Information processing device and program

Abstract

To estimate intimacy felt by a user.SOLUTION: An information processing device 1 has: 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 functions as: means (S2) for receiving data of a user's statement from the user terminal; means (S41) for identifying the degree of politeness for the wording of the user's statement; and means (S43, S44, S45) for estimating an intimacy score representing the degree of intimacy of the user based on the degree of politeness for the wording of the user's statement.SELECTED DRAWING: Figure 7

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. Under such circumstances, in EC sites as well as in physical stores, it is regarded as 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 desired by users, 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 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 utterance contents 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 an utterance sentence that the digital staff should say next. Since it is used in EC sites, 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 is good at advancing 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 because the utterances were generated only from the conversation history between the input digital staff and the customers.

Summary of the Invention

Problems to be Solved by the Invention

[0006] The purpose is to estimate the intimacy felt by the user.

Means for Solving the Problems

[0007] The information processing apparatus according to the present embodiment includes a memory that stores a program, a processor that executes the program, and a communication interface that communicates with a user terminal via an electrical public communication network. By executing the program, the processor functions as means for receiving data of the user's utterance from the user terminal, means for specifying the politeness level of the user's utterance, and intimacy estimation means for estimating an intimacy score representing the degree of intimacy of the user based on the politeness level of the user's utterance.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Embodiments 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 in which a virtual user (virtual user) constructed on a computer converses with an actual user. Here, a scenario will be described as an example in which 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. In this example, the customer corresponds to an actual user, and the digital staff corresponds to a virtual user. Of course, the scenario in which 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 line. 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 the 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'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, an increase in the reliability score, the empathy score, and the intimacy score, which will be described later, means an increase in the customer's reliability, empathy, and intimacy with respect to the digital staff.

[0013] By repeating the process, it is possible to determine how the empathy score and the reliability score change as the conversation progresses, and it is also possible 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 reducing 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. Identify the politeness level of the customer's statement, and determine the politeness level of the digital staff's statement based on the change in the politeness level of the identified customer's statement. When the politeness level of the customer's statement decreases, the politeness level of the digital staff's statement also decreases by one level from the current politeness level accordingly. When the politeness level of the customer's statement increases, the politeness level of the digital staff's statement also increases by one level from the current politeness level. In this way, in this embodiment, by changing the politeness level of the digital staff's statement according to the politeness level of the customer's statement, it is possible to increase the customer's sense of intimacy while suppressing its 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 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 this embodiment, programs for realizing other various 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 exemplified 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 follow 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 follows the theme of the conversation, the empathy level score calculation is not executed. For example, if the EC site is an apparel store, the apparel field is applied as the theme of the conversation. Specifically, if the nouns included in the digital staff's speech are apparel-related, it is determined that the content of the digital staff's speech follows the theme of the conversation. If the nouns included in the digital staff's speech are not apparel-related and are irrelevant to apparel, it is determined that the content of the digital staff's speech does not follow the theme of the conversation. This determination process may create an apparel-related term dictionary in advance and store it in the storage unit 14, and check whether the nouns included in the digital staff's speech are listed in the dictionary. Alternatively, it may be estimated using a learned model that has been machine-learned with the appropriateness of nouns and conversation themes 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 any positive expression or negative expression is included in the customer's speech. Alternatively, 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 speech follows the theme of the conversation, the reliability estimation processing unit 24 executes the calculation of the reliability score. When the content of the digital staff's speech does not follow the theme of the conversation, 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 familiarity 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 a familiarity score representing the degree of familiarity of the customer with 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 familiarity score to increase the familiarity 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 familiarity score to decrease the familiarity 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.

[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 processing 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 addition of 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 nouns with the content of the conversation of the digital staff that is considered to have induced the change in the change of 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 utterance 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 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. The digital staff's uttered sentences received from the uttered sentence generation device 2 are initially 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 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 the 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 digital staff changes the politeness level of the diction used accordingly. 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, changes in the empathy score, changes in the reliability score, changes in the intimacy score, and changes in the purchase probability score to the utterance generation device 2. The utterance generation device 2 can generate an utterance for 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] FIG. 4 shows the flow of the conversation processing of the information processing apparatus 1. 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 degree estimation processing unit 24 executes reliability degree estimation processing to calculate a reliability degree score (S5), and the intimacy degree estimation processing unit 25 executes intimacy degree estimation processing to calculate an intimacy degree score (S6). Each processing will be described later. Also, the purchase possibility estimation processing unit 26 calculates a purchase possibility score indicating the possibility that a customer purchases a product based on the empathy degree score, the reliability degree score, and the intimacy degree score (S7). The method of calculating a purchase possibility score indicating the possibility that a customer purchases a product on an EC site from the empathy degree score, the reliability degree score, and the intimacy degree score is arbitrary. For example, the current value of the purchase possibility score is added with the change amount of each of the empathy degree score, the reliability degree score, and the intimacy degree score, or a value obtained by multiplying the change amount by a predetermined coefficient such as 0.5, thereby calculating the purchase possibility score.

[0038] Next, the persona extension information update unit 28 generates persona extension information in which nouns in the digital staff's utterance made immediately before the customer's utterance indicating the change are associated with the changes in the empathy degree score, the reliability degree score, and the intimacy degree score respectively (S8). Thereby, when any of the customer's empathy degree, reliability degree, and intimacy degree changes, the nouns in the digital staff's utterance that caused the change can be recognized. For example, when the customer's reliability degree 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 preference can be specified.

[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 degree score, the transition of the reliability degree score, the transition of the intimacy degree score, and the transition of the purchase possibility 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 degree score, the transition of the reliability degree score, the transition of the intimacy degree score, and the transition of the purchase possibility score to the utterance sentence generation device 2 via the communication interface 17 (S9).

[0040] The reception processing unit 31 receives data of the speech text of the digital staff from the speech text generation device 2 via the communication interface 17 (S10). The diction conversion processing unit 29 converts the diction of the speech text of the digital staff into the diction with the set politeness level (S11). The transmission processing unit 32 transmits the data of the speech text 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), it returns to step S2 and waits 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 in step S4 of Fig. 4. Refer to Fig. 10 and Fig. 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 utterance is in line with the conversation topic 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 conversation topic is the apparel field. Typically, nouns included in the digital staff's utterance 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 utterance matches any of the terms listed in the apparel-related term dictionary, it is determined that the content of the digital staff's utterance is in line with the conversation topic (S21, YES). When a noun included in the digital staff's utterance 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 utterance is not in line with the conversation topic (S21, NO). When it is determined that the content of the digital staff's utterance is in line with the conversation topic (S21, YES), the system waits for the next utterance of the digital staff. When it is determined that the content of the digital staff's utterance is not in line with the conversation topic (S21, NO), the process proceeds to the next step S22 and the operation process of calculating the empathy score is started. Note that when it is determined that the digital staff's utterance is in line with the conversation topic, that is, the digital staff's utterance is within the scope of the conversation topic, 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 level estimation processing unit 23 distinguishes 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 checked against a dictionary that covers a plurality of pre-created affirmative expressions and a plurality of negative expressions to confirm whether they are listed in the dictionary, thereby distinguishing whether the customer's utterance is affirmative, negative, or neither.

[0044] When the customer's statement is positive, the empathy level estimation processing unit 23 adds a predetermined value I1 to the current value of the empathy level score to increase the empathy level score (S24). When the customer's statement is negative, the empathy level estimation processing unit 23 subtracts a predetermined value D1 from the current value of the empathy level score to decrease the empathy level score (S25). When the customer's statement is neither positive nor negative, assuming the customer is indifferent to the digital staff's statement, the empathy level 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 level score to further decrease the empathy level 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 digital staff's statement 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 digital staff's statement 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 digital staff's statement 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 digital staff's statement based on the analysis result of the natural language analysis processing unit 22. The method for 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 that 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 with a dictionary regarding a plurality of ending expressions related to each of a plurality of levels with different politeness levels 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] Figure 8 shows the flow of the diction change process. The diction change process is executed in parallel with the conversation process shown in Figure 4. The diction conversion processing unit 29 first initially sets the diction of the digital staff's utterance to "5" as the median of the politeness levels prepared in, for example, 11 levels prior to the conversation (S51). When the customer's utterance is received, the politeness level of the customer's utterance is specified (S52). Then, the diction conversion processing unit 29 determines whether the specified politeness level of the customer's utterance has changed from the politeness level of the previous customer's utterance (S53).

[0052] When the politeness level has not changed (S53, NO), the process returns to step S52 and waits for the next customer's utterance. When the politeness level has changed (S53, YES), it is determined whether the specified 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 specified 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 diction of the digital staff is accordingly changed to a lower politeness level by one step (S55). When it is determined that the specified 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 diction 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 diction change process ends.

[0055] As described above, according to the present embodiment, the empathy score, reliability score, and intimacy score of the customer with respect to the speech of the digital staff 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 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 (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 via an electric public communication network, wherein the processor, by executing the program, serves as means for receiving data of a user's utterance from the user terminal, serves as means for specifying the politeness level of the user's utterance, and functions as intimacy estimation means for estimating an intimacy score representing the degree of intimacy of the user based on the politeness level of the user's utterance.

2. The information processing apparatus according to claim 1, wherein the intimacy estimation means updates the intimacy score when the politeness level of the user's utterance changes.

3. The information processing apparatus according to claim 2, wherein the intimacy estimation means increases the intimacy score when the politeness level of the user's utterance decreases, and decreases the intimacy score when the politeness level of the user's utterance increases.

4. A program for causing 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 electric public communication network to serve as means for receiving data of a user's utterance from the user terminal, serve as means for specifying the politeness level of the user's utterance, and function as intimacy estimation means for estimating an intimacy score representing the degree of intimacy of the user based on the politeness level of the user's utterance.

Images