# US 2022/0036152 A1 · Electronic apparatus and method for controlling thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0036152 A1 - Document ID: US20220036152A1 - Status: Published application - Kind: A1 - Application number: 17/505,259 - Filed: 2021-10-19 - Published: 2022-02-03 - Earliest claimed: 2020-05-08 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Jinsu YEO; Youngyoon Lee; Youngcheon You; Jaechool Lee - Web page: https://patentgrep.com/patent/US20220036152A1 ## CPC - G06N 3/04: Architecture, e.g. interconnection topology - G06F 11/3409: {for performance assessment} - H03M 7/702: {Software} - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/08: Learning methods - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/09: Supervised learning - G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn - H03M 7/3059: {Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression} - H03M 7/70: {Type of the data to be coded, other than image and sound} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) ## Abstract A method for controlling an electronic apparatus is provided. The method for controlling an electronic apparatus includes the steps of selecting a generic-purpose artificial intelligence model, generating a compressed artificial intelligence model based on the selected generic-purpose artificial intelligence model, and generating a dedicated artificial intelligence model based on the generated compressed artificial intelligence model, and the step of generating a compressed artificial intelligence model includes the steps of acquiring a rank of a singular value decomposition (SVD) algorithm based on a compression rate, compressing and training the selected generic-purpose artificial intelligence model based on the acquired rank and converting the model into the compressed artificial intelligence model, determining the performance of the converted compressed artificial intelligence model based on a predetermined first threshold value, and based on the performance of the converted compressed artificial intelligence model being lower than the predetermined first threshold value, generating the dedicated artificial intelligence model. ## Claims ### Claim 1 A method for controlling an electronic apparatus, the method comprising: selecting a generic-purpose artificial intelligence model; generating a compressed artificial intelligence model based on the selected generic-purpose artificial intelligence model; and generating a dedicated artificial intelligence model based on the generated compressed artificial intelligence model, wherein the generating a compressed artificial intelligence model comprises: acquiring a rank of a singular value decomposition (SVD) algorithm based on a compression rate, compressing and training the selected generic-purpose artificial intelligence model based on the acquired rank and converting the model into the compressed artificial intelligence model, determining the performance of the converted compressed artificial intelligence model based on a predetermined first threshold value, and based on the performance of the converted compressed artificial intelligence model being lower than the predetermined first threshold value, generating the dedicated artificial intelligence model. ### Claim 2 (depends on 1) The controlling method of claim 1, wherein the generating a compressed artificial intelligence model comprises: based on the performance of the converted compressed artificial intelligence model being higher than or equal to the predetermined first threshold value, repeating the generating a compressed artificial intelligence model. ### Claim 3 (depends on 1) The controlling method of claim 1, wherein the generating the dedicated artificial intelligence model comprises: retraining the compressed artificial intelligence model based on a predetermined learning data set. ### Claim 4 (depends on 3) The controlling method of claim 3, wherein the generating the dedicated artificial intelligence model comprises: determining the performance of the retrained compressed artificial intelligence model in relation to a predetermined second threshold value; and based on the performance of the retrained compressed artificial intelligence model being higher than or equal to the predetermined second threshold value, additionally compressing the retrained compressed artificial intelligence model and generating the dedicated artificial intelligence model. ### Claim 5 (depends on 3) The controlling method of claim 3, wherein the generating the dedicated artificial intelligence model comprises: based on feature information of first learning data that trained the selected generic-purpose artificial intelligence model, classifying the first learning data into a plurality of learning data sets, and reconstituting at least one data set among the plurality of learning data sets as the predetermined learning data set. ### Claim 6 (depends on 5) The controlling method of claim 5, wherein the predetermined learning data set comprises at least one data set compressed to correspond to a target data input as an input value among the plurality of learning data sets. ### Claim 7 (depends on 3) The controlling method of claim 3, wherein the generating a compressed artificial intelligence model comprises: determining the performance of the converted compressed artificial intelligence model based on test data, and the generating a dedicated artificial intelligence model comprises: classifying the test data into a plurality of learning data sets, and reconstituting at least one data set among the plurality of learning data sets as the predetermined learning data set. ### Claim 8 (depends on 7) The controlling method of claim 7, wherein the test data includes test data that output a result within a predetermined range based on a target value. ### Claim 9 (depends on 3) The controlling method of claim 3, wherein the generating the dedicated artificial intelligence model comprises: based on receiving data setting information from a user, identifying a learning data set corresponding to the data setting information from a first learning data that trained the selected generic-purpose artificial intelligence model, and reconstituting the identified learning data set as the predetermined learning data set. ### Claim 10 An electronic apparatus comprising: a memory storing first learning data and a generic-purpose artificial intelligence model trained by the first learning data; and a processor, wherein the processor is configured to: select the trained generic-purpose artificial intelligence model, acquire a rank of a singular value decomposition (SVD) algorithm based on a compression rate, compress and train the selected generic-purpose artificial intelligence model based on the acquired rank and convert the model into the compressed artificial intelligence model, determine the performance of the converted compressed artificial intelligence model based on a predetermined first threshold value, and based on the performance of the converted compressed artificial intelligence model being lower than the predetermined first threshold value, generate a dedicated artificial intelligence model. ### Claim 11 (depends on 10) The electronic apparatus of claim 10, wherein the processor is configured to: based on the performance of the converted compressed artificial intelligence model being higher than or equal to the predetermined first threshold value, repeat the converting into the compressed artificial intelligence model. ### Claim 12 (depends on 10) The electronic apparatus of claim 10, wherein the processor is further configured to: retrain the compressed artificial intelligence model based on a predetermined learning data set. ### Claim 13 (depends on 12) The electronic apparatus of claim 12, wherein the processor is further configured to: determine the performance of the retrained compressed artificial intelligence model in relation to a predetermined second threshold value; and based on the performance of the retrained compressed artificial intelligence model being higher than or equal to the predetermined second threshold value, additionally compress the retrained compressed artificial intelligence model and generate the dedicated artificial intelligence model. ### Claim 14 (depends on 12) The electronic apparatus of claim 12, wherein the processor is further configured to: based on feature information of the first learning data that trained the selected generic-purpose artificial intelligence model, classify the first learning data into a plurality of learning data sets, and reconstitute at least one data set among the plurality of learning data sets as the predetermined learning data set. ### Claim 15 (depends on 12) The electronic apparatus of claim 12, wherein the processor is further configured to: determine the performance of the compressed artificial intelligence model based on test data; classify the test data into a plurality of learning data sets; and reconstitute at least one data set among the plurality of learning data sets as the predetermined learning data set.