the recommended features of the target cloud architecture. The recommendation features of the target cloud architecture can include identifier of the target cloud architecture, versions of the components of the target cloud architecture, methods for cloud migration, cloud target shapes/sizes, licensing impact, estimated time, etc. ”, where recommended features correspond to shape requirements for the recommended shape because it identifies the parameters that define the recommended target configuration.). Regarding claim 13, Shinde in view of Liu, further in view of Moghe teach the method of claim 12. Shinde teaches: wherein generating the recommended shape comprises identifying a plurality of potential shapes (Paragraph 71; “ In step 5424, the server can provide an option to re-assess the data in case the solution blueprint is not satisfactory. For example, the server can include a function to reprocess the request to generate one or more new recommendations for the target cloud architectures. ”, where the generation of one or more new recommendations for target cloud architectures based on the solution blueprint corresponds to identifying a plurality of potential shapes.). Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde in view of Liu, further in view of Moghe , further in view of Kouznetsov et al. ( US 20170097845 A1 ) hereafter Kouznetsov . Regarding claim 14, Shinde in view of Liu, further in view of Moghe teach the method of claim 13. Shinde teaches: potential shapes (Paragraph 71; “ In step 5424, the server can provide an option to re-assess the data in case the solution blueprint is not satisfactory. For example, the server can include a function to reprocess the request to generate one or more new recommendations for the target cloud architectures. ”, where the generation of one or more new recommendations for target cloud architectures based on the solution blueprint corresponds to identifying a plurality of potential shapes). Shinde in view of Liu, further in view of Moghe does not teach generating a compatibility score characterizing compatibility. However, Kouznetsov teaches: generating a compatibility score characterizing compatibility (Paragraphs 67-76; “ VM host compatibility scores based on existing placement rules are shown. In this example, VM-host compatibility scores are between 0 and 100, wherein 100 means fully compatible and 0 means incompatible. ”, “ When computing the compatibility scores, as shown by way of example below, when there is not full compatibility (with a score of 100) or complete incompatibility (with a score of zero), any one of a variety of scoring mechanisms can be used to assign a score between zero and 100 for partial compatibility. ”). Shinde, Liu, Moghe , and Kouznetsov are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shinde in view of Liu, further in view of Moghe to incorporate the teachings of Kouznetsov and generated a compatibility score characterizing compatibility for a plurality of potential shapes. A person of ordinary skill in the art would have recognized the known method of generating compatibility scores for each possible configuration would yield the predictable result of producing numeric scores for every shape, thus allowing the system to evaluate and select the most compatible shape. Regarding claim 15, Shinde in view of Liu, further in view of Moghe , further in view of Kouznetsov teach the method of claim 14. Kouznetsov teaches: selecting at least one of the options based at least partially on a compatibility score of that selected at least one of the options (Paragraphs 65-66; “ From the overall group-host compatibility scores (86), a VM sub-group is chosen to process (90). The group-host compatibility metrics and the number of allocated hosts are used to select the optimal host assignments for the group (92). This is done by comparing group-host scores to choose the most suitable hosts for a group of VMs 18. For example, the largest group may be chosen first. ” Discloses actively using compatibility scores to choose VM sub-groups and assign them to hosts, corresponding to selecting at least one of the options based at least partially on a compatibility score of that selected at least one of the options.) . Shinde teaches: the options may be potential shapes (Paragraph 71; “ In step 5424, the server can provide an option to re-assess the data in case the solution blueprint is not satisfactory. For example, the server can include a function to reprocess the request to generate one or more new recommendations for the target cloud architectures. ”, where the generation of one or more new recommendations for target cloud architectures based on the solution blueprint corresponds to identifying a plurality of potential shapes). Regarding claim 16, Shinde in view of Liu, further in view of Moghe , further in view of Kouznetsov teach the method of claim 15. Kouznetsov teaches: wherein the selected at least one of the options is fully compatible with the source asset (Paragraphs 67-76; “ Since V3 and V4 can only be placed on Host2, the normalized compatibility scores are 1 for both those cases ” , where 1 corresponds to a compatibility score of 100 with the VM, corresponding to the source asset, and the options are candidate host assignments for the VMs. ). Shinde teaches: the options are potential shapes (Paragraph 71; “ In step 5424, the server can provide an option to re-assess the data in case the solution blueprint is not satisfactory. For example, the server can include a function to reprocess the request to generate one or more new recommendations for the target cloud architectures. ”, where the generation of one or more new recommendations for target cloud architectures based on the solution blueprint corresponds to identifying a plurality of potential shapes). Regarding claim 17, Shinde in view of Liu, further in view of Moghe , further in view of Kouznetsov teach the method of claim 15. Kouznetsov teaches: wherein the selected at least one of the options is at least partially incompatible with the source asset (Paragraphs 67-76; “ Normalized score for V(n)−Host(n)=compatibility score of V(n)−Host(n)/sum of scores of V(n)−Host( i =1 to h). For example, the normalized score for V1−Host1=100/(100+0+100)=0.5. ”, where the source asset corresponds to the VMs. The disclosure provides examples of the possible options being fully compatible or partially compatible based on a calculated normalized compatibility score . The VMs correspond to the source asset and the options are candidate host assignments for the VMs. ). Shinde teaches: the options may be potential shapes (Paragraph 71; “ In step 5424, the server can provide an option to re-assess the data in case the solution blueprint is not satisfactory. For example, the server can include a function to reprocess the request to generate one or more new recommendations for the target cloud architectures. ”, where the generation of one or more new recommendations for target cloud architectures based on the solution blueprint corresponds to identifying a plurality of potential shapes). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sutton et al. ( US 9778952 B1 ) discusses migration of VM images through a user interface. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KENNETH P TRAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-6926 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-TH 4:30 a.m. - 12:30 p.m. PT, F 4:30 a.m. - 8:30 a.m. PT, or at Kenneth.Tran@uspto.gov . 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