WO2021039046A1 - Information processing device for advisor matching - Google Patents

Information processing device for advisor matching Download PDF

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WO2021039046A1
WO2021039046A1 PCT/JP2020/023933 JP2020023933W WO2021039046A1 WO 2021039046 A1 WO2021039046 A1 WO 2021039046A1 JP 2020023933 W JP2020023933 W JP 2020023933W WO 2021039046 A1 WO2021039046 A1 WO 2021039046A1
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advisor
information processing
user
score
advisors
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PCT/JP2020/023933
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French (fr)
Japanese (ja)
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良 岡崎
潤平 小林
吉英 鎌田
敏明 中澤
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株式会社ＳｈａｒｅＦａｉｒ
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services

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  • the present invention relates to an information processing device for matching advisors (experts).
  • the present application claims priority based on Japanese Patent Application No. 2019-153705 filed in Japan on August 26, 2019, the contents of which are incorporated herein by reference.
  • Patent Document 1 presents a list of advisors based on the keywords entered by the user (requester), but even if the advisors are selected based on the keywords, there are many mismatches, and the optimum advisor is presented. I can't say that.
  • Non-Patent Document 1 is a service for matching advisors, but since the matching is artificially performed based on the knowledge of the intermediary who mediates between the user (client) and the advisor, time and time for matching and The cost is very high, and the intermediary also causes variations in the matching quality, so there are cases where you cannot feel the benefits for the cost you paid.
  • one of the objects of the present invention is to provide an information processing device that presents a candidate list of optimal advisors to users.
  • one of the objects of the present invention is to provide an information processing apparatus that presents a more optimal advisor candidate list through introduction from an advisor.
  • a means of receiving questions entered by the user A means of calculating an advisor's score, at least based on the above questions and the attributes of the advisor, A means of selecting an advisor based on the calculated score, A means of receiving answers to questions entered by the selected advisor, A means of displaying a list of advisor candidates based on the received response, A means of receiving the identifier of the advisor selected by the user from the displayed candidate list, and Means for realizing a session with the selected advisor and A means of calculating incentive points, at least based on the conduct of the session, Provided is an information processing apparatus characterized by being equipped with.
  • a block diagram of an information processing apparatus according to an embodiment of the present invention is shown.
  • a flowchart of information processing according to an embodiment of the present invention is shown.
  • An overall view of an information processing system including an information processing apparatus according to an embodiment of the present invention is conceptually shown.
  • a data structure according to an embodiment of the present invention is shown.
  • FIG. 3 conceptually shows an overall view of the information processing system 3000 including the information processing apparatus according to the embodiment of the present invention.
  • the information processing device 1000, the user terminal 3100, and the advisor terminal 3200 (3200-1, 3200-2, ...) are connected via the network 3300.
  • FIG. Details An example of a block diagram of the information processing device 1000 is shown in FIG. Details will be described later.
  • the user terminal 3100 is a terminal (for example, a smartphone or a desktop computer) used by the user (client). You can log in to the service provided by the information processing device 1000 using the identifier (ID) and password assigned to each user.
  • ID identifier
  • password assigned to each user.
  • the advisor terminal 3200 is a terminal used by an advisor (expert). You can log in to the service provided by the information processing device 1000 using the identifier (ID) and password assigned to each advisor.
  • the network 3300 may be a dedicated wireless and / or wired line.
  • FIG. 1 shows a block diagram of an information processing apparatus 1000 according to an embodiment of the present invention.
  • the information processing device 1000 of this embodiment has an output (screen display) unit 1110, an input unit 1120, an input analysis unit 1130, a score calculation unit 1140, an advisor selection unit 1150, a user management unit 1160, an advisor management unit 1170, and an advisor management DB 1210.
  • the output (screen display) unit 1110 performs processing related to output.
  • the output process also includes displaying the output result on the screen.
  • the input unit 1120 performs processing related to input.
  • the input analysis unit 1130 analyzes the data processed by the input unit.
  • the score calculation unit 1140 calculates the score of the advisor based on the question from the user (client) and the attributes of the advisor.
  • Advisor selection department 1150 selects advisors.
  • the user management unit 1160 manages the user (client) ID and the inquiry history, collates the user ID, and processes the inquiry data.
  • the advisor management department 1170 manages the ID of the advisor (expert) and the history of inquiries and answers, collates the user ID, and processes the inquiry data.
  • the advisor management DB 1210 stores the advisor ID and the history of inquiries and answers.
  • An example of the data structure is shown in FIG. 4 (a).
  • Advisor attribute DB1220 stores so-called profiles such as advisor attributes, such as career, educational background, and fields where advice can be given (areas with knowledge). An example of the data structure is shown in FIG. 4 (b).
  • the advisor score DB1230 stores the score (evaluation value) calculated based on the attributes of the advisor. In addition, when the score is corrected after the fact by evaluation from the user, learning of the model, etc., the corrected score is also stored. In addition, the score correction history (time when the score was corrected and the amount of the corrected score) may be stored. An example of the data structure is shown in FIG. 4 (c).
  • the user management DB 1240 stores the user ID and inquiry history. An example of the data structure is shown in FIG. 4 (d).
  • the advisor evaluation DB1250 stores the evaluation from the user to the advisor after the session between the user and the advisor is completed.
  • An example of the data structure is shown in FIG. 4 (e).
  • the identifier of the advisor is stored in association with the identifier of another advisor introduced by the advisor and the consultation record of the introduced advisor.
  • the referral date may be stored.
  • An example of the data structure is shown in FIG. 4 (f).
  • the incentive point DB1270 stores points based on the advisor's performance.
  • An example of the data structure is shown in FIG. 4 (g). Further, the reason for giving points may be stored. Further, the point occurrence date as shown in FIG. 4 (g) may be stored in association with the consultation date in FIG. 4 (d) and the introduction date in FIG. 4 (f).
  • the control unit (processor) 1310 controls each unit and each DB inside the information processing device 1000.
  • the interface unit 1320 transmits / receives data to / from an external device of the information processing device 1000 via the network of FIG.
  • FIG. 2 shows a flowchart of information processing according to an embodiment of the present invention.
  • the user inputs the matter to be consulted with the advisor (hereinafter, "question"). Enter the question that the user has in a predetermined input item.
  • the input item may be one text box, but a plurality of text boxes may be provided so that the "question" can be easily analyzed.
  • the server analyzes the input items in response to the input "question", and from the trained model, the attribute information of each advisor registered in advance in the advisor database and the past. Calculate the score based on the information such as the score corrected to and the activity history.
  • the AI / statistical machine learning approach centered on natural language processing will be used.
  • traditional statistical methods may be used or a decision table may be used to generate the score.
  • there are a plurality of data items to be input by the user for example, an item for inputting a field to be inquired about, an item for actually asking a question, and the like.
  • the server extracts keywords for each input item and weights the keywords according to the input items (for example, the keywords input in the field items are more important. It may be judged that it is a thing, and a keyword with a more specific specialty may be judged to be more important), so that you can search for the best advisor.
  • the server generates a candidate list of (plural) advisors based on the calculated score. For example, generate a candidate list of the top five advisors with the highest calculated scores. Furthermore, the server may be excluded from the advisor candidate list if there is a possibility that a conflict of interest may occur due to the answer to the "question" based on the advisor's attribute information. The server sends the input "question" to the advisor terminal for each advisor on the candidate list.
  • the advisor may be searched under a plurality of conditions in order to avoid filling the candidate list with people of similar carriers.
  • a search is performed under certain search conditions to select the top one advisor, and then the search conditions such as changing the weighting of keywords are changed to search for another top.
  • a mechanism may be adopted in which one advisor is selected.
  • a candidate list that combines the search results under a plurality of conditions in S2020 may be created.
  • a candidate list that takes into account the diversity of advisors can be generated, which increases the possibility of inducing referrals through a wider variety of routes.
  • the search condition of S2020 may be configured to search not only the ability to answer the "question” but also an advisor who can introduce other advisors who may have such ability.
  • the introduction record of the introduction history DB in FIG. 4F may be quantified so that the search can be performed based on the results of the introduced advisor.
  • the selected advisor returns the answer to the "question" from the advisor terminal to the server. If it is possible to answer, the selected advisor will also answer (enter in the prescribed items). If the server receives a reply that cannot be answered, the advisor may be deleted from the candidate list and the score of the deleted advisor may be modified (for example, downward revision).
  • the server asks the selected advisor whether another advisor can be introduced.
  • another advisor means an advisor who seems to be more appropriate (has detailed knowledge) than himself / herself for the "question”.
  • another advisor's identifier eg, user ID, email address
  • the server transmits a "question” or the like to another advisor (for example, to the advisor terminal 3200-2 in FIG. 3) based on the input identifier of another advisor.
  • another advisor replies whether or not to answer the "question”. If another advisor can introduce another advisor, the same information processing as in S2050 may be performed. However, the referred advisor may be configured to prevent further referrals, and may be configured to limit the number of referral chains (such as a referral advisor referral to yet another advisor). You may.
  • S2070 it may be executed at the same time as S2060. If another adviser introduced is not registered in the database of the server, the other adviser introduced is prompted to enter attribute information (profile), etc., and a new ID and password are given. Issue. Furthermore, the score is calculated based on the input attribute information. In addition, the score may be revised upward in consideration of the points received. Then, if there is another advisor introduced, the same processing as in S2050 is performed for that advisor.
  • the server lists the advisor candidates including the reply from the advisor (the fact that the answer to the "question” is possible and the answer) and sends it to the user terminal.
  • the user terminal displays the transmitted candidate list on the screen. The user refers to the displayed candidate list and selects one or more advisers.
  • the user has a session (consultation) with the selected advisor.
  • the user evaluates the advisor after the session ends.
  • the evaluation method may be arbitrary, but for example, a predetermined item may be answered with YES / NO, or a score may be given.
  • the server stores the evaluation of the adviser in the advisor evaluation DB.
  • the logic itself is configured to update the advisors that can be introduced more accurately and the scoring that gives a high score to the advisors that can be answered by learning the sessions that were actually matched and their evaluations. You may.
  • the identifier of the introduced advisor and the identifier of the introduced advice are linked and stored in the introduction history DB. This allows you to calculate the number of advisors introduced by the advisor.
  • the referral record status is also linked and stored.
  • the referral record status indicates that "consultation has been completed” indicating that the session (consultation) was actually conducted with the introduced advisor, or that the user was actually introduced but was not selected by the user. There is a "consultation failure”. This makes it possible to calculate the number of consultations with the advisors actually introduced. By adopting such status, it is possible to evaluate the performance of referral of advisors.
  • the advisor can also evaluate the user.
  • incentive points are calculated based on the implementation of the session and the evaluation. For example, if the (first selected) advisor introduces another advisor and the introduced advisor conducts a session (consultation), +250 points will be added to the referred advisor. At the same time, +400 points will be added to the adviser introduced. Further, incentive points may be added or subtracted based on the evaluation from the user in S2100. In addition, the amount of compensation to the advisor may be calculated based on the incentive points. Further, the point grant date may be stored.
  • the functional blocks and the flowchart have been used, but as another embodiment, the hardware resources of the so-called five major computer devices (control device, arithmetic device, storage device, input device, output device). In addition, it can be realized by linking software.

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Abstract

Provided is an information processing device characterized by comprising: a means for receiving a query which is inputted by a user; a means for calculating the scores of advisors on the basis of at least the query and an attribute of the advisors; a means for selecting the advisors on the basis of the calculated scores; a means for receiving responses to the query which are inputted by the selected advisors; a means for displaying an advisor candidate list on the basis of the received responses; a means for receiving an identifier of the advisor selected by the user from the displayed candidate list; a means for implementing a session with the selected advisor; and a means for calculating incentive points on the basis of at least the session being conducted.

Description

アドバイザをマッチングするための情報処理装置Information processing device for matching advisors
　本発明は、アドバイザ（専門家）をマッチングするための情報処理装置に関する。
　本願は、２０１９年８月２６日に、日本に出願された特願２０１９－１５３７０５号に基づき優先権を主張し、その内容をここに援用する。 The present invention relates to an information processing device for matching advisors (experts).
The present application claims priority based on Japanese Patent Application No. 2019-153705 filed in Japan on August 26, 2019, the contents of which are incorporated herein by reference.
　ユーザ（クライアント）からの相談を解決するためのアドバイザをマッチングする手法については、従来から様々な形で提案されてきた。 The method of matching advisors for resolving consultations from users (clients) has been proposed in various forms.
　特許文献１は、ユーザ（依頼者）が入力したキーワードをベースにアドバイザのリストを提示しているが、キーワードに頼ってアドバイザを選定してもミスマッチであることが多く、最適なアドバイザを提示しているとは言えない。 Patent Document 1 presents a list of advisors based on the keywords entered by the user (requester), but even if the advisors are selected based on the keywords, there are many mismatches, and the optimum advisor is presented. I can't say that.
　非特許文献１は、アドバイザをマッチングするサービスであるが、ユーザ（クライアント）とアドバイザとを仲介する仲介人自身の知見に基づいて人為的にマッチングをしているので、マッチングのための時間的および費用的コストが非常に大きく、また仲介人によって属人的なマッチング品質のブレも生じるため、払ったコストの割には、メリットを感じられない場合もある。 Non-Patent Document 1 is a service for matching advisors, but since the matching is artificially performed based on the knowledge of the intermediary who mediates between the user (client) and the advisor, time and time for matching and The cost is very high, and the intermediary also causes variations in the matching quality, so there are cases where you cannot feel the benefits for the cost you paid.
特開２０１７－７３００７号公報JP-A-2017-73007
　上述したような従来技術に加えて、アドバイザをマッチングするには様々な問題がある。
　例えば、事業・技術領域の拡がりや進歩の加速により情報の陳腐化も早まるため、最適なアドバイザを自社のデータベースに登録し続ける難易度が上がり続けるという問題がある。
　更に、クライアントの具体的な「問い」が無い中での、主にインターネットを経由して「とりあえず様々な専門家をできるだけ多く集めよう」とするリクルーティングだけでは、焦点がずれた専門家がマッチングされやすいという問題もある。
　そもそも、一定数の専門家は、マッチングサービスを提供する業者からの登録依頼には応じないという問題もある。 In addition to the prior art as described above, there are various problems in matching advisors.
For example, as information becomes obsolete due to the expansion of business and technological fields and the acceleration of progress, there is a problem that the difficulty of continuing to register the optimum advisor in the company's database continues to increase.
Furthermore, in the absence of specific "questions" for clients, out-of-focus specialists will be matched only by recruiting, which mainly tries to "gather as many different specialists as possible" mainly via the Internet. There is also the problem that it is easy to do.
In the first place, there is also a problem that a certain number of specialists do not respond to registration requests from providers of matching services.
　本発明の一態様によると、ユーザへの最適なアドバイザの候補リストを提示する情報処理装置を提供することを本発明の目的のひとつとする。 According to one aspect of the present invention, one of the objects of the present invention is to provide an information processing device that presents a candidate list of optimal advisors to users.
　本発明の別の一態様によると、アドバイザからの紹介を通じて、より最適なアドバイザの候補リストを提示する情報処理装置を提供することを本発明の目的のひとつとする。 According to another aspect of the present invention, one of the objects of the present invention is to provide an information processing apparatus that presents a more optimal advisor candidate list through introduction from an advisor.
　本発明の一態様によると、
　ユーザが入力した問いを受信する手段と、
　少なくとも前記問いとアドバイザの属性とに基づき、アドバイザのスコアを計算する手段と、
　前記計算されたスコアに基づき、アドバイザを選定する手段と、
　前記選定されたアドバイザが入力した問いへの返答を受信する手段と、
　前記受信した返答に基づき、アドバイザの候補リストを表示する手段と、
　前記表示された候補リストから、ユーザが選択したアドバイザの識別子を受信する手段と、
　前記選択されたアドバイザとのセッションを実現する手段と、
　少なくとも前記セッションの実施に基づき、インセンティブポイントを計算する手段と、
を備えたことを特徴とする、情報処理装置を提供する。 According to one aspect of the invention
A means of receiving questions entered by the user,
A means of calculating an advisor's score, at least based on the above questions and the attributes of the advisor,
A means of selecting an advisor based on the calculated score,
A means of receiving answers to questions entered by the selected advisor,
A means of displaying a list of advisor candidates based on the received response,
A means of receiving the identifier of the advisor selected by the user from the displayed candidate list, and
Means for realizing a session with the selected advisor and
A means of calculating incentive points, at least based on the conduct of the session,
Provided is an information processing apparatus characterized by being equipped with.
　本実施例の一態様によると、ユーザに最適なアドバイザの候補リストを提供することができるようになる。更に、紹介やインセンティブを通して、優れたアドバイザを集めることができるようになる。更に、マッチングサービスを提供する業者からの登録依頼には応じない専門家も、友人である専門家からの紹介であれば応じる確率は上げることができるので、サービスの品質を向上させることができる。 According to one aspect of this embodiment, it becomes possible to provide the optimum advisor candidate list to the user. In addition, you will be able to gather excellent advisors through referrals and incentives. Furthermore, even experts who do not respond to registration requests from providers of matching services can increase the probability of responding if they are referred by a friend's expert, so the quality of service can be improved.
　本発明の他の目的、特徴及び利点は添付図面に関する以下の本発明の実施例の記載から明らかになるであろう。 Other objects, features and advantages of the present invention will become apparent from the following description of the embodiments of the present invention with respect to the accompanying drawings.
本発明の一実施例による情報処理装置のブロック図を示す。A block diagram of an information processing apparatus according to an embodiment of the present invention is shown. 本発明の一実施例による情報処理のフローチャートを示す。A flowchart of information processing according to an embodiment of the present invention is shown. 本発明の一実施例による情報処理装置を含めた情報処理システムの全体図を概念的に示す。An overall view of an information processing system including an information processing apparatus according to an embodiment of the present invention is conceptually shown. 本発明の一実施例によるデータ構造を示す。A data structure according to an embodiment of the present invention is shown.
　図３は、本発明の一実施例による情報処理装置を含めた情報処理システム３０００の全体図を概念的に示す。 FIG. 3 conceptually shows an overall view of the information processing system 3000 including the information processing apparatus according to the embodiment of the present invention.
　本実施例の情報処理システム３０００は、情報処理装置１０００と、ユーザ端末３１００と、アドバイザ端末３２００（３２００－１、３２００－２、・・・）とが、ネットワーク３３００を介して接続されている。 In the information processing system 3000 of this embodiment, the information processing device 1000, the user terminal 3100, and the advisor terminal 3200 (3200-1, 3200-2, ...) Are connected via the network 3300.
　情報処理装置１０００のブロック図の一例が図１で示される。詳細は、後述する。 An example of a block diagram of the information processing device 1000 is shown in FIG. Details will be described later.
　ユーザ端末３１００は、ユーザ（クライアント）が使用する端末（例えば、スマートフォンやデスクトップパソコン）である。ユーザ毎に割り振られる識別子（ＩＤ）やパスワードを使って、情報処理装置１０００が提供するサービスにログインできる。 The user terminal 3100 is a terminal (for example, a smartphone or a desktop computer) used by the user (client). You can log in to the service provided by the information processing device 1000 using the identifier (ID) and password assigned to each user.
　アドバイザ端末３２００は、アドバイザ（専門家）が使用する端末である。アドバイザ毎に割り振られる識別子（ＩＤ）やパスワードを使って、情報処理装置１０００が提供するサービスにログインできる。 The advisor terminal 3200 is a terminal used by an advisor (expert). You can log in to the service provided by the information processing device 1000 using the identifier (ID) and password assigned to each advisor.
　ネットワーク３３００は、無線および／または有線の専用回線などでもよい。 The network 3300 may be a dedicated wireless and / or wired line.
　図１は、本発明の一実施例による情報処理装置１０００のブロック図を示す。本実施例の情報処理装置１０００は、出力（画面表示）部１１１０、入力部１１２０、入力解析部１１３０、スコア計算部１１４０、アドバイザ選定部１１５０、ユーザ管理部１１６０、アドバイザ管理部１１７０、アドバイザ管理ＤＢ１２１０、アドバイザ属性ＤＢ１２２０、アドバイザスコアＤＢ１２３０、ユーザ管理ＤＢ１２４０、アドバイザ評価ＤＢ１２５０、紹介履歴ＤＢ１２６０と、制御部（プロセッサ）１３１０、インターフェイス部１３２０を備える。 FIG. 1 shows a block diagram of an information processing apparatus 1000 according to an embodiment of the present invention. The information processing device 1000 of this embodiment has an output (screen display) unit 1110, an input unit 1120, an input analysis unit 1130, a score calculation unit 1140, an advisor selection unit 1150, a user management unit 1160, an advisor management unit 1170, and an advisor management DB 1210. , Advisor attribute DB1220, advisor score DB1230, user management DB1240, advisor evaluation DB1250, referral history DB1260, control unit (processor) 1310, and interface unit 1320.
　出力（画面表示）部１１１０は、出力に関する処理をおこなう。出力処理には、出力結果を画面表示することも含む。 The output (screen display) unit 1110 performs processing related to output. The output process also includes displaying the output result on the screen.
　入力部１１２０は、入力に関する処理をおこなう。 The input unit 1120 performs processing related to input.
　入力解析部１１３０は、入力部で処理されたデータを解析する。 The input analysis unit 1130 analyzes the data processed by the input unit.
　スコア計算部１１４０は、ユーザ（クライアント）からの問いとアドバイザの属性とに基づき、アドバイザのスコアを計算する。 The score calculation unit 1140 calculates the score of the advisor based on the question from the user (client) and the attributes of the advisor.
　アドバイザ選定部１１５０は、アドバイザを選定する。 Advisor selection department 1150 selects advisors.
　ユーザ管理部１１６０は、ユーザ（クライアント）のＩＤや、問い合わせの履歴を管理しており、ユーザＩＤの照合や、問いあわせデータの加工処理をおこなう。 The user management unit 1160 manages the user (client) ID and the inquiry history, collates the user ID, and processes the inquiry data.
　アドバイザ管理部１１７０は、アドバイザ（専門家）のＩＤや、問い合わせ回答の履歴を管理しており、ユーザＩＤの照合や、問いあわせデータの加工処理をおこなう。 The advisor management department 1170 manages the ID of the advisor (expert) and the history of inquiries and answers, collates the user ID, and processes the inquiry data.
　アドバイザ管理ＤＢ１２１０は、アドバイザのＩＤや問い合わせ回答の履歴を記憶する。データ構造の一例を図４（ａ）に示す。 The advisor management DB 1210 stores the advisor ID and the history of inquiries and answers. An example of the data structure is shown in FIG. 4 (a).
　アドバイザ属性ＤＢ１２２０は、アドバイザの属性、例えば、経歴、学歴、アドバイスできる分野（知見のある領域）などのいわゆるプロフィールを記憶する。データ構造の一例を図４（ｂ）に示す。 Advisor attribute DB1220 stores so-called profiles such as advisor attributes, such as career, educational background, and fields where advice can be given (areas with knowledge). An example of the data structure is shown in FIG. 4 (b).
　アドバイザスコアＤＢ１２３０は、アドバイザの属性に基づいて計算されたスコア（評価値）を記憶する。また、ユーザからの評価およびモデルの学習等で、事後的にスコアが修正された場合には、修正されたスコアも記憶する。また、スコア修正履歴（スコアが修正された時期や修正されたスコア量）などが記憶されてもよい。データ構造の一例を図４（ｃ）に示す。 The advisor score DB1230 stores the score (evaluation value) calculated based on the attributes of the advisor. In addition, when the score is corrected after the fact by evaluation from the user, learning of the model, etc., the corrected score is also stored. In addition, the score correction history (time when the score was corrected and the amount of the corrected score) may be stored. An example of the data structure is shown in FIG. 4 (c).
　ユーザ管理ＤＢ１２４０は、ユーザのＩＤや問い合わせの履歴を記憶する。データ構造の一例を図４（ｄ）に示す。 The user management DB 1240 stores the user ID and inquiry history. An example of the data structure is shown in FIG. 4 (d).
　アドバイザ評価ＤＢ１２５０は、ユーザとアドバイザとのセッションが終了した後に、ユーザからアドバイザへの評価を記憶する。データ構造の一例を図４（ｅ）に示す。 The advisor evaluation DB1250 stores the evaluation from the user to the advisor after the session between the user and the advisor is completed. An example of the data structure is shown in FIG. 4 (e).
　紹介履歴ＤＢ１２６０は、アドバイザの識別子が、当該アドバイザが紹介した別のアドバイザの識別子と、紹介されたアドバイザの相談実績とを紐付けて記憶する。更に、紹介日が記憶されてもよい。データ構造の一例を図４（ｆ）に示す。 In the referral history DB1260, the identifier of the advisor is stored in association with the identifier of another advisor introduced by the advisor and the consultation record of the introduced advisor. In addition, the referral date may be stored. An example of the data structure is shown in FIG. 4 (f).
　インセンティブポイントＤＢ１２７０は、アドバイザの実績に基づくポイントを記憶する。データ構造の一例を図４（ｇ）に示す。更に、ポイント付与理由を記憶してもよい。更に、図４（ｄ）の相談日や図４（ｆ）の紹介日と関連付けて、図４（ｇ）に示すようなポイント発生日を記憶してもよい。 The incentive point DB1270 stores points based on the advisor's performance. An example of the data structure is shown in FIG. 4 (g). Further, the reason for giving points may be stored. Further, the point occurrence date as shown in FIG. 4 (g) may be stored in association with the consultation date in FIG. 4 (d) and the introduction date in FIG. 4 (f).
　制御部（プロセッサ）１３１０は、情報処理装置１０００内部の各部や各ＤＢを制御する。 The control unit (processor) 1310 controls each unit and each DB inside the information processing device 1000.
　インターフェイス部１３２０は、情報処理装置１０００の外部の装置と、図３のネットワークを介して、データを送受信する。 The interface unit 1320 transmits / receives data to / from an external device of the information processing device 1000 via the network of FIG.
　図２は、本発明の一実施例による情報処理のフローチャートを示す。 FIG. 2 shows a flowchart of information processing according to an embodiment of the present invention.
　Ｓ２０１０では、ユーザがアドバイザに相談したい事柄（以下、「問い」）を入力する。ユーザが有する疑問点を所定の入力項目に入力する。ここで、入力項目は、１つのテキストボックスでもよいが、複数のテキストボックスを設けて、「問い」を解析し易いように構成されてもよい。 In S2010, the user inputs the matter to be consulted with the advisor (hereinafter, "question"). Enter the question that the user has in a predetermined input item. Here, the input item may be one text box, but a plurality of text boxes may be provided so that the "question" can be easily analyzed.
　Ｓ２０２０では、入力された「問い」に対して、サーバ（例えば、情報処理装置１０００）が入力項目を解析して、学習済みモデルから、予めアドバイザデータベースに登録されている各アドバイザの属性情報および過去に修正されたスコアやアクティビティ履歴などの情報を基に、スコアを計算する。なお、スコアを生成するにあたっては、自然言語処理を中心としたＡＩ・統計的機械学習アプローチを活用する。別の実施例として、伝統的な統計的手法を用いてもよく、判定テーブルを用いてスコアを生成してもよい。また、ユーザが入力するデータ項目は、複数あり、例えば、問い合わせをしたい分野を入力する項目、実際に質問したい項目などである。サーバは、例えば、入力された項目毎にキーワードを抽出すると共に、入力された項目に応じて、キーワードに重み付けなどをしながら（例えば、分野の項目に入力されたキーワードの方が重要度が高いものと判断されてもよく、より具体的な専門性を持つキーワードの方が重要度が高いものと判断されてもよい）、最適なアドバイザを検索できるようにする。 In S2020, the server (for example, the information processing device 1000) analyzes the input items in response to the input "question", and from the trained model, the attribute information of each advisor registered in advance in the advisor database and the past. Calculate the score based on the information such as the score corrected to and the activity history. In generating the score, the AI / statistical machine learning approach centered on natural language processing will be used. As another example, traditional statistical methods may be used or a decision table may be used to generate the score. Further, there are a plurality of data items to be input by the user, for example, an item for inputting a field to be inquired about, an item for actually asking a question, and the like. For example, the server extracts keywords for each input item and weights the keywords according to the input items (for example, the keywords input in the field items are more important. It may be judged that it is a thing, and a keyword with a more specific specialty may be judged to be more important), so that you can search for the best advisor.
　Ｓ２０３０では、サーバは、計算されたスコアに基づき、（複数の）アドバイザの候補リストを生成する。例えば、計算されたスコアが高い上位５人のアドバイザの候補リストを生成する。更に、サーバは、アドバイザの属性情報に基づき、「問い」への回答によって利益相反が発生する可能性がある場合は、アドバイザの候補リストから除外してもよい。サーバは、候補リスト上のアドバイザそれぞれに対して、入力された「問い」をアドバイザ端末に送信する。 In S2030, the server generates a candidate list of (plural) advisors based on the calculated score. For example, generate a candidate list of the top five advisors with the highest calculated scores. Furthermore, the server may be excluded from the advisor candidate list if there is a possibility that a conflict of interest may occur due to the answer to the "question" based on the advisor's attribute information. The server sends the input "question" to the advisor terminal for each advisor on the candidate list.
　なお、Ｓ２０２０およびＳ２０３０の別の実施例として、似たようなキャリアの人で候補リストを埋め尽くしてしまうことを避けるために、複数の条件下でアドバイザを検索してもよい。例えば、Ｓ２０２０でアドバイザの検索において、ある検索条件下で検索して上位１名のアドバイザを選定し、その後に、キーワードへの重み付けを変更するなどの検索条件を変更して検索して別の上位１名のアドバイザを選定するような仕組みを採用してもよい。そして、Ｓ２０３０では、Ｓ２０２０における複数の条件下での検索結果を組み合わせた候補リストを作成してもよい。これによって、アドバイザの多様性も考慮した候補リストが生成できるので、より多様なルートでの紹介を引き起こすことができる可能性も高まる。 As another embodiment of S2020 and S2030, the advisor may be searched under a plurality of conditions in order to avoid filling the candidate list with people of similar carriers. For example, in the search of advisors in S2020, a search is performed under certain search conditions to select the top one advisor, and then the search conditions such as changing the weighting of keywords are changed to search for another top. A mechanism may be adopted in which one advisor is selected. Then, in S2030, a candidate list that combines the search results under a plurality of conditions in S2020 may be created. As a result, a candidate list that takes into account the diversity of advisors can be generated, which increases the possibility of inducing referrals through a wider variety of routes.
　更に別の実施例として、Ｓ２０２０の検索条件において、「問い」に対して回答できる能力だけでなく、そのような能力を持ち得る他のアドバイザを紹介できるアドバイザも検索するように構成されてもよい。例えば、紹介したアドバイザの実績に基づいて検索できるように、図４（ｆ）の紹介履歴ＤＢの紹介実績を数値化してもよい。 As yet another embodiment, the search condition of S2020 may be configured to search not only the ability to answer the "question" but also an advisor who can introduce other advisors who may have such ability. .. For example, the introduction record of the introduction history DB in FIG. 4F may be quantified so that the search can be performed based on the results of the introduced advisor.
　Ｓ２０４０では、選定されたアドバイザが、「問い」への回答可否をアドバイザ端末からサーバに返信する。また、回答可能の場合には、選定されたアドバイザは、その回答についても回答（所定の項目に入力）する。もし、サーバが回答不可の返信を受信したときには、候補リストから、該当するアドバイザを削除すると共に、当該削除されたアドバイザのスコアを修正（例えば、下方修正）してもよい。 In S2040, the selected advisor returns the answer to the "question" from the advisor terminal to the server. If it is possible to answer, the selected advisor will also answer (enter in the prescribed items). If the server receives a reply that cannot be answered, the advisor may be deleted from the candidate list and the score of the deleted advisor may be modified (for example, downward revision).
　Ｓ２０５０では、同時に、サーバは、選定されたアドバイザに対して、別のアドバイザが紹介可能かどうかを訊ねる。ここで、別のアドバイザとは、「問い」に対して、自身よりもより適切と思われる（詳しい知見を有する）アドバイザを意味する。選定されたアドバイザが、選定されたアドバイザ自身の端末（例えば、図３のアドバイザ端末３２００－１）から紹介可能である別のアドバイザの識別子（例えば、ユーザＩＤ、メールアドレス）を入力することにより、紹介手続が行われたと判断してもよい。サーバは、入力された別のアドバイザの識別子に基づいて、別のアドバイザへ「問い」等を（例えば、図３のアドバイザ端末３２００－２へ）送信する。 At the same time, in S2050, the server asks the selected advisor whether another advisor can be introduced. Here, another advisor means an advisor who seems to be more appropriate (has detailed knowledge) than himself / herself for the "question". By entering another advisor's identifier (eg, user ID, email address) that the selected advisor can introduce from the selected advisor's own terminal (eg, advisor terminal 3200-1 in FIG. 3). It may be determined that the referral procedure has been carried out. The server transmits a "question" or the like to another advisor (for example, to the advisor terminal 3200-2 in FIG. 3) based on the input identifier of another advisor.
　Ｓ２０６０では、別のアドバイザが「問い」への回答可否を返信する。なお、別のアドバイザが更に別のアドバイザが紹介可能である場合は、Ｓ２０５０と同様の情報処理をおこなえばよい。但し、紹介されたアドバイザが、更なるアドバイザの紹介ができないように構成してもよく、（紹介されたアドバイザが更に別のアドバイザを紹介するような）紹介の連鎖の回数を制限するように構成してもよい。 In S2060, another advisor replies whether or not to answer the "question". If another advisor can introduce another advisor, the same information processing as in S2050 may be performed. However, the referred advisor may be configured to prevent further referrals, and may be configured to limit the number of referral chains (such as a referral advisor referral to yet another advisor). You may.
　Ｓ２０７０では、Ｓ２０６０と同時に実行されてもよい。もし、紹介された別のアドバイザが、サーバのデータベースに登録されていなかった場合には、この紹介された別のアドバイザに対して、属性情報（プロフィール）等入力を促すと共に、新規ＩＤおよびパスワードを発行する。更に、入力された属性情報を基にスコアを計算する。更に、紹介を受けた点を考慮して、スコアを上方修正してもよい。そして、紹介された別のアドバイザがいる場合には、そのアドバイザに対してもＳ２０５０と同様の処理をおこなう。 In S2070, it may be executed at the same time as S2060. If another adviser introduced is not registered in the database of the server, the other adviser introduced is prompted to enter attribute information (profile), etc., and a new ID and password are given. Issue. Furthermore, the score is calculated based on the input attribute information. In addition, the score may be revised upward in consideration of the points received. Then, if there is another advisor introduced, the same processing as in S2050 is performed for that advisor.
Ｓ２０８０では、サーバは、アドバイザからの返信（「問い」への回答可である旨および回答）を含めて、アドバイザの候補をリスト化して、ユーザ端末に送信する。ユーザ端末は、送信された候補リストを、画面上に表示する。ユーザは、表示された候補リストを参照して、一人または複数人のアドザイザを選択する。 In S2080, the server lists the advisor candidates including the reply from the advisor (the fact that the answer to the "question" is possible and the answer) and sends it to the user terminal. The user terminal displays the transmitted candidate list on the screen. The user refers to the displayed candidate list and selects one or more advisers.
　Ｓ２０９０では、ユーザは、選択されたアドバイザとセッション（相談）する。 In S2090, the user has a session (consultation) with the selected advisor.
　Ｓ２１００では、セッション終了後、ユーザは、アドバイザを評価する。評価方法は、任意でよいが、例えば、所定の項目にＹＥＳ／ＮＯで回答してもよいし、点数をつけてもよい。そして、サーバは、アドザイザへの評価をアドバイザ評価ＤＢに記憶する。更に、実際にマッチングされ行われたセッションとその評価の学習により、より精確に、紹介できるアドバイザや、答えられるアドバイザに対し高スコアを与えられるようなスコアリングにロジック自体もアップデートするように構成されてもよい。 In S2100, the user evaluates the advisor after the session ends. The evaluation method may be arbitrary, but for example, a predetermined item may be answered with YES / NO, or a score may be given. Then, the server stores the evaluation of the adviser in the advisor evaluation DB. In addition, the logic itself is configured to update the advisors that can be introduced more accurately and the scoring that gives a high score to the advisors that can be answered by learning the sessions that were actually matched and their evaluations. You may.
　更に、今回のセッションが、紹介者に紹介されたアドバイザに基づく場合は、紹介履歴ＤＢに、紹介したアドバイザの識別子と、紹介されたアドバイスの識別子とを紐付け記憶する。これにより、アドバイザが紹介したアドバイザの数を計算することができる。更に、紹介実績ステータスも紐付けて記憶する。ここで、紹介実績ステータスとは、紹介したアドバイザと実際にセッション（相談）を行ったことを示す「相談実施済」や、実際に紹介されたが、ユーザに選定してもらえなかったことを示す「相談不成立」がある。これにより、実際に紹介されたアドバイザとの相談数も計算することができる。このようなステータスを採用することで、アドバイザの紹介に関する実績を評価することができる。 Furthermore, if this session is based on the advisor introduced to the introducer, the identifier of the introduced advisor and the identifier of the introduced advice are linked and stored in the introduction history DB. This allows you to calculate the number of advisors introduced by the advisor. In addition, the referral record status is also linked and stored. Here, the referral record status indicates that "consultation has been completed" indicating that the session (consultation) was actually conducted with the introduced advisor, or that the user was actually introduced but was not selected by the user. There is a "consultation failure". This makes it possible to calculate the number of consultations with the advisors actually introduced. By adopting such status, it is possible to evaluate the performance of referral of advisors.
　なお、アドバイザもユーザに対して評価することができる。 The advisor can also evaluate the user.
　Ｓ２１１０では、上記セッションの実施や上記評価に基づくインセンティブポイントが計算される。例えば、（最初に選定された）アドバイザが別のアドバイザを紹介して、その紹介されたアドバイザがセッション（相談）を実施した場合には、紹介をしたアドバイザに対して、＋２５０ポイントが加算されると共に、紹介されたアドバイザに対しては、＋４００ポイントが加算される。更に、Ｓ２１００におけるユーザからの評価に基づいて、インセンティブポイントが加算または減算されてもよい。更に、インセンティブポイントに基づいて、アドバイザへの報酬額を計算してもよい。更に、ポイント付与日を記憶してもよい。 In S2110, incentive points are calculated based on the implementation of the session and the evaluation. For example, if the (first selected) advisor introduces another advisor and the introduced advisor conducts a session (consultation), +250 points will be added to the referred advisor. At the same time, +400 points will be added to the adviser introduced. Further, incentive points may be added or subtracted based on the evaluation from the user in S2100. In addition, the amount of compensation to the advisor may be calculated based on the incentive points. Further, the point grant date may be stored.
　なお、本実施例では、機能ブロックとフローチャートを用いて説明したが、別の実施例としては、いわゆるコンピュータの五大装置（制御装置、演算装置、記憶装置、入力装置、出力装置）のハードウェア資源に、ソフトウェアを協働させても実現可能である。 In this embodiment, the functional blocks and the flowchart have been used, but as another embodiment, the hardware resources of the so-called five major computer devices (control device, arithmetic device, storage device, input device, output device). In addition, it can be realized by linking software.
　以上のように本発明の実施態様について説明したが、上述の説明に基づいて当業者にとって種々の代替例、修正又は変形が可能であり、本発明はその趣旨を逸脱しない範囲で前述の種々の代替例、修正又は変形を包含するものである。 Although the embodiments of the present invention have been described above, various alternative examples, modifications or modifications can be made to those skilled in the art based on the above description, and the present invention has various above-mentioned various examples without departing from the spirit thereof. It includes alternatives, modifications or modifications.

Claims (4)

  1. 　ユーザが入力した問いを受信する手段と、
    　少なくとも前記問いとアドバイザの属性とに基づき、アドバイザのスコアを計算する手段と、
    　前記計算されたスコアに基づき、アドバイザを選定する手段と、
    　前記選定されたアドバイザが入力した問いへの返答を受信する手段と、
    　前記受信した返答に基づき、アドバイザの候補リストを表示する手段と、
    　前記表示された候補リストから、ユーザが選択したアドバイザの識別子を受信する手段と、
    　前記選択されたアドバイザとのセッションを実現する手段と、
    　少なくとも前記セッションの実施に基づき、インセンティブポイントを計算する手段と、
    を備えたことを特徴とする、情報処理装置。     A means of receiving questions entered by the user,
    A means of calculating an advisor's score, at least based on the above questions and the attributes of the advisor,
    A means of selecting an advisor based on the calculated score,
    A means of receiving answers to questions entered by the selected advisor,
    A means of displaying a list of advisor candidates based on the received response,
    A means of receiving the identifier of the advisor selected by the user from the displayed candidate list, and
    Means for realizing a session with the selected advisor and
    A means of calculating incentive points, at least based on the conduct of the session,
    An information processing device characterized by being equipped with.
  2. 　前記選定されたアドバイザが、別のアドバイザの識別子を入力すると、当該別のアドバイザを前記候補リストに追加する手段を更に備え、
    　前記インセンティブを計算する手段は、前記選定されたアドバイザおよび前記別のアドバイザへの前記インセンティブポイントを更に計算することを特徴とする、請求項１に記載の情報処理装置。     When the selected advisor enters the identifier of another advisor, the selected advisor further provides a means for adding the other advisor to the candidate list.
    The information processing apparatus according to claim 1, wherein the means for calculating the incentive further calculates the incentive points for the selected advisor and the other advisor.
  3. 　紹介したアドバイザの識別子が、紹介されたアドバイザの識別子と紹介実績ステータスと共に紐付けて記憶する手段を更に備えた、請求項２に記載の情報処理装置。 The information processing device according to claim 2, further comprising a means for storing the introduced advisor's identifier in association with the introduced advisor's identifier and the referral record status.
  4. 　前記セッションにおけるアドバイザへの評価を記憶する手段を更に備え、
    　前記アドバイザのスコアを計算する手段は、前記評価にも基づいて、前記スコアを計算することを特徴とする、請求項１に記載の情報処理装置。     Further provided with a means of remembering the advisor's rating in the session
    The information processing apparatus according to claim 1, wherein the means for calculating the score of the advisor is to calculate the score based on the evaluation.
PCT/JP2020/023933 2019-08-26 2020-06-18 Information processing device for advisor matching WO2021039046A1 (en)

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