A computer-implemented method for collecting and adopting data for a learning of an entity, the method comprising the steps of: determining (201) the number (M) of epochs (203); recording (202) the system state (160, 170) of properties (181, 191) at least once during each epoch (203), wherein (M) number of epochs are performed; counting (205) how many times each property changed across all the epochs; dividing (206) all of the properties contained in the system into a number of (G) groups (208); translating (210) the groups (207) into factors; creating (211) a threshold factor; creating (212) an output measurement; and applying (213) a learning method.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for collecting and adopting data for a learning of an entity, the method comprising the steps of: determining (201) the number (M) of epochs (203); recording (202) the system state (160, 170) of properties (181, 191) at least once during each epoch (203), wherein (M) number of epochs are performed; counting (205) how many times each property changed across all the epochs; dividing (206) all of the properties contained in the system into a number of (G) groups (208); translating (210) the groups (207) into factors; creating (211) a threshold factor; creating (212) an output measurement; and applying (213) a learning method. 2. The method according to claim 1, wherein recorded system state (160, 170) comprises properties IDs (161-171) and their values (162-172). 3. The method according to claim 1, wherein counting (205) is performed using a comparator (131). 4. The method according to claim 1, wherein the result of counting (205) is in form of a non-volatile record (204) in the system storage (150) comprising a list of all properties and their respective change counts (214). 5. The method according to claim 1, wherein each group (208) is a set of properties and contains those properties whose number of changes across epochs meets criteria (209) of that group. 6. The method according to claim 1, wherein the properties are divided (206) into equally divided groups in the following manner: group   1  :  group   2 :   M / G ≤ c < 2  M / G … group   G :  ( G   …   1 )  M / G ≤ c ≤ M , wherein c indicates the number of changes across all epochs. 7. The method according to claim 1, wherein the output measurement is backup frequency or backup size or a size of an incremental backups. 8. The method according to claim 1, wherein the learning method is a specific experiment scheme selected from Design of Experiment theory. 9. The method according to claim 1, wherein the learning method is implemented by a neural network or a genetic algorithm. 10. A non-transitory computer readable medium storing computer-executable instructions performing all the steps of the computer-implemented method according to claim 1 when executed on a computer. 11. A system for collecting and adopting data for a learning of an entity, the system comprising: a resource provider (110, 120) providing properties; a storage (150) configured to store states (160, 170) of properties; a time resource (140) configured to be utilized for the purpose of creating subsequent epochs; a function resource (130) configured to count changes of properties; and a controller (101) configured to perform the steps of the method of claim 1. 12. The system according to claim 12, wherein the function resource (130) comprises a comparator (131) configured to compare two or more values of properties and provide a result determining whether the values are same or different.