Information processing system and information processing method

Abstract

SOLUTION: A UI generation processing unit 102 displays, based on a point-of-focus database 400 that records a plurality of points of focus 402 for each of a plurality of categories 401 set in advance for recording characteristics of a target person that a user observes, point-of-focus keys 603 so as to be selectable on a screen of a user terminal 10 and receives a selection of the point-of-focus key 603 from the user terminal 10. A sentence generation processing unit 103 inputs a point of focus indicated by the selected point-of-focus key 603 to predetermined machine learning to generate a recording document in consideration of the point of focus. A document determination processing unit 104 displays the generation recording document so as to be edited, on the screen of the user terminal 10, displays a decision key 607 for the recording document so as to be selected, and receives editing of the recording document and the selection of the decision key 607 from the user terminal 10. A document registration processing unit 105 registers, when the decision key 607 is selected, the recording document formed when the decision key 607 is selected, in a predetermined recording document database 500 so that the recording document can be displayed on another user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system and an information processing method. [Background technology]

[0002] Conventionally, observation records exist that record the daily behavior and words of subjects in childcare, nursing care, medical care, education, etc. using photographs, videos, and text, and there are various technologies related to information processing systems to support these. For example, JP 2021-092945 A (Patent Document 1) discloses an information processing device equipped with a reception unit, a memory unit, and a creation unit. The reception unit receives activity records input by the user for each specified activity. The memory unit stores the received activity records. The creation unit creates a document that includes at least a portion of one or more activity records extracted from the activity records stored in the memory unit. This is said to reduce the burden on childcare workers when creating childcare-related documents, while supporting them to focus on the activities of the childcare recipients.

[0003] Furthermore, Japanese Patent Application Laid-Open Publication No. 2022-184412 (Patent Document 2) discloses an information processing device including a first acquisition unit, a second acquisition unit, a third acquisition unit, and an output unit. The first acquisition unit acquires image data of a symbol attached to a user's belongings. The second acquisition unit acquires first identification information that identifies a group to which the user belongs. The third acquisition unit performs an analysis set for each piece of first identification information on the image data, and acquires second identification information that identifies contact information related to the user and group that is associated with the symbol. The output unit outputs the contact information identified by the second identification information acquired by the third acquisition unit. This allows contact information to be shared reliably between parties without being known to third parties.

[0004] Furthermore, Japanese Patent Laid-Open Publication No. 2022-071553 (Patent Document 3) discloses an image utilization system comprising an image acquisition means, an image processing means, and a service data generation means. The image acquisition means acquires an image including a child captured by a photographing means. The image processing means generates a processed image by performing predetermined processing on the image acquired by the image acquisition means, including adding data, deleting data, and / or correcting data. This enables the system to appropriately apply images to information management, sharing, transmitting, and distributing information at childcare and education facilities, thereby improving business efficiency.

[0005] Furthermore, Japanese Patent Application Laid-Open Publication No. 2022-020147 (Patent Document 4) discloses a device for supporting the creation of nursing care plans, which includes a processing unit and a memory unit. The processing unit includes one or more processors. The memory unit stores instructions to be executed by the processing object. The processing unit executes the following processes in response to the instructions: inputting text data including at least one of sentences and words expressing the nursing care service user's intentions regarding nursing care; acquiring keywords related to nursing care based on the input text data; extracting at least some of the existing plans that include the acquired keywords from multiple existing plans that are each already created; and displaying proposed contents to be entered for at least some items in the plan based on the extracted existing plans. This is said to enable support for the creation of nursing care plans so that plans can be created efficiently using techniques such as machine learning.

[0006] Furthermore, Japanese Patent Application Laid-Open Publication No. 2023-059685 (Patent Document 5) discloses an information processing device including an acquisition unit, a derivation unit, a generation unit, and a presentation unit. The acquisition unit acquires first medical document data including first information about a patient. The derivation unit derives second information about the patient that is different from the first information based on the first information included in the first medical document data. The generation unit generates second medical document data including the derived second information. The presentation unit presents the second medical document data generated by the generation unit. This makes it possible to support the creation of another medical document based on a previously created medical document. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent Publication No. 2021-092945 [Patent Document 2] Japanese Patent Publication No. 2022-184412 [Patent Document 3] Japanese Patent Publication No. 2022-071553 [Patent Document 4] Japanese Patent Publication No. 2022-020147 [Patent Document 5] Japanese Patent Publication No. 2023-059685 Summary of the Invention [Problem to be solved by the invention]

[0008] Here, observers write down contact information related to the observation and share that information with relevant parties, but this input is time-consuming and a burden on the observer. Furthermore, because each observer has different skills, their methods and approaches for responding to the subjects of observation vary, creating the issue of not being able to fully utilize each observer's skills. Furthermore, while sharing photos and videos of the subjects of observation makes it possible to share objective information, each observer has a different perspective for observing and checking the subjects' behavior, creating the issue of not fully utilizing each observer's skills.

[0009] The technologies described in Patent Documents 1-5 cannot solve the above-mentioned problems. The technology described in Patent Document 1 requires users to input the text for childcare records, which is not effective in reducing the workload or filling in the skills of each childcare worker. The technology described in Patent Document 2 automatically identifies user information from symbol images attached to the user's belongings, but does not reduce the workload of childcare record creation. The technology described in Patent Document 3 is a technology for processing images including children, but does not reduce the workload of creating childcare record documents. The technology described in Patent Document 4 is a technology that uses machine learning to generate care plans from documents or words indicating the intentions of care service users, but is not applicable to creating childcare record documents based on observations of children. The technology described in Patent Document 5 is a technology that automatically generates medical documents from patient information, but is not applicable to creating childcare record documents based on observations of children.

[0010] Therefore, the present invention has been made to solve the above-mentioned problems, and aims to provide an information processing system and information processing method that can reduce the workload and bridge the skill gap between observers in fields such as childcare, nursing care, medicine, and education by visualizing the skills (points of view) of observers and automatically generating record documents. [Means for solving the problem]

[0011] The present invention features the automatic generation and evaluation of observation records using a database of points of interest that indicate the observer's skill. The present invention also uses machine learning algorithms to generate sophisticated records based on observer input.

[0012] That is, the information processing system according to the present invention includes a UI generation processing unit, a document generation processing unit, a document determination processing unit, and a document registration processing unit. The UI generation processing unit selectably displays a focus key indicating a focus for each category on the screen of a user's user terminal based on a focus database that records multiple focus points for each category pre-defined for a user to record the characteristics of a subject observed by the user, and accepts selection of the focus key for each category from the user terminal. The document generation processing unit inputs the focus indicated by the focus key for each selected category to a predetermined machine learning unit, thereby generating a record document taking the focus points into consideration. The document determination processing unit displays the generated record document in an editable manner on the screen of the user terminal, and selectably displays a decision key for the record document, and accepts editing of the record document and selection of the decision key from the user terminal. When the decision key is selected, the document registration processing unit registers the record document at the time the decision key is selected in a predetermined record document database that can be displayed on other user terminals.

[0013] The information processing method according to the present invention is an information processing method for an information processing system, and includes a UI generation processing step, a record document generation processing step, and a document determination and registration processing step. Each processing step of the information processing method corresponds to each processing unit of the information processing system. [Effects of the Invention]

[0014] According to this invention, in fields such as childcare, nursing care, medicine, and education, the skills (points of view) of observers can be visualized and records can be automatically generated, thereby reducing the workload and bridging the skill gap between observers.

[0015] Furthermore, this invention can evaluate the generated records, encourage observers to improve their skills, and streamline information sharing with parents and other stakeholders. Furthermore, this invention provides the flexibility to adapt to real-world changes in analytical indicators in fields such as childcare, nursing care, medical care, and education by allowing users to add, delete, or adjust any categories and viewpoints they define. Furthermore, the use of machine learning algorithms enables document generation based on new data. Furthermore, even if the user interface is changed, the system's functionality can be maintained, enabling the design of a user interface that is tailored to the user's visibility. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a functional block diagram of an information processing system according to the present invention. [Figure 2] FIG. 1 is a diagram showing an execution procedure of an information processing method according to the present invention. [Figure 3] 3A is a diagram showing an example of a login screen of the information processing method according to the present invention, and FIG. 3B is a diagram showing an example of a user registration screen. [Figure 4] FIG. 10 is a diagram illustrating an example of a user registration database. [Figure 5] FIG. 10 is a diagram illustrating an example of a viewpoint database. [Figure 6] FIG. 10 is a diagram showing an example of an observation record input screen of the information processing method according to the present invention. [Figure 7] FIG. 10 is a diagram showing an example of an observation record input screen in the information processing method according to the present invention after a user has input a point of interest and selected a document generation key. [Figure 8] FIG. 10 is a diagram showing an example of the observation record input screen of the information processing method according to the present invention after the user has edited the generated document. [Figure 9] FIG. 2 is a diagram illustrating an example of a document record database. [Figure 10] FIG. 10 is a diagram showing an example of a document evaluation screen in the information processing method according to the present invention. [Figure 11]FIG. 10 is a diagram showing an example of a document record database including evaluation information. [Figure 12] FIG. 10 is a diagram illustrating an example in which a new viewpoint is added to the viewpoint database. [Figure 13] 10A and 10B are diagrams showing an example of a case where a document generation key and an additional memo key are displayed on an observation record input screen, and diagrams showing an example of a case where the document generation key and the additional memo key are selected. [Figure 14] FIG. 10 is a diagram showing an example of a five-area evaluation criteria database and a ten-attitude evaluation criteria database. [Figure 15] FIG. 10 is a diagram showing an example of calculating the scores of the area score table using a record document, a memo document, a five-area evaluation standard database, and a ten-attitude evaluation standard database. [Figure 16] FIG. 10 is a diagram showing an example of calculating the scores of an area score table from the observation record input screen and displaying a radar chart. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings to help understand the present invention. Note that the following embodiment is an example of the present invention, and is not intended to limit the technical scope of the present invention.

[0018] An information processing system 1 according to an embodiment of the present invention includes a user terminal 10, a server 11, and a network 12. The user terminal 10 is used by a user (e.g., a childcare worker) who creates an observation record, or a manager (e.g., the childcare worker's supervisor or evaluator) who manages the observation record.

[0019] The user terminal 10 includes a display unit (output unit) that displays a screen (window, screen), a reception unit (input unit) that receives input of predetermined instructions by user operation, a communication unit for wireless or wired communication, a storage unit that stores data, and a processing unit that controls each unit. The communication unit of the user terminal 10 can communicate with the server 11 via the network 12. The user terminal 10 includes, for example, a mobile terminal device (smartphone) with a touch panel, a tablet terminal device, a portable notebook computer, etc.

[0020] The server 11 is a commonly used computer or the like, and includes, for example, a communication unit for wireless and wired communication, a storage unit for storing data, and a processing unit for controlling each unit. The server 11 mainly transmits and receives data to and from the user terminal 10 via the network 12.

[0021] The network 12 is communicatively connected to each of the user terminal 10 and the server 11. The network 12 includes wireless communication networks such as Wi-Fi (registered trademark), a LAN (Local Area Network) via an access point, a WAN (Wide Area Network) via a wireless base station, a third generation (3G) communication method, a fourth generation (4G) communication method such as LTE, a fifth generation (5G) or later communication method, Bluetooth (registered trademark), and a specified low power wireless method.

[0022] The user terminal 10 and the server 11 each incorporate a CPU, ROM, RAM, SSD, etc. (not shown), and the CPU uses, for example, the RAM as a work area to execute programs stored in the ROM, SSD, etc. The CPU also executes programs to realize the functions of each processing unit (each processing unit) described below.

[0023] Next, the configuration and execution procedure according to an embodiment of the present invention will be described with reference to Figures 1 to 12. First, when a user (e.g., a childcare worker) uses a user terminal 10 to access a predetermined observation record system via a network 12, a user registration processing unit 101 of the server 11 accepts the access from the user terminal 10 and performs a login process (Figure 2: S101). For example, the user registration processing unit 101 displays a login screen 200 of the observation record system on the user terminal 10.

[0024] Here, there is no particular limitation on the display method of the user registration processing unit 101. For example, as shown in Fig. 3A, a login screen 200 displays a predetermined message 201 (e.g., "Please log in or register as a user"), an email address input field 202, a password input field 203, a login key 204, and a user registration key 205.

[0025] Here, if a user who has checked the login screen 200 has not yet completed user registration, when the user selects the user registration key 205, the user registration processing unit 101 accepts the selection of the user registration key 205 and displays the user registration screen 206 as shown in Figure 3B, and accepts input of user information from the user.

[0026] Here, user information means information that can uniquely identify a user, and examples include the user's name, a password that allows the user to log in, the user's email address, etc. Here, the user information is the user's name, email address, and password, and the user registration screen 206 displays a user name input field 207, an email address input field 208, a password input field 209, and a registration key 210.

[0027] Now, after checking the user registration screen 206, the user enters a user name (e.g., "xyz") in the user name input field 207, an email address (e.g., "abc@def") in the email address input field 208, and a password (e.g., "123456") in the password input field 209, and selects a registration key 210.The user registration processing unit 101 then accepts the selection of the registration key 210 and refers to a specified user registration database 300 on the server 11.

[0028] Here, there is no particular limitation on the user registration database 300, but for example, as shown in FIG. 4A, the user registration database 300 stores registered user names 301, registered email addresses 302, and registered registered passwords 303 in association with each other.

[0029] Then, the user registration processing unit 101 compares the registered email address 301 in the referenced user registration database 300 with the input email address ("abc@def"), and determines whether there is a registered email address that is the same as the input email address ("abc@def").

[0030] If the result of the determination is that a registered email address identical to the input email address ("abc@def") exists, the user registration processing unit 101 displays on the user terminal 10 a message indicating that the user is already registered in the observation record system, erases the input email address and input password, and prompts the user to enter a different email address and password.

[0031] On the other hand, if there is no registered email address that is the same as the input email address ("abc@def"), the user registration processing unit 101 displays a message on the user terminal 10 instructing the user to register a new user, and as shown in Fig. 4B, stores the input user name ("xyz"), input email address ("abc@def"), and input password ("123456") in association with the registered user name 301, registered email address 302, and registered password 303 in the user registration database 300. This completes the user's registration in the observation record system.

[0032] Then, the user registration processing unit 101 logs in the user terminal 10 of the registered user to the observation record system, thereby enabling the user to use the observation record system.

[0033] Incidentally, when a user who has already registered checks login screen 200, enters an email address and password, and selects login key 204, user registration processing unit 101 accepts the selection of login key 204 and refers to user registration database 300. Then, user registration processing unit 101 determines whether or not a registered email address 302 identical to the input email address ("abc@def") exists in the referenced user registration database 300, and whether or not the input password ("123456") matches the registered password 303 associated with the existing registered email address 302.

[0034] If, as a result of the determination, there is no registered email address 302 that is the same as the input email address ("abc@def"), or if the input password ("123456") does not match the registered password 303 associated with the existing registered email address 302, the user registration processing unit 101 displays an error screen on the user terminal 10 indicating that there is an error in the input email address ("abc@def") and input password ("123456"), and prompts the user to enter the correct email address and password.

[0035] On the other hand, if a registered email address 302 that is the same as the input email address ("abc@def") exists, and the input password ("123456") matches the registered password 303 associated with the existing registered email address 302, the user registration processing unit 101 determines that the input email address ("abc@def") and input password ("123456") are correct, and logs the user terminal 10 into the observation record system. This allows users who have already registered to use the observation record system.

[0036] When the user registration processing unit 101 logs in the user terminal 10, various operation keys are displayed selectably on the user terminal 10 and the selection of an operation key is accepted. Here, the operation keys may include, for example, a record document creation key and a record document evaluation key.

[0037] When the user selects a record document creation key, the user registration processing unit 101 accepts the selection of the creation key. Then, the UI generation processing unit 102 of the server 11 selectably displays, on the screen of the user terminal 10 of the user, a focus key 403 indicating a focus for each category, based on a focus database 400 that records a plurality of focus points for each of a plurality of categories that are set in advance to record the characteristics of the subject (e.g., kindergartener, infant) observed by the user (FIG. 2: S102).

[0038] Here, there is no particular limitation on the display method of the UI generation processing unit 102, but for example, the UI generation processing unit 102 refers to a viewpoint database 400. Here, there is no particular limitation on the viewpoint database 400, but for example, as shown in Fig. 5, a plurality of categories 401 (for example, "meals," "exercise," etc.) and viewpoints 402 for each category 401 are stored in association with each other. For example, the viewpoints 402 "baby food," "eat everything," "left food," "snack," "milk," "tasty," and "throw up" are stored in association with the category 401 "meals," and the viewpoints 402 "crawling on all fours," "crawling high," "rolling over," "lifting head," "crawling," and "standing up" are stored in association with each other. Also, category 401 "emotion" is associated with and stores "smiling," "smiling," "sleepy," "in a bad mood," "crying," "making eye contact," and "spaced out" from points of view 402, and category 401 "play" is associated with and stores "stuffed toy," "playing with hands," "shaking hands," "staring contest," "friends," and "building blocks" from points of view 402. The above are categories and points of view related to childcare.

[0039] It should be noted that the viewpoint database 400 here is based on categories 401 and viewpoints 402 related to childcare, but if the field of application changes to, for example, nursing care, medical care, education, etc., these categories 401 and viewpoints 402 will be designed according to that field.

[0040] Then, the UI generation processing unit 102 displays an observation record input screen 600 on the user terminal 10 (FIG. 2: S103). Here, there are no particular limitations on the display method of the UI generation processing unit 102, but, for example, as shown in FIG. 6, the UI generation processing unit 102 displays an input field 601 for date and time and an input field 602 for the name of the observed person, and selectably displays a plurality of focus keys 603 for each of a plurality of categories stored in the focus database 400 and a document generation key 604.

[0041] Therefore, when the user checks the observation record input screen 600, he or she enters a predetermined date and time (e.g., "January 23rd") in the date and time input field 601 and a predetermined name of the observed person (e.g., "uvw") in the observed person name input field 602, and selects a focus key 603a (e.g., "baby food," "leave food," "snack," "milk," "delicious," "throw up") for each category that corresponds to the observation result of the observed person from among multiple focus keys 603 (e.g., "baby food," "leave food," "snack," "milk," "tasty," "throw up"), and selects a document generation key 604.

[0042] The UI generation processing unit 102 then accepts the selection of the focus key 603a selected for each category and the document generation key 604, and the document generation processing unit 103 of the server 11 inputs the focus indicated by the selected multiple focus keys 603 into a predetermined machine learning unit, thereby generating a record document that takes the selected focus into consideration (Figure 2: S104).

[0043] Here, the machine learning unit can be a computer, device, software, etc. that uses a machine learning technique to discover certain rules from certain data and realize inferences and predictions for unknown data based on those rules. Known techniques can be used for machine learning, and although there are no particular limitations on the type of machine learning, for example, neural networks, genetic algorithms, reinforcement learning, etc. can be used.

[0044] Here, for example, the document generation processing unit 103 accesses a machine learning unit provided in another server and inputs the selected focus points for each category as keywords to the accessed machine learning unit, thereby causing the machine learning unit to generate a predetermined record document using the focus points for each category. Here, for the category "eating," the focus points "baby food" and "leaving food" are selected; for the category "exercise," the focus points "rolling over" and "standing up" are selected; for the category "emotion," the focus points "bad mood" and "crying" are selected; and for the category "play," the focus point "building blocks" is selected. Therefore, the machine learning unit uses the focus points for each category to generate a record document such as "bad food was left behind. The baby was rolling over. The baby was standing up. Today, the baby seemed to be in a bad mood and cried, but he was able to play with building blocks." This allows for the generation of a record document that takes the focus points into account.

[0045] Now, when the document generation processing unit 103 completes the generation of the record document, the document determination processing unit 104 of the server 11 displays the generated record document in an editable manner in the generated document display field 605 of the observation record input screen 600, and also displays the decision key 607 in a selectable manner, thereby accepting editing of the record document and selection of the decision key 607 from the user terminal 10 (Figure 2: S105).

[0046] Here, there is no particular limitation on the display method of the document determination processing unit 104, but as shown in FIG. 7, the document determination processing unit 104 displays the generated record document (for example, "The baby left the baby food. He rolled over. He learned to stand up. He seemed to be in a bad mood today and cried, but he was able to play with building blocks.") in the generated document display field 604, and displays an edit key 606 and a enter key 607 in a selectable manner.

[0047] Now, for example, if the user checks the displayed record document and determines that editing is not necessary, the user selects the decision key 607. Then, the document decision processing unit 104 accepts the selection of the decision key 607, and the document registration processing unit 105 of the server 11 associates the user information, date and time, the observed person, and the generated record document, and registers them in the record document database 500 (FIG. 2: S106).

[0048] Here, there are no particular limitations on the registration method of the document registration processing unit 105, but for example, the document registration processing unit 105 acquires the user name ("xyz") of the currently logged-in user based on the currently logged-in user terminal 10, as well as the input date and time ("January 23") and the observed person ("uvw").The document registration processing unit 105 then acquires the record document when the decision key 607 is selected ("The baby food was left. The baby was rolling over. The baby was standing up. The baby seemed to be in a bad mood today and cried, but was able to play with building blocks."), and references the record document database 500.

[0049] Here, the archived document database 500 is not particularly limited. For example, the archived document database 500 stores a user name 501, a subject name 502, a date and time 503, and an archived document 504 in association with each other. The document registration processing unit 105 stores the acquired user name ("xyz"), date and time ("January 23"), observed subject ("uvw"), and archived document in association with the user name 501, subject name 502, date and time 503, and archived document 504 in the archived document database 500. This makes it possible to accumulate the observer user, the observed subject, and the archived document. Furthermore, by having the user select multiple points of interest and then generating and storing the archived document, it becomes possible to visualize the skill (point of interest) of the observer user on what the observer user focuses on in the field through the archived document, which can improve the accuracy of the manager's evaluation of other users, as described below.

[0050] On the other hand, if the user checks the generated record document and determines that it needs to be edited, the user selects the edit key 606. The document determination processing unit 104 then makes the generated record document available for manual editing by the user in the generated document display field 605 of the observation record input screen 600. The user then manually edits the record document by using keyboard keys or the like to change or correct the record document. For example, the user might edit the record document to say, "He often leaves his baby food uneaten, but he drinks a lot of water. I was able to observe him rolling over on his own after waking up from his nap. He still lacks the stability to stand up, so he needs help, but he's getting better every day. He seemed to be in a bad mood today and cried a little, but he was able to concentrate and play with building blocks."

[0051] Then, after the user checks the edited record document, he or she selects the decision key 607. The document decision processing unit 104 then accepts the selection of the decision key 607, and the document registration processing unit 105 associates the user information, date and time, the observed person, and the created record document, and registers them in the record document database 500 (FIG. 2: S106).

[0052] Here, the document registration processing unit 105 stores the user name ("xyz"), date and time ("January 23"), observed person ("uvw"), and edited record document in the record document database 500, in association with the user name 501, the subject name 502, date and time 503, and record document 504. In other words, the document determination processing unit 104 accepts the user's editing operation, and allows the user to manually correct the record document generated by the document generation processing unit 103. This enables the user to accumulate record documents 504 that reflect their own opinions and skills.

[0053] Once the document registration processing unit 105 has completed registration, the registered record document can be transferred to a related party (e.g., an administrator) or a specified online service through user input. For example, the document registration processing unit 105 can send the record document to the guardian of the person being observed via email or to a communication service such as the ICT service "CoDOMN" through user input. This allows the user to easily share the record document with related parties and guardians.

[0054] Here, a document adjustment processing unit 106 may be provided that adjusts the record document registered in the record document database 500 to a predetermined format that is easy to view on other user terminals. For example, when the document registration processing unit 105 completes registration, the document adjustment processing unit 106 of the server 11 adjusts the record document 504 registered in the record document database 500 to a predetermined format that is easy to view on other user terminals 10 (FIG. 2: S107). For example, the document adjustment processing unit 106 converts the record document 504 into a Microsoft Word file, a PDF file, or an HTML file that is configured with a predetermined font and format, and adjusts it to the predetermined format. This allows the user to make the record document easier to view, making it easier for related parties and parents to understand.

[0055] Now, when another user (for example, an administrator) evaluates a record document registered by the above-mentioned user, the process is as follows. For example, when the other user accesses the above-mentioned observation record system via the network 12 using the other user's user terminal 10, the user registration processing unit 101 accepts the access from the user terminal 10 and performs login processing (FIG. 2: S201). The login processing is the same as described above.

[0056] Next, when the user registration processing unit 101 logs in the user terminal 10 of another user, various operation keys are displayed on the user terminal 10 so that they can be selected. Here, when the other user selects an evaluation key for the record document, the user registration processing unit 101 accepts the selection of the evaluation key. Then, the document evaluation processing unit 107 of the server 11 displays the record document 504 registered in the record document database 500 on the screen of the user terminal 10 of the other user who has logged in to the observation record system (FIG. 2: S202).

[0057] 10, the document evaluation processing unit 107 displays a date and time field 701 on the archive document evaluation screen 700 and accepts input of the date and time to be evaluated from another user. When another user inputs, for example, a predetermined date and time ("January 23rd") in the date and time field 701, the document evaluation processing unit 107 accepts the input date and time ("January 23rd") and retrieves the user name 501 ("xyz"), the subject name 502 ("uvw"), and the archive document 504 associated with the input date and time ("January 23rd") from the archive document database 500. Then, the document evaluation processing unit 107 uses the acquired user name 501 ("xyz"), subject name 502 ("uvw"), and record document 504 to display them in the user name column 702, subject name column 703, and record document column 704, respectively, on the record document evaluation screen 700.

[0058] Then, the document evaluation processing unit 107 displays an evaluator name input field 705, an evaluation score input field 706, an evaluation comment input field 705, and a send key 706 on the record document evaluation screen 700, and accepts input of evaluation information for the record document (Figure 2: S203).

[0059] Here, the document evaluation processing unit 107 may obtain the user name (e.g., "lmn") of the other logged-in user based on the other logged-in user terminal 10, and display the user name ("lmn") in the evaluator name input field 705 of the document evaluation screen 700.

[0060] Now, another user who evaluates the archive document checks the archive document field 704 on the archive document evaluation screen 700, inputs a predetermined evaluation score (e.g., "60 points") in the evaluation score input field 706, inputs a predetermined evaluation comment (e.g., "Do you understand why I'm in a bad mood?") in the evaluation comment input field 707, and selects the send key 707. The document evaluation processing unit 107 then accepts the evaluation score ("60 points"), the evaluation comment, and the selection of the send key 707 as input of evaluation information for the archive document, and stores the evaluator's name, evaluation score, and evaluation comment in association with each other in the archive document database 500 (FIG. 2: S204).

[0061] Here, the storage method of the document evaluation processing unit 107 is not particularly limited, and an example of such a method is to add and store various evaluation items in the archive document database 500. For example, the document evaluation processing unit 107 references the archive document database 500 and adds evaluation items, such as an evaluator 505, an evaluation score 506, and an evaluation comment 507, to the user name 501, the target person name 502, the date and time 503, and the archive document 504, as shown in Fig. 11. The document evaluation processing unit 107 then associates the evaluator's user name ("lmn"), the evaluation score ("60 points"), and the evaluation comment with the evaluator 505, the evaluation score 506, and the evaluation comment 507, which are associated with the evaluated user name 501 ("xyz"), the target person name 502 ("uvw"), the date and time 503 ("January 23"), and the archive document 504, and stores them. This allows the observer's record document information and the evaluator's evaluation information to be stored in association with each other. In addition, users can encourage other users to improve their skills by using the evaluation scores 506 and evaluation comments 507.

[0062] If no other users have made an evaluation, the document database 500 may have no data for the evaluator 505, evaluation score 506, and evaluation comment 507.

[0063] Now, when the document evaluation processing unit 107 has completed storing the evaluation information of other users associated with the record document, the viewpoint extraction processing unit 108 of the server 11 extracts important viewpoints 402 for recording the characteristics of the subject for each category 401 based on the record document 504 registered in the record document database 500 and the evaluation information associated with the record document 504 (e.g., evaluation comments 507) (Figure 2: S205).

[0064] Here, there are no particular limitations on the extraction method of the point of interest extraction processing unit 108, but it may be a manual method in which the point of interest extraction processing unit 108 accepts operational input from the user (observer) or another user (administrator) to extract specific words (characters, documents, etc.) in the record document 504 or the evaluation comments 507 as points of interest, or an automatic method in which the point of interest extraction processing unit 108 inputs the record document 504 or the evaluation comments 507 into a predetermined program to extract specific words output from the program as points of interest. Furthermore, these methods may be used in combination.

[0065] In the manual method, for example, first, the user or another user checks the recorded document 504 and the evaluation comments 507, selects a word that expresses a point of interest (e.g., "drink water often"), and selects a category 401 (e.g., "meals") related to the selected word from the existing categories 401. The point of interest extraction processing unit 108 then accepts input of the selected word ("drink water often") and the category 401 ("meals"), and can extract the predetermined word in the predetermined category 401 as a point of interest.

[0066] If a category related to the selected word does not exist in the existing categories 401, the user may add a new category corresponding to the selected word. In this case, the point of interest extraction processing unit 400 accepts an input of a new category from the user and extracts a predetermined word as a point of interest in the new category.

[0067] On the other hand, in the case of the automatic method, the viewpoint extraction processing unit 108 classifies the recorded document 504 and the evaluation comments 507 into multiple words such as nouns and verbs, compares the classified words with the viewpoints 402 in the viewpoint database 400 for each viewpoint 402, and determines whether the classified words exist in the viewpoints 402 in the viewpoint database 400.

[0068] If the result of the determination is that the classified word exists in the viewpoint 402 of the viewpoint database 400, the viewpoint extraction processing unit 108, for example, notifies the user terminal 10 that no new viewpoint exists, and terminates the processing without updating the viewpoint database 400.

[0069] On the other hand, if the classified word does not exist in the viewpoint 402 of the viewpoint database 400, the viewpoint extraction processing unit 108 notifies, for example, the user terminal 10 of the non-existent word as a candidate viewpoint. The user confirms the notification and determines whether to adopt the candidate viewpoint as a new viewpoint.

[0070] If the user decides to adopt a new point of view, the user selects a candidate point of view, and the point of view extraction processing unit 108 inquires as to which of the categories 401 in the point of view database 400 the selected candidate point of view corresponds. If the user selects, for example, a specific category 401, the point of view extraction processing unit 108 receives the selected candidate point of view and the category 401, and can extract a new point of view in the specified category 401.

[0071] In the above description, the user is allowed to select which category 401 the new point of interest falls into, but this is not limited to this. The point of interest extraction processing unit 108 may automatically determine the category to which the new point of interest belongs using a program such as machine learning.

[0072] As another automatic method, for example, in the above description, after one record document 504 is newly registered in the record document database 500, the focus extraction processing unit 108 extracts the focus 402, but this is not limited to this. For example, after a predetermined number (e.g., 10) of record documents 504 are newly registered in the record document database 500, the focus extraction processing unit 108 may extract frequently appearing words such as nouns and verbs as focus points from the predetermined number of record documents 504 using a program such as frequency analysis.

[0073] When the viewpoint extraction processing unit 108 completes the extraction, the viewpoint 402 for each category 401 in the viewpoint database 400 is updated based on the extracted viewpoint 402 for each category 401 (FIG. 2: S206).

[0074] Here, there is no particular limitation on the method of updating the point of interest extraction processing unit 108, but for example, the point of interest extraction processing unit 108 refers to the point of interest database 400, and as shown in Fig. 12, refers to a point of interest 402 belonging to an input (selected) category 401 (for example, "food") in the point of interest database 400, and associates the input (selected) word (for example, "drink water often") with the referenced point of interest 402 as a new point of interest 402a and stores it. In this way, the point of interest database 400 can be updated.

[0075] In the above description, the viewpoint extraction processing unit 108 updates the viewpoint database 400 by adding a new viewpoint 402a to the viewpoint database 400. However, this is not limited to this. For example, the viewpoint extraction processing unit 108 may update the viewpoint database 400 by extracting an existing, unused viewpoint that is rarely used from the document database 500 and deleting the unused viewpoint from the viewpoint database 400.

[0076] Now, once the viewpoint database 400 has been updated, next, in S102, the UI generation processing unit 102 displays, selectably, viewpoint keys 403 indicating viewpoints for each category on the screen of the user terminal 10 of the user based on the updated viewpoint database 400 (FIG. 2: S102). This allows the user to select newer viewpoints through the accumulation of record documents 504, thereby realizing the extraction of viewpoints closer to reality and the generation of record documents based thereon.

[0077] As described above, by extracting new points of focus, users can learn what they should pay attention to in situations such as childcare, nursing care, medical care, and education, thereby improving their skills and leading to the enrichment and improvement of the quality of their observation records (document records).

[0078] This invention features the automatic generation and evaluation of observation records using a database of points of view that indicate the observer's skills. Furthermore, this invention uses machine learning (algorithms) to generate sophisticated records based on the observer's input. This makes it possible to visualize the observer's skills (points of view) and automatically generate records in fields such as childcare, nursing care, medicine, and education, thereby reducing the workload and eliminating skill differences between observers.

[0079] Furthermore, with this invention, the user simply selects the corresponding item from a list of items that have been learned from the perspectives of many past observers to determine what aspects of the observed person should be recorded, and a record document is then automatically generated, thereby reducing the workload and bridging the skill gap between observers.

[0080] Furthermore, the present invention can evaluate the generated records, encourage the observer to improve his / her skills, and make it possible to share information with guardians and other related parties more efficient.

[0081] This invention allows users to add, delete, or adjust any categories and viewpoints they define in fields such as childcare, nursing care, medicine, and education, providing the flexibility to adapt to changes in analytical indicators in the real world. Furthermore, by using machine learning algorithms, document generation based on new data is realized.

[0082] Incidentally, in the present invention, after the record document 504 is recorded in the record document database 500, other users of other user terminals 10 are configured to evaluate the record document 504. However, this is not limited to this, and the UI generation processing unit 102 may accept input of any memo from the user, or the document evaluation processing unit 107 may numerically evaluate the record document or memo document using a predetermined evaluation criteria database.

[0083] For example, in S103, the UI generation processing unit 102 selectably displays an input field 601 for date and time, an input field 602 for the name of the observed person, a plurality of focus keys 603 for each of a plurality of categories, and a document generation key 604 on an observation record input screen 600, as shown in Fig. 13. At this time, the UI generation processing unit 102 further selectably displays an additional memo key 608 that allows the user to input a memo (Fig. 2: S103).

[0084] Therefore, the user inputs a predetermined date and time in the date and time input field 601, inputs a predetermined name of the observed person in the observed person name input field 602, selects a point of interest key 603 from multiple point of interest keys 603 that corresponds to the observation results of the observed person for each category, and selects a document generation key 604.

[0085] Then, the document generation processing unit 103 generates a record document taking into consideration the selected viewpoint (FIG. 2: S104), and the document determination processing unit 104 displays the generated record document in an editable manner in the generated document display field 605 of the observation record input screen 600 (FIG. 2: S105). Here, for example, the document determination processing unit 104 displays "I tried mat exercises..." in the generated document display field 605. The document determination processing unit 104 also displays an edit key 606 and a decide key 607 in a selectable manner, allowing the user to edit the generated document and decide on the generated document.

[0086] Furthermore, when the user selects the additional memo key 608, for example, and inputs a predetermined memo, the document determination processing unit 104 accepts the input memo and further displays "Regarding mat exercises..." in the memo display field 609. This allows the user to input memos in their own words and keep them as records, in addition to the generated documents generated by the machine learning unit.

[0087] Here, when the user selects the enter key 607 while looking at the generated document display field 605 or the memo document display field 609, the document registration processing unit 105 associates the user information, date and time, the observed person, and the generated record document, and registers them in the record document database 500 (FIG. 2: S106). In this case, the document registration processing unit 105 will register the memo document in addition to the record document in the record document database 500.

[0088] Here, when a record document or memo document is registered in the record document database 500, the document evaluation processing unit 107 can automatically evaluate the record document or memo document using a predetermined evaluation criteria database.

[0089] There are no particular limitations on the predetermined evaluation criteria database that serves as the evaluation criteria, but examples include a five-area evaluation criteria database 1400 and a ten-attitude evaluation criteria database 1401, as shown in FIG. 14. The five-area evaluation criteria database 1400 is composed of five areas that indicate the desired "attitudes that children should develop by the end of early childhood" based on the "Nursery School Childcare Guidelines," "Kindergarten Education Guidelines," and "Education and Childcare Guidelines for Certified Early Childhood Care Centers." Specifically, the five areas are categorized into five areas: "health," "human relationships," "environment," "language," and "expression." For example, the five-area evaluation criteria database 1400 stores five areas 1400a associated with specific content 1400b of the areas.

[0090] The 10-attitude evaluation criteria database 1401 is composed of 10 attitudes that show specific examples of "ideal attitudes to be achieved by the end of infancy" based on five areas, and specifically, the 10 attitudes are classified into "healthy mind and body," "independence," "cooperativeness," "emergence of morality and norm consciousness," "engagement with social life," "emergence of thinking ability," "engagement with nature and respect for life," "interest and sense in numbers, shapes, letters, etc.", "communication through words," and "rich sensitivity and expression." For example, the 10-attitude evaluation criteria database 1401 stores 10 attitudes 1401a in association with specific contents 1401b of the attitudes.

[0091] In this way, by configuring the evaluation criteria database with a five-area evaluation criteria database 1400 in line with the "childcare objectives" and a ten-attitude evaluation criteria database 1401, it is possible to make childcare workers in the field aware of the five areas and ten attitudes mentioned above.

[0092] There are no particular limitations on the method by which the document evaluation processing unit 107 evaluates a record document or a memo document using the five-area evaluation standard database 1400 and the ten-attitude evaluation standard database 1401. For example, when a record document or a memo document is registered, the document evaluation processing unit 107 refers to the five-area evaluation standard database 1400 and the ten-attitude evaluation standard database 1401, as shown in Fig. 15, and compares each word in the record document with each word in the specific content 1400b of the five-area evaluation standard database 1400, determines whether the words in the record document are similar to the words in the specific content 1400b, and calculates the similarity of the words in the record document for each of the five areas 1400a of the five-area evaluation standard database 1400. In addition, the document evaluation processing unit 107 determines whether the words in the recorded document are similar to the words in the specific content 1401b of the 10-attitude evaluation standard database 1401, and calculates the similarity of the words in the recorded document for each of the 10 attitudes 1401a in the 10-attitude evaluation standard database 1401.

[0093] Here, there is no particular limitation on the method for calculating the similarity of words, and examples include a method for calculating the degree of similarity between a string of characters in a record document and a string of characters of specific content, or a method for calculating the degree of similarity in meaning based on the context of the record document and the context of the specific content in the document.

[0094] Similarly, for memo documents, the document evaluation processing unit 107 compares each word in the memo document with each word in the specific content 1400b of the five-area evaluation standard database 1400 to determine whether the words in the memo document are similar to the words in the specific content 1400b, and calculates the similarity of the words in the memo document for each of the five areas 1400a. Also, the document evaluation processing unit 107 determines whether the words in the memo document are similar to the words in the specific content 1401b, and calculates the similarity of the words in the memo document for each of the ten aspects 1401a of the ten-attitude evaluation standard database 1401.

[0095] The document evaluation processing unit 107 then scores the record document and memo document based on the similarity for each of the five regions 1400a and the similarity for each of the ten shapes 1401a. There are no particular limitations on the scoring method. For example, since each of the ten shapes 1401a corresponds to a corresponding one of the five regions 1400a, the document evaluation processing unit 107 converts the similarity for each of the ten shapes 1401a into the similarity for the corresponding five regions 1400a and adds up the similarities for each of the five regions 1400a to calculate a score for each of the five regions 1400a. Here, by applying a predetermined weighting to the similarity for each of the ten shapes 1401a and converting it into the similarity for the five regions 1400a, it is possible to calculate a score for each of the five regions 1400a according to the weighting of the ten shapes 1401a.

[0096] The document evaluation processing unit 107 then creates an area score table 1500. As shown in FIG. 15, the area score table 1500 stores five areas 1500a and scores 1500b added up for the areas 1500a in association with each other. This makes it possible to automatically score and evaluate a record document or memo document selected by the user. In particular, the score 1500b for each of the five areas 1500a indicates the degree of sufficiency of each of the five areas, allowing the user to numerically confirm the degree of sufficiency for each of the five areas 1500a.

[0097] Furthermore, the document evaluation processing unit 107 can visualize records and memos based on five regions by creating an area score table 1500. For example, as shown in FIG. 16 , the document evaluation processing unit 107 calculates scores 1500b for each of the five regions 1500a in the area score table 1500 based on the registered records and memos, the five-area evaluation criteria database 1400, and the ten-attitude evaluation criteria database 1401. The document evaluation processing unit 107 then uses the area score table 1500 to create a graph of the scores 1500b for each of the five regions 1500a. While there are no particular limitations on the method for creating the graph, for example, as shown in FIG. 16 , the document evaluation processing unit 107 can create a radar chart 1600 in which the scores 1500b for the five regions 1500a are plotted on a regular polygon. This allows the user and other users to easily evaluate records and memos by checking the radar chart 1600.

[0098] Here, the document evaluation processing unit 107 may display, in addition to the radar chart 1600, specific contents 1400b of the five-area evaluation criteria database 1400 used for the scores 1500b for each of the five areas 1500a and specific contents 1401b of the ten-attitude evaluation criteria database 1401 as example sentences 1601 for each of the five areas 1500a. This allows the user and other users to check the "Nursery School Childcare Guidelines," "Kindergarten Education Guidelines," and "Education and Childcare Guidelines for Certified Early Childhood Care Centers" while looking at the radar chart 1600.

[0099] In this way, the present invention allows childcare workers to automatically record the children's behavior in written records with simple operations. Furthermore, by converting written records and memos into numerical values, the present invention allows childcare workers to check the children's daily growth in numerical form, and by accumulating the numerical values ​​over time, the childcare workers can also check the children's progress in numerical form. As a result, childcare workers can easily plan how to watch over the children and create future childcare plans, thereby enabling them to provide more fulfilling childcare.

[0100] Here, the document evaluation processing unit 107 creates and displays a radar chart 1600, but the present invention is not limited to this and a plurality of different user interface (UI) formats may be generated. In addition to radar charts, examples of user interfaces include pie charts, band charts, bar graphs, and line graphs. This makes it possible to maintain the functionality of the system even when the user interface is changed, and to design a user interface that is suited to the user's visibility.

[0101] In this invention, a five-area evaluation criteria database and a ten-attitude evaluation criteria database were set up for childcare, and records were quantified; however, this is not limited to this. In the field of nursing, medical care, or education, by creating an evaluation criteria database for each field, it is possible to quantify and evaluate records in each field. [Industrial Applicability]

[0102] As described above, the information processing system and information processing method according to the present invention are useful in fields where subjects are observed and recorded in settings such as childcare, nursing care, medical care, and education, and are effective as information processing systems and methods that can reduce the workload and bridge skill differences between observers by visualizing the observer's skills (points of view) and automatically generating record documents. Furthermore, the information processing system and information processing method according to the present invention can evaluate the generated record documents, encourage the observer to improve their skills, and also make information sharing with parents and other relevant parties more efficient. [Explanation of symbols]

[0103] 10 User terminal 11 Server 12 Network 101 User registration processing unit 102 UI generation processing unit 103 Document generation processing unit 104 Document determination processing unit 105 Document registration processing unit 106 Document Adjustment Processing Unit 107 Document Evaluation Processing Unit 108 Point of interest extraction processing unit 200 Login Screen 206 User registration screen 300 User Registration Database 400 Points of View Database 500 Archives Database 600 Observation record entry screen 700 Records Evaluation Screen

Claims

1. a UI generation processing unit that selectably displays, on a screen of a user terminal of a user, focus keys indicating focus points for each category, based on a focus database that records a plurality of focus points for each of a plurality of categories that are set in advance in order for a user to record characteristics of a subject being observed by the user, and receives selection of the focus key for each category from the user terminal; a document generation processing unit that generates a record document taking into consideration the viewpoints by inputting the viewpoints indicated by the viewpoint keys for each of the selected categories into a predetermined machine learning; a document determination processing unit that displays the generated record document in an editable manner on a screen of the user terminal, and also displays a selectable determination key for the record document, and accepts editing of the record document and selection of the determination key from the user terminal; a document registration processing unit that, when the decision key is selected, registers a record document at the time the decision key is selected in a predetermined record document database that can be displayed on other user terminals; An information processing system comprising:

2. a document adjustment processing unit that adjusts the document registered in the document database into a predetermined format that is easy to view on the other user terminal; Equipped with The information processing system according to claim 1 .

3. a document evaluation processing unit that displays a record document of the record document database on a screen of the other user terminal, and receives input of evaluation information for the record document, and when receiving the input of the evaluation information from the other user terminal, stores the evaluation information in association with the record document. Equipped with The information processing system according to claim 1 .

4. A document evaluation processing unit that uses a predetermined evaluation criteria database to numerically evaluate the records registered in the record database and the arbitrarily input memo documents. Equipped with The information processing system according to claim 1 .

5. a viewpoint extraction processing unit that extracts important viewpoints for recording the characteristics of the subject for each category based on the record documents registered in the record document database and the evaluation information associated with the record documents, and updates the viewpoints for each category in the viewpoint database based on the extracted viewpoints for each category; Equipped with The information processing system according to claim 1 .

6. a UI generation process for displaying selectable point-of-interest keys indicating points of interest for each category on a screen of a user's user terminal based on a point-of-interest database that records a plurality of points of interest for each of a plurality of categories that are preset in order for a user to record characteristics of a subject being observed by the user, and receiving selection of the point-of-interest keys for each category from the user terminal; a document generation process for generating a record document taking into consideration the viewpoints indicated by the viewpoint keys for each of the selected categories by inputting the viewpoints into a predetermined machine learning; a document determination process step of displaying the generated record document in an editable manner on a screen of the user terminal, and displaying a selectable determination key for the record document, and accepting editing of the record document and selection of the determination key from the user terminal; a document registration process step of, when the decision key is selected, registering a record document at the time the decision key is selected in a predetermined record document database that can be displayed on other user terminals; An information processing method for an information processing system comprising:

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