Information processing apparatus, information processing method, and program

Abstract

To appropriately recognize whether an object person is ready to make a call or not.SOLUTION: An information processing apparatus comprises: a schedule information acquisition unit which acquires schedule information of an object person; a result acquisition unit which acquires response information which is obtained by inputting the schedule information of the object person to a learning model and indicates whether the object person can make a call or not, the learning model trained by machine learning for correspondence between a schedule and response results indicating whether the object person responded to calls or not; and an output control unit which outputs the response information acquired by the result acquisition unit.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] There is known a technology that allows a user to grasp the status of another user in advance when communicating with the other user. For example, Patent Document 1 describes a technology that infers the status of the other user from the past behavioral patterns of the user and provides the information. Furthermore, for example, Patent Document 2 discloses a mobile phone that uses machine learning to learn keywords for a meeting schedule, so that the mobile phone's operating mode is set to silent mode when the meeting time arrives, but if an incoming call is answered during the meeting, the operating mode is set to normal mode even during the meeting. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-344389 [Patent Document 2] Patent No. 4511452 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in Patent Document 1, it is possible to guess the situation of the other user (target person), but the user who is trying to make a call cannot know whether the other user is in a situation where he or she can make a call. Also, in Patent Document 2, it is possible to switch the mode of the other user's mobile phone, but it is not possible for the user who is trying to make a call to know whether the other user is in a situation where he or she can make a call.

[0005] An object of the present invention is to provide an information processing device, an information processing method, and a program that can appropriately determine whether a target person is in a situation where they can make a phone call. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the information processing device of the present invention comprises a schedule information acquisition unit that acquires schedule information of a subject, a result acquisition unit that acquires response information indicating whether the subject is available to make a call, obtained by inputting the schedule information of the subject into a learning model that has been machine-learned to determine the correspondence between schedules and response results indicating whether the subject has answered the phone, and an output control unit that outputs the response information acquired by the result acquisition unit.

[0007] In order to solve the above-mentioned problems and achieve the object, the information processing method of the present invention includes a step of acquiring schedule information of a target person, a step of acquiring response information indicating whether the target person is available to take a call, which is obtained by inputting the schedule information of the target person into a learning model that has been machine-learned to determine the correspondence between the schedule and a response result indicating whether the target person has answered the call, and a step of outputting the response information acquired in the step of acquiring the response information.

[0008] In order to solve the above-mentioned problems and achieve the object, the program of the present invention causes a computer to execute the following steps: acquiring schedule information of a target person; acquiring response information indicating whether the target person is available to take a call, which is obtained by inputting the schedule information of the target person into a learning model that has undergone machine learning to determine the correspondence between the schedule and a response result indicating whether the target person has answered the call; and outputting the response information acquired in the step of acquiring the response information. [Effects of the Invention]

[0009] According to the present invention, it is possible to grasp the call status of the target person. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of the configuration of an information processing system according to the first embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of the mobile terminal according to the first embodiment. [Figure 3] FIG. 3 is a block diagram illustrating an example of the configuration of the information processing apparatus according to the first embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of a processing flow of the learning method for the learning model according to the first embodiment. [Figure 5] FIG. 5 is a diagram showing an example of a schedule. [Figure 6] FIG. 6 is a diagram showing an example of contact information of the mobile terminal according to the first embodiment. [Figure 7] FIG. 7 is a flowchart showing an example of a processing flow of response information according to the first embodiment. [Figure 8] FIG. 8 is a flowchart showing the flow of processing by the information processing device according to the second embodiment. [Figure 9] FIG. 9 is a diagram showing an example of contact information of the mobile terminal according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] (First embodiment) Next, embodiments of the present invention will be described in detail with reference to the drawings. Note that the present disclosure is not limited to the following detailed description of the invention (hereinafter referred to as the "embodiments"). Furthermore, the components in the following embodiments include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range.

[0012] (Information Processing System) 1 is a schematic diagram showing an example of the configuration of an information processing system 1 according to a first embodiment. The information processing system 1 includes a plurality of mobile terminals 10 and an information processing device 40. The information processing system 1 is an information processing system that displays, on the mobile terminal 10 of a user attempting to make a call, response information indicating whether or not the other user attempting to make a call can make a call via the mobile terminal 10.

[0013] In the following, the other party will be referred to as the target person as appropriate, the user attempting to make a call (the user of the mobile terminal 10 displaying the response information) will be referred to as the user as appropriate, and a user to whom the user can make a call will be referred to as the target person as appropriate. The target person may be, for example, a user registered in the contacts of the user's mobile terminal 10. The contacts here are information indicating users that the user can contact. Furthermore, the target person's mobile terminal 10 will be referred to as the called terminal as appropriate, and the user's mobile terminal 10 will be referred to as the calling terminal as appropriate. Note that when there is no need to distinguish between the called terminal (the target person's mobile terminal 10) and the calling terminal (the user's mobile terminal 10), they will be referred to as the mobile terminal 10.

[0014] (Mobile device) FIG. 2 is a block diagram showing an example of the configuration of a mobile terminal 10 according to the first embodiment. The mobile terminal 10 is, for example, a mobile terminal used for business purposes. The mobile terminals 10 communicate with each other and with external devices. The mobile terminals 10 can make calls and send and receive emails between each other. The mobile terminals 10 communicate with an information processing device 40. In the embodiment, the mobile terminal 10 is described as a smartphone, but is not limited to this and may be a tablet terminal or a wearable device with a calling function.

[0015] The mobile terminal 10 includes an input unit 12, an output unit 14, a storage unit 16, a communication unit 18, and a control unit 20.

[0016] The input unit 12 allows a user to input information and outputs various signals to the control unit 20. The input unit 12 can be realized by, for example, a touch panel, a button, a switch, a microphone, etc. The input unit 12 receives input operations of the mobile terminal 10 via, for example, a touch panel, and receives user voice via a microphone.

[0017] The output unit 14 outputs various types of information based on signals output from the control unit 20. The output unit 14 outputs sound. The output unit 14 outputs display images and sound. The output unit 14 is realized by, for example, a display including a liquid crystal display (LCD) or an organic electroluminescence (EL) display, a speaker, etc. The display images are, for example, images related to contacts or calls. The sound is, for example, a ringtone or the voice of the target person.

[0018] The storage unit 16 stores, for example, information such as the contents of calculations performed by the control unit 20 and programs. The storage unit 16 is configured, for example, with a main storage device such as a RAM (Random Access Memory) and a ROM (Read Only Memory), and a storage device such as an SSD (Solid State Drive) or an HDD (Hard Disk Drive). The storage unit 16 stores, for example, contact information.

[0019] The communication unit 18 is a communication unit for performing wired communication or wireless communication. The communication unit 18 performs communication using a communication method such as Wi-Fi (registered trademark) or a mobile phone line, and is connected to an intranet, the Internet, or an external device. The communication unit 18 communicates with other mobile terminals 10 or with the information processing device 40, for example.

[0020] The control unit 20 controls each unit of the mobile terminal 10. The control unit 20 has a control device such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The control unit 20 executes a program that controls the operation of the mobile terminal 10. The control unit 20 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 20 loads a stored program into memory and executes instructions included in the program. The control unit 20 includes an internal memory such as the RAM described above, and the internal memory is used for temporary storage of data in the control unit 20. The control unit 20 may be realized by a combination of hardware and software.

[0021] The control unit 20 includes an acquisition unit 22, a processing unit 24, and an output control unit 26. The control unit 20 implements the acquisition unit 22, the processing unit 24, and the output control unit 26 by reading and executing a program (software) from the storage unit 16. The control unit 20 may implement these processes using a single CPU, or may be equipped with multiple CPUs and implement the processes using the multiple CPUs. At least a portion of the acquisition unit 22, the processing unit 24, and the output control unit 26 may be implemented using hardware.

[0022] The acquisition unit 22 acquires various information. The acquisition unit 22 acquires information input to the mobile terminal 10. The acquisition unit 22 acquires response information indicating whether the target person is available for a call from the information processing device 40 via the communication unit 18. The response information is information indicating whether the target person is available for a call, and will be described in detail later.

[0023] The processing unit 24 processes various types of information. The processing unit 24 performs, for example, call processing. For example, when the target person operates the call button on the user's mobile terminal 10 upon receiving an incoming call, the processing unit 24 processes the user's mobile terminal 10 to a state where the target person can make a call. When the user operates the hang-up button upon receiving an incoming call on the user's mobile terminal 10, the processing unit 24 disconnects communication on the user's mobile terminal 10. When the user ignores the incoming call, the processing unit 24 generates information indicating that the user's mobile terminal 10 has received an incoming call. When the user performs an operation to display contacts on the user's mobile terminal 10, the processing unit 24 performs processing to display the contacts. The processing unit 24 outputs a signal generated based on the operation of the mobile terminal 10 to the output control unit 26.

[0024] The output control unit 26 controls the output based on the signal output from the processing unit 24. For example, when the mobile terminal 10 receives an incoming call, the output control unit 26 causes the output unit 14 to output a screen indicating that the incoming call has been received and a ringtone. For example, when the other user answers the phone in the user's mobile terminal 10, the output control unit 26 causes the output unit 14 to output a screen indicating that a call is in progress with the other user and causes the output unit 14 to output the voice of the other user. For example, when the user leaves an incoming call in the user's mobile terminal 10, the output control unit 26 causes the output unit 14 to output a display indicating that an incoming call has been received. When the user performs an operation to display contact information on the user's mobile terminal 10, the output control unit 26 causes the output unit 14 to display the contact information.

[0025] (Information processing device) Next, an information processing device 40 according to an embodiment will be described. FIG. 3 is a block diagram showing an example of the configuration of the information processing device 40. The information processing device 40 acquires schedule information indicating the schedule of a target person, inputs the schedule information into a machine-learned learning model N, and outputs response information indicating an estimated result of whether the target person is available for a call, that is, a prediction result indicating whether the target person is currently available for a call. The learning model N refers to a learning model in AI (Artificial Interigence). In this embodiment, the learning model N may be any supervised model. A detailed description of the schedule information, learning model N, response information, etc. will be given later.

[0026] The information processing device 40 includes an input unit 42 , an output unit 44 , a communication unit 46 , a storage unit 48 , and a control unit 50 .

[0027] The input unit 42 is operable and outputs various input signals to the control unit 20. The input unit 43 can be realized by, for example, a touch panel, a button, a switch, a keyboard, or the like.

[0028] The output unit 44 outputs various information based on signals output from the control unit 50. The output unit 14 is realized by, for example, a speaker or a display including a liquid crystal display or an organic EL display.

[0029] The communication unit 46 is a communication unit for performing wired communication or wireless communication. The communication unit 46 performs communication using a communication method such as Wi-Fi or a mobile phone line, and is connected to an intranet, the Internet, or an external device. The communication unit 46 communicates with the mobile terminal 10.

[0030] The storage unit 48 stores information such as the calculation contents and programs of the control unit 50. The storage unit 48 is configured, for example, with a RAM, a main storage device such as a ROM, and a storage device such as an SSD or HDD. The storage unit 48 stores the learning model N.

[0031] The control unit 50 controls each unit of the information processing device 40. The control unit 50 has a control device such as a CPU or an MPU. The control unit 50 executes a program that controls the operation of the information processing device 40. The control unit 50 may be realized by an integrated circuit such as an ASIC or an FPGA. The control unit 50 loads a stored program into memory and executes instructions included in the program. The control unit 50 includes an internal memory such as the RAM described above, and the internal memory is used for temporary storage of data in the control unit 50, etc. The control unit 50 may be realized by a combination of hardware and software.

[0032] The control unit 50 includes a learning unit 51, a schedule information acquisition unit 52, an attribute information acquisition unit 54, a result acquisition unit 56, and an output control unit 58. The control unit 50 reads and executes a program (software) from the storage unit 48, thereby realizing the learning unit 51, the schedule information acquisition unit 52, the attribute information acquisition unit 54, the result acquisition unit 56, and the output control unit 58 and executing the processes. The control unit 50 may execute these processes using a single CPU, or may be provided with multiple CPUs and execute the processes using the multiple CPUs. Furthermore, at least a portion of the learning unit 51, the schedule information acquisition unit 52, the attribute information acquisition unit 54, the result acquisition unit 56, and the output control unit 58 may be implemented by hardware.

[0033] In the present embodiment, the information processing device 40 has the functions of the learning unit 51, the schedule information acquisition unit 52, the attribute information acquisition unit 54, the result acquisition unit 56, and the output control unit 58, but the mobile terminal 10 may have these functions. That is, the mobile terminal 10 may function as the information processing device 40, and may include at least one of the learning unit 51, the schedule information acquisition unit 52, the attribute information acquisition unit 54, the result acquisition unit 56, and the output control unit 58, and may execute these processes.

[0034] Hereinafter, a learning method of the learning model by the information processing device 40 will be described, and then an output process using the learning model N that has undergone machine learning will be described.

[0035] (Learning model learning process) (Acquisition of training data) The learning unit 51 of the information processing device 40 acquires past schedule information indicating the user's past schedule and a response result indicating whether the user answered a phone call. The user's schedule refers to the user's situation, such as being in a meeting, at work, or out of the office. The learning unit 51 acquires the user's past conference information as past schedule information (the user's past schedule). The conference information is information indicating whether the user is in a meeting during a target time period (shown in the past schedule information in this example). Furthermore, the conference information preferably includes at least one of the type of the meeting (e.g., the subject), the time period during which the meeting is held, and the participants of the meeting, and more preferably includes all of these. The response result acquired by the learning unit 51 is information indicating whether the user answered a phone call during the time period during which the plan shown in the past schedule information was scheduled.

[0036] The learning unit 51 may acquire the past schedule information and the response result by any method. For example, the learning unit 51 may acquire the past schedule information of the user from the schedule of the user from which the response result is to be acquired. For example, the user's schedule may be stored in the storage unit 48 of the information processing device 40, and the learning unit 51 may read the schedule from the storage unit 48. Alternatively, the learning unit 51 may acquire the schedule from another device (server) or the cloud. Furthermore, for example, the learning unit 51 may acquire the call history of the user in the user's mobile terminal 10 and acquire the user's response result from the acquired call history. In this case, if the user's call history includes a history of a response during a time period when a schedule indicated in the past schedule information was scheduled, the learning unit 51 may acquire the response result corresponding to the past schedule information as information indicating that a response was made. Further, for example, when the user's call history does not include a history of an incoming call being received during a time period when the schedule indicated in the past schedule information was scheduled but the user answered the call, the learning unit 51 may acquire the response result corresponding to the past schedule information as information indicating that the call was not answered.Further, for example, when the user's call history includes a history of an incoming call being received during a time period when the schedule indicated in the past schedule information was scheduled but the user input an operation to reject the call, the learning unit 51 may acquire the response result corresponding to the past schedule information as information indicating that the call was not answered.

[0037] The learning unit 51 acquires a plurality of sets of teacher data, with the schedule (past schedule information) and response results acquired in this way being one set of teacher data.

[0038] More specifically, in this embodiment, the learning unit 51 also acquires user attribute information as training data. That is, the learning unit 51 acquires the user's past schedule information, the user's attribute information, and the user's response results as one set of training data. The attribute information is information that indicates the user's attributes, or in other words, information for distinguishing the user from other users. For example, the attribute information may include the user's affiliation in an organization (e.g., department), position in the organization, and number of years since joining the organization. The attribute information is preferably at least one of the affiliation, position, and number of years, and may be all of these. The attribute information may also be an identifier (such as name or employee number) that identifies the individual user.

[0039] The learning unit 51 acquires multiple sets of training data by using the acquired past schedule information, attribute information, and response results as one set of training data. In this case, the learning unit 51 acquires multiple sets of training data, which are data sets of past schedule information, attribute information, and response results, for each attribute information. That is, the learning unit 51 acquires multiple sets of training data for the same attribute information. The learning unit 51 also acquires training data with different attribute information. That is, in this embodiment, the learning unit 51 acquires multiple data sets with common attribute information but different past schedule information for each attribute information, and uses these as multiple sets of training data. This makes it possible to machine-learn the response results of users with various attributes, and to appropriately determine whether a user is in a position to make a call. However, acquiring attribute information and using it as training data is not essential.

[0040] (study) The learning unit 51 inputs a data set of past schedule information and response results (labels), set as training data, into the learning model, and causes the learning model to learn the correspondence between schedules and response results by machine learning, thereby obtaining a learning model N in which the correspondence between schedules and response results has been machine learned. The learning model N is a machine learning model, i.e., a program, that can output, when a schedule is input, response information indicating whether or not a person can respond during the time period in which the schedule falls (for example, a label that is an estimated result of whether or not a person can respond, and the probability that the label will be assigned). Any supervised model may be used as the model.

[0041] As described above, in this embodiment, a data set of past schedule information, attribute information, and response results is used as training data. In this case, the learning unit 51 inputs the data set of attribute information, past schedule information, and response results (labels) into the learning model, and causes the learning model to machine-learn the correspondence between the attributes, schedules, and response results, thereby obtaining a learning model N in which the correspondence between the attributes, schedules, and response results has been machine-learned. This makes it possible to perform machine learning for each attribute, and it is possible to appropriately determine whether users (target persons) with various attributes are in a situation where they can make a call. Note that when attribute information is not used, it is preferable to perform machine learning using, for example, training data of past schedule information and response results for users with the same attribute. In other words, in this case, it is preferable to construct a different learning model N for each attribute.

[0042] Next, a flow of a method for training the above-described learning model will be described with reference to a flowchart. FIG. 4 is a flowchart showing an example of a processing flow of the learning method for a learning model according to the first embodiment. As shown in FIG. 4, the information processing device 40 acquires a schedule (past schedule information) using the learning unit 51 (step S1) and acquires a response result corresponding to the schedule (step S2). The information processing device 40 causes the learning unit 51 to train the learning model using the schedule and the response result as training data (step S3). The learning unit 51 trains the learning model by inputting multiple sets of training data, each of which is a set of training data, into an untrained model, thereby generating a machine-learned learning model N. Note that in this embodiment, the learning unit 51 inputs a data set of attribute information, past schedule information, and response results into the learning model as described above, and trains the learning model to learn the correspondence between the attributes, schedule, and response results by machine learning, thereby obtaining a learning model N in which the correspondence between the attributes, schedule, and response results has been machine-learned.

[0043] As described above, in this embodiment, the information processing device 40 performs machine learning on the learning model to set the learning model N, but the entity that performs machine learning is not limited to the information processing device 40. For example, another device may perform machine learning on the learning model to set the learning model N in a similar manner to that described above, and the information processing device 40 may acquire the machine-learned learning model N from that device and store it in the storage unit 48.

[0044] (Response information acquisition process using learning model) Next, a method for acquiring response information indicating whether or not a target person can make a call using the learning model N will be described.

[0045] (Getting schedule information) The schedule information acquisition unit 52 of the information processing device 40 acquires schedule information indicating the schedule of the subject. In this embodiment, the schedule information acquisition unit 52 acquires the subject's current schedule as schedule information. This allows the information processing device 40 to estimate whether the subject is currently in a position to make a phone call. However, this is not limited thereto, and the schedule information acquisition unit 52 may also acquire the subject's future schedule as schedule information. This allows the information processing device 40 to estimate whether the subject will be in a position to make a phone call in the future. The schedule information includes conference information, as well as the past schedule information used in machine learning. The conference information here preferably includes at least one of the type of the conference (e.g., subject), the time period when the conference was held, and the participants of the conference, and more preferably includes all of these.

[0046] FIG. 5 is a diagram showing an example of a schedule. The schedule information acquisition unit 52 may acquire schedule information by any method. For example, the schedule information acquisition unit 52 may acquire schedule information of the subject from the subject's schedule. For example, the subject's schedule may be stored in the storage unit 48 of the information processing device 40, and the schedule information acquisition unit 52 may read the schedule from the storage unit 48. The schedule information acquisition unit 52 may also acquire the schedule from another device (server) or the cloud. Note that the schedule is information in which the user's (subject's) schedule for each time period is registered.

[0047] In the example shown in FIG. 5, for example, department manager A as a user (subject) has scheduled a regular meeting from 11:00 to 12:00 on Monday, a visitor from 13:00 to 14:00 on Tuesday, a department meeting from 11:00 to 12:00 on Thursday, and a department manager meeting from 15:00 to 16:00 on Friday. Also, for example, employee F as a user (subject) has scheduled a regular meeting on Monday, scheduled flextime work on Wednesday, and scheduled a department meeting on Thursday. Note that the schedules registered in the schedule shown in FIG. 5 are merely examples, and any content may be registered. For example, work content may be registered in the schedule. Document preparation, field work, etc. may also be registered in the schedule. The format of the schedule may be any format.

[0048] (Acquisition of attribute information) The attribute information acquisition unit 54 acquires attribute information of the target person. The attribute information acquisition unit 54 acquires the attribute information of the target person from a schedule. The attribute information acquisition unit 54 acquires attribute information about the target person whose schedule information has been acquired by the schedule information acquisition unit 52. As described above, attribute information is information indicating the attributes of a user, in other words, information for distinguishing a user from other users. For example, the attribute information may include an affiliation in an organization (e.g., a department), a position in the organization, and the number of years since the user joined the organization. The attribute information is preferably at least one of affiliation, position, and number of years, and may be all of these. The attribute information may also be an identifier (such as a name or employee number) that identifies an individual user. Note that the attribute information acquisition unit 54 is not limited to acquiring attribute information from a schedule, and may acquire attribute information from any database other than a schedule.

[0049] (Getting response results) The result acquisition unit 56 acquires response information indicating whether the target person is available for a call, which is obtained by inputting the target person's schedule information into a learning model N that has undergone machine learning to learn the correspondence between schedules and response results. The response information is information indicating the estimation result of whether the target person is available for a call during the time period indicated by the target person's schedule information (current time in this example).

[0050] In the present embodiment, the result acquiring unit 56 inputs the schedule information of the subject acquired by the schedule information acquiring unit 52 into the learning model N, and thereby acquires the response result (estimated result of whether or not to respond) output from the learning model N as the response information of the subject. That is, the result acquiring unit 56 reads out the trained learning model N generated by the learning unit 51 from the storage unit 48, and inputs the schedule information acquired by the schedule information acquiring unit 52 into the learning model N. Since the learning model N is a model that outputs an estimated result (label) of whether or not to respond when a schedule is input, when schedule information is input, it outputs an estimated result of whether or not to respond in the time period indicated by the schedule information, and the result acquiring unit 56 acquires the estimated result of whether or not to respond as response information.

[0051] Furthermore, in this embodiment, the result acquisition unit 56 acquires response information indicating an estimation result of whether or not a response is possible, which is obtained by inputting schedule information and attribute information into a learning model N that has undergone machine learning to learn the correspondence between attributes and schedules and response results. The result acquisition unit 56 inputs the schedule information of the subject acquired by the schedule information acquisition unit 52 and the attribute information of the subject acquired by the attribute information acquisition unit 54 into the learning model N, and thereby acquires the estimation result of whether or not a response is possible output from the learning model N as the response information of the subject. Note that when attribute information is not used, it is preferable that the result acquisition unit 56 use a learning model N constructed for users with the same attributes as the subject.

[0052] In this embodiment, the information processing device 40 performs the same process for each subject to acquire response information for each subject. That is, the information processing device 40 acquires schedule information and attribute information for each subject, inputs them into the learning model N, and acquires response information for each subject.

[0053] In this way, in this embodiment, the information processing device 40 inputs schedule information, etc. into the learning model N to obtain response information, but the entity that inputs schedule information, etc. into the learning model N is not limited to the information processing device 40. For example, another device may input schedule information, etc. into the learning model N in a similar manner to the above to obtain response information, and the information processing device 40 may obtain the response information from that device and store it in the memory unit 48.

[0054] (Response information output) The output control unit 58 outputs the response information acquired by the result acquisition unit 56. In this embodiment, the output control unit 58 outputs the response information to the calling terminal (user's mobile terminal 10) via the communication unit 46.

[0055] The output control unit 26 of the transmitting terminal (user's mobile terminal 10) causes the output unit 14 to display the response information of the target person (a user other than the user) acquired from the information processing device 40. When the response information indicates that the target person is available to respond, the output control unit 58 causes information indicating that the target person is available to respond to be displayed as the response information. On the other hand, when the response information indicates that the target person is unavailable to respond, the output control unit 58 causes information indicating that the target person is unavailable to respond to be displayed as the response information. The output control unit 58 may display the response information in any display format, but in this embodiment, the response information may be displayed together with the contact information. That is, the output control unit 58 causes information indicating the target person (a user other than the user) (e.g., name or job title) to be displayed as the contact information, in association with the response information of the target person.

[0056] As described above, the mobile terminal 10 may also have the functions of the information processing device 40. In this case, the mobile terminal 10 as the information processing device 40 causes the output unit 14 to display (output) the response information acquired by the result acquisition unit 56.

[0057] FIG. 6 is a diagram illustrating an example of contacts on a mobile device according to the first embodiment. FIG. 6 illustrates an example of contacts displayed on the user's mobile device 10 for the time period from 10:00 to 11:00 on Monday in the schedule of FIG. 5. In the example of FIG. 6, a regular meeting is scheduled between Manager A and Deputy Manager B. However, learning model N determines that Manager A and Deputy Manager B are available to answer the call. Therefore, the response information for Manager A and Deputy Manager B indicates that Manager A and Deputy Manager B are available to answer the call (in the example of FIG. 6, "Available to Call"). Manager C also has a regular meeting scheduled with employee F. However, learning model N determines that Manager F is unavailable to answer the call. Therefore, Manager C displays employee F's response information indicating that Manager A and Deputy Manager B are unavailable to answer the call (in the example of FIG. 6, "Unavailable to Call"). According to the present embodiment, machine learning is performed using attribute information. Therefore, even if the same schedule is scheduled, appropriate response information can be set and displayed for each individual. Note that in the example of FIG. 6, employee H is scheduled to make an outside call. Learning model N determines that Manager H is unavailable to answer the call. Therefore, manager C displays employee F's response information indicating that Manager H is unavailable to answer the call (in the example of FIG. 6, "Unavailable to Call"). Furthermore, employee G has no scheduled meetings and has been determined by learning model N to be available to respond, so it is displayed that employee G is available to respond. Furthermore, employee I has scheduled vacation and has been determined by learning model N to be unavailable to respond, so it is displayed that employee I is unavailable to respond. Note that, as shown in FIG. 6, when the response information indicates that an employee is unavailable to respond, the mobile terminal 10 may also display schedule information for that time period (e.g., the type of meeting) together with the response information. Furthermore, when the response information indicates that an employee is available to respond, the mobile terminal 10 may also display schedule information for that time period (e.g., the type of meeting) together with the response information.

[0058] Next, the flow of processing the response information described above will be described with reference to a flowchart. FIG. 7 is a flowchart showing an example of the processing flow of the response information according to the first embodiment. As shown in FIG. 7, the information processing device 40 acquires schedule information of the target person using the schedule information acquisition unit 52 (step S10). The information processing device 40 inputs the schedule information acquired by the schedule information acquisition unit 52 to a machine-learned learning model N using the result acquisition unit 56 (step S11). Furthermore, the information processing device 40 inputs the attribute information acquired by the attribute information acquisition unit 54 to the learning model N using the result acquisition unit 56. The learning model N receives the schedule information and the attribute information as input values, and outputs response information for the receiving terminal. The result acquisition unit 56 acquires the response information output from the learning model N as a result of whether or not the call is accepted (step S12). The information processing device 40 outputs the response information using the output control unit 58 (step S13). The output control unit 58 outputs the response information acquired by the result acquisition unit 56, indicating whether or not the call is accepted, to the calling terminal.

[0059] (effect) When a user wants to call a target person, they may check the target person's schedule on a calendar before deciding whether to actually call. Therefore, if the target person has an appointment such as a meeting on their calendar, it is expected that the user will refrain from calling. However, depending on the contents of the schedule, the target person may be able to answer the phone, and refraining from calling in such a case would result in the user having to wait unnecessarily. Therefore, it is necessary to properly understand whether the target person is in a situation where they can make a call.

[0060] In contrast, the information processing device 40 according to the present embodiment acquires response information indicating an estimated result of whether the target person can answer a call, using a learning model N that has already undergone machine learning to learn the correspondence between schedules and response results. Therefore, according to the present embodiment, it is possible to appropriately determine whether the target person is in a situation where they can make a call. That is, for example, in a case where the target person has an appointment but can answer a call, it is possible to prevent the target person from refraining from making a call. Furthermore, while a user may need to take the trouble of checking a schedule to determine whether to make a call, according to the present embodiment, the user can simply visually check the contact information to determine whether the target person is in a situation where they can make a call, thereby eliminating the need to check the schedule.

[0061] (Second embodiment) Next, a second embodiment will be described. The second embodiment differs from the first embodiment in that the result acquisition unit 56 performs a determination process before inputting schedule information into the learning model N. Processes common to the first embodiment are assigned the same reference numerals as those in the first embodiment, and descriptions thereof will be omitted.

[0062] Fig. 8 is a flowchart showing a processing flow of the information processing device according to the second embodiment Fig. 9 is a diagram showing an example of contacts of the mobile terminal according to the second embodiment.

[0063] (Determine whether there is a request to not make calls) In the second embodiment, the result acquisition unit 56 determines whether the target person has requested a call rejection (step S20). If the result acquisition unit 56 determines that the target person has requested a call rejection (step S20; Yes), the result acquisition unit 56 determines that the target person is unable to make a call (cannot respond in the response information) (step S21). The output control unit 58 outputs the determination to the mobile terminal (calling terminal) (step S22), and ends the processing. In the embodiment, the call rejection request from the target person may be registered in a contact list or a calendar. The result acquisition unit 56 may also determine that the call is rejected by detecting the power state (power OFF) or operating state, such as silent mode, of the target person's mobile terminal 10. As shown in FIG. 9, for example, when employee F has requested a call rejection, the output control unit 58 outputs a message indicating that the call is rejected to the calling terminal. The calling terminal acquires a signal from the information processing device 40 via the acquisition unit 22, and causes the output control unit 26 to display "call rejection" in the column for employee F in the contact list displayed by the output unit 14.

[0064] When the result acquiring unit 56 determines that there is no request for call prohibition from the target person (step S20; No), the process proceeds to step S23.

[0065] (Determination of working status) The result acquisition unit 56 determines whether the target person is working. The result acquisition unit 56 determines whether the target person's business terminal is turned on (step S23). If the result acquisition unit 56 determines that the target person's business terminal is not turned on (step S23; No), it determines that the call is unavailable (the response information indicates that the person is unable to respond) (step S21). The output control unit 58 outputs the determination to the calling terminal (step S22), and ends the processing. The result acquisition unit 56 may determine the target person's working status from a system that manages attendance. In the embodiment, the business terminal is, for example, an information processing device such as a PC (Personal Computer) or a WS (Work Station). The business terminal may also be a mobile terminal 10. As shown in FIG. 9, for example, if the person is on vacation, the output control unit 58 outputs a signal indicating "unavailable for calls (vacation)" to the calling terminal. The calling terminal acquires the signal from the information processing device 40 via the acquisition unit 22, and causes the output control unit 26 to display "unavailable for calls (vacation)" in the field for Manager C in the vacation contact list displayed by the output unit 14. If the call cannot be made because the user is not working, such as on vacation or has just finished work, the output control unit 58 may output only information indicating that the user is not working to the calling terminal. For example, the output control unit 58 may output information indicating that the user is on vacation or outside of working hours to the calling terminal.

[0066] If the result acquisition unit 56 determines that the business terminal of the subject is powered on (step S23; Yes), the process proceeds to step S24.

[0067] (Determining whether a call is in progress) The result acquisition unit 56 determines whether the mobile terminal 10 (called terminal) is currently on a call. The result acquisition unit 56 determines whether it is within a predetermined time period from immediately after the call ended (step S24). If the result acquisition unit 56 determines that it is within a predetermined time period from immediately after the call ended (step S24; Yes), it determines that the called terminal is currently on a call (a call is in progress in the response information) (step S25). The output control unit 58 outputs the determination to the calling terminal (step S22) and ends the process. In the embodiment, the predetermined time period is, for example, five minutes, but is not limited to this, and may be less than five minutes or more than five minutes. As shown in FIG. 9, for example, when employee E is currently on a call, the output control unit 58 outputs a signal indicating that the call is in progress to the calling terminal. The calling terminal performs the same process as in step S22 and displays "on a call" in the field for employee E in the contacts of the calling terminal. Furthermore, when employee G is currently on a call within a predetermined time period (less than five minutes) from immediately after the call ended, the output control unit 58 outputs a signal indicating that the call is in progress to the calling terminal. The calling terminal acquires the signal from the information processing device 40 using the acquisition unit 22, and the output control unit 26 causes the output unit 14 to display "in a call" in the column for employee G in the contact list. Note that the output control unit 58 may output "in a call" so as to distinguish between when an actual call is in progress and when the call has just ended.

[0068] If the result acquisition unit 56 determines that it is not within a predetermined time from immediately after the end of the call with the receiving terminal (step S24; No), the process proceeds to step S26.

[0069] The result acquisition unit 56 determines whether there is an appointment (step S26). The result acquisition unit 56 determines whether the target person has an appointment from the schedule. If the result acquisition unit 56 determines that there is no appointment (step S26; No), it determines that the target person is available for a call (available to respond in the response information) (step S27), and the output control unit 58 outputs the determination to the calling terminal (step S22), and ends the processing. For example, as shown in FIG. 9, if there is no appointment, the output control unit 58 outputs a signal indicating that a call is available to the calling terminal. The calling terminal acquires the signal from the information processing device 40 by the acquisition unit 22, and the output control unit 26 displays that a call is available in the column of the employee who has no appointment in the contact list displayed by the output unit 14.

[0070] If the result obtaining unit 56 determines that there is a plan (step S26; Yes), the process proceeds to step S28. Note that the process of step S26 may be performed by the plan information obtaining unit 52.

[0071] The processing from step S28 to step S31 is the same as the processing from step S10 to step S13 shown in FIG. 7, and therefore a description thereof will be omitted.

[0072] In the second embodiment, the information processing device 40 performs a process of determining whether an application has been made, whether the person is at work, and whether the person is on a call. Even in this case, the call status of the person can be grasped. Also, by displaying that the person is on a call for about five minutes immediately after the call, the person can concentrate on work related to the content of the call. In the embodiment, work related to the content of the call can be, for example, creating a memo.

[0073] (effect) As described above, the information processing device of the embodiment includes a schedule information acquisition unit 52 that acquires schedule information of a target person, a result acquisition unit 56 that acquires response information indicating whether the target person is available to make a call, which is obtained by inputting certain information about the target person into a learning model N that has undergone machine learning to determine the correspondence between the schedule and a response result indicating whether the target person has answered a call, and an output control unit 58 that outputs the response information acquired by the result acquisition unit 56. Therefore, it is possible to appropriately determine whether the target person is available to make a call.

[0074] The system further includes an attribute information acquisition unit 54 that acquires attribute information of the target person, and the learning model N has already machine-learned the schedule and attribute information and the response results, and the result acquisition unit 56 acquires the response information obtained by inputting the schedule information and attribute information of the target person. Therefore, it is possible to appropriately determine whether each target person is in a situation where they can make a phone call.

[0075] The schedule information also includes meeting information indicating information about the meeting, and the meeting information includes at least one of the subject, scheduled time, and participants of the meeting. Therefore, it is possible to appropriately grasp whether the target person is in a situation where they can make a phone call for each schedule.

[0076] The attribute information also includes the name and position of the target person, so it is possible to appropriately determine whether each target person is in a situation where they can make a call.

[0077] The information processing method of the embodiment includes a step of acquiring schedule information of a target person, a step of acquiring response information indicating whether the target person is available to make a call, which is obtained by inputting the schedule information of the target person into a learning model N that has undergone machine learning to determine the correspondence between the schedule and a response result indicating whether the target person has answered a call, and a step of outputting the response information acquired in the step of acquiring the response information. Therefore, it is possible to appropriately determine whether the target person is available to make a call.

[0078] The program of the embodiment causes a computer to execute the steps of: acquiring schedule information of a target person; acquiring response information indicating whether the target person is available to make a call, the response information being obtained by inputting the schedule information of the target person into a learning model N that has undergone machine learning to learn the correspondence between the schedule and a response result indicating whether the target person has answered a call; and outputting the response information acquired in the step of acquiring the response information. Therefore, it is possible to appropriately determine whether the target person is available to make a call. [Explanation of symbols]

[0079] 1. Information Processing Systems 10 Mobile devices 12, 42 Input section 14, 44 Output section 16, 48 Memory section 18, 46 Communications Department 20, 50 Control unit 22 Acquisition Department 24 Processing section 26, 58 Output control section 40 Information processing equipment 51 Learning Department 52 Schedule information acquisition unit 54 Attribute information acquisition section 56 Result acquisition part

Claims

1. a schedule information acquisition unit that acquires schedule information of a target person; a result acquisition unit that acquires response information indicating whether the target person can make a call, the response information being obtained by inputting the target person's schedule information into a machine-learned learning model that has undergone machine learning to learn a correspondence relationship between a schedule and a response result indicating whether the target person has answered a call; an output control unit that outputs the response information acquired by the result acquisition unit; Equipped with Information processing device.

2. An attribute information acquisition unit that acquires attribute information of the subject, The learning model has completed machine learning of the schedule, the attribute information, and the response result, the result acquisition unit acquires the response information obtained by inputting the schedule information and the attribute information of the subject; The information processing device according to claim 1 .

3. the schedule information includes meeting information indicating information about a meeting; the meeting information includes at least one of a subject, a scheduled time, and participants of the meeting; 3. The information processing device according to claim 1.

4. The attribute information includes the name and title of the subject. The information processing device according to claim 2 .

5. acquiring schedule information of the target person; a step of acquiring response information indicating whether the target person is available to make a call, the response information being obtained by inputting the target person's schedule information into a learning model that has undergone machine learning to determine the correspondence between a schedule and a response result indicating whether the target person has answered a call; outputting the response information acquired in the step of acquiring the response information; Including, Information processing methods.

6. acquiring schedule information of the target person; a step of acquiring response information indicating whether the target person is available to make a call, the response information being obtained by inputting the target person's schedule information into a learning model that has undergone machine learning to determine the correspondence between a schedule and a response result indicating whether the target person has answered a call; outputting the response information acquired in the step of acquiring the response information; The computer executes the program.

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