Information processing device and program

Abstract

To achieve a conversation for enhancing intimacy with an opposite party.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 statement sentence generation device (generative AI) via an electrical public communication network. By executing the program, the processor functions as: means (S9) for sending a request to generate a statement to the statement sentence generation device; means (S10) for receiving statement data from the statement sentence generation device; and means (S11) for converting the wording of the received statement into another wording with a different level of politeness.SELECTED DRAWING: Figure 4

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 opportunities 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 desired by users, 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 utterance contents for digital staff to carry on conversations with customers via chat or the like. The conversation history between the digital staff and the customer is sent to the generative AI together with the generation request. The generative AI analyzes the conversation history and creates an utterance sentence for the digital staff to 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 are good at carrying on 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 have a conversation that will increase intimacy with the other person. [Means for solving the problem]

[0007] The information processing device according to this embodiment has a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a statement generation device (generation AI) via an electrical public communication network. By executing the program, the processor functions as a means for sending a request to generate a statement to the statement generation device, a means for receiving statement data from the statement generation device, and a means for converting the wording of the received statement into another wording with a different level of politeness. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a configuration diagram of an entire system including an information processing device according to this embodiment. [Figure 2] FIG. 2 is a diagram showing the physical configuration of the information processing apparatus of FIG. [Figure 3] FIG. 3 is a diagram illustrating a functional configuration of the processor in FIG. [Figure 4] FIG. 4 is a flowchart showing the flow of conversation processing of the information processing device of FIG. [Figure 5] FIG. 5 is a flowchart showing the flow of the empathy level estimation process of FIG. [Figure 6] FIG. 6 is a flowchart showing the flow of the reliability estimation process of FIG. [Figure 7] FIG. 7 is a flowchart showing the flow of the intimacy degree estimation process of FIG. [Figure 8]FIG. 8 is a flowchart showing the flow of the diction conversion process by the information processing apparatus of FIG. 1. [Figure 9] FIG. 9 is a diagram showing an example of the persona information of the digital staff. [Figure 10] FIG. 10 is a diagram showing an example of the transition of the empathy score, reliability score, intimacy score, and purchase possibility score and the change in diction accompanying the progress of the conversation according to the present embodiment. [Figure 11] FIG. 11 is a diagram showing an example of the transition of the empathy score, reliability score, intimacy score, and purchase possibility score and the change in diction following FIG. 10.

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 scene where a virtual user (virtual user) constructed on a computer converses with an actual user. Here, a scene 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 scene where the actual user corresponds to the customer and the virtual user corresponds to the 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 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 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 history of the conversation 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 digital staff's statement, the reliability score or empathy score is increased, and when it is determined that the customer is negative, the reliability score or empathy score is decreased. Of course, as the reliability score, 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 reliability score change as the conversation progresses, and further to identify what kind of statements of the digital staff caused the change.

[0014] Furthermore, in an actual human relationship, as the intimacy increases, the politeness of speech 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 of speech 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 of the digital staff's statement generated by the statement generation device 2 is changed according to the situation so as to follow the change in the politeness of the words spoken by the customer. Identify the politeness of the customer's statement, and determine the politeness of the digital staff's statement based on the change in the politeness of the customer's identified statement. When the politeness of the customer's statement decreases, the politeness of the digital staff's statement is also decreased by one level from the current level accordingly. When the politeness of the customer's statement increases, the politeness of the digital staff's statement is also increased by one level from the current level. In this way, in the present embodiment, by changing the politeness of the digital staff's statement according to the politeness of the customer's statement, it is possible to increase the customer's sense of intimacy while suppressing the decrease.

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

[0017] When transmitting a 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 so as 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) executed by the processor 11, an operating system program (OS), a conversation processing program according to the present embodiment, programs for realizing various other 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] When the content of the digital staff's speech does not conform to the theme of the conversation, the empathy degree estimation processing unit 23 executes the calculation of the empathy score, and when the content of the digital staff's speech conforms to the theme of the conversation, the empathy score is not calculated. For example, if the EC site is an apparel store, the apparel field is applied to the theme of the conversation. Specifically, if the noun included in the digital staff's speech is related to apparel, it is determined that the content of the digital staff's speech conforms to the theme of the conversation, and if the noun included in the digital staff's speech is not related to apparel and is irrelevant to apparel, 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 an apparel-related term dictionary in advance and store it in the storage unit 14, and check whether the noun included in the digital staff's speech is listed in the dictionary, or it may be estimated using a learned model that has been machine-learned with the suitability of the noun and the conversation theme as teacher data.

[0025] When the customer is positive about the digital staff's speech, in order to increase the empathy score, a predetermined value is added to the current value of the empathy score. When the customer is negative about the digital staff's speech, in order to decrease the empathy score, a predetermined value is subtracted from the current value of the empathy 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 related to 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, 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 word usage of the customer's utterance with the word usage of the digital staff's utterance, and calculates an intimacy score representing the degree of intimacy of the customer with respect to the digital staff based on the comparison result. Specifically, when the politeness level of the word usage 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 level of the word usage 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 level of word usage, a dictionary regarding a plurality of ending 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, and the ending expression of the customer's utterance is collated with the dictionary to identify the politeness level of the corresponding ending expression.

[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 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 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 conversion processing unit 29 converts the diction of the digital staff's utterance sentence received from the utterance sentence generation device 2 into the diction of the set politeness level. Initially, the utterance sentences of the digital staff received from the utterance 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 conversion processing unit 29 converts the words and phrases of the digital staff's utterance sentence received from the utterance sentence generation device 2 into the set diction using the said dictionary.

[0033] 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 the decrease in the intimacy of the customer with respect to the digital staff can be suppressed.

[0034] The message generation request processing unit 30 transmits data on the conversation history between the customer and the digital staff member, including the customer's most recent statement, along with a request to generate a message from the digital staff member in response to the customer's statement, to the message generation device 2. Furthermore, in addition to the conversation history data, the message generation request processing unit 30 transmits persona information, persona extension information, changes in empathy score, changes in trust score, changes in intimacy score, and changes in purchase likelihood score to the message generation device 2. By referring to this information, the message generation device 2 can generate a message from the digital staff member that follows the flow of the conversation and that increases the empathy score and trust score rather than decreasing them.

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

[0036] Data on customer comments is received from the EC server 3 via the receiving processor 31 (S2). Data on digital staff comments is read from the memory unit 14. The customer comments and the digital staff comments are subjected to natural language analysis processing (S3). Parts of speech are broken down, nouns are extracted, and syntactic analysis is performed.

[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 processing 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 representing the possibility that a customer purchases a product 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 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 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 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 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 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 score, the transition of the intimacy 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 utterance text of the digital staff from the utterance text generation device 2 via the communication interface 17 (S10). The diction conversion processing unit 29 converts the diction of the utterance text of the digital staff into the diction of the set politeness level (S11). The transmission processing unit 32 transmits the data of the utterance 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 utterance. 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, and purchase possibility score and the change in diction.

[0042] Based on the analysis result of the natural language analysis processing unit 22, the empathy degree estimation processing unit 23 determines whether the content of the digital staff's speech is in line with the theme of the conversation (S21). For example, if the EC site is an apparel store, the theme of the conversation is the apparel field. Typically, the nouns included in the digital staff's speech are collated with an apparel-related term dictionary to check whether they are listed in the apparel-related term dictionary. When the nouns included in the digital staff's speech match any of the terms listed in the apparel-related term dictionary, it is determined that the content of the digital staff's speech is in line with the theme of the conversation (S21, YES). When the nouns included in the digital staff's speech do 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 is not in line with the theme of the conversation (S21, NO). When it is determined that the content of the digital staff's speech is in line with the theme of the conversation (S21, YES), it waits for the next digital staff's speech. When it is determined that the content of the digital staff's speech is not in line with the theme of the conversation (S21, NO), it proceeds to the next step S22 and starts the operation process of the empathy score. Note that when it is determined that the digital staff's speech is in line with 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 a casual conversation, the empathy score is calculated, and when the digital staff's speech 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 speech. In step S23, based on the analysis result of the natural language analysis processing unit 22, the empathy degree estimation processing unit 23 differentiates whether the customer's speech is affirmative, negative, or neither with respect to the digital staff's speech. Typically, the predicates included in the customer's speech are collated with a dictionary that covers 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 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 digital staff's statement, 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 processing in step S5 of FIG. 4. The reliability estimation processing unit 24 determines whether the content of the digital staff's statement follows the conversation topic 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 conversation topic (S31, YES), it waits for the next digital staff's statement. When it is determined that the content of the digital staff's statement follows the conversation topic (S31, NO), it proceeds to the next step S32 and starts the calculation processing 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 (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 processing in step S6 of 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 ending of the customer's statement is 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 or not 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 or not 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, for example, the middle level of politeness prepared in 11 levels prior to the conversation (S51). When receiving the customer's utterance, it identifies the degree of politeness of the customer's utterance (S52). Then, the diction conversion processing unit 29 determines whether the identified degree of politeness of the customer's utterance has changed from the degree of politeness of the previous customer's utterance (S53).

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

[0053] When it is determined that the identified degree of politeness of the customer's utterance has decreased compared to the degree of politeness 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 level of politeness by one step (S55). When it is determined that the identified degree of politeness of the customer's utterance has increased compared to the degree of politeness 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 level of politeness by one step (S56).

[0054] When the conversation continues (S57, NO), it 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 this embodiment, the empathy score, reliability score of the customer with respect to the speech of the digital staff, and the intimacy score of the customer with respect to the digital staff are calculated, and the changes in these empathy scores and reliability scores are sent to the speech generation device 2 together with the conversation history and the speech generation request. As a result, 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 this 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 the equivalent scope thereof.

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 comprising a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a speech generation device (generative AI) via an electrical public communication network, wherein the processor, by executing the program, has means for transmitting a speech generation request to the speech generation device, has means for receiving the speech data from the speech generation device, and functions as a speech style conversion means for converting the speech style of the received speech into another speech style with a different level of politeness.

2. The communication interface communicates with a user terminal via the electrical public communication network together with the speech generation device (generative AI), and the processor, has means for receiving user speech data from the user terminal, further functions as means for transmitting the converted speech data to the user terminal as a response to the user speech, and the speech style conversion means converts the speech style of the received speech according to the level of politeness of the user speech. The information processing apparatus according to Claim 1.

3. The speech style conversion means changes the level of politeness of the speech style following a change in the level of politeness of the user speech. The information processing apparatus according to Claim 2.

4. The speech style conversion means lowers the level of politeness of the speech style when the level of politeness of the user speech decreases, and raises the level of politeness of the speech style when the level of politeness of the user speech increases. The information processing apparatus according to Claim 3.

5. A program for causing an information processing apparatus comprising a memory for storing a program, a processor for executing the program, and a communication interface for communicating with a speech generation device (generative AI) via an electrical public communication network, to function as means for transmitting a speech generation request to the speech generation device, means for receiving the speech data from the speech generation device, and means for converting the speech style of the received speech into another speech style with a different level of politeness.

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