WO2022024356A1 - Organization attribute analysis system - Google Patents
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- the present invention relates to an tissue attribute analysis system.
- Patent Document 1 a technique for analyzing emotions received by others in response to a speaker's remark is known (see, for example, Patent Document 1). Further, a technique for analyzing emotions as an organization and an atmosphere in a group felt by an individual is also known (see, for example, Patent Documents 2 and 3).
- the tissue attribute analysis system of the present invention analyzes the reaction of the participants in the online session held in the organization, and analyzes the tissue attribute from the analysis result.
- organizational attributes can be analyzed through the reactions of participants in an online session held in an organization.
- the organization attribute analysis system of this embodiment is a system that analyzes the reactions of participants in an online session held in an organization and analyzes the attributes of the organization based on the analysis results.
- the reactions of participants are analyzed for each online session of multiple organizations, and the attributes of multiple organizations are analyzed by comparing the analysis results of each.
- the online sessions of a plurality of organizations to be analyzed are, for example, online sessions of a plurality of organizations in which the same user participates.
- the reaction may be analyzed for each of a plurality of users participating in the online session of the same organization, and the attributes of the organization may be analyzed based on the reaction common to the plurality of users.
- the online session performed in the present embodiment is, for example, an online conference, an online class, an online chat, etc., and terminals installed in a plurality of places are connected to a server via a communication network such as the Internet, and a plurality of terminals are connected through the server. It enables the exchange of moving images between them.
- the moving images handled in the online session include facial images and sounds of users (participants in the online session) who use the terminal.
- the moving image also includes an image such as a material shared and viewed by a plurality of users. It is possible to switch between the face image and the material image on the screen of each terminal to display only one of them, or to divide the display area and display the face image and the material image at the same time. Further, it is possible to display the image of one of a plurality of people on the full screen, or to display the image of a part or all of the users on a small screen.
- FIG. 1 is a block diagram showing a functional configuration example of the organization attribute analysis system according to the present embodiment.
- the tissue attribute analysis system of the present embodiment includes a moving image acquisition unit 11, a biological reaction analysis unit 12, and a tissue attribute analysis unit 13 as functional configurations.
- Each of the above functional blocks 11 to 13 can be configured by any of hardware, DSP (Digital Signal Processor), and software provided in the server device, for example.
- DSP Digital Signal Processor
- each of the above functional blocks 11 to 13 is actually configured to include a computer CPU, RAM, ROM, etc., and is a program stored in a recording medium such as RAM, ROM, a hard disk, or a semiconductor memory. Is realized by the operation of.
- the moving image acquisition unit 11 acquires a moving image obtained by photographing a plurality of users (participants) with a camera provided in each terminal during an online session from each terminal. It does not matter whether the moving image acquired from each terminal is set to be displayed on the screen of each terminal. That is, the moving image acquisition unit 11 acquires the moving image from each terminal, including the moving image being displayed on each terminal and the moving image being hidden.
- the biological reaction analysis unit 12 analyzes changes in the biological reaction of each of a plurality of persons based on the moving image acquired by the moving image acquisition unit 11.
- the biological reaction analysis unit 12 separates the moving image acquired by the moving image acquisition unit 11 into a set of images (a collection of frame images) and a voice, and analyzes changes in the biological reaction from each.
- the biological reaction analysis unit 12 analyzes the user's face image using the frame image separated from the moving image acquired by the moving image acquisition unit 11, and thereby at least one of the facial expression, the line of sight, the pulse, and the movement of the face. Analyze changes in biological reactions related to one. In addition, the biological reaction analysis unit 12 analyzes changes in the biological reaction regarding at least one of the user's speech content and voice quality by analyzing the voice separated from the moving image acquired by the moving image acquisition unit 11.
- a person's emotions feelings that occur for one's own or others' words and actions, such as comfort / discomfort or the degree thereof
- changes in biological reactions such as facial expressions, eyes, pulse, facial movements, speech content, and voice quality.
- changes in biological reactions caused by changes in the user's emotions are analyzed.
- the change in the user's emotion may be analyzed by analyzing the change in the biological reaction.
- the emotion analyzed in this case is, for example, the degree of comfort / discomfort.
- Analysis of changes in facial expressions is performed, for example, as follows. That is, for each frame image, a facial area is specified from the frame image, and the specified facial expressions are classified into a plurality of types according to an image analysis model trained in advance by machine learning. Then, based on the classification result, it is analyzed whether a positive facial expression change occurs between consecutive frame images, a negative facial expression change occurs, and how large the facial expression change occurs.
- Analysis of changes in the line of sight is performed, for example, as follows. That is, for each frame image, the area of the eyes is specified from the frame image, and the orientation of both eyes is analyzed to analyze where the user is looking. For example, it analyzes whether the speaker's face being displayed, the shared material being displayed, or the outside of the screen is being viewed. In addition, it may be possible to analyze whether the movement of the line of sight is large or small, and whether the movement is frequent or infrequent. The change in the line of sight is also related to the degree of concentration of the user.
- Analysis of pulse changes is performed, for example, as follows. That is, for each frame image, the face area is specified from the frame image. Then, using a trained image analysis model that captures the numerical value of the face color information (G in RGB), the change in the G color on the face surface is analyzed. By arranging the results along the time axis, a waveform showing the change in color information is formed, and the pulse is specified from this waveform. When a person is nervous, the pulse becomes faster, and when he / she feels calm, the pulse becomes slower.
- Analysis of changes in facial movement is performed, for example, as follows. That is, for each frame image, the area of the face is specified from the frame image, and the orientation of the face is analyzed to analyze where the user is looking. For example, it analyzes whether the speaker's face being displayed, the shared material being displayed, or the outside of the screen is being viewed. In addition, it may be possible to analyze whether the movement of the face is large or small, and whether the movement is frequent or infrequent. The movement of the face and the movement of the line of sight may be combined and analyzed. For example, it may be possible to analyze whether the speaker's face being displayed is viewed straight, whether the speaker is viewed with an upper eye or a lower eye, or whether the speaker is viewed from an angle.
- the content of the statement is analyzed as follows, for example. That is, the biological reaction analysis unit 12 converts the voice into a character string by performing a known voice recognition process on the voice for a specified time (for example, a time of about 30 to 150 seconds), and morphologically analyzes the character string. By doing so, words unnecessary for expressing conversation such as particles and acronyms are removed. Then, the remaining words are vectorized, and whether a positive emotional change is occurring, a negative emotional change is occurring, and the magnitude of the emotional change is analyzed.
- a specified time for example, a time of about 30 to 150 seconds
- Voice quality analysis is performed as follows, for example. That is, the biological reaction analysis unit 12 identifies the acoustic characteristics of the voice by performing a known voice analysis process on the voice for a specified time (for example, a time of about 30 to 150 seconds). Then, based on the acoustic characteristics, it is analyzed whether a positive voice quality change is occurring, a negative voice quality change is occurring, and how loud the voice quality change is occurring.
- a specified time for example, a time of about 30 to 150 seconds.
- the tissue attribute analysis unit 13 analyzes the attributes of the tissue based on the change in the biological reaction analyzed by the biological reaction analysis unit 12. For example, the organization attribute analysis unit 13 analyzes the attributes of an organization by analyzing how a plurality of organizations influence the communication style of the participants.
- the organization attribute analyzed by the organization attribute analysis unit 13 is, for example, an attribute of whether or not the organization is easy to communicate with. Alternatively, you may want to analyze the tissue attributes that are considered to be the factors that give good or bad performance.
- the organization attribute analysis unit 13 applies such an organization-specific attribute to, for example, a reaction common to a plurality of users participating in an online session of the organization, or a difference in a reaction in an online session of a plurality of organizations in which the same user participates. Analyze from such.
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Abstract
The present invention comprises: a moving image acquisition unit 11 that acquires a moving image obtained by imaging a participant in an online session; a biological reaction analysis unit 12 that analyzes, on the basis of the moving image acquired by the moving image acquisition unit 11, a change in a biological reaction for the participant; and an organization attribute analysis unit 13 that analyzes an attribute of an organization on the basis of the change in the biological reaction analyzed by the biological reaction analysis unit 12. The present invention analyzes the reaction of a participant in an online session being held in an organization, and on the basis of this analysis result, analyzes an attribute of the organization.
Description
本発明は、組織属性解析システムに関するものである。
The present invention relates to an tissue attribute analysis system.
従来、発言者の発言に対して他者が受ける感情を解析する技術が知られている（例えば、特許文献１参照）。また、組織としての感情や、個人が感じるグループ内の雰囲気を分析する技術も知られている（例えば、特許文献２，３参照）。
Conventionally, a technique for analyzing emotions received by others in response to a speaker's remark is known (see, for example, Patent Document 1). Further, a technique for analyzing emotions as an organization and an atmosphere in a group felt by an individual is also known (see, for example, Patent Documents 2 and 3).
本発明は、組織において行われるオンラインセッションにおける参加者の反応を通じて組織属性を解析できるようにすることを目的とする。
It is an object of the present invention to be able to analyze organizational attributes through the reactions of participants in an online session held in an organization.
上記した課題を解決するために、本発明の組織属性解析システムでは、組織において行われるオンラインセッションにおける参加者の反応を解析し、その解析結果から組織属性を解析する。
In order to solve the above-mentioned problems, the tissue attribute analysis system of the present invention analyzes the reaction of the participants in the online session held in the organization, and analyzes the tissue attribute from the analysis result.
上記のように構成した本発明によれば、組織において行われるオンラインセッションにおける参加者の反応を通じて組織属性を解析することができる。
According to the present invention configured as described above, organizational attributes can be analyzed through the reactions of participants in an online session held in an organization.
本実施形態の組織属性解析システムは、組織において行われるオンラインセッションにおける参加者の反応を解析し、その解析結果をもとに組織の属性を解析するシステムである。
The organization attribute analysis system of this embodiment is a system that analyzes the reactions of participants in an online session held in an organization and analyzes the attributes of the organization based on the analysis results.
一例として、複数の組織のオンラインセッションごとに参加者の反応をそれぞれ解析し、それぞれの解析結果を比較することを通じて、複数の組織の属性を解析する。ここで、解析対象とする複数の組織のオンラインセッションは、例えば、同じユーザが参加する複数の組織のオンラインセッションである。
As an example, the reactions of participants are analyzed for each online session of multiple organizations, and the attributes of multiple organizations are analyzed by comparing the analysis results of each. Here, the online sessions of a plurality of organizations to be analyzed are, for example, online sessions of a plurality of organizations in which the same user participates.
別の例として、同じ組織のオンラインセッションに参加する複数のユーザごとに反応をそれぞれ解析し、複数のユーザの間で共通した反応をもとに組織の属性を解析するようにしてもよい。
As another example, the reaction may be analyzed for each of a plurality of users participating in the online session of the same organization, and the attributes of the organization may be analyzed based on the reaction common to the plurality of users.
本実施形態において行うオンラインセッションは、例えばオンライン会議、オンライン授業、オンラインチャットなどであり、複数の場所に設置された端末をインターネットなどの通信ネットワークを介してサーバに接続し、当該サーバを通じて複数の端末間で動画像をやり取りできるようにしたものである。
The online session performed in the present embodiment is, for example, an online conference, an online class, an online chat, etc., and terminals installed in a plurality of places are connected to a server via a communication network such as the Internet, and a plurality of terminals are connected through the server. It enables the exchange of moving images between them.
オンラインセッションで扱う動画像には、端末を使用するユーザ（オンラインセッションの参加者）の顔画像や音声が含まれる。また、動画像には、複数のユーザが共有して閲覧する資料などの画像も含まれる。各端末の画面上に顔画像と資料画像とを切り替えて何れか一方のみを表示させたり、表示領域を分けて顔画像と資料画像とを同時に表示させたりすることが可能である。また、複数人のうち１人の画像を全画面表示させたり、一部または全部のユーザの画像を小画面に分割して表示させたりすることが可能である。
The moving images handled in the online session include facial images and sounds of users (participants in the online session) who use the terminal. In addition, the moving image also includes an image such as a material shared and viewed by a plurality of users. It is possible to switch between the face image and the material image on the screen of each terminal to display only one of them, or to divide the display area and display the face image and the material image at the same time. Further, it is possible to display the image of one of a plurality of people on the full screen, or to display the image of a part or all of the users on a small screen.
以下、本発明の一実施形態を図面に基づいて説明する。図１は、本実施形態による組織属性解析システムの機能構成例を示すブロック図である。図１に示すように、本実施形態の組織属性解析システムは、機能構成として、動画像取得部１１、生体反応解析部１２および組織属性解析部１３を備えている。
Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a functional configuration example of the organization attribute analysis system according to the present embodiment. As shown in FIG. 1, the tissue attribute analysis system of the present embodiment includes a moving image acquisition unit 11, a biological reaction analysis unit 12, and a tissue attribute analysis unit 13 as functional configurations.
上記各機能ブロック１１～１３は、例えばサーバ装置に備えられたハードウェア、ＤＳＰ（Digital Signal Processor）、ソフトウェアの何れによっても構成することが可能である。例えばソフトウェアによって構成する場合、上記各機能ブロック１１～１３は、実際にはコンピュータのＣＰＵ、ＲＡＭ、ＲＯＭなどを備えて構成され、ＲＡＭやＲＯＭ、ハードディスクまたは半導体メモリ等の記録媒体に記憶されたプログラムが動作することによって実現される。
Each of the above functional blocks 11 to 13 can be configured by any of hardware, DSP (Digital Signal Processor), and software provided in the server device, for example. For example, when configured by software, each of the above functional blocks 11 to 13 is actually configured to include a computer CPU, RAM, ROM, etc., and is a program stored in a recording medium such as RAM, ROM, a hard disk, or a semiconductor memory. Is realized by the operation of.
動画像取得部１１は、オンラインセッション中に各端末が備えるカメラにより複数のユーザ（参加者）を撮影することによって得られる動画像を各端末から取得する。各端末から取得する動画像は、各端末の画面上に表示されるように設定されているものか否かは問わない。すなわち、動画像取得部１１は、各端末に表示中の動画像および非表示中の動画像を含めて、動画像を各端末から取得する。
The moving image acquisition unit 11 acquires a moving image obtained by photographing a plurality of users (participants) with a camera provided in each terminal during an online session from each terminal. It does not matter whether the moving image acquired from each terminal is set to be displayed on the screen of each terminal. That is, the moving image acquisition unit 11 acquires the moving image from each terminal, including the moving image being displayed on each terminal and the moving image being hidden.
生体反応解析部１２は、動画像取得部１１により取得された動画像に基づいて、複数人のそれぞれについて生体反応の変化を解析する。本実施形態において生体反応解析部１２は、動画像取得部１１により取得された動画像を画像のセット（フレーム画像の集まり）と音声とに分離し、それぞれから生体反応の変化を解析する。
The biological reaction analysis unit 12 analyzes changes in the biological reaction of each of a plurality of persons based on the moving image acquired by the moving image acquisition unit 11. In the present embodiment, the biological reaction analysis unit 12 separates the moving image acquired by the moving image acquisition unit 11 into a set of images (a collection of frame images) and a voice, and analyzes changes in the biological reaction from each.
例えば、生体反応解析部１２は、動画像取得部１１により取得された動画像から分離したフレーム画像を用いてユーザの顔画像を解析することにより、表情、目線、脈拍、顔の動きの少なくとも１つに関する生体反応の変化を解析する。また、生体反応解析部１２は、動画像取得部１１により取得された動画像から分離した音声を解析することにより、ユーザの発言内容、声質の少なくとも１つに関する生体反応の変化を解析する。
For example, the biological reaction analysis unit 12 analyzes the user's face image using the frame image separated from the moving image acquired by the moving image acquisition unit 11, and thereby at least one of the facial expression, the line of sight, the pulse, and the movement of the face. Analyze changes in biological reactions related to one. In addition, the biological reaction analysis unit 12 analyzes changes in the biological reaction regarding at least one of the user's speech content and voice quality by analyzing the voice separated from the moving image acquired by the moving image acquisition unit 11.
人は感情（自分または他人の言動に対して起こる気持ち。快・不快またはその程度など）が変化すると、それが表情、目線、脈拍、顔の動き、発言内容、声質などの生体反応の変化となって現れる。本実施形態では、ユーザの感情の変化に起因する生体反応の変化を解析する。また、生体反応の変化を解析することを通じて、ユーザの感情の変化を解析するようにしてもよい。この場合に解析する感情は、一例として、快／不快の程度である。
When a person's emotions (feelings that occur for one's own or others' words and actions, such as comfort / discomfort or the degree thereof) change, it causes changes in biological reactions such as facial expressions, eyes, pulse, facial movements, speech content, and voice quality. Appears. In this embodiment, changes in biological reactions caused by changes in the user's emotions are analyzed. Further, the change in the user's emotion may be analyzed by analyzing the change in the biological reaction. The emotion analyzed in this case is, for example, the degree of comfort / discomfort.
表情の変化の解析は、例えば以下のようにして行う。すなわち、フレーム画像ごとに、フレーム画像の中から顔の領域を特定し、事前に機械学習させた画像解析モデルに従って特定した顔の表情を複数に分類する。そして、その分類結果に基づいて、連続するフレーム画像間でポジティブな表情変化が起きているか、ネガティブな表情変化が起きているか、およびどの程度の大きさの表情変化が起きているかを解析する。
Analysis of changes in facial expressions is performed, for example, as follows. That is, for each frame image, a facial area is specified from the frame image, and the specified facial expressions are classified into a plurality of types according to an image analysis model trained in advance by machine learning. Then, based on the classification result, it is analyzed whether a positive facial expression change occurs between consecutive frame images, a negative facial expression change occurs, and how large the facial expression change occurs.
目線の変化の解析は、例えば以下のようにして行う。すなわち、フレーム画像ごとに、フレーム画像の中から目の領域を特定し、両目の向きを解析することにより、ユーザがどこを見ているかを解析する。例えば、表示中の話者の顔を見ているか、表示中の共有資料を見ているか、画面の外を見ているかなどを解析する。また、目線の動きが大きいか小さいか、動きの頻度が多いか少ないかなどを解析するようにしてもよい。目線の変化はユーザの集中度にも関連する。
Analysis of changes in the line of sight is performed, for example, as follows. That is, for each frame image, the area of the eyes is specified from the frame image, and the orientation of both eyes is analyzed to analyze where the user is looking. For example, it analyzes whether the speaker's face being displayed, the shared material being displayed, or the outside of the screen is being viewed. In addition, it may be possible to analyze whether the movement of the line of sight is large or small, and whether the movement is frequent or infrequent. The change in the line of sight is also related to the degree of concentration of the user.
脈拍の変化の解析は、例えば以下のようにして行う。すなわち、フレーム画像ごとに、フレーム画像の中から顔の領域を特定する。そして、顔の色情報（ＲＧＢのＧ）の数値を捉える学習済みの画像解析モデルを用いて、顔表面のＧ色の変化を解析する。その結果を時間軸に合わせて並べることによって色情報の変化を表した波形を形成し、この波形から脈拍を特定する。人は緊張すると脈拍が速くなり、気持ちが落ち着くと脈拍が遅くなる。
Analysis of pulse changes is performed, for example, as follows. That is, for each frame image, the face area is specified from the frame image. Then, using a trained image analysis model that captures the numerical value of the face color information (G in RGB), the change in the G color on the face surface is analyzed. By arranging the results along the time axis, a waveform showing the change in color information is formed, and the pulse is specified from this waveform. When a person is nervous, the pulse becomes faster, and when he / she feels calm, the pulse becomes slower.
顔の動きの変化の解析は、例えば以下のようにして行う。すなわち、フレーム画像ごとに、フレーム画像の中から顔の領域を特定し、顔の向きを解析することにより、ユーザがどこを見ているかを解析する。例えば、表示中の話者の顔を見ているか、表示中の共有資料を見ているか、画面の外を見ているかなどを解析する。また、顔の動きが大きいか小さいか、動きの頻度が多いか少ないかなどを解析するようにしてもよい。顔の動きと目線の動きとを合わせて解析するようにしてもよい。例えば、表示中の話者の顔をまっすぐ見ているか、上目遣いまたは下目使いに見ているか、斜めから見ているかなどを解析するようにしてもよい。
Analysis of changes in facial movement is performed, for example, as follows. That is, for each frame image, the area of the face is specified from the frame image, and the orientation of the face is analyzed to analyze where the user is looking. For example, it analyzes whether the speaker's face being displayed, the shared material being displayed, or the outside of the screen is being viewed. In addition, it may be possible to analyze whether the movement of the face is large or small, and whether the movement is frequent or infrequent. The movement of the face and the movement of the line of sight may be combined and analyzed. For example, it may be possible to analyze whether the speaker's face being displayed is viewed straight, whether the speaker is viewed with an upper eye or a lower eye, or whether the speaker is viewed from an angle.
発言内容の解析は、例えば以下のようにして行う。すなわち、生体反応解析部１２は、指定した時間（例えば、３０～１５０秒程度の時間）の音声について公知の音声認識処理を行うことによって音声を文字列に変換し、当該文字列を形態素解析することにより、助詞、冠詞などの会話を表す上で不要なワードを取り除く。そして、残ったワードをベクトル化し、ポジティブな感情変化が起きているか、ネガティブな感情変化が起きているか、およびどの程度の大きさの感情変化が起きているかを解析する。
The content of the statement is analyzed as follows, for example. That is, the biological reaction analysis unit 12 converts the voice into a character string by performing a known voice recognition process on the voice for a specified time (for example, a time of about 30 to 150 seconds), and morphologically analyzes the character string. By doing so, words unnecessary for expressing conversation such as particles and acronyms are removed. Then, the remaining words are vectorized, and whether a positive emotional change is occurring, a negative emotional change is occurring, and the magnitude of the emotional change is analyzed.
声質の解析は、例えば以下のようにして行う。すなわち、生体反応解析部１２は、指定した時間（例えば、３０～１５０秒程度の時間）の音声について公知の音声解析処理を行うことによって音声の音響的特徴を特定する。そして、その音響的特徴に基づいて、ポジティブな声質変化が起きているか、ネガティブな声質変化が起きているか、およびどの程度の大きさの声質変化が起きているかを解析する。
Voice quality analysis is performed as follows, for example. That is, the biological reaction analysis unit 12 identifies the acoustic characteristics of the voice by performing a known voice analysis process on the voice for a specified time (for example, a time of about 30 to 150 seconds). Then, based on the acoustic characteristics, it is analyzed whether a positive voice quality change is occurring, a negative voice quality change is occurring, and how loud the voice quality change is occurring.
組織属性解析部１３は、生体反応解析部１２により解析された生体反応の変化に基づいて、組織の属性を解析する。例えば、組織属性解析部１３は、複数の組織が参加者のコミュニケーションスタイルにどう影響を与えているかを解析することにより、組織の属性を解析する。組織属性解析部１３が解析する組織属性は、例えば、コミュニケーションを行いやすい組織かどうかという属性である。または、良いまたは悪いパフォーマンスを出している要因と考えられる組織属性を解析するようにしてもよい。
The tissue attribute analysis unit 13 analyzes the attributes of the tissue based on the change in the biological reaction analyzed by the biological reaction analysis unit 12. For example, the organization attribute analysis unit 13 analyzes the attributes of an organization by analyzing how a plurality of organizations influence the communication style of the participants. The organization attribute analyzed by the organization attribute analysis unit 13 is, for example, an attribute of whether or not the organization is easy to communicate with. Alternatively, you may want to analyze the tissue attributes that are considered to be the factors that give good or bad performance.
同じユーザであっても、参加する組織のオンラインセッションの違いによって反応の仕方に違いが出ることがある場合、その組織のオンラインセッションに特有の雰囲気、つまりその組織が有している特有の属性に影響を受けている可能性がある。組織属性解析部１３は、このような組織特有の属性を、例えばその組織のオンラインセッションに参加する複数のユーザに共通する反応や、同じユーザが参加する複数の組織のオンラインセッションでの反応の違いなどから分析する。
If the same user may react differently depending on the online session of the participating organization, the atmosphere peculiar to the online session of the organization, that is, the peculiar attribute of the organization. May be affected. The organization attribute analysis unit 13 applies such an organization-specific attribute to, for example, a reaction common to a plurality of users participating in an online session of the organization, or a difference in a reaction in an online session of a plurality of organizations in which the same user participates. Analyze from such.
なお、上記実施形態は、何れも本発明を実施するにあたっての具体化の一例を示したものに過ぎず、これによって本発明の技術的範囲が限定的に解釈されてはならないものである。すなわち、本発明はその要旨、またはその主要な特徴から逸脱することなく、様々な形で実施することができる。
It should be noted that the above embodiments are merely examples of the embodiment of the present invention, and the technical scope of the present invention should not be construed in a limited manner. That is, the present invention can be implemented in various forms without departing from its gist or its main features.
１１ 動画像取得部
１２ 生体反応解析部
１３ 組織属性解析部 11 Movingimage acquisition unit 12 Biological reaction analysis unit 13 Tissue attribute analysis unit
１２ 生体反応解析部
１３ 組織属性解析部 11 Moving
Claims (8)
- 組織において行われるオンラインセッションにおける参加者の反応を解析し、その解析結果をもとに上記組織の属性を解析することを特徴とする組織属性解析システム。 An organization attribute analysis system characterized by analyzing the reactions of participants in an online session held in an organization and analyzing the attributes of the above organization based on the analysis results.
- 複数の組織のオンラインセッションごとに上記参加者の反応をそれぞれ解析し、それぞれの解析結果を比較することを通じて、上記複数の組織の属性を解析することを特徴とする請求項１に記載の組織属性解析システム。 The organizational attribute according to claim 1, wherein the reaction of the participants is analyzed for each online session of the plurality of organizations, and the attributes of the plurality of organizations are analyzed by comparing the analysis results of each. Analysis system.
- 同じユーザが参加する複数の組織のオンラインセッションごとに上記ユーザの反応をそれぞれ解析し、それぞれの解析結果を比較することを通じて、上記組織の属性を解析することを特徴とする請求項２に記載の組織属性解析システム。 The second aspect of claim 2, wherein the attributes of the organization are analyzed by analyzing the reaction of the user for each online session of a plurality of organizations in which the same user participates and comparing the analysis results of each. Organizational attribute analysis system.
- 同じ組織のオンラインセッションに参加する複数のユーザごとに反応をそれぞれ解析し、上記複数のユーザの間で共通した反応をもとに上記組織の属性を解析することを特徴とする請求項１に記載の組織属性解析システム。 The first aspect of claim 1, wherein the reaction is analyzed for each of a plurality of users participating in an online session of the same organization, and the attributes of the organization are analyzed based on the reaction common to the plurality of users. Organizational attribute analysis system.
- 上記複数の組織が参加者のコミュニケーションスタイルにどう影響を与えているかを解析することにより、上記組織の属性を解析することを特徴とする請求項１～４の何れか１項に記載の組織属性解析システム。 The organizational attribute according to any one of claims 1 to 4, wherein the attribute of the organization is analyzed by analyzing how the plurality of organizations influence the communication style of the participants. Analysis system.
- 上記オンラインセッション中に上記参加者を撮影することによって得られる動画像を取得する動画像取得部と、
上記動画像取得部により取得された動画像に基づいて、上記参加者について生体反応の変化を解析する生体反応解析部と、
上記生体反応解析部により解析された上記生体反応の変化に基づいて、上記組織の属性を解析する組織属性解析部とを備えた
ことを特徴とする請求項１～５の何れか１項に記載の組織属性解析システム。 A moving image acquisition unit that acquires a moving image obtained by photographing the participants during the online session, and a moving image acquisition unit.
Based on the moving image acquired by the moving image acquisition unit, the biological reaction analysis unit that analyzes the change in the biological reaction of the participants, and the biological reaction analysis unit.
The invention according to any one of claims 1 to 5, further comprising a tissue attribute analysis unit that analyzes the attributes of the tissue based on the change in the biological reaction analyzed by the biological reaction analysis unit. Tissue attribute analysis system. - 上記生体反応解析部は、上記動画像取得部により取得された動画像にける顔画像を解析することにより、表情、目線、脈拍、顔の動きの少なくとも１つに関する生体反応の変化を解析することを特徴とする請求項６に記載の組織属性解析システム。 The biological reaction analysis unit analyzes changes in the biological reaction related to at least one of facial expression, line of sight, pulse, and facial movement by analyzing the facial image in the moving image acquired by the moving image acquisition unit. 6. The tissue attribute analysis system according to claim 6.
- 上記生体反応解析部は、上記動画像取得部により取得された動画像にける音声を解析することにより、発言内容、声質の少なくとも１つに関する生体反応の変化を解析することを特徴とする請求項６または７に記載の組織属性解析システム。 The claim is characterized in that the biological reaction analysis unit analyzes changes in the biological reaction relating to at least one of the content of speech and voice quality by analyzing the voice in the moving image acquired by the moving image acquisition unit. The tissue attribute analysis system according to 6 or 7.
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