Information processing device and information processing method

Abstract

To provide an information processing device and an information processing method, etc., capable of appropriately utilizing the implicit knowledge.SOLUTION: An information processing device includes: a processing part for receiving the request for registration of the know-how information including the information in which the condition information representing a given starting condition and the assistance information representing the assistance actions performed when the starting condition is satisfied are associated with each other; and a storage part which stores a plurality of know-how information based on a plurality of registration requests. The processing part outputs any of the plurality of know-how information as a retrieval result based on the retrieval request including the retrieval information for specifying one of the starting condition and the assistance action.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] Conventionally, systems used in medical settings, nursing care facilities, etc. are known. Patent Document 1 discloses a method for matching a service provider with a request from a user of nursing care services. Patent Document 2 discloses a method for specifying a route for a home nursing care service. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-007574 [Patent Document 2] Japanese Patent Application Publication No. 2017-191416 Summary of the Invention [Problem to be solved by the invention]

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

[0005] The information processing device of this embodiment includes a memory unit that stores know-how information including information that corresponds condition information representing a given start condition with assistance information that represents assistance behavior to be performed when the start condition is satisfied, and that stores each of a plurality of users in association with the know-how information used by each of the plurality of users, and a processing unit that, when receiving a search for a recommended user or a recommended organization from a first user, determines the similarity between users based on the know-how information used by each of the plurality of users and the know-how information used by the first user, and suggests a second user from the plurality of users who is similar to the first user as the recommended user, or suggests an organization to which the second user belongs as the recommended organization. [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 14]FIG. 10 is a diagram illustrating the flow of a process for identifying recommended users. [Figure 15] 10 is a flowchart illustrating a second similarity determination process. [Figure 16] Example of facility listing information. [Figure 17] An example of a user page that displays information about a given user. [Figure 18] Examples of know-how information. [Figure 19A] An example of a user page that displays information about a given user. [Figure 19B] An example of a user page that displays information about a given user. [Figure 19C] An example of a user page that displays information about a given user. [Figure 20] 10 is an example of association between know-how information and multiple devices. [Figure 21] FIG. 10 is an explanatory diagram of a process for identifying a recommended supplier based on know-how information. [Figure 22] An example of a screen showing recommended suppliers. [Figure 23] An example of a screen displaying search results for recommended users. 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 the present embodiment digitalizes the intuition and tacit knowledge of caregivers in, for example, nursing care facilities, so that caregivers can provide appropriate care regardless of their level of expertise. 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, the present embodiment is not limited to this. For example, in home care where care is provided outside the facility, helpers' tacit knowledge related to caregiving may be accumulated and utilized. Furthermore, the method of the present embodiment provides the tacit knowledge of experts and others to other users, and is widely applicable 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 same applies to FIG. 2, FIG. 3, etc., which will be described later, in which modifications such as omissions and additions to 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 processing, which will be described later with reference to FIGS. 4, 6, 10, 12, 14, 15, etc. The multiple servers here may be physical servers or virtual servers. If a virtual server is used, the virtual server may be provided on a single 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] 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. 17 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.

[0017] 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.

[0018] 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.

[0019] 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 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. The storage unit 120 stores multiple pieces of know-how information 121 based on multiple registration requests.

[0020] 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.

[0021] 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.

[0022] The processing unit 110 includes, for example, a registration processing unit 111, a search processing unit 112, and a similarity determination unit 113.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] The similarity determination unit 113 may also perform a second similarity determination process to determine the similarity between two pieces of list information 123. When one or more pieces of know-how information 121 associated with a given facility are set as facility list information 124, the similarity determination unit 113 may perform a third similarity determination process to determine the similarity between the facility list information 124 and the list information 123. The similarity determination unit 113 performs a process to determine, for example, recommended users or recommended facilities based on the second similarity determination process or the third similarity determination process.

[0027] 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.

[0028] The storage unit 120 may store know-how information 121, registration information 122, list information 123, and facility list information 124. 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 assistance information are, for example, text.

[0029] The registered information 122 is 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. The list information 123 is a collection of one or more pieces of know-how information 121 that are being used by a given user. The facility list information 124 is a collection of one or more pieces of know-how information 121 registered by a user who belongs to a given facility. Details of each piece of information will be described later. The storage unit 120 may also store other information.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] The terminal device 200 may also include components not shown in Fig. 3, such as a light emitting unit, a vibration 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 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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, a neural network (NN) or the like may be made to perform machine learning using training data of know-how information and portions of the know-how information that require interpretation, and the portions that require interpretation may be automatically extracted using the trained model.

[0059] 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, a neural network (NN) or the like may be used to perform machine learning 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 trained model.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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. 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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'."

[0076] 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.

[0077] 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.

[0078] For example, 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The second processing algorithm is not limited to the above example, and a neural network may be used. Hereinafter, a neural network is abbreviated as NN. For example, the storage unit 120 may store multiple NNs with different structures as the multiple 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 CNN (Convolutional Neural Network). NN2 is, for example, a DNN (Deep Neural Network).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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."

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Furthermore, the technique of this embodiment can be applied to an information processing method that receives a registration request for know-how information 121 including information in which condition information representing a given start condition is associated with assistance information representing an assistance action to be executed when the start condition is satisfied, and outputs, as a search result, any one of a plurality of pieces of know-how information 121 stored based on a plurality of registration requests, based on a search request including search information that specifies either the start condition or the assistance action.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 .

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 3.3 Similarity determination during registration process In the above, an example has been described in which the search process utilizes the similarity determination process between the know-how information 121. However, the situation in which the result of the similarity determination process is utilized is not limited to this.

[0137] For example, when a registered user newly registers know-how information 121, similar know-how information similar to the know-how information 121 to be registered may be presented to the registered user. This process may be performed after step S103 in Fig. 4. Alternatively, it may be performed after step S216 in Fig. 6. For example, after the parameter calculation in step S216, when the accuracy rate is presented and a user selection regarding whether or not to adopt the parameter is accepted, the presentation of similar know-how information and the acceptance of a selection operation may be simultaneously performed.

[0138] When a registered user registers know-how information 121, the know-how information 121 is information that represents the registered user's tacit knowledge. Therefore, similar know-how information similar to the know-how information 121 is likely to be useful information for the registered user. Therefore, by presenting similar know-how information when registering the know-how information 121, it becomes possible to efficiently utilize tacit knowledge.

[0139] The similarity determination process here may be performed based on additional information as described above, or based on text mining, or based on the number of users who use the two pieces of know-how information 121 together. In this case, however, the know-how information 121, which is text, may have already been registered (step S104), but the association of devices and the like (step S217) may not have been completed. Therefore, there is a possibility that other users have not yet made progress in using the know-how information 121. Therefore, the similarity determination unit 113 may omit the determination based on the number of users who use the two pieces of know-how information 121 together in the similarity determination process, and various modifications of the specific similarity determination process are possible.

[0140] 3.4 Recommended Users and Facilities In addition, the processing unit 110 (similarity determination unit 113) may perform a second similarity determination process to determine the similarity between list information 123 corresponding to a first user among multiple users and list information 123 corresponding to a second user different from the first user.

[0141] As described above, the list information 123 of the first user is a collection of know-how information 121 currently being used by the first user, and the list information 123 of the second user is a collection of know-how information 121 currently being used by the second user. In other words, if the similarity between the list information 123 can be determined, users who use similar tacit knowledge can be identified.

[0142] For example, suppose there is a user who has provided care to a given person receiving care and whose care has been evaluated. The quality of the care may be evaluated by the person receiving care himself, by the person's family, or by a care manager. The quality of the care may also be evaluated based on the results of a facial image recognition process of the person receiving care, such as whether the person smiles frequently.

[0143] The user whose assistance content is being evaluated may be, for example, a home helper who provides home care or a family member of the person being assisted. In this case, from the perspective of providing appropriate assistance to the person being assisted, it is desirable for the user to provide assistance continuously. However, there may be situations in which the user is unable to provide assistance due to factors such as the family member having to attend to some business, the home helper taking a vacation, being transferred, or changing jobs. Furthermore, even if the user himself is able to provide assistance, there may be cases in which he or she has to find a different home helper due to factors such as cost.

[0144] In this case, the processing unit 110 (similarity determination unit 113) performs a second similarity determination process based on list information 123 corresponding to a user who has experience assisting a person being assisted and list information 123 of multiple users, and performs a process of determining a recommended user from the multiple users to assist the person being assisted based on the result of the second similarity determination process.

[0145] FIG. 14 is a diagram illustrating the flow of the process for determining a recommended user. First, a family member of the person being assisted or a care manager performs a search request operation for a recommended user. For example, the family member or the like performs an operation using the terminal device 200 to input a user ID that identifies the user who performed the desired assistance. For example, if the user performing the search is a family member of the person being assisted, they will input their own user ID if they provide regular assistance themselves, or if they request regular assistance from a home helper, they will input the user ID of the home helper. In step S501, the terminal device 200 accepts the search request operation.

[0146] In step S502, the terminal device 200 transmits a recommended user search request including the user ID to the server system 100. In step S503, the similarity determination unit 113 of the server system 100 identifies the list information 123 of the user who performed the desired assistance, based on the user ID transmitted from the terminal device 200. In step S504, the similarity determination unit 113 performs a second similarity determination process to determine the similarity between the list information 123 identified in step S503 and the list information 123 of another user.

[0147] FIG. 15 is a flowchart illustrating the second similarity determination process in step S504. In step S601, the similarity determination unit 113 extracts one piece of know-how information 121 included in one piece of list information 123. In step S602, the similarity determination unit 113 performs a first similarity determination process to determine the similarity between the know-how information 121 extracted in step S601 and one or more pieces of know-how information 121 included in the other piece of list information 123. In step S603, the similarity determination unit 113 stores the maximum value of the one or more calculated similarities as the score of the extracted know-how information 121.

[0148] In step S604, it is determined whether the calculation of scores has been completed for all of the know-how information 121 included in one of the list information 123. If the score calculation has not been completed, the similarity determination unit 113 returns to step S601, extracts other know-how information 121, and performs the first similarity determination process and records the score for the extracted know-how information 121.

[0149] When the calculation of scores has been completed for all know-how information 121, in step S605, the similarity determination unit 113 calculates a second similarity, which is the result of the second similarity determination process, based on the calculated score or scores. The second similarity here may be the sum or average of the score or scores, or other information such as the result of weighted addition. Note that Fig. 15 is an example of the second similarity determination process, and the specific process is not limited to this, and various modifications are possible.

[0150] For example, if the type of assistance to be used is known in advance, the similarity determination unit 113 may perform processing limited to that type. For example, it is assumed that the similarity determination unit 113 receives a search request for recommended users to provide meal assistance to the person being assisted in step S502. In this case, the similarity determination unit 113 may extract know-how information 121 associated with "meals" as additional information from each of the two list information 123 to be compared, and perform the second similarity determination process on the extracted results. Alternatively, the similarity determination unit 113 may obtain the second similarity based on a comparison process between distribution information representing the distribution of the know-how information 121 included in one list information 123 and distribution information of the other list information 123.

[0151] 15 completes the second similarity determination process between the list information 123 of the user who provided the desired assistance and the list information 123 of any one of the multiple users. The similarity determination unit 113 executes the same process as in FIG. 15 for the other users.

[0152] In step S505, the similarity determination unit 113 identifies the user corresponding to the list information 123 with the largest second similarity as the recommended user. In step S506, the server system 100 transmits information about the recommended user to the terminal device 200. In step S507, the display unit 240 of the terminal device 200 displays the information about the recommended user.

[0153] Alternatively, in step S505, the similarity determination unit 113 identifies a user corresponding to the list information 123 whose second similarity is equal to or greater than a given threshold as a recommended user. In this case, the number of recommended users is not limited to one. In step S506, the server system 100 transmits information about one or more recommended users to the terminal device 200. In step S507, the display unit 240 of the terminal device 200 displays the information about the recommended users.

[0154] In this way, it becomes possible to present information on users who are likely to provide similar care as users who have provided appropriate care in the past as recommended users to the family of the person being assisted and to care managers, etc. Therefore, by requesting assistance from the recommended users, it becomes possible to increase the satisfaction of the person being assisted and their family.

[0155] The storage unit 120 may also store, for each of a plurality of facilities, facility list information 124, which is a collection of know-how information 121 registered by one or more users belonging to the facility. The facility here refers to an organization to which a user who provides care to a person being assisted belongs, and may be a nursing home, a hospital, or another facility.

[0156] FIG. 16 is a diagram illustrating a process for obtaining facility list information 124. As shown in FIG. 16, the storage unit 120 stores registration information 122. Details of the registration information 122 are as described above with reference to FIG. 8, and the registration information 122 includes a registered user who has registered know-how information 121 and an ID that identifies the know-how information 121. The storage unit 120 may also store information that associates a facility with a user who belongs to the facility. In the example of FIG. 16, a user corresponding to UserID1 and a user corresponding to UserID4 belong to the facility corresponding to FacilityID1.

[0157] Based on these two pieces of information, the processing unit 110 obtains facility list information 124. In the example of Fig. 16, the user with UserID1 has registered know-how information 121 with ID1, and the user with UserID4 has registered know-how information 121 with ID4. Therefore, the facility list information 124 of the facility with FacilityID1 includes know-how information 121 with ID1 and ID4.

[0158] When a user registers know-how information 121, there is a high probability that the know-how information 121 corresponds to tacit knowledge acquired while working at the facility. In other words, the facility list information 124 can be said to be information representing tacit knowledge specific to the facility.

[0159] The processing unit 110 (similarity determination unit 113) may perform a third similarity determination process to determine the similarity between the list information 123 corresponding to a given user and the facility list information 124, and may perform a process to determine recommended facilities to be recommended to the given user or recommended users to be recommended to the given facility based on the results of the third similarity determination process. The specific flow of the third similarity determination process is the same as the second similarity determination process shown in FIG. 15. That is, the third similarity determination process is performed based on the similarity between the know-how information 121 included in the facility list information 124 and the know-how information included in the list information 123, and the third similarity is obtained as a result. Furthermore, various modifications of the third similarity determination process are possible, such as comparing the distribution information of the list information 123 with the distribution information of the facility list information 124.

[0160] For example, the similarity determination unit 113 performs a third similarity determination process based on list information 123 corresponding to users who have experience assisting persons receiving care and facility list information 124 of multiple facilities, and performs a process of determining a recommended facility for assisting the person receiving care from the multiple facilities based on the result of the third similarity determination process. In this way, it is possible to recommend a facility that can provide assistance suitable for the target person receiving care, thereby improving the satisfaction of the person receiving care and their family and improving the work efficiency of the care manager. As a result, it is possible to prevent early discharge from the facility.

[0161] Alternatively, the third similarity determination process may be used by users such as home helpers, caregivers, nurses, physical therapists, occupational therapists, speech-language-hearing therapists, etc. For example, when these users are job-hunting, it is desirable for them to select a facility whose care policy matches theirs. This has the advantage that users can utilize the care experience they have accumulated up to that point. Furthermore, if facilities can hire users who match their facility's policy, they can reduce the training burden and suppress turnover. According to the method of this embodiment, it is possible to match users and facilities based on the third similarity determination process based on the list information 123 and the facility list information 124.

[0162] The third similarity determination process may be triggered by a user such as a caregiver. For example, a user considering changing jobs may use the terminal device 200 to send a search request for recommended facilities to the server system 100. The server system 100 performs a third similarity determination process to determine the similarity between the target user's list information 123 and multiple facility list information 124. The server system 100 transmits information about facilities determined to have a high third similarity to the terminal device 200, and the display unit 240 of the terminal device 200 displays the information about the facilities. For example, the display unit 240 may display a list of facilities determined to have a third similarity equal to or greater than a threshold, or may display a list of a predetermined number of facilities in descending order of third similarity. Furthermore, facilities that satisfy the third similarity condition and are located within a predetermined threshold distance from the user's residence may be displayed together with a map.

[0163] Alternatively, the third similarity determination process may be triggered by a user such as a facility manager. For example, a person in charge of a facility recruiting personnel uses the terminal device 200 to send a search request for recommended users to the server system 100. The server system 100 performs the third similarity determination process to determine the similarity between the facility list information 124 of the target facility and a plurality of pieces of user list information 123. The server system 100 sends information about users determined to have a high third similarity to the terminal device 200, and the display unit 240 of the terminal device 200 displays the information about the users. For example, the display unit 240 may display a list of users determined to have a third similarity equal to or greater than a threshold, or may display a list of a predetermined number of users in descending order of third similarity. In addition to the condition of the third similarity, the search results may be narrowed down based on information such as the user's place of residence.

[0164] 17 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. As shown in Fig. 17, the user page may include an area RE1 for displaying know-how information 121 currently being used by the target user, an area RE2 for displaying registered know-how information 121, an area RE3 for displaying know-how information 121 recommended for use, and an area RE4 for displaying recommended facilities.

[0165] 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 included in the registration information 122 described above with reference to FIG. 8. RE3 displays, for example, the know-how information 121 currently in use or know-how information 121 similar to the registered know-how information 121. The know-how information 121 displayed in RE3 is determined, for example, based on the first similarity determination process described above. RE4 displays information about facilities determined to have a high third similarity by the third similarity determination process.

[0166] By displaying in this manner, not only can the registration and usage status of the know-how information 121 be easily grasped, but it is also possible to present unused know-how information 121 that has been determined to be suitable for the user, and to present facilities with care policies similar to that of the user. In other words, by using the screen shown in Fig. 17, useful information for the user can be presented in an easy-to-understand manner.

[0167] 17, 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.

[0168] 17, 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.

[0169] Furthermore, for know-how information 121 that is not yet ready for the user to add correct answer data because a 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.

[0170] 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.

[0171] 17, 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.

[0172] 3.5 HACs (Hospital Acquired Adverse Events) Hospital-acquired adverse events (HACs) are known to occur during hospitalization. HACs refer to the occurrence of illnesses other than those originally intended for treatment after hospitalization. HACs are preventable and represent a failure in patient management. For example, in the United States, hospitals are generally responsible for covering the medical costs of HACs, making it extremely important to prevent HACs.

[0173] HACs include events such as postoperative foreign body retention, air embolism, blood incompatibility, pressure ulcers, falls, trauma, fractures, dislocations, intracranial injuries, catastrophic injuries, burns, other injuries, inadequate blood glucose control, catheter-related urinary tract infections, catheter-associated infections, surgical site infections / mediastinitis after cardiac bypass, surgical site infections after bariatric surgery, laparoscopic gastric bypass, gastrostomy augmentation, laparoscopic limited gastric surgery, surgical site infections after plastic surgery, cardiac implantable electronic device surgical site infections, deep vein thrombosis / pulmonary embolism after plastic surgery, total knee replacement, hip replacement, and iatrogenic thoracic pneumothorax. Note that the above are examples of HACs, and various modifications are possible for specific events.

[0174] The know-how information 121 of this embodiment may include information representing in-hospital adverse events. FIG. 18 is an example of the know-how information 121. HACs1 and HACs2 shown in FIG. 18 each represent one of the above-mentioned events. As shown in FIG. 18, by associating HACs with the know-how information 121, it becomes possible to identify which types of HACs each piece of know-how information 121 is effective in suppressing. The information representing HACs may be acquired, for example, by making a series of utterances in step S101 of FIG. 4, such as, "To suppress HACs1, after doing xxx, do yyy." Alternatively, the server system 100 may ask a question such as, "What kind of HACs will this help suppress?" and acquire the information representing HACs as a response to the question.

[0175] For example, certified nursing assistants (CNAs) are the ones who actually provide care to patients in hospitals. Therefore, as described above with reference to FIG. 10, the CNAs add the necessary know-how information 121 to the list information 123 and use it in specific care situations. In this case, the search processing unit 112 may determine the know-how information 121 to be presented to the CNA based on a comparison process between HACs that the CNA has caused in the past and information representing HACs included in the know-how information 121. For example, by having a CNA who has caused a bedsore use know-how information 121 that is considered to be effective in preventing bedsores, it becomes possible to appropriately prevent HACs.

[0176] However, if the selection and use of know-how information 121 regarding HACs is left entirely up to the CNA, there is a risk that the know-how information 121 will not be utilized appropriately. For example, even if a CNA develops a pressure ulcer, the CNA may not recognize its importance, or may be overwhelmed by daily work and not have time to update the list information 123. However, HACs are not just an issue for the CNA as an individual; they are related to the reputation of the hospital to which the CNA belongs. Furthermore, it is known that medical expenses related to HACs can be enormous, and in countries where a system has been adopted in which hospitals bear the costs of HACs, as mentioned above, this could have an impact on hospital management.

[0177] Therefore, in this embodiment, when managing the list information 123 of users who directly provide assistance, it may be possible to allow operations not only by the users themselves but also by an administrative user who directs and supervises the users. For example, the multiple users in this embodiment include active users who are directed by the administrative user. An active user is a person who directly provides assistance to a person being assisted, such as the CNA described above.

[0178] The administrative user is a person who supervises the working users, such as a registered nurse (RN). However, the administrative user and the working users only need to have a hierarchical relationship, and their specific positions are not limited to RN and CNA. The method of this embodiment can also be extended to an organization with a chain of command with three or more levels.

[0179] The administrative user is a user who can use the information processing system 10 according to this embodiment and has the authority to view information about active users (for example, the user page of an active user as shown in FIG. 17). Note that the administrative user may be a user who, like the users described above, uses the know-how information 121 in the course of assistance by creating his or her own list information 123. Alternatively, the administrative user may be a user who mainly manages active users and does not actually provide assistance.

[0180] 19A to 19C are examples of user pages that are service usage screens of the information processing system 10 of this embodiment and display information about a given administrative user. As shown in Fig. 19A, the administrative user's user page displays objects used to transition to information related to the patient in charge, objects used to transition to information registered as favorites by the administrative user, objects used to transition to information about the organization to which the administrative user belongs, and the like. When a selection operation for each object is performed, a transition to a display screen for the corresponding information is performed.

[0181] FIG. 19B is an example of a screen that is displayed when a selection operation is performed on an object containing the text "My Patients" in FIG. 19A. In the example of FIG. 19B, the RN, who is the target administrative user, directs and supervises multiple CNAs. For example, Name1 to Name6 each represent the name of a CNA. Each CNA is responsible for assisting one or more patients. In other words, information related to the patients in charge may be information about multiple active users who are under the supervision of the administrative user and are directly responsible for assisting patients. By displaying in this manner, it is possible to present the active users who are the subject of management in an easy-to-understand manner to the administrative user.

[0182] The processing unit 110 may determine, from among a plurality of pieces of know-how information 121, recommended know-how information to be used by the active user based on the occurrence status of in-hospital adverse events of the active user, and may perform processing to present the determined recommended know-how information to the administrative user. For example, suppose a given CNA has a history of developing a pressure ulcer within a given period. Here, the given period is, for example, a period from the present to six months ago, but the specific period can be varied in various ways. In this case, to prevent the CNA from developing HACs again, it is preferable to have the CNA use the know-how information 121 associated with the pressure ulcer.

[0183] Therefore, the processing unit 110 may determine recommended know-how information based on, for example, HACs with occurrence histories and information about HACs included in the know-how information 121. For example, the processing unit 110 may display a warning when there is an active user who has an occurrence history of HACs but has not used the corresponding know-how information 121. In the example of Fig. 19B, an object containing the text "Warning" is displayed in the area corresponding to the CNA corresponding to Name1.

[0184] FIG. 19C is an example of a screen that is displayed when a selection operation for a given user is performed, for example, in FIG. 19B. As shown in FIG. 19C, when a given user is selected, the face photo, name, affiliation, etc. of the selected CNA are displayed, as well as know-how information related to the CNA. Specifically, the screen of FIG. 19C includes an area RE5 that displays know-how information 121 currently being used by the target CNA, and an area RE6 that displays registered know-how information 121. Furthermore, when a user for whom a warning display has been issued is selected, the screen of FIG. 19C may include an area RE7 that displays recommended know-how information. For example, "if7-then7" and "if10-then10" displayed in RE7 of FIG. 19C represent recommended know-how information.

[0185] In this way, a user who is in a position to manage one or more active users can properly grasp the occurrence status of HACs for each active user and the usage status of the know-how information 121. Furthermore, if the know-how information 121 for suppressing HACs is not being fully utilized, it becomes possible to notify the management user of this fact.

[0186] Furthermore, when the processing unit 110 receives a request from the administrative user to add recommended know-how information, the processing unit 110 may perform a process of adding the recommended know-how information to the list information 123 of the active user. More specifically, when the processing unit 110 receives a request from the administrative user to add recommended know-how information, the processing unit 110 may perform a process of adding the recommended know-how information to the list information 123 of the active user, even without the permission of the active user. In this way, the administrative user can manage the list information 123 of the active user. Therefore, even if the active user does not use appropriate know-how information 121 for reducing HACs for some reason, the administrative user can correct it. This avoids relying on the response of individual active users, making it possible to promote HACs reduction throughout the hospital. Furthermore, reducing HACs can also reduce medical costs and the number of patients throughout society.

[0187] For example, as shown in RE7 in FIG. 19C , an object including the text “Request to apply” may be displayed in an area corresponding to each piece of recommended know-how information. The object is used for an administrative user to request the addition of recommended know-how information. For example, when the administrative user performs a selection operation of the object on the screen of FIG. 19C , the processing unit 110 performs a process of adding the corresponding recommended know-how information to the list information 123 of the corresponding user. In this way, the administrative user can easily add required recommended know-how information while checking the use and registration status of the know-how information 121 of each active user. Note that, in FIG. 19 , the administrative user can request the active user to add recommended know-how information, but the present invention is not limited to this. For example, the administrative user may request the active user to add know-how information searched by the administrative user.

[0188] The above-described active user is an in-hospital user who is in charge of a given patient in the hospital. The multiple users in this embodiment may also include an out-of-hospital user who is in charge of providing care for the given patient outside the hospital. For example, if a given active user is in charge of a given hospitalized patient and another user provides care for the given patient after discharge, the other user is an out-of-hospital user.

[0189] For example, in FIG. 19B, two users marked with "I" (corresponding to Name1 and Name2) are in-hospital users, and the user marked with "O" (corresponding to Name3) is an out-of-hospital user. The out-of-hospital user, for example, provides home care for a patient. When an out-of-hospital user is selected, the know-how information 121 used by the out-of-hospital user, the registered know-how information 121, etc. are displayed, as in FIG. 19C. By displaying in this manner, it is possible to present to the administrative user in an easy-to-understand manner the usage status of the know-how information 121 for out-of-hospital users who are in charge of the same patient.

[0190] If an adverse event occurs to a patient under the care of a given active user, the RN who manages the patient and the entire hospital may be held responsible for the adverse event. For example, as mentioned above, HACs require the hospital to cover medical expenses. Adverse events in this case also include the patient being readmitted to hospital within a short period of time after discharge. For example, in the United States, if a discharged patient is readmitted within 30 days for the same illness, medical fees are reduced. In other words, just like HACs, short-term readmission is an important issue that should not be limited to measures at the active user level but should be prevented through organizational management.

[0191] Therefore, the processing unit 110 may perform a process of presenting to the administrative user second recommended know-how information, which is know-how information 121 recommended for use by users outside the hospital in order to prevent patients from being readmitted to the hospital. For example, the know-how information 121 may include information about the patient's illness. In this way, it is possible to identify know-how information 121 for providing appropriate care to a patient hospitalized due to a specific illness. The processing unit 110 may identify the second recommended know-how information based on the illness that caused the patient's hospitalization and the information about the illness included in the know-how information 121.

[0192] For example, the processing unit 110 determines whether the external user is using know-how information 121 that contributes to preventing re-admission of the patient, based on the patient's disease and the know-how information 121 that the external user is using. For example, the processing unit 110 may display a warning when there is an external user who is not using know-how information 121 corresponding to the patient's disease. For example, in the example of FIG. 19B, an object containing the text "Warning" is displayed in the area corresponding to the external user. Then, when an operation to select an external user is performed, a display process of second recommended know-how information recommended to the external user is performed, similar to RE7 in FIG. 19C.

[0193] In this way, the status of the assistance provided by the external user can be presented to the administrative user, allowing the administrative user to determine whether appropriate assistance is being provided to prevent a discharged patient from being readmitted to the hospital in a short period of time. Furthermore, similar to the above-described example, when the processing unit 110 receives a request from the administrative user to add second recommended know-how information, the processing unit 110 may perform processing to add the second recommended know-how information to the list information 123 of the external user without the permission of the external user.

[0194] The server system 100 of this embodiment may also include a billing processing unit (not shown). The billing processing unit performs processing to determine a fee for the user's use of the know-how information 121, and to bill and settle the determined fee. By requesting a fee for the use of the know-how information 121 in this way, it becomes possible to pay a reward to the user who has registered the know-how information 121, for example. This improves motivation to register the know-how information 121, making it possible to efficiently collect tacit knowledge.

[0195] In this case, since the external user is not a user employed by the hospital, it is assumed that the external user or the external user's direct employer pays the fee for using the know-how information 121. However, when the administrative user adds the second recommended know-how information to the external user list information 123, the billing processing unit may perform processing to bill the administrative user or the hospital to which the administrative user belongs. Since using the know-how information 121 benefits the hospital by reducing readmissions, the fee for this is considered a necessary expense for the hospital. In this way, by allowing the administrative user to edit the list information 123 of external users who are not directly subordinate to the administrative user and paying the fee for editing, the necessary know-how information 121 can be more easily provided to external users. Patients can receive appropriate care seamlessly during and after hospitalization, thereby reducing readmissions. In other words, the method of this embodiment enables the use of a wide range of tacit knowledge, including that of users outside the hospital, thereby making it possible to reduce medical costs, etc.

[0196] 3.6 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] FIG. 20 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. 20. Note that, as shown in FIG. 20, 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 21 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.

[0211] 21, 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.

[0212] 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.

[0213] 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.

[0214] In addition, when determining the fourth know-how information 121, the processing unit 110 may use the degree of use or popularity of each piece of know-how information 121. For example, the processing unit 110 may count the number of times each piece of know-how information 121 has been downloaded for use. The processing unit 110 may also count the number of users currently using each piece of know-how information 121. The number of downloads or the number of users indicates how many users have determined that the know-how information 121 in question is useful. In other words, know-how information 121 with a large number of downloads or the like is likely to be widely used, and there is a high demand for devices that can automate processing related to the know-how information 121. In other words, since the market size is expected to be relatively large, suppliers are also highly motivated to enter the market, making it easier for selected suppliers to actually start supplying devices.

[0215] Furthermore, in this embodiment, each user may be able to evaluate the know-how information 121 that they have used. Various modes of evaluation are possible, but for example, each user may assign a score to the know-how information 121. Know-how information 121 with a high score statistic (average value, etc.) is likely to be highly popular among users and to be widely used. Therefore, in this case as well, it is considered that there is a great need for a device that can automate the use of the know-how information 121.

[0216] FIG. 22 is an example of a screen presenting recommended suppliers. In the example of FIG. 22, 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. 22 represents a category determined based on a product classification code, such as UNSPSC. For example, in the example of FIG. 22, 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. 20, 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.

[0217] At this time, the processing unit 110 may present a ranking of each know-how information 121. As described above, the index for determining the ranking may be the number of downloads, the number of users, 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, rankings are useful for processing because they serve as a means to encourage suppliers to supply new devices. For example, FIG. 22 shows a screen displaying, in order of ranking, know-how information 121 that meets given conditions among multiple know-how information 121 that are not associated with devices.

[0218] 4. Variations <Data format> In the embodiment described above, know-how information 121, registration information 122, list information 123, facility list information 124, etc. are exemplified as data stored in the storage unit 120. However, the data format used in this embodiment is not limited to the above, and various modifications are possible. For example, data described as being divided into multiple tables may be combined into one table. Furthermore, data described as being one table may be divided into multiple tables. Furthermore, elements included in a given table may be added or omitted, or may be stored as elements of another table. Furthermore, in this embodiment, various tables have been used for explanation, but instead of storing these tables in a storage unit, the correspondence between each element contained in the table may be determined using machine learning such as a neural network.

[0219] For example, in the above, the registration information 122 is data related to registered users, and the list information 123 is data when users other than registered users use the know-how information 121. However, as can be seen from Figures 8 and 11, the data formats are similar, so there is no need to separate them. For example, the list information 123 may be information including know-how information 121 registered by the target user and know-how information 121 in use. For example, when registering know-how information 121, the registration processing unit 111 may store information identifying the know-how information, a device, a processing program, etc., in the list information 123 of the registered user.

[0220] In the above, an example has been described in which the list information 123 is a collection of know-how information 121 that is currently being used. However, the list information 123 may also include know-how information 121 that has a usage history but is not currently being used. For example, information such as downloaded but unused, in use, used in the past but not currently being used, etc. may be stored as the status of each know-how information 121 included in the list information 123. Furthermore, information such as the start date and time of use, the continuous usage time, the number of times used, and the evaluation score may also be stored for each know-how information 121.

[0221] In the above, an example has been described in which the facility list information 124 is a collection of know-how information 121 registered by the users who belong to the facility. However, the facility list information 124 may also include know-how information 121 that is currently being used by the users who belong to the facility.

[0222] <Use of know-how information 121> Also, in the above description, an example has been described in which the know-how information 121 is used individually. For example, suppose that p-th know-how information in which a start condition if_p and an assisting action then_p are associated with each other, and q-th know-how information in which a start condition if_q and an assisting action then_q are associated with each other are stored. Device_p is associated with the p-th know-how information 121, and Device_q is associated with the q-th know-how information 121. In this case, in the above example, a process of presenting information related to then_p when it is determined that if_p is satisfied using Device_p and a process of presenting information related to then_q when it is determined that if_q is satisfied using Device_q are performed independently.

[0223] However, the processing of this embodiment is not limited to this. For example, if the p-th know-how information and the q-th know-how information are similar, there is a possibility that if_p and then_q have some correlation. Similarly, there is a possibility that if_q and then_p have some correlation. Therefore, instead of processing these independently, composite processing may be performed.

[0224] For example, the processing unit 110 may receive sensor information from Device_p and sensor information from Device_q as inputs, and determine whether if_p is satisfied and whether if_q is satisfied based on both of them. For example, machine learning of a two-input, two-output NN is performed using both sample data and supervised answer data collected for the p-th know-how information and sample data and supervised answer data collected for the q-th know-how information. However, as described above, various modifications of the second processing algorithm are possible.

[0225] In this way, it is possible to improve the accuracy of the processing because the start conditions and the assistance actions can be determined taking into consideration the relevance between the know-how information 121. Note that although an example of combining two pieces of know-how information 121 has been described here, composite processing may be performed on three or more pieces of know-how information 121.

[0226] <Third Similarity Determination Process> The above describes an example in which the third similarity determination process is determined according to Fig. 15, similarly to the second similarity determination process. That is, each of the multiple know-how information 121 included in the facility list information 124 is subjected to the first similarity determination process with the know-how information 121 included in the list information 123. However, a specific example of the third similarity determination process is not limited to this.

[0227] For example, the facility list information 124 regarding a given facility may include information on the number of downloads of each piece of know-how information and the number of users. The similarity determination unit 113 may perform the third similarity determination process based on some of the know-how information 121 with high rankings determined by the number of downloads or the like, among the plurality of pieces of know-how information 121 included in the facility list information 124. For example, the similarity determination unit 113 targets a predetermined number of top-ranked pieces of know-how information 121 among the plurality of pieces of know-how information 121 included in the facility list information 124 for the third similarity determination process.

[0228] In this way, it is possible to limit the target of the third similarity determination process to information of higher importance among the plurality of pieces of know-how information 121 included in the facility list information 124. As a result, it is possible to more accurately determine the compatibility between the user and the facility.

[0229] <Recommended facilities suitable for those requiring assistance> In the above, an example has been described in which the third similarity between the list information 123 of users whose assistance content has been evaluated and the facility list information 124 is used to determine recommended facilities suitable for a given person being assisted.

[0230] However, the processing of this embodiment is not limited to this. The third similarity is an index from the perspective of whether the facility's assistance method is suitable for the target care recipient. However, the satisfaction of the care recipient when moving into the facility is not determined solely by the assistance method, but may be influenced by various factors such as the degree of progression of the care recipient's dementia and the level of ADL. For example, when a care recipient moves into a facility, if the level of the care recipient's ADL is relatively low compared to the ADL levels of the other residents, the care recipient may not be able to keep up with the recreational activities at the facility, which may result in a decrease in the care recipient's motivation and the care recipient may leave the facility.

[0231] Therefore, the processing unit 110 may perform processing to determine recommended facilities using information other than the list information 123 and the facility list information 124. For example, the inputs for the processing include the list information 123 of users whose assistance content has been evaluated, the facility list information 124 of the facility to be evaluated, dementia level information, and the ADL evaluation value. The output of the processing is information indicating the degree to which the person being assisted is suitable for the facility.

[0232] The dementia level information here is information that indicates the degree of progression of dementia of the person being assisted. For example, the dementia level information may be a score on the Mini-Mental State Examination (MMSE), a score on the Hasegawa Dementia Scale-Revised (HDS-R), or other information representing the results of a dementia test. The dementia level information may also be information based on brain images acquired using CT (Computed Tomography) or MRI (Magnetic Resonance Imaging). For example, the dementia level information may be information representing the results of a doctor's diagnosis based on the brain image, the brain image itself, or the results of some kind of image processing performed on the brain image.

[0233] The processing unit 110 may perform a process of determining recommended facilities using machine learning such as a neural network. For example, in the learning stage, the storage unit 120 associates, as correct answer data, information indicating the results when the care recipient moves into a given facility with the list information, facility list information, dementia level information, and ADL evaluation value.

[0234] The correct data here is, for example, information indicating whether the person receiving care continued to reside in the facility or whether they immediately left. For example, the correct data may be binary data that distinguishes between the two, or may be numerical data that indicates the duration of continuous residence.

[0235] Alternatively, the correct answer data may be information representing the degree of smile of the person being assisted while in the facility. For example, the degree of smile is calculated as the ratio of the number of times, frequency, and duration of smiles while in a normal state to the number of times the person smiles while in the facility. The normal state may be, for example, a state where the person is at home, or a state where the person is receiving care from a user whose care content has been evaluated.

[0236] The processing unit 110 performs machine learning based on the input data and correct answer data. For example, the processing unit 110 inputs the list information, facility list information, dementia level information, and ADL evaluation value into the NN, and performs forward calculations based on the weights at that time. The processing unit 110 then calculates an objective function based on the calculation results and correct answer data (occupancy status or degree of smile), and updates the weights based on the objective function.

[0237] In this way, a trained model can be generated based on the input data to estimate the occupancy status of the person receiving care when they move into the facility, or to estimate the degree of smiling. The processing unit 110 of this embodiment may find recommended facilities for the person being assisted based on the trained model. In this way, it becomes possible to determine the compatibility between the person being assisted and the facility from various perspectives, in addition to the method of assistance.

[0238] <Recommended user display screen> The above has described the process of determining a recommended user suitable for assisting a given person being assisted based on the second similarity determination process, and the process of determining a recommended user suitable for a given facility based on the third similarity determination process. Below, an example of a screen displaying information about the recommended users will be described.

[0239] Fig. 23 is a specific example of a screen displaying information about recommended users. As shown in Fig. 23, the display screen in this case includes an area RE11 for inputting a search key, a check box CB for indicating whether similarity is on or off, an area RE12 for displaying one or more recommended users as search results, and an area RE13 for displaying detailed information about a recommended user selected in RE12.

[0240] First, the user performing the search inputs a search key to be used in searching for recommended users into RE11. For example, as described above, the user inputs the user ID of a user whose assistance to the assisted person has been evaluated as the search key, and checks CB. When CB is checked, the processing unit 110 determines the recommended user based on a second similarity determination process that determines the similarity between users. That is, in this case, users who are determined to be similar to the user whose assistance to the assisted person has been evaluated are displayed in RE12 as recommended users. RE12 is an area that displays information on a predetermined number of recommended users, for example, in descending order of similarity.

[0241] In this embodiment, recommended users may be searched for from a different perspective than the second similarity determination process. For example, the user may uncheck CB and then input information such as the type of assistance (e.g., eating, toileting, etc.), the residence of the person being assisted, and the amount of remuneration as search keys. In this case, the processing unit 110 performs processing to display on the RE12, as recommended users, users who are skilled in a predetermined type of assistance, users who are active in the neighborhood, users who accept requests for remuneration equal to or less than the input amount, etc.

[0242] RE13 includes an area RE14 for displaying a user name, an area RE15 for displaying registered know-how information, an area RE16 for displaying a schedule, and an area RE17 for displaying the similarity for each category.

[0243] The know-how information 121 displayed on the RE 15 is determined based on the registration information 122 . 23, the number of downloads, review results by other users, rankings, etc. may be displayed for each piece of know-how information 121. In this way, it is possible to present to the user who performed the search how much useful tacit knowledge the target user possesses.

[0244] RE16 is an area that displays information indicating whether the target user can provide assistance services for each date. In the example of FIG. 23, it is displayed that the target user can provide assistance services from March 7th to March 13th, but cannot provide assistance services on March 14th. As shown in FIG. 23, the available service times for each date may also be displayed. In this way, the user who performed the search can determine the date and time to request assistance while looking at their own schedule, the schedule of the person being assisted, and the schedule of the recommended user.

[0245] The RE 17 includes an object that displays the degree of similarity between the user or facility identified by the search key and the recommended user for each assistance category. For example, categories 1 to 6 in FIG. 23 represent types of assistance, such as eating, toileting, and transfer, respectively. For example, the similarity determination unit 113 may classify multiple pieces of know-how information 121 included in the list information 123 into categories based on additional information of the know-how information 121. The similarity determination unit 113 then calculates, for each category, a second similarity between the list information 123 of the user corresponding to the search key and the list information 123 of other users. In the example of FIG. 23, the second similarity determination process is performed for each of six categories, thereby obtaining six second similarities, and the values are displayed as a graph. For example, the type of assistance required varies depending on the situation of the person being assisted and their family, such as requesting assistance with eating but not much assistance with transfers. In this regard, by presenting the degree of similarity for each category to the user as shown in FIG. 23, it becomes possible to more clearly indicate whether the recommended user is suitable for assisting the person being assisted. In the example of Figure 23, if you are requesting assistance corresponding to category 3 or 4, the corresponding user will be more likely to be selected, and if you are requesting assistance corresponding to category 2 or 6, the corresponding user will be less likely to be selected.

[0246] 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 together 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, terminal device, etc. are not limited to those described in the present embodiment, and various modifications are possible.

[0247] [Additional Notes] The information processing device of this embodiment includes a processing unit that accepts a registration request for know-how information including information that corresponds condition information representing a given start condition with assistance information representing an assistance action to be performed when the start condition is satisfied, and a memory unit that stores multiple pieces of know-how information based on multiple registration requests, and the processing unit outputs one of the multiple pieces of know-how information as a search result based on a search request that includes search information that identifies either the start condition or the assistance action. [Explanation of symbols]

[0248] 10...information processing system, 100...server system, 110...processing unit, 111...registration processing unit, 112...search processing unit, 113...similarity determination unit, 120...storage unit, 121...know-how information, 122...registration information, 123...list information, 124...facility list information, 130...communication unit, 200, 200-1, 200-2...terminal device, 210...processing unit, 220...storage unit, 230...communication unit, 240...display unit, 250...operation unit, 300, 300-1, 300-2...headset, RE1-RE7, RE11-RE17...area, CB...checkbox

Claims

1. a storage unit that stores know-how information including information in which condition information representing a given start condition is associated with assistance information representing an assistance action to be executed when the start condition is satisfied, and that stores each of a plurality of users in association with the know-how information used by each of the plurality of users; a processing unit that, when receiving a search for a recommended user or a recommended organization from a first user, determines a similarity between users based on the know-how information used by each of the plurality of users and the know-how information used by the first user, and suggests a second user similar to the first user from the plurality of users as the recommended user, or suggests an organization to which the second user belongs as the recommended organization; An information processing device comprising:

2. The information processing device according to claim 1 , further comprising a display unit that displays, for each assistance category, a degree of similarity between the know-how information used by the first user and the know-how information used by the recommended user as an object.

3. The storage unit storing list information including one or more pieces of know-how information currently being used for each of the plurality of users in association with each other; The processing unit The information processing apparatus according to claim 2 , wherein a similarity determination process is performed to determine a similarity between list information corresponding to the first user among the plurality of users and list information corresponding to a user other than the first user.

Images