Details
Claims 1-7, 10-11 and 14-23 are pending.
Claims 1-7, 10-11 and 14-23 are rejected.
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.
Claims 1-4, 7, 10-11, 14-16 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over HU al (Pub. No.: US 2017/0142177 A1) in view of Richter et al. (Patent No.: US 10,038,758 B1).
As per claim 1, HU discloses a content delivery network (CDN) node allocation method, comprising: - acquiring a first physical scene corresponding to a first network request (HU, Fig 1 S3, , paragraph 0033, wherein “the dispatch center receives a request from the user in the partition accessing the video and determines the priority of the user”; wherein the priority of the user can be the first physical scene corresponding to a first network request as claimed); - determining, in accordance with the first physical scene and a pre-established node quality score table (HU, Fig 1 S2, , paragraph 0032, wherein “the dispatch center establishes a mapping model between a user's priority and the service quality level”; wherein the mapping is pre-stablished since the establishing process if performed before receiving the request), first quality scores of a plurality of types of CDN nodes corresponding to the first physical scene, (HU, Fig 1 S1, , paragraph 0031, wherein “the dispatch center determines a service quality level of all edge nodes serving for a partition with respect to a video according to historical data of a blockage ratio and a slow speed ratio for a user in a partition accessing the video”; wherein the service quality level of all edge nodes serving for a partition with respect to a video according to historical data of a blockage ratio and a slow speed ratio for a user can be the first quality scores of a plurality of types of CDN nodes corresponding to the first physical scene as claimed);- evaluating a quality of the plurality of types of CDN nodes in accordance with the first quality scores of the plurality of types of CDN nodes to obtain a quality evaluation result (HU, Fig 2 S11, , paragraph 0043, wherein “the dispatching center retrieves the historical data having a blockage ratio and slow speed ratio for the user in the partition accessing the video through intelligent terminal and assigns corresponding weights to the historical data of blockage ratio and the historical data of the slow speed ratio. Then a weighted summing is conducted on these data to generate a service quality evaluation value”); and - selecting, in accordance with the numbers of nodes respectively corresponding to the plurality of types of candidate CDN nodes in a candidate CDN node set and the quality evaluation result, a CDN node of a first type from the candidate CDN node set, wherein the candidate CDN node of the first type has the highest quality (HU, Fig 1-2, paragraph 0044-0045, wherein “the dispatching center determines the service quality level of the edge node according to the service quality evaluation value, wherein the service quality evaluation value is inversely proportional to the level of the service quality. The dispatching center in the above-described embodiment determines level of quality service of the edge node providing video service by a weighted summing of historical data of a blockage ratio and slow speed ratio for the user accessing the video and a comparison. Since the dispatching center in the present embodiment determines whether the service quality of edge node is good or bad directly from the data information of user experience, the obtained evaluation of the service quality of the edge node is more reliable”; paragraph 0034, wherein “the dispatch center dispatches an edge node having a corresponding service quality level for the user based on the determined priority of the user and the mapping model”; paragraph 0055, wherein “As there may exist a plurality of edge nodes serving for a certain partition in a historical data, and the service quality provided by different edge nodes may also be close (i.e., the weighted sum of the blockage ratio and slow speed ratio of a served video is close, thus falling within the same threshold range), different edge nodes will be classified into to the same level of service quality. Under this scenario, when a user accesses a video, the dispatching center selects a edge node closer to the user”).HU does not explicitly disclose wherein the pre-established node quality score table comprises a mapping relation between the first physical scene and quality scores of the plurality of types of CDN nodes and the plurality of types of CDN nodes comprise CDN nodes of a plurality of different CDN providers, wherein the first network request is a video playback request, and the first physical scene corresponding to the first network request comprises one or more of video popularity, a video bitrate, and a startup type. However, Richter discloses wherein the pre-established node quality score table comprises a mapping relation between the first physical scene and quality scores of the plurality of types of CDN nodes (Richter, Fig 4, col 6 lines: 21-38, wherein “Media server 120 can analyze CDN performance data (310) and determine the CDN balancing ratios (315) that can be used to balance traffic between different CDNs. For example, media server 120 can receive data providing information regarding the performance of the CDNs from viewer devices and/or the CDNs themselves. FIG. 4 is a flowchart illustrating determining CDN balancing ratios for CDNs for media content playback. In FIG. 4, scores for each CDN can be determined for a variety of different metrics regarding the performances of the CDNs. CDN balancer 210 of FIG. 2 can determine scores for rebuffer rates (405), fatal error rates (410), average, median, etc. of bitrates corresponding to the fragments requested by viewer devices (415), scores for number of connections (i.e., number of viewing devices requesting fragments from the CDN) to CDNs (420), and scores corresponding to capacities of the CDNs (425). CDN balancing ratios can be generated based on the scores (315, 430)”) and the plurality of types of CDN nodes comprise CDN nodes of a plurality of different CDN providers (Richter, Fig 2, col 1 line: 61 – col 2 line: 9, wherein “This disclosure describes techniques for implementing content delivery network (CDN) balancing that can allow for improved playback of media content on viewer devices. For example, a video streaming service might employ several different CDNs to provide fragments of media content for playback on viewer devices. A CDN balancer can be used to analyze performance characteristics of the available CDNs to generate weights or ratios that can be used to distribute or balance the traffic (i.e., viewer devices requesting fragments) among the CDNs. The weights can be used to provide manifest files such that viewer devices request the fragments of the media content in accordance with the generated weights so that the traffic is better distributed among the CDNs, resulting in more reliable and higher quality playback of the media content”), wherein the first network request is a video playback request, and the first physical scene corresponding to the first network request comprises one or more of video popularity, a video bitrate, and a startup type (Richter, col 2 lines: 28-33, wherein “For example, viewer device 105 might request playback of media content (e.g., episode #1 of the television show Transparent) by providing a request to media server 120 for a manifest file indicating fragments, or segments, of the playback of the media content available at different quality levels based on bitrates and/or resolutions”; Richter, Fig 4, col 6 lines: 21-38, wherein “FIG. 4 is a flowchart illustrating determining CDN balancing ratios for CDNs for media content playback. In FIG. 4, scores for each CDN can be determined for a variety of different metrics regarding the performances of the CDNs. CDN balancer 210 of FIG. 2 can determine scores for rebuffer rates (405), fatal error rates (410), average, median, etc. of bitrates corresponding to the fragments requested by viewer devices (415), scores for number of connections (i.e., number of viewing devices requesting fragments from the CDN) to CDNs (420), and scores corresponding to capacities of the CDNs (425).).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate Richter teachings into HU to achieve the claimed limitations because this would have provided a way to improve the user experience and/or system performance by selecting the best CDN that can provide reliable and/or higher quality playback of the media content which allow for improved playback of media content on viewer devices (see Richter background and col 1 line: 61 – col 2 line: 9).
As per claim 2, claim 1 is incorporated and HU further discloses acquiring a plurality of pieces of historical network data, the physical scene corresponding to a single piece of the historical network data, and the type of the CDN node corresponding to the single piece of the historical network data; determining, in accordance with the single piece of the historical network data, the quality score of the CDN node of the type corresponding to the single piece of the historical network data; and establishing a mapping relation between the physical scene corresponding to the single piece of the historical network data and the quality score of the CDN node of the type corresponding to the historical network data to obtain the node quality score table (HU, paragraph 0087-0089, wherein “a historical data acquisition unit configured to retrieve the historical data of a blockage ratio and slow speed ratio in accessing a video through an intelligent terminal by a user in a partition. a service quality evaluation value calculation unit configured to assign corresponding weights to the historical data of blockage ratio and the historical data of the slow speed ratio obtained by the historical data acquisition unit for a weighted sum thereof to generate an service quality evaluation value; and a service quality level determination unit configured to determine the service quality level of the edge node according to the service quality evaluation value determined by the service quality evaluation value calculation unit”);
As per claim 3, claim 2 is incorporated and HU further discloses wherein the historical network data comprises historical video data; and the determining, in accordance with the single piece of the historical network data, the quality score of the CDN node of the type corresponding to the single piece of the historical network data comprises: acquiring, in accordance with the single piece of the historical video data, a first screen time and/or a lag duration and/or the number of lags of the single piece of the historical video data; and determining, in accordance with the first screen time and/or the lag duration and/or the number of lags, the quality score of the CDN node of the type corresponding to the historical video data (HU, paragraph 0087-0089, wherein “a historical data acquisition unit configured to retrieve the historical data of a blockage ratio and slow speed ratio in accessing a video through an intelligent terminal by a user in a partition. a service quality evaluation value calculation unit configured to assign corresponding weights to the historical data of blockage ratio and the historical data of the slow speed ratio obtained by the historical data acquisition unit for a weighted sum thereof to generate an service quality evaluation value; and a service quality level determination unit configured to determine the service quality level of the edge node according to the service quality evaluation value determined by the service quality evaluation value calculation unit”);
As per claim 4, claim 3 is incorporated and HU further discloses wherein the determining, in accordance with the first screen time, the lag duration, and the number of lags, the quality score of the CDN node of the type corresponding to the historical video data comprises: performing weighted average on the first screen time, the lag duration, and the number of lags to obtain the quality score of the CDN node of the type corresponding to the historical video data; or, processing, based on a pre-trained neural network score model, the first screen time, the lag duration, and the number of lags to obtain the quality score of the CDN node of the type corresponding to the historical video data (HU, paragraph 0087-0089, wherein “a historical data acquisition unit configured to retrieve the historical data of a blockage ratio and slow speed ratio in accessing a video through an intelligent terminal by a user in a partition. a service quality evaluation value calculation unit configured to assign corresponding weights to the historical data of blockage ratio and the historical data of the slow speed ratio obtained by the historical data acquisition unit for a weighted sum thereof to generate an service quality evaluation value; and a service quality level determination unit configured to determine the service quality level of the edge node according to the service quality evaluation value determined by the service quality evaluation value calculation unit”);
As per claim 7, claim 1 is incorporated and Richter further discloses acquiring requested data corresponding to the first network request through the CDN node of the first type, and returning the requested data to a terminal device which has transmitted the network request (Richter, col 7 lines: 62-64, wherein “As a result, the CDN can provide the requested fragments (340) and the viewer device can store the fragments in a buffer for playback (345)”);
Claims 10-11, 14-16 and 19-21 are rejected under the same rationale as claims 1-4 and 7.
Claims 5-6, 17-18 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over HU al (Pub. No.: US 2017/0142177 A1) in view of Richter et al. (Patent No.: US 10,038,758 B1) and KISA et al (Pub. No.: US 2022/0116316 A1).
As per claim 5, claim 1 is incorporated and HU and Richter do not explicitly disclose wherein the method further comprises: removing the selected candidate CDN node of the first type from the candidate CDN node set; and determining a current network request as the first network request, and returning to the step of acquiring the first physical scene corresponding to a first network request, until a total number of the candidate CDN nodes is 0. However, KISA further discloses wherein the method further comprises: removing the selected candidate CDN node of the first type from the candidate CDN node set; and determining a current network request as the first network request, and returning to the step of acquiring the first physical scene corresponding to a first network request, until a total number of the candidate CDN nodes is 0 (KISA, paragraph 0127, wherein “When generating ranking data as routing destination evaluation data, the routing destination evaluation unit 125 does not need to include all the edges in the ranking. In particular, the routing destination evaluation unit 125 may take the edges having the first to k-th scores into consideration for the ranking in accordance with the designated number k of edges, which is one of the parameters stored in the setting data storage unit 132, or may eliminate candidates that are not in an available state”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate KISA teachings into HU and Richter to achieve the claimed limitations because this would have provided a way to improve the user experience and/or system performance by dynamically evaluate candidates of a request routing destination to select the best CDN that can provide reliable and/or higher quality playback of the media content which allow for improved playback of media content on viewer devices (see KISA paragraph 0008-0009).
As per claim 6, claim 1 is incorporated and HU and Richter do not explicitly disclose determining, in accordance with overall quality of the plurality of types of CDN nodes and cost information of the plurality of types of CDN nodes, the numbers of nodes respectively corresponding to the plurality of types of candidate CDN nodes in the candidate CDN node set. However, KISA further discloses determining, in accordance with overall quality of the plurality of types of CDN nodes and cost information of the plurality of types of CDN nodes, the numbers of nodes respectively corresponding to the plurality of types of candidate CDN nodes in the candidate CDN node set (KISA, Fig 7, paragraph 0148, wherein “The metrics storage unit 232 may store, as metrics data, the routing destination candidates in association with multiple types of metrics values of the candidates and/or their scores at a given time point. FIG. 7 shows an example of RTT and cost metrics, as well as their scores, for each routing destination candidate (edge). Here, the metrics data is indicated in the form of a table, which is not a limitation. The metrics storage unit 232 stores a value and a score for each edge and for each item of the metrics”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate KISA teachings into HU and Richter to achieve the claimed limitations because this would have provided a way to improve the user experience and/or system performance by dynamically evaluate candidates of a request routing destination to select the best CDN that can provide reliable and/or higher quality playback of the media content which allow for improved playback of media content on viewer devices (see KISA paragraph 0008-0009).
Claims 17-18 and 22-23 are rejected under the same rationale as claims 5-6.
Response to Arguments
Applicant's arguments filed on 01/22/2026 have been fully considered but are now moot in light of the new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAMZA N ALGIBHAH whose telephone number is (571)270-7212. The examiner can normally be reached 7:30 am - 3:30 pm.
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/HAMZA N ALGIBHAH/Primary Examiner, Art Unit 2441