TWM576284U - Interactive appraisal system - Google Patents

Interactive appraisal system Download PDF

Info

Publication number
TWM576284U
TWM576284U TW107203643U TW107203643U TWM576284U TW M576284 U TWM576284 U TW M576284U TW 107203643 U TW107203643 U TW 107203643U TW 107203643 U TW107203643 U TW 107203643U TW M576284 U TWM576284 U TW M576284U
Authority
TW
Taiwan
Prior art keywords
reply message
message
module
interactive
text
Prior art date
Application number
TW107203643U
Other languages
Chinese (zh)
Inventor
鍾建屏
何健偉
王逸程
Original Assignee
荔枝智慧股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 荔枝智慧股份有限公司 filed Critical 荔枝智慧股份有限公司
Priority to TW107203643U priority Critical patent/TWM576284U/en
Publication of TWM576284U publication Critical patent/TWM576284U/en

Links

Abstract

An interactive appraisal system includes a user interface, a chatting robot device and a semantic analyzing engine. The chatting robot device receives a question message from an appraisal database to actively present the question message on the user interface. A reply message is sent to the semantic analyzing engine when the user interface receives the reply message. The semantic analysis engine extracts keywords from the reply message by using an algorithm module so as to parse the reply message. A classification module classifies the analyzed reply message into categories according to a field distribution. A score module gives scores according to contents of the classified reply message. A report generating device then compiles the reply message, the categories and the scores to generate a report.

Description

互動式檢核系統 Interactive check system

本創作係一種互動式檢核系統,其係透過使用者與聊天機器人提出答詢並將答詢結果經過解析以作為檢核統計使用。 This creation is an interactive check system that asks users and chat bots to answer and answer the results for use as check statistics.

檢核是用於收集使用者所回饋的意見,並將回饋意見予以分析以作為後續管理之用。一般來說,檢核係用於教學內容的課程意見、消費行為的後續追蹤、對於服務內容或產品的意見回饋等等。 The check is used to collect feedback from the user and analyze the feedback for subsequent management. In general, the check is used for course comments on teaching content, follow-up of consumer behavior, feedback on service content or products, and so on.

既有的檢核在實行上主要是使用紙本施測或線上填答兩種方式,但紙本施測在實際運用時容易受到時間與地點的限制,而使得填答的回覆率普遍較低,而且紙本所耗費的紙張也有環保方面的疑慮,另一方面,紙本施測因為欠缺與受測者之間的互動機制,在相同問卷之下,可能因為不同的問卷講解人的描述而造成偏誤,而問卷題目也沒有辦法提供其他解釋使受測者能夠藉由互動得到不同的題目描述,進而影響填答的準確率。 The existing inspections mainly use paper-based testing or online answering. However, paper-based testing is subject to time and place restrictions in actual application, and the response rate is generally lower. And the paper used in paper has environmental concerns. On the other hand, the paper is based on the lack of interaction with the subjects. Under the same questionnaire, it may be because of different questionnaires. There are no errors in the questionnaire, and there is no way to provide other explanations for the subject of the questionnaire to enable the subject to get different descriptions of the topics through interaction, thus affecting the accuracy of the answer.

線上填答雖然可以將紙本問卷的內容電子化而有更多表達效果及方式,且能夠減少受測者在時間與地點上的 使用侷限,但仍然無提供紙本填答過程中所欠缺的互動機制,受測者在無法理解問題時,無法即時地藉由互動機制得到對於題目更進一步的解釋,線上填答同樣無法大幅提高問卷的準確程度。此外,在質化性題目的填答上,也無法藉由互動的過程得到更多的資訊。 Online answering can electronically use the content of the paper questionnaire to have more expressions and ways, and can reduce the time and place of the subject. The use of limitations, but still does not provide the interaction mechanism that is lacking in the paper-based answering process. When the subject cannot understand the problem, the subject cannot be further explained by the interactive mechanism. The online answering cannot be greatly improved. The accuracy of the questionnaire. In addition, in the elaboration of qualitative questions, it is impossible to get more information through the process of interaction.

為解決前述問題,本創作提供一種互動式檢核系統,其包括:使用者介面;聊天機器人裝置,其係連接於該使用者介面以及檢核資料庫,並係由該檢核資料庫取得問題訊息以主動呈現該問題訊息於該使用者介面,俾於該使用者介面接收到回覆訊息後,將該回覆訊息傳送至語意解析引擎;該語意解析引擎,其係包括演算法模組、分類模組以及評分模組,其中,該演算法模組將該回覆訊息經過關鍵字提取以解析該回覆訊息,再將經解析的該回覆訊息傳送至該分類模組及該評分模組,以供該分類模組將經解析的該回覆訊息予以分類至依照領域配置的類別,並令該評分模組根據該分類後之回覆訊息中的內容給予分數;以及報表產生裝置,其彙整經該語意解析引擎所解析的該回覆訊息、該分類模組的分類結果及該評分模組所給予的分數以產生報表。 In order to solve the foregoing problems, the present invention provides an interactive check system, which includes: a user interface; a chat robot device connected to the user interface and the check database, and the problem is obtained by the check database. The message actively presents the problem message to the user interface. After receiving the reply message, the user message transmits the reply message to the semantic analysis engine. The semantic analysis engine includes an algorithm module and a classification module. And the scoring module, wherein the algorithm module extracts the reply message by keyword extraction to parse the reply message, and then transmits the parsed reply message to the classification module and the scoring module for the The classification module classifies the parsed reply message into a category configured according to the domain, and causes the scoring module to give a score according to the content in the classified reply message; and the report generating device, which collects the semantic parsing engine The reply message parsed, the classification result of the classification module, and the score given by the scoring module are used to generate a report.

前述的互動式檢核系統,進一步包括答覆資料庫,其係儲存經過該語意解析引擎所處理的該回覆訊息以提供該回覆訊息於報表產生裝置。 The interactive check system further includes a reply database that stores the reply message processed by the semantic parsing engine to provide the reply message to the report generating device.

前述的互動式檢核系統,進一步包括學習程序裝置, 其係透過模型將該語意解析引擎所解析結果進行機器學習,以將結果傳送至語意解析資料庫。 The aforementioned interactive check system further includes a learning program device, The machine learns the result of the semantic analysis engine through the model to transmit the result to the semantic analysis database.

前述的互動式檢核系統,其中,該聊天機器人裝置係依據該使用者介面所連續收到該回覆訊息的時間間隔,評斷此次檢核的有效性。 In the foregoing interactive check system, the chat robot device judges the validity of the check according to the time interval in which the user interface continuously receives the reply message.

前述的互動式檢核系統,其中,該聊天機器人裝置依據所取得之具有反向意含的問題訊息,評斷此次檢核的有效性。 In the foregoing interactive check system, the chat robot device judges the validity of the check according to the obtained problem message with a reverse intention.

前述的互動式檢核系統,其中,該聊天機器人裝置所呈現之該問題訊息係以文字按鈕、圖片選答或文字填答的方式呈現,其中,該文字按鈕的問題訊息係透過按鈕給予選項,供該聊天機器人裝置將該經由文字按鈕選擇的回覆訊息轉換為文字;該圖片選答的問題訊息係透過圖片提供選擇,供該聊天機器人裝置將該經由圖片選答的回覆訊息轉換為文字;以及該文字填答的問題訊息是透過文字形式表達該問題訊息,並接收文字形式的該回覆訊息。 In the foregoing interactive check system, the problem message presented by the chat robot device is presented by a text button, a picture selection or a text answering, wherein the question message of the text button is given by an button. The chat robot device converts the reply message selected by the text button into a text; the question message of the image answering mode provides a selection through the image for the chat robot device to convert the reply message via the picture answer to the text; The question message in the text is to express the question message in text form and receive the reply message in text form.

前述的互動式檢核系統,其中,該演算法模組將該回覆訊息透過語意解析資料庫執行關鍵字提取,而產生多維度斷詞的該回覆訊息,以在降低維度後解析該回覆訊息。 In the foregoing interactive check system, the algorithm module performs the keyword extraction through the semantic parsing database, and generates the reply message of the multi-dimensional word break to parse the reply message after reducing the dimension.

前述的互動式檢核系統,其中,該分類模組是根據在語意解析資料庫中的分類模型分類該回覆訊息中的內容,且該依照領域配置的類別是設定於該分類模組的類別資料庫中。 In the foregoing interactive check system, the classification module classifies the content in the reply message according to the classification model in the semantic analysis database, and the category configured according to the domain is the category data set in the classification module. In the library.

前述的互動式檢核系統,其中,該評分模組是根據該 回覆訊息中內容的情緒詞彙的正向或負向的值域給予分數。 The aforementioned interactive check system, wherein the scoring module is based on the A score is given to the positive or negative range of the emotional vocabulary of the content in the reply message.

本創作之雙向互動的智能檢核系統,在現行網路與智慧型行動裝置的普及下,檢核的受測者可以不受到填答的時間與地點的限制,而能夠以更直覺的互動式對話,在即時通訊平台上與聊天機器人進行互動答詢的檢核,不僅可以增加填答率,也能夠藉由即時的互動機制和檢核過程中判斷問卷有效性的機制來增加問卷的準確率。 The two-way interactive intelligent check system of this creation, under the popularization of the current network and smart mobile devices, the subjects who are checked can be more intuitively interactive without being limited by the time and place of the answer. Dialogue, the verification of interactive chat with chat robots on the instant messaging platform can not only increase the answer rate, but also increase the accuracy of the questionnaire by means of an instant interaction mechanism and a mechanism for judging the validity of the questionnaire during the check process. .

11‧‧‧使用者介面 11‧‧‧User interface

12‧‧‧聊天機器人裝置 12‧‧‧chat robot

13‧‧‧檢核資料庫 13‧‧‧Check database

14‧‧‧語意解析引擎 14‧‧ ‧ semantic analysis engine

15‧‧‧答覆資料庫 15‧‧‧Reply database

16‧‧‧學習程序裝置 16‧‧‧Learning program device

17‧‧‧報表產生裝置 17‧‧‧Report generation device

S1-S14‧‧‧步驟 S1-S14‧‧‧Steps

第1圖為本創作之互動式檢核系統的系統架構示意圖;第2至3圖為本創作之使用者介面及聊天機器人模組之使用示意圖;以及第4圖為本創作之互動式檢核系統的操作流程示意圖。 The first picture is a schematic diagram of the system architecture of the interactive check system of the creation; the second to third figures are the user interface of the creation and the use of the chat robot module; and the fourth picture is the interactive check of the creation Schematic diagram of the operation of the system.

本創作提供一種互動式檢核系統,如第1圖所示,該互動式檢核系統包括:使用者介面11、聊天機器人裝置12、檢核資料庫13、語意解析引擎14、答覆資料庫15、學習程序裝置16以及報表產生裝置17。 The present invention provides an interactive check system. As shown in FIG. 1 , the interactive check system includes: a user interface 11 , a chat robot device 12 , a check database 13 , a semantic analysis engine 14 , and a reply database 15 . The learning program device 16 and the report generating device 17 are provided.

使用者介面11是用於接收使用者的語音、文字、圖片、選項等回覆訊息的操作介面,其可以社交軟體、通訊平台、網站、聊天室、或應用程式,使用者可以透過使用 者介面11以語音輸入、文字輸入、點選等方式與聊天機器人對話或溝通。 The user interface 11 is an operation interface for receiving a reply message of a user's voice, text, pictures, options, etc., and can be used by a social software, a communication platform, a website, a chat room, or an application. The user interface 11 communicates or communicates with the chat robot by means of voice input, text input, and clicking.

聊天機器人裝置12係連接於使用者介面11以及檢核資料庫13,該聊天機器人裝置12由檢核資料庫13取得已經建置於檢核資料庫13中的問題訊息後,使用按鈕、文字、圖片、或其任意組合之形式主動呈現問題訊息於使用者介面11,如第2、3圖所示,俾於該使用者介面11接收到使用者的回覆訊息後,傳送該回覆訊息至語意解析引擎14。其中,當聊天機器人裝置12提供文字按鈕、圖片選答或文字填答的問題訊息時,該文字按鈕的問題訊息係透過按鈕或卡片的形式呈現選項,使用者根據選項選擇答案後,該聊天機器人裝置12將使用者所選擇的文字按鈕之回覆訊息轉化為文字並儲存於答覆資料庫15;該圖片選答的問題訊息係在圖片設計不同區塊表示不同選項提供選擇,使用者點選區塊以回答回覆訊息,該聊天機器人裝置12將使用者所選擇的圖片區塊之回覆訊息轉換為相對應的文字,並將相對應的文字儲存於答覆資料庫15;該文字填答的問題訊息是由聊天機器人裝置12傳送檢核資料庫13的問題訊息,而使用者以文字形式回答回覆訊息,聊天機器人裝置12將使用者的文字回覆訊息儲存於答覆資料庫15;答覆資料庫15儲存前述經過該語意解析引擎所處理的該回覆訊息以提供該回覆訊息於報表產生裝置17。 The chat robot device 12 is connected to the user interface 11 and the verification database 13. The chat robot device 12 obtains the problem message that has been built in the verification database 13 by the verification database 13, and uses buttons, texts, The image, or any combination thereof, actively presents the problem message to the user interface 11. As shown in the second and third figures, after the user interface 11 receives the reply message from the user, the reply message is sent to the semantic analysis. Engine 14. Wherein, when the chat robot device 12 provides a question message of a text button, a picture selection or a text answer, the question message of the text button presents an option in the form of a button or a card, and the user selects an answer according to the option, the chat robot The device 12 converts the reply message of the text button selected by the user into a text and stores it in the reply database 15; the question message of the picture pick-up is provided in different blocks of the picture design to indicate different options, and the user clicks the block to In response to the reply message, the chat bot 12 converts the reply message of the picture block selected by the user into a corresponding text, and stores the corresponding text in the reply database 15; the question message of the text is answered by The chat robot device 12 transmits the question message of the check database 13, and the user answers the reply message in text form, and the chat robot device 12 stores the user's text reply message in the reply database 15; the reply database 15 stores the foregoing The reply message processed by the semantic parsing engine to provide the reply message to the report Set 17.

語意解析引擎14包括演算法模組、分類模組以及評分模組,其中,該演算法模組將使用者的回覆訊息以自然 語言處理的演算法分析其語意,主要是透過關鍵字提取,透過大量文本依照詞彙的詞性所組成的語意解析資料庫,將該回覆訊息的文句予以斷詞,以解析該回覆訊息,並以相同方式產生用於模型訓練的詞彙,如下列表A所示,經過斷詞的語句為多維度架構,在經過降低維度後,使得語句更容易解析。 The semantic analysis engine 14 includes an algorithm module, a classification module, and a scoring module, wherein the algorithm module takes the user's reply message to the natural The language processing algorithm analyzes its semantics, mainly through keyword extraction, through a large amount of text according to the semantics of the lexical part of the semantic analysis database, the text of the reply message is broken, to resolve the reply message, and the same The way to generate vocabulary for model training, as shown in Listing A below, is that the sentence after the word break is a multi-dimensional architecture that makes the statement easier to parse after the reduced dimension.

語意解析引擎14的分類模組將經過前述解析的回覆訊息予以指派至依照產業領域配置的多個類別,分類模組包括類別資料庫以及分類機制,其中,類別資料庫配置有預先建置的不同類別以對應於檢核的不同產業領域,分類機制是建構於語意解析資料庫之上的分類模型,其對於各類別的相關連詞彙進行初步的指派與歸類,並藉由大量文本反覆訓練分類模型的準確度,以教育產業的學習成效檢核為例,其所使用的類別有「授課方式」、「課程內容」、「考試評分方式」等,以表A為例,該回覆訊息將被歸類於「授課方式」的分類。 The classification module of the semantic analysis engine 14 assigns the reply message parsed to the plurality of categories configured according to the industrial field, and the classification module includes a category database and a classification mechanism, wherein the category database is configured with different pre-built configurations. The classification is based on different industrial fields corresponding to the inspection. The classification mechanism is a classification model constructed on the semantic analysis database. It performs preliminary assignment and categorization of related vocabulary of each category, and repeats training classification through a large number of texts. The accuracy of the model is taken as an example of the examination of the learning effectiveness of the education industry. The categories used are "teaching method", "course content" and "examination scoring method". For example, in Table A, the reply message will be Classified into the classification of "teaching methods".

語意解析引擎14的評分模組根據經過分類模組所歸類的回覆訊息中的內容給予分數,例如,根據使用者的文字填答內容中的詞彙,特別是被定義帶有情緒意義的詞彙,判定其值域,值域分為正向與負向,且值域的量級與寬度將根據不同產業領域的檢核需求訂定,例如,以教育產業的學習成效檢核為例,值域可訂定於[-1,1]的區間內,如前述表A的範例,若「無趣」此一詞彙在計分的標籤資料庫中為負向的詞彙,則評分模組將第2圖中屬於「授 課方式」分類的語句,根據情緒詞彙的負向程度給予評分,例如,「-0.63分」,並將前述的數值彙整於報表產生裝置。 The scoring module of the semantic analysis engine 14 gives a score according to the content in the reply message classified by the classification module, for example, the vocabulary in the content is filled according to the user's text, especially the vocabulary with the emotional meaning defined. Determine the value range, the value range is divided into positive and negative directions, and the magnitude and width of the value domain will be determined according to the inspection requirements of different industrial fields. For example, taking the examination of the learning effectiveness of the education industry as an example, the value range Can be set in the interval of [-1,1], as in the example of Table A above, if the word "uninteresting" is a negative vocabulary in the scoring tag database, the scoring module will be the second picture. "grant The sentence of the class method is classified according to the degree of negative of the emotional vocabulary, for example, "-0.63 points", and the aforementioned numerical values are collected in the report generating device.

以下的表1為經過前述分類模組與評分模組所分類與評分的範例: Table 1 below is an example of classification and scoring by the above classification module and scoring module:

以下示例為語意解析引擎14的訓練階段以及預測階段的範例,在訓練階段中,使用者所輸入的語句經過斷詞、詞性分析後得出下列表2的矩陣O,而根據詞性不同,有可能得出多個矩陣(例如:O1,O2,...,On);在將前述斷詞後所得到的高維度矩陣進行降低維度而得到下列表3的矩陣S(根據詞性的不同,有可能得出矩陣S1,S2,...,Sn);將矩陣S與類別值C放入分類模組以建立分類模型而得出模型A;再依據每個類別值的使用者輸入的矩陣S與分數V,使用評分模組建立模型而得出模型B(根據類別的不同,有可能得出矩陣B1,B2,...,Bn)。 The following example is an example of the training phase and the prediction phase of the semantic analysis engine 14. In the training phase, the sentence input by the user is subjected to word segmentation and part of speech analysis to obtain the matrix O of the following list 2, and depending on the part of speech, it is possible Deriving a plurality of matrices (for example: O 1 , O 2 , ..., O n ); lowering the dimension of the high-dimensional matrix obtained after the above-mentioned word-breaking to obtain the matrix S of the following list 3 (according to the difference in part of speech) It is possible to derive the matrix S 1 , S 2 ,..., S n ); put the matrix S and the category value C into the classification module to establish a classification model to derive the model A; and then use the value according to each category The matrix S and the score V are input, and the model is built using the scoring module to obtain the model B (depending on the category, it is possible to derive the matrices B 1 , B 2 , ..., B n ).

在預測階段中,使用者所輸入的語句經過斷詞、詞性分析後得出前述的矩陣O(根據詞性不同,有可能得出多 個矩陣O1,O2,...,On);在將前述斷詞後所得到的高維度矩陣進行降低維度而得到矩陣S(根據詞性的不同,有可能得出矩陣S1,S2,...,Sn);將前述的矩陣S放入模型A中,以取得類別值C;根據前述的類別值C選擇對應的模型B,在透過矩陣S取得預測的分數。 In the prediction stage, the sentence input by the user is subjected to the word segmentation and part of speech analysis to obtain the aforementioned matrix O (depending on the part of speech, it is possible to derive a plurality of matrices O 1 , O 2 , ..., O n ); The matrix of the high-dimensional matrix obtained after the above-mentioned word-breaking is reduced to obtain a matrix S (depending on the part of speech, it is possible to obtain the matrix S 1 , S 2 , ..., S n ); placing the aforementioned matrix S The model A is acquired to obtain the category value C; the corresponding model B is selected based on the category value C described above, and the predicted score is obtained through the matrix S.

例如:使用者輸入:這堂課教的內容實在太有趣了 For example: user input: the content of this lesson is really interesting.

預測類別為:課程內容 Forecast category is: Course content

預測分數為:4 The forecast score is: 4

本創作的報表產生裝置17從答覆資料庫15中彙整前述經解析、斷詞的該回覆訊息、該分類模組的分類結果及該評分模組所給予的分數,經過統計後以文字或圖表形式產生報表,並藉由演算法模組歸納出利於決策的關鍵指標。 The report generating device 17 of the present creation summarizes the reply message of the parsed and broken words, the classification result of the classification module, and the score given by the scoring module from the reply database 15, and after statistics, in the form of text or graph Generate reports and summarize key indicators for decision making through algorithmic modules.

本創作更包括學習程序裝置16,其係透過模型將該語意解析引擎所解析結果進行機器學習,以將結果傳送至語意解析資料庫。 The creation further includes a learning program device 16 that performs machine learning through the model to analyze the results of the semantic analysis engine to transfer the results to the semantic analysis database.

為了確保檢核過程的有效以及確認使用者是否確實答覆問題,可以透過該聊天機器人裝置12係藉由該使用者介面所連續收到該回覆訊息的時間間隔長短,確認使用者填寫答覆的時間,以評斷此次檢核的有效性,該聊天機器人裝置12也可在傳送多個問題訊息之間,隨機傳送具有反向意含的問題訊息,以評斷此次檢核的有效性(例如,在第一個問題中詢問是否喜歡該項活動,並在第5題中反向訊問使用者是否不喜歡該項活動)。 In order to ensure the validity of the checking process and to confirm whether the user actually answers the question, the chat robot device 12 can confirm the time for the user to fill in the reply by the length of time that the user interface continuously receives the reply message. In order to judge the validity of the check, the chat bot 12 can also randomly transmit a problem message with a reverse meaning between the transmission of a plurality of question messages to judge the validity of the check (for example, at In the first question, ask if you like the event, and in the fifth question, ask the user if you don't like the event.

請參考第4圖的互動式檢核系統的操作流程示意圖,使用者啟用本創作之互動式檢核系統時,互動式檢核系統系統判斷使用者進入系統之途徑S1,由聊天機器人裝置會詢問使用者意圖S2,以進行身分之確認(若此次的檢核需要確認使用者身份則需提供同意身份綁定,若無此要求則不進行身分綁定)以判斷使用者進入的檢核系統,詢問意圖以進入檢核系統可為三種:提供代碼進入檢核、直述進入檢核(例如跟聊天機器人裝置說:我想填答OO講座的問卷),以及點選按鈕進入檢核(使用者與聊天機器人裝置 進行互動時,在聊天機器人裝置詢問使用者之意圖時,會提供使用者快速回覆之按鈕供使用者進行點按),則讓使用者進入檢核S4,聊天機器人裝置詢問檢核的類別S4,判斷是否有暫存S5,並根據該檢核是否具有可暫存之特性來檢視該檢核於該身分是否存在歷史填答紀錄,而詢問是否接續上次的填答S7,若繼續則接續上次的檢核S8;或判斷無暫存內容後,由聊天機器人裝置說明檢核內容S9,並與聊天機器人裝置互動檢核過程S10,在結束互動檢核過程後,檢核互動系統確認是否提交回覆訊息S11,若為是,則匯入資料庫S12,若否,則由聊天機器人裝置詢問意圖S2;接著在S12後,詢問是否繼續填答其他問題S13,若是,回到步驟S4,若為否,則結束本次檢核S14;若在詢問是否接續上次的填答S7或是否提交回覆訊息S11中選擇為否,則回到聊天機器人裝置會詢問使用者意圖S2。 Please refer to the operation flow diagram of the interactive check system in Figure 4. When the user activates the interactive check system of this creation, the interactive check system system judges the user's access to the system S1, and the chat robot device will ask User intention S2, to confirm the identity (if the verification needs to confirm the identity of the user, you need to provide consent binding, if there is no such requirement, do not bind the identity) to determine the user's access to the inspection system. There are three ways to ask for intent to enter the checkout system: provide code to enter the check, go straight to check (for example, with the chat robot, say: I want to answer the OO lecture), and click the button to enter the check (use Chatter device In the interaction, when the chat robot device asks the user's intention, the user's quick reply button is provided for the user to click, and the user is allowed to enter the check S4, and the chat robot device asks for the check category S4. Determining whether there is a temporary storage S5, and checking whether the check has a historical record in the identity according to whether the check has a temporary storage characteristic, and asking whether to continue the last answer S7, and if continuing, continue After the check S8; or after determining that there is no temporary storage content, the chat robot device describes the check content S9, and interacts with the chat robot device to check the process S10. After the interactive check process is finished, the check interaction system confirms whether to submit. Replying to the message S11, if yes, importing the database S12, if not, the chat robot device inquires about the intention S2; then after S12, asks whether to continue to answer the other question S13, and if so, returns to step S4, if If no, the current check S14 is ended; if it is asked whether to continue the last reply S7 or whether to submit the reply message S11, the answer back to the chat robot device will ask the user Intent S2.

上列詳細說明乃針對本創作之一可行實施例進行具體說明,惟該實施例並非用以限制本創作之專利範圍,凡未脫離本創作技藝精神所為之等效實施或變更,均應包含於本案之專利範圍中。 The detailed description above is intended to be illustrative of a possible embodiment of the present invention, and is not intended to limit the scope of the present invention. Any equivalent implementations or modifications that are not departing from the spirit of the present invention should be included in The patent scope of this case.

綜上所述,本案不僅於技術思想上確屬創新,並具備習用之傳統設備所不及之上述多項功效,已充分符合新穎性及進步性之法定創作專利要件,爰依法提出申請,懇請貴局核准本件創作專利申請案,以勵創作,至感德便。 To sum up, this case is not only innovative in terms of technical thinking, but also has many of the above-mentioned functions that are not in the traditional equipment used by the law. It has fully complied with the statutory creation patent requirements of novelty and progressiveness, and applied for it according to law. Approved this patent application for creation, in order to encourage creation, to the sense of virtue.

Claims (9)

一種互動式檢核系統,其包括:使用者介面;聊天機器人裝置,其係連接於該使用者介面,並係用以取得問題訊息以主動呈現該問題訊息於該使用者介面,俾於該使用者介面接收到回覆訊息後,傳送該回覆訊息;語意解析引擎,其係包括演算法模組、分類模組以及評分模組,其中,在接收該回覆訊息後,該演算法模組將該回覆訊息經過關鍵字提取以解析該回覆訊息,再將經解析的該回覆訊息傳送至該分類模組及該評分模組,以供該分類模組將該經解析的回覆訊息予以分類至依照領域配置的類別,並令該評分模組根據該分類後之回覆訊息中的內容給予分數;以及報表產生裝置,其彙整經該語意解析引擎所解析的該回覆訊息、該分類模組的分類結果及該評分模組所給予的分數以產生報表。 An interactive check system includes: a user interface; a chat bot device connected to the user interface and configured to obtain a problem message to actively present the question message to the user interface for use After receiving the reply message, the interface transmits the reply message; the semantic analysis engine includes an algorithm module, a classification module and a scoring module, wherein after receiving the reply message, the algorithm module will reply The message is extracted by the keyword to parse the reply message, and the parsed reply message is transmitted to the classification module and the scoring module, so that the classification module classifies the parsed reply message to be configured according to the domain. a category, and causing the scoring module to give a score based on the content in the reply message after the classification; and a report generating device that collects the reply message parsed by the semantic parsing engine, the classification result of the classification module, and the Score the score given by the module to generate a report. 根據申請專利範圍第1項所述的互動式檢核系統,進一步包括答覆資料庫,其係用以儲存經過該語意解析引擎所處理的該回覆訊息以提供該回覆訊息於報表產生裝置。 The interactive checking system of claim 1 further includes a replying database for storing the reply message processed by the semantic analysis engine to provide the reply message to the report generating device. 根據申請專利範圍第1項所述的互動式檢核系統,進一步包括學習程序裝置,其係透過模型將該語意解析引擎所解析結果進行機器學習,以將結果傳送至語意解析資 料庫。 According to the interactive inspection system of claim 1, further comprising a learning program device that performs machine learning through the model to analyze the result of the semantic analysis engine to transmit the result to the semantic analysis Library. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該聊天機器人裝置係依據該使用者介面所連續收到該回覆訊息的時間間隔,評斷此次檢核的有效性。 According to the interactive inspection system of claim 1, wherein the chat robot device judges the validity of the check according to the time interval in which the user interface continuously receives the reply message. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該聊天機器人裝置依據所取得之具有反向意含的問題訊息,評斷此次檢核的有效性。 According to the interactive inspection system of claim 1, wherein the chat robot device judges the validity of the check according to the obtained problem message with a reverse intention. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該聊天機器人裝置所呈現之該問題訊息係以文字按鈕、圖片選答或文字填答的方式呈現,其中,該文字按鈕的問題訊息係透過按鈕給予選項,供該聊天機器人裝置將該經由文字按鈕選擇的回覆訊息轉換為文字;該圖片選答的問題訊息係透過圖片提供選擇,供該聊天機器人裝置將該經由圖片選答的回覆訊息轉換為文字;以及該文字填答的問題訊息是透過文字形式表達該問題訊息,並接收文字形式的該回覆訊息。 According to the interactive inspection system of claim 1, wherein the problem message presented by the chat robot device is presented by a text button, a picture selection or a text, wherein the text button is The question message is an option given by the button for the chat robot device to convert the reply message selected by the text button into a text; the question message of the picture answer is provided through the picture for the chat robot device to select the picture via the picture The reply message is converted into text; and the question message that the text answers is to express the question message in text form and receive the reply message in text form. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該演算法模組將該回覆訊息透過語意解析資料庫執行關鍵字提取,而產生多維度斷詞的該回覆訊息,以在降低維度後,解析該回覆訊息。 According to the interactive checking system of claim 1, wherein the algorithm module performs the keyword extraction by using the semantic analysis database to generate the reply message of the multi-dimensional word segmentation, so as to After the dimension is lowered, the reply message is parsed. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該分類模組是根據在語意解析資料庫中的分類模型 分類該回覆訊息中的內容,且該依照領域配置的類別是設定於該分類模組的類別資料庫中。 According to the interactive inspection system described in claim 1, wherein the classification module is based on a classification model in a semantic analysis database. The content in the reply message is classified, and the category configured according to the domain is set in the category database of the classification module. 根據申請專利範圍第1項所述的互動式檢核系統,其中,該評分模組是根據該回覆訊息中內容的情緒詞彙的正向或負向的值域給予分數。 The interactive checking system according to claim 1, wherein the scoring module gives a score according to a positive or negative value range of the emotional vocabulary of the content in the reply message.
TW107203643U 2018-03-21 2018-03-21 Interactive appraisal system TWM576284U (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
TW107203643U TWM576284U (en) 2018-03-21 2018-03-21 Interactive appraisal system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
TW107203643U TWM576284U (en) 2018-03-21 2018-03-21 Interactive appraisal system

Publications (1)

Publication Number Publication Date
TWM576284U true TWM576284U (en) 2019-04-01

Family

ID=66997517

Family Applications (1)

Application Number Title Priority Date Filing Date
TW107203643U TWM576284U (en) 2018-03-21 2018-03-21 Interactive appraisal system

Country Status (1)

Country Link
TW (1) TWM576284U (en)

Similar Documents

Publication Publication Date Title
Hariri Unlocking the potential of ChatGPT: A comprehensive exploration of its applications, advantages, limitations, and future directions in natural language processing
Almatrafi et al. Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums
US9710829B1 (en) Methods, systems, and articles of manufacture for analyzing social media with trained intelligent systems to enhance direct marketing opportunities
Ralston et al. A voice interactive multilingual student support system using IBM Watson
US10949753B2 (en) Causal modeling and attribution
KR20180022762A (en) Method, system and computer-readable recording medium for providing customer counseling service using real-time response message generation
US11757807B2 (en) Interactive chatbot for multi-way communication
Kim et al. Towards identifying unresolved discussions in student online forums
Hasbullah et al. Automated content analysis: A sentiment analysis on Malaysian government social media
US20230237502A1 (en) Dynamic claims submission system
US10803247B2 (en) Intelligent content detection
Rizun et al. Business sentiment analysis. concept and method for perceived anticipated effort identification
Packowski et al. Using IBM watson cloud services to build natural language processing solutions to leverage chat tools
Rafaeli et al. Opportunities, tools, and new insights: Evidence on emotions in service from analyses of digital traces data
Myrendal Word meaning negotiation in online discussion forum communication
CN110532374A (en) The processing method and processing device of insurance information
Beresnev et al. Comparison of intelligent classification algorithms for workplace learning system in high-tech service-oriented companies
Mohamed et al. Analyzing the role of Sentiment Analysis in Public Relations: Brand Monitoring and Crisis Management
Pasat et al. An internship campaign case study showing results of enhanced recruitment processes using NLP
TWM576284U (en) Interactive appraisal system
TWI689843B (en) Intelligent appraisal system and method therof
Willis et al. Identifying domain reasoning to support computer monitoring in typed-chat problem solving dialogues
US20150304269A1 (en) System and method
Fang et al. Analyzing the intensity of complaints on social media
Howes Towards coherent co-presentation of expert evidence in criminal trials: Experiences of communication between forensic scientists and legal practitioners

Legal Events

Date Code Title Description
MM4K Annulment or lapse of a utility model due to non-payment of fees