Notification system

Abstract

We provide a notification system that can alert users to abnormalities in living organisms while protecting personal information. [Solution] The notification system comprises: a detection device installed in a target space that detects information within the target space; a skeleton data generation unit that estimates the skeleton of an organism present in the target space and generates skeleton data based on detection data showing the detection results of the detection device; a change data generation unit that generates change data showing changes in the organism based on the skeleton data; an abnormality detection unit that detects abnormalities in the organism based on the change data; and a notification unit that notifies the organism that an abnormality has been detected when such an abnormality is detected.

Description

Technical Field

[0006] , , ,

[0005] , , ,

[0001] This disclosure relates to a notification system.

Background Art

[0002] Patent Document 1 discloses a technique for analyzing the behavior of a mouse and monitoring the mouse based on the pose of the mouse, which is an animal, in a frame within a video clip.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] This disclosure provides a notification system that can notify of an abnormality of an organism while protecting personal information.

Means for Solving the Problems

[0005] The notification system in this disclosure includes a detection device provided in a target space for detecting information within the target space, a skeleton data generation unit that estimates the skeleton of an organism existing within the target space based on detection data indicating the detection result of the detection device and generates skeleton data, a change data generation unit that generates change data indicating a change in the organism based on the skeleton data, an abnormality detection unit that detects an abnormality of the organism based on the change data, and a notification unit that notifies that an abnormality of the organism has been detected when the abnormality is detected.

Effects of the Invention

[0006] The notification system in this disclosure can detect an abnormality of an organism based on the skeleton data of the organism, so it can detect an abnormality of an organism using data with less personal information. Therefore, it is possible to notify of an abnormality of an organism while protecting personal information. [Brief explanation of the drawing]

[0007] [Figure 1] Diagram showing the schematic configuration of the notification system in Embodiment 1. [Figure 2] An explanatory diagram illustrating biological sensing in Embodiment 1. [Figure 3] This figure shows an example of the structure of a wireless outlet in Embodiment 1. [Figure 4] This figure shows an example of the configuration of a wireless outlet in Embodiment 1. [Figure 5] Diagram showing the configuration of the main components of the server control system in Embodiment 1. [Figure 6] Flowchart showing the operation of the server and detection unit in Embodiment 1 [Modes for carrying out the invention]

[0008] (Knowledge and other information that formed the basis of this disclosure) At the time the inventors conceived of this disclosure, there was a technology, as described in Patent Document 1, that detected abnormalities in living organisms from video camera image data. However, in recent years, there has been a demand for a mechanism that can notify the public when an abnormality in a living organism is detected using this technology. However, in conventional technology, the video camera image data contains information other than that of living organisms. Therefore, the inventors discovered that in conventional technology, data containing information other than that of living organisms is directly used for detecting abnormalities, raising concerns from the perspective of protecting personal information. To solve this problem, they have come to form the subject of this disclosure. Therefore, this disclosure provides a notification system that can alert users to abnormalities in living organisms while protecting personal information.

[0009] The embodiments will be described in detail below with reference to the drawings. However, some unnecessarily detailed explanations may be omitted. For example, detailed explanations of already well-known matters or redundant explanations of substantially identical configurations may be omitted. The attached drawings and the following description are provided to enable those skilled in the art to fully understand this disclosure and are not intended to limit the subject matter described in the claims.

[0010] (Embodiment 1) Embodiment 1 will be described below with reference to Figures 1 to 6. [1-1. Structure] [1-1-1. Configuration of the notification system] Figure 1 shows the configuration of the notification system 1 in Embodiment 1. The notification system 1 of this embodiment detects the state of a target organism Q present in a space (for example, a living room) without using sensors, by using multiple electrical equipment 2 placed in that space. In this embodiment, Figure 1 illustrates the case where organism Q is a dog. The organism Q to be detected includes humans and animals. In this embodiment, an example of organism Q is a pet kept by user P. Furthermore, in this embodiment, a dog is used as an example of a pet. However, organism Q is not limited to dogs; it may also be a cat, a bird, etc.

[0011] The notification system 1 detects an abnormality in organism Q, and when an abnormality is detected, it notifies user P that there is an abnormality in organism Q. Furthermore, the notification system 1 of this embodiment notifies the type of abnormality in organism Q. The notification system 1 includes a terminal device 6 used by user P. In Figure 1, a portable smartphone equipped with a display is shown as an example of the terminal device 6. The terminal device 6 is not limited to this and may be a desktop computer, laptop computer, or other computer equipped with a notification means used by user P.

[0012] Hereinafter, the space to which the detection by the notification system 1 is applied is referred to as a target space 9. The target space 9 may be an enclosed space surrounded by walls and a ceiling, or may be a space with doors, windows, etc. open. Also, the use of the target space 9 is not limited. For example, the target space 9 may be a living space, a living area, a business space such as an office, a public space, or a space used for other purposes. Further, the target space 9 may be a breeding space in a zoo, a space in a hospital room in an animal hospital or a pet hotel, or a breeding space.

[0013] The target space 9 illustrated in FIG. 1 is an indoor space surrounded by a floor 91, a ceiling 92, and four walls 93, 94, 95, and 96.

[0014] The notification system 1 includes a plurality of electrical equipment materials 2 arranged in the target space 9 to which power is supplied, and a detection processing unit. In the notification system 1, among the plurality of electrical equipment materials 2, the state of a living creature Q existing in the space is detected without using a sensor by any two electrical equipment materials 2 having a function of transmitting and receiving radio waves (hereinafter also referred to as a wireless function).

[0015] In the present embodiment, as an example, the electrical equipment materials 2 having a wireless function are outlets 30A and 30B.

[0016] Also, in the present embodiment, the detection processing unit is provided in each of the outlets 30A and 30B, which are two electrical equipment materials 2 having a wireless function. However, the detection processing unit may be provided in another device that is not an electrical equipment material 2. For example, the notification system 1 may include a server 41 communicably connected to the outlets 30A and 30B via a relay device 40 and a network NW such as the Internet, and the server 41 may be configured to include the detection processing unit.

[0017] The electrical equipment material 2 includes members, equipment, machines, and appliances that are connected to a commercial power supply and fixedly installed in a building.

[0018] For example, in the target space 9 shown in FIG. 1, a lighting device 10, a switch 20, and outlets 30A and 30B are installed. Lighting fixtures 10, switches 20, and outlets 30A and 30B can be installed on the floor 91, ceiling 92, and the four walls 93, 94, 95, and 96 of the target space 9. A distribution board 50 is also installed outside the target space 9. Hereinafter, the surfaces of the floor 91, ceiling 92, and the four walls 93, 94, 95, and 96 that define the target space 9 will be collectively referred to as the walls of the target space 9.

[0019] Power lines 5A, 5B, and 5C are drawn into the target space 9 from the distribution board 50. Power lines 5A, 5B, and 5C are, for example, wires laid concealed within the walls of the target space 9, and are power cables such as VVF (Vinyl insulated Vinyl sheathed Flat-type cable) cable and CV (cross-linked polyethylene insulated vinyl sheath) cable. Hereafter, power lines 5A, 5B, and 5C will be referred to as power line 5 when not distinguished. The distribution board 50 branches off the service drop line 58, which is connected to the commercial power supply, and connects it to power line 5.

[0020] The lighting device 10 is mounted on the ceiling 92. A ceiling socket 112 is fixed to the ceiling 92, and the lighting device 10 is mounted on the ceiling socket 112. The lighting device 10 corresponds to an example of "lighting means".

[0021] The ceiling socket 112 is connected to power line 5A, and the lighting device 10 is connected to power line 5A by being attached to the ceiling socket 112. The ceiling socket 112 may be configured in a form called a lighting rosette.

[0022] The air conditioning unit 8 is mounted on the wall. The air conditioning unit 8 is a so-called air conditioner and corresponds to an example of a "blowing means." The blowing means may be a blowing device such as a fan.

[0023] The switch 20 has a switch body 21 that is connected to power line 5A. The switch 20 connects and disconnects power line 5A between the lighting device 10 and the distribution board 50. When the switch 20 is on, power is supplied to the lighting device 10. When the switch 20 is off, the power supply to the lighting device 10 is cut off, and the lighting device 10 turns off.

[0024] Outlets 30A and 30B are outlets that can be connected to load equipment that operates on commercial power. Outlets can also be called power outlets. For example, a single-phase 100V load is connected to outlets 30A and 30B. Outlet 30A is connected to the distribution board 50 by power line 5B, and outlet 30B is connected to the distribution board 50 by power line 5C.

[0025] The notification system 1 includes, for example, outlets 30A and 30B as two electrical equipment 2 equipped with the function of transmitting and receiving radio waves. In this embodiment, outlets 30A and 30B are located on opposing walls 93 and 95 of the target space 9, respectively. Furthermore, the height H1 from the floor 91 at the installation position of outlet 30A and the height H2 from the floor 91 at the installation position of outlet 30B are different from each other. Hereafter, when outlets 30A and 30B are not distinguished, they will be referred to as outlet 30.

[0026] Each of the outlets 30 is configured to transmit and receive radio waves in a predetermined frequency band to each other, and the state of organism Q in the target space 9 is detected by analyzing the state of the received radio waves.

[0027] Figure 2 is an explanatory diagram illustrating the sensing of organism Q by the notification system 1. In the notification system 1, the radio waves transmitted by one of the outlets 30 (outlet 30A in the example in Figure 2) propagate within the target space 9, and after being reflected once or multiple times by hitting the walls of the target space 9 or the surfaces of real objects such as living organisms Q present within the target space 9, they reach the other outlet 30 (outlet 30B in the example in Figure 2), or they reach the other outlet 30 directly without being reflected at all.

[0028] In other words, the other outlet 30B receives a direct wave DW (dashed line in the figure), which is a radio wave transmitted from one outlet 30A and reaching the other outlet 30B without reflection in the target space 9, and one or more indirect waves IW (dotted lines in the figure), which are radio waves that arrive after one or more reflections in the target space 9. Although Figure 2 is shown in plan view, the radio wave transmitted from outlet 30A can also propagate in the direction of the floor 91 and ceiling 92, generating indirect waves IW.

[0029] Therefore, by analyzing the amplitude and / or phase of the received direct wave DW and indirect wave IW at another outlet 30B, the three-dimensional position of one outlet 30A, which is the source of the radio waves, and the three-dimensional position and three-dimensional surface shape of organism Q present in the target space 9 can be obtained. The data showing the three-dimensional position and three-dimensional surface shape of organism Q present in the target space 9 corresponds to an example of "detection data". When outlet 30B transmits radio waves and outlet 30A receives those radio waves, three-dimensional information of the target space 9 can be obtained in outlet 30A in the same manner as described above.

[0030] [1-1-2. Outlet Configuration] In this embodiment, the electrical equipment 2 having wireless functionality, namely outlets 30A and 30B, have the same structure and configuration as an example. However, outlets 30A and 30B may have different structures and / or configurations, as long as they have functional elements similar to those of the detection unit 33 described later. The structure and configuration of outlet 30 will be described below. Figure 3 is a front view showing an example of the structure of the outlet 30. Figure 4 is a diagram showing an example of the configuration of the outlet 30.

[0031] The outlet 30 includes an outlet block 301. The outlet block 301 is a box-shaped enclosure made of insulating material and houses the wiring connected to each of the two terminals 32. A cover 302 with a pair of openings is positioned on the front of the outlet block 301. The terminals 32 are exposed at the back of the openings in the cover 302, and power is supplied to the load device by inserting the plug of the load device into the openings in the cover 302.

[0032] The outlet block 301 has a wire connection section 305 for connecting the power line 5B. The wire connection section 305 is a terminal into which the conductors of the power line 5B can be inserted, and the wire connection section 305 is provided with two wire connection sections 305 corresponding to the two conductors that make up the power line 5B. The terminals 32 of the outlet body 31 are connected to the wire connection section 305.

[0033] The outlet block 301 is fitted into the mounting frame 311. For example, the outlet block 301 is fixed to the mounting frame 311 by engaging a projection on the mounting frame 311 with a groove (not shown) formed on the side of the outlet block 301.

[0034] A rectangular hole is drilled in the wall material to which the outlet 30 is fixed. The support member 312 is placed behind the wall material, such as a board or gypsum board, through this hole. The mounting frame 311 is fixed to the building material by being connected to the support member 312 with the building material in between.

[0035] A microphone 38 is incorporated into the outlet block 301. The microphone 38 receives power by being connected to the wire connection part 305. Alternatively, the microphone 38 may receive power by being connected to a different power connection part than the wire connection part 305. The outlet block 301 incorporates a detection unit 33. The detection unit 33 corresponds to an example of a "detection device". The detection unit 33 is connected to the wire connection part 305 to receive power. An antenna cover 303 is placed on the surface of the outlet block 301 that is exposed to the target space 9, and an array antenna 37 used by the transmitter 35 and receiver 36 of the detection unit 33, which will be described later, is built into a position overlapping the antenna cover 303. The array antenna 37 may be configured as a single element in which multiple antenna elements are arranged in a line or in a grid.

[0036] The antenna cover 303 is a plate-shaped member that covers the surface of the array antenna 37 and is made of a material (for example, resin) that does not obstruct the propagation of radio waves transmitted or received by the array antenna 37.

[0037] The configuration in which the detection unit 33 is installed on the outlet block 301 is just one example. For example, the detection unit 33 may be configured as a separate unit from the outlet block 301, and the wiring inside the outlet block 301 may be connected to the detection unit 33. In this case, the outlet block 301 is provided with an array antenna 37, and the main body of the detection unit 33 can be positioned on the mounting frame 311 away from the cover 302 or in the vicinity of the mounting frame 311.

[0038] Referring to Figure 4, the detection unit 33 includes a control device 34, a transmitter 35, a receiver 36, an array antenna 37, and a microphone 38. The transmitter 35 transmits radio waves of a predetermined frequency band to the target space 9 using the array antenna 37. The receiver 36 receives the radio waves of the predetermined frequency band transmitted to the target space 9 via the array antenna 37. In this embodiment, the radio waves of the predetermined frequency band may be radio waves of the frequency band used in wireless Wi-Fi (registered trademark).

[0039] As described above, the array antenna 37 can be configured as a single element in which multiple antenna elements are arranged in a row or in a grid. The antenna elements may be, for example, a chip antenna, a microstrip antenna, etc. The array antenna 37 allows the transmitter 35 to transmit directional radio waves to the target space 9 in various directions. The receiver 36 can receive radio waves from each antenna element of the array antenna 37.

[0040] The control unit 34 is a computer equipped with a processor 340 such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit) and memory 341.

[0041] Memory 341 is a memory that stores programs and data. Memory 341 stores program 342. Memory 341 has a non-volatile storage area. Memory 341 also has a volatile storage area and constitutes the work area of ​​processor 340. Memory 341 is composed of, for example, ROM (Read Only Memory) and RAM (Random Access Memory).

[0042] The processor 340 includes, as functional units, a communication control unit 343, an acquisition unit 344, a detection processing unit 345, a radio wave separation unit 346, and an estimation unit 347. These functional elements of the processor 340 are realized, for example, by the processor 340 of the control device 34 (which is a computer) reading and executing a program 342 stored in memory 341. The program 342 can be stored in any storage medium that is readable by a computer. Alternatively, all or part of the functional elements of the processor 340 can be configured by hardware, each including one or more electronic circuit components.

[0043] The communication control unit 343 instructs the transmitter 35 to transmit radio waves in one or more directions toward the target space 9 via the array antenna 37. The communication control unit 343 also instructs the receiver 36 to receive radio waves from the array antenna 37. As described above, in this embodiment, the radio waves are radio waves in the frequency band used in wireless WiFi (hereinafter also referred to as WiFi radio waves). The communication control unit 343 performs WiFi communication using the transmitter 35 and receiver 36 in accordance with the WiFi communication standard. This allows the outlet 30 to transmit the processing results calculated by the detection processing unit 345 (described later) to other devices such as the server 41.

[0044] Furthermore, when a predetermined trigger occurs, the communication control unit 343 transmits transmission data, including detection data D1 and sound collection data D2, to the server 41 via the relay device 40 connected to the power outlet 30. In this embodiment, the predetermined trigger is the elapsed time (for example, 1 minute) since the transmission of the previous transmission data. The transmission data transmitted by the communication control unit 343, including detection data D1 and sound collection data D2, includes an identification ID that identifies the target space 9. The identification ID is information that uniquely identifies the detection unit 33 installed in the target space 9.

[0045] The acquisition unit 344 acquires radio wave state information, which is information about the state of the radio wave, based on the radio wave received by the receiver 36. The radio wave state information may include, for example, information about the amplitude and / or phase of the radio wave received by each antenna element of the array antenna 37. In this embodiment, the radio wave state information may be channel state information (CSI) acquired based on the WiFi radio wave, which is the radio wave received by the receiver 36. The acquisition unit 344 provides the acquired radio wave condition information to the detection processing unit 345.

[0046] Here, the acquisition unit 344 may store each acquired radio wave condition information data in a memory 341 or the like in a time series. The acquisition unit 344 may calculate a moving average value for each radio wave condition information data from the present to a predetermined time in the past, and provide the detection processing unit 345 with a dataset composed of the above moving average values ​​for each data as the current radio wave condition information. This reduces the noise contained in the radio wave condition information and enables more accurate detection of organism Q.

[0047] The detection processing unit 345 analyzes the radio waves received by the receiver 36 based on the radio wave state information acquired by the acquisition unit 344 to estimate the state of organism Q present in the target space 9. Specifically, the detection processing unit 345 analyzes the amplitude and / or phase state of the radio waves received by the receiver 36 based on the radio wave state information to estimate the state of organism Q present in the target space 9. The estimated state of organism Q detected by the detection processing unit 345 corresponds to the three-dimensional position and three-dimensional surface shape of organism Q and corresponds to detection data D1. That is, the detection processing unit 345 acquires the detection data D1 of organism Q.

[0048] The detection processing unit 345 estimates the state of organism Q present in the target space 9 using an estimation model that has been machine-learned to determine the relationship between the radio wave state information provided by the acquisition unit 344 and the state of organism Q present in the target space 9. For example, deep learning techniques are used for machine learning. This makes it possible to detect the state of organism Q with higher accuracy and ease by using an estimation model that has been machine-learned to determine the relationship between the information-rich radio wave state information and the detailed state of organism Q.

[0049] The radio wave separation unit 346 separates and extracts information on the direct wave DW and indirect wave IW contained in the radio waves received by the receiver 36 from the radio wave condition information provided by the acquisition unit 344.

[0050] The estimation unit 347 estimates the straight-line distance to the radio wave transmission source from the radio wave intensity of the direct wave DW, based on the information of the direct wave DW separated by the radio wave separation unit 346. The estimation unit 347 also estimates the incidence angle of the direct wave DW and indirect wave IW to the array antenna 37 from information such as the phase of the radio waves received at each antenna element of the array antenna 37. Then, the estimation unit 347 estimates the three-dimensional position of the radio wave transmission source in the target space 9 and the extent of the target space 9 from the estimated straight-line distance and incidence angle. Here, the extent of the target space 9 may be, for example, the three-dimensional position of each wall surface of the target space 9.

[0051] The sound detection processing unit 348 acquires sound collection data D2 via the microphone 38. Sound collection data D2 is data indicating the sound collected by the microphone 38.

[0052] In the notification system 1 having the above configuration, the state of organisms Q present in the target space 9 within the building can be detected without using sensors, by using electrical equipment 2 that can be distributed throughout the building.

[0053] [1-1-3. Mounted Sensors] A sensor 7 is attached to the animal Q, which is a dog. In this embodiment, the sensor 7 is a collar-type sensor. The sensor 7 comprises a processor, memory, communication means, and detection means. It detects the distance traveled, speed of movement, and acceleration of the animal Q. The sensor 7 transmits sensor data to the server 41 via a relay device 40 installed in the target space 9. The sensor data includes the detection results from the sensor 7.

[0054] [1-1-4. Server] Figure 5 is a block diagram showing the main components of the control system of server 41. Server 41 comprises a server control device 400 and a server communication device 401. The server control device 400 includes a server processor 410, which is a processor that executes programs such as a CPU and MPU, and a server memory 420. The server control device 400 controls various parts of the server 41 by having the server processor 410 read and execute the server program 421 stored in the server memory 420. By executing the server program 421 stored in the server memory 420, the server processor 410 functions as a server communication control unit 411, a management unit 412, a skeletal data generation unit 413, an activity level change data generation unit 414, an activity data generation unit 415, a sound data generation unit 416, an anomaly detection unit 417, and a notification unit 418. The activity level change data generation unit 414 and the behavior data generation unit 415 correspond to an example of a "change data generation unit".

[0055] The server memory 420 has memory for storing programs executed by the server processor 410 and data processed by the server processor 410. The server memory 420 stores the server program 421 executed by the server processor 410, the management DB (database) 422, and various other data. The server memory 420 has a non-volatile storage area for storing programs and data non-volatilely. The server memory 420 may also have a volatile storage area and constitute a work area for temporarily storing programs executed by the server processor 410 and data to be processed.

[0056] The management DB 422 is a database that stores data received from each target space 9, as well as data generated by the server control device 400, in chronological order from the past to the present. Each data entry stored in the management DB 422 is associated with an identification ID and communication information. The communication information is information for communicating with each part of the target space 9 and the terminal device 6 associated with the target space 9, with address information being an example. The communication information also includes a terminal identification ID that identifies the terminal device 6.

[0057] The server communication device 401 is a communication interface equipped with a wireless circuit, antenna, and other communication-related configurations in accordance with a predetermined communication standard, and communicates with each part of the target space 9 and the terminal device 6 associated with the target space 9 in accordance with the predetermined communication standard.

[0058] As described above, the server processor 410 functions as a server communication control unit 411, a management unit 412, a skeletal data generation unit 413, an activity level change data generation unit 414, an activity data generation unit 415, a sound data generation unit 416, an anomaly detection unit 417, and a notification unit 418.

[0059] The server communication control unit 411 communicates with each part of the target space 9 and the terminal device 6 associated with the target space 9 via the server communication device 401.

[0060] The management unit 412 performs operations on the management DB 422, such as storing data, identifying data, and retrieving data.

[0061] [1-1-4-1. Skeletal Data Generation Unit] The skeleton data generation unit 413 estimates the skeleton of organism Q based on the detection data D1 and generates skeleton data D3. In this embodiment, the skeleton data generation unit 413 generates skeleton data D3 from the detection data D1 that includes only the skeleton of organism Q and does not include the skeleton of user P. The skeleton data generation unit 413 may estimate the skeletons of organism Q and user P and then filter to include only the skeleton of organism Q to generate skeleton data D3, or it may estimate only the skeleton of organism Q and generate skeleton data D3.

[0062] The skeletal data generation unit 413 generates time-series skeletal data D3 in response to the time-series detection data D1 and stores it in the management DB 422. The skeletal data D3 is data that shows the time-series changes in the three-dimensional position of each part of the skeleton of organism Q in the target space 9.

[0063] The skeletal data generation unit 413 generates skeletal data D3 of organism Q present in the target space 9 by estimating the relationship between detection data D1 provided by the detection unit 33 and the state of organism Q present in the target space 9 using an estimation model that has been trained on machine learning. Machine learning can be performed using methods such as deep learning. This makes it possible to detect the skeletal state of organism Q with higher accuracy and ease by using an estimation model that has been trained on the relationship between the information-rich detection data and the skeletal state of organism Q.

[0064] Furthermore, the skeleton data generation unit 413 may be configured to generate skeleton data D3 based on the detection data D1, and then delete the corresponding detection data D1 from the management data DB 422. This ensures that detection data D1, which is unnecessary for the skeleton estimation by the skeleton data generation unit 413, does not remain in the server 41's memory 420, thereby protecting personal information.

[0065] [1-1-4-2. Activity level change data generation unit] The activity level change data generation unit 414 generates activity level change data D4 of organism Q based on the skeletal data D3. The activity level change data D4 corresponds to an example of "change data".

[0066] Activity change data D4 is data showing the time-series changes of multiple parameters, including the distance traveled, speed of movement, and acceleration of organism Q. Activity change data D4 may be generated for each data showing the changes in each part of the skeleton included in skeletal data D3, or activity change data D4 may be generated by combining the data showing the changes in each part of the skeleton included in skeletal data D3.

[0067] The activity level change data generation unit 414 generates activity level change data D4 of organism Q present in the target space 9 using an estimation model that has been machine-learned to determine the relationship between skeletal data D3 provided by the skeletal data generation unit 413 and activity level change data D4.

[0068] Furthermore, the activity level change data generation unit 414 generates activity level change data D4 of organism Q based on the sensor data provided from the attached sensor 7.

[0069] The activity level change data generation unit 414 may generate activity level change data D4 of organism Q based on at least one of the skeletal data D3 and the sensor data. In this case as well, the activity level change data generation unit 414 generates activity level change data D5 using an estimation model that has been machine-trained on the sensor data and the activity level change data D4.

[0070] [1-1-4-3. Behavioral Data Generation Unit] The behavioral data generation unit 415 generates behavioral data D5 of organism Q based on the skeletal data D3. Behavioral data D5 corresponds to an example of "change data".

[0071] Behavioral data D5 shows the time-series changes in the types of behaviors performed by organism Q. The types of behaviors include, for example, when a creature Q (a dog) bumps into an object, sniffs, walks, runs, jumps, or shows no interest in going for a walk. The types of behaviors are predetermined.

[0072] The behavioral data generation unit 415 generates behavioral data D5 of organism Q present in the target space 9 by using an estimation model that has been machine-learned to determine the relationship between skeletal data D3 provided by the skeletal data generation unit 413 and behavioral data D5.

[0073] Furthermore, the behavioral data generation unit 415 generates behavioral data D5 of organism Q based on sensor data provided from the attached sensor 7. By generating behavioral data D5 of organism Q based on skeletal data D3 and sensor data, the behavioral data generation unit 415 makes it easier to estimate the types of behavior outside the target space 9. In this case as well, the behavioral data generation unit 415 generates behavioral data D5 using an estimation model that has been machine-learned using sensor data and behavioral data D5. Behavior outside the target space 9 is, for example, behavior related to walking a dog, which is organism Q.

[0074] [1-1-4-4. Sound Data Generation Section] The sound data generation unit 416 generates sound data D6 of organism Q based on the sound collection data D2 provided by the microphone 38. The sound collection data D2 may include the direction of the voice, and may also include data showing the time-series changes in the voice waveform, separated by the direction of the voice.

[0075] Sound data D6 is data showing the time-series changes in the waveform of the sound emitted by organism Q. Sound data D6 includes the loudness, amplitude, and waveform of the sound emitted by organism Q.

[0076] The sound data generation unit 416 generates sound data D6 of organism Q present in the target space 9 using an estimation model that has been machine-learned to determine the relationship between the sound data D2 provided by the microphone 38 and the sound data D6. The sound data generation unit 416 selectively extracts the voice emitted by organism Q.

[0077] [1-1-4-5. Anomaly Detection Unit] Figure 5 illustrates the overview of the data flow for skeletal data D3, activity level change data D4, behavioral data D5, and sound data D6. Each of these data, along with the detection data D1 and sound collection data D2 received from the target space 9, are stored in the management DB 422.

[0078] Skeletal data D3 is generated by the skeletal data generation unit 413 based on detection data D1 transmitted from the detection unit 33 of the target space 9. The skeletal data D3 is provided to the activity level change data generation unit 414 and the behavior data generation unit 415.

[0079] Furthermore, the sound data D6 is generated by the sound data generation unit 416 based on the sound collection data D2 transmitted from the detection unit 33 provided in the target space 9.

[0080] The activity level change data D4 generated by the activity level change data generation unit 414, the behavior data D5 generated by the behavior data generation unit 415, and the sound data D6 generated by the sound data generation unit 416 are provided to the anomaly detection unit 417.

[0081] The anomaly detection unit 417 detects an anomaly in organism Q based on activity level change data D4, behavioral data D5, and sound data D6.

[0082] The anomaly detection unit 417 detects anomalies in organism Q present in the target space 9 by using an estimation model that has been machine-learned to determine the relationship between activity change data D4 provided by the activity change data generation unit 414, behavior data D5 provided by the behavior data generation unit 415, sound data D6 provided by the sound data generation unit 416, and the type of anomaly in organism Q. Alternatively, the anomaly detection unit 417 may detect anomalies in organism Q based on a dataset that associates the types of activity change data D4, behavior data D5, and sound data D6 with the abnormal state of organism Q.

[0083] In this embodiment, the anomaly detection unit 417 generates estimation data that estimates the probability of an anomaly occurring in organism Q as a percentage for each type of anomaly. The anomaly detection unit 417 detects that there is an anomaly of the corresponding type of organism Q if the percentage is above a predetermined threshold.

[0084] The types of abnormalities are predetermined. Examples of abnormalities include, for instance, a dog (organism Q) sleeping an unusually large amount, moving an unusually small amount, or being in an excited state and moving an unusually large amount. These are types of abnormalities that show a particularly strong correlation with activity level change data D4, but are detected by the abnormality detection unit 417 along with behavioral data D5 and sound data D6. For example, if sound data D6 indicates that there is a lot of barking, it is easy to infer that the activity level is high.

[0085] Furthermore, examples of abnormalities include, for example, a dog (organism Q) being reluctant to go for a walk, biting specific parts of its own body, trying to hide, frequently bumping into things, trembling, having seizures, tilting or shaking its head, and walking in circles. These are types of abnormalities that have a particularly strong correlation with behavioral data D5, but are detected by the abnormality detection unit 417 along with activity level change data D4 and sound data D6.

[0086] Furthermore, types of anomalies that show a strong correlation with activity level change data D4 take longer to detect compared to types of anomalies that show a strong correlation with behavioral data D5. As mentioned above, activity level change data D4 is data that shows the time-series changes of multiple parameters, including the distance traveled, speed of movement, and acceleration of organism Q. In order to determine whether organism Q is behaving differently than usual, data on the time-series changes of each parameter over a longer period is necessary. For example, in order for activity level change data D4 to detect that organism Q is moving an abnormally small amount compared to usual, activity level change data D4 corresponding to skeletal data D3 on a daily basis is required. On the other hand, in order to detect that organism Q is trembling as an anomaly, behavioral data D5 corresponding to skeletal data D3 on a minute-by-minute basis is sufficient, so the time to detect the anomaly is relatively short.

[0087] Furthermore, examples of abnormalities include, for instance, a dog (organism Q) groaning, making unusual noises, making sounds associated with severe pain, barking continuously and monotonously at night, or coughing persistently. These are types of abnormalities that show a particularly strong correlation with sound data D6, but are detected by the abnormality detection unit 417 along with activity level change data D4 and behavioral data D5.

[0088] The correlation between the estimated data generated by the anomaly detection unit 417 and the activity level change data D4, behavioral data D5, and sound data D6 is used in the machine learning of the anomaly detection unit 417 itself in order to improve the accuracy of anomaly detection.

[0089] Furthermore, based on the estimated data, the behavioral data generation unit generates behavioral data D5. More specifically, the estimated data is used for machine learning by the behavioral data generation unit 415. This can improve the accuracy of the anomaly detection unit 417 detecting anomalies in organism Q based on the behavioral data D5 generated by the behavioral data generation unit 415.

[0090] For example, even if the behavior data generation unit 415 generates behavior data D5 indicating that organism Q bumps into an object, if the anomaly detection unit 417 does not detect an anomaly in organism Q, it is highly likely that organism Q does not frequently bump into objects. This improves the accuracy of the behavior data generation unit 415's estimation of the type of behavior of organism Q, and consequently, the accuracy of detecting anomalies in organism Q.

[0091] [1-1-4-6. Hochi Department] When the anomaly detection unit 417 detects that there is an abnormality in organism Q, the notification unit 418 makes a predetermined notification. In this embodiment, the notification unit 418 transmits information including the corresponding type of abnormality to the terminal device 6 used by user P as a predetermined notification. In this way, the notification unit 418 notifies user P that an abnormality has been detected in organism Q and the type of abnormality.

[0092] [1-2. Operation] Figure 6 is a flowchart showing the operation of the server 41 and the detection unit 33. Flowchart FA shows the operation of server 41, and flowchart FB shows the operation of detection unit 33. Although not shown in the diagram, the attached sensor 7 transmits sensor data to server 41 at predetermined intervals (for example, every minute).

[0093] As shown in the flowchart FB, the communication control unit 343 of the detection unit 33 determines whether a trigger has occurred to send transmission data, including detection data D1 and sound collection data D2, to the server 41 (step SB1). If the trigger occurs (step SB1: YES), the communication control unit 343 sends the transmission data to the server 41 (step SB2). After step SB9 is completed, and if the communication control unit 343 determines that the trigger has not occurred (step SB1: NO), the detection unit 33 returns to step SB1.

[0094] As shown in flowchart FA, the server communication control unit 411 determines whether or not it has received new detection data D1 and sound collection data D2 (step SA1). When the server communication control unit 411 receives new detection data D1 and sound collection data D2, that is, when it receives new transmission data (step SA1: YES), the management unit 412 stores the new detection data D1 and sound collection data D2 in the management DB 422 (step SA2).

[0095] Next, the skeleton data generation unit 413 generates skeleton data D3 based on the detection data D1 received in step SA1 and stores it in the management DB 422 (step SA3). In step SA3, the skeleton data generation unit 413 may delete the detection data D1 stored in step SA2 from the management DB 422.

[0096] Next, the activity level change data generation unit 414 generates activity level change data D4 based on the sensor data and skeletal data D3 and stores it in the management DB 422 (step SA4).

[0097] Next, the behavior data generation unit 415 generates behavior data D5 based on the sensor data and skeletal data D3 and stores it in the management DB 422 (step SA5).

[0098] Next, the sound data generation unit 416 generates sound data D6 based on the sound collection data D2 and stores it in the management DB 422 (step SA6). In step SA6, the sound data generation unit 416 may delete the sound collection data D2 stored in step SA2 from the management DB 422.

[0099] Next, the anomaly detection unit 417 generates estimated data based on the activity level change data D4, behavior data D5, and sound data D6, and stores it in the management DB 422 (step SA7).

[0100] Next, the anomaly detection unit 417 determines whether or not it has detected an anomaly in organism Q based on the estimated data (step SA8). If the anomaly detection unit 417 detects an anomaly in organism Q (step SA8: YES), the notification unit 418 transmits information including the corresponding type of anomaly to the terminal device 6 used by user P (step SA9). After step SA9 is completed, and if the anomaly detection unit 417 determines that it has not detected any abnormality in organism Q (step SA8: NO), the server 41 returns to step SA1.

[0101] [1-3. Effects] The notification system 1 is provided in the target space 9 and includes a detection unit 33 that detects information within the target space 9, a skeleton data generation unit 413 that estimates the skeleton of organism Q present in the target space 9 and generates skeleton data based on detection data D1 indicating the detection result of the detection unit 33, an activity level change data generation unit 414 and an activity data generation unit 415 that generate change data indicating changes in organism Q based on the skeleton data D3, an abnormality detection unit 417 that detects abnormalities in organism Q based on the change data, and a notification unit 418 that notifies that an abnormality has been detected in organism Q when an abnormality is detected. According to this method, abnormalities in organism Q can be detected based on the skeletal data D3 of organism Q, thus enabling the detection of abnormalities in organism Q using data with minimal personal information. Therefore, abnormalities in organism Q can be reported while protecting personal information.

[0102] Furthermore, the system includes an activity level change data generation unit 414 as a change data generation unit, which generates activity level change data D4 as change data, showing the change in the activity level of organism Q. According to this method, abnormalities in organism Q can be detected based on changes in its activity level, thus enabling highly accurate detection of abnormalities in organism Q. Therefore, abnormalities in organism Q can be accurately reported while protecting personal information.

[0103] The system includes a behavioral data generation unit 415 as a change data generation unit, and the behavioral data generation unit 415 generates behavioral data D5 indicating the type of behavior of organism Q as change data. According to this method, abnormalities in organism Q can be detected based on its behavior, thus enabling highly accurate detection of abnormalities in organism Q. Therefore, abnormalities in organism Q can be accurately reported while protecting personal information.

[0104] The system includes a microphone 38 that collects sound from the target space 9, and a sound data generation unit 416 that extracts sound emitted by organism Q from the sound to generate sound data D6. The anomaly detection unit 417 detects anomalies based on change data and the sound data D6. According to this method, not only change data based on skeletal data D3, but also data on changes in the state of organism Q can be obtained from sound data D6. Therefore, the accuracy of anomaly detection is improved.

[0105] The detection unit 33 includes a transmitter 35 that transmits radio waves to the target space 9 and a receiver 36 that receives radio waves. Based on the received radio waves, it includes an acquisition unit 344 that acquires radio wave state information, which is information about the state of the radio waves, and an estimation unit 347 that analyzes the received radio waves based on the radio wave state information and estimates the state of organism Q present in the target space 9. The detection data D1 includes the state of the organism based on the radio waves, and the skeleton data generation unit 413 generates skeleton data D3 based on the state of organism Q included in the detection data D1. According to this method, detection data D1 can be obtained wirelessly. Furthermore, since the skeletal data D3 obtained wirelessly does not contain any information other than that of organism Q, personal information can be protected even in the estimation of the skeleton.

[0106] The target space 9 is provided with multiple electrical equipment 2 to which power is supplied, and a detection unit 33 is provided on at least one of the multiple electrical equipment 2. According to this, detection data D1 can be obtained by utilizing the electrical equipment 2 placed in the target space 9. Therefore, abnormalities in organism Q can be detected without having to place a dedicated component for obtaining detection data D1 in the target space 9.

[0107] The target space 9 is provided with multiple electrical equipment 2 to which power is supplied, and a microphone 38 is provided on at least one of the multiple electrical equipment 2. According to this, even without placing a dedicated component in the target space 9 to obtain detection data D1, abnormalities in organism Q can be detected by the sound in the target space 9.

[0108] Furthermore, organism Q is an animal other than a human. According to this, it is possible to detect abnormalities in organism Q that are more difficult for humans to notice than abnormalities in humans themselves.

[0109] Furthermore, it is equipped with a sensor 7 that is attached to organism Q and detects changes in organism Q. According to this, changes in biological Q can be detected with greater accuracy. Therefore, the accuracy of anomaly detection is improved.

[0110] Target space 9 is the space where pet animals reside. According to this, user P can detect abnormalities in their pet.

[0111] The behavioral data generation unit 415 further generates behavioral data D5 based on the estimated data generated when the anomaly detection unit 417 detects an anomaly in organism Q. According to this, the behavioral data generation unit 415 can generate behavioral data D5 that shows changes in organism Q based on the changes in organism Q that are likely to occur when there is an abnormality and the changes in organism Q that are likely to occur when there is no abnormality, thereby improving the accuracy of the behavioral data D5. Consequently, the accuracy of the abnormality detection unit 417's detection of abnormalities is improved. Furthermore, the behavioral data generation unit 415 may also generate behavioral data D5 using an estimation model that has been machine-learned from the estimation data generated when the anomaly detection unit 417 detects an anomaly in organism Q, and the behavioral data D5, in order to improve the accuracy of anomaly detection. As a result, the accuracy of behavioral data D5 is improved by using a model that has been machine-learned from the relationship between the estimation data generated by the anomaly detection unit 417, which has a large amount of information, and the behavioral data D5, so that anomalies in organism Q can be detected with higher accuracy and more easily.

[0112] (Other embodiments) As described above, Embodiment 1 has been presented as an example disclosed in this application. However, the technology in this disclosure is not limited to this and can be applied to embodiments that have been modified, replaced, added, or omitted. Furthermore, it is possible to combine the components described in Embodiment 1 to create new embodiments. Therefore, other embodiments are described below as examples.

[0113] In the above-described embodiment 1, outlets 30A and 30B were given as examples of electrical equipment 2 to which power is supplied, but the invention is not limited thereto. Electrical equipment 2 includes switches, outlets, and other power wiring components, as well as ventilation fans, lighting fixtures, intercoms, and other devices that consume power. Electrical equipment 2 also includes distribution boards, building energy management devices, and other power control equipment.

[0114] In the first embodiment described above, a detection unit 33 was given as an example of a detection device, but the invention is not limited to this. In other embodiments, the detection device may be a visible light camera. In this case, the detection data D1 is image data of a color video or photograph captured by the visible light camera. In other embodiments, the detection device may be an infrared camera. In this case, the detection data D1 is image data captured by the infrared camera. As described above, the notification system 1 of the embodiment described above is configured to acquire detection data D1 of organism Q without using sensors, but it is not limited to this and may be equipped with sensors such as visible light cameras or infrared cameras. These visible light cameras and infrared cameras may be installed on electrical equipment 2, mounted on surfaces, or installed on walls.

[0115] As described above, the detection device in the other embodiment is a visible light camera, and the detection data D1 is the image data captured by the visible light camera. According to this method, skeletal data can be obtained from video footage, potentially improving the accuracy of the skeletal data. Therefore, the accuracy of detecting abnormalities in living organisms may improve.

[0116] In another embodiment, the detection device is an infrared camera, and the detection data D1 is the image data captured by the infrared camera. This could improve the accuracy of skeletal data D3. Therefore, it could improve the accuracy of anomaly detection in organism Q. Furthermore, since the skeleton is estimated based on infrared camera imaging data, which contains less personal information than regular images, it is possible to protect the user's personal information even in skeletal estimation.

[0117] In the above-described Embodiment 1, the server 41 is configured to include a skeletal data generation unit 413, but the system is not limited to this. The skeletal data generation unit 314 may be provided in a detection device, a computer used by user P, or a computer installed in the electrical equipment 2. This ensures that the detection data D1 is not used by the server 41 or another server, but is used locally, thus further protecting personal information. This configuration is particularly suitable when the detection device is a visible light camera or an infrared camera, as the detection data D1 contains relatively more personal information than the detection data D1 of the detection unit 33 in Embodiment 1.

[0118] Furthermore, at least one of the activity level change data generation unit 414 and the behavior data generation unit 415, which are examples of change data generation units, may generate change data based on imaging data. The change data based on imaging data is used by the anomaly detection unit 417 to detect anomalies in organism Q. Thus, the anomaly detection unit 417 may detect anomalies not only based on the skeletal data of organism Q, but also based on the imaging data itself. From the viewpoint of protecting personal information, it is preferable that the imaging data is used without being stored in the server memory of server 41 or the memory of other clouds. In this case, the activity level change data generation unit 414 and the behavior data generation unit 415 are installed on any computer used by user P or on electrical equipment 2, and utilize the imaging data.

[0119] Furthermore, the detection unit 33 and microphone 38 may be installed on other electrical equipment 2, such as other lighting devices 10, or they may be installed on a wall surface.

[0120] The detection processing unit 345 may store each data point of the radio wave state information provided by the acquisition unit 344 in a time series. The detection processing unit 345 may extract the change region in the target space 9 from the time series changes in each data point of the radio wave state information and estimate the state of organism Q in the change region. This allows detection processing to be narrowed down to the change region within the target space 9, thus enabling faster detection.

[0121] The detection processing unit 345 may also store in advance in memory 341 or the like the radio wave state information when no organism Q is present in the target space 9 as reference channel information. The detection processing unit 345 can estimate the state of organism Q present in the target space 9 using the difference between the current radio wave state information and the reference channel information.

[0122] The detection processing unit 345 may also estimate the state of organism Q by using as additional information the three-dimensional position of the radio wave transmission source in the target space 9 and the extent of the target space 9, which are estimated by the estimation unit 347 described later. This makes it possible to improve the detection accuracy of the state of organism Q.

[0123] Alternatively, the arrangement of electrical equipment 2 in the target space 9 may be determined in advance during construction, and the arrangement information of each electrical equipment 2 in the target space 9 may be stored in memory 341 beforehand. The detection processing unit 345 can use the above arrangement information as additional information to estimate the state of organism Q. This makes it possible to further improve the detection accuracy of the state of organism Q.

[0124] Furthermore, in the above-described embodiment 1, the anomaly detection unit 417 is configured to estimate the type of anomaly that has a particularly strong correlation with the activity level change data D3, but the system is not limited to this. In other embodiments, the notification system 1 may include an activity estimation data generation unit as a functional unit that generates activity estimation data indicating the type of change in the activity of organism Q based on the activity level change data D4 generated by the activity level change data generation unit 414. The type of change in the activity of organism Q is the type of change in organism Q that has a strong correlation with the activity level change data D3, for example, sleeping more than usual. The type of change in activity does not necessarily correspond to the type of anomaly of organism Q estimated by the anomaly detection unit 417. In this case, the anomaly detection unit 417 detects an anomaly of organism Q based on the activity estimation data instead of the activity level data D4 in embodiment 1. Furthermore, the activity estimation data generation unit may generate activity estimation data using an estimation model that has been machine-learned from the activity estimation data and the estimation data generated by the anomaly detection unit 417. This allows estimated data to be fed back to generate activity estimation data, improving the accuracy of the activity estimation data and, consequently, the accuracy of anomaly detection.

[0125] Furthermore, in the above-described embodiment 1, the anomaly detection unit 417 is configured to estimate the type of anomaly that has a particularly strong correlation with the sound data D6, but the system is not limited to this. In other embodiments, the notification system 1 may include a sound estimation data generation unit as a functional unit that generates sound estimation data indicating the type of change related to the voice of the estimated organism Q based on the sound data D6 generated by the sound data generation unit 414. This type of change related to the voice of the organism Q is a type of change in the organism Q that has a strong correlation with the sound data D6, such as chirping more often than usual. Note that the type of change related to the voice does not necessarily correspond to the type of anomaly of the organism Q estimated by the anomaly detection unit 417. In this case, the anomaly detection unit 417 detects an anomaly of the organism Q based on the sound estimation data instead of the sound data D6 in embodiment 1. Furthermore, this sound estimation data generation unit may generate activity estimation data using an estimation model that has been machine-learned from the sound estimation data and the estimation data generated by the anomaly detection unit 417. As a result, estimated data is fed back to generate sound estimation data, improving the accuracy of the sound estimation data and, consequently, the accuracy of anomaly detection.

[0126] In the above-described embodiment 1, the detection processing unit 345, skeletal data generation unit 413, activity level change data generation unit 414, behavior data generation unit 415, sound data generation unit 416, and anomaly detection unit 417 are configured to use estimation models employing machine learning techniques, but are not limited to this. The estimation models of the detection processing unit 345, skeletal data generation unit 413, activity level change data generation unit 414, behavior data 415, sound data generation unit 416, and anomaly detection unit 417 may each be models employing techniques such as pattern matching, Bayesian estimation, or regression analysis.

[0127] In the above-described embodiment 1, the anomaly detection unit 417 uses a machine learning method to estimate the probability of an anomaly in each type of anomaly in organism Q as a percentage, and detects that there is an anomaly in the corresponding type of organism Q if the percentage is above a predetermined threshold. However, the anomaly detection unit 417 is not limited to this, and may detect anomalies based on a dataset that associates the types of combinations of activity change data D4, behavior data D5, and sound data D6 with the types of anomalies. Alternatively, the anomaly detection unit 417 may be configured to make a binary decision of whether or not there is an anomaly based on the activity change data D4, behavior data D5, and sound data D6. Furthermore, the anomaly detection unit 417 may calculate the probability of each type of anomaly in organism Q as a percentage using a regression analysis method instead of a machine learning method, based on the activity change data D4, behavior data D5, and sound data D6.

[0128] Furthermore, in the above-described embodiment 1, the server 41 is configured to include an anomaly detection unit 417, but the system is not limited to this. The anomaly detection unit 417 may be provided by a detection device, a computer used by user P, or a computer installed in the electrical equipment 2. Moreover, the detection device may be a visible light camera or an infrared camera, and the anomaly detection unit 417 may be configured to estimate abnormalities in organism Q using imaging data from the visible light camera or infrared camera in addition to activity level change data D4, behavior data D5, and sound data D6. In this case, the imaging data is not used by the server 41 or another server, but is used locally, thus further protecting personal information and potentially improving the accuracy of anomaly detection.

[0129] Furthermore, the anomaly detection unit 417 may learn the correlation between the feedback from user P regarding the anomaly and the estimated data. The feedback may include, for example, the results of a questionnaire administered by user P regarding the validity of the notification content, such as whether an anomaly in organism Q has been detected or the type of anomaly. This questionnaire may be answered, for example, by a terminal device 6 used by user P.

[0130] In the above-described embodiment 1, the notification unit 418 is configured to notify the user P of the corresponding type of abnormality, but is not limited to this. The notification unit 418 may also notify the user P of predetermined information related to the corresponding type of abnormality. In other embodiments, predetermined information may be, for example, the type of disease such as a physical disability or illness that has a strong correlation with the type of abnormality. For example, if the type of abnormality of a dog, which is organism Q, is an abnormally short sleep duration, the corresponding type of disease may be arthritis or the like, causing pain. The notification unit 418 may further notify the user P of the corresponding type of disease. In addition, the notification unit 418 or the terminal device 6 may notify the user P of promotional information that encourages the user P to access a site that lists the corresponding type of disease.

[0131] Furthermore, the anomaly detection unit 417 may be configured to estimate the type of disease corresponding to the type of anomaly using machine learning or other estimation models. Also, the type of disease may be included in the type of anomaly.

[0132] The management DB 422 may also contain background information about organism Q. Background information includes, for example, the breed of dog if organism Q is a dog. Other background information includes the age of organism Q and information about diseases or illnesses it already has. For example, if organism Q is elderly and has a herniated disc, the possibility of an abnormality occurring in organism Q is low, even if it moves around less than a dog without such a condition. The abnormality detection unit 417 can further improve its accuracy by detecting abnormalities in organism Q based on this background information.

[0133] In the above-described embodiment 1, the notification unit 418 is configured to transmit information including the corresponding type of abnormality to the terminal device 6 used by user P as a predetermined notification, but is not limited to this. The notification unit 418 may also notify that an abnormality has occurred in organism Q by blowing air using the air conditioner 8 used by user P. Alternatively, the notification unit 418 may also notify that an abnormality has occurred in organism Q by flashing the lighting device 10 used by user P. Furthermore, depending on the type of abnormality, the notification unit 418 may appropriately change the way the air conditioner 8 blows air and the way the lighting device 10 flashes.

[0134] The notification unit 418 may further notify the hospital that there is an abnormality in organism Q, depending on the type of abnormality in organism Q. In this case, the notification unit 418 transmits information including the corresponding type of abnormality to terminal devices installed in the hospital or terminal devices used by doctors in the hospital.

[0135] As described above, in the other embodiment, the target space 9 is provided with at least one of an air conditioner 8 as an example of a ventilation means and a lighting device 10 as an example of a lighting means, and the notification unit 418 notifies that an abnormality in organism Q has been detected via at least one of the ventilation by the air conditioner 8 and the flashing by the lighting device 10. According to this, it is possible to notify that an abnormality in a living organism has been detected using means that can be placed within the target space.

[0136] The processor 340 and server processor 410 may consist of a single processor or multiple processors. These processors may also be hardware programmed to implement the corresponding functional units. That is, these processors may consist of, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0137] The configurations of the detection unit 33 and server 41 shown in Figures 4 and 5 are examples, and the specific implementation is not particularly limited. In other words, it is not necessarily required that hardware corresponding to each part be implemented individually; it is also possible to configure the system so that a single processor executes a program to realize the functions of each part. Furthermore, some of the functions realized by software in the above-described embodiment may be implemented by hardware, or some of the functions realized by hardware may be implemented by software.

[0138] The operational step units shown in Figure 6 are divided according to the main processing content to facilitate understanding of the operation, and the operation is not limited by the way the processing units are divided or the names of the processing units. Depending on the processing content, it may be further divided into more step units. Alternatively, it may be divided so that one step unit includes even more processing. Furthermore, the order of the steps may be changed as appropriate, as long as it does not impede the intent of this disclosure.

[0139] Since the embodiments described above are for illustrative purposes of the technology described herein, various modifications, substitutions, additions, omissions, etc., can be made within the claims or their equivalents.

[0140] (Note) Based on the above description of embodiments, the following technologies are disclosed.

[0141] (Technology 1) A notification system comprising: a detection device provided in a target space for detecting information within the target space; a skeleton data generation unit that estimates the skeleton of an organism present in the target space and generates skeleton data based on detection data indicating the detection result of the detection device; a change data generation unit that generates change data indicating changes in the organism based on the skeleton data; an abnormality detection unit that detects abnormalities in the organism based on the change data; and a notification unit that notifies the organism of the detection of an abnormality when such an abnormality is detected. According to this method, abnormalities in organisms can be detected based on their skeletal data, allowing for the detection of abnormalities using data with minimal personal information. Therefore, it is possible to notify individuals of abnormalities in organisms while protecting their personal information.

[0142] (Technology 2) The notification system according to Technology 1, wherein the change data generation unit comprises an activity level change data generation unit, and the activity level change data generation unit generates activity level change data indicating a change in the activity level of the organism as the change data. According to this method, abnormalities in organisms can be detected based on changes in their activity levels, thus enabling highly accurate detection of these abnormalities. Therefore, it is possible to accurately notify individuals of abnormalities in organisms while protecting their personal information.

[0143] (Technology 3) The notification system according to Technology 1 or 2, wherein the change data generation unit comprises a behavior data generation unit, and the behavior data generation unit generates behavior data indicating the behavior of the organism as the change data. According to this method, abnormalities in organisms can be detected based on their behavior, allowing for highly accurate detection of these abnormalities. Therefore, it is possible to accurately report abnormalities in organisms while protecting personal information.

[0144] (Technology 4) A notification system according to any one of Techniques 1 to 3, comprising a microphone for collecting sound in the target space, and a sound data generation unit for extracting sound emitted by the organism from the sound and generating sound data, wherein the anomaly detection unit detects an anomaly based on the change data and the sound data. According to this method, data on changes in the biological state can be obtained not only from skeletal data but also from sound data. Therefore, the accuracy of anomaly detection is improved.

[0145] (Technical 5) The notification system according to any one of Technical 1 to 4, wherein the detection device comprises a transmitter that transmits radio waves into the target space and a receiver that receives the radio waves, an acquisition unit that acquires radio wave state information which is information about the state of the radio waves based on the received radio waves, and an estimation unit that analyzes the received radio waves based on the radio wave state information to estimate the state of the organism present in the target space, the detection data includes the state of the organism based on the radio waves, and the skeleton data generation unit generates the skeleton data based on the state of the organism included in the detection data. According to this method, detection data can be obtained wirelessly. Furthermore, since the skeletal data obtained wirelessly does not contain information other than that of living organisms, personal information can be protected even when estimating the skeleton.

[0146] (Technical 6) The notification system according to any one of Technical 1 to 4, wherein the detection device is a visible light camera, and the detection data is imaging data from the visible light camera. According to this method, skeletal data can be obtained from video footage, potentially improving the accuracy of the skeletal data. Therefore, the accuracy of detecting abnormalities in living organisms may improve.

[0147] (Technical 7) The notification system according to any one of Technical 1 to 4, wherein the detection device is an infrared camera, and the detection data is imaging data from the infrared camera. According to this method, skeletal data can be obtained from video footage, potentially improving the accuracy of the skeletal data. Therefore, the accuracy of detecting anomalies in living organisms may improve. Furthermore, since the skeleton is estimated based on infrared camera imaging data, which contains less personal information than regular images, the user's personal information can be protected even in skeletal estimation.

[0148] (Technical 8) The notification system according to any one of Technical 1 to 7, wherein the target space is provided with a plurality of electrical equipment to which power is supplied, and the detection device is provided on at least one of the plurality of electrical equipment. According to this method, detection data can be obtained by utilizing electrical equipment placed in the target space. Therefore, abnormalities in living organisms can be detected without having to place dedicated components for obtaining detection data in the target space.

[0149] (Technical 9) The notification system according to Technical 4, wherein the target space is provided with a plurality of electrical equipment to which power is supplied, and the microphone is provided on at least one of the plurality of electrical equipment. According to this method, it is possible to detect abnormalities in living organisms based on the sounds in the target space, without needing to place any special components in the target space to obtain detection data.

[0150] (Technical 10) The notification system according to any one of Technical 1 to 9, wherein the target space is provided with at least one of a blowing means and a lighting means, and the notification unit notifies that an abnormality of the organism has been detected via at least one of blowing air by the blowing means and flashing by the lighting means. According to this, it is possible to notify that an abnormality in a living organism has been detected using means that can be provided within the target space.

[0151] (Technical 11) The notification system according to any one of Technical 1 to 9, wherein the organism is an animal other than a human. According to this method, it can detect abnormalities in organisms that are more difficult for humans to notice than abnormalities in humans themselves.

[0152] (Technology 12) The notification system according to Technology 11, comprising a sensor attached to the organism for detecting changes in the organism. This allows for more accurate detection of biological changes, thus improving the accuracy of anomaly detection.

[0153] (Technical 13) The notification system according to Technical 11 or 12, wherein the target space is a space where pet animals are present. According to this, users can detect abnormalities in their pet animals.

[0154] (Technical 14) The notification system according to any one of Technical 1 to 13, wherein the change data generation unit further generates the change data based on the data generated when the abnormality detection unit detects an abnormality in the organism. According to this, the change data generation unit can generate change data indicating biological changes based on the biological changes that are likely to occur when there is an abnormality and the biological changes that are likely to occur when there is no abnormality, thereby improving the accuracy of the change data. Consequently, the accuracy of the abnormality detection unit's detection of abnormalities will also improve. [Industrial applicability]

[0155] As described above, the notification system according to the present invention can be used for the purpose of notifying that an abnormality has been detected in a living organism. [Explanation of Symbols]

[0156] 1. Notification System 2. Electrical equipment 6 Terminal devices 7. Mounting Sensor 8. Air conditioning system (air blower) 9. Target Space 10 Lighting device (lighting means) 20 switches 30 outlets 33. Detection Unit (Detection Device) 34 Control device 35 Transmitters 36 Receiver 38 Microphones 41 Servers 340 processors 341 memory 342 Programs 343 Communication Control Unit 344 Acquisition Department 345 Detection Processing Unit 346 Radio wave separation section 347 Estimation Department 348 Sound detection processing unit 400 Server Control Units 401 Server Communication Device 410 Server Processors 411 Server Communication Control Unit 412 Management Department 413 Skeleton Data Generation Unit 414 Activity level change data generation unit (change data generation unit) 415 Behavioral Data Generation Unit (Change Data Generation Unit) 416 Sound Data Generation Unit 417 Anomaly detection unit 418 Hochi Department 420 Server Memory 421 Server Program D1 detection data D2 Sound Collection Data D3 Skeleton Data D4 Activity level change data (change data) D5 Behavioral Data (Change Data) D6 Audio Data Q Biology

Claims

1. A detection device installed in the target space for detecting information within the target space, A skeleton data generation unit estimates the skeleton of an organism present in the target space and generates skeleton data based on detection data showing the detection results of the detection device, A change data generation unit that generates change data showing changes in the organism based on the skeletal data, An abnormality detection unit that detects abnormalities in the organism based on the aforementioned change data, The system includes a notification unit that notifies the user that an abnormality has been detected in the organism when such an abnormality is detected. Notification system.

2. The aforementioned change data generation unit includes an activity level change data generation unit. The activity level change data generation unit generates activity level change data that shows the change in the activity level of the organism as the change data. The notification system according to claim 1.

3. The aforementioned change data generation unit includes an action data generation unit, The behavioral data generation unit generates behavioral data that indicates the behavior of the organism as the change data. The notification system according to claim 1 or 2.

4. A microphone for collecting sound in the aforementioned target space, The system includes a sound data generation unit that extracts sounds emitted by the organism from the aforementioned sounds and generates sound data, The anomaly detection unit detects an anomaly based on the change data and the sound data. The notification system according to claim 3.

5. The detection device comprises a transmitter that transmits radio waves into the target space and a receiver that receives the radio waves. An acquisition unit that acquires radio wave status information, which is information about the state of the radio wave, based on the received radio wave, The system includes an estimation unit that analyzes the received radio waves based on the radio wave state information to estimate the state of the organism present in the target space, The detection data includes the state of the organism based on the radio waves. The skeletal data generation unit generates the skeletal data based on the state of the organism contained in the detection data. The notification system according to claim 1.

6. The detection device is a visible light camera, The detection data is the image data from the visible light camera. The notification system according to claim 1.

7. The detection device is an infrared camera, The detection data is the imaging data from the infrared camera. The notification system according to claim 1.

8. The aforementioned target space is provided with multiple electrical equipment and materials to which electricity is supplied. The detection device is provided on at least one of the multiple electrical equipment materials. The notification system according to claim 1.

9. The aforementioned target space is provided with multiple electrical equipment and materials to which electricity is supplied. The microphone is provided on at least one of the multiple electrical equipment materials. The notification system according to claim 4.

10. The aforementioned target space is provided with at least one of a blowing means and a lighting means. The notification unit notifies that an abnormality in the organism has been detected via at least one of the following: airflow from the blowing means and flashing from the lighting means. The notification system according to any one of claims 1, 2, 5 to 8.

11. The aforementioned organism is an animal other than a human. The notification system according to any one of claims 1, 2, 5 to 8.

12. The organism is fitted with a sensor that detects changes in the organism, The notification system according to claim 11.

13. The aforementioned space is a space where pet animals reside. The notification system according to claim 12.

14. The change data generation unit further generates the change data based on the data generated when the anomaly detection unit detects an anomaly in the organism. The notification system according to claim 3.

Images