JP7347794B2 - Interactive information acquisition device, interactive information acquisition method, and program - Google Patents

Description

The present invention relates to an interactive information acquisition device, an interactive information acquisition method, a program, and a recording medium.

2. Description of the Related Art Conventionally, techniques have been proposed that allow a user to have a simulated dialogue on a computer (for example, Patent Document 1).

In addition, in fields such as medical care and nursing care, it is necessary to ask users (patients, etc.) questions such as the presence or absence of life-sustaining treatment and matters related to personal care, and to obtain information such as the user's intentions in advance. .

Japanese Patent Application Publication No. 2004-178299

However, users feel psychological resistance to thinking about and answering questions about the availability of life-sustaining treatment and matters related to personal affairs. Therefore, it is difficult for the person asking the question (for example, staff at a hospital or nursing care facility) to obtain information from the user. Similarly, when the above-mentioned question is presented to the user through the technology disclosed in Patent Document 1, the user feels psychological resistance, making it difficult for the questioner to obtain information from the user. It is.

Therefore, an object of the present invention is to provide an interactive information acquisition device, an interactive information acquisition method, a program, and a recording medium that can easily acquire information.

In order to achieve the above object, the interactive information acquisition device of the present invention has the following features:
Including a general question presentation means, a general answer acquisition means, a clarity calculation means, an awareness degree calculation means, an implication ratio calculation means, a determination means, a purpose question presentation means, and a purpose answer acquisition means,
The general question presenting means refers to a general question database and presents general questions to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition means acquires a user's general answer to the general question,
The clarity calculation means calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
The awareness level calculation means calculates the awareness level for each of the question attributes using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation means calculates an implication ratio in the presented general question for each question attribute from the awareness level,
The determination means refers to a target question database and determines whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting means refers to the objective question database and presents the objective question linked to the set question attribute to the user,
The objective answer acquisition means acquires the user's objective answer to the objective question, and stores the acquired answer in a will database;
The intention database is a device that is a database that stores the user's objective answers to the objective questions.

The interactive information acquisition method of the present invention includes:
Including a general question presentation process, a general answer acquisition process, a clarity calculation process, an awareness level calculation process, an implication ratio calculation process, a determination process, a purpose question presentation process, and a purpose answer acquisition process,
The general question presenting step refers to a general question database and presents the general question to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition step acquires a user's general answer to the general question,
The clarity calculation step calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
In the awareness level calculation step, the awareness level is calculated for each question attribute using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation step calculates an implication ratio for each question attribute in the general question from the awareness level,
The determination step refers to a target question database to determine whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting step refers to the objective question database and presents the objective question linked to the set question attribute to the user;
The objective answer acquisition step acquires the user's objective answer to the objective question, and stores the acquired answer in a will database;
The intention database is a database that stores the user's objective answers to the objective questions, and the method is such that each of the steps are executed by a computer .

According to the present invention, for example, information can be easily acquired.

FIG. 1 is a block diagram showing the configuration of an example of an apparatus according to the first embodiment. FIG. 2 is a block diagram showing an example of the hardware configuration of the device according to the first embodiment. FIG. 3 is a flowchart illustrating an example of processing in the apparatus of the first embodiment. FIG. 4 is a schematic diagram showing an example of interaction between the device of Embodiment 1 and a user. FIG. 5 is a schematic diagram illustrating an example of interaction between the device of Embodiment 1 and a user. FIG. 6 is a block diagram showing the configuration of an example of a device according to the second embodiment. FIG. 7 is a block diagram showing an example of the hardware configuration of the device according to the second embodiment. FIG. 8 is a flowchart illustrating an example of processing in the apparatus of the second embodiment. FIG. 9 is a block diagram showing the configuration of an example of a device according to the third embodiment. FIG. 10 is a block diagram showing an example of the hardware configuration of the device according to the third embodiment. FIG. 11 is a flowchart illustrating an example of processing in the apparatus of the third embodiment. FIG. 12 is a schematic diagram showing an example of a calculation formula for calculating clarity, awareness level, and implication ratio. FIG. 13 is a schematic diagram showing an example of a calculation formula for calculating clarity, awareness level, and implication ratio.

In the interactive information acquisition device of the present invention, when the threshold is satisfied, the clarity calculation means further calculates the clarity from the objective answer, and the objective answer acquisition means further calculates the clarity from the objective answer and the objective answer. It may also be possible to store the target answer in association with the clarity level.

The interactive information acquisition device of the present invention further includes a machine learning means, and the machine learning means uses machine learning to calculate the information from the presentation of the general question to the presentation of the objective question based on the clarity of the objective answer. An aspect may also be adopted in which scenario data is generated by converting the scenario into data. In the aspect, the machine learning means may generate, by machine learning, the scenario data that takes the shortest time from the level of awareness to presenting the objective question.

In the interactive information acquisition device of the present invention, if the threshold is not satisfied, the general question presenting means may present a general question different from the presented general question.

In the interactive information acquisition device of the present invention, a plurality of question attributes are linked to one objective question and one general question, and the objective question presenting means selects a plurality of question attributes of the presented general question. It may also be possible to present objective questions that include all of the questions.

The interactive information acquisition device of the present invention further includes an attention degree calculation means, the attention degree calculation means calculates an attention degree indicating the number of times the question attribute is used, and the implication ratio calculation means calculates an attention degree indicating the number of times the question attribute is used. An aspect may be adopted in which a value obtained by multiplying the degree of awareness by the degree of attention is calculated as the implication ratio.

The interactive information acquisition device of the present invention further includes attribute information acquisition means, wherein the attribute information acquisition means acquires user attribute information, and the objective question includes a condition of the respondent for each objective question. The purpose question presenting means may present to the user a purpose question that satisfies the respondent's conditions based on the attribute information.

The interactive information acquisition device of the present invention further includes an audio input/output means, and the audio input/output means outputs each of the presentations in the general question presentation means and the objective question presentation means as audio data, and An aspect may be adopted in which each of the acquisitions by the general answer acquisition means and the objective answer acquisition means is input as audio data.

The interactive information acquisition device of the present invention further includes a purpose answer updating means, and the purpose answer acquiring means further stores time information at which the purpose answer is stored in the intention database in association with the purpose answer. The objective answer updating means may be configured to update at least one of the stored objective answer and the time information when a preset period has elapsed since the time information. In the aspect, the objective answer updating means may extend the period for objective questions whose objective answers are updated less frequently.

The clarity calculation device of the present invention includes a clarity calculation means, an answer time calculation means, and a clarity calculation means, and the clarity calculation means calculates the answer from the user's answer to at least one of the general question and the objective question. A degree of clarity indicating the degree of clarity is calculated, the answer time calculation means calculates the length of time until the answer is obtained, and the clarity calculation means calculates the degree of clarity and the length of time. is used to calculate clarity, and the clarity is a value indicating the degree of a user's willingness to interact with a question attribute that at least one of a general question and a target question has.

In the clarity calculation device of the present invention, the clarity calculation means calculates the clarity using an if-else type source code, and the if statement is a condition for linking arbitrary clarity to each arbitrary word. The else sentence may be a conditional sentence that associates arbitrary clarity with a word other than the arbitrary word.

In the clarity calculation device of the present invention, the response time calculation means may calculate the length of the time using the following (Formula 1).

tt=t/t' (Formula 1)

tt: Length of time calculated by the answer time calculation means t: Length of time required to obtain the answer t': Average length of time required to obtain the answer

In the clarity calculation device of the present invention, the clarity calculation means may calculate the clarity using the following (Formula 2).

Cl=1/(amb×tt) (Formula 2)

Cl: Clarity amb: The clarity tt calculated by the clarity calculation means: The length of time calculated by the response time calculation means

The clarity calculation device of the present invention may be used as the clarity calculation means in the interactive information acquisition device of the present invention.

The awareness level calculation device of the present invention includes a clarity level calculation means, a likelihood setting means, and an awareness level calculation means, and the clarity level calculation means calculates the clarity level from a user's answer to at least one of a general question and a purpose question. The clarity is a value indicating the degree of the user's willingness to interact with question attributes that at least one of the general question and the objective question has, and the likelihood setting means sets an arbitrary likelihood for each question attribute. is set, and the awareness level calculation means calculates the awareness level using the clarity level and the likelihood level, and the awareness level is a value indicating the level of awareness of the user in thinking about the question attribute. , is a device.

In the consciousness degree calculation device of the present invention, the consciousness degree calculation means may calculate the consciousness degree using the following (Equation 3).

int _n =Fv1+(cl-Fv2)×x _n (Formula 3)

int _n : Awareness level for question attribute n Fv1: Preset first fixed value Fv2: Preset second fixed value cl: Clarity calculated by the clarity calculation means x _n : Set by the likelihood setting means Likelihood at question attribute n

In the embodiment using the above (Formula 3), in the consciousness level calculation device of the present invention, the consciousness level calculation means may set the consciousness level calculated at the i-1st time (i≧2) as the Fv1.

In the consciousness level calculation device of the present invention, the clarity calculation means may calculate the clarity using the clarity calculation device of the present invention.

The awareness level calculation device of the present invention may be used as the awareness level calculation means in the interactive information acquisition device of the present invention.

In the interactive information acquisition method of the present invention, when the threshold is satisfied, the clarity calculation step further calculates the clarity from the purpose answer, and the purpose answer acquisition step further includes the purpose answer and the It may also be possible to store the target answer in association with the clarity level.

The interactive information acquisition method of the present invention further includes a machine learning step, and the machine learning step uses machine learning to determine the clarity of the objective answer from the presentation of the general question to the presentation of the objective question. An aspect may also be adopted in which scenario data is generated by converting the scenario into data. In the above aspect, the machine learning step may be such that the shortest scenario data from the level of awareness to presenting the objective question is generated by machine learning.

In the interactive information acquisition method of the present invention, if the threshold is not satisfied, the general question presenting step may present a general question different from the presented general question.

In the interactive information acquisition method of the present invention, a plurality of question attributes are linked to one objective question and one general question, and the objective question presenting step includes a plurality of question attributes of the presented general question. It may also be possible to present objective questions that include all of the questions.

The interactive information acquisition method of the present invention further includes an attention degree calculation step, the attention degree calculation step calculates an attention degree indicating the number of times the question attribute is used, and the implication ratio calculation step includes the An aspect may be adopted in which a value obtained by multiplying the degree of awareness by the degree of attention is calculated as the implication ratio.

The interactive information acquisition method of the present invention further includes an attribute information acquisition step, wherein the attribute information acquisition step acquires user attribute information, and the objective question includes a condition of the respondent for each objective question. The purpose question presenting step may be such that a purpose question that satisfies the respondent's conditions is presented to the user based on the attribute information.

The interactive information acquisition method of the present invention further includes an audio input/output step, and the audio input/output step outputs each of the presentations in the general question presentation step and the objective question presentation step as audio data, and An aspect may be adopted in which each of the acquisitions in the general answer acquisition step and the target answer acquisition step is input as audio data.

The interactive information acquisition method of the present invention further includes a purpose answer updating step, and the purpose answer acquiring step further includes storing time information at which the purpose answer is stored in the intention database in association with the purpose answer. The objective answer updating step may be such that when the time information has passed a preset period, at least one of the stored objective answer and the time information is updated. In the aspect, the objective answer updating step may extend the period for objective questions whose objective answers are updated less frequently.

The clarity calculation method of the present invention includes a clarity calculation step, an answer time calculation step, and a clarity calculation step, and the clarity calculation step calculates the answer from the user's answer to at least one of the general question and the objective question. Clarity indicating the degree of clarity is calculated, the answer time calculation step calculates the length of time until the answer is obtained, and the clarity calculation step calculates the clarity and the length of time. is used to calculate clarity, and the clarity is a value indicating the degree of a user's willingness to interact with a question attribute that at least one of a general question and a target question has.

In the clarity calculation method of the present invention, the clarity calculation step calculates the clarity using an if-else type source code, and the if statement is a condition for associating arbitrary clarity with each arbitrary word. The else sentence may be a conditional sentence that associates arbitrary clarity with a word other than the arbitrary word.

In the clarity calculation method of the present invention, the answer time calculation step may be such that the length of the time is calculated using the following (Formula 1).

tt=t/t' (Formula 1)

tt: Length of time calculated by the answer time calculation step t: Length of time required to obtain the answer t': Average length of time required to obtain the answer

In the clarity calculation method of the present invention, the clarity calculation step may be such that the clarity is calculated using the following (Formula 2).

Cl=1/(amb×tt) (Formula 2)

Cl: Clarity amb: The clarity tt calculated by the clarity calculation step: The length of time calculated by the response time calculation step

The clarity calculation method of the present invention may be used in the clarity calculation step in the interactive information acquisition method of the present invention.

The awareness calculation method of the present invention includes a clarity calculation step, a likelihood setting step, and an awareness calculation step, and the clarity calculation step calculates clarity from the user's answer to at least one of a general question and a target question. The clarity is a value that indicates the degree of the user's willingness to interact with question attributes that at least one of the general question and the objective question has, and the likelihood setting step includes setting an arbitrary likelihood for each question attribute. is set, and the awareness level calculation step calculates the awareness level using the clarity level and the likelihood level, and the awareness level is a value indicating the level of awareness of the user in thinking about the question attribute. , is the method.

In the consciousness level calculation method of the present invention, the consciousness level calculation step may be such that the consciousness level is calculated using the following (Equation 3).

int _n =Fv1+(cl-Fv2)×x _n (Formula 3)

int _n : Awareness level for question attribute n Fv1: Preset first fixed value Fv2: Preset second fixed value cl: Clarity calculated by the clarity calculation step x _n : Set by the likelihood setting step Likelihood at question attribute n

In the embodiment using the above (Formula 3), in the consciousness level calculation method of the present invention, the consciousness level calculation step may use the consciousness level calculated at the i-1st time (i≧2) as the Fv1.

In the consciousness level calculation method of the present invention, the clarity calculation step may be such that the clarity is calculated using the clarity calculation method of the present invention.

The awareness level calculation method of the present invention may be used in the awareness level calculation step in the interactive information acquisition method of the present invention.

The program of the present invention is a program that can execute at least one of the interactive information acquisition method, clarity calculation method, and awareness level calculation method of the present invention on a computer.

The recording medium of the present invention is a computer-readable recording medium that records the program of the present invention.

In the present invention, a "user" is a user who answers the presented questions (general questions and objective questions), and is also referred to as an answerer. Furthermore, in the present invention, "dialogue" may be, for example, a conversation between a user and the interactive information acquisition device of the present invention via a conversational interface (for example, LINE (registered trademark), etc.). However, it may also be a dialogue via an audio input/output device such as a speaker or a microphone.

The field of application of the present invention is not limited to the fields of medicine and nursing care, but can be applied to various fields where it is necessary to acquire information from a specific person. Specifically, the fields include, for example, medical care, nursing care, education, and counseling.

Embodiments of the present invention will be described using figures. The present invention is not limited to the following embodiments. In each figure below, the same parts are given the same reference numerals. In addition, the explanations of each embodiment can refer to each other unless otherwise mentioned, and the configurations of the embodiments can be combined unless otherwise mentioned.

[Embodiment 1]
FIG. 1 is a block diagram showing the configuration of an example of an interactive information acquisition device 10 of this embodiment. As shown in FIG. 1, the present device 10 includes a general question presentation means 11, a general answer acquisition means 12, a clarity calculation means 13, an awareness degree calculation means 14, an implication ratio calculation means 15, a determination means 16, and a purpose question presentation means. 17, and objective answer acquisition means 18. Further, the present device 10 may further include, for example, a machine learning means, an attention level calculation means, an attribute information acquisition means, a voice input/output means, an objective answer updating means, and the like.

The present device 10 may be, for example, a single device including the above-mentioned sections, or a device to which the above-mentioned sections can be connected via the communication line network 3. Further, the device 10 can be connected to an external device, which will be described later, via the communication line network. The communication line network 3 is not particularly limited, and any known network may be used, and may be wired or wireless, for example. Examples of the communication line network 3 include the Internet line, WWW (World Wide Web), telephone line, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), etc. . Examples of wireless communication include WiFi (Wireless Fidelity), Bluetooth (registered trademark), and the like. The wireless communication may be either direct communication between devices (Ad Hoc communication) or indirect communication via an access point. This device 10 may be incorporated into a server as a system, for example. Further, the device 10 may be, for example, a personal computer (PC, e.g., desktop type, notebook type), a smartphone, a tablet terminal, etc., on which the program of the present invention is installed.

FIG. 2 illustrates a block diagram of the hardware configuration of this device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, a communication device 107, and the like. Each part of the hardware configuration of the device 10 is interconnected via a bus 103 by respective interfaces (I/Fs).

A central processing unit (central processing unit) 101 is responsible for overall control of the device 10. In this device 10, a central processing unit 101 executes, for example, the program of the present invention and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 includes a general question presentation means 11, a general answer acquisition means 12, a clarity calculation means 13, an awareness degree calculation means 14, an implication ratio calculation means 15, a determination means 16, and a purpose question presentation means. It functions as a means 17 and a purpose answer acquisition means 18. In addition, when the present device 10 further includes a machine learning means, an attention level calculation means, an attribute information acquisition means, a voice input/output means, and an objective answer updating means, the central processing unit 101, for example, It functions as the attention level calculation means, the attribute information acquisition means, the voice input/output means, and the objective answer updating means.

For example, the bus 103 can also be connected to an external device. The external devices include, for example, an external database (for example, a general question database 40, a purpose question database 50, and an intention database 60, which will be described later), a printer, a clarity calculation device 20, which will be described later, and an awareness level calculation device 30, which will be described later. can be given. The device 10 can be connected to the communication network 3 by, for example, a communication device 107 connected to the bus 103, and can also be connected to an external device via the communication network 3.

An example of the memory 102 is a main memory (main storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs such as the program of the present invention stored in the storage device 104 (described later), and the central processing unit 101 reads various operating programs from the memory 102. Receive data and execute program. The main memory is, for example, a RAM (random access memory). Further, the memory 102 may be, for example, a ROM (read-only memory).

The storage device 104 is also referred to as a so-called auxiliary storage device in contrast to the main memory (main storage device), for example. As mentioned above, the storage device 104 stores operating programs including the program of the present invention. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads from and writes to the recording medium. The recording medium is not particularly limited, and may be of a built-in type or an external type, and examples include HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. It will be done. The storage device 104 may be, for example, a hard disk drive (HDD) in which a recording medium and a drive are integrated, or a solid state drive (SSD).

In the device 10, the memory 102 and the storage device 104 can also store access information and log information from an administrator, as well as information obtained from an external database (not shown).

The device 10 further includes an input device 105 and a display device 106, for example. The input device 105 is, for example, a touch panel, a keyboard, a mouse, or the like. Examples of the display device 106 include an LED display, a liquid crystal display, and the like.

As described above, the device 10 is capable of communicating with the general question database 40, the objective question database 50, and the intention database 60 via the communication line network 3 using the communication device 107. Further, the general question database 40, the purpose question database 50, and the intention database 60 may each be able to communicate via the communication line network 3, for example.

Next, an example of the interactive information acquisition method of this embodiment will be explained based on the flowchart of FIG. 3. The interactive information acquisition method of this embodiment is implemented as follows using, for example, the interactive information acquisition device 10 of FIG. 1. Note that the interactive information acquisition method of this embodiment is not limited to the use of the interactive information acquisition device 10 of FIG.

First, the general question presenting means 11 refers to the general question database 40 and presents a general question to the user (S1). The general question database 40 is a database that stores general questions for each preset question attribute. The general question is a question that is not a target question, which will be described later, and is not particularly limited, and may be, for example, a question that has the same question attributes as the target question, which will be described later. Further, the general question database 40 may further include, for example, small talk such as comments and greetings. Then, the general question presenting means 11 may present the above-mentioned small talk and the above-mentioned general questions together. The question attributes are not particularly limited, and can be arbitrarily set by, for example, the questioner, the administrator of the device 10, or the like. For example, a plurality of question attributes may be associated with one general question and a target question to be described later. The presentation format is not particularly limited, and for example, the general questions may be presented to the user as text data through a conversational interface. Further, if the device 10 further includes the voice input/output means, for example, the general question may be presented to the user as voice data as described later.

Next, the general answer acquisition means 12 acquires the user's general answer to the general question (S2). The format of the acquisition is not particularly limited, and for example, the general answer may be acquired from the user as text data through a conversational interface. Further, if the device 10 further includes the audio input/output means, for example, the general answer may be acquired from the user as audio data, as described later.

Next, the clarity calculation means 13 calculates the clarity from the general answer (S3). The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute. Specifically, for example, the clarity can be calculated using a clarity calculation device, which will be described later.

Next, the awareness level calculation means 14 calculates the awareness level for each of the question attributes using the question attributes set for the presented general question and the clarity level (S4). The awareness level is a value indicating the level of awareness of the user regarding the question attribute. Specifically, for example, the degree of consciousness can be calculated using a degree of consciousness calculation device described below. The awareness level may be stored in the general question database 40 in association with the general question, for example.

Next, the implication ratio calculating means 15 calculates the implication ratio in the presented general question for each of the question attributes from the awareness level (S5). The implication ratio is a value indicating the implication ratio of the question attribute in the presented general question.

Next, the determining means 16 refers to the objective question database 50 and determines whether the implication ratio satisfies a threshold (S6). The objective question database 50 is a database that stores objective questions and implicature ratio thresholds. The threshold value is not particularly limited, and can be arbitrarily set by, for example, the questioner, the administrator of the device 10, or the like. The objective question is linked to the general question by the question attribute. The purpose questions are questions that the questioner wants to ask the user, and are not particularly limited, such as whether or not the questioner wishes to receive life-prolonging treatment, whether or not they wish to donate organs, matters related to personal affairs, future career paths, and concerns at work or school. It is preferable that the question be a question that users such as the following would feel psychologically reluctant to answer. Further, the purpose question database 50 may further include, for example, small talk such as comments and greetings. Then, the purpose question presenting means 17, which will be described later, may present the above-mentioned small talk and the above-mentioned general question together.

If the threshold is satisfied (YES), the objective question presentation means 17 refers to the objective question database 50 and presents the objective question linked to the set question attribute to the user (S7). For example, when there are a plurality of question attributes as described above, the purpose question presenting means 17 may present a purpose question that includes all of the plurality of question attributes of the general question. Specifically, for example, if the question attribute of the general question presented above is (meal, family), the objective question is linked to the question attribute (meal, family), question attribute (meal, family, housework), etc. Present objective questions with specific objectives. The presentation format is not particularly limited, and for example, the objective question may be presented to the user as text data through a conversational interface. Further, if the device 10 further includes the voice input/output means, for example, the target question may be presented to the user as voice data as described later.

Next, the objective answer acquisition means 18 acquires the user's objective answer to the objective question (S8). Then, the objective answer acquisition means 18 stores the acquired answer in the intention database 60 (S9), and the process ends (END). The intention database 60 is a database that stores the user's objective answers to the objective questions. The format of the acquisition is not particularly limited, and for example, the objective answer may be acquired from the user as text data through a conversational interface. Furthermore, if the device 10 further includes the voice input/output means, for example, the target answer may be acquired from the user as voice data, as described later.

Further, in the device 10, for example, after the step (S8) and before the step (S9), the clarity calculation means 13 may further calculate the clarity from the objective answer. The objective answer acquisition means 18 may further store the objective answer and the clarity of the objective answer in association with each other. When calculating the clarity of the target answer, the device 10 may further include, for example, machine learning means. The machine learning means generates scenario data in which a scenario from presentation of the general question to presentation of the objective question is converted into data based on the clarity of the objective answer by machine learning. The machine learning is not particularly limited, and for example, known techniques can be used. The scenario will be specifically described later. Thereby, the present device 10 can generate, for example, scenario data in which the clarity of the objective question is likely to be high. Furthermore, the machine learning means may generate, by machine learning, the scenario data that takes the shortest time from the level of awareness to presenting the objective question. As a result, the present device 10 can reduce the number of general questions asked before presenting the objective question, and can suppress troublesomeness for the user.

On the other hand, if the threshold value is not satisfied (NO), for example, the process may be ended as is (END). Further, if the threshold value is not satisfied (NO), for example, the process may be returned to the step (S1) and repeated processing may be performed. In the step (S1) of the iterative process, for example, a general question different from the presented general question is presented. It is preferable that the different general question is, for example, a general question for which the target answer has not been obtained due to past processing, and for which the degree of awareness is the highest other than the presented general question.

The device 10 may further include, for example, an attention degree calculation means. For example, before the step (S5), the attention level calculating means calculates the attention level indicating the number of times the question attribute is used. That is, as the number of times increases, the degree of attention also increases. In this case, the implication ratio calculation means 15 calculates a value obtained by multiplying the awareness level and the attention level as the implication ratio.

The device 10 may further include attribute information acquisition means, for example. In this case, the device 10 acquires the user's attribute information using the attribute information acquisition means, for example, before the step (S7). The attribute information is not particularly limited, and includes, for example, age, gender, nationality, occupation, hobbies, family structure, friendships, medical history, work history, educational background, and the like. One piece of attribute information, or two or more pieces of attribute information may be acquired. The attribute information can be acquired via the input device 105, the communication device 107, etc., for example. The purpose question includes, for example, the conditions of the answerer for each purpose question. The answerer is a user who answers each question presented by the device 10. The conditions for the respondent are not particularly limited, and can be arbitrarily set by, for example, the questioner, the administrator of the device 10, and the like. Specifically, the conditions include, for example, age, gender, nationality, occupation, hobbies, family composition, friendships, medical history, work history, and educational background. These conditions may be used alone or in combination. In this case, the purpose question presentation means 17 presents the user with a purpose question that satisfies the respondent's conditions based on the attribute information. That is, if the attribute information is, for example, "65 years old, male, with children," the purpose question presenting means 17 will respond with answer conditions such as "60 to 70 years old," "60 to 70 years old, male," and "60 years old, male." It is possible to present objective questions such as ``Age 70, male, with children.''

The device 10 may further include audio input/output means, for example. The audio input/output means outputs each of the presentations in the general question presenting means 11 and the objective question presenting means 17 as audio data. Further, each of the above-mentioned acquisitions by the general answer acquisition means 12 and the objective answer acquisition means 18 is input as audio data.

The device 10 may further include, for example, the objective answer updating means. In this case, the objective answer acquisition means further stores time information at which the objective answer was stored in the intention database 60 in association with the objective answer. The objective answer updating means updates at least one of the stored objective answer and the time information when a preset period (for example, every six months, etc.) has elapsed since the stored time information. Specifically, for example, by re-presenting the general question and the objective question with reference to the scenario data when the objective answer was obtained for the objective answer after a preset period, The above update may be performed. Further, for the objective answer after a preset period has elapsed, only the objective question at the time when the objective answer was obtained may be re-presented. Thereby, the device 10 can, for example, periodically confirm the intention of the user (respondent).

For example, the objective answer updating means may further extend the period for objective questions whose objective answers are updated less frequently. The update frequency can be calculated, for example, from the log data of the objective answers stored in the intention database 60. Specifically, for example, depending on the update frequency, if the objective answer obtained two times before, the objective answer obtained last time, and the objective answer obtained this time are the same, the objective answer obtained next time will also be updated. It is possible to predict that the content is the same. That is, the present device 10 is capable of reading the changing tendency of the user's intention (purpose answer), for example, by the purpose answer updating means. Thereby, the present device 10 can save the user trouble and effort by, for example, extending the re-presentation period of the purpose question for which the user has a firm intention (that is, the update frequency is low).

Here, FIG. 4 shows an example in which the present invention is applied to a user. In FIG. 4, the device 10 is illustrated as a chatbot 1 in which the program of the present invention is installed, but this is merely an example and is not limited thereto. In FIG. 4, the left side shows an example of a dialogue between the chatbot 1 and the user 2, and the right side shows an example of a processing result within the chatbot 1 in the dialogue. As shown in FIG. 4, chatbot 1 first presents question A to user 2 as a general question. Next, the user 2 speaks the answer A to the chatbot 1 as an answer to the question A. The chatbot 1 obtains the answer A as a general answer. The question A and the answer A are collectively referred to as a dialogue A. Then, the chatbot 1 calculates the clarity, the awareness level, and the implication ratio from the answer A, respectively. A specific calculation method will be described later. As shown in FIG. 4, as a result of the calculations, the chatbot 1 determines that the conversation A does not satisfy the implication ratio threshold. Therefore, the chatbot 1 presents question B as the next general question to the user, and obtains answer B in the same manner as dialogue A (dialogue B). Similarly to the dialogue A, the dialogue B is determined not to satisfy the implicature ratio threshold. Therefore, the chatbot 1 presents question C as the next general question to the user, and obtains the answer C in the same manner as the dialogue A (dialogue C). As a result of each of the calculations, the chatbot 1 determines that the conversation C satisfies the threshold of the implication ratio, and refers to the purpose question database 50 to answer the purpose question "I will need assistance with my meals in the future." What if?” is presented. Note that in FIG. 4, chatbot 1 is communicating with other chatbots. Then, the machine learning means modifies the context of the objective question so that the objective answer with a high degree of clarity is easily obtained, and presents the modified objective question to the user 2 (Question D). The chatbot 1 then obtains the answer D from the user 2 and executes each calculation from the answer D. Here, in the present invention, a scenario refers to a series of flows from dialogue A in which an initial general question is presented to dialogue D in which a target question is presented, for example, as shown in FIG.

FIG. 5 shows another example in which the present invention is applied to a user. 5 is the same as the example shown in FIG. 4 except that the contents of dialogues A to D and the processing results are different.

According to the present embodiment, since the general question is presented and then the objective question is presented, information can be easily acquired through dialogue, for example. Further, the general question and the objective question are linked by the question attribute. Therefore, even if the user has a psychological resistance to answering a target question, as the user answers general questions with low psychological resistance, the user's psychological resistance is suppressed and it becomes easier to think about and answer the target question. .

[Embodiment 2]
FIG. 6 is a block diagram showing the configuration of an example of the clarity calculation device 20 of this embodiment. As shown in FIG. 6, the device 20 includes a clarity calculation means 21, a response time calculation means 22, and a clarity calculation means 23. This device 20 can be used, for example, as the clarity calculation means 13 in the interactive information acquisition device 10 of the first embodiment.

The present device 20 may be, for example, a single device including each of the units described above, or may be a device to which each of the units can be connected via a communication network. Although not shown, the device 20 can be connected to an external device, which will be described later, via the communication line network. The communication line network is not particularly limited, and any known network may be used, and may be wired or wireless, for example. The communication line network is, for example, the same as described above. The device 20 may be incorporated into a server as a system, for example. Further, the device 20 may be, for example, a personal computer (PC, e.g., desktop type, notebook type), a smartphone, a tablet terminal, etc., on which the program of the present invention is installed.

FIG. 7 illustrates a block diagram of the hardware configuration of this device 20. The device 20 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, a communication device 107, and the like. Each part of the hardware configuration of the device 20 is interconnected via a bus 103 by respective interfaces (I/Fs). For the explanation of each part of the hardware configuration of the present device 20, unless otherwise mentioned, for example, the explanation of each part of the hardware configuration of the interactive information acquisition device 10 of the first embodiment can be used.

The central processing unit (central processing unit) 101 functions as, for example, a clarity calculation means 21, an answer time calculation means 22, and a clarity calculation means 23.

Next, an example of the clarity calculation method of this embodiment will be explained based on the flowchart of FIG. 8. The clarity calculation method of this embodiment is implemented as follows using, for example, the clarity calculation device 20 of FIG. Note that the clarity calculation method of this embodiment is not limited to the use of the clarity calculation device 20 in FIG. 6 .

First, the intelligibility calculation means 21 calculates intelligibility indicating the degree of clarity of the answer from the user's answer to at least one of the general question and the objective question (S1). The general question and the objective question are, for example, the same as described above. The intelligibility calculating means 21 may calculate the intelligibility using, for example, an if-else type source code. In this case, the if statement is a conditional sentence that associates arbitrary intelligibility with each arbitrary word, and the else sentence is a conditional sentence that associates arbitrary intelligibility with words other than the arbitrary word. Each word in the if statement is, for example, a word included in a dictionary. Specifically, for example, the clarity level (amb) can be set using the following source code. Note that the source code below is an example and is not limited thereto. Furthermore, in the if statement of the source code below, there are multiple words associated with arbitrary intelligibility between the word "yes" and the word "um". The above-mentioned arbitrary intelligibility is evaluated on a scale of 10, with words having higher intelligibility given a smaller number in the source code below, but this is just an example and is not limited to this. Furthermore, in the source code below, the arbitrary clarity in the else statement is set to "2". For example, the intelligibility calculating means 21 refers to the following source code "Um: 10" from the answer B shown in FIG. Calculated as In other words, the clarity of Answer B can be said to be low.

amb=if (yes::1,...um:10) else 2

Next, the response time calculating means 22 calculates the length of time until the answer is obtained (S2). The units of time are not particularly limited, and include, for example, hours, minutes, and seconds, and preferably seconds. Specifically, for example, it may be calculated as the total length of time from when at least one of the general question and the objective question is presented to the user until an answer is obtained. Further, if the user uses the device 20 multiple times, the length of time may be calculated using the following (Formula 1), for example. The calculated length of time may be stored in the storage device 104, for example. The step (S2) may be performed, for example, before the step (S1) or in parallel with the step (S1).


tt=t/t' (Formula 1)

tt: Length of time calculated by the answer time calculation means 22 t: Length of time required to obtain the answer t': Average length of time required to obtain the answer

The t' may be an average value based on a specific user or an average value based on a plurality of users. If the t' is shorter than the t, it means that the user was able to answer with less trouble than usual, for example. Furthermore, when the t' is shorter than the t, the calculated value of tt is, for example, greater than or equal to 0 and less than or equal to 1. Therefore, when the device 20 calculates the clarity (cl) using, for example, (Equation 2) described later, the value obtained by multiplying the clarity (amb) by the tt becomes small, and the clarity cl becomes smaller. growing.

Then, the clarity calculation means 23 calculates the clarity using the clarity and the length of time (S3), and ends (END). As described above, the clarity is a value indicating the degree of the user's desire to interact with the question attribute of at least one of the general question and the objective question. Specifically, for example, the clarity may be calculated using (Equation 2) below.

Cl=1/(amb×tt) (Formula 2)

Cl: Clarity amb: The clarity tt calculated by the clarity calculation means 21: The length of time calculated by the response time calculation means 22

According to this embodiment, the degree of a user's desire to interact can be quantified. As a result, the interactive information acquisition device including the present device 20 can ask the highly motivated questions, which makes it easy to get answers from the user and easily acquire information. Although the interactive information acquisition device is not particularly limited, it is preferably the interactive information acquisition device of the present invention.

[Embodiment 3]
FIG. 9 is a block diagram showing the configuration of an example of the awareness level calculation device 30 of this embodiment. As shown in FIG. 9, the device 30 includes a clarity calculation means 31, a likelihood setting means 32, and an awareness calculation means 33. This device 30 can be used, for example, as the awareness level calculation means 14 in the interactive information acquisition device 10 of the first embodiment.

The present device 30 may be, for example, a single device including each of the units described above, or may be a device to which each of the units can be connected via a communication network. Although not shown, the device 30 can be connected to an external device, which will be described later, via the communication line network. The communication line network is not particularly limited, and any known network may be used, and may be wired or wireless, for example. The communication line network is, for example, the same as described above. This device 30 may be incorporated into a server as a system, for example. Further, the device 30 may be, for example, a personal computer (PC, e.g., desktop type, notebook type), a smartphone, a tablet terminal, etc., on which the program of the present invention is installed.

FIG. 10 illustrates a block diagram of the hardware configuration of this device 30. The device 30 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, a communication device 107, and the like. Each part of the hardware configuration of this device 30 is interconnected via a bus 103 by respective interfaces (I/Fs). For the explanation of each part of the hardware configuration of the present device 30, unless otherwise mentioned, for example, the explanation of each part of the hardware configuration of the interactive information acquisition device 10 of the first embodiment can be used.

The central processing unit (central processing unit) 101 functions as, for example, clarity calculation means 31, likelihood setting means 32, and awareness degree calculation means 33.

Next, an example of the method for calculating the degree of consciousness according to this embodiment will be explained based on the flowchart of FIG. 11. The consciousness level calculation method of this embodiment is carried out as follows using, for example, the consciousness level calculation device 30 of FIG. 9. Note that the clarity calculation method of this embodiment is not limited to the use of the consciousness level calculation device 30 in FIG. 9 .

First, the clarity calculation means 31 calculates the clarity from the user's answer to at least one of the general question and the objective question (S1). As described above, the clarity is a value indicating the degree of the user's desire to interact with a question attribute of at least one of the general question and the objective question. The clarity calculation means 31 may calculate the clarity using, for example, the clarity calculation device of the second embodiment.

Next, the likelihood setting means 32 sets an arbitrary likelihood for each of the question attributes (S2). The likelihood setting means 32 may set an arbitrary likelihood depending on, for example, the number of times the general question has been presented. Furthermore, different likelihoods may be set for the general question and the objective question. The step (S2) may be performed, for example, before the step (S1) or in parallel with the step (S1).

Then, the consciousness degree calculation means 33 calculates the consciousness degree using the clarity and the likelihood (S3), and ends (END). The awareness level is a value indicating the level of awareness of the user when thinking about the question attribute, as described above. The awareness level calculating means 33 may calculate the awareness level using, for example, the following (Equation 3).

int _n =Fv1+(cl-Fv2)×x _n (Formula 3)

int _n : Awareness level for question attribute n Fv1: Preset first fixed value Fv2: Preset second fixed value cl: Clarity calculated by the clarity calculation means x _n : Set by the likelihood setting means Likelihood at question attribute n

The Fv1 and Fv2 can be set to arbitrary values. Furthermore, for example, when the consciousness degree is calculated for the second time or later, the consciousness degree calculation means 33 may set the consciousness degree calculated at the i-1st time (i≧2) as the Fv1. In this case, Fv1 in the first calculation of the degree of consciousness is also referred to as an initial value, for example. If the initial value is set high, for example, when this device 30 is used as the awareness level calculation means 14 in the interactive information acquisition device 10 of the first embodiment, the objective answer can be acquired quickly.

According to this embodiment, it is possible to quantify the degree of awareness of the user regarding the question attributes. Thereby, the interactive information acquisition device including the present device 30 can ask the above-mentioned highly conscious questions, making it easier to obtain answers from the user and to easily acquire information. Although the interactive information acquisition device is not particularly limited, it is preferably the interactive information acquisition device of the present invention.

[Embodiment 4]
Next, a specific example of calculating the clarity, the degree of awareness, and the implication ratio will be shown using FIGS. 4 and 5 and FIGS. 12 and 13.

The question attributes of question A in FIG. 4 are, for example, "meal (question attribute 1), family (question attribute 2), and housework (question attribute 3)." Hereinafter, the question attribute "meal (question attribute 1)" will be explained using FIG. 12 as a representative example. First, calculation of clarity (cl) will be explained. In answer A, the above (Equation 2) assumes that the clarity (amb) is 5 and the time length (tt) is 0.25 (seconds), for example. Then, by substituting these values into the above (Equation 2), "cl = 1/(5 x 0.25)", and the clarity (cl) of "Meals (question attribute 1)" of the answer A is 0. .8. Next, calculation of the degree of consciousness (int ₁ ) using the calculated clarity (cl) will be explained. In answer A, the above (Formula 3) assumes that Fv1 (initial value) is 15, Fv2 is 0.5, and the likelihood (x ₁ ) is 50, for example. Then, by substituting these values into the above (Equation 3), it becomes "int ₁ = 15 + (0.8 - 0.5) x 50", and the awareness level of "Meals (question attribute 1)" of the answer A ( int ₁ ) is 30. Next, calculation of the implication ratio (th ₁ ) using the calculated awareness level (int ₁ ) will be explained. The degree of attention (Fr ₁ ) is, for example, once "breakfast" in question A, which is a word related to (similar to) the question attribute "meal (question attribute 1)." Then, when multiplying the calculated awareness level (int ₁ ) by the attention level, "th ₁ = 30×1" is obtained, and the implication ratio (th ₁ ) of "meal (question attribute 1)" of the answer A is , 30%. The determining means 16 of the interactive information acquisition device 10 determines whether the calculated implication ratio (30%) exceeds a preset threshold (eg, 50%). In the case of FIG. 4, since the threshold value is not exceeded, the process proceeds to dialogue B. Similarly, calculation formulas and results for dialogues B to D are shown in FIG.

The question attributes of question A in FIG. 5 are, for example, "family (question attribute 1), nursing care (question attribute 2), and will (question attribute 3)." Hereinafter, the question attribute "family (question attribute 1)" will be explained using FIG. 13 as a representative example. First, calculation of clarity (cl) will be explained. In answer A, the above (Equation 2) assumes that the clarity (amb) is 5 and the time length (tt) is 0.25 (seconds), for example. Then, by substituting these values into the above (Equation 2), "cl = 1/(5 x 0.25)" is obtained, and the clarity (cl) of "Family (question attribute 1)" of the answer A is 0. .8. Next, calculation of the degree of consciousness (int ₁ ) using the calculated clarity (cl) will be explained. In answer A, the above (Equation 3) assumes that Fv1 (initial value) is 15, Fv2 is 0.5, and the likelihood (x ₁ ) is 100, for example. Then, by substituting these values into the above (Equation 3), it becomes "int ₁ = 15 + (0.8 - 0.5) x 100", and the awareness level of the "family (question attribute 1)" of the answer A ( int ₁ ) is 45. Next, calculation of the implication ratio (th ₁ ) using the calculated awareness level (int ₁ ) will be explained. The degree of attention (Fr ₁ ) is, for example, one occurrence of "family" in question A, which is the same word as the question attribute "family (question attribute 1)." Then, multiplying the calculated awareness level (int ₁ ) by the attention level, "th ₁ = 45×1", and the implication ratio (th ₁ ) of "family (question attribute 1)" in answer A is , 45%. The determining means 16 of the interactive information acquisition device 10 determines whether the calculated implication ratio (45%) exceeds a preset threshold (for example, 50%). In the case of FIG. 5, since the threshold value is not exceeded, the process proceeds to dialogue B. Similarly, calculation formulas and results for dialogues B to D are shown in FIG.

[Embodiment 5]
The program of this embodiment is a program that can execute the methods of each of the embodiments described above on a computer. Furthermore, the program of this embodiment may be recorded on, for example, a computer-readable recording medium. The recording medium is not particularly limited, and includes, for example, HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, and the like.

Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. The configuration and details of the present invention can be modified in various ways that can be understood by those skilled in the art within the scope of the present invention.

<Additional notes>
Part or all of the above embodiments may be described as in the following additional notes, but are not limited to the following.
(Additional note 1)
Including a general question presentation means, a general answer acquisition means, a clarity calculation means, an awareness degree calculation means, an implication ratio calculation means, a determination means, a purpose question presentation means, and a purpose answer acquisition means,
The general question presenting means refers to a general question database and presents general questions to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition means acquires a user's general answer to the general question,
The clarity calculation means calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
The awareness level calculation means calculates the awareness level for each of the question attributes using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation means calculates an implication ratio in the presented general question for each question attribute from the awareness level,
The determination means refers to a target question database and determines whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting means refers to the objective question database and presents the objective question linked to the set question attribute to the user,
The objective answer acquisition means acquires the user's objective answer to the objective question, and stores the acquired answer in an intention database,
The intention database is an interactive information acquisition device, wherein the intention database is a database that stores the user's objective answers to the objective questions.
(Additional note 2)
If the threshold is satisfied,
The clarity calculation means further calculates the clarity from the objective answer,
The interactive information acquisition device according to appendix 1, wherein the objective answer acquisition means further stores the objective answer and the clarity of the objective answer in association with each other.
(Additional note 3)
Additionally, it includes machine learning means;
The interactive information according to appendix 2, wherein the machine learning means generates scenario data in which a scenario from presenting the general question to presenting the objective question is converted into data based on the clarity of the objective answer by machine learning. Acquisition device.
(Additional note 4)
The interactive information acquisition device according to appendix 3, wherein the machine learning means generates the shortest scenario data from the level of awareness to presenting the objective question by machine learning.
(Appendix 5)
If the above threshold is not met,
The interactive information acquisition device according to any one of appendices 1 to 4, wherein the general question presenting means presents a general question different from the presented general question.
(Appendix 6)
Multiple question attributes are linked to one objective question and general question,
6. The interactive information acquisition device according to any one of appendices 1 to 5, wherein the objective question presenting means presents an objective question that includes all of the plurality of question attributes of the presented general question.
(Appendix 7)
Furthermore, it includes an attention level calculation means,
The attention level calculation means calculates an attention level indicating the number of times the question attribute is used,
The interactive information acquisition device according to any one of Supplementary Notes 1 to 6, wherein the implication ratio calculation means calculates a value obtained by multiplying the awareness level and the attention level as the implication ratio.
(Appendix 8)
Furthermore, it includes an attribute information acquisition means,
The attribute information acquisition means acquires user attribute information,
The objective question includes conditions for the respondent for each objective question,
8. The interactive information acquisition device according to any one of Supplementary Notes 1 to 7, wherein the objective question presenting means presents an objective question that satisfies the respondent's conditions to the user based on the attribute information.
(Appendix 9)
Furthermore, it includes audio input/output means,
The audio input/output means outputs each of the presentations in the general question presentation means and the objective question presentation means as audio data,
The interactive information acquisition device according to any one of appendices 1 to 8, wherein each of the acquisitions by the general answer acquisition means and the objective answer acquisition means is input as audio data.
(Appendix 10)
Furthermore, it includes a purpose answer update means,
The objective answer acquisition means further stores time information at which the objective answer was stored in the intention database in association with the objective answer;
The interactive information acquisition according to any one of appendices 1 to 9, wherein the purpose answer updating means updates at least one of the stored purpose answer and the time information when a preset period has elapsed since the time information. Device.
(Appendix 11)
The interactive information acquisition device according to appendix 10, wherein the objective answer updating means extends the period for objective questions whose objective answers are updated less frequently.
(Appendix 12)
including a clarity calculation means, a response time calculation means, and a clarity calculation means,
The clarity calculation means calculates clarity indicating the degree of clarity of the answer from the user's answer to at least one of the general question and the objective question,
The answer time calculation means calculates the length of time until the answer is obtained,
The clarity calculation means calculates clarity using the clarity and the length of time,
The clarity calculation device is a clarity calculation device, wherein the clarity is a value indicating a degree of a user's willingness to interact with a question attribute that at least one of a general question and a target question has.
(Appendix 13)
The intelligibility calculating means calculates the intelligibility using an if-else type source code,
An if statement is a conditional statement that associates an arbitrary degree of intelligibility with an arbitrary word.
The clarity calculation device according to appendix 12, wherein the else sentence is a conditional sentence that associates arbitrary clarity with a word other than the arbitrary word.
(Appendix 14)
The clarity calculation device according to appendix 12 or 13, wherein the answer time calculation means calculates the length of time using the following (Formula 1).

tt=t/t' (Formula 1)

tt: Length of time calculated by the answer time calculation means t: Length of time required to obtain the answer t': Average length of time required to obtain the answer


(Appendix 15)
The clarity calculation device according to any one of appendices 12 to 14, wherein the clarity calculation means calculates the clarity using the following (Formula 2).

Cl=1/(amb×tt) (Formula 2)

Cl: Clarity amb: The clarity tt calculated by the clarity calculation means: The length of time calculated by the response time calculation means

(Appendix 16)
The clarity calculation device according to any one of appendices 12 to 15, which is used for the clarity calculation means in the interactive information acquisition device according to any one of appendices 1 to 11.
(Appendix 17)
including a clarity calculation means, a likelihood setting means, and an awareness calculation means,
The clarity calculation means calculates clarity from the user's answer to at least one of the general question and the objective question,
The clarity is a value indicating the degree of the user's willingness to interact with the question attributes of at least one of the general question and the objective question,
The likelihood setting means sets an arbitrary likelihood for each question attribute,
The awareness level calculating means calculates the awareness level using the clarity level and the likelihood level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute.
(Appendix 18)
The consciousness level calculation device according to appendix 17, wherein the consciousness level calculation means calculates the consciousness level using the following (Equation 3).

int _n =Fv1+(cl-Fv2)×x _n (Formula 3)

int _n : Awareness level for question attribute n Fv1: Preset first fixed value Fv2: Preset second fixed value cl: Clarity calculated by the clarity calculation means x _n : Set by the likelihood setting means Likelihood at question attribute n

(Appendix 19)
The consciousness level calculation device according to appendix 18, wherein the consciousness level calculation means sets the consciousness level calculated at the i-1st time (i≧2) as the Fv1.
(Additional note 20)
The degree of consciousness calculation device according to any one of appendices 17 to 19, wherein the clarity calculation means calculates the clarity using the clarity calculation device according to any one of appendices 12 to 16.
(Additional note 21)
The consciousness degree calculation device according to any one of appendices 17 to 20, which is used for the consciousness degree calculation means in the interactive information acquisition device according to any one of appendices 1 to 11.
(Additional note 22)
Including a general question presentation process, a general answer acquisition process, a clarity calculation process, an awareness level calculation process, an implication ratio calculation process, a determination process, a purpose question presentation process, and a purpose answer acquisition process,
The general question presenting step refers to a general question database and presents the general question to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition step acquires a user's general answer to the general question,
The clarity calculation step calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
In the awareness level calculation step, the awareness level is calculated for each question attribute using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation step calculates an implication ratio for each question attribute in the general question from the awareness level,
The determination step refers to a target question database to determine whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting step refers to the objective question database and presents the objective question linked to the extracted question attribute to the user;
The objective answer acquisition step acquires the user's objective answer to the objective question, and stores the acquired answer in an intention database;
In the interactive information acquisition method, the intention database is a database that stores a user's objective answers to the objective questions.
(Additional note 23)
If the threshold is satisfied,
The clarity calculation step further includes calculating the clarity from the objective answer,
23. The interactive information acquisition method according to appendix 22, wherein the objective answer acquisition step further stores the objective answer and the clarity of the objective answer in association with each other.
(Additional note 24)
Furthermore, it includes a machine learning process,
The interactive information according to appendix 23, wherein the machine learning step generates scenario data in which a scenario from presenting the general question to presenting the objective question is converted into data based on the clarity of the objective answer by machine learning. Acquisition method.
(Additional note 25)
25. The interactive information acquisition method according to appendix 24, wherein the machine learning step generates the shortest scenario data from the awareness level to presenting the objective question by machine learning.
(Additional note 26)
If the above threshold is not met,
26. The interactive information acquisition method according to any one of appendices 22 to 25, wherein the general question presenting step presents a general question different from the presented general question.
(Additional note 27)
Multiple question attributes are linked to one objective question and general question,
27. The interactive information acquisition method according to any one of appendices 22 to 26, wherein the objective question presenting step presents an objective question that includes all of the plurality of question attributes of the presented general question.
(Additional note 28)
Furthermore, it includes an attention level calculation step,
The attention level calculation step calculates an attention level indicating the number of times the question attribute has been used;
28. The interactive information acquisition method according to any one of appendices 22 to 27, wherein the implication ratio calculation step calculates a value obtained by multiplying the awareness level and the attention level as the implication ratio.
(Additional note 29)
Furthermore, it includes an attribute information acquisition step,
The attribute information acquisition step acquires user attribute information,
The objective question includes conditions for the respondent for each objective question,
29. The interactive information acquisition method according to any one of appendices 22 to 28, wherein the objective question presenting step presents an objective question that satisfies the respondent's conditions to the user based on the attribute information.
(Additional note 30)
Furthermore, it includes an audio input/output process,
The audio input/output step outputs each of the presentations in the general question presentation step and the objective question presentation step as audio data,
The interactive information acquisition method according to any one of appendices 22 to 29, wherein each of the acquisitions in the general answer acquisition step and the objective answer acquisition step is input as audio data.
(Appendix 31)
Furthermore, it includes a purpose answer update process,
The objective answer acquisition step further includes storing time information at which the objective answer was stored in the intention database in association with the objective answer;
The objective answer updating step is the interactive information acquisition according to any one of appendices 22 to 30, wherein when the time information has passed a preset period, at least one of the stored objective answer and the time information is updated. Method.
(Appendix 32)
32. The interactive information acquisition method according to appendix 31, wherein the objective answer updating step extends the period for objective questions whose objective answers are updated less frequently.
(Appendix 33)
Including a clarity calculation step, a response time calculation step, and a clarity calculation step,
The clarity calculation step calculates clarity indicating the degree of clarity of the answer from the user's answer to at least one of the general question and the objective question,
The answer time calculation step calculates the length of time until the answer is obtained,
The clarity calculation step calculates clarity using the clarity and the length of time,
The clarity calculation method is a clarity calculation method, wherein the clarity is a value indicating a degree of a user's willingness to interact with a question attribute that at least one of a general question and a target question has.
(Appendix 34)
The clarity calculation step calculates the clarity using an if-else type source code,
An if statement is a conditional statement that associates an arbitrary degree of intelligibility with an arbitrary word.
The clarity calculation method according to appendix 33, wherein the else sentence is a conditional sentence that associates arbitrary clarity with a word other than the arbitrary word.
(Appendix 35)
The clarity calculation method according to appendix 33 or 34, wherein the answer time calculation step calculates the length of time using the following (Formula 1).

tt=t/t' (Formula 1)

tt: Length of time calculated by the answer time calculation step t: Length of time required to obtain the answer t': Average length of time required to obtain the answer

(Appendix 36)
36. The clarity calculation method according to any one of appendices 33 to 35, wherein the clarity calculation step calculates the clarity using the following (Formula 2).

Cl=1/(amb×tt) (Formula 2)

Cl: Clarity amb: The clarity tt calculated by the clarity calculation step: The length of time calculated by the response time calculation step

(Additional note 37)
The clarity calculation method according to any one of appendices 33 to 36, which is used in the clarity calculation step in the interactive information acquisition method according to any one of appendices 22 to 32.
(Appendix 38)
including a clarity calculation step, a likelihood setting step, and an awareness calculation step,
The clarity calculation step calculates clarity from the user's answer to at least one of a general question and a purpose question,
The clarity is a value indicating the degree of the user's willingness to interact with the question attributes of at least one of the general question and the objective question,
The likelihood setting step sets an arbitrary likelihood for each question attribute,
In the consciousness level calculation step, the consciousness level is calculated using the clarity level and the likelihood level,
The awareness level is a value indicating the level of awareness of the user regarding the question attribute.
(Appendix 39)
The awareness level calculation method according to appendix 38, wherein the awareness level calculation step calculates the awareness level using the following (Equation 3).

int _n =Fv1+(cl-Fv2)×x _n (Formula 3)

int _n : Awareness level for question attribute n Fv1: Preset first fixed value Fv2: Preset second fixed value cl: Clarity calculated by the clarity calculation step x _n : Set by the likelihood setting step Likelihood at question attribute n

(Additional note 40)
The consciousness level calculation method according to appendix 39, wherein in the consciousness level calculation step, the consciousness level calculated at the i-1st time (i≧2) is set as the Fv1.
(Appendix 41)
The level of consciousness calculation method according to any one of appendices 38 to 40, wherein the clarity calculation step calculates the clarity using the clarity calculation method according to any one of appendices 33 to 37.
(Additional note 42)
The method for calculating the degree of consciousness according to any one of appendices 38 to 41, which is used in the degree of consciousness calculation step in the method for acquiring interactive information according to any one of appendices 22 to 32.
(Appendix 43)
A program capable of executing the method according to any one of appendices 22 to 33 on a computer.
(Appendix 44)
A computer-readable recording medium recording the program described in appendix 43.

According to the present invention, information can be easily acquired through dialogue, for example. Therefore, the present invention is particularly useful when, for example, a user asks a question that the user feels psychologically reluctant to answer.

1 Chatbot 2 User 10 Interactive information acquisition device 11 General question presentation means 12 General answer acquisition means 13 Clarity calculation means 14 Awareness degree calculation means 15 Implication ratio calculation means 16 Judgment means 17 Purpose question presentation means 18 Purpose answer acquisition means 20 Clarity calculation device 21 Clarity calculation means 22 Response time calculation means 23 Clarity calculation means 30 Consciousness calculation device 31 Clarity calculation means 32 Likelihood setting means 33 Consciousness calculation means 40 General question database 50 Purpose question database 60 Intention database 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Display device 107 Communication device

Claims (10)

Including a general question presentation means, a general answer acquisition means, a clarity calculation means, an awareness degree calculation means, an implication ratio calculation means, a determination means, a purpose question presentation means, and a purpose answer acquisition means,
The general question presenting means refers to a general question database and presents general questions to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition means acquires a user's general answer to the general question,
The clarity calculation means calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
The awareness level calculation means calculates the awareness level for each of the question attributes using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation means calculates an implication ratio in the presented general question for each question attribute from the awareness level,
The determination means refers to a target question database and determines whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting means refers to the objective question database and presents the objective question linked to the set question attribute to the user,
The objective answer acquisition means acquires the user's objective answer to the objective question, and stores the acquired answer in an intention database,
The intention database is an interactive information acquisition device, wherein the intention database is a database that stores the user's objective answers to the objective questions. If the threshold is satisfied,
The clarity calculation means further calculates the clarity from the objective answer,
2. The interactive information acquisition device according to claim 1, wherein the objective answer acquisition means further stores the objective answer and the clarity of the objective answer in association with each other. Additionally, it includes machine learning means;
3. The interactive method according to claim 2, wherein the machine learning means generates scenario data in which a scenario from presentation of the general question to presentation of the objective question is converted into data based on the clarity of the objective answer by machine learning. Information acquisition device. 4. The interactive information acquisition device according to claim 3, wherein the machine learning means generates the shortest scenario data from the level of awareness to presenting the objective question by machine learning. Furthermore, it includes an attention level calculation means,
The attention level calculation means calculates an attention level indicating the number of times the question attribute is used,
The interactive information acquisition device according to any one of claims 1 to 4, wherein the implication ratio calculation means calculates a value obtained by multiplying the awareness level and the attention level as the implication ratio. Furthermore, it includes an attribute information acquisition means,
The attribute information acquisition means acquires user attribute information,
The objective question includes conditions for the respondent for each objective question,
The interactive information acquisition device according to any one of claims 1 to 5, wherein the objective question presenting means presents to the user an objective question that satisfies the respondent's conditions based on the attribute information. Furthermore, it includes audio input/output means,
The audio input/output means outputs each of the presentations in the general question presentation means and the objective question presentation means as audio data,
The interactive information acquisition device according to any one of claims 1 to 6, wherein each of the acquisitions by the general answer acquisition means and the objective answer acquisition means is input as audio data. Furthermore, it includes a purpose answer update means,
The objective answer acquisition means further stores time information at which the objective answer was stored in the intention database in association with the objective answer;
8. The dialogue according to claim 1, wherein the purpose answer updating means updates at least one of the stored purpose answer and the time information when a preset period has elapsed since the time information. Type information acquisition device. Including a general question presentation process, a general answer acquisition process, a clarity calculation process, an awareness level calculation process, an implication ratio calculation process, a determination process, a purpose question presentation process, and a purpose answer acquisition process,
The general question presenting step refers to a general question database and presents the general question to the user;
The general question database is a database storing general questions for each preset question attribute,
The general answer acquisition step acquires a user's general answer to the general question,
The clarity calculation step calculates clarity from the general answer,
The clarity is a value indicating the degree of the user's willingness to interact regarding the question attribute,
In the awareness level calculation step, the awareness level is calculated for each question attribute using the question attribute set for the presented general question and the clarity level,
The awareness level is a value indicating the level of awareness of the user in thinking about the question attribute,
The implication ratio calculation step calculates an implication ratio for each question attribute in the general question from the awareness level,
The determination step refers to a target question database to determine whether the implication ratio satisfies a threshold;
The objective question database is a database that stores objective questions and implicature ratio thresholds,
The purpose question is linked to the general question by the question attribute,
If the threshold is satisfied,
The objective question presenting step refers to the objective question database and presents the objective question linked to the set question attribute to the user;
The objective answer acquisition step acquires the user's objective answer to the objective question, and stores the acquired answer in an intention database;
The intention database is a database that stores the user's objective answers to the objective questions,
An interactive information acquisition method in which each of the steps described above is executed by a computer . A program for causing a computer to execute the method according to claim 9.

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