Estimation device, estimation system and program

Abstract

To provide an estimation device allowing even non-experts such as owners to estimate expert knowledge of animals without special equipment such as a radio wave transmitter.SOLUTION: A time management unit 111 refers to a schedule DB 121 stored in a memory 12, at timing when a predetermined date and time arrives, reads out a question sentence from a question DB 124 and supplies the question sentence to a presentation unit 112. The presentation unit 112 causes a display unit 15 to present the supplied question sentence to the user. A video acquisition unit 113 controls a camera 16 to acquire video data representing video capturing a body part of an animal. A vital measurement unit 114 reads out video data from the video DB 122 and measures vital signs of the animal on the basis of the read video data. A situation information receiving unit 115 receives situation information representing a situation of the animal from the user via an operation unit 14. An estimation unit 116 searches a knowledge DB 126 to estimate knowledge of experts corresponding to a combination of the situation information and the vital signs.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] The present invention relates to an estimation device, an estimation system, and a program. [Background technology]

[0002] For pet owners, pets such as dogs and cats are like family members, and so "knowledge about pets" such as their moods, emotions, physical condition, and things they want to tell their owners are of great interest. However, since owners cannot communicate with their pets through words, it is difficult for them to know these things. Owners have a strong desire to understand their pets' emotions and to be aware of their pets' poor physical condition.

[0003] In order to correctly diagnose and understand "knowledge about pets," the knowledge of experts such as veterinarians and pet trainers is necessary. However, considering the absolute number of experts and the difficulty of making appointments, it is difficult to consult these experts on a daily basis. Therefore, there is a demand for a system that allows people to obtain reliable knowledge about their pets on a daily basis without having to consult an expert each time.

[0004] For example, Patent Document 1 discloses a non-contact type animal living body monitoring device that simultaneously or alternately transmits and receives a plurality of types of radio waves to read the breathing, heartbeat, and oxygen concentration of a living animal in a non-contact manner. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2022-151455 Summary of the Invention [Problem to be solved by the invention]

[0006] The technology described in Patent Document 1 requires a sensor unit that simultaneously or alternately transmits and receives multiple types of radio waves, resulting in high initial costs. In addition, this technology requires limitations on the placement of the sensor unit so that the distance to the animal does not vary significantly from the distance previously set in the measurement preparation.

[0007] One of the objects of the present invention is to enable even non-experts, such as pet owners, to estimate expert knowledge about animals without the need for special equipment such as a radio wave transmitter. [Means for solving the problem]

[0008] In one aspect, the present invention provides an estimation device that receives information indicating an animal's condition from a user, measures the animal's pulse based on a video of the animal's body, and estimates expert knowledge about the animal based on a combination of the information and the pulse. Effect of the Invention

[0009] According to the present invention, even non-experts such as pet owners can estimate expert knowledge about animals without special equipment such as a radio wave transmitter. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of an estimation device 1. [Diagram 2] FIG. 12 is a diagram showing an example of a schedule DB 121. [Diagram 3] FIG. 4 is a diagram showing an example of a video DB 122. [Figure 4] FIG. 1 is a diagram for explaining an example of the concept of video data. [Diagram 5] FIG. 12 shows an example of a vital sign DB123. [Figure 6] FIG. 13 is a diagram showing an example of a question DB 124. [Figure 7] FIG. 2 is a diagram showing an example of a branch DB 125. [Figure 8] FIG. 13 is a diagram showing an example of a knowledge DB 126. [Figure 9]FIG. 2 is a diagram showing an example of the functional configuration of the estimation device 1. [Figure 10] FIG. 2 is a diagram showing an example of the flow of operation of the estimation device 1. [Figure 11] FIG. 4 is a diagram showing an example of a display area R0. [Figure 12] FIG. 13 is a diagram showing an example of a display when a video of an animal is being captured. [Figure 13] FIG. 13 is a diagram showing an example of the overall configuration of an estimation system 9 according to a modified example. [Figure 14] FIG. 13 is a diagram showing an example of the configuration of a server device 2 according to a modified example. [Figure 15] FIG. 13 is a diagram showing an example of the configuration of an estimation device 1 according to a modified example. [Figure 16] FIG. 13 is a diagram showing an example of a question DB 124 according to a modified example. [Figure 17] FIG. 13 is a diagram showing an example of a knowledge DB 126 according to a modified example. [Figure 18] FIG. 13 is a diagram showing an example of a functional configuration of an estimation device 1 according to a modified example. [Figure 19] FIG. 11 is a diagram showing an example of the flow of operation of the estimation device 1 according to a modified example. [Figure 20] FIG. 13 is a diagram showing an example of a display region R10 according to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] <Embodiment> <Configuration of the estimation device> Fig. 1 is a diagram showing an example of the configuration of an estimation device 1. The estimation device 1 is a device that estimates expert knowledge about an animal based on a combination of information indicating the state of the animal received from a user such as the owner of the animal and an image of the body of the animal (hereinafter also referred to as an "animal image"). The estimation device 1 shown in Fig. 1 is a mobile terminal, for example, a smartphone.

[0012] 1, the estimation device 1 includes a processor 11, a memory 12, a communication unit 13, an operation unit 14, a display unit 15, and a camera 16. These are connected by a bus.

[0013] The processor 11 reads out and executes a program stored in the memory 12 to control each part of the estimation device 1. The processor 11 is, for example, a CPU (Central Processing Unit).

[0014] The communication unit 13 is a communication circuit that communicatively connects the estimation device 1 to another device by wire or wirelessly. For example, the communication unit 13 may have a circuit that complies with a wireless communication system standard such as IMT-2000, IMT-Advanced, IMT-2020, etc. In addition, for example, the communication unit 13 may have a circuit that complies with a wireless LAN standard such as IEEE802.11.

[0015] Furthermore, for example, the communication unit 13 may have a module that realizes near field communication (NFC), etc. Standards for this NFC include, for example, ISO / IEC18092 (NFCIP-1), ISO / IEC14443, ISO / IEC15693, and IEEE802.15.

[0016] The operation unit 14 includes operation buttons, a touch panel, and other operators for issuing various instructions, and receives an operation and sends a signal according to the operation content to the processor 11. This operation is, for example, pressing a button, a gesture on the touch panel, and the like.

[0017] The operation unit 14 may have a microphone for collecting voice. In this case, the processor 11 may perform voice recognition processing on the voice data representing the user's voice collected by the microphone, and accept the recognition result as the user's operation.

[0018] The display unit 15 has a display screen such as a liquid crystal display, and displays images under the control of the processor 11. A transparent touch panel of the operation unit 14 may be disposed on top of the display screen.

[0019] 1 has a display screen that displays images and the like that are visually perceived by the user, the display unit 15 may be a presentation unit that presents information by a medium that is perceived by the user other than by the user's vision. For example, the display unit 15 may have a speaker that emits sound instead of or in addition to the above-mentioned display screen.

[0020] The camera 16 is an imaging means, such as a digital still camera, that captures an image of the surroundings of the estimation device 1 and generates an image. The camera 16 includes an optical system such as a lens, as well as an imaging element such as a CMOS (Complementary Metal Oxide Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] 1 is a device that acquires image data by arranging a plurality of detection elements, each of which has sensitivity to light intensity in a wavelength region corresponding to each of the colors R (red), G (green), and B (blue), as one pixel unit. The wavelength regions corresponding to each color are preferably, for example, 580 nm to 680 nm for R (red), 500 nm to 630 nm for G (green), and 410 nm to 530 nm for B (blue).

[0022] The camera 16 may have a polarizing plate as an optical system. The polarizing plate is disposed in the direction in which the camera 16 photographs the object. This allows the camera 16 to remove reflected components from the surface of the object.

[0023] Furthermore, the camera 16 may detect light in a wavelength range other than the above-mentioned ranges. For example, the camera 16 may be a 5-band camera in which two sensitivities, cyan (C) and orange (O), are added to a normal RGB camera.

[0024] The memory 12 is a storage means for storing an operating system, various programs, data, etc., which are loaded into the processor 11. The memory 12 includes a RAM (Random Access Memory) and a ROM (Read Only Memory).

[0025] The memory 12 may include a solid state drive, a hard disk drive, etc. The memory 12 shown in FIG.

[0026] <Schedule DB configuration> 2 is a diagram showing an example of the schedule DB 121. The schedule DB 121 is a database that stores schedules for measuring the vital signs of animals in the estimation device 1.

[0027] The schedule DB 121 has a table structure that stores values ​​for each of the items that are associated with each other. The schedule DB 121 shown in FIG. 2 has items such as schedule ID, day of the week, and start time. The schedule ID is identification information assigned to each schedule for which vital signs measurement is performed. Vital signs measurement refers to measuring vital signs (hereinafter also referred to as vitals) that indicate the life signs and physical condition of an animal. The vital signs of this animal are measured based on video data (called video data) consisting of animal images taken periodically, for example, 30 frames per second. In the schedule DB 121 shown in FIG. 2, the date and time for performing vital signs measurement is determined by a combination of a day of the week associated with the schedule ID and a start time.

[0028] For example, a schedule for measuring vital signs at 7:00 a.m. every Monday through Friday is a schedule for measuring the vital signs of an animal when the animal is fed on weekday mornings when the user goes to work. The estimation device 1 performs vital sign measurements at the dates and times determined in accordance with this schedule DB 121.

[0029] The schedule for measuring vital signs may be associated with the timing of meals for the pet. In this case, the timing of meals may be determined according to the taxonomic classification, species, age, etc. of the pet. For example, the timing of meals may be determined as three times a day, morning, afternoon, and evening, for puppies, and twice a day, morning and evening, for adult dogs.

[0030] <Video DB configuration> Fig. 3 is a diagram showing an example of the video DB 122. The video DB 122 is a database that stores video data of an animal's body. The video DB 122 shown in Fig. 3 has a table structure that stores values ​​for each item that is associated with each other. The video DB 122 shown in Fig. 3 has the following items: video ID, start date and time, length, schedule ID, and video data.

[0031] In this video DB 122, the value of the video data is the video data itself. The value of the video ID is identification information for uniquely identifying the corresponding video data. The value of the start date and time is information on the date and time when shooting of the corresponding video data started (i.e., the start date and time). The value of the length is a numerical value indicating the length of the corresponding video data. The unit of length is, for example, minutes, seconds, or other time units. The value of the schedule ID is data that stores information on the schedule identified by which schedule ID defined in the schedule DB 121 the corresponding video data was shot according to.

[0032] FIG. 4 is a diagram for explaining an example of the concept of video data. Video data is composed of a number of still images taken at regular intervals. One frame of the number of still images is called a frame. The number of still images taken per unit time is called the frame rate. As shown in FIG. 4, video data is such that each of a number of ordered frames is associated with image data indicating a still image taken at each moment. Note that video data may be compressed as long as the image data for each frame can be acquired by decompressing it.

[0033] <Vital DB configuration> Fig. 5 is a diagram showing an example of the vital sign DB 123. The vital sign DB 123 is a database that stores vital signs of animals that have been measured in the past. The vital sign DB 123 shown in Fig. 5 has a table structure that stores values ​​for each item that is associated with each other. The vital sign DB 123 shown in Fig. 5 has the following items: video ID, pulse, heart rate variability, respiratory rate, LF, and HF. Of these items, the vital sign values ​​are the five items: pulse, heart rate variability, respiratory rate, LF, and HF.

[0034] Note that the vital signs measured by the vital sign measurement are not limited to the five items mentioned above, but include at least pulse rate.

[0035] The moving image ID is identification information for identifying the moving image data that was the subject of the measurement, and is common information with that stored in the moving image DB 122. From the moving image data identified by this moving image ID, the corresponding five vital signs are obtained.

[0036] The pulse value is the number of times the blood vessels in an animal's body pulsate in one minute. Pulse is synonymous with heart rate, since it is equal to the number of times the heart beats in one minute.

[0037] Heart rate variability is a numerical value that represents the fluctuation observed in the time interval between heartbeats, and is also called HRV (Heart Rate Variability). Heart rate variability reflects information related to biological control such as the autonomic nervous system, and it is known that its characteristics change with age and the presence or absence of illness in animals, just as in humans.

[0038] Respiratory rate is the number of breaths an animal takes per minute. This respiratory rate is measured from the changes in the outline of the animal's body captured in the animal image.

[0039] The LF and HF values ​​are both calculated values ​​obtained by performing power spectrum analysis on heartbeat interval fluctuations. For example, the LF value is the sum (integral value) of the intensities of low frequency components, and the HF value is the sum (integral value) of the intensities of high frequency components. It is said that the low frequency components correspond to blood pressure fluctuations, and the high frequency components correspond to respiratory fluctuations.

[0040] <Question DB configuration> Fig. 6 is a diagram showing an example of the question DB 124. The question DB 124 is a database that stores a group of questions related to a situation of a target animal, which are presented to a user such as an owner who understands the situation. The question DB 124 shown in Fig. 6 has a table structure that stores values ​​for each item that is associated with each other. The question DB 124 shown in Fig. 6 has items such as a question ID, a question sentence, and two or more options.

[0041] In the question DB 124, a question ID is identification information assigned to each question sentence that indicates the content of a question. A question sentence is data that expresses the content of a question using a character string or the like. An option is data that indicates a response that a user who is presented with a question sentence can select from in response to the question sentence.

[0042] For example, in the question DB 124 shown in FIG. 6, the question ID "Q1" is assigned to the question "What's in front?". In addition, in this question DB 124, five responses from options "A1" to "A5" are prepared for the question "What's the status of your tail?" identified by the question ID "Q2". For example, if the user selects option "A3" for the question "What's the status of your tail?", the response is "(Your pet is hiding its tail)." Note that the number of options for one question is not limited to five, and may be two or more.

[0043] <Branch DB configuration> FIG. 7 is a diagram showing an example of the branch DB 125. The branch DB 125 is a database that stores subsequent questions according to the response to a question. For example, if the response to the question "What is in front of you?" is "Nothing in particular," it is generally unnecessary to add a question about what is in front of the animal. On the other hand, if the response indicates the presence of something, such as "food" or "stuffed animal," adding a question about what is in front of the animal may be necessary to accurately determine the situation of the animal. Therefore, the branch DB 125 stores questions to be added according to the content of the user's response, for example, as data related by a tree structure.

[0044] 7, when the user's response to question ID "Q1" is option "A1", the estimating device 1 presents a question with question ID "Q2". On the other hand, when the user's response to question ID "Q1" is option "A2", the estimating device 1 presents a question with question ID "Q8". Note that in this branch DB 125, when there is no edge from a response to a subsequent question, that response is a terminal response.

[0045] <Knowledge DB Configuration> Fig. 8 is a diagram showing an example of the knowledge DB 126. The knowledge DB 126 is a database that stores knowledge that is presumed to be presented by an expert about an animal for each combination of information indicating the animal's condition indicated by a user's response (called situation information) and the measurement results of the animal's vital signs. The knowledge DB 126 shown in Fig. 8 has a table structure that stores values ​​for each item that is associated with each other. The knowledge DB 126 shown in Fig. 7 has the following items: situation information, measurement results, and knowledge information.

[0046] The situation information in the knowledge DB 126 is a field that stores situation information based on a response received from a user to whom a question was presented. For example, the situation information shown in Fig. 8 stores information such as "Q1A1 → Q2A4 → Q7A3 ...". This situation information indicates, for example, the following:

[0047] - The user's response to question ID "Q1" is option "A1." Next, question ID "Q2" is presented, and the user responds with option "A4." -Furthermore, question ID "Q7" is presented, and the user's response to this is option "A3."

[0048] From a list of the user's responses to these questions, the estimation device 1 identifies the animal's condition as understood by the user. Other examples of the animal's condition information include "lack of energy," "loss of appetite," "constipation (no bowel movements for a certain period of time)," "vomited after eating," "pupils are always open," "nictitating membrane (transparent or semi-transparent membrane that protects the eyeball) is exposed," and "abdomen is swollen."

[0049] The measurement results in the knowledge DB 126 are fields that store data showing the results of vital measurement of an animal. For example, the measurement results shown in Fig. 8 store information in which the pulse obtained by vital measurement is compared with a predetermined level and classified into an ordinal scale such as "high", "medium", ....

[0050] The measurement result may be only the current or most recent vital measurement result, but may also include a result of a vital measurement stored in the past, or a relative comparison of the current vital measurement result with the previously stored vital measurement result. In other words, the measurement result may be an evaluation of the deviation rate of the current measurement value, etc., when the average value, moving average value, seasonal mode value, etc. of the vital measurement of an individual animal is used as a standard.

[0051] The knowledge information in the knowledge DB 126 is associated with a combination of the above-mentioned situation information and the measurement result, and is represented by a character string or the like, indicating knowledge that is estimated to be presented by an expert for that combination. For example, in the knowledge information shown in FIG. 8, the knowledge information "is very scared of what is in front of him" is associated with the combination of the situation information "Q1A1→Q2A4→Q7A3..." and the measurement result "large". By presenting this knowledge information, the user who is the pet owner can know that the pet is scared of a stuffed animal or the like placed in front of it, for example, when the pet hides its tail and has a higher pulse rate than usual.

[0052] The knowledge information is not limited to the above-mentioned information about the animal's emotions. The knowledge information may be, for example, information about a sign of an animal's illness. For example, when the user responds that the animal "is lying down and not trying to move" and the result of vital signs measurement indicates that the breathing rate at rest has increased by 25 to 50% or exceeded 35 breaths per minute, the knowledge DB 126 may store knowledge information that "Congestive Heart Failure (CHF) is suspected" for this combination. Other examples of knowledge information include "Decreased tear secretion and dry eyes" and "Bradycardia and arrhythmia".

[0053] <Functional configuration of the estimation device> Fig. 9 is a diagram illustrating an example of a functional configuration of the estimation device 1. The processor 11 of the estimation device 1 reads out and executes a program stored in the memory 12, thereby functioning as a time management unit 111, a presentation unit 112, a video acquisition unit 113, a vital sign measurement unit 114, a situation information reception unit 115, and an estimation unit 116 illustrated in Fig. 9.

[0054] The time management unit 111 refers to the schedule DB 121 stored in the memory 12, and reads out a question from the question DB 124 at a timing when a predetermined date and time described therein comes. Then, the time management unit 111 supplies the read out question to the presentation unit 112.

[0055] Furthermore, the time management unit 111 refers to the branch DB 125 and checks whether there is an additional question, depending on the user's response received by the situation information receiving unit 115, which will be described later. If there is an additional question, the time management unit 111 reads out the “additional question text” and supplies the question text to the presentation unit 112.

[0056] The presentation unit 112 presents the question sentence supplied from the time management unit 111 to the user on the display screen of the display unit 15. Therefore, the estimation device 1 having the time management unit 111 and the processor 11 functioning as the presentation unit 112 is an example of an estimation device that presents questions according to a predetermined schedule.

[0057] Furthermore, the presenting unit 112 prompts the user, who is the pet owner, to take an image of the pet's body at a predetermined date and time around the time of presenting the question.

[0058] Here, an example of an image that can be used for vital measurement is a photograph of a human's face. However, in the case of animals, many of their faces are covered with animal hair, so photographs of the face often cannot be used for vital measurement. Therefore, the presentation unit 112 explains the part of the body of the animal that is the subject of the photograph, depending on the type of pet animal.

[0059] Here, the memory 12 stores in advance the types of animals that the user of the estimation device 1 is estimating, and stores information for each type of animal as to which part of the animal's body should be photographed so that the photographed image can be used for measuring vital signs. The presentation unit 112 simply presents this information stored in the memory 12 to the user.

[0060] For example, in the case of dogs and cats, the body part to be photographed is designated as the "plantar pads," which have thin fur and where blood components flowing through the blood vessels can be recognized under visible light. The plantar pads are raised, hairless parts on the soles of the animals' feet, and are also known as "paw pads." The part to be photographed for a photographic image that can be used to measure vital signs is not limited to the plantar pads, and may be, for example, the abdomen, which has thin fur and where blood vessels are visible.

[0061] The presentation unit 112 also presents knowledge information estimated by an estimation unit 116 (described later) to the user on the display screen of the display unit 15.

[0062] The video acquisition unit 113 controls the camera 16 to acquire video data showing a video of a body part of a specified animal captured by a user of the estimation device 1. The video data acquired by the video acquisition unit 113 is stored in the video DB 122 of the memory 12. In other words, the video acquisition unit 113 is an example of an acquisition means that is realized by the functioning of a computer and that acquires a video of an animal's body.

[0063] Vital sign measurement unit 114 reads video data from video DB 122 and measures the animal's vital signs based on this video data. The vital sign values ​​measured by vital sign measurement unit 114 are stored in vital sign DB 123 together with a video ID that identifies the video data, as shown in Fig. 5. In other words, this vital sign measurement unit 114 is an example of a measurement means that is realized by the functioning of a computer and that measures the pulse rate of an animal based on a video.

[0064] As described above, vital signs include at least pulse rate, and therefore the estimation device 1 having a processor 11 functioning as this vital sign measurement unit 114 is an example of an estimation device that measures the pulse rate of an animal based on a video of the animal's body.

[0065] The situation information receiving unit 115 receives "situation information" indicating the situation of an animal from a user of the estimation device 1 via the operation unit 14. Then, the situation information receiving unit 115 supplies the received situation information to the estimation unit 116. The situation information is composed of, for example, a list of combinations of question IDs and options stored in the above-mentioned question DB 124. The estimation device 1 having the processor 11 functioning as this situation information receiving unit 115 is an example of an estimation device that receives information indicating the situation of an animal from a user.

[0066] Moreover, the situation information receiving unit 115 receives situation information in response to a question presented by the presentation unit 112. In other words, the estimation device 1 having the processor 11 functioning as the situation information receiving unit 115 is an example of an estimation device that receives a response from a user to a question presented to the user regarding the situation of an animal, as information indicating the situation of the animal.

[0067] Moreover, this status information receiving unit 115 is an example of a receiving means that is realized by the functioning of a computer and that receives information indicating the status of an animal from a user.

[0068] The estimation unit 116 acquires the most recently measured vital signs of the animal from the vital signs DB 123, and also acquires the above-mentioned situation information from the situation information receiving unit 115. Then, the estimation unit 116 estimates knowledge that an expert may have regarding the current animal by searching and acquiring knowledge corresponding to a combination of the situation information and the vital signs from the knowledge DB 126. In other words, the estimation unit 116 is an example of an estimation means that is realized by the functioning of a computer and estimates the expert's knowledge regarding the animal according to a combination of information regarding the animal's situation and the measured pulse rate of the animal.

[0069] Then, the estimation unit 116 supplies data indicating the estimated expert knowledge to the presentation unit 112. As described above, the presentation unit 112 presents the estimated expert knowledge regarding the current animal on the display unit 15 based on the supplied data.

[0070] Therefore, the estimation device 1 having the processor 11 functioning as this estimation unit 116 is an example of an estimation device that estimates expert knowledge about an animal based on a combination of information indicating the animal's condition received from a user and the animal's pulse rate measured based on a video of the animal's body.

[0071] <Operation of the estimation device> 10 is a diagram showing an example of the flow of the operation of the estimation device 1. The processor 11 of the estimation device 1 checks the schedule stored in the schedule DB 121 (step S101), and judges whether the date and time scheduled for vital sign measurement has arrived by comparing with the current time (step S102). When it is judged that the date and time scheduled for measurement has not arrived (step S102; NO), the processor 11 returns the process to step S101.

[0072] On the other hand, if it is determined that the scheduled date and time for measurement has arrived (step S102; YES), the processor 11 refers to the question DB 124 in the memory 12 to present a question (step S103), and accepts the user's response to this question (step S104).

[0073] Then, when processor 11 receives the user's response, processor 11 refers to branch DB 125 to determine whether there is an additional question (step S105). If it is determined that there is an additional question (step S105; YES), processor 11 returns the process to step S103 and further presents the additional question.

[0074] On the other hand, if it is determined that there are no additional questions (step S105; NO), the processor 11 controls the display unit 15 to prompt the user to take a picture of, for example, an animal's paw, and obtains video data of the captured picture from the camera 16 (step S106).

[0075] Fig. 11 is a diagram showing an example of a display area R0. As shown in Fig. 11, a display unit 15 and a camera 16 are provided on the front side of the estimation device 1. As described above, the display unit 15 has a display screen such as a liquid crystal display. In the upper part of this display screen, a general information display area for displaying radio wave reception sensitivity, remaining battery power, time, etc. is provided, and in the lower part, a general operation area for receiving operations on the operating system is provided. Then, a display area R0 is provided between these areas. This display area R0 is assigned to a program for estimating expert knowledge about animals.

[0076] Fig. 12 is a diagram showing an example of a display when a video of an animal is being taken. When the processor 11 prompts the user to take a video of the pet's paws, a stop icon area R1, an explanation area R2, an animal body photography area R3, and a photography start button area R4 are displayed in the display area R0, as shown in Fig. 12.

[0077] The stop icon area R1 is an area for accepting an instruction to stop video recording.

[0078] The explanation area R2 is an area for displaying text etc. that explains the procedure for shooting a video. In the example shown in Fig. 12, the explanation area R2 displays the following explanation: "Hold your front paws over the smartphone, align your paw pads with the circle, and press the 'Start shooting' button."

[0079] The animal body shooting area R3 is an area that indicates the range in which the designated animal's body part (in this example, the paw pad) should fit within the image area that simultaneously displays the moving image being shot.

[0080] The shooting start button area R4 is an area for displaying a button for starting video shooting.

[0081] Returning to the description of the operation shown in Fig. 10, when the user presses the shooting start button to shoot the pads of the pet's front paws and video data is supplied from camera 16, processor 11 analyzes this video data and measures vital signs (step S107).

[0082] The processor 11 refers to the knowledge DB 126 in the memory 12 and estimates the expert knowledge corresponding to the combination of the animal's pulse rate obtained as a result of the vital sign measurement and the user's response to the posed question (step S108). Then, the processor 11 controls the display unit 15 to display the estimated expert knowledge to the user (step S109), and ends the process.

[0083] Through the operations described above, the estimation device 1 can estimate the knowledge of an expert regarding an animal without the intervention of an animal expert such as a veterinarian.

[0084] In particular, unlike humans, animals often have faces covered with fur, and it can be difficult to measure blood flow fluctuations from a video of the face. Since this estimation device 1 specifies the body parts to be photographed for each animal, the user can know which parts to photograph.

[0085] Furthermore, if an attempt is made to estimate an expert's knowledge about an animal based only on the response to a question, the inference may be incorrect depending on the accuracy of the response content. Since this estimation device 1 estimates an expert's knowledge based on a combination of situation information about the animal obtained from the user's response and the result of measuring the animal's vital signs, the possibility of inferring incorrect knowledge is reduced.

[0086] In other words, even if the user of this estimation device 1 is a non-expert, they can obtain reliable knowledge about pets with which they cannot communicate verbally, simply by answering questions and taking videos of specified body parts of the animal.

[0087] <Modification> The above is a description of the embodiment, but the contents of this embodiment may be modified as follows. In addition, the following modifications may be combined with each other.

[0088] In the above-described embodiment, the estimation device 1 has the processor 11 configured as a CPU, but the control means for controlling the estimation device 1 may have other configurations.

[0089] That is, the estimation device 1 may have, as the processor 11, various types of processors, such as a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a programmable logic device, etc., other than a CPU.

[0090] The operations of the processor in the above-described embodiments may not only be performed by one processor, but may also be performed by a plurality of processors located at physically separate locations working together.

[0091] Furthermore, the order of each operation of the processor is not limited to the order described in the above embodiment, and may be changed as appropriate.

[0092] In the above-described embodiment, the estimation device 1 estimates the expert's knowledge about animals according to a combination of the received situation information and the measured pulse rate, but when the situation information is related to the animal's diet, the estimation device 1 may identify the animal's tendency toward different types of diet based on the estimated knowledge. For example, when the type of diet given to the pet is dry or wet, as situation information obtained from the user's response, the estimation device 1 may identify which diet the pet prefers according to the magnitude of the pet's pulse rate. After identifying the pet's dietary preference (tendency), the estimation device 1 may display it on the display unit 15 to inform the user.

[0093] In the above-described embodiment, the knowledge DB 126 stored in the memory 12 has knowledge information associated with each combination of situation information and measurement result in a one-to-one correspondence, but the correspondence is not limited to this form. The knowledge DB 126 may be, for example, a machine learning model obtained by performing supervised learning using correct answer data in which a combination of situation information and measurement result is used as input data, and knowledge information presented by an expert for that combination is used as output data.

[0094] In this case, the knowledge DB 126 may be a parameter group capable of calculating one or more pieces of plausible knowledge information together with their likelihoods for a combination of situation information and measurement results. When two or more pieces of knowledge information that satisfy a predetermined condition are estimated for a combination of situation information and measurement results, the estimation device 1 may present them to a user together with their likelihoods.

[0095] In the above-described embodiment, the schedule DB 121 stored in the memory 12 of the estimation device 1 describes an actual time (also called an absolute time) as the start time, but a relative time indicating a time difference from a reference time may be described. For example, the schedule DB 121 may store, as the start time, the time elapsed from the time an animal wakes up, eats, goes to bed, etc.

[0096] In the above-described embodiment, the estimation device 1 is a single mobile terminal, but some of the functions may be realized by an external device.

[0097] 13 is a diagram showing an example of the overall configuration of an estimation system 9 according to a modified example. The estimation system 9 shown in FIG.

[0098] The estimation device 1 is a mobile terminal, such as a smartphone, and the server device 2 is a computer connected to one or more estimation devices 1 so as to be able to communicate with each other.

[0099] The communication line 3 is a line that communicatively connects the estimation device 1 and the server device 2. The communication line 3 may be, for example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, or a combination of these.

[0100] It should be noted that the respective numbers of the estimation devices 1 and the server devices 2 in the estimation system 9 are not limited to those shown in Fig. 13. For example, the server device 2 may be configured as a cluster system in which a plurality of devices share functions.

[0101] Fig. 14 is a diagram showing an example of the configuration of a server device 2 according to a modified example. As shown in Fig. 14, the server device 2 includes a processor 21, a memory 22, and a communication unit 23. These are connected by a bus.

[0102] The processor 21 reads and executes a program stored in the memory 22 to control each part of the estimation device 1. The processor 21 is, for example, a CPU.

[0103] The communication unit 23 is a communication circuit that communicatively connects the server device 2 to other devices via the communication line 3 in a wired or wireless manner.

[0104] The memory 22 is a storage means for storing an operating system, various programs, data, etc., which are loaded into the processor 21. The memory 22 includes a RAM and a ROM.

[0105] The memory 22 may include a solid state drive, a hard disk drive, etc. The memory 22 shown in Fig. 14 stores a schedule DB 221, a video DB 222, a vital DB 223, a question DB 224, a branch DB 225, and a knowledge DB 226. These are databases common to the schedule DB 121, the video DB 122, the vital DB 123, the question DB 124, the branch DB 125, and the knowledge DB 126 stored in the memory 12 of the estimation device 1 shown in the embodiment.

[0106] The processor 21 of the server device 2 may realize any of the functional configurations realized by the processor 11 in the embodiment, instead of the processor 11. For example, the processor 21 may function as an acquisition unit that acquires video data from the estimation device 1. In this case, the processor 11 of the estimation device 1 may transmit the video data to the server device 2 via the communication unit 13, the communication line 3, and the communication unit 23.

[0107] The processor 21 may also function as a measuring means for measuring the pulse rate of the animal based on the video data. The processor 21 may also function as a receiving means for receiving information indicating the state of the animal input by the user in the estimation device 1 via the communication unit 13, the communication line 3, and the communication unit 23.

[0108] In addition, the processor 21 may function as an estimation means for estimating expert knowledge regarding the animal based on a combination of situation information indicating the animal's situation input by a user in the estimation device 1 and the pulse rate measured from video data in the estimation device 1 or the server device 2.

[0109] The acquiring means, the measuring means, the accepting means, and the estimating means may be realized by either the estimation device 1 or the server device 2. As described above, the vital signs are measured values ​​that include at least information on the user's pulse rate.

[0110] In other words, in this modified example, the estimation system 9 having the estimation device 1 and the server device 2 is an example of an estimation system having a receiving means for receiving information indicating the animal's condition from a user, an acquisition means for acquiring a video of the animal's body, a measuring means for measuring the animal's pulse based on the video, and an estimation means for estimating expert knowledge about the animal based on the combination of the information and the pulse.

[0111] In addition, the program executed by the above-mentioned processor 11 or processor 21 is an example of a program for causing a computer to function as a receiving means for receiving information indicating the animal's condition from a user, an acquisition means for acquiring a video of the animal's body, a measurement means for measuring the animal's pulse based on the video, and an estimation means for estimating expert knowledge about the animal based on a combination of the information and the pulse.

[0112] The program for executing each of the above steps may be provided in a state stored in a computer-readable recording medium such as a magnetic recording medium such as a magnetic tape or a magnetic disk, an optical recording medium such as an optical disk, a magneto-optical recording medium, a semiconductor memory, etc. This program may also be downloaded via a communication line such as the Internet.

[0113] Furthermore, the estimation device 1 may change the questions asked to the user depending on the measurement results of vital signs including pulse rate.

[0114] Fig. 15 is a diagram showing an example of the configuration of an estimation device 1 according to a modified example. This estimation device 1 differs from the estimation device 1 according to Fig. 1 in that it does not have a schedule DB 121 and a branch DB 125. In addition, the question DB 124 and the knowledge DB 126 of this estimation device 1 have the following configurations.

[0115] 16 is a diagram showing an example of the question DB 124 according to the modified example. A question text and a plurality of options are described for each question number in the question DB 124. Each of the plurality of options is a combination of an option number and an option explanation that is a sentence explaining the option.

[0116] 17 is a diagram showing an example of knowledge DB 126 according to a modified example. This knowledge DB 126 has a category ID list 1261, a question pattern list 1262, and an answer pattern table 1263. The category ID list 1261 is a list in which combinations of categories of vital sign measurement results are associated with category IDs indicating categories (overall categories) determined according to the combinations.

[0117] The category ID list 1261 shows the category of the results for each measurement item of respiratory rate, pulse, and autonomic nerve as a measurement result category. Here, the autonomic nerve is, for example, an index showing the analysis result of the autonomic nerve derived based on the above-mentioned LF and / or HF, for example, LF / HF. In other words, this measurement item of the autonomic nerve is an index showing the state of the autonomic nerve derived from the result of frequency analysis of the heartbeat interval fluctuation.

[0118] The category ID list 1261 classifies the results of each of the above-mentioned measurement items into a plurality of categories. For example, the categories may be two categories, normal and abnormal, or three categories, "within normal range," "above normal range," and "below normal range." Similarly to the respiratory rate and heart rate, the above-mentioned indicators of the autonomic nerves are also classified into a plurality of categories by comparison with a predetermined threshold, range, formula, etc.

[0119] In the category ID list 1261 shown in FIG. 17, for example, if the respiratory rate category is "C1", the pulse rate category is "D1", and the autonomic nerve category is "E1", the category ID of the general category for this combination will be "C1D1E1".

[0120] The question pattern list 1262 is a list that stores patterns (called question patterns) of the type, order, etc. of questions to be presented according to the general category. This question pattern list 1262 is set for each category ID described in the category ID list 1261.

[0121] For example, the question pattern list 1262 shown in Fig. 17 is associated with the category ID "C1D1E1". This question pattern list 1262 stores question patterns to be presented when the general category is identified by the category ID "C1D1E1". This question pattern is, for example, a list in which the question IDs described in the question DB 124 are arranged in the order in which they are presented. Note that this question pattern may change depending on the answer to each question. In this case, the question DB 124 may include the contents of the branch DB 125 shown in the embodiment.

[0122] The answer pattern table 1263 is a table associated with each question pattern described in the question pattern list 1262 described above. This answer pattern table 1263 has items of answer pattern, product, and knowledge. The answer pattern is an item that lists all answer patterns (answer patterns) that a user may select for one question pattern described in the question pattern list 1262. The answer pattern table 1263 describes product and knowledge data for each of all answer patterns. The product item is an item that describes a product to be recommended corresponding to the answer pattern. The knowledge item is an item that describes expert knowledge corresponding to the answer pattern.

[0123] The estimation device 1 performs an interview by referring to the knowledge DB 126 shown in FIG. 17. For example, when the estimation device 1 determines a general category according to a combination of measurement results, it determines a question pattern corresponding to the general category, and presents questions to the user in order according to the determined question pattern. Then, when the option selected by the user is accepted as an answer, the estimation device 1 identifies an answer pattern from the accepted answer, and identifies information on a product and knowledge associated with the identified answer pattern. Here, one identified answer pattern is included in a plurality of answer patterns corresponding to one question pattern, and the one question pattern corresponds to one general category. That is, when the estimation device 1 determines a combination of measurement results, the estimation device 1 determines a general category and a question pattern. Then, when the estimation device 1 determines a user's answer to questions presented in order according to the determined question pattern, one answer pattern is determined. That is, the estimation device 1 measures an animal, conducts an interview according to the measurement results, and estimates the expert's knowledge from the results of the interview.

[0124] 18 is a diagram showing an example of a functional configuration of the estimation device 1 according to a modified example. In this case, the vital sign measurement unit 114 measures the vital signs of the animal based on the video data, and transmits the results to the presentation unit 112. The presentation unit 112 refers to the question DB 124 and the knowledge DB 126, and presents, on the display unit 15, a question according to the measurement results of the vital signs.

[0125] The situation information receiving unit 115 receives a user's operation from the operation unit 14. This operation includes information on the option selected by the user in response to the presented question. The estimation unit 116 obtains information on the option selected by the user in response to the question from the situation information receiving unit 115, and estimates knowledge about the animal's situation. At this time, the estimation unit 116 identifies knowledge information according to the interview result, and identifies a product to be recommended. The presentation unit 112 obtains the knowledge information and product information from the estimation unit 116, and causes the display unit 15 to present them.

[0126] 19 is a diagram showing an example of the flow of operations of the estimation device 1 according to the modified example. The processor 11 of the estimation device 1 controls the display unit 15 to prompt the user to take a picture of, for example, an animal's paw, and acquires video data of the video from the camera 16 (step S106).

[0127] When the user presses the shooting start button to shoot the pads of the pet's front paws and video data is supplied from the camera 16, the processor 11 analyzes the video data and measures the vital signs (step S107).

[0128] When the results of vital sign measurements are obtained, processor 11 determines a general category according to the combination of the results, and presents a question according to the general category (step S201). When a question is presented and the user operates operation unit 14 to select an option for the question, processor 11 accepts a response according to the operation (step S202).

[0129] Then, processor 11 estimates knowledge and products associated with the answer pattern determined by the option selected for the question (step S108), and displays (presents) information on the knowledge and products (step S109).

[0130] 20 is a diagram showing an example of a display area R10 according to a modified example, in which a medical inquiry and answer area R11, a product description area R12, a price area R13, and a purchase button area R14 are arranged.

[0131] The medical inquiry answer sentence region R11 is a region where a sentence indicating knowledge displayed according to an answer pattern is displayed. This sentence is constructed from knowledge extracted from the knowledge DB 126. The sentence is constructed, for example, by filling blanks provided in the basic sentence with words and phrases indicated by the estimated knowledge.

[0132] The product description region R12 is an area where a sentence indicating a product recommended according to the answer pattern is displayed. This sentence is constructed from product information extracted from the knowledge DB 126. Here, the "product" is a test kit or the like used for the test that the user's pet should undergo according to the interview result.

[0133] The price field R13 is a field for displaying the price of the above-mentioned product. This price is, for example, included in the above-mentioned product information.

[0134] The purchase button area R14 is an area where a button for accepting a user's purchase of a product is displayed. The user can purchase the recommended product by touching this button displayed on a terminal such as a smartphone with his / her finger.

[0135] Therefore, the estimation device of this modified example is an example of an estimation device that presents the user with questions regarding the animal's condition based on the measured pulse rate, accepts the user's response to the questions as information indicating the animal's condition, and presents expert knowledge regarding the estimated animal to the user.

[0136] Moreover, the estimation device according to this modified example is an example of an estimation device that measures the heart rate variability and respiratory rate of an animal based on a video of the animal's body, and presents a question according to a combination of the pulse rate and at least one of the heart rate variability and the respiratory rate.

[0137] Furthermore, the estimation device according to this modified example is an example of an estimation device that estimates an expert's knowledge about animals, identifies a product corresponding to that knowledge, and presents the identified product together with the knowledge to a user. [Explanation of symbols]

[0138] 1...estimation device, 11...processor, 111...time management unit, 112...presentation unit, 113...video acquisition unit, 114...vital measurement unit, 115...situation information reception unit, 116...estimation unit, 12...memory, 121...schedule DB, 122...video DB, 123...vital DB, 124...question DB, 125...branch DB, 126...knowledge DB, 1261...category ID list, 1262...question pattern list, 1263...answer pattern table, 13...communication unit, 14...operation unit, 15...display unit, 16...camera, 2...server device, 21...processor, 22...Memory, 221...Schedule DB, 222...Video DB, 223...Vitals DB, 224...Question DB, 225...Branch DB, 226...Knowledge DB, 23...Communication unit, 3...Communication line, 9...Estimation system, R0...Display area, R1...Cancel icon area, R2...Explanation area, R3...Animal body photography area, R4...Start photography button area, R2...Explanation area, R3...Animal body photography area, R4...Start photography button area, R10...Display area, R11...Questionnaire answer area, R12...Product description area, R13...Price area, R14...Purchase button area.

Claims

1. The method includes receiving information indicating the condition of an animal from a user, measuring the pulse of the animal based on a video of the body of the animal, and estimating expert knowledge about the animal based on a combination of the information and the pulse. Estimation device.

2. A response from the user to a question about the situation is received as the information. The estimation device according to claim 1 .

3. The questions are presented according to a pre-determined schedule. The estimation device according to claim 2 .

4. If the condition is related to diet, determining a tendency of the animal to a type of diet based on the inferred knowledge. The estimation device according to any one of claims 1 to 3.

5. presenting the user with a question regarding the state of the animal in accordance with the measured pulse rate, and accepting the user's response to the question as the information; Presenting the inferred knowledge to the user The estimation device according to claim 1 .

6. The heart rate variability and the respiratory rate of the animal are measured based on the video, and the question is presented according to a combination of the pulse rate and at least one of the heart rate variability and the respiratory rate. The estimation device according to claim 5 .

7. Inferring the knowledge and identifying products corresponding to the knowledge; Present the identified product together with the knowledge to the user. The estimation device according to claim 5 .

8. A receiving means for receiving information indicating the status of the animal from a user; An acquisition means for acquiring a video of the body of the animal; a measuring means for measuring the pulse rate of the animal based on the video; an estimation means for estimating an expert's knowledge about the animal according to a combination of the information and the pulse rate; An estimation system having the following:

9. Computer, A receiving means for receiving information indicating the status of the animal from a user; An acquisition means for acquiring a video of the body of the animal; a measuring means for measuring the pulse rate of the animal based on the video; an estimation means for estimating an expert's knowledge regarding the animal according to a combination of the information and the pulse rate; A program to function as a

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