Information processor and method for processing information

Abstract

To provide an information processor and a method for processing information that can use implicit knowledge properly.SOLUTION: The information processor includes: a processing unit for receiving a request to register know-how information including information in which output information and input information are related to each other, the output information being used for nursing care and being output to a care-giver and the input information being for outputting the output information; and a storage unit for storing the know-how information on the basis of the request of registration. The processing unit determines the importance level of the know-how information on the basis of relation information which relates the change of state information showing the state of a care-taker and the know-how information used to nurse the care-taker to each other.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

[0002] A system for use when a caregiver provides care to a care recipient has been known. Patent Document 1 discloses a method for generating information to be provided about the condition of a resident in a living space based on time-varying changes in detected information acquired by a sensor placed in the living space. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-18760 Summary of the Invention [Problem to be solved by the invention]

[0004] An information processing device, an information processing method, etc. that can appropriately utilize tacit knowledge are provided. [Means for solving the problem]

[0005] One aspect of the present disclosure includes a processing unit that evaluates the condition of the person being assisted with respect to physical functions, daily living functions, cognitive functions, and mental and behavioral disorders, as described in survey items by a certification investigator who determines the level of assistance or nursing care required, based on output data from sensors placed in the living environment of the person being assisted, and a memory unit that stores know-how information to be used in providing assistance to the person being assisted, wherein the processing unit is related to an information processing device that provides know-how information corresponding to the condition of the person being assisted based on the condition of the person being assisted. [Brief explanation of the drawings]

[0006] [Figure 1] 1 illustrates an example of an information processing system including an information processing apparatus according to an embodiment of the present invention. [Figure 2] 1 shows an example of a server system configuration. [Figure 3] 10 shows an example of the configuration of a terminal device. [Figure 4] FIG. 10 is a diagram illustrating the flow of a know-how information registration process. [Figure 5] Examples of know-how information. [Figure 6] FIG. 10 is a diagram illustrating a process flow for associating devices and the like with know-how information. [Figure 7] An example of the screen displayed when tagging correct data. [Figure 8] Example of registration information. [Figure 9] Example of registration information. [Figure 10] FIG. 10 is a diagram illustrating the flow of a search process. [Figure 11] Example of list information. [Figure 12] FIG. 10 is a diagram for explaining the flow of a process using know-how information. [Figure 13A] FIG. 10 is an explanatory diagram of a first similarity determination process. [Figure 13B] FIG. 10 is an explanatory diagram of a first similarity determination process. [Figure 14A] Example survey items for determining status information. [Figure 14B] Example survey items for determining status information. [Figure 15] FIG. 10 is an explanatory diagram of a process for determining importance based on changes in state information and used know-how information. [Figure 16] An example of a user page that presents recommended know-how information based on importance. [Figure 17] An example of neural network configuration. [Figure 18] Example of neural network input and output. [Figure 19] 10 is a flowchart illustrating a learning process. [Figure 20] 10 is a flowchart illustrating an inference process. [Figure 21] An example of neural network input and output when estimating evaluation results for multiple survey items. [Figure 22A]An example of know-how information used in meal assistance. [Figure 22B] An example of know-how information used in excretion assistance. [Figure 22C] Examples of know-how information used in transfer and mobility assistance. [Figure 23A] Examples of input data for each survey item. [Figure 23B] Examples of input data for each survey item. [Figure 23C] Examples of input data for each survey item. [Figure 23D] Examples of input data for each survey item. [Figure 23E] Examples of input data for each survey item. [Figure 24] 10A and 10B are diagrams for explaining a process of determining whether or not sensing data is insufficient. [Figure 25] Suggestions for when sensing data is insufficient and examples of items for which sensing data can be obtained. [Figure 26] 10 is an example of association between know-how information and multiple devices. [Figure 27] FIG. 10 is an explanatory diagram of a process for identifying a recommended supplier based on know-how information. [Figure 28] An example of a screen showing recommended suppliers. [Figure 29A] An example of a screen presenting information about a given user. [Figure 29B] An example of a screen presenting output information related to end-of-life care. [Figure 30A] An example of a screen displaying marks related to output information on nursing care software. [Figure 30B] An example of a screen on nursing care software that displays output information related to end-of-life care. [Figure 31] An example of a system configuration for providing meal assistance. [Figure 32A] 10 is an example of a screen displayed on the first terminal device. [Figure 32B] Another example of an object for output information. [Figure 33A] 10 is an example of a notification screen displayed on a second terminal device. [Figure 33B] 10 is an example of a status screen displayed on a second terminal device. [Figure 34A] An example of a screen presenting information about a given user. [Figure 34B] Examples of information needed to obtain appropriate device data (input information). [Figure 35] An example of a system configuration for providing assistance in the event of a fall. [Figure 36] An example of a screen displayed on a third terminal device. [Figure 37A] An example of a notification screen displayed on a fourth terminal device. [Figure 37B] An example of a notification screen displayed on a fourth terminal device. [Figure 37C] An example of a status screen displayed on the fourth terminal device. [Figure 37D] 10 is an example of a detailed screen displayed on the fourth terminal device. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, the present embodiment will be described with reference to the drawings. In the drawings, identical or equivalent elements are designated by the same reference numerals, and duplicate explanations will be omitted. Note that the present embodiment described below does not unduly limit the content described in the claims. Furthermore, not all of the configurations described in the present embodiment are necessarily essential components of the present disclosure.

[0008] 1. System configuration example FIG. 1 shows an example of the configuration of an information processing system 10 including an information processing device according to this embodiment. The information processing system 10 according to this embodiment provides information to caregivers, for example, in nursing care facilities, so that they can provide appropriate care regardless of their level of expertise by digitizing the "intuition" and "tacit knowledge" of caregivers performing tasks. Hereinafter, for simplicity's sake, "intuition" and "tacit knowledge" will also be referred to simply as "tacit knowledge." While the following describes a method for accumulating and utilizing tacit knowledge related to caregiving in nursing care facilities, hospitals, and the like, this embodiment is not limited to this. For example, in home care where care is provided outside the facility, tacit knowledge related to care provided by helpers may be accumulated and utilized. Furthermore, the method of this embodiment provides the tacit knowledge of experts and others to other users and can be widely applied to situations where tacit knowledge is used, such as schools, factories, and companies.

[0009] The information processing system 10 shown in FIG. 1 includes a server system 100, a terminal device 200, and a headset 300. In FIG. 1, two terminal devices 200, terminal device 200-1 and terminal device 200-2, are illustrated as the terminal devices 200, and headsets 300-1 and headsets 300-2 are illustrated as the headsets 300. However, the configuration of the information processing system 10 is not limited to that shown in FIG. 1, and various modifications are possible, such as omitting some components or adding other components. For example, the number of terminal devices 200 and headsets 300 may be three or more. The headset 300 may not be used, and may be substituted by the sound input unit or sound output unit of the terminal device 200. The same applies to FIG. 2, FIG. 3, etc., which will be described later, in which modifications such as omission or addition of components are possible.

[0010] Hereinafter, when there is no need to distinguish between the multiple terminal devices 200, they will be simply referred to as the terminal devices 200. Similarly, when there is no need to distinguish between the multiple headsets 300, they will be simply referred to as the headsets 300.

[0011] The information processing device of this embodiment corresponds to, for example, the server system 100. However, the method of this embodiment is not limited to this, and the processing of the information processing device may be executed by distributed processing using the server system 100 and other devices. For example, the information processing device of this embodiment may include the server system 100 and a terminal device 200. An example in which the information processing device is the server system 100 will be described below.

[0012] The server system 100 is connected to the terminal device 200 and the headset 300 via, for example, a network. The network here is, for example, a public communication network such as the Internet, but may also be a LAN (Local Area Network) or the like. For example, the terminal device 200 and the headset 300 are devices used by staff at a nursing facility or nurses at a hospital while on duty. Note that the headset 300 is not limited to a device that can be directly connected to the server system 100, and may also be a device that is connected to the server system 100 via the terminal device 200.

[0013] The terminal device 200 is not limited to a device that directly communicates with the server system 100. For example, a relay device (not shown) may be installed in a nursing care facility or the like. The relay device is a device that can communicate with the server system 100 via a network. The terminal device 200 and the headset 300 may be connected to the relay device using a LAN within the nursing care facility and connected to the server system 100 via the relay device. For example, it is assumed that multiple terminal devices 200 and multiple headsets 300 are used simultaneously in a nursing care facility or the like. The relay device may perform a process of selecting the terminal device 200 or headset 300 to which information from the server system 100 is to be transmitted. Alternatively, the relay device may be an administrator terminal used by an administrator of the nursing care facility and operate based on an operation input from the administrator. For example, information from the server system 100 may be displayed on a display unit of the relay device, and the administrator, viewing the displayed results, may select the terminal device 200 or headset 300 to which the information is to be transmitted. As described above, the information processing device of this embodiment can be implemented in various modifications. For example, the relay device may be included in the information processing device.

[0014] The server system 100 may be a single server or may include multiple servers. For example, the server system 100 may include a database server and an application server. The database server stores various data, which will be described later with reference to FIG. 2. The application server performs the processes, which will be described later with reference to FIGS. 4, 6, 10, 12, etc. The multiple servers here may be physical servers or virtual servers. Furthermore, when a virtual server is used, the virtual server may be provided on one physical server or may be distributed across multiple physical servers. As described above, the specific configuration of the server system 100 in this embodiment can be modified in various ways.

[0015] The terminal device 200 is a device used to present information provided by the server system 100 to a user and to allow the user of the terminal device 200 to input information. The user in this embodiment may be, for example, a caregiver who assists a person receiving care (a patient or resident) in a nursing facility or the like. Alternatively, the user of the terminal device 200 may be a home helper who provides home care, or a nurse, physical therapist, occupational therapist, or speech-language-hearing therapist who assists patients in a hospital or the like. Assistance in this embodiment refers to assistance with daily activities that the person is less able to perform on their own. Assistance includes various types of care for the person receiving care, such as assistance with eating, toileting, and transfers and mobility. Assistance in this embodiment may also be expanded to include caregiving and nursing.

[0016] Furthermore, the user in this embodiment is not limited to a user who provides care as a job, but may also be a family member of the person being assisted. Since the family member of the person being assisted may not have specialized knowledge regarding care, they do not register tacit knowledge, as will be described later with reference to Figures 4 and 6, for example, but instead use tacit knowledge registered by other users. However, this does not prevent the family member of the person being assisted from registering tacit knowledge.

[0017] Considering that the terminal device 200 will be used in situations where care is being provided, it may be, for example, a smartphone or tablet terminal that is easy to carry. However, the screens described later using Fig. 16 and the like may be viewed when the caregiver is not providing care, and the terminal device 200 may be a PC (Personal Computer) or the like.

[0018] The headset 300 also includes earphones or headphones for outputting sound and a microphone for converting sound into an electrical signal and outputting it as audio data. The headset 300 is a device that performs processing to output speech by the user as audio data and processing to present information from the server system 100 to the user as sound.

[0019] For example, a user such as a caregiver is provided with one terminal device 200 and one headset 300, and communicates with the server system 100 using the terminal device 200 and the headset 300. However, the method of the present embodiment is not limited to this, and the headset 300 may be omitted from the device used by the user, or another wearable device may be added. The wearable device here may be a glasses-type device, a wristwatch-type device, or a device of another shape.

[0020] 2 is a block diagram showing a detailed configuration example of the server system 100. The server system 100 includes, for example, a processing unit 110, a storage unit 120, and a communication unit 130. The processing unit 110 accepts a registration request for know-how information 121, which is information used in caregiving and includes output information to be output to a caregiver and input information for outputting the output information. The storage unit 120 stores multiple pieces of know-how information 121 based on multiple registration requests.

[0021] The output information here refers to information that allows the user, who is a caregiver, to utilize tacit knowledge by receiving the output information. The output information may be, for example, information to support the specific actions of the caregiver. The caregiver performs various types of assistance, such as meal assistance and toilet assistance. In addition, for each type of assistance, the caregiver performs specific actions to perform the assistance. For example, in meal assistance, the caregiver performs various assistance actions, such as sitting the person being assisted in a posture suitable for eating, picking up food with a spoon and bringing it to the person being assisted's mouth, etc. The output information may be assistance information that represents these assistance actions. For example, the output information may be information that represents whether or not an action should be performed, or information that identifies the correct timing, movement, posture, amount, etc. when performing an action.

[0022] Furthermore, experienced caregivers may have a hunch that a particular person being assisted "seems like he or she is prone to falling." This is thought to be because experienced caregivers have the ability to properly grasp the condition of the person being assisted, the living environment of the person being assisted, and naturally estimate the risk of the person being assisted. The output information of this embodiment may be information that indicates the risk of the person being assisted. In a narrow sense, the risk here refers to the risk of an incident occurring, and the incident refers to an incident that can be suppressed with appropriate assistance. The output information here does not need to instruct the caregiver to take specific actions. For example, even if the output information does not instruct specific actions to prevent falls, by properly grasping the risk of the person being assisted, the caregiver can determine actions that take the risk of falling into consideration.

[0023] As such, the output information of this embodiment may be assistance information for instructing the caregiver to take specific actions, or may be information that provides the caregiver with knowledge that is considered useful in providing assistance. When the output information is the latter, the specific action content is left to the caregiver, but it is possible to encourage the execution of assistance with higher quality than when there is no output information. Note that knowledge that is useful in assistance is not limited to risks. For example, in meal assistance, information presenting cooking styles may be used as output information. The output information may be information that represents other knowledge used in meal assistance, or may be information used in assistance other than meal assistance.

[0024] Furthermore, the input information in this embodiment may be information for outputting output information, specifically information representing elements that the expert considers in tacit knowledge. For example, if the output information is assistance information representing assistance actions to be performed by an assistant, the input information may be condition information representing the conditions for starting the assistance actions. In assistance, even the same assistance action may exist in some situations where it is useful and in others where it is not. For example, an expert provides high-quality assistance by adjusting the action according to the situation (condition), such as performing a first action when a first condition is met and performing a second action when a second condition is met. Therefore, by using the condition information as input information for the know-how information 121, it becomes possible to make the caregiver take appropriate action in appropriate situations.

[0025] However, just as the output information is not limited to assistance information representing assistance actions, the input information is not limited to condition information representing start conditions. For example, the input information may be one or more pieces of information that serve as indicators when determining output information. In the above example of risk, an expert can estimate the risk of the person being assisted by intuition or tacit knowledge, by comprehensively judging the physical function, cognitive function, specific behavioral content, etc. of the person being assisted. In this case, to use the expert's tacit knowledge as output information, information representing physical function, information representing cognitive function, information representing behavioral content, etc. may be used as input information. The same applies when information other than risk is used as output information, and the input information can widely include information that can serve as a judgment indicator when determining output information.

[0026] In the following description, an example will be described in which the input condition is condition information representing a given start condition. Also, an example will be described in which the output information is assistance information representing an assistance action to be executed when the start condition is satisfied. However, as explained above, the condition information in this specification can be extended to input information representing something other than the start condition. Furthermore, the assistance information in this specification can be extended to output information representing something other than an assistance action.

[0027] The processing unit 110 of this embodiment is configured by the following hardware. The hardware can include at least one of a circuit for processing digital signals and a circuit for processing analog signals. For example, the hardware can be configured by one or more circuit devices or one or more circuit elements mounted on a circuit board. The one or more circuit devices are, for example, an integrated circuit (IC), a field-programmable gate array (FPGA), etc. The one or more circuit elements are, for example, a resistor, a capacitor, etc.

[0028] The processing unit 110 may also be implemented by the following processor. The server system 100 of this embodiment includes a memory that stores information and a processor that operates based on the information stored in the memory. The information may be, for example, a program and various data. The processor includes hardware. Various types of processors may be used, such as a central processing unit (CPU), a graphics processing unit (GPU), or a digital signal processor (DSP). The memory may be a semiconductor memory such as a static random access memory (SRAM), a dynamic random access memory (DRAM), or a flash memory, or may be a register, a magnetic storage device such as a hard disk drive (HDD), or an optical storage device such as an optical disk drive. For example, the memory stores computer-readable instructions, and the processor executes the instructions to realize the functions of the processing unit 110 as processing. The instructions may be instructions from an instruction set that constitutes a program, or instructions that instruct the hardware circuitry of the processor to operate.

[0029] The processing unit 110 includes, for example, a registration processing unit 111 , a search processing unit 112 , a similarity determination unit 113 , a state determination unit 114 , and an importance determination unit 115 .

[0030] The registration processing unit 111 receives information corresponding to the user's tacit knowledge and performs processing to store the information in the storage unit 120. For example, the registration processing unit 111 performs processing to register the tacit knowledge as know-how information 121, as will be described later with reference to Fig. 4. Furthermore, the registration processing unit 111 may perform processing to create and update registration information 122 that associates devices, processing algorithms, parameters, etc. with the know-how information 121 in order to make the tacit knowledge easier to use, as will be described later with reference to Fig. 6.

[0031] When there is a user who wishes to use the know-how information 121 registered by the registration processing unit 111, the search processing unit 112 performs processing to accept a search request from the user and present the search results. When a user selects any of the know-how information 121 from the search results, the search processing unit 112 may also create and update list information 123, which is information that associates the user with the know-how information 121. This enables each user to use the know-how information 121 registered by others. More specifically, the list information 123 is a collection of one or more pieces of know-how information 121 that a given user is using.

[0032] The similarity determination unit 113 performs a first similarity determination process to determine the similarity between two pieces of know-how information 121. For example, when the search processing unit 112 receives the search request, it may obtain a result of the first similarity determination process from the similarity determination unit 113 and determine the know-how information 121 to be presented as a search result based on the result.

[0033] The status determination unit 114 performs a process of acquiring status information that indicates the status of the person being assisted. Details of the status information and the acquisition process will be described later.

[0034] The importance determination unit 115 performs processing to determine the importance of the know-how information 121 based on association information that associates changes in the condition information of the person being assisted with one or more pieces of know-how information 121 used to assist the person being assisted.

[0035] The storage unit 120 is a work area for the processing unit 110 and stores various types of information. The storage unit 120 can be realized by various types of memory, and the memory may be a semiconductor memory such as an SRAM, a DRAM, a ROM, or a flash memory, or may be a register, a magnetic storage device, or an optical storage device.

[0036] The storage unit 120 may store know-how information 121, registration information 122, and list information 123. The know-how information 121 is information in which condition information indicating a start condition is associated with assistance information indicating an assistance action to be performed when the start condition is satisfied. The condition information and the assistance information are, for example, text.

[0037] The registered information 122 includes one or more pieces of know-how information 121 registered by a user. The registered information 122 is, for example, information in which at least a device (sensor) for automating the determination of the start condition and specific processing content of the start determination are associated with the know-how information 121. However, the registered information 122 may include information that is not associated with a device or the like. The list information 123 is a collection of one or more pieces of know-how information 121 that a given user is using. Details of each piece of information will be described later. The storage unit 120 may also store other information.

[0038] The communication unit 130 is an interface for performing communication via a network, and includes, for example, an antenna, an RF (radio frequency) circuit, and a baseband circuit. The communication unit 130 may operate under the control of the processing unit 110, or may include a processor for communication control that is different from the processing unit 110. The communication unit 130 is an interface for performing communication in accordance with, for example, TCP / IP (Transmission Control Protocol / Internet Protocol). However, the specific communication method can be modified in various ways.

[0039] 3 is a block diagram showing a detailed configuration example of the terminal device 200. The terminal device 200 includes a processing unit 210, a storage unit 220, a communication unit 230, a display unit 240, and an operation unit 250, for example.

[0040] The processing unit 210 is configured by hardware including at least one of a circuit for processing digital signals and a circuit for processing analog signals. The processing unit 210 may also be realized by a processor. Various types of processors, such as a CPU, a GPU, or a DSP, can be used as the processor. The processor executes instructions stored in the memory of the terminal device 200, thereby realizing the functions of the processing unit 210 as processing.

[0041] The storage unit 220 is a work area for the processing unit 210, and is realized by various types of memory such as SRAM, DRAM, and ROM.

[0042] The communication unit 230 is an interface for performing communication via a network, and includes, for example, an antenna, an RF circuit, and a baseband circuit. The communication unit 230 performs communication with the server system 100 via, for example, the network.

[0043] The display unit 240 is an interface that displays various information and may be a liquid crystal display, an organic EL display, or another type of display. The operation unit 250 is an interface that accepts user operations. The operation unit 250 may be buttons or the like provided on the terminal device 200. The display unit 240 and the operation unit 250 may also be a touch panel that is integrally configured.

[0044] The terminal device 200 may also include components not shown in FIG. 3 , such as a light-emitting unit, a vibration unit, a sound input unit, and a sound output unit. The light-emitting unit is, for example, an LED (light emitting diode) and provides notification by emitting light. The vibration unit is, for example, a motor and provides notification by vibration. The sound input unit is, for example, a microphone, and the sound output unit is, for example, a speaker and provides notification by sound. The terminal device 200 may also include various sensors, such as motion sensors such as an acceleration sensor or a gyro sensor, an imaging sensor, a GPS (Global Positioning System) sensor, etc.

[0045] 2. Data registration 4 to 9, a process for registering know-how information 121 in the storage unit 120 of the server system 100 will be described below. The following process is a process for a user who is a caregiver or a nurse to accumulate his or her own tacit knowledge in a form that can be used by others.

[0046] 2.1 Text-based registration process First, the user inputs text including a start condition such as "when xxx occurs, do yyy" and an assistance action to be performed when the start condition is met. In this way, it becomes possible to store in the server system 100 situations and actions that the target user considers important in providing assistance.

[0047] 4 is a diagram illustrating the flow of the registration process of the know-how information 121. When this process starts, the user first speaks into the microphone of the headset 300, saying the above-mentioned content, "When xxx happens, do yyy." In step S101, the headset 300 converts the user's speech into voice data and transmits the voice data to the terminal device 200. Note that, prior to the voice input, a recognition process of a given trigger word may be performed, or an operation unit provided in the headset 300 may be operated.

[0048] In step S102, the terminal device 200 performs a speech recognition process on the speech data transmitted from the headset 300. In the speech recognition process, first, an acoustic analysis is performed to extract features from the speech data. Then, an acoustic model is used to identify phonemes with similar features based on the results of the acoustic analysis. Then, a pronunciation dictionary and a language model are used to convert the phonemes into words and sentences, thereby obtaining a speech recognition result. The speech recognition result is data representing the conversion result of converting speech data into text. Note that since a wide range of known methods can be applied to the speech recognition process of this embodiment, further detailed description will be omitted.

[0049] In step S103, the terminal device 200 transmits text, which is the result of the speech recognition processing, to the server system 100. The data transmitted in step S103 is, for example, text such as "If xxx, do yyy." Alternatively, the terminal device 200 may obtain two pieces of text representing the start condition and the assistance action by detecting words such as "if...", "in the case", and "when" in the speech recognition processing. In the above example, "did xxx" is the text representing the start condition, and "do yyy" is the text representing the assistance action.

[0050] In step S104, the registration processing unit 111 of the server system 100 performs a process of storing know-how information 121 in the storage unit 120 based on the acquired text. For example, the registration processing unit 111 stores information identifying the user who is the sender of the text. The information identifying the user may be identification information assigned to the headset 300 or identification information assigned to the terminal device 200. Alternatively, the information identifying the user may be a user ID that uniquely identifies the user. For example, the registration processing unit 111 stores information including the user ID, text representing the start condition, and text representing the assistance action in the storage unit 120 as know-how information 121.

[0051] Fig. 5 is an example of know-how information 121 stored in step S104. The ID in Fig. 5 is information that uniquely identifies the know-how information 121. Note that if the know-how information 121 can be uniquely identified by a combination of the start condition and the assisting action, the ID may be omitted. The registered user is a user ID or the like that indicates the user who registered the target know-how information 121. As described above, the start condition and the assisting action are text based on user input. The condition information representing the start condition is not limited to text only, but may also include voice data or the result of voice recognition processing. The result of voice recognition processing is, for example, the result of morphological analysis, such as information associating morphemes with parts of speech. The same is true for assistance information representing assistance actions, which may also include voice data or the result of voice recognition processing.

[0052] As shown in FIG. 5 , the registration processing unit 111 may also register information other than the above as the know-how information 121. For example, the know-how information 121 may include information identifying a situation in which an assistance action is performed. For example, the know-how information 121 may include information indicating which type of assistance the assistance action is to be performed in, among various types of assistance such as meal assistance, toilet assistance, and transfer / movement assistance. The know-how information 121 may also include information indicating attributes of the person being assisted, to whom the user provides assistance. The attributes here include information such as the age, sex, height, weight, medical history, and medication history of the person being assisted. The know-how information 121 may also include physical evaluation data indicating a physical evaluation of the person being assisted. The physical evaluation data includes information such as evaluation scores for Activities of Daily Living (ADL), rehabilitation history, risk of falling, and risk of bedsores. In this way, it is possible to store information regarding the type of assistance and the type of person being assisted that the know-how information 121 representing tacit knowledge should be used for.

[0053] The additional information, such as the type of assistance, the attributes of the person being assisted, and physical evaluation data, may be input voluntarily by the user. For example, the user may utter "When xxx occurs, do yyy" as well as other words such as "meal" and "tall." The user may also utter a series of utterances including additional information, such as "When a tall patient does xxx while eating, do yyy."

[0054] The server system 100 may also send questions such as "In what situations will you use this?" or "For what type of person will you use this?" to the headset 300. The user answers the questions by voice, and text representing the answer is sent to the server system 100, thereby acquiring the additional information.

[0055] As described above, in this embodiment, tacit knowledge is accumulated in the storage unit 120 of the server system 100 as know-how information 121 including text. The user can simply tweet the starting conditions and assistance actions that the user considers important while providing assistance using the headset 300, eliminating the need for complex input operations. This makes it easy to collect a large amount of tacit knowledge used in nursing care and medical care settings.

[0056] Furthermore, when the collection of know-how information 121 progresses, the server system 100 may perform an analysis process of the know-how information 121. For example, the processing unit 110 may perform a process of mapping each piece of know-how information 121 onto a feature amount space by obtaining a feature amount based on each piece of know-how information 121. For example, the processing unit 110 can estimate particularly important information among tacit knowledge by obtaining a dense region in the feature amount space.

[0057] 4 shows an example in which the voice recognition process is performed in the terminal device 200, but the present invention is not limited to this. For example, the terminal device 200 may transmit voice data received from the headset 300 to the server system 100. In this case, the processing unit 110 of the server system 100 may perform the voice recognition process. Alternatively, the voice recognition process may be performed in an external voice recognition server.

[0058] Furthermore, the user's input may be performed using text instead of voice. For example, the terminal device 200 may acquire text such as "When xxx occurs, do yyy" by accepting a character input operation by the user. The processing after acquiring the text is the same as in the example of FIG. 4.

[0059] 2.2 Devices and Mapping 4, know-how information 121 including, for example, a start condition of "if the patient's face starts to stagger while eating" and an assistance action of "stop serving the meal" is registered. These texts are meaningful information because they can alert users to cautions regarding meal assistance.

[0060] However, the method of this embodiment is not limited to storing tacit knowledge as text data, and further information may be associated with it. For example, the registration processing unit 111 associates information for automatically determining the start condition with the know-how information 121. In this way, the server system 100 can automatically determine whether the condition "the face began to stagger" is met. As a result, it is possible to suppress the variation in judgment among users, and it is possible to make less skilled users behave in the same way as more skilled users.

[0061] Specifically, in this embodiment, information specifying a device having a sensor that collects data, information specifying specific processing content for sensor information from the sensor, and the like may be associated with the know-how information 121. Hereinafter, this will be described with reference to FIGS. 6 to 8.

[0062] 6 is a diagram illustrating the flow of a process for collecting information for automation. First, in step S201, the registration processing unit 111 extracts a portion requiring interpretation from the condition information representing the start condition. Specifically, the portion requiring interpretation is a portion that is subject to automatic determination by the server system 100, such as text representing the movement, state, and environment of the person being assisted.

[0063] For example, if the start condition is "If the person's face starts to stagger while eating," the text "staggering" represents the movement of the person being assisted that should be detected in determining whether the start condition is met. The "face" part is information that identifies the body part that is the target of movement detection. Therefore, the registration processing unit 111 extracts the part "The person's face started to stagger" from "If the person's face starts to stagger while eating" as the part that requires interpretation.

[0064] Similarly, if the know-how information 121 associates the assisting action of "changing to a position that makes it easier to eat" with the starting condition of "if he only ate a little bit of rice from the spoon," then "did not eat" represents the direct movement of the person being assisted. In this case, "rice" represents the object to be eaten and is therefore used for the judgment. The part "of the spoon" also specifies the location of the rice and can be used for the judgment. The part "only a little bit" also provides a standard for the amount and can be used for the judgment. Therefore, the registration processing unit 111 extracts, for example, text "he only ate a little bit of rice from the spoon" as a part that requires interpretation.

[0065] As described above, the registration processing unit 111 may identify the portion requiring interpretation by, for example, performing morphological analysis in natural language processing. For example, the registration processing unit 111 first extracts words and phrases expressing motions and states based on the results of the morphological analysis as described above. The registration processing unit 111 may further perform processing to sequentially extract noun phrases that serve as objects, adverbial phrases and adjective phrases that modify the motions and states, and the like. For example, when morphological analysis is performed in the speech recognition processing shown in step S102 of FIG. 4, the registration processing unit 111 may extract the portion requiring interpretation based on the results of the speech recognition processing.

[0066] Alternatively, the storage unit 120 of the server system 100 may store in advance words that represent movements, states, etc. that are highly necessary to detect in caregiving. The registration processing unit 111 may extract portions that require interpretation based on a comparison process between these words and text that represents the start conditions. Various other variations on the process of extracting portions that require interpretation are possible. For example, machine learning may be performed on a neural network or the like using training data of know-how information and portions of that know-how information that require interpretation, and the portions that require interpretation may be automatically extracted using the trained model. Hereinafter, neural networks are abbreviated as NNs. An example of an NN will be described later using FIG. 17, etc.

[0067] In step S202, the registration processing unit 111 identifies devices including sensors necessary for processing based on the extraction results of the portion requiring interpretation. For example, when determining that the start condition is "the person's face has begun to stagger," the registration processing unit 111 determines that it is necessary to detect the movement of the person's face (head). For example, the registration processing unit 111 identifies, as devices necessary for processing, a camera capable of capturing an image of the person's face or a wearable device that can be worn on the head and includes a motion sensor. For example, the storage unit 120 of the server system 100 may pre-store one or more devices capable of detecting the movement of each body part of the person being assisted. Alternatively, for example, machine learning may be performed on a neural network or the like using text information of the start condition and training data of the devices necessary for processing, and the devices necessary for processing may be automatically identified using the learned model.

[0068] In addition, as a device for determining whether "only a small amount of rice was eaten from the spoon," a camera capable of capturing images of the assistant's hands or the mouth of the person being assisted is identified as a device that includes the sensor necessary for processing.

[0069] In step S203, the registration processing unit 111 determines whether the target user can use the specified device. The user here is a registered user who has registered the know-how information 121 by performing the process of FIG. 4, for example.

[0070] For example, the storage unit 120 stores in advance a list of devices available to each user. The device list is information that identifies specific devices such as smartphones, headsets, eyeglass-type wearable devices, watch-type wearable devices, and devices that can acquire biometric information of a person receiving care. More specifically, the device list may not only store information about a smartphone, but also the manufacturer of the smartphone, its model number, and the like.

[0071] Furthermore, devices available to a user are not limited to devices worn or carried by the user, but may also be devices located in a care facility, etc. For example, the device list may include cameras located in the target user's work environment, or other sensor devices. The sensors included in the sensor device can be modified in various ways, and various sensors such as a temperature sensor, a humidity sensor, an illuminance sensor, a barometric pressure sensor, an activity meter, and an odor sensor can be used.

[0072] In step S203, the registration processing unit 111 compares the device identified in step S202 with a list of devices available to registered users. In the above example, the registration processing unit 111 determines whether each device included in the device list can capture an image of the face of the person being assisted, or whether it includes a motion sensor and can be worn on the head.

[0073] If the registration processing unit 111 determines that the registered user cannot use the specified device, it skips the processing from step S204 onwards. In this case, the registered know-how information 121 is not associated with a device or the like. That is, the know-how information 121 is used in the form of text such as "do yyy when "do xxx", and automatic determination of start conditions, etc. is not performed.

[0074] The registration processing unit 111 may also instruct the terminal device 200 via the communication unit 130 to the effect that the identified device is not available, and the terminal device 200 may display a message. If it is determined that the registered user can use the identified device, the registration processing unit 111 determines information necessary to automatically determine the start condition. For example, the registration processing unit 111 determines a first processing algorithm for the device to collect device data, a second processing algorithm for processing the collected device data, and parameters to be used in the second processing algorithm. Specifically, the device data is sensor information detected by a sensor possessed by the device. For example, when a device with a camera such as a smartphone is identified, the device data (sensor information) is image data captured by the camera.

[0075] In step S204, the registration processing unit 111 determines the first processing algorithm, in other words, the registration processing unit 111 determines the processing content when acquiring sensor information.

[0076] For example, when automating the determination of the start condition that "the face is unsteady," the registration processing unit 111 needs to acquire, as sensor information, information that produces a distinguishable difference between cases where "the face is unsteady" and cases where "the face is not unsteady." That is, the sensor information in this case is information that represents the results of detecting the facial movement of the person being assisted, and may be, for example, a moving image of about one second capturing an image of an area including the head of the person being assisted, or time-series data of about one second from a motion sensor attached to the head of the person being assisted.

[0077] For example, the storage unit 120 may store a table in which multiple first processing algorithms are associated with words describing movements such as "unsteady." In the example of detecting whether a person's face is unsteady, the first processing algorithm may be an algorithm that causes a camera (image sensor) to capture video images, divide the captured images into one-second segments, and output the resulting images. Alternatively, the first processing algorithm may be an algorithm that causes a motion sensor to acquire acceleration data and angular velocity data in time series, divide the data into one-second segments, and output the resulting images. The registration processing unit 111 identifies a table based on the words extracted in step S201 and selects one of the first processing algorithms included in the table. Although not shown in FIG. 6, the registration processing unit 111 may display multiple first processing algorithms included in the identified table on the terminal device 200 and accept a user selection operation.

[0078] Determining the first processing algorithm enables the collection of device data (sensor information). Next, the processing unit 110 (registration processing unit 111) collects multiple pieces of device data acquired by the device and transmits a request to add a correct answer tag to each piece of device data to the terminal device 200 used by the registered user. The correct answer tag here represents the registered user's determination result as to whether each piece of device data satisfies the start condition. This allows information to be collected that associates input data in the start condition determination process with correct answer data that should be output when the input data is input. Based on this, the registration processing unit 111 can determine parameters to be used in the second processing algorithm. The parameters will be described later. According to the method of this embodiment, the registered user's determination criteria are reflected in the parameters, making it possible to appropriately digitize the registered user's tacit knowledge. Specific processing is described below.

[0079] In step S205, the registration processing unit 111 instructs the terminal device 200 to collect sample device data (hereinafter also referred to as sample data) via the communication unit 130. For example, the registration processing unit 111 may transmit a program that executes processing corresponding to the first processing algorithm described above. In step S206, the terminal device 200 instructs a sensor to collect sample data. Note that the sensor here may be included in the terminal device 200, or may be included in a sensor device different from the terminal device 200. That is, in step S206, the terminal device 200 may perform processing to control an internal sensor, or may transmit information instructing an external device to collect data. The terminal device 200 or the sensor device starts collecting sample data by installing the program transmitted from the registration processing unit 111.

[0080] In step S207, the sensor collects sample data. In step S208, the sensor transmits the collected sample data to the terminal device 200. In step S209, the terminal device 200 transmits the sample data to the server system 100. In step S210, the registration processing unit 111 of the server system 100 stores the received sample data in the storage unit 120.

[0081] By the processing of steps S207 to S210, one piece of sample data is stored in the storage unit 120 of the server system 100. In this embodiment, the processing of steps S207 to S210 is repeated until a predetermined number of sample data is accumulated. For example, after a registered user performs the processing shown in FIG. 4, the collection of sample data gradually progresses as the user continues with normal tasks. For example, if the start condition "the user's face began to stagger while eating" is registered, when the registered user provides meal assistance, the smartphone camera is turned on, and sample data capturing an image of the head of the person being assisted is automatically collected. Then, when the registered user continues tasks including meal assistance for a certain period of time, the collection of a predetermined number of sample data is completed.

[0082] If it is determined that collection of a predetermined number of sample data has been completed, in step S211, the registration processing unit 111 performs processing to generate screen information for displaying the sample data. In step S212, the registration processing unit 111 transmits the screen information to the terminal device 200. In step S213, the display unit 240 of the terminal device 200 displays the sample data. The screen information here may be the display screen itself, or may be information that can identify the display screen.

[0083] 7 is an example of a screen displayed on the display unit 240 in step S213. For example, if the sample data is a video of about one second capturing an image of the head of the person being assisted, the display unit 240 displays a screen including thumbnails of each video. The display unit 240 may also display a screen prompting the user to input whether or not each sample data satisfies the start condition. In the example of FIG. 7, the display unit 240 displays the text "Please select the data that shows 'the face is starting to stagger'."

[0084] In step S214, the terminal device 200 acquires correct answer data indicating whether each sample data satisfies the start condition. For example, the user performs an operation using the operation unit 250 to select the data "the face started to stagger" based on the screen of FIG. 7. For example, the terminal device 200 acquires a correct answer tag indicating that the selected sample data is correct as the correct answer data corresponding to the selected sample data. Furthermore, the terminal device 200 acquires an incorrect answer tag indicating that the correct answer data is incorrect as the correct answer data corresponding to sample data that has not been selected when the user operation is completed.

[0085] Furthermore, in step S214, the terminal device 200 may acquire information regarding the viewpoint of the user's judgment. For example, when judging whether or not the user's face is "unsteady," the maximum amount of movement from a reference position may be used as the judgment criterion. The reference position here may be, for example, the position of the face when the user is sitting upright in a chair or bed, or the center of the captured image. Alternatively, the amount of head movement in one go may be used as the judgment criterion, regardless of the reference position. In other words, even if the same word "unsteady" is extracted, the judgment on it may differ depending on the user.

[0086] The storage unit 120 may store a table in which information representing multiple viewpoints is associated with a word describing a movement, such as "unsteady." For example, the table stores two viewpoints: "the maximum angle of head movement relative to a reference position in the image is greater than a threshold value α" and "the angle of head movement per one movement is greater than a threshold value β." For example, in step S212, the registration processing unit 111 may transmit a display screen prompting the user to select one of the multiple viewpoints included in the table. Then, in step S214, the terminal device 200 receives information regarding the viewpoints for judgment along with the reception of the correct answer data.

[0087] In step S215, the terminal device 200 transmits the correct answer data to the server system 100. When a judgment viewpoint is input as described above, the terminal device 200 transmits information relating to the viewpoint to the server system 100.

[0088] In step S216, the registration processing unit 111 first receives device data as input and performs processing to determine a second processing algorithm for outputting output data indicating whether the start condition is satisfied. For example, the registration processing unit 111 determines the second processing algorithm based on the user input regarding the viewpoint of judgment acquired in step S215. For example, if the viewpoint "the maximum head movement angle relative to the reference position of the image is greater than a threshold value α" is selected, the second processing algorithm is an algorithm including processing to determine the "maximum head movement angle relative to the reference position of the image" and processing to compare the determined movement angle with the threshold value α. If the viewpoint "the head movement angle per one time is greater than a threshold value β" is selected, the second processing algorithm is an algorithm including processing to determine the "head movement angle per one time" and processing to compare the determined movement angle with the threshold value β. It is expected that the specific processing content will differ depending on whether a moving image is the target or time-series acceleration data, etc. is the target. Therefore, the second processing algorithm may be determined depending on the type of device (type of sensor) and the content of the first processing algorithm.

[0089] However, the parameters α and β in the second processing algorithm are unknown. Therefore, in step S216, the registration processing unit 111 performs processing to calculate parameters based on the sample data and the correct answer data. For example, the registration processing unit 111 determines the "maximum movement angle of the head relative to the reference position of the image" for the sample data in accordance with the second processing algorithm. Then, the registration processing unit 111 performs processing to determine the most likely α such that the movement angle is greater than α for sample data tagged as correct and is equal to or less than α for sample data tagged as incorrect.

[0090] For example, the registration processing unit 111 may classify sample data tagged with a correct answer tag and sample data tagged with an incorrect answer tag using a support vector machine (SVM). For example, the registration processing unit 111 obtains a hyperplane that separates sample data tagged with a correct answer tag from sample data tagged with an incorrect answer tag, and determines parameters such as α and β based on the hyperplane.

[0091] The second processing algorithm is not limited to the above example, and a neural network (NN) may be used. For example, the storage unit 120 may store a plurality of NNs with different structures as the plurality of second processing algorithms. For example, the storage unit 120 stores NN1, which is an NN suitable for processing with image data as input, and NN2, which is an NN suitable for processing with acceleration data and angular velocity data from a motion sensor as input. In step S216, the registration processing unit 111 performs processing to select one of a plurality of NNs including NN1 and NN2 automatically or based on user input. Note that NN1 is, for example, a convolutional neural network (CNN). NN2 is, for example, a deep neural network (DNN).

[0092] Then, in step S216, the registration processing unit 111 may perform learning processing using a neural network. For example, the registration processing unit 111 inputs sample data into the neural network and performs forward calculations using the weights obtained at that time to obtain output data. The registration processing unit 111 also obtains an objective function (for example, an error function such as a mean square error function) based on the output data and the correct answer data, and updates the weights to reduce the error using an error backpropagation method or the like. The registration processing unit 111 may store the neural network including the weights obtained when learning is completed in the storage unit 120 as a learned model. That is, when a neural network is used, the structure of the neural network corresponds to the second processing algorithm, and the weights correspond to the parameters.

[0093] In step S217, the registration processing unit 111 stores in the storage unit 120 the device used to acquire the sample data, the processing content for the device data (sensor information) of the device, and the identified parameters in association with the know-how information 121.

[0094] FIG. 8 is an example of the registration information 122 stored in step S217. As shown in FIG. 8, the registration information 122 includes a user ID representing a user who associated a device, an ID representing the know-how information 121, the device, and a processing program. The user here is, for example, a registered user who registered the know-how information 121 using FIG. 4. The device included in the registration information 122 is, for example, information such as the manufacturer and model number of the device, as described above. The processing program is, for example, a set of a second processing algorithm and parameters, and may be a neural network (NN) including weights. While an example will be described below in which the processing program included in the registration information 122 is a second processing algorithm and parameters, the processing program here may also include the first processing algorithm described above. In this way, the first processing algorithm for acquiring device data to be processed by the second processing algorithm can also be managed using the registration information 122.

[0095] The start condition can be automatically determined by using the know-how information 121 in Fig. 5 and the registration information 122 in Fig. 8. For example, in the case of the know-how information 121 corresponding to ID1 in Fig. 5, it is possible to automatically determine whether or not the start condition if1 is satisfied by performing processing according to PG1 on the device data of Device1.

[0096] As described above, the processing unit 110 (registration processing unit 111) performs text analysis processing on the condition information to identify a device used to determine whether the start condition represented by the condition information is met (for example, steps S202 and S203), and may associate information representing the identified device with the know-how information 121 (for example, step S217). In this way, the condition information is associated with a specific device, making it possible to determine whether the start condition is met using the device.

[0097] The know-how information 121 in this embodiment can be divided into three types from the viewpoint of association with devices. First, there is know-how information 121 for which it has been determined in step S203 that the device is usable and for which the processing in step S217 has been completed. This is know-how information 121 for which the start conditions can be automatically determined, since the second processing algorithm and parameters are specified in addition to the association with the device.

[0098] Second, there is know-how information 121 for which the device was determined to be unusable in step S203 and the processes from step S204 onward were not performed. Since the know-how information 121 is used in the form of text, whether or not the start condition is satisfied is determined by, for example, the user himself.

[0099] Thirdly, there is know-how information 121 for which it has been determined that the device is usable in step S203, but the processing in step S217 has not been completed. This is know-how information 121 for which sufficient sample data has not been collected to determine parameters. For example, the registration processing unit 111 may not generate registration information 122 for this know-how information 121, and may treat it in the same way as know-how information 121 for which it has been determined that the device is unusable in step S203. When sufficient sample data is accumulated in the future, the processing in step S217 will be completed and the registration information 122 will be generated, making it possible to automatically determine the start condition. There may also be cases where the collection of sample data is not completed even after the passage of time, and the registration information 122 will not be generated.

[0100] Note that FIG. 6 illustrates an example in which the parameters calculated in step S216 are stored as is in step S217. However, the processing of this embodiment is not limited to this. For example, the registration processing unit 111 may use a portion of the combination of sample data and correct data as validation data and use the validation data to calculate the accuracy rate of the determination processing using the second processing algorithm and parameters. The registration processing unit 111 transmits the accuracy rate to the terminal device 200. The terminal device 200 presents the accuracy rate and accepts user input regarding whether or not to adopt the parameters. Then, the registration processing unit 111 may perform the processing of step S217 when the user inputs that the parameters will be adopted. Furthermore, when the user inputs that the parameters will not be adopted, the registration processing unit 111 may, for example, reset the parameters and resume collection of sample data.

[0101] 2.3 Determine the correct action The above describes a method for automating the determination of the start condition among the start conditions and assistive actions included in the know-how information 121. However, the method of this embodiment is not limited to this, and processing related to assistive actions may also be automated. For example, if the know-how information 121 states that if "the person ate only a little rice from the spoon," "change the person being assisted into a position that makes it easier to eat," processing may be performed to determine the correct "posture that makes it easier to eat," or processing may be performed to issue a warning if the posture the user has the person assume deviates from the correct posture. By determining the correct answer for the assistive action, it becomes possible to have the user perform an assistive action in accordance with the registered know-how information 121, regardless of the user's level of proficiency.

[0102] The specific processing flow is the same as in Fig. 6. That is, the registration processing unit 111 extracts the portion requiring interpretation from the text representing the assistance action, as in step S201. In the above example, the registration processing unit 111 extracts "posture that makes it easy to eat."

[0103] Next, similar to steps S202 and S203, the registration processing unit 111 identifies a device for detecting an easy-to-eat posture, including a camera for capturing an image of the user and a motion sensor for detecting posture, and determines whether the device can be used.

[0104] If the data is available, the registration processing unit 111 collects sample data showing the posture of the person being assisted, and instructs the user to tag the collected data, as in steps S207 to S215. For example, the sample data is a still image of the user's entire body while eating, and the registration processing unit 111 performs processing to accept an operation to select a still image showing the user in a posture that is easy to eat from among the multiple still images.

[0105] The registration processing unit 111 determines parameters based on the assigned tags, similarly to step S216. The second processing algorithm representing the processing content for the device data may be a comparison process between the bending angle of a joint or the like and a threshold value, or may be other processing. Also, a neural network (NN) that receives the still image itself as input may be used. The parameters may be the threshold value or the weight of the NN.

[0106] The registration processing unit 111 associates the registration information 122, which includes the device, the second processing algorithm, and the parameters used when determining the assistance action, with the know-how information 121, in the same manner as in step S217.

[0107] 9 is another example of the registration information 122. As shown in FIG. 9, the registration information 122 includes a user ID representing a user who has associated a device, an ID representing the know-how information 121, a device In which is a device used for start determination, a processing program In which is a processing program used for start determination, a device Out which is a device used for determining an assisting action, and a processing program Out which is a processing program used for determining an assisting action. Like the device In, the device Out is, for example, information such as the manufacturer and model number of the device. Like the processing program In, the processing program Out is, for example, a set of a second processing algorithm and parameters, and may be a neural network including weights.

[0108] For example, in the case where the know-how information 121 indicates that "if the patient's face starts to become unsteady during a meal," the assistance action of "stop serving the meal" is easy to perform, and there is little need to find a correct action in the server system 100. Therefore, in this case, the above-described processing for the assistance action may be omitted. For example, as in the know-how information 121 of ID1 in Fig. 9, the device Out and processing program Out may have no data.

[0109] In addition, even when determining whether an assistive action is necessary, the device may be determined to be usable, but the second processing algorithm and parameters may not be determined due to factors such as insufficient sample data being collected. In this case, too, there will be no data in Device Out and Processing Program Out.

[0110] 3. Use of Data Through the above processing, the user's tacit knowledge is accumulated as know-how information 121. Furthermore, know-how information 121 for which the conditions are satisfied is associated with registration information 122 that identifies devices and the like for automating the determination of the start conditions and the determination of the assistance action. A method for using the acquired know-how information 121 will be described below.

[0111] 3.1 Search process If a user with low skill level can utilize the tacit knowledge of an expert, appropriate assistance can be provided regardless of the user's skill level. For example, each of multiple users who use the information processing system 10 selects one of the know-how information 121 stored in the storage unit 120 of the server system 100 and uses the selected know-how information 121.

[0112] 10 is a diagram illustrating the flow of a process in which each user selects and uses the know-how information 121. First, in step S301, the user performs a process of inputting search words using the headset 300. For example, the user speaks a start condition or an assistance action into the microphone of the headset 300.

[0113] In step S302, the terminal device 200 performs a voice recognition process to acquire text representing the start condition or text representing the assistance action, and in step S303, the terminal device 200 transmits the acquired text to the server system 100 as a search key.

[0114] In step S304, the server system 100 executes a search process using the acquired search key. That is, the processing unit 110 (search processing unit 112) of the server system 100 outputs, as a search result, any one of the plurality of know-how information 121 based on a search request including search information (search key) that is either text corresponding to the start condition or text corresponding to the assistance action. For example, the search processing unit 112 may output, as a search result, know-how information 121 that satisfies a condition such as the degree of match with the search key. Furthermore, as will be described later with reference to Figures 13A and 13B, the search processing unit 112 may output, as a search result, know-how information 121 that satisfies a given condition as a result of a first similarity determination process.

[0115] A situation in which a start condition is used as a search key is, for example, a situation in which the user is unable to determine an appropriate assistance action. For example, the user may recognize a situation, such as the person being assisted making a certain movement or the environment in which the person being assisted lives having changed in a certain way, but may not know what assistance action to take in that situation. In this case, by performing a search process using that situation as a start condition, know-how information 121 indicating an appropriate response in that situation is provided.

[0116] An assisting action refers to a specific action performed by a caregiver, such as feeding the person with a spoon, talking to the person, changing their posture, etc. For example, a user may recognize the actions required to assist the person being assisted with eating, toileting, etc., but may lack the ability to judge the situation and timing for performing these actions. In this case, by performing a search process based on the assisting action, know-how information 121 indicating the start conditions for performing the assisting action is provided.

[0117] In this way, according to the method of this embodiment, by performing a search process using the start condition or the assistance action as a search key, it becomes possible to determine and present know-how information 121 that is suitable for the user from among multiple pieces of know-how information 121 that represent tacit knowledge.

[0118] Note that the specific processing of step S304 can be modified in various ways. For example, when text representing a start condition is input as a search key, the search processing unit 112 may determine that the know-how information 121 satisfies the condition if at least a part of the text representing the start condition included in the know-how information 121 matches the search key. Furthermore, when text representing an assisting action is input as a search key, the search processing unit 112 may determine that the know-how information 121 satisfies the condition if at least a part of the text representing the assisting action included in the know-how information 121 matches the search key. Alternatively, the search processing unit 112 may determine the similarity between texts and determine that the condition is satisfied if the similarity is equal to or greater than a threshold.

[0119] In step S305, the server system 100 transmits one or more pieces of know-how information 121 determined to satisfy the conditions to the terminal device 200. In step S306, the display unit 240 of the terminal device 200 displays the acquired one or more pieces of know-how information 121. In step S307, the terminal device 200 accepts a selection operation by the user using, for example, the operation unit 250. That is, the user selects the know-how information 121 that he or she wishes to use from the know-how information 121 presented as the search results.

[0120] 5 is associated with the know-how information 121, selection of a specific device is required to fully utilize the know-how information 121. For example, suppose that the registered user who registered the target know-how information 121 uses a smartphone with model number BBB made by manufacturer AAA to determine the start condition, and registration information 122 indicating this is stored. However, a user who uses the know-how information 121 does not necessarily own a smartphone with model number BBB made by the same manufacturer AAA. Furthermore, if the smartphone camera is simply used, a product with a different model number from the same manufacturer may be used, or a product from another manufacturer may be used. Furthermore, for example, if the registered user's smartphone is used to capture an image of the face of a person being assisted, a camera installed on a nursing bed, a camera installed in a living room, a camera mounted on a mobile device, or the like may be used as a device for automatic determination. Therefore, when a user other than the registered user uses the know-how information 121 registered by the registered user, a device may be selected for each user.

[0121] In step S308, the terminal device 200 accepts a device selection operation by the user. For example, the storage unit 120 of the server system 100 may hold a device list indicating devices owned by the user who performed the search process, and may select and present devices similar to devices included in the registration information 122 from the device list. For example, as described above, if the device of the registered user is a smartphone, the search processing unit 112 may perform processing to display a list of smartphones and similar devices owned by the user who performed the search process on the display unit 240 of the terminal device 200. Note that if the registration information 122 is not associated with the know-how information 121, the processing of step S308 is omitted.

[0122] Next, in step S309, the terminal device 200 transmits the know-how information 121 selected by the user to the server system 100. In step S310, the server system 100 updates the list information 123 indicating the know-how information 121 currently being used by the target user. When the process of step S308 is performed, information on the selected device is also transmitted and added to the list information 123.

[0123] 10 , the processing unit 110 can acquire, from a plurality of users, requests to use any of the plurality of know-how information 121 stored in the storage unit 120. The storage unit 120 may store, for each of the plurality of users, list information 123 including one or more pieces of know-how information 121 currently in use in association with each other. In this way, it becomes possible to appropriately manage the know-how information 121 that each user is using among the large amount of know-how information 121 stored in the storage unit 120.

[0124] For example, a user with a low level of skill may increase the number of situations in which the tacit knowledge of experts can be utilized by proactively using know-how information 121. Also, when there is an excess of information and it is difficult to grasp it all, adjustments can be made such that the know-how information 121 to be used is limited to important information. Furthermore, since a user with a certain level of experience can properly perform assistance in many situations without using know-how information 121, the amount of know-how information 121 to be used may be reduced compared to a beginner.

[0125] FIG. 11 is an example of list information 123. The list information 123 includes information for identifying a user, information for identifying know-how information 121 being used by the user, and information for identifying a device for using the know-how information 121. In the example of FIG. 11, user a corresponding to User ID a is currently using know-how information 121 of ID 1 registered by a registered user corresponding to User ID 1. As shown in FIG. 8, the registered user has registered Device 1 to automate the know-how information 121 of ID 1. In contrast, user a has selected Device 1 a as a device for automating the know-how information 121 of ID 1, as shown in FIG. 11. That is, even for the same know-how information 121, the device used may differ depending on the user, and the storage unit 120 can store multiple devices associated with one know-how information 121.

[0126] In the example of FIG. 11, user a is currently using know-how information 121 of ID2 registered by a registered user corresponding to User ID2. Since neither the registered user nor a device is registered in the know-how information 121 of ID2, no device is associated with the know-how information 121 when used by user a. Note that know-how information 121 not associated with a device, such as ID2 in FIG. 11, is used in the form of text, for example. For example, if the know-how information 121 includes information such as "meal" as additional information, text corresponding to the know-how information 121 may be notified to the user at the timing of starting meal assistance.

[0127] On the other hand, in the know-how information 121 associated with a device, such as ID1 in FIG. 11, it is possible to automate the determination of the start conditions and the like.

[0128] 12 is a diagram illustrating the flow of a process for using know-how information 121 associated with a device. First, when know-how information 121 is added to list information 123, the corresponding device starts collecting sensor information in step S401. The sensor information may be collected continuously, or may be used in a specific situation related to the know-how information 121. For example, if the know-how information 121 includes information such as "excretion" as additional information, the device may start collecting sensor information when excretion assistance is started.

[0129] In step S402, the sensor transmits sensor information to the terminal device 200. If the device here is the terminal device 200, the processing of step S402 corresponds to the transfer of data from the sensor to a processor within the terminal device 200. In step S403, the terminal device 200 transmits the sensor information to the server system 100.

[0130] In step S404, the processing unit 110 automatically determines the start condition based on the sensor information. For example, the processing unit 110 determines the second processing algorithm and parameters based on the registration information 122 in Fig. 8. The processing unit 110 uses the sensor information as input data and performs processing in accordance with the second processing algorithm and parameters to obtain output data indicating whether the start condition is satisfied.

[0131] If it is determined that the start condition is satisfied, in step S405, the processing unit 110 identifies an assisting action based on the know-how information 121. In step S406, the processing unit 110 transmits information representing the identified assisting action to the terminal device 200. In step S407, the terminal device 200 transmits the information representing the assisting action to the headset 300. In step S408, the headset 300 announces the assisting action using a speaker. Note that the information representing the assisting action here may be text, and the processing in step S408 may be a voice readout process. However, as described above with reference to FIG. 9, the processing unit 110 may also find a correct answer to the assisting action and make a notification based on the correct answer.

[0132] As described above, the method of this embodiment digitizes the tacit knowledge of experienced users, enabling even less skilled users to provide appropriate care. For example, since less skilled users can provide care equivalent to that of an expert, the reproducibility of care is improved. Furthermore, the variation in care skills is reduced, facilitating organizational management, reducing the occurrence of incidents such as falls by care recipients. As a result, for example, in nursing care facilities, the occurrence of empty beds due to hospitalization and the occurrence of overtime work due to the preparation of accident reports can be reduced. Furthermore, reducing incidents prevents users from becoming overly sensitive to risk, thereby reducing stress and, as a result, reducing employee turnover. Furthermore, improving user skills and the working environment can increase the satisfaction of care recipients and their families and improve their quality of life (QOL).

[0133] Note that the information processing system 10, server system 100, terminal device 200, etc. of the present embodiment may implement part or most of their processing using a program. In this case, a processor such as a CPU executes the program to implement the information processing system 10, etc. of the present embodiment. Specifically, a program stored in a non-transitory information storage medium is read, and the read program is executed by a processor such as a CPU. Here, the information storage medium (computer-readable medium) stores programs, data, etc., and its functions can be implemented by an optical disk, a HDD, or memory (card-type memory, ROM, etc.). The processor such as a CPU then performs various processes of the present embodiment based on the program stored in the information storage medium. That is, the information storage medium stores a program for causing a computer to function as each part of the present embodiment.

[0134] The technique of this embodiment can also be applied to an information processing method including processing executed in the information processing device 10. The information processing method of this embodiment accepts a registration request for know-how information 121 including information in which condition information indicating a given start condition is associated with assistance information indicating an assistance action to be performed when the start condition is satisfied, and outputs, as a search result, any one of the plurality of pieces of know-how information 121 stored based on the plurality of registration requests, based on a search request including search information that specifies either the start condition or the assistance action.

[0135] 3.2 Similarity determination between know-how information Furthermore, the server system 100 (similarity determination unit 113) of this embodiment may perform a process of determining the similarity between given know-how information 121 and other know-how information 121. For example, in step S304 of Fig. 10, the search processing unit 112 may determine that not only the know-how information 121 extracted using the search key but also similar know-how information that is know-how information 121 similar to the given know-how information 121 satisfies the condition.

[0136] For example, the similarity determination unit 113 may determine the similarity between two pieces of know-how information 121 based on additional information included in the know-how information 121. For example, the additional information includes words that represent the type of assistance and attributes of the person being assisted, as shown in Fig. 5. The similarity determination unit 113 determines that the similarity is high when the same word is included in the two pieces of know-how information 121. Alternatively, synonyms may be defined for each word, and the similarity determination unit 113 may determine that the similarity between the two pieces of know-how information 121 is high when a synonym of a word included in one piece of know-how information 121 is included in the other piece of know-how information 121.

[0137] Alternatively, the similarity determination unit 113 may determine the similarity based on text mining. For example, the similarity determination unit 113 performs text mining on at least one of the text representing the start condition and the text representing the assistance action. The similarity determination unit 113 calculates tf-idf, which indicates the importance of each word extracted by text mining. tf represents the frequency of occurrence of a word, and idf represents the inverse document frequency. tf-idf is an index in which a word that appears frequently has a higher importance and a word that appears in many documents has a lower importance. For example, the similarity determination unit 113 calculates a vector in which tf-idf is associated with each word that appears in the know-how information 121. The similarity determination unit 113 calculates a vector for each of the two know-how information 121, and calculates the similarity between the two know-how information 121 based on the angle θ between the two calculated vectors. For example, the similarity is cosθ. However, various methods for determining the similarity between two documents are known, and these methods can be widely applied in this embodiment. Furthermore, the part that is the target of text mining is not limited to at least one of the start condition and the assistance action, and may include additional information.

[0138] The similarity determination unit 113 may also determine similar know-how information to a given know-how information 121 from the viewpoint of whether or not the know-how information is frequently used together with the given know-how information 121 .

[0139] For example, the plurality of know-how information 121 includes first know-how information, second know-how information, and third know-how information, and the processing unit 110 (similarity determination unit 113) performs a similarity determination process to determine the similarity between any two of the plurality of know-how information 121 stored in the storage unit 120. At this time, the similarity determination unit 113 determines the similarity based on the number of users whose list information 123 includes the first know-how information and the second know-how information, and the number of users whose list information 123 includes the first know-how information and the third know-how information.

[0140] 13A and 13B are diagrams illustrating the similarity determination process. Fig. 13A is an example of list information 123 related to users who are using both the first know-how information corresponding to IDa and the second know-how information corresponding to IDb. In the example of Fig. 13A, 100 users corresponding to UserIDx1 to UserIDx100 are using both the first know-how information and the second know-how information.

[0141] 13B is an example of list information 123 related to users who are using both the first know-how information corresponding to IDa and the third know-how information corresponding to IDc. In the example of FIG. 13B, only one user corresponding to UserIDy1 is using both the first know-how information and the third know-how information.

[0142] In this case, the second know-how information is likely to be used together with the first know-how information, and the third know-how information is unlikely to be used together with the first know-how information. The similarity determination unit 113 determines that the similarity between the first know-how information and the second know-how information is higher than the similarity between the first know-how information and the third know-how information.

[0143] In this way, it is possible to present to a user know-how information 121 that is useful to use together as similar know-how information. For example, it is possible to present a combination of useful know-how information 121 to a user who has performed the search process described above using FIG. 10. Note that the first know-how information and the second know-how information here may be different in the type of assistance. For example, if the first know-how information is related to meal assistance, the second know-how information is related to excretion assistance or transfer assistance. In this way, it is possible to include know-how information 121 that is determined to have a low similarity from the viewpoint of additional information, etc., in the similar know-how information.

[0144] 3.3 Determining the Importance of Know-How Information As described above, in this embodiment, a user can easily register know-how information 121. Furthermore, a search process allows users other than the registered user to use the know-how information 121. However, if it is easy to register know-how information 121, there is a high probability that a large amount of know-how information 121 will be accumulated in the storage unit 120. In this case, the importance of each piece of know-how information 121 may be determined in order to efficiently use the large amount of know-how information 121. The following describes the first importance from the perspective of the person being assisted who receives assistance and the second importance from the perspective of the caregiver who provides assistance using the know-how information 121. In the following description, the first importance will also be simply referred to as importance.

[0145] 3.3.1 Importance considering the person receiving care <Status information> The processing unit 110 of this embodiment may determine the importance of the know-how information 121 based on association information that associates a change in status information indicating the status of the person being assisted with the know-how information 121 used to assist the person being assisted. In this way, the importance can be determined from the perspective of how the assistance provided using the know-how information 121 affected the status of the person being assisted. In other words, the importance of the know-how information 121 is determined according to the degree of contribution to improving or maintaining (suppressing deterioration of) the status of the person being assisted.

[0146] The condition information here may be a level of care required, which indicates the degree to which the person being assisted needs care. Since various methods are known for calculating the level of care required as numerical data, it becomes possible to use an index that is easy to evaluate as the condition information.

[0147] The level of care required is an index used to determine whether a subject is in need of care or assistance, and the degree of need. A state requiring care is one in which, for example, constant care is required. A state requiring assistance is one in which assistance is required for daily life such as housework and dressing, and in which preventive care services are particularly effective.

[0148] For example, the level of care required may be information that represents the results of an assessment on a seven-level scale, from 1-2 requiring assistance and 1-5 requiring care, determined using the method described in https: / / www.mhlw.go.jp / stf / seisakunitsuite / bunya / hukushi_kaigo / kaigo_koureisha / nintei / gaiyo2.html. For example, the level of care required is determined based on the results of a survey conducted by a certified surveyor on various items such as physical function, daily living activities, daily living functions, cognitive function, mental and behavioral disorders, and adaptation to social life. Hereinafter, survey items will also be referred to simply as items.

[0149] Figures 14A and 14B show examples of items used to assess the level of care required. Each item is assessed using one of three evaluation criteria: ability, assistance, or presence / absence. Ability refers to the degree to which a person has the ability to perform the item. For example, for "Turning over in bed," listed in 1-3 of the Physical Functions and Activities of Daily Living, the evaluation result is one of the following levels: "Can do without holding on," "Can do with holding on to something," or "Cannot do." Assistance refers to the degree of assistance required to perform the item. For example, for "Body Washing," listed in 1-10 of the Physical Functions and Activities of Daily Living, the evaluation result is one of the following levels: "Independent," "Partial assistance," or "Full assistance." Presence / absence refers to the presence or absence of the event corresponding to the item. Each item is also classified into one of five categories based on its impact on the lives of patients and elderly people: ADL / Activities of Daily Living, Cognitive Function, Behavior, Social Life, and Medical Care.

[0150] These items are disclosed in, for example, https: / / www.mhlw.go.jp / file / 06-Seisakujouhou-12300000-Roukenkyoku / 0000077237.pdf, and may be used to determine the level of care required in this embodiment. The items for determining the level of care required may also include items not shown in Figures 14A and 14B, such as whether end-of-life care should be initiated, whether a lift, wheelchair, or walker should be used, and whether toilet guidance should be provided.

[0151] The condition information of the person being assisted may also be an evaluation result of the person being assisted's ADL (Activities of Daily Living). The evaluation of ADL may be obtained using, for example, the FIM (Functional Independence Measure). In the FIM, 13 items related to motor ADL and 5 items related to cognitive ADL are each evaluated on a 7-point scale from 1 to 7, thereby evaluating ADL as a numerical value from 18 to 126 points. Note that the evaluation method of ADL is not limited to the FIM, and various methods can be applied.

[0152] However, the status information in this embodiment is not limited to the above example and can be expanded to an index that indicates the status of the person being assisted. The status of assistance is, specifically, information that specifies whether assistance is needed, the type and degree of assistance needed, etc.

[0153] An example in which the status information is the level of care required will be described below. In the following, the status information is assumed to be any one of the numerical values 1 to 7. The numerical value 1 represents level 1 of support required, 2 represents level 2 of support required, 3 represents level 1 of care required, 4 represents level 2 of care required, 5 represents level 3 of care required, 6 represents level 4 of care required, and 7 represents level 5 of care required. In other words, in the following example, the smaller the numerical value representing the status information, the better the condition of the person being assisted.

[0154] <Flow of importance determination process> FIG. 15 is a diagram illustrating a method in this embodiment. The horizontal axis of FIG. 15 represents time, with t1 and t2 representing given timings. Furthermore, "state" represents the value of the status information at each timing. The status determination unit 114 of this embodiment sets a period of a given length and acquires status information of the person being assisted for each period. The given length here is, for example, one month, but may be a period of a different length. In the example of FIG. 15, the status determination unit 114 performs processing to acquire status information of the person being assisted at each of timings t1 and t2.

[0155] For example, the status information may be determined by a specialist such as a doctor based on the results of an investigation by a certified investigator. The server system 100 accepts input of the status information determined by the doctor or other expert. The status determination unit 114 acquires the transmitted status information. Alternatively, the status determination unit 114 may calculate the status information when the results of the investigation by the investigator are acquired. For example, the above-mentioned https: / / www.mhlw.go.jp / stf / seisakunitsuite / bunya / hukushi_kaigo / kaigo_koureisha / nintei / gaiyo2.html describes a method for estimating the reference time for nursing care certification, etc. using a tree model and the correspondence between the reference time for nursing care certification, etc. and the level of nursing care required. The status determination unit 114 may calculate the status information from the investigation results by performing a similar process. Alternatively, the status determination unit 114 may perform a process for estimating the evaluation results for each item, as described below with reference to FIG. 18, etc. In this case, the investigation by the investigator may be omitted.

[0156] Furthermore, in this embodiment, the processing unit 110 acquires know-how information 121 (tacit knowledge) used to assist each person being assisted. For example, the user who is the caregiver may be a family member of the person being assisted. It is highly likely that family members do not provide assistance as a job, but rather provide assistance only to the person being assisted. Therefore, it is possible to consider that the know-how information 121 included in the list information 123 of the family user has been used to assist the target person being assisted.

[0157] In addition, the user here may be a caregiver or a nurse working at a nursing facility or a hospital. However, if the user is in charge of two assisted persons A and B, not all of the know-how information 121 included in the list information 123 of the user is necessarily used in common for both assisted persons A and B. For example, the user may use part of the know-how information 121 included in the list information 123 to assist assisted person A and other part to assist assisted person B. For example, the storage unit 120 may store, in addition to the list information 123 shown in FIG. 11 , information that can identify the assisted person to which each piece of know-how information 121 applies. In this case, even if the user is in charge of multiple assisted persons, the know-how information 121 to be used for each assisted person can be identified.

[0158] Fig. 15 shows an example of three pieces of association information acquired for three persons receiving assistance during a period from t1 to t2. For example, a person receiving assistance whose status information value was 4 at t1 received assistance using four pieces of know-how information 121 with IDs of 1, 2, 34, and 56, respectively, during the period from t1 to t2, and the value of the status information at t2 became 5. In the following, to simplify the explanation, the know-how information 121 with ID i will also be referred to as ai, where i is an integer equal to or greater than 1.

[0159] In the example of Fig. 15, similarly, for example, a person being assisted whose status information value was 2 at t1 received assistance using three pieces of know-how information 121 with IDs of 1, 2, and 31, respectively, during the period from t1 to t2, and the value of the status information at t2 became 1. Similarly, a person being assisted whose status information value was 2 at t1 received assistance using know-how information 121 with ID 1 during the period from t1 to t2, and the value of the status information at t2 remained unchanged at 2.

[0160] In this way, one piece of association information is acquired by identifying the change in the condition information when one unit of time period has elapsed for one person receiving care and the know-how information 121 used during that one unit of time period. Although three pieces of association information are exemplified in Fig. 15, more pieces of association information can be acquired by increasing the number of people receiving care and the length of the target time period.

[0161] The importance determination unit 115 determines the importance of the know-how information 121 based on the association information. For example, if the condition of the person receiving care tends to improve when the know-how information 121 a1 is used, the importance determination unit 115 determines the importance of a1 to be high. In the above example, the higher the value of the condition information, the more assistance (care) is needed, so an improvement in the condition corresponds to a decrease in the value. However, as can be seen from the example of FIG. 15 , when assistance is provided using the know-how information 121 a1, the condition may improve, worsen, or remain the same. Furthermore, when multiple know-how information 121 are combined, it is necessary to identify which know-how information 121 contributes to the change or maintenance of the condition information. Furthermore, it is possible that using a given know-how information 121 in combination with other know-how information 121 contributes to the improvement or maintenance of the condition information more than using it alone. In other words, simply focusing on an individual know-how information 121 makes it difficult to determine the extent to which that know-how information 121 influences the change in the condition information.

[0162] Therefore, the processing unit 110 (importance determination unit 115) may perform processing to identify important know-how information based on regression analysis in which a value related to the state information is used as a response variable and information indicating use / non-use of the know-how information 121 is used as an explanatory variable. Specifically, the regression analysis here may be multiple regression analysis in which information indicating use / non-use of each of the multiple know-how information 121 is used as an explanatory variable. The important know-how information here refers to know-how information 121 whose importance is determined to be equal to or greater than a predetermined level.

[0163] The value related to the state information is, for example, a value based on the difference between the value of the state information at the start of the target period, Initial, and the value of the state information at the end, Output. As mentioned above, we are considering an example in which the better the state, the smaller the value, so the reward shown in the following formula (1) may be used as the value related to the state information. Reward is numerical data that increases as the degree of improvement in the state increases; it is a positive value when the state improves, 0 when the state remains unchanged, and a negative value when the state worsens. reward = Initial - Output …(1)

[0164] Also, let Ai be a variable indicating whether or not the know-how information 121 corresponding to ai is used. For example, Ai is a variable that is 1 when ai is used to assist the target person being assisted, and is 0 when it is not used. In this case, multiple regression analysis is performed based on the regression equation of the following equation (2). In the following equation (2), α1 to αn are partial regression coefficients, and β is a constant term. n is a positive integer indicating the number of know-how information 121. In a narrow sense, n represents the number of know-how information 121 that appears at least once in the multiple pieces of association information to be processed, out of the know-how information 121 stored in the storage unit 120. In other words, the importance determination unit 115 may exclude know-how information 121 that does not appear even once in the association information from the targets of importance determination. reward = β + α1×A1 + α2×A2 + … + αn×An …(2)

[0165] For example, importance determination unit 115 performs processing to find the most likely α1 to αn and β using the least squares method based on multiple pieces of association information. Multiple regression analysis is a well-known technique, so a detailed description will be omitted.

[0166] The importance determination unit 115 also tests the significance of each partial regression coefficient. The significance test is, for example, a t-test. The importance determination unit 115 performs a t-test on each partial regression coefficient to obtain a t-value, and then obtains a p-value from the t-value. If the p-value is smaller than a given threshold, the importance determination unit 115 determines that the target partial regression coefficient is significant, that is, the use / non-use of know-how information 121, which is an explanatory variable corresponding to the partial regression coefficient, affects changes in the state information, which is the objective variable. The given threshold here is, for example, 0.05, but other values may be used.

[0167] Furthermore, in the above formula (2), the better the condition of the person being assisted, the larger the value of the objective variable, reward. Therefore, if the partial regression coefficient is positive, the target know-how information 121 may contribute to improving the condition, and if the partial regression coefficient is negative, the target know-how information 121 may contribute to worsening the condition. Therefore, if the p-value is smaller than 0.05 and the partial regression coefficient is positive, the importance determination unit 115 determines that the importance of the corresponding know-how information 121 is high.

[0168] The importance determination unit 115 may determine the importance in two stages, high and low. In the above example, the know-how information 121 whose p-value is smaller than the threshold and whose partial regression coefficient is positive has a high importance, and the other know-how information 121 has a low importance. However, the importance determination unit 115 may determine the importance in three or more stages depending on the value of the p-value or the value of the partial regression coefficient.

[0169] In this embodiment, as described above, it is easy to register the know-how information 121 representing tacit knowledge. For example, when a large number of users register their own tacit knowledge as know-how information 121, the storage unit 120 can store a huge amount of know-how information 121. For example, the number of know-how information 121 may reach tens of thousands to hundreds of thousands. In this regard, the importance determination unit 115 can determine the importance of the know-how information 121 by performing the above-mentioned process.

[0170] Furthermore, the number of explanatory variables that significantly affect the above-mentioned objective variable and have a positive partial regression coefficient is not limited to one, and there may be multiple explanatory variables. That is, the importance determination unit 115 may identify multiple pieces of know-how information 121 as know-how information 121 with high importance. For example, it is assumed that the importance determination unit 115 determines that two pieces of know-how information 121, as and at, have high importance among n pieces of know-how information 121 corresponding to a1 to an. s and t are integers between 1 and n, and s≠t.

[0171] In this case, although "as" and "at" can be used independently, it is considered that the quality of care can be improved by using these two in combination. Therefore, when the importance determination unit 115 determines that the importance of a plurality of pieces of know-how information 121 is high in one multiple regression analysis, the importance determination unit 115 may store information specifying the combination in the storage unit 120.

[0172] <Attributes of the person receiving assistance> Through the above process, it is possible to identify, from among the multiple pieces of know-how information 121, know-how information 121 that is considered important for improving and maintaining the condition of the person receiving care. However, if the attributes of the people receiving care differ significantly, the type of assistance that is important for the target person receiving care may also differ. The attributes here refer to, for example, the condition information at the start of the target period. For example, a person receiving assistance level 1 who is currently in a relatively good condition can often do things on their own, and assistance to prevent them from transitioning to needing nursing care and assistance to encourage independence are effective. On the other hand, for a person receiving care level 3 or higher, extensive assistance at a facility is considered effective in preventing their condition from worsening. In other words, the important know-how information 121 may differ between a person receiving care in a relatively good condition and a person receiving care in a relatively poor condition.

[0173] Therefore, the storage unit 120 may store attribute information representing the attributes of the person being assisted. The processing unit 110 identifies important know-how information for assisting the person being assisted who has the given attribute based on the association information corresponding to the person being assisted who has been determined to have the given attribute based on the attribute information. Note that the important know-how information is information obtained based on, for example, regression analysis, and specifically, multiple association information corresponding to multiple people being assisted who have been determined to have the given attribute may be used in the processing.

[0174] For example, the attribute information is status information, and the importance determination unit 115 performs a process of identifying know-how information 121 that is important to an assisted person who is in the support level 1 state based on association information acquired for an assisted person whose status information is 1 (support level 1). The importance determination unit 115 also performs a process of identifying know-how information that is important to an assisted person who is in the support level 2 state based on association information acquired for an assisted person whose status information is 2 (support level 2). The same applies to other statuses, and the importance determination unit 115 determines the importance of the know-how information 121 for each of the care levels 1 to 5. Note that in this example, the status information is information in seven levels, and there are seven types of attributes represented by the attribute information. In this case, the importance determination unit 115 performs an importance determination for each of the seven types of attributes. However, the relationship between the status information and the attribute information can be modified in various ways, such as processing support level 1 and support level 2 together as a single attribute.

[0175] Considering the attributes of the person being assisted in this way makes it possible to improve the processing accuracy in the importance determination unit 115. That is, it becomes possible to appropriately identify know-how information 121 suitable for improving and maintaining the condition for each attribute.

[0176] Note that the attributes in this embodiment are not limited to status information at the start of the period. For example, as described above as the attributes of the person being assisted in the know-how information 121 of Fig. 5, the attributes here may include information such as the age, sex, height, weight, medical history, and medication history of the person being assisted. The attributes may also include physical evaluation data that indicates a physical evaluation of the person being assisted, and the physical evaluation data may include information such as rehabilitation history, fall risk, and bedsore risk.

[0177] <Example of processing using importance> The processing unit 110 may also perform processing to present recommended know-how information recommended for assisting a given person being assisted, based on the know-how information 121 used for assisting the given person being assisted and the know-how information 121 determined to be of high importance by the importance determination unit 115. In this way, it becomes possible to encourage the caregiver assisting the person being assisted to use the know-how information 121 that is highly important, that is, the know-how information 121 that is considered to be useful for improving and maintaining the condition of the person being assisted.

[0178] More specifically, the processing unit 110 may determine the recommended know-how information based on the attribute information of the person being assisted, as described above. For example, a first attribute to an m-th attribute are defined as attributes, and the importance determination unit 115 performs processing to determine know-how information 121 with a high degree of importance for each of the first attribute to the m-th attribute. m is an integer of 2 or more. If it is determined that the target person being assisted has the k-th attribute, the processing unit 110 identifies the recommended know-how information based on the know-how information 121 with a high degree of importance corresponding to the k-th attribute. k is an integer of 1 or more and m or less. In this way, it becomes possible to recommend know-how information 121 that is suited to the attributes of the person being assisted.

[0179] The recommended know-how information is, for example, know-how information 121 that is similar to the currently used know-how information 121 and has a high level of importance. The recommended know-how information is, for example, know-how information 121 that is effective when used in combination with the currently used know-how information 121. Each example will be described below using an example of a display screen.

[0180] Fig. 16 is a service usage screen of the information processing system 10 of this embodiment, and is an example of a user page on which information about a given user is displayed. The user here is a caregiver who provides care, and may be a family member of the person being assisted, or a caregiver working at a care facility or the like. The user page is displayed on the display unit 240 of the terminal device 200, for example, under the control of the processing unit 110 of the server system 100. The processing unit 110 may generate and transmit the image shown in Fig. 16. Alternatively, the processing unit 110 may transmit information for image generation to the terminal device 200, and the processing unit 210 of the terminal device 200 may generate the image.

[0181] 16, the user page includes an area RE1 that displays know-how information 121 currently being used by the target user, and an area RE2 that displays registered know-how information 121. RE1 displays, for example, the know-how information 121 included in the list information 123 described above with reference to FIG. 11. RE2 displays, for example, the know-how information 121 registered by the target user. By displaying in this manner, the user can easily grasp the registration and usage status of the know-how information 121.

[0182] The user page may also include a display for proposing replacement of the know-how information 121 currently in use. For example, the processing unit 110 identifies know-how information 121 similar to the know-how information 121 determined to have high importance. The importance determination unit 115 determines the importance as described above. The similar know-how information 121 is determined to have high importance as described above by the similarity determination unit 113. For convenience of explanation, the know-how information 121 determined to have high importance by the importance determination unit 115 is referred to as important know-how information. The know-how information determined to be similar to the important know-how information by the similarity determination unit 113 is referred to as replacement target know-how information.

[0183] Then, when the know-how information 121 currently being used by the target user includes know-how information to be replaced, the processing unit 110 proposes replacing the know-how information to be replaced with important know-how information. For example, the importance determination unit 115 determines by multiple regression analysis that the know-how information 121 corresponding to a34 has high importance. Furthermore, the similarity determination unit 113 determines that the similarity between a34 and a5 is high. In this case, a34 is important know-how information, and a5 is know-how information to be replaced.

[0184] As shown in FIG. 16, the target user is currently using know-how information 121 corresponding to a5. In FIG. 16, a5 is expressed as "if5-then5" using the start condition and the assistance action. The same applies to the other know-how information 121. Since a5 and a34 are similar, they can be used, for example, for the same type of assistance in a similar situation. For example, both a5 and a34 are know-how information for appropriately determining the pace of eating in meal assistance. Therefore, even if the know-how information 121 currently in use is replaced with similar know-how information 121, it is considered that the user will be able to use the know-how information 121 in a similar situation as before the replacement.

[0185] And a34 is determined to be more important than a5. That is, by replacing a5 with a34, the user can use the know-how information 121 in a similar situation and provide assistance that is more useful for improving and maintaining the condition of the person being assisted.

[0186] 16, "if5-then5" corresponding to the know-how information 121 currently in use is displayed in the area RE1, while "if34-then34" which is important know-how information recommended for replacement is displayed as Replaced tacit knowledge. By suggesting replacement with know-how information 121 that is similar to the know-how information 121 currently in use and has a high level of importance, it is possible to change the assistance provided by the user to one of higher quality that contributes more to the condition of the person being assisted.

[0187] The user page may also include a display for suggesting know-how information 121 that is recommended to be used in combination with the know-how information 121 currently in use.

[0188] As described above, when there are a plurality of pieces of know-how information 121 determined to have high importance, the importance determination unit 115 stores information specifying the combination in the storage unit 120. When the know-how information 121 currently being used by the user is included in the combination, the processing unit 110 performs processing to suggest to the user that the other know-how information 121 included in the combination be added.

[0189] For example, the importance determination unit 115 determines that four pieces of know-how information 121, a6, a7, a10, and a34, have high importance, and stores information about this combination in the storage unit 120. In the example of FIG. 16, the user is currently using the know-how information 121 of a6. Therefore, the processing unit 110 suggests adding a7, a10, and a34 and combining them with a6, rather than using a6 alone. Note that in the example of FIG. 16, a34 is displayed in RE1 as a replacement, and therefore the know-how information 121 to be added as a replacement may be two pieces of know-how information 121, a7 and a10.

[0190] For example, as shown in FIG. 16, the user page may include an RE3 that displays know-how information 121 that is recommended for use. In the RE3, for example, know-how information 121 that is recommended to be combined with the know-how information 121 currently in use is displayed as Recommend tacit knowledge. In the above example, "if7-then7" and "if10-then10" corresponding to a7 and a10 are displayed in the RE3. In this way, it is possible to suggest the combined use of multiple pieces of know-how information 121 that are considered to be useful for improving and maintaining the condition of the person being assisted, thereby further improving the quality of assistance.

[0191] 16, in addition to the know-how information 121 currently in use, the area shown in RE1 may display devices associated with the know-how information 121. The devices displayed here are devices included in the list information 123, as shown in FIG. 8 or 9. In this way, information on devices used in the know-how information 121 currently in use can be presented to the user in an easy-to-understand manner.

[0192] 16, the area shown in RE2 may display information indicating the registration status of devices related to the know-how information 121 registered by the target user. For example, for know-how information 121 that is not associated with the device in FIG. 6 (No in step S203), an object including text indicating this, "No device," is displayed.

[0193] Furthermore, for know-how information 121 that is not yet ready for the user to add correct answer data because the device has been associated (Yes in step S203) but not enough sample data has been collected (the loop of steps S207-S210 is ongoing), an object containing the text "Not ready" to that effect is displayed.

[0194] Furthermore, since sufficient sample data has been collected (the loop of steps S207-S210 ends), an object containing the text "ready" indicating that the know-how information 121 is ready for the user to add correct answer data is displayed in the know-how information 121. For example, when the user performs an operation to select the object displayed as "ready," the processing from step S211 onward in FIG. 6 is started.

[0195] 16, an object including the text "completed" indicating that the know-how information 121 has been completed after the user has added the correct answer data, determined the second processing algorithm and parameters (step S216), and created the registration information 122 (step S217) may be displayed. As described above, by displaying not only the information identifying the know-how information 121 but also the information associated with the know-how information 121, it is possible to clearly present the usage status and registration status of the know-how information 121 of the target user.

[0196] 16, the importance may be used to determine the priority of association with devices. By preferentially associating know-how information 121 with high importance with devices, it becomes easier to use know-how information 121 that is considered useful for improving or maintaining the condition of the person being assisted.

[0197] For example, the processing unit 110 may promote the collection of sample data by displaying a screen that prompts the user to proactively perform the target assistance for the know-how information 121 that is highly important but "Not ready." Also, the processing unit 110 may promote the association with the device by displaying a screen that prompts the user to perform the processes from step S211 onwards for the know-how information 121 that is highly important but "Ready."

[0198] <Variations in importance determination and result presentation> In the above, an example has been described in which the determination result of the importance determination unit 115 is binary data indicating whether or not it is important. Also, in the above description, know-how information 121 determined to be important in one regression analysis becomes important know-how information, and when multiple pieces of important know-how information are detected in the one regression analysis, it is determined that it is useful to use all of them in combination. However, whether or not know-how information is important and whether or not it is useful when combined may be determined using different conditions.

[0199] For example, the importance determination unit 115 may determine the importance in multiple stages of three or more stages as described above. For example, the importance determination unit 115 determines that the know-how information 121 whose importance is equal to or greater than a given first threshold value th1 is important know-how information. Furthermore, when there are multiple pieces of know-how information 121 whose importance is equal to or greater than a second threshold value th that is greater than the first threshold value th1, the importance determination unit 115 may determine that it is useful to use a combination of these pieces of know-how information 121. In other words, the importance determination unit 115 may determine that it is useful to combine some of the important know-how information 121 that has a particularly high importance.

[0200] For example, consider a case where the importance determination unit 115 determines that four pieces of know-how information 121, a6, a7, a10, and a34, are highly important, and further determines that the combination of a6 and a7 is useful. In this case, a6, a7, a10, and a34 become candidates for replacement. For example, as shown in FIG. 16, if a5 used by the user is similar to a34, information representing a34 is displayed in the Replaced know-how information field.

[0201] In the above example, a6 and a7 are candidates for the combined use of the know-how information 121 that is recommended. For example, as shown in Fig. 16, if the user is using a6, a7 is presented as the recommended know-how information 121. In this way, it becomes possible to flexibly determine the information to be displayed in RE1 and the information to be displayed in RE3 based on different conditions.

[0202] Further, the importance determination unit 115 may repeatedly execute regression analysis in a time series. For example, at the timing of tx, the importance determination unit 115 performs regression analysis based on the association information acquired during the period from t1 to tx, and at the timing of ty, performs regression analysis based on the association information acquired during the period from tx to ty. For example, x and y are integers satisfying x < y, and ty represents a timing later than tx. However, specific methods such as performing regression analysis based on the association information acquired during the period from t1 to ty at the timing of ty can be variously modified and implemented.

[0203] When the results of repeated regression analysis are obtained in this way, the importance determination unit 115 may make a determination by integrating the results of multiple regression analyses.

[0204] For example, the importance determination unit 115 determines the know-how information 121 determined to have a high importance in a predetermined ratio or more of the multiple regression analyses as important know-how information. For example, when the number of times the importance of a1 is determined to be high in multiple regression analyses is equal to or greater than a given threshold th3, the importance determination unit 115 determines a1 as important know-how information.

[0205] Furthermore, the importance determination unit 115 determines that a plurality of pieces of know-how information 121 whose simultaneous determination of high importance is equal to or greater than a given threshold value th4 are know-how information 121 that are useful to use in combination. For example, if a1 is determined to be important in a given regression analysis and a2 is also determined to be important in the same regression analysis, the importance determination unit 115 increments the count value for the combination (a1, a2). The importance determination unit 115 performs a count-up process for all combinations based on the results of multiple regression analyses. For example, if the given regression analysis determines that the importance of (a1, a2, a3) is high, the importance determination unit 115 may increment the count values for the combinations (a1, a2), (a1, a3), (a2, a3), and (a1, a2, a3). The importance determination unit 115 identifies combinations whose final count value is equal to or greater than th4 and presents the above-mentioned RE3 based on the identified combinations. Therefore, for example, in the judgment using the above th3, even if a1 is equal to or greater than th3 and a2 is equal to or greater than th3, if the number of times they appear together is small, the combination (a1, a2) will not be judged to be useful.

[0206] Even in this way, it is possible to flexibly determine the important know-how information and the know-how information 121 that is useful to use in combination.

[0207] The importance determination unit 115 may also limit in advance the know-how information 121 that is often used in combination. For example, the importance determination unit 115 identifies a combination of know-how information 121 that is frequently used based on a plurality of pieces of association information. For example, it is assumed that the number of pieces of association information that include all of the four pieces of know-how information 121 (a1, a2, a3, a4) among the plurality of pieces of association information is equal to or greater than a given threshold th5. In this case, the importance determination unit 115 may determine the importance of the four pieces of know-how information 121 (a1, a2, a3, a4) based on the plurality of pieces of association information that include the four pieces of know-how information 121 (a1, a2, a3, a4). Then, when a plurality of pieces of know-how information 121 whose importance is equal to or greater than a given threshold th6 is detected among (a1, a2, a3, a4), the importance determination unit 115 determines that the plurality of pieces of know-how information 121 are know-how information 121 that are useful to use in combination. For example, if the importance of a1 and a3 is equal to or greater than th6 and the importance of a2 and a4 is less than th6, the importance determination unit 115 determines that the combination of (a1, a3) is useful.

[0208] In this way, by determining the importance of a plurality of pieces of know-how information 121 that are frequently used in combination, it becomes possible to identify the know-how information 121 that is considered to be particularly important among the combinations. Note that although the combination (a1, a2, a3, a4) is used as an example here, similar processing is possible for other combinations.

[0209] Additionally, the method of this embodiment broadly includes determining the importance of the know-how information 121 based on the change in the state information and the know-how information 121 used, and the specific method is not limited to the above example.

[0210] 3.3.2 Importance based on caregiver preference The importance determination unit 115 may also determine the second importance based on the degree of use by users or the popularity of each piece of know-how information 121. While the above-mentioned importance is information that takes into consideration the condition of the person being assisted, the second importance is information that is based on the judgment of the caregiver providing the assistance.

[0211] For example, the importance determination unit 115 may count the number of times each piece of know-how information 121 has been downloaded for use. The number of downloads represents how many users have determined that the target know-how information 121 is useful. Therefore, the importance determination unit 115 determines that the second importance of the target know-how information 121 is higher as the number of downloads increases.

[0212] The importance determination unit 115 may also count the number of users currently using each piece of know-how information 121. The number of using users also indicates how many users have determined that the target know-how information 121 is useful. Therefore, the importance determination unit 115 determines that the second importance of the target know-how information 121 is higher as the number of using users is larger.

[0213] In this embodiment, each user may be able to evaluate the know-how information 121 that he or she has used. Various modes of evaluation are possible, and for example, each user may assign a score to the know-how information 121. The importance determination unit 115 may obtain a statistic (such as an average value) of the scores assigned to the target know-how information 121, and determine the second importance based on the statistic.

[0214] Furthermore, the number of downloads, the number of users, and the score representing the evaluation are not limited to being used alone, and two or more of them may be combined. For example, the importance determination unit 115 may perform processing to determine the second importance based on a given function that receives two or more of the number of downloads, the number of users, and the score representing the evaluation as input.

[0215] The processing unit 110 may also perform processing to present second recommended know-how information recommended for assisting a given person being assisted, based on one or more pieces of know-how information 121 used for assisting the given person being assisted and the know-how information 121 determined to have a high second importance by the importance determination unit 115. In this way, it is possible to encourage the use of know-how information 121 with a high second importance, i.e., know-how information 121 that the caregiver considers useful. The second recommended know-how information is, for example, know-how information 121 that is similar to the know-how information 121 currently being used and has a high second importance.

[0216] 3.4 Determining the condition of the person receiving care As described above, in the method of this embodiment, the condition information of the person being assisted may be used to determine the importance of the know-how information 121. The condition information is obtained based on, for example, the results of an investigation by an investigator. However, in this embodiment, part or all of the investigation by the investigator may be automated.

[0217] The information processing device of this embodiment may perform a process of estimating an evaluation result for each item in a survey. Hereinafter, machine learning will be described as a specific example of a method for estimating an evaluation result. However, the method of this embodiment is not limited to using machine learning, and various modifications are possible. Hereinafter, an example will be described in which NN is used as machine learning, but other methods such as SVM may be used for machine learning, or methods that are an extension of NN or SVM may be used.

[0218] FIG. 17 shows an example of the basic structure of a neural network (NN). Each circle in FIG. 17 is called a node or neuron. In the example of FIG. 17, the NN has an input layer, two or more hidden layers, and an output layer. The input layer is I, the hidden layers are H1 and Hn, and the output layer is O. In the example of FIG. 17, the number of nodes in the input layer is 2, the number of nodes in each hidden layer is 5, and the number of nodes in each layer can be modified in various ways. In addition, FIG. 17 shows an example in which each node in a given layer is connected to all nodes in the next layer, but this configuration can also be modified in various ways.

[0219] The input layer receives input values and outputs them to the intermediate layer H1. In the example of Fig. 17, the input layer I receives two types of input values. Each node in the input layer may perform some processing on the input value and output the processed value.

[0220] In a NN, a weight is set between two connected nodes. W1 in Figure 17 is the weight between the input layer I and the first hidden layer H1. W1 represents the set of weights between a given node included in the input layer and a given node included in the first hidden layer. For example, W1 in Figure 17 is information containing 10 weights.

[0221] Each node in the first hidden layer H1 performs a weighted sum of the outputs of the nodes in the input layer I connected to that node using a weight W1, and then adds a bias. Each node then applies a nonlinear activation function to the sum to determine the output of that node. The activation function may be a ReLU function, a sigmoid function, or another function.

[0222] The same is true for subsequent layers. That is, in a given layer, the output of the previous layer is weighted using the weight W, and then a bias is added and an activation function is applied to determine the output for the next layer. The output of the output layer is the output of the NN.

[0223] As can be seen from the above explanation, in order to obtain desired output data from input data using a NN, it is necessary to set appropriate weights and biases. In learning, training data is prepared in which given input data is associated with ground truth data that represents correct output data for the input data. The NN learning process is a process of determining the most likely weights based on the training data. Note that various learning methods, such as backpropagation, are known for the NN learning process. In this embodiment, these learning methods are widely applicable, so detailed description will be omitted. Furthermore, the NN is not limited to the configuration shown in FIG. 17, and a CNN, an RNN (Recurrent Neural Network), or the like may also be used.

[0224] FIG. 18 is a diagram illustrating input data and output data of a NN for determining the presence or absence of paralysis, etc., used to estimate an evaluation result regarding "presence or absence of paralysis, etc.," which is an example of a survey item. The input data here may include output data from a sensor placed in the living environment of the person being assisted. The output data from the sensor is referred to as sensing data. The input data may also include information regarding one or more pieces of know-how information 121 used in assisting the person being assisted.

[0225] For example, when estimating an evaluation result regarding the presence or absence of paralysis, the sensing data includes at least one of data detecting the muscle mass of the person being assisted, data detecting myoelectricity, and data capturing an image of a target body part. Muscle mass may be detected using, for example, a weighing scale (body composition monitor). Portable, compact body composition monitors have also become known in recent years, and the specific sensor shape can be modified in various ways. Myoelectricity is detected by fixing a sensor having multiple electrodes to the body surface of the person being assisted. Data capturing an image of the target body part is acquired by a camera (image sensor) capable of capturing an image of the person being assisted. The camera here may be fixed to the room or bed of the person being assisted, or may be mounted on the terminal device 200 or headset 300 used by the caregiver.

[0226] The input data also includes information indicating whether know-how information 121 regarding "how to change diapers when there is contracture or paralysis" has been used. This know-how information 121 is information used when changing the diaper of a person receiving care who has contracture or paralysis in their limbs, and for example, the assistance behavior includes correct actions for changing a diaper. When there is contracture or paralysis, the movement of the limbs of the person receiving care is restricted, so correct actions different from those used when there is no contracture or paralysis are used. When know-how information 121 regarding "how to change diapers when there is contracture or paralysis" is used, there is a high probability that the person receiving care has contracture or paralysis. Therefore, whether or not this know-how information 121 has been used is useful input data when estimating the evaluation result of the presence or absence of paralysis, etc. The value of the input data here is binary data that, for example, becomes a first value when the target know-how information 121 is being used and becomes a second value when it is not being used.

[0227] The input data also includes information indicating whether or not know-how information 121 on "how to change position when there is contracture or paralysis" has been used. This know-how information 121 is information used when changing the position of a person being assisted who has contracture or paralysis in the limbs, and for example, the assistance action includes the correct action for changing position. In this case, too, it is considered that the correct action will be different from that in the case where there is no contracture or paralysis. When know-how information 121 on "how to change position when there is contracture or paralysis" is used, there is a high probability that the person being assisted has contracture or paralysis. Therefore, whether or not this know-how information 121 has been used is useful input data when estimating the evaluation results of the presence or absence of paralysis, etc.

[0228] The input data also includes information indicating whether or not know-how information 121 on "how to do exercises and how to move the body to prevent contracture and paralysis" has been used. The assisting action in this case may be the correct exercise or body movement to be performed by the person being assisted, or the correct movement of the assistant when having the person being assisted perform the exercise. If know-how information 121 on "how to do exercises and how to move the body to prevent contracture and paralysis" is being used, there is a high probability that the person being assisted is at risk of developing paralysis, etc. Therefore, whether or not this know-how information 121 has been used is useful input data when estimating the evaluation result of the presence or absence of paralysis, etc.

[0229] The evaluation result of the presence or absence of paralysis etc. is the result of selecting all applicable numbers for, for example, "1. None," "2. Left upper limb," "3. Right upper limb," "4. Left lower limb," "5. Right lower limb," and "6. Other." Note that "6. Other" refers to loss of limbs etc.

[0230] Therefore, the output data is six pieces of data that respectively represent the likelihood that "1. No" will be selected, "2. Left upper limb" will be selected, "3. Right upper limb" will be selected, "4. Left lower limb" will be selected, "5. Right lower limb" will be selected, and "6. Other" will be selected. For example, each of the six pieces of data is numerical data between 0 and 1, and the closer the value is to 1, the more likely the target number should be selected.

[0231] The configuration of the NN for determining the presence or absence of paralysis or the like is not limited to that shown in FIG. 18, and various modifications of the input data and output data are possible. For example, some sensing data from the input data may be omitted, or other sensing data may be added to the input data. Also, with regard to the know-how information 121, input data relating to some know-how information 121 may be omitted, or input data relating to other know-how information 121 may be added.

[0232] In the learning stage, training data for creating an NN for determining the presence or absence of paralysis, etc., is acquired by associating correct answer data with the input data for a plurality of persons being assisted. For example, the input data is acquired based on sensing data from sensors placed in the living environment of the persons being assisted, or know-how information 121 used for the persons being assisted. The know-how information 121 is identified, for example, from list information 123 of caregivers who assist the target persons being assisted.

[0233] The correct answer data may also be assigned by an expert with specialized knowledge, such as an investigator. The expert will conduct an investigation, for example, following the certified investigator text shown in the URL above, and select all applicable numbers from the six above. For example, if the expert selects only "1. No" and does not select the other five, the correct answer data will be data in which the value corresponding to "1. No" is 1 and the values corresponding to the other five are 0.

[0234] For example, the processing unit 110 of the server system 100 acquires training data and performs machine learning based on the training data to create an NN for determining the presence or absence of paralysis, etc. Note that the machine learning may be performed by a device different from the server system 100.

[0235] FIG. 19 is a flowchart illustrating the learning process for generating an NN for determining the presence or absence of paralysis, etc. When this process starts, first, in step S501, the processing unit 110 acquires input data for learning. The input data here is as described above, and includes, for example, sensing data and information indicating whether or not know-how information 121 has been used. Note that the input data may also include information indicating the results of communication performed by the person being assisted. For example, the input data may include the output of a communication robot. Details of the communication robot will be described later.

[0236] In step S502, the processing unit 110 acquires the correct answer data associated with the input data. For example, the processing unit 110 executes the processes of steps S501 and S502 by reading out any one data set of the training data acquired in the learning stage.

[0237] In step S503, the processing unit 110 performs processing to update the weights of the NN. Specifically, the processing unit 110 inputs the input data acquired in step S501 to the NN for determining the presence or absence of paralysis, etc., and acquires output data by performing forward calculations using the weights at that stage. The processing unit 110 calculates an objective function based on the output data and the ground truth data. The objective function here is, for example, an error function based on the difference between the output data and the correct answer data, or a cross-entropy function based on the distribution of the output data and the distribution of the correct answer data.

[0238] The processing unit 110 updates the weights so that the error function decreases, for example. The above-mentioned backpropagation method and the like are known as weight updating methods, and these methods can be widely applied to this embodiment as well.

[0239] In step S504, the processing unit 110 determines whether to terminate the learning process. For example, multiple data sets included in the training data may be divided into learning data and validation data. The processing unit 110 may terminate the learning process when the weight updating process has been performed using all of the learning data, or may terminate the learning process when the accuracy rate based on the validation data exceeds a given threshold.

[0240] If the learning process is not to be ended, the processing unit 110 returns to step S501 to continue the process. That is, the processing unit 110 reads a new data set from the training data and performs a process of updating the weights based on the new data set.

[0241] When the learning process is terminated, the processing unit 110 stores the NN for determining the presence or absence of paralysis, etc. at that stage as a trained model in the storage unit 120. The trained model includes an algorithm for performing forward calculations and weighting coefficients. Note that FIG. 19 is an example of the learning process, and the method of this embodiment is not limited to this. For example, methods such as batch learning are also widely known in machine learning, and these methods can be widely applied in this embodiment.

[0242] 20 is a flowchart illustrating the processing of the status determination unit 114 in the inference stage. When this processing starts, first, in step S601, the status determination unit 114 determines whether the current timing is the timing to obtain status information of the person being assisted. For example, as described above with reference to FIG. 15, if the status information is obtained at predetermined intervals such as one month, the status determination unit 114 determines whether the predetermined interval has elapsed since the previous processing.

[0243] If it is determined that it is not the timing to obtain the state information, the state determination unit 114 ends the process without performing step S602 and subsequent steps.

[0244] If it is determined that it is time to obtain status information, in step S602, the status determination unit 114 acquires input data related to the person being assisted, which is the processing target. For example, the storage unit 120 acquires and stores sensor information collected by a group of sensors arranged in the living environment of the person being assisted. The status determination unit 114 performs a process of reading out data related to the target person being assisted, which is to be used as input data, from the collected data. For example, the status determination unit 114 reads the output of a body composition monitor, the output of a sensor that detects electromyography, the output of a camera that captures an image of the target body part, etc.

[0245] Further, the state determination unit 114 identifies the know-how information 121 used for the care recipient based on the list information 123 of the caregiver who cares for the care recipient, etc. Specifically, the state determination unit 114 determines for each of the three know-how information 121 of "how to change diapers in case of contracture or paralysis", "how to change body position in case of contracture or paralysis", and "how to do gymnastics for preventing contracture and paralysis, how to move the body" whether it has been used for the target care recipient.

[0246] In step S603, the state determination unit 114 reads out the NN for determining the presence or absence of paralysis, etc. from the storage unit 120. Then, the input data acquired in step S602 is input to the NN for determining the presence or absence of paralysis, etc., and output data is obtained by performing a forward calculation. The output data of the NN for determining the presence or absence of paralysis, etc. is, for example, six numerical data representing the probability that each of the six numbers is selected as described above. The state determination unit 114 determines that the target number is selected when the numerical value is equal to or greater than a threshold value th that satisfies 0 < th < 1. For example, when the numerical data corresponding to "1. None" is equal to or greater than th and the other five numerical data are less than th, the state determination unit 114 selects "1. None" as the evaluation result regarding the presence or absence of paralysis, etc., and outputs an estimation result of not selecting the other five.

[0247] As described above, according to the method of the present embodiment, it is possible to automatically estimate the evaluation result in the investigation item of the presence or absence of paralysis, etc. Although the specific item of the presence or absence of paralysis, etc. has been described as an example above, the point that the evaluation result can be estimated based on the presence or absence of use of the sensing data and the know-how information 121 is the same for other items.

[0248] FIG. 21 is a configuration example of the NN when estimating the evaluation results in p investigation items. FIG. 21 shows an example in which one NN is created for each item. Each NN acquires, as input data, sensing data, information indicating the presence or absence of use of the know-how information 121, and information representing the communication result using the communication robot. However, each NN does not necessarily need to receive all three types of these inputs, and some of them may be omitted.

[0249] In particular, as shown in Fig. 21, a filtering process may be performed to extract a portion of the know-how information 121 according to the target item regarding whether or not it has been used. As described above, there is a possibility that the amount of know-how information 121 will be enormous. If the filtering process is not performed, the majority of the input data will be determined by whether or not the know-how information 121 has been used, which may drastically reduce the influence of the sensing data and the results of the interview by the communication robot on the output. In this regard, by performing the filtering process, the amount of know-how information 121 that is input data is limited, making it possible to accurately estimate the evaluation result.

[0250] Furthermore, the sensors that output sensing data are not limited to the above-mentioned body composition monitors, electromyographic sensors, and imaging sensors (cameras), but various other sensors can be used, such as motion sensors such as acceleration sensors and angular velocity sensors, excretion detection sensors such as pressure sensors and odor sensors, position sensors such as GPS (Global Positioning System), sleep detection sensors that detect pulse and heart rate, temperature sensors, humidity sensors, illuminance sensors, and air pressure sensors. Each NN does not need to accept all of these as inputs, and may acquire only some of the sensing data, as will be described later with reference to Figures 23A to 23E.

[0251] As described above, various pieces of information are registered in the know-how information 121 by multiple users. FIGS. 22A to 22C are examples of the know-how information 121. FIG. 22A is an example of the know-how information 121 used in meal assistance, which involves helping a person receiving care eat. For example, in meal assistance, the caregiver understands the characteristics of the person receiving care and explains them to the person receiving care in an easy-to-understand manner, thereby performing an assistance action to facilitate the meal. For example, if the person receiving care has poor chewing ability, if the caregiver is aware of this, measures can be taken to prevent aspiration. It is also useful to advise the person receiving care, such as, "The rice has been softened, so chew it well." Number 2 in FIG. 22A is know-how information 121 for "communicating (helping the caregiver understand) the user's characteristics" to the caregiver. For example, when a predetermined start condition is met, the caregiver is prompted to perform an assistance action, such as obtaining and viewing data representing the characteristics of the person receiving care. As described above, the assistance behavior may include an action in which the caregiver conveys characteristics of the person being assisted to the person being assisted. The same applies to other know-how information 121. The know-how information 121 shown in Fig. 22A includes information for supporting various actions of the caregiver in meal assistance.

[0252] 22B is an example of know-how information 121 used in excretion assistance for assisting the excretion of a person being assisted. Note that excretion assistance may be performed in a toilet or using a diaper. Numbers 66-72 represent know-how information 121 for excretion assistance in a toilet, and Numbers 73-75 represent know-how information 121 for excretion assistance using a diaper.

[0253] Fig. 22C is an example of know-how information 121 used in transfer assistance and mobility assistance for assisting the transfer or movement of a person being assisted. The presence or absence of equipment or the type of equipment for transfer and mobility assistance varies depending on the condition of the person being assisted and the availability of lifts, etc. In the example of Fig. 22C, Numbers 92-103 represent know-how information 121 for assistance using a wheelchair, Numbers 104-107 represent know-how information 121 for assistance using a cane, and Numbers 108-112 represent know-how information 121 for assistance using a lift.

[0254] Furthermore, the know-how information 121 is not limited to this, and know-how information 121 used in situations other than eating, excretion, and transferring / moving may be used in the processing. Furthermore, "how to change diapers when there is contracture or paralysis" described above using Fig. 18 is know-how information 121 used in excretion assistance, and is information for dealing with more specific situations than the example shown in Fig. 22B. In this way, the know-how information 121 used in eating, excretion, and transferring / moving is not limited to Figs. 22A to 22C, and various modifications are possible.

[0255] A communication robot is a robot that communicates with a person receiving care. A communication robot may be, for example, a humanoid robot with two arms and a device capable of voice recognition and voice synthesis. When using such a communication robot, it is possible to have a conversation with a person receiving care, for example by bending its arms, and ask the person receiving care whether it can make the same movement. However, a communication robot is not limited to being humanoid, and may also be one that communicates through voice recognition and voice synthesis. In this case, the communication robot may be realized by a device such as a PC, and may be capable of conversing with an avatar displayed on a display. Communication with a person receiving care is not limited to voice conversation, and may also be text-based. For example, at least one of the speech by the communication robot and the response by the person receiving care may be text-based. The results of communication using a communication robot are useful primarily as input data for evaluating cognitive function.

[0256] 23A to 23E show examples of evaluation content indicating the type of evaluation performed for each item and specific input data used to estimate the evaluation results for the item. The input data is divided into the sensing target, tacit knowledge (know-how information 121), and communication robot. The column related to the communication robot is marked with a circle if it is used and left blank if it is not used. FIG. 23A shows items related to physical functions and daily living activities. FIG. 23B shows items related to daily living functions. FIG. 23C shows items related to cognitive functions. FIG. 23D shows items related to mental and behavioral disorders. FIG. 23E shows items related to adaptation to social life. Numbers such as 1-1 correspond to the numbers shown in FIG. 14A or FIG. 14B.

[0257] As shown in Figure 23A, the physical function and daily living activities section has many items for evaluating whether the person being assisted can perform a specific movement, and sensing data such as the movement of specific parts, contact with specific places, and load distribution during the movement are used as input data. For some items, the use or non-use of know-how information 121 is also used as input data. On the other hand, the output of the communication robot is only used for some items such as hearing ability.

[0258] As shown in FIG. 23B, the daily living functions are related to eating, excretion, transferring / moving, cleanliness, etc., and therefore many items use sensing data such as whether the person being assisted is receiving assistance. The sensing data may be, for example, captured images. Furthermore, the information shown in FIGS. 22A to 22C can be used to determine whether know-how information 121 is being used. Number in FIG. 23B represents the number in FIGS. 22A to 22C. The output of the communication robot is not used in the example of FIG. 23B.

[0259] As shown in FIG. 23C, the behavior of the person being assisted is important in estimating the evaluation results for cognitive function. Therefore, the output of the communication robot serves as input data for many items. The state determination unit 114 may also acquire and use responses to questions as sensing data using an image sensor, microphone, etc. Whether or not know-how information 121 is being used is not used in the example of FIG. 23C.

[0260] As shown in FIG. 23D, the behavior of the person being assisted is also important in estimating the evaluation results for items related to mental and behavioral disorders. Therefore, the output of the communication robot serves as input data for many items. The state determination unit 114 may also acquire and use responses to questions as sensing data using an image sensor, microphone, etc. The sensing data may also include detection results on how many times and how frequently a specific behavior was performed within a certain period of time. Whether or not know-how information 121 is used is not used in the example of FIG. 23D.

[0261] As shown in Fig. 23E, many of the items related to adaptation to social life use sensing data that indicates the detection results of how many times and how frequently a certain behavior was performed in a certain period of time. For medication, whether or not know-how information 121 was used is used. There may also be items that use the output of a communication robot, such as maladjustment to groups.

[0262] 21 and 23A to 23E, according to the method of this embodiment, it is possible to estimate the evaluation result for each item used when obtaining the condition information of the person being assisted from the use or non-use of sensing data and know-how information 121. In particular, by using the use or non-use of know-how information 121, it is possible to take into account the assistance being provided to the person being assisted, thereby improving the estimation accuracy.

[0263] For example, the processing unit 110 (status determination unit 114) may perform a process of calculating status information based on know-how information used in assisting the person being assisted and output data from sensors arranged in the living environment of the person being assisted. That is, the status determination unit 114 may calculate the status information based on the estimation result in addition to the process of estimating the evaluation result for the survey item. For example, when the status information is the level of care required, the status determination unit 114 obtains information indicating any one of levels 1 to 5 of care required as the status information. In this way, it becomes possible to automatically calculate the status information. Therefore, for example, it becomes possible to easily realize a process of determining the importance of the know-how information 121 based on a change in the status information.

[0264] However, the method of this embodiment is not limited to automatically calculating status information. For example, the estimated results for each item obtained by the method of this embodiment may be provided to an investigator. In this case, the investigator visits the person receiving care, as in the conventional method. However, since the investigator only needs to confirm whether the estimated results are appropriate, the burden on the investigator can be reduced compared to when each item is investigated from scratch, and the investigation time can be shortened.

[0265] The processing unit 110 may also perform a primary determination of the condition information based on the estimation results for each item, and provide the results of the primary determination to a specialist such as a doctor. In this case, the doctor or other specialist determines the final result of the condition information based on the results of the primary determination. Since the investigation by an investigator is omitted, it becomes easier to obtain the condition information.

[0266] <Suggested actions> As explained above, sensing data, which is output data from sensors, may be used as input data when estimating the evaluation results for a survey item. However, a certain amount of sensing data may be required to make a judgment using the sensing data.

[0267] For example, as shown in Fig. 23A, the state determination unit 114 may use sensing data that indicates fluctuations in body pressure or load when determining the evaluation result of "turning over" in 1-3. In this case, if the load on the bed decreases significantly, for example, it is considered that the person being assisted is holding onto something or is receiving assistance from a caregiver, and the state determination unit 114 estimates that the ability may be low.

[0268] However, being able to turn over on one's own is not limited to a case where the load does not decrease in all of the sensing data. For example, if the load does not decrease significantly in sensing data representing a predetermined percentage or more of the sensing data corresponding to multiple turns, the patient may be determined to be able to turn over on one's own. In this case, the percentage of sensing data acquired during the period from the previous state assessment to the current state assessment that meets a given condition is important, so it is desirable to have a relatively large number of sensing data. For example, if only one piece of sensing data is acquired when turning over, the result may be either an extreme result that the load decreases in all of the sensing data or that the load does not decrease in all of the sensing data, which may reduce the accuracy of the estimation result regarding the turning over.

[0269] The number of pieces of sensing data is also important for other types of sensing data, and using a small amount of sensing data as input data may result in a decrease in the estimation accuracy of the evaluation results.

[0270] Therefore, when it is determined that the number of sensor output data is insufficient in a given determination period, the processing unit 110 may output an instruction to the person being assisted or the caregiver assisting the person being assisted to take a specific action. This increases the opportunities to acquire sensing data, thereby increasing the input data to the state determination unit 114 and thereby improving the estimation accuracy.

[0271] Fig. 24 is a diagram illustrating the process of determining whether the number of pieces of sensing data is insufficient. The horizontal axis of Fig. 24 represents time, and the vertical axis represents the number of pieces of sensing data. t1 and t2 represent the timings for acquiring state information, similar to Fig. 15.

[0272] For example, the state determination unit 114 estimates an evaluation result based on sensing data from t1 to t2 in order to acquire state information at t2 in FIG. 24 . For example, the state determination unit 114 determines in advance the number of pieces of sensing data required for each item of input data. At a given timing between t1 and t2, the state determination unit 114 estimates the number of pieces of sensing data to be acquired at time t2 based on the number of pieces of sensing data acquired up to that point. For example, the state determination unit 114 may assume that the number of pieces of sensing data increases linearly as shown in FIG. 24 , or may calculate an approximation function that expresses the transition in the number of pieces of sensing data from t1 and estimate the number of pieces of sensing data at time t2 based on the approximation function. If the estimated number of pieces of sensing data at time t2 is less than the required number of pieces of data, the state determination unit 114 determines that there is a shortage of sensing data.

[0273] For example, if the state determination unit 114 determines that there is insufficient sensing data related to turning over, it may output a message suggesting to the person being assisted to turn over. Alternatively, the state determination unit 114 may output a message suggesting to the caregiver that the person being assisted turn over. In this way, sensing data can be acquired before the actual timing of t2, thereby improving the estimation accuracy of the evaluation result at t2.

[0274] Note that the instruction output by the state determination unit 114 is not limited to direct suggestions to turn over, get up, etc. For example, the state determination unit 114 may output suggestions to perform a series of actions related to multiple evaluation items.

[0275] 25 is a diagram showing the correspondence between the content of a suggestion made by the state determination unit 114 and the survey items for which sensing data can be acquired by the suggestion. For example, the state determination unit 114 may make a suggestion such as "Let's go shopping on XX day until XX." In order for the person being assisted to go shopping at a predetermined location, preparations such as brushing teeth, washing face, styling hair, clipping nails, and changing clothes are necessary. Furthermore, during the preparation stage and after going out, the user performs actions such as standing on both feet or one foot, walking, and standing up. That is, by making shopping suggestions, it becomes possible to collectively acquire sensing data related to multiple items shown in Fig. 25. That is, it becomes possible to efficiently collect a large amount of sensing data without making individual suggestions.

[0276] 25, the state determination unit 114 may also make suggestions for cleaning or getting ready to go out. If it is difficult to get ready to go out, the state determination unit 114 may also make suggestions for washing the mouth, styling hair, and putting on and taking off clothes. In this case, since carrying out each suggestion requires actions such as standing up or walking, it is possible to efficiently collect sensing data.

[0277] 3.5 Group Purchasing Organization (GPO) When medical facilities purchase medical equipment, they sometimes use GPOs (Group Purchasing Organizations). GPOs are in the business of specializing in price negotiations with manufacturers and other distributors, and offer members a service that lowers unit prices by committing to purchasing large lots. Even when the minimum purchase lot required by the manufacturer is large, by using a GPO, member medical facilities can keep costs down while purchasing only the necessary amount of high-priced products. For example, in the United States, many medical facilities are members of GPOs, and purchase various medical equipment through the GPO.

[0278] GPOs provide purchasing conditions (contracts), and when members use those contracts, they pay a portion of the purchase price to the GPO as a commission. The contract contents vary, such as setting prices for each manufacturer or setting discounts according to the purchase volume.

[0279] Computer systems and methods suitable for GPOs are described in U.S. patent application Ser. No. 15 / 783,992, filed October 13, 2017, entitled "COMPUTER-BASED SYSTEMS SPECIFICALLY CONFIGURED TO MANAGE SOFTWARE OBJECTS THAT ARE INTERRELATED VIA TRIGGER CONDITIONS AND METHODS OF USE THEREOF," and U.S. patent application Ser. No. 16 / 985,609, filed August 5, 2020, entitled "METHODS AND SYSTEMS FOR PROVIDING IMPROVED MECHANISM FOR UPDATING HEALTHCARE INFORMATION SYSTEMS," both of which are incorporated by reference in their entireties.

[0280] FIG. 6 of U.S. Patent Application No. 15 / 783,992 discloses an example of a screen for a buyer to create an RFP (Request For Proposal) by inputting contract parameters, discount conditions, etc. In this example, an RFP is created based on a product category specification using a product classification code such as the UNSPSC (United Nations Standard Products and Services Code), and the created RFP is sent to one or more suppliers.

[0281] When a supplier replies based on the RFP with specific products, the buyer is presented with a screen corresponding to FIG. 22, for example. FIG. 22 is the interface screen for selecting products. In FIG. 22, multiple products can be cross-referenced to select the product that best suits the buyer's needs.

[0282] FIG. 10 of US patent application Ser. No. 16 / 985,609 also discloses a screen for evaluating the effect of replacing a given product with another product.

[0283] As can be seen from these descriptions, it is important for a GPO to propose appropriate products in accordance with the buyer's requirements. The method of this embodiment may be used for consulting to a GPO, specifically for supporting product proposals by the GPO.

[0284] For example, when multiple devices are associated with given know-how information 121, the processing unit 110 may perform a process of presenting a device other than the first device among the multiple devices as an alternative device to the first device among the multiple devices.

[0285] FIG. 26 shows examples of the know-how information 121, the registration information 122, and the list information 123. As described above, when a registered user performs the process shown in FIG. 6, a device for determining either the start condition of the know-how information 121 or an assisting action is associated with the know-how information 121. Furthermore, when each user performs the process shown in FIG. 10 (particularly step S308), a device for determining either the start condition of the know-how information 121 or an assisting action is associated with the know-how information 121. This makes it possible to identify one or more devices associated with given know-how information 121, as shown in FIG. 26. Note that, as shown in FIG. 26, by further using the know-how information 121 itself, it is possible to associate more detailed information, such as a specific start condition or an assisting action, with a device.

[0286] As described above, Device 1 included in the registration information 122 is a device designated by a registered user to determine start conditions, etc. Device 1a included in the list information 123 is a device designated by another user to use the registered user's tacit knowledge. That is, the plurality of devices associated with the given know-how information 121 are all devices for determining the same starting conditions, etc., and therefore are highly likely to be similar. The same is true for Device 1b.

[0287] Therefore, for example, when a buyer is considering replacing Device 1, the processing unit 110 performs processing to propose Device 1a and Device 1b as alternative devices. In this way, it becomes possible to identify and present products that meet the user's requirements from a perspective different from that of product classification codes such as UNSPSC.

[0288] Furthermore, for example, when a buyer is considering replacing a device used in given know-how information 121, the processing unit 110 may propose one or more devices associated with similar know-how information similar to the know-how information 121 as alternative devices. The similar know-how information is determined based on the first similarity determination process, as described above. The know-how information 121 and the similar know-how information have high similarity, for example, between texts expressing start conditions and the types of assistance used. Therefore, the know-how information 121 and the similar know-how information are likely to be used in similar situations, and the device associated with the similar know-how information is also considered to be similar to the device to be replaced. By using similar know-how information in this way, it is possible to increase the number of devices that can be presented and support a wide range of proposals.

[0289] The method of this embodiment does not need to be fixed to the method of proposing a device using know-how information 121. For example, the processing unit 110 may be able to switch between a process of determining an alternative device using a code such as UNSPSC and a process of determining an alternative device using know-how information 121. For example, the processing unit 110 determines whether to use the code or the know-how information 121 based on a user input.

[0290] The processing unit 110 of this embodiment may also perform processing to identify fifth know-how information that is associated with a device and has a high similarity to fourth know-how information that is not associated with a device, from the plurality of know-how information 121. The processing unit 110 performs processing to determine a supplier of a device associated with the fifth know-how information as a supplier of a device for determining a start condition of the fourth know-how information.

[0291] 27 is a diagram illustrating a process of identifying a supplier based on the fourth know-how information and the fifth know-how information. For example, the know-how information 121 of ID43 corresponds to the fourth know-how information. The know-how information 121 of ID43 does not have corresponding registration information 122, and is not associated with a device. The similarity determination unit 113 finds similar know-how information similar to the know-how information 121 of ID43 based on the first similarity determination process. For example, the know-how information 121 of ID10 is similar know-how information and corresponds to the fifth know-how information.

[0292] 27, there is corresponding registration information 122 for know-how information 121 of ID10, and Device10 is associated as the device. The storage unit 120 also stores information that associates devices with suppliers that supply the devices. For example, Device10 is associated with Supplier10.

[0293] In this case, the processing unit 110 performs processing to propose Supplier 10 as a supplier of the device to be used in the know-how information 121 of ID 43. As described above, the know-how information 121 of ID 43 and the know-how information 121 of ID 10 are similar, and therefore, there is a high probability that a device similar to Device 10 can be used to automatically determine the know-how information 121 of ID 43. In other words, the device used to automatically determine the know-how information 121 of ID 43 has a high affinity with Supplier 10, and there is a possibility that Supplier 10 can develop and provide it.

[0294] As described above, since no device is associated with the know-how information 121 of ID43, there is a possibility that devices suitable for automatically determining the start conditions and assistance actions are not widely available on the market. However, since it is registered as know-how information 121, it represents the tacit knowledge of some user and may therefore be useful in assistance situations. In this regard, the method of this embodiment can present information for automating the processing of know-how information 121, which existing devices could not handle. As a result, it becomes possible to develop a new sensing device market.

[0295] FIG. 28 is an example of a screen presenting recommended suppliers. In the example of FIG. 28, the ID numbers of know-how information 121 to which no device is associated and the recommended suppliers associated with the know-how information are displayed. Note that "Category" in FIG. 28 represents a category determined based on a product classification code, such as UNSPSC. For example, in the example of FIG. 28, Supplier 1 and Supplier 2 are presented as suppliers of the device corresponding to the know-how information 121 with ID 11. Note that if there are multiple devices associated with the fifth know-how information as shown in FIG. 26, or if multiple pieces of fifth know-how information similar to one piece of fourth know-how information are selected, multiple suppliers may be recommended. Similarly, Supplier 10 is presented as the supplier of the device corresponding to the know-how information 121 with ID 43. The same applies to the other know-how information 121. In this way, it is possible to present recommended suppliers for each piece of know-how information 121 in an easy-to-understand manner.

[0296] At this time, the processing unit 110 may present a ranking (second importance) of each piece of know-how information 121. As described above, the index for determining the ranking may be the number of downloads, the number of users using the information, an evaluation value, or a combination of these. For example, the know-how information 121 of ID11 is highly ranked, and if a device that automates this determination were supplied, it is likely that many users would desire to use it. In this way, the ranking serves as a material for encouraging suppliers to supply new devices, and therefore it is useful to use it in processing. For example, FIG. 28 shows a screen displaying, in order of ranking, know-how information 121 that satisfies given conditions among multiple pieces of know-how information 121 that are not associated with devices.

[0297] The processing unit 110 may also present the importance of each piece of know-how information 121, taking into consideration the condition of the person being assisted. The importance here corresponds to Care to improve quality in FIG. 28, and in FIG. 28, the importance is displayed in three levels, A to C. As described above, the importance taking into consideration the condition of the person being assisted is determined, for example, by multiple regression analysis based on changes in condition information and whether or not know-how information is used. For example, the know-how information 121 of ID 11 is highly ranked, and also has a high degree of contribution to improving and maintaining the condition of the person being assisted. In other words, since it is clear that the information is highly important from the perspective of both the caregiver and the person being assisted, this serves as material to encourage suppliers to supply new devices.

[0298] On the other hand, there may be know-how information 121 that is highly rated by the caregiver but is determined to have a low level of importance when the condition information of the person being assisted is taken into consideration, such as the know-how information 121 of ID 43. Conversely, there may be know-how information 121 that is relatively low rated by the caregiver but is determined to have a high level of importance when the condition information of the person being assisted is taken into consideration, such as the know-how information 121 of ID 44. By displaying the importance from two perspectives as shown in Fig. 28, the amount of information regarding the degree of importance of the know-how information 121 increases, allowing the viewer (e.g., GPO) to make an appropriate decision.

[0299] Also, here, an example is shown in which two levels of importance are displayed on a screen presenting recommended suppliers to a GPO. However, the use of the first and second levels of importance is not limited to this. For example, in the search process described above with reference to FIG. 10, two levels of importance may be displayed when displaying search results. In this way, it is possible to present two levels of importance to various targets, such as caregivers working in nursing facilities or hospitals, managers who supervise the caregivers, home helpers, and people receiving care. Alternatively, the search processing unit 112 may perform a search process based on the levels of importance. Furthermore, two levels of importance may be used when determining the priority order when associating devices, etc. with the know-how information 121.

[0300] Furthermore, the screen on which the two levels of importance are presented is not limited to that shown in Fig. 28. For example, the level of importance of given know-how information 121 may be illustrated on a plane in which the number of downloads or the number of users is set as the first axis and the level of importance based on the status information of the person being assisted is set as the second axis. Alternatively, the processing unit 110 may calculate an overall level of importance based on the two levels of importance and perform processing to present the overall level of importance. In addition, various modifications of the specific content to be presented are possible.

[0301] 4. Variations In the above embodiment, several specific examples of the know-how information 121 were shown, but many other examples are conceivable, some of which are listed below. For example, the know-how information 121 may include (1) information for selecting an appropriate food form for each person being assisted, such as a patient, (2) information suggesting whether end-of-life care should be started after a predetermined period for each person being assisted, such as a patient, and information suggesting the timing for changing the content of care after end-of-life care has started, (3) information regarding the timing for the caregiver to stop providing meals, (4) information regarding the caregiver's response when the person being assisted aspirates while eating, and (5) information for detecting situations with a high risk of falling.

[0302] (1) Know-how information 121 for selecting an appropriate food type for each person being assisted, such as a patient, receives five types of input information: the results of a doctor's or other medical diagnosis regarding chewing and swallowing ability, the wishes of the person being assisted or their family, whether the person being assisted appears to be unable to chew the food provided to them, whether the person being assisted appears to be aspirating (choking, coughing, etc.) while eating, and whether the person being assisted expresses negative emotional information (indexed values of discomfort, disgust, sadness, surprise, fear, etc.) while eating. Furthermore, know-how information 121 for selecting an appropriate food type for each person being assisted, such as a patient, outputs the food type (for example, regular food, chopped food, extra chopped food, soft vegetables, blended food, thickened food, or jelly food) as output information.

[0303] Here, the diagnosis results of a doctor or other medical professional may be, for example, the number of times a person swallows saliva for 30 seconds in a repetitive saliva swallowing test (a test to see how many times the person can swallow saliva consecutively), or the doctor or other medical professional's judgment results may be categorized and tagged for each category and entered as information. The requests of the person being assisted or their family may be categorized and tagged for each category and entered as information. For example, a request such as "I would like the person to eat regular food if possible" and a request such as "I would like the person to eat safely" may be tagged differently. Whether the person being assisted is not chewing the food provided to them, whether they are aspirating (choking, coughing, etc.) while eating, and whether they are expressing negative emotions are entered as information by extracting images of the person not chewing the food or aspirating from a video of the person being assisted taken with a camera while eating.

[0304] (2) Know-how information 121 that suggests whether end-of-life care should be initiated for each patient or other care recipient after a predetermined period of time receives five types of input information: the amount or proportion of each meal type (e.g., main dish, side dish, or each ingredient such as meat or fish), the amount and timing of water intake, disease information, and weight (or BMI). Know-how information 121 that suggests whether end-of-life care should be initiated for each patient or other care recipient after a predetermined period of time and whether it is time to change the content of care after the start of end-of-life care outputs information indicating whether end-of-life care should be initiated after a predetermined period of time and whether it is time to change the content of care after the start of end-of-life care. End-of-life care refers to care for a care recipient who is considered likely to die in the near future. End-of-life care differs from regular care in that it emphasizes the alleviation of physical and mental pain and the support of the care recipient to live a dignified life. Furthermore, as end-of-life care is provided, the appropriate care for the patient may change over time as the patient's condition changes. In other words, by indicating when to begin end-of-life care and when to change the content of care during end-of-life care, it becomes possible to provide appropriate care to the patient until the end. For example, experienced caregivers have the tacit knowledge to estimate the timing and content of end-of-life care needed from various perspectives, such as the amount of food consumed. By digitizing this tacit knowledge, other caregivers can also provide appropriate end-of-life care.

[0305] A system for providing the caregiver with the above-described assistance information (1) and (2) includes a terminal device 200 and a server system 100, as shown in FIG. 1 . The terminal device 200 here is, for example, a PC. As described above, the information processing system 10 of this embodiment can be realized in various ways, and the server system 100 may be omitted. The terminal device 200 acquires input information including the above-described five types of information. The terminal device 200 may acquire the input information automatically using a sensor, or may acquire the input information based on an input operation by a caregiver. Furthermore, care software may be used in a care facility or the like, separate from the information processing system 10 according to this embodiment. The care software is software for storing the attributes and care history of the person being assisted. Various types of care software have been used in the past, and the care software described herein can be widely applied. The information processing system 10 may acquire input information including the above-described five types of information from the care software.

[0306] When the terminal device 100 receives an instruction to start analysis based on the input information, it sends each piece of input information to the server system 200 , and the server system 200 outputs the analysis results to the terminal device 100 . Fig. 29A is an example of a My Page in this case. The My Page in Fig. 29A is a screen that presents information about a given user, similar to the user page shown in Fig. 16, and displays know-how information 121 that the target user is using and know-how information 121 that the target user has registered.

[0307] 29A, the target user is currently using know-how information 121 for estimating whether it is the timing to start end-of-life care, which corresponds to (2) above. The My Page shown in FIG. 29A includes an object for uploading in an area corresponding to the know-how information 121. When a selection operation for the object is performed, the terminal device 200 uploads input information related to the target person being assisted to the server system 100.

[0308] The server system 100 obtains an analysis result by inputting the uploaded input information into, for example, a pre-created trained model related to end-of-life care. The server system 100 transmits the analysis result to the terminal device 200. The terminal device 200 presents information representing the analysis result to the user.

[0309] FIG. 29B is an example of a screen displaying the analysis results, such as a screen displayed on the display unit of the terminal device 200. As described above, the analysis results may include a determination result of whether end-of-life care should be initiated after a predetermined period of time, or a determination result of whether it is time to change the content of end-of-life care after the start of end-of-life care. For example, as shown in FIG. 29B, the analysis results may include a time-series change in feature values calculated based on input information and a determination result of whether end-of-life care should be initiated after a predetermined period of time. The feature values may be information determined to be important among the input information, such as a moving average of food intake, or information calculated based on the five pieces of input information. For example, when a neural network (NN) is used, the feature values may be the output of a given intermediate layer or output layer. In FIG. 29B, the actual values of the feature values up to a predetermined time (e.g., October) and estimated values of the feature values thereafter (e.g., November and thereafter) are presented, along with the timing of the start of end-of-life care determined using the estimated values. This makes it possible to appropriately present information regarding end-of-life care to the user.

[0310] The frequency with which it is necessary to determine the timing of starting end-of-life care, etc., is sufficiently lower than the frequency with which it is necessary to determine daily assistance such as eating, excretion, and transfer. Therefore, as described above, the communication load and processing load can be reduced by using a user operation on the terminal device 200 as a trigger for uploading and analyzing the data. However, the processing of the know-how information 121 related to end-of-life care is not limited to being triggered by a user operation, and may be automatically executed when the target input information is collected.

[0311] Furthermore, in the information (1) and (2) above, information corresponding to the input information may be managed using nursing care software. Therefore, as described above, part or all of the input information may be acquired via the nursing care software. In this case, the information processing system 10 according to the present embodiment may be linked to the nursing care software. For example, the analysis results by the server system 100 may be displayed on the display screen of the nursing care software.

[0312] FIG. 30A is an example of a display screen of the care software. For example, the care software may display a screen listing the assistance provided to a given care recipient in chronological order. However, the display screen of the care software is not limited to this, and various modifications are possible. For example, each time input information is entered into the care software, the input information may be sent to the server system 100 and automatically analyzed. The care software may display a mark indicating the analysis result in a predetermined field on the display screen. In the example of FIG. 30A, a mark including an exclamation mark is displayed in the field displaying the intake amount or intake rate for each type of food, which is used as one of the input information. FIG. 30B is an example of a display screen displayed when an operation to select the mark in FIG. 30A is performed. In the example shown in FIG. 30B, analysis results similar to those in FIG. 29B are displayed as a pop-up on the display screen of the care software. In this way, by linking the information processing system 10 according to this embodiment with the care software, for example, care records and analysis results can be displayed together, making it possible to present appropriate information to the user. Note that the method of displaying the analysis results in the care software is not limited to that shown in FIG. 30B, and various modifications are possible.

[0313] (3) In the know-how information 121 regarding the timing for the caregiver to stop providing a meal, three types of information are input as input information: “If the person being assisted continues to be unable to chew the food provided to them,” “If the person being assisted frequently aspirates (choking, coughing, etc.) while eating,” and “If the person being assisted appears sleepy while eating.” When the condition of any one of the three types of information is met, the output information is “Stop providing the meal.”

[0314] (4) In the know-how information 121 regarding the response of a caregiver when a person being assisted aspirates while eating, the input information is "If the person being assisted aspirates and loses their posture," and when this condition is met, the output information is "Please check their posture." The input information is "If the person being assisted aspirates and looks sleepy," and when this condition is met, the output information is "They seem sleepy. Please speak to them." The input information is "If the person being assisted aspirates and does not look sleepy or lose their posture" or "If the person being assisted aspirates and the food texture is incorrect," and when this condition is met, the output information is "Please check the food (e.g., food texture)." The input information is "If the person being assisted is aspirating more frequently (choking, coughing, etc.) while eating," and when this condition is met, the output information is "Stop providing the food."

[0315] Here, each piece of input information related to the know-how information 121 in (3) and (4) above is extracted from output data such as video captured by a camera and / or waveforms from a wearable device that measures swallowing (e.g., as described in U.S. Patent Application No. 16 / 276,768, filed February 15, 2019, entitled "Swallowing action measurement device and swallowing action support system." This patent application is incorporated by reference in its entirety into this specification).

[0316] As shown in FIG. 31, a system for providing the caregiver with assistance information related to (3) and (4) above includes a wearable device 400 that measures swallowing, a first terminal device 200A, a second terminal device 200B, and a server system 100. The person being assisted wears the wearable device 400 around their neck. The first terminal device 200A is placed, for example, on a table where the person being assisted eats, and has a function of capturing images of the person being assisted eating. The first terminal device 200A is, for example, a camera device or a smartphone with an app installed. The second terminal device 200B is a terminal carried by the caregiver, and has a function of receiving notifications via the app. The first terminal device 200A communicates with the wearable device 400 to exchange data. 31 shows an example in which the server system 100, the first terminal device 200A, and the second terminal device 200B are connected via a network NW such as a LAN or the Internet, and the second terminal device 200B and the wearable device 400 are directly connected using short-range wireless communication or the like. However, the specific connection mode of each device can be modified in various ways. In the embodiment shown below, an example in which the first terminal device 200A captures an image of one person being assisted will be described, but this is not limiting, and the first terminal device 200A may capture images of multiple people being assisted and output output information for each of the people being assisted.

[0317] The first terminal device 200A transmits an image of the person being assisted and output data of the wearable device 400 to the server system 100. Based on the image and output data, the server system 100 executes the process related to (3) or (4) above and requests output information such as "stop providing meal."

[0318] The app on the first terminal device 200A not only displays data from the wearable device 400 and captured images, but also displays output information. Fig. 32A is an example of a screen displayed on the display unit of the first terminal device 200A. The display unit here corresponds to, for example, the display unit 240 in Fig. 3. On the screen shown in Fig. 32B, an object OB1 indicating data measured by the wearable device 400 is superimposed on a partial area of the captured image IM. The information included in the object OB1 is, for example, a graph showing the time series changes in the measured values obtained by the wearable device 400.

[0319] In addition, an object OB2 indicating output information acquired from the server system 100 is superimposed on another part of the captured image IM. The output information includes, for example, as shown in (4) above, outputs such as "Please check your posture," "You seem sleepy. Please speak to the person," "Please check what you are eating," and "Please stop eating." The output information may also be capable of outputting information such as "Choking not detected," indicating that output of any of the above is unnecessary. For example, the object OB2 shown in FIG. 32A displays the text "Choking not detected" along with a first mark including a check mark indicating a normal state. FIG. 32B shows an example of other information displayed as the object OB2. For example, object OB2 may be an object that displays text such as "Check your posture," "Looks sleepy. Please speak up," or "Check your meal," along with a second mark that includes an exclamation mark. Object OB2 may also be an object that displays text such as "Please stop eating," along with a third mark that includes an exclamation mark. In the example of FIG. 32B, the second mark is an exclamation mark surrounded by a circle, and the third mark is an exclamation mark surrounded by a triangle.

[0320] "No choking detected" is information presented when no abnormality is detected in the person being assisted, and is of low urgency. The other four messages are relatively urgent because some abnormality has been detected. For example, "Please check your posture," "Looks sleepy. Please speak to the person," and "Check the person's eating" correspond to situations requiring attention when providing food. Furthermore, "Please stop eating" corresponds to situations where choking occurs frequently, and is therefore of particularly high urgency. As described above with reference to Figures 32A and 32B, by using the first to third marks along with their corresponding text, it is possible to appropriately communicate the condition of the person being assisted to the caregiver. Furthermore, the presentation method according to the level of urgency is not limited to using different marks or text; the color of the object OB2 may also be changed. For example, an object OB2 containing the first mark may be displayed in green, an object OB2 containing the second mark in yellow, and an object OB2 containing the third mark in red.

[0321] By displaying this information on the first terminal device 200A, the caregiver (caregiver A in FIG. 31) who is nearby providing meal assistance to the person being assisted can properly understand the condition of the person being assisted. Specifically, the caregiver can not only visually check the person being assisted, but also view the sensor output (object OB1) and the output information (object OB2) from the server system 100, and therefore can provide appropriate assistance.

[0322] The terminal device 200 may also include a light (light-emitting unit) such as an LED. The terminal device 200 may control the light in accordance with the output information. Specifically, when it is OK to continue providing food to the person being assisted (when normal), the light displays green; when attention is required when providing food to the person being assisted, the light displays yellow; and when food provision should be stopped, the light displays red. As a result, the caregiver assisting the person being assisted with food (caregiver A in FIG. 31) can recognize the notification based on the output information presented using the light of the first terminal device 200A. Note that although the above example shows the server system 100 calculating the output information, this is not limiting, and the first terminal device 200A may calculate the output information. In this case, the first terminal device 200A performs a display process for the output information calculated by itself.

[0323] Furthermore, the output information is not limited to that presented on the first terminal device 200A. For example, the output information may be presented on the second terminal device 200B used by a caregiver (caregiver B in FIG. 31) who is in a position where he or she cannot directly see the first terminal device 200A. For example, caregiver B is responsible for assisting the person being assisted with eating, and the person being assisted is in relatively good condition and is likely to be able to eat independently. In this case, caregiver B may set the first terminal device 200A in a predetermined position and have the person being assisted start eating, and then perform other assistance with a higher priority. In this case, by presenting the output information on the second terminal device 200B carried by caregiver B, caregiver B can return to assisting the person being assisted with eating as needed. Alternatively, caregiver B does not have to be the person originally responsible for assisting the person being assisted with eating. For example, if assistant A, who is in charge, is currently assisting another person being assisted, assistant B can provide appropriate help by presenting output information on the second terminal device 200B carried by assistant B.

[0324] 33A and 33B are examples of screens displayed on the second terminal device 200B. FIG. 33A is an example of a lock screen (notification screen) of the second terminal device 200B. For example, as shown in FIG. 33A, the second terminal device 200B may display objects OB21-OB24 corresponding to output information. All of the objects OB21-OB24 correspond to the object OB2 in FIGS. 32A and 32B. The object OB21 corresponds to "Please stop eating" in the output information described above. The object OB22 corresponds to "Please check your eating." The object OB23 corresponds to "You look sleepy. Please speak to me." The object OB24 corresponds to "Please check your posture."

[0325] The notifications corresponding to objects OB21-OB24 are not limited to being all notification targets. For example, output information to be notified may be selected depending on the user's level of proficiency. For example, a setting may be made to notify all output information to users with low proficiency in order to alert the person being assisted as much as possible, while notifications may be limited to output information with high urgency to users with high proficiency. In the above example, "Please stop eating" is more urgent than the other three, so for example, when targeting a user with high proficiency, object OB21 may be a display target, and objects OB22-OB24 may not be display targets.

[0326] The second terminal device 200B may also display a status screen showing detailed information about the output information. Fig. 33B is an example of the status screen. For example, the status screen may include items such as monitor type, user or device, status, and response.

[0327] The monitor type is information that identifies the situation (assistance) to be monitored, which in the above example is "eating." The user or device is information that identifies the target person being assisted. This may be the name of the person being assisted, or the ID of the device used to assist the target person being assisted. The device here may be the wearable device 400, the first terminal device 200A, or another device. The status represents output information. For example, "Chewing not detected" corresponds to normal. "Check posture," "Looks sleepy. Please speak to the person," and "Check eating" correspond to warnings. "Please stop eating" corresponds to stop.

[0328] The response is information that indicates the response status of the caregiver. If the status is normal, no action by the caregiver is required. If the status is caution, action by the caregiver is not necessarily required, and for example, only notification of output information may be performed. In this case, whether or not a notification was made may be displayed as the response, and information identifying the user who was notified may be displayed. If the status is canceled, it is highly likely that action by the caregiver is required. Therefore, information identifying whether action has been taken or not may be displayed as the response. Furthermore, if action has not been taken, an object indicating that the caregiver will take action may be displayed, as shown in FIG. 33B. For example, if a user with the username "Sato" performs this operation, the information indicating the response is updated to information indicating "Sato has taken action." This prevents missed responses in emergencies and prevents multiple caregivers from providing assistance to a specific person being assisted in the same way.

[0329] 33B, it is possible to properly grasp the eating status of the person being assisted and to immediately determine whether or not an action by the caregiver is required. Note that, although the above describes a form in which notification is made using the display unit of the second terminal device 200B, the form of notification is not limited to this, and the caregiver may be notified by voice via the headset 300, for example.

[0330] Further, using the know-how information 121 of (3) and (4) above as an example, an example of a screen displayed when using given know-how information 121 will be described. As shown in step S203 of FIG. 6, when registering the know-how information 121, a device may be associated with the know-how information 121. Furthermore, as shown in step S216 of FIG. 6, processing content for device data may be associated with the know-how information 121. In the case of the know-how information 121 of (3) and (4) above, the device is a camera (terminal device 200) or a wearable device 400. Furthermore, the processing content may be processing to detect the posture, etc. of the person being assisted from a captured image, or processing to detect the amplitude, frequency, etc. of a waveform output from the wearable device 400.

[0331] However, even for similar know-how information 121, the required device data may differ depending on the know-how information 121. For example, in the know-how information 121 shown in (3) and (4) above or similar know-how information 121, the posture of the person being assisted is detected based on an image captured by a camera. The device data in this case may be image data that can detect the posture of the person being assisted, and may be data captured from the front of the person being assisted, data captured from the side of the person's profile, or data captured from an intermediate direction (diagonal direction). For example, the content of the device data differs depending on the direction from which the experienced user who registers the know-how information 121 thinks the person being assisted should be observed. Alternatively, it is conceivable that the device data will differ depending on the facilities of the care facility or the like to which the registered user belongs, as the positions at which cameras can be installed will differ.

[0332] That is, the know-how information 121 shown in (3) and (4) above is not limited to one, and may include multiple pieces of know-how information 121 with different device data registered by multiple registered users. Also, one registered user may register multiple pieces of know-how information 121 with different device data in order to provide the same or similar assistance.

[0333] When using such know-how information 121, it is important to align the device data. For example, when using know-how information 121 to determine whether a person receiving care should stop eating by performing image processing on device data captured from the front, using device data captured from the side or obliquely as input data may result in reduced processing accuracy. This is because the device data processing content of the know-how information 121 is intended to target data captured from the front of the person receiving care. Therefore, the know-how information 121 may include information for acquiring appropriate device data. For example, if the device is a camera, the information for acquiring appropriate device data may be information specifying the imaging direction when capturing an image of the subject. Furthermore, the information for acquiring appropriate device data may be information specifying the imaging range, such as whether the imaging range includes the entire body of the person receiving care, a bust-up, or the face, or other information for determining the characteristics of the captured image. Furthermore, when a device other than a camera is used, the know-how information 121 may also include information for acquiring appropriate device data.

[0334] Fig. 34A is another example of My Page. As in Fig. 29A, My Page includes know-how information 121 currently in use and registered know-how information 121. In the example of Fig. 34A, the user is currently using know-how information 121 corresponding to (3) or (4) above. My Page also includes a camera start object that instructs the start of a camera as an object for starting use of the know-how information 121.

[0335] FIG. 34B is an example of a display screen when an operation is performed on the camera activation object shown in FIG. 34A. The screen shown in FIG. 34B includes information for acquiring appropriate device data. The information for acquiring appropriate device data is, for example, a guide object for indicating the face direction of the person being assisted in the image. The guide objects in FIG. 34B include object OB5 representing an outline and object OB6 representing an illustration of a face. However, either object OB5 or OB6 may be omitted. Furthermore, information other than both objects OB5 and OB6 may be used to indicate the appropriate face direction. For example, text such as "Please capture the person being assisted from the front" may be displayed, or the text may be output as audio using the terminal device 200 or the headset 300. Various modifications are possible to the method of presenting information for acquiring appropriate device data.

[0336] For example, the know-how information 121 is associated with information for acquiring appropriate device data based on sample data accumulated by the processing of steps S207-S210 in Fig. 6. For example, when the server system 100 acquires a plurality of images as sample data, it determines an average face direction based on the plurality of images. The server system 100 may determine objects OB5 and OB6 as information for acquiring appropriate device data based on the determined average face direction, and associate the determined objects OB5, OB6, etc. with the know-how information 121. When using the know-how information 121, the terminal device 200 presents information including objects OB5 and OB6 based on the information associated with the know-how information 121, as illustrated in Fig. 34B.

[0337] In this way, for example, a user who uses the know-how information 121 can appropriately adjust the placement of the camera or the like in accordance with the know-how information 121 to be used. Therefore, it becomes possible to acquire input information suitable for the know-how information 121 to be used.

[0338] Alternatively, information about device data may be presented in the search process for know-how information 121 shown in FIG. 10 . For example, in step S309 of FIG. 10 , know-how information 121 using data captured from the front, know-how information 121 using data captured from the side, and know-how information 121 using data captured from an oblique angle are presented as different pieces of know-how information 121. In this case, for example, sample data acquired in step S210 of FIG. 6 or information for acquiring appropriate device data may be displayed in association with the know-how information 121. A user who has performed a search can consider a device data acquisition method when determining the know-how information 121 to use, such as selecting know-how information 121 that uses data suitable for the user's environment. For example, if a camera capable of capturing an image of a person being assisted from the front is already installed in the user's environment, the user can adopt know-how information 121 that uses data captured from the front to utilize the camera, or can adopt know-how information 121 that uses data captured from an oblique angle to utilize highly rated know-how information 121. In the latter case, for example, the user can make a decision such as "Since the existing camera is not compatible, I will use the camera of the terminal device 200, which is easy to adjust the position."

[0339] Here, using the know-how information 121 of (3) and (4) as an example, we have explained a method of associating information for obtaining appropriate device data with the know-how information 121, and a method of presenting information for obtaining appropriate device data when using the know-how information 121. However, these methods can be widely applied to the know-how information 121 according to this embodiment, and are not limited to the know-how information 121 of (3) or (4).

[0340] (5) In the know-how information 121 for detecting situations where there is a high risk of falling, three types of information are input as input information: “If the person cannot walk on his / her own but starts walking without assistance,” “If the person has difficulty understanding the situation he / she is in and his / her physical functions and spatial awareness have deteriorated so that he / she cannot recognize steps,” and “If the person does not know where the toilet is.” When the condition of any one of the three types of information is met, the output information is that “this is a situation where there is a high risk of falling.”

[0341] Here, the above three types of information are extracted, for example, from information from foot pressure sensors (for example, pressure sensors are provided in multiple locations on the insole, and the time series changes in each pressure sensor and the time series changes in the center of gravity position) and video captured by a camera. Only when it is determined that there is a "high risk of falling," for example, is the output information notified to the caregiver via headset 300, along with information about the person being assisted who is determined to be at high risk of falling. The caregivers who are notified may only be those who are near or in the vicinity of the person being assisted who is determined to be at high risk of falling.

[0342] As shown in FIG. 35, the system for providing the caregiver with assistance information (output information) related to (5) above includes a camera 500, a third terminal device 200C, a fourth terminal device 200D, a server system 100, and a foot pressure sensor 600. The camera 500 is, for example, a wide-area camera and is installed on the ceiling of a room or on a wall near the ceiling. There may be multiple cameras 500, and FIG. 35 shows two cameras: a camera 500-1 installed on the ceiling and a camera 500-2 installed on a wall. The third terminal device 200C is placed, for example, in a station and has a function to display the state of the person being assisted captured by the camera 500. The fourth terminal device 200D is a terminal device used by a given caregiver. Note that FIG. 35 shows an example in which the server system 100, the third terminal device 200C, the fourth terminal device 200D, the camera 500, and the foot pressure sensor 600 are connected via a network NW such as a LAN or the Internet. However, the specific connection mode of each device can be modified in various ways.

[0343] The camera 500 transmits an image of the person being assisted to the server system 100. The foot pressure sensor 600 also transmits the sensing results to the server system 100. Based on the acquired information, the server system 100 executes the process related to (5) above and obtains output information indicating the risk of falling.

[0344] FIG. 36 is an example of a screen displayed on the display unit of the third terminal device 200C. The third terminal device 200C displays information about the foot pressure sensor 600 of the person being assisted and information indicating the degree of risk in association with the image of the person being assisted. In the example of FIG. 36, two people being assisted are imaged on the screen, and objects OB31 and OB32 indicating information about the foot pressure sensor 600 and objects OB41 and OB42 indicating the degree of risk are displayed near each person being assisted. The information indicating the degree of risk corresponds to the output information of (5) above. Note that the number of people being assisted captured on the image may be one, or three or more. The information indicating the information about the foot pressure sensor 600 and the information indicating the degree of risk may be displayed for all or some of the people being assisted. Note that a known face recognition technology, for example, may be used as a means for identifying the person being assisted.

[0345] For example, cameras 500 are placed in various locations in a care facility or the like, and foot pressure sensors 600 are attached to individuals being assisted who are being monitored for falls. The third terminal device 200C is placed at a station in the care facility or the like and used by a manager or the like. Therefore, by displaying the image shown in Fig. 36 on the third terminal device 200C, it becomes possible to properly grasp the risk of falls within the facility.

[0346] Furthermore, the presentation of the output information is not limited to that performed by the third terminal device 200C. For example, the output information may be presented by the fourth terminal device 200D. FIGS. 37A-37D are examples of screens displayed on the fourth terminal device 200D. FIGS. 37A and 37B are examples of lock screens (notification screens) of the fourth terminal device 200D. For example, as shown in FIG. 37A, the fourth terminal device 200D may display information indicating that a specific person being assisted is about to fall or that the specific person being assisted should be monitored to prevent falls, based on the output information (5) above.

[0347] While it is important to determine the level of risk of falling at a given time, from the perspective of preventing injuries due to falls, it is also important to observe the gait status of a given person over time. In other words, when providing assistance to prevent falls, chronological information, including past fall risk history, is important. Therefore, the fourth terminal device 200D may notify the chronological output information of the target person as shown in FIG. 37B. For example, when an operation to select "Respond" included in a given notification is performed on the screen shown in FIG. 37A, a display such as that shown in FIG. 37B may be possible. For example, in FIG. 37B, a notification of monitoring is issued at 17:27, and output information calculated for the target person after the notification is also notified within the same object. For example, the person to be monitored is determined to be at risk of falling at 17:28 and 17:29, and output information indicating this is displayed together with the output information notified at 17:27.

[0348] The fourth terminal device 200D may also display a status screen showing detailed processing information related to the output information. For example, the status screen shown in Fig. 33B above includes information related to a fall. The status screen related to a fall includes items such as location, time, person who detected it, status, response, and details.

[0349] "Location" indicates the location where the person being assisted is at risk of falling. For example, the server system 100 stores information in advance that associates the ID of the camera 500 with the installation location, and identifies the location based on this information. "Time" is information indicating the timing at which it was determined that there was a high possibility of falling. "Detector" is information indicating the person being assisted who has been determined to have a high possibility of falling, such as the name of the person being assisted. However, "Detector" may also be other information that can be used to identify the person being assisted, such as the ID of the foot pressure sensor 600. "Status" is information indicating whether or not the caregiver has taken action, and includes information such as "taken action" or "not taken action." "Response" is an area where an object is displayed to indicate that the caregiver is assisting the person being assisted. For example, an object containing the text "respond" is displayed in the response column. "Details" is detailed information showing the condition of the person being assisted who has been determined to have a high risk of falling, and may be, for example, link information for transitioning to a screen described later with reference to FIG. 37D. By displaying such a status screen, it is possible to properly present to the user when, where, and who is in danger of falling, and what response is being taken in response.

[0350] FIG. 37C is another example of a status screen. As shown in FIG. 37C, the status screen may include time-series information regarding falls of a given assisted person. This makes it possible to clearly present the progression of the assisted person's fall risk and the caregiver's response at each timing. In the example of FIG. 37C, the caregiver did not respond to the fall risk at 17:23, for example, because the caregiver determined that there was no problem based on visual inspection. In addition, the fall risks that occurred to the same assisted person at 17:27, 17:28, and 17:29 were responded to by a caregiver with the user name Sato, for example.

[0351] Considering the risk of fractures and other injuries to the person being assisted, it is important to prevent falls before they occur. Therefore, when safety-conscious control is performed, a notification that a fall is imminent may be sent even in a situation where the person does not actually fall. In other words, even in a situation where the server system 100 determines that there is a high possibility of a fall, there may be cases where support for the person being assisted is truly necessary, and there may also be cases where simply watching over the person is sufficient.

[0352] Therefore, the user of the fourth terminal device 200D may view detailed information when it is determined that the likelihood of falling is high. For example, when a selection operation is performed on an object including the text "View" displayed in the details field in Fig. 37C, the fourth terminal device 200D may display detailed information and receive feedback based on the detailed information.

[0353] Fig. 37D is an example of a screen that displays detailed information. As shown in Fig. 37D, the detailed information may be a moving image of the person being assisted. The moving image here may be the captured moving image itself, or may be information including an object (corresponding to OB31, etc.) showing information from the foot pressure sensor 600 or an object (corresponding to OB41, etc.) showing the degree of risk, as shown in Fig. 36. For example, if the server system 100 determines output information indicating the risk of falling based on 10 seconds of moving image, the moving image here is 10 seconds of moving image when it is determined that the risk of falling is high.

[0354] After viewing the video, the user of the fourth terminal device 200D inputs in Fig. 37D whether the scene indicated that the person being assisted was in danger of falling or whether the scene indicated that there was no particular problem and no intervention by a caregiver was required. The input information may be stored in the nursing facility or may be fed back to the server system 100 and used to update the trained model that determines the risk of falling.

[0355] Although the above describes a form in which notification is made using the display unit of the fourth terminal device 200D, the form of notification is not limited to this, and the caregiver may be notified by voice via the headset 300, for example.

[0356] In the above, for example, a status screen is displayed on each terminal device 200 for the information related to (3) to (5) to check the status of each person being assisted, but a status screen may be displayed for the information related to (1) and (2) above. Also, the know-how information 121 to be displayed on the status screen is not limited to (1) to (5), and devices such as the terminal device 200 may display a status screen related to other information.

[0357] Although the present embodiment has been described in detail above, those skilled in the art will readily understand that many modifications are possible without substantially departing from the novel features and advantages of the present embodiment. Therefore, all such modifications are intended to be included within the scope of the present disclosure. For example, a term described at least once in the specification or drawings with a different term having a broader or equivalent meaning may be replaced with that different term anywhere in the specification or drawings. Furthermore, all combinations of the present embodiment and modifications are also intended to be included within the scope of the present disclosure. Furthermore, the configurations and operations of the information processing system, information processing device, server system, and terminal device are not limited to those described in the present embodiment, and various modifications are possible.

[0358] [Additional Notes] One aspect of the present disclosure includes a processing unit that accepts a registration request for know-how information that includes information used in assistance, where output information that is output to an assistant is associated with input information for outputting the output information, and a memory unit that stores the know-how information based on the registration request, and the processing unit is related to an information processing device that determines the importance of the know-how information based on association information that associates changes in status information that represent the status of a person being assisted with know-how information used in assisting the person being assisted. [Explanation of symbols]

[0359] 10...information processing system, 100...server system, 110...processing unit, 111...registration processing unit, 112...search processing unit, 113...similarity determination unit, 114...status determination unit, 115...importance determination unit, 120...storage unit, 121...know-how information, 122...registration information, 123...list information, 130...communication unit, 200, 200-1, 200-2...terminal device, 200A...first terminal device, 200B...second terminal device, 200C...third terminal device, 2 00D...fourth terminal device, 210...processing unit, 220...storage unit, 230...communication unit, 240...display unit, 250...operation unit, 300, 300-1, 300-2...headset, 400...wearable device, 500, 500-1, 500-2...camera, 600...foot pressure sensor, NW...network, RE1, RE2, RE3...area, OB1, OB2, OB21-OB24, OB31, OB32, OB41, OB42...object

Claims

1. a processing unit that evaluates the condition of the person being assisted with respect to physical function, daily living function, cognitive function, and mental / behavioral disorder, which are described in the survey items by a certification surveyor who determines the level of need for assistance or care, based on output data from sensors placed in the living environment of the person being assisted; a storage unit for storing know-how information to be used in providing assistance to the person being assisted; Including, The processing unit An information processing device that provides know-how information corresponding to the condition of the person being assisted based on the condition of the person being assisted.

2. The physical functions include the presence or absence of paralysis, the presence or absence of contracture, and at least one function of turning over in bed, getting up, maintaining a sitting position, maintaining a standing position on both feet, walking, standing up, and standing on one foot; The function of daily living includes at least one of transfer, mobility, swallowing, eating, urination, and defecation; The cognitive function includes at least one of the following functions: saying one's date of birth, saying one's own name, understanding the current season, and understanding location; The mental and behavioral disorder includes at least one of the following features: emotional instability, day and night reversal, repeating the same thing over and over, shouting, restlessness, collecting things, and forgetfulness; The information processing device according to claim 1 .

3. The processing unit Acquires association information that associates a change in the state of the person being assisted acquired at a first timing with a change in the state of the person being assisted acquired at a second timing different from the first timing, with the know-how information used to assist the person being assisted between the first timing and the second timing; The processing unit determining the importance of the know-how information so that the importance of the know-how information when the degree of improvement in the condition of the person being assisted between the first timing and the second timing is high is higher than the importance of the know-how information when the degree of improvement in the condition of the person being assisted is low; The information processing device according to claim 2 .

Images