Information processing apparatus and program

Abstract

To generate persona expansion information of a user.SOLUTION: A processor of an information processing apparatus 1 executes a program to function as: means 31 for receiving data of a speech of a real user from a user terminal; means 23, 24 for calculating the empathy score and trust score based on the speech of the real user, the empath score representing how the real user empathizes with a virtual user, the trust score indicating how much the real user trust the virtual user; means 28 for generating the changes of the empathy score and the trust score, in association with the contents of the speech of the virtual user, as persona expansion information related to the real user; means 30 for transmitting a request to a speech sentence generation apparatus to generate a speech of the virtual user, together with the personal expansion information and data of histories of speeches of the real user and the virtual user; means 31 for receiving data of a speech of the virtual user from the speech sentence generation apparatus; and means 32 of transmitting the received data of the speech of the virtual user to the user terminal.SELECTED DRAWING: Figure 3

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 in the future, it is expected that 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 for staff to introduce recommended products through conversations with customers, to dig out customer needs and materialize products that users desire, and to provide one-on-one customer service for 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 instead of actual staff, digital staff serves customers (referred to as actual users) one-on-one.

[0004] The use of generative AI is expected for creating the speech content for digital staff to carry on 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 utter next. Since it is used in an EC site, the ultimate goal is sales, and for that purpose, high conversation skills and customer service capabilities like those of excellent actual staff are required. Excellent actual staff are good at carrying on conversations to build an intimate relationship while gaining empathy and trust from customers, whether consciously or unconsciously.

[0005] However, it was not possible to break away from mechanical conversations by generating utterances only from the conversation history between the input digital staff and the customer.

Summary of the Invention

Problems to be Solved by the Invention

[0006] The objective is to generate persona expansion information of the user.

Means for Solving the Problems

[0007] The information processing apparatus according to the present 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 an utterance generation device (generative AI) via an electrical public communication network, and a virtual user converses with an actual user. By executing the program, the processor functions as means for receiving data of an actual user's utterance from the user terminal, means for calculating a sympathy score representing the degree of sympathy of the actual user with respect to the virtual user and a trust score representing the degree of trust of the actual user with respect to the virtual user based on the actual user's utterance, means for generating, in association with the content of the virtual user's utterance, changes in each of the sympathy score and the trust score as persona expansion information regarding the actual user, means for transmitting a generation request for the virtual user's utterance to the utterance generation device together with data of the utterance histories of the actual user and the virtual user and the persona expansion information, means for receiving data of the virtual user's utterance from the utterance generation device, and means for transmitting the received data of the virtual user's utterance to the user terminal.

Brief Description of the Drawings

[0008]

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DETAILED DESCRIPTION OF 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 has a conversation with an actual user. Here, a scenario where a customer who has visited an EC site has a conversation 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 scenario where the actual user corresponds to the customer and the virtual user corresponds to the digital staff is not limited.

[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 purchases a product while conversing with a digital staff (virtual user). The information processing apparatus 1 directly receives the customer's speech data 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 above 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's speech and the digital staff's 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 sympathy score is calculated to estimate the degree to which the customer shows sympathy for the digital staff's speech, and based on the determination result, a reliability score is calculated to estimate the degree to which the customer shows a sense of trust in the digital staff's speech. Whether to apply the determination result of positive or negative regarding the customer's speech to either the sympathy score or the reliability 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 respect to the digital staff increase.

[0013] By repeating the said process, it is possible to obtain how the empathy score and the reliability score change respectively as the conversation progresses, and further, it is possible to specify 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 usage 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 usage 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 the present 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 regarding the customer's statement is specified, and based on the change in the politeness level of the words regarding the specified customer's statement, the politeness level of the digital staff's words is determined. When the politeness level of the words regarding the customer's statement decreases, the politeness level of the words regarding the digital staff's statement is also decreased by one level from the current politeness level accordingly. When the politeness level of the words regarding the customer's statement increases, the politeness level of the words regarding the digital staff's statement is also increased by one level from the current politeness level accordingly. Thus, in the present embodiment, by changing the politeness level of the digital staff's words to match the politeness level of the customer's words, it becomes possible to increase the customer's sense of intimacy while suppressing the decrease.

[0016] Also, in this 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 utterance generation request to the utterance 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 utterance 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 a main memory, a work area, etc. of the processor 11. The ROM 13 or the storage unit 14 stores a BIOS (Basic Input Output System), an operating system program (OS), a conversation processing program according to this embodiment, programs for realizing various other functions, and various data required for those processes.

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

[0021] As shown in FIG. 3, by executing the conversation processing program, the processor 11 functions as a control unit 21, a natural language analysis processing unit 22, a 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 diction conversion processing unit 29, a speech generation request processing unit 30, a reception processing unit 31, and a transmission processing unit 32.

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

[0023] The natural language analysis processing unit 22 performs natural language analysis processing on the data of the customer's speech received via the reception processing unit 31, decomposes it into parts of speech, extracts particularly the string of nouns, and performs syntactic analysis. Also, the natural language analysis processing unit 22 performs natural language analysis processing on the speech of the digital staff transmitted to the EC server 3 via the transmission processing unit 32, decomposes it into parts of speech, extracts the string of nouns, and performs syntactic analysis. The parts of speech decomposed, the nouns extracted, and the syntactic analysis results by the natural language analysis processing unit 22 are used in the respective 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] When the content of the digital staff's speech does not conform to the theme of the conversation, the empathy level estimation processing unit 23 executes the calculation of the empathy level score. When the content of the digital staff's speech conforms to the theme of the conversation, the empathy level score is not calculated. The theme of the conversation is, for example, if the EC site is a clothing store, the clothing field is applied. Specifically, if the nouns included in the digital staff's speech are related to clothing, it is determined that the content of the digital staff's speech conforms to the theme of the conversation. If the nouns included in the digital staff's speech are not related to clothing and are irrelevant to clothing, it is determined that the content of the digital staff's speech does not conform to the theme of the conversation. This determination process may create a clothing-related term dictionary in advance, store it in the storage unit 14, and check whether the nouns included in the digital staff's speech are listed in the dictionary, or it may be estimated using a learned model that has been machine-learned with the appropriateness of the nouns and the conversation theme as teacher data.

[0025] When the customer is positive about the digital staff's speech, a predetermined value is added to the current value of the empathy level score to increase the empathy level score. When the customer is negative about the digital staff's speech, a predetermined value is subtracted from the current value of the empathy level score to decrease the empathy level 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. The determination process for identifying whether the customer's speech is positive or negative about the digital staff's speech may create a dictionary that covers a plurality of positive expressions and a plurality of negative expressions in advance and store it in the storage unit 14, and determine whether the customer's speech includes any positive expression or negative expression, or it may be estimated using a learned model that has been machine-learned with a plurality of expressions and the possibility of being positive or negative as teacher data.

[0026] When the content of the digital staff's utterance 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 utterance is not in line with the conversation topic, the reliability score calculation is not executed. When the customer makes an affirmative utterance in response to the digital staff's utterance, a predetermined value is added to the current value of the reliability score to increase the reliability score. When the customer makes a negative utterance in response to the digital staff's utterance, 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 diction of the customer's utterance with the diction of the digital staff's utterance, 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 degree of the diction of the customer's utterance decreases, a predetermined value is added to the current value of the intimacy score to increase the intimacy score. When the politeness degree of the diction of the customer's utterance 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 degree of diction, a dictionary regarding a plurality of inflectional expressions corresponding to each of a plurality of levels with different politeness degrees from honorific language to friendly language is created in advance and stored in the storage unit 14. The inflectional expression of the customer's utterance is collated with the dictionary, and the politeness degree of the corresponding inflectional expression is specified to perform the determination.

[0029] It is assumed that as the empathy score, reliability score, and intimacy score increase, the likelihood that a customer will purchase a product increases. Therefore, the purchase likelihood estimation processing unit 26 calculates a purchase likelihood score (overall score) that indicates the likelihood that a customer will purchase a product based on the calculated empathy score, reliability score, and intimacy score. For example, the purchase likelihood score is calculated by adding the current value of the purchase likelihood score to the amount of change in the empathy score, reliability score, and intimacy score, or a value obtained by multiplying the amount of change by a predetermined coefficient such as 0.5. Note that the purchase likelihood score may be calculated by simply adding the empathy score, reliability score, and intimacy score, or by weighted addition of the empathy score, reliability score, and intimacy score. The calculation method may be changed as desired.

[0030] The persona information update unit 27 generates new persona information related to the place of origin, preferences, etc. based on the parts of speech and syntactic analysis results decomposed by the natural language analysis processing unit 22, and adds it to the existing persona information related to the customer to update it. The persona information is initially 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 memory unit 14.

[0031] The persona extension information update unit 28 generates persona extension information that associates changes in the empathy score and the reliability score with the content of the digital staff member's conversation, in this case, nouns, that are thought to have caused those changes. In other words, the persona extension information is generated as information that can identify whether nouns in the digital staff member's statements lead to an increase or decrease in the empathy score and the reliability score. For example, persona extension information for a certain customer is generated by associating an increase in the reliability score with "blue" and "jacket." Persona extension information is generated as appropriate as the conversation progresses and is stored in the memory unit 14.

[0032] The language conversion processing unit 29 converts the language used in the digital staff member's message received from the message generation device 2 into language with a set level of politeness. Initially, the digital staff member's message received from the message generation device 2 is often mechanical, businesslike, and dry. As it is, it does not indicate a high level of intimacy. Therefore, a dictionary is created in advance to convert the language into multiple levels of politeness, from honorific language to friendly language, and is stored in the memory unit 14. The language conversion processing unit 29 converts the words and phrases used in the digital staff member's message received from the message generation device 2 into language with a set level of politeness, using the dictionary.

[0033] As will be described in more detail below, the politeness level of the customer's language is identified, and when that level changes as the conversation progresses, the politeness level of the language used by the digital staff is changed to reflect that change. For example, when the politeness level of the customer's language decreases, that is, when the customer's intimacy with the digital staff increases, the politeness level of the language used by the digital staff is decreased by one level from the current level. When the politeness level of the customer's language increases, that is, when the customer's intimacy with the digital staff decreases, the politeness level of the language used by the digital staff is increased by one level from the current level. By changing the politeness level of the language used by the digital staff in this way to reflect changes in the politeness level of the customer, the customer's intimacy with the digital staff can be increased and a decrease in the customer's intimacy with the digital staff can be prevented.

[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 of the digital staff in response 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. The utterance generation device 2 can generate an utterance of the digital staff by referring to these pieces of information 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 degree estimation processing unit 23 executes empathy degree estimation processing to calculate an empathy degree 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 process will be described later. Also, the purchase possibility estimation processing unit 26 calculates a purchase possibility score representing the possibility that a customer purchases a product based on the empathy degree score, the reliability score, and the intimacy score (S7). The method of calculating a purchase possibility score indicating the possibility that a customer purchases a product on the EC site from the empathy degree score, the reliability score, and the intimacy score is arbitrary. For example, the purchase possibility score is calculated by adding to the current value of the purchase possibility score a value obtained by multiplying the change amount of each of the empathy degree score, the reliability score, and the intimacy score or 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 degree score, the reliability score, and the intimacy score respectively (S8). Thereby, when any of the customer's empathy degree, reliability, and intimacy changes, the nouns in the digital staff's utterance that caused the change can be recognized. For example, when the customer's reliability score increases, it can be known 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 specified.

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

[0040] The receiving processing unit 31 receives the data of the digital staff member's message from the message generation device 2 via the communication interface 17 (S10). The language conversion processing unit 29 converts the language of the digital staff member's message to a set level of politeness (S11). The sending processing unit 32 transmits the data of the digital staff member's message, with the language converted, to the EC server 3 via the communication interface 17 (S12). If the conversation is continuing (S13, NO), the process returns to step S2 and waits for the next customer's message. If the conversation has ended (S13, YES), the conversation process ends.

[0041] Fig. 5 shows the flow of the empathy estimation process in step S4 in Fig. 4. Please refer to examples of changes in empathy score, trust score, intimacy score, and purchase likelihood score, as well as changes in wording, shown in Figs. 10 and 11.

[0042] The empathy estimation processing unit 23 determines whether the content of the digital staff's utterance is in line with 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 a clothing store, the theme of the conversation is the clothing field. Typically, the nouns included in the digital staff's utterance are compared with a dictionary of clothing-related terms to check whether they are listed in the dictionary of clothing-related terms. When the noun included in the digital staff's utterance matches any of the terms listed in the dictionary of clothing-related terms, it is determined that the content of the digital staff's utterance is in line with the theme of the conversation (S21, YES). When the noun included in the digital staff's utterance does not match any of the terms listed in the dictionary of clothing-related terms, it is determined that the content of the digital staff's utterance is not in line with the theme of the conversation (S21, NO). When it is determined that the content of the digital staff's utterance is in line with the theme of the conversation (S21, YES), waiting for the next digital staff's utterance. When it is determined that the content of the digital staff's utterance is not in line with 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 utterance is in line with the theme of the conversation, that is, when the digital staff's utterance 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 utterance is a casual conversation, the empathy score is calculated, and when the digital staff's utterance is not a casual conversation 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 utterance. In step S23, the empathy estimation processing unit 23 differentiates whether the customer's utterance is affirmative, negative, or neither with respect to the digital staff's utterance based on the analysis result of the natural language analysis processing unit 22. Typically, the predicates included in the customer's utterance are compared with a dictionary covering a plurality of pre-created affirmative expressions and a plurality of negative expressions to check whether they are listed in the dictionary, thereby differentiating whether the customer's utterance 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, it is assumed that the customer is indifferent to the statement of the digital staff, and 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 (S1) S23.

[0047] When the customer's comment is positive, the reliability estimation processing unit 24 adds a predetermined value I11 to the current value of the reliability score, thereby increasing the reliability score (S34). When the customer's comment is negative, the reliability estimation processing unit 24 subtracts a predetermined value D11 from the current value of the reliability score, thereby decreasing the reliability score (S35). When the customer's comment is neither positive nor negative, the reliability estimation processing unit 24 determines that the customer is indifferent to the comments made by the digital staff, and subtracts a predetermined value D12, the absolute value of which is greater than the predetermined value D11, from the current value of the reliability score, thereby further decreasing the reliability score (S36).

[0048] Fig. 7 shows the flow of the intimacy estimation process in step S6 in Fig. 4. The intimacy estimation processor 25 identifies the level of politeness of the language used in the customer's speech (S41). The determination of the level of politeness is performed by comparing the endings of the customer's speech with a dictionary of multiple endings associated with multiple levels of politeness, from honorific language to friendly language, and checking the level of politeness of the endings listed in the dictionary that match the endings of the customer's speech.

[0049] The intimacy estimation processor 25 determines whether the politeness level of the specified customer's speech has changed from the politeness level of the previous customer's speech (S42). If the politeness level has not changed (S42, NO), the process returns to step S41 and waits for the next customer's speech. If the politeness level has changed (S42, YES), the process determines whether the politeness level of the specified customer's speech has decreased compared to the politeness level of the previous customer's speech (S43).

[0050] When it is determined that the level of politeness of the specified customer's speech has decreased compared to the level of politeness of the previous customer's speech (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 level of politeness of the specified customer's speech has increased compared to the level of politeness of the previous customer's speech (S43, NO), a predetermined value D21 is subtracted from the current value of the intimacy score to decrease the intimacy score (S45).

[0051] Figure 8 shows the flow of the word usage change process. The word usage change process is executed in parallel with the conversation process shown in Figure 4. The word usage conversion processing unit 29 first initially sets the word usage of the digital staff's utterance to "5" as, for example, the median of the politeness levels prepared in 11 steps before the conversation (S51). When receiving the customer's utterance, it identifies the politeness level of the customer's utterance (S52). Then, the word usage conversion processing unit 29 determines whether the identified politeness level of the customer's utterance has changed from the politeness level of the previous customer's utterance (S53).

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

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

[0054] When the conversation continues (S57, NO), the process returns to step S52 and waits for the next customer's utterance. When the conversation has ended (S57, YES), the word usage 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 are transmitted to the speech generation device 2 together with the conversation history and the speech generation request. By doing so, the speech generation device 2 can generate a speech of the digital staff that increases 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 increased 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 (generative 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 extension 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, score calculation means for calculating a sympathy score representing the degree of sympathy of the actual user with respect to the virtual user and a reliability score representing the degree of trust of the actual user with respect to the virtual user based on the speech of the actual user, persona extension information generation means for generating, in association with the content of the speech of the virtual user, changes in each of the sympathy score and the reliability score as persona extension information regarding the actual user, means for transmitting a generation request for the speech of the virtual user to the speech generation device together with data of the speech history of each of the actual user and the virtual user and the persona extension information, means for receiving data of the speech of the virtual user from the speech generation device, An information processing apparatus that functions as means for transmitting the received data of the speech of the virtual user to the user terminal.

2. The information processing apparatus according to claim 1, wherein the persona extension information generation means associates a noun included in the speech of the virtual user as a factor for changes in each of the sympathy score and the reliability score with the changes in each of the sympathy score and the reliability score.

3. The information processing apparatus according to claim 1, wherein the score calculation means determines whether the speech of the virtual user follows the theme of the conversation, calculates the sympathy score when the speech of the virtual user does not follow the theme of the conversation, and calculates the reliability score when the speech of the virtual user follows the theme of the conversation.

4. The information processing apparatus according to claim 3, wherein the score calculation means determines whether the actual user is affirmative or negative with respect to the speech of the virtual user based on the speech of the actual user, and calculates the sympathy score and the reliability score based on the result of the determination.

5. The information processing apparatus according to claim 4, wherein the score calculation means increases the empathy score or the reliability score when it is determined that the real user is positive about the statement of the virtual user, and decreases the empathy score or the reliability score when it is determined that the real user is negative about the statement of the virtual user.

6. 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 first user terminal and a second user terminal via an electric public communication network, wherein the processor executes the program to receive means for receiving data of a statement of a first user and data of a statement of a second user from the first user terminal and the second user terminal, respectively; score calculation means for calculating an empathy score representing the degree of empathy of the first user with respect to the second user and a reliability score representing the degree of trust of the first user with respect to the second user based on the statement of the first user; An information processing apparatus that functions as persona extension information generation means for generating changes in the empathy score and the reliability score in association with the content of the statement of the second user as persona extension information regarding the first user.

7. An information processing apparatus having a communication interface for communicating with a user terminal and a statement generation device (generative AI) via an electric public communication network, for a virtual user to have a conversation with a real user, means for receiving data of a statement of the real user from the user terminal; means for calculating an empathy score representing the degree of empathy of the real user with respect to the virtual user and a reliability score representing the degree of trust of the real user with respect to the virtual user based on the statement of the real user; A program that functions as means for generating changes in the empathy score and the reliability score in association with the content of the statement of the virtual user as persona extension information regarding the real user.

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