# US 11,122,329 B2 · Predicting multimedia session MOS > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,122,329 B2 - Document ID: US11122329B2 - Status: Granted - Kind: B2 - Application number: 16/078,201 - Filed: 2017-02-22 - Published: 2019-02-14 - Granted: 2021-09-14 - Earliest claimed: 2016-02-25 - Assignees: Telefonaktiebolaget LM Ericsson (publ) - Applicants: Telefonaktiebolaget LM Ericsson (publ) - Inventors: Jing Fu; Junaid Shaikh; Tomas Lundberg; Gunnar Heikkilä - Web page: https://patentgrep.com/patent/US11122329B2 ## CPC - H04N 21/44209: {Monitoring of downstream path of the transmission network originating from a server, e.g. bandwidth variations of a wireless network (arrangements for maintenance or administration in data switching networks involving bandwidth and capacity management H04L41/0896)} - H04L 65/80: Responding to QoS - H04N 21/2402: {Monitoring of the downstream path of the transmission network, e.g. bandwidth available (traffic monitoring in data switching networks H04L43/00; monitoring data switching networks utilization H04L43/0876)} - H04N 21/4392: {involving audio buffer management} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique. ## Claims ### Claim 1 A method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS, wherein the multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality, the method comprising: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique. ### Claim 2 (depends on 1) The method according to claim 1, wherein the video features comprise a feature being a first percentile of the per unit time scores of the video quality. ### Claim 3 (depends on 1) The method according to claim 1, wherein the video features comprise a feature being a fifth percentile of the per unit time scores of the video quality. ### Claim 4 (depends on 1) The method according to claim 1, wherein the video features comprise a feature being a fifteenth percentile of the per unit time scores of the video quality. ### Claim 5 (depends on 1) The method according to claim 1, wherein the step of estimating is based on a random forest based model. ### Claim 6 (depends on 1) The method according to claim 1, wherein the buffering features comprise a feature being total buffering time. ### Claim 7 (depends on 1) The method according to claim 1, wherein the buffering features comprise a feature being number of rebuffering events. ### Claim 8 (depends on 1) The method according to claim 1, wherein the buffering features comprise a feature being percentage of buffering time divided by video time. ### Claim 9 (depends on 1) The method according to claim 1, wherein the buffering features comprise a feature being number of rebuffering events per video length. ### Claim 10 (depends on 1) The method according to claim 1, wherein the buffering features comprise a feature being last seen rebuffering from the end of the video. ### Claim 11 A MOS, Mean Opinion Score, estimator for predicting a multimedia session MOS, wherein the multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality and an initial buffering event and rebuffering events in the video and wherein audio quality is represented by a list of per time unit scores of audio quality, the MOS estimator comprising processing means and a memory comprising instructions which, when executed by the processing means, causes the MOS estimator to: generate video features from the input list of per time unit scores of the video quality; generate audio features from the input list of per time unit scores of the audio quality; generate buffering features from the initial buffering event and rebuffering events in the video; and estimate a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique. ### Claim 12 (depends on 11) The MOS estimator according to claim 11, wherein the video features comprise a feature being a first percentile of the per unit time scores of the video quality. ### Claim 13 (depends on 11) The MOS estimator according to claim 11, wherein the video features comprise a feature being a fifth percentile of the per unit time scores of the video quality. ### Claim 14 (depends on 11) The MOS estimator according to claim 11, wherein the video features comprise a feature being a fifteenth percentile of the per unit time scores of the video quality. ### Claim 15 (depends on 11) The MOS estimator according to claim 11, wherein the instructions to estimate comprise instructions which, when executed by the processing means, causes the MOS estimator to estimate using a random forest based model. ### Claim 16 (depends on 11) The MOS estimator according to claim 11, wherein the buffering features comprise a feature being total buffering time. ### Claim 17 (depends on 11) The MOS estimator according to claim 11, wherein the buffering features comprise a feature being number of rebuffering events. ### Claim 18 (depends on 11) The MOS estimator according to claim 11, wherein the buffering features comprise a feature being percentage of buffering time divided by video time. ### Claim 19 (depends on 11) The MOS estimator according to claim 11, wherein the buffering features comprise a feature being number of rebuffering events per video length. ### Claim 20 (depends on 11) The MOS estimator according to claim 11, wherein the buffering features comprise a feature being last seen rebuffering from the end of the video. ### Claim 21 A non-transitory computer-readable storage medium comprising a computer program product including instructions to cause at least one processor to: generate video features from the input list of per time unit scores of the video quality; generate audio features from the input list of per time unit scores of the audio quality; generate buffering features from the initial buffering event and rebuffering events in the video; and estimate a multimedia session MOS, Mean Opinion Score, from the generated video features, generated audio features and generated buffering features by using machine learning technique.