Support system, support method, and support program

Abstract

To enable grouping by an intended item as compared with the case of using unsupervised learning, and to eliminate complication of work as compared with the case of using supervised learning, when supporting decision making of a response to a reception history.SOLUTION: The assistance system includes a processor, and the processor acquires a response history including an inquiry from a customer and an answer to the inquiry, acquires a feature word predefined as a characteristic word of each topic for each of a plurality of topics, classifies the response history into any one of the plurality of topics based on the feature word, and outputs information according to a response history classification result obtained by classifying the response history.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an assistance system, an assistance method, and an assistance program. [Background technology]

[0002] Patent Document 1 describes "a method for supporting the registration of frequently asked questions (FAQs) using machine learning to determine whether or not to register new FAQs, a method for automatic learning from FAQs, and a computer system." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-36210 Summary of the Invention [Problem to be solved by the invention]

[0004] Customer needs are contained in response history, especially in the content of customer inquiries. Effectively utilizing this information to meet those needs will differentiate a company from its competitors. However, response history contains a huge amount of data, making it difficult to manually review and analyze the content comprehensively. Therefore, there is a need for data analysis technology to capture the overall picture of customer needs and support decision-making regarding how to respond to response history.

[0005] In response to this, Patent Document 1 discloses a technology that uses machine learning to support the creation of FAQs. In Patent Document 1, inquiries are grouped using clustering. However, when unsupervised learning is used, it is not possible to group response histories by intended items. On the other hand, when supervised learning is used, it is necessary to assign labels, which requires a lot of work, and it is difficult to define classes in the first place.

[0006] The present disclosure has been made in consideration of these circumstances, and aims to provide a system, method, and program that, when assisting in decision-making regarding how to respond to response history, can group items by intended items compared to when unsupervised learning is used, and can eliminate the complexity of the work compared to when supervised learning is used. [Means for solving the problem]

[0007] The support system according to the first aspect of the present disclosure includes a processor that acquires a response history including inquiries from customers and responses to the inquiries, acquires predefined feature words for each of a plurality of topics as words characteristic of each topic, classifies the response history into one of the plurality of topics based on the feature words, and outputs information according to the response history classification result obtained by classifying the response history.

[0008] A support system according to a second aspect of the present disclosure is the support system according to the first aspect, wherein the processor performs a Pareto analysis on the response history classification results and visualizes the analysis results of the Pareto analysis.

[0009] A support system according to a third aspect of the present disclosure is a support system according to the first or second aspect, in which the processor performs trend analysis of the response history classification results based on the trends of the inquiries or the responses, and outputs information according to the analysis results of the trend analysis.

[0010] A support system according to a fourth aspect of the present disclosure is the support system according to the third aspect, wherein the processor visualizes the results of the trend analysis in the form of a scatter plot including parameters of at least one of the response time from the inquiry to the response and the first-line response rate.

[0011] A support system according to a fifth aspect of the present disclosure is the support system according to the third or fourth aspect, wherein the processor selects a target topic to be turned into an FAQ from among the plurality of topics based on the results of the trend analysis, and outputs information according to the target topic.

[0012] A support system according to a sixth aspect of the present disclosure is a support system according to any one of the first to fifth aspects, in which the processor acquires existing FAQs that have already been converted into FAQs, classifies the existing FAQs into one of the plurality of topics using a classification model generated when classifying the response history, and outputs information according to the FAQ classification result into which the existing FAQs have been classified.

[0013] A support system according to a seventh aspect of the present disclosure is the support system according to the sixth aspect, wherein the processor performs a Pareto analysis on each of the response history classification results and the FAQ classification results, compares the response history analysis results obtained by Pareto analysis of the response history classification results with the FAQ analysis results obtained by Pareto analysis of the FAQ classification results, and outputs information according to the comparison results.

[0014] An assistance system according to an eighth aspect of the present disclosure is the assistance system according to the seventh aspect, wherein the processor determines, based on the comparison result, a priority order among the topics to be turned into FAQs for the plurality of topics, and outputs information according to the priority order among the topics.

[0015] A support system according to a ninth aspect of the present disclosure is the support system according to the seventh or eighth aspect, wherein the processor generates a plurality of similar groups for at least one of the plurality of topics based on the similarity of the response history and the existing FAQs, determines the priority within the topics to be turned into FAQs based on the number of response history entries and the number of existing FAQs in each of the plurality of similar groups, and outputs information according to the priority within the topic.

[0016] A support system according to a tenth aspect of the present disclosure is a support system according to any one of the first to ninth aspects, wherein the processor subdivides at least one of the plurality of topics into a plurality of detailed topics based on the similarity of the response history, and outputs information according to the plurality of detailed topics.

[0017] An assistance method according to an eleventh aspect of the present disclosure includes a computer acquiring a response history including inquiries from customers and responses to the inquiries, acquiring, for each of a plurality of topics, predefined characteristic words that are characteristic of each topic, classifying the response history into one of the plurality of topics based on the characteristic words, and outputting information according to the response history classification result into which the response history has been classified.

[0018] An assistance program according to a twelfth aspect of the present disclosure causes a computer to perform the following processes: acquiring a response history including inquiries from customers and responses to the inquiries; acquiring, for each of a plurality of topics, predefined feature words that are characteristic of each topic; classifying the response history into one of the plurality of topics based on the feature words; and outputting information according to the response history classification results obtained by classifying the response history. [Effects of the Invention]

[0019] According to the support system, support method, and support program disclosed herein, when supporting decision-making regarding how to respond to response history, it is possible to group by intended items compared to when unsupervised learning is used, and it is possible to eliminate the complexity of the work compared to when supervised learning is used. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram showing an example of an outline of decision-making using the support system 100 according to the present embodiment. [Figure 2] 1 is a diagram illustrating an example of a hardware configuration of a support system 100 according to an embodiment of the present invention. [Figure 3] 1 is a diagram illustrating an example of a functional configuration of a support system 100 according to the present embodiment. [Figure 4] FIG. 10 is a diagram illustrating an example of a response history. [Figure 5] FIG. 10 is a diagram showing an example of a feature word. [Figure 6] FIG. 10 is a diagram illustrating an example of an existing FAQ. [Figure 7] FIG. 2 is a diagram showing a processing flow executed by the support system 100 according to the first embodiment. [Figure 8] FIG. 10 is a diagram showing details of the response history classification process. [Figure 9] FIG. 2 is a diagram showing a first example of visualization by the support system 100 according to the first embodiment. [Figure 10] FIG. 10 is a diagram showing a second example of visualization by the support system 100 according to the first embodiment. [Figure 11] FIG. 10 is a diagram showing a third example of visualization by the support system 100 according to the first embodiment. [Figure 12] FIG. 10 is a diagram showing a processing flow executed by the support system 100 according to the second embodiment. [Figure 13] FIG. 10 is a diagram showing details of the target topic selection process. [Figure 14] FIG. 10 is a diagram showing a first example of visualization by the support system 100 according to the second embodiment. [Figure 15] FIG. 10 is a diagram showing a second example of visualization by the support system 100 according to the second embodiment. [Figure 16] FIG. 10 is a diagram showing a processing flow executed by the support system 100 according to the third embodiment. [Figure 17] FIG. 10 is a diagram showing details of existing FAQ classification processing. [Figure 18] FIG. 10 is a diagram showing details of the topic comparison analysis process. [Figure 19] FIG. 10 is a diagram showing a first example of visualization by the support system 100 according to the third embodiment. [Figure 20] FIG. 10 is a diagram showing a second example of visualization by the support system 100 according to the third embodiment. [Figure 21] FIG. 10 is a diagram illustrating details of the similar group generation process. [Figure 22] FIG. 10 is a diagram showing a third example of visualization by the support system 100 according to the third embodiment. [Figure 23] FIG. 10 is a diagram showing a fourth example of visualization by the support system 100 according to the present embodiment. [Figure 24] FIG. 10 is a diagram showing a processing flow executed by the support system 100 according to the fourth embodiment. [Figure 25] FIG. 10 is a diagram showing details of detailed topic classification processing. [Figure 26] FIG. 10 is a diagram showing a first example of visualization by the support system 100 according to the fourth embodiment. [Figure 27] FIG. 10 is a diagram showing a processing flow executed by the support system 100 according to the fifth embodiment. [Figure 28] FIG. 13 is a diagram showing a processing flow executed by the support system 100 according to the sixth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of the present disclosure will be described below with reference to the drawings. In each drawing, the same or equivalent components and parts are designated by the same reference numerals. Furthermore, the dimensional proportions in the drawings are exaggerated for the sake of explanation and may differ from the actual proportions.

[0022] 1 is a diagram showing an example of an outline of decision-making using a support system 100 according to this embodiment. Note that this diagram comprehensively shows all processes that can be executed by the support system 100. However, the support system 100 does not necessarily need to execute all of the processes shown in this diagram, and may execute at least some of the processes shown in this diagram.

[0023] The support system 100 may perform at least one of the following processes: a response history classification process shown in step S10, a target topic selection process shown in step S20, an FAQ classification process shown in step S30, a topic comparison and analysis process shown in step S40, a similar group generation process shown in step S50, and a detailed topic classification process shown in step S60. Each process will be described in detail below.

[0024] In this way, the support system 100 may support the user in making decisions to turn the questions into FAQs in order of priority. The support system 100 may also support the user in making decisions by checking the contents and considering countermeasures. FAQ is an abbreviation for Frequently Asked Question, and is a collection of frequently asked questions about products and services and their answers.

[0025] 2 is a diagram showing an example of the hardware configuration of the assistance system 100 according to this embodiment. The assistance system 100 may be a computer. The assistance system 100 includes a processor 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a storage 104, a communication interface 105, and a user interface 106. These components are connected to each other via a bus 109 so as to be able to communicate with each other.

[0026] The processor 101 executes various programs and controls each component. Here, the processor 101 is assumed to be a CPU (Central Processing Unit). The ROM 102 stores various programs and various data. The RAM 103 temporarily stores programs or data as a working area. The storage 104 is configured with an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data.

[0027] In the assistance system 100 according to this embodiment, an assistance program is stored in the ROM 102 or the storage 104. The processor 101 reads the assistance program from the ROM 102 or the storage 104 and expands it in the RAM 103 as a working area, whereby the computer controls each component and executes various arithmetic processing.

[0028] The communication interface 105 is an interface through which the assistance system 100 communicates with other devices. The user interface 106 is an input / output interface through which the assistance system 100 exchanges information with a user. The user interface 106 may include input devices such as a mouse, keyboard, touch panel, and microphone, and output devices such as a monitor and speaker.

[0029] 3 is a diagram showing an example of the functional configuration of the assistance system 100 according to this embodiment. The assistance system 100 includes an input / output unit 110, a storage unit 130, a data processing unit 150, and a display unit 170. The processor 101 may read out an assistance program from the ROM 102 or the storage 104 and load it into the RAM 103, thereby implementing each functional unit in the computer.

[0030] The input / output unit 110 inputs various data from the outside and outputs various data to the outside.

[0031] The storage unit 130 stores input data required for processing and also stores output data corresponding to the processing results. The storage unit 130 may store, as input data, data related to at least one of response history, parameters, a technical term dictionary, a synonym dictionary, unnecessary words, characteristic words, and existing FAQs. The storage unit 130 may also store, as output data, data related to at least one of classification results, analysis results, classification models, comparison results, and similar groups.

[0032] The data processing unit 150 executes data processing using input data. The data processing unit 150 may include at least one of an analysis target extraction unit 151, a morphological analysis unit 152, a noise removal unit 153, a synonym processing unit 154, an unnecessary word removal unit 155, a classification unit 156, a Pareto analysis unit 157, a visualization unit 158, a trend analysis unit 159, a target topic selection unit 160, a classification model application unit 161, a topic comparison analysis unit 162, a similar group generation unit 163, and a detailed topic classification unit 164.

[0033] The display unit 170 displays various data. It may have at least one of the display unit 170, parameter display unit 171, response history display unit 172, feature word display unit 173, classification result display unit 174, analysis result display unit 175, existing FAQ display unit 176, target topic display unit 177, comparison result display unit 178, similar group display unit 179, and detailed topic display unit 180.

[0034] The following describes input data used for data processing by such support system 100. The response history is data including inquiries from customers and responses to those inquiries. FIG. 4 is a diagram showing an example of the response history. The response history may include items such as reception number, reception date and time, inquiry content, response content, first-line response, response lead time, and information source.

[0035] The reception number is an identification number that identifies the response history. The reception numbers may be assigned in ascending order starting from the earliest reception date and time. The inquiry content is an item that indicates the content of the inquiry from the customer related to a specific product or service. The response content is an item that indicates the content of the response to the inquiry from the customer.

[0036] The first-line response is an item that indicates whether the problem was resolved on the first contact. The first-line response may be marked with "O" if the problem was resolved without escalation or return contact, or "X" if the problem was not resolved. The first-line response is also called primary resolution. The response lead time is an item that indicates the response time from the inquiry to the response. The information source is an item that indicates the source that served as the basis for the response.

[0037] A response history that may include such items may be data collected and accumulated at a contact center such as a call center. Note that the response history is data that has not yet been converted into an FAQ, and can be said to be data that will become the basis for converting into an FAQ.

[0038] The parameters are data for extracting a target product or service from the response history, and may be data such as the product name or model of a photoelectric sensor or the like.

[0039] The technical term dictionary and the synonym dictionary are, as the name suggests, dictionary data that lists technical terms and synonyms. The unnecessary words are data that define words that are unnecessary for data processing. The unnecessary words may be data such as greetings, particles, and conjunctions.

[0040] The feature words are data containing predefined feature words for each of multiple topics. Figure 5 shows an example of feature words. The feature words may include items such as topic numbers, topic names, and feature words.

[0041] The topic number is an identification number that identifies a topic. The topic name is an item that indicates the name of the topic. The topic name may indicate a theme, subject, or field, such as "product selection" or "settings."

[0042] A characteristic word is an item that indicates a word characteristic of a topic and a predefined word. For example, if the topic name is "product selection," the characteristic words may be "selection," "substitution," "discontinuation," etc. Also, if the topic name is "settings," the characteristic words may be "adjustment," "range," "day of the week," "parameter," etc. In other words, characteristic words can be said to be predefined words that are likely to appear when the subject matter is the theme, subject, or field indicated by the topic name.

[0043] Such feature words may be defined by the user. In this case, the user may define the feature words from scratch, or the user may define the feature words by selecting words that have been mechanically extracted based on probability.

[0044] Existing FAQs are data on existing FAQs that have already been compiled. Figure 6 shows an example of an existing FAQ. An existing FAQ may include items such as an FAQ number, question, answer, product category, and creation date.

[0045] The FAQ number is an identification number that identifies the FAQ. The questions and answers are items that indicate the questions and answers that have been made into FAQs. The product classification is an item that indicates the classification of the product or service to which the FAQ relates. The creation date is an item that indicates the date on which the FAQ was made into an FAQ.

[0046] By turning response history into FAQs in this way, customers can solve their own questions, reducing the number of similar inquiries. This reduces the burden on operators and improves work efficiency. However, it is not a good idea to create FAQs indiscriminately. If there are too many FAQs, customers will not be able to find the information they are looking for. Also, FAQs need to be updated regularly, which can be costly.

[0047] Therefore, it is important to create an FAQ that contains just the right amount of useful information from the accumulated response history. The support system 100 according to this embodiment supports decision-making on how to respond to such response history. Below, the processing executed by the support system 100 according to this embodiment will be described in detail using six embodiments as examples.

[0048] First Embodiment 7 is a diagram showing a processing flow executed by the support system 100 according to the first embodiment. In the first embodiment, the support system 100 executes only the response history classification process shown in step S10.

[0049] FIG. 8 is a diagram showing details of the response history classification process. In step S11, the processor 101 reads each piece of data. At this time, the processor 101 reads at least the response history and characteristic word data. That is, the processor 101 acquires the response history including inquiries from customers and responses to those inquiries, and acquires characteristic words predefined as characteristic words for each of multiple topics. In addition, the processor 101 may also read data on parameters, a technical term dictionary, a synonym dictionary, and unnecessary words.

[0050] In step S12, the processor 101 extracts an analysis target as the analysis target extraction unit 151. For example, the processor 101 extracts an analysis target from a response history using parameters. As an example, the processor 101 may extract, as the analysis target, a response history related to a photoelectric sensor from a mixture of response histories related to various products and services.

[0051] In step S13, the processor 101 performs morphological analysis as the morphological analysis unit 152. For example, the processor 101 uses a technical terminology dictionary to divide the response history extracted as the analysis target into morphemes, which are the smallest units of language, and analyzes the part of speech of each morpheme, etc.

[0052] In step S14, the processor 101 removes noise as the noise removal unit 153. For example, the processor 101 removes, as noise, those of the divided morphemes that do not have meaning on their own (numbers, symbols, punctuation marks, etc.).

[0053] In step S15, the processor 101 executes synonym processing as the synonym processing unit 154. For example, the processor 101 uses a synonym dictionary to standardize the expressions of words that are treated as synonyms.

[0054] In step S16, the processor 101 functions as the unnecessary word removal unit 155 to remove unnecessary words. For example, the processor 101 uses unnecessary words to remove greetings, particles, conjunctions, and the like. The processor 101 may preprocess the response history to be analyzed in this manner, for example. This allows the processor 101 to improve the accuracy of classification.

[0055] In step S17, the processor 101, functioning as the classification unit 156, classifies the response history into one of a plurality of topics based on the feature words. For example, the processor 101 may calculate the probability that the response history belongs to a plurality of topics for each reception number. As an example, the processor 101 may calculate the probability that the response history with reception number 00001 belongs to each of topic numbers 0 to 7. Then, for example, if the probability of belonging to topic number 2 is the highest, the processor 101 may classify the response history with reception number 00001 into topic number 2. The topic classified in this way becomes the belonging topic. The processor 101 may execute a similar classification process sequentially for each reception number. In this way, the processor 101 can generate a classification model.

[0056] In step S18, the processor 101, as the Pareto analysis unit 157, performs a Pareto analysis on the response history classification results obtained by classifying the response histories. For example, the processor 101 may count the number of response histories for each topic. Next, the processor 101 may sort the topics in descending order of the number of cases. Furthermore, the processor 101 may calculate the ratio for each topic when the total number of cases is 100. Then, the processor 101 may calculate the cumulative ratio in descending order of the number of cases.

[0057] In step S19, the processor 101, functioning as the visualization unit 158, visualizes the above-described processing results in various ways. FIG. 9 is a diagram showing a first example of visualization by the support system 100 according to the first embodiment. As shown in the diagram, the processor 101 may visualize the response history and the classification results in a table format in association with each other. In this case, the classification results may include the associated topics and the probability of each topic belonging to the topic.

[0058] FIG. 10 is a diagram showing a second example of visualization by the support system 100 according to the first embodiment. In this diagram, the horizontal axis indicates the topic number. In addition, in this diagram, the left vertical axis indicates the number of cases, and a bar graph indicates the number of cases. In addition, in this diagram, the right vertical axis indicates the cumulative ratio, and a line graph indicates the cumulative ratio. As shown in this diagram, the processor 101 may visualize the analysis results obtained by the Pareto analysis.

[0059] FIG. 11 is a diagram showing a third example of visualization by the support system 100 according to the first embodiment. This diagram shows an example in which words used in classified response histories for each of a plurality of topics are expressed as a word cloud. A word cloud is a visualization of frequently used words, extracted from text data such as sentences and color-coded according to the number of times they appear. As shown in this diagram, the processor 101 may visualize the words used in the response histories in a manner according to the number of times they appear. In this way, the processor 101 can output information according to the response history classification results obtained by classifying the response histories.

[0060] <Second embodiment> 12 is a diagram showing a processing flow executed by the support system 100 according to the second embodiment. In the second embodiment, the support system 100 executes a target topic selection process shown in step S20 in addition to the response history classification process shown in step S10. The response history classification process may be the same as in the first embodiment, and therefore a description thereof will be omitted here.

[0061] 13 is a diagram showing details of the target topic selection process. In step S21, the processor 101 acquires a response history classification result obtained by classifying the response history. For example, the processor 101 may acquire data in which the response history and the classification result are associated with each other, as shown in FIG.

[0062] In step S22, the processor 101, as the trend analysis unit 159, performs trend analysis on the response history classification results based on the trends of inquiries or responses. For example, the processor 101 may calculate the average response time by averaging the response lead time for response histories categorized into the same belonging topic. The processor 101 may also calculate the first-line response rate by tallying the success or failure of first-line responses for response histories categorized into the same belonging topic.

[0063] The processor 101 may perform such trend analysis for each of a plurality of topics. In the above description, an example in which trend analysis is performed on a topic-by-topic basis has been shown, but the present invention is not limited to this. The processor 101 can also perform trend analysis of the response history based on the elapsed time since the sale of a product or service. In addition, the processor 101 can also perform trend analysis of the response history in comparison with sales data. In this case, classification by product group, inquiry classification, inquiry category, or the like may be used.

[0064] In step S23, the processor 101, as the target topic selection unit 160, selects a target topic to be turned into an FAQ from among the multiple topics based on the results of the trend analysis. For example, the processor 101 may select, from among the multiple topics, a topic whose average response time is less than a predetermined threshold as the target topic. Alternatively, or in addition, the processor 101 may select, from among the multiple topics, a topic whose first-line response rate is equal to or greater than a predetermined threshold as the target topic.

[0065] In step S24, the processor 101, functioning as the visualization unit 158, visualizes the above-described processing results in various ways. For example, the processor 101 may display the topic number of the topic selected as the target topic. In this way, the processor 101 can output information according to the target topic.

[0066] 14 is a diagram showing a first example of visualization by the support system 100 according to the second embodiment. In this diagram, the horizontal axis represents the first-line response rate, and the vertical axis represents the average response time. As shown in this diagram, the processor 101 may visualize the results of the trend analysis in the form of a scatter plot whose parameters include at least one of the response time from inquiry to response and the first-line response rate.

[0067] 15 is a diagram showing a second example of visualization by the support system 100 according to the second embodiment. In this diagram, the horizontal axis represents the year and month, and the vertical axis represents the number of inquiries. In this way, the processor 101 can output information according to the analysis results of the trend analysis.

[0068] <Third embodiment> 16 is a diagram showing a processing flow executed by the support system 100 according to the third embodiment. In the third embodiment, the support system 100 executes an existing FAQ classification process shown in step S30, a topic comparison and analysis process shown in step S40, and a similar group generation process shown in step S50, in addition to the response history classification process shown in step S10 and the target topic selection process shown in step S20. The response history classification process and the target topic selection process may be the same as those in the second embodiment, and therefore will not be described here.

[0069] 17 is a diagram showing details of the existing FAQ classification process. In step S31, the processor 101 reads each piece of data. At this time, the processor 101 reads at least the data of the existing FAQs and the classification model. That is, the processor 101 may acquire the existing FAQs that have already been converted into FAQs and the classification model that was generated when classifying the response history. In addition to this, the processor 101 may read data of a technical term dictionary, a synonym dictionary, and unnecessary words.

[0070] Steps S32 to S35 may be the same as steps S13 to S16 except that the target changes from response history to existing FAQs, and therefore a description thereof will be omitted here.

[0071] In step S36, the processor 101, functioning as the classification model application unit 161, applies the classification model generated in step S17 to the existing FAQ. As a result, the processor 101 classifies the existing FAQ into one of a plurality of topics using the classification model generated when classifying the response history.

[0072] Steps S37 and S38 may be similar to steps S18 and S19, except that the target changes from the response history classification result obtained by classifying the response history to the FAQ classification result obtained by classifying the existing FAQs, and therefore a description thereof will be omitted here. In this way, for example, the processor 101 can output information according to the FAQ classification result obtained by classifying the existing FAQs.

[0073] 18 is a diagram showing details of the topic comparison analysis process. In step S41, the processor 101 acquires each analysis result obtained by performing a Pareto analysis on each of the response history classification result and the FAQ classification result.

[0074] In step S42, the processor 101, as the topic comparison and analysis unit 162, compares and analyzes the response history analysis result obtained by performing a Pareto analysis on the response history classification result and the FAQ analysis result obtained by performing a Pareto analysis on the FAQ classification result. For example, the processor 101 may calculate the total number and difference between the number of response histories and existing FAQs for each topic.

[0075] In step S43, the processor 101 determines the priority order among the multiple topics to be turned into FAQs based on the comparison results. For example, the processor 101 may determine the priority order so that the priority is higher in descending order of the difference. Note that, although the above description has been given as an example of determining the priority order based on the difference, the present invention is not limited to this. The processor 101 may also determine the priority order using, for example, the FAQ usage record.

[0076] In step S44, the processor 101, functioning as the visualization unit 158, visualizes the above-described processing results in various ways. FIG. 19 is a diagram showing a first example of visualization by the support system 100 according to the third embodiment. As shown in this diagram, the processor 101 may visualize, in a table format, the number of response history cases, the number of existing FAQ cases, the total, the difference, and the priority for each topic. In this way, the processor 101 can output information according to the priority between topics, for example.

[0077] FIG. 20 is a diagram showing a second example of visualization by the support system 100 according to the third embodiment. In this diagram, the horizontal axis represents the topic number. In addition, in this diagram, the left vertical axis represents the total, and the bar graph represents the number of cases. Note that the bar graph with dots represents the number of existing FAQs, and the bar graph without dots represents the number of response history cases, so that the breakdown can be seen. In addition, in this diagram, the right vertical axis represents the difference, and the line graph represents this difference. In this way, for example, the processor 101 can output information according to the comparison result obtained by comparing the response history analysis result and the FAQ analysis result.

[0078] 21 is a diagram showing details of the similar group generation process. In step S51, the processor 101 acquires the classification results obtained by classifying the response histories and the existing FAQs, that is, the response history classification result and the FAQ classification result.

[0079] In step S52, the processor 101, as the similarity group generation unit 163, generates multiple similarity groups for at least one topic among the multiple topics based on the similarity between the response history and the existing FAQ. For example, when a target topic has been selected by the target topic selection process, the processor 101 may select the topic selected as the target topic from the multiple topics. Then, the processor 101 may generate multiple similarity groups for the topic based on, for example, the Levenshtein distance, which calculates the similarity between character strings.

[0080] In step S53, the processor 101 determines the priority within the topic to be turned into an FAQ based on the number of response logs and the number of existing FAQs in each of the multiple similar groups. For example, the processor 101 may determine the priority so that for a group with no existing FAQs, the priority is given to the group with the largest number of response logs.

[0081] In step S54, the processor 101, functioning as the visualization unit 158, visualizes the above-described processing results in various ways. Fig. 22 is a diagram showing a third example of visualization by the support system 100 according to the third embodiment. As shown in this figure, the processor 101 may visualize the reception number, FAQ number, and group number in a table format in association with each other.

[0082] Here, the group number is an identification number that identifies a similar group. That is, the processor 101 may display which response history belongs to which similar group and which existing FAQ belongs to which similar group.

[0083] FIG. 23 is a diagram showing a fourth example of visualization by the support system 100 according to this embodiment. While FIG. 22 shows details of similar groups, FIG. 23 shows a summary of similar groups. The group size indicates the size of the similar group, and here indicates the total number of existing FAQs and response histories belonging to the same similar group. Mean and Std indicate the average and standard deviation, and here indicate values ​​internally processed when calculating the Levenshtein distance. In this way, for example, the processor 101 can output information according to the priority within a topic.

[0084] <Fourth embodiment> 24 is a diagram showing a processing flow executed by the support system 100 according to the fourth embodiment. In the fourth embodiment, the support system 100 executes a detailed topic classification process shown in step S60 in addition to the response history classification process shown in step S10 and the target topic selection process shown in step S20. The response history classification process and the target topic selection process may be the same as those in the second embodiment, and therefore a description thereof will be omitted here.

[0085] 25 is a diagram showing details of the detailed topic classification process. In step S61, the processor 101 acquires a response history classification result obtained by classifying the response history. For example, the processor 101 may acquire data in which the response history and the classification result are associated with each other, as shown in FIG.

[0086] In step S62, the processor 101, as the detailed topic classification unit 164, classifies at least one of the multiple topics into multiple detailed topics based on the similarity of the response histories. At this time, for example, when a target topic has been selected by the target topic selection process, the processor 101 may select a topic from the multiple topics that was not selected as the target topic. Then, the processor 101 may classify the topic into multiple detailed topics using an algorithm such as clustering.

[0087] In step S63, the processor 101, functioning as the visualization unit 158, visualizes the above-described processing results in various ways. FIG. 26 is a diagram showing a first example of visualization by the support system 100 according to the fourth embodiment. As shown in this figure, the processor 101 may visualize the detailed topic number and the number of handling history records for each of a plurality of detailed topics. In response to this, the user may review the handling history records categorized into the detailed topics and input the content and product analysis / fault-related items.

[0088] <Fifth embodiment> 27 is a diagram showing a processing flow executed by the support system 100 according to the fifth embodiment. In the fifth embodiment, the support system 100 may be the same as that in the third embodiment except that it does not execute the target topic selection process shown in step S20, and therefore, a description thereof will be omitted here.

[0089] Sixth Embodiment 28 is a diagram showing a processing flow executed by the support system 100 according to the sixth embodiment. In the sixth embodiment, the support system 100 may be the same as that of the fourth embodiment except that it does not execute the target topic selection process shown in step S20, and therefore, a description thereof will be omitted here.

[0090] In this way, the support system 100 of this embodiment acquires a response history including inquiries from customers and responses to the inquiries, acquires pre-defined characteristic words for each of multiple topics as words characteristic of each topic, classifies the response history into one of multiple topics based on the characteristic words, and outputs information according to the response history classification results obtained by classifying the response history.

[0091] As a result, the support system 100 according to this embodiment classifies call histories into one of multiple topics through semi-supervised learning using feature words, enabling grouping by intended items compared to unsupervised learning. In other words, the support system 100 according to this embodiment utilizes a priori knowledge from a contact center or the like to classify customer needs by intended items and outputs information based on the results, thereby providing more useful information for decision-making. In this case, the user only needs to set feature words, eliminating the need for extensive labeling. Therefore, the support system 100 according to this embodiment can reduce the complexity of the work compared to supervised learning.

[0092] Furthermore, according to the support system 100 of this embodiment, when creating an FAQ, information is output that takes into consideration not only the response history but also the type and volume of existing FAQs, so that information that is useful for creating an FAQ can be provided starting from the topics with the highest priority.

[0093] Furthermore, with the support system 100 according to this embodiment, topics that require a response other than creating an FAQ can be subdivided into detailed topics, making it possible to provide information that makes it easy to understand the content and plan countermeasures. In this case, because a priori knowledge is used, the time required to check the content can be significantly reduced.

[0094] Additionally, by using the support system 100 according to this embodiment, the user can grasp the magnitude of needs for each category. Furthermore, the user can determine whether or not a FAQ should be created for each category. Furthermore, since information about similar groups generated by mixing response history and existing FAQs is output, the user can grasp the response history content for which a new FAQ should be created without performing extensive processing. Additionally, the user can also check the overlap of existing FAQs.

[0095] The above-described processing can also be realized by dedicated hardware circuits. In this case, the processing may be performed by a single piece of hardware or by multiple pieces of hardware.

[0096] In addition, in the above explanation, the term "processor" refers to a processor in a broad sense, and includes general-purpose processors (e.g., CPU: Central Processing Unit, etc.) and dedicated processors (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0097] Furthermore, the processor operations described above may not only be performed by a single processor, but may also be performed by multiple processors located at physically separate locations working together. The order of the processor operations is not limited to the above-described order, and may be changed as appropriate.

[0098] The above-mentioned program may be provided by a computer-readable non-transitory recording medium such as a USB (Universal Serial Bus) memory, a flexible disk, or a CD-ROM (Compact Disc Read Only Memory), or may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable non-transitory recording medium is typically transferred and stored in a memory or storage device. The program may be provided as standalone application software, or may be incorporated into the software of each device as a function of the device.

[0099] The above-described program can be provided as a program product. The program product includes any product for providing the program. For example, the program product includes a program provided over a network such as the Internet, and a non-transitory computer-readable recording medium such as a CD-ROM or DVD on which the program is stored.

[0100] The present disclosure is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit of the present disclosure. [Explanation of symbols]

[0101] 100 Support System 101 processors 102 ROM 103 RAM 104 Storage 105 Communication Interface 106 User Interface 109 Bus 110 Input / output section 130 Storage section 150 Data Processing Unit 170 Display section

Claims

1. a processor, the processor comprising: Acquire a response history including inquiries from customers and responses to the inquiries; For each of a plurality of topics, characteristic words defined in advance as characteristic words of each topic are obtained, classifying the response history into one of the plurality of topics based on the characteristic words; outputting information according to the response history classification result obtained by classifying the response history; Support system.

2. The processor: Pareto analysis of the response history classification results; Visualizing the analysis results of the Pareto analysis. The assistance system according to claim 1 .

3. The processor: analyzing the response history classification results based on the trends of the inquiries or the responses; outputting information according to the trend analysis results; The assistance system according to claim 1 or 2.

4. The processor: The analysis result of the trend analysis is visualized in the form of a scatter diagram including at least one of the response time from the inquiry to the response and the first-line response rate as a parameter. The assistance system according to claim 3 .

5. The processor: Selecting a target topic to be turned into an FAQ from among the plurality of topics based on the trend analysis results; outputting information according to the target topic; The assistance system according to claim 3 .

6. The processor: Obtain existing FAQs that have already been made into FAQs, classifying the existing FAQ into one of the plurality of topics using a classification model generated when classifying the response history; outputting information according to the FAQ classification results obtained by classifying the existing FAQs; The assistance system according to claim 1 .

7. The processor: performing a Pareto analysis on each of the response history classification result and the FAQ classification result; A response history analysis result obtained by performing a Pareto analysis on the response history classification result is compared with a FAQ analysis result obtained by performing a Pareto analysis on the FAQ classification result; outputting information according to the comparison result; The assistance system according to claim 6.

8. The processor: determining a priority order among the plurality of topics to be turned into FAQs based on the comparison result; outputting information according to the priority order among the topics; The assistance system according to claim 7.

9. The processor: generating a plurality of similar groups based on similarities between the response history and the existing FAQ for at least one topic among the plurality of topics; determining a priority order within the topics to be turned into FAQs based on the number of the response histories and the number of the existing FAQs in each of the plurality of similar groups; outputting information according to the priority within the topic; The assistance system according to claim 7.

10. The processor: subclassifying at least one of the plurality of topics into a plurality of detailed topics based on the similarity of the response history; outputting information according to the plurality of detailed topics; The assistance system according to claim 1 or 2.

11. The computer Acquiring a response history including inquiries from customers and responses to the inquiries; Acquiring predefined feature words as characteristic words for each of a plurality of topics; classifying the response history into one of the plurality of topics based on the characteristic words; and outputting information according to a response history classification result obtained by classifying the response history. How to help.

12. On the computer, A process of acquiring a response history including inquiries from customers and responses to the inquiries; A process of acquiring, for each of a plurality of topics, characteristic words that have been defined in advance as characteristic words for each topic; A process of classifying the response history into one of the plurality of topics based on the characteristic words; and outputting information according to the response history classification result obtained by classifying the response history. Support programs.

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