# US 12,181,965 B2 · Data center monitoring and management operation including data center alert prioritization by shaping rewards > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,965 B2 - Document ID: US12181965B2 - Status: Granted - Kind: B2 - Application number: 18/100,855 - Filed: 2023-01-24 - Published: 2024-07-25 - Granted: 2024-12-31 - Earliest claimed: 2023-01-24 - Assignees: Dell Products L.P. - Applicants: Dell Products L.P. - Inventors: Raja Neogi; Khayam Anjam - Web page: https://patentgrep.com/patent/US12181965B2 ## CPC - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)} - G06F 11/0709: {in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems} - G06F 11/0781: {Error filtering or prioritizing based on a policy defined by the user or on a policy defined by a hardware/software module, e.g. according to a severity level} - G06F 11/0784: {Routing of error reports, e.g. with a specific transmission path or data flow} - G06F 11/3055: {Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available (error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction of the data by redundancy in operations G06F11/14; error detection or correction by redundancy in hardware G06F11/16)} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data; assigning the data center data to a vectorized input space; reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension; decoding the latent space to provide a vectorized decoded output space; performing a data center asset operational status forecasting operation using the vectorized decoded output space. ## Claims ### Claim 1 A computer-implementable method for performing a data center management and monitoring operation, comprising: receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data; assigning the data center data to a vectorized input space; reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension; decoding the latent space to provide a vectorized decoded output space; performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data; performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and, prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations; the reward shaping operation is used to train a policy; and, the policy is trained using one of semi supervised learning and unsupervised learning. ### Claim 2 (depends on 1) The method of claim 1, wherein: the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations. ### Claim 3 (depends on 2) The method of claim 2, wherein: the reward shaping operation determines a value of a reward; the reward includes at least one of a service factor reward and a criticality reward. ### Claim 4 (depends on 1) The method of claim 1, wherein: the policy training is adaptive. ### Claim 5 A system comprising: a processor; a data bus coupled to the processor; a data center asset client module; and, a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data; assigning the data center data to a vectorized input space; reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension; decoding the latent space to provide a vectorized decoded output space; performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data; performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and, prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations; the reward shaping operation is used to train a policy; and, the policy is trained using one of semi supervised learning and unsupervised learning. ### Claim 6 (depends on 5) The system of claim 5, wherein: the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations. ### Claim 7 (depends on 6) The system of claim 6, wherein: the reward shaping operation determines a value of a reward; the reward includes at least one of a service factor reward and a criticality reward. ### Claim 8 (depends on 5) The system of claim 5, wherein: the policy training is adaptive. ### Claim 9 A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data; assigning the data center data to a vectorized input space; reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension; decoding the latent space to provide a vectorized decoded output space; performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data; performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and, prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations; the reward shaping operation is used to train a policy; and, the policy is trained using one of semi supervised learning and unsupervised learning. ### Claim 10 (depends on 9) The non-transitory, computer-readable storage medium of claim 9, wherein: the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations. ### Claim 11 (depends on 10) The non-transitory, computer-readable storage medium of claim 10, wherein: the reward shaping operation determines a value of a reward; the reward includes at least one of a service factor reward and a criticality reward. ### Claim 12 (depends on 9) The non-transitory, computer-readable storage medium of claim 9, wherein: the policy training is adaptive. ### Claim 13 (depends on 9) The non-transitory, computer-readable storage medium of claim 9, wherein: the computer executable instructions are deployable to a client system from a server system at a remote location. ### Claim 14 (depends on 9) The non-transitory, computer-readable storage medium of claim 9, wherein: the computer executable instructions are provided by a service provider to a user on an on-demand basis.