Patent ID: 12203764

DESCRIPTION OF EMBODIMENTS

Some embodiments according to the present invention are described below with reference to the drawings. The same elements illustrated in the drawings are denoted by the same reference numerals, and overlapping explanations are not made below.

Entire Outline

The entire outline of the present embodiment is described below with reference toFIG.1. As illustrated inFIG.1, a vehicle40communicates with a computer20via a communication network30.

The computer20includes a central processing unit (CPU)21, a memory22, a communication I/F23, and a storage24, which are electrically connected to each other via buses (not illustrated). An installed position of the computer20can be any location determined as appropriate.

The CPU21reads, into the memory22, various kinds of programs stored in the storage24, for example, and executes various types of instructions included in the programs. The memory22is a storage medium such as a read-only memory (ROM) and a random-access memory (RAM). The storage24is a storage medium such as a hard disk drive (HDD). The functions of the computer20may be provided through applications such as software as a service (SaaS) provided on the communication network30. The computer20may be a server instead.

The communication I/F23is implemented by hardware such as a network adopter, various kinds of software for communication, or a combination thereof, and is configured so as to execute wired or wireless communication via the communication network30and the like.

The communication network30may be configured in either a wireless or wired mode or in both wireless and wired modes. The communication network30may also include the Internet. The computer20and the vehicle40in the present embodiment are connected to the communication network30in a wireless communication mode.

<Configuration Example of Information Processing Device>

An example of a configuration of the information processing device100mounted on the vehicle40is described below with reference toFIG.2.

As illustrated inFIG.2, the information processing device100includes a sensor group50, a GPS receiver51, a microphone52, a storage device53, a speaker54, and a controller60.

The information processing device100may be mounted on either a vehicle equipped with an automated driving function or a vehicle without being equipped with an automated driving function. The information processing device100may be mounted on a vehicle capable of switching between automated driving and manual driving. The term “automated driving” as used in the present embodiment refers to a state in which at least any of a brake actuator, an accelerator actuator, and a steering wheel actuator is controlled without the intervention of operation of the user. The automated driving thus can include a state in which other actuators are operated by the user. The automated driving also refers to a state in which any control such as acceleration/deceleration control and lateral positioning control is executed. The term “manual driving” as used in the present embodiment refers to a state in which the user operates the brake pedal, the accelerator pedal, and the steering wheel, for example.

The sensor group50(a ride detection device) is used to detect a ride of the user in the vehicle. The sensor group50includes a pressure-sensitive sensor (also referred to as a seating-sensitive sensor) provided at a seat cushion, a camera for capturing the user in the compartment, and a sensor for detecting open and closed states of doors. The controller60uses signals obtained from the respective sensors so as to detect the ride of the user in the vehicle.

The GPS receiver51receives radio waves from an artificial satellite so as to detect positional information of the vehicle40on the ground. The positional information of the vehicle40detected by the GPS receiver51includes the latitude information and the longitude information. The GPS receiver51outputs the detected positional information of the vehicle40to the controller60. The means of detecting the positional information of the vehicle40is not limited to the GPS receiver51. For example, a means called odometry may be used to estimate the position of the vehicle40. The odometry is a means of obtaining a moving amount and a moving direction of the vehicle40in accordance with a rotation angle and an angular velocity of the vehicle40so as to estimate the position of the vehicle40.

The microphone52is used for inputting voice of the user.

The storage device53is a device different from a memory of the controller60, and is a hard disk or a solid-state drive, for example. The storage device53stores a map database53a, an action history database53b, and a question database53c.

The map database53astores map information, such as road information and facility information, necessary for a route guidance. The road information refers to information on the number of lanes on a road, boundary lines on a road, and a relation of connection of lanes. The map database53aoutputs the map information to the controller60in response to the request from the controller60. While the present embodiment is illustrated with the case in which the information processing device100includes the map database53a, the information processing device100does not necessarily include the map database53a. The map information may be acquired through vehicle-to-vehicle communications or road-to-vehicle communications. When the map information is stored in a server externally located (for example, the computer20illustrated inFIG.1), the information processing device100may acquire the map information from the server through communications as necessary. The information processing device100may regularly acquire the latest map information from the server so as to update the stored map information.

The map database53aalso stores information regarding points of interest (POIs). The term “POI” as used in the present embodiment refers to data indicating a specific point location. The POI includes at least an attribute and positional information (latitude, longitude) of the location. The attribute refers to information used for classifying POIs, such as a restaurant, a shopping mall, and a park. The POI may also include a name, an address, a phone number, and an icon of the location. The term “name” as used herein refers to, when the attribute is a restaurant, a specific name of the restaurant. The positional information of the POI may include an altitude in addition to a latitude and a longitude.

The action history database53bstores actions of the user at POIs in association with the positional information of the vehicle40or the POIs. The actions of the user at the POIs are put in database form as a history.

The question database53cstores the attribute of the respective POIs associated with questions regarding the actions of the user taken at the POIs.

The controller60is a general-purpose microcomputer including a central processing unit (CPU), a memory, and an input-output unit. A computer program is installed on the microcomputer so as to function as the information processing device100. The microcomputer functions as a plurality of information processing circuits included in the information processing device100when the computer program is executed. While the present embodiment is illustrated with the case in which the software is installed to fabricate the respective information processing circuits included in the information processing device100, dedicated hardware for executing each information processing as described below can be prepared to implement the information processing circuits. The respective information processing circuits may be composed of individual hardware. The controller60includes, as examples of the respective information processing circuits, a user identification unit61, a destination arrival determination unit62, a voice analysis unit63, an action history update unit64, an action estimation unit65, a question selection unit66, a ride detection unit67, and a question output unit68.

The speaker54is installed in the compartment of the vehicle40so as to output voice.

The user identification unit61identifies the user by use of a face image of the user captured when riding in the vehicle40. In particular, the user identification unit61determines whether the captured face image conforms to or is similar to a face image preliminarily registered in the storage device53. When the captured face image conforms to or is similar to the face image preliminarily registered in the storage device53, the user identification unit61identifies the user riding in the vehicle40as a user preliminarily registered. The face identification described above can also be executed when the ignition is OFF.

Another method of identifying the user may use an ID of an intelligence key (also referred to as a smart key). A system using the intelligence key includes an antenna that transmits radio waves and a receiver that receives the radio waves, which are provided in both the vehicle and the key. When the user presses a switch provided on a door handle or a trunk, for example, the radio waves are transmitted from the antenna of the vehicle, and the key when receiving the radio waves automatically returns the radio waves. The receiver of the vehicle then receives the radio waves, so as to lock or unlock the doors. The radio waves transmitted from the key include an identification cipher that is preliminarily registered in the vehicle. Associating the identification cipher with the user information can identify the user.

The destination arrival determination unit62determines whether the vehicle40has arrived at a destination. The term “destination” as used in the present embodiment refers to a target location set by the user through a navigation device (not illustrated). The positional information on the destination is stored in the map database53a. The destination arrival determination unit62checks the positional information of the vehicle40acquired from the GPS receiver51with the positional information of the destination stored in the map database53a, and determines that the vehicle40has arrived at the destination when both information conforms to or substantially conforms to each other.

The voice analysis unit63analyzes voice of the user input via the microphone52. The analyzing method as used herein is a conventionally-known method.

The action history update unit64stores actions that the user takes at POIs in the action history database53bin association with the positional information of the vehicle40or the POIs, and updates the action history of the user at the POIs.

The action estimation unit65estimates the action of the user taken before riding in the vehicle. The term “action of the user taken before riding in the vehicle” as used in the present embodiment refers to an action that the user takes before the user rides in the vehicle40. In particular, the action of the user taken before riding in the vehicle refers to an action that the user takes at a destination before riding in the vehicle40.

The action estimation unit65also acquires schedule data of the user from the computer20through the communication network30. The schedule data of the user includes information on an action plan of the user, such as a time “when”, a place “where”, and a thing “what”. The schedule data may be acquired from a terminal (such as a smartphone) that the user holds.

The question selection unit66refers to the action estimated by the action estimation unit65and the question database53cso as to choose a question put to the user. When the question selection unit66chooses the question, a signal indicating the chosen question is output to the question output unit68.

The ride detection unit67detects a ride of the user in the vehicle in accordance with a signal acquired from the sensor group50. In particular, the ride detection unit67detects the user riding in the vehicle when acquiring a signal indicating a change in resistance value from the pressure-sensitive sensor. The ride detection unit67also detects the user riding in the vehicle when detecting the presence of the user in the compartment through the analysis of the camera image. The ride detection unit67may detect the ride of the user when acquiring the signal indicating the change in the resistance value from the pressure-sensitive sensor after the detection of the open or closed state of the door. When the ride detection unit67detects the user riding in the vehicle, a signal indicating the detection of the ride is output to the question output unit68.

The question output unit68outputs the question chosen by the question selection unit66when receiving the signal indicating the detection of the ride of the user80. The question may be output by voice via the speaker54, or may be output by text information to a display (such as a display of the navigation device). The present embodiment is illustrated with the case in which the question is output by voice via the speaker54.

An example of a method of storing the action of the user taken at a POI is described below with reference toFIG.3.

The situation illustrated inFIG.3is a case in which the vehicle40arrives at a destination (a ramen shop90in this case) set by the user80, and is parked in a parking space at the ramen shop90. The user80gets out of the vehicle40and enters the ramen shop90. The user80then leaves the ramen shop90and rides in the vehicle40after finishing things to be done at the ramen shop90.

When the destination arrival determination unit62determines that the vehicle40has arrived at the destination, a signal indicating the determination result is output to the action estimation unit65. The action estimation unit65when receiving the signal estimates the action of the user80taken before riding in the vehicle. As described above, the action of the user80taken before riding in the vehicle refers to an action that the user80takes at the destination when the user80is out of the vehicle40. Since the destination of the user80in the case illustrated inFIG.3is the ramen shop90, the action of the user80taken before riding in the vehicle refers to the action that the user80has taken at the ramen shop90.

A method of estimating the action of the user80by the action estimation unit65is described below. The action estimation unit65first acquires the positional information of the vehicle40from the GPS receiver51. In particular, the action estimation unit65acquires the positional information of the vehicle40when the vehicle40is determined to have arrived at the destination by the destination arrival determination unit62.

The action estimation unit65checks the positional information of the vehicle40with the map database53a, and acquires the POI at the current position (the parked position of the vehicle40). In particular, as illustrated inFIG.3, the action estimation unit65acquires the positional information of the POI (the latitude and the longitude) and the attribute of the POI (the ramen shop). In the case illustrated inFIG.3, since the vehicle40is parked in the parking space at the ramen shop90, the positional information of the vehicle40is presumed to conform to the positional information of the POI. The action estimation unit65acquires the positional information of the POI that conforms to or substantially conforms to the positional information of the vehicle40. The action estimation unit65acquires the attribute of the POI associated with the positional information of the POI.

As described above, the action estimation unit65checks the positional information of the vehicle40with the map database53a, so as to recognize that the current position is the ramen shop90. The action estimation unit65then refers to table data in which the attribute of the POI is associated with the action type of the user stored in the storage device53so as to estimate the action of the user80taken before riding in the vehicle. The action type of the user as used in the present embodiment is a classification of the actions that the user could take at the POI. For example, when the attribute of the POI is a restaurant, two actions of taking a meal and having a meeting are stored in association with the restaurant as the action type of the user. The action type of the user does not necessarily include plural actions, and may include a single action. When the attribute of the POI is a ramen shop as illustrated inFIG.3, the action of taking a meal is only associated with the ramen shop as the action type of the user. The action of the user80taken before riding in the vehicle in the case illustrated inFIG.3estimated by the action estimation unit65is thus to take a meal.

A signal indicating the estimated result obtained by the action estimation unit65is output to the question election unit66. The question selection unit66when receiving the signal from the action estimation unit65refers to the action of the user80(taking a meal) taken before riding in the vehicle and the question database53cso as to choose a question put to the user80. As described above, the question database53cstores the attribute of the POI in association with the questions regarding the actions of the user at the POI. The question election unit66refers to the action of the user80(taking a meal) taken before riding in the vehicle and the question database53c, so as to choose a question regarding a meal. For example, a question with two choices that the user can answer with either YES or NO, such as “Did you have something for a meal?”, is stored as a question regarding a meal. A question asking a particular answer, such as “What did you have for a meal?”, is also stored. The question election unit66can optionally choose any question regarding a meal. When the question election unit66chooses the question, a signal indicating the chosen question is output to the question output unit68.

The timing of asking the user the question can be, but not necessarily, immediately after the user80rides in the vehicle40. The reason for this is that the probability that the user80could forget the action taken at the POI increases as the time elapsed after the user80rides in the vehicle40is longer. The ride detection unit67thus detects the ride of the user in accordance with the signal acquired from the sensor group50. When the ride detection unit67detects the ride of the user80, a signal indicating the detection result is output to the question output unit68.

The question output unit68, when receiving the signal indicating the detection of the ride of the user80, asks the user the question chosen by the question selection unit66by voice via the speaker54. The user80in this case is then asked a question, “Did you have something for a meal?”, for example.

The user80answers the question by voice via the microphone52. The voice analysis unit63analyzes the voice data of the user80. When the user80answers “YES”, the user80is determined to have taken a meal as the action taken at the ramen shop90through the voice analysis. The action history update unit64associates the positional information of the vehicle40with the action indicating that the user80has taken a meal at the ramen shop90, and stores the associated information in the action history database53b. The action history update unit64may also associate the POI with the action indicating that the user80has taken a meal at the ramen shop90and store the associated information in the action history database53b. The present embodiment thus can store the action of the user80taken at the POI.

The data stored in the action history database53bincludes the date and time when the answer of the user is input, the positional information of the vehicle40, the POI (the positional information and the attribute), and the action taken at the POI.

Another example of the method of storing the action of the user taken at a POI is described below with reference toFIG.4.

The situation illustrated inFIG.4is a case in which the vehicle40arrives at a destination (a shopping mall91in this case) set by the user80, and is parked in a parking space at the shopping mall91. The user80gets out of the vehicle40and enters the shopping mall91. The user80then leaves the shopping mall91and rides in the vehicle40after finishing things to be done at the shopping mall91.

The action estimation unit65checks the positional information of the vehicle40with the map database53a, and recognizes that the current position is the shopping mall91, in the same manner as in the case illustrated inFIG.3. The action estimation unit65then refers to the table data in which the attribute of the POI is associated with the action type of the user80, so as to estimate the action of the user80.

The shopping mall91in the present embodiment is a commercial complex occupied by a plurality of stores, restaurants, beauty salons, travel agencies, and fitness gyms, for example. The shopping mall is also referred to as a shopping center.

The type of the action of the user taken at the shopping mall91includes a plurality of actions such as taking a meal, doing shopping, waiting for someone, having a haircut, and taking exercise. To accurately estimate the action of the user80among the plural actions, the action estimation unit65refers to the schedule data of the user80acquired from the computer20. As described above, since the schedule data includes the information on the action plan of the user80, the action estimation unit65referring to the schedule data can estimate the action of the user80taken at the shopping mall91with a high accuracy. The schedule data in this case is assumed to include the information “taking a meal at the shopping mall91” as the action plan of the user80, for example. The action estimation unit65then refers to the schedule data so as to estimate that the action of the user80taken at the shopping mall91is to take a meal.

The question selection unit66when receiving the signal from the action estimation unit65refers to the action of the user80taken before riding in the vehicle and the question database53cso as to choose a question to be put to the user80. The question to be put to the user80in this case is assumed to be a question asking the user “Did you have something for a meal?”, as in the case illustrated inFIG.3.

The user80answers the question by voice via the microphone52. The voice analysis unit63analyzes the voice data of the user80. When the user80answers “YES”, the user80is determined to have taken a meal at the shopping mall91through the voice analysis. The action history update unit64associates the positional information of the vehicle40with the action indicating that the user80has taken a meal at the shopping mall91, and stores the associated information in the action history database53b. The action history update unit64may also associate the POI with the action indicating that the user80has taken a meal at the shopping mall91and store the associated information in the action history database53b. Referring to the schedule data thus can store the action of the user80taken at the POI with a high accuracy if there are plural kinds of actions that the user80would take at the POI.

The action estimation unit65may refer to the action history of the user80stored in the action history database53bso as to estimate the action of the user80. For example, the action estimation unit65may choose an action with a high frequency so as to estimate the action of the user80among the actions stored in the action history.

An example of operation of the information processing device100is described below with reference to the flowchart shown inFIG.5.

In step S101, the user identification unit61identifies the user80riding in the vehicle40by use of the face image captured by the camera, the identification cipher transmitted from the intelligence key, and the like. The process proceeds to step S103, and the destination is then set by the user80.

The process proceeds to step S105, and the destination arrival determination unit62checks the positional information of the vehicle40acquired from the GPS receiver51with the positional information of the destination stored in the map database53aso as to determine whether the vehicle40has arrived at the destination. When the vehicle40is determined to have arrived at the destination (YES in step S105), the process proceeds to step S107. When the vehicle40is determined not to have arrived at the destination yet (NO in step S105), the process is on standby.

In step S107, the action estimation unit65estimates the action of the user80taken before riding in the vehicle. In particular, the action estimation unit65checks the positional information of the vehicle40acquired from the GPS receiver51with the map database53a, and acquires the POI at the current position (the parked position of the vehicle40). The action estimation unit65thus can recognize that the current position is the ramen shop90(refer toFIG.3) or the current position is the shopping mall91(refer toFIG.4). The action estimation unit65refers to the table data in which the attribute of the POI (the ramen shop or the shopping mall) is associated with the type of the action of the user80, and estimates the action of the user80taken before riding in the vehicle. The signal indicating the estimation result obtained by the action estimation unit65is output to the question selection unit66.

The process proceeds to step S109, and the question selection unit66when receiving the signal from the action estimation unit65refers to the action of the user80taken before riding in the vehicle and the question database53cso as to choose the question to be put to the user80. The signal indicating the question chosen by the question selection unit66is output to the question output unit68.

The process proceeds to step S111, and the ride detection unit67detects the ride of the user80in accordance with the signal acquired from the sensor group50. When the ride of the user is detected by the ride detection unit67, the signal indicating the detection of the ride of the use80is output to the question output unit68.

The process proceeds to step S113, and the question output unit68, when receiving the signal indicating the detection of the ride of the user80, asks the user80the question chosen by the question selection unit66by voice via the speaker54.

When the answer is input by the voice via the microphone52(YES in step S115), the process proceeds to step S117. When the answer is not returned from the user80(NO in step S115), the process is on standby. The means of obtaining the answer from the user80is not limited to the voice, and the answer from the user80may be obtained through the operation on the touch panel.

In step S117, the voice analysis unit63analyzes the voice data of the user80. The action history update unit64associates the positional information of the vehicle40with the action that the user has taken at the POI in accordance with the analyzed result obtained by the voice analysis unit63, and stores the associated information in the action history database53b. The action history update unit64may associate the POI with the action that the user80has taken at the POI and store the associated information in the action history database53b.

Operational Effects

As described above, the information processing device100according to the present embodiment can achieve the following operational effects.

The information processing device100includes the controller60, the ride detection device (the sensor group50) that detects the ride of the user80in the vehicle40, the storage device (the action history database53b) that stores the action data of the user80, the output device (the speaker54) that outputs the question data for requesting the answer from the user80, and the input device (the microphone52) that receives the input from the user80.

The controller60, when detecting the ride of the user80by the signal obtained from the ride detection device, outputs the output data including at least the question regarding the action of the user80taken before riding in the vehicle40from the output device in accordance with the positional information of the vehicle40. The controller60acquires the answer to the question from the user80as the input data via the input device. The controller60associates the input data with the positional information of the vehicle40or the POI, and stores the associated information in the storage device. The controller60thus can store the action of the user80taken at the POI with a high accuracy.

The controller60determines the question depending on the attribute of the POI determined in accordance with the positional information of the vehicle40. Since the vehicle40in the case illustrated inFIG.3is parked in the parking space at the ramen shop90, the attribute of the POI is determined to be the ramen shop90in accordance with the positional information of the vehicle40. The storage device (the question database53c) stores the attribute of the POI (the ramen shop) in association with the question regarding the action of the user (taking a meal) at the POI, so that the controller60can determine the question in accordance with the attribute of the POI.

The controller60may estimate the action of the user80taken before riding in the vehicle in accordance with the action history stored in the action history database53bor the attribute of the POI. When the attribute of the POI is the ramen shop as illustrated inFIG.3, the controller60can estimate that the action of the user taken before riding in the vehicle is to take a meal. The controller60may determine the action having a higher frequency as the estimated action of the user80among the actions stored in the action history. The controller60thus can estimate the action of the user80taken before riding in the vehicle with a high accuracy.

The controller60may acquire the data regarding the action plan of the user80(the schedule data of the user80) through the communication, and refer to the schedule data so as to determine the question. The controller60thus can determine the appropriate question if there are several types of actions that the user would take at the POI, and can store the action of the user80at the POI with a high accuracy. The term “communication” as used herein refers to the communication with the computer20(the server) or the communication with the terminal that the user80holds.

The input data is data converted from the voice of the user80, or data generated through the operation made by the user80. The user80uses the microphone52or the touch panel as a means for answering the question. The user80using the microphone52or the touch panel can answer the question more easily.

The controller60detects the user80getting off the vehicle40after arriving at the destination set by the user80, and then outputs the question when detecting the user80riding in the vehicle40. The controller60thus can ask the user80the question at an appropriate timing.

The POI may be any of a POI located around the positional information of the vehicle40, a destination set through the navigation device equipped in the vehicle40, a POI included in the data regarding the action plan of the user80acquired through the communication, or a POI included in the data regarding the action history of the user80before riding in the vehicle acquired through the communication.

Modified Example

A modified example of the present embodiment is described below.

As illustrated inFIG.6, an information processing device101according to the modified example further includes an accuracy calculation unit69. The accuracy calculation unit69calculates an accuracy indicating likelihood (probability) of the action of the user80estimated by the action estimation unit65. The estimation accuracy may be evaluated according to a three-grade level of a low level, a middle level, and a high level, or may be evaluated according to a probability (0% to 100%).

An example of a method of storing the action that the user80has taken at a POI is described below with reference toFIG.7.

FIG.7shows the attribute of the POI illustrating a restaurant, a shopping mall, and a class. The term “class” as used in the present embodiment includes a swimming class a dance class, and a flower-arrangement class. The type of the action that the user takes at a restaurant includes taking a meal and having a meeting. The type of the action that the user takes at a shopping mall includes a taking a meal, doing shopping, waiting for someone, having a haircut, and taking exercise, as described above. The type of the action that the user takes at a class includes taking exercise and taking a lesson.

As described above, since the attribute of the POI is associated with the type of the action of the user, the action estimation unit65estimates the action of the user80from the type of the action described above, as shown inFIG.7. The estimated action of the user80at the shopping mall in the case shown inFIG.7is any of taking a meal, doing shopping, and waiting for someone for illustration purposes.

While the accuracy of the action estimated by the action estimation unit65(also simply referred to below as an “estimated action) needs to be high when there are several possible actions to be estimated as shown inFIG.7, the accuracy of the estimation is not always high. The reason for requiring the high accuracy for the estimation of the action is that the question put to the user80would be an inappropriate question if the estimated action is wrong. The action of the user, however, is not always estimated with a high accuracy, as described above.

To deal with this, the information processing device101according to the modified example calculates the accuracy of the estimated action, and determines the question depending on the calculated accuracy. The action history of the user80stored in the action history database53bis used so as to calculate the accuracy of the estimated action. The modified example is illustrated below with two cases in which there is no action history of the user80stored in the action history database53band in which the action history of the user80stored in the action history database53bincludes actions taken with a predetermined number of times (for example, ten times).

First, the case in which there is no action history of the user80stored in the action history database53bis described below.

When the destination of the user80is a restaurant, the action estimated by the action estimation unit65is either to take a meal or to have a meeting, as shown inFIG.7. The action estimation unit65then estimates the action of the user that is either to take a meal or to have a meeting. The action estimation unit65may estimate, but not necessarily, the action of the user that is typically presumed to have a high probability, for example. An action having a high probability typically taken at a restaurant is to take a meal. This is also stored in the table data. The action estimation unit65thus may refer to the table data so as to estimate that the action of the user80is to take a meal when the attribute of the POI is a restaurant.

Alternatively, the action estimation unit65may refer to the action history of the user80stored in the action history database53bso as to estimate the action of the user80. For example, the action estimation unit65may choose an action having a high frequency so as to estimate the action of the user80among the actions stored in the action history. This method cannot be used when there is no action history.

The accuracy calculation unit69calculates the accuracy of the action of taking a meal estimated by the action estimation unit65. The accuracy calculation unit69first refers to the action history database53b, and acquires the action history of the user80taken at the restaurant. Since there is no action history of the user80at the restaurant in this case, the accuracy calculation unit69determines that the user80comes to the restaurant for the first time. The accuracy calculation unit69in this case determines that the probability is low that the action that the user80has taken at the restaurant is to take a meal. The reason for this is that the determination of whether the action that the user80takes at the restaurant is to take a meal or have a meeting is difficult. Although the action of taking a meal is typically presumed to have a higher probability, there is still a probability that the action of the user is to have a meeting. The accuracy calculation unit69then calculates that the accuracy of the action of taking a meal estimated by the action estimation unit65is low. A signal indicating the calculation result is then output to the question selection unit66.

The question selection unit66when receiving the signal from the accuracy calculation unit69recognizes that the accuracy of the action of taking a meal estimated by the action estimation unit65is low. This recognition allows the question election unit66to avoid choosing the question regarding a meal and choose a question for asking the action of the user80itself. The reason for choosing the question for asking the action of the user80itself is to accumulate the action history in preparation for a case in which the user80would come to the restaurant again in the future.

An example of the question for asking the action itself is a question asking “What did you do there?”, as shown inFIG.7. The answer to the question from the user80is associated with the positional information of the vehicle40or the POI, and is stored in the action history database53b. Asking the action of the user80itself when there is no action history of the user80stored in the action history database53bcan accumulate the action history in preparation for a case in which the user80would come to the restaurant again in the future. Asking the action of the user80itself can also avoid asking an inappropriate question.

Next, the case in which the action history of the user80stored in the action history database53bincludes the actions taken with a predetermined number of times (for example, ten times) is described below.

As in the case in which there is no action history of the user80stored in the action history database53b, the destination of the user80in the following case is assumed to be a restaurant. The accuracy calculation unit69refers to the action history database53b, and acquires the action history of the user80taken at the restaurant. A case is assumed below in which ten times of the actions in the action history include seven times of the actions of taking a meal and three times of the actions of having a meeting. Since the number of times of the actions of taking a meal is greater than the number of times of the actions of having a meeting, the accuracy calculation unit69calculates to determine that the probability corresponds to the middle level that the action of the user80taken at the restaurant is to take a meal. In other words, the accuracy calculation unit69calculates to determine that the accuracy of the action of taking a meal estimated by the action estimation unit65corresponds to the middle level. A signal indicating the calculation result is then output to the question selection unit66. The accuracy of the action of taking a meal estimated by the action estimation unit65can be calculated as a probability. In the case in which ten times of the actions in the action history include seven times of the actions of taking a meal and three times of the actions of having a meeting, the probability can be calculated to be 70% that the action of the user80taken at the restaurant is to take a meal. The phrase “the accuracy of the action estimated by the action estimation unit65corresponds to the middle level” conforms to about 70% in terms of the probability.

The question selection unit66when receiving the signal from the accuracy calculation unit69determines that the probability that the action of the user80taken at the restaurant is to take a meal is high, and then chooses the question regarding a meal. Examples of the question regarding a meal in this case include a question asking “Did you eat something?” and a question asking “Did you drink something?”, as shown inFIG.7. The answer to the question from the user80is associated with the positional information of the vehicle40or the POI, and is stored in the action history database53b.When ten times of the actions in the action history include three times of the actions of taking a meal and seven times of the actions of having a meeting, the question selection unit66chooses the question regarding a meeting. Examples of the question regarding a meeting in this case include a question asking “Are you having a meeting for something?” and a question asking “Are you having a meeting with someone?”, as shown inFIG.7.

While the above case is illustrated with the predetermined number of times that is ten times, the predetermined number of times is not limited to ten times. The greater number of times of the actions of the user80in the action history stored in the action history database53bcontributes to an improvement in the accuracy of the estimated action. The accuracy of the estimated action fluctuates as the number of times of the actions of the user80in the action history is smaller. When the number of times of the actions of the user80in the action history stored in the action history database53bis less than five times, the number of times of the actions of the user80in the action history stored in the action history database53bmay be considered to be zero.

Similarly, when the destination of the user80is a shopping mall, and the accuracy of the action of the user80(taking a meal) estimated by the action estimation unit65is calculated to be the middle level, the question selection unit66chooses the question regarding a meal. The question regarding a meal is the same as described above, and explanations are not repeated below. When the accuracy of the action of the user80(doing shopping) estimated by the action estimation unit65is calculated to be the middle level, the question selection unit66chooses the question regarding shopping. Examples of the question regarding shopping in this case include a question asking “Did you buy something?” and a question asking “Did you find something?”, as shown inFIG.7. When the accuracy of the action of the user80(waiting for someone) estimated by the action estimation unit65is calculated to be the middle level, the question selection unit66chooses the question regarding an appointment. Examples of the question regarding an appointment in this case include a question asking “Are you waiting for someone?” and a question asking “Are you meeting Mr. (Mrs.) . . . ?”, as shown inFIG.7.

Similarly, when the destination of the user80is a class, and the accuracy of the action of the user80(taking exercise) estimated by the action estimation unit65is calculated to be the middle level, the question selection unit66chooses the question regarding exercise. Examples of the question regarding exercise include a question asking “Did you take exercise?” and a question asking “Swimming? Yoga? Dancing?”, as shown inFIG.7. When the accuracy of the action of the user80(taking a lesson) estimated by the action estimation unit65is calculated to be the middle level, the question selection unit66chooses the question regarding a lesson. Examples of the question regarding a lesson include a question asking “Taking a lesson?” a question asking “Flower arrangement? Tea ceremony? Calligraphy?”, as shown inFIG.7.

Calculating the accuracy of the action of the user80estimated by the action estimation unit65and determining the question depending on the calculated accuracy as described above can avoid asking an inappropriate question.

A case is assumed below in which ten times of the actions in the action history at the restaurant include ten times of the actions of taking a meal and no actions of having a meeting. Since all of the actions in the action history are to take a meal, the accuracy calculation unit69calculates to determine that the probability is high that the action of the user80taken at the restaurant is to take a meal. In other words, the accuracy calculation unit69calculates to determine that the accuracy of the action of the user80(taking a meal) estimated by the action estimation unit65is high. A signal indicating the calculation result is then output to the question selection unit66. The probability in this case can be calculated to be 100% that the action of the user80taken at the restaurant is to take a meal.

The question election unit66when receiving the signal from the accuracy calculation unit69determines that the probability that the action of the user80taken at the restaurant is to take a meal is quite high, and then chooses, from the questions regarding a meal, a question delving deeply more than the question asked when the accuracy corresponds to the middle level. Examples of the question delving deeply in this case include a question asking “Was it good?” and a question asking “Do you want to come to have it again?”, as shown inFIG.7. The voice analysis unit63can classify the answer to this question from the user80into a positive answer and a negative answer. The action history update unit64can associate the classified result with the POI as classification data so as to store the data into the action history database53b. Classifying the answer of the user80into a positive answer and a negative answer can ask the user80the question delving more deeply next time.

When ten times of the actions in the action history include no actions of taking a meal and ten times of the actions of having a meeting, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding a meeting. Examples of the question delving deeply in this case include a question asking “Finished well?” and a question asking “Was Mr. (Mrs.) . . . fine?”, as shown inFIG.7.

Similarly, when the destination of the user80is a shopping mall, and the accuracy of the action of the user80(taking a meal) estimated by the action estimation unit65is calculated to be high, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding a meal. The question delving deeply is the same as described above, and explanations are not repeated below. When the accuracy of the action of the user80(doing shopping) estimated by the action estimation unit65is calculated to be high, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding shopping. Examples of the question delving deeply in this case include a question asking “Did you find something reasonable?” and a question asking “Could you buy something good?”, as shown inFIG.7. When the accuracy of the action of the user80(waiting for someone) estimated by the action estimation unit65is calculated to be high, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding an appointment. Examples of the question delving deeply in this case include a question asking “Meet all right?” and a question asking “Was Mr. (Mrs.) . . . fine?”, as shown inFIG.7.

Similarly, when the destination of the user80is a class, and the accuracy of the action of the user80(taking exercise) estimated by the action estimation unit65is calculated to be high, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding exercise. Examples of the question delving deeply in this case include a question asking “Sweat well?” and a question asking “Refresh yourself?”, as shown inFIG.7. When the accuracy of the action of the user80(taking a lesson) estimated by the action estimation unit65is calculated to be high, the question selection unit66chooses the question delving deeply more than the question asked when the accuracy corresponds to the middle level among the questions regarding a lesson. Examples of the question delving deeply in this case include a question asking “Make progress?” and a question asking “Spiritually awakened?”, as shown inFIG.7.

Calculating the accuracy of the action of the user80estimated by the action estimation unit65and determining the question depending on the calculated accuracy as described above can stimulate the conversations with the user80.

The accuracy calculation unit69may refers to the schedule data of the user80when calculating the accuracy of the estimated action. Referring to the schedule data can improve the accuracy to be calculated. When there is no action history of the user80stored in the action history database53b, the accuracy of the estimated action (taking a meal) is determined to be low, as described above. The action estimation unit65in this case refers to the schedule data when including the information “taking a meal at a restaurant”, so as to calculate the accuracy of the estimated action (taking a meal) to be the middle level or to be high.

Next, an example of a structure of the questions stored in the question database53cis described below with reference toFIG.8.

As illustrated inFIG.8, the questions are classified into several layers (the first layer to the third layer), and are stored in the question database53c. The question selection unit66chooses a question belonging to any of the layers depending on the accuracy of the estimated question calculated by the accuracy calculation unit69. In particular, the question selectin unit66chooses a question included in the first layer when the accuracy of the estimated action is smaller than a first predetermined value. As shown inFIG.8, the question included in the first layer is to ask the action of the user80itself.

The question selection unit66chooses a question included in the second layer when the accuracy of the estimated action is greater than or equal to the first predetermined value and less than or equal to a second predetermined value (the first predetermined value<the second predetermined value). As shown inFIG.8, the question included in the second layer is to confirm the estimated action.

The question selection unit66chooses a question included in the third layer when the accuracy of the estimated action is greater than the second predetermined value. The question included in the third layer is to request the user80to answer an impression or an evaluation regarding the action taken before riding in the vehicle, as shown inFIG.8. The third layer may include a question for requesting the user80to answer an impression or an evaluation regarding the POI. Examples of the question for requesting the user80to answer the impression or the evaluation regarding the action taken before riding in the vehicle include a question asking “Was it good?” and a question asking “Do you want to come to have it again?” when the action taken before riding in the vehicle is to take a meal, as shown inFIG.8. An example of the question for requesting the user80to answer the impression or the evaluation regarding the POI is a question asking “Was the inside clean enough?” when the attribute of the POI is a restaurant, as shown inFIG.8.

The probability calculated by the accuracy calculation unit69may be used as the first predetermined value and the second predetermined value shown inFIG.8. For example, the first predetermined value may be set to 30%, and the second predetermined value may be set to 70%. The first layer corresponds to the state in which the estimated accuracy is low in the case shown inFIG.7. The second layer corresponds to the state in which the estimated accuracy is at the middle level in the case shown inFIG.7. The third layer corresponds to the state in which the estimated accuracy is high in the case shown inFIG.7.

The question delves more deeply as the classification proceeds from the first layer to the third layer, namely, as the accuracy of the estimated action is higher. The action of the user80taken at the POI is stored in the action history database53aso as to accumulate the action history. As the action history is accumulated more, the accuracy of the estimated action calculated by the accuracy calculation unit69is higher, and the question delves more deeply. The increase in the accumulation of the action history can stimulate the conversations with the user80. While the questions shown inFIG.8are classified into the three layers, the classification is not limited to this case. The questions may delve more deeply so as to be classified further into the fourth layer and the fifth layer. The respective layers are set depending on the degree of abstraction or specificity of the questions allotted to the respective layers. In other words, the abstraction of the question is increased as the layer is higher, and the specificity of the question is increased as the layer is lower.

Next, a relationship between the action history stored in the action history database53band the accuracy of the estimated action calculated by the accuracy calculation unit69is described below with reference toFIG.9.

As described above, the accuracy of the estimated action is lower as the action history stored in the action history database53bis smaller. The accuracy of the estimated action is higher as the action history stored in the action history database53bis greater.

Similarly, the accuracy of the estimated action is lower as the probability of the action history stored in the action history database53bis lower. The accuracy of the estimated action is higher as the probability of the action history stored in the action history database53bis greater. The above explanations are made with the case in which the probability that the action of the user80taken at the restaurant is to take a meal is 70% when ten times of the actions in the action history include seven times of the actions of taking a meal and three times of the actions of having a meeting. The probability of 70% corresponds to the probability of the action history.

The accuracy calculation unit69does not necessarily need to refer to the action history stored in the action history database53bupon the calculation of the accuracy of the estimated action. It is difficult to estimate the action that the user80has taken at the POI when the attribute is a shopping mall as described above, since there are a lot of possible actions that the user80would take. The accuracy of the estimated action is thus decreased. When the attribute of the POI is a ramen shop, the accuracy of the estimated action is increased since the action that the user80would take can be specified. When the attribute of the POI is a supermarket, the action that the user80would take can be substantially limited to shopping, and the accuracy of the estimated action is thus determined to correspond to the middle level. The accuracy calculation unit69thus can calculate the accuracy of the estimated action only by use of the attribute of the POI without referring to the action history stored in the action history database53b.

An example of operation of the information processing device101according to the modified example is described below with reference to the flowchart shown inFIG.10. The processing in steps S201to207and219to225is the same as the processing in steps S101to S107and S111to S117, and overlapping explanations are not repeated below.

In step S209, the accuracy calculation unit69calculates the accuracy indicating the likelihood (the probability) of the action of the user80estimated by the action estimation unit65.

The process proceeds to step S211, and the question selection unit66chooses the question belonging to any of the layers depending on the accuracy of the estimated action calculated by the accuracy calculation unit69(refer toFIG.8). In particular, the question selection unit66chooses the question for asking the action of the user80itself when the accuracy of the estimated action is smaller than the first predetermined value (step S213). The question selection unit66chooses the question for confirming the estimated action when the accuracy of the estimated action is greater than or equal to the first predetermined value and less than or equal to the second predetermined value (in step S215). The question selection unit66chooses the question for requesting the user80to answer the impression or the evaluation regarding the action taken before riding in the vehicle or the question for requesting the user80to answer the impression or the evaluation regarding the POI when the accuracy of the estimated action is greater than the second predetermined value (step S217).

Operational Effects

As described above, the information processing device101according to the modified example can achieve the following operational effects.

The controller60calculates the accuracy of the estimated action in accordance with the action history stored in the action history database53bor the attribute of the POI, and determines the question depending on the calculated accuracy of the estimated action. This can avoid asking an inappropriate question.

The questions are classified into the plural layers (refer toFIG.8). The controller60determines the question belonging to any of the layers as output data depending on the accuracy of the estimated action. The determination of the question depending on the accuracy of the estimated action can stimulate the conversations with the user80. The controller60may determine the abstraction of the question depending on the accuracy of the estimated action so as to define the determined question as the output data.

The output data includes the question for requesting the user80to answer the action taken before riding in the vehicle, the question for requesting the user80to answer the impression or the evaluation regarding the action taken before riding in the vehicle, and the question for requesting the user80to answer the impression or the evaluation regarding the POI. Using various kinds of questions can stimulate the conversations with the user80.

The controller60classifieds the answer of the user80into the positive answer and the negative answer, and associates the classified result with the POI to store the classification data in the action history database53b. The classification of the answer of the user80into the positive answer and the negative answer can provide the question delving more deeply when asking the user80next time.

The controller60may output the classification data associated with the POI in accordance with the positional information of the vehicle40. In a case in which the answer of the user80regarding a meal at the ramen shop90is assumed to be the positive answer indicating “It was good”, the controller60may output the voice saying “The ramen was good, wasn't it?” when the vehicle40is traveling around the ramen shop90. This can stimulate the conversations with the user80.

The respective functions described in the embodiment above can be implemented by one or plural processing circuits. The respective processing circuits include a programed processing device, such as a processing circuit including an electrical circuit. The respective processing circuits also include an application-specific integrated circuit (ASIC) configured to execute the functions described above and a device such as a conventional circuit component.

While the present invention has been described above by reference to the embodiment, it should be understood that the present invention is not intended to be limited to the descriptions and the drawings composing part of this disclosure. Various alternative embodiments, examples, and technical applications will be apparent to those skilled in the art according to this disclosure.

FIG.3illustrates the case in which the vehicle40is parked in the parking space at the ramen shop90, and the positional information of the vehicle40thus can be presumed to conform to the positional information of the POI, as described above. However, the destination does not always provide a parking space. When there is no parking space at the destination, the user80would try to park the vehicle40in a coin-operated parking lot around the destination, and then walk toward the destination from the parking lot, as illustrated inFIG.11, for example. The positional information of the vehicle40in this case does not conform to the positional information of the destination (the positional information of the POI). The positional information of the vehicle40associated with the action of the user80taken before riding in the vehicle40and stored together thus cannot be used as the effective data.

To deal with this, the information processing device100may associate the positional information of the terminal held by the user (referred to as a “user's terminal”) with the action of the user80taken before riding in the vehicle, and store the information in the action history database53b. In particular, the information processing device100acquires the positional information of the user's terminal through the communication when the user arrives at the destination. The information processing device100is herein presumed to be configured to be able to communicate with the user's terminal. For example, the information processing device100includes a receiver that receives data transmitted from the user's terminal. The information processing device100refers to information in a guiding application installed in the user's terminal, so as to determine whether the user80has arrived at the destination.

The information processing device100estimates the action of the user80at the destination in accordance with the positional information acquired from the user's terminal. The information processing device100asks the user80a question regarding the estimated action. The information processing device100then associates the positional information acquired from the user's terminal with the action of the user80taken before riding in the vehicle40, and stores the information in the action history database53b. This can accurately store the action of the user80taken at the POI if the parked position of the vehicle40is away from the position of the destination (the position of the POI).

REFERENCE SIGNS LIST

40VEHICLE50SENSOR GROUP51GPS RECEIVER52MICROPHONE53STORAGE DEVICE53aMAP DATABASE53bACTION HISTORY DATABASE53cQUESTION DATABASE54SPEAKER60CONTROLLER61USER IDENTIFICATION UNIT62DESTINATION ARRIVAL DETERMINATION UNIT63VOICE ANALYSIS UNIT64ACTION HISTORY UPDATE UNIT65ACTION ESTIMATION UNIT66QUESTION SELECTION UNIT67RIDE DETECTION UNIT68QUESTION OUTPUT UNIT69ACCURACY CALCULATION UNIT100,101INFORMATION PROCESSING DEVICE