Patent ID: 12260859

MODE FOR CARRYING OUT THE INVENTION

Embodiments of the present disclosure will be described in detail below on the basis of the drawings. Note that in each of the embodiments below, the same parts are designated by the same reference numerals and duplicate description will be omitted.

Embodiment

[Outline of the Information Processing System According to the Embodiment]

FIG.1is a diagram for describing an example of the information processing system according to the embodiment. An information processing system1illustrated inFIG.1is an interaction processing system that realizes an interaction between a user U and an agent apparatus10. The user U is an example of a speaker of the agent apparatus10in the information processing system1. For example, there is a possibility that the user U gets bored if there is no originality or freshness in the interaction with the agent apparatus10. For example, the user U does not use the agent apparatus10with which the user U has gotten bored. Therefore, the information processing system1has an episode talk function of realizing an interaction based on an episode of the user U so that the user U does not get bored with the interaction with the agent apparatus10and has a sense of affinity.

The information processing system1includes the agent apparatus10and an information processing apparatus100. The information processing system1may include, for example, a plurality of agent apparatuses10that interacts with each of a plurality of users U. The agent apparatus10and the information processing apparatus100are, for example, configured to be able to communicate via a network or directly communicate without the network. The information processing apparatus100may, for example, be incorporated in the agent apparatus10. In the description described below, an example of the information processing system1in which the agent apparatus10interacts with the user U by cooperation between the agent apparatus10and the information processing apparatus100will be described.

The agent apparatus10is a so-called Internet of Things (IoT) device, and performs various types of information processing in cooperation with the information processing apparatus100. There is a case where the voice recognition, the interaction processing by voice, and the like executed by the agent apparatus10are referred to as an agent function. For example, the agent apparatus10can make various responses to the utterance of the user U. For example, the response includes a response based on episode, a response based on scenario, a response based on the verbalization of situation, a knowledge-based response, a chat response, and the like.

The agent apparatus10includes, for example, an autonomous mobile body, a smartphone, a tablet terminal, a game device, a home speaker, a home electrical appliance, an automobile, and the like. The smartphone, the tablet terminal, or the like realizes the agent function described above by executing a program (application) having the agent function. In the present embodiment, a case where the agent apparatus10is an autonomous mobile body will be described.

In the example illustrated inFIG.1, the agent apparatus10can be various apparatuses that perform an autonomous operation based on environment recognition. The agent apparatus10is a long-ellipsoidal agent-type robot apparatus that autonomously travels by wheels. The agent apparatus10includes, for example, two wheels and a drive mechanism for driving the two wheels. The agent apparatus10controls the drive mechanism to move while maintaining the upright state. The agent apparatus10realizes various communication with the user U, for example, by performing an autonomous operation according to the user U, the surroundings, or the own situation. The agent apparatus10may be, for example, a small robot having such a size and weight that the user U can easily lift with one hand.

In the embodiment, the agent apparatus10executes information processing with respect to a collected voice. For example, the agent apparatus10recognizes an utterance of the user U and executes information processing with respect to the utterance. In the example illustrated inFIG.1, it is assumed that the agent apparatus10is installed at the home, company, school, or the like of the user U who uses the agent apparatus10.

The agent apparatus10may, for example, not only collect surrounding sounds, but include various sensors for acquiring other various types of information. For example, in addition to the microphone, the agent apparatus10may include a camera for acquiring a space, an illuminance sensor for detecting illuminance, a gyro sensor for detecting inclination, an infrared sensor for detecting an object, and the like.

The information processing apparatus100is a so-called cloud server, and is a server apparatus that executes information processing in cooperation with the agent apparatus10. The information processing apparatus100acquires utterance data of the user U collected by the agent apparatus10, and analyzes the utterance data using natural language understanding (NLU), natural language processing (NLP), or the like. After extracting the topic information from the utterance data, the information processing apparatus100acquires episode data regarding the topic information from an episode database (DB). The topic information includes, for example, information indicating classification of episodes, words such as keywords, character strings, and the like. That is, the topic information is information for acquiring the episode data. The episode DB has, for example, episode data related to the user U and is stored in a storage apparatus. The information processing apparatus100has a function of interacting with the user U so as to include an episode based on the episode data.

In the example illustrated inFIG.1, the agent apparatus10utters an utterance C11instructed by the information processing apparatus100in a case where the user U who has been absent is recognized. The utterance C11is, for example, “Welcome back.”. The user U utters an utterance C12in response to the utterance C11. The utterance C12is, for example, “I went to work at the company.”. The agent apparatus10transmits the utterance data of the utterance C12to the information processing apparatus100.

The information processing apparatus100analyzes the utterance data of the utterance C12and extracts topic information “company” and “went”. The information processing apparatus100acquires the episode data corresponding to the extracted topic information from the episode DB, and instructs the agent apparatus10to make an utterance C13based on the episode data. The information processing apparatus100acquires, for example, episode data regarding “company” and “go”. In this case, the episode data is assumed to be, for example, data indicating an episode regarding that the speaker went to the company yesterday and the day before yesterday. The agent apparatus10utters the utterance C13to the user U. The utterance C13is, for example, “I see. You go every day.”. Note that an interaction method based on the episode data will be described later. The user U utters an utterance C14in response to the utterance C13. The utterance C14is, for example, “That is work.”. The agent apparatus10transmits the utterance data of the utterance C14to the information processing apparatus100.

The information processing apparatus100analyzes the utterance data of the utterance C14and controls the agent apparatus10to utter a response to the user U. For example, the information processing apparatus100generates utterance data suitable for the character of the agent apparatus10on the basis of an interaction model, an interaction scenario, or the like for responding to the utterance data. In the example illustrated inFIG.1, the agent apparatus10utters an utterance C15according to the utterance data of the utterance C14. The utterance C15is, for example, “Cheer up for me.”.

As described above, when acquiring the episode data regarding the topic information included in the utterance data of the user U, the information processing apparatus100according to the embodiment can cause the agent apparatus10to execute an interaction including an episode of the user U based on the episode data. Therefore, the information processing apparatus100can provide the episode based on the episode data of the user U in the interaction with the user U. As a result, the information processing apparatus100can realize an interaction with which the user U has a sense of affinity by including the episode of the user U in the interaction.

[Configuration Example of the Agent Apparatus According to the Embodiment]

FIG.2is a diagram illustrating an example of a configuration of the agent apparatus10according to the embodiment. As illustrated inFIG.2, the agent apparatus10includes a sensor unit11, an input unit12, a light source13, an output unit14, a drive unit15, a control unit16, and a communication unit17.

The sensor unit11has a function of collecting various sensor information regarding the user U and the surroundings. The sensor unit11according to the present embodiment includes, for example, a camera, a time of flight (ToF) sensor, a microphone, an inertial sensor, and the like. The sensor unit11may include various sensors such as, for example, a geomagnetic sensor, a touch sensor, an infrared sensor, a temperature sensor, and a humidity sensor. The sensor unit11supplies the collected sensor information to the control unit16. The sensor unit11has a function of collecting a voice and the like with the microphone. The sensor unit11can store the collected voice and the like in a storage apparatus.

The input unit12has a function of detecting a physical input operation by the user U. The input unit12includes, for example, a button such as a power switch. The input unit12supplies input information indicating the detected input operation to the control unit16.

The light source13expresses an eyeball movement of the agent apparatus10, which is an autonomous mobile body. The light source13includes, for example, two eye portions. The light source13expresses rich eyeball movement according to the situation and movement in accordance with an instruction from the control unit16.

The output unit14has a function of outputting various sounds including voices. The output unit14includes, for example, a speaker, an amplifier, and the like. The output unit14outputs a sound instructed by the control unit16.

The drive unit15expresses an operation by driving the drive mechanism of the agent apparatus10, which is an autonomous mobile body. The drive unit15includes, for example, two wheels, a plurality of motors, and the like. The drive unit15is driven in accordance with an instruction from the control unit16.

The control unit16controls the agent apparatus10. The control unit16has a function of controlling each configuration included in the agent apparatus10. The control unit16controls, for example, activation and stop of each configuration. The control unit16controls the light source13, the output unit14, the drive unit15, and the like on the basis of control information and the like from the information processing apparatus100.

In a case where an utterance of the user U is collected by the sensor unit11, the control unit16performs control to transmit utterance data indicating the utterance to the information processing apparatus100. The control unit16controls the output unit14to output the interaction data instructed from the information processing apparatus100. The control unit16realizes the interaction with the user U by outputting the interaction data for the collected utterance data.

The communication unit17communicates with, for example, the information processing apparatus100, another external apparatus, or the like. The communication unit17transmits and receives various data via, for example, a wired or wireless network. For example, in a case where an utterance is collected, the communication unit17transmits utterance information to the information processing apparatus100. For example, the communication unit17may transmit not only the utterance data but also identification information for identifying the user U, or the like to the information processing apparatus100. The communication unit17supplies, for example, various data received from the information processing apparatus100to the control unit16.

Heretofore, a functional configuration example of the agent apparatus10according to the present embodiment has been described. Note that the aforementioned configurations described in conjunction withFIG.3are mere examples, and the functional configuration of the agent apparatus10according to the present embodiment is not limited to the above example. The functional configuration of the agent apparatus10according to the present embodiment can be flexibly modified depending on the specification or operation.

[Structure Example of the Agent Apparatus According to the Embodiment]

FIG.3is a diagram illustrating an example of a structure of the agent apparatus10according to the present embodiment. The left diagram inFIG.3is a side view diagram illustrating the posture of the agent apparatus10in a standing state (including the time of staying and the time of traveling). The right diagram inFIG.3is a side view diagram illustrating the posture of the agent apparatus10in a sitting state.

As illustrated in the right diagram ofFIG.3, in the present embodiment, the agent apparatus10causes a part of the bottom portion to be grounded to the floor surface when resting in the sitting state. Therefore, at least three points including the two wheels570and the grounded bottom portion come into contact with the floor surface, and the agent apparatus10is in a sitting state at the three points. Therefore, the agent apparatus10can stably rest in the sitting state. Furthermore, the agent apparatus10does not cause the bottom portion to be set on the floor surface when moving in the standing state.

A center of gravity CoG of the agent apparatus10is located on a perpendicular line V1above the axle of the wheels570when the agent apparatus10is in a forward tilting posture (standing state) (see the left diagram ofFIG.3). Therefore, the balance of the agent apparatus10is kept and the standing state is maintained.

On the other hand, in the sitting state, as illustrated in the right diagram ofFIG.3, by tilting the agent apparatus10backward, the at least three points including the two wheels570and the bottom portion (protrusion portion701) are brought into contact with the floor surface. At this time, the center of gravity CoG of the agent apparatus10is located between the perpendicular line V1passing through the axle of the wheels570and a perpendicular line V2passing through a contact portion between the bottom portion (protrusion portion701) and the floor surface. When the agent apparatus10is in the sitting state, the agent apparatus10can stably rest in the sitting state by positioning the center of gravity CoG of the agent apparatus10between the perpendicular line V1passing through the axle of the wheels570and the perpendicular line V2passing through the contact portion between the bottom portion (protrusion portion701) and the floor surface.

[Configuration Example of the Information Processing Apparatus According to the Embodiment]

FIG.4is a diagram illustrating an example of a configuration of the information processing apparatus100according to the embodiment. As illustrated inFIG.4, the information processing apparatus100includes a communication unit110, a storage unit120, and a control unit130. The control unit130is electrically connected to the communication unit110and the storage unit120.

The communication unit110communicates with, for example, the agent apparatus10, another external apparatus, or the like. The communication unit110transmits and receives various data via, for example, a wired or wireless network. The communication unit110supplies, for example, data received from the agent apparatus10to the control unit130. For example, the communication unit110transmits data instructed by the control unit130to an instructed transmission destination.

The storage unit120stores various data and programs. For example, the storage unit120is, for example, a semiconductor memory element such as random access memory (RAM), flash memory or the like, a hard disk, an optical disk, or the like. The storage unit120stores information received via the communication unit110. The storage unit120stores various data such as, for example, episode data D1, template data D2, user data D3, question data D4, interaction data D5, knowledge data D10, management data D20, and utterance data D30.

The episode data D1is data indicating an episode of the user U. The episode includes, for example, information regarding a past event of the user U, a future schedule of the user U, a hobby of the user U, and the like. The template data D2is data indicating an algorithm for generating the interaction data D5from the episode data D1. The user data D3is data indicating information regarding the user U, and the information regarding the user U includes, for example, personal information, authentication information, setting information, and the like of the user U. The question data D4is data indicating a question or the like for drawing out an episode from the user U. The question data D4is data used at the time of generating the episode data D1or the like. The interaction data D5is data used for an interaction with the user U. The knowledge data D10is data for realizing an interaction based on knowledge, a scenario, and the like. The knowledge data D10includes, for example, information such as intention identification, common sense knowledge, specialized knowledge, and corpus. The management data D20is data indicating a correspondence relationship between the user U and the agent apparatus10used by the user U. The utterance data D30is utterance data from the agent apparatus10, and is associated with an analysis result.

In the present embodiment, the information processing apparatus100realizes the episode DB by storing and managing the episode data D1in the storage unit120. The information processing apparatus100may be configured to store the episode DB in a storage apparatus or the like external to the own apparatus.

The control unit130is, for example, a dedicated or general-purpose computer. The control unit130is, for example, an integrated control unit that controls the information processing apparatus100. The control unit130includes each functional unit that cooperates with the agent apparatus20so that the agent apparatus10realizes an interaction with the user U.

The control unit130includes functional units of a recognition unit131, an acquisition unit132, an interaction control unit133, a generation unit134, a collection unit135, and an operation control unit140. Each functional unit of the control unit130is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), or the like executing a program stored inside the information processing apparatus100using the RAM or the like as a work area. Furthermore, each functional unit may be realized by an integrated circuit such as an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like.

The recognition unit131has a function of authenticating the user U on the basis of the sensor information of the agent apparatus10. The recognition unit131performs personal authentication by, for example, face authentication, voice authentication, fingerprint authentication, or the like, and recognizes the user U whom the agent apparatus10interacts. In a case where the user U who can use the agent apparatus10is authenticated on the basis of the sensor information of the agent apparatus10, the user data D3, the management data D20, and the like, the recognition unit131enables an interaction between the user U and the agent apparatus10.

The recognition unit131converts the utterance data received from the agent apparatus10into character strings. For example, the recognition unit131can use automatic speech recognition (ASR) processing or the like. The recognition unit131analyzes the recognized character strings using NLU, NLP processing, or the like. In the present embodiment, the recognition unit131analyzes the dependency structure of the character strings, and stores analysis information indicating the analysis result in the storage unit120in association with the utterance data D30. The analysis information includes, for example, information indicating a dependency structure such as a phrase and a case.

The acquisition unit132acquires the episode data D1regarding the topic information included in the utterance data D30of the user U from the storage unit120that stores the episode data D1of the user U. For example, the acquisition unit132extracts the topic information from the utterance data D30. The topic information is information for acquiring an episode of the user U, and includes information such as a preset keyword, word, character string, or the like.

The acquisition unit132acquires the episode data D1in which the topic information satisfies interaction conditions. The interaction conditions are set in, for example, the user data D3, the management data D20, or the like. The interaction conditions include, for example, a condition for extracting the episode data D1on the basis of the topic information. For example, in a case where the condition is that the set number or more of words or character strings included in the episode data D1match the topic information, the acquisition unit132acquires the episode data D1satisfying the interaction conditions from the storage unit120.

In a case where the user U is recognized, the acquisition unit132acquires the episode data D1satisfying acquisition conditions regardless of the utterance of the user U. The acquisition conditions include, for example, conditions for acquiring the episode data D1indicating the latest episode, an episode of time zone, an episode of date or season of the user U, or the like. The latest episode is, for example, the newest episode of the user U. For example, in a case where the time zone is morning, the episode of time zone includes an episode regarding a schedule of today, an event of yesterday, and the like. For example, in a case where the time zone is daytime, the episode of time zone includes an episode regarding an afternoon schedule, a night schedule, and the like. For example, in a case where the time zone is night, the episode of time zone includes an episode regarding an event of today, a schedule of tomorrow, and the like. The episode of date or season includes, for example, an episode in a set period of time. The acquisition unit132supplies the acquired episode data D1to the interaction control unit133. In a case where the acquisition unit132has failed to acquire the episode data D1, the acquisition unit132notifies the interaction control unit133of the fact that the acquisition has failed.

The interaction control unit133controls an interaction with the user U (speaker) so as to include an episode based on the episode data D1acquired by the acquisition unit132. Controlling the interaction includes, for example, controlling to utter an utterance to the user U, controlling to face and talk to the user U, and the like. For example, the interaction control unit133controls the agent apparatus10to generate the interaction data D5including an episode and output a voice based on the interaction data D5. The interaction control unit133instructs the agent apparatus10to output the interaction data D5by voice via the communication unit110. Therefore, the agent apparatus10realizes an interaction with the user U by outputting the voice based on the interaction data D5from the output unit14.

The interaction control unit133generates the interaction data D5including an episode on the basis of, for example, the episode data D1and an algorithm for generating an interactive sentence. The algorithm can be realized by, for example, a template, an application, a program, or the like. In the present embodiment, a case where the interaction data D5is generated using the template data D2will be described.

The interaction control unit133generates the interaction data D5by setting the data of the episode data D1to the template. The interaction control unit133controls an interaction with the user U such that the agent apparatus10spontaneously utters an episode. For example, in a case where the user U is recognized, the interaction control unit133controls the agent apparatus10to utter a voice of the episode based on the episode data D1satisfying the acquisition condition.

The interaction control unit133controls an interaction with the user U on the basis of the interaction data D5different from the episode in a case where the acquisition unit132has not acquired the episode data D1. That is, the interaction control unit133controls the agent apparatus10to generate the interaction data D5not including an episode on the basis of the knowledge data D10and output a voice based on the interaction data D5. The interaction data D5not including an episode is, for example, data for performing a response based on the knowledge data D10such as scenario interaction, verbalization of situation, knowledge-based interaction, and a chat response. The scenario interaction includes, for example, a response according to a scenario designed in advance. The verbalization of situation includes, for example, a response based on a corpus, a situation definition, and the like for spontaneous speaking according to the current situation. The knowledge-based interaction includes, for example, a personalized response based on the knowledge data D10. The interaction control unit133selects the scenario interaction, the verbalization of situation, the knowledge-based interaction, and the chat response according to the interaction with the user U, and generates the interaction data D5indicating the selected knowledge-based response.

In a case where the user U of the agent apparatus10is authenticated, the interaction control unit133controls the interaction with the user U on the basis of the question data D4in order to draw out an episode of the user U. For example, the interaction control unit133can select the question data D4according to the time zone. The interaction control unit133controls the agent apparatus10so as to output a voice based on the question data D4.

The generation unit134generates the episode data D1on the basis of the analysis result of the utterance data D30. For example, the generation unit134generates the episode data D1on the basis of the analysis result of the utterance data D30and the state and situation of the agent apparatus10. The generation unit134focuses on the state and situation of the agent apparatus10to generate the episode data D1on the basis of information that cannot be recognized from the analysis result of the utterance data D30.

The generation unit134generates the episode data D1on the basis of the question data D4that the interaction control unit133is asking the user U and the analysis result of the utterance data D30corresponding to the question. For example, the generation unit134generates the episode data D1by combining the question content of the question data D4and the answer of the user U. For example, in the case of the question data D4“Where are you going today?”, the generation unit134can generate the episode data D1indicating today's episode of the user on the basis of the response to the question of the user U. That is, the generation unit134can generate the episode data D1on the basis of the response drawn out from the user U by the question. The generation unit134stores the generated episode data D1in the storage unit120in association with the user U.

The collection unit135collects related information regarding the recognized user U. The related information includes, for example, information indicating a past situation or the like of the user U. The collection unit135collects, for example, information indicating a situation of the user U recognized on the basis of the sensor information of the agent apparatus10. The collection unit135collects, for example, related information indicating “Mr. Suzuki came to our home yesterday”. The collection unit135collects, for example, related information indicating “Mother was watching TV on the weekend”. The collection unit135can collect, for example, the schedule of the user U, information of the user U on a social network, and the like as the related information. The collection unit135supplies the collected related information to the generation unit134.

The generation unit134generates the episode data D1of the user U on the basis of the collected related information and stores the episode data D1in the storage unit120. For example, the generation unit134analyzes the related information and generates the episode data D1on the basis of information of a person, an animal, or the like related to the user U.

The operation control unit140controls the operation of the agent apparatus10. The operation control unit140controls the operation of the agent apparatus10on the basis of, for example, an action plan based on the situation of the user U, learning knowledge, or the like. For example, the operation control unit140performs control to move and operate the agent apparatus10having a long ellipsoidal outer shape while maintaining the standing state. The movement operation includes, for example, a forward and back motion, a turning motion, a rotation motion, and the like. The operation control unit140performs control to cause the agent apparatus10to actively execute an inducement operation that induces communication between the user U and the agent apparatus10. The inducement operation includes, for example, an action for inducing an interaction between the user U and the agent apparatus10.

Heretofore, a functional configuration example of the information processing apparatus100according to the embodiment has been described. Note that the aforementioned configuration described in conjunction withFIG.4is a mere example, and the functional configuration of the information processing apparatus100according to the embodiment is not limited to the above example. The functional configuration of the information processing apparatus100according to the embodiment can be flexibly modified depending on the specification or operation.

[Data Structure Example of the Episode Data]

FIG.5is a diagram illustrating an example of the episode data D1according to the embodiment. As illustrated inFIG.5, the information processing apparatus100has a plurality of pieces of episode data D1regarding the user U. In the example illustrated inFIG.5, a case of having six pieces of episode data D1will be described, but the number of pieces of episode data D1varies depending on the number of created and registered pieces of episode data. The episode data D1includes, for example, items such as Ep_id, When, Who, Where, Action, State, Target, Why, How, With, Cause, and the like.

In the item Ep_id, information with which it is possible to identify the episode data D1of the user U is set. In the item When, information regarding date and time, season, and the like of the episode data D1is set. In the item Who, information regarding a name, a proper noun, and the like of the episode data D1is set. In the item Where, information regarding the location of the episode data D1is set. In the item Action, information regarding a predicate of the episode data D1is set. In the item State, information regarding the state of the episode data D1is set. In the item Target and in the item Why, information regarding the cause of the episode data D1is set. In the item How, information regarding a means, a method, a procedure, and the like of the episode data D1is set. In the item With, information regarding a person, an animal, an object, and the like of the episode data D1is set. In the item Cause, information regarding the cause of the episode data D1is set. That is, in the item Cause, information for associating the pieces of episode data D1with each other is set. In the episode data D1, information is set to an item corresponding to an episode among a plurality of items, and an item not corresponding to the episode is blank.

In the example illustrated inFIG.5, the information processing apparatus100manages six pieces of episode data D1of Ep1to Ep6as the episode data D1regarding the user U. The episode data D1of the item Ep_id being Ep1is data indicating an episode “Tanaka pats the aibo at home yesterday”. The episode data D1of Ep1indicates that it is the cause of the episode data D1of Ep2because “Ep2” is set in the item Cause. The episode data D1of the item Ep_id being Ep2is data indicating an episode “The aibo is happy at home yesterday”. That is, the episode data D1of Ep1and EP2indicate the episodes that the Aibo was happy because Tanaka patted the aibo at home yesterday. The episode data D1of the item Ep_id being Ep3is data indicating an episode “Because Tanaka is hungry this morning, Tanaka eats curry quickly with Yamada”. The episode data D1of Ep3indicates that it is not related to any other episode data D1because the item Cause is blank.

The episode data D1of the item Ep_id being Ep4is data indicating an episode “Recently, Yamada runs and makes a dash for a toilet”. The episode data D1of Ep4indicates that it is the cause of the episode data D1of Ep5because “Ep5” is set in the item Cause. The episode data D1of the item Ep_id being Ep5is data indicating an episode “Yamada falls down”. That is, the episode data D1of Ep4and EP5indicate the episodes that Yamada made a dash for a toilet and fell down. The episode data D1of the item Ep_id being Ep6is data indicating a future episode “Tomorrow, everyone will get into a hot spring in Izu”. The episode data D1of Ep6indicates that it is not related to any other episode data D1because the item Cause is blank.

In the present embodiment, a case where information such as yesterday, this morning, or the like is set to the item When of the episode data D1will be described in order to simplify the description, but date and time information may be set. In this case, it is sufficient if the information processing apparatus100replaces the date and time with yesterday, the day before yesterday, tomorrow, or the like when creating the interaction data D5on the basis of the episode data D1.

The aforementioned configuration described in conjunction withFIG.5is a mere example, and the data structure of the episode data D1according to the embodiment is not limited to the above example. The data structure of the episode data D1according to the embodiment can be flexibly modified depending on the specification or operation. For example, the episode data D1may have items such as a purpose, a destination, and the like regarding the episode.

[Data Structure Example of the Template Data]

FIG.6is a diagram illustrating an example of the template data D2according to the embodiment. As illustrated inFIG.6, the template data D2includes, for example, items such as a classification, a template, or the like. The classification includes, for example, classification of episodes such as past, future, during conversation (currently recognizing), and the like. The template has data for performing an interaction based on the items of the episode data D1. The template is an example of an algorithm. In the present embodiment, a case where the information processing apparatus100generates the interaction data D5using a template will be described, but the information processing apparatus100may generate the interaction data D5using an application, a module, or the like for executing an algorithm.

In the example illustrated inFIG.6, the template data D2of the classification being past (1) indicates an algorithm“(when) (who) (where+de) (why+kara) (with)to(target) (how) (action+ta) yone”. The template data D2of the classification being past (2) indicates an algorithm“(when) (who) (where+de) (why+kara) (with)to(target) (how) (action+te) (state+ta)yone”. The template data D2of the classification being future indicates an algorithm“(when) (who) (where+de) (why+kara) (with)to(target) (how) (action end-form)ndesho”. The template data D2of the classification being during conversation indicates an algorithm“(when) (who) (where+de) (why+kara) (with)to(target) (how) (action end-form)ne”.

The information processing apparatus100generates the interaction data D5indicating an episode by setting the information of the item set in the episode data D1to the corresponding item of the algorithm of the template data D2. For example, in the case of the episode data D1of Ep1illustrated inFIG.5, the information processing apparatus100generates the interaction data D5of “Tanaka patted the aibo at home yesterday” using the template data D2of the past. The information processing apparatus100stores the template data D2corresponding to the character of the agent apparatus10in the storage unit120to enable an interaction of an episode suitable for the character. The information processing apparatus100stores the template data D2according to the language or the like of the region where the user U lives in the storage unit120to enable an interaction of an episode with a sense of affinity.

[Example of Generation of the Episode Data]

FIGS.7and8are diagrams for describing an example of generation of the episode data D1based on the utterance data D30according to the embodiment.

In the example illustrated in the left diagram ofFIG.7, the dependency structure of “yesterday”, “Taro”, “at home”, “because it was cold”, “heater”, “immediately”, and “turned on” in the utterance data30is analyzed by syntactic analysis. Regarding “yesterday”, the phrase is a noun phrase, and the case is a time case. Regarding “Taro”, the phrase is a noun phrase, and the case is a nominative case. Regarding “at home”, the phrase is a noun phrase, and the case is a causal case. Regarding “because it was cold”, the phrase is an adjective phrase, and the case is a modifier clause of an inflectable word. Regarding “heater”, the phrase is a noun phrase, and the case is an objective case. Regarding “immediately”, the phrase is an adverbial phrase, and the case is a modifier clause of an inflectable word. Regarding “turned on”, the phrase is a verbal phrase and the case is a predicate clause. In this case, the information processing apparatus100generates the episode data D1illustrated in the right diagram ofFIG.7on the basis of the analysis result of the utterance data D30, and stores the episode data D1in the storage unit120as the episode data D1of the user U. In the episode data D1, “yesterday” is set to the item When, “Taro” is set to the item Who, “turn on” is set to the item Action, “heater” is set to the item Target, “because it is cold” is set to the item Why, and “immediately” is set to the item How, and the other items are blank.

In the example illustrated in the left diagram ofFIG.8, the dependency structure of “last weekend”, “with family”, “zoo”, and “went” in the utterance data D30is analyzed by syntactic analysis. Regarding “last weekend”, the phrase is a noun phrase, and the case is a time case. Regarding “with family”, the phrase is a noun phrase, and the case is a causal case. Regarding “zoo”, the phrase is a noun phrase, and the case is an objective case. Regarding “went”, the phrase is a verbal phrase and the case is a predicate clause. In this case, the information processing apparatus100generates the episode data D1illustrated in the right diagram ofFIG.8on the basis of the analysis result of the utterance data D30, and stores the episode data D1in the storage unit120as the episode data D1of the user U. In the episode data D1, “last weekend” is set to the item When, “speaker” is set to the item Who, “go” is set to the item Action, and “zoo” is set to the item Target, and the other items are blank. In a case where the nominative case is not included in the utterance data D30, the speaker of the agent apparatus10is set to the item Who of the episode data D1.

[Processing Procedure Example (1) of the Information Processing Apparatus According to the Embodiment]

FIG.9is a flowchart illustrating processing procedure example (1) of the information processing apparatus100according to the embodiment. The processing procedure illustrated inFIG.9is realized by the control unit130of the information processing apparatus100executing a program. The processing procedure illustrated inFIG.9is repeatedly executed in a case where the interaction of the agent apparatus10is controlled by the information processing apparatus100. That is, the processing procedure illustrated inFIG.9is repeatedly executed by the information processing apparatus100, for example, during the interaction with the user U.

As illustrated inFIG.9, the control unit130of the information processing apparatus100extracts topic information from the analysis result of the utterance data D30(step S101). For example, the control unit130extracts, as the topic information, a word, a character string, or the like that satisfies extraction conditions such as when, where, who, what is done, in what state, to what, what, with whom, why, how, a purpose, and a result thereof. After storing the extraction result in the storage unit120, the control unit130proceeds the processing to step S102.

The control unit130acquires the episode data D1regarding the topic information from the storage unit120(step S102). For example, the control unit130searches for the episode data D1having the topic information and acquires the episode data D1satisfying the interaction condition from the storage unit120. The interaction condition includes, for example, that the number, ratio, and the like of matching between the words included in the episode data D1and the topic information are greater than or equal to a preset threshold value. In a case where the plurality of pieces of episode data D1is acquired, the control unit130uses the episode data D1having the highest degree of matching or uses the episode data D1not used in the past interaction. After storing the acquisition result indicating whether or not the episode data D1has been acquired in the storage unit120, the control unit130proceeds the processing to step S103.

The control unit130determines whether or not the episode data D1has been acquired on the basis of the acquisition result of the storage unit120(step S103). In a case where it is determined that the episode data D1has been acquired (Yes in step S103), the control unit130proceeds the processing to step S104. The control unit130generates the interaction data D5on the basis of the episode data D1and the template data D2(step S104). For example, the control unit130acquires the template data D2of the classification corresponding to the information of the item when of the episode data D1from the storage unit120. In a case where the item when indicates the past, the control unit130acquires the template data D2of the classification being the past. The control unit130generates the interaction data D5by setting the information set in the item of the episode data D1to the template of the acquired template data D2. After storing the generated interaction data D5in the storage unit120, the control unit130proceeds the processing to step S106described later.

Furthermore, in a case where it is determined that the episode data D1has not been acquired (No in step S103), the control unit130proceeds the processing to step S105. The control unit130generates the interaction data D5different from the episode (step S105). For example, the control unit130selects one of the scenario interaction, the verbalization of situation, the knowledge-based interaction, and the chat on the basis of the history of previous interactions with the user U, and generates the interaction data D5. In a case where the scenario interaction is selected, the control unit130generates the interaction data D5based on the scenario corresponding to the utterance content of the user U. In a case where the verbalization of situation is selected, the control unit130generates the interaction data D5corresponding to the current situation recognized via the agent apparatus10. In a case where the knowledge-based interaction is selected, the control unit130generates the interaction data D5based on the utterance content of the user U and the knowledge data D10. In a case where the chat is selected, the control unit130generates the interaction data D5responding to the utterance content of the user U. After storing the generated interaction data D5in the storage unit120, the control unit130proceeds the processing to step S106.

The control unit130controls the interaction with the speaker on the basis of the generated interaction data D5(step S106). For example, the control unit130instructs the agent apparatus10to perform an interaction based on the interaction data D5via the communication unit110. Therefore, the agent apparatus10realizes an interaction with the user U by making an utterance based on the instructed interaction data D5. When the processing of step S106ends, the control unit130ends the processing procedure illustrated inFIG.9.

In the processing procedure illustrated inFIG.9, the control unit130functions as the acquisition unit132by executing step S102. The control unit130functions as the interaction control unit133by executing the processing from step S103to step S106.

As described above, when acquiring the episode data D1regarding the topic information included in the utterance data D30of the user U, the information processing apparatus100can realize an interaction including the episode of the speaker based on the episode data D1. The information processing apparatus100can provide the episode based on the episode data D1of the user U in the interaction with the user U. As a result, the information processing apparatus100can realize an interaction with which the user U has a sense of affinity by including the episode of the user U in the interaction.

[Processing Procedure Example (2) of the Information Processing Apparatus According to the Embodiment]

FIG.10is a flowchart illustrating processing procedure example (2) of the information processing apparatus100according to the embodiment. The processing procedure illustrated inFIG.10is realized by the control unit130of the information processing apparatus100executing a program. The processing procedure illustrated inFIG.10is executed by the information processing apparatus100in order to spontaneously interact with the user U in a case where the user U (speaker) of the agent apparatus10is recognized and the interaction with the user U is not performed.

As illustrated inFIG.10, the control unit130of the information processing apparatus100determines whether or not the speaker of the agent apparatus10has been recognized (step S201). For example, the control unit130acquires the sensor information of the agent apparatus10via the communication unit110, and performs processing of recognizing the face, voiceprint, and the like of the speaker on the basis of the sensor information and the user data D3associated with the agent apparatus10. In a case where it is determined that the speaker is not recognized on the basis of the result of the recognition processing (No in step S201), the control unit130ends the processing procedure illustrated inFIG.10.

In a case where it is determined that the speaker is recognized on the basis of the result of the recognition processing (Yes in step S201), the control unit130proceeds the processing to step S202. The control unit130acquires the episode data D1satisfying the acquisition condition from the storage unit120(step S202). The acquisition condition includes, for example, a condition for acquiring the latest episode of the speaker, an episode according to a time zone, or the like. For example, in a case where the acquisition condition is a condition for acquiring the latest episode, the control unit130acquires the newest episode data D1from the storage unit120from the storage unit120. For example, in a case where the acquisition condition is a condition for acquiring an episode according to a time zone, the control unit130refers to the item When of the episode data D1and the like, and acquires the episode data D1suitable for the time zone from the storage unit120. For example, in a case where the time zone is morning, the control unit130acquires the episode data D1indicating a schedule of today, an event of yesterday, and the like from the storage unit120. For example, in a case where the time zone is daytime, the control unit130acquires the episode data D1indicating a schedule of afternoon, a schedule of night, and the like from the storage unit120. For example, in a case where the time zone is night, the control unit130acquires the episode data D1indicating an event of today, a schedule of tomorrow, and the like from the storage unit120. When the processing of step S202ends, the control unit130proceeds the processing to step S203.

The control unit130generates the interaction data D5on the basis of the episode data D1and the template data D2(step S203). For example, the control unit130acquires the template data D2of the classification corresponding to the information of the item when of the episode data D1from the storage unit120. The control unit130generates the interaction data D5for the agent apparatus10to spontaneously make an utterance by setting the information set in the item of the episode data D1to the template of the acquired template data D2. After storing the generated interaction data D5in the storage unit120, the control unit130proceeds the processing to step S204.

The control unit130controls the interaction with the speaker on the basis of the generated interaction data D5(step S204). For example, the control unit130instructs the agent apparatus10to perform an interaction based on the interaction data D5via the communication unit110. Therefore, the agent apparatus10spontaneously performs an interaction with the user U by making an utterance based on the instructed interaction data D5. When the processing of step S204ends, the control unit130ends the processing procedure illustrated inFIG.10.

In the processing procedure illustrated inFIG.10, the control unit130functions as the acquisition unit132by executing step S202. The control unit130functions as the interaction control unit133by executing the processing from step S203to step S204.

As described above, the information processing apparatus100can acquire the episode data D1satisfying the acquisition condition regardless of the utterance data D30of the speaker and spontaneously provide the episode based on the episode data D1. Furthermore, since the information processing apparatus100can acquire various episode data D1depending on the acquisition condition, it is possible to spontaneously provide various episodes. As a result, since the information processing apparatus100can provide the episode of the user U even in a state of not interacting with the user U, it is possible to give the user U a sense of affinity.

[Data Structure Example of the Question Data]

FIG.11is a diagram illustrating an example of the question data D4according to the embodiment. As illustrated inFIG.11, the question data D4includes items such as, for example, time zone, question content, or the like. The time zone is, for example, a time zone in which a question is made, such as morning, daytime, or night. The question content includes data for asking a question according to the time zone.

In the example illustrated inFIG.11, the question data D4of the time zone being morning has question contents such as “Where are you going today?”, “What's your plan for today?”, “What do you do today?”, “What did you do yesterday?”, “Where did you go yesterday?”, and the like. The question data D4of the time zone being daytime has question contents such as “What do you eat at night?”, “What are you doing this afternoon?”, and the like. The question data D4of the time zone being night has question contents such as “What will you do tomorrow?”, “What did you do today?”, and the like.

[Processing Procedure of the Information Processing Apparatus Regarding Generation of the Episode Data Using Question]

FIG.12is a flowchart illustrating a processing procedure of the information processing apparatus100regarding generation of the episode data using a question. The processing procedure illustrated inFIG.12is realized by the control unit130of the information processing apparatus100executing a program. The processing procedure illustrated inFIG.12is executed by the information processing apparatus100in a case where the user U (speaker) of the agent apparatus10is recognized.

As illustrated inFIG.12, the control unit130of the information processing apparatus100determines whether or not the speaker of the agent apparatus10has been recognized (step S301). In a case where it is determined that the speaker is not recognized (No in step S301), the control unit130ends the processing procedure illustrated inFIG.12. Furthermore, in a case where it is determined that the speaker is recognized (Yes in step S301), the control unit130proceeds the processing to step S302.

The control unit130controls the interaction with the speaker on the basis of the question data D4(step S302). For example, the control unit130acquires the question data D4corresponding to the current time zone from the storage unit120, and instructs the agent apparatus10to perform an interaction based on the question data via the communication unit110. Therefore, the agent apparatus10utters a question sentence to the user U by making an utterance based on the instructed interaction data D5. When the processing of step S302ends, the control unit130proceeds the processing to step S303.

The control unit130acquires an analysis result of the utterance data D30corresponding to the question data D4(step S303). For example, the control unit130acquires an analysis result of the utterance data D30after instructing the interaction based on the question data D4. After acquiring the analysis result of the utterance data D30, the control unit130proceeds the processing to step S304.

The control unit130generates the episode data D1on the basis of the analysis result of the utterance data D30(step S304). For example, the control unit130generates the episode data D1on the basis of the analysis result of the utterance data D30as illustrated inFIGS.7and8. For example, in a case where the analysis result of the utterance data D30includes information regarding an episode, the control unit130generates the episode data D1. For example, in a case where the analysis result of the utterance data D30does not include information for generating an episode, the control unit130does not generate the episode data D1. The information for generating an episode includes, for example, information corresponding to the items such as Action, When, and the like. Returning toFIG.12, the control unit130functions as the generation unit134by executing step S304. When the processing of step S304ends, the control unit130proceeds the processing to step S305.

The control unit130determines whether or not the episode data D1has been generated in step S304(step S305). In a case where it is determined that the episode data D1is not generated (No in step S305), the control unit130ends the processing procedure illustrated inFIG.12. Furthermore, in a case where it is determined that the episode data D1has been generated (Yes in step S305), the control unit130proceeds the processing to step S306. The control unit130stores the generated episode data D1in the storage unit120in association with the speaker (step S306). For example, the control unit130stores the episode data D1in the storage unit120in association with the user data D3of the recognized speaker. When the processing of step S306ends, the control unit130ends the processing procedure illustrated inFIG.12.

[Example of Generation of the Episode Data Using Question]

FIG.13is a diagram illustrating an example of generation of the episode data using a question of the information processing apparatus100according to the embodiment.

In the example illustrated inFIG.13, the agent apparatus10utters an utterance C21instructed by the information processing apparatus100and based on the question data D4when the user U is recognized. The utterance C21is, for example, “Where are you going today?”. The user U utters an utterance C22in response to the utterance C21. The utterance C22is, for example, “A picnic with friends.”. The agent apparatus10transmits the utterance data of the utterance C22to the information processing apparatus100.

The information processing apparatus100analyzes the utterance data D30of the utterance C22and extracts topic information “friends” and “picnic”. Furthermore, the question sentence of the utterance C21includes topic information of “today” and “go”. In this case, the information processing apparatus100generates the episode data D1illustrated in the lower right ofFIG.13on the basis of the analysis result of the utterance data D30and the question data D4, and stores the episode data D1in the storage unit120as the episode data D1of the user U. In the episode data D1, “today” is set to the item When, “speaker” is set to the item Who, “go” is set to the item Action, “picnic” is set to the item Target, and “friends” is set to the item With, and the other items are blank.

In this manner, the information processing apparatus100can ask the user U a question on the basis of the question data D4and generate the episode data D1from the analysis result of the utterance data D30and the question data D4according to the question, so that it is possible to construct various episode data D1of the user U. As a result, the information processing apparatus100can maintain the freshness of the episode provided to the user U by enriching the episodes that can be provided to the user U.

[Processing Procedure of the Information Processing Apparatus Regarding Generation of the Episode Data Using Related Information]

FIG.14is a flowchart illustrating a processing procedure regarding generation of episode data based on related information of the information processing apparatus100. The processing procedure illustrated inFIG.14is realized by the control unit130of the information processing apparatus100executing a program. The processing procedure illustrated inFIG.14is executed by the information processing apparatus100in a case where the user U (speaker) of the agent apparatus10is recognized.

As illustrated inFIG.14, the control unit130of the information processing apparatus100collects related information regarding the user U via the communication unit110(step S401). The related information includes, for example, sensor information of the agent apparatus10indicating the situation of the user U, or the like. The related information is information for speaking the past situation of the user U as an episode. The related information includes, for example, information indicating that a friend of the user U has come, information indicating that a parent, a relative, or the like has watched television on a weekend, and the like. The related information may include, for example, information such as a schedule of the user U, a social networking service (SNS), or the like. After storing the collected related information in the storage unit120, the control unit130proceeds the processing to step S402.

The control unit130generates the episode data D1on the basis of the collected related information (step S402). For example, the control unit130analyzes the related information and generates the episode data D1indicating the past situation on the basis of the analysis result. For example, in a case where the related information is information indicating that “A friend came yesterday”, the control unit130generates the episode data D1in which “yesterday” is set to the item When, “friend” is set to the item Who, “come” is set to the item Action, “home” is set to the item Where, and the other items are blank. When the processing of step S402ends, the control unit130proceeds the processing to step S403.

The control unit130determines whether or not the episode data D1has been generated in step S402(step S403). In a case where it is determined that the episode data D1is not generated (No in step S403), the control unit130ends the processing procedure illustrated inFIG.14. Furthermore, in a case where it is determined that the episode data D1has been generated (Yes in step S403), the control unit130proceeds the processing to step S404. The control unit130stores the generated episode data D1in the storage unit120in association with the speaker (step S404). When the processing of step S404ends, the control unit130ends the processing procedure illustrated inFIG.14.

[Example of Use of the Episode Data Based on the Related Information]

FIG.15is a diagram illustrating an example of use of the episode data based on the related information of the information processing apparatus100according to the embodiment.

In the example illustrated inFIG.15, the information processing apparatus100generates the episode data D1illustrated in the lower right ofFIG.15on the basis of the collected related information, and stores the episode data D1in the storage unit120as the episode data D1of the user U. In the episode data D1, “yesterday” is set to the item When, “friend” is set to the item Who, “home” is set to the item Where, and “come” is set to the item Action, and the other items are blank.

When recognizing the user U via the agent apparatus10, the information processing apparatus100generates the interaction data D5on the basis of the episode data D1ofFIG.15indicating the past situation of the user U and the template data D2. The information processing apparatus100instructs the agent apparatus10to utter based on the generated interaction data D5. The agent apparatus10utters an utterance S31based on the interaction data D5. The utterance C31is, for example, “A friend came to our home yesterday.”. The user U utters an utterance C32in response to the utterance C31. The utterance C32is, for example, “That's right.”.

In this manner, since the information processing apparatus100can collect the related information of the user U and generate the episode data D1on the basis of the related information, it is possible to construct the episode data D1according to the past situation of the user U. As a result, the information processing apparatus100can maintain the freshness of the episode provided to the user U by further enriching the episodes that can be provided to the user U.

[Episode Data Generalization Function of the Information Processing Apparatus]

The information processing apparatus100further includes a function of generating generalized episode data D1on the basis of common data of the plurality of pieces of episode data D1.FIG.16is a diagram for describing generalization of the plurality of pieces of episode data D1. A illustrated inFIG.16, the information processing apparatus100has episode data D1of the item Ep_id being Ep11, Ep12, and Ep13as the episode data D1of the user U. The episode data D1of Ep11is data indicating an episode “Yamada goes to the company by taxi on September 1st”. The episode data D1of Ep12is data indicating an episode “Yamada goes to the company with a subordinate because they ran into each other on September 2nd”. The episode data D1of Ep13is data indicating an episode “Yamada goes to the company late on September 3rd”.

In this case, the information of the items Who, Where, and Action in the episode data D1of Ep11, Ep12, and Ep13are coincident. When the control unit130of the information processing apparatus100detects a group of episode data D1in which the number of matching items is equal to or larger than a preset threshold value from among the plurality of pieces of episode data D1, the control unit130performs generalization of the episode data D1. In the example illustrated inFIG.16, the information processing apparatus100generates the episode data D1obtained by generalizing the three items Who, Where, and Action as the item Ep_id of Ep20. That is, in the episode data D1of Ep20, “Yamada” is set to the item Who, “company” is set to the item Where, and “go” is set to the item Action, and the other items are blank.

As described above, in a case where there is a plurality of pieces of similar episode data D1, the information processing apparatus100can generate the episode data D1in which the behavioral tendency of the user U is generalized. As a result, the information processing apparatus100can realize an interaction with the user U based on the generalized episode using the generalized episode data D1. For example, in a case where the generalized episode data D1of Ep20is generated, the information processing apparatus100can realize an interaction based on the interaction data D5such as “Are you going to the company today?”, “Are you not going to the company today?”, “How was the company today?”, or the like.

[Hardware Configuration]

An information device of the information processing system1according to the above-described embodiment is realized by, for example, a computer1000having a configuration as illustrated inFIG.17. Next, the information processing apparatus100according to the embodiment is described as an example.FIG.17is a hardware configuration diagram illustrating an example of the computer1000that realizes the functions of the information processing apparatus100. The computer1000includes a CPU1100, RAM1200, read only memory (ROM)1300, a hard disk drive (HDD)1400, a communication interface1500, and an input/output interface1600. Each unit of the computer1000is connected by a bus1050.

The CPU1100operates on the basis of a program stored in the ROM1300or the HDD1400, and controls each unit. For example, the CPU1100loads a program stored in the ROM1300or the HDD1400onto the RAM1200, and executes processing corresponding to various programs.

The ROM1300stores a boot program such as a basic input output system (BIOS) executed by the CPU1100when the computer1000is activated, a program depending on hardware of the computer1000, and the like.

The HDD1400is a computer-readable recording medium that non-transitory records a program executed by the CPU1100, data used by the program, and the like. Specifically, the HDD1400is a recording medium that records an information processing program according to the present disclosure, which is an example of program data1450.

The communication interface1500is an interface for the computer1000to connect to an external network1550(for example, the Internet). For example, the CPU1100receives data from another device or transmits data generated by the CPU1100to another device via the communication interface1500.

The input/output interface1600is an interface for connecting an input/output device1650and the computer1000. For example, the CPU1100receives data from the input device such as a keyboard and a mouse via the input/output interface1600. Furthermore, the CPU1100transmits data to the output device such as a display, a speaker, or a printer via the input/output interface1600. Furthermore, the input/output interface1600may function as a media interface that reads a program or the like recorded in a predetermined recording medium (media). The media is, for example, an optical recording medium such as a digital versatile disc (DVD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, semiconductor memory, or the like.

For example, in a case where the computer1000functions as the information processing apparatus100according to the embodiment, the CPU1100of the computer1000executes the information processing program loaded on the RAM1200to realize the functions of the recognition unit131, the acquisition unit132, the interaction control unit133, the generation unit134, the collection unit135, the operation control unit140, and the like. Furthermore, the HDD1400stores the information processing program according to the present disclosure and data in the storage unit120. Note that the CPU1100reads the program data1450from the HDD1400and executes the program data, but as another example, the program may be acquired from another apparatus via the external network1550.

In the present embodiment described above, the case where the agent apparatus10and the information processing apparatus100cooperate with each other to execute the interaction processing in the information processing system1has been described, but it is not limited thereto. For example, in the information processing system1, the agent apparatus10may independently execute the interaction processing. In this case, in the agent apparatus10, it is sufficient if the acquisition unit132, the interaction control unit133, the generation unit134, the collection unit135, the operation control unit140, and the like of the information processing apparatus100are realized by the control unit16.

The preferred embodiment of the present disclosure has been described above with reference to the accompanying drawings, while the technical scope of the present disclosure is not limited to the above examples. It is apparent that a person having normal knowledge in the technical field of the present disclosure may find various alterations and modifications within the scope of the technical idea stated in the claims, and it should be understood that they will naturally come under the technical scope of the present disclosure.

Furthermore, the effects described in the present specification are merely illustrative or exemplified effects, and are not limitative. That is, with or in the place of the above effects, the technology according to the present disclosure may achieve other effects that are clear to those skilled in the art from the description of the present specification.

Furthermore, it is also possible to create a program for causing the hardware such as the CPU, the ROM, and the RAM built in the computer to exhibit a function equivalent to the configuration of the information processing apparatus100, and a computer-readable recording medium recording the program can also be provided.

Furthermore, each step of the processing of the information processing apparatus100of the present specification is not necessarily needed to be processed in chronological order along the order described in the flowcharts. For example, each step of the processing of the information processing apparatus100may be processed in an order that differs from the order described in the flowcharts or may be processed in parallel.

The case where the information processing apparatus100of the present specification provides the episode data D1by interaction (voice) with the user U via the agent apparatus10has been described, but it is not limited thereto. For example, the information processing apparatus100may be configured to provide the episode data D1via the display apparatus or provide the episode data D1by a combination of display and voice output.

(Effects)

The information processing apparatus100includes the acquisition unit132that acquires, from the storage unit120that stores the episode data D1of the speaker, the episode data D1regarding the topic information included in the utterance data D30of the speaker, and the interaction control unit133that controls an interaction with the speaker so as to include an episode based on the episode data D1.

Therefore, when acquiring the episode data D1regarding the topic information included in the utterance data D30of the speaker, the information processing apparatus100can realize an interaction including the episode of the speaker based on the episode data D1. The information processing apparatus100can provide the episode based on the episode data D1of the speaker in the interaction with the speaker. As a result, the information processing apparatus100can realize an interaction with which the speaker has a sense of affinity by including the episode of the speaker in the interaction.

In the information processing apparatus100, the interaction control unit133generates the interaction data D5including an episode on the basis of the episode data D1and an algorithm for generating an interactive sentence, and controls the interaction on the basis of the interaction data D5.

Therefore, the information processing apparatus100can generate the interaction data D5including the episode of the speaker on the basis of the episode data D1and the algorithm, so that the configuration of the episode data D1can be simplified. As a result, since the information processing apparatus100can construct various episode data D1of the speaker, it is possible to include various episodes in the interaction with the speaker, and it is possible to realize an interaction with which the speaker has a further sense of affinity.

In the information processing apparatus100, the algorithm includes a template according to the classification of the episode, and the interaction control unit133sets the data of the episode data D1to the template and generates the interaction data D5.

Therefore, the information processing apparatus100can generate the interaction data D5by setting the data of the episode data D1to the template, so that the processing of generating the episode data D1can be simplified. Furthermore, the information processing apparatus100can acquire the episode data D1suitable for the utterance content of the speaker by classifying the episode data D1. As a result, since the episode suitable for the utterance content of the speaker can be included in the interaction, the information processing apparatus100can realize an interaction with which the speaker has a further sense of affinity.

In the information processing apparatus100, the acquisition unit132acquires the episode data D1in which the topic information satisfies the interaction condition.

Therefore, the information processing apparatus100can acquire the episode data D1suitable for the topic information according to the interaction condition and generate the interaction data D5based on the episode data D1. As a result, since the episode satisfying the interaction condition can be included in the interaction, the information processing apparatus100can realize an interaction with which the speaker has a further sense of affinity.

In the information processing apparatus100, in a case where the speaker is recognized, the acquisition unit132acquires the episode data D1satisfying the acquisition condition, and the interaction control unit133controls the interaction with the speaker so as to spontaneously utter the episode indicated by the acquired episode data D1.

Therefore, the information processing apparatus100can acquire the episode data D1satisfying the acquisition condition regardless of the utterance of the speaker and spontaneously provide the episode based on the episode data D1. Furthermore, since the information processing apparatus100can acquire various episode data D1depending on the acquisition condition, it is possible to spontaneously provide various episodes. As a result, since the information processing apparatus100can provide the episode of the speaker even in a state of not interacting with the speaker, it is possible to give the speaker a sense of affinity.

In the information processing apparatus100, the interaction control unit133controls an interaction with the speaker on the basis of the interaction data D5different from the episode in a case where the acquisition unit132has not acquired the episode data D1.

Therefore, when not acquiring the episode data D1suitable for the utterance content of the speaker, the information processing apparatus100can realize the interaction based on the interaction data D5not including the episode. As a result, in a case where there is no episode suitable for the utterance content of the speaker, the information processing apparatus100can suppress interruption of the interaction with the speaker by performing an interaction different from the episode.

The information processing apparatus100further include the generation unit134that generates the episode data D1on the basis of the analysis result of the utterance data D30and the storage unit120that stores the episode data D1in association with the speaker.

Therefore, when generating the episode data D1on the basis of the analysis result of the utterance data D30of the speaker, the information processing apparatus100can store the episode data D1in association with the speaker in the storage unit120. As a result, the information processing apparatus100can enrich the episodes to be included in the interaction with the speaker by constructing the episode data D1according to the utterance content of the speaker, and thus, it is possible to realize an interaction with which the speaker has a further sense of affinity.

In the information processing apparatus100, the interaction control unit133controls the interaction with the speaker on the basis of the question data D4for asking the speaker a question, and the generation unit134generates the episode data D1on the basis of the analysis result of the utterance data D30corresponding to the question data D4and the question data D4.

Therefore, the information processing apparatus100can ask the speaker a question on the basis of the question data D4and generate the episode data D1from the analysis result of the utterance data D30and the question data D4according to the question, so that it is possible to construct various episode data D1of the speaker. As a result, the information processing apparatus100can maintain the freshness of the episode provided to the speaker by enriching the episodes that can be provided to the speaker.

The information processing apparatus100further includes the collection unit135that collects the related information regarding the past situation of the speaker, and the generation unit134generates the episode data D1on the basis of the related information.

Therefore, since the information processing apparatus100can collect the related information of the speaker and generate the episode data D1on the basis of the related information, it is possible to construct the episode data D1according to the past situation of the speaker. As a result, the information processing apparatus100can maintain the freshness of the episode provided to the speaker by further enriching the episodes that can be provided to the speaker.

In the information processing apparatus100, the generation unit134generates generalized episode data d1on the basis of common data of the episode data D1of the storage unit120.

Therefore, when data common to the episode data D1of the speaker is included, the information processing apparatus100can generate the episode data D1obtained by generalizing the common data. As a result, since the information processing apparatus100can realize an interaction including generalized episodes of the speaker, it is possible to provide an interaction with a sense of affinity without interrupting the interaction with the speaker.

The information processing system1is an information processing system1including the agent apparatus10that collects the utterance data D30of the speaker and the information processing apparatus100, in which the information processing apparatus100includes the acquisition unit132that acquires, from the storage unit120that stores the episode data D1of the speaker, the episode data D1regarding the topic information included in the utterance data D30, and the interaction control unit133that controls an interaction with the speaker so as to include an episode based on the episode data D1.

Therefore, when acquiring the episode data D1regarding the topic information included in the utterance data D30of the speaker, the information processing system1can realize an interaction including the episode of the speaker based on the episode data D1. The information processing system1can provide the episode based on the episode data D1of the speaker in the interaction with the speaker. As a result, the information processing system1can realize an interaction with which the speaker has a sense of affinity by including the episode of the speaker in the interaction.

In the information processing system1, the agent apparatus10is a movable robot, and the interaction control unit133controls an interaction with the speaker via the agent apparatus10.

Therefore, the information processing system1can collect the utterance data D30of the speaker via the agent apparatus10and realize an interaction including an episode of the speaker based on the episode data D1. As a result, the information processing system1can realize an interaction with a sense of affinity between the speaker and the robot by including the episode of the speaker in the interaction with the movable robot.

The information processing method includes, by a computer, acquiring, from the storage unit120that stores the episode data D1of the speaker, the episode data D1regarding the topic information included in the utterance data D30of the speaker, and controlling an interaction with the speaker so as to include an episode based on the episode data D1.

Therefore, when acquiring the episode data D1regarding the topic information included in the utterance data D30of the speaker, the information processing method can realize, by a computer, an interaction including the episode of the speaker based on the episode data D1. The information processing method can provide the episode based on the episode data D1of the speaker in the interaction with the speaker. As a result, the information processing method can realize, by a computer, an interaction with which the speaker has a sense of affinity by including the episode of the speaker in the interaction.

Note that the configuration below also falls within the technical scope of the present disclosure.

(1)

An information processing apparatus including:an acquisition unit that acquires, from a storage unit that stores episode data of a speaker, the episode data regarding topic information included in utterance data of the speaker; andan interaction control unit that controls an interaction with the speaker so as to include an episode based on the episode data.
(2)

The information processing apparatus according to (1), in whichthe interaction control unit generates interaction data including the episode on the basis of the episode data and an algorithm for generating an interactive sentence, and controls the interaction on the basis of the interaction data.
(3)

The information processing apparatus according to (2), in whichthe algorithm includes a template according to a classification of the episode, andthe interaction control unit sets data of the episode data to the template and generates the interaction data.
(4)

The information processing apparatus according to any of (1) to (3), in whichthe acquisition unit acquires the episode data in which the topic information satisfies an interaction condition.
(5)

The information processing apparatus according to (4), in whichin a case where the speaker is recognized, the acquisition unit acquires the episode data satisfying an acquisition condition, andthe interaction control unit controls an interaction with the speaker so as to spontaneously utter the episode indicated by the acquired episode data.
(6)

The information processing apparatus according to any of (1) to (5), in whichthe interaction control unit controls an interaction with the speaker on the basis of interaction data different from the episode in a case where the acquisition unit has not acquired the episode data.
(7)

The information processing apparatus according to any of (1) to (6), further including:a generation unit that generates the episode data on the basis of an analysis result of the utterance data; anda storage unit that stores the episode data in association with the speaker.
(8)

The information processing apparatus according to (7), in whichthe interaction control unit controls an interaction with the speaker on the basis of question data for asking the speaker a question, andthe generation unit generates the episode data on the basis of the analysis result of the utterance data corresponding to the question data and the question data.
(9)

The information processing apparatus according to (8), further including:a collection unit that collects related information regarding a past situation of the speaker, in whichthe generation unit generates the episode data on the basis of the related information.
(10)

The information processing apparatus according to (8) or (9), in whichthe generation unit generates the episode data generalized on the basis of common data of the episode data of the storage unit.
(11)

An information processing system including an agent apparatus that collects utterance data of a speaker and an information processing apparatus, the information processing apparatus including:an acquisition unit that acquires, from a storage unit that stores episode data of the speaker, the episode data regarding topic information included in the utterance data; andan interaction control unit that controls an interaction with the speaker so as to include an episode based on the episode data.
(12)

The information processing system according to (11), in whichthe agent apparatus is a movable robot, andthe interaction control unit controls an interaction with the speaker via the agent apparatus.
(13)

An information processing method including, by a computer:acquiring, from a storage unit that stores episode data of a speaker, the episode data regarding topic information included in utterance data of the speaker; andcontrolling an interaction with the speaker so as to include an episode based on the episode data.
(14)

A computer-readable recording medium recording a program for causing a computer to realize:acquiring, from a storage unit that stores episode data of a speaker, the episode data regarding topic information included in utterance data of the speaker; andcontrolling an interaction with the speaker so as to include an episode based on the episode data.
(15)

A program for causing a computer to realize:acquiring, from a storage unit that stores episode data of a speaker, the episode data regarding to topic information included in utterance data of the speaker; andcontrolling an interaction with the speaker so as to include an episode based on the episode data.

REFERENCE SIGNS LIST

1Information processing system10Agent apparatus11Sensor unit12Input unit13Light source14Output unit15Drive unit16Control unit17Communication unit100Information processing apparatus110Communication unit120Storage unit130Control unit131Recognition unit132Acquisition unit133Interaction control unit134Generation unit135Collection unit140Operation control unitD1Episode dataD2Template dataD3User dataD4Question dataD5Interaction dataD10Knowledge dataD20Management dataD30Utterance data