Information processing device, information processing method, and information processing program

Abstract

To easily manage a measurement result of a body composition meter that performs measurement for various measurement items.SOLUTION: An information processing device comprises: a storage unit which stores association of associating a set of a numerical range of a measurement value and a unit with a measurement item of body measurement and a measurement result of the body; an imaging unit which acquires an image obtained by imaging the measurement result of the body displayed on a measurement device; a character acquisition unit which acquires the set of the measurement value and the unit from the acquired image; and an item specification unit which specifies the measurement item of the acquired set of the measurement value and the unit on the basis of the association and stores the specified measurement item and the acquired set of the measurement value and the unit in the storage unit in association with each other.SELECTED DRAWING: Figure 2

Description

[Technical field]

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

[0002] In recent years, application programs that centrally manage various measurement values ​​obtained by a body composition analyzer or the like have come into use.

[0003] For example, a technology has been proposed in which measurement values ​​and units are obtained from an image of the measurement results displayed on a display device using optical character recognition (OCR) or the like, and the measurement item of the measurement value is determined based on the obtained unit (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2002-150465 A Summary of the Invention [Problem to be solved by the invention]

[0005] Body composition monitors can measure various items, and some items are measured in the same units. Therefore, it is difficult to correctly determine the measurement items based on the units of the measurement results alone. For example, the units of weight and muscle mass are often expressed in "kg (kilograms)", so it is not possible to determine whether a measurement is weight or muscle mass just because the unit of the measurement is "kg (kilograms)". Therefore, the user has to visually check the measurement, which is a burden for the user.

[0006] One aspect of the disclosed technology has an object to provide an information processing device, an information processing method, and an information processing program that can easily manage the measurement results of a body composition meter that measures various measurement items. [Means for solving the problem]

[0007] One aspect of the disclosed technology is exemplified by an information processing device as follows: The information processing device includes a correspondence relationship that associates pairs of numerical ranges of measurement values ​​and units with measurement items of physical measurement, a storage unit that stores the physical measurement results, an imaging unit that acquires an image of the physical measurement results displayed on the measurement device, a character acquisition unit that acquires pairs of measurement values ​​and units from the acquired image, and an item identification unit that identifies the measurement items of the acquired pairs of measurement values ​​and units based on the correspondence relationship, and associates the identified measurement items with the acquired pairs of measurement values ​​and units and stores them in the storage unit. Effect of the Invention

[0008] According to the disclosed technology, it is possible to easily manage the measurement results of a body measurement device that measures various measurement items. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a hardware configuration of a smartphone according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a processing block of the smartphone according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of a numerical range management table stored in the management database. [Figure 4] FIG. 4 is a diagram illustrating an example of the first correlation management table stored in the management database. [Diagram 5] FIG. 5 is a diagram illustrating an example of the second correlation management table stored in the management database. [Figure 6] FIG. 6 is a diagram showing an example of a measurement result management table stored in the management database. [Figure 7] FIG. 7 is a first diagram showing an example of the measurement result displayed on the display unit of the body composition monitor. [Figure 8]FIG. 8 is a second diagram showing an example of the measurement result displayed on the display unit of the body composition monitor. [Figure 9] FIG. 9 is a diagram illustrating an example of a processing flow of the smartphone according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a processing flow of a process in which the identifying unit associates a measurement result with a measurement item. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] <Embodiment> The configurations of the embodiments shown below are examples, and the disclosed technology is not limited to the configurations of the embodiments. The information processing device according to the embodiment has, for example, the following configuration. The information processing device has a correspondence relationship that associates a pair of a numerical range of a measurement value and a unit with a measurement item of a physical measurement, a storage unit that stores the physical measurement result, an imaging unit that acquires an image of the physical measurement result displayed on the measurement device, a character acquisition unit that acquires a pair of a measurement value and a unit from the acquired image, and an item identification unit that identifies the measurement item of the acquired pair of the measurement value and the unit based on the correspondence relationship, and associates the identified measurement item with the acquired pair of the measurement value and the unit and stores them in the storage unit.

[0011] A body measurement device exemplified by a body composition meter can measure various measurement items such as body weight, body fat percentage, body fat mass, lean body mass, muscle mass, and skeletal muscle percentage. The information processing device stores in a storage unit the correspondence between the numerical range of the measurement value and the unit for such measurement items. The information processing device then identifies the measurement item based on the above correspondence and a pair of the measurement value and the unit acquired from an image captured by an imaging unit of the measurement result displayed on the measurement device. For example, the information processing device extracts the measurement item associated with the unit combined with the measurement value by the above correspondence. The information processing device then compares the measurement value with the numerical range associated with the extracted measurement item, and identifies the measurement item whose measurement value is within the numerical range. By performing such processing, the information processing device can identify the measurement item with higher accuracy than a technology that identifies the measurement item with the unit of the measurement value. The information processing device then associates the identified measurement item, measurement value, and unit and stores them in a storage unit, thereby easily managing the measurement results of the body measurement device that measures various measurement items.

[0012] Hereinafter, an embodiment in which the above-mentioned information processing device is applied to a smartphone will be further described with reference to the drawings. FIG. 1 is a diagram showing an example of a hardware configuration of a smartphone 100 according to an embodiment. For example, the smartphone 100 takes an image of a display of a body composition scale on which various measurement values ​​are displayed, acquires the measurement values ​​and units from the captured image, and stores them in a database. The smartphone 100 includes a Central Processing Unit (CPU) 101, a main memory unit 102, an auxiliary memory unit 103, and a camera 104. The CPU 101, the main memory unit 102, the auxiliary memory unit 103, and the camera 104 are connected to each other by a connection bus.

[0013] The CPU 101 is also called a microprocessor unit (MPU) or a processor. The CPU 101 is not limited to a single processor, and may have a multiprocessor configuration. A single CPU 101 connected to a single socket may have a multicore configuration. At least a part of the processing executed by the CPU 101 may be performed by a processor other than the CPU 101, for example, a dedicated processor such as a digital signal processor (DSP), a graphics processing unit (GPU), a numerical calculation processor, a vector processor, or an image processing processor. At least a part of the processing executed by the CPU 101 may be executed by an integrated circuit (IC) or other digital circuit. At least a part of the CPU 101 may include an analog circuit. The integrated circuit includes a large scale integrated circuit (LSI), an application specific integrated circuit (ASIC), and a programmable logic device (PLD). The PLD includes, for example, a field-programmable gate array (FPGA). The CPU 101 may be a combination of a processor and an integrated circuit. The combination is called, for example, a microcontroller unit (MCU), a system-on-a-chip (SoC), a system LSI, a chipset, etc. In the smartphone 100, the CPU 101 deploys a program stored in the auxiliary memory unit 103 in a working area of ​​the main memory unit 102, and controls peripheral devices through the execution of the program. This enables the smartphone 100 to execute processing that matches a predetermined purpose. The main memory unit 102 and the auxiliary memory unit 103 are recording media that the smartphone 100 can read.

[0014] The main storage unit 102 is exemplified as a storage unit directly accessed by the CPU 101. The main storage unit 102 includes a Random Access Memory (RAM) and a Read Only Memory (ROM).

[0015] The auxiliary storage unit 103 stores various programs and various data in a readable and writable recording medium. The auxiliary storage unit 103 is also called an external storage device. An operating system (OS), various programs, various tables, and the like are stored in the auxiliary storage unit 103. The OS includes a communication interface program that transfers data to and from external devices, etc. connected via the communication unit 104. The external devices, etc. include, for example, other information processing devices and external storage devices connected via a computer network, etc. The auxiliary storage unit 103 may be, for example, a part of a cloud system, which is a group of computers on a network.

[0016] The auxiliary storage unit 103 is, for example, an erasable programmable ROM (EPROM), a solid state drive (SSD), a hard disk drive (HDD), or the like.

[0017] The camera 104 is an imaging device including a lens and an image sensor. Examples of the image sensor include a Charge-Coupled Device (CCD) image sensor and a Complementary Metal Oxide Semiconductor (CMOS) image sensor. In the camera 104, the image sensor converts light incident through the lens into an electrical signal. The camera 104 generates still image data and video data based on the electrical signal converted by the image sensor. The camera 104 stores the generated still image data and video data in the auxiliary storage unit 103. The camera 104 storing the still image data and video data in the auxiliary storage unit 103 is also referred to as the camera 104 capturing a still image or video.

[0018] The smartphone 100 may further include, for example, an input unit that accepts operation instructions from a user, etc. Examples of such an input unit include input devices such as a keyboard, a pointing device, a touch panel, an acceleration sensor, or a voice input device.

[0019] The smartphone 100 may include, for example, an output unit that outputs data processed by the CPU 101 and data stored in the main memory unit 102. Examples of such an output unit include output devices such as a Liquid Crystal Display (LCD), a Plasma Display Panel (PDP), an Electroluminescence (EL) panel, or an organic EL panel.

[0020] <Processing block of smartphone 100> 2 is a diagram showing an example of a processing block of the smartphone 100 according to the embodiment. The smartphone 100 includes an imaging unit 11, a character acquisition unit 12, an item identification unit 13, and a management database 14. The smartphone 100 executes processes as each unit of the smartphone 100, such as the imaging unit 11, the character acquisition unit 12, the item identification unit 13, and the management database 14, by the CPU 101 executing a computer program deployed in an executable manner in the main storage unit 102.

[0021] The management database 14 is a database constructed in the auxiliary storage unit 103. FIG. 3 is a diagram showing an example of a numerical range management table 141 stored in the management database 14. The numerical range management table 141 includes the items of "measurement item", "unit", and "numerical range". The "measurement item" stores information indicating the measurement item measured by the body composition meter. The "unit" stores information indicating the unit attached to the measurement value of the associated measurement item. Note that the unit of the measurement value may be written differently depending on the model or manufacturer of the body composition meter, such as "Kg", "kg", and "kilogram". In consideration of such differences in writing, each unit may be registered in "unit" with multiple notations. The "numerical range" stores the numerical range of the measurement value when the associated measurement item is measured. In this way, in the numerical range management table 141, the unit of each measurement item and the numerical range of the measurement value are associated with each other. Note that the measurement items registered in the numerical range management table 141 may be appropriately selected according to the measurement target items of the body composition meter used.

[0022] FIG. 4 is a diagram showing an example of a first correlation management table 142 stored in the management database 14. The first correlation management table 142 includes the items of a "first measurement item" and a "second measurement item." The "first measurement item" and the "second measurement item" each store information indicating a measurement item to which the same unit is assigned. The "first measurement item" stores a measurement item whose measurement value is greater than that of the "second measurement item" when the same person is measured. That is, the first correlation management table 142 manages the magnitude relationship between the measurement items.

[0023] 5 is a diagram showing an example of the second correlation management table 143 stored in the management database 14. The first correlation management table 142 includes the items of "measurement item" and "approximation formula". "Measurement item" stores information indicating the measurement item calculated by the formula stored in "approximation formula". "Approximate formula" stores a formula for calculating an approximate value of the measurement item stored in "measurement item". In other words, the second correlation management table 143 is a table that associates correlations between each measurement item by mathematical formulas.

[0024] FIG. 6 is a diagram showing an example of a measurement result management table 144 stored in the management database 14. The measurement result management table 144 includes the items of "date," "measurement item," and "measurement result." "Date" stores information indicating the date on which the measurement was performed. "Measurement item" stores information indicating the measurement item that was measured. "Measurement result" stores information indicating the display unit of the body composition analyzer. The measurement results displayed in the measurement result management table 144 are stored in the measurement result management table 144. The measurement results are registered in the measurement result management table 144 by the item specifying unit 13.

[0025] The imaging unit 11 captures still images or videos using the camera 104 in response to a user's operation. The imaging unit 11 captures, for example, the measurement results displayed on the body composition meter. FIG. 7 is a first diagram showing an example of the measurement results displayed on the display unit 500 of the body composition meter. In the example of FIG. 7, the measurement results (measurements and units) of each measurement item are displayed in a list on the display unit 500. When the measurement results of each measurement item are displayed in a list in this way, the imaging unit 11 may capture a still image or video of the display unit 500 displayed in a list in response to a user's operation. FIG. 8 is a second diagram showing an example of the measurement results displayed on the display unit 500 of the body composition meter. In the example of FIG. 8, the measurement results of each item of muscle mass, body fat percentage, body water percentage, and body weight are displayed on the display unit 500 while being switched in this order. When the measurement results of each measurement item are displayed in order on the display unit 500 in this way, the imaging unit 11 may capture a still image of the display unit 500 on which each measurement result is displayed. Furthermore, the imaging unit 11 may capture a moving image of the display unit 500 in which the display of each measurement item is switched. Hereinafter, in this specification, the term "image" includes a still image and a moving image.

[0026] The character acquisition unit 12 acquires the measurement value and unit measured by the body composition monitor from the image captured by the imaging unit 11. The character acquisition unit 12 acquires a pair of the measurement value and the unit included in the image by, for example, OCR.

[0027] The item identification unit 13 identifies the measurement item corresponding to the pair of the measurement value and unit acquired by the character acquisition unit 12 based on the information stored in the management database 14. The item identification unit 13 registers the pair of the measurement value and unit acquired by the character acquisition unit 12 in the measurement result management table 144 in association with the identified measurement item.

[0028] <Processing flow of smartphone 100> Fig. 9 is a diagram showing an example of a processing flow of the smartphone 100 according to the embodiment. Hereinafter, an example of a processing flow of the smartphone 100 will be described with reference to Fig. 9.

[0029] At T1, the imaging unit 11 captures an image of the measurement results displayed on the display unit 500 of the body composition monitor using the camera 104. The imaging unit 11 stores the captured image in the auxiliary storage unit 103, for example.

[0030] At T2, the character acquisition unit 12 acquires a measurement result including a pair of a numerical value and a unit from the image captured at T1. At T3, the item identification unit 13 identifies a measurement item corresponding to the pair of the measurement value and the unit acquired at T2. Details of T3 will be described with reference to FIG. 10. At T4, the item identification unit 13 registers the measurement item and the measurement result in the measurement result management table 144 in association with each other.

[0031] FIG. 10 is a diagram showing an example of a processing flow of the process in which the item identifying unit 13 associates the measurement results with the measurement items. The processing in FIG. 10 shows an example of a detailed flow of the processing in T3 in FIG. 9. In the following processing flow, it is assumed that four pairs, namely, a first pair of "30" and "Kg", a second pair of "50" and "%", a third pair of "55" and "%", and a fourth pair of "120" and "Kg", are acquired as the measurement results in T2 in FIG. 9. It is also assumed that the contents of the numerical range management table 141, the first correlation management table 142, and the second correlation management table 143 are the same as those shown in FIGS. 3, 4, and 5. Hereinafter, an example of a processing flow of the process in which the item identifying unit 13 associates the measurement results with the measurement items will be described with reference to FIG. 10.

[0032] In T31, the item specifying unit 13 specifies the measurement item corresponding to the measurement result obtained in T2 of FIG. , and narrows down the results based on the unit of the measurement item. In the numerical range management table 141 illustrated in FIG. 3, the measurement items corresponding to the unit "Kg" are body weight and muscle mass. Therefore, the item identification unit 13 associates the first and fourth sets of measurement items whose unit is "Kg" with body weight and muscle mass. Also, in the numerical range management table 141 illustrated in FIG. 3, the measurement items corresponding to the unit "%" are body fat percentage and body water percentage. Therefore, the item identification unit 13 associates the second and third sets of measurement items whose unit is "%" with body fat percentage and body water percentage.

[0033] In T32, the item identifying unit 13 associates the measurement items narrowed down in T31 based on the numerical range of the measurement value. For example, the measurement items of the first set of "30" and "Kg" and the fourth set of "120" and "Kg" are associated with body weight and muscle mass. Here, in the numerical range management table 141 illustrated in FIG. 3, the numerical range of body weight is "10-200", and the numerical range of muscle mass is "3-100". The numerical value "30" of the first set belongs to both the numerical range of body weight and muscle mass. In addition, the numerical value "120" of the second set also belongs to both the numerical range of body weight and muscle mass. Therefore, at this stage, the item identifying unit 13 associates the measurement items of the measurement results indicated by the first set and the measurement results indicated by the fourth set with body weight and muscle mass.

[0034] Also, for example, the measurement items of the second set of "50" and "%" and the third set of "55" and "%" are associated with the body fat percentage and the body water percentage. Here, in the numerical range management table 141 illustrated in FIG. 3, the numerical range of the body fat percentage is "3-40", and the numerical range of the body water percentage is "45-70". The numerical value "50" of the second set belongs to both the numerical range of the body fat percentage and the body water percentage. Also, the numerical value "55" of the third set belongs to both the numerical range of the body fat percentage and the body water percentage. Therefore, at this stage, the item identification unit 13 associates each of the measurement items of the measurement results indicated by the second set and the measurement results indicated by the third set with the body fat percentage and the body water percentage.

[0035] In T33, the item identification unit 13 determines whether or not the pair of measurement value and unit for each pair indicating all measurement results is associated with one measurement item. If it is associated with one measurement item (YES in T33), the process ends. If it is not associated with one measurement item (NO in T33), the process proceeds to T34. In this example, since there are multiple associated measurement items, the process proceeds to T34.

[0036] In T34, the item identifying unit 13 associates the measurement items using the magnitude relationship between the measurement items registered in the first correlation management table 142. As described above, the measurement items associated with the first and fourth sets of measurement results are body weight and muscle mass. The item identifying unit 13 refers to the first correlation management table 142 illustrated in FIG. 4 to understand that the measurement value of body weight is greater than that of muscle mass. The item identifying unit 13 associates the fourth set, which has the greater measurement value, with body weight, and associates the first set, which has the smaller measurement value, with muscle mass. That is, by this process, the item identifying unit 13 can identify the measurement item of the first set as muscle mass and the measurement item of the fourth set as body weight. Note that the magnitude relationship between the body fat percentage and the body water percentage is not registered in the first correlation management table 142 illustrated in FIG. 4. Therefore, the item identifying unit 13 does not perform the process in T34 for the second and third sets.

[0037] In T35, item identification unit 13 determines whether or not the pair of measurement values ​​and units for all pairs indicating the measurement results has been associated with one measurement item. If it has been associated with one measurement item (YES in T35), processing ends. If it has not been associated with one measurement item (NO in T35), processing proceeds to T36. In this example, the second and third pairs have not been associated with one measurement item, so processing proceeds to T36.

[0038] In T36, the item specifying unit 13 narrows down the measurement items using the approximation formula registered in the second correlation management table 143. The second and third sets each include body fat percentage and body water content. The second correlation management table 143 is associated with the body fat percentage and has not yet been associated with one measurement item. Therefore, the item identifying unit 13 selects an approximate equation for calculating the body fat percentage and an approximate equation for calculating the body water percentage from among the approximate equations registered in the second correlation management table 143. Here, the second correlation management table 143 illustrated in FIG. 5 has an approximate equation for calculating the body fat percentage registered therein, but has not registered an approximate equation for calculating the body water percentage. Therefore, the item identifying unit 13 selects an approximate equation for calculating the body fat percentage. Note that, when a plurality of approximate equations for calculating the body fat percentage are registered in the second correlation management table 143, the item identifying unit 13 may select an approximate equation that can calculate the body fat percentage using the measured values ​​that have already been associated with the measurement items.

[0039] The item specifying unit 13 calculates the body fat percentage using the selected estimation formula. The body fat percentage calculated using the estimation formula is called the estimated body fat percentage. The item specifying unit 13 associates the first set with the body weight and the fourth set with the muscle mass. Then, the item specifying unit 13 substitutes the body weight and the muscle mass into the selected estimation formula to calculate that the estimated body fat percentage is 50%. The item specifying unit 13 associates the set of measurements of the second and third sets, the measurement value of which is closer to the estimated body fat percentage, with the body fat percentage. In this example, the measurement value "50" of the second set is closer to the estimated body fat percentage than the measurement value "55" of the third set. Then, the item specifying unit 13 associates the second set with the body fat percentage and the remaining third set with the body water percentage.

[0040] <Effects of the embodiment> The smartphone 100 according to the embodiment associates the measurement result with the measurement item based on the unit and the numerical range of the measurement result by referring to the numerical range management table 141. Therefore, it is possible to identify the measurement item with higher accuracy than a technique that identifies the measurement item of a measurement value based only on the unit.

[0041] Furthermore, the smartphone 100 associates the measurement results with the measurement items by using the magnitude relationship between the measurement values ​​of the measurement items (T34 in FIG. 10) or by using an approximate calculation formula (T36 in FIG. 10). By performing such processing, it is possible to identify the measurement items that correspond to the measurement results with even higher accuracy.

[0042] <Modification> In the embodiment described above, the measurement items corresponding to the measurement values ​​and units of the measurement results are narrowed down by referring to the numerical range management table 141. Furthermore, narrowing down is performed using the magnitude relationship between the measurement values ​​of the measurement items (T34 in FIG. 10) and narrowing down is performed using an approximate expression (T36 in FIG. 10). However, in this embodiment, at least one of the process of T34 and the process of T36 in FIG. 10 may be omitted. Even in such a case, the smartphone 100 according to this embodiment can identify the measurement item with higher accuracy than a technology that identifies the measurement item of the measurement value using only the unit.

[0043] In the embodiment described above, the smartphone 100 photographs the display unit 500 using the camera 104. However, the smartphone 100 may receive an input of an image of the display unit 500 of the body composition monitor photographed by another device. In such a case, the smartphone 100 does not need to include the camera 104.

[0044] The embodiments and modifications disclosed above can be combined with each other.

[0045] <<Computer-readable recording medium>> An information processing program that causes a computer or other machine or device (hereinafter, computer, etc.) to realize any of the above functions can be recorded on a recording medium that can be read by the computer, etc. Then, the computer, etc. can provide the function by reading and executing the program from the recording medium.

[0046] Here, a computer-readable recording medium refers to a recording medium that stores information such as data and programs electrically, magnetically, optically, mechanically, or chemically and can be read by a computer. Among such recording media, those that can be removed from a computer include, for example, flexible disks, magneto-optical disks, Compact Disc Read Only Memory (CD-ROM), Compact Disc-Recordable (CD-R), Compact Disc-ReWriterable (CD-RW), Digital Versatile Disc (DVD), Blu-ray Disc (BD), Digital Audio Tape (DAT), 8mm tape, memory cards such as flash memory, etc. Furthermore, examples of recording media that are fixed to a computer include hard disks and ROMs. [Explanation of symbols]

[0047] 100·Smartphone 101··CPU 102...Main memory 103...Auxiliary storage section 104··Camera 11 Imaging unit 12...Character acquisition part 13...Item specification part 14. Management Database 141 Numeric range management table 142 First correlation management table 143 Second correlation management table 144 Measurement result management table 500...Display section

Claims

1. a storage unit for storing a correspondence relationship that associates a set of a numerical range and a unit of a measurement value with a measurement item of physical measurement, the physical measurement results, and an approximation formula for calculating an approximate value of a second measurement item using the measurement value of a first measurement item; an imaging unit for acquiring an image of the body measurement results displayed on the measurement device; A character acquisition unit that acquires a pair of a measurement value and a unit from the acquired image; an item specifying unit that specifies a measurement item of a pair of the acquired measurement value and a unit based on the correspondence relationship, and stores the specified measurement item in the storage unit in association with the acquired measurement value and a pair of the unit; the item identifying unit further executes a process of associating a measurement value that is closest to the estimated value calculated using the estimation formula with the second measurement item. Information processing device.

2. The storage unit further stores a magnitude relationship between measurement values ​​of measurement items having a common unit, the item identification unit further executes a process of associating the acquired measurement value with a measurement item based on the magnitude relationship. The information processing device according to claim 1 .

3. acquiring an image of the body measurement results displayed on the measuring device; obtaining pairs of measurements and units from the obtained image; A process of identifying a measurement item of the acquired pair of measurement value and unit based on a correspondence relationship that associates the pair of the numerical range and unit of the measurement value stored in the storage unit with the measurement item of the physical measurement; and storing the identified measurement item in the storage unit in association with a set of the acquired measurement value and unit, The process of storing in the storage unit further includes a process of associating a measurement value that is closest to an approximate value calculated using an estimation formula that calculates an approximate value of a second measurement item using a measurement value of a first measurement item with the second measurement item. A computer implemented information processing method.

4. acquiring an image of the body measurement results displayed on the measuring device; obtaining pairs of measurements and units from the obtained image; A process of identifying a measurement item of the acquired pair of measurement value and unit based on a correspondence relationship that associates the pair of the numerical range and unit of the measurement value stored in the storage unit with the measurement item of the physical measurement; and storing the identified measurement item in the storage unit in association with a set of the acquired measurement value and unit, The process of storing in the storage unit further includes a process of associating a measurement value that is closest to an approximate value calculated using an estimation formula that calculates an approximate value of a second measurement item using a measurement value of a first measurement item with the second measurement item. An information processing program executed by a computer.

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