Patent application EP18199264A (EP), filed 2018-10-09
Publication number: EP3499375A1
Title: PREDICTING QUALITY OF AN INFORMATION SYSTEM USING SYSTEM DYNAMICS MODELLING AND MACHINE LEARNING
Abstract: A method of performing predictive analysis on an external index number describing quality of an information system based on a dynamic causal loop diagram of a plurality of quality meters describing the information system is disclosed. In an aspect, one or more quality meters are selected (402) from the plurality of quality meters for predicting values of an external index number. Values of selected quality meters and the external index number maintained (401) are processed (403) using one or more statistical analysis methods to determine dependencies between the plurality of quality meters and the external index number. Based on the processing, a prediction model for predicting external index number values is established (404). Said model may be trained (406) based on received training data (405) using machine learning tools.
IPC codes: G06F  11/    34            A I
G06N  20/    00            A I
G06N   3/    08            A I
Claims: number: 1
text: A computer implemented method of performing predictive analysis on an external index number describing quality of an information system based on a dynamic causal loop diagram of a plurality of quality meters describing the information system, the information system comprising one or more of information technology systems, communications systems and access networks and producing information quantifiable by the plurality of quality meters and the dynamic causal loop diagram of the plurality of quality meters comprising present information on values of the plurality of quality meters and interactions with their strengths between the plurality of quality meters, the method comprising:
number: 2
text: A method according to claim 1, wherein the outputting of the present predicted external index number value comprises displaying the present predicted external index number to one or more users via one or more displays of one or more user equipment.
number: 3
text: A method according to claim 1 or 2, wherein the calculating the present predicted external index number value is based also on one or more sets of past values of the one or more quality meters.
number: 4
text: A method according to any preceding claim, further comprising:
number: 5
text: A method according to any preceding claim, further comprising:
number: 6
text: A method according to claim 5, further comprising:
number: 7
text: A method according to any preceding claim, further comprising after the establishing, training, by the analysis system, the prediction model using the one or more machine learning tools based on the history data.
number: 8
text: A method according to any preceding claim, wherein the one or more statistical analysis methods comprise one or more methods based on one or more of the following: regression analysis, correlation analysis and structural equations modelling.
number: 9
text: A method according to any preceding claim, wherein the determining the dependencies comprises calculating values for a dependency coefficient indicating an extent of a statistical relationship involving dependence between a quality meter of the plurality of quality meters and the external index number, the dependency coefficient being one of the following types: a linear correlation coefficient, a non-linear correlation coefficient, a linear regression coefficient and a non-linear regression coefficient.
number: 10
text: A method according to claim 9, further comprising
before the establishing, removing, by the analysis system, each quality meter of the one or more quality meters for which the dependency coefficient has a value or an absolute value below a pre-defined dependency threshold from the one or more quality meters.
number: 11
text: A method according to claim 9 or 10, further comprising:
number: 12
text: A method according to any preceding claim, wherein the external index number, the history data and the training data comprise metadata, the metadata comprising at least information on one of the following: data type, location where data was acquired, time of data and to which pre-defined category data belongs.
number: 13
text: A method according to claim 12, further comprising
in response to receiving incomplete training data comprising at least one value for the external index number, retrieving, by the analysis system, at least one set of values for the one or more quality meters corresponding to the at least one value for the external index number based on the metadata of the incomplete training data and the metadata of the history data and training, by the analysis system, the prediction model using the one or more machine learning tools based on the incomplete training data and the at least one set of values for the one or more quality meters.
number: 14
text: A method according to any preceding claim, further comprising:
number: 15
text: A method according to claim 14, further comprising:
number: 16
text: A method according to claim 14 or 15, further comprising:
number: 17
text: A method according to any preceding claim, wherein the external index number is the Net Promoter Score or some other survey-based customer satisfaction metric.
number: 18
text: An apparatus comprising means for carrying out the method according to any of claims 1 to 17.
number: 19
text: A computer program product comprising program instructions configuring a computer to perform a method according to any of claims 1 to 17 when the computer program is run.
Office action: search_report, dated 2019-06-19
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: US2017243122A1
ref_type: patent
severity: background
ref_id: EP2770462A1
ref_type: patent
severity: background
ref_id: US2017052867A1
ref_type: patent
severity: background
ref_id: US2012269441A1
ref_type: patent
severity: background
ref_id: EP3188093A1
ref_type: patent
severity: background
ref_id: US2015199148A1
ref_type: patent
severity: background
ref_id: US2012166888A1
ref_type: patent
severity: background
ref_id: US2012166888
ref_type: patent
severity: background
ref_id: US2012269441
ref_type: patent
severity: obviousness
ref_id: EP2770462
ref_type: patent
severity: background
ref_id: US2015199148
ref_type: patent
severity: background
ref_id: US2017052867
ref_type: patent
severity: background
ref_id: EP3188093
ref_type: patent
severity: background
ref_id: US2017243122
ref_type: patent
severity: obviousness
Final disposition: pending (2019-06-19)