Patent application EP17189765A (EP), filed 2017-09-07
Publication number: EP3454506A1
Title: METHOD AND DEVICE FOR MONITORING A TELECOMMUNICATION NETWORK
Abstract: [0001]    Embodiments relate to a method for monitoring a telecommunication network (1) comprising at least one access node (2), a plurality of client devices (3), and a plurality of telecommunication lines (4) connecting said at least one access node (2) to respective client devices (3). The method may comprise:
- determining (S2) a training set associated with the telecommunication network (1), said training set including a plurality of training examples, a training example specifying features related to the functioning of a telecommunication line (4) and a label,
- clustering (S3) the training examples associated with a label, thereby determining a plurality of clusters associated with said label,
- determining (S4) weights associated with the respective clusters, wherein the weight associated with a cluster is determined as a function of a position of said cluster in a feature space and of said label,
- resampling (S5) said clusters according to said weights, thereby determining resampled clusters,
- determining (S6) a resampled training set according to said resampled clusters,
- training (S7) a machine learning model according to said resampled training set, thereby determining a trained machine learning model configured for determining or predicting a label as a function of operational data related to the functioning of a telecommunication line.
IPC codes: H04L  12/    24            A I
H04L  12/    26            A I
G06N  20/    00            A I
Claims: number: 1
text: Method for monitoring a telecommunication network (1) comprising at least one access node (2), a plurality of client devices (3), and a plurality of telecommunication lines (4) connecting said at least one access node (2) to respective client devices (3), the method being executed by a monitoring device (5) and comprising:
number: 2
text: Method according to claim 1, wherein a training example specifies one or more features related to a speed of the telecommunication line and/or one or more features related to a stability of a telecommunication line.
number: 3
text: Method according to one of claims 1 to 2, comprising receiving (S1) ticketing data specifying complaints received by a customer service associated with said telecommunication network (1), wherein the label of a training example specifies the presence and/or type of a complain.
number: 4
text: Method according to one of claims 1 to 3, wherein, for a label specifying an absence of complain, the weight associated with a cluster is determined by:
number: 5
text: Method according to one of claims 1 to 4, wherein, for a label specifying a complain related to a speed of a telecommunication line, the weight associated with a cluster is determined by:
number: 6
text: Method according to one of claims 1 to 5, wherein, for a label specifying a complain related to a stability of a telecommunication line, the weight associated with a cluster is determined by:
number: 7
text: Method according to one of claims 1 to 6, comprising determining (T3) a Quality of Experience of a telecommunication line and/or predicting that a user of a telecommunication line will complain, by using the trained machine learning model.
number: 8
text: Method according to claim 7, comprising performing (T4) a corrective action for the telecommunication network (1) in response to determining low QoE or predicting a user complain.
number: 9
text: Computer program (P) comprising instructions for performing the method of one of claims 1 to 8 when said instructions are executed by a computer.
number: 10
text: Monitoring device (5) for monitoring a telecommunication network (1) comprising at least one access node (2), a plurality of client devices (3), and a plurality of telecommunication lines (4) connecting said at least one access node (2) to respective client devices (3), the monitoring device (5) comprising means (6, 7, P) configured for:
Office action: search_report, dated 2019-03-13
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: US9152925B2
ref_type: patent
severity: background
ref_id: US2014310396A1
ref_type: patent
severity: background
ref_id: - KARAN MITRA ET AL: "Proactive QoE Provisioning in Heterogeneous Access Networks using Hidden Markov Models and Reinforcement Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 25 December 2016 (2016-12-25), XP080742916
ref_type: npl
severity: background
ref_id: - NITESH V CHAWLA ET AL: "SMOTE: Synthetic Minority Over-sampling Technique", JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 16, 31 January 2002 (2002-01-31), pages 321 - 357, XP055096179
ref_type: npl
severity: background
ref_id: US2014310396
ref_type: patent
severity: obviousness
ref_id: US9152925
ref_type: patent
severity: obviousness
ref_id: - KARAN MITRA ET AL, "Proactive QoE Provisioning in Heterogeneous Access Networks using Hidden Markov Models and Reinforcement Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20161225), XP080742916 [Y] 1,2,7-10 * chapters 2-4 * * figures 1-9 *
ref_type: npl
severity: obviousness
ref_id: - NITESH V CHAWLA ET AL, "SMOTE: Synthetic Minority Over-sampling Technique", JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, (20020131), vol. 16, pages 321 - 357, XP055096179 [A] 1-10 * chapters 1-5 *
ref_type: npl
severity: background
Final disposition: pending (2019-03-13)