JP6241698B1 - Evaluation support system and evaluation support apparatus - Google Patents

Abstract

An evaluation support system and an evaluation support apparatus capable of quantitatively evaluating the contents of a presentation of a proposer are provided.
An evaluation support system 100 for evaluating the content of a presentation of a proposer via a network 4, an acquisition means S110 for obtaining target information D generated based on the video of the presentation of the proposer, Reference database in which past target information acquired in advance, reference information used for evaluation of the past target information, and three or more levels of association between the past target information and the reference information are stored Evaluation means S120 for referring to the reference database, obtaining evaluation information including first relations of three or more stages between the target information D and the reference information, and an evaluation result R based on the evaluation information And output means S130 for outputting the evaluation result R.
[Selection] Figure 1

Description

  The present invention relates to an evaluation support system and an evaluation support apparatus for evaluating the contents of a presentation of a proposer.

  2. Description of the Related Art In recent years, services on the Internet have been actively developed, for example, based on the social trust of users (proposers), which can promote the enjoyment of services among individuals. For example, services that issue virtual stocks based on the social trust of the proposer and other users buy and sell virtual stocks are attracting attention. In such a field, the social trust of the proposer is evaluated by the degree of attention to the proposer in other social networking services (SNS: Social Networking Service), such as Twitter (registered trademark) and Facebook (registered trademark). It is evaluated by the number of followers.

  On the other hand, when issuing a virtual stock or the like based on the proposer, a user who buys or sells the virtual stock or the like considers buying or selling of the virtual stock or the like mainly based on the idea including the future potential of the proposer. For this reason, for example, when the degree of attention to the proposer in other social networking services is high, even if the idea of the proposer is skeptical, the social credibility of the proposer may be appreciated more than necessary. There is. In addition, in the proposer who does not use other social networking services, even if the idea is promising, the social trust of the proposer may be evaluated low. For this reason, it is difficult for the conventional evaluation method to appropriately evaluate the social credibility of the proposer.

  As a method for appropriately evaluating the social trust of the proposer, there is a method for evaluating a presentation including the idea of the proposer. However, it is necessary for an operator or the like to spend enormous amounts of time and labor costs of the evaluator in order to evaluate each proposer's presentation. Moreover, since it is assumed that the evaluation dispersion | variation for every evaluator will also become large, quantitative evaluation is mentioned as a subject.

  In this regard, in order to evaluate the presentation of the proposer, for example, a presentation evaluation apparatus disclosed in Patent Document 1 has been proposed.

  The presentation evaluation apparatus disclosed in Patent Document 1 displays a slide when a presentation is started. Then, the evaluation of the line of sight detected by the line-of-sight direction detection device is added. Then, this apparatus checks the timing of turning each page. Based on this result, the page turning timing is compared with the time extracted from the agenda. As a result, when a large deviation occurs, a warning indicating that effect is displayed on the presentation screen. For example, if the next page has been advanced earlier than the scheduled time, “Proceeded early (X seconds)” is displayed.

JP 2007-219161 A

  However, with the disclosed technique of Patent Document 1, it is only possible to evaluate based on how long a lecture was spent on the presentation material, and the content of the presentation cannot be evaluated. Under such circumstances, it is desired to be able to quantitatively evaluate the content of the proposer's presentation.

  Therefore, the present invention has been devised in view of the above-described problems, and an object of the present invention is to provide an evaluation support system and an evaluation support apparatus that can quantitatively evaluate the content of the presentation of the proposer. It is in.

  An evaluation support system according to a first aspect of the present invention is an evaluation support system that evaluates the content of a presentation of a proposer via a network, and obtains target information generated based on the video of the presentation of the proposer And past target information acquired in advance, reference information used for evaluation of the past target information, and three or more levels of association between the past target information and the reference information are stored. A reference database, evaluation means for referring to the reference database, obtaining evaluation information including first relations of three or more stages between the target information and the reference information, and an evaluation result based on the evaluation information. Output means for generating and outputting the evaluation result.

  The evaluation support system according to a second invention is the evaluation support system according to the first invention, wherein the acquisition means generates question information to the proposer based on the target information and outputs the question information; and the question information And an additional means for acquiring reply information based on the answer of the proposer.

  The evaluation support system according to a third invention is characterized in that, in the second invention, the questioning means generates the question information with reference to the evaluation information acquired by the evaluation means.

  The evaluation support system according to a fourth invention is the evaluation support system according to any one of the first invention to the third invention, wherein the acquiring means is content data relating to the content of the material used for the presentation of the proposer from the target information, the proposer At least one of progress data regarding how to proceed with the presentation, voice data regarding the voice of the proposer, and facial expression data regarding the feature of the face of the proposer.

  The evaluation support system according to a fifth aspect of the present invention is the evaluation support system according to the fourth aspect, wherein the facial expression data includes first facial expression data relating to characteristics of the eyes of the proposer and facial data relating to characteristics of the entire face of the proposer. The evaluation result has an estimated health value.

  The evaluation support system according to a sixth aspect of the present invention is the evaluation support system according to any one of the first to fifth aspects, wherein the acquisition means generates the target information based on a video of the suggestor's presentation for 30 seconds to 30 minutes. It is characterized by acquiring.

  In any one of the first to sixth inventions, the evaluation support system according to a seventh aspect of the present invention is configured such that, when a relationship between the past target information and the reference information is newly acquired, the relationship is It is further characterized by further comprising updating means for reflecting the association degree.

  An evaluation support system according to an eighth invention is characterized in that, in any one of the first to seventh inventions, the acquisition means acquires the degree of attention of the proposer in a social networking service.

  An evaluation support apparatus according to a ninth aspect of the present invention is an evaluation support apparatus that evaluates the content of a suggestor's presentation, an acquisition unit that acquires target information generated based on the video of the proposer's presentation, and acquired in advance Past reference information, reference information used in the evaluation of the past target information, and a reference database in which three or more levels of association between the past target information and the reference information are stored; Referring to the reference database, generating an evaluation result based on the evaluation information, an evaluation unit that acquires evaluation information including first relations of three or more stages between the target information and the reference information, And an output unit that outputs the evaluation result.

  According to 1st invention-8th invention, an evaluation means refers to a reference database, and acquires the evaluation information containing the 1st relevance between object information and reference information. The target information is generated based on the video of the proposer's presentation. For this reason, a quantitative evaluation result can be acquired with respect to the contents of the presentation of the proposer. Thereby, it becomes possible to connect the quantitative evaluation result to the social credibility of the proposer.

  In particular, according to the second invention, the question means generates question information for the proposer and outputs the question information. Further, the adding means acquires answer information based on the answer of the proposer to the question information. For this reason, even when the amount of information included in the presentation of the proposer is small, the answer information can be acquired using the question information, and the amount of information can be supplemented. Thereby, the accuracy of the evaluation result can be improved.

  In particular, according to the third aspect, the question means generates the question information with reference to the evaluation information. For this reason, the question information according to the evaluation information can be generated. Thereby, the accuracy of the evaluation result can be further improved.

  In particular, according to the fourth invention, the acquisition means extracts at least one of content data, progress data, voice data, and facial expression data from the target information. For this reason, it is possible to generate an independent evaluation result for each data and to generate an evaluation result combining the data. Thereby, it is possible to generate an optimal evaluation result according to the field and situation of the presentation.

  In particular, according to the fifth invention, the evaluation result has an estimated health value of the proposer. For this reason, the health condition at the time of presentation of a proposer can also be included in an evaluation standard. Thereby, the accuracy of the evaluation result can be further improved.

  In particular, according to the sixth aspect, the acquisition means acquires the target information generated based on the video of the suggestor's presentation in 30 seconds or more and 30 minutes or less. For this reason, the variation in the amount of information included in each piece of target information can be suppressed. Thereby, it is possible to easily obtain a quantitative evaluation result.

  In particular, according to the seventh aspect, the update unit reflects the relationship in the association degree when the relationship between the past target information and the reference information is newly acquired. For this reason, the association degree can be easily updated, and the accuracy of the evaluation result can be further increased.

  According to the ninth aspect, the evaluation unit refers to the reference database, and acquires evaluation information including the first association degree between the target information and the reference information. The target information is generated based on the video of the proposer's presentation. For this reason, a quantitative evaluation result can be acquired with respect to the contents of the presentation of the proposer. Thereby, it becomes possible to connect the quantitative evaluation result to the social credibility of the proposer.

It is a mimetic diagram showing the example of composition of the evaluation support system in an embodiment. FIG. 2A is a schematic diagram illustrating an example of the target information, and FIG. 2B is a schematic diagram illustrating an example of the evaluation result. FIG. 3A is a schematic diagram illustrating an example of a configuration of the evaluation support apparatus in the embodiment, and FIG. 3B is a schematic diagram illustrating an example of a function of the evaluation support apparatus in the embodiment. FIG. 4 is a schematic diagram illustrating an example of a reference database according to the embodiment. FIG. 5 is a schematic diagram illustrating a first modification of the reference database according to the embodiment. FIG. 6 is a schematic diagram illustrating a second modification of the reference database according to the embodiment. FIG. 7 is a schematic diagram illustrating an example of question information and answer information. FIG. 8 is a schematic diagram illustrating an example of a question database according to the embodiment. FIG. 9 is a flowchart illustrating an example of the operation of the evaluation support system in the embodiment. FIG. 10 is a flowchart illustrating a first modification of the operation of the evaluation support system in the embodiment. FIG. 11 is a flowchart illustrating a second modification of the operation of the evaluation support system in the embodiment. FIG. 12 is a schematic diagram illustrating a modified example of the question database according to the embodiment.

  Hereinafter, an example of an evaluation support system according to an embodiment of the present invention will be described with reference to the drawings.

(Embodiment: Configuration of Evaluation Support System 100)
With reference to FIG. 1, an example of a configuration of an evaluation support system 100 in the embodiment will be described. FIG. 1 is a block diagram showing the overall configuration of the evaluation support system 100 in the present embodiment.

  As shown in FIG. 1, the evaluation support system 100 includes an evaluation support apparatus 1. The evaluation support apparatus 1 may be connected to, for example, the server 3 or the like in addition to being connected to the user terminal 2 via the public communication network 4 (network), for example.

  The evaluation support system 100 is mainly used for developing services on the Internet. The evaluation support system 100 can output a quantitative evaluation result R for the target information D generated based on the video of the presentation of the user (proposer). Therefore, the administrator or the like of the evaluation support system 100 can consider the social credit of the proposer based on the evaluation result R, for example, and can set virtual stocks or the like according to the social credit. In addition, the proposer can consider improving the presentation based on the evaluation result R, for example. Further, the target information D and the evaluation result R need not be disclosed on the Internet. For this reason, it is possible to evaluate the contents of the presentation of the proposer in an unknown state.

<Evaluation support apparatus 1>
The evaluation support apparatus 1 in this embodiment is mainly used by an administrator of the evaluation support system 100 or the like. The evaluation support apparatus 1 acquires the target information D and outputs an evaluation result R for the target information D. The manager or the like can examine the social trust of the proposer based on the evaluation result R obtained by the evaluation support apparatus 1.

  The evaluation support apparatus 1 acquires the target information D generated by the user terminal 2 from the user terminal 2 as shown in FIG. The target information D is generated based on the video of the suggestor's presentation using the camera and microphone of the user terminal 2. The target information D includes, for example, at least one of presentation data P, audio data S, and facial expression data F. The evaluation support apparatus 1 extracts presentation data P and the like from the acquired target information D as necessary.

  The presentation data P includes, for example, at least one of content data relating to the content of the material used for the presentation by the proposer and progress data relating to how the proposer proceeds with the presentation. The content data may include data obtained by converting the content of the material into text data, and may include image data such as a figure or a graph. The progress data includes data related to the progress time for explaining each material and the progress time of the entire presentation. The progress data includes, for example, data related to the order in which materials are used.

  The voice data S includes, for example, at least one of speech data related to the speech of the proposer and tone data related to the tone of the proposer. The utterance data includes, for example, data obtained by converting the utterance of the proposer into text data. The tone data includes, for example, data related to the speed of the utterance of the proposer, the volume of voice, intonation, time between utterances, and the like.

  The facial expression data F includes, for example, at least one of first facial expression data regarding the characteristics of the eyes of the proposer and facial data regarding the characteristics of the entire face of the proposer. The first facial expression data includes data relating to features such as the eyes of the proposer, eyesight, and line of sight. The face data includes data relating to body characteristics such as the face color, wrinkles, hair, mouth, and nose of the proposer.

  For example, as illustrated in FIG. 2B, the evaluation support apparatus 1 generates an evaluation result R for the target evaluation D and outputs the evaluation result R to the user terminal 2. The evaluation result R has, for example, at least one of planability, sustainability, future potential, presentation ability, estimated health value, estimated age, and evaluation price. In addition to generating a score for each item, the evaluation result R may generate a comment.

  Regarding planability, sustainability, and future prospects, the evaluation of the proposals by the proponents is mainly shown. The planability can be used, for example, for evaluation of plan omissions and omissions in the proposal of the proposer. Sustainability can be used, for example, for evaluating whether or not the proposal can be realized in a proposal. The future can be used, for example, for objective future evaluation in the proposal of the proposer.

  Among the reference information, regarding the presentation ability, the estimated health value, and the estimated age, evaluation for the proposer itself is shown. The presentation ability can be used, for example, for evaluating the proposal progress and explanation method of the proposer. The estimated health value can be used to evaluate the health condition obtained from the appearance of the proposer. The estimated age can be used to evaluate the estimated age obtained from the appearance of the proposer, for example.

  FIG. 3A is a schematic diagram illustrating an example of the configuration of the evaluation support apparatus 1. As the evaluation support apparatus 1, an electronic device such as a personal computer (PC) is used. The evaluation support apparatus 1 includes a housing 10, a CPU 101, a ROM 102, a RAM 103, a storage unit 104, and I / Fs 105 to 107. Each component 101 to 107 is connected by an internal bus 110.

  A CPU (Central Processing Unit) 101 controls the entire evaluation support apparatus 1. A ROM (Read Only Memory) 102 stores an operation code of the CPU 101. A RAM (Random Access Memory) 103 is a work area used when the CPU 101 operates. The storage unit 104 stores various information such as the target information D. As the storage unit 104, for example, an HDD (Hard Disk Drive), a data storage device such as an SSD (Solid State Drive) or a floppy disk is used. For example, the evaluation support apparatus 1 may include a GPU (Graphics Processing Unit) (not shown). By having a GPU, it is possible to perform higher-speed arithmetic processing than usual.

  The I / F 105 is an interface for transmitting and receiving various types of information to and from the user terminal 2 via the public communication network 4. The I / F 106 is an interface for transmitting / receiving information to / from the input unit 108. For example, a keyboard is used as the input part 108, and an administrator of the evaluation support system 100 inputs various information or a control command of the evaluation support apparatus 1 through the input part 108. The I / F 107 is an interface for performing transmission / reception of various information with the output unit 109. The output part 109 outputs various information stored in the storage unit 104 or the processing status of the evaluation support apparatus 1. A display is used as the output portion 109, and for example, a touch panel type may be used.

  FIG. 3B is a schematic diagram illustrating an example of functions of the evaluation support apparatus 1. The evaluation support apparatus 1 includes an acquisition unit 11, an evaluation unit 12, an output unit 14, an input unit 15, and an information DB 16. The evaluation support apparatus 1 may include an update unit 13, for example. Note that the function shown in FIG. 3B is realized by the CPU 101 executing a program stored in the storage unit 104 or the like using the RAM 103 as a work area. Moreover, each structure 11-16 may be controlled by artificial intelligence, for example. Here, “artificial intelligence” may be based on any known artificial intelligence technology.

<Information DB16>
The information DB 16 includes a reference database in which past target information acquired in advance and reference information used for evaluation of past target information are stored. The information DB 16 includes, in addition to storing evaluation information obtained by evaluating the target information D, for example, a database storing various information such as a format for displaying the evaluation result R based on the evaluation information. The reference database and the database are stored in the storage unit 104 realized by an HDD, an SSD, or the like. Each structure 11-15 memorize | stores various information in information DB16 as needed, or takes out various information.

  In the reference database, for example, as shown in FIG. 4, three or more levels of association between past target information and reference information are stored. The past target information and reference information have a plurality of data, and are linked with the association degree indicating the degree of the relationship, for example, the degree of relevance of three or more levels such as 10 levels and 5 levels (in FIG. Displayed with line features). For example, “content A” included in past content data indicates an association degree “80%” with “plan A” included in the planability of the reference information, and “sustainability” included in the sustainability of the reference information. The degree of association with “A” is “10%”.

  The reference database has, for example, an algorithm that can calculate the association degree. As the reference database, for example, a function (classifier) optimized based on past target information, reference information, and association degree may be used.

  The past target information includes data corresponding to the target information D described above, and includes, for example, at least one of past presentation data, past audio data, and past facial expression data. Since the characteristics of each data are the same as each data which the target information D mentioned above has, the description is omitted.

  The reference information includes data corresponding to the evaluation result R, and includes at least one of planability, sustainability, future potential, presentation ability, estimated health value, estimated age, and evaluation price, for example. As reference information, a result obtained by evaluating past target information is used. Since the characteristics of each data are the same as each data which the evaluation result R mentioned above has, the description is omitted.

  Since the reference information has the data as described above, an evaluation result R for the presentation of the proposer can be generated. Each of the above data is an example, and can be arbitrarily set as necessary.

  The past target information and reference information may be stored in the reference database in a video or sound data format, for example, or may be stored in a data format such as a numerical value, a matrix (vector), or a histogram.

  The association degree is calculated based on the relationship between past target information and reference information. The association degree is calculated using, for example, machine learning. For example, deep learning is used for machine learning.

  For example, as shown in FIG. 5, the association degree may be calculated based on a relationship between a combination of two or more data included in past target information and reference information. For example, a combination of “content A” included in past content data of past target information and “progress B” included in past progress data is between “plan A” included in the planability of the reference information. The degree of association is “80%”, and the degree of association with “plan B” is “10%”. In this case, when generating the evaluation result R, it is possible to improve accuracy and expand the range of options.

  The past target information may include intermediate information associated with each piece of data and reference information included in the past target information, for example, as shown in FIG. In this case, the intermediate information is associated with at least one of past presentation data, past audio data, and past facial expression data through three or more levels of similarity, and is associated with reference information through association. ing. Thereby, even when at least one of past presentation data, past voice data, and past facial expression data is updated, added, etc., it is not necessary to update the association degree. As a result, it is possible to significantly reduce the time spent for reconstructing the reference database associated with the change of the evaluation object.

<Acquisition unit 11>
The acquisition unit 11 acquires the target information D. In addition to acquiring the target information D from the user terminal 2, the acquisition unit 11 may acquire the target information D from a storage medium such as a portable memory. Note that the data format of the target information D is arbitrary. For example, the acquisition unit 11 may convert the data into an arbitrary data format.

  The acquisition unit 11 may extract only specific data included in the target information D, for example, and extracts at least one of content data, progress data, audio data S, and facial expression data F, for example. Data to be extracted can be arbitrarily set by an administrator or the like.

<Question part 11a>
The acquisition unit 11 includes, for example, a question unit 11a. The question unit 11 a generates question information Q for the proposer based on the target information D, and outputs the question information Q to the user terminal 2 or the like via the output unit 14. The user terminal 2 or the like displays the acquired question information Q as shown in FIG. 7, for example. In addition to generating and outputting the question information Q in the text data format, the question unit 11a may generate and output the question information Q in the voice data format, for example. The question unit 11a may generate and output the question information Q during the acquisition of the target information D (for example, during the presentation of the proposer), for example, after acquiring the target information D (for example, the target person's presentation) The question information Q may be generated and output after the presentation). The number of times the question unit 11a generates and outputs the question information Q is arbitrary.

  The question unit 11a generates the question information Q with reference to, for example, a question database. The question database has past target information acquired in advance and past question information used for questions of past target information, and is stored in the information DB 16.

  For example, as shown in FIG. 8, the question database stores three or more levels of question relations between past target information and past question information. The past target information and the past question information are linked by the question relation indicating the degree of the relationship, for example, the degree of relevance of three or more levels such as 10 levels or 5 levels (in FIG. Display). For example, “content A” included in past content data indicates a question association degree “60%” with “question A” included in past question information, and question association with “question C”. Degree indicates “30%”. For example, the question unit 11a generates and outputs past question information having the highest degree of question association as question information Q. In addition, when the question part 11a produces | generates the question information Q, it can set so that the question relevance degree in arbitrary threshold values or ranges may be referred.

  The question database has, for example, an algorithm that can calculate the question association degree. As the question database, for example, functions (classifiers) optimized based on past target information, past question information, and question relations may be used.

  The past target information and the past question information are stored in the question database in a video or sound data format, for example, and may be stored in a data format such as a numerical value, a matrix (vector), or a histogram.

  The question association degree is calculated based on the relationship between past target information and past question information. The question association degree is calculated using, for example, machine learning. For example, deep learning is used for machine learning. In the question database, as in the reference database shown in FIG. 5, the question association degree may be calculated based on a combination of two or more data included in past target information.

<Addition part 11b>
The acquisition unit 11 includes, for example, an addition unit 11b. The adding unit 11b acquires answer information A based on the answer of the proposer with respect to the question information Q. For example, as shown in FIG. 7, the answer information A is generated based on the content that the proposer has answered to the question information Q displayed on the user terminal 2.

  In addition to extracting the response information A from the acquired target information D, the adding unit 11b may acquire the response information A as information different from the target information D, for example. The adding unit 11b may acquire the response information A in the text data format, or may acquire the response information A in the audio data format, for example. After the adding unit 11b acquires the answer information A, for example, the question unit 11a may generate the question information Q for the proposer again based on the answer information A. For example, the adding unit 11b may acquire the answer information A as a part of the target information D, and may classify the answer information A into any one of the presentation data P, the voice data S, and the facial expression data F, for example. When the adding unit 11b acquires the answer information A as information different from the target information D, the reference database includes past answer information corresponding to the answer information A.

  In addition to the target information D, the acquisition unit 11 may acquire, for example, personal information regarding the target person stored in the target information D. Thereby, it becomes possible to output the evaluation result R corresponding to the person information.

<Evaluation unit 12>
The evaluation unit 12 acquires evaluation information including the first association degree of three or more stages between the target information D and the reference information. The evaluation unit 12 refers to the reference database stored in the storage unit 104, selects past target information that matches or is similar to the target information D, and determines the association degree associated with the selected past interview data. Calculated as the first degree of association. In addition, the evaluation unit 12 may calculate the first association degree between the target information D and the reference data using, for example, a reference database as a classifier algorithm or an optimized function.

  For example, when the reference database shown in FIG. 4 is used, when the presentation data P included in the target information D matches or is similar to “content A”, “80%”, “ First association degrees of “10%” for “Sustain A” and “1%” for “Presentation B” are calculated. Further, when the voice data S is similar to “speech A” and “speech B”, for example, the association degree between “speech A” and “speech B” and the reference information is multiplied by an arbitrary coefficient. A value is calculated as the first association degree. Further, when the target information D includes a plurality of data, for example, a first association degree corresponding to each of the plurality of data is calculated.

  After calculating the first association degree, the evaluation unit 12 acquires evaluation information including the target information D, the reference information, and the first association degree. Note that the evaluation unit 12 may calculate the first association degree with reference to, for example, the reference database illustrated in FIG.

<Update unit 13>
For example, when the relationship between the past target information and the reference information is newly acquired, the update unit 13 reflects the relationship in the association degree. As data to be reflected in the association degree, for example, update data including target information D newly acquired by an administrator or the like and reference information corresponding to the evaluation result R of the target information D is used. In addition, as data to be reflected in the association degree, for example, learning data or the like created based on the evaluation result R by an administrator or the like is used.

<Output unit 14>
The output unit 14 generates an evaluation result R based on the evaluation information, and outputs the evaluation result R. The output unit 14 generates an evaluation result R for the target information D based on, for example, the first association degree of the evaluation information. The output unit 14 may generate the evaluation result R without performing, for example, processing of the evaluation information.

  The output unit 14 outputs the generated evaluation result R. The output unit 14 may output the evaluation result R to the output unit 109 via the I / F 107, or may output the evaluation result R to an arbitrary device such as the user terminal 2 via the I / F 105, for example.

<Input unit 15>
The input unit 15 receives the target information D transmitted from the user terminal 2 via the I / F 105, and receives various types of information input from the input part 108 via the I / F 106, for example. In addition, the input unit 15 may receive, for example, target information D stored in the server 3. The input unit 15 may receive the target information D and the like via a storage medium such as a portable memory. The input unit 15 receives, for example, update data created by an administrator or the like based on the evaluation result R, learning data used to update the association degree, and the like.

<User terminal 2>
The user terminal 2 is held by a proposer who uses the evaluation support system 100. In addition to a personal computer (PC), for example, an electronic device such as a smartphone or a tablet terminal is used as the user terminal 2. The user terminal 2 includes a camera and a microphone for acquiring a video of the proposer's presentation, and a generation unit that generates the target information D based on the acquired video. In addition, the user terminal 2 may have a structure and function equivalent to the evaluation support apparatus 1 mentioned above, for example. That is, for example, the evaluation support system 100 according to the present embodiment may use the user terminal 2 instead of the evaluation support apparatus 1.

<Server 3>
The server 3 stores data (database) related to various types of information. In this database, for example, information sent via the public communication network 4 is accumulated. The server 3 may store information similar to the information DB 16, for example, and may transmit and receive various types of information to and from the evaluation support apparatus 1 via the public communication network 4. As the server 3, for example, a database server on a network may be used. The server 3 may be used instead of the storage unit 104 and the information DB 16 described above.

<Public communication network 4>
The public communication network 4 (network) is an Internet network or the like to which the evaluation support apparatus 1 or the like is connected via a communication circuit. The public communication network 4 may be a so-called optical fiber communication network. The public communication network 4 is not limited to a wired communication network, and may be realized by a wireless communication network.

(Embodiment: Operation of Evaluation Support System 100)
Next, an example of the operation of the evaluation support system 100 in the present embodiment will be described. FIG. 9 is a flowchart showing an example of the operation of the evaluation support system 100 in the present embodiment.

<Acquisition means S110>
First, target information D to be evaluated is acquired (acquiring unit S110). The acquisition unit 11 may acquire the target information D via a storage medium such as a portable memory, for example, in addition to acquiring the target information D generated by the user terminal 2 via the input unit 15. The acquisition unit 11 may acquire, for example, person information in addition to the target information D. The acquisition unit 11 may store the acquired target information D and the like in the information DB 16.

  For example, the acquisition unit 11 extracts specific data included in the target information D. In this case, the acquisition unit 11 may store the extracted data in the information DB 16.

  For example, the acquisition unit 11 may acquire each target information D stored for a certain period in addition to continuously acquiring the target information D generated by the user terminal 2. That is, the acquisition unit 11 may acquire the target information D in a lump after the suggestion of the presenter ends, in addition to dividing and acquiring the target information D in a state close to real time during the suggestion of the presenter. The timing at which the acquisition unit 11 acquires the target information D from the user terminal 2 and the number of divisions are arbitrary.

<Evaluation means S120>
Next, with reference to the reference database, evaluation information including the first association degree between the target information D and the reference information is acquired (evaluation means S120). The evaluation unit 12 acquires the target information D from the acquisition unit 11 or the information DB 16 and acquires a reference database from the information DB 16.

  The evaluation unit 12 can calculate the first association degree between the target information D and the reference information by referring to the reference database. For example, the evaluation unit 12 selects past target information that matches, partially matches, or is similar to the target information D, selects reference information associated with the selected past target information, and selects the selected past information. The first association degree is calculated based on the association degree between the target information and the reference information. Note that the evaluation unit 12 may store the calculated first association degree and the acquired evaluation information in the information DB 16.

  For example, the evaluation unit 12 may refer to the reference database illustrated in FIG. 5 to calculate the first association degree between each combination of data included in the target information D and the reference information. Further, the evaluation unit 12 may calculate the first association degree via the intermediate information with reference to, for example, the reference database illustrated in FIG.

<Output means S130>
Next, an evaluation result R is generated based on the evaluation information, and the evaluation result R is output (output unit S130). The output unit 14 may acquire evaluation data from the evaluation unit 12 or the information DB 16, and may acquire a format for displaying the evaluation result R from the information DB 16, for example.

  The output unit 14 generates an evaluation result R with reference to, for example, a format based on the evaluation information. For example, as illustrated in FIG. 2B, the output unit 14 generates, as an evaluation result R, a result of calculating scores (scores) for evaluation items such as “planning” and “sustainability” based on the evaluation information. And output. The output unit 14 may calculate, for example, a value for each first association degree associated with the reference information as a score, or may calculate a value combining the first association degree, an average value, or the like. The output unit 14 may select, for example, text data based on the evaluation information, and generate and output it as a comment of the evaluation result R. In this case, the text data is stored in the storage unit 104 in a state of being linked with the reference information in advance.

  Thereafter, the output unit 14 outputs the evaluation result R. The output unit 14 outputs the evaluation result R to the user terminal 2 or the output part 109. The output unit 14 may output the evaluation result R based on, for example, a result obtained by comparing a preset threshold value with the first association degree. In this case, for example, when the threshold is set to “80% or more”, the evaluation result R is output only when the first association degree is 80% or more. The threshold condition can be set arbitrarily.

<Update means S140>
Thereafter, for example, when a relationship between past target information and reference information is newly acquired, the relationship may be reflected in the association degree (update unit S140). For example, the update unit 13 acquires update data newly acquired by an administrator or the like, and reflects the update data in the association degree. In addition, the update unit 13 acquires, for example, learning data created by the administrator based on the evaluation result R and reflects it in the association degree.
The update unit 13 calculates and updates the degree of association using, for example, machine learning, and deep learning is used for machine learning, for example.

  Thereby, the operation of the evaluation support system 100 in the present embodiment is completed. Note that whether or not the above-described updating unit S140 is performed is arbitrary.

  According to the present embodiment, the evaluation unit S120 refers to the reference database and acquires evaluation information including the first association degree between the target information D and the reference information. The target information D is generated based on the video of the proposer's presentation. For this reason, the quantitative evaluation result R can be acquired with respect to the content of the presentation of the proposer. Thereby, it is possible to connect the quantitative evaluation result R to the social credibility of the proposer.

  Further, according to the present embodiment, the acquisition unit S110 extracts at least one of content data, progress data, audio data S, and facial expression data F from the target information D. For this reason, it is possible to generate an independent evaluation result R for each data and to generate an evaluation result R that combines the data. This makes it possible to generate an optimal evaluation result R according to the field and situation of the presentation.

  Further, according to the present embodiment, the evaluation result R has an estimated health value of the proposer. For this reason, the health condition at the time of presentation of a proposer can also be included in an evaluation standard. Thereby, the accuracy of the evaluation result R can be further improved.

  In addition, according to the present embodiment, the acquisition unit S110 acquires the target information D generated based on the video of the proposer's presentation in 30 seconds or more and 30 minutes or less. For this reason, the variation in the amount of information included for each target information D can be suppressed. Thereby, it is possible to easily obtain the quantitative evaluation result R.

  Further, according to the present embodiment, when the relationship between the past target information and the reference information is newly acquired, the updating unit S140 reflects the relationship in the association degree. For this reason, the association degree can be easily updated, and the accuracy of the evaluation result R can be further increased.

(First Modification of Operation of Evaluation Support System 100)
Next, a first modified example of the operation of the evaluation support system 100 in the present embodiment will be described with reference to FIG. The difference between the above-described operation and the operation of the first modified example is that the questioning unit S110a and the adding unit S110b are performed. Since the other points are the same as the above-described operation, description thereof will be omitted as appropriate. Note that the inquiry unit S110a and the addition unit S110b may be implemented after the acquisition unit S110, for example, in the acquisition unit S110.

<Question means: S110a>
The question means S110a generates question information Q for the proposer based on the acquired target information D, and outputs the question information Q. The question unit 11a refers to, for example, the question database shown in FIG. 8, obtains three or more levels of first question association between the target information D and past question information, and obtains past question information and first question Question information Q is generated based on the association degree. The question unit 11a may generate and output question information Q corresponding to a plurality of questions, for example. For example, the question unit 11a stores the generated question information Q in the information DB 16.

<Additional means: S110b>
Next, the adding unit S110b acquires answer information A based on the answer of the proposer with respect to the question information Q. The adding unit 11b may add the answer information A to the target information D when acquiring the answer information A as information different from the target information D. In this case, the evaluation unit S120 is performed using the target information D to which the answer information A is added. The adding unit 11b may extract the answer information A from the target information D, for example. In this case, in the adding unit S110b, the acquisition unit 11 acquires the target information D including the answer information A.

  Thereafter, the above-described evaluation unit S120 and the like are performed, and the operation of the evaluation support system 100 in the present embodiment ends.

  According to the first modified example of the operation in the present embodiment, the evaluation unit S120 refers to the reference database, and includes the first association degree between the target information D and the reference information, as in the above-described operation. Get information. The target information D is generated based on the video of the proposer's presentation. For this reason, the quantitative evaluation result R can be acquired with respect to the content of the presentation of the proposer. Thereby, it is possible to connect the quantitative evaluation result R to the social credibility of the proposer.

  In particular, according to the first modified example of the operation in the present embodiment, the question unit S110a generates the question information Q for the proposer and outputs the question information Q. Further, the adding unit S110b obtains answer information A based on the answer of the proposer with respect to the question information Q. For this reason, even when the amount of information included in the presentation of the proposer is small, the answer information A can be acquired using the question information Q, and the amount of information can be supplemented. Thereby, the accuracy of the evaluation result R can be improved.

(Second Modification of Operation of Evaluation Support System 100)
Next, with reference to FIG. 11, the 2nd modification of operation | movement of the evaluation assistance system 100 in this embodiment is demonstrated. The difference between the operation of the first modification described above and the operation of the second modification is that after the evaluation unit S120 is performed, the question unit S110a and the addition unit S110b are performed, and the evaluation unit S120 is performed again. . Since the other points are the same as the above-described operation, description thereof will be omitted as appropriate.

  The question unit S110a refers to the question database shown in FIG. 12, for example, and generates the question information Q. The question database shown in FIG. 12 is different from the question database shown in FIG. 8 in that the reference information held in the reference database is linked to the past question information by the question relevance. That is, based on the evaluation information acquired in the evaluation unit S120, the question unit S110a and the addition unit S110b are implemented.

  In the operation of the second modification, in the adding unit S110b, the acquisition unit 11 acquires the target information D including the answer information A. Thereafter, the evaluation unit S120 is performed again based on the target information D including the answer information A.

  Thereafter, the above-described output means S130 and the like are implemented, and the operation of the evaluation support system 100 in the present embodiment ends.

  According to the second modified example of the operation in the present embodiment, the evaluation unit S120 refers to the reference database, and includes the first association degree between the target information D and the reference information, as in the above-described operation. Get information. The target information D is generated based on the video of the proposer's presentation. For this reason, the quantitative evaluation result R can be acquired with respect to the content of the presentation of the proposer. Thereby, it is possible to connect the quantitative evaluation result R to the social credibility of the proposer.

  In particular, according to the second modification of the operation in the present embodiment, the question unit S110a generates the question information Q with reference to the evaluation information. For this reason, the question information Q according to the evaluation information can be generated. Thereby, it becomes possible to further improve the accuracy of the evaluation result R.

  According to the above-described embodiment, the target information D can be evaluated based on the association degrees set in three or more stages (first association degree, question association degree, first question association degree, similarity degree). There are features. The association degree can be described by a numerical value of 0 to 100%, for example, and may be configured at any stage as long as it can be described by three or more numerical values.

  In the reference information selected as a candidate of the evaluation result R for the target information D based on such association degree, reference information and the like (past question information, intermediate information) may be displayed in descending order of association degree. It becomes possible. Thus, by displaying in the order of the association degrees and the like, the manager or the like can preferentially select a tendency that is highly likely to correspond to the proposer. On the other hand, since a tendency that is unlikely to be a proposal can be displayed without being excluded, the administrator or the like can make a selection without missing it.

  In addition to the above, according to the above-described embodiment, even when the relevance or the like is extremely low such as 1%, it is possible to evaluate without overlooking. Even reference information or the like having an extremely low association degree indicates that it is connected as a slight sign, and it is possible to suppress oversight and misperception.

  Moreover, according to embodiment mentioned above, in the acquisition means S110, the acquisition part 11 may acquire the attention degree of the proposer in a social networking service, for example. For example, the number of followers such as Twitter (registered trademark) and Facebook (registered trademark) is used as the degree of attention of the proposer, and the degree of connection with other users is used. For this reason, information other than the content of the presentation of the proposer can be targeted for the evaluation result R. As a result, it is possible to easily generate an optimal evaluation result R according to the field and situation of the presentation.

  Further, according to the above-described embodiment, the video of the proposer is desirably 30 seconds or more and 30 minutes or less, and more preferably 2 minutes or more and 5 minutes or less. Thereby, the evaluation variation for every proposer can be suppressed and the quantitative evaluation result R can be easily realized.

  Further, according to the above-described embodiment, the language used by the proposer for the presentation is arbitrary, and the quantitative evaluation result R can be realized in any language. That is, the target information D may be generated based on a presentation using a native language or multilingual by the proposer.

  Further, according to the above-described embodiment, the target information D may be generated based on a presentation video of one or a plurality of proposers, a company introduction, or the like. That is, the proposer does not have to be taken in the presentation video.

  In addition, according to the above-described embodiment, for example, the proposer may be able to continue the presentation multiple times (for example, every day, every week, etc.). In this case, in addition to generating different target information D for each presentation, target information D in which a plurality of presentations are collected may be generated.

  Further, according to the above-described embodiment, the target information D may be generated, for example, based on a presentation video for the purpose of funding or supporting a world adventure that the proposer takes several months to several years.

  In addition, according to the above-described embodiment, the target information D may be generated based on a video of a presentation for the purpose of procuring funds necessary for an athlete, for example, if the proposer is an athlete or an athlete supporter. Good.

  Further, according to the above-described embodiment, the target information D may be generated based on, for example, a presentation video for the purpose of requesting or providing support other than funds by the proposer. In addition, at least one of credit, electronic money, points, and virtual currency is used, for example, human support, educational support, technical guidance, and the like may be used.

  In addition, according to the above-described embodiment, the target information D may be generated based on a video of a presentation for the purpose of searching for a side job, a job search, or a human support at a time when the proposer is vacant, for example. Good.

  Further, according to the above-described embodiment, the target information D may be generated, for example, based on a video of a presentation for the purpose of providing or providing a human request such as care or nursing by the proposer.

  Further, according to the above-described embodiment, the proposal information D may be generated based on, for example, a presentation video for the purpose of buying and selling merchandise.

  Further, according to the above-described embodiment, the proposal information D may be generated based on a video of a presentation for the purpose of obtaining an estimate of a place where the proposer wants to go and where to stay.

  Further, according to the above-described embodiment, the target information D may be generated based on a presentation video for the purpose of exchanging time with the proposer and buying and selling time.

  Further, according to the above-described embodiment, the target information D may be generated based on a presentation video for the purpose of providing or providing a human request such as a periodic request such as a babysitter by the proposer.

  Further, according to the above-described embodiment, the target information D may be generated based on a presentation video for the purpose of supporting a person, service, system, or the like that the proposer wants to support.

  In addition, according to the above-described embodiment, the target information D may be generated based on a video in which a performance such as a suggestor's performance or performance is used as a presentation. In this case, the proposer generates the target information D for the purpose of attracting customers or funding, for example.

  Moreover, according to embodiment mentioned above, based on the numerical value of the evaluation result R acquired for every proposer, you may enable it to trade a numerical value freely between proposers like a virtual currency. For example, a proposer having a high evaluation result R may be able to transfer, lend, deposit, etc. the evaluation result R with respect to a proposer with a low evaluation result R. In addition, based on the value of the evaluation result R of the proposer, purchase evaluation of other proposers is purchased, and even if the value of the evaluation result R on hand temporarily becomes negative, replenishment, appropriation, and settlement within a certain period Etc. may be made possible.

  In the above, for example, when there is a proposer whose numerical value of the evaluation result R is extremely high, there is a possibility that numerical fluidity cannot be expected. For this reason, the proposer may be able to transfer the numerical value to another proposer or user. In this case, for example, the proposer can be transferred to a proposer in a field having a market value equivalent to the field of the numerical value owned.

  Although the embodiments of the present invention have been described, the embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

1: Evaluation support device 2: User terminal 3: Server 4: Public communication network 10: Case 11: Acquisition unit 11a: Questioning unit 11b: Addition unit 12: Evaluation unit 13: Update unit 14: Output unit 15: Input unit 16 : Information DB
100: Evaluation support system 101: CPU
102: ROM
103: RAM
104: Storage unit 105: I / F
106: I / F
107: I / F
108: input portion 109: output portion 110: internal bus S110: acquisition means S110a: inquiry means S110b: addition means S120: evaluation means S130: output means S140: update means

Claims (9)

An evaluation support system for evaluating the contents of a proposal of a proposer via a network,
Acquisition means for acquiring target information generated based on the video of the presenter's presentation;
Reference database in which past target information acquired in advance, reference information used for evaluation of the past target information, and three or more levels of association between the past target information and the reference information are stored When,
An evaluation unit that refers to the reference database and obtains evaluation information including first relations of three or more stages between the target information and the reference information;
Output means for generating an evaluation result based on the evaluation information and outputting the evaluation result;
An evaluation support system comprising: The acquisition means includes
Based on the target information, generating question information to the proposer, and a question means for outputting the question information;
Additional means for obtaining answer information based on the answer of the proposer to the question information;
The evaluation support system according to claim 1, further comprising: The evaluation support system according to claim 2, wherein the question unit generates the question information with reference to the evaluation information acquired by the evaluation unit. The acquisition means includes, from the target information, content data relating to the content of the material used for the presentation of the proposer, progress data relating to how to proceed the presentation of the proposer, audio data relating to the voice of the proposer, and the proposer The evaluation support system according to any one of claims 1 to 3, wherein at least one of facial expression data relating to the facial features of the facial expression is extracted. The facial expression data is
First facial expression data relating to the eye features of the proposer;
Face data relating to characteristics of the entire face of the proposer;
Have
The evaluation support system according to claim 4, wherein the evaluation result has an estimated health value. The evaluation support according to any one of claims 1 to 5, wherein the acquisition unit acquires the target information generated based on a video of the presentation of the proposer for 30 seconds to 30 minutes. system. The update means for reflecting the relationship in the association degree when a relationship between the past target information and the reference information is newly acquired is further provided. The evaluation support system according to claim 1. The evaluation support system according to claim 1, wherein the acquisition unit acquires a degree of attention of the proposer in a social networking service. An evaluation support device that evaluates the content of a presentation of a proposer,
An acquisition unit for acquiring target information generated based on the video of the presentation of the proposer;
Reference database in which past target information acquired in advance, reference information used for evaluation of the past target information, and three or more levels of association between the past target information and the reference information are stored When,
An evaluation unit that refers to the reference database and obtains evaluation information including first relations of three or more stages between the target information and the reference information;
An output unit that generates an evaluation result based on the evaluation information and outputs the evaluation result;
An evaluation support apparatus comprising:

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