A platform for providing a task is provided based on a deep learning neural network. A method implemented by a computer includes extracting conversation information from at least one message application installed in an electronic device, extracting task information from the conversation information by using a convolutional neural network (CNN) learning model for natural language processing and a recurrent neutral network (RNN) learning model for creating a sentence, and providing the task information through a task providing application installed in the electronic device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method implemented by a computer, the method comprising: extracting conversation information from at least one message application installed in an electronic device; extracting task information from the conversation information by using a convolutional neural network (CNN) learning model for natural language processing and a recurrent neutral network (RNN) learning model for creating a sentence; and providing the task information through a task providing application installed in the electronic device. 2. The method of claim 1, wherein the extracting of the task information includes: extracting a core word corresponding to a task-related word from a sentence included in the conversation information through the CNN learning model in which the task-related word is learned; and creating a task sentence by using the core word through the RNN learning model. 3. The method of claim 1, wherein the extracting of the task information includes: analyzing a positive expression and a negative expression in a sentence included in the conversation information through the RNN learning model including a learning model specialized in analyzing emotion to create a confirmation sentence having no the negative expression; and extracting a core word corresponding to a task-related word from the confirmation sentence through the CNN learning model in which the task-related word is learned. 4. The method of claim 1, wherein the extracting of the task information includes: extracting a task, based on 5W1H (when, where, who, what, why, and how) attributes, from a sentence included in the conversation information through the CNN learning model in which a task-related word is learned. 5. The method of claim 1, wherein the extracting of the task information includes: analyzing a positive expression and a negative expression in a sentence included in the conversation information through a learning model specialized in analyzing emotion to reflect an analysis result of the positive and negative expressions on extraction of a task. 6. The method of claim 1, wherein the CNN learning model is constructed through learning performed by defining a target function based on a task-related word after words of learning data are expressed to a word vector through a word embedding scheme and by adjusting a distance between embedding vectors to distinguish between the task-related word and a task-unrelated word. 7. The method of claim 6, wherein the CNN learning model is constructed through the learning performed by defining the target function based on words which correspond to a place, time, and a goal and are included in the target-related word. 8. The method of claim 1, wherein the CNN learning model and the RNN learning model are reinforced through reinforcement learning models created by creating a cache model for learning data, which is added, when the learning data is added and then merging the cache model with an existing learning model. 9. A computer program recorded in a computer-readable recording medium to perform a task providing method, wherein the task providing method includes: extracting conversation information from at least one message application installed in an electronic device; extracting task information from the conversation information by using a CNN learning model for natural language processing and an RNN learning model for creating a sentence; and providing the task information through a task providing application installed in the electronic device. 10. A system implemented with a computer, the system comprising: at least one processor implemented to execute a computer-readable instruction, wherein the at least one processor includes: a conversion extracting unit configured to extract conversation information from at least one message application installed in an electronic device; a task extracting unit configured to extract task information from the conversation information by using a CNN learning model for natural language processing and an RNN learning model for creating a sentence; and a task providing unit configured to provide the task information through a task providing application installed in the electronic device.