Patent application EP21179992A (EP), filed 2021-06-17
Publication number: EP3929829A1
Title: INFORMATION PROCESSING DEVICE, REGRESSION MODEL GENERATION METHOD, AND REGRESSION MODEL GENERATION PROGRAM PRODUCT
Abstract: [0001]    An information processing device: acquires data and prepares a data set with the acquired data; generates a regression model using the data set; calculates an optimum solution from the generated regression model; repetitively acquires the data based on the calculated optimum solution and updating the data set; and repetitively generates the regression model using the updated data set. The acquiring of data and the preparing of data set are executed based on a different criterion in response to a satisfaction of a predetermined condition, and the generating of regression model is executed using the data set prepared based on the different criterion.
IPC codes: G06N  10/    00            A I
G06N  20/    00            A I
G06N   5/    00            A I
Claims: number: 1
text: An information processing device comprising:
number: 2
text: The information processing device according to claim 1, wherein
the data acquisition unit (20) acquires the data in random manner in response to the satisfaction of the predetermined condition, and
the regression model generation unit (22) adds the data acquired in random manner to the data set and generates the regression model using the data set.
number: 3
text: The information processing device according to claim 1 or 2, wherein
the predetermined condition is satisfied when a repetition number of data acquisition reaches a predetermined count.
number: 4
text: The information processing device according to any one of claims 1 to 3, wherein
the predetermined condition is satisfied when a performance of the generated regression model reaches a predetermined level.
number: 5
text: The information processing device according to any one of claims 1 to 4, wherein
the predetermined condition is satisfied when the data acquired by the data acquisition unit (20) is already included in the data set.
number: 6
text: The information processing device according to any one of claims 1 to 5, wherein
the regression model generation unit (22) generates the regression model using a subset of the data set in response to the satisfaction of the predetermined condition.
number: 7
text: The information processing device according to any one of claims 1 to 6, wherein
the predetermined condition is changed according to a repetition number of data acquisition.
number: 8
text: A regression model generation method comprising:
number: 9
text: A program product stored in a computer readable non-transitory tangible storage medium, the program product comprising instructions to be executed by a computer, the instructions comprising:
Office action: search_report, dated 2021-12-29
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: - KOKI KITAIJIANG GUOSHENGHONG JUSHU TANAKAKOJI TSUDAJUNICHIRO SHIOMIRYO TAMURA: "PHYSICAL REVIEW RESEARCH", vol. 2, 2020, article "Designing metamaterials with quantum annealing and factorization machines"
ref_type: npl
severity: background
ref_id: [XDI]     - "PHYSICAL REVIEW RESEARCH", vol. 2, 16 March 2020, article KITAI KOKI ET AL: "Designing metamaterials with quantum annealing and factorization machines", XP055848128
ref_type: npl
severity: background
ref_id: - DE ATH GEORGE ET AL: "Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation", 28 November 2019 (2019-11-28), pages 1 - 37, XP055848007, Retrieved from the Internet <URL:https://arxiv.org/vc/arxiv/papers/1911/1911.12809v1.pdf> [retrieved on 20211005], DOI: 10.1145/3425501
ref_type: npl
severity: background
ref_id: - UENO TSUYOSHI ET AL: "COMBO: An efficient Bayesian optimization library for materials science", MATERIALS DISCOVERY, vol. 4, 1 June 2016 (2016-06-01), pages 18 - 21, XP055848035, ISSN: 2352-9245, DOI: 10.1016/j.md.2016.04.001
ref_type: npl
severity: background
ref_id: - KIM JUNGTAEK ET AL: "On Local Optimizers of Acquisition Functions in Bayesian Optimization", 16 June 2020 (2020-06-16), Cham, XP055848028, ISBN: 978-3-030-67660-5, Retrieved from the Internet <URL:https://arxiv.org/pdf/1901.08350.pdf> [retrieved on 20211005], DOI: 10.1007/978-3-030-67661-2_40
ref_type: npl
severity: background
ref_id: US2021295157A1
ref_type: patent
severity: background
ref_id: - Kitai Koki ET AL, "Designing metamaterials with quantum annealing and factorization machines", PHYSICAL REVIEW RESEARCH, (20200316), vol. 2, XP055848128 [XD] 1,2,5,8,9 * Section I, III.C * [I] 3,4,6,7
ref_type: npl
severity: novelty_destroying
ref_id: - De Ath George ET AL, "Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation", doi:10.1145/3425501, (20191128), pages 1 - 37, URL: https://arxiv.org/vc/arxiv/papers/1911/1911.12809v1.pdf, (20211005), XP055848007 [A] 3,4 * Sections 1-3 *
ref_type: npl
severity: background
ref_id: - UENO TSUYOSHI ET AL, "COMBO: An efficient Bayesian optimization library for materials science", MATERIALS DISCOVERY, (20160601), vol. 4, doi:10.1016/j.md.2016.04.001, ISSN 2352-9245, pages 18 - 21, XP055848035 [A] 1,8,9 * Section 1-2 *
ref_type: npl
severity: background
ref_id: - Kim Jungtaek ET AL, "On Local Optimizers of Acquisition Functions in Bayesian Optimization", Cham, doi:10.1007/978-3-030-67661-2_40, ISBN 978-3-030-67660-5, (20200616), URL: https://arxiv.org/pdf/1901.08350.pdf, (20211005), XP055848028 [A] 1,8,9 * Section 2 *
ref_type: npl
severity: background
Final disposition: pending (2021-12-29)