# US 12,184,467 B2 · Artificial intelligent enhanced data sampling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,184,467 B2 - Document ID: US12184467B2 - Status: Granted - Kind: B2 - Application number: 18/351,244 - Filed: 2023-07-12 - Published: 2023-11-02 - Granted: 2024-12-31 - Earliest claimed: 2019-02-27 - Assignees: Huawei Technologies Co., Ltd. - Applicants: Huawei Technologies Co., Ltd. - Inventors: Ming Li; Katherine Zhao - Web page: https://patentgrep.com/patent/US12184467B2 ## CPC - H04L 27/2663: {Coarse synchronisation, e.g. by correlation} - H04L 41/16: using machine learning or artificial intelligence - H04L 27/2665: {Fine synchronisation, e.g. by positioning the FFT window} - H04L 43/024: by adaptive sampling - H04L 43/0864: Round trip delays - H04L 49/205: {Quality of Service based} - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - H04L 43/022: by sampling - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Monitoring an operational characteristic of a data communication device within a network includes sampling an operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval to produce fine-grain samples of the operational characteristic of the data communication device, training a machine learning algorithm using the fine-grain samples of the operational characteristic of the data communication device, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate, sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval to produce coarse-grain samples of the operational characteristic of the data communication device, and using the machine learning algorithm to process the coarse-grain samples of the operational characteristic of the data communication device to produce accuracy-enhanced samples of the operational characteristic of the data communication device. ## Claims ### Claim 1 A method for monitoring an operational characteristic of a data communication device within a network, the method comprising: obtaining first fine-grain samples of the operational characteristic of the data communication device, wherein the first fine-grain samples are produced by sampling the operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval; training a machine learning algorithm using the first fine-grain samples, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate; obtaining coarse-grain samples of the operational characteristic of the data communication device, wherein the coarse-grain samples are produced by sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval; and using the machine learning algorithm to process the coarse-grain samples to produce accuracy-enhanced samples of the operational characteristic of the data communication device. ### Claim 2 (depends on 1) The method of claim 1, wherein the operational characteristic of the data communication device includes a round-trip communication interval between a data collection communication device and the data communication device. ### Claim 3 (depends on 1) The method of claim 1, wherein the operational characteristic of the data communication device includes an average free buffer level of the data communication device. ### Claim 4 (depends on 1) The method of claim 1, wherein the operational characteristic of the data communication device includes at least one health related parameter. ### Claim 5 (depends on 1) The method of claim 1, wherein the operational characteristic of the data communication device includes at least one network state parameter. ### Claim 6 (depends on 1) The method of claim 1, wherein the operational characteristic of the data communication device includes at least one network latency parameter. ### Claim 7 (depends on 1) The method of claim 1, further comprising: obtaining second fine-grain samples of the operational characteristic of the data communication device, wherein the second fine-grain samples are produced by sampling the operational characteristic of the data communication device at the fine-grain sample rate over a third sampling interval; and retraining the machine learning algorithm using the second fine-grain samples of the operational characteristic of the data communication device. ### Claim 8 A communication device comprising: a processing circuitry; a memory; and a communications circuitry coupled to the processing circuitry and the memory, the communications circuitry configured to: obtain first fine-grain samples of an operational characteristic of a data communication device, wherein the first fine-grain samples are produced by sampling the operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval; train a machine learning algorithm using the first fine-grain samples, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate; obtain coarse-grain samples of the operational characteristic of the data communication device, wherein the coarse-grain samples are produced by sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval; and use the machine learning algorithm to process the coarse-grain samples to produce accuracy-enhanced samples of the operational characteristic of the data communication device. ### Claim 9 (depends on 8) The communication device of claim 8, wherein the operational characteristic of the data communication device includes a round-trip communication interval between a data collection communication device and the data communication device. ### Claim 10 (depends on 8) The communication device of claim 8, wherein the operational characteristic of the data communication device includes an average free buffer level of the data communication device. ### Claim 11 (depends on 8) The communication device of claim 8, wherein the operational characteristic of the data communication device includes at least one health related parameter. ### Claim 12 (depends on 8) The communication device of claim 8, wherein the operational characteristic of the data communication device includes at least one network state parameter. ### Claim 13 (depends on 8) The communication device of claim 8, wherein the operational characteristic of the data communication device includes at least one network latency parameter. ### Claim 14 (depends on 8) The communication device of claim 8, wherein the communications circuitry is further configured to: obtain second fine-grain samples of the operational characteristic of the data communication device, wherein the second fine-grain samples are produced by sampling the operational characteristic of the data communication device at the fine-grain sample rate over a third sampling interval; and retrain the machine learning algorithm using the second fine-grain samples of the operational characteristic of the data communication device. ### Claim 15 A computer program product comprising computer-executable instructions that are stored on a non-transitory computer storage medium and that, when executed by one or more processors, cause a communication device to: obtain first fine-grain samples of an operational characteristic of a data communication device, wherein the first fine-grain samples are produced by sampling the operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval; train a machine learning algorithm using the first fine-grain samples, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate; obtain coarse-grain samples of the operational characteristic of the data communication device, wherein the coarse-grain samples are produced by sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval; and use the machine learning algorithm to process the coarse-grain samples to produce accuracy-enhanced samples of the operational characteristic of the data communication device. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the operational characteristic of the data communication device includes at least one of: a round-trip communication interval between a data collection communication device and the data communication device; or an average free buffer level of the data communication device. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the operational characteristic of the data communication device includes at least one health related parameter. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the operational characteristic of the data communication device includes at least one network state parameter. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the operational characteristic of the data communication device includes at least one network latency parameter. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the computer-executable instructions, when executed by the one or more processors, cause the communication device to: obtain second fine-grain samples of the operational characteristic of the data communication device, wherein the second fine-grain samples are produced by sampling the operational characteristic of the data communication device at the fine-grain sample rate over a third sampling interval; and retrain the machine learning algorithm using the second fine-grain samples of the operational characteristic of the data communication device.