TWI511124B - Selection method based on speech recognition and mobile terminal device and information system using the same - Google Patents

Description

Voice recognition based selection method and its mobile terminal device and information system

The present invention relates to a selection method and a mobile terminal device and information system thereof, and more particularly to a voice recognition based selection method and a mobile terminal device and information system thereof.

In the computer's Nature Language Understanding, a specific grammar is usually used to capture the intent or information of the user's input statement. Therefore, if there are enough data of the user input sentence stored in the database, a reasonable judgment can be made.

In the existing practice, one uses the built-in fixed word list to capture the user's input sentence, and the fixed word list contains specific intentions or specific terms used by the information, and the user needs to express according to the specific term. Their intent or information, their intent or information can be correctly identified by the system. However, forcing the user to remember each specific term of the fixed word list is quite unhuman. For example: existing The use of fixed word lists in technology requires users to ask: “What is the weather in Shanghai (or Beijing) tomorrow (or the day after tomorrow)?”, and users want to ask the weather if they use other more natural colloquial expressions. In the situation, for example, "How is Shanghai tomorrow?", because the "weather" does not appear in the statement, the existing technology will be understood as "there is a place in Shanghai called tomorrow", which obviously does not capture the true intention of the user. In addition, the types of statements used by users are very complicated, and often change from time to time. Sometimes users may enter incorrect statements. In this case, the user's input statements must be fetched by fuzzy matching. . Therefore, the effect of a fixed word list that only provides rigid input rules is even worse.

In addition, when using natural language understanding to handle multiple types of user intent, the grammatical structure of some different intents is the same. For example, when the user's input statement is "I want to see the Romance of the Three Kingdoms", the user's intention may be If you want to see a movie from the Romance of the Three Kingdoms, or if you want to read a book about the Romance of the Three Kingdoms, it is usually in this case that there are two possible intents to match the user's choice. However, in many cases, it is superfluous and inefficient to provide unnecessary possible intent to make the user's choice. For example, when the user's input sentence is "I want to see Super Star Avenue", it is not necessary to match the user's intentions to the book or painting of the Super Star Avenue (because the Super Star Avenue is a TV show).

Furthermore, in general, the search results obtained in full-text search are unstructured data. The information in unstructured data is scattered and unrelated. For example, after a keyword is input by a search engine such as Google or Baidu, the obtained web search result is unstructured data, because the search result must be read by human item by item. Reading can find useful information, and this method not only wastes the user's time, but also may miss the information you want, so it will be greatly limited in practicality.

The invention provides a voice recognition-based selection method, a mobile terminal device and an information system thereof, which can improve the convenience of the user operation.

The present invention provides a voice recognition-based selection method, including: receiving a first voice input; performing voice recognition on the first voice input to generate a first keyword; generating at least one first reward answer according to the first keyword; When the number of the answer answers is 1, the corresponding operation is performed according to the data type of the selected first return answer; when the number of the first return answer is greater than 1, the first candidate list including the first return answer is displayed and the first Two voice input; performing voice recognition on the second voice input to generate a second keyword; and selecting a second reward answer from the first report answer displayed by the first candidate list according to the second keyword.

The invention provides a mobile terminal device, which comprises a voice receiving unit, a display unit, a storage unit and a data processing unit. The voice receiving unit receives the first voice input and the second voice input. The display unit is used to display a candidate list containing a reward answer. The storage unit is used to store multiple materials. The data processing unit is coupled to the voice receiving unit, the display unit, and the storage unit. The data processing unit performs voice recognition on the first voice input to generate a first keyword, and selects a corresponding first reward answer according to the first keyword. Data processing unit when the number of first answer answers selected is one The corresponding operation is performed according to the type of the first return answer selected. When the number of selected first reward answers is greater than 1, the data processing unit controls the display unit to display the first candidate list including the first reward answer. The data processing unit performs voice recognition on the second voice input to generate a second keyword, and selects a second reward answer from the first report answer of the first candidate list according to the second keyword.

The invention provides an information system, including a server and a mobile terminal device. The server is used to store multiple data and has voice recognition function. The mobile terminal device includes a voice receiving unit, a display unit, and a data processing unit. The voice receiving unit receives the first voice input and the second voice input. The display unit is used to display a candidate list containing a reward answer. The data processing unit is coupled to the voice receiving unit, the display unit, and the server. The data processing unit performs voice recognition on the first voice input through the server to generate the first keyword, and the server selects the corresponding first reward answer according to the first keyword and transmits the first report answer to the data processing unit. When the number of selected first return answers is 1, the data processing unit performs a corresponding operation according to the type of the selected first return answer. When the number of selected first return answers is greater than 1, the data processing unit controls the display unit to display a first candidate list including the first report answer, and the data processing unit performs voice recognition on the second voice input through the server to generate a second a keyword, and the server selects the second report answer from the first report answer of the first candidate list according to the second keyword and transmits the result to the data processing unit.

The present invention provides a voice recognition-based selection method, including: searching according to the first keyword in a structured database to obtain at least one first reward answer; when the number of selected first reward answers is greater than 1, Show one contains this a first candidate list of the first answer; receiving a second voice input after displaying the first candidate list, and performing voice recognition on the second voice input to generate a second keyword; and, according to the second user It is intended to select a second reward answer from the first reward answer of the first candidate list.

Based on the above, the voice recognition-based selection method and the mobile terminal device and the information system thereof perform voice recognition and natural language processing on the first voice input and the second voice input to confirm the first voice input and the second The corresponding keyword is input by voice, and the reward answer is selected according to the keyword corresponding to the first voice input and the second voice input. Thereby, the convenience of the user's operation can be improved.

The above described features and advantages of the invention will be apparent from the following description.

100, 520, 520’, 720, 720’ ‧ ‧ natural language understanding system

102, 503, 503', 703, 902, 902'‧‧‧ request information

104‧‧‧Analysis results

106‧‧‧ possible intent grammar information

108, 509, 509’, 711, 904, 904’ ‧ ‧ keywords

110‧‧‧Responding results

112‧‧‧Intentional information

114‧‧‧Determining intent grammar information

116‧‧‧Analysis Results Output Module

200‧‧‧Search System

220‧‧‧ Structured Database

240‧‧‧Search Engine

260‧‧‧Search interface unit

280‧‧‧Guide data storage device

300‧‧‧Natural Language Processor

302, 832, 834, 836, 838 ‧ ‧ records

304‧‧‧ title field

306‧‧‧Content field

308‧‧ ‧ field

310‧‧‧Guide field

312‧‧‧Value field

314‧‧‧Source field

316‧‧‧heat field

318, 852, 854‧‧‧ favorite fields

320, 862, 864‧‧‧ aversive field

400‧‧‧Knowledge-assisted understanding module

500, 500’, 700, 700’‧‧‧ Natural Language Dialogue System

501, 701‧‧‧ voice input

507, 507’, 707‧ ‧ voice response

510, 710‧‧‧ voice sampling module

511, 511', 711, 906, 906' ‧ ‧ return answers

513, 513’, 713 ‧ ‧ voice

522, 722‧‧‧ voice recognition module

524, 724‧‧‧ natural language processing module

526, 726‧‧‧Speech synthesis module

530, 740‧‧‧Speech synthesis database

702‧‧‧voice integrated processing module

715‧‧‧User preference information

717‧‧‧User preference record

730‧‧‧Characteristic Database

872, 874‧‧‧ fields

900, 1010‧‧‧ mobile terminal devices

908, 908’‧‧‧ Candidate List

910, 1011‧‧‧ voice receiving unit

920, 1013‧‧‧ Data Processing Unit

930, 1015‧‧‧ display unit

940‧‧‧ storage unit

1000‧‧‧Information System

1020‧‧‧Server

SP1‧‧‧ first voice

SP2‧‧‧second voice

1200, 1300‧‧‧ voice control system

1210‧‧‧Auxiliary starter

1212, 1222‧‧‧ wireless transmission module

1214‧‧‧ Trigger Module

1216‧‧‧Wireless rechargeable battery

12162‧‧‧ battery unit

12164‧‧‧Wireless charging module

1220, 1320‧‧‧ mobile terminal devices

1221‧‧‧ voice system

1224‧‧‧Voice sampling module

1226‧‧‧Speech synthesis module

1227‧‧‧Voice output interface

1228‧‧‧Communication Module

1230‧‧‧ (cloud) server

1232‧‧‧Voice Understanding Module

12322‧‧‧Voice recognition module

12324‧‧‧Voice Processing Module

S410~S450‧‧‧ steps of a retrieval method according to an embodiment of the present invention

S510~S590‧‧‧ steps of the working process of the natural language understanding system according to an embodiment of the invention

S602, S604, S606, S608, S610, S612‧‧‧ steps of modifying the voice response

S802~S890‧‧‧ steps of a natural language dialogue method according to an embodiment of the present invention

S1100~S1190‧‧‧ steps of a speech recognition based selection method according to an embodiment of the invention

S1402~S1412‧‧‧ steps of a voice manipulation method according to an embodiment of the present invention

1 is a block diagram of a natural language understanding system in accordance with an embodiment of the present invention.

2 is a diagram showing the analysis results of various request information of a user by a natural language processor according to an embodiment of the present invention.

3A is a schematic diagram of a plurality of records having a particular data structure stored by a structured database, in accordance with an embodiment of the present invention.

3B is a schematic diagram of a plurality of records having a particular data structure stored by a structured database in accordance with another embodiment of the present invention.

FIG. 3C is a schematic diagram of the guidance material stored by the guidance data storage device according to an embodiment of the invention.

4A is a flow chart of a retrieval method in accordance with an embodiment of the present invention.

4B is a flow chart showing the operation of a natural language understanding system in accordance with another embodiment of the present invention.

FIG. 5A is a block diagram of a natural language dialogue system according to an embodiment of the invention.

FIG. 5B is a block diagram of a natural language understanding system according to an embodiment of the invention.

FIG. 5C is a block diagram of a natural language dialogue system according to another embodiment of the present invention.

FIG. 6 is a flow chart of a method for modifying a voice response according to an embodiment of the invention.

FIG. 7A is a block diagram of a natural language dialogue system according to an embodiment of the invention.

FIG. 7B is a block diagram of a natural language dialogue system according to another embodiment of the present invention.

FIG. 8A is a flowchart of a natural language dialogue method according to an embodiment of the invention.

8B is a schematic diagram of a plurality of records having a particular data structure stored by a structured database in accordance with yet another embodiment of the present invention.

FIG. 9 is a schematic diagram of a system of a mobile terminal device according to an embodiment of the invention.

FIG. 10 is a schematic diagram of a system of an information system according to an embodiment of the invention.

11 is a flow chart of a voice recognition based selection method in accordance with an embodiment of the present invention.

FIG. 12 is a block diagram of a voice control system according to an embodiment of the invention.

FIG. 13 is a block diagram of a voice control system according to another embodiment of the invention.

FIG. 14 is a flowchart of a voice control method according to an embodiment of the invention.

Since the existing implementation of the fixed word list can only provide rigid input rules, the judgment ability of the user's variable input sentence is very insufficient, so often the user's intention is judged incorrectly and the required information cannot be found, or The problem of outputting unnecessary information to the user due to insufficient judgment. In addition, the existing search engine can only provide users with scattered and unrelated search results, so users have to spend time and one by one to filter out the required information, not only wasting time but also missing the required information. The present invention provides a method and system for retrieving structured data for the aforementioned problems of the prior art. The structured data provides a specific field to store different types of data elements, and provides a user with natural voice input information for searching. Quickly and correctly judge the user's intent, thereby providing the required information to the user, or providing a more accurate message for selection.

1 is a block diagram of a natural language understanding system in accordance with an embodiment of the present invention. As shown in FIG. 1 , the natural language understanding system 100 includes a retrieval system 200 , a natural language processor 300 , and a knowledge assisted understanding module 400 . The knowledge assisted understanding module 400 is coupled to the natural language processor 300 and the retrieval system 200 , and the retrieval system 200 The structure database 220, the search engine 240, and the search interface unit 260 are further included. The search engine 240 is coupled to the structured database 220 and the retrieval interface unit 260. In the present embodiment, the retrieval system 200 includes a retrieval interface unit 260, but is not intended to limit the present invention. In some embodiments, the retrieval interface unit 260 may not be present, and the search engine 240 may otherwise cause the search engine 240 to perform the structured database 220. Full Text Search.

When the user issues the request information 102 to the natural language understanding system 100, the natural language processor 300 can analyze the request information 102 and send the analyzed possible intent grammar data 106 to the knowledge assisted understanding module 400, where the grammar data may be intended 106 includes a keyword 108 and an intent profile 112. Subsequently, the knowledge assisted understanding module 400 retrieves the keywords 108 in the possible intent grammar material 106 and sends them to the retrieval system 200 and stores the intent data 112 within the knowledge assisted understanding module 400, while the search engine 240 in the retrieval system 200 will After the full-text search is performed on the structured database 220 according to the keyword 108, the response result 110 of the full-text search is transmitted back to the knowledge assisted understanding module 400. Then, the knowledge assisted understanding module 400 compares the stored intent data 112 according to the response result 110, and sends the determined determined intent grammar data 114 to the analysis result output module 116, and the analysis result output module 116 Then, based on the determined intent grammar data 114, the analysis result 104 is transmitted to the server (not shown), and then sent to the user after the information required by the user is queried. Should pay attention to Yes, the analysis result 104 may include the keyword 108, and may also output partial information (eg, the number of the record 302) of the record containing the keyword 108 (eg, the record of FIG. 3A/3B), or all of the information. In addition, the analysis result 104 can be directly converted into a voice output to the user by the server, or after a specific process, and then the corresponding voice is output to the user (the specific method and the content and information included later will be described in detail later). The information output by the retrieval system 200 can be designed by a person skilled in the art according to actual needs, which is not limited by the present invention.

The analysis result output module 116 may be combined with other modules, for example, may be incorporated into the knowledge assisted understanding module 400 in one embodiment, or separated from the natural language understanding system 100 in another embodiment. Located in the server (eg, including the natural language understanding system 100), the server will then receive the intent grammar data 114 for processing. In addition, the knowledge assisted understanding module 400 can store the intent data 112 in a storage device inside the module, in the natural language understanding system 100, in the server (for example, including the natural language understanding system 100), or in any The storage for the knowledge-assisted understanding module 400 can be retrieved, which is not limited by the present invention. Furthermore, the natural language understanding system 100, including the retrieval system 200, the natural language processor 300, and the knowledge-assisted understanding module 400 can be constructed by hardware, software, firmware, or various combinations of the above, and the present invention is not This is a limitation.

The aforementioned natural language understanding system 100 may be located in a cloud server, or may be located in a server in a local area network, or even in a personal computer, a mobile computer device (such as a notebook computer) or a mobile communication device (such as a mobile phone). Natural language understanding The components in the system 100 or the retrieval system 200 do not necessarily need to be disposed in the same machine, but may be dispersed in different devices or systems through various communication protocols depending on actual needs. For example, the natural language understanding processor 300 and the knowledge assisted understanding module 400 can be configured in the same smart phone, and the retrieval system 200 can be configured in another cloud server; or, the retrieval interface unit 260, natural language understanding processing The device 300 and the knowledge assisted understanding module 400 can be configured in the same notebook computer, and the search engine 240 and the structured database 220 can be configured in another server in the local area network. In addition, when the natural language understanding system 100 is located at the server (whether it is a cloud server or a local area network server), the retrieval system 200, the natural language understanding processor 300, and the knowledge assistance understanding module 400 may be configured differently. In the host computer, the server main system coordinates the transmission of information and data between them. Of course, the retrieval system 200, the natural language understanding processor 300, and the knowledge-assisted understanding module 400 can also combine two or all of them into a computer host according to actual needs, and the present invention does not limit the configuration of this part.

In an embodiment of the present invention, the user can send request information to the natural language processor 300 in various ways, such as sending a request message by means of a spoken voice input or a text description. For example, if the natural language understanding system 100 is located in a server (not shown) in the cloud or regional network, the user may first input the request by using a mobile device (such as a mobile phone, PDA, tablet, or the like). The information 102 is then transmitted to the natural language understanding system 100 in the server through the telecommunication system operator to allow the natural language processor 300 to perform the analysis of the request information 102. Finally, after confirming the user's intention, the server Output through analysis results After the module 116 passes the corresponding analysis result 104 to the server, the module 116 transmits the information requested by the user back to the user's mobile device. For example, the request information 102 may be a question that the user desires to obtain an answer by the natural language understanding system 100 (eg, "How is the weather in Shanghai tomorrow"), and the natural language understanding system 100 analyzes the user's intention is When the weather of Shanghai Tomorrow is queried, the weather data to be queried will be sent to the user as the output result 104 through the analysis result output module 116. In addition, if the user's instruction to the natural language understanding system 100 is "I want to see the bullet fly", "I want to listen to the days I walked together", because "let the bullets fly" or "the days that have passed together" Different fields may be included, so the natural language processor 300 will parse the user's request information 102 into one or more possible intent grammar materials 106, which may include the keyword 108 and the intent data 112, and then After the full-text search is performed on the structured material 240 in the search system 220, the user's intention is further confirmed.

Further, when the user's request information 102 is "How is the weather in Shanghai tomorrow?", the natural language processor 300 can generate a possible intent grammar data 106 after analysis: "<queryweather>, <city>= Shanghai, <time> = tomorrow.

In an embodiment, if the natural language understanding system 100 considers that the intention of the user is quite clear, the user's intention (ie, querying the weather in Shanghai tomorrow) can be directly output to the server through the analysis result output module 116. And the server can transmit the user to the user in the weather specified by the user. For another example, when the user's request information 102 is "I want to see the Romance of the Three Kingdoms", the natural language processor 300 After analysis, three possible intent grammar data 106 can be generated: "<readbook>, <bookname>=Three Kingdoms"; "<watchTV>, <TVname>=Three Kingdoms"; and "<watchfilm>, <filmname> = The Romance of the Three Kingdoms."

This is because the keywords 108 (ie, "Three Kingdoms") in the possible grammar material 106 may belong to different fields, that is, books (<readbook>), television dramas (<watchTV>), and movies (<readfilm>). There are three fields, so one request information 102 can be analyzed into a plurality of possible intent grammar materials 106, so further analysis by the knowledge assisted understanding module 400 is needed to confirm the user's intention. As another example, if the user enters "I want to see the bullet fly", because the "let the bullet fly" may be the movie name or the book name, there may be at least the following two possible intent grammars. Source 106: "<readbook>, <bookname>=Let the bullet fly"; and "<watchfilm>, <filmname>=Let the bullet fly"; they belong to two areas of books and movies. The above-mentioned possible intent grammar data 106 is then further analyzed by the knowledge assisted understanding module 400, and the determined intent grammar data 114 is obtained therefrom to express the explicit intent of the user's request information. When the knowledge assisted understanding module 400 analyzes the possible intent grammar data 106, the knowledge assisted understanding module 400 can transmit the keyword 108 (eg, "Three Kingdoms" or "Let the bullet fly") to the retrieval system 200 via the search interface 260. The structured repository 220 in the retrieval system 200 stores a plurality of records having a particular data structure, and the search engine 240 can retrieve the keywords 108 received by the interface unit 260. The structured database 220 performs a full-text search, and transmits the response result 110 obtained by the full-text search to the knowledge-assisted understanding module 400, and then the knowledge-assisted understanding module 400 can obtain the determined intent grammar by responding to the result 110. Information 114. As for the full-text search of the structured repository 220 to determine the details of the intent grammar data 114, a more detailed description will be provided later through FIGS. 3A, 3B and related paragraphs.

In the concept of the present invention, the natural language understanding system 100 can first retrieve the keyword 108 in the user's request information 102, and determine the domain attribute of the keyword 108 by the full-text search result of the structured database 220, such as the above. When you enter "I want to see the Romance of the Three Kingdoms", it will generate possible intent grammar materials 106 belonging to the three fields of books, TV series, and movies, and then further analyze and confirm the user's clear intentions. Therefore, users can easily express their intentions or information in a colloquial manner, without having to memorize specific terms, such as specific terms in the existing practice regarding fixed word lists.

2 is a diagram showing the results of analysis of various request information of a user by the natural language processor 300, in accordance with an embodiment of the present invention.

As shown in FIG. 2, when the user's request information 102 is "How is the weather in Shanghai tomorrow?", after the natural language processor 300 analyzes, the possible intent grammar data 106 can be generated as: "<queryweather>, <city >=Shanghai, <time>=Tomorrow"

The intent data 112 is "<queryweather>", and the keyword 108 is "Shanghai" and "tomorrow". Since only a set of intent grammar data 106 (query weather <queryweather>) is obtained after analysis by the natural language processor 300, in an embodiment The knowledge assisted understanding module 400 can directly retrieve the keywords 108 "Shanghai" and "Tomorrow" as the analysis result 104 and send them to the server to query the weather information (for example, query the weather profile of Shanghai tomorrow, including weather, temperature, etc. Information), without having to perform a full-text search on the structured repository 220 to determine user intent. Of course, in an embodiment, the structured database 220 can still be subjected to full-text search for more accurate user intent determination, and those skilled in the art can make changes according to actual needs.

In addition, when the user's request information 102 is "I want to see the bullet fly", because two possible intent grammar materials 106 can be generated: "<readbook>, <bookname>=let the bullet fly"; and "<watchfilm> , <filmname>=let the bullet fly"; with two corresponding intent materials 112"<readbook>" and "<watchfilm>", and two identical keywords 108 "let the bullet fly" to indicate that the intention may be Look at the book "Let the bullets fly" or watch the movie "Let the bullets fly." To further confirm the user's intent, the keyword 108 "Let the bullet fly" be sent to the search interface unit 260 via the knowledge assisted understanding module 400, and then the search engine 240 uses the keyword 108 "Let the bullet fly" to structure the data. The library 220 performs a full-text search to confirm that "let the bullet fly" should be the title of the book or the name of the movie to confirm the user's intention.

Furthermore, when the user's request information 102 is "I want to listen to the day I walked together", two possible intent grammar materials 106 can be generated: "<playmusic>, <singer>= walked together, <songname>= Day ";"<playmusic>, <songname>=days passed together"

Two corresponding identical intent materials 112"<playmusic>", and two The group corresponding to the keyword 108 "walked together" with "days" and "the days that have passed together" to indicate that their intention may be to listen to the song "Day" that the singer "walked together" or listen to the song. "Days passed together", at this time, the knowledge assisted understanding module 400 can transmit the first set of keywords 108 "walking together" to the "days" and the second set of keywords "todays" to the search interface unit 260, to confirm whether there is a "day" song sung by the singer (the user's intention implied by the first set of keywords), or whether there is a "day of the past" song (the first) The user intent implied by the two sets of keywords) to confirm the user's intention. However, the present invention is not limited to the format and name corresponding to each possible intent grammar material and intent material represented herein.

3A is a diagram of a plurality of records having a particular data structure stored by structured database 220, in accordance with an embodiment of the present invention.

In general, in some existing full-text search methods, the search results obtained are unstructured data (such as those searched through Google or Baidu), because the information of the search results is scattered and unrelated. Therefore, the user must review each piece of information one by one, thus causing practical limitations. However, in the concept of the present invention, the efficiency and correctness of the retrieval can be effectively improved by the structured database. Because the numerical data contained in each record in the structured database disclosed by the present invention is related to each other, and the numerical data is used together to express the attributes of the record. Therefore, when the search engine performs a full-text search on the structured database, when the recorded numerical data matches the keyword, the guidance data corresponding to the numerical data is output as the intention to confirm the requested information. The implementation details of this section will be further described through the following examples.

In the embodiment of the present invention, each record 302 stored in the structured database 220 includes a title field 304 and a content field 306. The title field 304 includes a plurality of field positions 308, each of which includes a guide. Field 310 and value field 312, the navigation field 310 of the plurality of records 302 is used to store the guidance data, and the value field 312 of the plurality of records 302 is used to store the numerical data. Here, the record 1 shown in FIG. 3A is exemplified, and the three sub-fields 308 in the title field 304 of the record 1 are respectively stored: "singerguid: Andy Lau", "songnameguid: the days that have passed together"; "songtypeguid: RTHK, Cantonese, popular"; the guide field 310 of each sub-field 308 stores the guide data "singerguid", "songnameguid" and "songtypeguid", respectively, and the corresponding field 312 of the corresponding sub-field 308 The numerical data "Andy Lau", "the days that passed together" and "Hong Kong and Taiwan, Cantonese, popular" were stored. In the field of the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer, the singer The domain type of "representative numerical data", Hong Kong and Taiwan, Cantonese, and popular is the song type. The various reference materials herein may be represented by different specific strings or words, respectively, and are not limited thereto. The content field 306 of the record 1 is the lyrics content of the song that stores the "days that have passed together" or stores other materials (such as composer/writer, etc.), but in the content field 306 of each record. Real information is not the focus of the invention, so This is only schematically illustrated in Figure 3A.

In the foregoing embodiment, each record includes a title field 304 and a content field 306, and the field 308 in the title field 304 includes a guide field 310 and a value field 312, but is not limited to the present invention. There may also be no content field 306 in some embodiments, or even some of the embodiments may have no navigation field 310.

In addition, in the embodiment of the present invention, a first special character is stored between the data of each sub-field 308 to separate the data of each sub-field 308 in the navigation field 310 and the value field 312. The second special character is stored between the data to separate the information of the guide field and the value field. For example, as shown in FIG. 3A, between "singerguid" and "Andy Lau", "songnameguid" and "days passed together", and "songtypeguid" and "Hong Kong and Taiwan, Cantonese, popular" are The second special character ":" is used for the separation, and the respective sub-fields 308 of the record 1 are separated by the first special character "∣", but the invention is not limited to ":" or "∣" "Let's be used as a special character to separate.

On the other hand, in the embodiment of the present invention, each of the sub-fields 308 in the title field 304 may have a fixed number of bits. For example, the fixed number of bits of each sub-field 308 may be 32 words, and the guide bar therein. The fixed number of bits of bit 310 can be 7 or 8 bits (used to direct 128 or 256 different profiles), and in addition, the number of bits required for the first special character and the second special character can be Fixed, so the fixed number of digits of the sub-field 308 can be stored in the remaining digits after deducting the number of bits occupied by the index field 310, the first special character, and the second special character. Numerical data for value field 312. Furthermore, since the number of digits of the sub-field 308 is fixed, the sub-field 308 is added. The content of the stored data may be the guidance field 310 (indicator of the guidance data), the first special character, the numerical data of the numerical field 312, and the second special character, as shown in FIG. 3A, and as described above. The number of bits of the four data is also fixed, so the bits of the hint field 310 can be skipped (eg, skipping the first 7 or 8 bits) and the number of bits of the second special character (eg, After skipping 1 word, that is, 8 bits, and then deducting the number of bits occupied by the first special character (for example, the last 1 word, 8 bits), the value field can be directly obtained. The numerical data of 312 (for example, the numerical data is directly taken out in the first sub-field 308 of the record 1), and there are 32-3=29 words for storing the numerical data of the numerical field 312 in the calculation formula. 3 (ie, 1+1+1) represents the guidance data of the directed field 310, the first special character, and the second special character respectively, and then the required field type Just judge. Therefore, after the currently obtained numerical data comparison is completed (regardless of whether the comparison is successful or not), the numerical data of the next sub-field 308 can be taken out according to the above-mentioned method for taking out the numerical data (for example, in the second of the record 1). In the sub-field 308, the numerical data "days passed together" is directly taken out to compare the types of the comparison fields. The above method of taking out the numerical data can be compared from the record 1, and after comparing all the numerical data of the record 1, the numerical data of the first sub-field 308 in the title field 308 of the record 2 is taken out (for example "Feng Xiaogang") for comparison. The above comparison program will continue until all recorded values have been compared.

It should be noted that the number of bits in the above-mentioned column 308, and the number of bits used in the index field 310, the first special character, and the second special character may be changed according to actual applications, and the present invention does not Limit it. The foregoing uses the alignment to extract the numerical data. The manner of the present invention is only an embodiment, but is not intended to limit the present invention, and another embodiment may be performed by using a full-text search. In addition, the implementation of the skip pointer field 310, the second special character, and the first special character may be achieved by using a bit translation (eg, division), which may be implemented by hardware, software, or The combination of the two can be carried out by a person skilled in the art according to actual needs. In another embodiment of the present invention, each of the sub-fields 308 in the title field 304 may have a fixed number of digits, the index field 310 in the sub-field 308 may have another fixed number of digits, and the title field 304 The first special character and the second special character may not be included. Since the number of bits in each of the sub-fields 308 and the index fields 310 is fixed, the method of skipping the specific number of bits or using the bit shifting may be used. The method (for example, division) is used to directly retrieve the guidance data or numerical data in each of the sub-fields 308.

It should be noted that since the sub-field 308 has been mentioned to have a certain number of bits, a counter can be used in the natural language understanding system 100 (or in a server including the natural language understanding system 100) to record the current ratio. It is a sub-field 308 of a record. In addition, the aligned records may also use another counter to store their order. For example, when a first counter record is used to indicate the currently aligned recording order, and a second counter is used to indicate the currently aligned column order, if the current comparison is the one of FIG. 3A When the third sub-column 308 of record 2 is 308 (that is, the comparison "filenameguid: Huayi Brothers"), the value stored in the first counter will be 2 (indicating that the current comparison is record 2), the second counter is The stored value is 3 (indicating that the third sub-column 308 is currently aligned). Furthermore, the above manner of storing the guidance data of the guidance field 310 only by 7 or 8 bits is intended to be large in the sub-field 308. The multi-digits are used to store the numerical data. The actual guidance data can be used as an indicator through the 7 and 8 digits, and the actual guidance data can be read from the guidance data storage device 280 stored in the retrieval system 200. The guidance material is stored in a tabular manner, but any other means available for access by the retrieval system 200 can be used in the present invention. Therefore, in actual operation, in addition to directly extracting the numerical data for comparison, when the matching result is generated, the guidance data may be directly taken out as the response result 110 to the knowledge assisting understanding mode according to the values of the two counters. Group 400. For example, when the second sub-field 308 of record 6 (ie, "songnameguid: betrayal") is successfully matched, it will be known that the current first counter/second counter have values of 6 and 2, respectively, so According to these two values, the guidance data storage device 280 shown in FIG. 3C is stored, and the guidance data is queried from the sub-field 2 of the record 6 as "songnameguid". In an embodiment, after the number of digits of the sub-field 308 is fixed, all the bits of the sub-field 308 are used to store the numerical data, so that the guide field, the first special character, and the Two special characters, and the search engine 240 only knows that each time the fixed number of digits is crossed, it is another sub-field 308, and one of the second counters is added (of course, the first one is required for each record change) The stored value of the counter is incremented by one), which provides more bits to store the value data.

Another example is given to illustrate the process of returning the matching record 110 to the knowledge assisted understanding module 400 for further processing when the matching result is generated. Corresponding to the data structure of the above record 302, in the embodiment of the present invention, when the user's request information 102 is "I want to see the bullet fly", two possible intent grammar materials may be generated. 106: "<readbook>, <bookname> = let the bullet fly"; and "<watchfilm>, <filmname> = let the bullet fly"; the search engine 240 retrieves the bullet by the keyword 108 received by the retrieval interface unit 260 The "fly" is used to perform a full-text search on the title field 304 of the record stored in the structured database 220 of FIG. 3A. In the full-text search, the record 5 storing the numerical data "Let the bullet fly" is found in the title field 304, and thus a matching result is produced. Next, the retrieval system 200 will return the guidance material "filmnameguid" corresponding to the keyword 108 "Let the bullet fly" in the header field 304 of the backhaul record 5 to the knowledge assisted understanding module 400. Since the title data of the record 5 contains the guide material "filmnameguid" corresponding to the numerical data "let the bullet fly", the knowledge assisted understanding module 400 compares the guide material "filmnameguid" of the record 5 with the above-mentioned possible intention grammar data. 106 previously stored intent data 112"<watchfilm>" or "<readbook>", it can be determined that the determination information of the request information is 114 "<watchfilm>, <filmname>= let the bullet fly" (because Both contain "film" in it). In other words, the information described in the user's request information 102 "let the bullet fly" is the movie name, and the data user's request information 102 is intended to watch the movie "let the bullet fly" instead of reading the book.

Give another example for further explanation. When the user's request information 102 is "I want to listen to the day I walked together", two possible intent grammar materials 106 may be generated: "<playmusic>, <singer>= walked together, <songname>=day"; versus "<playmusic>, <songname>=days passed together"; the search engine 240 retrieves the two sets of keywords 108 received by the interface unit 260: "walking together" and "days"; and "walking together" Days"

The full-text search is performed on the title field 304 of the record stored in the structured database 220 of FIG. 3A. Due to the full-text search, the matching results corresponding to the first group of keywords 108 "going through" and "days" are not found in the title field 304 of all the records, but the corresponding corresponding to the second group of keywords 108 is found. Record 1 of "Days Walking Together", then the retrieval system 200 records the guidance material "songnameguid" corresponding to the second group of keywords 108 in the title field 304 as the matching record 110 and returns it to the knowledge-assisted understanding mode. Group 400. Next, after receiving the guidance material "songnameguid" corresponding to the value data "days passed together", the knowledge assisted understanding module 400 walks with the possible intention grammar material 106 (ie, "playmusic>, <singer>=" After that, <songname>=days are compared with the intent data 112 (ie, <singer>, <songname>, etc.) in "<playongic>, <songname>=days passed together), so this is found. The sub-user's request information 102 does not describe the material of the singer's name, but describes the material whose song name is "the day that passed together" (because only <songname> is successful). Therefore, the knowledge assisted understanding module 400 can determine, by the above comparison, that the determined intent grammar data 114 of the request information 102 is "<playmusic>, <songname>=days passed together", and the user requests information. The intent of 102 is to listen to the song "the days that have passed together."

In another embodiment of the present invention, the retrieved response result 110 may be an exact match record that exactly matches the keyword 108, or a partial match record that partially matches the keyword 108. For example, if the user's request information 102 is "I want to listen to Xiao Jingteng's betrayal", similarly, the natural language processor 300 analyzes to generate two possible intent grammar materials 106: "<playmusic>, <singer> = Xiao Jingteng, <songname>=betrayal; and "<playmusic>, <songname>=Xiao Jingteng's betrayal"; and send two sets of keywords 108: "Xiao Jingteng" and "betrayal"; and "Xiao Jingteng's betrayal"; The interface unit 260, the search engine 240 then performs a full-text search of the title field 304 of the record 302 stored in the structured database 220 of FIG. 3A by the keyword 108 received by the search interface unit 260. Since in the full-text search, the second group of keywords 108 "Xiao Jingteng's betrayal" did not match any records, but the first group of keywords 108 "Xiao Jingteng" and "betrayal" found the matching result of record 6 and record 7. . Since the second group of keywords 108 "Xiao Jing Teng" and "Betrayal" only match the numerical data "Xiao Jing Teng" in Record 6, but do not match other numerical data "Yang Zongwei" and "Cao Ge", record 6 is a partial matching record. (Please note that the above-mentioned corresponding request information 102 "I want to see the bullet 5" and the corresponding request information "I want to listen to the day that I walked together" record 1 is a partial match record), and the keyword "Xiao Jingteng" and "Betrayal" exactly matches the value of record 7 (because the second set of keywords 108 "Xiao Jingteng" and "Betrayal" match successfully), so record 7 is an exact match record. In the invention In the embodiment, when the search interface unit 260 outputs the plurality of matching records 110 to the knowledge assisted understanding module 400, the complete matching records (that is, all the numerical data are matched) and the partial matching records may be sequentially output (ie, Only partial data is matched to the matching record 110, wherein the priority of the exact match record is greater than the priority of the partial match record. Therefore, when the search interface unit 260 outputs the matching record 110 of the record 6 and the record 7, the output priority order of the record 7 is greater than the output priority order of the record 6, because all the numerical data "Xiao Jingteng" and "Betrayal" of the record 7 are generated. Match the results, but record 6 also contains "Yang Zongwei" and "Cao Ge" did not produce a match. That is, the higher the degree to which the records stored in the structured repository 220 match the keywords 108 in the request information 102, the easier it is to be prioritized for the user to review or select the corresponding determined intent grammar data 114. In another embodiment, the matching record 110 corresponding to the record with the highest priority can be directly output as the determination of the intended grammar data 114. The foregoing is not intended to limit the invention, as in another embodiment it may be possible to take the form of the search as long as the matching record is found (for example, "I want to hear Xiao Jingteng's betrayal" as the request information 102, when the record 6 is retrieved That is, when the matching result is generated, the guidance data corresponding to the record 6 is output as the matching record 110), and the ordering of the priority order is not included to speed up the retrieval. In another embodiment, the corresponding processing mode can be directly executed and provided to the user for the record with the highest priority. For example, when the movie with the highest priority is the movie of the Three Kingdoms, the movie and the user can be directly played. In addition, if the highest priority is the betrayal of Xiao Jingteng's singing, the song can be played directly with the user. It should be noted that the present invention is described herein only and is not intended to be limiting.

In still another embodiment of the present invention, if the user's request information 102 is "I want to listen to Andy Lau's betrayal", then one of the possible intent grammar materials 106 is: "<playmusic>, <singer> = Andy Lau, <songname>=betrayal; if the search interface unit 260 enters the keywords 108 "Andy Lau" and "betrayal" into the search engine 240, no matching results will be found in the database of FIG. 3A. In still another embodiment of the present invention, the search interface unit 260 may input the keywords 108 "Andy Lau" and "betrayal" into the search engine 240, respectively, and respectively determine that "Andy Lau" is the singer name (guide material singerguid) and " "Betrayal" is the name of the song (the guide information is songnameguid, and the singer may be Cao Ge or Xiao Jingteng, Yang Zongwei and Cao Ge chorus). At this time, the natural language understanding system 100 can further remind the user: "Is the betrayal of this song sung by Xiao Jingteng (according to the matching result of record 7)?", or, "Whether it is chorus by Xiao Jingteng, Yang Zongwei and Cao Ge (according to record 6) Match the result)?".

In still another embodiment of the present invention, the record stored in the structured database 220 may further include a source field 314 and a heat field 316. The database shown in FIG. 3B includes, in addition to the fields of FIG. 3A, a source field 314 heat field 316, a favorite field 318, and an aversive field. The source field 314 of each record can be used to store which structured database the record originated from (in this figure only the structured database 220 is displayed, but in fact there can be more different structured databases), or Which user and server provide the source value. Moreover, the natural language understanding system 100 can retrieve a structured repository of a particular source based on preferences that the user has disclosed in the previous request message 102 (eg, by requesting the keyword 108 in the information 102). When the text search produces a match, the heat value of the record is incremented by one). The heat field 316 of each record 302 is used to store the search heat value or the popularity value of the record 302 (for example, the number of matches or the probability that the record is matched by a single user, a specific user group, all users at a specific time). For the reference of the knowledge assisted understanding module 400 to determine the user's intention, the use of the favorite field 318 and the aversion field will be described in detail later. In detail, when the user's request information 102 is "I want to see the Romance of the Three Kingdoms", the natural language processor 300 can generate a plurality of possible intent grammar materials 106 after analysis: "<readbook>, <bookname>=Three Kingdoms The Romance ";" <watchTV>, <TVname>=The Romance of the Three Kingdoms; and "<watchfilm>, <filmname>=The Romance of the Three Kingdoms".

If the natural language understanding system 100 is in the history of the user's request information 102 (for example, by using the heat field 316 to store the number of times the record 302 was clicked by a certain user), most of the requests are counted as movies. The natural language understanding system 100 can perform a search for the structured database storing the movie records (at this time, the source value in the source field 314 is the code for recording the structured database storing the movie records), so that the priority can be determined. "<watchfilm>, <filmname>=Three Kingdoms" is used to determine the intent grammar material 114. For example, in an embodiment, each record 302 can also be matched once, and one of the subsequent heat fields 316 can be added as a history of the user. Therefore, when the full-text search is performed according to the keyword 108 "Three Kingdoms", the record 302 having the highest value among the heat field 316 can be selected from all the matching results as the judgment of the user's intention. In an embodiment, if the natural language understanding system 100 searches for the "Three Kingdoms" in the search result of the keyword "Three Kingdoms", If the search heat value stored in the hot field 316 of the program has the highest value, then "<watchTV>, <TVname>=Three Kingdoms" can be preferentially determined to determine the intentional grammar data 114. In addition, the manner of changing the value stored in the heat field 316 can be changed by the computer system in which the natural language understanding system 100 is located, which is not limited by the present invention. In addition, the value of the heat field 316 may also decrease with time to indicate that the user's heat for a certain record 302 has gradually decreased, and the present invention does not limit this portion.

As another example, in another embodiment, since the user may particularly like to watch the drama of the Three Kingdoms in a certain period of time, since the length of the drama may be long and the user cannot read it for a short time, in a short time It is possible to repeat the selection (assuming that the value in the heat field 316 is incremented by one each time it is matched), thus causing a certain record 302 to be repeatedly matched, which can be known by analyzing the data of the heat field 316. Moreover, in another embodiment, the telecommunications operator may also utilize the popularity field 316 to indicate the popularity of the material provided by a source, and the data provider's code may be stored in the source field 314. For example, if a supplier who supplies the "Three Kingdoms TV series" has the highest probability of being selected, when a user enters the "I want to see the Romance of the Three Kingdoms" request information 102, although in the database of Figure 3B When you are doing a full-text search, you will find a book reading the Romance of the Three Kingdoms (Record 8), watching the drama of the Three Kingdoms (Record 9), and watching the Three Kingdoms (10), but the data in the Heat 316 shows the Three Kingdoms. The TV series is now the most popular option (that is, the values of the heat fields of records 8, 9, and 10 are 2, 5, and 8 respectively), so the guidance data of the record 10 will be provided first to make the matching record 110 output to The knowledge assisted understanding system 400 serves as the top priority for determining user intent. In one embodiment, the data of the source field 314 can be displayed to the user at the same time, allowing the user to determine whether the television show he wants to watch is provided by a provider. It should be noted that the information stored in the source field 314 and the manner of changing the same may also be changed through the computer system in which the natural language understanding system 100 is located, which is not limited by the present invention. It should be noted that those skilled in the art should further further cut the information stored in the heat field 316, the favorite field 318, and the aversion field 320 in FIG. 3B into related to the user and related to all users. The two parts, and the heat field 316, the favorite field 318, and the aversion field 320 information related to the user are stored in the user's mobile phone, and the server stores the heat field 316 associated with all users, and the favorite field. 318, aversion to the field 320 and other information. In this way, information about personal preferences related only to the user's personal choice or intent is stored only in the user's personal mobile communication device (eg, mobile phone, tablet, or small notebook, etc.), and the server The information related to the user is stored, which not only saves the storage space of the server, but also preserves the privacy of the user's personal preference.

Obviously, the numerical data contained in each record in the structured database disclosed by the present invention is related to each other (for example, the numerical data in the record 1 "Andy Lau", "days passed together", "port Taiwan, Cantonese, popular "is used to describe the characteristics of record 1," and these numerical data are used together to express the intent of the request information from the user (for example, when the "days passed together" are matched. Indicates that the user's intention may be access to the data of record 1), so when the search engine performs a full-text search on the structured database, the recorded numerical data may be At the time of the distribution, the guidance data corresponding to the numerical data (for example, the output "songnameguid" as the response result 110) is output, thereby confirming the intention of the requested information (for example, the comparison is performed in the knowledge assisted understanding module 400).

Based on the disclosure or teachings of the above exemplary embodiments, FIG. 4A is a flowchart of a retrieval method in accordance with an embodiment of the present invention. Referring to FIG. 4A, the retrieval method of the embodiment of the present invention includes the following steps: providing a structured database, and the structured database stores a plurality of records (step S410); receiving at least one keyword (step S420); The word is used to perform full-text search on the title fields of the plurality of records (step S430). For example, the keyword 108 is input into the search interface unit 260 to cause the search engine 240 to perform a full-text search on the title field 304 of the plurality of records 302 stored in the structured database 220. The search mode may be as shown in FIG. 3A or FIG. The search method performed by 3B or the manner in which the spirit is not changed is performed; whether or not the full-text search has a matching result is determined (step S440). For example, the search engine 240 determines whether the full-text search corresponding to the keyword 108 has a matching result; and if there is a matching result, the exact match record and the partial match record are sequentially output (step S450). For example, if there is a record matching the keyword 108 in the structured database 220, the search interface unit 260 sequentially outputs the matching data in the exact matching record and the partial matching record matching the keyword 108 (via the pair of FIG. 3C). The guidance data storage device 280 is obtained as a response result 110 sent to the knowledge assistance understanding system 400, wherein the priority order of the exact match record is greater than the priority order of the partial match record.

On the other hand, if there is no matching result, the user can be directly notified of the matching failure and the process is terminated, the user is notified that the matching result is not found, and further input is required, or the possible options are listed to further select the user (for example, the foregoing). Liu Dehua and "Betrayal" do not produce a matching result in the full-text search) (step 460).

The foregoing process steps are not intended to limit the present invention, and some steps may be omitted or removed. For example, in another embodiment of the present invention, a matching judgment module located outside the retrieval system 200 may be used (not shown in the figure). Step S440 is performed; or in another embodiment of the present invention, the above step S450 may be omitted, and the actions of sequentially outputting the exact match record and the partial match record may be output by the matching result located outside the retrieval system 200. The module (not shown in the figure) performs the actions of sequentially outputting the exact match record and the partial match record in step S450.

4B is a flowchart of the operation of the natural language understanding system 100 in accordance with another embodiment of the present invention, based on what is disclosed or taught by the above-described exemplary embodiments. Referring to FIG. 4B, the natural language understanding system 100 of the other embodiment of the present invention includes the following steps: receiving request information (step S510). For example, the user transmits the request information 102 having the voice content or the text content to the natural language understanding system 100; provides a structured database, and the structured database stores a plurality of records (step S520); grammatically digitizes the request information ( Step S530). For example, a natural language processor 300 analyzes the user's request information 102, and then converts to the corresponding possible intent grammar data 106; identifies the possible attributes of the keyword (step S540). For example, the knowledge assisted understanding module 400 identifies possible attributes of at least one of the keywords 108 that may be intended to be grammar material 106, for example, the keyword 108 "Three Kingdoms" may be a book, a movie, and a television program; The full-text search is performed on the title field 304 of the plurality of records (step S550). For example, the keyword 108 is input into the search interface unit 260 to cause the search engine 240 to perform full-text search on the title field 304 of the plurality of records stored in the structured database 220; whether the full-text search has a matching result (step S560) . For example, the search engine 240 determines whether the full-text search corresponding to the keyword 108 has a matching result; if there is a matching result, sequentially outputs the matching data corresponding to the full matching record and the partial matching record (step S570). Response result 110. For example, if a record in the structured database 220 matches the keyword 108, the search interface unit 260 sequentially outputs the guide data corresponding to the match record and the partial match record of the keyword 108 as the response result 110. The priority order of the exact match records is greater than the priority order of the partial match records; and the corresponding determined intent grammar data is sequentially output (step S580). For example, the knowledge-assisted understanding module 400 outputs the matched records and partial matches in sequence. Recording, by which the corresponding determined intent grammar data 114 is output.

On the other hand, if the matching result is not generated in step S560, it may be processed in a manner similar to step S460, for example, directly notifying the user that the matching fails and ending the process, notifying the user that the matching result is not found and requesting further input, or The possible options are listed for the user to make further choices (for example, the foregoing example in which "Andy Lau" and "Betrayal" do not produce a matching result in the full-text search) (step S590).

The foregoing process steps are not intended to limit the invention, and some steps may be omitted or removed.

In summary, the present invention performs full-text search by extracting keywords included in the user's request information and for the title field of the record having the specific data structure in the structured database, and if a matching result is generated, Determine the category of the domain to which the keyword belongs to determine the intent of the user to request the information.

Next, we will explain more about the application of the above structured database in speech recognition. Firstly, in the natural language dialogue system, the wrong voice response is corrected according to the user's voice input, and further possible answers are found to report the application to the user.

As mentioned earlier, today's mobile communication devices have been able to provide natural language conversations to allow users to voice to communicate with mobile communication devices. However, in the current voice dialogue system, when the user's voice input is not clear, since the same sentence voice input may mean a plurality of different intentions or purposes, the system may easily output a voice response that does not conform to the voice input. Therefore, in many conversation situations, it is difficult for a user to obtain a voice response that meets his or her intention. To this end, the present invention proposes a modified speech response The method and the natural language dialogue system, wherein the natural language dialogue system can repair the wrong voice response according to the user's voice input, and further find other possible answers to report back to the user. In order to clarify the content of the present invention, the following specific examples are given as examples in which the present invention can be implemented.

FIG. 5A is a block diagram of a natural language dialogue system according to an embodiment of the invention. Referring to FIG. 5A, the natural language dialogue system 500 includes a speech sampling module 510, a natural language understanding system 520, and a speech synthesis database 530. In an embodiment, the voice sampling module 510 is configured to receive the first voice input 501 (eg, voice from the user), and then parse it to generate the first request information 503, and the natural language understanding system 520 will again A request information 503 is parsed to obtain the first keyword 509 therein, and after finding the first return answer 511 that meets the first request information 503 (according to the description of FIG. 1, the first request information 503 can be the same as the request information 102 The method of processing, that is, the request information 102 will generate the possible intent grammar data 106 after the analysis, and the keyword 108 therein will be used to perform a full-text search on the structured database 220 to obtain the response result 110, and the response result 110 The determined intent grammar data 114 is generated by comparison with the intent data 112 in the possible intent grammar data 106, and finally the analysis result 104 is sent by the analysis result output module 116, and the analysis result 104 can be used as the first return answer 511 in FIG. 5A. According to the first return answer 511, the voice synthesis database 530 performs a corresponding voice query (because the analysis result 104 as the first answer 511 can be included) / Partial matching records related information 302 (e.g., stored in the guidance information guidance field 310, the numerical value data field 312, and information in the content field 306 ... etc.), and therefore can use these The data is queried, and the first voice 513 that is queried is output to generate a first voice response 507 corresponding to the first voice input 501 to the user. Wherein, if the user believes that the first voice response 507 output by the natural language understanding system 520 does not conform to the first request information 503 in the first voice input 501, the user inputs another voice input, such as the second voice input 501', To indicate this. The natural language understanding system 520 processes the second voice input 501' using the same processing method as the first voice input 501 to generate the second request information 503', and then parses the second request information 503' to obtain the first The second keyword 509' finds the second return answer 511' that matches the second request information 503', finds the corresponding second voice 513', and finally generates the corresponding second voice response 507' output according to the second voice 513'. To the user, as a correction to the first return answer 511. Obviously, the natural language understanding system 520 can be based on the natural language understanding system 100 of FIG. 1, and a new module (which will be explained in conjunction with the subsequent FIG. 5B) is added to correct the erroneous voice response according to the user's voice input. the goal of.

The components in the aforementioned natural language dialogue system 500 can be configured in the same machine. For example, the speech sampling module 510 and the natural language understanding system 520 are, for example, disposed on the same electronic device. The electronic device may be a mobile phone (Cell phone), a Personal Digital Assistant (PDA) mobile phone, a smart phone, a mobile communication device, a Pocket PC, or a tablet (Tablet). PC), notebook computers, personal computers, or other electronic devices that have communication functions or are equipped with communication software are not limited in scope here. In addition, the above electronic device can use the Android operating system, Microsoft The operating system, the Android operating system, the Linux operating system, and the like are not limited thereto. Of course, the components in the aforementioned natural language dialogue system 500 do not necessarily need to be disposed in the same machine, but may be distributed in different devices or systems and connected through various different communication protocols. For example, the natural language understanding system 520 can be located in a cloud server or a server in a local area network. Moreover, the various components of the natural language understanding system 520 can also be distributed among different machines. For example, the components of the natural language understanding system 520 can be located in the same or different machines as the speech sampling module 510.

In this embodiment, the voice sampling module 510 is configured to receive voice input. The voice sampling module 510 can be a device for receiving audio such as a microphone, and the first voice input 501 / the second voice input 501 ′ can be Voice from the user.

In addition, the natural language understanding system 520 of the present embodiment can be implemented by a hardware circuit composed of one or several logic gates. Alternatively, in another embodiment of the present invention, the natural language understanding system 520 can be implemented by computer code. For example, the natural language understanding system 520 is, for example, a code segment written by a programming language, implemented in an application, an operating system, or a driver, and the code segments are stored in a storage unit and processed by the processing unit. (not shown in Figure 5A) to perform. In order to enable those skilled in the art to further understand the natural language understanding system 520 of the present embodiment, an example will be described below. However, the present invention is intended to be illustrative only, and is not limited thereto. For example, the invention may be implemented by using hardware, software, firmware, or a combination of the three embodiments.

FIG. 5B is a block diagram of a natural language understanding system 520, in accordance with an embodiment of the invention. Referring to FIG. 5B, the natural language understanding system 520 of the present embodiment may include a voice recognition module 522, a natural language processing module 524, and a voice synthesis module 526. The voice recognition module 522 receives the request information transmitted from the voice sampling module 510, such as the first request information 503 for parsing the first voice input 501, and extracts one or more first keywords 509 (for example, The keyword 108 or sentence of FIG. 1A, etc.). The natural language processing module 524 can further parse the first keywords 509 to obtain a candidate list that includes at least one reward answer (the same as the processing of FIG. 5A, that is, for example, through the retrieval system 200 of FIG. 1A). The database 220 performs full-text search, and after obtaining the response result 110 and comparing the intent data 112, the determined intent grammar data 114 is generated, and finally the analysis result 104 sent by the analysis result output module 116 generates a reward answer), and An answer that is more in line with the first voice input 501 is selected from all of the candidate answers of the candidate list as the first return answer 511 (eg, picking a perfect match record, etc.). Since the first return answer 511 is an internal analysis of the natural language understanding system 520, it must also be converted into a speech output before being output to the user so that the user can make a judgment. The speech synthesis module 526 then queries the speech synthesis database 530 according to the first reward answer 511. The speech synthesis database 530 records, for example, text and corresponding speech information, so that the speech synthesis module 526 can find out The first speech 513 corresponding to the first reward answer 511 is used to synthesize the first speech response 507. Thereafter, the speech synthesis module 526 can transmit the synthesized first voice response 507 through a voice output interface (not shown) (wherein the voice output interface is, for example, a speaker, a speaker, or a headphone, etc.) The device) is output to the user. It should be noted that when the speech synthesis module 526 queries the speech synthesis database 530 according to the first return answer 511, the first report answer 511 may need to be format converted first, and then through the interface specified by the speech synthesis database 530. call. Since the need to perform format conversion when calling the speech synthesis database 530 is related to the definition of the speech synthesis database 530 itself, since this portion belongs to a technique well known to those skilled in the art, it will not be described in detail herein.

Next, an example is given to illustrate that if the user inputs the first voice input 501 of "I want to see the Romance of the Three Kingdoms", the voice recognition module 522 receives the first voice input 501 from the voice sampling module 510. The parsed first request information 503 is then taken out, for example, as a first keyword 509 containing "Three Kingdoms". The natural language processing module 524 can then parse the first keyword 509 "Three Kingdoms" (for example, the full-text search of the structured database 220 through the retrieval system 200 of FIG. 1A, and obtain the response result 110 and the intent The data 112 is compared to generate the determined intent grammar data 114, and finally the analysis result 104 sent by the analysis result output module 116), thereby generating a reward answer containing the three intent options of the "Three Kingdoms" and integrating them into a candidate. List (assuming that each intent option has only one return answer, which is categorized as "reading", "watching TV", and "watching movies"), and then selecting one of the three return answers from the candidate list. The heat field 316 has the highest value (e.g., the record 10 of Figure 3B is selected) as the first return answer 511. In an embodiment, the corresponding manner in which the heat field 316 has the highest value may be directly executed (for example, the previously mentioned "betrayal" sung by Xiao Jingteng to the user), which is not limited by the present invention.

In addition, the natural language processing module 524 can also determine the previous number by parsing the subsequently received second voice input 501' (because it is fed into the voice sampling module 510 in the same manner as the previous voice input 501). A return answer 511 is correct. Because the second voice input 501' is a response by the user to the first voice response 507 previously provided to the user, it includes information that the user considered the previous first voice response 507 correct or not. If after analyzing the second voice input 501', it indicates that the user thinks that the first report answer 511 is incorrect, the natural language processing module 524 can select other reward answers in the candidate list as the second report answer 511', for example, from the candidate. After the first return answer 511 is removed from the list, and a second return answer 511' is re-selected in the remaining return answer, the speech synthesis module 526 is used to find the second speech 513' corresponding to the second return answer 511'. Finally, the second voice 513' is synthesized into a second voice response 507' to be played to the user through the voice synthesis module 526.

Continuing the previous user input "I want to see the Romance of the Three Kingdoms", if the user wants to watch the TV series of the Three Kingdoms, the option of recording 10 in Figure 3B previously output to the user (because it is a movie of "Three Kingdoms") Not what the user wants, so the user may enter "I want to watch the drama of the Three Kingdoms" (the user clearly pointed out that he wants to watch a TV series), or "I don't want to watch the Romance of the Three Kingdoms" (the user only denies the current option)... Etc. as the second voice input 501'. Then, after the second voice input 501' obtains its second request information 503' (or the second keyword 509'), it will find that the second keyword 509' in the second request information 503' will contain " The TV series (the user has a clear indication) or "Do not want the movie" (the user only denies the current option), so it will be judged that the first return answer 511 does not meet the user's needs. Therefore, at this time, you can re-list from the candidate list. Select another return answer as the second return answer 511' and output a corresponding second voice response 507', for example, output "I now play the second voice response 507 for you in the Three Kingdoms TV series" (if the user explicitly points out that he wants to watch The Three Kingdoms TV series), or the second voice response 507' that outputs "what option do you want" (if the user only negates the current option), and combines other options in the candidate list for the user to select (eg "choose the heat bar" The second highest return answer 511') is the second highest answer 511'). In another embodiment, if the second voice input 501' input by the user includes a "select" message, for example, "Show the Three Kingdoms" When the user chooses to enter the second voice input 501' of "I want to watch a movie", the user will enter the second choice of "I want to watch the movie", the second choice of the "Royal Book", "Watch the Three Kingdoms TV series", and "Watch the Three Kingdoms" After the second request information 503' of the voice input 501' and the user's intention is found (for example, from the second keyword 509', the user selects "watch movie") Then the second voice input 501' will, after parsing to obtain its second request information 503', output the second voice response 507' of "I am currently playing the Three Kingdoms movie for you" (if the user wants to watch the Three Kingdoms movie) and then directly Play the movie to the user. Of course, if the user enters the "I want the third option" (assuming that the user chooses to read the book at this time), the application corresponding to the third selection will be executed, that is, output " What you want is to read the book of the Three Kingdoms' "Second Voice Response 507" and combine it with the e-book showing the Romance of the Three Kingdoms.

In this embodiment, the voice recognition module 522, the natural language processing module 524, and the voice synthesis module 526 in the natural language understanding system 520 can be disposed in the same machine as the voice sampling module 510. In other embodiments, speech recognition The module 522, the natural language processing module 524, and the speech synthesis module 526 can also be distributed among different machines (eg, computer systems, servers, or the like). For example, the natural language understanding system 520' shown in FIG. 5C, the speech synthesis module 526 can be disposed in the same machine 502 as the speech sampling module 510, and the speech recognition module 522 and the natural language processing module 524 can be configured on another machine. . In addition, under the architecture of FIG. 5C, the natural language processing module 524 transmits the first return answer 511 / the second return answer 511 ' to the speech synthesis module 526, which then returns the answer 511 / the second return answer. The 511' is sent to the speech synthesis database to find the corresponding first speech 513/second speech 513' as a basis for generating the first speech response 507/second speech response 507'.

FIG. 6 is a flow chart of a method for modifying a first voice response 507 according to an embodiment of the invention. In the method for correcting the first voice response 507 in this embodiment, when the user thinks that the currently played first voice response 507 does not match the first request information 503 that was previously input, the second voice input 501 is re-entered. 'When fed into the speech sampling module 510, and then analyzed by the natural language understanding system 520 to know that the first speech response 507 previously played to the user does not conform to the user's intention, the natural language understanding system 520 can output the second again. The voice response 507' is used to correct the original first voice response 507. For convenience of explanation, only the natural language dialogue system 500 of Fig. 5A is taken as an example, but the method of correcting the first voice response 507 of the present embodiment can also be applied to the above-described natural language dialogue system 500' of Fig. 5C.

Referring to FIG. 5A and FIG. 6 simultaneously, in step S602, the voice sampling module 510 receives the first voice input 501 (also fed into the voice sampling module 510). The first voice input 501 is, for example, a voice from a user, and the first voice input is The entry 501 can also have the user's first request information 503. Specifically, the first voice input 501 from the user may be an inquiry sentence, a command sentence, or other request information, such as "I want to see the Romance of the Three Kingdoms", "I want to listen to the music of the water" or "A few degrees of today's temperature", etc. Wait.

In step S604, the natural language understanding system 520 parses the at least one first keyword 509 included in the first voice input 501 to obtain a candidate list, wherein the candidate list has one or more reward answers. For example, when the first voice input 501 of the user is "I want to see the Romance of the Three Kingdoms", the first keyword 509 obtained by the natural language understanding system 520 after analysis is, for example, "The Romance of the Three Kingdoms" and "Look". . For another example, when the first voice input 501 of the user is "I want to listen to the song of forgetting the water", the first keyword 509 obtained by the natural language understanding system 520 after analysis is, for example, "forget the water" and "listen". ,"song"".

After that, the natural language understanding system 520 can perform a query from the structured database 220 according to the first keyword 509 to obtain at least one search result (for example, the analysis result 104 of FIG. 1), which is used as a reward in the candidate list. answer. The manner of selecting the first return answer 511 from the plurality of reward answers may be as described in FIG. 1A and will not be described herein. Since the first keyword 509 may contain different knowledge areas (such as movies, books, music or games, etc.), and the same knowledge field may be further divided into multiple categories (for example, different authors of the same movie or book name) , different singers of the same song name, different versions of the same game name, etc.), so for the first keyword 509, the natural language understanding system 520 can query one or more related documents in the structured database. Search of the first keyword 509 Finding results (eg, analysis results 104), wherein each of the search results may include guidance material related to the first keyword 509 (eg, "Xiao Jingteng", "Betrayal" as a key 108 in the structuring of FIGS. 3A, 3B When the database 220 performs full-text search, for example, the matching results of the records 6 and 7 of FIG. 3A are obtained, which respectively contain the guidance materials of “singerguid” and “songnameguid”, which are the data stored in the guide field 310) And other information. The other information is, for example, in the search result, in addition to other keywords related to the first keyword 709, etc. (for example, "days passed together" as a keyword and in the structured database 220 of FIG. 3A. When the full-text search and record 1 is the matching result, "Andy Lau" and "Hong Kong, Taiwan, Cantonese, and popular" are other materials. Therefore, from another point of view, when the first voice input 501 input by the user has multiple first keywords 509, it indicates that the first request information 503 of the user is clear, so that the natural language understanding system 520 can query more. The search result that is close to the first request information 503.

For example, when the first keyword 509 is "Three Kingdoms" (for example, when the user inputs "I want to see the voice of the Three Kingdoms"), the natural language understanding system 520 may generate three possible intent grammar materials 106 after analysis ( As shown in Figure 1): "<readbook>, <bookname>=Three Kingdoms"; "<watchTV>, <TVname>=Three Kingdoms"; and "<watchfilm>, <filmname>=Three Kingdoms".

Therefore, the search results of the inquiry are about "..."The Romance of the Three Kingdoms..."Books" (intent information is <readbook>), "..."The Romance of the Three Kingdoms..."TV drama" (intention) The information is <watchTV>), "..."The Romance of the Three Kingdoms...""Movie"" The record of <watchfilm>) (for example, records 8, 9, and 10 of FIG. 3B), wherein "TV drama", "book", and "movie" respectively list corresponding user intentions). For another example, when the first keyword 509 is ""forget the water", "music"" (for example, the user inputs "speaking of the music I want to listen to", the natural language understanding system 520 may generate the following after analysis. Possible intent grammar data: "<playmusic>, <songname>=forget the water"; the search results of the inquiry are, for example, records of "... "forget the water"... "Andy Lau"" (for example, the record of Figure 3B) 11), "... "Forget the water"... "Li Junjun" record (for example, record 12 of Figure 3B), in which "Andy Lau" and "Li Yujun" are the intent data corresponding to the user. In other words, each search result may include a first keyword 509 and an intent data related to the first keyword 509, and the natural language understanding system 520 converts the data included in the search result into a search result according to the query. Return the answer and record the return answer in the candidate list for use in subsequent steps.

In step S606, the natural language understanding system 520 selects at least one first reward answer 511 from the candidate list, and outputs a corresponding first voice response 507 according to the first reward answer 511. In the present embodiment, the natural language understanding system 520 can arrange the reward answers in the candidate list in order of priority, and select a reward answer from the candidate list according to the priority order, thereby outputting the first voice response 507.

For example, when the first keyword 509 is "Three Kingdoms", it is assumed that the natural language understanding system 520 queries a lot of records about "..."Three Kingdoms"... "Books" (ie, by query) The number of materials received is prioritized), followed by "..."The history of the Romance of the Three Kingdoms..."Music", and the "Music of the Three Kingdoms"... "TV drama" has the fewest records, then the Natural Language Understanding System 520 will "Three Kingdoms" "The Book of Romance" as the first return answer (the most preferred return answer), "the music of the Romance of the Three Kingdoms" as the second return answer (the second preferred choice of the answer), "the drama of the Three Kingdoms" as the first Three return answers (the third preferred return answer). Of course, if the first return answer related to the "Book of the Three Kingdoms" is not only a record, the first return answer 511 can also be selected according to the order of precedence (for example, the number of times selected). The relevant details have been mentioned above. I will not repeat them here.

Next, in step S608, the speech sampling module 510 receives the second speech input 501', and the natural language understanding system 520 parses the second speech input 501' and determines whether the previously selected first reward answer 511 is correct. Here, the voice sampling module 510 parses the second voice input 501 ′ to parse the second keyword 509 ′ included in the second voice input 501 ′, wherein the second keyword 509 ′ is, for example, further by the user. Keywords provided (such as time, intent, knowledge area, etc.). Moreover, when the second keyword 509' in the second voice input 501' does not match the intent data associated with the first reward answer 511, the natural language understanding system 520 determines that the previously selected first reward answer 511 is Incorrect. The manner in which the second request information 503' of the second voice input 501' is included to include the "correct" or "negative" first voice response 507 has been mentioned above and will not be described herein.

Further, the second speech input 501' parsed by the natural language understanding system 520 may or may not include an explicit second keyword 509'. For example, the voice sampling module 510 receives, for example, what the user said, "I don't mean the Three Kingdoms. "Book of Righteousness" (Case A), "I am not referring to the books of the Romance of the Three Kingdoms, I am referring to the TV series of the Romance of the Three Kingdoms" (Case B), "I refer to the TV series of the Romance of the Three Kingdoms" (Case C) and so on. The second keyword 509' in the above case A is, for example, ""No", "Three Kingdoms", "Book"", and the keyword 509 in Case B is, for example, "No", "Romance of the Three Kingdoms", "Book" "Yes", "Three Kingdoms", "TV drama", and the second keyword 509' in case C is, for example, "Yes", "Romance of the Three Kingdoms", "TV drama". For convenience of explanation, only the cases A, B, and C are exemplified above, but the embodiment is not limited thereto.

Next, the natural language understanding system 520 determines whether the related intent data in the first reward answer 511 is correct based on the second keyword 509' included in the second speech input 501'. That is to say, if the first return answer 511 is "a book of the Romance of the Three Kingdoms" and the second keyword 509' is "the Romance of the Three Kingdoms" or "TV drama", the natural language understanding system 520 will judge the first return. The related intent data in answer 511 (ie, the user wants to see the "National Romance" "book") does not conform to the second keyword 509' from the user's second voice input 501' (ie, the user wants to see the "Three Kingdoms" "TV drama"), thereby judging the A return answer 511 is incorrect. Similarly, if the answer to the answer is "the book of the Romance of the Three Kingdoms" and the second keyword 509' is "No", "Romance of the Three Kingdoms" and "Book", the Natural Language Understanding System 520 will also determine the A return answer 511 is incorrect.

After the natural language understanding system 520 parses the second voice input 501 and determines that the previously output first voice response 501 is correct, the natural language understanding system 520 makes a corresponding second voice input 501' as shown in step S610. Back should. For example, if the second voice input 501' from the user is "Yes, a book of the Three Kingdoms", the natural language understanding system 520 may be a second voice response outputting "a book that is helping you to open the Romance of the Three Kingdoms". 507'. Alternatively, the natural language understanding system 520 can load the book content of the Three Kingdoms directly through the processing unit (not shown) while playing the second voice response 507'.

However, after the natural language understanding system 520 parses the second voice input 501', it is determined that the previously output first voice response 507 (ie, the return answer 511) is incorrect, then the natural language understanding system 520 will perform as shown in step S612. The other one other than the first return answer 511 is selected from the candidate list, and the second voice response 507' is output according to the selected result. Here, if the second voice input 501' provided by the user does not have the explicit second keyword 509' (such as the second voice input 501' of the above case A), the natural language understanding system 520 can be based on the priority order. A second preferred selection of return answers is selected from the candidate list. Alternatively, if the second voice input 501' provided by the user has an explicit second keyword 509' (such as the second voice input 501' of the above cases B and C), the natural language understanding system 520 can directly refer to the user's The second keyword 509' of the guide selects the corresponding reward answer from the candidate list.

On the other hand, if the second voice input 501' provided by the user has an explicit second keyword 509' (such as the second voice input of cases B and C above), the natural language understanding system 520 checks in the candidate list. If there is no return answer corresponding to the second keyword 509, the natural language understanding system 520 outputs a third voice response, such as "Check this book" or "I don't know".

In order to make the correction language of this embodiment be further understood by those skilled in the art. The method of sound response and the natural language dialogue system will be described in detail below with reference to an embodiment.

First, it is assumed that the first voice input 501 received by the voice sampling module 510 is "I want to see the Romance of the Three Kingdoms" (step S602). Then, the natural language understanding system 520 can be interpreted as ""seeing" and "the Romance of the Three Kingdoms". a first keyword 509 and obtaining a candidate list having a plurality of first reward answers, wherein each of the reward answers has associated keywords and other materials (other materials may be stored in content field 306 of FIG. 3A/3B, or Is a portion of the value field 312 of each record 302 (step S604), as shown in Table 1 (assuming that the books/television/music/movie for the Romance of the Three Kingdoms have only one piece of information).

Next, the natural language understanding system 520 will select the desired reward answer in the candidate list. Assuming that the natural language understanding system 520 sequentially selects the reward answer a in the candidate list as the first reward answer 511, the natural language understanding system 520 outputs, for example, "whether or not the book of the Three Kingdoms is played", that is, the first voice response 507 ( Step S606).

At this time, if the second voice input 501 ′ received by the voice sampling module 510 is “Yes” (step S608 ), the natural language understanding system 520 determines that the above-mentioned reward answer a is correct, and the natural language understanding system 520 Another voice response 507 "Please wait" (ie, the second voice response 507') is output, and the book content of the Three Kingdoms is loaded through the processing unit (not shown) (step S610).

However, if the second voice input 501' received by the voice sampling module 510 is "I don't refer to the book of the Three Kingdoms" (step S608), the natural language understanding system 520 determines that the above-mentioned reward answer a is incorrect, and The natural language understanding system 520 will then select another return answer from the return answer b~e of the candidate list as the second return answer 511', which is, for example, the "TV drama of whether to play the Three Kingdoms" of the answer b. If the user continues to answer "not a TV show", the natural language understanding system 520 will choose to report one of the answers c~e to report. In addition, if the reward answers a~e in the candidate list are returned to the user by the natural language understanding system 520, and the return answers a~e do not match the user's voice input 501, the natural language understanding system 520 outputs "check". The voice response 507 without any data (step S612).

In another embodiment, in the above step S608, if the voice sampling module 510 receives the user's second voice input 501' as "I mean the comics of the Three Kingdoms". Here, since there is no return answer about the comics in the candidate list, the natural language understanding system 520 will directly output "check no data. The second voice response 507'.

Based on the above, the natural language understanding system 520 can output a corresponding first voice response 507 in accordance with the first voice input 501 from the user. When the first voice response 507 output by the natural language understanding system 520 does not meet the request information 503 of the first voice input 501 of the user, the natural language understanding system 520 can correct the first voice response 507 that is originally output, and according to the user. The second voice input 501' provided subsequently further outputs a second voice response 507' that is more consistent with the user first request information 503. As such, if the user is still dissatisfied with the answers provided by the natural language understanding system 520, the natural language understanding system 520 can automatically correct and report a new voice response to the user in order to enhance the user and natural language dialogue system 500. Convenience in conversation.

It is worth mentioning that, in step S606 and step S612 of FIG. 6, the natural language understanding system 520 can also sort the reward answers in the candidate list according to different methods for evaluating the priority order, according to the priority list from the candidate list. The answer is selected and the voice response corresponding to the answer is output.

For example, the natural language understanding system 520 can prioritize the first return answer 511 in the candidate list according to the usage habits of the people, wherein the more frequently the answers are frequently used by the people. For example, taking the first keyword 509 as the "Three Kingdoms" as an example, assume that the natural language understanding system 520 finds the answer to the drama of the Three Kingdoms, the books of the Three Kingdoms, and the music of the Three Kingdoms. its In the case of the "Three Kingdoms", when people refer to the "Three Kingdoms", less people will refer to the "Three Kingdoms" TV series, and fewer people will refer to the "Three Kingdoms" music (for example, using Figure 3C). When the value stored in the heat field 316 represents the matching situation of all users, the value of the heat field 316 is the highest in the "book" record of the "Three Kingdoms", then the natural language understanding system 520 will be prioritized. Sort the answers to the questions about "books," "television," and "music." That is to say, the natural language understanding system 520 preferentially selects the "book of the Three Kingdoms" as the first return answer 511, and outputs the first voice response 507 according to the first return answer 511.

In addition, the natural language understanding system 520 can also determine the priority order for returning answers based on user habits. In particular, the natural language understanding system 520 can record voice input (including the first voice input 501, the second voice input 501', or any voice input input by the user) that has received the user from the feature database. (For example, as shown in FIG. 7A/7B), wherein the property database can be stored in a storage device such as a hard disk. The feature database can record information about the user's preferences, habits, and the like, such as the first keyword 509 and the response record generated by the natural language understanding system 520 when the natural language understanding system 520 parses the user's voice input 501. The storage and retrieval of user preferences/customary data will be further explained later through FIG. 7A/7B/8. Moreover, in one embodiment, when the value stored in the heat field 316 in FIG. 3C is related to the user's habits (eg, the number of matches), the value of the heat field 316 can be used to determine the user's usage habits or prioritization. Therefore, the natural language understanding system 520 can select the user records recorded in the property database 730 when selecting the reward answer. The information is used to report the answers in order of priority, so as to output a voice response 507 that is more in line with the user's voice input 501. For example, in Figure 3B, the value stored in the 8/9/10 heat field 316 is 2/5/8, which can represent the "books", "TV series", "Three Kingdoms" respectively. The number of matches for the movie is 2/5/8, so the answer to the movie corresponding to "The Movie of the Three Kingdoms" will be preferred.

On the other hand, the natural language understanding system 520 can also select a reward answer based on user habits. For example, suppose that when a user engages in a conversation with the natural language understanding system 520, he often mentions "I want to read the books of the Romance of the Three Kingdoms", and less mentions "I want to watch the TV series of the Romance of the Three Kingdoms", and less mention "I To see the music of the Romance of the Three Kingdoms" (for example, there are 20 records of the "Book of the Three Kingdoms" recorded in the user dialogue database (for example, the favorite field 318 of record 8 in Figure 3B), 8 TV series about "The Romance of the Three Kingdoms" The record (for example, the preference field 318 of record 9 in Figure 3B) and one record of the "Music of the Three Kingdoms", the priority order of the return answers in the candidate list will be followed by the "Three Kingdoms" Books, "TV dramas of the Romance of the Three Kingdoms" and "Music of the Romance of the Three Kingdoms". That is to say, when the first keyword 509 is "The Romance of the Three Kingdoms", the natural language understanding system 520 selects "the book of the Romance of the Three Kingdoms" as the first return answer 511, and outputs the first according to the first return answer 511. Voice response 507.

It is worth mentioning that the natural language understanding system 520 can also determine the priority order of returning answers according to user preferences. Specifically, the user dialogue database can also record keywords that the user has expressed, such as "like", "idol", "disgust" or "hate". Therefore, the natural language understanding system 520 can wait for a while. In the selection list, the return answers are sorted according to the number of times the above keywords are recorded. For example, if there are more times in the return answer related to "like", then the return answer will be selected first. Or, suppose that the number of times in the return answer related to "disgust" is more, then it is selected later.

For example, suppose that when a user engages in a dialogue with the natural language understanding system 520, he often mentions "I hate watching TV dramas of the Three Kingdoms" and less mentions "I hate listening to the music of the Three Kingdoms" and less mentions " I hate listening to the books of the Romance of the Three Kingdoms. (For example, there are 20 records in the user dialogue database about "I hate to watch the drama of the Three Kingdoms" (for example, the aversive field 320 recorded in Figure 3B is recorded), 8 Regarding the record of "I hate listening to the music of the Romance of the Three Kingdoms" and a book about "I hate to read the Romance of the Three Kingdoms" (for example, by recording the disgusting field 320 of record 8 in Figure 3B), the return answer in the candidate list. The order of priority is "The Books of the Romance of the Three Kingdoms", "The TV Drama of the Three Kingdoms" and "The Music of the Romance of the Three Kingdoms". That is to say, when the first keyword 509 is "Three Kingdoms", the natural language understanding system 520 selects the "Three Kingdoms" book as the first return answer 511, and outputs the first according to the first return answer 511. Voice response 507. In an embodiment, an "disgusting field 320" may be added to the heat field 316 of FIG. 3B to record the user's "degree of disgust." In another embodiment, one (or other value) may be directly subtracted from the heat field 316 (or favorite field 318) of the corresponding record when parsing the user's "disgust" information for a record. Record user preferences when no fields are added. Various embodiments for recording user preferences may be applied to the embodiments of the present invention, and the present invention is not limited thereto. Other about users An embodiment of the recording and application of customary materials, as well as user/common usage habits and preferences, etc., to provide answers and reward answers, will be explained in more detail in later FIG. 7A/7B/8.

On the other hand, the natural language understanding system 520 can also provide a return before the answer is provided by the user prior to the natural language dialogue system 500 (eg, before the first voice input 501 is played, at which time the user does not know which return the natural language dialogue system 500 will provide. The answer is chosen by the input) of the input voice to determine the priority of at least one of the answers. That is, if the voice input (eg, the fourth voice input) is received by the voice sampling module 510 earlier than the first voice input 501, the natural language understanding system 520 can also analyze the fourth voice input. The fourth keyword is selected in the candidate list, and the fourth reward answer corresponding to the fourth keyword is preferentially selected, and the fourth voice response is output according to the fourth reward answer.

For example, assume that the natural language understanding system 520 first receives the first voice input 501 of "I want to watch a TV show", and after a short time (eg, after a few seconds), assume that the natural language understanding system 520 receives "help me. The fourth voice input 501 puts the Romance of the Three Kingdoms. At this time, the natural language understanding system 520 can recognize the first keyword 509 of the "drama" in the first voice input 501, and then recognize the "Three Kingdoms" in the fourth keyword. Therefore, the natural language understanding system 520 selects the return answers for the "Three Kingdoms" and "TV series" from the candidate list, and outputs a fourth voice response to the user based on the fourth return answer.

Based on the above, the natural language understanding system 520 can be based on voice input from the user, and can be used according to the usage habits, user preferences, user habits, or users. The front-to-back dialogue and the like, and output a voice response that is more in line with the request information of the voice input to the user. The natural language understanding system 520 can prioritize the reward answers in the candidate list according to different sorting methods, such as the usage habits, user preferences, user habits, or the user's forward and backward conversations. Thereby, if the voice input from the user is relatively unclear, the natural language understanding system 520 can determine the meaning of the user's voice input 501 by referring to the usage habits, user preferences, user habits, or the user's conversations. Intent (e.g., attributes of the keyword 509 in the first voice input 501, knowledge fields, etc.). In other words, if the reward answer is close to what the user has expressed/intended by the person, the natural language understanding system 520 will prioritize the return answer. In this way, the voice response output by the natural language dialogue system 500 can be more consistent with the user's request information.

In summary, in the method for correcting a voice response and the natural language dialogue system of the present embodiment, the natural language dialogue system can output a corresponding first voice response 507 according to the first voice input 501 from the user. Wherein, when the first voice response 507 output by the natural language dialogue system does not match the first request information 503 or the first keyword 509 of the first voice input 501 of the user, the natural language dialogue system can correct the first voice originally output. In response to 507, and according to the second voice input 501' provided by the user, a second voice response 507' that is more in line with the user's needs is further selected. In addition, the natural language dialogue system can also preferentially select a more appropriate return answer according to the usage habits of the people, the user's preference, the user's habits or the user's talks, and the like, and output a corresponding voice response to the user. In this way, if the user is not satisfied with the answer provided by the natural language dialogue system, the natural language dialogue system can The user automatically corrects the request according to the request information that is spoken by the user, and returns a new voice response to the user, thereby improving the convenience of the user in the dialogue with the natural language dialogue system.

Then, the architecture and components such as the system 100 and the structured database 220 are understood in a natural language, and are applied to an example of providing a response and returning an answer according to the conversation scene and context of the user, the user's usage habits, the usage habits of the user, and the user's preference. Description.

FIG. 7A is a block diagram of a natural language dialogue system according to an embodiment of the invention. Referring to FIG. 7A, the natural language dialogue system 700 includes a speech sampling module 710, a natural language understanding system 720, a feature database 730, and a speech synthesis database 740. In fact, the speech sampling module 710 in FIG. 7A is the same as the speech sampling module 510 of FIG. 5A, and the natural language understanding system 520 is the same as the natural language understanding system 720, so the functions performed are the same. In addition, when the natural language understanding system 720 analyzes the request information 703, the user's intention can also be obtained by performing full-text search on the data database 220 of FIG. 1. This part of the technology has been described above with respect to FIG. 1 and related descriptions. No longer. The feature database 730 is used to store the user preference information 715 sent by the natural language understanding system 720, or to provide the user preference record 717 to the natural language understanding system 720, which will be described in more detail later. The speech synthesis database 740 is equivalent to the speech synthesis database 530 for providing voice output to the user. In this embodiment, the voice sampling module 710 is configured to receive the voice input 701 (ie, the first/second voice input 501/501' of FIG. 5A/B is the voice from the user), and the natural language understanding system 720 Analysis The request information 703 in the voice input (ie, the first/second request information 503/503' of FIG. 5A/B), and outputs a corresponding voice response 707 (ie, the first/second voice response 507/ of FIG. 5A/B). 507'). The components in the aforementioned natural language dialogue system 700 can be configured in the same machine, which is not limited by the present invention.

The natural language understanding system 720 receives the request information 703 from the speech sampling module 710 that parses the speech input 701, and the natural language understanding system 720 will generate one or more keywords 709 based on the speech input 701. A candidate list including at least one reward answer is generated, and one of the candidate keywords 709 is found as the reward answer 711, and the speech synthesis database 740 is queried to find the voice 713 corresponding to the reward answer 711. Finally, the voice response 707 is output according to the voice 713. In addition, the natural language understanding system 720 of the present embodiment may be implemented by a hardware circuit composed of one or several logic gates, or implemented by computer code, which is merely an example, and is not limit.

Figure 7B is a block diagram of a natural language dialog system 700', in accordance with another embodiment of the present invention. The natural language understanding system 720' of FIG. 7B can include a speech recognition module 722 and a natural language processing module 724, and the speech sampling module 710 can be combined with the speech synthesis module 726 in a speech synthesis processing module 702. The voice recognition module 722 receives the request information 703 sent from the voice sampling module 710 to parse the voice input 701 and converts it into one or more keywords 709. The natural language processing module 724 further processes the keywords 709 to obtain at least one candidate list, and selects one of the candidate lists that is more in line with the voice input 701 as the reward answer 711. Since this reward answer 711 is the natural language understanding system 720 The internal analysis of the answer, so must also be converted to text or voice output to be output to the user, then the speech synthesis module 726 will query the speech synthesis database 740 according to the return answer 711, and the speech synthesis database 740, for example The recorded text and its corresponding voice information enable the speech synthesis module 726 to find the speech 713 corresponding to the reward answer 711, thereby synthesizing the speech response 707. Thereafter, the speech synthesis module 726 can output the synthesized speech through a voice output interface (not shown), wherein the voice output interface is, for example, a device such as a speaker, a speaker, or a headset, to output a voice to the user. It should be noted that in FIG. 7A, the natural language understanding system 720 incorporates the speech synthesis module 726 (eg, the architecture of FIG. 5B, but the speech synthesis module 726 is not shown in FIG. 7A), while the speech synthesis module The group will use the reward answer 711 to query the speech synthesis database 740 to obtain the speech 713 as a basis for synthesizing the speech response 707.

In this embodiment, the speech recognition module 722, the natural language processing module 724, and the speech synthesis module 726 in the natural language understanding system 720 are respectively equivalent to the speech recognition module 522 and the natural language processing module of FIG. 5B. Group 524 and speech synthesis module 526 provide the same functionality. In addition, the speech recognition module 722, the natural language processing module 724, and the speech synthesis module 726 can be disposed in the same machine as the speech sampling module 710. In other embodiments, the speech recognition module 722, the natural language processing module 724, and the speech synthesis module 726 may also be dispersed in different machines (eg, a computer system, a server, or the like). For example, the natural language understanding system 720' shown in FIG. 7B, the speech synthesis module 726 can be disposed in the same machine 702 as the speech sampling module 710, and the speech recognition module 722 and the natural language processing module 724. Can be configured on another machine. It should be noted that in the architecture of FIG. 7B, since the speech synthesis module 726 and the speech sampling module 710 are disposed in a machine 702, the natural speech understanding system 720 needs to transmit the reward answer 711 to the machine 702, and The speech synthesis module 726 sends the reward answer 711 to the speech synthesis database 740 to find the corresponding speech 713 as a basis for generating the speech response 707. In addition, when the speech synthesis module 726 calls the speech synthesis database 740 according to the reward answer 711, it may be necessary to first convert the response answer 711 and then call through the interface specified by the speech synthesis database 740, because this part belongs to the present. Techniques well known to those skilled in the art are not described in detail herein.

The natural language dialogue method will be described below in conjunction with the natural language dialog system 700 described above in connection with FIG. 7A. FIG. 8 is a flowchart of a natural language dialogue method according to an embodiment of the invention. For convenience of explanation, only the natural language dialogue system 800 of Fig. 7A is taken as an example, but the natural language dialogue method of the present embodiment can also be applied to the above-described natural language dialogue system 700' of Fig. 7B. Compared with FIG. 5/6, the information outputted by the user's voice input is automatically corrected according to the user's voice input, but FIG. 7A/7B/8 deals with recording the user's preference according to the feature database 730. The data 715 is selected from the candidate list to report the answer 711 and play the corresponding voice to the user. In fact, the embodiments of Figures 5/6 and 7A/7B/8 may alternatively or coexist, and the invention is not limited thereto.

Referring to FIG. 7A and FIG. 8 simultaneously, in step S810, the voice sampling module 710 receives the voice input 701. The voice input 701 is, for example, a voice from a user, and the voice input 701 may also have a request information 703 of the user. Specifically In other words, the voice input 701 from the user may be an inquiry sentence, a command sentence or other request information, such as the example mentioned above, "I want to see the Romance of the Three Kingdoms", "I want to listen to the music of the water" or "A few degrees of today's temperature" and many more. It should be noted that steps S802-S806 are the flow of the natural language dialogue system 700 storing the user preference information 715 for the user's previous voice input, and the subsequent steps S810-S840 are based on the user preferences previously stored in the feature database 730. The data 715 is operated. The details of steps S802-S806 will be described later in detail, and the operation contents of steps S820-S840 will be described below.

In step S820, the natural language understanding system 720 parses at least one keyword 709 included in the first voice input 701 to obtain a candidate list, wherein the candidate list has one or more reward answers. In detail, the natural language understanding system 720 parses the speech input 701 and obtains one or more keywords 709 of the speech input 701. For example, when the user's voice input 701 is "I want to see the Romance of the Three Kingdoms", the keyword 709 obtained by the natural language understanding system 720 after analysis is, for example, "The Romance of the Three Kingdoms" and "Look" (as before) Said, but also analyze the user wants to see books, TV series, or movies). For another example, when the user's voice input 701 is "I want to listen to the song of forgetting the water", the keyword 709 obtained by the natural language understanding system 720 after analysis is, for example, "forget the water", "listen", "song" (As mentioned earlier, you can re-analyze what the user wants to hear is the version that Andy Lau or Li Yijun sang). After that, the natural language understanding system 720 can perform full-text search from the structured database according to the above keyword 709, and obtain at least one search result (which may be at least one of the records in FIG. 3A/3B), as a candidate list. The answer in the return. due to A keyword 709 may belong to different knowledge areas (such as movies, books, music, games, etc.), and may be further divided into multiple categories in the same knowledge field (for example, different authors of the same movie or book name, the same Different singers of song names, different versions of the same game name, etc.), so for a keyword 709, the natural language understanding system 720 can get a stroke after analysis (eg, full-text search of the structured repository 220) A plurality of search results related to this keyword 709, which include other information than the keyword 709 and the keyword 709, etc. (the contents of other information are as shown in Table 1). Therefore, from another point of view, when the first voice input 701 input by the user has multiple keywords 709, it indicates that the user's request information 703 is clearer, so that the natural language understanding system 720 can analyze the request information 703. Close search results (because if the natural language understanding system 720 can find the exact match result, it should be the option that the user wants).

For example, when the keyword 709 is "Three Kingdoms", the search result analyzed by the natural language understanding system 720 is, for example, "..."Three Kingdoms"... "TV drama", "..." The records of the "Romance of the Three Kingdoms"... "Books" (where "TV dramas" and "books" are the user's intentions in response to the results). For another example, when the keyword 709 is "Forget Water" or "Music", the user's intention analyzed by the natural language understanding system 720 may be "... "forgetting water"... "music"... "Andy Lau", "... "forget the water"... "Music"... "Li Yujun" record, in which "Andy Lau" and "Li Yujun" are search results to indicate the user's intention. In other words, after the natural language understanding system 720 performs a full-text search on the structured database 220, each search result may include a keyword 709 and a keyword 709. Other information (as shown in Table 1), and the natural language understanding system 720 converts the search results into at least one candidate answer list for use in subsequent steps based on the analyzed search results.

In step S830, the natural language understanding system 720 records the user preference record 717 sent by the feature database 730 (for example, according to the result of storing the user preference data 715 stored therein, which will be described later). A return answer 711 is selected from the candidate list, and a voice response 707 is output according to the return answer 711. In the present embodiment, the natural language understanding system 720 can select the reward answer 711 from the candidate list in a prioritized order (which is included in the priority order). In step S840, based on the report answer 711, the voice response 707 is output (step S840).

For example, in an embodiment, the number of search results may be prioritized. For example, when the keyword 709 is "Three Kingdoms", it is assumed that the natural language understanding system 720 finds in the structured database 220 after analysis. ... "The Romance of the Three Kingdoms"... "Books" has the largest number of records, followed by "..."The Romance of the Three Kingdoms..."Music", and "..."The Romance of the Three Kingdoms." . . . "TV drama" has the fewest records, then the natural language understanding system 720 will record the records related to the "Book of the Three Kingdoms" as the first priority return answer (for example, sorting all the books about the Romance of the Three Kingdoms into one candidate) List, and can be sorted according to the value of the heat field 316), the record related to the "Music of the Three Kingdoms" as the second priority return answer, the record related to the "TV drama of the Three Kingdoms" as the third priority return answer . It should be noted that in addition to the number of search results, the priority may be based on the user's preference. Good, user habits, or habits of everyone, the relevant narrative will be detailed later.

In order to enable those skilled in the art to further understand the natural language dialogue method and the natural language dialogue system of the present embodiment, an embodiment will be described in detail below.

First, it is assumed that the first voice input 701 received by the voice sampling module 710 is "I want to see the Romance of the Three Kingdoms" (step S810), and then, the natural language understanding system 720 can be interpreted as ""seeing" and "the Romance of the Three Kingdoms". Keyword 709, and obtain a candidate list with multiple reward answers, each of which has an associated keyword (step S820) and other information, as shown in Table 1 above.

Next, the natural language understanding system 720 will select the reward answer in the candidate list. Assuming that the natural language understanding system 720 selects the reward answer a in the candidate list (refer to Table 1) as the first reward answer 711, the natural language understanding system 720 outputs, for example, "whether or not the book of the Three Kingdoms is played" as a voice response. 707 (steps S830 to S840).

As described above, the natural language understanding system 720 can also sort the reward answers in the candidate list in accordance with different methods of evaluating the priority order, and accordingly output a voice response 707 corresponding to the reward answer 711. For example, the natural language understanding system 720 can determine user preferences based on a plurality of conversation records with the user (eg, using the user's positive/negative terms as mentioned above), and can also use the user preference record 717 to determine the return answer. 711 priority order. Before explaining the usage of the user's positive/negative terms, the user preference information 715 is first described in terms of storing the user/person's preferences/dislikes or habits.

The manner in which the user preference profile 715 is stored is now determined in accordance with steps S802-806. In an embodiment, before receiving the voice input 701 in step S810, a plurality of voice inputs, that is, previous history conversation records, may be received in step S802, and user preferences are captured based on the previous plurality of voice inputs 701. The data 715 (step S804) is then stored in the property database 730. In fact, the user preference profile 715 can also be stored in the structured repository 220 (or in a manner that incorporates the feature repository 730 into the structured repository 220). For example, in one embodiment, the popularity field 316 of FIG. 3B can be utilized directly to record the user's preferences, as the recording mode of the heat field 316 has been previously mentioned (eg, when a record 302 is matched, the heat is about to be used. The field is incremented by one) and will not be repeated here. Of course, it is also possible to store the user preference information 715 in the structured database 220, for example, by using keywords (such as "Three Kingdoms"), combined with user preferences (for example, when the user mentions "like" and the like. In terms of terms and negative terms such as "disgusting", the values of the favorite field 318 and the disgusting field 320 of FIG. 3B may be respectively added to one), and then the number of favorites (for example, the number of positive and negative terms) may be calculated. Therefore, when the natural language understanding system 720 queries the structured database 200 for the user preference record 717, the value of the preference field 318 and the aversion field 320 can be directly queried (the number of positive terms and other negative terms can be queried), and then According to the judgment of the user's preference (the statistical value of the positive term and the negative term is also transmitted as the user preference record 717 to the natural language understanding system 720).

The case where the user preference information 715 is stored in the feature database 730 (i.e., the feature database 730 is not incorporated into the structured library 220) will be described below. In an embodiment, the user preference information 715 can use the keyword and the user's "hi" for the keyword. A good way to store, for example, the user preference information 715 can be stored directly using the favorite field 852 and the aversive field 862 of FIG. 8B to record the user's personal preference and dislike of a keyword, and the favorite column Bit 854 and disgusting field 864 are used to record the preferences and dislikes of the group of keywords. For example, in FIG. 8B, record 832 stores the keyword "The Romance of the Three Kingdoms" and "Book" corresponding to the favorite field 852 and The values of the dislike field 862 are 20 and 1, respectively, and the values of the favorite field 852 and the disgusting field 862 corresponding to the keyword "Three Kingdoms" and "TV drama" stored in the record 834 are 8 and 20, respectively. The values of the favorite field 852 and the disgusting field 862 corresponding to the keyword "Three Kingdoms" and "Music" stored in the record 836 are 1 and 8, respectively, which indicate the user's personal preference for related keywords. Aversion data (for example, the higher the value of the favorite field 852, the more like, the higher the value of the field 862 is, the more disgusting it is). In addition, the values of the favorite field 854 and the aversion field 864 corresponding to the record 832 are 5 and 3, respectively, and the values of the favorite field 854 and the aversive field 864 corresponding to the record 834 are respectively 80 and 20, and the record 836 corresponds to The values of the favorite field 854 and the aversion field 864 are 2 and 10, respectively, which indicate the preference and disgusting information of the relevant keywords (referred to as "favorite indication"), so that the user's preference can be used. Increase the value of the favorite field 852 and the aversion field 862. Therefore, if the user inputs the voice "I want to watch the TV series of the Three Kingdoms", the natural language understanding system 720 can use the "keyword" ""Three Kingdoms", "TV drama"" and the "favorite indication" to increase the value of the favorite field. The merged user preference data 715 is sent to the feature database 730, so that the feature database 730 can be added to the value of the favorite field 852 of the record 834 (because the user wants to see "Three Kingdoms", "TV" "The drama" indicates that its preference has increased). According to the above method of recording user preference data, when the user inputs the relevant keyword again, for example, when the user inputs "I want to see the Romance of the Three Kingdoms", the natural language understanding system 720 can be based on the keyword "Three Kingdoms". The feature database 730 of 8B queries three records 832/834/836 related to "Three Kingdoms", and the feature database 730 can use the value of the favorite field 852 and the aversion field 862 as the user preference record 717 back. To the natural language understanding system 720, the natural language understanding system 720 can then use the user preference record 717 as a basis for determining the user's personal preferences. Of course, the feature database 730 can also return the value of the favorite field 854 and the dislike field 864 as the user preference record 717 to the natural language understanding system 720, but the user preference record 717 will be used as a basis for judging the preference of the person. The present invention does not limit the user preference record 717 to represent the preferences of the user or the individual.

In another embodiment, the values of the preference field 852 and the aversion field 862 can also serve as a basis for judging the user/people's habits. For example, the natural language understanding system 720 can determine the difference between the preference field 852/854 and the aversion field 862/864 after receiving the user preference record 717. If the two values differ by a certain threshold, Indicates that the user is accustomed to using a specific way to conduct a dialogue. For example, when the value of the favorite field 852 is greater than the value of the aversive field 862 by more than 10 times, the user particularly likes to use the "positive language" for the dialogue (this is "user habit" A way of recording), therefore, the natural language understanding system 720 can only select the reward answer in the preferred field 852 in this case. When the natural language understanding system 720 uses the value of the favorite field 854/aversion field 864 stored in the property database 730, it indicates the judged It is a favorite record of all users of the feature database 730, and the judgment result can be used as a reference material for everyone's usage habits. It should be noted that the user preference record 717 returned by the feature database 730 to the natural language understanding system 720 can include both the user's personal preference record (eg, the value of the favorite field 852/disgusting field 862) and the preference record of the individual. (For example, the value of the favorite field 854/aversion field 864), the present invention is not limited thereto.

As for the storage of the user preference data 715 obtained based on the current voice input, the candidate list may be generated in step S820 (whether it is an exact match or a partial match), and the natural language dialogue system 700 stores the current voice input in the user. User preference data 715 obtained. For example, in step S820, whenever the keyword can generate a matching result in the structured database 220, it can be determined that the user has a preference for the matching result, so "keyword" and "favorite indication" can be used. After sending to the feature database 730 and finding the corresponding record therein, the corresponding record records the corresponding favorite field 852/854 or the aversion field 862/864 (for example, when the user inputs "I want to see the books of the Three Kingdoms" At that time, the value of the favorite field 852/854 of the record 832 of FIG. 8B may be incremented by one). In another embodiment, the natural language dialogue system 700 may also store the user preference information 715 after the user selects a reward answer in step S830. In addition, if a corresponding keyword is not found in the property database 730, a new record can be created to store the user preference profile 715. For example, when the user inputs the voice "I listen to Andy Lau's forgotten water" and generates the keyword "Andy Lau", "forget the water", if the corresponding record is not found in the feature database 730 when storing, the feature data will be Library 730 creates a new record 838 and in its corresponding favorite field The value of 852/854 is increased by one. The above-mentioned user preference information 715 storage timing and storage mode are for illustrative purposes only, and those skilled in the art can change the embodiment shown in the present invention according to the actual application, but all equivalent modifications should not be deviated from the spirit of the present invention. It is included in the claims of the present invention.

Further, although the format in which the records 832-838 are stored in the property database 730 shown in FIG. 8B is different from the recording format of the structured library 220 (for example, as shown in FIGS. 3A/3B/3C), the present invention The storage format of the record is not limited. Moreover, although the above embodiment only describes the storage and use of the favorite field 852/854 and the aversive field 862/864, in another embodiment, the field 872/874 can be additionally opened in the property database 730. Store other habits of the user/person, such as the number of times the data corresponding to the record is downloaded, quoted, recommended, commented, or referred to... In another embodiment, the number or data of these downloads, citations, recommendations, comments, or referrals may also be stored in the favorite field 852/854 and the aversive field 862/864, for example, each time the user pairs an item. If you record a good comment or refer it to someone else's reference, you can add one to the value of the favorite field 852/854. If the user gives a bad comment on a record, you can add one to the value of the aversion field 862/864. The present invention does not limit the number of records and the numerical recording method of the fields. It should be noted that those skilled in the art should understand that since the favorite field 852, the aversion field 862, etc. in FIG. 8B are only related to the user's personal choices and preferences, the personal selection of these users can be The favorite/disgusting information is stored in the user's mobile communication device, and information such as the favorite field 854 and the aversion field 864... associated with all users are stored in the server, thereby saving storage space of the server. It also preserves the privacy of the user's personal preferences.

The actual use of the user will be further explained below using FIG. 7A and FIG. 8B. Based on the conversation content of the plurality of voice inputs 701, it is often mentioned that "the user hates to watch the drama of the Three Kingdoms" when the user engages in dialogue with the natural language understanding system 720, and less "I hate listening to the music of the Three Kingdoms". And less mentioned "I hate listening to the Romance of the Three Kingdoms" (for example, the feature database 730 records 20 records about "I hate to watch the drama of the Three Kingdoms" (that is, in Figure 8B, "Three Kingdoms" plus The number of negative terms in "TV series" is 20), and the number of negative words in "I hate listening to the music of the Three Kingdoms" (that is, in Figure 8B, the number of negative terms in "Three Kingdoms" plus "Music" is 8) And one of the "books that I hate listening to the Romance of the Three Kingdoms") (that is, in Figure 8B, the number of negative terms in the "Three Kingdoms" plus "books" is 1) because of the return from the feature database 730. The user preference record 717 will contain the number of these three negative terms (ie, 20, 8, 1), and the natural language understanding system 720 will sequentially order the priority of the return answer 711 in the candidate list as "the book of the Three Kingdoms". , "The Music of the Romance of the Three Kingdoms" and "The TV Drama of the Romance of the Three Kingdoms." That is to say, when the keyword 709 is "Three Kingdoms", the natural language understanding system 720 selects the "Three Kingdoms" book as the reward answer 711, and outputs a voice response 707 based on the reward answer 711. It should be noted that although the above is a prioritized use of the statistical values of the negative terms used by the user, in another embodiment, the statistical values of the positive terms used by the user may still be used alone to prioritize ( For example, as previously mentioned, the value of the favorite field 852 is above a certain threshold of the aversion field 862).

It is worth mentioning that the natural language understanding system 720 can also be used at the same time. The amount of positive and negative terms used by the household to determine the priority of the return answer. Specifically, the feature database 730 can also record keywords that the user has expressed, such as "like", "idol" (above is positive), "disgust" or "hate" (above negative words), etc. Wait. Therefore, in addition to comparing the number of times the user uses "like" and "disgust", the natural language understanding system 720 can also directly report the reward answer according to the number of positive/negative terms corresponding to the keyword from the candidate list. Sorting (that is, comparing positive or negative terms to which the number of citations is more). For example, if there are more times in the return answer related to "like" (that is, if the number of citations of the positive term is more, or if the value of the favorite field 852 is larger), the answer will be selected first. Or, assuming that there are more times in the return answer related to "disgust" (that is, the number of references to negative terms is greater, or the value of the aversion field 862 is larger), then it is selected later, so the natural language understanding system 720 All of the reward answers can be sorted into a candidate list according to the above prioritization. Since some users may prefer to use positive terms (for example, the value of the favorite field 852 is particularly large), while others are accustomed to using negative terms (for example, the value of the aversive field 862 is particularly large), in the above embodiment, the user The preference record 717 will reflect the usage habits of individual users, so it is possible to provide options that are more in line with the user's habits for selection.

In addition, the natural language understanding system 720 can also prioritize the reward answers 711 in the candidate list according to the usage habits of the people, wherein the more frequently the answers frequently used by the people are prioritized (for example, using the heat field 316 of FIG. 3C). recording). For example, when the keyword 709 is "Three Kingdoms", assuming natural language theory The reward answers found by the solution system 720 are, for example, the TV series of the Romance of the Three Kingdoms, the books of the Romance of the Three Kingdoms, and the music of the Romance of the Three Kingdoms. Among them, if the people refer to the "Three Kingdoms", they usually refer to the "Three Kingdoms" TV series. Less people will refer to the "Three Kingdoms" movie, and fewer people will refer to the "Three Kingdoms" book (for example, Figure 8B). In the case where the values of the related records in the favorite field 854 are 80, 40, and 5, respectively, the natural language understanding system 720 sorts the return answers 711 regarding "drama", "movie", and "book" in order of priority. That is to say, the natural language understanding system 720 preferentially selects the "TV drama of the Three Kingdoms" as the reward answer 711, and outputs a voice response 707 based on the reward answer 711. As for the above-mentioned "priority of the answers frequently used by the people", the heat field 316 of FIG. 3C can be used for recording, and the recording mode has been disclosed in the relevant paragraph of FIG. 3C above, and will not be described herein.

In addition, the natural language understanding system 720 can also determine the priority order of the reward answer 711 according to the frequency of use of the user. Specifically, since the natural language understanding system 720 can record the voice input 701 that has been received from the user in the property database 730, the property database 730 can record the natural language understanding system 720 that resolves the user's voice input 701. The keyword 709 and the natural language understanding system 720 all generate response messages such as the answer 711. Therefore, when the natural language understanding system 720 selects the reward answer 711 in the future, it can be based on the response information recorded in the feature database 730 (for example, user preference/disgust/habit, even everyone's preference/disgust/habit...) According to the prioritization, a reward answer 711 that is more in line with the user's intention (determined by the user's voice input) is found, and the corresponding voice response is obtained. As for the above-mentioned "priority order of returning the answer 711 according to the user's habits", it is also possible to make Recording is performed using the heat field 316 of FIG. 3C, and the recording mode has been disclosed in the relevant paragraph of FIG. 3C above, and will not be described herein.

In summary, the natural language understanding system 720 can store the user preference attributes (eg, positive and negative terms), user habits, and usage habits described above into the feature database 730 (step S806). That is, in step S802, step S804, and step S806, the user preference information 715 is obtained from the user's previous historical conversation record, and the collected user preference data 715 is added to the feature database 730, and The user is accustomed to storing the usage habits to the feature database 730, allowing the natural language understanding system 720 to utilize the rich information in the feature database 730 (eg, user preference record 717) to provide a more accurate response from the user.

Next, the details of step S830 are further described. In step S830, after receiving the voice input in step S810 and parsing the keyword 709 of the voice input at S820 to obtain the candidate list, the natural language understanding system 720 then follows the user preferences such as user preference, user habits, or crowd use habits. Record 717, determining at least one priority order for returning the answer (step S880). As mentioned above, the priority order can be based on the number of records searched, the positive/negative terms of the user or the individual. Then, a return answer 711 is selected from the candidate list according to the priority order (step S890), and the selection of the return answer may also be the highest match or the highest priority as described above. Thereafter, based on the return answer 711, the voice response 707 is output (step S840).

On the other hand, the natural language understanding system 720 can also determine the priority order of at least one reward answer based on the voice input 701 entered by the user earlier. That is It is said that the natural language understanding system 720 can also analyze this by assuming that another voice input 701 (such as the aforementioned fourth voice input) is received by the voice sampling module 710 in advance of the voice response 707 being played. The keyword in the voice input 701 (that is, the fourth voice input) (that is, the fourth keyword 709), and in the candidate list, the return answer corresponding to the keyword is preferentially selected as the reward answer 711, and This reward answer 711 outputs a voice response 707.

For example, assume that the natural language understanding system 720 first receives the voice input 701 of "I want to watch a TV show", and after a few seconds, assume that the natural language understanding system 720 receives the "Help me put the Romance of the Three Kingdoms". Voice input 701. At this time, the natural language understanding system 720 can recognize the keyword (first keyword) of the "TV drama" in the first voice input 701, and recognize the keyword of the "Three Kingdoms" (fourth keyword) Therefore, the natural language understanding system 720 selects the intent data from the candidate list as the return answer for the "Three Kingdoms" and "TV drama", and returns the answer 711 to output the voice response 707 to the user.

Based on the above, the natural language understanding system 720 can output the request information 703 that conforms to the voice input 701 according to the voice input from the user and the information of the user's usage habits, user preferences, user habits, or the user's talks. The voice response 707 is given to the user. The natural language understanding system 720 can prioritize the reward answers in the candidate list according to different sorting methods, such as the usage habits, user preferences, user habits, or the user's forward and backward conversations. Thereby, if the voice input 701 from the user is less clear, the natural language understanding system 720 can refer to the usage habits, user preferences, user habits or users. The contextual conversation is said to determine the intended intent of the user's speech input 701 (eg, the attributes of the keyword 709 in the speech input, the knowledge domain, etc.). In other words, if the reward answer 711 is close to what the user has expressed/intended by the person, the natural language understanding system 720 will prioritize the reward answer 711. In this way, the voice response 707 output by the natural language dialogue system 700 can be more consistent with the user's request information 703.

It should be noted that although the feature database 730 and the structured database 220 are described in different databases, the two databases may be combined, and those skilled in the art may select according to actual applications.

In summary, the present invention provides a natural language dialogue method and system thereof, and the natural language dialogue system can output a corresponding voice response according to a voice input from a user. The natural language dialogue system of the present invention can also preferentially select a more appropriate return answer according to the usage habits of the people, the user's preference, the user's habits or the user's talks, etc., thereby outputting a voice response to the user, thereby enhancing The convenience of the user in a conversation with the natural language dialogue system.

Then, the architecture and components such as the system 100 and the structured database 220 are understood in a natural language, and the number of reward answers obtained by analyzing the requested information according to the user's voice input is determined, and the operation is directly performed according to the data type, or the user is required to provide Further instructions, then when there is only one return answer, you can also directly explain the example of the operation based on the data type. The benefit of providing the user with this choice is that the system does not have to filter the answer for the user, but instead provides the candidate list with the return answer directly to the user, and allows the user to select the answer by themselves. Decide which software you want to execute or which service to offer in order to provide a user-friendly interface.

FIG. 9 is a schematic diagram of a system of a mobile terminal device according to an embodiment of the invention. Referring to FIG. 9 , in the embodiment, the mobile terminal device 900 includes a voice receiving unit 910 , a data processing unit 920 , a display unit 930 , and a storage unit 940 . The data processing unit 920 is coupled to the voice receiving unit 910, the display unit 930, and the storage unit 940. The voice receiving unit 910 is configured to receive the first input voice SP1 and the second input voice SP2 and transmit the data to the data processing unit 920. The first voice input SP1 and the second voice input SP2 described above may be voice inputs 501, 701. The display unit 930 is configured to be controlled by the material processing unit 920 to display the first/second candidate list 908/908'. The storage unit 940 is configured to store a plurality of materials, and the data may include the data of the structured database 220 or the characteristic database 730, and details are not described herein. In addition, the storage unit 940 can be any type of memory in a server or computer system, such as dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, and read-only memory. (ROM), etc., the present invention is not limited thereto, and those skilled in the art can select according to actual needs.

In the present embodiment, the data processing unit 920 functions as the natural language understanding system 100 of FIG. 1, and performs speech recognition on the first input speech SP1 to generate the request information 902, and then analyzes the first request information 902 and the natural language. Processing to generate a first keyword 904 corresponding to the first input speech SP1, and according to the first keyword 904 corresponding to the first input speech SP1, the data from the storage unit 940 (eg, the search engine 240 according to the keyword 108 to the structured database) 220 full-text search) Find the first return answer 906 (for example, the first return answer 511/711). When the number of the first reward answers 906 found is 1, the data processing unit 920 can directly perform the corresponding operation according to the type of the data corresponding to the first reward answer 906; when the number of the first reward answers 906 is greater than 1, the data Processing unit 920 can organize the first reward answer 906 into a first candidate list 908, and then control display unit 940 displays first candidate list 908 to the user. In the case that the first candidate list 908 is displayed for the user to make further selection, the data processing unit 920 receives the second input voice SP2 and performs voice recognition to generate the second request information 902', and then the second request information. 902' performs natural language processing to generate a second keyword 904' corresponding to the second input speech SP2, and selects a corresponding portion from the first candidate list 908 according to the second keyword 904' corresponding to the second input speech SP2. The first keyword 904 and the second keyword 904' may be composed of a plurality of keywords. The manner of analyzing the second speech input SP2 to generate the second request information 902' and the second keyword 904' may use the manner in which the second speech input is analyzed by using FIGS. 5A and 7A, and thus will not be described again.

Similarly, when the number of second reward answers 906 is 1, the data processing unit 920 performs corresponding operations according to the type of the second reward answer 906; when the number of second reward answers 906' is greater than 1, the data processing unit 920 The second reward answer 906' is further organized into a second candidate list 908' and the display unit 940 is controlled to display. Then, according to the next input voice of the user, the corresponding part is selected, and then the corresponding operation is performed according to the number of subsequent feedback answers. This can be referred to the analogy of the above description, and will not be described here.

Further, the data processing unit 920 will map the plurality of records 302 of the structured repository 220 (eg, the numeric data of each of the sub-fields 308 in the title field 304) to the first keyword 904 corresponding to the first input speech SP1. The alignment is performed (as described above with respect to Figures 1, 3A, 3B, 3C). When the structured database 220 has a record 302 that is at least partially matched with the first key 904 of the first input speech SP1, then the record 302 is considered to be the matching result produced by the first input speech SP1 (eg, FIG. 3A/ 3B produces a matching result). Wherein, if the type of the material is a music file, the record 302 may include a song name, a singer, an album name, a publishing time, a play order, ..., etc.; if the type of the material is an image file, the record 302 may include a movie name, Publishing time, staff (including performers), ..., etc.; if the type of the material is a web file, the record 302 may include the website name, the web page type, the corresponding user account, ..., etc.; For the image file, the record 302 may include a picture name, picture information, ..., etc.; if the type of the data is a business card file, the record 302 may include a contact name, a contact phone number, a contact address, ..., etc. . The foregoing record 302 is illustrated by way of example, and the record 302 may be determined according to an actual application, and the embodiment of the present invention is not limited thereto.

Next, the material processing unit 920 can determine whether the second keyword 904' corresponding to the second input speech SP2 contains a sequential vocabulary indicating the order (for example, "I want a third option" or "I choose the third one"). When the second keyword 904' corresponding to the second input speech SP2 includes the sequential vocabulary indicating the order, the material processing unit 920 selects the material located at the corresponding position from the first candidate list 908 according to the sequential vocabulary. When the second keyword 904' corresponding to the second input speech SP2 does not include the sequential vocabulary indicating the order, it indicates that the user may directly select a certain first return in the first candidate list 908. In answer 906, the data processing unit 920 compares the record 302 corresponding to each first return answer 906 in the first candidate list 908 with the second keyword 904' to determine the first return answer 906 and the second input speech SP2. Corresponding degree, according to the degree of correspondence, determines whether a certain first return answer 906 corresponds to the second input speech SP2 in the first candidate list 908. In an embodiment of the present invention, the data processing unit 920 may determine the first candidate list 906 according to the degree of correspondence of the first reward answer 906 to the second keyword 904' (eg, the degree of complete matching or partial matching). Whether there is a certain first return answer 906 corresponding to the second input speech SP2, thereby simplifying the selection process. The data processing unit 920 can select the highest degree of correspondence in the data to correspond to the second input voice SP2.

For example, if the first input voice SP1 is “What is the weather today”, after performing voice recognition and natural language processing, the first keyword 904 corresponding to the first input voice SP1 may include “Today” and “Weather”, The data processing unit 920 reads the data corresponding to today's weather and displays the weather data as the first candidate list 908 through the display unit 930. Then, if the second input speech SP2 is "I want to see the third data" or "I choose the third pen", after the speech recognition and natural language processing, the second input speech SP2 corresponds to the second keyword 904' Will include "3rd", where "3rd" will be interpreted as a sequential vocabulary indicating the order, so the data processing unit 920 will read the third data in the first candidate list 908 (ie, the first candidate) The third pen in the list 908 first returns the answer 906), and the corresponding weather information is displayed again through the display unit 930. Or, if the second input voice SP2 is "I want to see the weather in Beijing" or "I choose the weather in Beijing", after performing speech recognition and natural language processing, The second keyword 904' corresponding to the two-input voice SP2 will include "Beijing" and "weather", so the data processing unit 920 will read the data corresponding to Beijing in the first candidate list 908. When the number of the first reward answers 906 corresponding to the selection is 1, the corresponding weather information may be directly displayed through the display unit 930; when the selected number of the first reward answers 906 is greater than 1, the second second is displayed. The candidate list 908' (containing at least one second reward answer 906') is further selected by the user.

If the first input voice SP1 is "I want to call the old card", after performing voice recognition and natural language processing, the first keyword 904 corresponding to the first input voice SP1 may include "telephone" and "sheet", The data processing unit 920 reads the contact information of the corresponding last name "Zhang" (the full-text search can be performed through the structured database 220, and then the detailed information corresponding to the record 302 is obtained), and the contact information is displayed through the display unit 930. The first candidate list 908 (ie, the first return answer 906). Then, if the second input voice SP2 is "3rd old" or "I choose the third", after performing speech recognition and natural language processing, the second keyword 904' corresponding to the second input speech SP2 may include "3rd", where "3rd" will be interpreted as a sequential vocabulary indicating the order, so the data processing unit 920 will read the third data in the first candidate list 908 (ie, the third A return answer 906), and dialing based on the selected data. Alternatively, if the second input speech SP2 is "I choose 139 at the beginning", after performing speech recognition and natural language processing, the second keyword 904' corresponding to the second input speech SP2 may include "139" and "beginning". Here, "139" is not interpreted as the sequential vocabulary indicating the order, so the material processing unit 920 reads the contact information of the first candidate list 908 whose telephone number is 139; if the second input voice SP2 is "I" For the old Zhang of Beijing, after the speech recognition and natural language processing, the second keyword 904' corresponding to the second input speech SP2 will include "Beijing" and "Zhang", and the data processing unit 920 will read the first candidate. The address in list 908 is the contact information of Beijing. When the number of selected first return answers 906 is 1, the dialing is performed according to the selected data; when the number of selected first return answers 906 is greater than 1, then The first return answer 906 selected at this time is taken as the second return answer 906', and is organized into a second candidate list 908' for display to the user for selection.

If the first input voice SP1 is "I am looking for a restaurant", after performing voice recognition and natural language processing, the first keyword 904 of the first input voice SP1 will include "restaurant", and the data processing unit 920 will read all corresponding The first report answer 906 is returned in the restaurant. Since such an indication is not very clear, the first candidate list 908 (including the first return answer 906 corresponding to all restaurant materials) will be displayed to the user through the display unit 930, and the user is further advanced. Instructions. Next, if the user inputs "3rd restaurant" or "I choose the 3rd" through the second input voice SP2, after the voice recognition and natural language processing, the second keyword 904' corresponding to the second input voice SP2 Will include "3rd", where "3rd" will be interpreted as a sequential vocabulary indicating the order, so the data processing unit 920 will read the third data in the first candidate list 908, and according to the selected The data is displayed. Alternatively, if the second input speech SP2 is "I select the nearest one", after performing speech recognition and natural language processing, the second keyword 904' corresponding to the second input speech SP2 may include "recent", so the data processing unit 920 will read the restaurant information of the first candidate list 908 with the address closest to the user; if the second input voice SP2 is "I want a restaurant in Beijing", the speech recognition and natural language are performed. After the word processing, the second keyword 904' corresponding to the second input voice SP2 will include "Beijing" and "restaurant", so the data processing unit 920 will read the restaurant material in the first candidate list 908 with the address of Beijing. When the number of selected first reward answers 906 is 1, it is displayed according to the selected data; when the number of selected first reward answers 906 is greater than 1, the first return answer 906 selected at this time is taken as the first The second answer 906' is returned and organized into a second candidate list 908' for display to the user for selection.

In accordance with the above, data processing unit 920 can perform corresponding operations in accordance with the type of material of the selected first reward answer 906 (or second reward answer 906'). For example, when the type of the data corresponding to the selected first reward answer 906 is a music file, the data processing unit 920 performs music playback according to the selected data; when the type of the selected data is an image file, the data The processing unit 920 performs video playback according to the selected data; when the type of the selected data is a web file, the data processing unit 920 displays according to the selected data; when the type of the selected data is an image file, The data processing unit 920 performs picture display according to the selected data; when the type of the selected data is a business card file, the data processing unit 920 performs dialing according to the selected data.

FIG. 10 is a schematic diagram of a system of an information system according to an embodiment of the invention. Referring to FIG. 9 and FIG. 10, in the embodiment, the information system 1000 includes a mobile terminal device 1010 and a server 1020. The server 1020 may be a cloud server, a regional network server, or the like, but The embodiments of the invention are not limited thereto. The mobile terminal device 1010 includes a voice receiving unit 1011, a data processing unit 1013, and a display unit 1015. The data processing unit 1013 is coupled to the voice receiving unit 1011. The display unit 1015 and the server 1020. The mobile terminal device 1010 may be a mobile communication device such as a mobile phone (Cell phone), a personal digital assistant (PDA) mobile phone, or a smart phone, and the present invention does not limit this. The function of the voice receiving unit 1011 is similar to that of the voice receiving unit 910, and the function of the display unit 1015 is similar to that of the display unit 930. The server 1020 is configured to store a plurality of materials and has a voice recognition function.

In this embodiment, the data processing unit 1013 performs voice recognition on the first input voice SP1 through the server 1020 to generate the first request information 902, and then performs natural language processing on the first request information 902 to generate a corresponding first input voice. The first keyword 904 of SP1, and the server 1020 performs a full-text search on the structured database 220 according to the first keyword 904 to find the first return answer 906 and transmits it to the data processing unit 1013. When the number of the first report answer 906 is 1, the data processing unit 1013 performs a corresponding operation according to the data type corresponding to the first report answer 906; when the number of the first report answer 906 is greater than 1, the data processing unit 1013 At this time, the selected first return answer 906 is organized into the first candidate list 908, and then the control display unit 1015 displays to the user, and waits for further instructions from the user. After the user inputs the indication, the data processing unit 1013 then performs voice recognition on the second input voice PS2 through the server 1020 to generate the second request information 902', and then analyzes and natural language processing the second request information 902'. To generate a second keyword 904' corresponding to the second input speech SP2, and the server 1020 selects a corresponding first reward answer 906 from the first candidate list 908 according to the second keyword 904' corresponding to the second input speech SP2. The second reward answer 906', and transmitted to the capital Material processing unit 1013. Similarly, when the number of corresponding second reward answers 906 is 1 at this time, the data processing unit 920 performs corresponding operations according to the type of the data corresponding to the second reward answer 906; when the number of second reward answers 906 is greater than At 1 o'clock, the data processing unit 1013 sorts the second return answer 906 selected at this time into a second candidate list 908', and then controls the display unit 1015 to display the user for further selection. Then, the server 1020 selects the corresponding part according to the subsequent input voice, and the data processing unit 1013 performs the corresponding operation according to the number of selected materials. This can be referred to the analogy of the above description, and will not be described herein.

It should be noted that, in an embodiment, if the number of first reward answers 906 selected according to the first keyword 904 corresponding to the first input voice SP1 is 1, the operation corresponding to the data may be directly performed. Moreover, in another embodiment, a prompt may be output to the user to inform the user that the corresponding operation of the selected first reward answer 906 will be performed. Furthermore, in another embodiment, when the number of second reward answers 906 selected according to the second keyword 904' corresponding to the second input voice SP2 is 1, the operation corresponding to the data may be directly performed. Of course, in another embodiment, a prompt may be output to the user to notify the user that the corresponding operation of the selected material is to be performed, and the present invention does not limit this.

Further, the server 1020 compares the respective records 302 of the structured database 220 with the first keywords 904 corresponding to the first input speech SP1. When each record 302 is at least partially matched to the first keyword 904, then this record 302 is considered to be the material matched by the first input speech SP1 and this record 302 is taken as one of the first reward answers 906. If the first input voice SP1 corresponds to the first When the number of first reward answers 906 selected by the keyword 904 is greater than one, the user may input an indication through the second input speech SP2. Since the indication input by the user at this time through the second input voice SP2 may include an order (to indicate the order of selecting the first item in the information, etc.), directly select one of the displayed information (for example, directly indicating a certain information) Content), or determining the user's intention according to the instruction (for example, selecting the nearest restaurant, the user is displayed with the "recent" restaurant), and then the server 1020 will then determine the second keyword 904 corresponding to the second input voice SP2. 'Do you include a sequential vocabulary indicating the order. When the second keyword 904' corresponding to the second input speech SP2 includes the sequential vocabulary indicating the order, the server 1020 selects the first return answer 906 located at the corresponding position from the first candidate list 908 according to the sequential vocabulary. When the second keyword 904 ′ corresponding to the second input speech SP2 does not include the sequential vocabulary indicating the order, the server 1020 associates each first return answer 906 in the first candidate list 908 with the second input speech SP2. The two keywords 904' are compared to determine the degree of correspondence between the first return answer 906 and the second input speech SP2, and may determine, according to the degree of correspondence, whether the first return answer 906 in the first candidate list 908 corresponds to the second input speech. SP2. In an embodiment of the present invention, the server 1020 may determine, according to the degree of correspondence between the first return answer 906 and the second keyword 904', those first return answers 906 in the first candidate list 908 corresponding to the second input speech SP2, To simplify the process of selection. The server 1020 can select the one with the highest degree of correspondence in the first report answer 906 as corresponding to the second input voice SP2.

11 is a flow chart of a voice recognition based selection method in accordance with an embodiment of the present invention. Referring to FIG. 11, in this embodiment, the first input voice is received (step Step S1100), and performing speech recognition on the first input speech SP1 to generate the first request information 902 (step S1110), and then analyzing the first request information 902 to perform natural language processing to generate a first keyword corresponding to the first input speech. 904 (step S1120). Next, the corresponding first reward answer 906 is selected from the plurality of materials according to the first keyword 904 (step S1130), and it is determined whether the number of the selected first reward answers 906 is 1 (step S1140). When the number of selected first return answers 906 is 1, that is, the determination result of step S1140 is YES, the corresponding operation is performed according to the data type corresponding to the first return answer 906 (step S1150). When the number of selected first return answers 906 is greater than 1, that is, the determination result of step S1140 is "NO", the first candidate list 908 is displayed according to the selected first return answer 906 and the second input speech SP2 is received (step S1160). And performing voice recognition on the second input voice to generate second request information 902' (step S1170), and analyzing and natural language processing on the second request information 902' to generate a second keyword corresponding to the second input voice 904' (step S1180). Next, the corresponding portion is selected from the first report answer 906 in the first candidate list 908 according to the second request information 902, and then the process returns to step S1140 to determine whether the number of selected first report answers 906 is 1 (step S1190). The order of the above steps is for illustration, and the embodiment of the present invention is not limited thereto. For details of the above steps, reference may be made to the embodiments of FIG. 9 and FIG. 10, and details are not described herein again.

In summary, the voice recognition-based selection method and the mobile terminal device and the information system thereof in the embodiment of the present invention perform voice recognition and natural language processing on the first input voice and the second input voice to confirm the first input voice and Second input The keyword corresponding to the sound is selected according to the keyword corresponding to the first input voice and the second input voice. Thereby, the convenience of the user's operation can be improved.

Next, an operation example combining the architecture and components of the natural language understanding system 100 and the structured database 220 and the auxiliary activation device disclosed in the present invention will be described.

FIG. 12 is a block diagram of a voice control system according to an embodiment of the invention. Referring to FIG. 12, the voice control system 1200 includes an auxiliary activation device 1210, a mobile terminal device 1220, and a server 1230. In the present embodiment, the auxiliary activation device 1210 activates the voice system of the mobile terminal device 1220 by wirelessly transmitting signals, so that the mobile terminal device 1220 communicates with the server 1230 according to the voice signal.

In detail, the auxiliary activation device 1210 includes a first wireless transmission module 1212 and a trigger module 1214 , wherein the trigger module 1214 is coupled to the first wireless transmission module 1212 . The first wireless transmission module 1212 supports, for example, Wireless Fidelity (Wi-Fi), Worldwide Interoperability for Microwave Access (WiMAX), Bluetooth, and Ultra-wideband. , UWB) or radio frequency identification (RFID) communication protocol device, which can transmit a wireless transmission signal to establish a wireless connection with another wireless transmission module. The trigger module 1214 is, for example, a button, a button, or the like. In this embodiment, after the user presses the trigger module 1214 to generate a trigger signal, the first wireless transmission module 1212 receives the trigger signal and starts, and the first wireless transmission module 1212 The wireless transmission signal is transmitted, and the wireless transmission signal is transmitted to the mobile terminal device 1220 through the first wireless transmission module 1212. In an embodiment, the auxiliary activation device 1210 may be a Bluetooth headset.

It should be noted that although some hands-free headsets/microphones currently have a design to activate certain functions of the mobile terminal device 1220, in another embodiment of the present invention, the auxiliary activation device 1210 may be different from the earphone/microphone described above. The above-mentioned earphone/microphone performs listening/talking by replacing the earphone/microphone on the mobile terminal device 1220 by connecting with the mobile terminal device, and the activation function is an additional design, but the auxiliary starting device 1210 of the present invention is "only" used. The voice system in the mobile terminal device 1220 is turned on, and does not have the function of listening/talking, so the internal circuit design can be simplified and the cost is low. In other words, with respect to the hands-free headset/microphone described above, the auxiliary activation device 1210 is another device, that is, the user may have both a hands-free headset/microphone and the auxiliary activation device 1210 of the present invention.

In addition, the shape of the auxiliary starting device 1210 described above may be an item that is accessible to the user, such as a ring, a watch, an earring, a necklace, an eyeglass, etc., that is, various portable items, or a mounting member, for example, a configuration. The driving accessories on the steering wheel are not limited to the above. That is to say, the auxiliary activation device 1210 is a "living" device, and through the setting of the internal system, the user can easily touch the trigger module 1214 to turn on the voice system. For example, when the shape of the auxiliary activation device 1210 is a ring, the user can easily move the finger to press the trigger module 1214 of the ring to be triggered. On the other hand, when the shape of the auxiliary starting device 1210 is a device disposed on the driving accessory, the user can also easily trigger during driving. The trigger module 1214 of the driving accessory device. In addition, the auxiliary activation device 1210 of the present invention can be used to turn on the voice system in the mobile terminal device 1220, and even turn on the amplification function, compared to the discomfort of listening/talking with the earphone/microphone. Therefore, the user can still listen/talk directly through the mobile terminal device 1220 without wearing the earphone/microphone. In addition, for the user, these "living" auxiliary starting devices 1210 are items that would otherwise be worn or used, so there is no problem of uncomfortable or uncomfortable use, that is, no flowers are needed. Time to adapt. For example, when a user is cooking in a kitchen and needs to dial a mobile phone placed in a living room, assuming that it is wearing the auxiliary starting device 1210 of the present invention having a ring, a necklace or a watch shape, the ring and the necklace can be lightly touched. Or watch to turn on the voice system to ask for friend recipe details. Although some earphones/microphones with start-up function can achieve the above purposes, in the process of cooking, not every time you need to call a friend, so for the user, wear headphones at any time. It is quite inconvenient to cook the microphone in order to control the mobile terminal device at any time.

In other embodiments, the auxiliary activation device 1210 can also be configured with a wireless rechargeable battery 1216 for driving the first wireless transmission module 1212. Further, the wireless charging battery 1216 includes a battery unit 12162 and a wireless charging module 12164. The wireless charging module 12164 is coupled to the battery unit 12162. Here, the wireless charging module 12164 can receive energy supplied from a wireless power supply device (not shown) and convert the energy into power to charge the battery unit 12162. As such, the first wireless transmission module 1212 of the auxiliary activation device 1210 can be conveniently charged through the wireless rechargeable battery 1216.

On the other hand, the mobile terminal device 1220 is, for example, a Cell phone, a Personal Digital Assistant (PDA) mobile phone, a smart phone, or a Pocket PC equipped with a communication software (Pocket PC). ), Tablet PC or laptop, and more. The mobile terminal device 1220 may be any portable mobile device having a communication function, and the scope thereof is not limited herein. Further, the mobile terminal device 1220 may use an Android operating system, a Microsoft operating system, an Android operating system, a Linux operating system, and the like, and is not limited to the above.

The mobile terminal device 1220 includes a second wireless transmission module 1222, and the second wireless transmission module 1222 can match the first wireless transmission module 1212 of the auxiliary activation device 1210, and adopts a corresponding wireless communication protocol (for example, wireless compatibility). A communication protocol such as authentication, global interoperability microwave access, Bluetooth, ultra-wideband communication protocol or radio frequency identification is established to establish a wireless connection with the first wireless transmission module 1212. It should be noted that the "first" wireless transmission module 1212 and the "second" wireless transmission module 1222 are used to describe that the wireless transmission module is configured in different devices, and is not intended to limit the present invention.

In other embodiments, the mobile terminal device 1220 further includes a voice system 1221. The voice system 1221 is coupled to the second wireless transmission module 1222. Therefore, after the user triggers the trigger module 1214 of the auxiliary activation device 1210, the user can transmit the first The wireless transmission module 1212 and the second wireless transmission module 1222 wirelessly activate the voice system 1221. In an embodiment, the voice system 1221 can include a voice sampling module 1224, a voice synthesis module 1226, and a voice output interface 1227. Voice sampling module The 1224 is configured to receive a voice signal from a user. The voice sampling module 1224 is, for example, a device for receiving audio such as a microphone. The speech synthesis module 1226 can query a speech synthesis database, and the speech synthesis database is, for example, information recorded with text and corresponding speech, so that the speech synthesis module 1226 can find the speech corresponding to the specific text message. Synthesize text messages into speech. Thereafter, the speech synthesis module 1226 can output the synthesized speech through the speech output interface 1227 for playback to the user. The voice output interface 1227 described above is, for example, a speaker or an earphone.

In addition, the mobile terminal device 1220 may also be configured with a communication module 1228. The communication module 1228 is, for example, an element capable of transmitting and receiving wireless signals, such as a radio frequency transceiver. Further, the communication module 1228 enables the user to answer or make a call through the mobile terminal device 1220 or use other services provided by the carrier. In this embodiment, the communication module 1228 can receive the response information from the server 1230 through the Internet, and establish a call connection between the mobile terminal device 1220 and the at least one electronic device according to the response information, wherein the electronic device The device is for example another mobile terminal device (not shown).

The server 1230 is, for example, a network server or a cloud server, and has a voice understanding module 1232. In this embodiment, the voice recognition module 1232 includes a voice recognition module 12322 and a voice processing module 12324. The voice processing module 12324 is coupled to the voice recognition module 12322. Here, the speech recognition module 12322 receives the speech signal transmitted from the speech sampling module 1224 to convert the speech signal into a plurality of segmentation semantics (eg, keywords or words, etc.). Voice processing module 12324 The meanings represented by the segmentation semantics (eg, intent, time, location, etc.) can be parsed according to the segmentation semantics, and the meaning represented in the speech signal can be determined. In addition, the voice processing module 12324 also generates corresponding response information according to the parsed result. In this embodiment, the speech understanding module 1232 can be implemented by a hardware circuit composed of one or several logic gates, or can be implemented by a computer program code. It is worth mentioning that in another embodiment, the voice understanding module 1232 can be configured in the mobile terminal device 1320, such as the voice control system 1300 shown in FIG. The operation of the speech understanding module 1232 of the server 1230 can be as shown in the natural language understanding system 100 of FIG. 1A and the natural language dialogue system 500/700/700' of FIGS. 5A/7A/7B.

The method of voice manipulation will be described below in conjunction with the voice control system 1200 described above. FIG. 14 is a flowchart of a voice control method according to an embodiment of the invention. Referring to FIG. 12 and FIG. 14 simultaneously, in step S1402, the auxiliary activation device 1210 transmits a wireless transmission signal to the mobile terminal device 1220. The detailed description is that when the first wireless transmission module 1212 of the auxiliary activation device 1210 is triggered by receiving a trigger signal, the auxiliary activation device 1210 transmits a wireless transmission signal to the mobile terminal device 1220. Specifically, when the trigger module 1214 in the auxiliary activation device 1210 is pressed by the user, the trigger module 1214 is triggered by the trigger signal, and the first wireless transmission module 1212 sends the wireless transmission signal to the mobile terminal. The second wireless transmission module 1222 of the device 1220 is configured to enable the first wireless transmission module 1212 to be coupled to the second wireless transmission module 1222 via a wireless communication protocol. The above-mentioned auxiliary starting device 1210 is only used to turn on the voice system in the mobile terminal device 1220, and does not have the function of listening/talking, so the internal circuit design can be simplified and the cost is low. In other words, The auxiliary activation device 1210 is another device with respect to the hands-free headset/microphone attached to the general mobile terminal device 1220, that is, the user may have both a hands-free headset/microphone and the auxiliary activation device 1210 of the present invention.

It is worth mentioning that the above-mentioned auxiliary starting device 1210 can be a user's easy-to-access item, such as a ring, a watch, an earring, a necklace, a pair of glasses, and the like, or a mounting member, for example, a configuration. The driving accessories on the steering wheel are not limited to the above. That is to say, the auxiliary activation device 1210 is a "living" device, and through the setting of the internal system, the user can easily touch the trigger module 1214 to turn on the voice system 1221. Therefore, the auxiliary system 1210 of the present invention can be used to turn on the voice system 1221 in the mobile terminal device 1220, and even turn on the sound amplification function (described later in detail), so that the user can still wear the earphone/microphone without using the earphone/microphone. Listening/talking is performed directly through the mobile terminal device 1220. In addition, for the user, these "living" auxiliary starting devices 1210 are items that would otherwise be worn or used, so there is no problem of uncomfortable or uncomfortable use.

In addition, the first wireless transmission module 1212 and the second wireless transmission module 1222 can be in a sleep mode or an operating mode. The sleep mode refers to that the wireless transmission module is in a closed state, that is, the wireless transmission module does not receive/detect wireless transmission signals, and cannot be connected with other wireless transmission modules. The working mode refers to the wireless transmission module being turned on, that is, the wireless transmission module can continuously detect wireless transmission signals, or can transmit wireless transmission signals at any time, and can be connected with other wireless transmission modules. Here, when the trigger module 1214 is triggered, if the first wireless transmission module 1212 In the sleep mode, the trigger module 1214 wakes up the first wireless transmission module 1212, causes the first wireless transmission module 1212 to enter the working mode, and causes the first wireless transmission module 1212 to transmit the wireless transmission signal to the second wireless transmission mode. The group 1222 allows the first wireless transmission module 1212 to be coupled to the second wireless transmission module 1222 of the mobile terminal device 1220 via a wireless communication protocol.

On the other hand, in order to prevent the first wireless transmission module 1212 from continuously maintaining the operating mode and consuming excessive power, the preset time (for example, 5 minutes) after the first wireless transmission module 1212 enters the working mode, if triggered If the module 1214 is not triggered again, the first wireless transmission module 1212 enters the sleep mode from the working mode and stops connecting with the second wireless transmission module 1220 of the mobile terminal device 1220.

Thereafter, in step S1404, the second wireless transmission module 1222 of the mobile terminal device 1220 receives the wireless transmission signal to activate the voice system 1221. Next, in step S1406, when the second wireless transmission module 1222 detects the wireless transmission signal, the mobile terminal device 1220 can activate the voice system 1221, and the 1221 sampling module 1224 of the voice system can start receiving the voice signal, for example, "Today How many degrees?", "Call to Pharaoh.", "Please check the phone number."

In step S1408, the voice sampling module 1224 transmits the voice signal to the voice understanding module 1232 in the server 1230 to analyze the voice signal and generate response information through the voice understanding module 1232. Further, the speech recognition module 12322 in the speech understanding module 1232 receives the speech signal from the speech sampling module 1224 and divides the speech signal into a plurality of segmentation semantics, and the speech processing module 12324 then Segmentation semantics for speech understanding to generate responses The response information of the tone signal.

In another embodiment of the present invention, the mobile terminal device 1220 can further receive the response information generated by the voice processing module 12324, and output the content in the response information or perform the operation performed by the response information through the voice output interface 1227. In step S1410, the voice synthesis module 1226 of the mobile terminal device 1220 receives the response information generated by the voice understanding module 1232, and performs voice synthesis according to the content (for example, vocabulary or sentence) in the response information to generate a voice response. . And, in step S1412, the voice output interface 1227 receives and outputs the voice response.

For example, when the user presses the trigger module 1214 in the auxiliary activation device 1210, the first wireless transmission module 1212 sends a wireless transmission signal to the second wireless transmission module 1222, so that the mobile terminal device 1220 activates the voice system. Voice sampling module 1224 of 1221. Here, assuming that the voice signal from the user is a query sentence, such as "Today's temperature is a few degrees?", the voice sampling module 1224 receives and transmits the voice signal to the voice understanding module 1232 in the server 1230. The speech understanding module 1232 can transmit the response information generated by the parsing back to the mobile terminal device 1220. Assuming that the content of the response information generated by the speech understanding module 1232 is "30 ° C", the speech synthesis module 1226 synthesizes the "30 ° C" message into a speech response, and the speech output interface 1227 can respond to the speech. Broadcast to the user.

In another embodiment, assuming that the voice signal from the user is a command sentence, such as "calling to Pharaoh.", the voice understanding module 1232 can recognize the command sentence as "calling the phone to Pharaoh." request". In addition, the speech understanding module 1232 will generate new response information, such as "Please confirm whether to dial to Pharaoh", and this The new response information is transmitted to the mobile terminal device 1220. Here, the speech synthesis module 1226 synthesizes the new response information into a voice response, and broadcasts the message to the user through the voice output interface 1227. Further, when the user's response is a positive answer to the class of "Yes", similarly, the voice sampling module 1224 can receive and transmit the voice signal to the server 1230 for the voice understanding module 1232 to parse. . After the speech understanding module 1232 has finished parsing, a dialing instruction information is recorded in the response information and transmitted to the mobile terminal device 1220. At this time, the communication module 1228 queries the phone number of the "Pharaoh" according to the contact information recorded in the phone database to establish a connection connection between the mobile terminal device 1220 and another electronic device, that is, Dial to "Pharaoh."

In other embodiments, the above-described operation method may be performed by using the voice control system 1300 or other similar system in addition to the voice control system 1200 described above, and is not limited to the above embodiments.

In summary, in the voice control system and method of the embodiment, the auxiliary activation device can wirelessly turn on the voice function of the mobile terminal device. Moreover, the shape of the auxiliary starting device may be a "living" item accessible by the user, such as a ring, a watch, an earring, a necklace, a pair of glasses, etc., that is, various portable items, or a mounting member. For example, the traveling accessory disposed on the steering wheel is not limited to the above. As a result, it is more convenient to use the auxiliary activation device 1210 of the present invention to turn on the voice system in the mobile terminal device 1220 compared to the current discomfort of wearing the hands-free headset/microphone.

It should be noted that the above-mentioned server 1230 having a speech understanding module can be Can be a web server or a cloud server, and the cloud server may involve the user's privacy issues. For example, the user needs to upload a complete address book to the cloud server in order to complete operations related to the address book such as making a call, sending a text message, and the like. Even if the cloud server uses an encrypted connection and is not saved for immediate use, it is still difficult to eliminate the user's superiority. Accordingly, the following provides another voice control method and a corresponding voice interaction system thereof, and the mobile terminal device can perform a voice interaction service with the cloud server without uploading the complete address book. In order to clarify the content of the present invention, the following specific examples are given as examples in which the present invention can be implemented.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention, and any one of ordinary skill in the art can make some changes and refinements without departing from the spirit and scope of the present invention. The scope of the invention is defined by the scope of the appended claims.

S1100, S1110, S1120, S1130, S1140, S1150, S1160, S1170, S1180, S1190‧ ‧ ‧ steps of a speech recognition based selection method according to an embodiment of the present invention

Claims (20)

A voice recognition-based selection method includes: receiving a first voice input; performing voice recognition on the first voice input to generate a first keyword; performing natural language processing on the first keyword to generate a corresponding first a first user intent of the voice input; selecting at least one first reward answer according to the first user intention; when the number of the selected first reward answer is 1, corresponding to the selected type of the first reward answer An operation of displaying a first candidate list including the first report answer when the number of the first report answers selected is greater than one; and performing a second voice input to perform voice recognition on the second voice input to generate a second keyword; performing natural language processing on the second keyword to generate a second user intent corresponding to the second voice input; and selecting from the first report answer of the first candidate list according to the second user intent The second reward answer. The speech recognition-based selection method of claim 1, wherein the step of selecting the first reward answer according to the first user intention comprises: recording the record stored in a structured database with the first user intent Performing an alignment; and when the record is at least partially matched with the first user intent, then the record is The first return answer corresponding to the first voice input is considered. The speech recognition-based selection method of claim 1, wherein the step of selecting the second reward answer from the first reward answer in the first candidate list according to the second user intention comprises: determining the Whether the second user intention includes a sequential vocabulary indicating the order; when the second user intends to include the sequential vocabulary indicating the order, the vocabulary according to the order is selected according to the order to select the first position at the corresponding position from the first candidate list Returning an answer; when the second user intention does not include the sequential vocabulary indicating the order, comparing the records corresponding to each of the first return answers in the first candidate list with the second user intention; The comparison result determines which of the first candidate answers of the first candidate list corresponds to the second speech input. The voice recognition-based selection method of claim 3, wherein determining, according to the comparison result, which of the first candidate answers in the first candidate list corresponds to the second voice input comprises: selecting the first The highest degree of matching in the answer is the corresponding second voice input. The speech recognition-based selection method according to claim 1, wherein the step of performing the corresponding operation according to the selected type of the first reward answer comprises: when the selected first return answer type is one Music file, then choose The first return answer is played by the music; when the selected first return answer type is an image file, the selected first return answer is played by the image; when the selected first return answer is of the type a webpage file, wherein the selected first reward answer is displayed; when the type of the selected first reward answer is an image file, the selected first reward answer is displayed; and when the selected first When the type of the answer is a business card file, the selected first return answer is dialed. A mobile terminal device includes: a voice receiving unit, receiving a first voice input and a second voice input; a display unit; a storage unit for storing a plurality of materials; and a data processing unit coupled to the voice a receiving unit, the display unit and the storage unit, the data processing unit performs voice recognition on the first voice input to generate a first keyword, and performs natural language processing on the first keyword to generate a corresponding first voice a first user intent, and selecting a first reward answer according to the first user intention, when the number of the selected first reward answers is 1, the data processing unit corresponds according to the selected type of the first reward answer And when the number of the selected first return answers is greater than 1, the data processing unit controls the display unit to display the first candidate list including the first report answer, and the data processing unit performs the second voice Speech recognition to generate a second keyword, The second keyword performs natural language processing to generate a second user intent corresponding to the second speech input, and selects a second reward answer from the first reward answer in the first candidate list in accordance with the second user intent. The mobile terminal device of claim 6, wherein the data processing unit compares the record corresponding to each of the first reward answers with the first user intention, and each of the records and the first user intention When at least partially matched, the record is considered to be the first return answer corresponding to the first voice input. The mobile terminal device of claim 6, wherein the data processing unit determines whether the second user intention includes a sequential vocabulary indicating an order, and when the second user intention includes the sequential vocabulary indicating the order And the data processing unit vocabulary selects the first return answer located at the corresponding position from the first candidate list according to the sequence, and when the second user intention does not include the sequential vocabulary indicating the order, the data processing The unit compares the record corresponding to each of the first report answers in the first candidate list with the second user intention to determine a degree of matching between the first report answer and the second voice input, and according to the matching degree Determining which of the first return answers in the first candidate list corresponds to the second voice input. The mobile terminal device of claim 8, wherein the data processing unit selects the highest degree of matching in the first report answer to correspond to the second voice input. The mobile terminal device of claim 6, wherein when the selected type of the first return answer is a music file, the data processing unit performs music playback according to the selected first return answer. The first return selected The type of the answer is an image file, and the data processing unit performs image playback according to the selected first return answer. When the type of the selected first answer answer is a web file, the data processing unit selects according to the selected image. The first return answer is displayed. When the type of the first return answer selected is an image file, the data processing unit performs image display according to the selected first return answer, and when the selected When the type of the answer is a business card file, the data processing unit dials according to the selected first return answer. An information system includes: a server for storing a plurality of materials and having a voice recognition function; and a mobile terminal device comprising: a voice receiving unit, receiving a first voice input and a second voice input; and displaying a data processing unit coupled to the voice receiving unit, the display unit, and the server, the data processing unit performing voice recognition on the first voice input through the server to generate a first keyword, for the first The keyword performs natural language processing to generate a first user intent corresponding to the first voice input, and the server selects a corresponding at least one first reward from the records included in the structured database according to the first user intent. The answer is transmitted to the data processing unit. When the number of the first return answer selected is 1, the data processing unit performs a corresponding operation according to the selected type of the first return answer, when the first return is selected. When the number of answers is greater than 1, the data processing unit controls the display unit to display the inclusion according to the selected first return answer. The first candidate list of the first return answers, as well as the data processing unit Performing voice recognition on the second voice input through the server to generate a second keyword, performing natural language processing on the second keyword to generate a second user intent corresponding to the second voice input, and the server is based on The second user intends to select a second reward answer from the first reward answer in the first candidate list and transmit to the data processing unit. The information system of claim 11, wherein the server compares the record of each of the first return answers with the first user intent, wherein each of the records and the first user intention are at least partially When matching, the record is regarded as the first return answer corresponding to the first voice input. The information system of claim 11, wherein the server determines whether the second user intention includes a sequential vocabulary indicating the order, and when the second user intends to include the sequential vocabulary indicating the order, Determining, according to the sequence, the first return answer located at the corresponding position from the first candidate list, and when the second user intention does not include the sequential vocabulary indicating the order, the server Each of the records in the candidate list is compared with the second user intent to determine a degree of matching between the first report answer and the second voice input, and determining which of the first candidate list is the first according to the matching degree The reward answer corresponds to the second voice input. The information system of claim 13, wherein the server selects the highest degree of matching in the first report answer to correspond to the second voice input. For example, the information system described in claim 11 of the patent scope, wherein The type of the first return answer is a music file, and the data processing unit performs music playing according to the selected first return answer. When the selected first return answer type is an image file, the data is The processing unit performs image playback according to the selected first reward answer. When the type of the selected first return answer is a webpage file, the data processing unit displays according to the selected first reward answer. If the type of the first return answer selected is an image file, the data processing unit performs image display according to the selected first return answer, and when the selected first return answer type is a business card file, The data processing unit performs dialing according to the selected first return answer. A voice recognition-based selection method includes: performing voice recognition on a first voice input to generate a first keyword; performing a search in a structured database according to the first keyword to obtain at least one first reward answer; When the number of the selected first reward answers is greater than 1, displaying a first candidate data including the first reward answer; receiving a second voice input after displaying the first candidate list, and inputting the second voice input Performing speech recognition to generate a second keyword; and selecting a second reward answer from the first reward answer of the first candidate list according to the second user intent. The speech recognition-based selection method of claim 16, wherein the step of searching the first keyword in a structured database to obtain at least one first reward answer comprises: When the record of the structured database is at least partially matched to the first keyword, the record is considered to be the first return answer corresponding to the first voice input. The speech recognition-based selection method of claim 16, wherein the step of selecting the second reward answer from the first reward answer in the first candidate list according to the second keyword comprises: when When the second keyword includes the sequential vocabulary indicating the order, the first return answer located at the corresponding position is selected from the first candidate list according to the order; when the second keyword does not include the order indicating the order When the vocabulary is vocabulary, compare the record corresponding to each of the first return answers in the first candidate list with the second keyword; and determine which first return of the first candidate list according to the comparison result The answer corresponds to the second voice input. The voice recognition-based selection method of claim 18, wherein determining, according to the comparison result, which of the first candidate answers in the first candidate list corresponds to the second voice input comprises: selecting the first The highest degree of matching in the answer is the corresponding second voice input. The speech recognition-based selection method according to claim 16, wherein the step of performing the corresponding operation according to the selected type of the first reward answer comprises: when the selected first return answer type is one a music file, the music is played on the selected first return answer; When the selected first return answer type is an image file, the selected first return answer is played by the image; when the selected first return answer type is a web file, the selected first Returning the answer for display; when the selected first return answer type is an image file, performing a picture display on the selected first return answer; and when the selected first return answer type is a business card file, Dial the selected first return answer.