JP3939613B2 - Race game apparatus, race game control method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a racing game device, a racing game control method and a program, for exciting a game player's further eagerness to win the game value by outputting a sentence stimulating the emotion of the game player according to the speech contents spoken by the game player in the process of a game so that the same game can be played with consistency. <P>SOLUTION: This racing game device includes: a topic retrieving part 360 for collating a first morpheme information extracted by a sentence structure analyzing part 430 with each previously stored topic title, retrieving the topic title including the first morpheme information from the topic titles and obtaining a first numerical value information related to the retrieved topic title; and a talking sentence retrieving part 371 for collating the first numerical value information with each second numerical value information according to the first numerical value information obtained by the topic retrieving part 360, retrieving the second numerical value information conforming to the first numerical value information from the second numerical value information, and obtaining a talking sentence corresponding to the retrieved second numerical value information. <P>COPYRIGHT: (C)2004,JPO

Description

[0001]
BACKGROUND OF THE INVENTION
In the present invention, a plurality of movement models traveling on a predetermined traveling path are arranged, and a player makes predictions about the arrival order of each movement model for each race, and after the input about the prediction is completed, each movement model When a race that competes for the order of arrival by running on the road and the order of arrival of each movement model matches the player's expectation, the player's expectation is correct and the predetermined game value is discharged. The present invention relates to a racing game apparatus, a racing game control method, and a program.
[0002]
[Prior art]
In recent years, among various game devices arranged in a game hall, there is a horse racing game device that allows a plurality of players to participate. This horse racing game apparatus is intended to run a plurality of movement models on an elliptical traveling path, and the player is placed at the station portion arranged around the traveling path before the start of each movement model. Predict the order of arrival of each moving model that finishes in. If the player's prediction matches the arrival order set in the horse racing game apparatus, the player can acquire a predetermined number of game values (medals and the like).
[0003]
[Problems to be solved by the invention]
However, in the above-described horse racing game apparatus, a player often pays a large amount of game fee before acquiring the game value, so even if his / her prediction is correct in a certain race, he / she feels lost. I often had. On the other hand, there is a horse racing game device that pays a large number of game values with a high payout rate when a player's prediction is right. According to this, even if the player's own prediction is not satisfied in a plurality of races, if the payout rate in one race is high, the player can play the game with the enthusiasm for recovering the loss at a stroke. .
[0004]
However, after the player has acquired the desired game value, the player has moved to another game machine because the player has achieved his or her purpose. In addition, when there is only one player, even if the player is pleased that the desired game value has been acquired, there is no one who can share the joy, and thereafter, the player moves to another game machine. There were also many.
[0005]
Therefore, the present invention has been made in view of the above points, and outputs to the player a sentence that stimulates the player's emotion based on the utterance content uttered by the player during the game. There are provided a racing game apparatus, a racing game control method, and a program capable of stimulating the desire to acquire the gaming value and allowing the same game to be played with continuity.
[0006]
[Means for Solving the Problems]
The present invention has been made in order to solve the above-described problems, and a plurality of movement models traveling on a predetermined traveling path are provided, and a player makes predictions about the arrival order of each movement model for each race. After completing the input for, each mobile model runs on the road and competes for the order of arrival, and if the order of arrival of each model matches the expectation, the player's prediction is correct. As a result, when discharging a predetermined game value, the second morpheme information indicating a morpheme composed of one character, a plurality of character strings, or a combination thereof is associated with first numerical information such as a numerical value and a sign. A plurality of the second morpheme information is stored in advance, and a sentence to be spoken to the player is associated with second numerical information such as a numerical value and a code as a reference for searching the sentence, and a plurality of the sentences are stored in advance. Corresponding to the utterance input from the user Based on the character string, each morpheme constituting the minimum unit of the character string is extracted as first morpheme information, the extracted first morpheme information is compared with each second morpheme information stored in advance, The second morpheme information including the morpheme constituting the first morpheme information is searched from the morpheme information, the first numerical information associated with the searched second morpheme information is acquired, and the acquired first numerical information The first numerical information is compared with each second numerical information stored in advance, and the second numerical information that matches the first numerical information is searched from each second numerical information, A sentence associated with the second numerical information is acquired.
[0007]
According to the invention according to the present application, the racing gaming device searches for the second morpheme information including the first morpheme information (morpheme constituting the utterance content from the player) from each second morpheme information. Based on the first numerical information associated with the searched second morpheme information, a sentence associated with the second numerical information that matches the first numerical information can be acquired. A specific sentence can be output based on the utterance content from the player. As a result, if the sentence output from the racing gaming device is a sentence that stimulates the player's emotions, the player will be further motivated to acquire game values (medals, etc.) and continue. The same game can be performed happily with sex.
[0008]
In particular, if the second numerical information is the emotion level held for the player and the rank of the image held for the player is set in a good direction when the emotion level is high, the racing gaming device searches for the second If the emotional level corresponding to the numerical information is high, the player has a good image, so the sentence associated with the high emotional level (second numerical information), for example, a sentence that encourages the player (“Next is more Let ’s take a medal ”, etc.), and the player can enjoy the game while enjoying the thrill and excitement.
[0009]
For the above configuration, each second morpheme information is associated with the first numerical information, and the sentence indicates the second numerical information and the number of times that the player's prediction is likely to be correct in each race. A number of sentences are stored in advance for each second numerical information and each assumed number of times, and the number of times the player's prediction has been hit is counted, and the obtained first numerical information and the number of times counted Based on the second numerical information stored in advance and each assumed number of times, the second numerical information and the assumed number of times that match the first numerical information and the number of times, respectively, It is preferable to acquire a sentence associated with the assumed number of times.
[0010]
In this case, each of the second numerical information stored in advance and each assumed number of times is calculated based on the first numerical information acquired and the number of times counted (the number of times the player's prediction is correct). The second numerical information and the assumed number of times that match each of the first numerical information and the counted number of times can be searched, and the sentence associated with the searched second numerical information and the assumed number of times can be acquired. Therefore, the racing game apparatus can change the content of the sentence to be spoken according to not only the acquired first numerical information but also the counted number of times.
[0011]
In this case, if the player's expectation is not correct, the player's money will decrease, but depending on the number of times the competitive gaming device has been hit, a specific sentence (for example, “this” If the player's own money is reduced, the player's willingness to acquire more game value is stimulated by the output sentence, and the same game is played with continuity. It can be done happily.
[0012]
In addition, if the second numerical information is regarded as an emotional level held to the player, the content of the talking sentence changes depending on the level of the emotional level. Is The contents of the sentence to be spoken can be changed one by one depending on the degree of emotion corresponding to the acquired first numerical information. Therefore, when the level of emotion held for a player is high, the racing game device can apply a word of encouragement to the player, and the player can have more fun with the output spoken sentence. Can play games.
[0013]
Further, with regard to the above configuration, a plurality of second morpheme information is associated with a discourse range indicating a morpheme that is related to the utterance content that will be input by the player or the response content to the player, In the information, the first numerical information is associated with each, and a plurality of the discourse ranges are stored in advance, the extracted first morpheme information and each stored discourse range are collated, and from each discourse range, Search for the discourse range that matches the morpheme included in the first morpheme information, collate each second morpheme information associated with the searched discourse range with the extracted first morpheme information, and each second morpheme information It is preferable to search the second morpheme information including the morpheme constituting the first morpheme information, and obtain the first numerical information associated with the searched second morpheme information.
[0014]
In this case, the racing gaming device does not need to collate all the second morpheme information with the extracted first morpheme information, and each second morpheme information associated only with the searched “discourse range” Since it is only necessary to collate with the extracted first morpheme information, the second morpheme information including the morpheme constituting the first morpheme information is searched from each second morpheme information in a short time (pinpoint search). can do.
[0015]
As a result, the prize discharging device can retrieve the corresponding second morpheme information in a short time, and therefore, the first numerical information that matches the first numerical information is based on the first numerical information associated with the second morpheme information. Sentences associated with binary information can be output quickly.
[0016]
It is preferable that numerical values corresponding to the acquired first numerical information are sequentially accumulated, and the accumulated numerical values are used as the first numerical information. As a result, the racing gaming apparatus can use the numerical value accumulated sequentially as the first numerical information, so if the second numerical information that matches the accumulated first numerical information is the above emotion level, The corresponding sentence can be output on the basis of the overall emotional level.
[0017]
DETAILED DESCRIPTION OF THE INVENTION
[First embodiment]
(Basic configuration of conversation control system)
A conversation control system according to the present invention will be described with reference to the drawings. FIG. 1 is a schematic configuration diagram of a conversation control system having a conversation control apparatus 1 according to the present embodiment.
[0018]
As shown in the figure, the conversation control device 1 includes an input unit 100, a speech recognition unit 200, a conversation control unit 300, a sentence analysis unit 400, a conversation database 500, an output unit 600, and a speech recognition dictionary storage. Part 700.
[0019]
In this embodiment, for the convenience of explanation, the description is limited to the utterance content of the user (player) (this utterance content is a kind of input information), but is limited to the utterance content of the user. Instead, it may be input information input from a keyboard or the like. Therefore, the “utterance content” shown below can be described by replacing “utterance content” with “input information”.
[0020]
Similarly, in the following description, for convenience of explanation, the description will be limited to the “spoken sentence type” (speech type), but is not limited to this “spoken sentence type”, and input from a keyboard or the like. It may be an “input type” indicating the type of input information. Accordingly, the following “speech sentence type” (speech type) can be described by replacing “speech type” with “input type”.
[0021]
The input unit 100 is an acquisition unit that acquires input information from a user. In the present embodiment, a microphone, a keyboard, and the like are used. The input unit 100 is also a character recognition unit that specifies a character string indicating input information based on input information input from a user.
[0022]
Here, the input information means characters, symbols, voices and the like input through a keyboard or the like. Specifically, the input unit 100 acquires user input information (other than voice), and outputs the acquired input information to the conversation control unit 300. Further, the input unit 100 that has acquired the utterance content from the user (the utterance content is composed of voice and is a kind of input information) with a microphone or the like, the voice constituting the acquired utterance content is a voice signal. To the voice recognition unit 200.
[0023]
The voice recognition unit 200 is a character recognition unit that identifies a character string corresponding to the utterance content based on the utterance content acquired by the input unit 100. Specifically, the speech recognition unit 200 to which a speech signal is input from the input unit 100 analyzes the input speech signal, and a character string corresponding to the analyzed speech signal is stored in the speech recognition dictionary storage unit 700. The specified character string is output to the conversation control unit 300 as a character string signal. The speech recognition dictionary storage unit 700 stores a dictionary corresponding to standard speech signals.
[0024]
The sentence analysis unit 400 analyzes a character string specified by the input unit 100 or the speech recognition unit 200. In the present embodiment, as shown in FIG. 2, a morpheme extraction unit 410 and a phrase analysis unit 420 are included. A sentence structure analysis unit 430, an utterance type determination unit 440, a morpheme database 450, and an utterance type database 460.
[0025]
The morpheme extraction unit 410 is a morpheme extraction unit that extracts each morpheme constituting a minimum unit of a character string as first morpheme information based on the character string specified by the input unit 100 or the speech recognition unit 200.
[0026]
Specifically, the morpheme extraction unit 410 to which the character string signal is input from the management unit 310 extracts each morpheme from the character string corresponding to the input character string signal. Here, in this embodiment, the morpheme means the minimum unit of the word structure represented in the character string. As the minimum unit of the word structure, as shown in FIG. 3, for example, parts of speech such as nouns, adjectives and verbs can be cited. In the present embodiment, each morpheme is expressed as m1, m2,.
[0027]
That is, the morpheme extraction unit 410 collates a character string corresponding to the input character string signal with a morpheme group such as nouns, adjectives, and verbs stored in advance in the morpheme database 450, and the morpheme is extracted from the character string. Each morpheme (m1, m2,...) That matches the group is extracted, and each extracted morpheme is output to the phrase analysis unit 420 as an extraction signal.
[0028]
The phrase analysis unit 420 is a conversion unit that converts each morpheme into a phrase format based on each morpheme extracted by the morpheme extraction unit 410. Specifically, the phrase analysis unit 420 to which the extraction signal is input from the morpheme extraction unit 410 uses the morphemes corresponding to the input extraction signal to combine them into a phrase format.
[0029]
Here, in this embodiment, the phrase format is a sentence in which the independent grammar or one or more attached words are attached to the independent grammar in the Japanese grammar, or a character string that does not destroy the meaning of the Japanese grammar. Means a sentence that is separated as finely as possible. This clause is expressed as p1, p2,... Pk in this embodiment.
[0030]
That is, the phrase analysis unit 420, as shown in FIG. 4, based on each morpheme corresponding to the input extracted signal, the dependency element of each morpheme (for example, (m2), (m4), (m5) ) ・ ・） Is extracted, and each morpheme is grouped into each phrase based on the extracted dependency elements. “T” shown in the figure means transposition.
[0031]
The phrase analysis unit 420 that collects each morpheme into each phrase includes a sentence structure analysis unit 430 and an utterance type determination unit 440 using sentence pattern information including each phrase that combines each morpheme and each morpheme constituting each phrase as a sentence pattern signal. Output to.
[0032]
The sentence structure analysis unit 430 is a classification unit that classifies each morpheme of the first morpheme information segmented by the phrase analysis unit 420 into attributes such as subject case and target case. Specifically, the sentence structure analysis unit 430, to which the sentence pattern signal is input from the phrase analysis unit 420, determines each morpheme included in the phrase based on each morpheme corresponding to the input sentence pattern signal and the phrase composed of each morpheme. Determine the “case composition”.
[0033]
Here, the “case structure” means a case (attribute) indicating a substantial concept in the clause. In the present embodiment, for example, a subject (subject) that represents a subject / subject, Examples include a meaning object (target case), an action meaning an action, a time meaning (tense, aspect), a location meaning a place, and the like. In this embodiment, each morpheme associated with the “case” (case configuration) of the three elements of the subject, the object, and the action is used as the first morpheme information. That is, as shown in FIG. 5, for example, when the dependency element of each morpheme is “” or “is”, the sentence structure analysis unit 430 applies the subject (subject) to the morpheme before the dependency element. Or a leading character). In addition, the sentence structure analysis unit 430
For example, if the dependency element of each morpheme is “NO” or “NO”, it is determined that the morpheme before the dependency element is an object (target).
[0034]
Further, for example, when the dependency element of each morpheme is “Yes”, the sentence structure analysis unit 430 includes an action (predicate; this predicate is composed of a verb, an adjective, and the like). It is determined that
[0035]
The sentence structure analysis unit 430 that has determined the “case structure” of each morpheme that constitutes each clause determines the range of topics (topics) to be described later based on the first morpheme information associated with the determined “case structure”. A topic search command signal for specifying is output to the reflective determination unit 320.
[0036]
The utterance type determination unit 440 is a type specifying unit that specifies an utterance type (input type) indicating the type of utterance content (input information) based on the phrase specified by the phrase analysis unit 420. Specifically, “speech sentence type” (speech type) is determined based on each morpheme corresponding to the sentence pattern signal input from the phrase analysis unit 420 and a phrase composed of each morpheme.
[0037]
In this embodiment, as shown in FIG. 6, the “spoken sentence type” is a statement sentence (D; Declaration), an impression sentence (I; Impression), a conditional sentence (C; Condition), and a result sentence ( E; Effect, time sentence (T; Time), location sentence (L; Location), repulsive sentence (N; Negation), and the like.
[0038]
The statement sentence means a sentence composed of a user's opinion or idea, and in this embodiment, as shown in FIG. 6, for example, a sentence such as “I like Sato” can be cited. An impression sentence means the sentence which consists of an impression which a user holds. A place sentence means a sentence made up of place elements.
[0039]
A result sentence means a sentence composed of sentences including a result element for a topic. A time sentence means a sentence composed of sentences including temporal elements related to a topic.
[0040]
The conditional sentence means a sentence composed of sentences including elements such as a premise of a topic, a condition and a reason why the topic is established, when one utterance is regarded as a topic. The repulsive sentence means a sentence composed of a sentence including an element that repels the utterance partner. An example sentence for each “spoken sentence type” is as shown in FIG.
[0041]
That is, the utterance type determination unit 440 collates each phrase with each dictionary stored in the utterance type database 460 based on each phrase corresponding to the input sentence pattern signal, and from each phrase, Extract sentence elements related to the dictionary. The utterance type determination unit 440 that extracts sentence elements related to each dictionary from each phrase determines “spoken sentence type” based on the extracted sentence elements. The sentence element means a type for identifying the type of character string, and the sentence element in the present embodiment includes the above-described definition phrase (which means “-”).
[0042]
Here, as shown in FIG. 7, the utterance type database 460 includes a definition expression example dictionary having a dictionary related to a definition phrase (for example,), an affirmative phrase (for example, approval, sympathy, ping pong). positive cases dictionary with a dictionary related to, a result clause (for example, so, so) the results representation case dictionary with a dictionary related to, greeting clause (for example, Hello) greeting case dictionary with a dictionary related to, It is composed of a negative example dictionary having a dictionary related to a negative phrase (for example, it is not idiot, the opposite), and each dictionary is associated with “type of spoken sentence”.
[0043]
As a result, the utterance type determination unit 440 compares the phrase with each dictionary stored in the utterance type database 460, extracts the sentence element related to each dictionary from the phrase, and associates it with the extracted sentence element. The “spoken sentence type” can be determined by referring to the determined type.
[0044]
The utterance type determination unit 440 outputs an answer search command signal for causing the corresponding user to search for a specific answer sentence to the answer sentence search unit 370 based on an instruction from the topic search unit 360 described later.
[0045]
The conversation database 500 stores in advance a plurality of second morpheme information indicating each morpheme composed of one character, a plurality of character strings, or a combination thereof, and a plurality of reply contents to the user with respect to the utterance contents. Storage means (discourse storage means). The conversation database 500 is also an answer storage unit (discourse storage unit) that stores in advance a plurality of answer types indicating the types of answer contents associated with a plurality of answer contents in association with the second morpheme information.
[0046]
Furthermore, the conversation database 500 stores in advance a plurality of discourse ranges (keywords) indicating morphemes that constitute a range relevant to the input content that will be input by the user or the response content to the user. But there is. A plurality of second morpheme information indicating a morpheme made up of one character, a plurality of character strings, or a combination thereof is associated with the discourse range (keyword). Associated with each The It has been.
[0047]
Furthermore, the conversation database 500 is also an answer storage means (discourse storage means) for classifying and storing each element constituting the second morpheme information into attributes such as each subject consisting of a main case and a target case consisting of a target case. .
[0048]
As shown in FIG. 8, in the present embodiment, the conversation database 500 is roughly divided in the present embodiment, which means a range that is related to the content of the speech that will be spoken by the user or the content of the response to the user. It consists of a range (discourse) and a topic (topic) that means a range that is most closely related to the content the user is speaking. As shown in the figure, “discourse range” is positioned as a superordinate concept of “topic” in the present embodiment.
[0049]
Each discourse range can be configured to have a hierarchical structure as shown in FIG. As shown in the figure, for example, a higher level discourse range (entertainment) for a certain discourse range (movie) is positioned in the upper hierarchical structure, and a lower level discourse range (movie) for the discourse range (movie). The attribute (movie) can be located in the lower hierarchical structure. That is, in the present embodiment, each discourse range can be arranged at a hierarchical position where the relationship between the higher concept, the lower concept, the synonym, and the synonym becomes clear with other discourse ranges.
[0050]
As described above, the discourse range is composed of topics, and in this embodiment, for example, if the discourse range is an A movie name, it includes a plurality of topics related to the A movie name.
[0051]
This topic means a morpheme composed of a single character, a plurality of character strings, or a combination thereof, that is, each morpheme constituting speech content that will be uttered by the user. Each morpheme is associated with a subject (subject), an object (target case), and an action “case” (attribute). In this embodiment, each morpheme associated with these three elements is expressed as a topic title (this topic title corresponds to a subordinate concept of “topic”) (second morpheme information). .
[0052]
The topic title is not limited to each morpheme associated with the above three elements, but other “cases”, that is, time (tense, aspect) meaning time, location meaning place, You may have each morpheme matched with the condition which means a condition, the impression which means an impression, the effect which means a result, etc.
[0053]
In this embodiment, the topic title (second morpheme information) is stored in advance in the conversation database 500, and the first morpheme information (derived from the utterance content uttered by the user) is It is a distinction.
[0054]
For example, if the talk range is “A movie name”, as shown in FIG. 10, the subject title is subject (A movie name), object (director), action (great) {this is “A movie name” The director of the name is composed of "meaning great".
[0055]
If there is no morpheme associated with “case composition” (subject, object, action, etc.) among the topic titles, “*” is indicated for the portion in the present embodiment.
[0056]
For example, {A What is a movie name? } Is converted into a topic title (subject; object; action). }, “A movie name” can be specified as a subject, but “object” and “action” are not elements of the sentence, so the topic title is “subject” (A movie name). “No object” (*); “No action” (*) (see FIG. 10).
[0057]
The answer sentence means an answer sentence (answer contents) to be answered to the user, and is associated with each topic title (second morpheme information) in this embodiment (see FIG. 8). In this embodiment, as shown in FIG. 11, the answer sentence is a statement sentence (D; Declaration), an impression sentence in order to make an answer corresponding to the type of utterance sentence uttered by the user. Types such as (I; Impression), conditional statement (C; Condition), result statement (E; Effect), time statement (T; Time), location statement (L; Location), negative statement (N; Negation) Type).
[0058]
That is, as shown in FIG. 12, each answer sentence is associated with, for example, a discourse range (Sato) {subordinate concept; home run, superordinate concept; grass baseball, synonym; panda Sato, Sato player, panda} and each topic title. It has been.
[0059]
As shown in the figure, for example, the topic title 1-1 is {(Sato; *; I like): this consists of the order of (subject; object; action) as described above. If the order is the same below, the answer sentence 1-1 corresponding to the topic title 1-1 is (DA; statement affirmation “I like Sato”), (IA; comment affirmation) “I like Sato very much”), (CA; conditional affirmation “Sato ’s home run is very impressive”), (EA; a result affirming “I always watch Sato's games on TV”), (TA: Time affirmative sentence "I actually like it from the five-bats continual refrain in Koshien"), (LA; Place affirmative sentence "I like the serious face when standing on the blow"), (NA A repulsive affirmative sentence "I don't want to talk to people who don't like Sato, goodbye").
[0060]
In the present embodiment, as shown in FIG. 2, the conversation control unit 300 includes a management unit 310, a reflexive determination unit 320, a turnback determination unit 330, a discourse range determination unit 340, and an abbreviated sentence complement unit 350. , A topic search unit 360 and an answer sentence search unit 370.
[0061]
The management unit 310 controls the entire conversation control unit 300. Specifically, the management unit 310 to which a character string is input from the input unit 100 or the voice recognition unit 200 outputs the input character string to the morpheme extraction unit 410 as a character string signal. In addition, the management unit 310 outputs the answer sentence searched by the answer sentence search unit 370 to the output unit 600.
[0062]
The reflexive determination unit 320 is a fixed form acquisition unit that collates the first morpheme information extracted by the morpheme extraction unit 410 with each fixed content, and searches the fixed content including the first morpheme information from each fixed content. .
[0063]
Here, the standard content means reflection element information for replying a standard content to the utterance content from the user, and this reflection element information is stored in the reflection element database 801 (standard storage means) in advance. A plurality are stored. As the reflective element information, in the present embodiment, as shown in FIG. 13, for example, "Good morning", "Hello", "Good evening", "greeting elements" such as "Hey", "I see", "Really?""Typicalelements" such as
[0064]
Specifically, the reflexive determination unit 320 to which the topic search command signal is input from the sentence structure analysis unit 430 receives the first morpheme information included in the input topic search command signal and the reflection element database 801. The reflection element information is collated, the reflection element information including the first morpheme information is searched from the reflection element information, and the searched reflection element information is output to the management unit 310.
[0065]
That is, assuming that the reflection element information is D1 and the first morpheme information is W, the reflective determination unit 320 determines that the relationship of W∩D1 ≠ φ (φ; empty set) is established. Process to give a typical answer.
[0066]
For example, when the user utters the utterance content “Good morning”, the reflexive determination unit 320 collates the utterance content “Good morning” with each reflection element information, and the utterance content from each reflection element information. The reflection element information “good morning” including (matching) “good morning” is searched, and the searched reflection element information “good morning” is output to the management unit 310.
[0067]
When the reflection determination unit 320 cannot search the reflection element information including the utterance content from the reflection element information, the reflex determination unit 320 returns the topic search command signal input from the sentence structure analysis unit 430. Output to.
[0068]
The return determination unit 330 collates the current first morpheme information extracted by the morpheme extraction unit 410 with the past response content stored in the return element database 802, and the current first morpheme information is a past response. If included in the content, it is a fixed form acquisition means for acquiring the content of the agreement.
[0069]
Here, “turnback” means to say back the content of the user's utterance as it is (or content close to it) in this embodiment. In this embodiment, the return element is composed of the first morpheme information that constitutes the response content output from the conversation control device 1 immediately before. For example, as shown in FIG. (Horse; *; beautiful), “I like Sato” (Sato; *; I like).
[0070]
Further, the return element database 802 is also an agreement storage means for preliminarily storing agreement contents for agreeing to input information input by the user. The content of the agreement includes, for example, the input information previously input by the user (if the previous input information by the user is “A film name director is Mr. S”, the content of the agreement is “ The director of the A movie name is Mr. S)), or “Yes”, “It is true”.
[0071]
Specifically, the return determination unit 330 to which the topic search command signal is input from the reflective determination unit 320 configures the first morpheme information and the return element included in the input topic search command signal for each return element. Each morpheme is collated, and it is determined whether or not the first morpheme information is included in the return element (see FIG. 14).
[0072]
If it is determined that the first morpheme information is included in each return element, the return determination unit 330 acquires the content of the agreement and outputs an answer sentence including the acquired content of the agreement to the management unit 310 (return) Process). That is, assuming that the return element (such as the previous answer sentence) is S and the first morpheme information is W, the return determination unit 330 indicates the above when the relationship of W⊂S and W ≠ φ is established. Performs a reversal process.
[0073]
For example, the conversation control device 1 responds with “The director of the A movie name is Mr. S” (Director of the A movie name; Mr. S; *) (the order is the subject, the object, the order of the action, and so on. ) Is output, and then the user responds with a response if the utterance is “is the director of A movie name Mr. S” (director of A movie name; Mr. S; *)? Since the determination unit 330 matches the first morpheme information of the user (Director of A movie name; Mr. S; *) and each morpheme of the answer sentence (Director of A movie name; Mr. S; *), The user concludes that he / she is replying to the contents of the reply, obtains the stored agreement contents “Yes”, and outputs the obtained agreement contents.
[0074]
Further, the return determination unit 330 collates the current first morpheme information extracted by the morpheme extraction unit 410 with the past first morpheme information stored in the return element database 802 to obtain the current first morpheme information. Is included in the past first morpheme information, it is also a fixed form acquisition means for acquiring the repulsion content.
[0075]
Specifically, when the user utters the utterance content “Horse is beautiful” and the conversation control device 1 outputs the content “Horse is good” as the response content When the person repeats the utterance content that “the horse is beautiful”, the turn-back determination unit 330 determines that each morpheme (first morpheme information) {horse; *; beautiful} constituting the current utterance content “horse is beautiful” Each morpheme (first morpheme information) {horse; *; beautiful} that constitutes the content of the previous utterance “Horse is beautiful” matches the content of the response from the conversation control device 1 to the user. You can conclude that you have not heard at all about "I feel good."
[0076]
In this case, since the user has not heard the response content from the conversation control device 1, the return determination unit 330 acquires the stored repulsion content (for example, do not repeat the same content), and the acquired repulsion The contents can be output.
[0077]
On the other hand, if the reversion determining unit 330 determines that the first morpheme information is the same as the content of the previous answer sentence or the first morpheme information is not the same as the previous first morpheme information, the reflex determination unit 320 The input topic search command signal is output to the conversation range determination unit 340.
[0078]
In addition, although the said return determination part 330 has shown the process in case a user makes a return with respect to "the reply content of the conversation control apparatus 1," the following processes can also be performed. For example, when the output unit 600 outputs an answer sentence “Horse is beautiful”, the user answers “Why is the horse beautiful?”, “Why is it beautiful?”, Or “Why?” This is a process of the turn-back determination unit 330 performed when the utterance is made.
[0079]
In this case, the return determination unit 330 outputs the response sentence S “the horse is beautiful” and the utterance content W from the user (“Why is the horse beautiful? (Question sentence)” or “Why is it beautiful? (Question sentence) ”), (W−c) ⊂S, S ≠ φ, c ≠ φ (where c means the utterance type of W, and this utterance type is determined by the utterance type determination unit 440 described later. As will be described later, for example, a question sentence can be cited as an utterance type, so a “conditional” reversal processing (the user asks the question contents for the answer contents). (Processing when a flip is performed).
[0080]
For example, when the conversation control device 1 outputs an answer sentence “The horse is beautiful”, the above-mentioned user utters the content of the utterance “Why is the horse beautiful?”. At that time, in order to resolve the user's question, the return determination unit 330 acquires an answer sentence such as “Even the horse is not beautiful” from the return element database 802, and the acquired answer sentence is sent to the management unit 310. Process to output.
[0081]
The discourse range determination unit 340 collates the first morpheme extracted by the phrase analysis unit 420 with each discourse range, and searches the discourse range for a discourse range that matches the morpheme included in the first morpheme information. It is a discourse search means.
[0082]
Specifically, the conversation range determination unit 340 to which the topic search command signal is input from the turn-back determination unit 330 determines the user's conversation range based on the input talk search command signal. That is, based on the input search command signal, the conversation range determination unit 340 searches the conversation database 500 for a range (discourse range) relevant to the content spoken by the user.
[0083]
For example, the discourse range determination unit 340 includes the first morpheme information included in the input topic search command signal (an interesting movie; *; is there) {is there an interesting movie? }, The first morpheme information is compared with the discourse range group, and if the discourse range group includes a morpheme (for example, “movie”) constituting the first morpheme information, The “movie” included in the information is determined as the discourse range. In this case, since the discourse range “movie” is included in the first morpheme information, the discourse range determination unit 340 includes the input first morpheme information in the topic search command signal and outputs it to the topic search unit 360.
[0084]
On the other hand, when the first morpheme information does not include a discourse range group, the conversation range determination unit 340 includes the input first morpheme information in the topic search command signal and outputs it to the abbreviated sentence complement unit 350.
[0085]
Thereby, the topic search unit 360 described later collates each “topic title” belonging to the “discourse range” determined by the conversation range determination unit 340 with the first morpheme information specified by the sentence structure analysis unit 430. Therefore, it is unnecessary to collate “all” “topic titles” (second morpheme information) with the first morpheme information, and the answer sentence search unit 370 to be described later will search for the final answer sentence. Can be shortened.
[0086]
As described above, the discourse range determination unit 340 collates the first morpheme information and the discourse range group, and if the discourse range group includes the morpheme of the first morpheme information, determines the morpheme as the discourse range. However, the present invention is not limited to this, and the morpheme of the return element retrieved immediately by the return determination unit 330 or the morpheme constituting the utterance content uttered by the user may be determined as the discourse range. . The abbreviated sentence complementing unit 350, which will be described later, can add the discourse range to the first morpheme information from which the morpheme is omitted, using the discourse range determined by the discourse range determination unit 340.
[0087]
The abbreviated sentence complementing unit 350 searches the attributes (subjects, objects, actions, etc.) constituting the first morpheme information based on the first morpheme information extracted by the phrase analysis unit 420 for attributes that do not include a morpheme. Attribute search means. The abbreviated sentence complementing unit 350 is also a morpheme adding unit that adds a morpheme constituting the discourse range searched by the discourse range determining unit 340 to the attribute based on the searched attribute.
[0088]
Specifically, the abbreviated sentence complementing unit 350 to which the topic search command signal is input from the discourse range determination unit 340 is based on the first morpheme information included in the input discourse search command signal, and the utterance including the first morpheme information. It is determined whether or not the content is an abbreviated sentence. If the utterance content including the first morpheme information is an abbreviated sentence, the morpheme in the discourse range to which the first morpheme information belongs is added to the first morpheme information.
[0089]
For example, the abbreviation sentence complementing unit 350 has a morpheme constituting the first morpheme information included in the input topic search command signal (director; *; *) (director?) (This sentence is “what” director If it is unknown, it means an abbreviated sentence.), The discourse range determined by the discourse range determination unit 340 (A movie name; this A movie name indicates the title of the movie) If it is the first morpheme information belonging to A), the determined discourse range (A movie name) is added to the first morpheme information to the morpheme constituting the first morpheme information (director of “A movie name”; *; *) Yes.
[0090]
That is, assuming that the first morpheme information is W and the determined discourse range is D, the abbreviated sentence complementing unit 350 adds the discourse range D to W and includes the added first morpheme information in the topic search command signal. The information is output to the topic search unit 360.
[0091]
Thereby, even if the first morpheme information is an abbreviated sentence and it is not clear as Japanese, the abbreviated sentence complementing unit 350, when the first morpheme information belongs to a certain discourse range, for example, The discourse range D (A movie name) is added to the first morpheme information W (director; *; *), and the first morpheme information is added to W ′ (A movie name director; *; *). ? }, Even if the user's utterance content is an abbreviated sentence, the abbreviated sentence can be supplemented based on the previously determined discourse range, and the abbreviated sentence should be clarified Can do.
[0092]
For this reason, the first morpheme information is set so that the abbreviated sentence complementing unit 350 has the proper utterance contents constituting the first morpheme information even if the utterance contents constituting the first morpheme information are abbreviated sentences. Since the specific morpheme can be complemented, the topic search unit 360 acquires the optimal “topic title” (second morpheme information) related to the first morpheme information based on the first morpheme information after the complement. The answer sentence search unit 370 can output the answer content more suitable for the user's utterance content based on the “topic title” acquired by the topic search unit 360.
[0093]
The topic search unit 360 collates the first morpheme information extracted by the phrase analysis unit 420 or the first morpheme information supplemented by the abbreviated sentence complement unit 350 with each second morpheme information, It is a search means for searching second morpheme information including morphemes constituting the first morpheme information.
[0094]
Specifically, the topic search unit 360 to which the topic search command signal is input from the talk range determination unit 340 or the abbreviated sentence complement unit 350 is based on the first morpheme information included in the input topic search command signal. The “topic title” including the morpheme of the first morpheme information is searched from each “topic title” (second morpheme information) belonging to the discourse range determined by the determination unit 340, and the search result is used as a search result signal. The information is output to the answer sentence search unit 370 and the utterance type determination unit 440.
[0095]
For example, when the “case configuration” constituting the first morpheme information is (Sato; *; I like) {I like Sato}, the topic search unit 360, as shown in FIG. Each morpheme (Sato; *; I like) belonging to the “composition” is compared with each topic title 1-1 to 1-4 belonging to the discourse range (Sato), and from among the topic titles 1-1 to 1-4 A topic title 1-1 (Sato; *; likes) that matches (or approximates) each morpheme (Sato; *; likes) belonging to the “case composition” is searched, and this search result is used as a search result signal as a response sentence. The data is output to the search unit 370 and the utterance type determination unit 440.
[0096]
The utterance type determination unit 440 to which the search result signal is input from the topic search unit 360, based on the input search result signal, an answer search command for searching for a specific answer sentence that answers to the corresponding user. A signal (this answer search command signal also includes the determined “sentence type”) is output to the answer sentence search unit 370.
[0097]
The answer sentence search unit 370 is an answer acquisition unit that acquires an answer sentence associated with the second morpheme information based on the second morpheme information (topic title) searched by the topic search unit 360. In addition, the answer sentence search unit 370 collates the utterance type of the identified user with each answer type associated with the second morpheme information based on the second morpheme information searched by the topic search unit 360, It is also a second search means for searching for an answer type that matches the user's utterance type from among the answer types.
[0098]
Specifically, the answer sentence search unit 370, to which the search result signal from the topic search unit 360 and the answer search command signal from the utterance type determination unit 440 are input, the topic title (search result) corresponding to the input search result signal. Based on the second morpheme information) and the “speech sentence type” (utterance type) corresponding to the answer search command signal, the answer sentence group (each answer content) associated with the “topic title”. Among them, an answer sentence having an answer type (this answer type means “type of answer sentence” shown in FIG. 11) that matches the “sentence sentence type” (DA, IA, CA, etc.) is searched.
[0099]
For example, when the topic title corresponding to the search result is the topic title 1-1 (Sato; *; I like) shown in FIG. 12, the answer sentence search unit 370 is associated with the topic title 1-1. Of the reply sentences 1-1 (DA, IA, CA, etc.), the reply composed of the reply type (DA) that matches the “spoken sentence type” (for example, DA; utterance type) determined by the utterance type determination unit 440. The sentence 1-1 (DA; (I also like Sato) is searched), and the searched answer sentence is output to the management unit 310 as an answer sentence signal.
[0100]
The management unit 310 to which the answer sentence signal is input from the answer sentence search unit 370 outputs the input answer sentence signal to the output unit 600. In addition, the management unit 310 to which the reflection element information is input from the reflective determination unit 320 or the contents of the turn-back process from the turn-back determination unit 330, the answer sentence corresponding to the input reflection element information, the content of the turn-back process that is input To the output unit 600.
[0101]
The output unit 600 is an output unit that outputs the answer text acquired by the answer text search unit 370. In this embodiment, for example, a speaker, a display, and the like can be given. Specifically, the output unit 600 to which an answer sentence is input from the management unit 310 outputs the input answer sentence {for example, I also like Sato}.
[0102]
(Conversation control method using conversation control device)
The conversation control method by the conversation control apparatus 1 having the above configuration can be implemented by the following procedure. FIG. 15 is a flowchart showing the procedure of the conversation control method according to the present embodiment.
[0103]
First, the input unit 100 performs a step of acquiring the utterance content from the user (S101). Specifically, the input unit 100 acquires the voice that constitutes the utterance content of the user, and outputs the acquired voice to the voice recognition unit 200 as a voice signal. Further, the input unit 100 identifies a character string corresponding to the input information (other than the voice) based on the input information (other than the voice) input from the user, and the conversation control unit uses the identified character string as a character string signal. Output to 300.
[0104]
Next, the voice recognition unit 200 performs a step of specifying a character string corresponding to the utterance content based on the utterance content acquired by the input unit 100 (S102). Specifically, the speech recognition unit 200 to which a speech signal is input from the input unit 100 analyzes the input speech signal, and a character string corresponding to the analyzed speech signal is stored in the speech recognition dictionary storage unit 700. The specified character string is output to the conversation control unit 300 as a character string signal.
[0105]
And the morpheme extraction part 410 performs the step which extracts each morpheme which comprises the minimum unit of a character string as 1st morpheme information based on the character string specified by the speech recognition part 200 (S103).
[0106]
Specifically, the morpheme extraction unit 410 to which the character string signal is input from the management unit 310 includes a character string corresponding to the input character string signal and nouns, adjectives, verbs, and the like stored in advance in the morpheme database 450. The morpheme group is collated, each morpheme (m1, m2,...) Matching the morpheme group is extracted from the character string, and each extracted morpheme is output to the phrase analysis unit 420 as an extraction signal.
[0107]
Then, the phrase analysis unit 420 performs a step of grouping each morpheme into a phrase format based on each morpheme extracted by the morpheme extraction unit 410 (S104). Specifically, the phrase analysis unit 420 to which the extraction signal is input from the morpheme extraction unit 410 uses the morphemes corresponding to the input extraction signal to combine them into a phrase format.
[0108]
That is, as shown in FIG. 4, the phrase analysis unit 420 extracts and extracts the dependency elements (for example, gahahahahaha ...) of each morpheme based on each morpheme corresponding to the input extraction signal. Each morpheme is grouped into each clause based on the dependency element. The phrase analysis unit 420 that collects each morpheme into each phrase includes a sentence structure analysis unit 430 and an utterance type determination unit 440 using sentence pattern information including each phrase that combines each morpheme and each morpheme constituting each phrase as a sentence pattern signal. Output to.
[0109]
Thereafter, the sentence structure analysis unit 430 performs a step of classifying each morpheme of the first morpheme information segmented by the phrase analysis unit 420 into attributes such as subject case and target case (S105). Specifically, the sentence structure analysis unit 430, to which the sentence pattern signal is input from the phrase analysis unit 420, determines each morpheme included in the phrase based on each morpheme corresponding to the input sentence pattern signal and the phrase composed of each morpheme. Determine the “case composition”.
[0110]
That is, as shown in FIG. 5, for example, when the dependency element of each morpheme is “” or “is”, the sentence structure analysis unit 430 applies the subject (subject) to the morpheme before the dependency element. Or a leading character). In addition, the sentence structure analysis unit 430
For example, if the dependency element of each morpheme is “NO” or “NO”, it is determined that the morpheme before the dependency element is an object (target).
[0111]
Further, for example, when the dependency element of each morpheme is “Yes”, the sentence structure analysis unit 430 includes an action (predicate; this predicate is composed of a verb, an adjective, and the like). It is determined that
[0112]
The sentence structure analysis unit 430 that has determined the “case structure” of each morpheme that constitutes each clause determines the range of topics (topics) to be described later based on the first morpheme information associated with the determined “case structure”. A topic search command signal for specifying is output to the topic search unit 360.
[0113]
Next, the utterance type determination unit 440 performs a step of specifying an utterance type indicating the type of utterance content based on the phrase specified by the phrase analysis unit 420 (S106). Specifically, “speech sentence type” (speech type) is determined based on each morpheme corresponding to the sentence pattern signal input from the phrase analysis unit 420 and a phrase composed of each morpheme.
[0114]
That is, the utterance type determination unit 440 collates each phrase with each dictionary stored in the utterance type database 460 based on each phrase corresponding to the input sentence pattern signal, and from each phrase, Extract sentence elements related to the dictionary. The utterance type determination unit 440 that extracts sentence elements related to each dictionary from each phrase determines “spoken sentence type” based on the extracted sentence elements.
[0115]
The utterance type determination unit 440 outputs an answer search command signal for causing the corresponding user to search for a specific answer sentence to the answer sentence search unit 370 based on an instruction from the topic search unit 360 described later.
[0116]
Next, the reflexive determination unit 320 compares the first morpheme information extracted by the morpheme extraction unit 410 with each fixed content, and performs a step of searching for fixed content including the first morpheme information from each fixed content. (S107: reflective processing).
[0117]
Specifically, the reflexive determination unit 320 to which the topic search command signal is input from the sentence structure analysis unit 430 receives the first morpheme information included in the input topic search command signal and the reflection element database 801. The reflection element information (standard content) is collated, the reflection element information including the first morpheme information is searched from each reflection element information, and the searched reflection element information is output to the management unit 310.
[0118]
When the reflection element information cannot be searched for the reflection element information including the first morpheme information from the reflection element information, the reflection determination unit 320 determines whether to return the topic search command signal input from the sentence structure analysis unit 430. To the unit 330.
[0119]
Next, the return determination unit 330 collates the first morpheme information extracted by the morpheme extraction unit 410 with each return element, and performs a step of searching for the return element including the first morpheme information from each return element ( S108;
[0120]
When it is determined that the first morpheme information is included in each return element, the return determination unit 330 acquires the return element including the first morpheme information, and manages the response sentence including the acquired return element. The data is output to the unit 310 (turnback processing). That is, if the return element (answer sentence output last time, utterance content spoken by the previous user, etc.) is S and the first morpheme information is W, the return determination unit 330 has a relationship of W⊂S and W ≠ φ. If it is established, the turn-back process shown above is performed.
[0121]
On the other hand, when determining that the first morpheme information is not included in each return element, the return determination unit 330 sends the topic search command signal input from the reflective determination unit 320 to the conversation range determination unit 340. Output.
[0122]
Then, the discourse range determination unit 340 collates the first morpheme extracted by the phrase analysis unit 420 with each discourse range, and searches (determines) the discourse range including the first morpheme information from each discourse range. A step is performed (S109).
[0123]
Specifically, the conversation range determination unit 340, to which the topic search command signal is input from the reversal determination unit 330, is based on the input search command signal, and the content the user is speaking from the conversation database 500. Search for a relevant range (discourse range).
[0124]
For example, the discourse range determination unit 340 includes the first morpheme information included in the input topic search command signal (an interesting movie; *; is there) {is there an interesting movie? }, The first morpheme information is compared with the discourse range group, and if the discourse range group includes a morpheme (for example, “movie”) constituting the first morpheme information, The “movie” included in the information is determined as the discourse range. In this case, since the discourse range “movie” is included in the first morpheme information, the discourse range determination unit 340 includes the input first morpheme information in the topic search command signal and outputs it to the topic search unit 360.
[0125]
On the other hand, when the first morpheme information does not include a discourse range group, the conversation range determination unit 340 includes the input first morpheme information in the topic search command signal and outputs it to the abbreviated sentence complement unit 350.
[0126]
Next, the abbreviation sentence complementing unit 350 includes attributes that do not include a morpheme among the attributes (subject, object, action, etc.) constituting the first morpheme information based on the first morpheme information extracted by the phrase analysis unit 420. Perform the step of searching. Thereafter, the abbreviated sentence complementing unit 350 performs a step of adding the morpheme constituting the discourse range searched by the discourse range determining unit 340 to the attribute based on the attribute not including the searched morpheme (S110; abbreviated sentence). Complement).
[0127]
Specifically, the abbreviated sentence complementing unit 350 to which the topic search command signal is input from the discourse range determination unit 340 is based on the first morpheme information included in the input discourse search command signal, and the utterance including the first morpheme information. It is determined whether or not the content is an abbreviated sentence. If the utterance content including the first morpheme information is an abbreviated sentence, the morpheme in the discourse range to which the first morpheme information belongs is added to the first morpheme information.
[0128]
For example, the abbreviation sentence complementing unit 350 has a morpheme constituting the first morpheme information included in the input topic search command signal (director; *; *) (director?) (This sentence is “what” director If it is unknown, it means an abbreviated sentence.), The conversation range previously determined by the conversation range determination unit 340 (A movie name; this A movie name is the title of the movie) The first morpheme information belonging to the first morpheme information is added to the first morpheme information ("A movie name"). Director; *; *).
[0129]
That is, assuming that the first morpheme information is W and the determined discourse range is D, the abbreviated sentence complementing unit 350 adds the discourse range D to the first morpheme information W, and uses the first morpheme information after the addition as a topic search command. It is included in the signal and output to the topic search unit 360.
[0130]
Next, the topic search unit 360 collates the first morpheme information extracted by the phrase analysis unit 420 or the first morpheme information supplemented by the abbreviated sentence complement unit 350 with each second morpheme information, and each second morpheme A step of searching for second morpheme information including morpheme constituting the first morpheme information from the information is performed (S111).
[0131]
Specifically, the topic search unit 360 to which the topic search command signal is input from the talk range determination unit 340 or the abbreviated sentence complement unit 350 is based on the first morpheme information included in the input topic search command signal. The “topic title” including the morpheme of the first morpheme information is searched from each “topic title” (second morpheme information) belonging to the discourse range determined by the determination unit 340, and the search result is used as a search result signal. The information is output to the answer sentence search unit 370 and the utterance type determination unit 440.
[0132]
For example, when the “case configuration” constituting the first morpheme information is (Sato; *; I like) {I like Sato}, the topic search unit 360, as shown in FIG. Each morpheme (Sato; *; I like) belonging to the “composition” is compared with each topic title 1-1 to 1-4 belonging to the discourse range (Sato), and from among the topic titles 1-1 to 1-4 A topic title 1-1 (Sato; *; likes) that matches (or approximates) each morpheme (Sato; *; likes) belonging to the “case composition” is searched, and this search result is used as a search result signal as a response sentence. The data is output to the search unit 370 and the utterance type determination unit 440.
[0133]
The utterance type determination unit 440 to which the search result signal is input from the topic search unit 360, based on the input search result signal, an answer search command for searching for a specific answer sentence that answers to the corresponding user. A signal (this answer search command signal also includes the determined “sentence type”) is output to the answer sentence search unit 370.
[0134]
Then, the answer sentence search unit 370 collates the utterance type of the identified user with each answer type associated with the second morpheme information based on the second morpheme information searched by the topic search unit 360, A search is made for a response type that matches the user's utterance type from each response type, and an answer sentence associated with the response type is obtained based on the searched response type (S112).
[0135]
Specifically, the answer sentence search unit 370, to which the search result signal from the topic search unit 360 and the answer search command signal from the utterance type determination unit 440 are input, the topic title (search result) corresponding to the input search result signal. Based on the second morpheme information) and the “speech sentence type” (utterance type) corresponding to the answer search command signal, the answer sentence group (each answer content) associated with the “topic title”. Among them, an answer sentence having an answer type (this answer type means “type of answer sentence” shown in FIG. 11) that matches the “sentence sentence type” (DA, IA, CA, etc.) is searched.
[0136]
For example, when the topic title corresponding to the search result is the topic title 1-1 (Sato; *; I like) shown in FIG. 12, the answer sentence search unit 370 is associated with the topic title 1-1. Of the reply sentences 1-1 (DA, IA, CA, etc.), the reply composed of the reply type (DA) that matches the “spoken sentence type” (for example, DA; utterance type) determined by the utterance type determination unit 440. The sentence 1-1 (DA; (I also like Sato) is searched), and the searched answer sentence is output to the management unit 310 as an answer sentence signal.
[0137]
Next, the management unit 310 to which the answer sentence signal is input from the answer sentence search unit 370 outputs the input answer sentence signal to the output unit 600. In addition, the management unit 310 to which the reflection element information is input from the reflective determination unit 320 or the contents of the turn-back process from the turn-back determination unit 330, the answer sentence corresponding to the input reflection element information, the content of the turn-back process that is input Is output to the output unit 600 (S113). The output unit 600 to which an answer sentence is input from the management unit 310 outputs the input answer sentence {for example, I also like Sato}.
[0138]
(Operation and effect of conversation control device and conversation control method)
According to the invention according to the present application having the above-described configuration, the reflexive determination unit 320 collates the first morpheme information constituting the utterance content uttered by the user with each fixed content stored in advance, and each fixed content from among, it is possible to find the standard content including a first morpheme information, reflexive determination unit 320, for example when the first morpheme information is a fixed content such as "Hello" includes the standard content it is possible to answer the same boilerplate content "Hello" or the like.
[0139]
In addition, when the user's utterance content is a fixed content, the reflexive determination unit 320 replies with the fixed content (greeting etc.), so that the user first contacts the conversation control device 1. You can enjoy a sense of communication.
[0140]
Further, the reversal determination unit 330 compares the current first morpheme information with the past response content, and if the current first morpheme information is not included in the past response content, the agreement stored in advance is stored. Since the contents can be acquired, if the input information currently input from the user matches the past response contents, the return determination unit 330 returns the user to the previous response contents (use It is possible to conclude that the user has input the input information that the person is listening to the response content.
[0141]
In this case, the return determination unit 330 acquires the contents of the agreement that has been stored and the acquired agreement (for example, “That ’s right”) because the user has made a response to the previous response. Can be output. As a result, if the user does not understand the meaning of the answer content output from the conversation control device 1, he can listen again and listen again to the answer content, so he is also talking to other users. You can taste a feeling like this.
[0142]
Further, the reversal determination unit 330 collates the current first morpheme information with the past first morpheme information, and if the current first morpheme information is included in the past first morpheme information, the repulsion content is acquired. Therefore, when the input information input last time is included in the input information input this time, the user may repeatedly input the same content as the previous input information. Therefore, it can be determined that the user has not properly answered the response content from the conversation control device 1.
[0143]
In this case, since the user does not appropriately reply to the previous response content, the reversal determination unit 330 repels the user, so the stored repulsion content is acquired, and the acquired repulsion content Is output. As a result, if the user does not input appropriate input information with respect to the response content from the conversation control device 1, the repulsion content is output from the conversation control device 1. You can taste a sense of being.
[0144]
Further, in order to search for the second morpheme information approximate to the first morpheme information, the topic search unit 360 may collate each second morpheme information belonging to the “discourse range” with the first morpheme information. Since it is not necessary to collate the second morpheme information with the first morpheme information, the time until the second morpheme information approximate to the first morpheme information can be shortened.
[0145]
As a result, the topic search unit 360 can search (pinpoint search) the second morpheme information that approximates the first morpheme information in a short time, so that the answer sentence search unit 370 is the topic search unit 360. An answer sentence associated with the second morpheme information can be acquired in a short time based on the searched second morpheme information, and the conversation control device 1 quickly answers the utterance content from the user. be able to.
[0146]
Moreover, the topic search part 360 searches the 2nd morpheme information which contains the morpheme (element which comprises a user's utterance content) which comprises 1st morpheme information from each 2nd morpheme information, and an answer sentence search part Since 370 can acquire the answer content associated with the second morpheme information based on the second morpheme information searched by the topic search unit 360, the answer sentence search unit 370 can obtain the utterance content of the user. Based on each constituent morpheme (first morpheme information), it is possible to take into account the semantic spaces (subjects, objects, etc.) constructed by each morpheme and obtain the response contents created in advance based on such semantic spaces Thus, it is possible to acquire the answer content more suitable for the utterance content, rather than simply using the entire utterance content as a keyword and obtaining the answer content associated with the keyword.
[0147]
Further, since the topic search unit 360 searches for the second morpheme information including the first morpheme information, it is not necessary to search for the second morpheme information that completely matches the utterance content of the user, and the conversation control device 1 is developed. The developer who does not need to memorize a huge amount of answer contents corresponding to the utterance contents that will be uttered by the user in advance, and can reduce the capacity of the storage unit.
[0148]
Furthermore, the answer sentence search unit 370 selects the answer type that matches the user's utterance type from the answer types (description, affirmation, location, repulsion, etc.) associated with each second morpheme information belonging to the “discourse range”. Since the response content associated with the response type can be acquired based on the searched response type, the conversation control device 1 can determine the utterance type that constitutes the user's conversation content, for example, the user Answers that match the user's utterance type out of multiple answers based on what is simply stated, what the user has to say, what the user describes the location element, etc. As a result, it is possible to obtain an optimal answer to the corresponding user.
[0149]
Furthermore, the answer sentence search unit 370 matches the user's utterance type from the answer types associated with the second morpheme information belonging only to the “discourse range” searched by the discourse range determination unit 340. Since it is only necessary to search for the type (pinpoint search is possible), it is not necessary to search for the response type associated with the “all” second morpheme information and the user's utterance type one by one. Can be obtained in a short time.
[0150]
Finally, the abbreviated sentence complementing unit 350 belongs to the discourse range in which the first morpheme information exists even if the first morpheme information constituting the utterance content of the user is an abbreviated sentence and is not clear as Japanese. If so, the discourse range can be added to the first morpheme information, and the first morpheme information consisting of abbreviated sentences can be complemented.
[0151]
Thus, the abbreviated sentence complementing unit 350 allows the first morpheme information so that the utterance contents constituting the first morpheme information are appropriate Japanese even if the utterance contents constituting the first morpheme information are abbreviated sentences. Since a specific morpheme (such as a morpheme constituting a discourse range) can be complemented, the topic search unit 360 is based on the first morpheme information after completion supplemented by the abbreviated sentence complement unit 350. The optimal second morpheme information related to the information can be acquired, and the answer sentence search unit 370 can select the answer content more suitable for the user's utterance content based on the second morpheme information acquired by the topic search unit 360. Can be output.
[0152]
As a result, even if the input information from the user is an abbreviated sentence, the conversation control device 1 does not use a function such as a neuro network or an AI intelligence, and does not mean what the abbreviated sentence means through past search results. Since the developer of the conversation control device 1 does not need to install a neuro network or AI intelligence, the system of the conversation control device 1 can be constructed more simply.
[0153]
[Example of change]
In addition, this invention is not limited to the said embodiment, The change as shown below can be added.
[0154]
(First change example)
In this modification, the conversation database 500 may be an element storage unit that stores a plurality of pieces of element information indicating the entire set group including a plurality of morpheme sets in association with the set group. Further, the morpheme extraction unit 410 collates the morpheme extracted from the character string with each set group, selects a set group including the extracted morpheme from each set group, and selects an element associated with the selected set group. Information may be extracted as first morpheme information.
[0155]
As shown in FIG. 16, some morphemes included in the character string uttered by the user are similar. For example, as shown in FIG. 16, if the element information indicating the entire group is “gift”, the “gift” is similar to a present, gift, year-end gift, mid-year gift, celebration, etc. (group). Therefore, when there is a morpheme similar to “gift” (such as the present), the morpheme extraction unit 410 can handle the similar morpheme as “gift”.
[0156]
That is, for example, when the morpheme extracted from the character string is “present”, the morpheme extraction unit 410 has “present” as the element information representing “present” as shown in FIG. “Present” can be replaced with “Gift”.
[0157]
As a result, the morpheme extraction unit 410 can sort out morphemes that are similar to each other. Therefore, the developer who develops the conversation control device 1 corresponds to the semantic space that is grasped from the mutually similar first morpheme information. It is not necessary to create the second morpheme information and the response contents related to the second morpheme information one by one, and as a result, the amount of data stored in the storage unit can be reduced.
[0158]
(Second modified example)
As shown in FIG. 17, in the present modification example, the topic search unit 360 may include a ratio calculation unit 361 and a selection unit 362.
[0159]
The ratio calculation unit 361 compares the first morpheme information extracted by the morpheme extraction unit 410 with each second information, and the ratio of the first morpheme information to the second morpheme information for each second morpheme information. Is a calculation means for calculating.
[0160]
Specifically, the ratio calculation unit 361 to which the topic search command signal is input from the sentence structure analysis unit 430, based on the first morpheme information included in the input topic search command signal, as shown in FIG. One morpheme information is collated with each topic title (second morpheme information) belonging to the discourse range stored in the conversation database 500, and the first morpheme information occupies in each topic title for each topic title. Calculate the percentage.
[0161]
For example, as shown in FIG. 18, when the first morpheme information constituting the utterance sentence uttered by the user is (Sato; *; I like) {I like Sato}, the ratio calculation unit 361 Each morpheme belonging to “case composition” is matched with each morpheme belonging to “case composition” by comparing each morpheme belonging to “case composition” (Sato; *; likes) and each morpheme included in the topic title (Sato; *; likes). The ratio of (Sato; *; I like) is calculated to be 100%. The ratio calculation unit 361 performs these calculations for each topic title, and outputs each calculated ratio to the selection unit 362 as a ratio signal.
[0162]
The selection unit 362 is a selection unit that selects one second morpheme information from each second morpheme information according to the size of each rate calculated by the rate calculation unit 361 for each second morpheme information. is there.
[0163]
Specifically, the selection unit 362 to which the ratio signal is input from the ratio calculation unit 361 selects from among the ratios included in the input ratio signal (element of “case configuration” / element of “topic title” × 100). For example, a topic title with a high ratio is selected (see FIG. 18). The selection unit 362 that has selected a topic title with a high ratio outputs the selected topic title to the answer sentence search unit 370 and the utterance type determination unit 440 as a search result signal. The answer sentence search unit 370 acquires an answer sentence associated with the topic title based on the topic title selected by the selection unit 362.
[0164]
Accordingly, the selection unit 362 calculates the ratio of the first morpheme information to the second morpheme information for each second morpheme information, and the size of each ratio calculated for each second morpheme information. Accordingly, one second morpheme information can be selected from each second morpheme information, so that the selection unit 362 has, for example, first morpheme information (which constitutes the user's utterance content). If second morpheme information with a large proportion of second morpheme information can be acquired from a plurality of second morpheme information groups, the second morpheme information that follows the semantic space grasped from the first morpheme information is more As a result, the answer sentence search unit 370 can make an optimum answer to the utterance content of the user.
[0165]
Further, since the selection unit 362 can select a topic title having a high ratio calculated by the ratio calculation unit 361 from a plurality of topic titles, the selection unit 362 belongs to the “case configuration” included in the user's utterance sentence. Even if each morpheme and each topic title stored in the conversation database 500 do not completely match, it is possible to acquire a topic title closely related to each morpheme belonging to “case composition”.
[0166]
As a result, since the selection unit 362 can acquire a topic title closely related to the “case configuration” constituting the first morpheme information, the developer who develops the conversation control device 1 configures the first morpheme information. Since it is not necessary to store topic titles that completely match “case configuration” in the conversation database 500, the capacity of the conversation database 500 can be reduced.
[0167]
Further, the ratio calculation unit 361 calculates the ratio of the first morpheme information to the second morpheme information for each second morpheme information belonging only to the “discourse range” searched by the discourse range determination unit 340. Therefore, it is not necessary to calculate the ratio of the first morpheme information to the “all” second morpheme information, and the second morpheme information that follows the semantic space composed of the first morpheme information is acquired in a shorter time. As a result, it is possible to quickly output the optimum answer contents for the utterance contents from the user based on the acquired second morpheme information.
[0168]
The ratio calculation unit 361 collates each morpheme of the first morpheme information belonging to each classified attribute and each morpheme of each second morpheme information belonging to each attribute stored in advance for each attribute. It may be a first search means for searching for second morpheme information including each morpheme of the first morpheme information in at least one attribute from the second morpheme information.
[0169]
Specifically, the ratio calculation unit 361 to which the topic search command signal is input, for each “case” (subject; object; action) of the “case configuration” included in the input topic search command signal. ”And each morpheme belonging to“ case ”of the topic title composed of the same“ case ”are collated, and it is determined whether or not the morphemes constituting each“ case ”are the same.
[0170]
For example, as illustrated in FIG. 19, when the “case” morpheme of “case configuration” is (dog; person; bitten) {dog bites a person}, the ratio calculation unit 361 displays those morphemes. Match "dog", "person", and "chewed" with the morpheme "dog", "person", and "chewed" of the topic title consisting of the same "case" that constitutes the morpheme Then, among the morphemes “dog”, “people”, and “chewing” that make up the topic title, the “case composition” morpheme “dog” consisting of the same “case” corresponding to each morpheme, The ratio of “people” and “biting” is calculated (100%).
[0171]
If the element constituting the topic title is (person; dog; bite) {person bites the dog}, the ratio calculation unit 361 performs morphemes belonging to two cases by the same procedure as described above. Therefore, the degree of coincidence of each “case” between the morpheme constituting “case composition” and “topic title” is calculated to be 33% (see FIG. 19).
[0172]
The ratio calculation unit 361 that has calculated the ratio selects a topic title with a high ratio from the ratios, and outputs the selected topic title to the answer sentence search unit 370 and the utterance type determination unit 440 as a search result signal.
[0173]
As a result, the ratio calculation unit 361 displays, for each “case”, each morpheme of the first morpheme information belonging to each classified “case configuration” (subject case, target case, etc.) and a pre-stored topic title. Since the second morpheme information including each morpheme of the first morpheme information in at least one “case” can be searched from each topic title, the ratio calculator 361 is different from the normal word order. If the utterance content is composed of things, for example, “a person bites a dog”, the morpheme of the subject case is “person” and the morpheme of the subject case is “dog”. You can search for matching second morpheme information, and obtain the response contents {"really?" Or "I don't know the meaning"} associated with the second morpheme information (person; dog; bite) can do.
[0174]
That is, since the ratio calculation unit 361 can identify utterance contents that are difficult to identify, for example, “a person bites a dog” and “a dog bites a person”, an optimal answer to the identified utterance contents, For the former, for example, “Really?”, For the latter, for example, “Are you okay?”
[0175]
Further, the ratio calculation unit 361 may search for the second morpheme information including the morpheme of the first morpheme information in at least one attribute from the respective second morpheme information belonging to the “discourse range”. From the second morpheme information, it is no longer necessary to acquire one second morpheme information, the second morpheme information that follows the semantic space composed of the first morpheme information can be acquired in a shorter time, As a result, the conversation control device 1 can quickly output the optimum answer content for the utterance content from the user based on the acquired second morpheme information.
[0176]
Note that the selection unit 362 may select one topic title from among the topic titles according to a predetermined priority order. This priority order means a priority for selection as a topic title. This priority order is predetermined by the developer at the development stage.
[0177]
(Third change example)
As shown in FIG. 20, the present modification is not limited to the above-described embodiment and each modification, and data is transmitted and received via the communication network 800 in the conversation control devices 1 a and 1 b and the communication network 1000. Communication unit 900, conversation databases 500b to 500d connected to communication unit 900, and servers 2a to 2c (conversation control system).
[0178]
Here, the communication network 1000 means a communication network that transmits and receives data. In the present embodiment, for example, the Internet is used. In this modification, the conversation databases 500b to 500d and the servers 2a to 2c are limited for convenience. However, the present invention is not limited to this, and other conversation databases may be provided.
[0179]
Thereby, the conversation control unit 300 refers not only to the conversation database 500a arranged inside the conversation control apparatus 1a but also to other conversation control apparatuses 1b, other conversation databases 500b to 500d, and servers 2a to 2c. Therefore, for example, it is a case where it is not possible to search from the conversation database 500a the discourse range related to each morpheme (first morpheme information) belonging to the “case configuration” included in the topic search command signal. However, by referring to the other conversation control device 1b, conversation databases 500b to 500d, and servers 2a to 2c, it is possible to search the range of conversation related to the above “case structure” and the like based on the user's utterance sentence. A suitable answer sentence can be searched.
[0180]
(Fourth change example)
The sentence structure analysis unit 430 may store in the conversation database 500 each “case configuration” and each “case configuration” constituting the identified first morpheme information. The answer sentence search unit 370 may store each “case structure” and each “morpheme” associated with each “case structure” constituting the searched answer sentence in the conversation database 500.
[0181]
The conversation range determination unit 340 may store the searched conversation range in the conversation database 500. The topic search unit 360 may store the searched second morpheme information in the conversation database 500.
[0182]
The first morpheme information, the second morpheme information, each “case composition” that constitutes the first morpheme information or the second morpheme information, each morpheme associated with each “case composition”, and the retrieved answer sentence Each "case structure" and each morpheme associated with each "case structure" and the searched discourse range are associated with each other and stored in the conversation database 500 or the reversal element database 802 as history morpheme information. be able to.
[0183]
The abbreviated sentence complementing unit 350 does not include a morpheme from each attribute (subject, object, action, etc .; case configuration) constituting the first morpheme information based on the first morpheme information extracted by the phrase analysis unit 420. The attribute is searched, and the history morpheme information stored in the conversation database 500 or the return element database 802 is added to the attribute based on the searched attribute.
[0184]
Specifically, the abbreviated sentence complementing unit 350 to which the topic search command signal is input from the discourse range determination unit 340 is based on the first morpheme information included in the input discourse search command signal, and the utterance including the first morpheme information. If the content is an abbreviated sentence, and the utterance content made up of the first morpheme information is an abbreviated sentence (for example, the subject, object, or action does not have a predetermined morpheme), the conversation database 500 or The history form information stored in the return element database 802 is added to the first morpheme information.
[0185]
That is, assuming that the subject included in the history form information is S1, the object is O1, the action A1, the discourse range is D1, and the omitted first morpheme information is W, the supplemented first morpheme information W1 is S1ＳW , O1∪W, A1∪W, or D1∪W.
[0186]
The topic search unit 360 collates the first morpheme information W1 supplemented by the abbreviated sentence complement unit 350 and each second morpheme information, and from each “topic title” (second morpheme information), the first morpheme information The second morpheme information including W1 is searched, and the searched topic title is output to the answer sentence search unit 370 and the utterance type determination unit 440 as a search result signal.
[0187]
Thereby, even if the utterance content composed of the first morpheme information is an abbreviated sentence and is not clear as Japanese, the abbreviated sentence complementing unit 350 uses the history form information stored in the conversation database 500, Since the omitted morpheme of the first morpheme information can be complemented, the utterance content including the omitted first morpheme information can be clarified.
[0188]
For this reason, when the abbreviated sentence complementing part 350 is the abbreviated sentence which comprises 1st morpheme information, 1st morpheme information is set so that the utterance content which consists of 1st morpheme information becomes appropriate Japanese. Since the morpheme omitted in FIG. 6 can be supplemented, the topic search unit 360 is based on the first morpheme information supplemented with the morpheme, and the optimal “topic title” (second morpheme) related to the first morpheme information. Information) can be acquired, and the answer sentence search unit 370 can output the answer content more suitable for the user's utterance content based on the optimal “topic title” acquired by the topic search unit 360. .
[0189]
(Fifth change example)
The topic search unit 360 may include a deletion unit 363 and a discourse addition unit 364, as shown in FIG. The deletion unit 363 collates the second morpheme information with the discourse range searched by the discourse range determination unit 340 based on the searched second morpheme information, and determines the discourse from each morpheme constituting the second morpheme information. It is a deletion means for deleting a morpheme that matches the range.
[0190]
Specifically, the topic search unit 360 to which the topic search command signal is input from the abbreviated sentence complement unit 350 receives the first morpheme information included in the input topic search command signal and the discourse determined by the discourse range determination unit 340. Each second morpheme information belonging to the range is collated, and second morpheme information matching the first morpheme information is searched from each second morpheme information.
[0191]
Then, based on the searched second morpheme information, the deletion unit 363 collates the second morpheme information with the morphemes constituting the discourse range determined by the discourse range determination unit 340, and stores the second morpheme information. Then, the morpheme that matches the morpheme constituting the discourse range is deleted, and the second morpheme information from which the morpheme has been deleted is output to the discourse adding unit 364 as a delete signal.
[0192]
That is, the deletion unit 363 removes (removes) the current discourse range D2 (this D2 is made of morpheme) determined by the discourse range determination unit 340 from each morpheme t1 constituting the second morpheme information. If the result is t2, t2 = t1-D2).
[0193]
The discourse adding unit 364 acquires another discourse range associated with the discourse range searched by the discourse range determining unit 340 based on the second morpheme information from which the morpheme has been deleted by the deletion unit 363, and the acquired other This is a discourse adding means for adding the morpheme constituting the discourse range to the second morpheme information.
[0194]
More specifically, if the current discourse range D2 is related to the answer sentence K1, and the discourse range is DK, other discourses related to the answer sentence K1 or the current discourse range D2 (those that have a sibling relationship). Since the range D3 can be expressed as D3 = D2∪DK, the second morpheme information W2 after adding the morpheme constituting the other discourse range D3 can be set to W2 = t2∪D3.
[0195]
For example, each morpheme t1 constituting the second morpheme information is (A movie name; *; interesting) {A movie name is interesting? }, And the current discourse range D2 determined by the discourse range determination unit 340 is (A movie name), the deletion unit 363 first starts from each morpheme t1 (A movie name; *; interesting). The conversation range D2 (A movie name) is deleted, and the deleted result is set to t2 (*; *; interesting) (t2 = t1-D2).
[0196]
When the other conversation range D3 related to the current conversation range D2 (A movie name) is “B movie name”, the second morpheme information after adding the morpheme constituting the other conversation range D3 W2 is t2∪D3, so (B movie name; *; funny) {B movie name is interesting? }.
[0197]
Thereby, when the user's utterance content is “A movie name is interesting?”, The discourse adding unit 364 matches each morpheme (A movie name; *; interesting) constituting the user's utterance content. Second morpheme information (A movie name; *; interesting), other second morpheme information (B movie name; *; interesting) {B movie name is interesting? }, The answer sentence search unit 370 acquires the answer sentence (for example, “B movie name is interesting”) associated with the second morpheme information changed by the discourse adding part 364, The acquired answer text can be output.
[0198]
As a result, the answer sentence search unit 370 does not directly output the answer sentence to the user's utterance contents, but based on the second morpheme information including the morpheme added by the discourse addition part 364, the utterance contents Therefore, the output unit 600 can output a more humane answer sentence based on the answer sentence searched by the answer sentence search unit 370.
[0199]
Note that the discourse adding unit 364 is not limited to adding the other discourse range to the second morpheme information from which the morpheme has been deleted, but to the second morpheme information from which the morpheme has been deleted. (Stored in the database 500) may be added.
[0200]
(Sixth change example)
When the topic search unit 360 cannot search the second morpheme information including the first morpheme information from the second morpheme information, the topic search unit 360 collates the first morpheme information with each answer content, When the answer content including the first morpheme information can be searched from among the first morpheme information, the first search unit may acquire the second morpheme information associated with the searched answer content.
[0201]
Specifically, the topic search unit 360, to which the topic search command signal is input from the abbreviated sentence complement unit 350, based on the first morpheme information included in the input topic search command signal, If the second morpheme information that matches the first morpheme information cannot be obtained from each second morpheme information by collating with the morpheme information, as shown in FIG. The answer sentence associated with the second morpheme information is collated.
[0202]
When the topic search unit 360 determines that the morpheme constituting the first morpheme information (the action or the morpheme associated with the action) is included in the answer sentence by this collation, the answer sentence includes Search for associated second morpheme information.
[0203]
Thereby, even if the topic search unit 360 cannot search the second morpheme information that matches the first morpheme information from each second morpheme information, Since the answer sentence including the morpheme (action or the morpheme associated with the action) that constitutes the user can be identified and the second morpheme information associated with the identified answer sentence can be searched, the user's utterance content 2nd morpheme information corresponding to the 1st morpheme information which constitutes can be searched appropriately.
[0204]
As a result, since the topic search unit 360 can search for the optimal second morpheme information corresponding to the first morpheme information, the answer sentence search unit 370 can search for the optimal second morpheme information searched by the topic search unit 360. Based on the above, it is possible to acquire appropriate answer contents for the user's utterance contents.
[0205]
[program]
The contents described in the conversation control system and the conversation control method can be realized by executing a dedicated program for using a predetermined program language in a general-purpose computer such as a personal computer.
[0206]
Here, as the programming language, the topic that the user wants, the user's emotional level for a certain matter, or the type of statement sentence, affirmative sentence, question sentence, repulsive sentence, etc. are associated with the morpheme according to the semantic content In this embodiment, for example, DKML (Discourse Knowledge Markup Language), XML (eXtensible Markup Language), C language, etc. developed by the inventors can be used.
[0207]
That is, the conversation control device 1 stores data stored in each of the conversation databases 500a to 500d (stored information such as second morpheme information, fixed content, answer sentence, answer type, set group, discourse range, element information, etc.), etc. These parts can be constructed by DKML (Discourse Knowledge Markup Language), XML (eXtensible Markup Language), or the like, and can be realized by executing a program for using the constructed storage information.
[0208]
According to such a program according to the present embodiment, each morpheme constituting the utterance content of the user is identified, the semantic content grasped from each identified morpheme is analyzed, and associated with the analyzed semantic content. A conversation control device, a conversation control system, and a conversation control method that have the effect of being able to output the optimum answer contents corresponding to the user's utterance contents by outputting the answer contents prepared in advance. It can be easily realized by a general purpose computer.
[0209]
In addition, since the developer who develops the conversation control device 1 can hierarchically construct the second morpheme information and the like for searching the answer contents for the user's utterance contents using the language in the database. The conversation control device 1 can acquire the response content for the utterance content from the database through a hierarchical procedure based on the utterance content of the user.
[0210]
In other words, the conversation control device 1 has determined and determined the hierarchy of the user's utterance content (for example, whether the second morpheme information stored in the database is in a higher concept or a lower concept). Appropriate answer contents can be acquired from the answer contents accumulated in advance based on the hierarchy.
[0211]
For this reason, the conversation control device 1 does not collate the first morpheme information composed of the user's utterance content and the previously stored “all” second morpheme information one by one. Since the second morpheme information and the first morpheme information may be collated, the second morpheme information approximate to the first morpheme information can be acquired in a short time.
[0212]
Furthermore, communication between the communication unit 800 and the communication unit 900 may be performed by transmitting and receiving data via a communication network 1000 using a protocol such as DKML. Thereby, for example, when there is no answer content suitable for the user's utterance content in the conversation control device 1, the conversation control device 1 is suitable for the user's utterance content through the communication network 1000 according to the convention such as DKML. It is possible to retrieve the answer contents (described in DKML or the like) and obtain the retrieved answer contents (see FIG. 20).
[0213]
The program can be recorded on a recording medium. As shown in FIG. 23, examples of the recording medium include a hard disk 1100, a flexible disk 1200, a compact disk 1300, an IC chip 1400, and a cassette tape 1500. According to the recording medium on which such a program is recorded, the program can be easily stored, transported, sold, and the like.
[0214]
[Second Embodiment]
(Basic configuration of racing game equipment)
A second embodiment of the present invention will be described with reference to the drawings. FIG. 24 is a diagram showing an internal configuration of the racing game apparatus 3 according to the present embodiment.
[0215]
In this embodiment, as shown in FIG. 25 and FIG. 25, the racing game apparatus 3 is provided with a plurality of movement models 3520 that travel on a predetermined travel path 3510. In this racing game apparatus 3, the player makes predictions about the arrival order of each movement model 3520 for each race, and after the input about the prediction is completed, each movement model travels on the traveling path 3510 and determines the arrival order. Run a competing race. Then, when the arrival order of each movement model matches the player's expectation, the racing game apparatus 3 discharges a predetermined game value because the player's expectation is correct.
[0216]
In the present embodiment, the race performed on the game board 3500 is described as a horse racing race, but the race is not limited to this. It may be a car race in which a plurality of moving vehicles compete for the arrival order.
[0217]
As shown in the figure, the internal configuration of the racing game apparatus 3 is almost the same as the internal configuration of the conversation control apparatus 1 in the first embodiment, but the spoken sentence search unit 371, the spoken sentence database 501 and the counting unit. 2000 and the voting processing unit 3000, and the function of the topic search unit 360 is slightly different from that of the first embodiment. Since it is the same as the structure of 1st embodiment and a modified example except this difference, description about structures other than a difference is abbreviate | omitted.
[0218]
In the first embodiment, the conversation control device 1 performs the process of acquiring the optimum answer sentence corresponding to the utterance content based on the utterance content from the player. However, in this embodiment, the racing gaming device 3 The difference is that a process of outputting a specific sentence (talking sentence) to the player is performed based on the utterance content from the player and the number of wins counted by the counting unit 2000. The specific explanation is as follows.
[0219]
As shown in FIG. 25, the voting processing unit 3000 includes input / output units 3100a to 3100c, a voting acquisition unit 3200, a voting determination unit 3300, a drive control unit 3400, a game board 3500, and a voting storage unit 3600. It has.
[0220]
The input / output units 3100a to 3100c include operation units 3110a to 3110c, coin discharge units 3120a to 3120c, and voice input / output units 3130a to 3130c. Each player can participate in the game by sitting in front of the input / output units 3100a to 3100c and performing a specific operation. Although there are a plurality of these input / output units 3100a to 3100c in the present embodiment, these are collectively described as the input / output unit 3100 for convenience of explanation.
[0221]
The operation unit 3110 is used to input the arrival order of each moving model 3520 predicted by the player. Specifically, the operation unit 3110 that causes the player to input the arrival order predicted by the player as an arrival order vote, outputs the input arrival order vote to the vote acquisition unit 3200. The coin discharge unit 3120 discharges a predetermined number of game values based on an instruction from the vote determination unit 3300. Here, the game value includes a medal in the present embodiment.
[0222]
The voice input / output unit 3130 acquires voice uttered by the player, and in this embodiment, a microphone or the like is used. Specifically, the voice input / output unit 3130 that has acquired the player's voice outputs the acquired voice to the voice recognition unit 200 as a voice signal. The voice input / output unit 3130 also outputs the spoken sentence searched by the spoken sentence search unit 371 by voice. Note that the processing from the voice recognition unit 200 is the same as that in the first embodiment, and thus description thereof is omitted.
[0223]
The vote acquisition unit 3200 accepts the arrival order of each moving model predicted by each player for each race. In addition, the vote acquisition unit 3200 manages the time (acceptance time) for accepting the prediction from the player. Specifically, the vote acquisition unit 3200 receives the arrival order vote input from the operation unit 3110 until the reception time managed by the own unit elapses.
[0224]
The vote acquisition unit 3200 that has received the arrival order vote from the operation unit 3110 outputs the received arrival order vote to the vote determination unit 3300 as an acceptance signal. When the reception time has elapsed, the vote acquisition unit 3200 performs processing for invalidating the arrival order vote input from the operation unit 3110.
The voting determination unit 3300 determines the arrival order of each moving model 3520 traveling on the traveling path 3510. As shown in FIG. 26, the determination of the arrival order is performed using a “arrival order table” in the present embodiment. This “arrival order table” is stored in advance in the voting storage unit 3600.
[0225]
In the present embodiment, the “arrival order random number” shown in the figure is a value that is continuously counted within a range of 1 to 70. In this embodiment, this arrival order random number is associated with the number of each moving model 3520 that finishes in the top two in a certain race. Here, in the present embodiment, the race means from the start of the time for accepting a vote from the player to the end of the competition of each moving model 3520 performed on the game board 3500.
[0226]
Specifically, the voting determination unit 3300 to which the reception signal is input from the vote acquisition unit 3200 extracts one random number from the arrival order random numbers based on the input reception signal as shown in FIG. The number of each movement model associated with the extracted random number is acquired. The vote acquisition unit 3200 that acquired the number of each moving model 3520 outputs the acquired number of each moving model 3520 to the drive control unit 3400 as an acquisition number signal.
[0227]
Also, the voting determination unit 3300 determines that the player's prediction has been correct when the arrival order of each moving model 3520 matches the player's prediction. Specifically, the voting determination unit 3300, to which the acceptance signal is input from the voting acquisition unit 3200, first uses the “arrival order table” shown in FIG. 26 to determine the number of each moving model 3520 that finishes in the top two in the race. To get.
[0228]
The vote determination unit 3300 that acquired the number of each moving model 3520 collates the acquired number with the arrival order vote corresponding to the reception signal, and when each of the numbers and the arrival order vote match, It is determined that the player's expectation is correct. The voting determination unit 3300 that has made this determination outputs a hit signal indicating that the player's prediction has been hit to the count unit 2000.
[0229]
Further, the voting determination unit 3300 that has made the above determination outputs a discharge command signal for causing the corresponding player to discharge a predetermined game value to the corresponding coin discharge unit 3120. The coin discharge unit 3120 to which the discharge command signal is input from the voting determination unit 3300 discharges the game medium corresponding to the discharge command signal to the outside based on the input discharge command signal.
[0230]
Regarding the game value discharge, in the present embodiment, the coin discharge unit 3120 discharges medals, but is not limited to this. As this game value, for example, a point corresponding to the game value may be given to a magnetic card or the like, or a prize or the like may be discharged.
[0231]
The drive control unit 3400 controls the movement of each moving model 3520 provided on the game board 3500 based on an instruction from the vote determination unit 3300. Specifically, the drive control unit 3400 to which the acquisition number signal is input from the voting determination unit 3300 has each movement model 3520 corresponding to each number based on each number corresponding to the input acquisition number signal. The movement of each moving model 3520 is controlled so that it finishes in the top two.
[0232]
The counting unit 2000 is a counting unit that counts the number of times the player's prediction has been hit. Specifically, the counting unit 2000 to which the hit signal is input from the vote determination unit 330 counts the number of times the hit signal is input based on the input hit signal.
[0233]
In this embodiment, the number of times the hit signal is input means the number of hits made by the player. The counting unit 2000 that counts the number of hits outputs the number of counted times to the talking sentence search unit 371 as a count signal. In the present embodiment, the counting unit 2000 counts the number of times that the player has hit continuously.
[0234]
Note that when a plurality of players are participating in the game, the counting unit 2000 can count, for each player, the number of times the player's prediction has been hit. Specifically, the voting determination unit 3300 compares each arrival order vote of each player corresponding to the input reception signal with each number determined in the “arrival order determination table”, and each of the above arrival order votes. Is determined for each vote in order of arrival.
[0235]
The voting determination unit 3300 that performs the determination for each arrival order vote outputs a determination result (a result of only the match) to the counting unit 2000 that the two match each other for each arrival order vote. If the counting unit 2000 counts the determination result performed by the vote determining unit 3300 for each arrival order vote, it counts the number of times that the player's prediction is correct for each player.
[0236]
As a result, since the counting unit 2000 can count the number of hits made for each player, the talking sentence searching unit 371 determines a specific talking for each player according to the number counted for each player. You can search for sentences.
[0237]
Here, first numerical information such as a numerical value and a sign is associated with a topic title (second morpheme information) indicating a morpheme composed of one character, a plurality of character strings, or a combination thereof. The conversation database 500 (morpheme storage means) stores a plurality of the topic titles in advance.
[0238]
In the present embodiment, as shown in FIG. 27, a plurality of the topic titles are associated with the discourse range indicating the morpheme that is related to the utterance content that will be input from the player or the response content to the player. ing. Each topic title is associated with the first numerical information. In the present embodiment, it is assumed that a plurality of conversation ranges are stored in advance in the conversation database 500.
[0239]
That is, in this embodiment, as shown in FIG. 28, the discourse range is “today”, and each topic title associated with the discourse range “today” is (today; *; attached), (today). ; *; Can be taken a lot), (Today; *; Cannot be taken), (Today; *; The first numerical information associated with each topic title is +1, +1, -1, -1. In addition to the above conversation range, a plurality of other conversation ranges are stored in the conversation database 500 in advance.
[0240]
In the present embodiment, as described above, if the topic title is, for example, a phrase that praises the racing game apparatus 3 (today is attached) (today; *; attached), the first numerical information associated with the topic title Is +1. On the other hand, if the topic title is, for example, a phrase that repels the race gaming device 3 (cannot be taken today) (today; *; cannot be taken), the first numerical information associated with the topic title is -1.
[0241]
In the present embodiment, the magnitude of the first numerical information can be associated with the magnitude of the emotion level (second numerical information) held by the player. Specifically, as shown in FIG. 29, for example, if the first numerical information is “−1”, the emotion level associated with this is “bad”, and if the first numerical information is “0”, The degree of emotion associated with this can be treated as “normal”. Furthermore, if the first numerical information is “+1”, the emotion level associated with this can be “good”. A spoken sentence search unit 371, which will be described later, can select a sentence to be spoken to the player in accordance with the magnitude of the emotion level.
[0242]
For example, when the emotion level associated with the topic title is “+1”, the spoken sentence search unit 371 described later will please the player because the emotion level is in the positive direction. You can select a sentence.
[0243]
The spoken sentence database 501 (sentence storage means) stores a plurality of talk sentences in advance in association with the second numerical information (for example, the emotion level) such as a numerical value and a sign as a reference for searching the sentence. is there. In the present embodiment, as shown in FIG. 30, in the talk sentence, the emotion level is associated with an estimated number of times indicating the number of times that the player's prediction is likely to be correct in each race. A plurality of such spoken sentences are stored in advance for each second numerical information and each assumed number of times (refer to the “spoken sentence table” shown in the figure).
[0244]
In the present embodiment, the content of the talking sentence differs depending on the number of times that the player's prediction is correct. This is because if the player's expectation is lost, the player's money will decrease, but depending on the number of times the competitive gaming device 3 has hit the above, a specific talk sentence (for example, “this tone” etc.) ) Is output, the player is motivated to focus on the next race even if his money is reduced, and the same game can be enjoyed with continuity.
[0245]
In addition, since the content of this talking sentence also changes depending on the level of emotion, the racing game apparatus 3 changes the content of the sentence to be spoken in accordance with the level of emotion corresponding to the acquired first numerical information. Can do. Therefore, the racing game apparatus 3 can apply a word of encouragement to the player when the emotion level of the player is high, and the player can further apply the output spoken sentence to the player. Have fun playing games.
[0246]
The topic search unit 360 collates the first morpheme information extracted by the sentence structure analysis unit 430 with each topic title stored in advance, and the topic including the morpheme constituting the first morpheme information from each topic title. A title is searched, and first numerical information associated with the searched topic title is acquired.
[0247]
Further, the topic search unit 360 collates each topic title associated with the conversation range searched by the conversation range determination unit 340 with the first morpheme information extracted by the sentence structure analysis unit 430, and determines each topic title. A topic title including a morpheme constituting the first morpheme information is searched from inside, and first numerical information associated with the searched topic title is acquired.
[0248]
Specifically, the topic search unit 360, to which the topic search command signal is input from the talk range determination unit 340, sets the talk range based on the talk range corresponding to the input topic search command signal and the first morpheme information. A topic title that matches the first morpheme information is searched from the associated topic titles, and first numerical information associated with the searched topic title is acquired. The topic search unit 360 that has acquired the first numerical information outputs the acquired first numerical information to the talking sentence search unit 371 as a numerical signal.
[0249]
Based on the first numerical information acquired by the topic search unit 360, the spoken sentence search unit 371 collates the first numerical information with each of the second numerical information stored in advance. The second acquisition means for searching for second numerical information that matches the first numerical information and acquiring a talk sentence associated with the searched second numerical information.
[0250]
The spoken sentence search unit 371 also uses the first numerical information acquired by the topic search unit 360 and the number of times counted by the count unit 2000 to store each second numerical information and each assumption stored in the conversation database 500 in advance. The second numerical information and the assumed number of times corresponding to the first numerical information and the number of times are searched from the number of times, and a talk sentence associated with the searched second numerical information and the assumed number of times is acquired. Specifically, the spoken sentence search unit 371 to which the count signal from the count unit 2000 and the numerical signal from the topic search unit 360 are input, as shown in FIG. 30, is the number of hits corresponding to the input count signal ( Based on the first numerical information (for example, “+1”) corresponding to the input numerical signal and the number of times (two times) and the assumed number of times corresponding to each of the first numerical information (+1) (Twice) and emotion level (+1) are searched, and the spoken sentence (great condition!) Associated with the searched number of times (twice) and emotion level (+1) is acquired from the spoken sentence database 501. .
[0251]
The spoken sentence search unit 371 that has acquired the spoken sentence outputs the acquired spoken sentence to the voice input / output unit 3130, and the voice input / output unit 3130 to which the spoken sentence is input from the spoken sentence search unit 371 receives the input spoken phrase. Sentence is output with voice or displayed on the screen.
[0252]
The spoken sentence search unit 371 may sequentially accumulate numerical values corresponding to the first numerical information acquired by the topic searching unit 360, and the sequentially accumulated numerical values may be used as the first numerical information. As a result, the spoken sentence search unit 371 can use the numerical values accumulated sequentially as the first numerical information, so if the second numerical information that matches the first numerical information is set as the emotion level, the past to the present It is possible to search for the corresponding spoken sentence based on the emotional level across the whole.
[0253]
(A racing game control method using a racing game apparatus)
The race game control method by the race game apparatus having the above-described configuration can be implemented by the following procedure. FIG. 31 is a flowchart showing the procedure of the racing game control method according to the present embodiment.
[0254]
As shown in the figure, first, the voice input / output unit 3130 performs a step of acquiring the utterance content input from the player (S201). And the morpheme extraction part 410 specifies the character string which comprises utterance content based on the utterance content acquired by the audio | voice input / output part 3130, and performs the step which extracts each morpheme from the specified character string ( S202, S203).
[0255]
Thereafter, the phrase analysis unit 420 performs a step of grouping each morpheme into a phrase format based on each morpheme extracted by the morpheme extraction unit 410 (S204). In this embodiment, each morpheme belonging to one phrase format is collectively referred to as first morpheme information. The processing of S201 to S204 is the same as the processing of S101 to S104 described in the first embodiment. For this reason, the detailed description about these processes is abbreviate | omitted.
[0256]
Next, the topic search unit 360 performs a step of acquiring the corresponding first numerical information (S205). Specifically, the topic search unit 360, to which the topic search command signal is input from the talk range determination unit 340, sets the talk range based on the talk range corresponding to the input topic search command signal and the first morpheme information. A topic title that matches the first morpheme information is searched from the associated topic titles, and first numerical information associated with the searched topic title is acquired. The topic search unit 360 that has acquired the first numerical information outputs the acquired first numerical information to the talking sentence search unit 371 as a numerical signal.
[0257]
Next, the counting unit 2000 performs a step of counting the number of times the player's prediction has been hit (S206). Specifically, the counting unit 2000 to which the hit signal is input from the vote determination unit 330 counts the number of times the hit signal is input based on the input hit signal.
[0258]
In this embodiment, the number of times the hit signal is input means the number of hits made by the player. The counting unit 2000 that counts the number of hits outputs the number of counted times to the talking sentence search unit 371 as a count signal. In the present embodiment, the counting unit 2000 counts the number of times that the player has hit continuously.
[0259]
Thereafter, the spoken sentence search unit 371 collates the first numerical information with each second numerical information stored in advance based on the first numerical information acquired by the topic search unit 360, and The second numerical information that matches the first numerical information is searched from the inside, and a step of acquiring a talk sentence associated with the searched second numerical information is performed (S207).
[0260]
In other words, in the present embodiment, the spoken sentence search unit 371 stores each second stored in the conversation database 500 in advance based on the first numerical information acquired by the topic search unit 360 and the number of times counted by the count unit 2000. From the numerical information and each assumed number of times, search the second numerical information and the assumed number of times that match the first numerical information and the number of times, respectively, and obtain a talk sentence associated with the searched second numerical information and the assumed number of times To do.
[0261]
Specifically, the spoken sentence search unit 371 to which the count signal from the count unit 2000 and the numerical signal from the topic search unit 360 are input, as shown in FIG. 30, is the number of hits corresponding to the input count signal ( Based on the first numerical information (for example, “+1”) corresponding to the input numerical signal and the number of times (two times) and the assumed number of times corresponding to each of the first numerical information (+1) (Twice) and emotion level (+1) are searched, and the spoken sentence (great condition!) Associated with the searched number of times (twice) and emotion level (+1) is acquired from the spoken sentence database 501. .
[0262]
The spoken sentence search unit 371 that has acquired the spoken sentence outputs the acquired spoken sentence to the voice input / output unit 3130, and the voice input / output unit 3130 to which the spoken sentence is input from the spoken sentence search unit 371 receives the input spoken phrase. Sentence is output with voice or displayed on the screen.
[0263]
Note that the process for determining whether or not the player's prediction is correct can be performed in the following procedure. First, the player performs a step of inputting his / her expected arrival order of each moving model 3520 via the operation unit 3110. The operation unit 3110 that causes the player to input the arrival order predicted by the player as an arrival order vote is output to the vote acquisition unit 3200.
[0264]
And the vote acquisition part 3200 performs the step which receives the arrival order of each movement model which each player anticipated for every race. Specifically, the vote acquisition unit 3200 that has received the arrival order vote from the operation unit 3110 outputs the received arrival order vote to the vote determination unit 3300 as an acceptance signal. When the reception time managed by the vote acquisition unit 3200 has elapsed, the vote acquisition unit 3200 performs processing for invalidating the arrival order vote input from the operation unit 3110.
Thereafter, the voting determination unit 3300 performs a step of determining the arrival order of each moving model 3520 traveling on the traveling path 3510. Specifically, the voting determination unit 3300 to which the reception signal is input from the vote acquisition unit 3200 extracts one random number from the arrival order random numbers based on the input reception signal as shown in FIG. The number of each movement model associated with the extracted random number is acquired.
[0265]
The vote acquisition unit 3200 that acquired the number of each moving model 3520 outputs the acquired number of each moving model 3520 to the drive control unit 3400 as an acquisition number signal. The drive control unit 3400 to which the acquisition number signal is input from the voting determination unit 3300 is based on each number corresponding to the input acquisition number signal, and each moving model 3520 corresponding to each of the numbers is the second highest. The movement of each moving model 3520 is controlled so as to make a goal.
[0266]
Further, the voting determination unit 3300 that acquired the number of each moving model 3520 collates the acquired number with the arrival order vote corresponding to the reception signal, and when each of the numbers matches the arrival order vote Determines that the player's expectation was correct. The voting determination unit 3300 that has made this determination outputs a hit signal indicating that the player's prediction has been hit to the count unit 2000.
[0267]
(Operations and effects of the race game apparatus and race game control method)
According to the invention having the above-described configuration, the spoken sentence search unit 371 is based on the first numerical information acquired by the topic search unit 360, and the talking is associated with the second numerical information that matches the first numerical information. Since the sentence can be acquired, the talking sentence search unit 371 can output a specific talking sentence corresponding to the utterance content from the player. As a result, if the sentence output from the racing game apparatus 3 is a sentence that stimulates the player's emotions, the player is motivated to acquire game value by the output sentence, and has continuity. Can enjoy the same game.
[0268]
In particular, if the second numerical information is the emotion level held for the player and the rank of the image held for the player is set in a good direction when this emotion level is high, the spoken sentence search unit 371 searches. If the emotional level corresponding to the second numerical information is high, the player has a good image, so a spoken sentence associated with the high emotional level, for example, a sentence that encourages the player (“Next, more medals "Let's take it"), and the player can enjoy the game while enjoying the thrill and excitement.
[0269]
Further, the spoken sentence search unit 371 stores each pre-stored value based on the first numerical information acquired by the topic search unit 360 and the number of times counted by the count unit 2000 (the number of times the player's prediction has been hit). The second numerical information and the assumed number of times are searched for the second numerical information and the assumed number of times corresponding to the first numerical information and the number of times, respectively, and the talk associated with the searched second numerical information and the assumed number of times. Since the sentence can be acquired, the talking sentence searching unit 371 can change the talking sentence to be acquired not only according to the acquired first numerical information but also according to the counted hit count.
[0270]
As a result, if the player's desired gaming value is not discharged, the player's money will decrease, but a specific talking sentence (in accordance with the number of prizes discharged by the competitive gaming apparatus 3) For example, if “this tone” is output, even if the player's money is reduced, the player's willingness to acquire more game value is stimulated by the output spoken sentence and has continuity. A similar game can be enjoyed.
[0271]
In addition, since the content of this talking sentence also changes depending on the level of emotion, the racing game apparatus 3 changes the content of the sentence to be spoken in accordance with the level of emotion corresponding to the acquired first numerical information. Can do. Therefore, the racing game apparatus 3 can apply a word of encouragement to the player when the emotion level of the player is high, and the player can further apply the output spoken sentence to the player. Have fun playing games.
[0272]
Furthermore, the topic search unit 360 does not need to collate all the topic titles with the extracted first morpheme information, and each topic title associated only with the searched “discourse range” and the extracted first morpheme information. Since only the morpheme information is collated, a topic title including the first morpheme information can be searched (pinpoint search) in a short time from each topic title.
[0273]
As a result, the spoken sentence search unit 371 can search for the corresponding topic title in a short time, so that the spoken sentence search unit 371 determines the first numerical information associated with the searched topic title. It is possible to quickly search for a spoken sentence associated with second numerical information that matches the single numerical information.
[0274]
【The invention's effect】
As described above, according to the present invention, the player can acquire more game value by outputting a sentence that stimulates the player's emotion based on the utterance content uttered by the player during the game. It can motivate you and let you play the same game with continuity.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a schematic configuration of a conversation control system according to a first embodiment.
FIG. 2 is a block diagram showing an internal structure of a conversation control unit and a sentence analysis unit in the first embodiment.
FIG. 3 is a diagram showing the contents of each morpheme extracted by a morpheme extraction unit in the first embodiment.
FIG. 4 is a diagram showing the contents of each phrase extracted by a phrase analysis unit in the first embodiment.
FIG. 5 is a diagram showing the contents of “case” specified by the sentence structure analysis unit in the first embodiment.
FIG. 6 is a diagram showing an “uttered sentence type” specified by an utterance type determining unit in the first embodiment.
FIG. 7 is a diagram showing the contents of each dictionary stored in the utterance type database in the first embodiment.
FIG. 8 is a diagram showing the contents of a hierarchical structure built inside the conversation database in the first embodiment.
FIG. 9 is a diagram showing a detailed relationship of a hierarchical structure built inside the conversation database in the first embodiment.
FIG. 10 is a diagram showing the content of a “topic title” constructed within the conversation database in the first embodiment.
FIG. 11 is a diagram showing the content of “answer sentence type” associated with “topic title” built inside the conversation database in the first embodiment.
FIG. 12 is a diagram showing the contents of “topic title” and “answer sentence” belonging to “discourse range” built inside the conversation database in the first embodiment.
FIG. 13 is a diagram showing the contents of reflection element information stored in the reflection element database in the first embodiment.
FIG. 14 is a diagram showing the contents of a wrapping element and a morpheme of the wrapping element stored in the wrapping element database in the first embodiment.
FIG. 15 is a flowchart showing a procedure of a conversation control method according to the first embodiment.
FIG. 16 is a diagram showing utterance contents organized by a morpheme extraction unit in the first modification.
FIG. 17 is a diagram illustrating an internal configuration of a topic search unit in a second modified example.
FIG. 18 is a diagram illustrating a state in which the ratio calculation unit in the second modification collates each morpheme belonging to “case configuration” and each “topic title” for each “topic title”.
FIG. 19 is a diagram illustrating a state in which the ratio calculation unit in the second modified example collates each morpheme belonging to “each component” and each morpheme belonging to “topic title” for each “case”.
FIG. 20 is a diagram showing a schematic configuration of a conversation control system in a third modified example.
FIG. 21 is a diagram illustrating an internal configuration of a topic search unit in a fifth modification example.
FIG. 22 is a diagram illustrating a state in which the topic search unit in the sixth modification collates first morpheme information with a topic title or an answer sentence.
FIG. 23 is a diagram showing a recording medium for storing a program in the first embodiment.
FIG. 24 is a diagram showing an internal structure of the racing game apparatus according to the second embodiment.
FIG. 25 is a diagram showing an internal structure of a voting processor in the second embodiment.
FIG. 26 is a diagram showing the contents of the “arrival order table” stored in the vote storage unit in the second embodiment.
FIG. 27 is a diagram showing a detailed relationship of a hierarchical structure of discourse range groups constructed inside a conversation database in the second embodiment.
FIG. 28 is a diagram showing the topic title and the contents of the first numerical information associated with the discourse range in the second embodiment.
FIG. 29 is a diagram showing a correspondence relationship between first numerical information and second numerical information in the second embodiment.
FIG. 30 is a diagram showing the contents of a spoken sentence table stored inside a spoken sentence database in the second embodiment.
FIG. 31 is a flowchart showing a procedure of a racing game control method according to the second embodiment.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Conversation control apparatus, 2 ... Server, 3 ... Competition game apparatus, 100 ... Input part, 200 ... Speech recognition part, 300 ... Conversation control part, 310 ... Management part, 320 ... Reflective determination part, 330 ... Determination part, 330 ... Voting determination unit, 340 ... Discourse range determination unit, 350 ... Abbreviated sentence complementation unit, 360 ... Topic search unit, 361 ... Ratio calculation unit, 362 ... Selection unit, 363 ... Deletion unit, 364 ... Discourse addition unit, 370 ... Answer sentence search unit, 371 ... sentence search part, 400 ... sentence analysis part, 410 ... morpheme extraction part, 420 ... phrase analysis part, 430 ... sentence structure analysis part, 440 ... utterance type determination part, 450 ... morpheme database, 460 ... Speech type database, 500 ... Conversation database, 501 ... Spoken sentence database, 600 ... Output unit, 700 ... Speech recognition dictionary storage unit, 800 ... Communication unit, 801 ... Reflection element database 802 ... Return element database, 900 ... Communication unit, 1000 ... Communication network, 1100 ... Hard disk, 1200 ... Flexible disk, 1300 ... Compact disk, 1400 ... IC chip, 1500 ... Cassette tape, 2000 ... Counting unit, 3000 ... Voting processing unit 3100 ... Input / output unit, 3110 ... Operation unit, 3120 ... Coin ejecting unit, 3130 ... Voice input / output unit, 3200 ... Voting acquisition unit, 3300 ... Voting determination unit, 3400 ... Drive control unit, 3500 ... Game board, 3510 ... Traveling path, 3520 ... moving model, 3600 ... voting storage unit

Claims (6)

A plurality of movement models traveling on a predetermined road are provided, and the player makes predictions about the order of arrival of each movement model for each race, and after the input about the prediction is completed, each movement model is A race that runs on the road and competes for the order of arrival, and when the order of arrival of each movement model matches the above-mentioned prediction, the race that discharges a predetermined game value as the player's prediction is correct A gaming device,
The second morpheme information indicating a morpheme composed of one character, a plurality of character strings, or a combination thereof is associated with first numerical information such as a numerical value and a sign, and a plurality of morpheme information is stored in advance. Storage means;
Sentences that speak to the player are associated with second numerical information such as numerical values and codes that are used as a reference for searching the sentences, and a sentence storage unit that stores a plurality of the sentences in advance.
Morpheme extraction means for extracting each morpheme constituting the minimum unit of the character string as first morpheme information based on the character string corresponding to the utterance content input from the player;
The first morpheme information extracted by the morpheme extraction means is compared with each second morpheme information stored in advance, and the morpheme constituting the first morpheme information is determined from the second morpheme information. First acquisition means for searching for the second morpheme information including, and acquiring the first numerical information associated with the searched second morpheme information;
Based on the first numerical information acquired by the first acquisition means, the first numerical information is compared with each of the second numerical information stored in advance, and from among the second numerical information, Searching for the second numerical information that matches the first numerical information, and acquiring the sentence associated with the searched second numerical information;
Each of the second morpheme information is associated with the first numerical information,
The statement assumes said second numerical information, wherein the prediction of the player in each race is correlated and supposed number indicating the number of times is assumed to hit,該文within each said second numerical information The number of times is set and stored in advance.
Having a counting means to count the number of times the player's prediction has been hit,
The second acquisition means includes the second numerical information stored in advance and the estimated number of times based on the first numerical information acquired by the first acquisition means and the number of times counted by the counting means. From the second numerical information and the assumed number of times that match the first numerical value information and the number of times, the second numerical information searched and the sentence associated with the assumed number of times is obtained,
A plurality of the second morpheme information is associated with a discourse range consisting of morphemes indicating morphemes related to the utterance content that will be input from the player or the response content to the player, and each of the second morpheme information , The first numerical information is associated with each, a plurality of the conversation range is stored in advance,
The first morpheme information extracted by the morpheme extraction means is collated with each discourse range stored in advance, and the discourse that matches the morpheme included in the first morpheme information is selected from the discourse ranges. It has a discourse search means for searching a range
Wherein the first obtaining means, said respective second morpheme information verification associated with the discourse range searched in the discourse retrieval means is executed in the discourse retrieval means, said first extracted by the morpheme extracting unit Collate with one morpheme information,
The second morpheme information including the morpheme constituting the first morpheme information is searched from the second morpheme information, and the first numerical information associated with the searched second morpheme information is obtained. A racing game apparatus characterized by that. The racing game apparatus according to claim 1,
The second acquisition means sequentially accumulates numerical values corresponding to the first numerical information acquired by the first acquisition means, and uses the accumulated numerical values as the first numerical information. . A plurality of movement models traveling on a predetermined traveling path are provided, and the player makes predictions about the arrival order of the respective movement models for each race, and after the input about the prediction is completed, the respective movement models are A race that runs on the road and competes for the order of arrival, and when the order of arrival of each movement model matches the above-mentioned prediction, the race that discharges a predetermined game value as the player's prediction is correct A game control method comprising:
The second morpheme information indicating a morpheme composed of one character, a plurality of character strings, or a combination thereof is associated with first numerical information such as a numerical value and a sign, and a step of storing a plurality of the second morpheme information in advance When,
A sentence to be spoken to the player is associated with the second numerical information such as a numerical value and a code serving as a reference for searching the sentence, and a plurality of the sentences are stored in advance.
Extracting each morpheme constituting the minimum unit of the character string as first morpheme information based on the character string corresponding to the utterance content input from the player;
The extracted first morpheme information is compared with each second morpheme information stored in advance, and the second morpheme includes the morpheme constituting the first morpheme information from the second morpheme information. Retrieving information and obtaining the first numerical information associated with the retrieved second morpheme information;
Based on the acquired first numerical information, the first numerical information and the second numerical information stored in advance are collated, and the second numerical information matches the first numerical information. Retrieving the second numerical information, and acquiring the sentence associated with the searched second numerical information,
Each of the second morpheme information is associated with the first numerical information,
The statement assumes said second numerical information, wherein the prediction of the player in each race is correlated and supposed number indicating the number of times is assumed to hit,該文within each said second numerical information The number of times is set and stored in advance.
Counting the number of times a player's expectation has been hit,
Based on the acquired first numerical information and the counted number of times, the first numerical information and the number of times that coincide with each of the first numerical information and the number of times out of the previously stored second numerical information and each assumed number of times. Searching for binary numerical information and the assumed number of times, and obtaining the sentence associated with the searched second numerical information and the assumed number of times,
A plurality of the second morpheme information is associated with a discourse range consisting of morphemes indicating morphemes related to the utterance content that will be input from the player or the response content to the player, and each of the second morpheme information , The first numerical information is associated with each, a plurality of the conversation range is stored in advance,
The extracted first morpheme information is collated with each pre-stored discourse range,
Searching the discourse range that matches the morpheme included in the first morpheme information from the discourse ranges;
The second morpheme information associated with the searched discourse range is compared with the first morpheme information extracted by the morpheme extraction unit, and the first morpheme is extracted from the second morpheme information. And a step of retrieving the second morpheme information including the morpheme constituting the information and obtaining the first numerical information associated with the retrieved second morpheme information. A race game control method according to claim 3,
A racing game control method characterized by sequentially accumulating numerical values corresponding to the acquired first numerical information, and using the accumulated numerical values as the first numerical information . A plurality of movement models traveling on a predetermined traveling path are provided, and the player makes predictions about the arrival order of the respective movement models for each race, and after the input about the prediction is completed, the respective movement models are A race that runs on the road and competes for the order of arrival, and when the order of arrival of each movement model matches the above-mentioned prediction, the race that discharges a predetermined game value as the player's prediction is correct A game control device program comprising:
On the computer,
The second morpheme information indicating a morpheme composed of one character, a plurality of character strings, or a combination thereof is associated with first numerical information such as a numerical value and a sign, and a step of storing a plurality of the second morpheme information in advance When,
A sentence to be spoken to the player is associated with the second numerical information such as a numerical value and a code serving as a reference for searching the sentence, and a plurality of the sentences are stored in advance.
Extracting each morpheme constituting the minimum unit of the character string as first morpheme information based on the character string corresponding to the utterance content input from the player;
The extracted first morpheme information is compared with each second morpheme information stored in advance, and the second morpheme includes the morpheme constituting the first morpheme information from the second morpheme information. Retrieving information and obtaining the first numerical information associated with the retrieved second morpheme information;
Based on the acquired first numerical information, the first numerical information and the second numerical information stored in advance are collated, and the second numerical information matches the first numerical information. Retrieving the second numerical information, and acquiring the sentence associated with the searched second numerical information,
Each of the second morpheme information is associated with the first numerical information,
The statement assumes said second numerical information, wherein the prediction of the player in each race is correlated and supposed number indicating the number of times is assumed to hit,該文within each said second numerical information The number of times is set and stored in advance.
Counting the number of times a player's expectation has been hit,
Based on the acquired first numerical information and the counted number of times, the first numerical information and the number of times that coincide with each of the first numerical information and the number of times out of the previously stored second numerical information and each assumed number of times. Searching for binary numerical information and the assumed number of times, and obtaining the sentence associated with the searched second numerical information and the assumed number of times,
A plurality of the second morpheme information is associated with a discourse range consisting of morphemes indicating morphemes related to the utterance content that will be input from the player or the response content to the player, and each of the second morpheme information , The first numerical information is associated with each, a plurality of the conversation range is stored in advance,
Collating the extracted first morpheme information with each of the previously stored discourse ranges, and searching the discourse range that matches the morpheme included in the first morpheme information from the discourse ranges When,
The second morpheme information associated with the searched discourse range is compared with the first morpheme information extracted by the morpheme extraction unit, and the first morpheme is extracted from the second morpheme information. The second morpheme information including the morpheme constituting the information is retrieved, and the first numerical information associated with the retrieved second morpheme information is acquired. The program according to claim 5,
  A program for executing a process of sequentially accumulating numerical values corresponding to the acquired first numerical information and using the accumulated numerical values as the first numerical information.

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