Mat Tuoda Tari Tutumie Un Us 20180276201a1 All The Tituli

Sep . 27 , 2018 US 2018/0276201 A1 METHOD OF THEREOF AND dicting information, including knowledge/probability based inference , optimization prediction, preference -based plan NON - TRANSITORY COMPUTER READABLE ning , recommendation , and the like . Knowledge represen RECORDING MEDIUM tation is a technique for automating human experience information into knowledge data , including knowledge CROSS -REFERENCE TO RELATED building (data generation / classification ) , knowledge man ELECTRONIC APPARATUS , CONTROLLING APPLICATIONS agement (data utilization ), and the like.Motion control is a [ 0001] This application is based on and claims priority under 35 U . S . C . 119 to Korean Patent Application No . 10 -2017 -0037129, filed on Mar. 23 , 2017 , and Korean Patent Application No. 10 - 2017 - 0155897 , filed on Nov . 21 , 2017 , in the Korean Intellectual Property Office, the disclo vehicle and the motion of the robot, including motion control (navigation , collision and running ), operation con trol (behavior control), and the like . [0007 ] An existing chatting server could not do much sures of which are incorporated by reference herein in their entireties . technique for controlling an autonomous running of a more than retrieving and providing a pre - stored response for not fully understanding user utterance . Since all the chatting servers are embodied to have the same specification , BACKGROUND resources are overly used when even a simple question is processed . 1 . Field SUMMARY 10002] The disclosure relates to an electronic apparatus, a controlling method thereof, and a non - transitory computer readable recording medium , and for example , to an elec tronic apparatus capable of matching a chatting server at a difficulty level of user utterance , a controlling method thereof and a non -transitory computer readable recording medium . [0003] In addition , the present disclosure relates to an Artificial Intelligence ( AI) system that imitates the functions of human brain such as recognition and determination using a machine learning algorithm and applications thereof. 2 . Description of Related Art [0004 ] The Artificial Intelligence (AI) system is a com puter system that may implement a human -level intelli gence , and unlike the conventional rule -based smart system , the Artificial Intelligence ( AI) system is a smart system where a machine trains and determines by itself and gets smarter. The use of artificial intelligence system increases a recognition rate and facilitates understanding a user ' s taste [0008 ] An aspect of the disclosure relates to providing an electronic apparatus capable of evaluating a difficulty level of natural language input by a user based on artificial intelligence technology and matching a chatting server hav ing an optimal specification based on the evaluated difficulty level with the input natural language, a controlling method thereof and a non -transitory computer readable recording medium . [0009] According to an example embodiment, an elec tronic apparatus is provided , the electronic apparatus includ ing an input unit comprising input circuitry configured to receive a natural language input, a communicator compris ing communication circuitry configured to perform commu nication with a plurality of external chatting servers , and a processor configured to analyze a characteristic of the natu ral language and a characteristic of a user and determine a chatting server corresponding to the natural language from among the plurality of external chatting servers, and to control the communicator to transmit the natural language to respect to the natural language . [0010 ] The processor may be further configured to per form a preprocessing of the natural language , to analyze a characteristic of the preprocessed natural language and the more accurately . Therefore, the conventional rule-based smart system has been gradually replaced by a deep - learning based artificial intelligence system . [0005 ] The artificial intelligence technology includes a the determined chatting server to receive a response with elemental technologies which utilize the machine learning characteristic of the user and to determine a chatting server corresponding to the preprocessed natural language from machine learning ( ex . deep - learning ) technology and technology . The machine learning technology may refer, for example , to an algorithm technique which sorts character istics of input data and trains by itself. The elemental technology uses a machine learning algorithm such as the deep -learning and includes the techniques such as linguistic understanding , visual understanding, inference /prediction , knowledge representation , motion control, etc . [ 0006 ] The artificial intelligence technology is applied to various technical fields. For example , linguistic understand ing is a technique for recognizing and applying processing human language / characters and includes the functions of natural language processing, machine translation , dialogue system , query response , voice recognition /synthesis , and the like. Visual understanding is a technique for recognizing and object recognition, object tracking , image search , human recognition , scene understanding, spatial understanding, image enhancement, and the like . The inference /prediction is a technique for determining, logically inferring and pre processing objects from a human ' s point of view , including among the plurality of external chatting servers, and to control the communicator to transmit the preprocessed natu ral language to the determined chatting server. [0011] The processor may be further configured to with respect to each of the plurality of chatting servers, calculate (determine ) at least one of a first matching score with respect to the preprocessed natural language, a second matching score with respect to a dialogue pattern including the pre processed natural language, a third matching score with respect to emotion of the user and a fourth matching score with respect to the characteristic of the user, with respect to each of the plurality of chatting servers , to calculate ( deter mine ) a final matching score with respect to each of the plurality of chatting servers using at least one of the first to fourth matching scores, and to determine a chatting server having a highest final matching score from among the plurality of chatting servers as the chatting server corre sponding to the preprocessed natural language .

Sep . 27 , 2018 US 2018/0276201 A1 [0012 ] The first matching score may be calculated (deter - mined ) based on weighted values given to words included in the preprocessed natural language and a Term Frequency / Inverse Document Frequency (TF /IDF ) value , wherein the second matching score is calculated (determined ) based on a similarity to pre -stored dialogue pattern data , wherein the third matching score is calculated (determined ) based on sentiment analysis and emotion analysis of the preprocessed natural language , and wherein the fourth matching score is calculated (determined ) based on at least one of: age , gender , region and education of the user . [0013] The electronic apparatus may further include a memory configured to store history information of dialogues performed with the chatting server corresponding to the natural language and context information with respect to a situation to which the natural language is input. [0014 ] The processor may be further configured to in response to receiving an additional natural language through the input unit after receiving the natural language , determine a chatting server corresponding to the additional natural language from among the plurality of external chatting servers by analyzing a characteristic of the additional natural language and the characteristic of the user, and in response to the chatting server corresponding to the natural language being different from the chatting server corresponding to the additional natural language , control the communicator to transmit the additional natural language to the chatting server corresponding to the additional natural language . [ 0015 ] The processor may be further configured to control the communicator to transmit history information of dia logues performed with the chatting server corresponding to the natural language and context information along with the additional natural language to the chatting server corre sponding to the additional natural language. [ 0016 ] The plurality of chatting servers may include two or more of a first chatting server, a second chatting server chatting servers , and wherein the transmitting includes transmitting the preprocessed natural language to the deter mined chatting server. [0020 ] The determining may further include with respect to each of the plurality of chatting servers, calculating (determining) a first matching score with respect to the preprocessed natural language, a second matching score with respect to a dialogue pattern including the preprocessed natural language, a third matching score with respect to emotion of the user and a fourth matching score with respect plurality of chatting servers, calculating (determining) a to the characteristic of the user, with respect to each of the final matching score for each of the plurality of chatting servers using at least one of the first to fourth matching scores, and determining a chatting server having a highest final matching score from among the plurality of external chatting servers as the chatting server corresponding to the preprocessed natural language. 10021] The first matching score may be calculated (deter mined )based on weighted values given to words included in the preprocessed natural language and a Term Frequency / Inverse Document Frequency (TF/IDF) value, wherein the second matching score is calculated (determined ) based on similarity to pre - stored dialogue pattern data , wherein the third matching score is calculated (determined ) based on sentiment analysis and emotion analysis of the preprocessed natural language , and wherein the fourth matching score is calculated ( determined ) based on at least one of: age , gender, region and education of the user. [0022] The method may further include storing history information of dialogues performed with the chatting server corresponding to the natural language and context informa tion with respect to a situation to which the natural language is input. [0023] The methodmay further include receiving an addi tional natural language from the user after receiving the respect to the natural language input based on the charac natural language , determining a chatting server correspond ing to the additional natural language from among the teristic of the natural language input and the characteristic of the user. teristic of the additional natural language and the character [0017 ] The first chatting server may be a chatting server that provides a response corresponding to the natural lan guage input using a pre - stored response database , wherein the second chatting server is a chatting server that provides language , transmitting the additional natural language to the input using a first response model, and wherein the third guage. and a third chatting server that provide the response with a response by determining a context of the natural language chatting server is a chatting server that provides a response by inferring a question included in the natural language input using a second response model. [0018 ] According to an example embodiment, a method for controlling an electronic apparatus including receiving a natural language input is provided , the method including , determining a chatting server corresponding to the natural language from among a plurality of external chatting servers by analyzing a characteristic of the natural language and a characteristic of the user, and transmitting the natural lan guage to the determined chatting server to receive a response plurality of external chatting servers by analyzing a charac istic of the user, and in response to the chatting server corresponding to the natural language being different from the chatting server corresponding to the additional natural chatting server corresponding to the additional natural lan [0024 ] The transmitting of the additional natural language performed with the chatting server corresponding to the natural language and context information along with the additional natural language to the chatting server corre sponding to the additional natural language . may include transmitting history information of dialogues [0025 ] The plurality of chatting servers may include two or more of a first chatting server, a second chatting server and a third chatting server that provide the response with respect to the natural language input based on the charac teristic of the natural language input and the characteristic of with regard to the natural language . the user. [0019]. The method may further include performing a [0026 ] The first chatting server may be a chatting server that provides a response corresponding to the natural lan mining includes analyzing a characteristic of the prepro cessed natural language and the characteristic of the user and determining a chatting server corresponding to the prepro - guage input using a pre -stored response database , wherein preprocessing of the natural language , wherein the deter cessed natural language from among the plurality of external the second chatting server is a chatting server that provides a response by determining a context of the natural language input using a first response model, and wherein the third

Sep . 27 , 2018 US 2018/0276201 A1 chatting server is a chatting sever that provides a response by inferring a question included in the natural language input using a second response model. [ 0027 ] According to an example embodiment, a computer program product is provided , the computer program product comprising commands, which when executed by a proces sor, cause an electronic apparatus to preform operations comprising : receiving a natural language input, determining a chatting server corresponding to the natural language from among a plurality of external chatting servers by analyzing a characteristic of the natural language and a characteristic of the user, and transmitting the natural language to the [0042 ] FIG . 13 is a flowchart illustrating an example method of controlling a matching bot according to an embodiment of the present disclosure . DETAILED DESCRIPTION [0043 ] All the terms used in this disclosure including technical and scientific terms have the same meanings as would be generally understood by those skilled in the related art. However, these terms may vary depending on the intentions of the person skilled in the art, legal or technical interpretation , and the emergence of new technologies . In to the natural language . addition , some terms may be arbitrarily selected . These terms may have a meaning defined herein and , unless otherwise specified , may be construed based on the entire disclosure, an optimal chatting server may be provided to a contents of this disclosure and common technical knowledge in the art. a natural language input by a user. In addition , when a chatting- based user response service is provided , unneces be used to describe a variety of elements , but the elements sary use of resources may be reduced . simply to distinguish one element from other elements . For example , without departing from the scope of the present determined chatting server to receive a response with respect [0028] According to various embodiments of the present user by dynamically changing a chatting server according to BRIEF DESCRIPTION OF THE DRAWINGS [ 0029 ] The above and other aspects, features and attendant advantages of the present disclosure will become more apparent from the following detailed description , taken in conjunction with the accompanying drawings , in which like reference numerals refer to like elements , and wherein : [0030] FIG . 1 is a diagram illustrating an example concept of a chatting bot system according to an embodiment of the present disclosure ; [0031] FIG . 2 is a block diagram illustrating an example configuration of an electronic apparatus according to an embodiment of the present disclosure ; [ 0032 ] FIG . 3 is a block diagram illustrating an example of an electronic apparatus according to an embodiment of the present disclosure ; [0033 ] FIG . 4 is a block diagram illustrating an example processor according to some embodiments of the present (0044 ] The terms such as “ first,” “ second,” and so on may should not be limited by these terms. The terms are used disclosure , the first component may be referred to as a second component, and similarly , the second component may also be referred to as a first component. The term " and /or” may include any combination of a plurality of related items or any of a plurality of related items. [0045 ] The terms used in the application are merely used to describe particular example embodiments , and are not intended to limit the disclosure . Singular forms in the disclosure are intended to include the plural forms as well, unless the context clearly indicates otherwise . It will be further understood that terms such as “ including ” or “ hav ing ,” etc ., are intended to indicate the existence of the disclosed features , numbers , operations , actions, compo nents , parts , or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, operations , actions , components, parts, or combi nations thereof may exist or may be added . disclosure ; [0046 ] In an example embodiment, ' a module ', ' a unit', or ' a part' perform at least one function or operation , and may data training unit according to some embodiments of the present disclosure ; be realized as hardware, such as a processor or integrated [0034 ] FIG . 5A is a block diagram illustrating an example [ 0035 ] FIG . 5B is a block diagram illustrating an example data recognition unit according to some embodiments of the present disclosure; [0036 FIG . 6 is a table illustrating an example method for determining a difficulty level of a natural language accord ing to an embodiment of the present disclosure; [ 0037 ] FIGS. 7A and 7B are tables illustrating an example method for calculating matching scores by analyzing a natural language according to an embodiment of the present disclosure; 0038 ] FIG . 8 is a table illustrating an example method for calculating matching scores by analyzing a dialogue pattern according to an embodiment of the present disclosure ; [ 00391 FIG . 9 is a block diagram illustrating an example configuration of a chatting bot system according to another embodiment of the present disclosure ; [0040 ] FIGS. 10 and 11 are flowcharts illustrating an example method for controlling an electronic apparatus according to various embodiments of the present disclosure ; [0041 ] FIGS. 12A and 12B are diagrams illustrating an example concept of a chatting bot system according to another embodiment of the present disclosure; and circuit, software that is executed by a processor, or any combination thereof. In addition , a plurality of ‘modules ' , a plurality of ‘ units ’, or a plurality of ‘parts’may be integrated into at least one module and may be realized as at least one processor except for ‘modules', 'units ' or ' parts ’ that should be realized in a specific hardware . [0047 ] Hereinafter, various example embodiments will be described in greater detail with reference to the accompa nying drawings. [0048 ] FIG . 1 is a diagram illustrating an example concept of a chatting bot system according to an embodiment of the present disclosure . [0049 ] Referring to FIG . 1 , a chatting bot system 1000 and third chatting servers 210 , 220 and 230 . A plurality of chatting servers 210 , 220 and 230 may have different specifications , resources and response processing models. For example , the plurality of chatting servers 210 , 220 and 230 may have different CPU functions. [0050 ] The chatting bot system 1000 may include the electronic apparatus 100 capable of responding to a natural language input by a user and a server 200 . For example , examples of the electronic apparatus 100 may , for example , may include an electronic apparatus 100 and first, second

Sep . 27 , 2018 US 2018/0276201 A1 and without limitation , include a PC , a smart TV , a smart phone, a tablet PC , a voice recognition device, an audio device , or the like. The server 200 may include a series of chatting server devices, or may be embodied as a single device including a plurality of processors, each operating as a separate chatting bot. the electronic apparatus 100 may include an input unit (e. g., including input circuitry ) 110 , a communicator (e.g., includ ing communication circuitry ) 120 and a processor ( e. g ., including processing circuitry ) 130 . [0058 ] The input unit 110 may include various input user input in text format. The sentences the user uttered or entered may include both a subject and a verb , or may include one or more of subject, verb , or object. Alternatively , the sentences uttered or input by the user may include only circuitry and receive a natural language from a user. For example , and without limitation , the input unit 110 may be embodied as a microphone, or the like , to receive a voice uttered by the user as a natural language . According to another non - limiting example, the input unit 110 may be embodied as a keyboard , a touchscreen , or the like , to receive a natural language input by the user in the text format, instead of the voice format. 10059 ) The communicator 120 may include various com munication circuitry and perform communication with a [ 0052 ] For example , the electronic apparatus 100 may determine whether the natural language relates to a simple query or requires language understanding through inference . municator 120 may receive a response with respect to the [0051] According to an embodiment, the electronic appa ratus 100 may select one of the plurality of chatting servers 210, 220 and 230 based on the natural language inputby the user. The natural language input by the user, for example , may be a sentence the user uttered in voice or a sentence the specific words. Based on the result of determination , the electronic appara tus 100 may select one of the plurality of chatting servers 210, 220 and 230 . 10053] The electronic apparatus 100 may transmit the natural language input by the user to one of the plurality of chatting servers 210 , 220 and 230 selected by the electronic apparatus. One of the plurality of chatting servers 210 , 220 and 230 selected by the electronic apparatus may generate a response with respect to the natural language input by the user. Each of the plurality of chatting servers 210 , 220 and 230may include a differentmodel for generating a response . For example , a beginner level model for generating a response to a specific natural language , an intermediate level model for grasping a context and an advanced level model for understanding a language by inference may , for example , and without limitation , be respectively stored in the plurality of chatting servers 210 , 220 and 230 . [ 0054 ] According to another embodiment of the present disclosure , the electronic apparatus 100 may perform only an input/output function, and the server 200 may match a corresponding chatting server by analyzing a natural lan guage. plurality of external chatting servers . For example , the communicator 120 may transmit the natural language input through the input unit 110 to a chatting server. The com

acteristic of the user and to identify a chatting server corresponding to the natural language from among the plurality of chatting servers , and to control the communi cator to transmit the natural language to the identified chatting server in order to receive a response with respect to the natural language . ( 21 ) Appl . No . : 15 / 922 , 014