# US 2020/0401945 A1 · Data Analysis Device and Multi-Model Co-Decision-Making System and Method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401945 A1 - Document ID: US20200401945A1 - Status: Published application - Kind: A1 - Application number: 17/007,910 - Filed: 2020-08-31 - Published: 2020-12-24 - Earliest claimed: 2018-03-30 - Applicants: Huawei Technologies Co., Ltd. - Inventors: Yixu Xu; Zhongyao Wu; Yuanyuan Wang - Web page: https://patentgrep.com/patent/US20200401945A1 ## CPC - H04L 41/16: using machine learning or artificial intelligence - G06F 8/61: Installation - G06F 18/20: Analysing - G06F 18/213: Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06N 20/00: Machine learning - H04W 76/14: Direct-mode setup - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract This application provides a data analysis device and a multi-model co-decision-making system and method. The data analysis device trains a target service based on a combinatorial learning algorithm, to obtain a plurality of learning models, generates an installation message used to indicate to install the plurality of learning models, and sends the installation message to another data analysis device. The installation message is used to trigger installation of the plurality of learning models and predictions and policy matching that are based on the plurality of learning models. ## Claims ### Claim 1 A device, comprising: a communications interface, configured to establish a communication connection to another device; a processor; and a non-transitory computer-readable storage medium storing a program to be executed by the processor, the program including instructions for: training a target service based on a combinatorial learning algorithm, to obtain a plurality of learning models, wherein the plurality of learning models comprise a primary model and a secondary model; generate an installation message indicating to install the plurality of learning models, wherein the installation message comprises description information of the primary model, description information of the secondary model, and voting decision-making information of voting on prediction results of the plurality of learning models; and send the installation message to the another device through the communications interface. ### Claim 2 (depends on 1) The device according to claim 1, wherein: the voting decision-making information comprises averaging method voting decision-making information, and the averaging method voting decision-making information comprises simple averaging voting decision-making information or weighted averaging decision-making information; or the voting decision-making information comprises vote quantity—based decision-making information, and the vote quantity—based decision-making information comprises majority voting decision-making information, plurality voting decision-making information, or weighted voting decision-making information. ### Claim 3 (depends on 1) The device according to claim 1, wherein the program further includes instructions for: obtaining statistical information through the communications interface, wherein the statistical information comprises an identifier of the primary model, a total quantity of prediction times of model predictions performed by each learning model within a preset time interval, a prediction result of each prediction performed by each learning model, and a final prediction result of each prediction; and optimizing a model of the target service based on the statistical information. ### Claim 4 (depends on 3) The device according to claim 3, wherein the installation message further comprises: first periodicity information, wherein the first periodicity information indicates a feedback periodicity for the another device to feed back the statistical information, and the preset time interval is the same as the feedback periodicity. ### Claim 5 (depends on 1) The device according to claim 1, wherein the program further includes instructions for: sending a request message to the another device through the communications interface, wherein the request message requests to subscribe to a feature vector, the request message comprises second periodicity information, and the second periodicity information indicates a collection periodicity for collecting data related to the feature vector. ### Claim 6 (depends on 1) The device according to claim 1, wherein the device is a radio access network data analysis network element or a network data analysis network element. ### Claim 7 A device, comprising: a communications interface, configured to establish a communication connection to another device; a processor; and a non-transitory computer-readable storage medium storing a program to be executed by the processor, the program including instructions for: receiving an installation message from the another device through the communications interface; installing a plurality of learning models based on the installation message, wherein the plurality of learning models comprises a primary model and a secondary model, and the installation message comprises description information of the primary model, description information of the secondary model, and voting decision-making information of voting on prediction results of the plurality of learning models; performing predictions based on the plurality of learning models; and matching a policy with a target service based on a prediction result of each of the plurality of learning models and the voting decision-making information. ### Claim 8 (depends on 7) The device according to claim 7, wherein the program comprises instructions for: receiving, from the another device through the communications interface, a request message, wherein the request message requests to subscribe to a feature vector, and the request message comprises identification information of the primary model and identification information of the feature vector; parsing the installation message, installing the plurality of learning models based on the parsed installation message; using the feature vector as an input parameter of the plurality of learning models; obtaining a prediction result after a prediction is performed by each of the plurality of learning models; determining a final prediction result based on the prediction result of each learning model and the voting decision-making information; and matching, based on the final prediction result, the policy with the target service corresponding to the identification information of the primary model. ### Claim 9 (depends on 8) The device according to claim 8, wherein the request message further comprises second periodicity information, and the second periodicity information indicates indicate a collection periodicity for collecting data related to the feature vector; and wherein the program includes instructions for collecting data from a network device based on the collection periodicity. ### Claim 10 (depends on 7) The device according to claim 7, wherein the program further includes instructions for: feeding back statistical information to the another device through the communications interface, wherein the statistical information comprises an identifier of the primary model, a total quantity of prediction times of model predictions performed by each learning model within a preset time interval, a prediction result of each prediction performed by each learning model, and a final prediction result of each prediction. ### Claim 11 (depends on 10) The according to claim 10, wherein the installation message further comprises first periodicity information, the first periodicity information indicates a feedback periodicity for feeding back the statistical information, and the preset time interval is the same as the feedback periodicity; and wherein the program includes instructions for feeding back the statistical information to the another device based on the feedback periodicity. ### Claim 12 (depends on 7) The device according to claim 7, wherein the device is a radio access network element. ### Claim 13 (depends on 7) The device according to claim 7, wherein the device is a network element. ### Claim 14 A method, comprises: training, by a device, a target service based on a combinatorial learning algorithm, to obtain a plurality of learning models; generating an installation message indicating to install the plurality of learning models; and sending the installation message to another device, wherein the installation message triggers installation of the plurality of learning models, and triggers predictions and policy matching that are based on the plurality of learning models, and wherein the plurality of learning models comprise a primary model and a secondary model, the installation message comprises description information of the primary model, description information of the secondary model, and voting decision-making information of voting on prediction results of the plurality of learning models. ### Claim 15 (depends on 14) The method according to claim 14, wherein: the voting decision-making information comprises averaging method voting decision-making information, and the averaging method voting decision-making information comprises simple averaging voting decision-making information. ### Claim 16 (depends on 14) The method according to claim 14, wherein: the voting decision-making information comprises averaging method voting decision-making information, and the averaging method voting decision-making information comprises weighted averaging decision-making information. ### Claim 17 (depends on 14) The method according to claim 14, wherein: the voting decision-making information comprises vote quantity—based decision-making information; and the vote quantity—based decision-making information comprises majority voting decision-making information, plurality voting decision-making information, or weighted voting decision-making information. ### Claim 18 (depends on 14) The method according to claim 14, further comprising: obtaining, by the device, statistical information; and optimizing, by the device, a model of the target service based on the statistical information, wherein the statistical information comprises an identifier of the primary model, a total quantity of prediction times of predictions performed by each learning model within a preset time interval, a prediction result of each prediction performed by each learning model, and a final prediction result of each prediction. ### Claim 19 (depends on 18) The method according to claim 18, wherein the installation message further comprises first periodicity information, the first periodicity information indicates a feedback periodicity for the another device to feed back the statistical information, and the preset time interval is the same as the feedback periodicity. ### Claim 20 (depends on 14) The method according to claim 14, further comprising: sending, by the device, a request message to the another device, wherein the request message requests to subscribe to a feature vector, the request message comprises second periodicity information, and the second periodicity information indicates a collection periodicity for collecting data related to the feature vector.