# US 2021/0279524 A1 · Training model creation system and training model creation method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279524 A1 - Document ID: US20210279524A1 - Status: Published application - Kind: A1 - Application number: 17/015,585 - Filed: 2020-09-09 - Published: 2021-09-09 - Earliest claimed: 2020-03-04 - Assignees: Hitachi, Ltd. - Applicants: Hitachi, Ltd. - Inventors: Shimei KO; Kazuaki TOKUNAGA; Toshiyuki UKAI - Web page: https://patentgrep.com/patent/US20210279524A1 ## CPC - G06N 3/084: Backpropagation, e.g. using gradient descent - G06F 18/213: Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/253: {of extracted features} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/091: Active learning - G06N 3/096: Transfer learning - G06N 3/098: Distributed learning, e.g. federated learning - G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A training model creation system includes a first server (a mother server 100) that diagnoses a state of an inspection target in a first base (a mother base) using a first model (a mother model) of a neural network and a plurality of second servers (child servers 200) that diagnose a state of an inspection target in each base of the plurality of second bases using a second model (a child model) of the neural network. In the training model creation system, the first server receives feature values of the trained second model from the respective plurality of second servers, merges a received plurality of feature values of the second model and a feature value of the trained first model, and reconstructs and trains the first model based on a merged feature value. ## Claims ### Claim 1 A training model creation system that inspects, with a neural network, a process carried out in a plurality of bases including a first base and a plurality of second bases, the training model creation system comprising: a first server that diagnoses a state of an inspection target in the first base using a first model of the neural network; and a plurality of second servers that diagnose a state of an inspection target in each base of the plurality of second bases using a second model of the neural network, wherein the first server receives feature values of the trained second model from the respective plurality of second servers, merges a received plurality of feature values of the second model and a feature value of the trained first model, and reconstructs and trains the first model based on a merged feature value. ### Claim 2 (depends on 1) The training model creation system according to claim 1, wherein the feature values of the first and the second models are represented by, in a tier structure of the models, combinations of weights of tiers representing characteristics of bases or processes in which the models are operated. ### Claim 3 (depends on 1) The training model creation system according to claim 1, wherein after constructing and training an initial model, the first server shares the trained initial model with the plurality of second servers, and after capturing characteristics of the own bases and constructing and training the second model based on the initial model shared from the first server, the second servers extract the feature values from the trained second model and transmit the feature values to the first server. ### Claim 4 (depends on 1) The training model creation system according to claim 1, wherein the first server shares, with the plurality of second servers, a third model, which is a trained model of the reconstructed first model, and the first server and the plurality of second servers apply the common third model to the neural network for diagnosing an inspection target of the own bases. ### Claim 5 (depends on 4) The training model creation system according to claim 4, wherein the first server applies the third model to the neural network for diagnosing an inspection target of the first base and, when accuracy of a reasoning result by the third model after the application satisfies a predetermined accuracy standard, shares the third model with the plurality of second servers, and the second servers apply the third model to the neural network for diagnosing an inspection target of the second bases. ### Claim 6 (depends on 4) The training model creation system according to claim 4, wherein the first server shares the third model with the plurality of second servers, the second servers apply the third model to the neural network for diagnosing an inspection target of the second bases, and when accuracy of a reasoning result by the third model after the application satisfies a predetermined accuracy standard in the second servers, the first server applies the third model to the neural network for diagnosing an inspection target of the first base. ### Claim 7 (depends on 3) The training model creation system according to claim 3, wherein the second servers transmit sample data obtained by extracting characteristic information of the own bases from inspection data collected in the own bases to the first server together with the feature values extracted from the trained second model, and the first server reconstructs and trains the first model based on the received sample data and a feature value obtained by merging the received plurality of feature values and the feature value of the trained first model. ### Claim 8 (depends on 1) The training model creation system according to claim 1, wherein respective factories or respective lines provided in the factories are units of the first base and the plurality of second bases. ### Claim 9 A training model creation method by a system that inspects, with a neural network, a process carried out in a plurality of bases including a first base and a plurality of second bases, the system including: a first server that diagnoses a state of an inspection target in the first base using a first model of the neural network; and a plurality of second servers that diagnose a state of an inspection target in each base of the plurality of second bases using a second model of the neural network, the training model creation method comprising: a feature value receiving step in which the first server receives feature values of the trained second model from the respective plurality of second servers; a feature value merging step in which the first server merges a plurality of feature values of the second model received in the feature value receiving step and a feature value of the trained first model; and a common model creating step in which the first server reconstructs and trains the first model based on the feature value merged in the feature value merging step. ### Claim 10 (depends on 9) The training model creating method according to claim 9, wherein the feature values of the first and the second models are represented by, in a tier structure of the models, combinations of weights of tiers representing characteristics of bases or processes in which the models are operated. ### Claim 11 (depends on 9) The training model creating method according to claim 9, further comprising, before the feature value receiving step; an initial model sharing step in which, after constructing and training an initial model, the first server shares the trained initial model with the plurality of second servers; and a feature value transmitting step in which, after capturing characteristics of the own bases and constructing and training the second model based on the initial model shared in the initial model sharing step, the second servers extract the feature values from the trained second model and transmit the feature values to the first server. ### Claim 12 (depends on 9) The training model creating method according to claim 9, further comprising, after the common model creating step: a common model sharing step in which the first server shares, with the plurality of second servers, a third model, which is a trained model of the first model reconstructed in the common model creating step; and a common model operation step in which the first server and the plurality of second servers apply the common third model to the neural network for diagnosing an inspection target of the own bases. ### Claim 13 (depends on 11) The training model creating method according to claim 11, wherein in the feature value transmitting step, the second servers transmit sample data obtained by extracting characteristic information of the own bases from inspection data collected in the own bases to the first server together with the feature values extracted from the trained second model, and in the common model creating step, the first server reconstructs and trains the first model based on the received sample data and a feature value merged in the feature value merging step.