Method for automating recording of service provision in elderly welfare facility, information processing system, and computer program

Abstract

This method for automating recording of service provision in an elderly welfare facility comprises the steps of: obtaining first state data of beneficiaries of an elderly welfare facility; establishing a customized service provision plan for each beneficiary on the basis of the first state data; and generating a service provision list for each worker of the elderly welfare facility on the basis of the customized service provision plan.

Description

Method for Automating Service Provision Records in Elderly Welfare Facilities, Information Processing System, and Computer Program

[0001] The present disclosure relates to a method for automating the recording of service provision in elderly welfare facilities, an information processing system, and a computer program.

[0002] Elderly welfare facilities provide various services to meet the physical, cognitive, and emotional needs of the elderly. It is desirable that these services be customized according to the individual circumstances of the recipients (e.g., the elderly). Furthermore, appropriate service schedules need to be allocated based on the staff's situation and capabilities.

[0003] Staff at elderly welfare facilities must provide services according to a given schedule and record the details. However, performing such service provision and recording in real time can place a significant workload on employees. Previously, these service records were primarily managed through manual methods or simple computerized systems. However, manual recording consumed a significant amount of time and effort while failing to guarantee accuracy. Since staff had to manually record service details every time, a time delay could occur between service provision and recording, increasing the likelihood of important information being omitted or recorded inaccurately. Consequently, there was a risk that the consistency and reliability of service provision within the elderly welfare facilities would be compromised.

[0004] While the introduction of computerized systems resolved some issues, limitations still existed in terms of real-time management and data accuracy. In computerized systems, manual work was required during the data entry process, and real-time updates were often difficult. Furthermore, even with the use of computerized systems, the time burden on employees was not reduced because they had to manually input records, and the possibility of errors during data entry remained. These problems could ultimately lead to a decline in the quality of service provision.

[0005] Furthermore, since multiple staff members work in shifts to provide various services in elderly welfare facilities, maintaining record consistency is a critical task. Consistency in service provision records is essential for accurately assessing the recipient's condition and establishing future service plans. However, under manual or simple computerized recording methods, inconsistencies frequently occurred due to discrepancies in records among staff members. Additionally, the inability to standardize records was a problem, as various staff members could write them in different ways.

[0006] The need for a new system to address these issues emerged, and an efficient method to record service provision details in real time was required. In particular, there was a need for development in a direction that reduces the workload of employees and enhances the accuracy and efficiency of services through a system capable of automatically recording and managing service provision details.

[0007] The aforementioned background technology is one that the inventor possessed or acquired in the process of deriving the contents of the disclosure of the present application, and it cannot be considered as prior art disclosed to the general public prior to the filing of this application.

[0008] One objective according to the embodiments of the present disclosure is to provide a method for automating the recording of service provision in elderly welfare facilities, an information processing system, and a computer program.

[0009] The objective according to the embodiments of the present disclosure is to provide a method for automating service provision records in an elderly welfare facility, an information processing system, and a computer program that can improve the accuracy and efficiency of services by automatically establishing a service provision plan tailored to individual recipients based on their needs and generating and distributing an optimized service provision schedule according to the work schedule of employees.

[0010] A method for automating the recording of service provision in an elderly welfare facility according to one embodiment is a method for automating the recording of service provision in an elderly welfare facility, and may include the steps of: acquiring first status data of a recipient of the elderly welfare facility; establishing a customized service provision plan for each of the recipients based on the first status data; and generating a service provision list for each of the employees of the elderly welfare facility based on the customized service provision plan.

[0011] In one embodiment, the method may further include the step of transmitting the service provision list to each of the employees' employee terminals.

[0012] In one embodiment, the step of establishing a service provision plan for each of the recipients based on the first state data may further include the step of inputting the first state data into an artificial neural network model and the step of obtaining a service provision plan for each of the recipients output from the artificial neural network model based on the first state data.

[0013] In one embodiment, the step of establishing a service provision plan for each of the recipients based on the first state data may include the step of inputting natural language data, including the first state data and natural language spoken by the recipient, into an artificial neural network model, and the step of obtaining a service provision plan for each of the recipients output from the artificial neural network model based on the first state data and the natural language data.

[0014] In one embodiment, the natural language data may be at least one of natural language spoken by the recipient and received by an employee terminal, and voice recognized by the recipient's recipient terminal.

[0015] In one embodiment, the step of generating a service provision list for each employee of the elderly welfare facility based on the customized service provision plan may include the step of generating a service provision list to be provided to the employee based on the customized service provision plan and second status data of the employee of the elderly welfare facility.

[0016] In one embodiment, each recipient of the elderly welfare facility has a corresponding recipient terminal and each employee has an employee terminal, and the method may include the steps of generating a log when one of the recipient terminals and one of the employee terminals approach within a predetermined distance and recording in a database that a specific service among the service provision list has been performed based on the generated log.

[0017] In one embodiment, prior to the step of recording in a database that a specific service among the service provision list has been fulfilled based on the generated log, the method may further include a step of determining which service among the service provision list the generated log corresponds to.

[0018] In one embodiment, the step of determining which service among the service provision list the generated log corresponds to may include the step of determining which service among the service provision list the generated log corresponds to based on past data associated with one of the recipient terminals and one of the employee terminals.

[0019] In one embodiment, the step of determining which service among the service provision list the generated log corresponds to may include the step of inputting past data associated with one of the recipient terminals and one of the worker terminals into an artificial neural network model—the past data includes at least one of a log generated in the past and a history of provided services between one of the recipient terminals and one of the worker terminals—and the step of obtaining a service corresponding to the generated log output from the artificial neural network model based on the past data.

[0020] In one embodiment, the method may include the steps of: acquiring biometric data measured by at least one of the recipient terminal and the worker terminal; determining, based on the biometric data, whether the stress index of at least one of the recipient corresponding to a specific recipient terminal or the worker corresponding to a specific worker terminal has exceeded a threshold; and adjusting the service provision plan of the recipient or the service provision list of the worker based on the determination that the stress index has exceeded the threshold.

[0021] In one embodiment, the method further comprises the steps of generating plan implementation data including whether a service provision list for at least one of the workers has been implemented, and displaying the plan implementation data, wherein the plan implementation data includes at least one of the name of a specific service, a recipient, and a time of implementation, and the list of implemented and unimplemented items among the service provision lists may be displayed separately.

[0022] In one embodiment, the method further includes the step of displaying first state data for each of the recipients on a display device, and the screen of the display device may be partitioned to display first state data for each of the recipients.

[0023] In one embodiment, the method may further include the step of generating plan implementation data including whether a service provision list for at least one of the employees has been implemented, and the step of generating a warning message and transmitting it to an administrator terminal when it is determined that an unimplemented service has occurred based on the plan implementation data.

[0024] In one embodiment, the first state data may include basic evaluation data including at least one of the recipient's health state, cognitive function, or emotional state.

[0025] In one embodiment, the service provision list may be a list of services that each of the workers is required to provide to one or more recipients during the day.

[0026] In one embodiment, the recipient includes an elderly patient receiving services from the elderly welfare facility, and the worker may be a person providing care services to the recipient of the elderly welfare facility.

[0027] A computer program according to one embodiment may be stored on a computer-readable recording medium to execute a method for automating the recording of service provision at an elderly welfare facility on a computer.

[0028] An information processing system according to one embodiment includes a memory and a processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program may include instructions for acquiring first state data of a recipient of an elderly welfare facility, establishing a customized service provision plan for each of the recipients based on the first state data, and generating a service provision list for each of the employees of the elderly welfare facility based on the customized service provision plan.

[0029] The method for automating service provision records in an elderly welfare facility, the information processing system, and the computer program according to one embodiment have the advantage of improving the accuracy and efficiency of services by automatically establishing a customized service provision plan for recipients based on the needs of individual recipients and generating and distributing an optimized service provision schedule according to the work schedule of employees.

[0030] The effects of the method for automating the record of service provision in elderly welfare facilities, the information processing system, and the computer program according to the embodiments are not limited to those mentioned above, and other effects not mentioned will be clearly understood by a person skilled in the art from the description below.

[0031] The following drawings attached to this specification illustrate preferred embodiments of the present disclosure and serve to further enhance understanding of the technical concept of the present disclosure together with the detailed description of the invention; therefore, the present disclosure should not be interpreted as being limited only to the matters described in such drawings.

[0032] FIG. 1 is a drawing illustrating an automated system for recording service provision at an elderly welfare facility according to one embodiment of the present disclosure.

[0033] FIG. 2 is a hardware configuration diagram of an information processing system that performs an automated method for recording the provision of services at an elderly welfare facility according to one embodiment of the present disclosure.

[0034] FIG. 3 is a flowchart of a method for automating the recording of service provision at an elderly welfare facility according to one embodiment of the present disclosure.

[0035] FIG. 4 is a diagram illustrating the process of creating and providing a customized service provision plan and the process of creating and providing a service provision list according to one embodiment of the present disclosure.

[0036] FIG. 5 is a diagram illustrating an exemplary Long Short-Term Memory (LSTM) model applicable to one embodiment of the present disclosure.

[0037] FIG. 6 is a flowchart of an automated method for recording whether a service is performed according to one embodiment of the present disclosure.

[0038] FIG. 7 is a diagram illustrating an automated process for recording whether a service is performed according to one embodiment of the present disclosure.

[0039] FIG. 8 is a diagram illustrating an exemplary Temporal Convolutional Network (TCN) model applicable to one embodiment of the present disclosure.

[0040] FIGS. 9 to 24 are drawings illustrating an exemplary user interface (UI) provided by an information processing system according to one embodiment of the present disclosure.

[0041] The various embodiments described in this specification are illustrative for the purpose of clearly explaining the technical concept of this disclosure and are not intended to limit it to specific embodiments. The technical concept of this disclosure includes various modifications, equivalents, alternatives, and embodiments optionally combined from all or part of each embodiment described in this specification. Furthermore, the scope of the technical concept of this disclosure is not limited to the various embodiments presented below or the specific descriptions thereof.

[0042] Terms used in this specification, including technical or scientific terms, may have the meaning generally understood by those skilled in the art to which this disclosure pertains, unless otherwise defined.

[0043] Expressions used herein such as “comprising,” “may compose,” “possessing,” “possessing,” “having,” and “possessing” imply the existence of the subject feature (e.g., function, operation, or component, etc.) and do not exclude the existence of other additional features. That is, such expressions should be understood as open-ended terms implying the possibility of including a second embodiment.

[0044] In this specification, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout the specification, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0045] Additionally, the terms 'module' or 'part' as used in the specification refer to software or hardware components, and the 'module' or 'part' performs certain roles. However, the meaning of 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, as an example, the 'module' or 'part' may include components such as software components, object-oriented software components, class components, and task components, and at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within the 'module' or 'part' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0046] According to one embodiment of the present disclosure, a ‘module’ or ‘part’ may be implemented as a processor and memory. The term ‘processor’ should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the term ‘processor’ may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. The term ‘processor’ may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations. Additionally, the term ‘memory’ should be broadly interpreted to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as Random Access Memory (RAM), Read-Only Memory (ROM), Non-Volatile Random Access Memory (NVRAM), Programmable Read-Only Memory (PROM), Erasable-Programmable Read-Only Memory (EPROM), Electrically Erasable PROM (EEPROM), Flash Memory, Magnetic or Optical Data Storage Devices, Registers, etc. If a processor can read information from memory and / or write information to memory, the memory is said to be in an electronic communication state with the processor. Memory integrated into a processor is in an electronic communication state with the processor.

[0047] Expressions such as "first," "second," or "first," "second" as used in this specification are used to distinguish one object from another when referring to a plurality of objects of the same kind, unless otherwise indicated by the context, and do not limit the order or importance of said objects.

[0048] Expressions used herein such as “A, B, and C,” “A, B, or C,” “A, B, and / or C,” or “at least one of A, B, and C,” “at least one of A, B, or C,” “at least one of A, B, and / or C,” “at least one selected from A, B, and C,” “at least one selected from A, B, or C,” “at least one selected from A, B, and / or C,” etc., may mean each of the listed items or all possible combinations of the listed items. For example, “at least one selected from A and B” may refer to (1) A, (2) at least one of A, (3) B, (4) at least one of B, (5) at least one of A and at least one of B, (6) at least one of A and B, (7) at least one of B and A, and (8) all of A and B.

[0049] As used herein, the expression “based on” is used to describe one or more factors affecting an act or action of a decision or judgment described in the phrase or sentence containing such expression, and such expression does not exclude additional factors affecting said act or action of a decision or judgment.

[0050] As used in this specification, the expression that a certain component (e.g., a first component) is "connected" or "connected" to another component (e.g., a second component) may mean that the said certain component is not only directly connected or connected to the said other component, but is also connected or connected through a new other component (e.g., a third component).

[0051] As used herein, the expression "configured to" may have meanings such as "set to," "capable of," "modified to," "made to," or "capable of." Such expression is not limited to the meaning of "specifically designed in hardware," and, for example, a processor configured to perform a specific operation may mean a generic-purpose processor capable of performing that specific operation by executing software.

[0052] Various embodiments of the present disclosure will be described below with reference to the accompanying drawings. In the accompanying drawings and the description thereof, identical or substantially equivalent components may be given the same reference numerals. Furthermore, in the description of the various embodiments below, the description of identical or corresponding components may be omitted, but this does not mean that such components are not included in the embodiments.

[0053] FIG. 1 is a drawing illustrating an automated system for recording service provision at an elderly welfare facility according to one embodiment of the present disclosure.

[0054] Referring to FIG. 1, an automated system for recording service provision in an elderly welfare facility according to one embodiment of the present disclosure may include an information processing system (100), a terminal (200), an external server (300), and a network (400).

[0055] Here, the automated system for recording service provision at an elderly welfare facility illustrated in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment illustrated in FIG. 1 and may be added, changed, or deleted as needed.

[0056] In one embodiment, the information processing system (100) can perform an automated service for recording the provision of services at an elderly welfare facility.

[0057] More specifically, the information processing system (100) can establish a customized service provision plan for a recipient based on at least one of the information of a recipient receiving services at an elderly welfare facility and an employee providing services.

[0058] In addition, the information processing system (100) can generate a list of services provided by an employee (daily, weekly, monthly, etc.) based on a customized service provision plan for the recipient and information on the employee's work schedule (e.g., work schedule), and can provide the generated list of services provided to the employee. Through this, the quality of service can be greatly improved by providing services tailored to the individual recipient's needs in a timely manner, and the accuracy and efficiency of the service can be maximized.

[0059] In addition, the information processing system (100) can automatically store in a database the details of services provided by the employee and / or the details of services received by the recipient. The employee can conveniently record the details of services provided using their employee terminal. Furthermore, as will be described in detail later, when the employee terminal and the recipient terminal approach within a certain distance (i.e., when the recipient and the employee come into contact), it can be automatically entered that a service has been provided to the recipient. Through this, the workload of the employee can be reduced and recording errors can be minimized.

[0060] Additionally, the information processing system (100) can provide contact history between the recipient and the worker, service omission history, etc., in the form of a visualized dashboard. For example, the information processing system (100) can provide a user interface (UI) (e.g., FIG. 9 to 24) that displays and outputs contact history between the recipient and the worker, service omission history, etc., in the form of a dashboard. Through this, the overall service provision status can be monitored, and immediate response can be enabled through real-time warnings when a problem occurs.

[0061] In one embodiment, the information processing system (100) may be connected to a terminal (200) (e.g., a recipient terminal (e.g., 201 in FIG. 4) and / or an employee terminal (e.g., 202 in FIG. 4)) through a network (400), and may provide an automated service for recording the provision of elderly welfare facility services to the terminal (200).

[0062] Here, the terminal (200) may refer to any form of entity(s) in a system having a mechanism for communicating with an information processing system (100). For example, such a terminal (200) may include a PC (personal computer), a notebook, a mobile terminal, a smartphone, a tablet PC, and a wearable device such as a smart ring or a smart watch, and may include any type of terminal capable of connecting to a wired / wireless network. Additionally, the terminal (200) may include any computing device implemented by at least one of an agent, an API (Application Programming Interface), and a plug-in. Additionally, the terminal (200) may include an application source and / or a client application.

[0063] Additionally, the network (400) may refer to a connection structure capable of exchanging information between each node, such as multiple terminals and servers. For example, the network (400) may include a Local Area Network (LAN), a Wide Area Network (WAN), the World Wide Web (WWW), a wired / wireless data network, a telephone network, a wired / wireless television network, a Controller Area Network (CAN), and Ethernet.

[0064] Wireless data communication networks may include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, NFC (Near-Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc.

[0065] In one embodiment, an external server (300) may be connected to an information processing system (100) via a network (400), and may store and manage various information and data (e.g., status data of recipients and / or employees, etc.) necessary for the information processing system (100) to perform an automated method for recording service provision in an elderly welfare facility, or receive, store, and manage various information and data (e.g., customized service provision plans for each recipient, service provision lists for each employee, plan implementation data including service provision history, etc.) derived as the information processing system (100) performs the automated method for recording service provision in an elderly welfare facility.

[0066] For example, the external server (300) may be an operating server of a social welfare facility that can provide information required by the information processing system (100), or a server of a medical service provider.

[0067] As another example, the external server (300) may be a storage server separately provided outside the information processing system (100). However, it is not limited thereto. Hereinafter, with reference to FIG. 2, the hardware configuration of the information processing system (100) for performing the method of automating the record of providing services for the elderly welfare facility will be described.

[0068] FIG. 2 is a hardware configuration diagram of an information processing system that performs an automated method for recording the provision of services at an elderly welfare facility according to one embodiment of the present disclosure.

[0069] FIG. 2 is a hardware configuration diagram of an information processing system that performs an automated method for recording the provision of services at an elderly welfare facility according to one embodiment of the present disclosure.

[0070] Referring to FIG. 2, an information processing system (100) according to another embodiment of the present disclosure may include one or more processors (110), a memory (120) for loading a computer program (151) executed by the processor (110), a bus (130), a communication interface (140), and a storage (150) for storing the computer program (151). Here, FIG. 2 illustrates only the components related to the embodiments of the present disclosure. Accordingly, a person skilled in the art to which the present disclosure pertains will understand that other general-purpose components may be included in addition to the components illustrated in FIG. 2.

[0071] The processor (110) controls the overall operation of each component of the information processing system (100). The processor (110) may be configured to include a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any form of processor well known in the art of the present disclosure.

[0072] Additionally, the processor (110) may perform operations for at least one application or program for executing the method according to the embodiments of the present disclosure, and the information processing system (100) may have one or more processors.

[0073] In one embodiment, the processor (110) may further include RAM (Random Access Memory, not shown) and ROM (Read-Only Memory, not shown) for temporarily and / or permanently storing signals (or data) processed within the processor (110). Additionally, the processor (110) may be implemented in the form of a system-on-chip (SoC) comprising at least one of a graphics processing unit, RAM, and ROM.

[0074] Memory (120) stores various data, instructions and / or information. Memory (120) may load a computer program (151) from storage (150) to execute a method / operation according to various embodiments of the present disclosure. When the computer program (151) is loaded into memory (120), the processor (110) may perform the method / operation by executing one or more instructions constituting the computer program (151). Memory (120) may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0075] The bus (130) provides communication functions between components of the information processing system (100). The bus (130) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0076] The communication interface (140) supports wired and wireless internet communication of the information processing system (100). Additionally, the communication interface (140) may support various communication methods other than internet communication. To this end, the communication interface (140) may be configured to include a communication module well known in the art of the present disclosure. In some embodiments, the communication interface (140) may be omitted.

[0077] Storage (150) can store computer programs (151) non-temporarily. When performing an automated process for recording the provision of services at an elderly welfare facility through an information processing system (100), storage (150) can store various information necessary to provide the automated process for recording the provision of services at an elderly welfare facility.

[0078] The storage (150) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which the present disclosure belongs.

[0079] A computer program (151) may include one or more instructions that cause a processor (110) to perform a method / operation according to various embodiments of the present disclosure when loaded into memory (120). That is, the processor (110) may perform the method / operation according to various embodiments of the present disclosure by executing the one or more instructions.

[0080] In one embodiment, the computer program (151) may include one or more instructions for performing an automated method for recording service provision in an elderly welfare facility, comprising the steps of acquiring first status data of a recipient of an elderly welfare facility, establishing a customized service provision plan for each recipient based on the first status data, and generating a service provision list for each employee of the elderly welfare facility based on the customized service provision plan.

[0081] The steps of the method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present disclosure belongs.

[0082] The components of the present disclosure may be implemented as a program (or application) and stored on a medium to be executed in combination with a computer, which is hardware. The components of the present disclosure may be executed as software programming or software elements, and similarly, embodiments may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as a combination of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors. Hereinafter, with reference to FIGS. 3 through 8, a method for automating the recording of service provision for elderly welfare facilities performed by an information processing system (100) will be described.

[0083] FIG. 3 is a flowchart of a method for automating the recording of service provision in an elderly welfare facility according to one embodiment of the present disclosure, and FIG. 4 is a diagram illustrating the process of creating and providing a customized service provision plan and the process of creating and providing a service provision list according to one embodiment of the present disclosure.

[0084] Referring to FIG. 3, in step S110, the information processing system (100) can obtain the recipient's first state data.

[0085] Here, the beneficiary is a person receiving services from an elderly welfare facility; for example, the beneficiary may be an elderly patient receiving services from an elderly welfare facility, but is not limited thereto.

[0086] Additionally, the recipient's first state data is data containing information regarding the recipient's state, for example, the recipient's first state data may include at least one of a health state, cognitive function, or emotional state.

[0087] Such primary status data of the recipient may be generated and acquired through surveys and self-reports of the recipient, observational evaluations by staff, physical and health examinations performed by medical professionals, and cognitive function tests and psychological tests performed by the recipient. However, this is not limited to these sources, and existing medical records or health records issued by a hospital may also be acquired from an electronic health record system.

[0088] According to one embodiment, the first state data of such a recipient may correspond to the recipient's basic evaluation data. According to another embodiment, the recipient's basic evaluation data may be generated based on the recipient's first state data. For example, an artificial neural network model trained to generate the recipient's basic evaluation data when the first state data is input may be used. The recipient's basic evaluation data may include at least one of physical function data, cognitive function data, health status data, and social environment data.

[0089] In step S120, the information processing system (100) can establish a customized service provision plan for the recipient based on the recipient's first status data obtained through step S110.

[0090] Here, the customized service provision plan for the recipient may refer to planning information related to service provision, such as what services to provide to the recipient, at what level, and when to provide those services, based on the recipient's first status data.

[0091] In one embodiment, the information processing system (100) can obtain a customized service provision plan for a recipient by analyzing the first state data using a first artificial neural network model. For example, the information processing system (100) can obtain a customized service provision plan for a recipient output by the first artificial neural network model based on the first state data by inputting the first state data into the first artificial neural network model.

[0092] Here, the first artificial neural network model may be a model trained using training data in which the recipient's state data (health, cognitive function, emotional state, etc.) is used as input data and service provision records (service type and provision time, etc.) based on the state data are used as ground truth data.

[0093] An artificial neural network model (e.g., a neural network) consists of one or more network functions, and one or more network functions may consist of a set of interconnected computational units that can generally be referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. One or more network functions are composed of at least one node. The nodes (or neurons) constituting one or more network functions may be interconnected by one or more 'links'.

[0094] In an artificial neural network model, one or more nodes connected via links can form a relative relationship between an input node and an output node. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As previously mentioned, the input node versus output node relationship can be generated based on links. One or more output nodes may be connected to a single input node via links, and vice versa.

[0095] In a relationship between input and output nodes connected through a single link, the value of the output node can be determined based on data input to the input node. Here, the nodes interconnecting the input and output nodes may have weights. These weights can be variable and may be varied by a user or an algorithm to enable the artificial neural network model to perform the desired function. For example, if one or more input nodes are interconnected to a single output node via respective links, the output node value can be determined based on the values ​​input to the input nodes connected to the output node and the weights set on the links corresponding to each input node.

[0096] As described above, an artificial neural network model consists of one or more nodes interconnected through one or more links, forming input-output node relationships within the model. The characteristics of an artificial neural network model can be determined by the number of nodes and links within the model, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two artificial neural network models exist with the same number of nodes and links but different weight values ​​between the links, the two models may be recognized as different from each other.

[0097] Some of the nodes constituting an artificial neural network model may form a layer based on their distances from the initial input node. For example, a set of nodes with a distance of n from the initial input node may form n layers. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach that node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the order of a layer within an artificial neural network model may be defined in a way different from that described above. For example, a layer of nodes may be defined by its distance from the final output node.

[0098] The initial input node may refer to one or more nodes within an artificial neural network model to which data is directly input without passing through links in relation to other nodes. Alternatively, within the artificial neural network model, it may refer to nodes that do not have other input nodes connected by links in relation to nodes based on links. Similarly, the final output node may refer to one or more nodes within an artificial neural network model that do not have output nodes in relation to other nodes. Additionally, the hidden node may refer to nodes constituting the artificial neural network model that are neither the initial input node nor the final output node. An artificial neural network model according to one embodiment of the present disclosure may have more nodes in the input layer than nodes in the hidden layer that are close to the output layer, and may be an artificial neural network model in which the number of nodes decreases as it progresses from the input layer to the hidden layer.

[0099] An artificial neural network model may include one or more hidden layers. The hidden nodes of a hidden layer can take the output of the previous layer and the output of neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes in the input layer can be determined based on the number of data fields in the input data and may be the same or different from the number of hidden nodes. The input data fed into the input layer can be processed by the hidden nodes of the hidden layer and output by the fully connected layer (FCL), which is the output layer.

[0100] In one embodiment, the artificial neural network model may be a deep learning model.

[0101] A deep learning model (e.g., a deep neural network (DNN)) can refer to an artificial neural network model that includes multiple hidden layers in addition to input and output layers. Using a deep neural network, one can identify the latent structures of data. That is, one can identify the latent structures of photos, text, videos, voice, and music (e.g., what objects are in a photo, what the content and emotions of the text are, what the content and emotions of the voice are, etc.).

[0102] Deep neural networks may include, but are not limited to, convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, Generative Adversarial Networks (GAN), restricted Boltzmann machines (RBM), deep belief networks (DBN), Q networks, U networks, and Siamese networks.

[0103] In one embodiment, the network function may include an autoencoder. Here, the autoencoder may be a type of artificial neural network for outputting output data similar to the input data.

[0104] An autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between the input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical. Additionally, the autoencoder can perform non-linear dimensionality reduction. The number of input and output layers may correspond to the number of sensors remaining after the preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layers included in the encoder may have a structure where it decreases as it moves away from the input layer. Since the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and the decoder) may not transmit a sufficient amount of information if it is too small, it may be maintained at a certain number or higher (e.g., more than half the number of the input layer).

[0105] In one embodiment, the first artificial neural network model may be an LSTM model as shown in FIG. 5, but is not limited thereto.

[0106] In one embodiment, the information processing system (100) inputs not only the recipient's first state data but also natural language data into the first artificial neural network model, thereby obtaining a customized service provision plan output by the first artificial neural network model based on the first state data and natural language data.

[0107] Here, the natural language data includes natural language spoken by a recipient, for example, data generated as a result of receiving natural language spoken by a recipient via an employee terminal (202), or data generated as a result of voice recognition by a recipient terminal (201), but is not limited thereto.

[0108] In one embodiment, the information processing system (100) determines the condition of the recipient based on the recipient's biometric data and can adjust the customized service provision plan for the recipient based on the recipient's condition.

[0109] For example, the information processing system (100) calculates a stress index of a recipient corresponding to a specific recipient terminal (201) based on the recipient's biometric data, and if it is determined that the recipient's stress index exceeds a threshold, it may adjust the customized service provision plan for the recipient. For example, if the recipient's stress index exceeds a threshold, the information processing system (100) calculates the predicted time required until the recipient's stress index becomes below the threshold, and may cancel or postpone the service provision plan to be provided to the recipient during the calculated time. This stress index of the recipient may be caused by contact with a specific employee, in which case the information processing system (100) may adjust the customized service provision plan so that the employee does not provide services to the recipient or provides only minimal services.

[0110] Here, the recipient's biometric data is data measured through the recipient's terminal (201), and may be biometric information measured through a wearable device such as a smart watch or smart ring, but is not limited thereto.

[0111] In step S130, the information processing system (100) can generate a list of services provided to employees based on the customized service provision plan generated through step S120.

[0112] Here, an employee is a person who provides services at an elderly welfare facility; for example, an employee may be a person who provides care services to a recipient of an elderly welfare facility, but is not limited thereto.

[0113] Additionally, the service provision list for the worker may be, but is not limited to, a list of services that the worker is required to provide to one or more recipients during the day, and the service provision list for the worker may include a daily list, a weekly list, a monthly list, and an annual list.

[0114] In one embodiment, the information processing system (100) can collect second status data of an employee of an elderly welfare facility and can generate a list of services to be provided to the employee based on a customized service provision plan for the recipient and the employee's second status data.

[0115] Here, the employee's second status data may include, but is not limited to, basic information such as the employee's name, role, and working hours, and work information (e.g., daily / weekly / monthly work schedules (day and night)), and may include the employee's biometric information (e.g., blood pressure, heart rate, stress index, etc.) and health information (e.g., existing diseases / conditions, information on medications being taken, etc.).

[0116] In one embodiment, the information processing system (100) can generate a list of services to be provided to an employee by analyzing the employee's customized service provision plan and the employee's second state data using a first artificial neural network model.

[0117] To this end, the first artificial neural network model (LSTM model) has a multi-input structure and may be a multi-task trained model.

[0118] Here, the multi-input structure is a structure designed to receive different types of data as inputs, and multi-task learning may involve performing optimization separately for each function by using a separate task-specific loss function at the final output stage of the first artificial neural network model so that multiple tasks can be performed depending on the type of input data.

[0119] For example, the first artificial neural network model (LSTM model) can be further trained using second training data, which is used to derive a service provision list for the worker based on the worker's second state data and the customized service provision plan for the worker, in addition to the first training data, which is used to derive a customized service provision plan for the worker based on the worker's first state data.

[0120] Here, the first training data may be training data in which the recipient's status data (health, cognitive function, emotional state, etc.) is used as input data and service provision records (service type and provision time, etc.) corresponding to the status data are used as correct answer data, and as the first artificial neural network model learns from this training data, it is possible to derive a customized service provision plan corresponding to the first status data.

[0121] In addition, the second training data may be training data that uses a customized service provision plan and employee's second state data (e.g., basic information such as employee's name, role, and working hours, and work information (e.g., daily / weekly / monthly work schedule (day and night))) as input data, and a service provision list as ground truth data, and enables the first artificial neural network model to derive a service provision list corresponding to the customized service provision plan and the second state data as it learns from this training data.

[0122] Here, the LSTM model, which is the first artificial neural network model, is a model that learns time series data and performs time series prediction. It has the advantage of being able to derive more accurate result data by sharing the learning content of the time series features in the first training data and the time series features in the second training data.

[0123] However, not limited to this, the first artificial neural network model for deriving a customized service provision plan and the first artificial neural network model for deriving a service provision list may be different models trained using different training data, with only their structures being identical.

[0124] In one embodiment, the information processing system (100) determines the condition of an employee based on the employee's biometric data and can adjust the list of services provided to the employee based on the employee's condition.

[0125] For example, the information processing system (100) calculates a stress index of an employee corresponding to a specific employee terminal (202) based on the employee's biometric data, and if it is determined that the employee's stress index exceeds a threshold, it may adjust the service provision list for the employee. For example, if the employee's stress index exceeds a threshold, the information processing system (100) calculates the time predicted to be required until the employee's stress index becomes below the threshold, and may cancel, postpone, or assign the service to be performed by the employee to another employee during the calculated time. This employee's stress index may be caused by contact with a specific recipient, in which case the information processing system (100) may adjust the employee's service provision list so that the employee does not provide services to the recipient or provides only minimal services.

[0126] Here, the biometric data of the worker is data measured through the worker's terminal (202), and may be biometric information measured through a wearable device such as a smartphone, smart watch, or smart ring, but is not limited thereto.

[0127] In step S140, the information processing system (100) can provide the service provision list generated through step S130 to the employee.

[0128] In one embodiment, the information processing system (100) may provide a service provision list to each employee's employee terminal (202). The provision of such service provision list may be achieved by transmitting data to the employee terminal (202) through a network.

[0129] In another embodiment, the information processing system (100) may send a list of services provided by a specific worker to the specific worker's terminal (202) when a Bluetooth contact log occurs between the specific worker's terminal (202) and a Bluetooth recognition device. Here, the Bluetooth recognition device may be a device deployed in an elderly welfare facility, but is not limited thereto.

[0130] In step S150, the information processing system (100) can automatically record whether the service is performed by the employee based on the service provision list provided through step S140. This will be explained in more detail below with reference to FIGS. 6 and FIGS. 7.

[0131] FIG. 6 is a flowchart of a method for automating the recording of whether a service is performed according to one embodiment of the present disclosure, and FIG. 7 is a diagram illustrating the process of automating the recording of whether a service is performed according to one embodiment of the present disclosure.

[0132] Referring to FIGS. 6 and FIGS. 7, in step S210, the information processing system (100) can obtain a log between the worker terminal (202) and the recipient terminal (201).

[0133] Here, the log between the worker terminal (202) and the recipient terminal (201) is generated in response to the worker terminal (202) and the recipient terminal (201) approaching within a predetermined distance. For example, the log between the worker terminal (202) and the recipient terminal (201) may be a contact log generated based on Bluetooth communication as the worker terminal (202) and the recipient terminal (201) come into contact. This contact log may include information such as identification information, contact time, and contact location, but is not limited thereto.

[0134] In step S220, the information processing system (100) can determine the service performed by the worker on the recipient based on the log obtained through step S210.

[0135] In one embodiment, the information processing system (100) can determine which of the services included in the service provision list corresponds to the log between the employee terminal (202) and the recipient terminal (201).

[0136] In one embodiment, the information processing system (100) can determine which of the plurality of services the log between the worker terminal (202) and the recipient terminal (201) corresponds to, based on past data associated with one of the recipient terminals (201) and one of the worker terminals (202).

[0137] Here, past data may include at least one of logs and service history generated in the past between one of the recipient terminals (201) and one of the worker terminals (202), but is not limited thereto.

[0138] For example, the information processing system (100) identifies a log identical to a specific log based on logs and service history generated in the past between a specific worker's worker terminal (202) and a specific recipient's recipient terminal (201), and if the identified log is a log corresponding to a specific service, the service corresponding to the specific log can also be determined to be a specific service.

[0139] In one embodiment, the information processing system (100) can obtain a service corresponding to a log output based on past data through the first artificial neural network model by inputting past data associated with one of the recipient terminals (201) and one of the employee terminals (202) into the first artificial neural network model.

[0140] To this end, the first artificial neural network model may be further trained using third training data, which is utilized for the purpose of determining the service received by the recipient from the worker or the service provided by the worker to the recipient, based on past data between the recipient and the worker, in addition to the first training data and second training data mentioned above.

[0141] Here, the third learning data may be learning data in which a plurality of logs generated between the recipient terminal (201) and the worker terminal (202) are used as input data, and information regarding the type of service corresponding to each of the plurality of logs is used as correct answer data, and as the first artificial neural network model learns from this learning data, it is possible to derive the type of service corresponding to a specific log from past data.

[0142] In one embodiment, the information processing system (100) determines a service corresponding to a specific log by analyzing past data between a recipient and an employee through a first artificial neural network model, and if the type of service corresponding to the specific log is not specified, the specific log may be withheld.

[0143] Subsequently, when a specific log is withheld, the information processing system (100) can determine the service corresponding to the withheld specific log by inputting past data between the recipient and the worker into the second artificial neural network model.

[0144] Meanwhile, when a specific log is withheld because the type of service corresponding to the specific log is not specified, the information processing system (100) may provide a notification to the employee terminal (202) corresponding to the specific log indicating that the log is an abnormal log, and may receive feedback from the employee terminal (202) in response to the provided notification as to what service corresponding to the specific log that was withheld is, but is not limited thereto.

[0145] Here, the second artificial neural network model is a model trained using the third training data, and may be a TCN model capable of analyzing long-term time series patterns compared to the first artificial neural network model (e.g., FIG. 8), but is not limited thereto.

[0146] In step S230, the information processing system (100) can store the execution history of the service determined through step S220.

[0147] In one embodiment, the information processing system (100) may record in a database that the worker has provided a specific service to the recipient based on a log generated between one of the recipient terminals (201) and one of the worker terminals (202), if it is determined that the service provided to the recipient by the worker or the service provided by the worker to the recipient is a specific service among a plurality of services included in the worker's service provision list.

[0148] In one embodiment, the information processing system (100) can extract a service provision pattern of an employee by analyzing past data over a predetermined period through a second artificial neural network model (TCN), and can supplement a missing service provision history based on the extracted service provision pattern.

[0149] For example, the information processing system (100) may, based on the service provision pattern of an employee extracted through a second artificial neural network model, extract a pattern in which an employee provides a specific service to a specific recipient around 9:00 AM on Monday every week, but if no record of the employee performing a specific service to a specific recipient at 9:00 AM on Monday of a specific date is recorded, supplementary records may be made that the employee provided a specific service to a specific recipient based on this service provision pattern.

[0150] In addition, an information processing system (100) according to one embodiment of the present disclosure can perform various operations to provide optimal elderly welfare facility services to a recipient and to assist an employee in providing optimal elderly welfare facility services to a recipient.

[0151] FIGS. 9 to 24 are drawings illustrating an exemplary user interface (UI) provided by an information processing system according to one embodiment of the present disclosure.

[0152] As a non-limiting example, first, referring to FIG. 9, the information processing system (100) can display various data of an elderly welfare facility in the form of a dashboard. The displayed data may include, for example, the total number of recipients of the elderly welfare facility, active staff (caregivers), completed services, unfulfilled services, etc. Referring to FIG. 10, examples of services provided on each day may be displayed.

[0153] Referring to FIG. 11, in one embodiment, an information processing system (100) may display first status data of each recipient on a display device. At this time, the screen of the display device may display the first status data of the currently selected recipient, for example, age, health status, recent evaluation, etc. As another example, the screen of the display device may be partitioned to display biometric data (for example, heart rate, oxygen saturation, blood pressure, body temperature, etc.) among the first status data for each recipient. Meanwhile, such a display device may include a manager terminal of a manager of an elderly welfare facility. The manager terminal may correspond to a smart device possessed by the manager, but is not limited thereto. In one embodiment, the manager terminal may be installed in each room of the elderly welfare facility, and in this case, the manager terminal may include a display device such as a tablet PC or a smart TV.

[0154] Referring to FIGS. 12 and 13, in one embodiment, an information processing system (100) may display basic evaluation data for each recipient. The basic evaluation data may include at least one of physical function data, cognitive function data, health status data, emotional data, and social environment data.

[0155] Referring to FIGS. 14 and 15, in one embodiment, the information processing system (100) can display an overview of the service provision plan and the service provision plan for each recipient.

[0156] Referring to FIG. 16, in one embodiment, the information processing system (100) can display the basic evaluation data of each recipient in detail.

[0157] Referring to FIG. 17, in one embodiment, an information processing system (100) can generate plan implementation data (e.g., including at least one of the name of a specific service, a recipient, and a time of implementation) including whether a service provision list for at least one of the workers has been implemented, and can display the plan implementation data. At this time, the information processing system (100) displays the plan implementation data, but distinguishes between the list of implemented services and the list of unimplemented services so that it can intuitively recognize what the implemented services and unimplemented services are.

[0158] In one embodiment, the information processing system (100) generates plan implementation data including whether a service provision list for at least one of the employees has been implemented, and based on the plan implementation data, if it is determined that an unimplemented service has occurred, it generates a warning message and transmits it to the manager's terminal (200), thereby enabling the manager to recognize the employee's failure to perform the service.

[0159] In one embodiment, the information processing system (100) can perform a service provision evaluation for an employee by comparing the employee's service provision list with a log generated from the employee terminal (202).

[0160] More specifically, the information processing system (100) can evaluate the service provision of an employee by comparing the time of occurrence of a log generated from an employee terminal (202) during a unit period (e.g., one day) with the employee's service provision list.

[0161] For example, the information processing system (100) can determine whether the worker has provided the service to the recipient at the correct time by comparing the time the worker is required to provide each service according to the worker's service provision list with the time of occurrence of the log generated from the worker terminal (202).

[0162] As another example, the information processing system (100) can determine whether the worker has faithfully provided the service to the recipient by comparing the type of service provided by the worker and the time required to provide the service and the time of occurrence of the log generated before and after the time of providing the service, according to the worker's service provision list.

[0163] Evaluations of such employees may be determined in the form of scores or grades, and penalties or advantages may be provided based on the evaluation scores or grades.

[0164] For example, the information processing system (100) may receive preferred service types and preferred service provision times from each employee, and accordingly generate a service provision list for each employee, and may prioritize the allocation of preferred service provision times and preferred types of services starting from employees with high evaluation scores or evaluation grades.

[0165] Additionally, the information processing system (100) may provide a job change right to an employee with a high evaluation score or evaluation grade, and may grant the authority to freely change jobs with an employee with a lower evaluation score or evaluation grade than oneself using the job change right.

[0166] Referring to FIGS. 18 to 20, in one embodiment, the information processing system (100) can display the records of each recipient's meal intake, records of feces and urine excretion, stress levels, etc.

[0167] Referring to FIG. 21, in one embodiment, the information processing system (100) can display a list of services that each worker must perform.

[0168] Referring to FIG. 22, in one embodiment, the information processing system (100) can display statistical data such as key performance indicators of services provided by an elderly welfare facility, time pattern charts, etc.

[0169] Referring to FIG. 23, in one embodiment, an information processing system (100) may receive a service provision plan in a chatbot manner based on an artificial neural network model for a specific recipient.

[0170] Referring to FIG. 24, in one embodiment, the information processing system (100) can monitor and display indicators such as the fulfillment rate and omission rate of services provided in an elderly welfare facility in real time.

[0171] As explained above, a person skilled in the art to which this disclosure pertains will understand that this disclosure may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. The scope of this disclosure is defined by the claims set forth below rather than by the detailed description, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts should be interpreted as being included within the scope of this disclosure.

[0172] The features and advantages described herein are not all included, and in particular, many additional features and advantages will become apparent to those skilled in the art by considering the drawings, the specification, and the claims. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes and may not be chosen to describe or limit the subject matter of this disclosure.

[0173] The foregoing description of the embodiments of the present disclosure is provided for illustrative purposes only. It is not intended to limit the present disclosure to the exact form disclosed or to make it incomplete. Those skilled in the art will understand that many modifications and variations are possible in light of the foregoing disclosure.

[0174] Therefore, the scope of the present disclosure is not limited by the detailed description but by any of the claims of the application based thereon. Accordingly, the disclosure of embodiments of the present disclosure is illustrative and does not limit the scope of the present disclosure as set forth in the following claims.

Claims

1. As a method for automating records regarding the provision of services at elderly welfare facilities, A step of obtaining first state data of a recipient of the above-mentioned elderly welfare facility; A step of establishing a customized service provision plan for each of the recipients based on the first state data; and Based on the above customized service provision plan, the step of generating a service provision list for each employee of the above elderly welfare facility A method for automating records of service provision in elderly welfare facilities, including 2. In Paragraph 1, The above method is, The step of transmitting the service provision list to each of the above employees' employee terminals. A method for automating records of service provision in elderly welfare facilities, including further 3. In Paragraph 1, The step of establishing a service provision plan for each of the above recipients based on the above first state data is, The step of inputting the above-mentioned first state data into an artificial neural network model; and A step of obtaining a customized service provision plan for each of the recipients output based on the first state data in the artificial neural network model. A method for automating records of service provision in elderly welfare facilities, including 4. In Paragraph 1, The step of establishing a service provision plan for each of the above recipients based on the above first state data is, A step of inputting natural language data, including the first state data and natural language spoken by the recipient, into an artificial neural network model; and A step of obtaining a customized service provision plan for each of the recipients output based on the first state data and the natural language data in the artificial neural network model. A method for automating records of service provision in elderly welfare facilities, including 5. In Paragraph 4, The above natural language data is, A method for automating the provision of services in an elderly welfare facility, wherein the natural language spoken by the recipient is input by an employee terminal, and at least one of the voice recognized by the recipient's terminal.

6. In Paragraph 1, Based on the above customized service provision plan, the step of generating a service provision list for each employee of the above elderly welfare facility is: A step of generating a list of services to be provided to the employee based on the above customized service provision plan and the second status data of the employee of the above elderly welfare facility. A method for automating records of service provision in elderly welfare facilities, including 7. In Paragraph 1, Each beneficiary of the above-mentioned elderly welfare facility has a corresponding beneficiary terminal, Each of the above-mentioned workers has a worker terminal, A step of generating a log when one of the above-mentioned recipient terminals and one of the above-mentioned employee terminals approach within a predetermined distance; and A step of recording in the database that a specific service among the service provision list has been performed based on the generated log. A method for automating records of service provision in elderly welfare facilities, including 8. In Paragraph 7, Prior to the step of recording in the database that a specific service among the service provision list has been fulfilled based on the generated log, A method for automating service provision records for elderly welfare facilities, further comprising the step of determining which service among the service provision list the generated log corresponds to.

9. In Paragraph 8, The step of determining which service among the service provision list the above-mentioned generated log corresponds to is: A step of determining which service among the service provision list corresponds to the generated log based on past data associated with one of the aforementioned recipient terminals and one of the aforementioned employee terminals. including, Method for automating service provision records in elderly welfare facilities.

10. In Paragraph 8, The step of determining which service among the service provision list the above-mentioned generated log corresponds to is: A step of inputting past data associated with one of the aforementioned recipient terminals and one of the aforementioned worker terminals into an artificial neural network model - said past data includes at least one of logs generated in the past and service history provided between one of the aforementioned recipient terminals and one of the aforementioned worker terminals -; and A step of obtaining a service corresponding to the generated log output based on the past data in the artificial neural network model above. A method for automating records of service provision in elderly welfare facilities, including 11. In Paragraph 1, The above method is, A step of acquiring biometric data measured by at least one of the above-mentioned recipient terminal and the above-mentioned employee terminal; A step of determining whether the stress index of at least one of a recipient corresponding to a specific recipient terminal or a worker corresponding to a specific worker terminal has exceeded a threshold based on the above biometric data; and A step of adjusting the service provision plan of the aforementioned recipient or the service provision list of the aforementioned worker based on the determination that the stress index has exceeded a threshold. A method for automating records of service provision in elderly welfare facilities, including 12. In Paragraph 1, The above method is, A step of generating plan implementation data including whether a service provision list for at least one of the above-mentioned employees has been implemented; and Step of displaying the above plan implementation data Includes more, The above-mentioned plan implementation data includes at least one of the name of a specific service, a recipient, and an implementation time, and A method for automating the recording of service provision in an elderly welfare facility, wherein fulfilled and unfulfilled lists among the above service provision lists are displayed separately.

13. In Paragraph 1, The above method is, A step of displaying the first state data of each of the above recipients on a display device Includes more, A method for automating the provision of services in an elderly welfare facility, wherein the screen of the display device is partitioned to display first status data for each of the recipients.

14. In Paragraph 1, The above method is, A step of generating plan implementation data including whether a service provision list for at least one of the above-mentioned employees has been implemented; and Based on the above plan implementation data, if it is determined that an unimplemented service has occurred, a step of generating a warning message and sending it to the administrator terminal A method for automating records of service provision in elderly welfare facilities, including further 15. In Paragraph 1, The above first state data is, A method for automating the provision of services in an elderly welfare facility, comprising basic evaluation data including at least one of the health status, cognitive function, or emotional status of the recipient.

16. In Paragraph 1, A method for automating the record of service provision in an elderly welfare facility, wherein the above service provision list is a list of services that each of the above employees must provide to one or more beneficiaries during the day.

17. In Paragraph 1, The above beneficiaries include elderly patients receiving services from the above elderly welfare facility, and The above-mentioned worker is a person who provides care services to the above-mentioned beneficiary of the above-mentioned elderly welfare facility, Method for automating service provision records in elderly welfare facilities.

18. A computer program stored on a computer-readable recording medium for executing a method according to any one of paragraphs 1 through 17 on a computer.

19. In information processing systems, Memory; and A processor connected to the memory and configured to execute at least one computer-readable program contained in the memory. Includes, The above at least one program is, Acquire first-state data of a recipient of an elderly welfare facility, and Based on the above first state data, a customized service provision plan for each of the above recipients is established, and Based on the above customized service provision plan, including instructions for generating a service provision list for each employee of the above elderly welfare facility, Information processing system.

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