A method and apparatus for generating optimized rules by comparing result data obtained using an existing rule set and result data obtained using a rule set learned through deep learning is provided. A rule generation method of a rule generation apparatus comprises obtaining first result data by executing a rule engine on input data based on a predetermined first rule set, generating a training rule set by analyzing the input data using a deep learning module, obtaining second result data by executing the rule engine on the input data based on the generated training rule set comparing the first result data and the second result data and based on a result of the comparison, updating the predetermined first rule set to a second rule set using the training rule set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A rule generation method of a rule generation apparatus, the rule generation method comprising: obtaining first result data by executing a rule engine on input data based on a predetermined first rule set; generating a training rule set by analyzing the input data using a deep learning module; obtaining second result data by executing the rule engine on the input data based on the generated training rule set; comparing the first result data and the second result data; and based on a result of the comparison, updating the predetermined first rule set to a second rule set using the training rule set. 2. The rule generation method of claim 1, wherein the first result data is obtained by executing a first rule included in the predetermined first rule set on the input data, and the second result data is obtained by executing a second rule included in the training rule set on the input data, and wherein the updating of the predetermined first rule set to the second rule set comprises clustering the input data if a difference level greater than a predetermined reference level is identified between the first result data and the second result data. 3. The rule generation method of claim 1, wherein the updating the predetermined first rule set to the second rule set comprises storing the second result data if a difference level less than a predetermined reference level is identified between the first result data and the second result data. 4. The rule generation method of claim 1, wherein the first result data is obtained by executing a first rule included in the predetermined first rule set, and the second result data is obtained by executing a second rule included in the training rule set; and wherein the updating of the predetermined first rule set to the second rule set comprises deleting the first rule included in the predetermined first rule set if a difference level greater than a predefined reference level is identified between the first result data and the second result data. 5. The rule generation method of claim 1, wherein the generating of the training rule set comprises: clustering the input data into m groups by analyzing the input data; and generating the training rule set including m rules from the m groups. 6. The rule generation method of claim 5, further comprising: calculating result data corresponding to the clustered input data using the deep learning module; in response to an accuracy value of the calculated result data exceeding a predetermined threshold value, replacing the first result data with the calculated result data; analyzing a second rule included in the training rule set based on the calculated result data; and including the analyzed second rule in the training rule set. 7. The rule generation method of claim 6, wherein the analyzing the second rule comprises: extracting a rule function input to a first rule included in the predetermined first rule set for the input data; and updating the second rule using the extracted rule function. 8. The rule generation method of claim 7, further comprising: calculating analysis result data corresponding to the clustered input data using predetermined analytic functions; selecting an analytic function from among the predetermined analytic functions, wherein the selected analytic function yields analysis result data having a highest accuracy from among accuracies corresponding to the predetermined analytic functions; analyzing the second rule based on the selected analytic function; and including the analyzed second rule in the training rule set. 9. The rule generation method of claim 5, further comprising: calculating result data using an analytic function selected in advance, according to an attribute of the clustered input data; in response to an accuracy value of the calculated result data exceeding a predetermined threshold value, replacing the first result data with the calculated result data; analyzing a second rule included in the training rule set based on the calculated result data; and including the analyzed second rule in the training rule set.