Information processing apparatus and program

Abstract

To estimate the level of trust users feel.SOLUTION: A processor of an information processing apparatus 1 executes a program to function as: means (S2) for receiving data of a speech of a real user from a user terminal; means (S9) for transmitting, to a speech sentence generation apparatus, a request to generate a speech of a virtual user together with data of histories of speeches of the real user and the virtual user; means (S10) for receiving data of a speech of the virtual user from the speech sentence generation apparatus; means (S12) for transmitting, to the user terminal, the received data of the speech of the virtual user; means (S33) for determining, based on the speech of the real user, whether the real user agrees with or disagrees with the speech of the virtual user; and means (S34, S35, S36) for calculating, based on a result of the determination, the trust score indicating how much the real user trusts the virtual user.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus and a program.

Background Art

[0002] Currently, EC sites (online sales) such as apparel have become widespread, and it is expected that in the future, the opportunity to open virtual stores in shopping malls in virtual spaces (metaverses) on the Internet will increase. In such a situation, as in physical stores, it is important from the perspective of sales promotion to provide one-on-one customer service in which staff introduce recommended products through conversations with customers, dig out customer needs, materialize products that users desire, and respond to various inquiries.

[0003] Generally, the number of users visiting EC sites is much larger than the number of customers visiting physical stores. Therefore, it is not realistic for actual staff to serve each user visiting the EC site individually. Thus, it is assumed that virtual staff (referred to as virtual users or digital staff) is constructed on a computer, and the digital staff serves customers (referred to as actual users) one-on-one instead of actual staff.

[0004] The use of generative AI is expected for creating the speech content for digital staff to have conversations with customers via chat or the like. The generative AI is sent the conversation history between the digital staff and the customer together with the generation request. The generative AI analyzes the conversation history and creates the speech sentence that the digital staff should speak next. Since it is used in an EC site, the ultimate goal is sales, and for this purpose, high conversation skills and customer service capabilities like those of excellent actual staff are required. Excellent actual staff is good at advancing conversations to build an intimate relationship while gaining empathy and trust from customers, whether consciously or unconsciously.

[0005] However, generating utterances based solely on the input conversation history between the digital staff and the customer was not enough to move away from a mechanical conversation. Summary of the Invention [Problem to be solved by the invention]

[0006] The goal is to estimate the degree of trust felt by users. [Means for solving the problem]

[0007] The information processing device according to this embodiment includes a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a user terminal and a statement generation device (generation AI) via an electrical public communication network. By executing the program, the processor functions as: means for receiving data on real user utterances from the user terminal; means for sending a request for generating a virtual user utterance together with data on the utterance histories of the real user and the virtual user to the statement generation device; means for receiving data on the virtual user utterances from the statement generation device; means for sending the received data on the virtual user utterances to the user terminal; means for making a determination based on the real user utterances as to whether the real user is positive or negative about the virtual user utterance; and means for calculating a reliability score representing the degree of trust the real user has in the virtual user based on the determination result. [Brief explanation of the drawings]

[0008]

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MODE FOR CARRYING OUT THE INVENTION

[0009] Hereinafter, an information processing apparatus according to an embodiment of the present invention will be described with reference to the drawings. The information processing apparatus according to the present embodiment is applied to a scenario where a virtual user (virtual user) constructed on a computer converses with an actual user. Here, a scenario where a customer who has visited an EC site converses with a virtual staff (digital staff) constructed on a computer in a chat room prepared on the EC site will be described as an example. In this example, the customer corresponds to an actual user, and the digital staff corresponds to a virtual user. Of course, the present invention is not limited to a scenario where the actual user is a customer and the virtual user is a digital staff.

[0010] As shown in FIG. 1, the information processing apparatus 1 according to the present embodiment is connected to a speech generation apparatus 2 that functions as a generation AI, an EC server 3, and a user terminal 4 via an electrical public communication network 5 such as the Internet. The user terminal 4 is connected to the EC server 3, and a customer (actual user) visits the EC site operated by the EC server 3. A chat room or the like is prepared on the EC site, and the customer makes a purchase while conversing with a digital staff (virtual user). The information processing apparatus 1 directly receives the speech data of the customer input from the user terminal 4 from the user terminal 4 or indirectly via the EC server 3, creates a speech of the digital staff in response to the customer's speech, and directly transmits it to the user terminal 4 or indirectly via the EC server 3 to the user terminal 4. By repeating the process, the conversation between the customer and the digital staff progresses on the chat room. The information processing apparatus 1 transmits a speech generation request to the speech generation apparatus 2 together with the ordered customer speech and digital staff speech, that is, the conversation history between the customer and the digital staff.

[0011] In the present embodiment, based on the customer's speech, it is determined whether the customer is positive or negative about the digital staff's speech, and based on the determination result, a degree of empathy score is calculated to estimate to what extent the customer shows empathy for the digital staff's speech, and based on the determination result, a degree of trust score is calculated to estimate to what extent the customer shows a sense of trust for the digital staff's speech. Whether to apply the determination result of positive or negative regarding the customer's speech to either the empathy score or the trust score is selected according to whether the digital staff's speech follows the theme of the conversation. For example, if the EC site is a clothing store, clothing is applied as the theme of the conversation.

[0012] When it is determined that the customer is positive about the statement of the digital staff, the reliability score or the empathy score is increased, and when it is determined that the customer is negative, the reliability score or the empathy score is decreased. Of course, as the reliability score, the empathy score, and the intimacy score (to be described later) increase, it means that the customer's reliability, empathy, and intimacy with the digital staff increase.

[0013] By repeating this process, it is possible to obtain how the empathy score and the reliability score change as the conversation progresses, and further to identify what kind of statement content of the digital staff caused the change.

[0014] Furthermore, in an actual human relationship, as the intimacy increases, the politeness level of the language gradually decreases, and there is a tendency to gradually change from honorific language to friendly language. It is considered that the intimacy can be deepened by decreasing the politeness level of the language and changing from honorific language to friendly language. In other words, if the customer uses friendly language while the digital staff continues to use honorific language, the customer may lose the sense of intimacy.

[0015] Therefore, in this embodiment, the politeness level of the statement of the digital staff generated by the statement generation device 2 is changed according to the situation so as to follow the change in the politeness level of the words uttered by the customer. The politeness level of the words related to the customer's statement is specified, and based on the change in the politeness level of the words related to the specified customer's statement, the politeness level of the digital staff's words is determined. When the politeness level of the words related to the customer's statement decreases, the politeness level of the words related to the digital staff's statement also decreases by one level from the current politeness level accordingly. When the politeness level of the words related to the customer's statement increases, the politeness level of the words related to the digital staff's statement also increases by one level from the current politeness level accordingly. In this way, in this embodiment, by changing the politeness level of the digital staff's words according to the politeness level of the customer's words, it is possible to increase the customer's sense of intimacy while suppressing the decrease.

[0016] In addition, in the present embodiment, the customer intimacy score for the digital staff is calculated based on the change in the politeness level of the customer's speech. When the politeness level of the customer's speech decreases, the customer intimacy score for the digital staff is increased. When the politeness level of the customer's speech increases, the customer intimacy score for the digital staff is decreased.

[0017] When transmitting the speech generation request to the speech generation device 2, in addition to the conversation history between the customer and the digital staff, by transmitting the transitions of the empathy score, the trust score, and the intimacy score respectively, the speech of the digital staff can be generated to follow the flow of the conversation and increase the empathy score and the trust score without decreasing them.

[0018] As shown in FIG. 2, the information processing device 1 as a conversation device has a RAM 12, a ROM 13, a storage unit 14, an input device 15, a display 16, and a communication interface 17 connected to a processor 11 via a system bus 10. The processor 11 is composed of, for example, a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The processor 11 executes a conversation program loaded from the storage unit 14 and the ROM 13 into the RAM 12 to execute conversation processing between the customer and the digital staff.

[0019] The RAM 12 functions as the main memory, work area, etc. of the processor 11. The ROM 13 or the storage unit 14 stores the BIOS (Basic Input Output System), the operating system program (OS), the conversation processing program according to the present embodiment, programs for realizing other various functions, and various data required for those processes.

[0020] The input device 15 comprises a keyboard (KB) and a pointing device such as a mouse or touch panel. The display 19 is typically realized by an LCD (Liquid Crystal Display). In addition to the conversation processing program, the memory unit 14 stores data related to the conversation history between the customer and the digital staff, customer persona information and persona extension information, exemplified in Figure 9, etc.

[0021] As shown in Figure 3, by executing the conversation processing program, processor 11 functions as a control unit 21, a natural language analysis processing unit 22, an empathy estimation processing unit 23, a reliability estimation processing unit 24, an intimacy estimation processing unit 25, a purchase possibility estimation processing unit 26, a persona information update unit 27, a persona extension information update unit 28, a wording conversion processing unit 29, a statement generation request processing unit 30, a receiving processing unit 31, and a transmitting processing unit 32.

[0022] The data of the comment input by the customer from the user terminal 4 is received by the receiving processing unit 31 via the EC server 3. Note that the data of the comment input by the customer from the user terminal 4 may be received directly from the user terminal 4 to the receiving processing unit 31 without going through the EC server 3.

[0023] The natural language analysis processing unit 22 performs natural language analysis on the data of customer utterances received via the reception processing unit 31, breaking it down into parts of speech, extracting character strings of nouns in particular, and performing syntactic analysis. The natural language analysis processing unit 22 also performs natural language analysis on the utterances of digital staff members transmitted to the EC server 3 via the transmission processing unit 32, breaking it down into parts of speech, extracting character strings of nouns, and performing syntactic analysis. The parts of speech broken down by the natural language analysis processing unit 22, the extracted nouns, and the syntactic analysis results are used in the processes of the empathy estimation processing unit 23, the reliability estimation processing unit 24, the persona information update unit 27, and the persona extension information update unit 28.

[0024] The empathy estimation processing unit 23 calculates an empathy score when the content of the digital staff member's statement is not in line with the topic of the conversation, and does not calculate an empathy score when the content of the digital staff member's statement is in line with the topic of the conversation. For example, if the e-commerce site is an apparel shop, the topic of the conversation is the apparel field. Specifically, if the nouns included in the digital staff member's statement are apparel-related, the content of the digital staff member's statement is determined to be in line with the topic of the conversation. If the nouns included in the digital staff member's statement are not apparel-related and unrelated to apparel, the content of the digital staff member's statement is determined to be in line with the topic of the conversation. This determination process may involve creating an apparel-related terminology dictionary in advance and storing it in the storage unit 14, comparing the nouns included in the digital staff member's statement with the dictionary to determine whether they are listed in the apparel-related terminology dictionary, or it may involve using a trained model that uses training data to estimate the suitability of nouns and conversation topics.

[0025] When a customer responds positively to a comment made by the digital staff member, a predetermined value is added to the current value of the empathy score to increase the empathy score. When a customer responds negatively to a comment made by the digital staff member, a predetermined value is subtracted from the current value of the empathy score to decrease the empathy score. Typically, the added value and the subtracted value are the same, but the absolute value of the added value may be higher or lower than the absolute value of the subtracted value. The determination process for identifying whether a customer's comment is positive or negative in response to a comment made by the digital staff member may involve creating a dictionary containing multiple positive expressions and multiple negative expressions in advance and storing it in the memory unit 14, and determining whether the customer's comment contains any positive or negative expression. Alternatively, a trained model trained by machine learning may be used to estimate multiple expressions and the likelihood of a comment being positive or negative as training data.

[0026] When the content of the digital staff's speech is in line with the conversation topic, the reliability estimation processing unit 24 executes the calculation of the reliability score; when the content of the digital staff's speech is not in line with the conversation topic, the reliability score calculation is not executed. When the customer makes an affirmative speech in response to the digital staff's speech, a predetermined value is added to the current value of the reliability score to increase the reliability score. When the customer makes a negative speech in response to the digital staff's speech, a predetermined value is subtracted from the current value of the reliability score to decrease the reliability score. Typically, the addition value and the subtraction value are the same value, but the absolute value of the addition value may be higher or lower than the absolute value of the subtraction value.

[0027] The intimacy estimation processing unit 25 compares the word usage of the customer's speech with the word usage of the digital staff's speech, and calculates an intimacy score representing the degree of intimacy of the customer towards the digital staff based on the comparison result. Specifically, when the politeness level of the word usage of the customer's speech decreases, a predetermined value is added to the current value of the intimacy score to increase the intimacy score. When the politeness level of the word usage of the customer's speech increases, a predetermined value is subtracted from the current value of the intimacy score to decrease the intimacy score. Typically, the addition value and the subtraction value are the same value, but the absolute value of the addition value may be higher or lower than the absolute value of the subtraction value.

[0028] Typically, for the determination of the politeness level of word usage, a dictionary regarding a plurality of inflectional expressions corresponding to each of a plurality of levels with different politeness levels from honorific language to friendly language is created in advance and stored in the storage unit 14. The inflectional expression of the customer's speech is collated with the dictionary, and the politeness level of the corresponding inflectional expression is specified to perform the determination.

[0029] It is assumed that when the empathy score, reliability score, and intimacy score increase, the likelihood of a customer purchasing a product increases. Therefore, the purchase probability estimation unit 26 calculates a purchase probability score (comprehensive score) representing the likelihood of a customer purchasing a product based on the calculated empathy score, reliability score, and intimacy score. For example, the purchase probability score is calculated by adding to the current value of the purchase probability score the change amount of each of the empathy score, reliability score, and intimacy score, or a value obtained by multiplying the change amount by a predetermined coefficient such as 0.5. Note that the purchase probability score may be calculated by simply adding the empathy score, reliability score, and intimacy score, or by weighted adding the empathy score, reliability score, and intimacy score. The calculation method can be arbitrarily changed.

[0030] The persona information update unit 27 generates new persona information regarding the place of origin, hobbies, etc. based on the part-of-speech and syntactic analysis results decomposed by the natural language analysis processing unit 22, and updates the existing persona information regarding the customer by adding it. The persona information is initially the user information registered on the EC site. As the conversation progresses, new persona information is generated and added. The persona information is stored in the storage unit 14.

[0031] The persona extended information update unit 28 generates persona extended information associating the nouns in the content of the conversation of the digital staff that is considered to have induced the change with the changes in the empathy score and the reliability score. That is, persona extended information is generated as information that can identify whether the nouns in the digital staff's speech lead to an increase or decrease in the empathy score and the reliability score. For example, as persona extended information related to a certain customer, an increase in the reliability score is associated with "blue" and "jacket" and generated. The persona extended information is appropriately generated as the conversation progresses and accumulated in the storage unit 14.

[0032] The diction usage conversion processing unit 29 converts the diction of the digital staff's uttered sentence received from the uttered sentence generation device 2 into the diction of the set politeness level. Initially, the uttered sentences of the digital staff received from the uttered sentence generation device 2 are often mechanical, business-like, and dull. As they are, the intimacy level is low. Therefore, a dictionary for converting into diction of multiple levels with different politeness levels from honorific language to friendly language is created in advance and stored in the storage unit 14. The diction usage conversion processing unit 29 converts the words and phrases of the digital staff's uttered sentence received from the uttered sentence generation device 2 into the set diction using the dictionary.

[0033] Although details will be described later, when the politeness level of the customer's diction is specified and the politeness level changes during the conversation process, the politeness level of the diction used by the digital staff is changed following the change. For example, when the politeness level of the customer's diction decreases, that is, when the intimacy of the customer with respect to the digital staff increases, the politeness level of the diction regarding the digital staff's utterance is decreased by one level from the current politeness level. When the politeness level of the customer's diction increases, that is, when the intimacy of the customer with respect to the digital staff decreases, the politeness level of the diction regarding the digital staff's utterance is increased by one level from the current politeness level. By changing the politeness level of the diction used by the digital staff following the change in the politeness level of the customer's diction in this way, the intimacy of the customer with respect to the digital staff can be increased, and a decrease in the intimacy of the customer with respect to the digital staff can be suppressed.

[0034] The utterance generation request processing unit 30 transmits data on the conversation history between the customer and the digital staff including the customer's previous utterance to the utterance generation device 2 together with a request to generate an utterance for the digital staff to respond to the customer's utterance. Further, in addition to the conversation history data, the utterance generation request processing unit 30 transmits persona information, persona extension information, the transition of the empathy score, the transition of the reliability score, the transition of the intimacy score, and the transition of the purchase probability score to the utterance generation device 2. In the utterance generation device 2, by referring to these pieces of information, it is possible to generate an utterance for the digital staff so as to follow the flow of the conversation and increase the empathy score and the reliability score without decreasing them.

[0035] Figure 4 shows the flow of the conversation processing of the information processing apparatus 1. Along with the start of the conversation, the empathy estimation processing unit 23, the reliability estimation processing unit 24, the intimacy estimation processing unit 25, and the purchase probability estimation processing unit 26 initialize their respective scores under the control of the control unit 21 (S1). For example, the empathy score, the reliability score, and the intimacy score are initialized to zero values, and the purchase probability score is initialized to 50 as the median value. When the conversation is repeated with the same customer and digital staff, the values of the empathy score, the reliability score, the intimacy score, and the purchase probability score at the end of the previous conversation may be carried over.

[0036] Data on the customer's utterance is received from the EC server 3 via the reception processing unit 31 (S2). Data on the digital staff's utterance is read from the storage unit 14. The customer's utterance and the digital staff's utterance are subjected to natural language analysis processing (S3). Part-of-speech analysis is performed, nouns are extracted, and syntactic analysis is executed.

[0037] Using the natural language parsing result, the empathy level estimation processing unit 23 executes empathy level estimation processing to calculate an empathy level score (S4), the reliability estimation processing unit 24 executes reliability estimation processing to calculate a reliability score (S5), and the intimacy estimation processing unit 25 executes intimacy estimation processing to calculate an intimacy score (S6). Each processing will be described later. Also, the purchase likelihood estimation processing unit 26 calculates a purchase likelihood score representing the likelihood that a customer will purchase a product based on the empathy level score, the reliability score, and the intimacy score (S7). The method of calculating the purchase likelihood score indicating the likelihood that a customer will purchase a product on the EC site from the empathy level score, the reliability score, and the intimacy score is arbitrary. For example, the purchase likelihood score is calculated by adding to the current value of the purchase likelihood score the change amount of each of the empathy level score, the reliability score, and the intimacy score, or a value obtained by multiplying the change amount by a predetermined coefficient such as 0.5.

[0038] Next, the persona extension information update unit 28 generates persona extension information associating nouns in the digital staff's utterance made immediately before the customer's utterance indicating the change with the changes in the empathy level score, the reliability score, and the intimacy score respectively (S8). Thereby, when any of the customer's empathy level, reliability, or intimacy changes, it is possible to recognize the nouns in the digital staff's utterance that caused the change. For example, when the customer's reliability score increases, it can be determined that the increase is caused by "blue" and "jacket", in other words, the customer is interested in "blue" and "jacket", or the customer's preferences can be identified.

[0039] In the next step S9, the utterance sentence generation request processing unit 30 generates an utterance sentence generation request together with the conversation history, the persona information, the persona extension information, the transition of the empathy level score, the transition of the reliability score, the transition of the intimacy score, and the transition of the purchase likelihood score. The transmission processing unit 32 transmits the utterance sentence generation request together with the data of the conversation history, the persona information, the persona extension information, the transition of the empathy level score, the transition of the reliability score, the transition of the intimacy score, and the transition of the purchase likelihood score to the utterance sentence generation device 2 via the communication interface 17 (S9).

[0040] The receiving processing unit 31 receives data of the speech of the digital staff from the speech generation device 2 via the communication interface 17 (S10). The diction conversion processing unit 29 converts the diction of the speech of the digital staff into the diction of the set politeness level (S11). The transmission processing unit 32 transmits data of the speech of the digital staff with the converted diction to the EC server 3 via the communication interface 17 (S12). When the conversation is continuing (S13, NO), return to step S2 and wait for the next customer's speech. When the conversation has ended (S13, YES), the conversation processing ends.

[0041] FIG. 5 shows the flow of the empathy estimation process of step S4 in FIG. 4. Refer to FIGS. 10 and 11 for an example of the transition of the empathy score, reliability score, intimacy score, purchase possibility score, and the change in diction.

[0042] The empathy level estimation processing unit 23 determines whether the content of the digital staff's speech follows the theme of the conversation based on the analysis result of the natural language analysis processing unit 22 (S21). For example, if the EC site is an apparel store, the theme of the conversation is the apparel field. Typically, nouns included in the digital staff's speech are checked against an apparel-related term dictionary to confirm whether they are listed in the dictionary. When a noun included in the digital staff's speech matches any of the terms listed in the apparel-related term dictionary, it is determined that the content of the digital staff's speech follows the theme of the conversation (S21, YES). When a noun included in the digital staff's speech does not match any of the terms listed in the apparel-related term dictionary, it is determined that the content of the digital staff's speech does not follow the theme of the conversation (S21, NO). When it is determined that the content of the digital staff's speech follows the theme of the conversation (S21, YES), the system waits for the next speech of the digital staff. When it is determined that the content of the digital staff's speech does not follow the theme of the conversation (S21, NO), the process proceeds to the next step S22, and the calculation process of the empathy score is started. Note that when it is determined that the digital staff's speech follows the theme of the conversation, that is, when the digital staff's speech is within the scope of the theme of the conversation, the empathy score is not calculated, but the reliability score is calculated. In other words, when the digital staff's speech is small talk, the empathy score is calculated, and when the digital staff's speech is not small talk and is in line with the theme to be consulted originally, the reliability score is calculated.

[0043] In step S22, the natural language analysis processing unit 22 analyzes the customer's speech. In step S23, the empathy level estimation processing unit 23 distinguishes whether the customer's speech is affirmative, negative, or neither with respect to the digital staff's speech based on the analysis result of the natural language analysis processing unit 22. Typically, by checking the predicates included in the customer's speech against a dictionary that covers a plurality of pre-prepared affirmative expressions and a plurality of negative expressions and confirming whether they are listed in the dictionary, it is distinguished whether the customer's speech is affirmative, negative, or neither.

[0044] When the customer's statement is positive, the empathy degree estimation processing unit 23 adds a predetermined value I1 to the current value of the empathy degree score to increase the empathy degree score (S24). When the customer's statement is negative, the empathy degree estimation processing unit 23 subtracts a predetermined value D1 from the current value of the empathy degree score to decrease the empathy degree score (S25). When the customer's statement is neither positive nor negative, assuming that the customer is indifferent to the statement of the digital staff, the empathy degree estimation processing unit 23 subtracts a predetermined value D2 whose absolute value is larger than the predetermined value D1 from the current value of the empathy degree score to further decrease the empathy degree score (S26).

[0045] FIG. 6 shows the flow of the reliability estimation process in step S5 of FIG. 4. The reliability estimation processing unit 24 determines whether the content of the statement of the digital staff follows the theme of the conversation based on the analysis result of the natural language analysis processing unit 22 (S31). The determination method is the same as that in step S21. When it is determined that the content of the statement of the digital staff does not follow the theme of the conversation (S31, YES), it waits for the next statement of the digital staff. When it is determined that the content of the statement of the digital staff follows the theme of the conversation (S31, NO), it proceeds to the next step S32 and starts the calculation process of the reliability score.

[0046] In step S32, the natural language analysis processing unit 22 analyzes the customer's statement. In step S33, the reliability estimation processing unit 24 distinguishes whether the customer's statement is positive, negative, or neither with respect to the statement of the digital staff based on the analysis result of the natural language analysis processing unit 22. The method of this distinction is also the same as that in (S1) S23.

[0047] When the customer's statement is positive, the reliability estimation processing unit 24 adds a predetermined value I11 to the current value of the reliability score to increase the reliability score (S34). When the customer's statement is negative, the reliability estimation processing unit 24 subtracts a predetermined value D11 from the current value of the reliability score to decrease the reliability score (S35). When the customer's statement is neither positive nor negative, assuming the customer is indifferent to the digital staff's statement, the reliability estimation processing unit 24 subtracts a predetermined value D12, whose absolute value is larger than the predetermined value D11, from the current value of the reliability score to further decrease the reliability score (S36).

[0048] FIG. 7 shows the flow of the intimacy estimation process of step S6 in FIG. 4. The intimacy estimation processing unit 25 identifies the politeness level of the customer's statement (S41). For the determination of the politeness level, the endings of the customer's statement are collated against a dictionary regarding a plurality of ending expressions related to each of a plurality of levels of politeness ranging from honorific language to friendly language, and the politeness level of the ending expression listed in the dictionary that matches the ending of the customer's statement is confirmed.

[0049] The intimacy estimation processing unit 25 determines whether the identified politeness level of the customer's statement has changed from the politeness level of the previous customer's statement (S42). When the politeness level has not changed (S42, NO), the process returns to step S41 and waits for the next customer's statement. When the politeness level has changed (S42, YES), it is determined whether the identified politeness level of the customer's statement has decreased compared to the politeness level of the previous customer's statement (S43).

[0050] When it is determined that the identified politeness level of the customer's statement has decreased compared to the politeness level of the previous customer's statement (S43, YES), a predetermined value I21 is added to the current value of the intimacy score to increase the intimacy score (S44). When it is determined that the identified politeness level of the customer's statement has become higher than the politeness level of the previous customer's statement (S43, NO), a predetermined value D21 is subtracted from the current value of the intimacy score to decrease the intimacy score (S45).

[0051] FIG. 8 shows the flow of the wording change process. The wording change process is executed in parallel with the conversation process shown in FIG. 4. Prior to the conversation, the wording conversion processing unit 29 first initially sets the wording of the digital staff member's speech to, for example, "5," which is the middle level of politeness out of 11 levels (S51). When receiving a customer's speech, the wording conversion processing unit 29 identifies the level of politeness of the customer's speech (S52). Then, the wording conversion processing unit 29 determines whether the level of politeness of the identified customer's speech has changed from the level of politeness of the previous customer's speech (S53).

[0052] If the politeness level has not changed (S53, NO), the process returns to step S52 and waits for the next customer's comment. If the politeness level has changed (S53, YES), it is determined whether the politeness level of the specified customer's comment has decreased compared to the politeness level of the previous customer's comment (S54).

[0053] When it is determined that the level of politeness in the speech of the identified customer has decreased compared to the level of politeness in the speech of the previous customer (S54, YES), that is, when the customer's intimacy with the digital staff member has increased, the digital staff member's language is accordingly changed to a level of politeness that is one level lower (S55).When it is determined that the level of politeness in the speech of the identified customer has increased compared to the level of politeness in the speech of the previous customer (S54, NO), that is, when the customer's intimacy with the digital staff member has decreased, the digital staff member's language is accordingly changed to a level of politeness that is one level higher (S56).

[0054] If the conversation is continuing (S57, NO), the process returns to step S52 and waits for the next customer utterance. If the conversation has ended (S57, YES), the wording change process ends.

[0055] As described above, according to the present embodiment, the empathy score, reliability score, and intimacy score of the customer towards the digital staff's speech are calculated, and the changes in these empathy scores and reliability scores, together with the conversation history, are transmitted to the speech generation device 2 along with the speech generation request. As a result, the speech generation device 2 can generate a speech for the digital staff that enhances the empathy and reliability and suppresses the decrease in empathy and reliability.

[0056] Also, according to the present embodiment, by changing the politeness level of the digital staff's language following the change in the politeness level of the customer's speech, the intimacy can be enhanced and the decrease in intimacy can be suppressed.

[0057] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, as well as in the invention described in the claims and its equivalent scope.

Explanation of Reference Numerals

[0058] 1... Information processing device, 2... Speech generation device (generating AI), 3... EC server, 4... User terminal, 11... Processor, 10... System bus, 12... RAM, 13... ROM, 14... Storage unit, 15... Input device, 16... Display, 17... Communication interface, 21... Control unit, 22... Natural language analysis processing unit, 23... Empathy estimation processing unit, 24... Reliability estimation processing unit, 25... Intimacy estimation processing unit, 26... Purchase possibility estimation processing unit, 27... Persona information update unit, 28... Persona extended information update unit, 29... Language conversion processing unit, 30... Speech generation request processing unit, 31... Reception processing unit, 32... Transmission processing unit.

Claims

1. An information processing apparatus having a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a user terminal and a speech generation device (generating AI) via an electrical public communication network, for a virtual user to have a conversation with an actual user, by executing the program, the processor means for receiving data of the speech of the actual user from the user terminal, means for transmitting a generation request for the speech of the virtual user, together with data of the speech history of each of the actual user and the virtual user, to the speech generation device, means for receiving data of the speech of the virtual user from the speech generation device, means for transmitting the received data of the speech of the virtual user to the user terminal, means for determining, based on the speech of the actual user, whether the actual user is affirmative or negative about the speech of the virtual user, An information processing apparatus that functions as a reliability score calculation means for calculating a reliability score indicating the degree of trust of the actual user in the virtual user based on the result of the determination.

2. The processor further functions as means for determining whether the speech of the virtual user is along the theme of the conversation, The reliability score calculation means calculates the reliability score when the speech of the virtual user is along the theme of the conversation, and does not calculate the reliability score when the speech of the virtual user is not along the theme of the conversation. The information processing apparatus according to claim 1.

3. The reliability score calculation means increases the reliability score when it is determined that the actual user is affirmative about the speech of the virtual user, and decreases the reliability score when it is determined that the actual user is negative about the speech of the virtual user. The information processing apparatus according to claim 1.

4. An information processing apparatus having a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a user terminal via an electrical public communication network, by executing the program, the processor means for receiving data of the speech of the user from the user terminal, means for determining, based on the speech of the user, whether the speech of the user is affirmative or negative. An information processing apparatus that functions as means for calculating a reliability score of the user based on the result of the determination.

5. An information processing apparatus having a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a user terminal and a speech generation device (generating AI) via an electrical public communication network, for a virtual user to have a conversation with an actual user. Means for receiving data of the speech of the actual user from the user terminal. Means for transmitting a generation request for the speech of the virtual user, together with data of the speech history of each of the actual user and the virtual user, to the speech generation device. Means for receiving data of the speech of the virtual user from the speech generation device. Means for transmitting the received data of the speech of the virtual user to the user terminal. Means for executing a determination as to whether the actual user is positive or negative about the speech of the virtual user based on the speech of the actual user. A program that functions as means for calculating a reliability score representing the degree of trust of the actual user in the virtual user based on the result of the determination.

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