JP2008033806A - Communication support device, program, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a communication support device, a program, and a recording medium to present information required for smoothing communication in mutual communication. <P>SOLUTION: A communication type determination part 10 determines a user type, a customer type and a rival type by a determining sentence on the basis of type classification by a Herrmann model and NLP. An action content determination part 20 computes a customer weight vector indicating significance for an evaluation item from a customer's viewpoint, a user weight vector indicating significance for an evaluation item from the user's viewpoint in relation to a rival and a rival weight vector indicating significance for an evaluation item from the rival is viewpoint in relation to the user about the selected customer, computes the total evaluation score of the user and the total evaluation score of the rival to the customer from the weight vectors and determines action content on the basis of the customer type and customer intimacy. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a technology for supporting communication with a partner in a sales situation, and more particularly to a support technology for smoothly communicating in consideration of internal factors such as psychological and emotional aspects of the self and the partner. .

  Conventionally, as a support tool for performing appropriate sales activities based on customer information, for example, there is one described in Patent Document 1. This sales support system patterns customers into multiple types based on customer data obtained through sales activities and contract information subscribed by customers, and extracts sales support information suitable for those types from the sales support information storage database To do. Thereby, it is possible to realize optimal sales support for the sales staff.

  Further, as a support tool for conducting sales activities, for example, there is one described in Patent Document 2. This sales support system is a system for medical specialists to obtain and analyze necessary information when necessary. For each drug, the total number of prescriptions for doctors and the number of prescriptions for specific company products are distinguished. In this way, usage information is obtained and analyzed. In addition, it identifies the clinic that is the sales target, and identifies contact behavior and products such as visits to doctors. Thereby, the optimal sales support of medical products for doctors can be realized.

  Such a sales support system supports appropriate sales activities based on customer information, but also focuses on communication between the customer and the customer so that smooth communication can be performed. A system is also disclosed. For example, Patent Document 3 describes a communication support system that supports a series of discussion development by using an input interface and link information in which words corresponding to both convergence and divergence of a discussion are defined. ing. Further, Patent Document 4 categorizes various types of data such as individual regionality, blood, and consciousness according to attributes, and transmits the attribute data to recognize a person having the same attribute and to make communication objective and efficient. A communication system to be established is described.

JP 2005-276236 A JP 2002-56103 A JP 2005-56403 A JP 2000-68864 A

  For example, a communication support system for sales is generally based on the classification of self, customers, and the like, which are the targets of communication, into several types and individually responding to customers according to the types. Here, the types of customers are classified by, for example, job title, gender, age, family structure, occupation, address, and the like. In general, a system that supports communication from the customer to the customer classifies the customer as seen from the customer into several types.

  However, there is a problem that such communication support based only on the customer classification data is insufficient to realize smooth communication. In this case, it is desirable to consider other classification data in addition to the customer classification data, and provide communication support based on the plurality of classification data. That is, it is expected that communication will be smoother not only by classifying the customer with respect to the customer viewed from the self but also by taking into account the factors that classify the self with respect to the self viewed from the customer. Also, in actual sales activities, there are competing opponents, so communication can be made even smoother by taking into account the factors that classify the opponents against the opponents seen by customers. Be expected.

  Accordingly, the present invention has been made to solve the above-mentioned problems, and its purpose is communication that can present information necessary for facilitating communication when communicating between itself and the other party. To provide a support device, a program, and a recording medium.

  In order to achieve the above object, a communication support device according to the present invention is a device that supports communication between a self and a partner. , A communication type determination unit that makes a determination based on a determination sentence that reflects the internal factors of human psychology and emotion, and how to view the problem for a given problem As for the evaluation item indicating how to solve, the opponent weight vector indicating the importance for the evaluation item, the self-weight vector indicating the importance for the evaluation item viewed from the relationship with the opponent, and the relationship with the self A weight vector calculation unit that calculates an opponent weight vector indicating the importance of the evaluation item viewed from the opponent, and the weight vector calculation Based on the opponent weight vector, the self-weight vector and the opponent weight vector calculated by the unit, a comprehensive evaluation point for calculating the overall evaluation point indicating the superiority of the opponent over the opponent and the overall evaluation point indicating the superiority of the opponent over the opponent It is characterized by comprising: a calculation unit; and an action content determination unit that determines an action content of the other party based on the partner type and a preset familiarity between the self and the other party.

  In the communication support apparatus according to the present invention, the communication type determination unit may determine the self type, the opponent type, and the opponent type based on a determination sentence based on a Herman model and a type classification of NLP (Neuro Linguistic Programming). Features.

  Further, in the communication support apparatus according to the present invention, the comprehensive evaluation calculation unit shows its superiority over the opponent based on the opponent weight vector, the self-weight vector, and the opponent weight vector by AHP (Analytic Hierarchy Process) analysis. Comprehensive evaluation points and comprehensive evaluation points indicating the superiority of the opponent over the opponent are calculated.

  The communication support apparatus according to the present invention further calculates CI (Contency Index), which is an index of uncertainty in AHP analysis, and calculates the customer weight vector, self-weight vector, and opponent calculated by the weight vector calculation unit. A CI determination unit that determines the consistency of the weight vectors is provided.

  Although the present invention has been described as a communication support apparatus, the present invention can also be realized as a program and a recording medium substantially equivalent to these, and the present invention records a communication support program and the communication support program. Recording media are also included.

  According to the present invention, the types of self, opponents and opponents classified by internal elements such as psychological and emotional aspects are determined, and a comprehensive evaluation value indicating the superiority of the self and opponents against the opponents is calculated, The content of self action is determined. Thereby, the self can obtain the reference information required when communicating with the other party. That is, it is possible to determine the action content that the person actually performs with respect to the other party with reference to the information on the type, the comprehensive evaluation value, and the action content. Therefore, it is possible to facilitate communication between the self and the other party.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
〔Constitution〕
First, the configuration of the communication support apparatus according to the embodiment of the present invention will be described. FIG. 1 is a configuration diagram illustrating an entire system including a communication support apparatus. This communication support system is a system for determining the action content for the user to smoothly communicate with the customer. In the actual business environment, there are not only self and customers but also competitors. Therefore, in this communication support system, the content of actions necessary to smoothly advance own sales activities is determined on the premise of the existence of the opponent. Specifically, the communication support system determines the communication type of the self, the customer, and the opponent, evaluates the customer against the value judgment based on the customer communication type, and also evaluates the opponent against the customer. Based on these evaluations, the self-action content is determined.

  Referring to FIG. 1, this communication support system includes a communication support device 1, which is an application server connected to a LAN (Local Area Network) 5, a terminal 2, a Web server 3, and a mobile phone connected to the Internet 6. 4 is configured. The communication support apparatus 1 includes a communication type determination unit 10, an action content determination unit 20, a data maintenance unit 30, a storage unit 40, and a control unit 50.

  The communication type determination unit 10 determines the communication types of the self, the customer, and the opponent (hereinafter referred to as “self type, customer type, and opponent type”) based on the answer to the question for determining the communication type.

  The action content determination unit 20 calculates a weight vector for the value determination element (evaluation item) of the customer type for the self type, the customer type, and the opponent type determined by the communication type determination unit 10, respectively. A comprehensive evaluation value indicating the superiority of the person (self and opponent viewed from the customer) is calculated, and the action content of the person is determined by the partner type and closeness.

  Here, the comprehensive evaluation value calculated by the action content determination unit 20 is based on an AHP (Analytic Hierarchy Process) analysis. This AHP analysis is an analysis method for deriving sensory information such as uncertain experience and intuition that cannot be measured objectively as a comprehensive judgment result that does not fall into biased subjectivity. L. It was developed by Professor Saaty. For details, please refer to “AHP Theory and Practice” (Eizo Kinoshita, Nikka Giren Publisher).

  The data maintenance unit 30 adds, changes, deletes data in various tables stored in the storage unit 40. The storage unit 40 is an HD, a RAM, or the like, and stores various tables to be described later. The control unit 50 comprehensively controls the communication type determination unit 10, the action content determination unit 20, the data maintenance unit 30, and the storage unit 40, and mediates data input / output as necessary. In addition, data is transmitted to and received from the Web server 3 via the LAN 5. For example, data input by the operator in the mobile phone 4 is transmitted to the Web server 3 via the Internet 6 and is transmitted to the control unit 50 of the communication support apparatus 1 via the LAN 5. And the control part 50 outputs the said data to the communication type determination part 10, the action content determination part 20, or the data maintenance part 30 according to the content of data.

  The terminal 2 transmits / receives data to / from the communication support apparatus 1 via the Web server 3 and the LAN 5 by an operation of the operator (operator) who performs business activities and the like. Data transmitted from the communication support apparatus 1 is displayed on the screen, or data input by an operator's operation is transmitted to the communication support apparatus 1.

  The mobile phone 4 has the same function as that of the terminal 2, and is operated between itself and the communication support apparatus 1 via a telephone communication network (not shown), the Internet 6, the Web server 3, and the LAN 5 by an operation of the mobile phone 4. Send and receive data. Note that the mobile phone 4 may have functions different from those of the terminal 2 because it is easy to carry and handle. For example, the terminal 2 has a function of performing a self-type determination operation and a data setting operation regarding a customer and an opponent, and the mobile phone 4 performs an operation of a customer type and an opponent type determination, a display of self-action content, You may make it have a function which performs input operation of an action result.

  FIG. 2 is a block diagram showing the configuration of the communication type determination unit 10 shown in FIG. The communication type determination unit 10 includes a self type determination unit 11 that determines a self type, a customer type determination unit 12 that determines a customer type, and an opponent type determination unit 13 that determines an opponent type.

  FIG. 3 is a block diagram showing a configuration of the action content determination unit 20 shown in FIG. This action content determination unit 20 is a comprehensive evaluation means 21 that calculates a comprehensive evaluation value of the customer for the customer and a comprehensive evaluation value of the opponent for the customer, and an action for determining the action content of the customer using the customer type and the intimacy. Content determination means 22.

  The comprehensive evaluation means 21 includes a weight vector calculation stage 23, a comprehensive evaluation calculation stage 24, and a CI (Continuity Index) determination stage 25. The weight vector calculation stage 23 calculates a customer weight vector indicating the importance of the evaluation item viewed from the customer, using the evaluation points for the evaluation item stored in the customer weight vector table. In addition, using the evaluation points for the communication type stored in the evaluation vector table, the self-weight vector indicating the importance (dominance) of the evaluation item viewed from the self in relation to the opponent, and the relationship with the self An opponent weight vector indicating the importance (dominance) of the evaluation item viewed from the opponent is calculated. A larger vector value means higher importance or superiority.

  In the comprehensive evaluation calculation stage 24, when the customer weight vector, the self-weight vector, and the opponent weight vector calculated by the weight vector calculation stage 23 are determined to be consistent weight vectors by the CI determination stage 25 described later. Then, based on the customer weight vector, the self-weight vector, and the opponent weight vector, a self-evaluation point for the customer and a total evaluation point for the opponent for the customer are calculated.

  The CI determination stage 25 determines whether or not the customer weight vector, the self-weight vector, and the opponent weight vector calculated by the weight vector calculation stage 23 are consistent. Specifically, the CI determination stage 25 calculates the CI value of the customer weight vector based on the customer weight vector and the evaluation points for the evaluation items stored in the customer weight vector table. Also, the CI value of the self-weight vector is calculated based on the self-weight vector and the evaluation point for the communication type stored in the evaluation vector table. Further, the CI value of the opponent weight vector is calculated based on the opponent weight vector and the evaluation point for the communication type stored in the evaluation vector table. Then, for each weight vector, the CI value is compared with a preset value, and when the CI value is smaller than the set value, it is determined that the weight vector is consistent, and the CI value is greater than or equal to the set value. In some cases, it is determined that the weight vector is not consistent.

  As described above, the action content determination unit 22 determines its own action contents based on the customer type and the familiarity with the customer. The actual action content of the customer with respect to the customer is determined by the self with reference to various data calculated and determined by the communication support apparatus 1.

  4A to 4D are configuration diagrams illustrating items of various tables stored in the storage unit 40. FIG. 4A is a table regarding communication types. The type determination table includes a “determination item ID”, a “determination sentence” that is a question and an answer for determining a communication type, and a “type classification” corresponding to the answer of the “determination sentence”. The “determination item ID”, “determination sentence”, and “type classification” are classified into those based on the Herman model and those based on NLP (Neuro Linguistic Programming). For example, “type classification” is B, G, R, and Y when the Herman model is used, and V, A, and K when the NLP is used. There are a plurality of “determination sentences” corresponding to these “type classifications”.

Here, the Herman model is a scientific tool based on brain research originating from the cerebral physiology theory of Nobel Prize-winning scholars, and is also called brain superiority survey. NLP is a neurolinguistic program developed by Richard Bundler and John Grinder in the United States, and belongs to the field of human communication systematized based on psychology and linguistics. See below for details on the Herman model.
http://www.herrmann.co.jp/p01.html
For details of NLP, refer to the following.
http://www.nlpij.co.jp/whats_nlp/nlp_theory.html

  The type list table consists of “type code”, “type name”, “type details 1” indicating basic characteristics, and “special details” that combine Herman's “type classification” and NLP “type classification” in the type determination table. “Type details 2” indicating a surface and “Type details 3” indicating a weak surface. The “type code” is BV, BA, BK, GV or the like in the above example. This “type code” corresponds to “self type”, “customer type”, and “competitor type” described later.

  FIG. 24 is a diagram showing types classified by Herman and NLP. In FIG. 24, from the left, “type classification” by Herman, “type classification” by NLP, basic characteristics of “type code” including these “type classifications”, and the like are shown. Thus, there are a total of 12 types classified by Herman and NLP, such as BV and BA.

  FIG. 4-2 is a table relating to action contents, opponents, and customers. The action content table is a master table related to the action content, “behavior classification code” indicating the action content classification, “behavior code” indicating the action content code, “action name” indicating the action content name, and action It consists of “details of action” indicating the specific details of the contents. One or more “behavior codes”, “behavior names”, and “behavior details” correspond to “behavior classification codes”. The behavior classification table is composed of “customer familiarity” indicating “intimacy” with the customer, “behavior classification code”, and “behavior classification name” indicating the classification name of the behavior content. The “behavior classification name” is, for example, product description, entertainment, or fishing. One or more “behavior classification codes” and “behavior classification names” correspond to “customer intimacy”.

  Individual BP (Best Practice) table (individual action content table) includes “Employee Code”, “Self Type”, “Customer Code”, “Customer Type”, “Customer Parent” "Density", "Behavior code" indicating the content of the action that the customer performs to the customer, and the behavior content of the "Behavior code" ("Behavior classification name" in the behavior classification table, "Behavior name" " It consists of “action completion date” completed by executing “action details”). One or more “self-types” correspond to the “employee code”. Further, one or more “customer codes”, “customer types”, and “customer intimacy” correspond to “self type”. Also, one or more “action code” and “action completion date” correspond to “customer code”, “customer type”, and “customer closeness”.

  The self type determined by the self type determination unit 11 of the communication type determination unit 10 is stored in “self type” of the individual BP table. Further, the customer types determined by the customer type determining means 12 are stored in “customer type” in the individual BP table and “customer type” in the customer information table described later. Further, the action content determined by the action content determination unit 20 is stored as an “action code” in the individual BP table in the order in which the action contents are executed or together with an index indicating the order. Here, the action content is “behavior classification code” (ie, “behavior classification name” in the action classification table) “action name” “action code” in the action content table corresponding to “behavior code” in the individual BP table. Action details ”.

  The opponent information table includes the own “employee code”, the customer “customer code” common to the self and the opponent when conducting business activities, the opponent “counter code”, the “counter name”, the “counter” "Person type" and "customer closeness" between the opponent and the customer. One or more “customer codes” correspond to “employee codes”. Further, one or more “counter code”, “counter name”, “counter type”, and “customer closeness” correspond to “customer code”. The opponent type determined by the opponent type determination unit 13 of the communication type determination unit 10 is stored in the “counter type” of the opponent information table.

  The customer information table is a master table related to customer information, and includes a customer “customer code”, “customer name”, and “customer type”.

  FIG. 4-3 is a table relating to the results of self-action content. The sales activity table shows the “employee code” of the customer, the “date” completed by the user, the “customer code”, the “customer name”, and the product of the customer who executed the action. It consists of “advertisement product code 1” in the case of advertising, and “action code” of the action content completed by itself. One or more “years” correspond to the “employee code”. In addition, one or more “customer code”, “customer name”, “advertisement product code 1” and the like and “action code” correspond to “year / month”.

  The sales performance table is composed of the own “employee code”, the “year / month” in which the sales are totaled, the “product name” of the product sold by the user, and the “sales (amount)” of the product. One or more “years” correspond to the “employee code”. Further, one or more “product names” and “sales (amount)” correspond to “year / month”.

  FIG. 4-4 is a table relating to weight vectors and evaluation items. The customer evaluation item table is composed of “customer type”, “evaluation item code” that uniquely indicates a code corresponding to the evaluation item for each “customer type”, and “evaluation item” that indicates points that the customer emphasizes. The One or more “evaluation item codes” and “evaluation items” correspond to “customer type”. For example, as shown in FIG. 16, when “customer type” is BK, “evaluation item code” is a1,..., A5, and “evaluation item” corresponds to “evaluation item code”. “Logical explanatory material” “Neat and soft clothing” “Summary in literature” “Hands-up lumber sample” “MR does not talk too much”. FIG. 16 is a model diagram showing how the “customer type” BK captures the problem and how to solve it. Specifically, it solves the problem of “saving time”. It shows that the method (evaluation item) is the aforementioned “evaluation item”.

  The customer weight vector table is a table that expresses which evaluation item is more important between two evaluation items by the customer of the “customer type” by using a relative “evaluation point”. , “Target evaluation item code” corresponding to the row side (vertical side) of the evaluation matrix necessary for calculating the customer weight vector, “reference evaluation item code” corresponding to the column side (horizontal side) of the evaluation matrix, and , “Evaluation point” of “Target evaluation item code” when the evaluation item of “Target evaluation item code” is viewed from the evaluation item of “Standard evaluation item code”. This “evaluation score” is 0 to 9, which means that the larger evaluation is higher. One or more “target evaluation item codes”, “reference evaluation item codes”, and “evaluation points” correspond to “customer type”.

  FIG. 17 shows an example in which the customer weight vector table is shown as a table when the customer type is BK. A large evaluation point 7 (upper right of the matrix) indicates that the customer of the “customer type” is on the vertical side of the evaluation item of the “standard evaluation item code” on the horizontal side (evaluation item “not too much talk (a5)”). This means that the evaluation item of the “target evaluation item code” (the evaluation item “deliver logical explanation material (a1)”) is emphasized. In addition, a small evaluation score of 0.2 (approximately the center of the matrix) indicates that the customer of the “customer type” evaluates the evaluation item of the “standard evaluation item code” on the side (“summarize literature (a3)”) This means that the evaluation item of the “target evaluation item code” on the vertical side (the evaluation item “decent but soft clothing (a2)”) is not considered more important than the item). The evaluation score between the same evaluation items is 1.

  Returning to FIG. 4-4, in the evaluation item of the “evaluation item code”, which of the two types of the self type and the opponent type is more suitable for the evaluation vector table ( This is a table that shows relative “evaluation points” in terms of relative “evaluation points”, and is the row side (vertical side) of the evaluation matrix required when calculating “evaluation item code”, self-weight vector or opponent weight vector ) “Target type code” indicating the type corresponding to), “Standard type code” indicating the type corresponding to the column side (horizontal side) of the evaluation matrix, and “Target type code” from the “Standard type code” It consists of “evaluation point” of “target type code”. The “evaluation score” is 0 to 9, which means that the larger evaluation is high (dominance is high). One or more “target type code”, “reference type code”, and “evaluation point” correspond to “evaluation item code”.

  FIG. 18 is an example in which the evaluation vector table is shown as a table when the customer type is BK, the self type is BV, the opponent type 1 is RA, and the opponent type 2 is GK. For example, in the evaluation item “deliver logical explanation material”, the larger evaluation point 5 (upper right of the matrix) is more adapted to the self on the vertical side than the opponent 2 on the horizontal side (higher advantage) ) Means. In addition, a small evaluation score of 1/5 (lower left of the matrix) means that the opponent 2 on the vertical side is not adapted (less advantageous) than the self on the horizontal side. The evaluation score between the same types is 1. That is, on the basis of self, in the evaluation item “deliver logical explanation material”, the opponent 1 has a low advantage (evaluation point 1/5) and the opponent 2 also has a low advantage (evaluation point 1 / 5) Means that.

  FIG. 4-5 is a diagram illustrating a relationship among the personal BP table, the behavior classification table, and the behavior content table illustrated in FIG. From FIG. 4-5, “customer intimacy” in the personal BP table and “customer intimacy” in the behavior classification table are associated, and “behavior classification code” in the behavior classification table and “behavior classification table in the behavior content table”. "Is related.

[Operation]
Next, the operation of the communication support apparatus 1 shown in FIG. 1 will be described. In the following description, displaying on the screen means displaying on the screen of the terminal 2 via the LAN 5 or displaying on the screen of the mobile phone 4 via the LAN 5, the Web server 3 and the Internet 6. In addition, inputting data by the operation of the operator who is the self means that the communication support apparatus 1 inputs the operation data by operating the terminal 2 or the mobile phone 4.

  FIG. 5 is a flowchart for explaining self-type determination processing. When there is an authentication input by an operation of the operator who is the self, the self-type determining unit 11 of the communication type determining unit 10 performs self-authentication and specifies his / her employee code (step S501). The self type determination unit 11 reads “determination item ID” and “determination sentence” from the type determination table, and displays a question of “determination sentence” on the screen (step S502). If there is an answer input as a self-answer to the question (step S503), it is determined whether or not the answer has been input for all the questions in the read “determination sentence” (step S504). If all the questions have not been answered, the process proceeds to step 502, and if already entered, the process proceeds to step 505.

  The self type determination means 11 reads “determination sentence” and “type classification” from the type determination table, compares each answer in step 503 with the answer in “determination sentence” (step S505), and determines the optimum self type. Determination is made (step S506). Then, the latest self type is stored in the “self type” in the “employee code” of the individual BP table (step S507). Then, when there is an input of “customer closeness” between the self and the customer by the operation of the self operator, the “employee code” and the “customer code” of the customer are displayed in the individual BP table. Stored in “customer closeness” (step S508). Further, the determined optimum self-type, customer intimacy, etc. are displayed on the screen (step S509).

  FIG. 6 is a flowchart for explaining the details of the optimum type determination process (a) in steps 505 to 507 shown in FIG. The self type determination unit 11 reads “determination sentence” and “type classification” from the type determination table (step S601). Then, for the “type classification” corresponding to the answer to the question by Herman in the “determination sentence”, the number of answers of the same “type classification” is totaled (step S602). The “type classification” in which the number of answers is 6 or more is determined as the first type of the user (step S603). In this case, a plurality of types may be determined as the first type of itself. Here, in the case of a question by Herman, the type is, for example, none, B, G, or BG by four types of answers to two questions as a pair. Thus, 24 questions (pairs) are prepared.

  In addition, the self type determination unit 11 adds up the number of answers of the same “type classification” for the “type classification” corresponding to the answer of the question by the NLP in the “determination sentence” (step S604). The “type classification” that maximizes the number of answers is determined as its second type (step S605). Here, in the case of NLP questions, 13 single selection questions are prepared. Then, the self-type determining unit 11 sets the combination of the first type and the second type as a self-type, and stores it in the “self-type” in the “employee code” of the individual BP table (step S606). ). For example, when the first type is B, G, and the second type is V, the self type is BV and GV.

  FIG. 7 is a flowchart for explaining the customer type / counter type determination process. The customer type determination unit 12 or the opponent type determination unit 13 of the communication type determination unit 10 authenticates itself and specifies its employee code when there is an authentication input by an operation of the operator who is the self (step S701). . Then, the customer is selected by own operation (step S702). The customer type determination unit 12 or the opponent type determination unit 13 reads “determination item ID” and “determination sentence” from the type determination table, and displays a question of “determination sentence” on the screen (step S703). Then, an answer is input as a customer answer or an opponent answer to the question (step S704).

  The customer type determination unit 12 or the opponent type determination unit 13 reads the “determination sentence” and the “type classification” from the type determination table, and compares each answer in step 704 with the answer in “determination sentence” (step In step S705, the optimum customer type or opponent type is determined (step S706). Then, in the read “determination sentence”, the number of answers is compared with a preset number for each type of question (step S707). If there is a question type with a smaller number of answers than a preset number, the process proceeds to step 703. If the number of answers is greater than or equal to the preset number for all question types, the process proceeds to step 708. The customer type determination unit 12 stores the optimum type determined in step 706 as the latest customer type in the “customer type” corresponding to the “customer code” of the customer selected in step 702 in the customer information table. (Step S708). Further, in the individual BP table, it is stored in the “customer type” corresponding to the customer “employee code” and the customer “customer code” selected in step 702 (step S708). On the other hand, the opponent type determination unit 13 sets the optimum type determined in step 706 as the latest opponent type, and in the opponent information table, the own “employee code” and the customer “customer code” selected in step 702 are displayed. Is stored in the “counter type” corresponding to (step S708). Further, the determined optimum customer type or opponent type is displayed on the screen (step S709).

  FIG. 8 is a flowchart for explaining the details of the optimum type determination process (b) in steps 705, 706, and 708 shown in FIG. The customer type determination unit 12 or the opponent type determination unit 13 reads “determination sentence” and “type classification” from the type determination table (step S801). Then, for the “type classification” corresponding to the answer of the question by Herman in the “determination sentence”, the number of answers of the same “type classification” is totaled (step S802). The “type classification” that maximizes the number of responses is determined as the first type of customer or opponent (step S803).

  Further, the customer type determination means 12 or the opponent type determination means 13 sums up the number of answers of the same “type classification” for the “type classification” corresponding to the answer of the question by the NLP in the “determination sentence” (step S804). ). The “type classification” that maximizes the number of responses is determined as the second type of customer or opponent (step S805). Then, the customer type determination unit 12 sets the combination of the first type and the second type as a customer type, and in the customer information table, the “customer type” corresponding to the “customer code” of the customer selected in step 702 (Step S806). Further, in the individual BP table, it is stored in the “customer type” corresponding to the “employee code” of the customer and the “customer code” of the customer selected in step 702 (step S806). On the other hand, the opponent type determination means 13 sets the combination of the first type and the second type as the opponent type, and sets the “employee code” of the customer selected in step 702 in the opponent information table. The data is stored in “counter type” corresponding to “customer code” (step S806).

  9A and 9B are flowcharts for explaining the action content determination process. When there is an authentication input by the operation of the operator who is the self, the action content determination unit 20 performs self-authentication and specifies its own employee code (step S901). Then, a customer selection screen is displayed (step S902), and if there is a customer selection input by the operation of the operator who is the user (step S903), the “employee code” and the customer from the personalized BP table And “action completion date” are read out, and it is determined whether or not there is an “action code” in which “action completion date” is not stored. If there is an “action code” that does not contain an “action completion date”, the action content indicating that there is an unimplemented action content and the “action name” (read from the action content table) corresponding to the “action code” A summary of the contents is displayed (step S904).

  The action content determination unit 20 determines whether or not to set / review the self type, the customer type, the opponent type, the customer familiarity, or the action content based on the operation input of the operator who is the self (step S905). When setting / reviewing is performed, the process proceeds to step 906, and when setting / reviewing is not performed, the process proceeds to step 918. The action content determination unit 20 reads the “customer type” of the customer selected in step 903 from the customer information table (step S906).

  The self type determination unit 11 of the communication type determination unit 10 performs the self type determination process shown in FIG. 5 (step S908) when setting / reviewing the self type in step 905 (step S907). Further, when the customer type is set / reviewed in step 905 (step S909), the customer type determination unit 12 performs the customer type determination process shown in FIG. 7 (step S910). Further, when setting / reviewing the opponent type in step 905 (step S911), the opponent type determination means 13 performs the opponent type determination process shown in FIG. 7 (step S912). Further, when setting / reviewing the customer intimacy between the self and the customer in step 905 (step S913), the self type determination unit 11 performs a customer intimacy input process (step S914). Similarly, when setting / reviewing the customer intimacy between the opponent and the customer in step 905 (step S913), the opponent type determination unit 13 performs a customer intimacy input process (step S914). Details of the customer closeness input process (h) in step 914 will be described later.

  Then, when the action content is set / reviewed in step 905 (step S915), the action content determination unit 20 calculates a comprehensive evaluation value of the self and the opponent for the customer (step S916), and determines the action content. The “behavior code” is stored in the individual BP table (step S917). Details of the comprehensive evaluation value calculation process (c) in step 916 and the action content determination process (d) in step 917 will be described later.

  The action content determination unit 20 reads “action code” and “action completion date” from the individual BP table, and “action content” (action content) corresponding to the latest “action code” in which the “action completion date” is not stored. Read from the table and action classification table) is displayed on the screen (step S918). Here, the “behavior content” corresponding to the latest “behavior code” is the action content of the “behavior code” stored in order in the individual BP table, that is, “behavior name”, “behavior details”, “behavior classification”. “Name”, which is the newest in order among the unexecuted action contents for which “action completion date” is not stored.

  Then, when the action content is executed and completed according to the screen display (step S919), the action content determination unit 20 performs the action content and the product data when the product is sold by the operation of the operator who is self. A series of sales activity results (behavior contents results) such as quantity and the like are input from the result input screen (steps S920 and 921). The action content determination unit 20 sets and stores the “action completion date” in the “action code” of the individual BP table for the action content result (step S922). Further, based on the action content result, various data such as the advertised product, the sold product, and the sales amount are stored in the sales activity table and the sales performance table (step S923). Then, the action content determination unit 20 determines the action contents, and stores the “behavior code” in the individual BP table (step S924). Details of the action content determination process (d) in step 924 will be described later.

In this step 924, for the behavior classification corresponding to the customer type and customer familiarity, the behavior details such as behavior details corresponding to the behavior classification are stored in the individual BP table as the behavior details that have not been implemented. . Normally, the process of step 924 is performed in the following cases.
(1) When customer intimacy between self and customer changes (2) When customer intimacy between opponent and customer changes (3) Customer type, self type or opponent type changes Case (4) When the data of the type determination table, type list table, action content table, action classification table customer weight vector table or evaluation vector table changes, that is, the processing of step 924 is the case of (1) to (4) That is, it is performed after the data maintenance unit 30 rewrites the data in the table stored in the storage unit 40 by an operation from the terminal 2 or the mobile phone 4 by the operator.

  FIG. 15 is a flowchart for explaining the details of the customer closeness input process (h) in step 914 shown in FIG. In the customer intimacy input screen (step S1501), the self type determination means 11 will input its own “employee code” in the individual BP table when there is an input of the customer intimacy between the self and the customer by its own operation. And “customer closeness” corresponding to the customer's “customer code” (step S1502). Further, when the opponent type determination means 13 inputs an opponent closeness between the opponent and the customer by his / her own operation, his / her “employee code” and customer “customer code” are entered in the opponent information table. ”And“ customer closeness ”corresponding to the“ counter code ”of the opponent (step S1503).

  FIGS. 10A and 10B are flowcharts illustrating details of the comprehensive evaluation calculation process (c) in Step 916 shown in FIG. 9B. The weight vector calculation stage 23 of the comprehensive evaluation means 21 in the action content determination unit 20 specifies “customer type” from the customer information table for the customer selected in step 903 in FIG. The “target evaluation item code”, “reference evaluation item code” and “evaluation point” corresponding to “customer type” are read, and a customer weight vector is calculated (step S1001). Here, specific evaluation items corresponding to “target evaluation item code” and “reference evaluation item code” correspond to “evaluation item” corresponding to “evaluation item code” in the customer evaluation item table. Details of the weight vector calculation process (e) will be described later.

  Then, the weight vector calculation stage 23 reads the “self type” stored in the individual BP table for the “customer type”, calculates the number of the “self type”, and for each “self type”. (Steps S1002, 1011, 1012) For the “customer type”, the number of evaluation item codes is calculated from the customer weight vector table, and for each evaluation item (Steps S1005, 1008, 1009), the self and the opponent The weight vector is calculated.

  Specifically, the weight vector calculation stage 23 reads the m-th “self type” in the “customer type” from the individual BP table (step S1003). Then, with respect to the “evaluation item code” read out in step 1001, “target type code”, “reference type code” and “evaluation point” are read out from the evaluation vector table, and its own weight vector is calculated (step S1004). Details of the weight vector calculation process (e) will be described later.

  Further, the weight vector calculation stage 23 reads out the “counter type” in the “customer code” of the customer from the opponent information table (step S1006). In this case, when a plurality of opponents exist for one customer, a plurality of “countertypes” are read out. Then, for the nth “evaluation item code” read in step 1001, “target type code”, “reference type code”, and “evaluation point” are read from the evaluation vector table, and a weight vector of the opponent is calculated (step S1007). ). Details of the weight vector calculation process (e) will be described later.

  The comprehensive evaluation calculation stage 24 of the action content determination unit 20 calculates, for each “self type” (steps S1002, 1011, 1012), the comprehensive evaluation value of the customer for the self and the comprehensive evaluation value of the opponent for the customer ( Step S1010). Details of the comprehensive evaluation value calculation process (g) will be described later.

  FIG. 11 is a flowchart showing details of the weight vector calculation processing (e) in steps 1001, 1004, and 1007 shown in FIGS. 10-1 and 10-2. By this weight vector calculation process (e), the customer weight vector, the self-weight vector, and the opponent weight vector are calculated. When calculating the customer weight vector, the weight vector calculation stage 23 reads data from the customer weight vector table and generates, for example, the evaluation matrix shown in FIG. Here, the customer type is BK as shown in FIG. 17, the target evaluation items are a1 to a5 on the vertical side, and the reference target items are a1 to a5 on the horizontal side. The value model of the customer type BK can be represented by a model that shows how the problem is captured and how it is solved, as shown in FIG. Specifically, for the problem of “saving time”, the solution method (evaluation item) is “logical explanation material” (a1), “appropriately soft clothing” (a2), “ It is shown that there is a summary in the literature (a3), “get a lumber sample” (a4), and “MR does not talk too much” (a5).

  The weight vector calculation stage 23 reads data from the evaluation vector table when calculating the self-weight vector and the opponent weight vector, and generates an evaluation matrix for each evaluation item as shown in FIG. In this case, the opponent types read out in step 1006 of FIG. 10A are two types of RA, which is the opponent 1 type, and GK type, which is the opponent 2 type.

各列の合計ｓ_１，ｓ_２，・・・，ｓ_ｎを以下の式により算出する（ステップＳ１１０１）。

In FIG. 11, the evaluation matrix is expressed as

Total _s 1 in each _column, s _{2, ···,} is calculated by the following equation _{s n} (step S1101).

Then, all elements are divided by the sum of each column in the equation (2) (step S1102), an average value of each row is calculated, and a weight vector is calculated by the following equation (step S1103).

The customer weight vector is calculated after the evaluation matrix is directly generated from the customer weight vector table in steps 1101 to 1103.

On the other hand, for the self-weight vector and the opponent weight vector, an evaluation matrix for each evaluation item is generated from the evaluation vector table in steps 1101 to 1103, and the weight vector of the above-described equation (3) is calculated for each evaluation item. Is done. That is, the self-weight vector and the opponent weight vector are set to the second evaluation item in the first evaluation item (W ₁₁ , W ₂₁ ,..., W _r1 ) by the above-described equation (3) for each evaluation item. Then, (W ₁₂ , W ₂₂ ,..., W _r2 ),..., (W _1n , W _2n ,..., W _rn ) are calculated for the nth evaluation item. This weight vector can be expressed as the following equation by arranging evaluation items on the horizontal side and self and opponent on the vertical side.

尚、後述する総合評価値算出処理（ｇ）における総合評価値は、（４）式に表した顧客重みベクトル、及び（５）式に示した自己及び対抗者重みベクトルを用いて算出される。 Incidentally, the self-weight vector can be expressed by equation (6), the opponent 1 weight vector can be expressed by equation (7), and the opponent r-1 vector can be expressed by equation (8).

In addition, the comprehensive evaluation value in the comprehensive evaluation value calculation process (g) described later is calculated using the customer weight vector represented by the equation (4) and the self and opponent weight vectors represented by the equation (5).

  Then, in order to determine the uncertainty of the weight vector calculated by the weight vector calculation stage 23 in step 1103, the CI determination stage 25 calculates CI (Saight consistency in AHP) that is an index of the uncertainty. (Step S1104) When the CI is 0.1 or more (Step S1105), it is determined that the weight vector is inconsistent (Step S1106). In this case, the data maintenance unit 30 corrects the “evaluation point” of the customer weight vector table and the “evaluation point” of the evaluation vector table stored in the storage unit 40 by the operation of the operator who is himself. If the CI is smaller than 0.1 (step S1105), it is determined that the weight vector is consistent, and the weight vector calculation stage 23 stores the customer weight vector, self and opponent weight vector in the storage unit 40. To store. These weight vectors are used for calculation of the comprehensive evaluation value in step 1010 of FIG.

尚、（４）式は顧客重みベクトルであるが、自己及び対抗者重みベクトルについても同様である。（５）式に示した自己及び対抗者重みベクトルの場合は、縦毎の評価項目毎に重みベクトルが算出されるから、この単位でＣＩが算出される。 FIG. 12 is a flowchart showing details of the CI calculation process (f) in step 1104 shown in FIG. The CI value is calculated by this CI calculation process (f). The CI determination stage 25 multiplies each element of the weight matrix of the equation (4) for each column of the evaluation matrix of the equation (1) described above to calculate an intermediate matrix as the following equation (step S1201).

Note that equation (4) is a customer weight vector, but the same applies to the self and opponent weight vectors. In the case of the self and opponent weight vector shown in the equation (5), the weight vector is calculated for each evaluation item for each vertical length, so the CI is calculated in this unit.

そして、以下の式によりＣＩを算出する（ステップＳ１２０３）。

ここで、ｎは顧客重みベクトルの場合は評価項目の合計数であり、自己及び対抗者重みベクトルの場合は自己及び対抗者の合計数である。 Then, the CI determination stage 25 calculates the average of all the elements of the intermediate matrix and calculates the maximum eigenvalue as the following equation (step S1202).

Then, CI is calculated by the following equation (step S1203).

Here, n is the total number of evaluation items in the case of the customer weight vector, and is the total number of the self and the opponent in the case of the self and the opponent weight vector.

ここで、ｅ_１は、顧客に対する自己の総合評価値、ｅ_２−１は顧客に対する対抗者１の総合評価値、・・・、ｅ_{２−（ｒ−１）}は顧客に対する対抗者ｒ−１の総合評価値である。 FIG. 13 is a flowchart showing details of the comprehensive evaluation value calculation process (g) in step 1010 shown in FIG. 10-2. By this comprehensive evaluation value calculation process (g), the customer's own comprehensive evaluation value and the customer's total evaluation value for the customer are calculated. Specifically, the comprehensive evaluation calculation stage 24 calculates a comprehensive evaluation value by the following expression using the customer weight vector of the expression (4) and the self and opponent weight vectors of the expression (5) (step) S1301). Then, the comprehensive evaluation calculation stage 24 stores the calculated comprehensive evaluation value in the storage unit 40.

Here, e ₁ is a comprehensive evaluation value of the customer, e _2-1 is a comprehensive evaluation value of the opponent 1 against the customer, ..., e _2-(r-1) is an opponent r-1 against the customer. It is a comprehensive evaluation value.

  FIG. 19 is a diagram illustrating the comprehensive evaluation values in the examples illustrated in FIGS. 16, 17, and 18. As shown in FIG. 19, the overall evaluation value of the customer type BK is 0.54, the overall evaluation value of the opponent 1 is 0.16, and the overall evaluation value of the opponent 2 is 0.29. According to this comprehensive evaluation value, since the comprehensive evaluation value of the self is larger than the comprehensive evaluation value of the opponent 1 and the opponent 2, when viewed from the customer of the customer type BK, the self is more than the opponent 1 and the opponent 2. It can be seen that the evaluation is high (has superiority).

  FIG. 14 is a diagram showing details of the action content determination process (d) in steps 917 and 924 shown in FIG. 9-2. By this action content determination process (d), the self action content is determined. The action content determination means 22 reads the customer weight vector, the self and opponent weight vector, the self evaluation value for the customer, and the counter evaluation value for the customer stored in the storage unit 40 (step S1401). And the action content determination means 22 determines whether it is an evaluation item as an own strength and an evaluation item as an own weakness for every evaluation item (step S1402, 1407). .

Specifically, the action content determination means 22 determines the ratio of its own weight vector to its own weight vector and the opponent's weight vector for the nth (n = 1 to 1) evaluation item, that is, n. It is determined whether or not the self weight for the second evaluation item is 66% (0.66) or more (step S1403). _{Expressed by the} formula, it is determined whether W _1n / (W _1n + W _2n +... + W _rn ) is 66% or more. When it is 66% or more, the evaluation item is stored in the storage unit 40 as an item of its own strength (step S1404).

  In addition, the action content determination unit 22 determines the ratio of its own weight vector to its nth (n = 1 to 1) evaluation item, that is, the ratio of its own weight vector among its own weight vector and the opponent's weight vector. It is determined whether or not the self weight for the item is 34% (0.34) or less (step S1405). If it is 34% or less, the evaluation item is stored in the storage unit 40 as an item of weakness (step S1406).

  When the processing of steps 1402 to 1407 is completed for all the evaluation items, the action content determination means 22 reads out the evaluation item of its own strength and the evaluation item of its own weakness from the storage unit 40, and the customer between itself and the customer The familiarity is read from the individual BP table, and the customer familiarity between the opponent and the customer is read from the opponent information table and displayed on the screen (step S1408).

  Then, the action content determination means 22 determines the “action code” of the action contents according to the “customer type” of the customer and the “customer closeness” between the customer and the customer, and the individual BP in the determined order. It is stored in the “behavior code” in the table (step S1409). Specifically, the behavior content determination means 22 reads “behavior classification code” and “behavior category name” in “customer closeness” between the customer and the customer from the behavior classification table. Here, the “behavior classification name” is “product description”, “entertainment”, “fishing”, for example. Then, the action content determination means 22 reads “action code”, “action name”, and “action details” corresponding to the “action classification code” from the action content table. For example, the “behavior name” corresponding to “entertainment”, which is the “behavior classification name”, is “meal”, and the “behavior details” is “let's go to the restaurant where the customer goes”. That is, the behavior content determination means 22 uses the behavior classification table and the behavior content table for the “action code” of the behavior content according to the “customer type” of the customer and the “customer closeness” between the customer and the customer. The order read out from the action content table (the stored order or the order indicated by the index when stored together with the index) is stored in the individual BP table as the order in which the action content is executed. To do. In this case, no data is stored in the “action completion date”.

  The action content determination means 22 displays one or more “behavior classification codes” and “behavior classification names” corresponding to “customer closeness” in the action classification table on a screen as a list (step S1410). If there is a selection input of “behavior classification code” and “behavior classification name” by the selection operation of the operator who is the self (step S1411), “behavior code” “behavior name” “behavior details” corresponding to the “behavior classification code” "Is read from the action content table and displayed on the screen (step S1412).

  FIG. 20 is a diagram showing an overall configuration of a screen displayed on the terminal 2 shown in FIG. The screen configuration shown here is an example of a screen displayed on the terminal 2. In FIG. 20, the screen displayed on the terminal 2 includes an initial screen, a self-analysis screen, a self-analysis result screen, a customer / counter setting screen, a customer / counter analysis screen, and a customer / counter analysis result screen. ing.

  The initial screen is the screen shown in FIG. 22-1, and is displayed as the initial screen of the self-type determination process of FIG. The self-analysis screen is the screen shown in FIG. 22-2 and is displayed in steps 502 and 503 shown in FIG. The self-analysis result screen is a screen shown in FIG. 22-3 and is displayed in step 509 shown in FIG.

  The customer / competitor setting screen is the screen shown in FIG. 22-4, and is displayed in step 702 shown in FIG. The customer / competitor analysis screen is displayed in steps 703 and 704 shown in FIG. The customer / counterpart analysis result screen is displayed in step 709 shown in FIG.

  FIG. 21 is a diagram showing an overall configuration of a screen displayed on the mobile phone 4 shown in FIG. The screen configuration shown here is an example of a screen displayed on the mobile phone 4. In FIG. 21, the screen displayed on the mobile phone 4 includes a customer selection screen, an analysis target selection screen, a customer / counterpart analysis screen, a customer / counterpart analysis result screen, and an instruction display / result input screen. .

  The customer selection screen is a screen shown in FIG. 23A and is displayed in steps 902 and 903 shown in FIG. By the button input of “analysis” and “instruction display” displayed on this screen, the branch of step 905 shown in FIG. 9-1 is made. The analysis target selection screen is a screen shown in FIG. 23-2 and is displayed in step 906 shown in FIG. In this screen, the list of opponents and the customer familiarity for the customer are read from the opponent information file and the individual BP table and displayed.

  The customer / counterpart analysis screen is a screen shown in FIG. 23-3, and is displayed in steps 910 and 912 shown in FIG. The customer / competitor analysis result screen is the screen shown in FIG. 23-4 and is displayed in steps 910 and 912 shown in FIG. The instruction display / result input screen is the screen shown in FIGS. 23-5, 23-6, and 23-7, and is displayed in step 917 shown in FIG. 9-2. The information on the strengths and weaknesses of the screen shown in FIG. 23-6 and the screen information shown in FIG. 23-7 are the evaluation items of the own strength and the weakness evaluation items determined in steps 1404 and 1406 of FIG. And is stored in the recording unit 40.

  As described above, according to the communication support device 1 according to the embodiment of the present invention, the communication type determination unit 10 determines the self type, the customer type, and the competition based on the “determination sentence” based on the Herman model and the NLP type classification. The person type is determined. And the action content determination part 20 is the customer weight vector which shows the importance with respect to the evaluation item seen from the customer about the selected customer, and the self-weight vector which shows the importance with respect to the evaluation item seen from the self in relation to the opponent , And calculate an opponent weight vector indicating the importance of the evaluation items viewed from the opponent in relation to the self, and calculate an overall evaluation score of the customer and an overall evaluation score of the opponent from these weight vectors, Action content is determined based on customer type and customer familiarity. As a result, the self can obtain reference information necessary when actually communicating with the customer. That is, referring to the information on the self type, the customer type, the opponent type, the comprehensive evaluation value, and the action content, it is possible to determine the action content that the self actually performs on the other party. Therefore, it is possible to facilitate communication between the self and the other party.

  The present invention has been described with reference to the embodiment. However, the present invention is not limited to the above embodiment, and various modifications can be made without departing from the technical idea thereof. For example, the communication type determination unit 10 determines the self type, the customer type, and the opponent type based on the Herman model and the “determination sentence” for type classification by NLP cerebral physiology. And NLP need not be used. For example, the type may be determined on the basis of a “determination sentence” for type classification in the field of psychology, eg, somatotype, egogram, E / P theory, enneagram, or MBTI (personality type examination).

  Moreover, although the action content determination part 20 of the communication assistance apparatus 1 calculated the self comprehensive evaluation point with respect to a customer, and the comprehensive evaluation point of the opponent with respect to a customer based on AHP analysis, it is not necessarily required based on AHP analysis. There is no. In short, any analysis technique that can calculate the numerical value of the relative superiority of the self and the opponent with respect to the customer in the relationship between the self, the customer and the opponent.

  Moreover, in the said embodiment, although the communication assistance apparatus 1 decided the self action content in the relationship of self, a customer, and an opponent, it was not only the scene of a sales activity, but the scene of the hierarchical relationship of a company organization. It can also be applied. In this case, the aforementioned customer corresponds to the boss, and the opponent corresponds to the colleague. In addition, this communication support apparatus 1 is used not only for sales activities and company organization hierarchical relationships, but also between self, customers, and other opponents who are in an equal or opposing relationship. It can be applied to scenes involving the three parties.

  20 and 21, the terminal 2 and the mobile phone 4 display different screens, respectively, but may display equivalent screens having the same function. Further, the configuration is not limited to the configuration of the communication support system illustrated in FIG. 1. For example, the storage unit 40 of the communication support device 1 may be configured to be provided in another device different from the communication support device 1. Good.

It is a block diagram which shows the whole system containing the communication assistance apparatus by embodiment of this invention. It is a block diagram which shows the structure of the communication type determination part of FIG. It is a block diagram which shows the structure of the action content determination part of FIG. It is a block diagram which shows the item of the table memorize | stored in the memory | storage part of FIG. It is a block diagram which shows the item of the table memorize | stored in the memory | storage part of FIG. It is a block diagram which shows the item of the table memorize | stored in the memory | storage part of FIG. It is a block diagram which shows the item of the table memorize | stored in the memory | storage part of FIG. It is a figure which shows the relationship between a personal BP table, an action classification table, and an action content table. It is a flowchart figure explaining a self type determination process. It is a flowchart figure explaining the detail of the optimal type determination process (a) of FIG. It is a flowchart figure explaining a customer type / opponent type determination process. It is a flowchart figure explaining the detail of the optimal type determination process (b) of FIG.7 and FIG.8. It is a flowchart figure explaining action content determination processing. It is a flowchart figure explaining action content determination processing. It is a flowchart figure explaining the detail of the comprehensive evaluation calculation process (c) of FIG. 9-2. It is a flowchart figure explaining the detail of the comprehensive evaluation calculation process (c) of FIG. 9-2. FIG. 11 is a flowchart for explaining details of a weight vector calculation process (e) of FIGS. 10-1 and 10-2. It is a flowchart figure explaining the detail of CI calculation process (f) of FIG. It is a flowchart figure explaining the detail of the comprehensive evaluation value calculation process (g) of FIG. 10-2. It is a flowchart figure explaining the detail of the action content determination process (d) of FIG. 9-2. It is a flowchart figure explaining the detail of the customer familiarity input process (h) of FIGS. 9-1. It is a figure which shows the example of a value model of a customer type. It is a figure which shows the example of the evaluation matrix for calculating a customer weight vector. It is a figure which shows the example of the evaluation matrix for calculating a self-weight vector and an opponent weight vector. It is a figure which shows the example of a comprehensive evaluation value. It is a figure which shows the whole structure of the screen displayed on the terminal of FIG. It is a figure which shows the whole structure of the screen displayed on the mobile phone of FIG. It is an example of a screen displayed on a terminal. It is an example of a screen displayed on a terminal. It is an example of a screen displayed on a terminal. It is an example of a screen displayed on a terminal. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is an example of a screen displayed on a mobile phone. It is a figure which shows the type distinguished by Herman and NLP.

Explanation of symbols

1 Communication support device 2 Terminal 3 Web server 4 Mobile phone 5 LAN
6 Internet 10 Communication type determination unit 11 Self-type determination unit 12 Customer type determination unit 13 Opponent type determination unit 20 Action content determination unit 21 Comprehensive evaluation unit 22 Action content determination unit 23 Weight vector calculation stage 24 Comprehensive evaluation calculation stage 25 CI determination Stage 30 Data maintenance unit 40 Storage unit 50 Control unit

Claims (6)

In a device that supports communication between self and the other party,
A communication type determination unit that determines a self-type, a partner type, and a type of an opponent who is opposed to the other person based on a determination sentence that reflects human psychological and emotional internal factors;
Viewed from the other party, with respect to an evaluation item indicating how to solve the problem and how to solve the predetermined problem, an opponent weight vector indicating the importance of the evaluation item, the relationship with the opponent A weight vector calculation unit for calculating a self-weight vector indicating the importance for the evaluation item viewed from the self and a counter-weight vector indicating the importance for the evaluation item viewed from the opponent in relation to the self,
Based on the opponent weight vector, the self-weight vector, and the opponent weight vector calculated by the weight vector calculation unit, an overall evaluation point indicating the superiority of the opponent over the opponent and an overall evaluation score indicating the superiority of the opponent over the opponent are obtained. A comprehensive evaluation calculation unit for calculating,
A communication support apparatus, comprising: an action content determination unit that determines an action content of the other party based on the partner type and a preset familiarity between the self and the partner. The communication support apparatus according to claim 1,
The communication type determination unit determines a self type, a partner type, and an opponent type based on a determination sentence based on a Herman model and a type classification of NLP (Neuro Linguistic Programming). The communication support apparatus according to claim 1,
The comprehensive evaluation calculation unit, based on the partner weight vector, the self-weight vector, and the opponent weight vector, based on the AHP (Analytic Hierarchy Process) analysis, the comprehensive evaluation point indicating the superiority of the opponent over the opponent and the superiority of the opponent over the opponent A communication support apparatus characterized by calculating a comprehensive evaluation score indicating sex. The communication support apparatus according to claim 3,
In addition, CI (Continuity Index) that is an index of uncertainty in AHP analysis is calculated, and CI determination for determining the consistency of the customer weight vector, self-weight vector, and opponent weight vector calculated by the weight vector calculation unit A communication support apparatus characterized by having a section. A program by a device that supports communication between self and the other party, the computer constituting the communication support device,
A process of determining the self type, the opponent type, and the type of the opponent who is in a counter relationship with the other person based on a determination sentence that reflects human psychological and emotional internal factors;
Viewed from the other party, with respect to an evaluation item indicating how to solve the problem and how to solve the predetermined problem, an opponent weight vector indicating the importance of the evaluation item, the relationship with the opponent A process of calculating a self-weight vector indicating the importance of the evaluation item viewed from the self and a opponent weight vector indicating the importance of the evaluation item viewed from the opponent in relation to the self;
Based on the opponent weight vector, the self-weight vector, and the opponent weight vector, a process for calculating a comprehensive evaluation point indicating the superiority of the opponent over the opponent and an overall evaluation point indicating the superiority of the opponent over the opponent;
A communication support program for determining a content of an action for a partner based on the partner type and a closeness between the self and the partner set in advance.   A recording medium on which the communication support program according to claim 5 is recorded.

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