Internal intention collection support program, internal intention collection support device, internal intention collection support method, and recording medium

Abstract

To provide an internal intention collection support program for generating an intention dataset available for learning that considers the internal intention accompanying a behavior.SOLUTION: An internal intention collection support program of the present disclosure includes a behavior information acquisition procedure, an internal intention acquisition procedure, and a dataset generation procedure. The behavior information acquisition procedure acquires a user's behavior information, the internal intention acquisition procedure acquires the user's internal intention present in the behavior information, and the dataset generation procedure records the behavior information and the internal intention in linkage with each other to generate an intention dataset.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an implicit intention collection support program, an implicit intention collection support device, an implicit intention collection support method, and a recording medium. [Background technology]

[0002] As a mechanism for outputting information in response to a user's behavior, an information processing device is known that includes a behavior history acquisition unit that acquires behavior history information including a behavior type, which is the type of user behavior, the title of the target of the behavior, and the time point at which the behavior was performed, and a model generation unit that uses multiple pieces of behavior history information in a chronological order as training data and generates a model that outputs recommended information indicating the target corresponding to the behavior history information when the behavior history information is input (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-163557 Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, when people decide to act, there are cases where their inherent will (intention), known as experiential knowledge or tacit knowledge, is strongly involved. However, there are challenges with regard to inherent intention, such as people not even being aware of it themselves, or even if they are aware, finding it difficult to verbalize it.

[0005] Therefore, an object of the present disclosure is to provide an implicit intention collection support program, an implicit intention collection support device, an implicit intention collection support method, and a recording medium that can support awareness of implicit intentions accompanying actions. [Means for solving the problem]

[0006] In order to achieve the above object, the implicit intention collection support program of the present disclosure includes: A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. The present invention provides an implicit intention collection support program for causing a computer to execute each of the above procedures.

[0007] The present disclosure provides an implicit intention collection support device, A recommended action output unit and a hypothesis reason information output unit are included, the recommended action output unit outputs a recommended action to be recommended to the user; The hypothesis reason information output unit outputs hypothesis reason information indicating a hypothesis reason for recommending the recommended action.

[0008] The method for supporting collection of implicit intentions disclosed herein includes: a recommended action output step and a hypothesis reason information output step; The recommended action output step outputs a recommended action to be recommended to the user, The hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. Each of the steps is a computer-implemented method.

[0009] The recording medium of the present disclosure includes: A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. A computer-readable recording medium on which is recorded an implicit intention collection support program for causing a computer to execute each of the above procedures. [Effects of the Invention]

[0010] The present disclosure can provide an opportunity to become aware of the inherent intentions that accompany actions. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration of an example of an implicit intention collection support device according to the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of the implicit intention collection support device of the present disclosure. [Figure 3] FIG. 3 is a flowchart showing an example of a procedure according to the implicit intention collection support program of the present disclosure. [Figure 4] FIG. 4 is a block diagram showing an example of the configuration of an implicit intention collection support device according to the present disclosure. [Figure 5] FIG. 5 is a flowchart showing an example of a procedure according to the implicit intention collection support program of the present disclosure. [Figure 6] FIG. 6 is an example of a screen for explaining an example of a behavioral information acquisition procedure of the implicit intention collection support program of the present disclosure. [Figure 7] FIG. 7 is an example of a screen for explaining an example of an implicit intention acquisition procedure of the implicit intention collection support program of the present disclosure. [Figure 8] FIG. 8 is an example of a screen for explaining an example of an implicit intention acquisition procedure of the implicit intention collection support program of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. In the following drawings, identical parts are designated by the same reference numerals. Furthermore, the descriptions of the embodiments can be used interchangeably unless otherwise specified, and the configurations of the embodiments can be combined unless otherwise specified. In the present disclosure, each drawing may apply to one or more embodiments.

[0013] [Embodiment 1] The implicit intention collection support program of the present disclosure is a program for causing a computer to execute a recommended action output procedure and a hypothetical reason information output procedure. The implicit intention collection support program of the present disclosure can also be said to be a program for causing a computer to function as the recommended action output procedure and the hypothetical reason information output procedure. Furthermore, the implicit intention collection support program of the present disclosure can also be said to be a program for causing a computer to execute, for example, each step of the implicit intention collection support method described below.

[0014] the recommended action output step outputs a recommended action to be recommended to the user; The hypothetical reason information output step outputs hypothetical reason information indicating a hypothetical reason why the recommended action is recommended.

[0015] For example, the "procedure" in each of the steps can be read as "processing." The implicit intention collection support program of the present disclosure may be recorded on a computer-readable recording medium, for example. The recording medium is, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples thereof include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., SSD (Solid State Drive), USB flash memory, SD / SDHC card, etc.), optical disk (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), and floppy disk (FD). The implicit intention collection support program of the present disclosure (also referred to as a programming product or program product, for example) may be distributed from an external computer. The "distribution" may be, for example, distribution via a communication network or a device connected via a wire. The implicit intention collection support program of the present disclosure may be installed and executed on a device to which it is distributed, or may be executed without being installed. An information processing device capable of executing the implicit intention collection support program of the present disclosure can be referred to as, for example, an implicit intention collection support device of the present disclosure.

[0016] Next, the configuration of an example of an implicit intention collection support device according to the present disclosure will be described with reference to FIG. 1. FIG. 1 is a block diagram showing an example of the configuration of an implicit intention collection support device 10 according to the present disclosure (hereinafter also referred to as the present device 10). As shown in FIG. 1, the present device 10 includes a recommended action output unit 11 and a hypothetical reason information output unit 12. Furthermore, although not shown, the present device 10 may include, for example, an input unit, an output unit, a display unit, and / or a storage unit. The recommended action output unit 11 and the hypothetical reason information output unit 12 can respectively execute, for example, a recommended action output procedure and a hypothetical reason information output procedure in the implicit intention collection support program according to the present disclosure.

[0017] The device 10 may be, for example, a single device including the above-described units, or a device in which the units can be connected via a communication network. The device 10 can also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and any known network can be used, for example, a wired or wireless network. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, and LPWA. Examples of the wireless communication include direct communication between devices (Ad Hoc communication), infrastructure communication, and indirect communication via an access point. The device 10 may be incorporated into a server as a system. Furthermore, the present device 10 may be, for example, a personal computer (PC, for example, desktop or notebook type) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, etc. The present device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the above-mentioned units is located on a server and the other units are located on a terminal.

[0018] 2 shows a block diagram of the hardware configuration of the 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, an output device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0019] The central processing unit 101 cooperates with other components via a controller (such as a system controller or an I / O controller) and controls the entire device 10. In the device 10, the central processing unit 101 executes, for example, the program of the present disclosure (the implicit intention collection support program) and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 functions as a recommended action output unit 11 and a hypothetical reason information output unit 12. The device 10 may include, as a computing device, other computing devices such as a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0020] The bus 103 can also be connected to, for example, external devices. Examples of the external devices include an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. The device 10 can be connected to an external network (the communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0021] The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from the memory 102 and executes the programs. The main memory may be, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

[0022] The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing data from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) or a solid-state drive (SSD) in which the recording medium and drive are integrated. When the device 10 includes the storage unit, for example, the storage device 104 functions as the storage unit. The storage unit can record, for example, behavioral information, implicit intentions, recommended actions, and hypothetical reason information, which will be described later.

[0023] In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store, for example, the above-mentioned information on the user of the present device. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0024] The device 10 further includes, for example, an input device 105 and an output device 106. Examples of the input device 105 include pointing devices such as a touch panel, track pad, and mouse; a keyboard; imaging means such as a camera and scanner; card readers such as an IC card reader and a magnetic card reader; and audio input means such as a microphone. Examples of the output device 106 include display devices such as an LED display and a liquid crystal display; audio output devices such as a speaker; a printer; and the like. In the first embodiment, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated device, such as a touch panel display.

[0025] An example of processing by the implicit intention collection support program of the present disclosure will be described more specifically with reference to Fig. 3. Fig. 3 is a flowchart showing an example of each procedure of the implicit intention collection support program of the present disclosure.

[0026] The recommended action output unit 11 outputs a recommended action for the user (S1, recommended action output step). The recommended action is not particularly limited, and for example, is information indicating the type of action recommended to the user, and its content is not particularly limited. The recommended action may include, for example, the same information as the type of the action information, or may include different information. Specific examples of the recommended action include, for example, information recommending an operation of a specific device, text information to be input by the user, etc. The recommended action may be, for example, an option regarding the user's action presented by the device 10. Furthermore, the recommended action output unit 11 may output the recommended action based on premise information, for example. The premise information is, for example, information that is a premise for the user's action, and is not particularly limited. Examples of the premise information include information such as a user's dialogue log and user attributes.

[0027] The hypothesis reason information output unit 12 outputs hypothesis reason information explaining the reason why the recommended action was recommended (S2, hypothesis reason information output step). The hypothesis reason information is, for example, information in which the reason for recommendation is associated with each recommended action. The hypothesis reason information may be, for example, recorded in the storage unit of the device 10A or an external recording medium. The hypothesis reason information can be generated, for example, by using a machine learning model that performs machine learning on an intention dataset. The intention dataset is, for example, a set of data in which at least one of behavior information and recommended actions is associated with hypothesis reason information and recorded. The intention dataset will be described later in embodiment 2. The hypothesis reason information output unit 12 may, for example, output the hypothesis reason information using a rule-based algorithm or may output the hypothesis reason information using a large-scale language model. As the rule-based algorithm, for example, hypothesis reason information corresponding to the recommended action may be associated and recorded in advance, and the hypothesis reason information corresponding to the recommended action may be read and output. In this case, the recommended action and the hypothesis reason information may be recorded in the storage unit of the device 10A or an external recording medium. The large-scale language model is not particularly limited, and examples thereof include ChatGPT by Open AI, Bing by Microsoft, Bard by Google (Alphabet Inc.), and LLM by NEC Corporation. In this case, the hypothesis reason information output unit 12 can generate and output the hypothesis reason information by providing the large-scale language model with, for example, the recommended action and a prompt on which a constraint is imposed so as to output the hypothesis reason information.

[0028] The implicit intention collection support method of the present disclosure is, for example, a method implemented by replacing each "procedure" in the implicit intention collection support program of the present disclosure with a "step." Specifically, the implicit intention collection support method of the present disclosure includes a recommended action output step and a hypothetical reason information output step, The recommended action output step outputs a recommended action to be recommended to the user, and the hypothetical reason information output step outputs hypothetical reason information indicating a hypothetical reason for recommending the recommended action. The implicit intention collection support method of the present disclosure can be implemented, for example, using the implicit intention collection support device 10 of the present disclosure shown in FIG. 1 or FIG. 2. Note that the implicit intention collection support method of the present disclosure is not limited to, for example, a method using the implicit intention collection support device 10. For the implicit intention collection support method of the present disclosure, for example, the descriptions of the implicit intention collection support program and the implicit intention collection support device of the present disclosure can be cited.

[0029] According to the implicit intention collection support program of the present disclosure, the recommended action output procedure can output a recommended action to be recommended to the user, and the hypothetical reason information output procedure can output hypothetical reason information indicating a hypothetical reason for recommending the recommended action. As described above, people are not always able to simply and appropriately verbalize the intentions of their actions. On the other hand, when the implicit intention collection support program of the present disclosure outputs recommended actions and hypothetical reason information, people can compare and consider their own intentions for the actions with the hypothetical reason information. Therefore, the implicit intention collection support program of the present disclosure makes it easier for users to verbalize the implicit intentions that serve as guidelines for their actions. Therefore, the implicit intention collection support program of the present disclosure can provide an opportunity for users to become aware of the implicit intentions associated with actions.

[0030] [Embodiment 2] Another example of the implicit intention collection support program of the present disclosure will be described.

[0031] The implicit intention collection support program of this embodiment is similar to the implicit intention collection support program of the first embodiment, except that it includes, in addition to the configuration of the implicit intention collection support program of the first embodiment, a behavioral information acquisition step, a match determination step, and an implicit intention acquisition step, and the description thereof can be cited. The implicit intention collection support program of this embodiment includes, for example, a behavioral information acquisition step, a match determination step, and an implicit intention acquisition step, where the behavioral information acquisition step acquires user behavior information, the match determination step determines whether the recommended behavior and the behavior information match, and the implicit intention acquisition step acquires the user's implicit intention implicit in the behavior information when the recommended behavior and the behavior information do not match.

[0032] The implicit intention collection support program of this embodiment may further include, for example, a data set generation procedure, in which, when the recommended action matches the action information, the action information and / or the recommended action are linked to the hypothetical reason information and recorded to generate an intention data set, and when the recommended action does not match the action information, the action information is linked to the implicit intention and recorded to generate an intention data set.

[0033] Next, the implicit intention collection support device of this embodiment will be described with reference to Fig. 4. The implicit intention collection support device 10A of the present disclosure is similar to the implicit intention collection support device 10 of the first embodiment, except that it includes a behavioral information acquisition unit 13, a match determination unit 14, an implicit intention acquisition unit 15, and optionally a data set generation unit 16 in addition to the configuration of the implicit intention collection support device 10 of the first embodiment, and the description thereof can be cited. The implicit intention collection support device 10A of this embodiment includes, for example, the match determination unit 14 and the implicit intention acquisition unit 15. The behavioral information acquisition unit 13 acquires behavioral information of a user. The match determination unit 14 determines whether the recommended behavior matches the behavioral information. If the recommended behavior does not match the behavioral information, the implicit intention acquisition unit 15 acquires the user's implicit intention implicit in the behavioral information. Furthermore, in the case where the device 10A includes the data set generation unit 16, for example, when the recommended action matches the action information, the data set generation unit 16 links at least one of the action information and the recommended action with the hypothetical reason information and records them to generate an intention data set, and when the recommended action does not match the action information, the data set generation unit 16 links the action information with the implicit intention and records them to generate an intention data set.

[0034] 4, the implicit intention collection support device 10A includes, in addition to the configuration of the implicit intention collection support device 10 of embodiment 1, a behavioral information acquisition unit 13, a match determination unit 14, an implicit intention acquisition unit 15, and optionally, a data set generation unit 16. The hardware configuration of the implicit intention collection support device 10A is the same as that of the implicit intention collection support device 10 of FIG. 2, except that the central processing unit 101 includes the configuration of the implicit intention collection support device 10A of FIG. 4 instead of the configuration of the implicit intention collection support device 10 of FIG. 1.

[0035] An example of processing by the implicit intention collection support program of the present disclosure will be described more specifically with reference to Fig. 5. Fig. 5 is a flowchart showing an example of each procedure of the implicit intention collection support program of the present disclosure.

[0036] First, steps S1 and S2 are carried out in the same manner as steps S1 and S2 in the first embodiment.

[0037] Next, the behavioral information acquisition unit 13 acquires behavioral information of the user (S11, behavioral information acquisition step). The behavioral information is not particularly limited and may be, for example, information indicating the type of user behavior, and the content thereof is not particularly limited. The behavioral information may be, for example, information acquired directly or indirectly about the user's behavior. Examples of the directly acquired information include operation information of a specific device and information about the user's behavior input by the user. Examples of the indirectly acquired information include information about the user's movement based on the user's location information. The behavioral information may be, for example, selection information selected by the user from options related to the user's behavior presented by the device 10. Note that the behavioral information acquisition unit 13 may, for example, acquire the user's behavioral information in real time, or may acquire behavioral information (behavior history) recorded in the memory unit of the device 10 or an external storage device. Note that if the recommended behavior is an option of the user's behavior, the behavioral information acquisition unit 13 may, for example, acquire the selection result of the option as the user's behavioral information.

[0038] The match determination unit 14 determines, for example, whether the recommended action matches the action information (S12, match determination step). When the recommended action is an option for the user's action and the action is a selection result of the option, the match determination unit 14 can determine whether the recommended action matches the action information by determining, for example, whether the option recommended as the recommended action matches the option selected by the user.

[0039] For example, if the recommended action and the behavioral information do not match (S12, No), the implicit intention acquisition unit 15 can acquire the user's implicit intention implicit in the behavioral information (S14, implicit intention acquisition step). The implicit intention is information about the user's intention (will) when the user performed the behavioral information. The implicit intention acquisition unit 15 can acquire the implicit intention, for example, by outputting a query to the user inquiring about the implicit intention implicit in the behavioral information and having the user input the user's implicit intention as a response to the query. The implicit intention acquisition unit 15 may output the query using, for example, a rule-based algorithm or a large-scale language model. The rule-based algorithm may, for example, associate and record query information corresponding to the behavioral information in advance, and read and output the query information corresponding to the user's behavioral information. In this case, the behavioral information and query information may be recorded in a storage unit of the device 10 or an external recording medium. The large-scale language model is not particularly limited, and examples thereof include ChatGPT by Open AI, Bing by Microsoft, Bard by Google (Alphabet, Inc.), and LLM by NEC Corporation. In this case, the implicit intention acquisition unit 15 can generate and output the query by providing the large-scale language model with, for example, the behavioral information and a prompt with constraints imposed so as to output the query.

[0040] In this embodiment, the hypothetical reason information output unit 12 may output, for example, inferred reason information that infers the user's underlying intention underlying the behavioral information. Examples of the inferred reason information include, but are not limited to, a sentence such as, "The reason you selected XX (behavior) is because XXX (reason), right?" The hypothetical reason information output unit 12 may output the inferred reason information using, for example, a rule-based algorithm, or may output the inferred reason information using a large-scale language model. The rule-based algorithm may, for example, associate and record in advance the inferred reason information corresponding to the behavioral information, and read and output the inferred reason information corresponding to the user's behavioral information. In this case, the behavioral information and the inferred reason information may be recorded in a storage unit of the device 10A or in an external recording medium. The large-scale language model is not particularly limited, and examples thereof include ChatGPT by Open AI, Bing by Microsoft, Bard by Google (Alphabet Inc.), and LLM by NEC Corporation. In this case, the implicit intention acquisition unit 15 can generate and output the query by, for example, providing the large-scale language model with the behavioral information and a prompt on which a constraint is imposed so as to output the inferred reason information. Furthermore, the hypothetical reason information output unit 12 may, for example, inquire about the accuracy of the inferred reason information. If the inferred reason information matches the user's implicit intention, the hypothetical reason information output unit 12 may, for example, acquire the inferred reason information again as the user's implicit intention. If the inferred reason information does not match the user's implicit intention, the implicit intention acquisition unit 15 can acquire the implicit intention by, for example, outputting a query to the user inquiring about the implicit intention implicit in the behavioral information and having the user input the user's implicit intention as a response to the query.

[0041] For example, if the recommended action matches the action information (S12, Yes), the dataset generation unit 16 associates at least one of the action information and the recommended action with the hypothetical reason information and records them to generate an intention dataset (S13). If the recommended action does not match the action information (S12, No), the dataset generation unit 16 associates the action information with the implicit intention and records them to generate an intention dataset (S15) (dataset generation procedure). For example, the dataset generation unit 16 may associate the action information with the implicit intention and record them in a storage unit of the device 10 to generate the intention dataset, or may associate the action information with the implicit intention and record them in a database external to the device 10. Furthermore, for example, if the user did not input an implicit intention in step S14, the dataset generation unit 16 may associate the action information with the implicit intention and record information such as “no implicit intention input,” “no response,” or “no intention” in association with the action information. Furthermore, the dataset generation unit 16 may, for example, further link and record the premise information. Furthermore, the dataset generation unit 16 may, for example, further link and record information on the success or failure of the recommendation in the intention dataset. The success or failure information may, for example, be information indicating that the recommended action matches the behavioral information (recommendation successful), or information indicating that the recommended action does not match the behavioral information (recommendation failed). Furthermore, in the case of a recommendation failure, the presence or absence of input of an implicit intention may be further recorded.

[0042] In this embodiment, the hypothetical reason information output unit 12 may update the hypothetical reason information based on, for example, the intention dataset. The hypothetical reason information output unit 12 may, for example, analyze each piece of information recorded in the intention dataset and update the hypothetical reason information based on the analysis results. The analysis may, for example, be an analysis using natural language processing. Specifically, the hypothetical reason information output unit 12 may convert the information recorded in the intention dataset into vectors using W2V (Word2Vec) and perform analysis of variance based on the converted vectors. This allows the information recorded in the intention dataset to be classified into successful recommendations (recommended actions adopted by the user), corrective recommendations (recommended actions not adopted by the user, but for which an implicit intention was input), ignored recommendations (recommended actions not adopted by the user, and for which no implicit intention was input), etc. Furthermore, the recommended behavior output unit 11 may, for example, pass the intention dataset to an LLM to analyze the linguistic information (behavioral information, recommended actions, hypothetical reason information, and implicit intention) included in the intention dataset. In this case, the hypothesis reason information output unit 12 can, for example, pass a prompt with constraints imposed on it to classify the information in the intention dataset (e.g., the success recommendation, the correction recommendation, and the ignore recommendation) and the information in the intention dataset to the LLM for analysis.

[0043] Next, a specific example of the processing of the device 10A will be described with reference to Figures 6 to 10. In the following description, the device 10A will be described as being incorporated into a chat system used for consultation between a mentor and a client, but the present disclosure is not limited to the following example.

[0044] First, a mentor, which is the device 10A, is consulting with a client in the chat system shown in FIG. 6. The recommended action output unit 11 of the device 10A monitors the interaction between the mentor (the user of the device 10A) and the client in the chat system shown in FIG. 6, receives the client's consultation ("What I want to achieve: Implementing the technology I'm researching in a useful way in society." To achieve this, the first action is to "clarify who it will be useful for.") and determines a recommended action (selection of the "WILL" button and "fundamental reason") using the consultation content as premise information. Then, the recommended action output unit 11 displays candidate buttons for replying to the client to the mentor, as shown in FIG. 6. At this time, the hypothetical reason information output unit 12 also outputs hypothetical reason information (it is recommended that you ask WILL for the fundamental reason for the reason XXX) as the basis for the recommended action.

[0045] Next, the behavioral information acquisition unit 13 monitors the behavior (input to the chat system) of the mentor (user of the device 10) in the chat system shown in Figure 7. The mentor checks the consultation's request ("What I want to achieve: Implement the technology I'm researching in a useful way in society." To achieve this, the first action is to "clarify who it will be useful for"), the recommended action ("WILL" button and "fundamental reason") presented by the device 10A, and the hypothetical reason information (for some reason, it is recommended that you ask the fundamental reason for WILL), and then inputs a question ("What does 'research' mean?"). The behavioral information acquisition unit 13 of the device 10 acquires the question from the mentor ("What does 'research' mean?") as the user's behavioral information.

[0046] Because the recommended action (the "WILL" button and the "fundamental reason") does not match the user's action (the user inputs a question ("What does 'research' mean?"), the match determination unit 14 of the device 10A executes processing by the implicit intention acquisition unit 15, as shown in FIG. 7. Specifically, the implicit intention acquisition unit 15 presents the user, or mentor, with a question asking about the intention of the behavioral information (if you don't mind, please tell us why you selected the button: "Teach" or "Don't teach"). When the mentor presses the "Teach" button, the implicit intention acquisition unit 15 displays a pop-up window on the chat system for inputting an implicit intention, as shown in FIG. 8, and prompts the mentor to input the intention of the behavior. Then, the implicit intention acquisition unit 15 can acquire, for example, the input intention as the user's implicit intention implicit in the behavioral information. Then, the dataset generation unit 16 of the device 10A associates and records the behavioral information with the implicit intention, thereby generating an intention dataset.

[0047] According to the implicit intention collection support program of the present embodiment, when acquiring behavioral information in a scene involving complex decision-making, the behavioral information can be linked to the underlying intention. Therefore, by using the intention dataset generated by the implicit intention collection support program of the present disclosure, it becomes possible to learn behavioral information with higher accuracy.

[0048] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0049] <Additional Notes> Some or all of the above embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. An inherent intention collection support program for causing a computer to execute each of the above procedures. (Appendix 2) The method includes a behavioral information acquisition procedure, a matching determination procedure, and an implicit intention acquisition procedure. The behavioral information acquisition step acquires behavioral information of a user, the matching determination step determines whether the recommended action matches the action information; 2. The implicit intention collection support program according to claim 1, wherein the implicit intention acquisition step acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information are inconsistent. (Appendix 3) 3. The implicit intention collection support program according to claim 2, wherein the hypothetical reason information output step outputs presumed reason information for presuming the user's implicit intention underlying the behavior information. (Appendix 4) Includes a dataset generation procedure, The data set generation procedure includes: If the recommended action matches the action information, at least one of the action information and the recommended action is associated with the hypothetical reason information and recorded to generate an intention dataset; 4. The implicit intention collection support program according to claim 3, wherein, if the recommended behavior does not match the behavior information, the behavior information and the implicit intention are linked and recorded to generate an intention dataset. (Appendix 5) 5. The implicit intention collection support program according to claim 4, wherein the hypothesized reason information output step updates the hypothesized reason information based on the intention dataset. (Appendix 6) A recommended action output unit and a hypothesis reason information output unit are included, the recommended action output unit outputs a recommended action to be recommended to the user; The hypothetical reason information output unit outputs hypothetical reason information indicating a hypothetical reason for recommending the recommended action. (Appendix 7) The system includes a behavioral information acquisition unit, a match determination unit, and an implicit intention acquisition unit, the behavioral information acquisition unit acquires behavioral information of a user; the matching determination unit determines whether the recommended behavior matches the behavior information; 7. The implicit intention collection support device according to claim 6, wherein the implicit intention acquisition unit acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information do not match. (Appendix 8) 8. The implicit intention collection support device according to claim 7, wherein the hypothetical reason information output unit outputs presumed reason information for presuming the user's implicit intention underlying the behavior information. (Appendix 9) a dataset generation unit; The dataset generation unit If the recommended action matches the action information, at least one of the action information and the recommended action is associated with the hypothetical reason information and recorded to generate an intention dataset; 9. The implicit intention collection support device according to claim 8, wherein, if the recommended behavior does not match the behavior information, the behavior information and the implicit intention are linked and recorded to generate an intention dataset. (Appendix 10) 10. The implicit intention collection support device according to claim 9, wherein the hypothetical reason information output unit updates the hypothetical reason information based on the intention dataset. (Appendix 11) a recommended action output step and a hypothesis reason information output step; The recommended action output step outputs a recommended action to be recommended to the user, The hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. The method for supporting collection of implicit intentions, wherein each of the steps is executed by a computer. (Appendix 12) The method includes a behavioral information acquisition step, a matching determination step, and an implicit intention acquisition step, The behavioral information acquisition step acquires behavioral information of a user, the matching determination step determines whether the recommended behavior matches the behavior information; 12. The implicit intention collection support method according to claim 11, wherein the implicit intention acquisition step acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information are inconsistent. (Appendix 13) 13. The method for supporting collection of implicit intentions according to claim 12, wherein the hypothetical reason information output step outputs presumed reason information for presuming the user's implicit intention underlying the behavior information. (Appendix 14) A data set generation step is included, The data set generation step includes: If the recommended action matches the action information, at least one of the action information and the recommended action is associated with the hypothetical reason information and recorded to generate an intention dataset; The implicit intention collection support method according to claim 13, wherein, if the recommended behavior and the behavioral information do not match, the behavioral information and the implicit intention are linked and recorded to generate an intention dataset. (Appendix 15) The implicit intention collection support method according to claim 14, wherein the hypothetical reason information output step updates the hypothetical reason information based on the intention dataset. (Appendix 16) A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. A computer-readable recording medium on which is recorded an implicit intention collection support program for causing a computer to execute each of the above procedures. (Appendix 17) The method includes a behavioral information acquisition procedure, a matching determination procedure, and an implicit intention acquisition procedure. The behavioral information acquisition step acquires behavioral information of a user, the matching determination step determines whether the recommended action matches the action information; 17. The recording medium according to claim 16, wherein the implicit intention acquisition step acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information are inconsistent. (Appendix 18) 18. The recording medium according to claim 17, wherein the hypothetical reason information output step outputs hypothetical reason information that hypothesizes an underlying intention of the user that is implicit in the behavior information. (Appendix 19) Includes a dataset generation procedure, The data set generation procedure includes: If the recommended action matches the action information, at least one of the action information and the recommended action is associated with the hypothetical reason information and recorded to generate an intention dataset; 19. The recording medium according to claim 18, wherein, if the recommended action does not match the action information, the action information and the implicit intention are linked and recorded to generate an intention dataset. (Appendix 20) 20. The recording medium according to claim 19, wherein the hypothesis reason information output step updates the hypothesis reason information based on the intention dataset. [Industrial Applicability]

[0050] According to the present disclosure, when acquiring behavioral information in a scene involving complex decision-making, the behavioral information can be linked to the underlying intention. Therefore, by using the intention dataset generated by the implicit intention collection support program of the present disclosure, it becomes possible to learn behavioral information with higher accuracy. Therefore, the present disclosure can be widely and usefully used in various fields that use machine learning. [Explanation of symbols]

[0051] 10, 10A Inherent intention collection support device 11 Recommended Action Output Section 12 Hypothesis reason information output section 13 Behavioral information acquisition unit 14 Match determination section 15. Intention Acquisition Unit 16 Dataset Generation Unit 101 Central Processing Unit 102 memory 103 Bus 104 Storage device 105 Input Device 106 Output Device 107 Communication Devices

Claims

1. A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. An inherent intention collection support program for causing a computer to execute each of the above procedures.

2. The method includes a behavioral information acquisition procedure, a matching determination procedure, and an implicit intention acquisition procedure. The behavioral information acquisition step acquires behavioral information of a user, the matching determination step determines whether the recommended behavior matches the behavior information; 2. The implicit intention collection support program according to claim 1, wherein the implicit intention acquisition step acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information do not match.

3. 3. The program for supporting collection of implicit intentions according to claim 2, wherein the hypothetical reason information output step outputs presumed reason information for presuming the user's implicit intention underlying the behavior information.

4. Includes a dataset generation procedure, The data set generation procedure includes: If the recommended action matches the action information, at least one of the action information and the recommended action is associated with the hypothetical reason information and recorded to generate an intention dataset; 4. The implicit intention collection support program according to claim 3, wherein, when the recommended action does not match the action information, the action information and the implicit intention are recorded in association with each other to generate an intention data set.

5. 5. The program for supporting collection of implicit intentions according to claim 4, wherein the hypothetical reason information output step updates the hypothetical reason information based on the intention data set.

6. A recommended action output unit and a hypothesis reason information output unit are included, the recommended action output unit outputs a recommended action to be recommended to the user; The hypothetical reason information output unit outputs hypothetical reason information indicating a hypothetical reason for recommending the recommended action.

7. The system includes a behavioral information acquisition unit, a match determination unit, and an implicit intention acquisition unit, the behavioral information acquisition unit acquires behavioral information of a user; the matching determination unit determines whether the recommended behavior matches the behavior information; The implicit intention collection support device according to claim 6 , wherein the implicit intention acquisition unit acquires the user's implicit intention that is implicit in the behavior information when the recommended behavior and the behavior information do not match.

8. The implicit intention collection support device according to claim 7 , wherein the hypothetical reason information output unit outputs presumed reason information for presuming the user's implicit intention that is implicit in the behavior information.

9. a recommended action output step and a hypothesis reason information output step; The recommended action output step outputs a recommended action to be recommended to the user, The hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. The method for supporting collection of implicit intentions, wherein each of the steps is executed by a computer.

10. A procedure for outputting recommended actions and a procedure for outputting hypothesis reason information are included. the recommended action output step outputs a recommended action to be recommended to the user; the hypothesis reason information output step outputs hypothesis reason information indicating a hypothesis reason why the recommended action is recommended. A computer-readable recording medium on which is recorded an implicit intention collection support program for causing a computer to execute each of the above procedures.

Images