JP2023082970A - expert answer system - Google Patents

Abstract

To provide an expert answer system capable of pseudo-outputting an answer from an individual expert to a user who desires to obtain an answer to a question from the individual expert in a specific field.SOLUTION: An expert answer prediction system 1 includes a machine learning device 10. The machine learning device 10 includes: a learning model storage unit 11 that stores, as a digital clone Ci of each individual expert Ei, a learning model built to be able to predict an answer that each individual expert Ei will give to a question by learning life logs of each of individual experts E1 to EN; a question input unit 12 that inputs question data for any individual expert Ex into the digital clone Cx corresponding to the individual expert Ex; and an answer output unit 13 that outputs an answer predicted as an answer of the individual expert Ex by the digital clone Cx according to the input question data.SELECTED DRAWING: Figure 1

Description

The present invention relates to a system for predicting answers to questions given by individual experts having expertise in specific fields and outputting the predicted answers.

2. Description of the Related Art Conventionally, as a system for outputting answers to questions given by users, for example, a system seen in Patent Literature 1 is known. In this system, answers to various questions are prepared and stored in advance, and answers matching the content of the questions given by the user are extracted and output.

Japanese Patent No. 6770283

By the way, in many fields, users with questions about a field often want answers (including advice, recommendations, etc.) from a reputable individual expert in that field.

However, it is generally difficult for a general user to contact a prominent individual expert and obtain an answer from the individual expert. In addition, in the system as seen in Patent Document 1, in many cases, only general-purpose answers can be obtained, and the usefulness and quality of the answers tend to be poor for the user.

The present invention has been made in view of this background, and is capable of outputting a pseudo-response from an individual expert in a specific field to a user who wishes to obtain an answer to a question from the individual expert. The purpose is to provide an expert response system that can

In order to achieve the above object, the expert answer prediction system of the present invention is an expert answer prediction system that predicts and outputs answers from individual experts who have expertise in a specific field,
comprising a machine learning device that constitutes a digital clone of said expert;
The machine learning device
A learning model memory that stores a learning model constructed so as to be able to predict an answer to a question by the individual expert by learning the life log of the individual expert as a digital clone of the individual expert. Department and
a question input unit that acquires questions for the individual expert and inputs question data indicating the contents of the questions into the learning model;
An answer output unit for outputting an answer predicted by the learning model as an answer of the individual expert according to question data input from the question input unit.

According to the present invention, since the digital clone is a learning model constructed by learning the lifelog of an individual expert in a specific field, it can simulate the thinking and behavior of the individual expert. Become. Therefore, when the question data is input to the learning model, the learning model can predict a highly reliable answer as an answer issued by the individual expert, and the highly reliable answer can be used as a question. It can be output from the output section.

Therefore, according to the present invention, it is possible to output a pseudo-response from an individual expert to a user who wishes to obtain an answer to a question from an individual expert in a specific field.

Further, in the present invention, the machine learning device stores a plurality of learning models respectively corresponding to a plurality of individual experts in the learning model storage unit, and the question input unit stores the acquired question item or according to a request given by the questioner regarding the question item, one learning model is selected from among the plurality of learning models, and the question data is input to the selected learning model. can be configured as

According to this, it is possible to output highly reliable answers from the question output unit to questions in various fields as answers from individual experts in each field. In particular, when selecting a learning model that inputs question data in response to a request from the questioner, it is possible to present the questioner with a pseudo-response from an individual expert desired by the questioner. Become.

The block diagram which shows the structure of the expert answer prediction system of embodiment of this invention.

One embodiment of the invention is described below with reference to FIG. The expert answer prediction system 1 of this embodiment includes a machine learning device 10 that configures digital clones C1, C2, . . . , CN of a plurality of experts E1, E2, . Hereinafter, any one of the experts E1, E2, .

Each expert Ei is a real individual expert with expertise in a particular field. The individual expert can be, for example, a widely known (prominent) expert in a particular field or in the general public.

A specific field in which each expert Ei has expertise (hereinafter sometimes referred to as a specialized field) may be any field regardless of its type or breadth. The specialized fields include, for example, academic fields such as physics and linguistics, fields related to specific organisms such as dogs, cats, fish, and insects, fields related to daily life such as cleaning, cooking, and laundry, and each expert It may be a field that Ei claims to be his/her own field of expertise or field of expertise, or a field that is generally recognized as a field of expertise or specialty of each expert Ei.

The specialist fields of the experts E1 to EN may be different from each other, but the specialist fields of the experts E1 to EN may overlap in whole or in part. Further, the specialized field of each expert Ei may span multiple fields.

The machine learning device 10 is composed of, for example, one or more computers including a processor such as a microcomputer, a memory, an interface circuit, a communication device, etc., and an external network NW (wide area network) composed of the Internet, a telephone communication network, etc. It is configured to be able to communicate with the communication terminal 20 used by the user P via the external network NW.

Then, the machine learning device 10 can build (generate) a digital clone Ci as a learning model for each expert Ei by the functions implemented by the implemented hardware configuration and program (software configuration). and functions as a learning model storage unit 11 for storing and holding the digital clones Ci of each expert Ei, and question data indicating the content of questions given by the user P via the communication terminal 20 are stored digitally. A function as a question input unit 12 for inputting to a required digital clone Cx (=C1, or C2, or, . . . , or CN) among clones C1 to CN, and a function as an answer output unit 13 for outputting the answers received.

In this case, the machine learning device 10 uses the lifelog of each expert Ei as learning data for each expert Ei to construct a digital clone Ci of each expert Ei through machine learning processing. The lifelog of each expert Ei is information that includes a lot of information unique to each expert Ei or characteristic information. The life log includes, for example, attribute information of each expert Ei (age, gender, location, specialty field, hobby, etc.), SNS (Social Networking Service) information sent by each expert Ei, and meetings of each expert Ei. It is possible to use recording data or video recording data etc. at meetings such as.

The worker can input such a life log to the machine learning device 10 through an appropriate input device at any time. Alternatively, the machine learning device 10 can automatically collect information from the external network NW such as the Internet for each expert Ei.

The machine learning device 10 constructs a learning model corresponding to each expert Ei by machine learning processing using life logs given to each expert Ei as learning data, and applies the learning model to each expert Ei is stored in the learning model storage unit 11 as a digital clone Ci of . In this case, the learning model as a digital clone Ci of each expert Ei functions as artificial intelligence capable of simulating the thinking and behavior of the expert Ei. is given, an answer (response information) predicted as the answer of the expert Ei himself can be generated.

The input of new lifelogs of each expert Ei to the machine learning device 10 and the machine learning process for each expert Ei are continuously performed. Along with this, the digital clone Ci of each expert Ei is updated so as to increase its reliability as an artificial intelligence that simulates the thoughts and behaviors of each expert Ei.

As described above, the machine learning device 10 constructs a plurality of digital clones C1 to CNi as learning models for the respective experts E1 to EN, and stores and holds them in the learning model storage unit 11. FIG. As the technology for generating the digital clones C1 to CN, for example, the technology proposed in Japanese Unexamined Patent Application Publication No. 2018-190457 can be adopted.

The communication terminal 20 used by the user P is composed of, for example, a smartphone, a tablet terminal, a personal computer, or the like, and includes a display section 20a composed of a liquid crystal display or the like, and a sound generating section 20b composed of a speaker or the like. A predetermined service application (hereinafter simply referred to as a service application) for using the machine learning device 10 is pre-installed in the communication terminal 20 .

Then, the communication terminal 20 can communicate with the machine learning device 10 via the external network NW while the service application is activated. In this case, the communication terminal 20 has a function of receiving a question from the user P and transmitting it to the machine learning device 10, or receiving an answer corresponding to the question from the machine learning device 10 and notifying the user P It has functions.

Next, the operation of the expert answer prediction system 1 of this embodiment will be described. When a user P wants to obtain an answer to a required question from one of a plurality of experts E1 to EN, an expert Ex, the user P who asks the question connects the Launch an installed service app. Then, the user P inputs a question to the communication terminal 20 by operating the operation unit (touch panel, operation switch, keyboard, etc.) of the communication terminal 20 or by voice input operation. When the user P wants to designate a desired expert as the question target expert Ex, the user P inputs the identification information (name, etc.) of the desired expert into the communication terminal 20 together with the questions.

For example, when user P wants to ask a specific expert who is good at cleaning and tidying up how to tidy up his room, he says, "Mr. I think so, but what should I do?” is input into the communication terminal 20.

Also, for example, when user P wants to ask a prominent expert in drama critics, etc., about the evaluation of a particular drama, he can say, "Mr. XX (expert's name), How is the content?” is input into the communication terminal 20.
If the expert to be questioned is not specified, the input of identification information such as the name of the expert is omitted.

The communication terminal 20 to which the questions are input as described above transmits question transmission data indicating the input questions to the machine learning device 10 via the external network NW. The question transmission data is transmitted to the machine learning device 10 as text data or voice signal data, for example.

In the machine learning device 10 that has received the question transmission data, the question input unit 12 generates text-format question data indicating the content of the question from the received question transmission data. When the received question transmission data is text data, the question transmission data may be used as it is as the question data.

Then, if a desired expert is specified in the received question transmission data, the question input unit 12 selects the specified digital clones C1 to CN stored and held in the learning model storage unit 11. Enter the question data into the digital clone Cx of the expert Ex. As a result, the question data is input to the digital clone Cx determined according to the request of the user P who is the questioner.

In addition, if the desired expert is not specified in the received question transmission data, the question input unit 12, from the question transmission data or the generated question data, based on the keyword etc. included in the question content, Identify the field of question content. Then, the question input unit 12 selects a digital clone of one expert corresponding to the specified field from among the digital clones C1 to CN stored and held in the learning model storage unit 11. Clone Cx is selected, and question data is input to the selected digital clone Cx. As a result, the question data is input to the digital clone Cx determined according to the content of the question.

The digital clone Cx to which the question data is input generates, in text data format, an answer that is expected to be answered by the expert Cx to the question content indicated by the question data, and the answer output unit 13 receives the generated answer. hand over. The reply output unit 13 transmits reply transmission data indicating the contents of the reply to the communication terminal 20 of the user P via the external network NW. The reply transmission data is transmitted to the communication terminal 20 as text data or voice signal data, for example.

Then, the communication terminal 20 that has received the reply transmission data notifies the user P of the content of the reply indicated by the reply transmission data. In this case, the content of the answer is displayed as character information on the display unit 20a of the communication terminal 20, or is output from the pronunciation unit 20b of the communication terminal 20 as voice information. When notifying the user P of the content of the answer, the image of the questioned expert Ex may be displayed on the display unit 20a of the communication terminal 20. FIG. Further, when the content of the answer is output from the communication terminal 20 as voice information, an image that looks as if the expert Ex is actually speaking may be displayed on the display unit 20a.

As described above, the user P can obtain an answer corresponding to the question to the question target expert Ex from the digital clone Cx corresponding to the expert Ex. In this case, since the digital clone Cx is constructed by machine learning processing so as to be able to simulate the thinking and behavior of the expert Ex, the user P can give an answer equivalent to the answer obtained from the expert Ex himself. can be obtained artificially. In particular, when the user P designates an expert Ex to be questioned, the user P obtains an answer from the digital clone Cx as if an answer was actually obtained from the desired expert Ex. can.

In the above-described embodiment, the communication terminal 20 used by the user P is used to input questions and notify the content of answers. 10 may be attached.

Further, in the above-described embodiment, even if the user P does not specify the desired expert, for example, the user P can specify (request) attributes such as the gender and location of the expert to be questioned. Question data may be input to a digital clone corresponding to an expert selected in accordance with a specified attribute from experts corresponding to the field of question content.

1... expert answer prediction system, 10... machine learning device, 11... learning model storage unit, 12... question input unit, 13... answer output unit.

Claims (2)

An expert answer prediction system that predicts and outputs answers from individual experts who have expertise in a specific field,
comprising a machine learning device that constitutes a digital clone of said individual expert;
The machine learning device
A learning model memory that stores a learning model constructed so as to be able to predict an answer to a question by the individual expert by learning the life log of the individual expert as a digital clone of the individual expert. Department and
a question input unit that acquires questions for the individual expert and inputs question data indicating the contents of the questions into the learning model;
An expert answer prediction system, comprising: an answer output unit for outputting an answer predicted by the learning model as an answer of the individual expert according to question data input from the question input unit. In the expert answer prediction system according to claim 1,
The machine learning device stores a plurality of learning models corresponding to each of a plurality of individual experts in the learning model storage unit, and the question input unit responds to the content of the acquired question item, Alternatively, one learning model is selected from the plurality of learning models in response to a request given by the questioner regarding the question item, and the question data is input to the selected learning model. An expert answer prediction system characterized by:

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