JPWO2016104736A1 - Communication providing system and communication providing method - Google Patents

Abstract

One of the purposes is to provide a communication system and a communication method that can appropriately express the individuality and personality of each user. Social data of a first user registered in one or more social networking services, social data of a plurality of users registered in a plurality of social networking services, and a plurality of social data A plurality of text data collected from a networking service; a database storing information; and a second user query for the first user based on at least a portion of the information stored in the database. Thus, there is provided a communication providing system including an information processing module having a function as an artificial intelligence that infers or learns and determines an answer to a question.

Description

  The present invention relates to an interactive system for automatically communicating with a user who has accessed through a telecommunication line such as the Internet.

  With the spread of social networking services, it is possible for a plurality of users to transmit and share information using devices such as smartphones, tablet terminals, and personal computers. Thereby, the amount of information that an individual can obtain or transmit has increased, and its range has been significantly expanded. By operating application software installed in the terminal device, information can be easily shared simultaneously with a plurality of people. The content of communication is not limited to voice and text, but communication using still images and moving images is also possible, and it can be said that the amount and quality of information is increasing.

  Social networking services can overcome time and distance obstacles and share a large amount of information with each other in real time, but each user has private time. In reality, it is not always possible to exchange information in real time. In addition, users of social networking services can simultaneously send information to multiple people associated as friends, but it is difficult to communicate with each of multiple friends individually and simultaneously. is there.

  By the way, an automatic interactive system is disclosed that stores document data of answers to expected questions on a recording medium and outputs answers in response to user questions (see Patent Documents 1 and 2). ). However, in these automatic interactive systems, since answer documents are prepared in advance, the dialog is uniform and it is impossible to have a conversation according to the social situation that changes every moment. In addition, there is a problem that it is not possible to express the personality and emotions of the other party who asks the question.

JP 2006-072787 A JP 2006-172280 A

  In view of such a problem, an embodiment of the present invention has an object to provide a communication system and a communication method that can express individual personality and personality of a user.

  According to one embodiment of the present invention, a first user's social data registered with one or more social networking services and a plurality of uses registered with multiple social networking services. A database in which information of a user's social data and a plurality of text data collected from a plurality of social networking services is stored, and a second user's question for the first user is stored in the database There is provided a communication providing system including an information processing module having a function as an artificial intelligence that infers or learns an answer to a question and determines based on at least a part of the information being asked.

  In one embodiment of the present invention, the database stores a first database that stores social data of a first user, a second database that stores social data of a plurality of users, and a plurality of text data. The third database may be hierarchized.

  In one embodiment of the present invention, an information processing module having a function as an artificial intelligence sends an appropriate answer to a question from data recorded in the first database, the second database, and the third database. Inference may be made based on at least part of the information recorded in the upper database.

  In one embodiment of the present invention, an information processing module having a function as artificial intelligence performs trend analysis on answers to questions from social data of a plurality of users stored in the first database, and infers answers to the questions. Or may be determined by learning.

  In one embodiment of the present invention, the trend analysis may analyze a temporal change in answers of a plurality of users to a question.

  In one embodiment of the present invention, the first user's social data and the plurality of users' social data are text data and text data generated from one or more of audio data, photo data, and video data. Things are included.

  In one embodiment of the present invention, an information processing module including a similar database in which similar questions are grouped as similar questions and an answer corresponding to the similar question is associated, and having a function as an artificial intelligence is obtained from the similar database. An answer to the question may be selected.

  In one embodiment of the present invention, the similar database may include a similar question database that groups and records the contents of similar questions, and a similar answer database that stores answers corresponding to the similar questions.

  In one embodiment of the present invention, the similar database includes a first similar database in which data categorizing the contents of a question and an answer among social data of the first user, and social data of a plurality of users. A second similar database that stores data that classifies the contents of questions and answers from data, and a third similar database that stores data that classifies the contents of questions and answers from text data; It may be hierarchized.

  In one embodiment of the present invention, an evaluation module that acquires information evaluated by the second user and assigns an evaluation value to the answer may be included.

  In one embodiment of the present invention, the evaluation module may include a notification module for notifying the first user of the evaluation result of the answer.

  In one embodiment of the present invention, a first user who has received a notification from the notification module edits the content of the answer to the question, and the content of the answer to the question based on the content edited by the editing module. And an update module to be updated.

  In one embodiment of the present invention, the evaluation module assigns a priority level based on the evaluation result, and the information processing module having a function as artificial intelligence infers or learns an appropriate answer to the question based on the priority. May be determined.

  In one embodiment of the present invention, the database may have a function of updating and accumulating the first user's social data, the second user's social data, and text data over time.

  In one embodiment of the present invention, an image data generation module that generates three-dimensional image data of the first user may be included.

  In one embodiment of the present invention, the database includes the first user's voice data, and generates a phoneme data generation module that generates phoneme data from the voice data, and a voice generation module that generates voice of conversation using the phoneme data. And may be included.

  In one embodiment of the present invention, an information processing module having a function as artificial intelligence may have a function of analyzing a frequency of answers to a question and recording a question corresponding to a high-frequency answer in a similar database. Good.

  In one embodiment of the present invention, an information processing module having a function as artificial intelligence has a function of parsing text data included in personal social data and inferring or learning an answer to a question. It may be.

  According to one embodiment of the present invention, a question that a virtual personal image of the first user is generated based on the registration information of the first user and is transmitted to the virtual individual by the second user. The answer to the question is determined by inferring or learning based on one type of social data of the first user, social data of a plurality of users or a plurality of text data registered in advance, A communication providing method for providing the determined answer to the second user is provided.

  In one embodiment of the present invention, first, the first user's social data is searched for an answer to the question, and when an appropriate answer cannot be obtained, the second is searched for a plurality of users' social data. Thirdly, when an appropriate answer cannot be obtained, a plurality of social data registered in advance may be searched.

  In one embodiment of the present invention, the answer to the question may be determined by trend analysis of the answer to the question from the social data of a plurality of users, and inferring or learning the answer to the question.

  In one embodiment of the present invention, the trend analysis may analyze a temporal change in answers of a plurality of users to a question.

  In one embodiment of the present invention, the first user's social data and the plurality of users' social data may be text data and text data generated from audio data, photo data, and video data.

  In an embodiment of the present invention, similar questions are grouped as similar questions, answers corresponding to similar questions are stored in a similar database in association with each other, and answers to the questions are selected from the similar database. May be.

  In one embodiment of the present invention, data similar to the contents of questions and answers is stored in the similar database from the social data of the first user, and the questions and answers are stored from the social data of a plurality of users. May be stored, and data in which the contents of questions and answers are categorized from text data may be stored.

  In an embodiment of the present invention, information evaluated by the second user may be acquired and an evaluation value may be attached to the answer.

  In one embodiment of the present invention, the evaluation result of the answer may be notified to the first user.

  In one embodiment of the present invention, the first user who receives the notification from the notification module receives the edited content of the answer to the question and updates the content of the answer to the question based on the edited content. It may be.

  In one embodiment of the present invention, a priority level may be assigned based on the evaluation result, and an appropriate answer to the question may be inferred or learned based on the priority order.

  In one embodiment of the present invention, the social data of the first user, the social data of the second user, and the text data may be updated and accumulated over time.

  In an embodiment of the present invention, three-dimensional image data of the first user may be generated.

  In one embodiment of the present invention, the first user's voice data may be stored, phoneme data may be generated from the voice data, and conversational voice may be generated using the phoneme data.

  In one embodiment of the present invention, the frequency of answers to a question may be analyzed, and a question corresponding to a high-frequency answer may be recorded in a similar database.

  In one embodiment of the present invention, text data included in personal social data may be parsed and an answer to a question may be inferred or learned to be determined.

  According to an embodiment of the present invention, artificial intelligence (AI) learns a user's thoughts by acquiring data from various social networking services used by the user and storing it in a database. A system that provides appropriate answers to questions can be provided.

It is a figure which shows the relationship between the communication provision system which concerns on one Embodiment of this invention, a social networking service, and the user who utilizes this communication provision system. It is an example of the screen for registering in the communication provision system which concerns on one Embodiment of this invention. It is an example of the screen for registering in the communication provision system which concerns on one Embodiment of this invention. It is an example of the screen for registering in the communication provision system which concerns on one Embodiment of this invention. (D) It is an example of the screen for registering in the communication provision system which concerns on one Embodiment of this invention. It is an example of the screen for registering in the communication provision system which concerns on one Embodiment of this invention. It is a figure which shows the relationship between the communication provision system which concerns on one Embodiment of this invention, a social networking service, and the user who utilizes this communication provision system. In the communication provision system which concerns on one Embodiment of this invention, it is an example figure of the screen for asking a question. In the communication provision system which concerns on one Embodiment of this invention, it is an example figure of the screen for asking a question. In the communication provision system which concerns on one Embodiment of this invention, it is an example figure of the screen by which an answer is provided. In the communication providing system according to an embodiment of the present invention, when a virtual first user answers a question asked by a plurality of second users at a terminal of the first user, a plurality of second It is a figure explaining the function to set automatic answer propriety for every user. In the communication provision system which concerns on one Embodiment of this invention, it is a flowchart explaining the flow of the process which judges whether a reply is possible for every use which asked a question, and answers. It is a figure which shows the functional structure of the communication provision system which concerns on this embodiment. It is a figure explaining the structure of the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the structure of the communication provision system which concerns on one Embodiment of this invention. It is a flowchart explaining the flow of the process performed in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the similar database contained in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the database contained in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the database contained in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the aspect which evaluates a reply in the communication provision system which concerns on one Embodiment of this invention. It is a flowchart explaining the flow of the process performed in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the aspect which edits with the candidate of the reply with respect to a question in the communication provision system which concerns on one Embodiment of this invention. In the communication provision system which concerns on one Embodiment of this invention, when a question is received, it is a figure which shows the structure for searching the reply from the past conversation, and predicting a suitable reply. It is a flowchart explaining the flow of the process performed in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the aspect which ranks the reply with respect to a question in the communication provision system which concerns on one Embodiment of this invention. It is a figure explaining the aspect which ranks the reply with respect to a question in the communication provision system which concerns on one Embodiment of this invention. The communication provision system which concerns on one Embodiment of this invention WHEREIN: The aspect which integrates a context by grouping the question and answer in conversation is shown. In the communication provision system which concerns on one Embodiment of this invention, it is a figure which illustrates what kind of keyword order is important when seeking a match. In the communication provision system which concerns on one Embodiment of this invention, a question and an answer show the aspect clustered by the similarity of the answer, and it is a figure which shows the aspect that each cluster comprises one group. In the communication provision system which concerns on one Embodiment of this invention, it is a figure explaining the vector (keyword, concept, type) which comprises each question has the prediction factor which estimates an equivalent answer vector. It is a figure explaining the structure of the communication provision system which concerns on one Embodiment of this invention.

  Embodiments of the present invention will be described below with reference to the drawings. However, the present invention can be implemented in many different modes and should not be construed as being limited to the description of the embodiments exemplified below. In addition, the drawings may be schematically represented with respect to the width, thickness, shape, and the like of each part in comparison with actual aspects for the sake of clarity of explanation, but are merely examples, and the interpretation of the present invention is not limited. It is not limited. In addition, in the present specification and each drawing, elements similar to those described above with reference to the previous drawings are denoted by the same reference numerals, and detailed description may be omitted as appropriate.

[System Overview]
FIG. 1A shows a relationship between a communication providing system according to an embodiment of the present invention, a social networking service (hereinafter also referred to as “SNS”), and a user who uses this communication providing system. The communication providing system 100 according to the present embodiment has a function of acquiring data from various SNS used by a user and storing it in a database. The communication providing system 100 further has a function (function as an artificial intelligence) for inferring and learning a user's thought based on information stored in the database, and can provide an appropriate answer to the question. Details will be described below.

  In FIG. 1A, the communication providing system 100 is placed in a state where it can be accessed with a plurality of users 202 through a telecommunication line. A plurality of users 202 can communicate with each other directly and indirectly by a service provided by the communication providing system 100.

  FIG. 1A shows that a plurality of users 202 are placed in a state where each user terminal 200 can communicate with the communication providing system 100 and both via an electric communication line. Here, the plurality of users 202 is preferably a set of users registered in advance to receive services provided via the communication providing system 100. By using the service provided via the communication providing system 100 as a registration system, the safety and reliability of communication can be improved.

  Note that it is not always necessary that all of the plurality of users 202 be registered in advance in order to receive a service provided via the communication providing system 100. For example, at least one of the plurality of users 202 may be able to use the communication providing system 100 as an anonymous user.

  The communication providing system 100 is placed in a state where it can communicate with one or more of the SNS 300 provided to the public. Examples of the SNS 300 include Twitter (registered trademark), Facebook (registered trademark), and various communication services provided on the Internet. Note that communication modes provided by the SNS are various and are not limited to the services exemplified above. In the present embodiment, the SNS 300 that cooperates with the communication providing system 100 only needs to be provided with social data of the user, and preferably has a mode in which information can be shared among a plurality of users.

  In this specification, “social data” refers to characters (including emoticons) that a user sends to a specific person (for example, another user registered as a friend) or an unspecified person through some kind of SNS. ), Information such as symbols, sounds, still images, and moving images. Further, “personal social data” refers to social data of a specific individual among a plurality of users.

  The communication providing system 100 acquires social data accumulated in at least one, preferably a plurality of SNSs 300. The acquisition of social data performed by the communication providing system 100 may be executed in real time, or may be performed in a timely manner or at regular intervals. The communication providing system 100 accumulates the acquired social data in a database. In order to increase the amount and quality of information stored in the database, the communication providing system 100 preferably acquires social data in cooperation with a plurality of SNSs 300.

  Specific examples of the process of acquiring social data and the acquired social data in the database include the following. First, the first user 204 registers the attributes of the first user 204 in the communication providing system 100 using a personal computer, a smartphone, or the like. For this purpose, for example, as shown in FIG. 1B, the first user 204 displays the screen shown in “Welcome to Personalized AI” using a browser or an application, and clicks the “Register” button 1. Push. Then, a screen for inputting the attributes of the first user 204 is displayed. In the attribute of the first user 204, information (SNS specifying information) for specifying from which SNS of the SNS 300 social data is acquired, and the first user 204 in the SNS acquiring social data. Authentication information may be included. For example, as shown in FIG. 1C, SNS identification information is input in field 2, and a user name and a password are input in fields 3 and 4 as authentication information. Then, the “Register” button 5 is pressed.

  The attributes of the first user 204 are stored in a storage device of the communication providing system 100, for example, a secondary storage that stores a database or the like. The communication providing system 100 uses the SNS specifying information and the authentication information of the SNS to access the SNS specified by the SNS specifying information, and logs in to the SNS using the authentication information of the first user 204. Thereafter, the communication providing system 100 acquires information on the first user 204 stored in the SNS, that is, social data. Information related to the first user 204 includes messages exchanged by the first user 204 with a specific user in the SNS, articles, photos, audio information, and videos posted by the first user 204 on the SNS. (Which may include audio information).

  The communication providing system 100 stores information on the first user 204 acquired from the SNS in a database. In this case, the communication providing system 100 can directly use the information regarding the first user 204 acquired from the SNS as a database. Or the communication provision system 100 may convert the information regarding the 1st user 204 into another information according to the attribute and content of the acquired information, and may accumulate | store it in a database.

  For example, when information indicating that the first user 204 in the SNS has answered a question from another user is acquired, a question from another user and the first user 204 corresponding thereto. Can be stored in the database in association with each other. In this case, the database is composed of a plurality of areas (such as sub-databases or tables), and can include (1) an area for storing questions and (2) an area for storing answers. Therefore, a question from another user is accumulated in the area where the question is accumulated, and the answer of the first user 204 is accumulated in the area where the answer is accumulated. Further, a question from another user and the answer of the first user 204 are associated with each other, and for example, information for association is stored in the third area. For example, a set of a primary key for a question and a primary key for an answer is stored in the third area.

  Note that the communication providing system 100 acquires the first information stored in the database when all the information related to the first user 204 is acquired from the SNS specified by the SNS specifying information and is not stored in the database. The ratio of all information related to the user 204 may be presented to the first user 204. As a result, the first user 204 can know the degree of information accumulation. For example, the communication providing system 100 responds to a question from one of the plurality of users 202 by the first user 204. It is possible to decide whether or not to reply on behalf of In addition, it is possible to predict the appropriateness of an answer to a question by presenting the degree of accumulation of such information to a plurality of users 202.

  For example, as shown in FIG. 1D, when the ratio of all information related to the first user 204 in the information accumulated in the database is displayed as a pie chart 6, 60% of the information is accumulated in the database. Is displayed as “You are 60% of yours”. If the first user 204 thinks that it should be 60% or more, the “public” button 7 is pressed. Thereby, the communication providing system 100 answers questions from other users using the information stored in the database.

  If the first user 204 is dissatisfied with 60%, then all (100%) information is accumulated in the database. For example, as shown in Fig. 1E, "I have become you" Is displayed. If the first user 204 is satisfied with this, the “public” button 8 is pressed. Thereby, the communication providing system 100 answers questions from other users using the information stored in the database.

  Note that the first user 204 may be able to ask the communication providing system 100 several questions. After seeing the answers to the questions of some attempts, the “Publish” buttons 7 and 8 may be pressed.

  Even with social data provided by SNS, the privacy of the user should be ensured. The communication providing system 100 according to the present embodiment can reflect information individually set by the user in each SNS 300. For example, when a user who uses a certain SNS 300 sets his / her personal profile private and sets a message posted to the SNS to be public, a range of disclosure or private / public is set for each message. In this case, the communication providing system 100 can take over the setting.

  Therefore, when the communication providing system 100 acquires information related to the first user 204 and stores it in the database, the disclosure range of the information is also stored in the database. For example, as shown in FIG. 1F, if the one-to-one message exchange 301-1 with the user 207 in the SNS 6 (301), the question and answer by the message exchange 301-1 are sent to the user 207. Only disclosed. In other words, if the question made by the user A other than the user 207 using the communication providing system 100 appears only when the first user 204 exchanges a one-to-one message with the user 207. The answer to the question is not provided to the user A.

  Even if the first user 204 answers only in a specific group in the SNS 6, even if a user other than the user belonging to the specific group makes a question, the answer is not provided. For example, as shown in FIG. 1F, when the first user 204 receives and answers a question in the group 301-2 in the SNS 6, the answer is sent to the user 208 belonging to the group 3010-2. Disclosed.

  On the other hand, when the first user 204 makes an answer to a question in a state where anyone can see it in the SNS, no matter which user makes the question, the answer is sent to the communication providing system 100. Provided by. For example, if the first user 204 answers a question under a state (301-3) that can be viewed by any user belonging to SNS6 (301), the first user 204 If a similar question is received from the user 209 belonging to the same SNS 6, the answer is provided.

  In this embodiment, it is preferable that the plurality of users 202 to whom the service is provided from the communication providing system 100 are users of one or a plurality of SNSs 300 at the same time. This is because the communication providing system 100 acquires social data of each of the plurality of users 202 from the SNS 300 provided to the public. That is, a plurality of users 202 can receive service provision via the communication providing system 100 by participating in the community of the SNS 300.

  FIG. 1A shows a mode in which the second user 206 interacts with the first user 204 via the communication providing system 100. However, one of the features in the present embodiment is that the first user 204 and the second user 206 do not directly exchange messages, but the virtual user generated by the communication providing system 100. A typical first user 204b and a real second user 206 interact with each other. As a result, even if the same question is made to the first user 204 a plurality of times, the communication providing system 100 answers as the virtual first user 204b on behalf of the first user 204. It can be carried out. Thereby, the effort of the 1st user 204 can be reduced. For example, when the first user 204 is a celebrity, even if many users ask the same question, the first user 204 can be prevented from becoming a burden. Even if a special question is asked, if the first user 204 has answered such a question in the past, the first user 204 does not look for a past answer, The communication providing system 100 can make an answer as the virtual first user 204b. This can also reduce the effort of the first user 204.

  An example of the dialogue in this case is as follows. First, the second user 206 accesses the communication providing system 100, logs in if necessary, and then specifies the other party (first user 204b) who wants to interact. For example, as shown in FIG. 1G, a screen for asking a question is displayed, and a “login” button 9 is pressed to log in. Alternatively, when asking the question as an anonymous user, the “anonymous” button 10 is pressed. Further, in order to specify a partner who desires a dialog, as shown in FIG. 1H, the user name of the partner who desires a dialog is input in the field 11. Alternatively, a list of acquaintances in the communication providing system 100 of the second user 206 can be displayed to specify a partner with whom a conversation is desired.

  And the question with respect to the other party (1st user 204b) is transmitted from the user terminal 200. FIG. For example, as shown in FIG. 1H, the question is received by the communication providing system 100 by inputting the question in the field 12 and pressing the enter key or the like. The communication providing system 100 that has received the question transmits an appropriate answer to the question to the user terminal 200. For example, if a question “Who are you?” Is input in the field 12, an answer “I am the president” is displayed in the field 14, as shown in FIG. 1J. At this time, the face photograph data 13 of the first user 204 may be displayed. The face photograph data 13 may be data generated by three-dimensional data, as will be described later.

  At this time, the answer transmitted by the communication providing system 100 is generated based on the personal social data of the first user 204 as a first example. As a second example, when the communication providing system 100 cannot find an appropriate answer from personal social data, the communication providing system 100 generates an appropriate answer by referring to social data of a plurality of users and responds.

  In this case, due to the characteristics of the computer system, the response of the virtual first user 204b is extremely fast with respect to the time required for the second user 206 to ask a question (time for operating the user terminal 200). It becomes. Therefore, a plurality of users 202 can interact with the virtual first user 204b at the same time as described above.

  In the present embodiment, the virtual first user 204b generated based on the personal social data of the first user 204 can be considered to be embodied via the communication providing system 100. From another point of view, it can be considered that the virtual first user 204b exists on the computer program or application program executed by the communication providing system 100. Alternatively, the virtual first user 204b can be regarded as an entity embodied by hardware resources constituting the communication providing system 100 and software resources executed on the hardware resources.

  FIG. 2 shows a functional configuration of the communication providing system 100 according to the present embodiment. The communication providing system 100 includes an information processing module 104 having a function as artificial intelligence and a database 102.

  The communication providing system 100 shown in FIG. 2 has a database 102 realized by a memory module or storage device such as a hard disk, a semiconductor memory, and a magnetic memory, and a central processing unit (CPU) or an arithmetic processing circuit having an equivalent function. The information processing module 104 can be realized by a device realized by the above. Here, the information processing module can be regarded as a functional block realized by hardware resources or hardware resources and software resources, and is sometimes called an information processing unit or information processing means. Hereinafter, the same applies to what is denoted as a module in this specification.

  Here, the artificial intelligence means that intelligent functions such as inference and judgment are realized by using hardware resources and software resources, and may be recognized as a concept including a database for storing data as knowledge. . Artificial intelligence also has a learning function, which may have the ability to predict the future from past information (data). Also in the present embodiment, the information processing module 104 having a function as artificial intelligence includes at least one having a function as normal artificial intelligence as described above.

  The database 102 has an area for storing at least social data. FIG. 2 shows an aspect in which the database 102 includes a first database 102 a and a second database 102 b that store social data acquired from the SNS 300. Similarly, the third database 102c may include social data, or words, vocabularies, and fixed phrases used in communication may be stored in advance.

  The first database 102a, the second database 102b, and the third database 102c are stored in association with the contents of the dialogue included in the social data, such as questions and answers (or questions and responses). Moreover, social data transmitted unilaterally like a personal tweet (tweet) may be included.

  In general, tweets (tweets) are often not answers to questions. In this case, the tweet (tweet) is parsed and decomposed into adverb phrases representing the subject, object, place, time, and form, and adverb phrases representing the subject, object, place, time, and form, etc. Information about the first user 204 may be accumulated in the database. For example, suppose the first user 204 tweeted, “My sister signed a contract to purchase a car yesterday.” At this time, the subject is “my sister”, the object is “car” and “contract”, and the adverb phrase representing time is “yesterday”. Therefore, the question “Who did it?” Is generated, the answer “I am my sister.” Is generated, the question “When did you do it?” Is generated, and the answer “Yesterday” is given. Generate a question “What did you make a contract to purchase?”, Generate an answer “I was a car”, generate a question “What did you do?” Can be generated and stored in the database.

  The similar database 106 stores similar questions as one group, and answers to the group are associated with each other. The similar database 106 may be divided into a first similar database 106a, a second similar database 106b, and a third similar database 106c corresponding to the first to third databases described above.

  The information processing module 104 having a function as an artificial intelligence performs a function as an artificial intelligence in cooperation with the database 102 and the similar database 106, and also functions for editing a message (answer information, etc.) and a message (answer information, etc.). It includes an evaluation function, an evaluation result notification function, a phoneme generation function for reproducing the user's voice, a question generation function for generating a new question, and a 3D imaging function for generating a 3D video of the user.

  The communication providing system 100 is placed in a state capable of bidirectional communication with a plurality of users (user terminals 202a and 202b illustrated in FIG. 2). In other words, the user terminals 202a and 202b can communicate with the communication providing system 100 and the SNS 300. On the other hand, when viewed from the communication providing system 100, the user terminals 202a and 202b can communicate with a plurality of user terminals 202a and 202b. It is in an available state.

  The communication providing system 100 can generate a virtual first user 204b for the first user 204 on the system, and can have a conversation with a plurality of other users sequentially or simultaneously. is there. In this case, the virtual first user 204b can have an equal conversation with all accessing users. On the other hand, for the first user 204, among the plurality of second users 202, depending on closeness (family, friendship), organizational relationship (whether they belong to the same corporation or group), etc. There is a request to limit the scope of responses. That is, the first user 204 may want to reserve an answer to the question in relation to the second user 206.

  In the communication providing system 100 according to the present embodiment, whether or not the first user 204 may automatically answer a plurality of users 202 by the virtual first user 204b. It has a function to set. FIG. 1K shows an example of a screen display of a user terminal operated by the first user 204. The screen display shown in FIG. 1K indicates whether or not the first user 204 may make an automatic answer for each registered user to receive the service provided via the communication providing system 100. The stage to set up is shown. In FIG. 1K, a display 210 for identifying a user based on a user name, a thumbnail image of the user, and the like, and an answer availability selection switch 211 are shown. The first user 204 can set automatic answer permission for each user while viewing such a screen display. Note that such settings can be changed in a timely manner, and the first user 204 can change the settings by operating the selection switch 211 for timely answer availability.

  FIG. 1L shows the operation of the communication providing system 100 in the case where answering permission / inhibition is individually set for a plurality of second users 202. For example, when a question is made by the second user 206 (S31), the user is identified and an automatic answer is judged (S32). When the second user 206 who made the question is set as “response possible” by the first user 204, the virtual first user 204b generates an answer (S33), An answer is made to the user 206 (S34). On the other hand, when the second user 206 who has made the question is set as “response not possible” by the first user 204 (S32), the virtual first user 204b holds the answer. Become.

  As described above, the communication providing system 100 can provide a function in which the first user 204 individually sets whether or not an answer can be made to a plurality of second users 202. In other words, the communication providing system 100 has a function of setting whether or not to generate an answer in the information processing module 104 for the second usage question. Thereby, the range of communication by the virtual first user 204b can be set. As a result, communication with a specific user can be restricted for the first user 204. In the communication providing system 100, the virtual first user 204b is a plurality of second users. Since it is not necessary to answer all the questions 202 and all the questions, the load on the system can be reduced.

  Hereinafter, a configuration for generating a virtual individual of the user by the communication providing system 100 and enabling the virtual individual to communicate with other users will be described in detail.

[Database]
FIG. 3A is a diagram illustrating the configuration of the database 102 in the communication providing system 100 according to an embodiment of the present invention. As shown in FIG. 3A, the database 102 may be constructed in a plurality of layers. The first database 102a is a database corresponding to each user, and stores personal social data. The personal social data stored in the first database 102a is also used as basic data when generating virtual user data in the communication providing system 100. Therefore, personal social data and information generated therefrom are stored in the database for each user. The information generated from personal social data includes vectors obtained by analyzing personal social data using keywords, vectors obtained by analyzing personal social data using concepts, and personal social data, depending on the type. One or more of the analyzed vector and the vector obtained by analyzing the personal social data by the group are included.

  The data stored in the first database 102a includes personal social data acquired from a plurality of SNSs 300. For example, information such as a comment posted by a certain user on the SNS, communication contents exchanged on the SNS between the user and other users, and tweets (tweets) of the user are stored. Yes. Also, as described above, questions and answers can be generated and stored.

  The second database 102b stores social data of a plurality of users. The data stored in the second database 102b includes social data of each user acquired from the plurality of SNSs 300. The second database 102b stores a larger amount of data than the first database 102a. The second database 102b is hierarchically positioned below the first database 102a.

  Note that the data stored in the second database 102b can be changed based on the change in the social data stored in the first database 102a. For example, when it is detected that a large number of users change the length, style, and contents of answers to similar questions, the data stored in the second database 102b can be changed according to the change. Is possible. That is, as shown in FIG. 3B, it is possible to perform a trend analysis on the first database 102a and update the second database 102b. As an example of such a case, in response to the question "What kind of person are you?" In contrast to responding, recently, when changes such as “I am a company employee” are detected that responds to occupations, the second database is responded to this change. The data stored in 102b may be changed so that the occupation is answered in response to the question "What kind of person are you?" Such a change may be referred to as “analyzing answers to questions”.

  The third database 102c stores a plurality of text data. The data stored in the third database 102c may be any text data set in advance, or may be a collection of texts that appear frequently among texts that appear on the SNS. . For example, texts having a high frequency of appearance may be collected from real-time communication called chat that is exchanged in SNS.

  Specifically, the first user's social data stored in the first database 102a includes real-time communication data called chat, tweets uploaded to Twitter (registered trademark), Facebook, etc. The uploaded comments and comments of other users with respect to the comments are stored. The social data of a plurality of users stored in the second database 102b may include data similar to the above, and further may store a question and text data of an answer to the question in association with each other. Also in the third database 102c, a plurality of text data may be stored in association with each other in the form of a question and an answer to the question.

  The social data stored in the first database 102a and the second database 102b is preferably text data. However, this text data may include not only text data created by the SNS user but also text data generated from audio data, photo data, and video data.

  For example, audio data included in audio data and video data can be converted into text and stored in a database as text data. In addition, when a user updates a photo or video to the SNS, a comment may be attached at the same time, and another user's comment may be attached to the comment. In addition, in some cases, a photo location, a content, or a content is added to a photo, a moving image, or a sound. Therefore, in generating text data from photo data and video data, data such as tags and comments attached thereto can be stored as text data in a database. In addition, position information (country name, place name, etc.) and date information from which photo data and video data are acquired can be stored in a database as text data. In other words, text data can be generated and stored in a database based on one or more of audio data, photo data, and video data.

  As described with reference to FIG. 2, such a database 102 operates in cooperation with or in cooperation with the information processing module 104 having a function as artificial intelligence. Since the user's personal social data is stored in the first database 102a, what uses this hierarchy may be expressed as “private AI” or “personalized AI” as artificial intelligence reflecting the individual. it can. Further, since the second database stores social data of the entire user, what is realized using this hierarchy can also be expressed as “everyone AI” or “common sense AI”.

[Information processing module (artificial intelligence)]
The information processing module 104 having a function as artificial intelligence generates a virtual individual using social data stored in the database 102 and communicates with a real user. The information processing module 104 having a function as artificial intelligence has, for example, a function of recognizing text data stored in the database 102 and creating or generating an answer to a question. At this time, the information processing module 104 having a function as artificial intelligence has a function of inferring or learning an appropriate answer to a question from text data stored in the database 102 and determining it.

  The information processing module 104 having a function as artificial intelligence receives a question of the second user 206 with respect to the virtual first user 204b, and creates or generates an answer to the question. At this time, the information processing module 104 having a function as artificial intelligence performs processing for obtaining answers in order from the upper hierarchy of the database 102.

  FIG. 4 is a flowchart showing an example of a process in which the information processing module 104 having a function as artificial intelligence searches the database 102 and creates an answer to a question. Hereinafter, the flow of processing will be described with reference to FIG.

  First, when an information processing module 104 having a function as artificial intelligence creates or generates an answer to a question, first, an appropriate answer to the question is searched from the first database 102a (S01). If an answer is obtained from the first database 102a, it will most accurately reflect the thoughts, thoughts, feelings, etc. of the first user 204 (S06).

  When searching for a suitable answer to the question from the first database 102a, the question stored in the first database 102a is searched for a question that matches the question as a character string. The answers associated with the asked questions can be retrieved. Alternatively, even if the question stored in the first database 102a does not completely match the question as a character string, a search is performed by determining whether or not they match in consideration of a certain degree of notation fluctuation. You can also. It is also possible to search for a question having the largest number of words included in the question among the questions stored in the first database 102a. In this case, in addition to the number of words, the search can be performed in consideration of the order of words. It is also possible to analyze the meaning of the question stored in the first database 102a and the question and search for the closest meaning. Semantic analysis can include, for example, deriving a logical outcome from an answer as a conclusion or deriving a condition assumed by the answer based on a predetermined logic system. As described above, when an answer is detected, if it includes a case where the character string is not completely matched, there is a case where the answer is inferred and determined. In addition, when there is a complete match as a character string, there is a case of “learning and determining an answer”.

When the information processing module 104 having the function as artificial intelligence cannot obtain an answer from the first database 102a (S02), the information processing module 104 searches the second database 102b in the lower hierarchy (S03). The answers obtained from the second database 102b can know the tendency of how the majority of users are answering a specific question, and can obtain an average and appropriate answer. For example,
"who are you"
In response to the question
“I am XX (name).”
The information processing module 104 having a function as artificial intelligence can determine a similar answer as an answer to the question. The above is an example of a simple question. However, based on the second database 102b, the response tendency of a large number of users is reflected, so that it is possible to obtain an answer in time (S06).

  When the information processing module 104 having the function as the artificial intelligence cannot find an answer in the second database 102b (S04), the information processing module 104 searches the third database 102c (S05). Since an enormous number of text data is stored in the third database 102c, an answer can be selected from the stored text data. Then, the selected one is set as an answer (S06).

[Judging similar questions]
The database 102 stores a question and a response to the question in association with each other. However, questions (questions) and responses (responses) to communication in human society are usually not uniform. For example, when trying to ask the name of a person you meet for the first time, you may ask “Who are you?” Or “Please tell me your name”.

  In one embodiment of the present invention, when such a similar question is made, a mechanism for selecting a response to the corresponding question is provided. For example, in response to the above-mentioned questions “Who are you?” And “Tell me your name”, there is no sense of incongruity even if you reply “I am Annie.” This mechanism may be constructed as the similar database 106 as described in FIG.

  FIG. 5 shows an example of the similar database 106. The similar database 106 includes a question database 107 that stores questions and a response database 108 that stores responses. Alternatively, an area for storing a question and an area for storing a response may be provided in the similar database 106.

  The question database 107 stores a plurality of questions as data. The plurality of questions are stored as one group in association with similar questions. For example, the above-mentioned “Who are you?” And “Tell me your name” are associated and stored as similar questions.

  The response database 108 stores the response content to the question, that is, a plurality of responses as data. For example, the above-mentioned reply “I am Annie” is stored. In the response database 108, the response data is associated with a specific question. According to the example above, the questions “Who are you?” And “Tell me your name” are grouped together and the response is associated with the response “I am Annie.” ing.

  FIG. 6A shows another aspect of the similarity database 106. FIG. 6A shows an aspect in which the similar database 106 is hierarchized. For example, the similar database 106 includes a first similar database 106a corresponding to the first database 102a and a second similar database corresponding to the second database 102b. The database 106b and the third similar database 106c corresponding to the third database 102c may be hierarchized.

  The first similar database 106a is created for each specific user based on the personal social data of the user. Text data corresponding to a question sentence and text data corresponding to a reply sentence are stored from text data by chat or the like included in the personal social data or the contents of the textized conversation. In this case, as described with reference to FIG. 5, among the text data corresponding to the question sentence, similar question sentence data is grouped and stored in the question database, and the corresponding reply sentence data is stored in the response database and associated. It has been.

  The second similar database 106b is created based on social data of a plurality of users. Text data corresponding to a question sentence and text data corresponding to a reply sentence are stored from text data by chat or the like included in social data of a plurality of users, or from the contents of a textized conversation. In the second similar database 106b, as described with reference to FIG. 5, similar text data corresponding to the question text is grouped and stored in the question database, and response text data corresponding thereto is stored in the response database. Stored and associated.

  Note that the data stored in the second similar database 106b can be changed based on the change in the social data stored in the first similar database 106a. That is, as shown in FIG. 6B, the trend analysis of the first similar database 106a can be performed, and the second similar database 106b can be updated using the result. For example, suppose that there are cases where a large number of users respond to nominations for each of the questions “Who are you?” And “Tell me your name”. In this case, for a large number of users, if it is detected in the first similar database 106a that “Who are you?” And “Tell me your name” are similar questions, In the second similar database 106b, the second similar database 106b may be updated so that both the question “Who are you?” And “Tell me your name” are similar questions. it can.

  The third similar database 106c stores a plurality of text data. The plurality of text data may be prepared as a question text and text data of a response text to the question text. These text data may be collected text data having a high appearance frequency among text data appearing on the SNS. In the third similar database 106c, as described with reference to FIG. 5, similar text data corresponding to the question text is grouped and stored in the question database, and response text data corresponding thereto is stored in the response database. Stored and associated.

  As described above, the similar database 106 includes the first similar database in which the data categorizing the contents of the question and the answer among the social data of the first user and the social data of a plurality of users are stored. Is divided into a second similar database that stores data that classifies the contents of questions and answers, and a third similar database that stores data that classifies the contents of questions and answers from text data It is preferable that

  According to this embodiment, what is determined to be the same response for a plurality of questions is associated as a combination of the question and the response. By such a procedure, it is not necessary to memorize all combinations of questions and responses to the questions, and the storage capacity can be saved. It also provides quick access to answers to questions.

  Furthermore, in the communication providing system according to the present embodiment, when the information processing module 104 having a function as artificial intelligence refers to the second database 102b and there are many identical contents in answers from many users. When it is determined, a question item for the answer may be automatically added to the similar database 106 and updated. In this way, by updating the similar database with reference to the social data of multiple users, the combination of questions and answers that are determined to be similar can be more accurately and accurately, and the accuracy of communication can be improved. it can.

[Generate phonemes]
In the communication providing system according to the present embodiment, in the mode shown in FIG. 1A, the second user 206 recognizes the voice by speaking with the voice, and the voice of the first user 204 (or the pseudo user) A function of responding with the voice of the person himself / herself. Alternatively, when the second user 206 makes a non-voice question using text information or the like, it has a function of responding with the voice of the first user 204 (or a pseudo person's voice). It may be. As such a voice response method, a method can be used in which a large number of voices of the first user 204 are recorded in advance and a response sentence is created as voice data using the voice. However, such a method forces the user himself / herself to input a large amount of audio data in advance, which is not preferable for all users and impairs the user's convenience.

  Therefore, in the communication providing system 100 according to the present embodiment, communication data based on the user's voice performed via the SNS is recorded as needed, and phoneme data is created based on the data. For example, a word spoken by a user in voice communication is taken out and a set of a plurality of words is created.

  This set of words is stored as data for creating phonemes. In order to create phonemes, first, a feature (waveform) of voice data prepared in advance and an actual user's voice waveform are compared in accordance with user attributes (for example, sex, age, etc.). Then, the waveform of the voice data prepared in advance is adjusted so as to approach the waveform of the user's voice. The adjusted voice data is used as the phoneme data of the user.

  Alternatively, when a method is used in which a large number of voices of the first user 204 are recorded in advance and a response sentence is created as voice data using the voices, the following may be possible. . That is, when a large number of voices of the first user 204 are recorded in advance, it is assumed that voice data is acquired by causing the first user 204 to read a predetermined sentence. At this time, communication data by voice of the user performed via the SNS is recorded at any time, and voice generated by the first user 204 is recognized. When a predetermined sentence or a part of a word constituting a part thereof is recognized, the voice data of the recognized part is acquired and stored. If the voice data of the entire sentence determined in advance is obtained by repeating this process and appropriately connecting the acquired and stored voice data, the connected voice data is registered. As a result, even if a method of recording a large number of voices of the first user 204 in advance and creating voice data is used, the first user 204 can be used without burdening the first user 204. It is possible to obtain voice data in which many voices of the person 204 are recorded.

  The creation of such phoneme data may be performed by the information processing module 104 having a function as artificial intelligence and the database 102, or may be executed in cooperation with an external server that creates phonemes.

  In the information processing module 104 having a function as artificial intelligence, when the second user 206 makes a question by voice, the text data of the reply sentence is converted into voice data by using the phoneme data created as described above. Create Then, the created voice data is transmitted to the second user 206 as a response sentence.

  As described above, the communication providing system 100 can acquire the voice data of the conversation performed via the SNS and create phoneme data that meets each user without giving a special load to the user. Even if it is pseudo, voice data close to the user can be created and voice communication can be established.

[Evaluation of answer]
The communication providing system according to the present embodiment can include an evaluation module in which the second user 206 evaluates the content of the answer transmitted from the communication providing system 100. The answer can be evaluated by operating the user terminal 200 of the second user 206. The user terminal 200 can display the answer contents on the screen and display an icon (or button) for performing the evaluation, and the second user 206 can execute the evaluation by performing an operation according to the screen display. it can. The evaluation screen can be constructed by displaying icons (or buttons) indicating “Like” and “Dislike” (buttons 15 and 16 in FIG. 1J) together with the reply message. Further, the evaluation may be in a form in which the user gives a score instead of the two-choice set.

  FIG. 7 is a diagram for explaining an aspect in which an answer answered from the communication providing system 100 is evaluated with respect to a question made by the second user 206 to the virtual first user 204b.

  In FIG. 7, the second user 206 (any of Mr. A, Mr. B, and Mr. C) has a question to communicate with the virtual first user 204 b generated on the communication providing system 100. The case is shown (left side in the figure). The second user 206 (any one of Mr. A, Mr. B, and Mr. C) evaluates the answer provided from the communication providing system 100. For example, in the case of Mr. A, it is evaluated as “Dislike”, and in the case of Mr. B or Mr. C, it is evaluated as “Like” (approximately the center in the figure).

  The information processing module 104 having a function as artificial intelligence can total the evaluation information input to the evaluation module for each response sentence and accumulate it as evaluation data. For example, the evaluation data may be stored in a point system in correspondence with the response text data. Based on the evaluation value, the information processing module 104 having a function as an artificial intelligence can rank the response data having a high evaluation value so that the response data with a high evaluation value is preferentially selected.

  Further, when the second user 206 gives a low evaluation for a certain response content, the information may be notified to the first user 204. Upon receiving the notification, the first user 204 can edit the response content and update the response text.

[Edit answer to question]
The communication providing system according to the present embodiment includes an editing module that allows the first user 204 to edit the reply sentence created by the communication providing system 100 by itself. For example,
"Who are you" or "Tell me your name"
In response to the question
“I am XX (name).”
It is assumed that the information processing module 104 having a function as artificial intelligence is learned so as to respond. However, some users may think that the response content thus derived is not appropriate. For example, to the above question:
“I am the CEO of XX Company.”
May be considered appropriate.

  Further, as shown in FIG. 7, the first user 204 may receive a low evaluation notification for the response content (left side in the figure). In that case, the first user 204 may want to modify and edit the response content.

  In such a case, the communication providing system 100 according to the present embodiment individually sets a response sentence when the user wants to set a response sentence that is different from the response sentence created by the information processing module 104 having a function as an artificial intelligence. It can be edited by the function of the editing module.

  As a method of editing the response content, the user may input text data of the response content from the user terminal 200 and update the content by the update module. As a preferred mode, the user may learn by interacting with the information processing module 104 having a function as artificial intelligence.

For example, the first user 204 accesses the communication providing system 100 using the user terminal 200, and gives a pseudo first user 204 created on the communication providing system 100 to, for example,
"Tell me your name"
Ask. The information processing module 104 having the function as artificial intelligence has a prepared answer as follows:
“I am XX (name).”
Responds. The first user 204 can determine that there is no problem with the response content. In this case, the first user 204 need not use the editing function. Next, another question is "Who are you?"
In response to the question, the information processing module 104 having a function as artificial intelligence,
“I am XX (name).”
When the response is made, the first user 204 determines that this response is not appropriate, and uses the editing function. For example, the icon is operated according to the screen display of the user terminal 200 to set the editing mode. Then, the response content “I am XX (name)” is set to the response content “I am the representative director of XX company”. This allows you to answer the question “Who are you?” To answer “I am the representative director of XX company”.

  In this case, when “Tell me your name” and “Who are you” are registered in the same similar group 106 in the similar database 106 shown in FIG. 5, the contents of the similar database 106 are updated. For example, “Please tell me your name” should be answered “I am XX (name).”, And “Who are you?” It can be re-registered as another group.

  FIG. 8A shows a flowchart describing the evaluation of questions and answers by the second user 206 and the manner in which the first user 204 performs editing.

  In FIG. 8A, the second user 206 accesses the virtual first user 204b in which the communication providing system 100 exists and asks a question (question) (S11). The communication providing system 100 generates an answer to the question (S12) and transmits the answer to the second user 206 (S13). Then, the second user 206 evaluates the obtained answer (S14).

  The communication providing system 100 totals the evaluation values (S15). If the evaluation value is normal or high, or the evaluation value is higher than a certain level, the answer may be weighted (S16, S17), assuming that the answer is a preferable answer. In this case, the weighted answer may have a high probability of being selected at the next search.

  In addition, if the evaluation of the answer is low, the first user 204 is notified to that effect (S16, S18). The first user 204 that has received the notification (S19) edits the content of the response so that a favorable response is obtained (S20). When the communication providing system 100 receives the edited answer, the communication providing system 100 corrects and updates the corresponding answer recorded in the database (S21). If the first user 204 determines that there is no need to correct the content of the answer, the first user 204 can leave it as it is.

  In FIG. 8A, the second user 206 asks a question and the first user 204 edits the answer to the question. However, one embodiment of the present invention is not limited to this, and the first user 204 has a conversation with the virtual first user 204b appearing in the communication providing system 100, asks himself a question, and evaluates himself. And editing can be performed. By such processing, the first user 204 can know the content of the answer to the question, and can evaluate the answer and edit the answer so that a preferable answer can be obtained. That is, the information processing module 104 having a function as artificial intelligence can be learned.

  As described above, the communication providing system 100 according to the present embodiment can create a new response sentence by a user editing data associated with a question and a response. In this case, the user can learn the artificial intelligence function to respond appropriately to the question while interacting with the pseudo self generated on the communication providing system 100.

  In this case, the first user 204 edits the answer to the question while interacting with the virtual first user 204b generated on the communication providing system 100, and functions as an artificial intelligence. The information processing module 104 can be learned.

[Display and selection of answer candidates]
FIG. 8B shows an aspect in which the communication providing system 100 generates a candidate for an answer to a question and the user selects it. The communication providing system 100 generates candidate answers to the question based on the first database 102a in which personal social data is stored. At this time, the first information processing module 104a having a function as artificial intelligence has a function of generating a plurality of answer candidates based on different logics. The first information processing module 104a having a function as artificial intelligence generates answer candidates by weighting the contents of recent conversations from the personal social data of the first user, and generates another logic. As a result, answer candidates are created based on the content of the conversation with a high appearance frequency from all the data. For example, in response to a question about favorite food, there is logic for answering food that has been eaten recently and logic for answering food that the first user has long liked.

  Further, the information processing module 104b having a function as artificial intelligence is stored in the second database 102b when the first information processing module 104a cannot find an appropriate answer in the first database 102a. Create candidate answers based on the data. In the second database 102b, in addition to storing social data of the entire user, for example, various information on the Internet is collected and stored by crawling. The answer to the question generated by the information processing module 104b having the function as artificial intelligence is not an answer reflecting the individual personality of the user, but a generalized answer for social wisdom. For example, an answer that is pleasing is generated for a question about a player in the home country that won the international match.

  The answer candidates generated by the information processing module 104 having a function as artificial intelligence are output to the terminal 204a of the first user. For example, the first information processing module 104a having a function as artificial intelligence generates seven types of answer candidates and outputs the answer candidates to the first user terminal 204a. FIG. 8B shows a mode in which the answers A1 to A7 are displayed on the terminal 204a of the first user. In the first user's terminal 204a, the answer candidates generated by the information processing module 104 having the function as artificial intelligence are in order of highest priority (the order of answers judged to be most accurate). Answer candidates A1 to A7 are displayed. The first user can refer to these answers A1 to A7 and change the order of answers based on his / her own judgment. In FIG. 8B, the order of the answer candidates A1 and A3 is switched.

  The order of answer candidates changed by the first user is reflected in the information processing module 104 having a function as artificial intelligence. As a result, when the same question is asked next time, answer candidates are generated in the order of A3, A2, and A1. This result is used as a response of the pseudo first user 204b generated by the information processing module 104 having a function as artificial intelligence. In this way, the information processing module 104 having a function as artificial intelligence generates a plurality of answer candidates for a question, and the first user selects the answer candidate, thereby making a pseudo first user. The answer of 204b can be made more appropriate.

  Such an answer candidate editing function can edit an answer candidate by the function of the editing module described above, and can update an answer candidate for the question based on the contents edited by the editing module by the function of the update module.

[Three-dimensional user photos]
The communication providing system 100 according to the present embodiment can acquire a user's appearance as image data, convert it into three-dimensional image data, and display it on a user terminal. For example, in the configuration shown in FIG. 1A, when the second user 206 interacts with the virtual first user generated on the communication providing system 100, the video of the first user is displayed on the user terminal 200. Can be displayed.

  User image data can be acquired using the imaging function of the user terminal 200. For example, the user takes a picture of himself / herself using the user terminal 200 and uploads the image data to the communication providing system 100. Alternatively, in the case where information on a user's photographing of the user and exchanging messages with other users is recorded in SNS or the like, image data may be extracted using the photographed information. it can. Thereby, the latest image data of the user can be acquired, and the three-dimensionalization of the user photo can be realized based on the latest image data of the user.

  The communication providing system 100 can generate 3D image data by estimating and calculating the 3D shape of a person shown in the 2D photo data. As the method, for example, three-dimensional image data is created by a method for obtaining depth information from the density distribution of the texture of the object or a method for calculating a normal direction from the intensity of light reflection on the object surface.

  The user's three-dimensional image data may be appropriately updated as described above. As another configuration, for example, image data is retrieved from the personal social data of a specific user recorded in the first database 102a and the user is captured using the face recognition function. Is identified. And the structure which selects the newest data from the image data in which a user is reflected, and updates a user's three-dimensional image data based on the data can also be provided.

  In this way, when interacting with a virtual individual existing on the communication providing system 100, the other party's video is displayed on the user terminal as a three-dimensional image, thereby giving a sense of reality to the communication. In this case, the 3D image of the virtual individual is updated to the most recent data, so that the appearance of the other party becomes more specific, and the feeling of intimacy can be further enhanced through pseudo communication.

[Automatic question generation]
The communication providing system 100 according to the present embodiment has a function of automatically generating and registering a question sentence using social data stored in the database 102.

  For example, in the first database 102 a, many personal “tweets” may be recorded as personal social data of the first user 204. Personal “tweets” include, for example, “I went shopping in Tokyo today” and “I'm waiting in Shibuya”. Such personal social data does not have a form of response to the inquiry, but “tweet” includes at least one piece of information.

  In the above sentence example, information on place names such as “Tokyo” and “Shibuya” is included. In addition, various information such as food, weather, clothes, stock prices, news keywords, and names can be found from personal social data.

  The information processing module 104 having a function as an artificial intelligence creates and registers question data from information obtained by decomposing text data of such personal social data into nouns, verbs, adjectives, etc., and syntactic interpretation. It has a function to keep. The information processing module 104 having a function as artificial intelligence refers to social data of a plurality of users registered in the second database 102b and learns what kind of conversation is being made. For example, in response to the question “Where did you go?”, If there are many conversations such as “The function went to XX (place name).” ? "Is generated and registered in the first database 102. If a plurality of question sentences can be created, they can be registered in the similar database 106.

  As described above, the information processing module 104 having a function as artificial intelligence automatically creates a question sentence from personal social data, thereby enhancing the dialogue ability.

[How to express humanity]
You may make it give fluctuation to the reply to a question. For example,
"How are you?"
In response to the question
"I'm fine. How are you?"
Even if a reply is set, this reply may not necessarily be made. For example, when many other users are replying “very good!”, They may be influenced and respond in the same way.

  For this fluctuation, parameters such as a person who has a high probability of occurrence of fluctuation or a person who is not easily influenced by other users may be assigned to the user based on a personality analysis of the user. Thereby, the personality of each user or the userness can be produced depending on how the fluctuation is reflected. Such parameters can be stored in the communication providing system 100 as user attributes at the time of registration in the communication providing system 100 and read out as necessary. In addition, as a result of such parameters and personality analysis, the result of detecting the temporal change of the user's answer accumulated in the first database 102a is stored in the communication providing system 100, and if necessary, Can be read. Similarly, the result of detecting temporal changes in the information related to the user accumulated in the first similar database 106a can be stored in the communication providing system 100 and read out as necessary.

[Derivation of answers to questions]
The information processing module 104 having a function as artificial intelligence can perform a response to the inquiry as follows.

-Overall configuration-
For example, it is assumed that the user 1 and the user 2 have the following conversation. Here, T1, T2,... Represent time.

User 1: "Hello" [T1]
User 2: "Hello" [T2]
User 1: “How are you?” [T3]
User 2: “You are fine, you are” [T4]
User 1: “Today is wonderful” [T5]

  The contents of such a conversation between the user 1 and the user 2 are summarized as shown in Tables 1 and 2.

  In such a typical conversation between the user 1 and the user 2, for example, the answer to the question of the user 1 is "How are you?" I understand that. This is, the user 1 to the user 2, can be understood from the context in which has already said, "Hello".

  In some cases, the user gives an answer without receiving a question, that is, a one-way conversation such as a tweet. For example, the following tweet:

User 1: “What a great game” [T1]
User 1: “The future of computing is ubiquitous artificial intelligence” [T2]

  Table 3 summarizes such tweets by user 1.

  A combination of questions and answers can be obtained from past conversations. The communication providing system 100 can learn past conversations. The communication providing system 100 according to the present embodiment can generate new questions and infer and predict responses by learning past conversation data (social data accumulated in a database). FIG. 9 shows a configuration for retrieving an answer from a past conversation when a question is received and predicting an appropriate answer.

  An important aspect for predicting answers is where artificial intelligence must mimic human behavior. The response to a question depends on the person asking the question. Therefore, in order to predict a response to a question made by a certain user, the artificial intelligence needs to refer to the user's past conversation data and predict a response content with a high priority obtained from the data.

  However, if an appropriate response sentence cannot be detected from past conversation data, the artificial intelligence finds an appropriate response sentence from the conversation that the first user exchanges with another user. There is a need. When doing this, it is necessary to be careful not to leak confidential matters such as personal information of other users by searching for conversations with other users.

  In such a case, in order to eliminate the risk of leakage of personal information, the artificial intelligence estimates the confidentiality score of each answer in the content of the conversation exchanged by other users.

  For example, an answer sentence that has already been shared with another user has a low confidentiality score. Moreover, even if a general answer sentence as in the above example sentence is not shared with other users, the confidentiality score can be low.

  FIG. 10 shows a flow of processing for selecting an answer to a question from the user 2 to the user 1. The artificial intelligence first searches for a suitable answer from conversations between the first user and the second user exchanged in the past (S21). As a result, when a suitable answer is found, it is selected as an answer (S22, S26). On the other hand, when a suitable answer is not found, one or a plurality of answers learned from the conversations of all users are selected (S22, S23). The confidentiality score is evaluated for the answer obtained here (S24). Then, one with a low confidentiality score is selected and selected as an answer (S25, S26).

-Search for answers-
Moreover, as shown in FIG. 11, the answers to the questions may be ranked as follows. For example, as shown in FIG. 11, when an answer is found in a conversation that the first user and the second user have exchanged in the past, the answer can be shared between the two at the “personal level”. Can be an answer. Further, when finding an appropriate response sentence from the conversation in which the first user exchanges with other users, the confidentiality score is evaluated for one or a plurality of obtained answers. An answer with a low confidentiality score can be set as a “public level” answer. Furthermore, as shown in FIG. 12, the social data of a plurality of users is learned, the confidentiality score of one or a plurality of answers obtained therefrom is evaluated, and an answer with a low score is assigned to a “shared level”. Can be an answer.

ここで、

は、利用者ｉと利用者ｊとの会話を表すサブモデルである。Such a user can be expressed by the following equation.

here,

Is a submodel representing a conversation between user i and user j.

は次式で表すことができる。

ここで、Ｑ（ｉ，ｊ）は利用者ｊから利用者ｉに対してなされた質問を表し、Ａ（ｉ、ｊ）は利用者ｉから利用者ｊになされた回答を示し、ｔは対応する時間を示す。And a public submodel that represents all user conversations

Can be expressed as:

Here, Q (i, j) represents a question made from user j to user i, A (i, j) represents an answer made from user i to user j, and t corresponds. Indicates the time to do.

ここで、ｑは利用者ｉに対する利用者ｊの質問（またはクエリ）であり、Ａは予測される回答である。The relationship of the answers to the questions can be expressed as follows using the function f.

Here, q is a question (or query) of user j with respect to user i, and A is a predicted answer.

For example, when the confidentiality score of the answer is higher than the first threshold, the predicted answer is an individual level answer. Further, when the confidentiality score is equal to or lower than the first threshold value and the confidentiality score is lower than the second threshold value, the predicted answer is a public level answer. If it does not correspond to these, it can be treated as a shared level answer. Note that the confidentiality score being lower than the second threshold may be a publicity score that is a concept that forms a pair with confidentiality. For example, if the confidentiality score is C _private and the publicity score is C _public , it can be defined as C _private = 1-C _public .

ここで、Ｃprivateは個人レベルの回答に対する機密性のスコアを与え、Ｃ_{ｐｕｂｌｉｃ}は公開性のスコアを与える。Such an aspect of the answer level can be expressed by the following equation.

Here, Cprivate gives a confidentiality score for the personal level answer, and C _public gives a publicity score.

ここで、ｓｉｍ（ｑ，Ｑｘ）は、ｑ及びｔｕｐｌｅ（Ｑｘ，Ａｘ，ｔｘ）との間の類似性又はマッチングを示す関数である。There is one answer that is judged to be the most appropriate answer for the question, and can be expressed by the following equation.

Here, sim (q, Qx) is a function indicating the similarity or matching between q and tuple (Qx, Ax, tx).

-Integration of context-
When predicting answers to questions, it is important to understand the context. Answers to the same question can rely on past questions and answers made in the conversation. This is especially true for general questions, such as answering yes. The answers to such questions can be applied to the functions described above. In order to integrate the context, it is possible to apply the learning data of questions and answers in a conversation made in the past as a group. FIG. 13 illustrates how contexts are integrated by grouping questions and answers in a conversation. It is shown that the matching of the query with the question and the answer is a weighted sum.

ここで、ａiは重み係数であり、Ｗは文脈ウィンドウに含まれているタプル数を示す。The similarity when integrating contexts can be expressed as:

Here, a i is a weighting factor, and W indicates the number of tuples included in the context window.

-Matching function-
To find the relevance of questions and answers from past conversations, it is necessary to evaluate the similarity between them. Questions and answers can be represented in each query as a set of vectors. Similarity between two sets of vectors can be expressed as a weighted sum of similarities between corresponding vectors. Similarity between vectors can be estimated as cosine distance, Euclidean distance, Mahalanobis distance.

Each query for a question and an answer is composed of a set of vectors of keyword (W), concept (C), type (T), and group (G) as shown in the following equation.

Keywords Keywords are obtained by building words from all messages exchanged between users. Vector similarity between two keywords is weighted (TF-IDF: Robertson, SE, Walker, S., Beaulieu, MM, Gatford, M., & Payne, A. Okapi at TREC-4. In Proceedings of the 4th Text REtrieval Conference (TREC-4), pp. 73-96, 1995]). Or, OkapiBM25 (Harman, D. Ranking algorithms. In WB Frakes & R. Baeza-Yates (Eds.), Information retrieval: Data structures and algorithms, pp. 363-392. Englewood Cliffs, New Jersey, USA: Prentice Hall, 1992]), or any other ranking function used in information retrieval. Similarity between keywords includes semantic similarity. It is considered that two keywords that are semantically related are similar. This semantic relationship is described by WordNet (GA Miller, R. Beckwith, CD Fellbaum, D. Gross, K. Miller. WordNet: An online lexical database. Int. J. Lexicograph. 3, 4, pp. 235-244, 1990), BabelMet (R. Navigli and S. P Ponzetto. BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. Artificial Intelligence, 193, Elsevier, pp. 217-250, 2012), co-appearance Analysis (Harris ZS Co-occurrence and transformation in linguistic structure, Language, 33, pp. 283-340., 1957). When estimating similarity, the order and location of keywords in the document must be considered. As a measure to integrate the order in the system, the distribution of indexes representing locations is used. FIG. 14 shows what keyword order is important when seeking a match.

・ Concepts Concepts can be obtained by compressing the vocabulary dimension. Latent Semantic Analysis can be used to provide a limited list of concepts (Thomas K. Landauer, Peter W. Foltz et Darrell Laham. Introduction to Latent Semantic Analysis, Discourse Processes, vol. 25, p. 259-284, 1998). Each question and response is projected into the concept space. This projection gives a concept vector. The cosine distance can estimate the similarity between concept vectors.

• Types Types are defined as different groups of messages from the perspective of grammatical structure and reputation analysis. This group includes questions, people, rough places, order, romantic messages, happy messages, and more. These classifications can be obtained by the artificial intelligence learning of manually labeled feature vectors, grammatical data, concepts and keywords.

Group Tuples (Q, A) of all users are clustered based on the similarity of text between answers. Each cluster is given a name. The group vector is a list of clusters to which the tuple (Q, A) belongs. Both have similar answers, so obviously two questions in the same group are similar. FIG. 15 shows a mode in which questions and answers are clustered by the similarity of the answers, and each cluster forms a group.

・ Generation of templates Artificial intelligence can be learned using all available data. The input vector is a question and the output vector is an answer. In order to obtain the answer to any question, the learned artificial intelligence estimates the answer template. Combined with query matching along with the questions stored in the database, the answers in this template can be matched to the answers in the database. This is important in order to be able to find an answer when a question is not found in the data. Any supervised algorithm can be used in this case as a neural network, support vector machine, k-Nearest-Neighbors, Gaussian mixture model.

  Co-occurrence analysis on keywords between questions and answers can be performed on some of the keywords in the answer template. In FIG. 16, the vectors (keywords, concepts, types) constituting each question have a predictor for estimating an equivalent answer vector. The answer template group is equivalent to that in the question template.

  As described above, in the communication providing system according to the embodiment of the present invention, the communication providing system acquires data from various SNSs and accumulates them in a database, and an information processing module having a function as artificial intelligence is used by a user. Learning thoughts can provide appropriate answers to questions.

  The communication providing system according to an embodiment of the present invention collects social data from a plurality of SNSs, and an information processing module having a function as artificial intelligence infers and learns it, so that one user can conceive. You can also offer no thoughts.

  Furthermore, according to an embodiment of the present invention, if a database (first database 102a) that generates a virtual user generated by the communication providing system 100 is made to acquire specialized knowledge, the virtual user is acquired. Various services can be provided. Such expertise may be acquired from an SNS used by the corresponding user.

  For example, if at least one of the specialized knowledge in each field such as natural science, humanities, psychology, law, finance, engineering, etc. is stored in a database that generates virtual users, the applicable field Expertise can be made available to virtual users. In addition, it is preferable that the expertise in this case includes not only uniform expertise accumulated by books and the like, but also information on expertise used by natural persons as a conversation on a social network. By including information related to such conversational expertise, the communication providing system 100 can effectively use various expertise as an answer to a question.

  Note that such specialized knowledge can be acquired as pseudo first user knowledge generated by artificial intelligence, for example, if the first user desires. In this case, the pseudo first user can not only acquire specific specialized knowledge but also use the first user in consideration of the individuality of the first user. For example, if the first user has knowledge in the medical field, the pseudo first user also has expertise in the medical field, and the pseudo first user has further specialized knowledge. If you acquire expertise in the legal field, you will be able to demonstrate your abilities in the field of forensic medicine.

  In this case, the expertise stored in the first database 102a may be stored in a question and answer (ie Q & A) format. The social data (knowledge) stored in the first database 102 can be updated in a timely manner, and the data is not lost, so that the amount of knowledge can be increased.

  Further, by reflecting the information of the second database 102b in which social data of all users is stored in the first database 102a in which such expertise is stored, a more appropriate answer can be obtained by enhancing the learning effect. Can be output.

  As specialized knowledge, it is possible to deal with knowledge in various fields such as justice, administration, medical care, environment, economy, etc. according to user attributes. That is, according to an embodiment of the present invention, a virtual individual having high expertise can appear on the communication providing system in accordance with the user's attributes, and the service can be provided to other users.

[Knowledge sharing by group: Group AI]
FIG. 2 shows an information processing module (first information processing module) having a function as artificial intelligence configured based on personal social data and a function as artificial intelligence configured based on social data of the entire user. And the information processing module (second information processing module) having The present invention can further include an information processing module (third information processing module) having a function as an artificial intelligence in which a plurality of first information processing modules are aggregated to share information or a knowledge level.

  FIG. 17 shows a mode in which there are a plurality of first information processing modules 104a having functions as artificial intelligence configured based on personal social data (first information processing modules 104a_1 to 104a_n (n is 2 or more). integer)). As described with reference to FIG. 2, the first information processing module 104a generates pseudo users called “private AI” and “personalized AI”. On the other hand, the second information processing module 104b linked to the first information processing module 104a is based on a fusion of knowledge obtained by crawling social data of the entire user and various information on the social network. It is artificial intelligence and is called “Everyone AI”, “Common Sense AI” or “Moral AI”.

  FIG. 17 shows that a plurality of first information processing modules 104a_1 to 104a_n form one set 110. A third information processing module 112 having a function as an artificial intelligence that shares knowledge and information is connected to the set 110. The third information processing module 112 is artificial intelligence generated by a common knowledge level generated by the plurality of first information processing modules 104a_1 to 104a_n. That is, the third information processing module 112 is a pseudo user having all the knowledge, personality, and personality of the plurality of pseudo users generated by the plurality of first information processing modules 104a_1 to 104a_n. It can be said that there is. Such third information processing module 112 can be referred to as a “group AI”.

  The third information processing module 104c can demonstrate the knowledge and individuality of each pseudo user generated by the plurality of first information processing modules 104a_1 to 104a_n. From another viewpoint, the pseudo user generated by the third information processing module 112 is different from the pseudo user generated by the first information processing module 104a. Since a plurality of individuals are generated together, it can be said that they have a corporate character.

  The plurality of first information processing modules 104a_1 to 104a_n can have a conversation with the third information processing module 112. Thereby, the plurality of first information processing modules 104a_1 to 104a_n can share each other's information and knowledge. Even if one of the pseudo users generated by the first information processing modules 104a_1 to 104a_n leaves the group, the third information processing module 112 can share the knowledge and personality of the detached user. Since it can hold | maintain, the loss of intelligence by member withdrawal can be prevented. For example, when the third information processing module 112 having a function as artificial intelligence is operated as a function of a part of a certain corporation, even if a member (natural person) belonging to the corporation leaves in the middle, the member who has left You can keep your skills in that corporation.

  The third information processing module 104c can acquire new information from the plurality of first information processing modules 104a_1 to 104a_n, whereby artificial intelligence grows. That is, as the skill of each pseudo individual generated by the first information processing modules 104a_1 to 104a_n is improved, the skill becomes a skill of a pseudo user generated by the third information processing module 112. Reflected.

  Note that, as shown in FIG. 17, the third information processing module 112 may be placed in an accessible state with the second information processing module 102b through an electric communication line. Thereby, the third information processing module 103c can collect a wide range of information.

  As described with reference to FIG. 17, according to the present invention, a plurality of pseudo users generated by artificial intelligence can be used to determine the knowledge, intelligence, and personality of a plurality of users forming the set. A pseudo user with everything can be formed and used.

DESCRIPTION OF SYMBOLS 100 ... Communication provision system, 102 ... Database, 104 ... Information processing module, 106 ... Similarity database, 107 ... Question database, 108 ... Response database, 200 ... User terminal, 202 ... Plural users, 204 ... First user, 206 ... Second user, 300 ... SNS

Claims (38)

Social data of a first user registered in one or more social networking services; social data of a plurality of users registered in the plurality of social networking services; A plurality of text data collected from social networking services and a database in which information is stored;
As artificial intelligence for determining a second user's question for the first user by inferring or learning an answer to the question based on at least a portion of the information stored in the database An information processing module having a function.   The database includes a first database that stores social data of the first user, a second database that stores social data of the plurality of users, and a third database that stores the plurality of text data. The communication providing system according to claim 1, wherein the communication providing system is hierarchized into a database.   The information processing module having the function as the artificial intelligence is configured to send an appropriate answer to the question from among the data recorded in the first database, the second database, and the third database. The communication providing system according to claim 2, wherein the inference is generated based on at least a part of information recorded in the database.   The information processing module having the function as the artificial intelligence is configured to analyze an answer to a question from the social data of the plurality of users stored in the first database, and infer or learn an answer to the question. The communication providing system according to claim 3, wherein the communication providing system is determined.   The communication providing system according to claim 4, wherein the trend analysis is to analyze temporal changes in the answers of the plurality of users with respect to the question.   The social data of the first user and the social data of the plurality of users are text data and text data generated from any one or more of audio data, photo data, and video data. The communication providing system described. Grouping similar questions as similar questions, and including a similar database in which answers corresponding to the similar questions are associated,
The communication providing system according to claim 6, wherein the information processing module having a function as the artificial intelligence selects an answer to the question from the similar database.   The communication providing system according to claim 7, wherein the similar database includes a similar question database that records similar question contents in a group, and a similar answer database that stores answers corresponding to the similar questions.   The similar database includes a first similar database in which data categorizing the contents of a question and an answer among social data of the first user is stored, and a question from social data of the plurality of users. And a second similar database storing data typifying the contents of answers and a third similar database storing data categorizing the contents of questions and answers from the text data The communication providing system according to claim 8.   The communication providing system according to claim 1, further comprising an evaluation module that obtains information obtained by evaluating the answer by the second user and assigns an evaluation value to the answer.   The communication providing system according to claim 10, wherein the evaluation module includes a notification module that notifies the first user of an evaluation result of the answer.   The first user who has received the notification from the notification module edits the content of the answer to the question, and updates to update the content of the answer to the question based on the content edited by the editing module The communication providing system according to claim 11, comprising a module.   The evaluation module assigns a priority level based on the evaluation result, and the information processing module having a function as the artificial intelligence infers or determines an appropriate answer to the question based on the priority The communication providing system according to claim 10.   The communication providing system according to claim 1, wherein the database updates and accumulates the social data of the first user, the social data of the second user, and the text data over time.   The communication providing system according to claim 1, further comprising an image data generation module that generates three-dimensional image data of the first user. The database includes phonetic data of the first user and generates phoneme data from the voice data;
The communication providing system according to claim 1, further comprising: a voice generation module that generates voice of conversation using the phoneme data.   The communication providing system according to claim 7, wherein the information processing module having the function as the artificial intelligence analyzes a frequency of the answer to the question and records a question corresponding to a frequently-answered answer in the similar database.   The information processing module having the function as the artificial intelligence parses text data included in the social data of the first user, and infers or learns an answer to the question, and determines it. The communication providing system described.   The communication providing system according to claim 1, wherein the communication providing system has a function of setting whether or not to generate an answer in the information processing module with respect to the question by the second use.   The information processing module generates a plurality of answer candidates for the second user's question, and the first user edits the answer candidate for the question, and the contents edited by the editing module The communication providing system according to claim 1, further comprising: an update module that updates a content of an answer to the question based on the information. Generating a virtual personal image of the first user based on the registration information of the first user;
Accepts a question sent by the second user to the virtual individual;
An answer to the question is determined by inferring or learning based on any one of the first user's social data, a plurality of users' social data or a plurality of pre-registered text data,
The communication providing method, wherein the determined answer is provided to the second user.   First, search the social data of the first user for an answer to the question, and secondly search the social data of the plurality of users when an appropriate answer cannot be obtained, and provide an appropriate answer. The communication providing method according to claim 21, wherein thirdly, a plurality of pre-registered social data are searched when they cannot be obtained.   The communication providing method according to claim 22, wherein an answer to the question is determined by trend analysis of the answer to the question from the social data of the plurality of users, and inferring or learning the answer to the question.   The communication providing method according to claim 23, wherein the trend analysis analyzes temporal changes of the answers of the plurality of users with respect to the question.   The social data of the first user and the social data of the plurality of users are text data and text data generated from one or more of audio data, photo data, and video data. How to provide communication. Group similar questions as similar questions, associate corresponding answers to the similar questions and store them in a similar database;
The communication providing method according to claim 21, wherein an answer to the question is selected from the similar database.   Data similar to the contents of the questions and answers from the social data of the first user is stored in the similar database, and the contents of the questions and answers are classified from the social data of the plurality of users 27. The communication providing method according to claim 26, wherein stored data is stored, and data in which questions and answers are classified from the text data is stored.   The communication providing method according to claim 21, wherein the second user acquires information evaluated by the answer and assigns an evaluation value to the answer.   The communication providing method according to claim 28, wherein the first user is notified of the evaluation result of the answer.   The first user who has received the notification receives content obtained by editing the content of the answer to the question, and updates the content of the answer to the question based on the edited content. How to provide communication.   30. The communication providing method according to claim 29, wherein a priority level is assigned based on the evaluation result, and an appropriate answer to the question is inferred or learned based on the priority order.   The communication providing method according to claim 21, wherein the social data of the first user, the social data of the second user, and the text data are updated and accumulated over time.   The communication providing method according to claim 21, wherein three-dimensional image data of the first user is generated.   The communication providing method according to claim 21, wherein the first user's voice data is stored, phoneme data is generated from the voice data, and a voice of conversation is generated using the phoneme data.   27. The communication providing method according to claim 26, wherein the frequency of the answer to the question is analyzed, and a question corresponding to a frequently-answered answer is recorded in the similar database.   The communication providing method according to claim 21, wherein the text data included in the social data of the first user is parsed, and an answer to the question is inferred or learned.   The communication providing method according to claim 21, wherein whether to generate an answer to the question by the second use is set. A plurality of answer candidates for the second user's question are generated, and the first user edits the answer candidate for the question, and updates the content of the answer to the question based on the edited content The communication providing method according to claim 21.

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