Information processing system and program

Abstract

To provide an information processing system for identifying a state of an organism according to the progress of states of biomolecules over time.SOLUTION: An information processing system includes: acquisition means for acquiring distribution images indicating both distributions of first physical amount and second physical amount different from the first physical amount in a plurality of biomolecules of a specific organism for the distributions at a plurality of time points; and identification means for identifying a state of the specific organism on the basis of the plurality of distribution images acquired for the distributions at the plurality of time points.SELECTED DRAWING: Figure 8

Description

[Technical Field]

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

[0002] Patent Document 1 discloses an influenza infection test agent and test method that uses the nucleoprotein (NP) of influenza virus as an antigen for detecting AIV antibodies. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-285749 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a technique for diagnosing that a living organism is in a particular state when a predetermined biomolecule is detected from the living organism. Here, it is desirable to identify the state of a living organism according to the change in the state of biomolecules over time.

[0005] The present disclosure aims to identify the state of a living organism according to the change in the state of biomolecules over time. [Means for solving the problem]

[0006] The information processing system of the present disclosure comprises an acquisition means for acquiring distribution images showing the distribution of both a first physical quantity and a second physical quantity different from the first physical quantity for multiple biomolecules of a specific organism at multiple time points, and an identification means for identifying a state of the specific organism based on the multiple distribution images acquired for the distribution at multiple time points, wherein the distribution images are images in which the first physical quantity and the second physical quantity are identified for the multiple biomolecules from molecular images displayed as images showing the biomolecules, and the molecular images are displayed in an area of the distribution image that corresponds to at least the first physical quantity, and the identification by the identification means is performed so as to identify the state depending on which area of the distribution image satisfies a condition defined for a change in the second physical quantity identified from the multiple distribution images. [Effects of the Invention]

[0007] According to the present disclosure, the state of a living organism can be identified based on the change in the state of biomolecules over time. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the overall configuration of a condition specifying system. [Figure 2] FIG. 2 illustrates an example of the hardware configuration of a server device and a terminal. [Figure 3] FIG. 2 illustrates an example of a functional configuration of a server device. [Figure 4] FIG. 10 is a diagram illustrating an example of a biological information management table. [Figure 5] 10 is a flowchart showing the flow of a statistics creation process. [Figure 6] FIG. 10 is a diagram showing an example of how statistical information is created. [Figure 7] FIG. 10 is a diagram showing an example of how statistical information is created. [Figure 8] 10 is a flowchart showing the flow of a state identification process. [Figure 9]10 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. [Figure 10] 10 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. [Figure 11] 10 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. [Figure 12] FIG. 10 is a diagram showing a status notification screen. [Figure 13] 10A and 10B are diagrams showing an example of how the state of a target living organism is identified by the state identification process. [Figure 14] 10A and 10B are diagrams showing an example of how the state of a target living organism is identified by the state identification process. [Figure 15] 10A and 10B are diagrams showing an example of how the state of a target living organism is identified by the state identification process. [Figure 16] 10A and 10B are diagrams showing an example of how the state of a target living organism is identified by the state identification process. [Figure 17] 10 is a flowchart showing the flow of a state identification process according to the second embodiment. [Figure 18] 10A and 10B are diagrams illustrating images displayed on a display unit of a terminal when a state identification process according to a second embodiment is performed. [Figure 19] FIG. 10 is a diagram showing an example of how the state of a target living organism is identified by the state identifying process according to the second embodiment. [Figure 20] (A) is a diagram showing a two-dimensional electrophoresis image showing each protein contained in serum obtained from a horse at a single time point, and (B) is a diagram showing the two-dimensional electrophoresis image of Figure 20(A) divided into multiple regions. [Figure 21] (A) is a graph showing the body temperature of the subject horses, and (B) is a graph showing the amount of amyloid A substance obtained from the subject horses. [Figure 22] This figure shows statistics calculated for the amount of each protein contained in the serum of each subject horse. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. First Embodiment FIG. 1 is a diagram showing an example of the overall configuration of a condition specifying system 1. As shown in FIG. A condition identification system 1, which is an example of an information processing system, is a system that identifies the condition of a living organism. Examples of living organisms include animals, plants, and microorganisms. Examples of animals include terrestrial animals and aquatic animals. Animals also include humans. Plants also include agricultural crops.

[0010] Examples of the state of a living organism include the health state of the living organism, the cosmetic state of the living organism, the physical state of the living organism when the living organism is an animal, the brain state of the living organism when the living organism is an animal, the quality state of the living organism when the living organism is food, and the growth state of the living organism when the living organism is a plant or microorganism. Examples of living organisms that are food include agricultural crops and animals. Examples of the health state of a living organism include the state of injury or illness of the living organism, and the state of fatigue of the living organism. Examples of the physical state of a living organism include the state of height of the living organism, the state of weight of the living organism, the state of body fat percentage of the living organism, the state of muscle mass of the living organism, and the state of the living organism. The state of fatigue of a living organism may also be considered as the physical state of the living organism. Examples of the brain state of a living organism include the state of memory of the living organism, the state of thinking ability of the living organism, and the like. Examples of the quality state of a living organism include the state of taste of the living organism. Furthermore, the growth state of a living organism may include, for example, the state regarding the size of each part or the whole of the organism, the state regarding the color of the organism, the state regarding the smell of the organism, the state regarding the withering of the organism, and the state regarding the bacteria and viruses that the organism possesses.

[0011] The condition specifying system 1 includes a server device 10, a biomolecule analyzer 20, and a terminal 30. The server device 10, the biomolecule analyzer 20, and the terminal 30 are connected via a network.

[0012] The server device 10 compares a biomolecule of one organism with another biomolecule different from the biomolecule, and identifies the state of the one organism based on the comparison result. The organism whose state is identified by the server device 10 may be referred to as a target organism below. Also, the organism that the server device 10 compares with the target organism may be referred to as a comparison organism below. The server device 10 acquires information indicating the physical quantities of biomolecules. Examples of biomolecules include proteins, amino acids, and peptides. Examples of the physical quantities of biomolecules include the amount of substance of the biomolecule, the molecular weight of the biomolecule, the isoelectric point of the biomolecule, and the charge amount of the biomolecule. The isoelectric point is the pH at which the average charge of the biomolecule after ionization is zero. The information indicating the physical quantities of the biomolecules also includes, for example, information indicating the distribution of the physical quantities of the biomolecules. Hereinafter, information indicating the physical quantities of the biomolecules may be referred to as molecular information. Hereinafter, information indicating the physical quantities of the biomolecules of a target organism may be referred to as target molecular information. Hereinafter, information indicating the physical quantities of the biomolecules of a comparison organism may be referred to as comparison molecular information.

[0013] Examples of proteins include hemoglobin, albumin, transferrin, haptoglobin, globulin, lipoprotein, peptide hormone, cytokine, prealbumin, ubiquitin, lactalbumin, glutathione peroxidase, superoxide dismutase, thrombin, prothrombin, actin, acylphosphatase, adrenodoxin, avidin, aldolase, uteroglobin, elastase, ovalbumin, parvalbumin, plastocyanin, relaxin, myoglobin, and leghemoglobin. The protein may be a protein contained in a body fluid such as blood or saliva. The protein may also be a protein contained in serum. The protein may also be a protein contained in a part of a living organism. In other words, the protein may be any protein contained in a component of a living organism. The protein may also be a protein different from any of the above examples. Examples of amino acids include valine, isoleucine, leucine, methionine, lysine, phenylalanine, tryptophan, threonine, histidine, etc. The amino acid may be an amino acid contained in a body fluid such as blood or saliva. The amino acid may also be an amino acid contained in serum. The amino acid may also be an amino acid contained in a part of a living organism. In other words, the amino acid may be any amino acid contained in a component of a living organism. The amino acid may also be an amino acid different from any of the above examples.

[0014] The server device 10 acquires multiple pieces of comparative molecular information. These pieces of comparative molecular information are molecular information that indicate biomolecules at different time points for one comparison organism. The server device 10 also associates the state of the comparison organism over a period that includes these multiple time points with the acquired pieces of comparative molecular information. The server device 10 acquires the above-described pieces of comparative molecular information and associates the state of the comparison organism with the pieces of comparative molecular information for each comparison organism. As a result, the server device 10 stores multiple pieces of comparative molecular information associated with the state of the organism for each comparison organism. The server device 10 also acquires a plurality of pieces of target molecule information, each of which indicates a biomolecule at a different time point for a single target organism.

[0015] The server device 10 detects changes in biomolecules over time from multiple pieces of target molecular information. The server device 10 then compares the detected biomolecules with the biomolecules indicated in the comparative molecular information to extract, from the comparative molecular information stored in the server device 10, comparative molecular information indicating changes in biomolecules corresponding to the changes in biomolecules detected in the target organism. The server device 10 then identifies the state of the comparative organism associated with the extracted comparative molecular information as the state of the target organism. The server device 10 then outputs information regarding the identified state to the terminal 30.

[0016] The server device 10 is realized, for example, by a computer. The server device 10 may be configured by a single computer, or may be realized by distributed processing using multiple computers. Furthermore, the server device 10 may be realized on virtual hardware provided by cloud computing.

[0017] The biomolecule analyzer 20 analyzes biomolecules contained in components collected from a living organism. More specifically, the biomolecule analyzer 20 detects the distribution of biomolecules by analyzing the biomolecules contained in the components collected from a living organism. For example, a two-dimensional electrophoresis device is used as the biomolecule analyzer 20. This two-dimensional electrophoresis device separates each biomolecule contained in the components collected from a living organism by molecular weight and by isoelectric point, thereby creating a two-dimensional electrophoresis image showing the molecular weight distribution and isoelectric point distribution of each biomolecule. In the two-dimensional electrophoresis image, for example, the positions at which pixels constituting the image showing the biomolecules are arranged are defined in a two-dimensional Cartesian coordinate system in which the x-axis represents the distribution of isoelectric points of biomolecules and the y-axis represents the distribution of molecular weights of biomolecules. That is, in the two-dimensional electrophoresis image, information indicating the presence or absence of a biomolecule is written for each pixel coordinate. This two-dimensional electrophoresis image can be regarded as an example of molecular information. Furthermore, information showing the distribution of biomolecules, such as a two-dimensional electrophoresis image, may be referred to as "distribution information" below. Information showing the distribution of biomolecules in a target organism may be referred to as "target distribution information" below. Information showing the distribution of biomolecules in a comparison organism may be referred to as "comparison distribution information" below. The two-dimensional electrophoretic image also includes information indicating the distribution of the amounts of biomolecules. That is, the distribution of biomolecules includes the distribution of molecular weights of biomolecules, the distribution of isoelectric points of biomolecules, the distribution of the presence of biomolecules, and the distribution of the amounts of biomolecules. The information indicating the amounts of biomolecules in the two-dimensional electrophoretic image may be displayed, for example, by the shading of the image showing the biomolecules. For example, a higher density of the image showing the biomolecules in the two-dimensional electrophoretic image may indicate a higher amount of biomolecules.

[0018] Furthermore, a biosensor, for example, may be used as the biomolecule analyzer 20. This biosensor identifies biomolecules in a living organism. The biosensor also separates the identified biomolecules by molecular weight and by isoelectric point, and creates an image showing the distribution of molecular weights and isoelectric points of the biomolecules. Therefore, an image created using the biosensor can also be considered as distribution information. Furthermore, the image created by the biosensor may include information showing the distribution of the substance amounts of the biomolecules.

[0019] In this embodiment, a user of the condition identification system 1 uses the biomolecule analyzer 20 to analyze components of a comparison organism obtained at multiple time points during a single period, and creates comparative distribution information for each of the multiple time points, showing the distribution of each biomolecule contained in the components. Each of these multiple time points is a time point at which the state of the comparison organism is identified. A single period is a period during which a state encompassing the states of the comparison organism at multiple time points is identified. Hereinafter, individual states of the comparison organism at multiple time points may be referred to as individual states. Hereinafter, a state of the comparison organism encompassing the individual states at multiple time points may be referred to as a comprehensive state. An example of a comprehensive state is a state in which the comparison organism has influenza. An example of an individual state is a state in which the comparison organism has influenza, such as a state infected with the influenza virus or a state with a fever. Furthermore, the user creates, for each comparison organism, a plurality of pieces of comparative distribution information, each representing a different time point at which the components of the comparison organism, including the biomolecule, were obtained, using the biomolecule analyzer 20. This allows a plurality of pieces of comparative distribution information, each showing the biomolecules in their individual states at a plurality of time points, to be created for each comparison organism.

[0020] In particular, in this embodiment, the user uses the biomolecular analysis device 20 to analyze the biomolecules obtained at each point in time when multiple comparison organisms have the same comprehensive state and individual state, and creates comparison molecule information. Furthermore, the user analyzes components of the target organism obtained at multiple time points using the biomolecule analyzer 20, and creates target distribution information for each of the multiple time points showing the distribution of each biomolecule contained in the components. These multiple time points are the target time points at which the condition identification system 1 identifies the condition of the target organism.

[0021] When the biomolecule analysis device 20 creates the distribution information, it transmits the created distribution information to the server device 10 together with date and time information indicating the date and time when the biomolecule identified from the distribution information was obtained and bioinformation indicating the organism that has the biomolecule indicated by the distribution information. Note that the organism that "has" the biomolecule includes not only an organism that has the target biomolecule, but also an organism that had the target biomolecule until it was collected, even though the organism no longer has the target biomolecule after the target biomolecule has been collected from the organism.

[0022] The biomolecule analyzer 20 is not limited to one that analyzes each biomolecule contained in a component collected from a living organism. For example, if the biomolecule analyzer 20 can analyze each biomolecule in a living organism and generate distribution information without collecting a component containing the biomolecule from the living organism, such a biomolecule analyzer 20 may be used.

[0023] The terminal 30 has a display unit 31 that displays information. When the terminal 30 acquires information from the server device 10, the terminal 30 displays the acquired information on the display unit 31. The terminal 30 is realized by, for example, a computer, a tablet information terminal, or another information processing device. The terminal 30 may be, for example, a smartphone. That is, the terminal 30 may be any type of terminal.

[0024] The type of network used to connect the server device 10 with the biomolecule analyzer 20 and the terminal 30 is not particularly limited as long as it allows data transmission and reception, and may be, for example, the Internet, a LAN (Local Area Network), a WAN (Wide Area Network), etc. The communication line used to transmit and receive data may be wired or wireless. Furthermore, a configuration in which each device is connected via multiple networks or communication lines may be used.

[0025] <Hardware configuration example> FIG. 2 is a diagram showing an example of the hardware configuration of the server device 10 and the terminal 30. As shown in FIG. 2, the server device 10 and the terminal 30 each include a CPU (Central Processing Unit) 10a, which is a computing means, and a memory 10c, which is a main storage means. Each device also includes external devices such as a nonvolatile storage device 10g, a network interface 10f, a display mechanism 10d, an audio mechanism 10h, and an input device 10i, such as a keyboard or a mouse.

[0026] The memory 10c and the display mechanism 10d are connected to the CPU 10a via the system controller 10b. The network interface 10f, the nonvolatile storage device 10g, the audio mechanism 10h, and the input device 10i are connected to the system controller 10b via the bridge controller 10e. Each component is connected by various buses, such as a system bus and an input / output bus.

[0027] The nonvolatile storage device 10g stores programs for implementing various functions. These programs are loaded into the memory 10c, and the CPU 10a executes processes based on these programs, thereby implementing various functions. Examples of the nonvolatile storage device 10g include semiconductor memories such as SSDs (Solid State Drives) and magnetic disk devices such as HDDs (Hard Disk Drives).

[0028] <Server device functional configuration> Next, the functional configuration of the server device 10 will be described. FIG. 3 is a diagram showing an example of the functional configuration of the server device 10. As shown in FIG. The server device 10 includes an information acquisition unit 101, a memory unit 102, an area identification unit 103, a partition unit 104, a substance quantity identification unit 105, a statistics unit 106, a detection unit 107, a target determination unit 108, a state identification unit 109, and an output unit 110.

[0029] The information acquisition unit 101, which is an example of an acquisition means, acquires distribution information from the biomolecule analysis device 20. More specifically, the information acquisition unit 101 acquires distribution information consisting of target distribution information or comparison distribution information, along with date and time information and bioinformation. The information acquisition unit 101 also acquires information indicating the state of a comparison organism. Information indicating the state of a comparison organism may be referred to as state information below. Information indicating the individual states of a comparison organism may be referred to as individual state information below. Information indicating the overall state of a comparison organism may be referred to as overall state information below. A user of the condition identification system 1 inputs individual state information corresponding to each piece of comparative distribution information into the terminal 30 for each piece of comparative distribution information. The individual state information corresponding to the comparative distribution information is information indicating the state of each individual state at the time when the biomolecule indicated in the comparative distribution information was obtained. The user also inputs comprehensive state information corresponding to multiple pieces of comparative distribution information into the terminal 30. The comprehensive state information corresponding to multiple pieces of comparative distribution information is information indicating the comprehensive state over a period including multiple time points at which the biomolecules indicated in the multiple pieces of comparative distribution information were obtained.

[0030] When the information acquisition unit 101 acquires the comparative distribution information and the state information, the information acquisition unit 101 stores the acquired state information in the storage unit 102 in association with the comparative distribution information corresponding to this state information. In this embodiment, as described above, for multiple comparison organisms whose comprehensive state and individual state are the same, comparative molecular information is created for each time point when the individual state was in that state. Therefore, even if the comparative distribution information relates to different comparison organisms, these comparative molecular information are associated with individual state information indicating the same individual state and comprehensive state information indicating the same comprehensive state. Note that the information acquisition unit 101 can also be considered as molecular information acquisition means for acquiring molecular information.

[0031] The storage unit 102 stores the distribution information, date and time information, biological information, status information, etc. acquired by the information acquisition unit 101. The contents stored in the storage unit 102 will be described in detail later.

[0032] The region identification unit 103 identifies a region in the distribution information where a biomolecule exists. The region identification unit 103 uses both the target distribution information and the comparison molecule information as targets for region identification. An example of a method for identifying regions where biomolecules exist in distribution information by the region identification unit 103 will be described below. The region identification unit 103 detects images showing biomolecules in the distribution information and identifies the coordinates where the detected images are shown, thereby identifying the regions where biomolecules exist. When multiple biomolecules exist in the distribution information, the region identification unit 103 identifies a region in which the biomolecule exists for each existing biomolecule. Furthermore, the region identification unit 103 identifies a region in which the biomolecule exists for each distribution information. When the region identification unit 103 identifies a region in which the biomolecule exists in the distribution information of the target, it associates region information indicating the identified region with the distribution information of the target.

[0033] The partitioning unit 104 partitions regions in the distribution information where biomolecules exist. More specifically, the partitioning unit 104 partitions regions in the molecular information where biomolecules exist, based on the region information created by the region identification unit 103. The partitioning unit 104 also partitions regions in which biomolecules exist for each piece of distribution information. The partitioning unit 104 may partition a region in which one biomolecule exists in the molecular information into a single region, or may divide the region into multiple regions. The partitioning unit 104 may also partition a region in the distribution information where no biomolecules exist. The regions partitioned by the partitioning unit 104 may be referred to as partitioned regions hereinafter. The dividing unit 104 divides the regions based on both the target distribution information and the comparative molecule information.

[0034] The substance amount specifying unit 105 specifies the substance amount of the biomolecule indicated in the distribution information. More specifically, the substance amount specifying unit 105 specifies the substance amount of the biomolecule included in the partitioned region in the distribution information for each partitioned region. After specifying the substance amount of the biomolecule included in a target partitioned region in the distribution information, the substance amount specifying unit 105 associates substance amount information indicating the specified substance amount with the target partitioned region. The substance amount specifying unit 105 uses both the target distribution information and the comparison molecule information as targets for specifying the substance amount of the biomolecule.

[0035] The statistics section 106 calculates statistics of the substance amounts of biomolecules indicated in each of the comparative distribution information associated with the same individual state information. The statistics unit 106 extracts comparative distribution information associated with the same individual state information from the comparative distribution information stored in the storage unit 102. Then, the statistics unit 106 calculates, for each partitioned region, statistics of the amounts of substance of biomolecules included in the partitioned region in each of the extracted comparative distribution information. More specifically, the statistics unit 106 calculates statistics of the amounts of substance of biomolecules included in partitioned regions with the same coordinates in each of the extracted comparative distribution information based on the amount of substance information associated with the partitioned region. Furthermore, the statistics unit 106 calculates statistics of the amounts of substance of biomolecules for each of the comparative distribution information associated with the same individual state information. In this way, statistics of the amounts of substance of biomolecules are calculated for each individual state.

[0036] The statistics unit 106 may calculate statistics of the amounts of substance of biomolecules by calculating the average values of the amounts of substance of the biomolecules that are the subject of statistics. Alternatively, the statistics unit 106 may calculate statistics of the amounts of substance of biomolecules by calculating the median values of the amounts of substance of the biomolecules that are the subject of statistics. In this way, any method may be used as the statistical method used by the statistics unit 106. Furthermore, when the statistics unit 106 calculates statistics of the substance amounts of biomolecules for each individual state included in one comprehensive state, it associates each calculated statistic with the corresponding individual state information. As a result, information indicating the statistics calculated by the statistics unit 106 is created for each individual state. Hereinafter, the information indicating the statistics calculated by the statistics unit 106 for each individual state may be referred to as statistical information. The statistics unit 106 associates the created statistical information with partitioned region information that identifies the partitioned region that is the subject of this statistical information, and stores the created statistical information in the storage unit 102. Statistical information is information that indicates the statistical amounts of biomolecules, and therefore, statistical information can also be broadly considered as molecular information.

[0037] The detection unit 107 detects changes in biomolecules of a target organism. The detection unit 107 compares the amounts of substance of biomolecules indicated in the two pieces of target distribution information based on the substance amount information associated with the two pieces of target distribution information. The two pieces of target distribution information are distribution information indicating biomolecules obtained from a target organism at different times. Of the two pieces of target distribution information to be compared, the one indicating the biomolecule obtained earlier may be referred to as "early distribution information" below. Of the two pieces of target distribution information to be compared, the one indicating the biomolecule obtained later may be referred to as "late distribution information" below. The target biomolecules to be compared are biomolecules included in partitioned regions with the same coordinates in the two pieces of target distribution information.

[0038] The detection unit 107 determines whether the biomolecules being compared satisfy a change condition. The change condition is a condition established for detecting changes in biomolecules accompanying changes in the state of the target organism. In this embodiment, the change condition is established as the difference in the amount of substance between the two biomolecules being compared being equal to or greater than a predetermined value. The detection unit 107 detects two biomolecules that satisfy the change condition as biomolecules whose amount of substance has changed. Note that, hereinafter, the two biomolecules detected by the detection unit 107 as satisfying the change condition may be referred to as detected molecules. Also, hereinafter, the difference in the amount of substance between the two biomolecules detected by the detection unit 107 may be referred to as a detection value. When biomolecules in multiple divided regions satisfy the change condition, the detection unit 107 detects all of the biomolecules in the multiple divided regions.

[0039] The target determining unit 108 determines, from the statistical information stored in the storage unit 102, statistical information to be compared with the detection molecule. The target determination unit 108 determines statistical information for a partitioned area having the same coordinates as the partitioned area in which the detected molecule is displayed as statistical information to be compared with the detected molecule. The target determination unit 108 references partitioned area information associated with the statistical information and extracts statistical information for a partitioned area having the same coordinates as the partitioned area in which the detected molecule is displayed. Furthermore, when the detected molecule is detected in multiple partitioned areas, the target determination unit 108 determines statistical information to be compared for each detected molecule. Note that the statistical information determined by the target determination unit 108 to be compared with the detected molecule may be referred to as comparison statistical information hereinafter.

[0040] The state specifying unit 109, which is an example of a specifying means, specifies the state of the target living organism. The state identification unit 109 extracts biomolecules that satisfy the identity similarity condition from among the biomolecules indicated in the comparison statistical information. The identity similarity condition is a condition established to identify changes in biomolecules that are identical or similar to changes in the amount of substance of the biomolecules indicated by the detection value of the detected molecule. In this embodiment, the identity similarity condition is established such that the changes in the amount of substance of the biomolecules indicated in the comparison statistical information and the changes in the amount of substance of the biomolecules indicated by the detection value are identical or similar. An example of an identity similarity condition is that the difference between the change in the amount of substance of the biomolecule indicated in the comparison statistical information and the detection value is less than or equal to a predetermined value. The state identification unit 109 identifies the comprehensive state associated with the comparison statistical information that satisfies the identity similarity condition as the state of the target organism. Comparison statistical information that satisfies the identity similarity condition is comparison statistical information that indicates biomolecules that satisfy the identity similarity condition.

[0041] The output unit 110, which is an example of an output means, outputs information relating to the state identified by the state identification unit 109 to the terminal 30.

[0042] <Storage contents of memory unit> Next, the contents of the information stored in the storage unit 102 will be described. 4(A) and 4(B) are diagrams showing examples of biometric information management tables. The biometric information management table is a table for managing information about a comparison organism. This biometric information management table is provided for each type of organism. FIG. 4(A) shows a biometric information management table for "humans," a type of animal, and FIG. 4(B) shows a biometric information management table for "bananas," a type of agricultural crop.

[0043] In the biological information management table shown in FIG. 4(A), the name of the comparison biological body is shown in the "name" field. Additionally, the "date and time" indicates date and time information. Furthermore, comparative distribution information is shown in "distribution." More specifically, "distribution" shows the distribution of the physical quantities of biomolecules identified from the distribution information for each biomolecule.

[0044] In addition, individual condition information is shown in "individual condition." In this embodiment, the "individual condition" is divided into four items: "health condition," "physical condition," "brain condition," and "beauty condition." "Health condition" indicates the health condition of the comparison organism. "Physical condition" indicates the physical condition of the comparison organism. "Brain condition" indicates the brain condition of the comparison organism. "Cosmetic condition" indicates the cosmetic condition of the comparison organism. "Individual condition" may indicate the growth condition of the comparison organism. Additionally, "Comprehensive Status" indicates comprehensive status information.

[0045] The "individual status" and "comprehensive status" in the biological information management table shown in FIG. 4(A) will be specifically described. For example, the comparative distribution information for organism "A" is associated with the "general condition" "influenza," as well as "health conditions" such as "infection" and "fever." "Influenza" is the condition in which the comparative organism has influenza. "Infection" is the condition in which the comparative organism is infected with the influenza virus. "Fever" is the condition in which the comparative organism has a fever. For example, the comparative distribution information of organism "H" is associated with the "comprehensive state" of "anticancer drug effective," as well as "health states" such as "anticancer drug intake" and "fatigue." "Anticancer drug effective" is a state in which the comparative organism is experiencing the effects of taking an anticancer drug. "Anticancer drug intake" is a state in which the comparative organism has taken an anticancer drug. "Fatigue" is a state in which the comparative organism is experiencing fatigue. For example, the comparative distribution information of organism "I" is associated with the "general state" of "no effect of anticancer drug," as well as "health states" such as "taking anticancer drug" and "headache." "No effect of anticancer drug" is a state in which the comparative organism has no effect from taking anticancer drug. "Headache" is a state in which the comparative organism has a headache.

[0046] For example, the comparative distribution information of organism "J" is associated with the "general state" "training effective," as well as "physical states" such as "training performed" and "muscle pain." "Training effective" is a state in which the comparative organism has experienced the effects of training. "training performed" is a state in which the comparative organism has trained. "muscle pain" is a state in which the comparative organism has experienced muscle pain. For example, the comparative distribution information of organism "J" is associated with the "general state" of "no training effect," as well as "physical states" such as "training performed" and "fatigue." "No training effect" is a state in which the comparative organism has no effect from training. "Fatigue" is a state in which the comparative organism feels fatigued. For example, the comparative distribution information of organism "K" is associated with "memory improvement," which is a "comprehensive state," as well as with "physical states" such as "8 hours of sleep" and "brain states" such as "3 hours of study." "Memory improvement" is a state in which the memory of the comparison organism has improved. "8 hours of sleep" is a state in which the comparison organism has slept for 8 hours. "3 hours of study" is a state in which the comparison organism has studied for 3 hours. For example, the comparative distribution information of organism "L" is associated with "memory decline" which is a "comprehensive state," as well as with "physical states" such as "4 hours of sleep" and "brain states" such as "3 hours of study." "Memory decline" is a state in which the memory of the comparative organism has declined. "4 hours of sleep" is a state in which the comparative organism has slept for 4 hours.

[0047] For example, the comparative distribution information of organism "M" is associated with a "general condition" of "skin age 40 years old" as well as "beauty condition" such as "use of skin cream A." "Skin age 40 years old" is a condition in which the skin age of the comparative organism is identified as 40 years old. "Use of skin cream A" is a condition in which the comparative organism has used "skin cream A." For example, the comparative distribution information of organism "M" is associated with a "general condition" of "skin age 25 years old" as well as "beauty condition" such as "use of skin cream B." "Skin age 25 years old" is a condition in which the skin age of the comparative organism is identified as 25 years old. "Use of skin cream B" is a condition in which the comparative organism has used "skin cream B."

[0048] In the biometric information management table shown in Fig. 4(B), the name that identifies the comparison organism is shown in "identification name." In the illustrated example, the "identification name" shows the name that identifies each "banana." Additionally, the "date and time" indicates date and time information. In addition, in the "Distribution" section, comparative distribution information is shown, similar to the example in FIG. 4(A). Furthermore, "individual condition" indicates individual condition information. In this embodiment, "quality condition" is indicated as "individual condition." "Quality condition" indicates the quality condition of the comparison living body. Additionally, "Comprehensive Status" indicates comprehensive status information.

[0049] The "individual status" and "comprehensive status" in the biological information management table shown in FIG. 4(B) will be specifically described. The comparative distribution information for organism "X" is associated with the "overall state" of "good quality" as well as with "quality states" such as "sugar content 20 degrees." "Good quality" is a state in which the quality of the comparative organism is identified as good. Furthermore, "sugar content 20 degrees" is a state in which the sugar content of the comparative organism is identified as 20 degrees. Furthermore, the comparative distribution information for organism "Y" is associated with the "general state" of "poor quality," as well as with "quality states" such as "sugar content 10 degrees." "Poor quality" is a state in which the quality of the comparative organism is identified as poor. Furthermore, "sugar content 10 degrees" is a state in which the sugar content of the comparative organism is identified as 10 degrees. Although not shown in FIGS. 4A and 4B, the storage unit 102 is provided with a biometric information management table for each type of comparison biometric.

[0050] <Statistics creation process> Next, the flow of the statistics creation process will be described with reference to an example up to the creation of statistical information. The statistics creation process is a process in which the server device 10 creates statistical information. In this embodiment, the statistics creation process starts when a user of the condition identification system 1 designates one comprehensive state and instructs the server device 10 to execute the statistics creation process. Note that, hereinafter, one comprehensive state designated by the user may be referred to as a designated state. In addition, hereinafter, a case in which the designated state is "influenza" will be described. Fig. 5 is a flowchart showing the flow of the statistics creation process, and Figs. 6 and 7 are diagrams showing an example of the process up to the creation of statistical information.

[0051] First, the server device 10 designates one of the comparison biometrics that were in the designated state (step (hereinafter referred to as "S") 101). The server device 10 identifies the comparison biometric that was in the designated state from the biometric information associated with comprehensive state information that indicates the designated state. Note that the one comparison biometric designated by the server device 10 in step 101 may be referred to as the designated biometric below. Next, the server device 10 designates one of the comparative distribution information associated with the comprehensive state information indicating the designated state (S102). The comparative distribution information designated by the server device 10 in step 102 is comparative distribution information indicating the biomolecules of the designated organism. In addition, the comparative distribution information designated by the server device 10 in step 102 may be referred to as designated distribution information hereinafter.

[0052] In this example, as described above, the designated state is "influenza." In this case, when the designated organism becomes organism "A," comparative distribution information for organism "A" associated with "influenza" is designated in step 102, as shown in FIG. 6(A). The designated distribution information shown in FIG. 6(A) is one of the comparative distribution information associated with organism "A" and "influenza" in the biological information management table for "humans" (see FIG. 4(A)). This designated distribution information is information indicating the distribution of the substance amount of a biomolecule when the isoelectric point of the biomolecule is the horizontal axis and the molecular weight of the biomolecule is the vertical axis. In addition, in the designated distribution information shown in FIG. 6(A), an image indicated by diagonal lines and lines surrounding the diagonal lines represents a biomolecule. The distribution information shown in FIG. 6(A) shows seven different biomolecules. In the illustrated example, the biomolecules are spaced apart in the distribution information, but multiple biomolecules may be adjacent to each other in the distribution information.

[0053] Next, the region identifying unit 103 identifies a region in which a biomolecule exists in the designated distribution information (S103). The partitioning unit 104 partitions the area identified by the area identifying unit 103 in the designated distribution information (S104). In this example, in step 104, the partitioning unit 104 partitions the region in which the biomolecule is indicated in the designated distribution information into twenty regions consisting of region 1 to region 20, as shown in FIG. 6(B). In the example shown in FIG. 6(B), one biomolecule is partitioned into five regions consisting of region 1 to region 5. One biomolecule is also partitioned into one region consisting of region 6. One biomolecule is also partitioned into one region consisting of region 7. One biomolecule is also partitioned into six regions consisting of region 8 to region 13. One biomolecule is also partitioned into two regions consisting of region 14 and region 15. One biomolecule is also partitioned into four regions consisting of region 16 to region 19. One biomolecule is also partitioned into one region consisting of region 20.

[0054] Next, the substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each divided region (S105). In this example, in step 105, the substance amount determination unit 105 determines the substance amount of the biomolecule contained in the partitioned region, as shown in FIG. 6(C). FIG. 6(C) is a diagram showing biomolecules present in region 20, a partitioned region, with the time at which the biomolecule was obtained represented on the horizontal axis and the substance amount of the biomolecule represented on the vertical axis. In FIG. 6(C), the symbol "●" represents the biomolecule of organism "A." The biomolecule shown in FIG. 6(C) is associated with one of the individual states, "infection." This "infection" is one of the individual states (see FIG. 4(A)) associated with the comparative distribution information showing the biomolecule of organism "A" identified in FIG. 6(C). Note that in the example of FIG. 6(C), only the biomolecule in region 20 is shown, but in step 105, the substance amount of the biomolecule is determined for each of regions 1 to 20, i.e., for all of the partitioned regions in the designated distribution information.

[0055] Next, the server device 10 determines whether or not the substance amount of the biomolecule has been specified for all the comparative distribution information related to the designated organism that is associated with comprehensive state information indicating the designated state (S106). If the substance amount of the biomolecule is not specified for any of the comparative distribution information associated with the comprehensive state information indicating the specified state (NO in S106), the server device 10 newly specifies one of the comparative distribution information for which the substance amount is not specified (S102). Then, the processing from step 103 onwards is performed.

[0056] In this way, the processes from step 102 onward are repeated until the amounts of substance of biomolecules are identified for all comparative distribution information associated with comprehensive state information indicating the designated state among the comparative distribution information related to the designated organism. As a result, in this example, as shown in FIG. 6(D), the amounts of substance of biomolecules present in the partitioned areas are identified for all comparative distribution information associated with "influenza" identified for organism "A." FIG. 6(D) is a diagram showing biomolecules present in area 20 at each time point at which biomolecules were obtained during the period identified as "influenza," with the horizontal axis representing the time at which the biomolecules were obtained and the vertical axis representing the amount of substance of the biomolecules. Each biomolecule shown in FIG. 6(D) is associated with an individual state. This individual state is the individual state (see FIG. 4(A)) associated with the comparative distribution information showing the biomolecules of organism "A" identified in FIG. 6(D). Each individual state also indicates the number of days, from "1 day" to "14 days." This number of days refers to the number of days since organism "A" was infected with the influenza virus. In the example of FIG. 6(D), only the biomolecules in region 20 are shown, but by repeating steps 102 to 106, the amounts of substance of the biomolecules in all the divided regions are identified.

[0057] When the substance amounts of the biomolecules are identified for all of the comparative distribution information related to the designated organism that is associated with comprehensive state information indicating the designated state (YES in S106), the server device 10 proceeds to the next step: S107, where the server device 10 determines whether the substance amounts of the biomolecules in the comparative distribution information are identified for all of the comparison organisms that were in the designated state. If the substance amount of the biomolecule in the comparison distribution information is not specified for any of the comparison organisms that were in the designated state (NO in S107), the server device 10 newly designates one comparison organism for which the substance amount of the biomolecule in the comparison distribution information is not specified (S101). After this, the processing from step 102 onwards is performed.

[0058] In this manner, the processing from step 101 onward is repeated until the amount of substance of the biomolecule in the comparative distribution information is determined for all comparison organisms that were designated. As a result, in this example, as shown in FIG. 7(E), the amount of substance of the biomolecule present in the partitioned area is determined for all comparative distribution information associated with "influenza" for all comparison organisms designated, namely, organisms "A," "B," and "C." FIG. 7(E) shows the biomolecules present in area 20 for organisms "A," "B," and "C" at each time point at which the biomolecule was obtained during the period identified as "influenza." In FIG. 7(E), the horizontal axis represents the time point at which the biomolecule was obtained, and the vertical axis represents the amount of substance of the biomolecule. Also, in FIG. 7(E), the symbol "●" represents the biomolecule of organism "A," the symbol "▲" represents the biomolecule of organism "B," and the symbol "■" represents the biomolecule of organism "C." Furthermore, the amount of substance of the biomolecules of organism "A," the amount of substance of the biomolecules of organism "B," and the amount of substance of the biomolecules of organism "C" are each associated with the same individual state information. This individual state is the individual state (see FIG. 4(A)) associated with the comparative distribution information showing the biomolecules of organism "A," organism "B," and organism "C" identified in FIG. 7(E). Furthermore, in the example of FIG. 7(E), only the biomolecules in region 20 are shown, but by repeating steps 101 to 107, the amounts of substance of the biomolecules in all partitioned regions can be identified.

[0059] When the amounts of substance of biomolecules in the comparison distribution information are identified for all comparison organisms that were in the specified state (YES in S107), the statistics unit 106 calculates statistics of the amounts of substance of biomolecules associated with the same state among the individual states. More specifically, the statistics unit 106 calculates statistics of the amounts of substance of biomolecules associated with the same state among the individual states for each individual state, thereby creating statistical information indicating statistics of the amounts of substance of biomolecules for each individual state (S108). In addition, the statistics unit 106 creates statistical information for each partitioned region. In this example, the statistical unit 106 calculates statistics of the amounts of substance of biomolecules of organism "A", organism "B", and organism "C" that are associated with the same state among the individual states, for each individual state, thereby creating the statistical information shown in Fig. 7(F). Fig. 7(F) shows statistics of the amounts of substance of biomolecules in region 20. This statistical information shows statistics of the amounts of substance of biomolecules of each of organism "A", organism "B", and organism "C" for each individual state.

[0060] <Status identification process> Next, the flow of the condition identification process will be described with reference to an example up to the point where information on the condition identified by the condition identification process is output to the terminal 30. The condition identification process is a process in which the server device 10 identifies the condition of the target living organism. In this embodiment, the condition identification process is started when a user of the condition identification system 1 specifies target distribution information to be used in the condition identification process and instructs the server device 10 to execute the condition identification process. Note that the target distribution information specified by the user as the target distribution information to be used in the condition identification process may be referred to as usage distribution information hereinafter.

[0061] Fig. 8 is a flowchart showing the flow of the state identification process. Figs. 9 to 12 are diagrams showing an example of the process up to when information relating to the state identified in the state identification process is output to the terminal 30. In the following, an example will be described in which the target organism is organism "D" and the two pieces of target distribution information shown in Figure 9(A) are usage distribution information. Figure 9(A) shows early distribution information and late distribution information for organism "D" as usage distribution information. This early distribution information and late distribution information each show the distribution of seven different biomolecules, similar to the distribution information shown in Figure 6(A). Furthermore, organism "D" is, for example, a human being.

[0062] First, the region identifying unit 103 identifies a region in which a biomolecule exists in the usage distribution information for each piece of usage distribution information (S201). The partitioning unit 104 partitions the areas identified by the area identification unit 103 for each piece of usage distribution information (S202). In this example, in step 202, the partitioning unit 104 partitions the regions in which biomolecules are shown in the early distribution information and the later distribution information into 20 regions consisting of regions 1 to 20, as shown in Figure 9(B). That is, the partitioning unit 104 partitions the regions in which biomolecules are shown in the usage distribution information in the same manner as the partitioning performed on the comparison distribution information (see Figure 6(B)).

[0063] Next, the substance amount identification unit 105 identifies the substance amount of the biomolecule contained in each partitioned region for each use distribution information (S203). As a result, in this example, as shown in FIG. 9(C), the substance amounts of the biomolecules indicated in the early distribution information and the later distribution information for organism "D" are identified for each partitioned region. FIG. 9(C) is a diagram showing the biomolecules present in each partitioned region, with the horizontal axis representing the time at which the biomolecule was obtained and the vertical axis representing the amount of substance of the biomolecule. In FIG. 9(C), the symbol "●" indicates a biomolecule indicated in the early distribution information, and the symbol "▲" indicates a biomolecule indicated in the later distribution information.

[0064] Next, the detection unit 107 detects biomolecules that satisfy the change condition (S204). More specifically, the detection unit 107 determines whether or not the biomolecules satisfy the change condition for each partitioned area, and detects biomolecules that are determined to satisfy the change condition. In this example, in regions 14, 15, and 20 shown in FIG. 9(C), the amounts of substance of biomolecules indicated in the later distribution information have changed from the amounts of substance of biomolecules indicated in the earlier distribution information. More specifically, in region 14, the amounts of substance of biomolecules indicated in the later distribution information have increased compared to the amounts of substance of biomolecules indicated in the earlier distribution information. Furthermore, in regions 15 and 20, the amounts of substance of biomolecules indicated in the later distribution information have decreased compared to the amounts of substance of biomolecules indicated in the earlier distribution information. The biomolecules contained in regions 14, 15, and 20 are all detected by detection unit 107 as satisfying the change condition.

[0065] Note that the multiple biomolecules that satisfy the change condition, i.e., the biomolecules contained in regions 14 and 15 and the biomolecules contained in region 20, are different biomolecules. An example of different biomolecules is hemoglobin and transferrin. Another example of different biomolecules is hemoglobin HbA and hemoglobin HbA2. In other words, different biomolecules may be different as long as the molecules that make up the biomolecules are not identical.

[0066] Next, the target determining unit 108 determines the comparison statistical information to be compared with the detected molecule (S205). The target determining unit 108 determines, from the statistical information stored in the storage unit 102, statistical information that targets a partitioned area having the same coordinates as the partitioned area in which the detected molecule is indicated, as the comparison statistical information. In this example, the target determining unit 108 determines each piece of statistical information targeting region 20 as comparative statistical information for the detection molecules shown in region 20 for organism "D." This comparative statistical information also includes statistical information associated with the inclusive state "influenza" and targeting region 20, as shown in FIG. 10(D). Furthermore, although not shown, the target determining unit 108 determines each piece of statistical information targeting region 15 as comparative statistical information for the detection molecules shown in region 15 for organism "D." Furthermore, the target determining unit 108 determines each piece of statistical information targeting region 14 as comparative statistical information for the detection molecules shown in region 14 for organism "D."

[0067] Next, the state identification unit 109 extracts biomolecules that satisfy the same or similar condition from among the biomolecules indicated in the comparison statistical information (S206). More specifically, the state identification unit 109 compares the detected molecules with the biomolecules indicated in the comparison statistical information to extract biomolecules that satisfy the same or similar condition from among the biomolecules indicated in the comparison statistical information. In this example, as shown in FIG. 10(E), the state identification unit 109 determines that the biomolecules shown in the two pieces of comparative statistical information both satisfy the same-similarity condition with respect to the detected molecules included in region 20 for organism "D." One of these two pieces of comparative statistical information is comparative statistical information associated with the generic state "influenza" and targeting region 20. The dotted-line portion A in this comparative statistical information shows a decrease in the amount of substance of the biomolecule over time, and the state identification unit 109 determines that the biomolecule shown in this portion A satisfies the same-similarity condition with respect to the detected molecules included in region 20 for organism "D." Note that this comparative statistical information associated with "influenza" and targeting region 20 is the statistical information shown in FIG. 7(F). The other of the two pieces of comparative statistical information is comparative statistical information associated with the generic state "COVID (Corona Virus Disease)-19" and targeting region 20. The dotted line point B in the figure of this comparison statistical information also shows a decrease in the amount of substance of the biomolecule over time, and the state identification unit 109 determines that the biomolecule shown in this point B satisfies the same similarity condition with the detected molecule contained in area 20 for organism "D."

[0068] In this way, it may be determined that the biomolecules indicated in the multiple pieces of comparative statistical information all satisfy the same or similar condition for the detected molecules contained in a single partitioned area. Additionally, influenza virus and COVID-19 have in common the fact that they are both infectious diseases, and specific biomolecules in a target organism may show the same or similar changes when the target organism is infected with influenza virus and when the target organism is infected with COVID-19. In this case, the state identification unit 109 compares the biomolecules indicated in the extracted comparative statistical information with the detected molecules contained in partitioned areas that have not yet been compared. In this example, as shown in FIG. 11(F), the state identification unit 109 compares the detection molecules shown in region 14 for organism "D" with the biomolecules shown in the comparative statistical information associated with "influenza" and targeting region 14. The state identification unit 109 also compares the detection molecules shown in region 14 for organism "D" with the biomolecules shown in the comparative statistical information associated with "COVID-19" and targeting region 14. The state identification unit 109 also compares the detection molecules shown in region 15 for organism "D" with the biomolecules shown in the comparative statistical information associated with "influenza" and targeting region 15. The state identification unit 109 also compares the detection molecules shown in region 15 for organism "D" with the biomolecules shown in the comparative statistical information associated with "COVID-19" and targeting region 15.

[0069] Here, the dashed line portion C in the comparative statistical information associated with "influenza" and targeting region 14 shows an increase in the amount of substance of the biomolecule over time. The state identifying unit 109 then determines that the biomolecule shown in this portion C satisfies the same similarity condition with respect to the detected molecule shown in region 14 for organism "D." Furthermore, the dashed line portion D in the comparative statistical information associated with "influenza" and targeting region 15 shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 then determines that the biomolecule shown in this portion D satisfies the same similarity condition with respect to the detected molecule shown in region 15 for organism "D." On the other hand, the comparative statistical information associated with "COVID-19" and targeting region 14 indicates that the quantity of substance of the biomolecule does not change over time. The state identifying unit 109 then determines that this biomolecule does not satisfy the same-similar condition with respect to the detected molecule shown in region 14 for organism "D." The comparative statistical information associated with "COVID-19" and targeting region 15 also indicates that the quantity of substance of the biomolecule does not change over time. Therefore, the state identifying unit 109 then determines that this biomolecule also does not satisfy the same-similar condition with respect to the detected molecule shown in region 15 for organism "D." In this case, the state identification unit 109 extracts the biomolecule indicated in the statistical information associated with "influenza" as a biomolecule that satisfies the same or similar condition. Note that the comparison statistical information indicating the biomolecule extracted by the state identification unit 109 as satisfying the same or similar condition may be referred to as extracted statistical information hereinafter.

[0070] Next, the state identification unit 109 identifies the state of the target organism from the extracted statistical information. More specifically, the state identification unit 109 identifies the comprehensive state associated with the extracted statistical information as the state of the target organism (S207). Note that the state of the target organism identified by the state identification unit 109 may be referred to as a specific state hereinafter. In this example, the condition identifying unit 109 identifies "influenza," which is a comprehensive condition associated with the extracted statistical information, as the condition of the living body "D."

[0071] In this example, it has been explained that the biomolecules shown in the comparative statistical information associated with "influenza" and the biomolecules shown in the comparative statistical information associated with "COVID-19" both satisfy the same similarity conditions for the detected molecules included in region 20.

[0072] Here, Figure 11(G) shows biomolecules included in region 7 of the early distribution information and the late distribution information, comparative statistical information associated with "influenza" and targeting region 7, and comparative statistical information associated with "COVID-19" and targeting region 7. In each diagram shown in Figure 11(G), the horizontal axis represents the time point at which the biomolecule was obtained, and the vertical axis represents the amount of substance of the biomolecule. Also, in Figure 11(G), the symbol "●" represents a biomolecule shown in the early distribution information, and the symbol "▲" represents a biomolecule shown in the late distribution information. As shown in FIG. 11(G), in region 7 for organism "D," there is no change between the amount of substance of the biomolecules shown in the early distribution information and the amount of substance of the biomolecules shown in the later distribution information. Furthermore, in the comparative statistical information associated with "influenza" and targeting region 7, there is no change in the amount of substance of the biomolecules over time. On the other hand, the comparative statistical information associated with "COVID-19" and targeting region 7 shows an increase in the amount of substance of the biomolecules over time at dashed point E in the figure. Therefore, when, for organism "D," the biomolecules in region 20 show the change at point B in FIG. 10(E) and the biomolecules in region 7 show the change at point E in FIG. 11(G), the state identification unit 109 identifies the state of organism "D" as "COVID-19."

[0073] In this embodiment, the state identifying unit 109 identifies the state of the target organism as "influenza" when a biomolecule indicated in the comparative statistical information associated with "influenza" satisfies the same-similar condition with respect to each of the detected molecules included in region 20, region 15, and region 14. Here, there are cases where a biomolecule indicated in the comparative statistical information associated with "influenza" satisfies the same-similar condition with respect to the detected molecules included in region 20 and region 15, but does not satisfy the same-similar condition with respect to the detected molecules included in region 14. In this case, the state of the target organism may be identified as a state other than "influenza."

[0074] Next, the output unit 110 outputs the information about the specific state to the terminal 30 (S208). In this example, the output unit 110 displays a status notification screen 40 on the display unit 31 of the terminal 30, as shown in Fig. 12. The status notification screen 40 is a screen for notifying the user of a specific status. The status notification screen 40 displays a biometric identification section 41, a status notification section 42, a timing notification section 43, and a future status notification section 44. In the following description, it is assumed that the date on which the status notification screen 40 is displayed is the same as the date on which the biomolecules indicated in the later distribution information were obtained.

[0075] Information for identifying the target living organism is displayed in the biometric identification section 41. In the illustrated example, the biometric identification section 41 displays the text "Mr. D" including information for identifying the target living organism. The specific status is displayed in the status notification section 42. In the illustrated example, the status notification section 42 displays the text "Current Status" and the text "Influenza", which is the specific status.

[0076] The time notifying unit 43 indicates the time when the target organism entered the specific state. In the illustrated example, the time notifying unit 43 displays the text "time when current state was reached" and the text "one day ago," which is the time when the target organism entered the specific state. The time when the target organism entered the specific state is identified by the state identifying unit 109 based on the relationship between the time when the comparison organism entered the inclusive state associated with the extracted statistical information and the time when a biomolecule satisfying the identical similarity condition was obtained. In this example, in the extracted statistical information shown in FIG. 7(F), the time point when the biomolecule satisfying the identical similarity condition, i.e., the biomolecule shown at dashed line point A in the figure, was obtained is the time point "1 day", which is one of the individual states. Furthermore, the time point when the comparison organism became "influenza" is the time point when the biomolecule associated with "infection", which is one of the individual states, was obtained, which is one day before the time point "1 day". Therefore, the state identification unit 109 identifies the time when the target organism became the specified state as "1 day ago", which is displayed in the time notification unit 43.

[0077] The future state notification unit 44 displays the future state of the target organism identified by the state identification unit 109, along with the time when this future state will be achieved. The future state of the target organism identified by the state identification unit 109 will hereinafter be referred to as the future state. In the illustrated example, the future state notification unit 44 displays the text "future state," as well as the text "1 day later: fever" and the text "5 days later: normal temperature," which include the future state and the time when the future state will be achieved. Note that the future state is a state associated with a time later than the time when a biomolecule satisfying the same similarity condition was obtained, among the individual states associated with the extracted statistical information. The time when the future state will be achieved is identified by the state identification unit 109 based on the relationship between the time when a biomolecule satisfying the same similarity condition was obtained and the time when the future state was associated. 7(F), the time points when the future states "fever" and "normal temperature" are associated are "day 2" and "day 6," which are one day and five days after the time points when the biomolecules satisfying the same similarity conditions are obtained. Therefore, the state specifying unit 109 specifies the time points when the target organism will reach the future states of "fever" and "normal temperature" as "one day later" and "five days later," which are displayed on the future state notifying unit 44.

[0078] As described above, in this embodiment, the detection unit 107 detects biomolecules that satisfy a change condition among the multiple biomolecules indicated in the target distribution information. Furthermore, the target determination unit 108 determines comparative statistical information according to a partitioned region containing biomolecules that satisfy the change condition. The state identification unit 109 then identifies the state of the target organism based on the relationship between the biomolecules that satisfy the change condition and the biomolecules indicated in the comparative statistical information. That is, in this embodiment, the state identification unit 109 identifies the state of the target organism according to the target biomolecules among the multiple biomolecules that satisfy conditions defined for the change in the biomolecules identified from the molecular information. Examples of conditions defined for the change in the biomolecules include change conditions. Furthermore, examples of the target biomolecules include biomolecules included in the partitioned region among the multiple biomolecules. In this case, the state of the target organism can be identified using not only one biomolecule but also multiple biomolecules. Furthermore, a method for improving the state of the target organism may be determined based on the identified state of the target organism. In other words, the result of identifying the state of the target organism may be used to determine how to interact with the target organism.

[0079] In particular, the condition identification system 1 of this embodiment creates a two-dimensional electrophoresis image using components collected from the target organism without predetermining which biomolecules to collect from the target organism, and identifies the condition of the target organism from each of the biomolecules shown in the created two-dimensional electrophoresis image. Here, a method can be considered in which biomolecules to be collected from a target organism are determined in advance according to the target state of the target organism, and the state of the target organism is determined using only the determined biomolecules. However, in this case, even if other biomolecules different from the determined biomolecules change over time, the state of the target organism cannot be determined according to the other biomolecules. Therefore, in this embodiment, the state of the target organism is identified using all biomolecules contained in the components obtained from the target organism.

[0080] Note that the state specifying unit 109 in this embodiment specifies not only the current state of the target organism but also the future state of the target organism (see FIG. 12). That is, the above-mentioned "specifying the state of the target organism" includes not only specifying the current state of the target organism but also specifying the future state of the target organism.

[0081] Furthermore, in this embodiment, the biomolecules shown in the two-dimensional electrophoretic image include not only biomolecules with known functions but also biomolecules with unknown functions. Here, a method of identifying the state of a target organism using only biomolecules with known functions is also conceivable. However, in this case, if none of the biomolecules with known functions satisfy the change conditions, the state of the target organism cannot be identified. Therefore, in this embodiment, the state of the target organism is identified using not only biomolecules with known functions but also biomolecules with unknown functions.

[0082] Furthermore, in this embodiment, the output unit 110 outputs information according to the target of biomolecules that satisfy the conditions set for changes in biomolecules identified from molecular information, regarding the state of the target organism. In this case, information regarding the state of the target organism can be output using not only one biomolecule but multiple biomolecules.

[0083] In particular, in this embodiment, the target of the biomolecule is a portion of the biomolecule that satisfies the change condition. In this case, even if the biomolecules as a whole do not satisfy the conditions set for the changes in the biomolecules, the state of the target organism can be identified.

[0084] Furthermore, in this embodiment, for organism "A," the state of this organism "A" is identified as "influenza" when one biomolecule included in region 14 and region 15 satisfies the change condition and one biomolecule included in region 20 satisfies the change condition. Also, as described above, for organism "A," when one biomolecule included in region 20 satisfies the change condition and one biomolecule included in region 7 satisfies the change condition, the state of organism "A" is identified as "COVID-19." That is, in this embodiment, the state identification unit 109 identifies the state of the target organism depending on which of the multiple biomolecules the target biomolecule satisfies the change condition. In this case, the state of the target organism can be identified according to a plurality of biomolecules.

[0085] In this embodiment, the distribution information includes information about biomolecules in each of a plurality of sections divided according to the physical quantities of the biomolecules. A change condition is defined for each of the plurality of sections. Examples of the physical quantities of the biomolecules include molecular weight and isoelectric point. Examples of the sections include partitioned regions. In this case, the state of the target organism can be identified according to the target physical quantity of the biomolecule that satisfies the change condition.

[0086] Furthermore, in this embodiment, for organism "A," the state of this organism "A" is identified as "influenza" when one biomolecule contained in region 20 satisfies the change condition and one biomolecule contained in regions 14 and 15 satisfies the change condition. That is, in this embodiment, the state identifying unit 109 identifies the state of the target organism when both the specific target of the first biomolecule and the specific target of the second biomolecule in the target organism satisfy the change condition. In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified based only on whether a specific target in a single biomolecule satisfies a change condition.

[0087] In particular, in this embodiment, for organism "A," one biomolecule with a decreased amount of substance in region 20 satisfies the change condition, and one biomolecule with an increased amount of substance in region 14 satisfies the change condition. That is, in this embodiment, an increase in the physical quantity of a specific target in a first biomolecule satisfies the change condition, and a decrease in the physical quantity of a specific target in a second biomolecule satisfies the change condition. In this case, even if the tendency of change in the physical quantity of the first biomolecule differs from the tendency of change in the physical quantity of the second biomolecule, the state of the target organism can be identified.

[0088] Furthermore, in this embodiment, the state of organism "A" was identified as "influenza" when one biomolecule included in region 20 and one biomolecule included in region 14 satisfied the change condition, and one biomolecule included in region 7 did not. It was also described that the state of organism "A" was identified as "COVID-19" when one biomolecule included in region 20 and one biomolecule included in region 7 satisfied the change condition, and one biomolecule included in region 14 did not satisfy the change condition. That is, in this embodiment, the state identification unit 109 identifies the state of the target organism as the first state when a specific target in the first biomolecule and a specific target in the second biomolecule of the target organism satisfy the change condition, and a specific target in the third biomolecule does not satisfy the change condition. Furthermore, the state identification unit 109 identifies the state of the target organism as the second state when a specific target in the first biomolecule and a specific target in the third biomolecule of the target organism satisfy the change condition, and a specific target in the second biomolecule does not satisfy the change condition. In this case, even if a common biomolecule between the first state and the second state satisfies a condition set for a change in the biomolecule, the state of the target organism can be identified.

[0089] Furthermore, in this embodiment, for organism "A," the state of this organism "A" is identified as "influenza" when the portion of one biomolecule included in region 14 and the portion included in region 15 satisfy the change condition. That is, in this embodiment, the state identifying unit 109 identifies the state of the target organism when both the first portion and the second portion of the specific biomolecule satisfy the change condition. In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified based only on one portion of one biomolecule satisfying a change condition.

[0090] In particular, in this embodiment, for organism "A," the state of this organism "A" is identified as "influenza" when an increase in the amount of substance in a portion of one biomolecule included in region 14 satisfies the change condition, and a decrease in the amount of substance in a portion included in region 15 satisfies the change condition. That is, in this embodiment, an increase in a specific physical quantity in the first portion satisfies the change condition, and a decrease in the physical quantity in the second portion satisfies the change condition. In this case, even if the tendency of change in the physical quantity in the first portion differs from the tendency of change in the physical quantity in the second portion, the state of the target living organism can be identified.

[0091] The method of identifying the state by the state identifying unit 109 is not limited to the above example. For example, for a biomolecule consisting of regions 1 to 5, the state identified for the target organism may differ depending on whether the change in the amount of substance of the portion included in region 1 satisfies the change condition or the change in the amount of substance of the portion included in region 5 satisfies the change condition based on the relationship with the biomolecules shown in the comparison statistical information. That is, the state identifying unit 109 may identify the state of the target organism as a first state when a first portion of the specific biomolecule of the target organism satisfies the change condition but a second portion does not, and may identify the state of the target organism as a second state different from the first state when the first portion of the specific biomolecule of the target organism does not satisfy the change condition but a second portion does. The state of a biomolecule may differ depending on the site of post-translational modification. For example, when a phosphate group is added to a specific biomolecule containing an amino acid, the activation state and function of the specific biomolecule will differ if the 20th amino acid is phosphorylated compared to if the 45th amino acid is phosphorylated. In this embodiment, the respective states of the target organism can be identified from such different states of the biomolecule. More specifically, the respective states of the target organism can be identified from changes in the physical quantities of the biomolecules in each different state.

[0092] (Another example of state identification processing) Next, an example of the state identification process that is performed when object distribution information different from the example shown in FIG. 9(A) is used will be described. 13 and 14 are diagrams showing an example of how the state of a target living organism is identified by the state identification process.

[0093] In the following, an example will be described in which the target organism is organism "E" and the two pieces of target distribution information shown in FIG. 13(A) are use distribution information. In FIG. 13(A), early distribution information and late distribution information for organism "E" are shown as use distribution information. This early distribution information and late distribution information each show the distribution of seven different biomolecules, similar to the distribution information shown in FIG. 6(A). The early distribution information shown in FIG. 13(A) is target distribution information showing each biomolecule contained in a component obtained from organism "E" the day before organism "E" takes an anticancer drug. The late distribution information shown in FIG. 13(A) is target distribution information showing each biomolecule contained in a component obtained from organism "E" the day after organism "E" takes an anticancer drug. The organism "E" is, for example, a human. The region identification unit 103 identifies the region where the biomolecule exists in each piece of usage distribution information. The partitioning unit 104 partitions the region where the biomolecule exists in each piece of usage distribution information into 20 regions consisting of Region 1 to Region 20, in the same way as the partitioning performed on the comparison distribution information (see FIG. 6(B)).

[0094] The substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each partitioned region for each piece of usage distribution information. The detection unit 107 determines whether the biomolecule satisfies a change condition for each partitioned region, and detects the biomolecule determined to satisfy the change condition. In this example, in regions 6, 19, and 20 shown in FIG. 13(A), the amounts of substance of biomolecules indicated in the later distribution information have changed from the amounts of substance of biomolecules indicated in the early distribution information. More specifically, in regions 6 and 19, the amounts of substance of biomolecules indicated in the later distribution information have decreased compared to the amounts of substance of biomolecules indicated in the early distribution information. Furthermore, in region 20, the amounts of substance of biomolecules indicated in the later distribution information have increased compared to the amounts of substance of biomolecules indicated in the early distribution information. The biomolecules indicated in the early distribution information and the later distribution information for regions 6, 19, and 20 are detected by the detection unit 107 as satisfying the change condition.

[0095] Next, the target determining unit 108 determines the comparison statistical information to be compared with the detected molecule, which is a biomolecule that satisfies the change condition. In this example, as shown in FIG. 13(B), the target determining unit 108 determines each piece of statistical information targeting region 20 as comparative statistical information for the detected molecules shown in region 20 for organism "E." The target determining unit 108 also determines each piece of statistical information targeting region 19 as comparative statistical information for the detected molecules shown in region 19 for organism "E," and determines each piece of statistical information targeting region 6 as comparative statistical information for the detected molecules shown in region 6 for organism "E." As shown in FIG. 13(B), these comparative statistical information pieces include statistical information associated with the comprehensive state "anticancer drug effective" and statistical information associated with the comprehensive state "anticancer drug ineffective." FIG. 13(B) is a diagram showing information compared by the state identifying unit 109. More specifically, FIG. 13(B) is a diagram showing biomolecules included in the early distribution information and the late distribution information, comparative statistical information associated with "anticancer drug effective," and comparative statistical information associated with "anticancer drug ineffective." In addition, in FIG. 13(B), the symbol "●" indicates a biomolecule shown in the early distribution information, and the symbol "▲" indicates a biomolecule shown in the later distribution information.

[0096] Next, the state specifying unit 109 extracts biomolecules that satisfy the same similarity condition from the comparison statistical information. In this example, the dashed line portion a in the comparative statistical information associated with "anticancer drug effective" and targeting region 20 shows an increase in the amount of a biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion a satisfies the same similarity condition with respect to the detected molecule included in region 20 for organism "E." The dashed line portion b in the comparative statistical information associated with "anticancer drug ineffective" and targeting region 20 also shows an increase in the amount of a biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion b satisfies the same similarity condition with respect to the detected molecule included in region 20 for organism "E." In this case, the state identifying unit 109 compares the biomolecule shown in the extracted comparative statistical information with the detected molecule shown in the partitioned region that has not yet been compared. More specifically, the state identifying unit 109 compares the detected molecule included in region 19 for organism "E" with the comparative statistical information targeting region 19. Furthermore, the state specifying unit 109 compares the detected molecules contained in the region 6 for the organism "E" with the comparative statistical information for the region 6.

[0097] Here, the dotted line portion c in the comparative statistical information associated with "anticancer drug effective" and targeting region 19 shows a decrease in the substance amount of the biomolecule over time. The state identifying unit 109 identifies the biomolecule shown in this portion c as satisfying the same similarity condition with the detected molecule included in region 19 for organism "E." Also, the dotted line portion d in the comparative statistical information associated with "anticancer drug effective" and targeting region 6 shows a decrease in the substance amount of the biomolecule over time. The state identifying unit 109 identifies the biomolecule shown in this portion d as satisfying the same similarity condition with the detected molecule included in region 6 for organism "E." On the other hand, the comparative statistical information associated with "no anticancer drug effect" and targeting region 19 indicates that the amount of substance of the biomolecule does not change over time. The state identifying unit 109 then determines that this biomolecule does not satisfy the same or similar condition for the detected molecule shown in region 19. Furthermore, the comparative statistical information associated with "no anticancer drug effect" and targeting region 6 indicates that the amount of substance of the biomolecule does not change over time. The state identifying unit 109 then determines that this biomolecule does not satisfy the same or similar condition for the detected molecule shown in region 6.

[0098] In this case, the state identifying unit 109 extracts the biomolecule indicated in the comparative statistical information associated with "anticancer drug effective" as a biomolecule that satisfies the same similarity condition. The state identifying unit 109 also identifies "anticancer drug effective," which is the comprehensive state associated with the extracted comparative statistical information, as the state of organism "E." Furthermore, the output unit 110 causes the display unit 31 of the terminal 30 to display a status notification screen 40 (see FIG. 12) relating to the status identified by the status identification unit 109.

[0099] In this example, it has been explained that the biomolecules shown in the comparative statistical information associated with "anticancer drug effective" and the biomolecules shown in the comparative statistical information associated with "anticancer drug ineffective" both satisfy the same similarity condition for the detected molecules contained in region 20.

[0100] Here, Figure 14(C) shows biomolecules included in region 1 of the early distribution information and the late distribution information, comparative statistical information associated with "anticancer drug effective" and targeting region 1, and comparative statistical information associated with "anticancer drug ineffective" and targeting region 1. In each diagram shown in Figure 14(C), the horizontal axis represents the time point at which the biomolecule was obtained, and the vertical axis represents the amount of substance of the biomolecule. Also, in Figure 14(C), the symbol "●" represents a biomolecule shown in the early distribution information, and the symbol "▲" represents a biomolecule shown in the late distribution information. As shown in FIG. 14(C), in region 1 for organism "E," there is no change between the amount of substance of the biomolecule indicated in the early distribution information and the amount of substance of the biomolecule indicated in the later distribution information. Furthermore, in the comparative statistical information associated with "anticancer drug effective" and targeting region 1, there is no change in the amount of substance of the biomolecule over time. On the other hand, the comparative statistical information associated with "anticancer drug ineffective" and targeting region 1 shows an increase in the amount of substance of the biomolecule over time at dashed line portion e in the figure. Therefore, for organism "E," if the biomolecules included in region 20 show the change at portion b in FIG. 13(B) and the biomolecules included in region 1 show the change at portion e in FIG. 14(C), the state identifying unit 109 identifies the state of organism "E" as "anticancer drug ineffective."

[0101] As described above, in this embodiment, the information acquisition unit 101 acquires usage distribution information that can identify the state of a biomolecule over time from before a specific action occurs in the target organism to after the specific action occurs. An example of the specific action is the ingestion of an anticancer drug. The state identification unit 109 identifies the state of the target organism as a first state related to the specific action when a specific target in the specific biomolecule satisfies a change condition, and identifies the state of the target organism as a second state related to the specific action when the specific target in the specific biomolecule does not satisfy the change condition. An example of the specific target in the specific biomolecule is the portion of the biomolecule shown in region 19 (see FIGS. 13(A) and 13(B)). An example of the first state is a state in which the target organism is benefited by the ingestion of an anticancer drug. An example of the second state is a state in which the target organism is not benefited by the ingestion of an anticancer drug. In this case, it is possible to identify the effect on the target organism caused by a specific action being exerted on the target organism.

[0102] In particular, in this embodiment, the state identifying unit 109 identifies the state of the target organism as a first state when the specific target in the first biomolecule satisfies the change condition and the specific target in the second biomolecule does not satisfy the change condition, and identifies the state of the target organism as a second state when the specific target in the first biomolecule does not satisfy the change condition and the specific target in the second biomolecule satisfies the change condition. An example of the specific target in the second biomolecule is the portion of the biomolecule shown in region 1 (see Figures 13(A) and 14(C)). In this case, the accuracy of identifying the state of the target organism as the second state can be improved compared to a configuration in which the state of the target organism is identified as the second state solely based on the fact that a specific target in the first biological molecule does not satisfy the change condition.

[0103] In this embodiment, the state determination unit 109 determines that the target organism is in the first state when the biomolecules contained in region 20, the biomolecules contained in region 19, and the biomolecules contained in region 6 in the usage distribution information all satisfy the change conditions, but this is not limited to this. For example, in the usage distribution information, if a biomolecule included in region 19 satisfies the change condition and a biomolecule included in region 6 does not satisfy the change condition, and if a biomolecule associated with "anticancer drug effective" and shown in comparative statistical information targeting region 19 satisfies the same or similar condition, the state identifying unit 109 may identify the target organism as "anticancer drug effective." That is, if a specific target in the first biomolecule and a specific target in the second biomolecule both satisfy the change condition, the state identifying unit 109 may identify the state of the target organism as the first state. Alternatively, if a specific target in the first biomolecule does not satisfy the change condition but a specific target in the second biomolecule does satisfy the change condition, the state of the target organism may be identified as the first state. An example of the specific target in the first biomolecule is a portion of the biomolecule included in region 19. An example of the specific target in the second biomolecule is a portion of the biomolecule included in region 6 (see FIGS. 13(A) and 13(B)). In this case, it is possible to prevent the target living organism from being identified as being in a state different from the first state even though the target living organism is in the first state.

[0104] Furthermore, in this embodiment, the condition specifying unit 109 specifies the condition related to the specific action of the target living organism as a comprehensive condition associated with the health condition as an individual condition, but the present invention is not limited to this. For example, the state identifying unit 109 may identify the state of the target organism as a comprehensive state associated with individual physical states based on changes in biomolecules of the target organism. As one example, the state identifying unit 109 may identify the state of the target organism as "training effective" associated with the physical state "muscle pain" (see FIG. 4(A)). As another example, the state identifying unit 109 may identify the state of the target organism as "training ineffective" associated with the physical state "fatigue." "Training effective" and "training ineffective" are examples of states related to the implementation of training by the target organism. Implementation of training is also an example of a specific action. Furthermore, the state identification unit 109 may identify the state of the target organism as a comprehensive state associated with a brain state as an individual state, for example, based on changes in biomolecules of the target organism. For example, the state identification unit 109 may identify the state of the target organism as "improved memory," which is associated with a brain state of "studying for 3 hours" (see FIG. 4(A)) and a physical state of "sleeping for 8 hours." For another example, the state identification unit 109 may identify the state of the target organism as "deteriorated memory," which is associated with a brain state of "studying for 3 hours" and a physical state of "sleeping for 4 hours." In this way, two or more pieces of information from the individual state information may be associated with the comprehensive state information. "Improved memory" and "deteriorated memory" are examples of states related to learning by the target organism. Learning is also an example of a specific action. Furthermore, the state identified by the state identification unit 109 as the state of the target organism in which a specific action has been applied may not only be a state in which the specific action has an effect or an effect, but also a state in which the specific action has had a positive effect or an adverse effect. Furthermore, the condition identification unit 109 may identify the condition of the target organism as a comprehensive condition associated with individual cosmetic conditions, for example, based on changes in biomolecules of the target organism. For example, the condition identification unit 109 may identify the condition of the target organism as "skin age 40 years old" associated with the cosmetic condition "skin cream A used" (see FIG. 4(A)). For another example, the condition identification unit 109 may identify the condition of the target organism as "skin age 25 years old" associated with the cosmetic condition "skin cream B used." "Skin age 40 years old" and "skin age 25 years old" are examples of conditions related to the use of skin cream by the target organism. The use of skin cream is also an example of a specific action.

[0105] (Another example of state identification processing) Next, an example of the state specification process that is performed when target distribution information different from either the example shown in FIG. 9(A) or the example shown in FIG. 13(A) is used will be described. 15 and 16 are diagrams showing an example of how the state of a target living organism is identified by the state identification process.

[0106] In the following, an example will be described in which the target organism is organism "F" and the four pieces of target distribution information shown in FIG. 15 are usage distribution information. In FIG. 15, four pieces of target distribution information are shown as usage distribution information, each of which shows a different time point at which the biomolecules were obtained for organism "F." Each of these four pieces of usage distribution information shows the distribution of seven different biomolecules, similar to the distribution information shown in FIG. 6(A). Hereinafter, the four pieces of usage distribution information shown in FIG. 15 may be referred to as first time point distribution information, second time point distribution information, third time point distribution information, and fourth time point distribution information, in order from the earliest time point at which the biomolecules were obtained. Note that organism "F" is, for example, a banana.

[0107] In this example, the partitioning unit 104 partitions all regions in each piece of usage distribution information. Specifically, as shown in FIG. 15, each piece of usage distribution information is a two-dimensional Cartesian coordinate system in which the horizontal axis indicates the isoelectric point of the biomolecule by coordinates ranging from 1 to 11, and the vertical axis indicates the molecular weight of the biomolecule by coordinates ranging from 1 to 9. The larger the coordinate on the horizontal axis, the higher the isoelectric point of the biomolecule, and the larger the coordinate on the vertical axis, the higher the molecular weight of the biomolecule. The partitioning unit 104 then partitions the region indicated in each piece of usage distribution information into regions corresponding to the coordinates. In the example shown in FIG. 15, all regions in each piece of usage distribution information are partitioned, resulting in partitioned regions where biomolecules are present and partitioned regions where biomolecules are not present.

[0108] Although not shown in the figures, in this example, the partitioning unit 104 partitions all regions of the comparison distribution information stored in the storage unit 102 in the same manner as the use distribution information shown in Fig. 15. Furthermore, the substance amount identification unit 105 identifies the substance amounts of biomolecules contained in the partitioned regions in the comparison distribution information for each partitioned region. Furthermore, the statistics unit 106 calculates statistics of the substance amounts of biomolecules for each partitioned region and creates statistical information for each partitioned region.

[0109] The substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each partitioned region for each piece of usage distribution information. The detection unit 107 determines whether the biomolecule satisfies a change condition for each partitioned region, and detects the biomolecule determined to satisfy the change condition. In this example, in the region where the horizontal coordinate is "9" and the vertical coordinate is "5," i.e., region (9, 5), the amount of substance of the biomolecule indicated in the second time point distribution information is decreased compared to the amount of substance of the biomolecule indicated in the first time point distribution information. Furthermore, in region (9, 5), the amount of substance of the biomolecule indicated in the fourth time point distribution information is increased compared to the amount of substance of the biomolecule indicated in the third time point distribution information. Furthermore, in region (3, 7), the third time point distribution information does not indicate a biomolecule, while the fourth time point distribution information indicates a biomolecule. In this case, the detection unit 107 detects the biomolecule indicated in the first time point distribution information and the second time point distribution information for region (9, 5) as satisfying the change condition. Furthermore, the detection unit 107 detects the biomolecule indicated in the third time point distribution information and the fourth time point distribution information for region (9, 5) as satisfying the change condition. Furthermore, the detection unit 107 detects the biomolecule indicated in the fourth time point distribution information of the region (3, 7) as satisfying the change condition. In region (3, 7), there is no change in the amount of biomolecules in the first, second, and third time point distribution information. More specifically, in region (3, 7), no biomolecules are shown in the first, second, or third time point distribution information.

[0110] Next, the target determining unit 108 determines the comparison statistical information to be compared with the detected molecule, which is a biomolecule that satisfies the change condition. In this example, as shown in FIG. 16, the target determining unit 108 determines each piece of statistical information targeting region (9, 5) as comparative statistical information for the detected molecule shown in region (9, 5) for organism "F." The target determining unit 108 also determines each piece of statistical information targeting region (3, 7) as comparative statistical information for the detected molecule shown in region (3, 7) for organism "F." As shown in FIG. 16, these pieces of comparative statistical information include statistical information associated with the comprehensive state "good quality" and statistical information associated with the comprehensive state "poor quality." FIG. 16 is a diagram showing information compared by the state identifying unit 109. More specifically, FIG. 16 is a diagram showing biomolecules shown in each piece of usage distribution information, comparative statistical information associated with "good quality," and comparative statistical information associated with "poor quality."

[0111] In addition, in the biomolecules of organism "F" shown in Figure 16, the symbol ● represents the biomolecule shown in the first time point distribution information, the symbol ▲ represents the biomolecule shown in the second time point distribution information, the symbol ■ represents the biomolecule shown in the third time point distribution information, and the symbol ★ represents the biomolecule shown in the fourth time point distribution information.

[0112] Next, the state determining unit 109 extracts biomolecules that satisfy the same similarity condition from the comparison statistical information. In this example, the dashed line portion A in the comparative statistical information associated with the comprehensive state "good quality" and targeting the region (9, 5) shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion A satisfies the same-similar condition with respect to the biomolecule shown in the region (9, 5) of the first time point distribution information and the second time point distribution information. The dashed line portion B in the comparative statistical information associated with the comprehensive state "poor quality" and targeting the region (9, 5) shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion B satisfies the same-similar condition with respect to the biomolecule shown in the region (9, 5) of the first time point distribution information and the second time point distribution information.

[0113] In this example, the comparative statistical information associated with the comprehensive state "good quality" and targeting the region (9, 5) shows an increase in the amount of substance of the biomolecule over time at the dashed line portion C in the figure. The state identifying unit 109 determines that the biomolecule shown in this portion C satisfies the same-similar condition with respect to the biomolecule shown in the region (9, 5) of the third time point distribution information and the fourth time point distribution information. The comparative statistical information associated with the comprehensive state "good quality" and targeting the region (3, 7) also shows an increase in the amount of substance of the biomolecule over time at the dashed line portion D in the figure. The state identifying unit 109 determines that the biomolecule shown in this portion D satisfies the same-similar condition with respect to the biomolecule shown in the region (3, 7) of the fourth time point distribution information. On the other hand, the comparative statistical information associated with the inclusive state "poor quality" and targeting the region (9, 5) does not show an increase in the amount of substance of the biomolecule over time. In this case, the state identifying unit 109 determines that the biomolecule shown in this comparative statistical information does not satisfy the same-similar condition with respect to the biomolecule shown in the region (9, 5) of the third time point distribution information and the fourth time point distribution information. Furthermore, the comparative statistical information associated with the inclusive state "poor quality" and targeting the region (3, 7) shows that the amount of substance of the biomolecule does not change over time. In this case, the state identifying unit 109 determines that the biomolecule shown in this comparative statistical information does not satisfy the same-similar condition with respect to the biomolecule shown in the region (3, 7) of the fourth time point distribution information.

[0114] In this case, the state identifying unit 109 extracts the biomolecule indicated in the comparison statistical information associated with "good quality" as a biomolecule that satisfies the same similarity condition. Furthermore, the state identifying unit 109 identifies "good quality," which is the comprehensive state associated with the extracted comparison statistical information, as the state of biomolecule "F." Furthermore, the output unit 110 causes the display unit 31 of the terminal 30 to display a status notification screen 40 (see FIG. 12) relating to the status identified by the status identification unit 109.

[0115] As described above, no biomolecules are shown in the region (3, 7) in the first time point distribution information, the second time point distribution information, and the third time point distribution information of the usage distribution information shown in Fig. 15. That is, in this embodiment, the usage distribution information includes information on the presence of biomolecules in each of the multiple partitioned regions at multiple time points, and the multiple partitioned regions include a region where no biomolecules of the target organism are present at at least one of the multiple time points. In this case, the state of the target organism can be identified based on changes in the biomolecules in the area where no biomolecules were present.

[0116] Furthermore, in this embodiment, the information acquisition unit 101 acquires information including information that can identify changes in biomolecules of a target organism during a first time period and information that can identify changes in biomolecules during a second time period that follows the first time period. The first time period may be, for example, the period from when the biomolecules shown in the first time point distribution information are obtained to when the biomolecules shown in the second time point distribution information are obtained. The second time period may be, for example, the period from when the biomolecules shown in the third time point distribution information are obtained to when the biomolecules shown in the fourth time point distribution information are obtained. The state identification unit 109 then identifies the state of the target organism based on the biomolecular targets that satisfy the change condition for changes during the first time period and the biomolecular targets that satisfy the change condition for changes during the second time period. In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified based only on whether biomolecules change over a single period of time and satisfy a change condition.

[0117] In particular, in this embodiment, the target biomolecules that satisfy the change condition for the change in the first period are the same as the target biomolecules that satisfy the change condition for the change in the second period. Examples of the target biomolecules include biomolecules included in region (9, 5) (see FIGS. 15 and 16). In this case, the state of the target organism can be identified based on changes in a specific biomolecule over multiple periods.

[0118] <Second embodiment> Next, a second embodiment will be described. The second embodiment is common to the first embodiment in that the condition specifying system 1 specifies the condition of a target living organism. On the other hand, the second embodiment differs from the first embodiment in that the user of the condition identifying system 1 can select what type of condition the condition identifying unit 109 will identify for the target living organism. The second embodiment will be described below. The second embodiment has the same configuration as the first embodiment except for the points described below. In addition, the following will describe configurations that are different from the first embodiment, and descriptions of configurations that are the same as the first embodiment may be omitted.

[0119] Fig. 17 is a flowchart showing the flow of the condition identification process of the second embodiment. Fig. 18 is a diagram showing an image displayed on the display unit 31 of the terminal 30 when the condition identification process of the second embodiment is performed. Fig. 19 is a diagram showing an example of how the condition of the target living organism is identified by the condition identification process of the second embodiment. In the following, an example will be described in which the target living organism is living organism "G". Furthermore, living organism "G" is, for example, a human being. In this embodiment as well, when a user of the condition identification system 1 specifies target distribution information to be used in the condition identification process and instructs the server device 10 to execute the condition identification process, the condition identification process is started.

[0120] First, the information acquiring unit 101 determines whether or not an instruction to identify the state of the target living organism has been given (S301 in FIG. 17). When the user inputs an instruction to identify the state of the target living organism into the terminal 30, the terminal 30 notifies the server device 10 that the instruction has been given. The information acquiring unit 101 determines whether or not an instruction to identify the state of the target living organism has been given based on whether or not this notification has been received. Furthermore, while a negative result continues, the information acquiring unit 101 repeats the processing of step 301.

[0121] When the terminal 30 notifies the server device 10 that an instruction to identify the state of the target living organism has been issued (YES in S301), the information acquiring unit 101 determines whether or not a selection has been made as to which type of state the state identifying unit 109 is to identify (S302). When the user selects, on the terminal 30, which type of state the state identifying unit 109 is to identify, type information indicating the type of selected state is transmitted from the terminal 30 to the server device 10. The information acquiring unit 101 determines whether or not a selection has been made as to which type of state the state identifying unit 109 is to identify, depending on whether or not this type information has been acquired. Furthermore, while a negative result continues, the information acquiring unit 101 repeats the processing of step 302.

[0122] In this example, in the condition identification process, when the user inputs an instruction to identify the condition of the target living organism into the terminal 30, a selection screen 50 is displayed on the display unit 31 of the terminal 30, as shown in Fig. 18(A). The selection screen 50 is a screen for allowing the user to select which type of condition the condition identification unit 109 will identify. The selection screen 50 shows a selection prompting section 51, an item section 52, and a decision section 53. Selection prompting section 51 displays information for prompting the user to select which type of state to have state identification section 109 identify. In the illustrated example, selection prompting section 51 displays the text "Please select the type of state you want to know about."

[0123] Items that can be selected by the user are displayed in the item section 52. In the item section 52, an item selection section 521 is displayed. The item selection section 521 displays items classified according to the type of condition identified by the condition identification section 109. In the illustrated example, the item selection section 521 displays items including "health condition," "physical condition," "brain condition," and "beauty condition," which are classified according to the type of condition identified by the condition identification section 109. In the illustrated example, "health condition" is selected in the item selection section 521.

[0124] When the user selects decision section 53 while "health condition" is selected in item selection section 521, a selection item section 522 and a detailed selection section 523 are displayed in item section 52 of selection screen 50, as shown in Fig. 18(B). In addition, a return section 54 and a decision section 55 are displayed on selection screen 50.

[0125] The selection item section 522 of the item section 52 shows the item selected in the item selection section 521. In the illustrated example, the selection item section 522 shows "health condition." The detailed selection section 523 of the item section 52 displays items that are further categorized from the items displayed in the selection item section 522. The items displayed in the detailed selection section 523 are items that the user can select. In the illustrated example, the detailed selection section 523 displays items including "influenza," "COVID-19," "norovirus," and "hepatitis," which are further categorized from the "health condition" displayed in the selection item section 522. Also, in the illustrated example, "influenza" is selected in the detailed selection section 523.

[0126] When the user selects the return section 54, the selection screen 50 shown in FIG. 18(A) is displayed again on the display section 31 of the terminal 30. Furthermore, when "influenza" is selected in the detailed selection section 523 and the user selects the decision section 55, type information indicating "influenza", which is the type of the selected condition, is transmitted from the terminal 30 to the server device 10.

[0127] When the information acquiring unit 101 acquires the type information (YES in S302), the process proceeds to the next step. The target determining unit 108 extracts, from the statistical information stored in the storage unit 102, statistical information to which the type information acquired by the information acquiring unit 101 is associated as comprehensive state information, as statistical information to be compared with the biomolecules of the target organism (S303). More specifically, the target determining unit 108 extracts statistical information to which type information is associated, the statistical information targeting a region in which biomolecules satisfying the extraction conditions are displayed. The extraction conditions are conditions determined so that the target determining unit 108 can extract statistical information to be compared with the biomolecules of the target organism. The extraction conditions of this embodiment are determined from the perspective of extracting statistical information targeting a region in which biomolecules whose substance amounts have changed over time are displayed. Furthermore, in this embodiment, the extraction condition is determined to be that the substance amounts of the biomolecules included in the statistical information have changed over time by more than a predetermined value.

[0128] FIG. 19A is a diagram showing an example of statistical information stored in the storage unit 102. In this example, as shown in FIG. 19A, the storage unit 102 stores statistical information in which type information "influenza" is associated as comprehensive state information. Furthermore, the dashed line portion I in the statistical information associated with "influenza" and targeting region 20 indicates a decrease in the substance amount of the biomolecule over time. Furthermore, the dashed line portion II in the statistical information associated with "influenza" and targeting region 15 indicates a decrease in the substance amount of the biomolecule over time. Furthermore, the dashed line portion III in the statistical information associated with "influenza" and targeting region 14 indicates an increase in the substance amount of the biomolecule over time. In this case, the target determination unit 108 extracts the statistical information associated with region 20, the statistical information associated with region 15, and the statistical information associated with region 14 as statistical information that satisfies the extraction condition.

[0129] Next, the substance amount specifying unit 105 specifies, for each partition area, the substance amount of the biomolecule shown in the partition area for each piece of usage distribution information (S304). Next, the target determining unit 108 determines (S305) a biomolecule of a target organism to be compared with the biomolecule indicated in the statistical information extracted in step 303. More specifically, the target determining unit 108 determines a biomolecule included in a partitioned area whose coordinates are the same as those of the target in the statistical information extracted in step 303 as the biomolecule to be compared.

[0130] In this example, as described above, statistical information for region 20, statistical information for region 15, and statistical information for region 14 are extracted. In this case, the target determining unit 108 determines the biomolecules included in region 20, the biomolecules included in region 15, and the biomolecules included in region 14, among the biomolecules shown in the usage distribution information for organism "G," as the biomolecules to be compared, respectively. FIG. 19(B) is a diagram showing the statistical information extracted in step 303 and the biomolecules of the target organism determined by the target determining unit 108 as targets for comparison with this statistical information. FIG. 19(B) shows statistical information for region 20, statistical information for region 15, and statistical information for region 14, each associated with "influenza" as statistical information. FIG. 19(B) also shows the biomolecules included in region 20, region 15, and region 14 in each usage distribution information as biomolecules of organism "G." In each diagram shown in FIG. 19(B), the horizontal axis represents the time point at which the biomolecule was obtained, and the vertical axis represents the amount of substance of the biomolecule.

[0131] Next, the state identification unit 109 compares the statistical information with the biomolecules of the target organism, and identifies the state of the target organism from the comparison result (S306). More specifically, the state identification unit 109 identifies the state of the target organism based on whether the biomolecules determined by the target determination unit 108 as the comparison targets satisfy a specific condition. The specific condition is a condition determined so that the state identification unit 109 identifies the state of the target organism as a comprehensive state associated with the statistical information. In this embodiment, the specific condition is determined to be that the difference between the value of the change in the substance amount of the biomolecules in the target organism over time and the value of the change in the substance amount of the biomolecules that satisfy the extraction condition in the statistical information is equal to or less than a predetermined value.

[0132] If the biomolecules of the target organism satisfy the specific conditions, the state identifying unit 109 identifies the inclusive state associated with the statistical information extracted in step 303 as the state of the target organism. Furthermore, if the biomolecules of the target organism do not satisfy the specific conditions, the state identifying unit 109 identifies a non-inclusive state associated with the statistical information extracted in step 303 as the state of the target organism. Note that the biomolecules of the target organism determined as the comparison targets by the target determining unit 108 may be biomolecules in multiple partitioned regions. In this case, the state identifying unit 109 determines that the biomolecules of the target organism satisfy the specific condition if all of the biomolecules contained in each partitioned region satisfy the specific condition. Furthermore, the state identifying unit 109 determines that the biomolecules of the target organism do not satisfy the specific condition if any of the biomolecules contained in each partitioned region do not satisfy the specific condition.

[0133] In this example, as shown in FIG. 19(B), the amount of substance of the biomolecules contained in the region 20 for the organism "G" does not change over time. Furthermore, the amount of substance of the biomolecule in region 15 for organism "G" increases over time. This change in the amount of substance of the biomolecule is the opposite trend to the decrease in the amount of substance of the biomolecule associated with "influenza" and shown in the statistical information for region 15. Furthermore, the amount of substance of the biomolecules contained in region 14 for organism "G" does not change over time. In this case, the state identifying unit 109 determines that the biomolecules of the target organism do not satisfy the specific condition. Furthermore, the state identifying unit 109 identifies the organism "G" as being in a state other than "influenza" associated with the statistical information.

[0134] Next, the output unit 110 outputs information relating to the state identified by the state identification unit 109 to the terminal 30 (S307). 18(C), the output unit 110 displays a status notification screen 40 on the display unit 31 of the terminal 30. The status notification screen 40 of this embodiment includes a biometric identification unit 41, a status notification unit 42, a warning unit 45, a detail prompting unit 46, a negation unit 47, and a determination unit 48.

[0135] The biometric identification section 41 displays the text "Mr. G" including information for identifying the target biometric. The status notification section 42 displays the text "Current Status" and the text "Not influenza" indicating the status identified by the status identification section 109.

[0136] Information for alerting the user about the state of the target living organism is displayed in the attention-calling unit 45. In this embodiment, the output unit 110 displays the attention-calling unit 45 when a biomolecule included in any partitioned area in the usage distribution information satisfies a change condition. When the amount of a biomolecule in a target organism changes, this change may cause a change in the state of the target organism. Therefore, the server device 10 of this embodiment alerts the user to the state of the target organism when the biomolecule indicated in the usage distribution information satisfies the change condition, even if the biomolecule of the target organism does not satisfy the specific condition.

[0137] In this example, as shown in FIG. 19(B), among the biomolecules contained in region 15 for organism G, the biomolecule shown in dashed line section IV in the figure has an increasing amount of substance over time, and the biomolecule shown in section IV satisfies the change condition. Therefore, a warning section 45 is displayed on status notification screen 40. In the illustrated example, warning section 45 displays the text "Caution" and the text "Your health condition may be in another unhealthy state."

[0138] The detail prompting section 46 displays information that prompts the user to have the condition specifying section 109 specify a detailed condition of the target living organism. In the illustrated example, the detail prompting section 46 displays the text "Do you want to specify another condition?" When the user selects the negation section 47, the detailed state of the target living body is not identified, and the state identification process ends. Furthermore, when the user selects the determination unit 48, the condition identification process shown in Fig. 8 is executed. As a result, the detailed condition of the target living organism is identified, and a condition notification screen 40 (see Fig. 12) relating to the identified condition is displayed on the display unit 31 of the terminal 30.

[0139] As described above, in this embodiment, when a user selects which type of state the state identification unit 109 is to identify for a target organism, the information acquisition unit 101 acquires type information indicating the selected type of state. Then, the target determination unit 108 determines usage distribution information to be used to identify the state of the target organism from the type of state identified from the type information. Furthermore, the state identification unit 109 identifies the state of the target organism from changes in biomolecules indicated in the usage distribution information determined by the target determination unit 108. That is, in this embodiment, the information acquisition unit 101 acquires item information regarding the target item identified by the state identification system 1 from among multiple items classified for the state of the organism. Then, the state identification unit 109 identifies the state of the target organism according to changes identified from the usage distribution information for the target biomolecule determined according to the item identified from the item information. Examples of the item information include type information. The information acquisition unit 101 can also be considered as item information acquisition means for acquiring item information. In this case, the state of the target organism can be identified according to the biomolecule, among the plurality of biomolecules, that is determined by the item of the target state identified for the target organism.

[0140] 9(A) and "influenza" is selected as the type of condition to be identified by the condition identification unit 109, the condition identification unit 109 identifies the condition of the target living organism as "influenza." In this case, the output unit 110 displays the condition notification screen 40 shown in FIG. 12 on the display unit 31 of the terminal 30. 13(A) and “anticancer drug effect” is selected as the type of condition identified by the condition identification unit 109, the condition identification unit 109 identifies the condition of the target organism as “anticancer drug effect.” The output unit 110 displays a condition notification screen 40 related to the condition identified by the condition identification unit 109 on the display unit 31. 15 and “quality” is selected as the type of condition identified by the condition identification unit 109, the condition identification unit 109 identifies the condition of the target organism as “good quality.” The output unit 110 displays a condition notification screen 40 related to the condition identified by the condition identification unit 109 on the display unit 31.

[0141] Next, an example carried out by the present inventor will be described.

[0142] [Example] The present inventors prepared 2D electrophoresis images showing multiple biomolecules from a living organism, each for biomolecules obtained at multiple time points, and examined the changes over time in the amount of each biomolecule shown in the 2D electrophoresis images. In this example, the living organisms to be examined for changes in the amount of biomolecules were three racehorses. When describing these three horses individually, they may be referred to as "Horse A," "Horse B," and "Horse C." When describing the horses to be examined for changes in the amount of biomolecules without distinction, they may be referred to as the "subject horse." The present inventors used serum obtained from the subject horses to prepare 2D electrophoresis images showing each protein contained in the serum. At the time the serum was obtained, all of the subject horses were 2 years old. At the time the serum was obtained from the subject horses, Horse A's weight was 348 kg, Horse B's weight was 379 kg, and Horse C's weight was 377 kg.

[0143] Figure 20(A) is a diagram showing a two-dimensional electrophoretic image of each protein contained in serum obtained from Horse A at a single time point. In this two-dimensional electrophoretic image, the distribution of the substance amount of each protein is shown in a two-dimensional orthogonal coordinate system in which the horizontal axis shows the distribution of the isoelectric point of the protein and the vertical axis shows the distribution of the molecular weight of the protein. Each black image shown in this two-dimensional electrophoretic image represents a protein. Furthermore, the higher the density of this black image, the greater the substance amount of each protein. The inventors obtained serum from each subject horse at 15 time points and created a 2D electrophoresis image for each serum obtained. More specifically, each subject horse was infected with influenza virus, serum was obtained from the subject horse on each day from the day of infection until 14 days later, and a 2D electrophoresis image was created for each serum obtained.

[0144] The inventors also divided the region shown in the created two-dimensional electrophoretic image into a plurality of regions. Figure 20(B) is a diagram showing the state in which the two-dimensional electrophoretic image of Figure 20(A) has been divided into a plurality of regions. In the example shown, the region shown in the two-dimensional electrophoretic image is divided into 338 regions. Furthermore, the twenty regions, which are an example of the divided regions, are numbered 1 to 20, respectively. The inventors divided the regions for each of the created two-dimensional electrophoretic images in the same manner as the example shown in Figure 20(B).

[0145] The inventors also identified the amount of protein contained in each divided region of each of the created 2D electrophoresis images. Furthermore, the inventors calculated statistics for each subject horse regarding the amount of protein contained in each divided region. More specifically, the inventors calculated statistics for the amount of protein contained in each divided region of the 2D electrophoresis image for each day since the subject horse was infected with influenza virus. These statistical calculations were performed for each divided region of the 2D electrophoresis image. The inventors then evaluated the change in the amount of protein contained in each divided region over time based on the calculated statistics.

[0146] (Comparative Example) In addition, as a comparative example to the examples, the body temperature of each subject horse was measured every day up to 14 days after the subject horse was infected with the influenza virus, and the amount of amyloid A substance, an example of a protein, obtained from the subject horse was measured. Figure 21(A) is a diagram showing the body temperatures of the subject horses. In Figure 21(A), the horizontal axis shows the number of days that have passed since the subject horse was infected with the influenza virus, and the vertical axis shows the body temperature of the subject horse. Also, in Figure 21(A), the symbol "●" indicates horse A, the symbol "▲" indicates horse B, and the symbol "■" indicates horse C. Figure 21(B) is a diagram showing the amount of amyloid A substance obtained from the subject horses. In Figure 21(B), the horizontal axis shows the number of days since the subject horse was infected with influenza virus, and the vertical axis shows the amount of amyloid A substance in the subject horse. In Figure 21(B), the symbol "●" indicates horse A, the symbol "▲" indicates horse B, and the symbol "■" indicates horse C.

[0147] As shown in Figure 21(A), for each of the subject horses, an increase in body temperature was confirmed two days after infection with the influenza virus. Therefore, the horse is identified as being infected with the influenza virus based on the increase in body temperature two days after infection with the influenza virus. In other words, it takes two days for a user to determine that a horse is infected with the influenza virus from its body temperature.

[0148] Furthermore, Figure 21(B) shows that for each of the subject horses, an increase in the amount of amyloid A substance was confirmed three days after infection with the influenza virus. Therefore, the horse is identified as being infected with the influenza virus based on the increase in the amount of amyloid A substance three days after infection with the influenza virus. In other words, for a user to identify that a horse is infected with the influenza virus from the amount of amyloid A substance in the horse, it takes three days after the horse is infected with the influenza virus. The method described in the comparative example can be considered as an example of a method for identifying the state of a subject organism using only biomolecules with known functions.

[0149] Figure 22 shows statistics calculated for the amount of each protein contained in the serum of each subject horse. Figure 22 shows statistics for the amount of protein contained in regions where changes in protein amount were confirmed, out of 338 regions defined in the two-dimensional electrophoresis image. In each of the graphs shown in Figure 22, the horizontal axis shows the number of days since the subject horse was infected with influenza virus, and the vertical axis shows the amount of protein contained in the region defined in the two-dimensional electrophoresis image. In the illustrated example, the protein substance amount of each subject horse and statistics on the protein substance amount of each subject horse are shown for each number of days since the subject horse was infected with the influenza virus, for regions 11, 18, 20, 25, 37, 38, 42, 56, 58, 59, 80, 101, 105, 106, 107, 108, 116, 119, 124, 125, 136, 137, and 169. The number "1" in each area of Figure 22 represents the amount of protein substance of horse A. The number "2" in each area of Figure 22 represents the amount of protein substance of horse B. The number "3" in each area of Figure 22 represents the amount of protein substance of horse C. The solid lines in each area of Figure 22 represent statistics calculated for the amount of protein substance of each subject horse.

[0150] Figure 22 confirms that for all subject horses, the amount of protein changed over time after influenza virus infection. In particular, statistics for regions 11, 18, 20, 101, 105, 106, 107, 119, 124, 125, 136, and 137 confirmed a decrease in the amount of protein one day after influenza virus infection. Furthermore, statistics for regions 37, 38, 42, 56, 58, 80, and 116 confirmed an increase in the amount of protein one day after influenza virus infection. Therefore, based on the change in the protein mass shown in Figure 22 one day after the horse was infected with the influenza virus, the horse is identified as being infected with the influenza virus. That is, it takes one day after the horse is infected with the influenza virus for the user to identify the horse as being infected with the influenza virus from the change in the protein mass shown in Figure 22. In other words, when a user uses a 2D electrophoresis image showing biomolecules to identify the state of a living organism, the state of the living organism can be identified more quickly than when using the body temperature or the amount of amyloid A in the living organism. Furthermore, because the state of a living organism is identified from changes in each biomolecule shown in the 2D electrophoresis image, the accuracy of identifying the state of a living organism can be improved compared to a configuration in which the state of a living organism is identified from changes in a single biomolecule. The method described in this example can be considered as an example of a method for identifying the state of a target living organism using not only biomolecules with known functions but also biomolecules with unknown functions.

[0151] Although the embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear from the claims that various modifications and improvements to the above embodiments are also included in the technical scope of the present invention.

[0152] In the above-described embodiment, the state identified by the state identification unit 109 for the target organism varies depending on the usage distribution information used in the state identification process (see FIGS. 9(A), 13(A), and 15). Here, the biomolecules shown in each usage distribution information are all biomolecules contained in the components obtained from each target organism, and it is not predetermined which biomolecules are obtained from the target organism.

[0153] In the above-described embodiment, the information acquiring unit 101 acquires a two-dimensional electrophoretic image containing information about a plurality of biomolecules as molecular information, but the invention is not limited to this. For example, the information acquiring unit 101 may acquire molecular information, which is information about a single biomolecule, for each biomolecule. That is, the information acquiring unit 101 acquiring molecular information about multiple biomolecules includes both acquiring molecular information, which is information about multiple biomolecules, and acquiring multiple pieces of molecular information for each biomolecule.

[0154] Furthermore, in the above-described embodiment, it was explained that the information indicating the distribution of the physical quantities of biomolecules is information indicating the molecular weight of the biomolecules, information indicating the isoelectric point of the biomolecules, information indicating the amount of substance of the biomolecules, etc., but it is not limited to this. The information indicating the distribution of the physical quantities of biomolecules may be, for example, information indicating the distribution of the charge amounts of biomolecules. Furthermore, for example, in a two-dimensional electrophoretic image, instead of the distribution of the isoelectric points of biomolecules, the distribution of the charge amounts of biomolecules may be shown. Furthermore, the information indicating the distribution of the physical quantities of biomolecules may be, for example, information indicating the distribution of the hydrophobicity of biomolecules or information indicating the three-dimensional structure of biomolecules. In this case, the two-dimensional electrophoretic image may be an image in which the distribution of the hydrophobicity of biomolecules is shown on one of the x-axis and y-axis, and the distribution of the molecular weight of biomolecules is shown on the other. Furthermore, the two-dimensional electrophoretic image may be an image in which the three-dimensional structure of biomolecules is shown on one of the x-axis and y-axis, and the distribution of the isoelectric points of biomolecules is shown on the other. Additionally, in a two-dimensional electrophoretic image, the information indicated on the x-axis and the information indicated on the y-axis may be information indicating any physical property, as long as they are information indicating different physical properties. Furthermore, the biomolecule analyzer 20 may create a three-dimensional electrophoretic image in which three pieces of information indicating different physical properties are plotted on the x-axis, y-axis, and z-axis, respectively, from among the pieces of information indicating the physical properties described above. This three-dimensional electrophoretic image may then be used to identify the state of a living organism.

[0155] In the above-described embodiment, the distribution information indicates the distribution of biomolecules, but the present invention is not limited to this. For example, information indicating the amount of substance of a biomolecule may be created for each isoelectric point or each charge amount of the biomolecule. Furthermore, information indicating the amount of substance of a biomolecule may be created for each molecular weight of the biomolecule. The server device 10 may then identify the distribution of biomolecules from the information created for each isoelectric point of the biomolecule, the information created for each charge amount of the biomolecule, the information created for each molecular weight of the biomolecule, etc.

[0156] Furthermore, in the above-described embodiment, the molecular information is described as information indicating the physical quantities of biomolecules, but the present invention is not limited to this. The molecular information may be, for example, information indicating whether or not a biomolecule is present. In other words, the molecular information may be any information relating to a biomolecule.

[0157] Furthermore, in the above-described embodiment, the biomolecules to be statistically analyzed by the statistical unit 106 are biomolecules that are associated with the same state among the individual states, but this is not limited to this. The biomolecules to be counted by the statistical unit 106 may be, for example, biomolecules obtained from multiple organisms when the number of days that have passed since the multiple organisms entered a specific inclusion state is the same.

[0158] Furthermore, in the above-described embodiment, the biomolecules to be statistically analyzed by the statistics unit 106 are biomolecules of different organisms, but the present invention is not limited to this. The biomolecules to be counted by the statistics unit 106 may include biomolecules of the same organism.

[0159] In the above-described embodiment, the distribution information used by the detection unit 107 for comparison is described as two pieces of distribution information from among three or more pieces of usage distribution information, the time points at which the biomolecules were obtained being closest to each other. However, this is not limited to this. The distribution information used by the detection unit 107 for comparison may be two pieces of distribution information from among three or more pieces of usage distribution information, the time points at which the biomolecules were obtained being far apart. As an example, the detection unit 107 may use the first time point distribution information and the fourth time point distribution information shown in FIG. 15 for comparison to detect a change from the biomolecule indicated in the first time point distribution information to the biomolecule indicated in the fourth time point distribution information.

[0160] Furthermore, in the above-described embodiment, the biomolecules compared by the detection unit 107 and the biomolecules determined as the comparison targets by the target determination unit 108 are each described as being biomolecules contained in partitioned areas with the same coordinates, but this is not limited to this. The biomolecules compared by the detection unit 107 and the biomolecules determined as comparison targets by the target determination unit 108 may be biomolecules contained in partitioned regions with different coordinates, as long as they are contained in corresponding partitioned regions. In this case, the detection unit 107 and the target determination unit 108 may identify the biomolecules contained in the corresponding partitioned regions on the condition that the biomolecules to be compared are each shown in a predetermined coordinate range. Furthermore, the detection unit 107 and the target determination unit 108 may identify the biomolecules contained in the corresponding partitioned regions based on the positional relationship in the distribution information between the biomolecules to be compared and other biomolecules.

[0161] Furthermore, in the above-described embodiment, the state identification unit 109 identifies the state of the target organism as a comprehensive state associated with statistical information indicating biomolecules that satisfy the same similarity condition, but this is not limited to this. The state specifying unit 109 may specify the state of the target organism as any one of the individual states associated with statistical information indicating biomolecules that satisfy the same similarity condition.

[0162] Furthermore, in the above-described embodiment, the state specifying unit 109 specifies the state of the target organism in accordance with changes in the substance amount of biomolecules in the target organism, but the present invention is not limited to this. For example, the detection unit 107 may detect, from the molecular information related to the target organism, a change in the molecular weight of the biomolecule, a change in the isoelectric point of the biomolecule, a change in the charge amount of the biomolecule, a change in the presence or absence of the biomolecule, etc. Then, the state identification unit 109 may extract molecular information related to a comparison organism that has experienced a change corresponding to the change detected by the detection unit 107, and identify the state of the comparison organism associated with the extracted molecular information as the state of the target organism. In other words, the change in the biomolecule used to identify the state of the target organism is not limited to a change in the amount of substance of the biomolecule.

[0163] Furthermore, in the above-described embodiment, the server device 10 acquires multiple pieces of molecular information for a single organism at different times when the biomolecules were obtained, and identifies changes in the biomolecules over time from the acquired multiple pieces of molecular information, but this is not limited to this. The server device 10 may acquire molecular information indicating changes in biomolecules over time, such as information indicating changes in biomolecules over a specific period of time. Examples of changes in biomolecules include changes in specific physical quantities of biomolecules and changes in the presence or absence of biomolecules. Examples of specific physical quantities include the amount of substance, molecular weight, isoelectric point, and charge amount. The server device 10 may then identify changes in biomolecules over time from the acquired molecular information. In other words, the molecular information may be any type of information as long as it allows the server device 10 to identify changes in biomolecules.

[0164] Furthermore, in the above-described embodiment, the change condition is defined as the change of a biomolecule, but the change condition is not limited to this. For example, the change condition may be that the biomolecules do not change. As one example, the change condition may be that the difference in the amount of substance between two biomolecules is equal to or less than a predetermined value. Then, the state identification unit 109 may extract molecular information indicating a biomolecule of a comparison organism that corresponds to a biomolecule that satisfies the change condition, and identify the state associated with the extracted molecular information as the state of the target organism. 9(B), if the change condition is satisfied by the fact that the amount of substance of the biomolecule contained in region 20 does not change, and if the change condition is satisfied by the fact that the amount of substance of the biomolecule contained in region 15 does not change, the state of the target organism may be identified as another state other than "influenza" or "COVID-19." Examples of other states include a state in which the target organism is healthy.

[0165] Furthermore, the molecular information described above that can identify the state of a biomolecule over time from before a specific action is exerted on a target organism to after this specific action is exerted includes not only information that can identify that a biomolecule has changed over time, but also information that can identify that a biomolecule has not changed over time.

[0166] Furthermore, the detection unit 107 may detect the degree of change in the biomolecules. More specifically, the detection unit 107 may detect, from each use distribution information, which of multiple stages the degree of change in the biomolecules included in the partitioned area is at. Then, the state identification unit 109 may extract distribution information related to a comparison organism that exhibits a change corresponding to the stage detected by the detection unit 107, and identify the state associated with the extracted distribution information as the state of the target organism.

[0167] Furthermore, although the state specifying unit 109 has been described as comparing the detected molecules with the biomolecules indicated in the statistical information, the present invention is not limited to this. The state identification unit 109 may, for example, compare the detected molecule with a biomolecule indicated in comparative distribution information for one comparison organism, and if the biomolecule indicated in the comparative distribution information satisfies the identity similarity condition, may identify the state associated with the comparative distribution information as the state of the target organism.

[0168] Furthermore, in the above-described embodiment, the subject organism is different from the comparison organism, but this is not limiting. The subject organism may be the same organism as the comparison organism.

[0169] Furthermore, in the above-described embodiment, the state identification unit 109 identifies the state of the target organism by comparing the biomolecules of the target organism with the biomolecules of a comparison organism, which is an organism with the same attributes as the target organism, but this is not limited to this. The condition identification unit 109 may identify the condition of the target organism by comparing the biomolecules of the target organism with the biomolecules of a comparison organism, which is an organism with different attributes from the target organism. For example, the condition identification unit 109 may identify the condition of the target organism by comparing the biomolecules of a human target organism with the biomolecules of a horse comparison organism. In this case, the server device 10 may identify characteristics of the physical quantities of the biomolecules for each attribute of the organism, and correct the physical quantities of the biomolecules shown in the comparison molecule information for each attribute of the comparison organism based on the identified characteristics. The server device 10 may then identify the condition of the target organism by comparing the biomolecules of the target organism with the corrected biomolecules in the comparison molecule information.

[0170] Furthermore, the condition specifying unit 109 may specify the condition of the target living organism based on the results of machine learning. The state identification unit 109 learns the relationship between the distribution of biomolecules and the state of a living organism using, as training data, distribution information and individual state information that associate the distribution of the substance amount of each biomolecule shown in the 2D electrophoresis image with the state of the living organism at the time the biomolecule shown in the 2D electrophoresis image was obtained from the living organism. Based on the learning results, the state identification unit 109 generates a learning model that inputs the distribution information and outputs individual state information. Then, the state identification unit 109 may identify the state of the target living organism from the distribution information related to the target living organism based on the generated learning model. In other words, the state identification unit 109 may identify the state of the target living organism from the distribution pattern of biomolecules shown in the distribution information related to the target living organism by performing pattern recognition by associating the distribution of biomolecules shown in the input distribution information with the individual state information. In this way, the state identification unit 109 may identify the state of the target living organism without comparing the biomolecules of the target living organism with other biomolecules. The biomolecules that are pattern-recognized by the state specifying unit 109 in the distribution information are regarded as target biomolecules that satisfy the conditions defined for the change in the biomolecules identified from the molecular information. The conditions defined for the change in the biomolecules identified from the molecular information are that the biomolecules identified from the distribution information are pattern-recognized by the state specifying unit 109. The target biomolecules that satisfy the conditions are the distribution of biomolecules shown in the distribution information.

[0171] Furthermore, in the above-described embodiment, the condition specifying system 1 is configured to include the server device 10 and the biomolecule analyzer 20, but the present invention is not limited to this. For example, a device in which the server device 10 and the biomolecule analysis device 20 are integrated may be provided in the condition determination system 1. That is, a single device provided in the condition determination system 1 may be used to analyze biomolecules, create molecular information, and determine the condition of the target organism.

[0172] Furthermore, in the present embodiment, the user inputs the individual status information and the comprehensive status information by operating the terminal 30 or the server device 10, but the present invention is not limited to this. For example, a living body may be photographed using a photographing means such as a video camera. Furthermore, an analyzing means for analyzing a living body may be used to analyze the living body shown in the photographed video, and individual status information or comprehensive status information indicating the status of the living body may be created from the analysis results, and the created information may be transmitted to the information acquiring unit 101. In this way, the information acquiring unit 101 may acquire information from a functional means different from the terminal 30.

[0173] In addition, in the present embodiment, the server device 10 is configured to identify the state of the target living organism, but the present invention is not limited to this. For example, the terminal 30 may have the functions of the server device 10. In other words, the terminal 30 may have the functions of the server device 10, such as the information acquisition unit 101, the storage unit 102, the region identification unit 103, the partition unit 104, the substance amount identification unit 105, the statistics unit 106, the detection unit 107, the target determination unit 108, the state identification unit 109, and the output unit 110.

[0174] Furthermore, a program for realizing an embodiment of the present invention may be provided in a state where it is stored on 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, or a semiconductor memory. It may also be provided via a communication means such as the Internet.

[0175] Furthermore, although multiple embodiments have been described above, the configuration included in one embodiment may be interchanged with the configuration included in another embodiment, or the configuration included in one embodiment may be added to another embodiment. [Explanation of symbols]

[0176] 1...Status identification system, 10...Server, 20...Biomolecular analysis device, 30...Terminal, 40...Status notification screen, 101...Information acquisition unit, 102...Storage unit, 109...Status identification unit, 110...Output unit

Claims

1. an acquisition means for acquiring a distribution image showing the distribution of both a first physical quantity and a second physical quantity different from the first physical quantity for a plurality of biomolecules of a specific organism at a plurality of time points; an identification means for identifying the state of the specific living body based on a plurality of distribution images acquired for the distribution at a plurality of time points; Equipped with the distribution image is an image in which the first physical quantity and the second physical quantity are identified for the plurality of biomolecules from a molecular image displayed as an image showing the biomolecules, the molecular image is displayed in an area of the distribution image that corresponds to at least the first physical quantity, the identification by the identification means is performed so as to identify the state according to an area in the distribution images where a condition defined for a change in the second physical quantity identified from the plurality of distribution images is satisfied.

2. The identification means If the condition is satisfied in a first region in the distribution image, the state is identified as a state indicating a relationship with a first item among the items classified regarding the state of the living body; The information processing system of claim 1, wherein when the condition is satisfied in a second area different from the first area in the distribution image, the state is identified as a state indicating a relationship with a second item among the items that is not related to the first item.

3. The information processing system according to claim 1 , wherein the specifying means specifies the state in response to the condition being satisfied in an area in the distribution image where the molecular image showing a part of one biomolecule is displayed.

4. the specifying means specifies the state in accordance with the fact that the condition is satisfied in a third region in the distribution image in which the molecular image showing a first portion of one biomolecule is displayed, and the fact that the condition is satisfied in a fourth region in which the molecular image showing a second portion of the one biomolecule different from the first portion is displayed; the third region is a region in which the condition is satisfied by an increase in the second physical quantity, The information processing system according to claim 1 , wherein the fourth region is a region in which the condition is satisfied by a decrease in the second physical quantity.

5. the plurality of distribution images include a first distribution image and a second distribution image acquired for the distribution at a time point different from the distribution shown in the first distribution image, the specifying means specifies the state in response to the condition being satisfied in a specific region in the second distribution image; the molecular image is not displayed in an area of the first distribution image corresponding to the specific area, The information processing system according to claim 1 , wherein the molecular image is displayed in the specific region in the second distribution image.

6. The plurality of biomolecules includes a first biomolecule and a second biomolecule different from the first biomolecule, 2. The information processing system according to claim 1, wherein the identification means identifies the state in accordance with the fact that the condition is satisfied in an area in the distribution image where the molecular image showing the first biomolecule is displayed, and the condition is satisfied in an area in the distribution image where the molecular image showing the second biomolecule is displayed.

7. The plurality of biomolecules further includes a third biomolecule different from both the first biomolecule and the second biomolecule; The identification means When the condition is satisfied in a region in the distribution image where the molecular image representing the first biomolecule is displayed and a region in which the molecular image representing the second biomolecule is displayed, and the condition is not satisfied in a region in which the molecular image representing the third biomolecule is displayed, the state is identified as a first state; 7. The information processing system of claim 6, wherein if the condition is met in the area in the distribution image where the molecular image showing the first biomolecule is displayed and the molecular image showing the third biomolecule is displayed, and the condition is not met in the area in which the molecular image showing the second biomolecule is displayed, the state is identified as a second state different from the first state.

8. The information processing system according to claim 6 , wherein the identifying means identifies the state depending on whether the condition is satisfied in an area in the distribution image where the molecular image showing the second biomolecule is displayed.

9. an acquisition means for acquiring a distribution image showing the distribution of both a first physical quantity and a second physical quantity different from the first physical quantity for a plurality of biomolecules of a specific organism at a plurality of time points; an output means for outputting information corresponding to a plurality of distribution images acquired for the distribution at a plurality of time points; Equipped with the distribution image is an image in which the first physical quantity and the second physical quantity are identified for the plurality of biomolecules from a molecular image displayed as an image showing the biomolecules, the molecular image is displayed in an area of the distribution image that corresponds to at least the first physical quantity, the output by the output means is performed so that the information is output according to an area in the distribution image in which a condition defined for a change in the second physical quantity identified from the plurality of distribution images is satisfied.

10. A program for causing a computer to function as the information processing system according to any one of claims 1 to 9.

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