Information processing system and information processing method

Abstract

To enable objective evaluation of characteristics of a flavor derived from target tea leaves.SOLUTION: An information processing system (100) disclosed herein comprises an odor measurement device (30) having multiple odor sensor elements, and an estimation unit (13) configured to input a measurement signal output from the odor measurement device (30) to a trained model to output an estimation result obtained by estimating an evaluation of characteristics of a flavor derived from target tea leaves.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

[0002] In recent years, devices have been developed that evaluate specific objects based on their odors. For example, Patent Document 1 describes an odor identification device that uses coke odors and tar odors collected from potential odor sources in steel plants as reference odors and identifies the source and cause of unknown odors in steel plants and the like.

[0003] However, it is difficult to objectively estimate the aroma of tea leaves using conventional techniques. It is also difficult to know the taste of tea beverages extracted from tea leaves from the tea leaves themselves. Therefore, in tea leaf retailers, for example, tea leaves are handed directly to customers, customers are allowed to sample tea beverages actually extracted from tea leaves, and the characteristics of the tea beverage, such as its aroma and taste, are explained. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2006-017467 A Summary of the Invention [Problem to be solved by the invention]

[0005] Using specialized equipment such as gas chromatography, it is possible to identify the components contained in tea leaves and tea drinks, but the results of such analysis do not necessarily coincide with consumer preferences.

[0006] One aspect of the present invention is to provide an information processing system, an information processing method, etc. for objectively evaluating the flavor characteristics derived from target tea leaves. [Means for solving the problem]

[0007] An information processing system according to one embodiment of the present invention comprises: an acquisition unit that acquires a measurement signal from an odor measuring device that measures an odor derived from target tea leaves and outputs the measurement signal; and an estimation unit that inputs the feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves into a trained model obtained by machine learning using training data that includes explanatory variables including feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves, and a target variable including evaluation information indicating the results of a sensory evaluation test regarding the flavor characteristics derived from the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics derived from the target tea leaves, wherein the odor measuring device comprises a plurality of odor sensor elements, and the measurement signal is output from each of the plurality of odor sensor elements.

[0008] An information processing method according to one aspect of the present invention includes an acquisition step in which a computer acquires a measurement signal from an odor measuring device that measures the odor derived from target tea leaves; and an estimation step in which the computer inputs the feature quantities based on the measurement signal output from the odor measuring device that measured the odor derived from the target tea leaves into a trained model obtained by machine learning using training data including explanatory variables including feature quantities based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves, and a target variable including evaluation information indicating the results of a sensory evaluation test regarding the flavor characteristics derived from the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics derived from the target tea leaves, wherein the odor measuring device has a plurality of odor sensor elements, and the measurement signal is output from each of the plurality of odor sensor elements. [Effects of the Invention]

[0009] According to one aspect of the present invention, it is possible to provide an information processing system and an information processing method for objectively estimating the flavor characteristics derived from target tea leaves. [Brief explanation of the drawings]

[0010] [Figure 1]1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 2] 1 is a functional block diagram showing an example of an odor measurement device according to an embodiment of the present invention. [Figure 3] FIG. 2 is a top view showing an example of the configuration of an odor sensor element. [Figure 4] 1 is a functional block diagram showing an example of the configuration of an estimation device according to an embodiment of the present invention. [Figure 5] 10 is a flowchart showing an example of the processing flow in which an information processing system according to one embodiment of the present invention estimates the taste characteristics of a tea beverage extracted from target tea leaves. [Figure 6] 1 is a functional block diagram showing an example of the configuration of an estimation device according to an embodiment of the present invention. [Figure 7] 1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 8] 1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 9] 1 is a functional block diagram showing an example of a configuration of an information processing system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] One embodiment of the present invention will be described below, but the present invention is not limited thereto. Furthermore, unless otherwise specified in this specification, the expression "A to B" representing a range of numerical values ​​means "A or more and B or less."

[0012] [Embodiment 1] (Information processing system 100) First, an overview of an information processing system 100 according to one embodiment of the present invention will be described using Fig. 1. Fig. 1 is a schematic diagram showing an example of the configuration of an information processing system 100 according to one embodiment of the present invention.

[0013] As shown in Fig. 1, the information processing system 100 includes an odor measurement device 30 capable of outputting a detection signal based on an odorous substance corresponding to the odor derived from target tea leaves, an estimation device 10 that outputs an estimation result estimating the flavor characteristics derived from the target tea leaves based on the detection signal, and a user terminal 50 that transmits the detection signal to the estimation device and receives the estimation result. As shown in Fig. 1, the information processing system 100 may be configured such that the user terminal 50 and the estimation device 10 are connected via a wide area communication network 40. Alternatively, the user terminal 50 and the odor measurement device 30 may be connected via a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0014] The wide area communication network 40 is not particularly limited, and may be the Internet, a telephone line network, a mobile communication network, a CATV communication network, a satellite communication network, etc. The wide area communication network 40 may further be connected to a cloud server that stores the detection signal output by the odor measuring device 30 and the estimation result output by the estimation device 10. When the wide area communication network 40 is connected to a cloud server, the estimation device 10 may be realized as the cloud server.

[0015] According to the above configuration, the information processing system 100 inputs features based on measurement signals measuring the odor derived from the target tea leaves into a trained model to estimate the flavor characteristics derived from the target tea leaves. Here, the trained model is machine-learned using training data including explanatory variables including features based on measurement signals measuring the odor derived from sample tea leaves (e.g., tea leaves whose taste is already well known) and objective variables including evaluation information indicating the results of a sensory evaluation test regarding the flavor characteristics derived from the sample tea leaves.

[0016] This allows the information processing system 100 to objectively evaluate the flavor characteristics of the target tea leaves based on the measurement results of the odor derived from the target tea leaves. For example, the information processing system can be used as a substitute for a questionnaire to identify tea leaf preferences. The information processing system can also be used to objectively compare the odor derived from newly harvested tea leaves with the odor derived from tea leaves harvested in the past, and therefore can be effectively used by tea producers when determining the quality of new tea leaves.

[0017] In this specification, "tea leaves" may refer to fresh tea leaves or dried tea leaves. Furthermore, tea leaves may be fermented tea leaves, unfermented tea leaves, or semi-fermented tea leaves.

[0018] In this specification, the "flavor characteristics derived from tea leaves" to be estimated may be, for example, the odor characteristics of the tea leaves themselves, the odor characteristics of a tea beverage extracted from the tea leaves, or the taste characteristics of the tea beverage. For example, the objective variable in the trained model may be the odor of the sample tea leaves themselves, and the information processing system 100 may estimate the odor of the target tea leaves themselves. Furthermore, the objective variable in the trained model may be the odor of the tea beverage extracted from the sample tea leaves, and the information processing system 100 may estimate the odor of the tea beverage extracted from the target tea leaves. Furthermore, the objective variable in the trained model may be the taste of the tea beverage extracted from the sample tea leaves, and the information processing system 100 may estimate the taste of the tea beverage extracted from the target tea leaves.

[0019] The information processing system may output the estimation results as, for example, multiple parameters, or may output a result that combines multiple parameters. The multiple parameters may include taste, odor, and appearance. More specifically, the information processing system may output estimation results for the odor and taste of the target tea leaves themselves, and the odor, taste, and appearance of a tea beverage extracted from the target tea leaves. Based on these estimation results, the information processing system may output a different type of tea leaf that has a flavor similar to the target tea leaves.

[0020] The information processing system 100 may be used by tea sellers to estimate consumer preferences. Also, the information processing system 100 may be used by tea consumers to estimate the aroma of target tea leaves in order to estimate their own preferences.

[0021] In the information processing system 100, the odor measurement device 30 includes multiple sensor elements. The multiple sensor elements may be capable of measuring different odor substances, or the same odor substances. The measurement signal is output from each of the multiple odor sensor elements. The sensor elements will be described below. After that, the odor measurement device 30 incorporating the odor sensor element 31 and the information processing system 100 including the odor measurement device 30 and the estimation device 10 will be described in detail.

[0022] (Odor measuring device 30) The outline and effects of an odor measurement device 30 employing an odor sensor element 31 will be described below with reference to Fig. 2. Fig. 2 is a functional block diagram showing an example of the configuration of an information processing system 100 equipped with an odor measurement device 30 employing an odor sensor element 31. The odor measurement device 30 includes an odor sensor element 31 that detects odor substances, a power source 32 (power supply), a clock 33 (timer), a control unit 34, and a communication unit 35. As described above, the odor measurement device 30 may be connected to a user terminal 50.

[0023] The power supply 32 is a power source for supplying power to the odor sensor element 31. The constant voltage power supply 32 supplies a constant current (for example, a direct current of 1 mA) via lead wires to the odor sensor element 31. The voltage value supplied by the constant voltage power supply 32 is 0.5 V to 10 V, for example, 2.5 V or 5.0 V.

[0024] The clock 33 measures the time. The clock 33 transmits the measured time to the control unit 34. The clock 33 may be a clock whose time is set by the user, or may be a radio-controlled clock.

[0025] The control unit 34 controls all the components of the odor measuring device. The control unit 34 also outputs the odor detected by the odor sensor element 31 as a measurement signal. The control unit 34 may output the odor measurement signal according to the time measured by the clock 33.

[0026] The communication unit 35 transmits the measurement signal output by the control unit 34. The communication unit 35 transmits the measurement signal to the wide area communication network 40, and the transmitted measurement signal is acquired by the estimation device 10.

[0027] The odor measuring device 30 may further include a housing, although this is not an essential component. The housing is a container capable of containing air containing odor substances. When the odor measuring device 30 includes a housing, the odor sensor element 31 is installed inside the housing.

[0028] The odor measuring device 30 outputs a measurement signal that indicates the change over time in the electrical conductivity of the odor sensor element 31 before and after an odor substance is adsorbed to the odor sensor element 31. This makes it possible to detect and distinguish various odor substances.

[0029] <Odor sensor element 31> 3 is a top view showing an example of the configuration of an odor sensor element 31. The odor sensor element 31 includes an odorant receiving layer 315 containing the above-described resin composition, a first metal wiring 313A, and a second metal wiring 313B. In the following, when there is no need to distinguish between the first metal wiring 313A and the second metal wiring 313B, they may be referred to as metal wiring 313.

[0030] The first metal wiring 313A and the second metal wiring 313B are each metal wirings that function as electrodes for measuring changes in the electrical conductivity of the odorant receiving layer 315 (i.e., the resin composition). That is, the first metal wiring 313A and the second metal wiring 313B are spaced apart from each other, and the odorant receiving layer 315 is in contact with at least a portion of the first metal wiring and at least a portion of the second metal wiring. In one example, the first metal wiring 313A and the second metal wiring 313B are metal wirings that are not in direct contact with each other, and may be metal wirings that are approximately parallel to each other, as shown in FIG. 3.

[0031] 3, metal wiring 313 including first metal wiring 313A and second metal wiring 313B may be disposed on substrate 311. Substrate 311 may be a substrate such as glass epoxy commonly used in electronic circuits. Metal wiring 313 may be metal wiring such as copper or gold. The thickness of each of first metal wiring 313A and second metal wiring 313B as viewed in a direction perpendicular to the surface of the substrate may be, for example, 10 μm to 2 mm.

[0032] The odorant receiving layer 315 may be in contact with at least a portion of the first metal wiring 313A and at least a portion of the second metal wiring 313B. The odorant receiving layer 315 may be arranged to fill the area between the first metal wiring 313A and the second metal wiring 313B, as shown in FIG.

[0033] When the electrical conductivity of the odorant receiving layer 315 (i.e., the electrical conductivity of the odor sensor element 31) is low, it is desirable that the distance between the first metal wiring 313A and the second metal wiring 313B be a predetermined distance (for example, 500 μm) or less.

[0034] The odorant receiving layer may contain a resin composition. The resin composition includes a resin and may further contain one or more types selected from a surfactant and a filler (e.g., a conductive carbon material). In this specification, "odorant receiving layer" refers to a layer that adsorbs the odorant to be identified. The odorant receiving layer 315 is formed from the above-mentioned resin composition. The odorant receiving layer 315 may be provided as part of the odor sensor element 31. The electrical resistance of this odorant receiving layer 315 changes in response to the adsorption of odorants, etc. In other words, the odor sensor element 31 is an odor detection device equipped with such an odorant receiving layer 315, and the odor measurement method of the odor sensor element 31 may be a chemiresistor type. Furthermore, the odor sensor element 31 is not limited to the above-mentioned chemiresistor type odor sensor element, and may include one or more types of odor sensor elements used in known odor sensors, etc.

[0035] When the odor sensor element 31 is a chemiresistor type containing a resin composition, the change in electrical conductivity over time differs between when odorant A is adsorbed and when odorant B, which is different from odorant A, is adsorbed, making it possible to detect and distinguish various odorants. In the odor measurement device 30 described below, multiple odor sensor elements 31 are arranged, each including a substrate 311 on which a structure for detecting odorants (metal wiring 313 and an odorant receiving layer 315) is provided. Each substrate 311 is provided with multiple sets including multiple odorant receiving layers 315. Each of the multiple odor sensor elements 31 may be equipped with a constant-voltage power supply and a voltmeter. In the odor measurement device 30, each substrate 311 may be provided with one structure for detecting odorants (metal wiring 313 and an odorant receiving layer 315). Alternatively, in the odor measurement device 30, multiple sets of structures for detecting odorants (metal wiring 313 and an odorant receiving layer 315) may be arranged on a single substrate 311. In the latter case, a constant voltage power supply and a voltmeter are connected to each of the sets provided on the substrate 311 .

[0036] The resin compositions contained in the odorant receiving layers 315 of the multiple odor sensor elements 31 included in the odor measurement device 30 may be the same or different. If the odorant receiving layers 315 included in the multiple odor sensor elements 31 have the same composition, each of the multiple odorant receiving layers 315 can detect the same odorant. Furthermore, if the multiple odor sensor elements 31 each include odorant receiving layers 315 with different compositions, each of the multiple odorant receiving layers 315 will respond differently to the odorant. In this way, by providing multiple sets of components for detecting odorants, the accuracy of odorant identification in the odor measurement device 30 can be improved.

[0037] The odor measuring device 30 described above can output the change in electrical conductivity of the odor sensor element 31 over time for each odor substance adsorbed to the odor sensor element 31. By applying this odor measuring device 30, it is possible to compare the change in electrical conductivity of the odor sensor element 31 over time when odor substance A is adsorbed to the odor sensor element 31 with the change in electrical conductivity of the odor sensor element 31 over time when odor substance B is adsorbed to the odor sensor element 31. Based on the results of such comparison, it is possible to realize an estimation device 10 that can estimate an evaluation of the flavor characteristics derived from target tea leaves, as described below, from the odor substances adsorbed to the odor sensor element 31.

[0038] (Estimation device 10) The following describes the overview and effects of the estimation device 10. The estimation device 10 is a device that estimates an evaluation of the flavor characteristics derived from the target tea leaves from the measurement signal output by the above-mentioned odor measurement device 30. The estimation device 10 uses a trained model obtained by machine learning, and is therefore able to determine odor substances with high accuracy.

[0039] 4 is a functional block diagram showing an example of the configuration of the estimation device 10. The estimation device 10 includes a control unit 1 that controls each unit of the estimation device 10, a storage unit 2 that stores various data used by the estimation device 10, and a communication unit 14, but is not limited to this configuration. For example, the storage unit 2 may be an external device attached to the estimation device 10. Furthermore, the estimation device 10 may be connected to a wide area communication network 40 as described above.

[0040] The communication unit 14 transmits the estimation result estimated by the estimation unit 13 to the user terminal 50 via the wide area communication network 40 based on the user terminal data 22. The communication unit 14 may transmit the estimation result directly to the user terminal 50, or may transmit the estimation result to a web page or the like on the wide area communication network 40 that is accessible by the user terminal 50. Alternatively, the communication unit 14 may be another device that is capable of acquiring the estimation result from the estimation device 10 and has the functionality of the communication unit 14. In this case, the estimation device 10 may transmit the result estimated by the estimation unit 13 via the other device.

[0041] <Control Unit 1> First, we will explain the control unit 1. The control unit 1 includes an acquisition unit 11, an extraction unit 12, and an estimation unit 13. Furthermore, some of the blocks included in the control unit 1 may be omitted from the control unit 1 by assigning their functions to another device that can communicate with the estimation device 10.

[0042] The acquisition unit 11 acquires the measurement signal output from the odor measuring device 30. Specifically, the acquisition unit 11 acquires the detection signal from each of the plurality of odor sensor elements 31. The acquisition unit 11 may acquire the measurement signal in real time, or may acquire the measurement signal at predetermined time intervals (e.g., 0.1 second intervals). The acquisition unit 11 preferably acquires the measurement signal every first time. The acquisition unit 11 may store the acquired measurement signal data in the storage unit 2.

[0043] The first time period may be, for example, but is not particularly limited to, 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 8 hours, 10 hours, or more.

[0044] The extraction unit 12 extracts feature quantities that indicate the characteristics of the aroma of the target tea leaves from the measurement signals acquired by the acquisition unit 11. The feature quantities extracted by the extraction unit 12 may be, for example, values ​​related to at least one of the signal intensity and the change over time of the measurement signals.

[0045] More specifically, the feature amount may be a signal intensity ratio of the measurement signals output by the plurality of odor sensor elements, or a difference in the signal intensity of the measurement signals output by a specific odor sensor element relative to the signal intensity of the measurement signals output by other odor sensor elements. Furthermore, the feature amount may be a waveform showing the change over time of the measurement signals or a pattern of the change over time of the measurement signals.

[0046] The estimation unit 13 inputs the measurement signal data into the trained model 21 to estimate the flavor characteristics derived from the target tea leaves. The estimation unit 13 may perform the estimation by, for example, class classification. Alternatively, the estimation unit 13 may perform the estimation by regression analysis. The estimation unit 13 may further estimate the type and quality of the target tea leaves. The estimation unit 13 may store the estimation result data in the storage unit 2.

[0047] <Storage section 2> Next, a description will be given of the storage unit 2. The storage unit 2 may store a trained model 21. Furthermore, measurement signal data and estimation result data may also be stored as necessary.

[0048] The trained model 21 is trained by machine learning using training data. The training data includes the following explanatory variables and objective variables. The explanatory variables include features based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves. The objective variable includes evaluation information indicating the results of a sensory evaluation test on the flavor characteristics derived from the sample tea leaves. The sample tea leaves may be the same type of tea leaves as the target tea leaves.

[0049] The trained model 21 may be generated using a known machine learning algorithm. Examples of machine learning algorithms that can be used to generate the trained model 21 include the k-nearest neighbor method, logistic regression, support vector machines, random forests, and neural networks.

[0050] The user terminal data 22 is data related to a terminal owned by a user of the information processing system 100. The user terminal data 22 is data for linking the user terminal 50 with the measurement signal transmitted from the user terminal 50. Examples of users of the information processing system 100 include tea leaf sellers, tea leaf producers, tea leaf consumers, etc.

[0051] (Processing performed by information processing system 100) An overview of an information processing method according to one embodiment of the present invention will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an overview of the information processing method by the information processing system 100. Note that what is estimated in the flowchart of Fig. 5 are the taste characteristics of a tea beverage extracted from target tea leaves.

[0052] In step S1, the odor measuring device 30 measures the odor derived from the target tea leaves and outputs a measurement signal. At this time, the odor measuring device 30 may output the odor measurement result to the estimation device 10 in real time, or may output it every first hour as described above.

[0053] In step S2, the acquiring unit 11 acquires the measurement signal output from the odor measuring device 30 (acquisition step). The acquiring unit 11 may acquire the measurement signal in real time, or may acquire the measurement signal at predetermined time intervals (e.g., 0.1 second intervals). The acquiring unit 11 may store the acquired measurement signal in the memory unit 2.

[0054] In step S3, the extraction unit 12 extracts a feature quantity based on the measurement signal. For example, the extraction unit 12 may extract, as the feature quantity, a ratio or difference of the measurement signals, a waveform indicating a change in the measurement signal over time, or a pattern of the change in the measurement signal over time.

[0055] In step S4, the estimation unit 13 inputs the feature amounts into the trained model 21 to estimate the flavor characteristics derived from the target tea leaves and outputs the estimation result (estimation step). At this time, the trained model 21 is obtained by machine learning using training data. The explanatory variables and objective variables included in the training data are as described above. The estimation unit 13 may store the output estimation result in the memory unit 2.

[0056] In step S5, the communication unit 14 transmits (outputs) the estimation result output by the estimation unit 13 to the user terminal 50 via the wide area communication network 40 based on the user terminal data 22.

[0057] Because the smell derived from the target tea leaves does not necessarily match the analysis results of the odorous substances contained in the smell derived from the target tea leaves, it has been difficult to estimate an objective evaluation of the tea smell of tea leaves and to estimate the smell of tea drinks from the tea leaves alone.By using the information processing system 100, the estimation unit 13 makes estimations using a trained model 21 that has been trained based on the results of a sensory evaluation test of the smell derived from the tea leaves, making it possible to objectively evaluate the flavor characteristics derived from the target tea leaves.

[0058] [Embodiment 2] An outline of an estimation device 10a according to another embodiment of the present invention will be described below with reference to Fig. 6. Note that descriptions of matters that have already been described will be omitted.

[0059] (Configuration of information processing system 100a) FIG. 6 is a functional block diagram showing an example of the configuration of an estimation device 10a different from that of the first embodiment.

[0060] The estimation device 10a includes a control unit 1a that controls all components of the estimation device 10a, a memory unit 2a that stores various data used by the information processing device, and an input unit 15. In addition to an acquisition unit 11, an extraction unit 12, and an estimation unit 13a, the control unit 1a also includes an identification unit 18 that identifies tea leaves that suit the subject's preferences from among multiple types of target tea leaves. The memory unit 2a stores a trained model 21a and estimation result data 23.

[0061] The input unit 15 is for receiving various input operations from the user and may be, for example, a keyboard, a mouse, a touch panel, etc. The input unit 15 may be used to input information including, for example, at least one of information regarding the subject's tea beverage preference, information regarding the subject's preference for sample tea leaves, and information regarding the subject's preference for other foods.

[0062] The subject may be a tea consumer or a tea purchaser, and the user and the subject may be the same or different.

[0063] <Control unit 1a> The estimation unit 13a estimates the flavor characteristics derived from a plurality of types of target tea leaves, and stores estimation result data 23 in the storage unit 2a.

[0064] The identification unit 18 identifies, from among the plurality of types of target tea leaves, tea leaves that match the subject's preferences regarding tea leaf-derived flavor characteristics, based on the estimation result data 23 for the plurality of types of target tea leaves. The identification unit 18 may, for example, compare information input from the input unit 15 with the estimation result data 23, and identify the target tea leaves that have the most in common as the tea leaves that match the subject's preferences. The identification unit 18 may also identify tea leaves that match the subject's preferences based on the subject's preferences for sample tea leaves.

[0065] <Storage section 2a> In addition to the items described above as explanatory variables, trained model 21a further includes at least one of information regarding tea beverage preferences, information regarding preferences for sample tea leaves, and information regarding other foods. By including these pieces of information as explanatory variables in trained model 21a, it becomes possible to more accurately identify tea leaves that suit the subject's preferences from among the target tea leaves based on the information input via input unit 15.

[0066] The estimation result data 23 is the result of estimating the flavor characteristics derived from the tea leaves, output from the estimation unit 14. The estimation result data 23 may be labeled with data such as human tendencies to prefer the target tea leaves and sample tea leaves similar to the target tea leaves.

[0067] [Embodiment 3] An overview of an information processing system 100a according to another embodiment of the present invention will be described below with reference to Fig. 7. Fig. 7 is a schematic diagram showing an example of the configuration of an information processing system 100a different from those of embodiments 1 and 2. Note that descriptions of matters that have already been described will be omitted.

[0068] 7, in the information processing system 100a, the measurement signal output by the odor measuring device 30 is transmitted to the estimation device 10 via a wide area communication network 40. In addition, the user terminal 50 receives the estimation result output from the estimation device 10 based on the measurement signal via the wide area communication network 40.

[0069] The information processing system 100a includes an estimation device 10, an odor measurement device 30, and a user terminal 50. The information processing system 100a may also include a wide area communication network 40, as necessary. As described above, in the information processing system 100a, the odor measurement device 30, the estimation device 10, and the user terminal 50 may be connected via the wide area communication network 40.

[0070] In the information processing system 100a, the user terminal data 22 may further include data linking the device number of the odor measuring device 30 with the terminal number of the user terminal 50, in addition to the data described above.

[0071] With this configuration, it is possible to transmit the estimation results to a user in an area distant from the odor measuring device 30, for example, to a person who plans to purchase tea leaves using the Internet, etc. Furthermore, if tea leaves are produced or sold at multiple locations and the information processing system 100a is equipped with multiple odor measuring devices 30 installed at each of the multiple locations, it is possible to transmit the estimation results based on the measurement signals measured at the multiple locations. Furthermore, if the information processing system 100a is used by users at multiple locations, the information processing system 100a is equipped with multiple user terminals 50, and the estimation results based on the measurement signals can be transmitted to each of the user terminals 50 at the multiple locations.

[0072] [Embodiment 4] An overview of an information processing system 100b according to another embodiment of the present invention will be described below with reference to Fig. 8. Fig. 8 is a schematic diagram showing an example of the configuration of the information processing system 100b. Note that descriptions of matters that have already been described will be omitted.

[0073] As shown in Fig. 8, in the information processing system 100b, the measurement signal output by the odor measurement device is directly transmitted to the estimation device 10b. The estimation device 10b outputs an estimation result of the evaluation of the flavor characteristics derived from the target tea leaves based on the acquired measurement signal. In other words, in the information processing system 100b, the estimation device 10b can be said to be integrated with the user terminal 50. Furthermore, in the information processing system 100b, the estimation device 10b and the odor measurement device 30 may be connected via a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0074] (Configuration of information processing system 100b) 9 is a functional block diagram showing an example of the configuration of an information processing system 100b different from those of Embodiments 1 and 2. The information processing system 100b includes an estimation device 10b and an odor measurement device 30. The information processing system 100b may also include a wide area communication network 40 as necessary.

[0075] The estimation device 10b includes a control unit 1a that controls all the components of the estimation device 10b, a storage unit 2 that stores various data used by the estimation device 10b, and an output unit 17 that outputs the estimation results.

[0076] <Control unit 1a> The control unit 1 a includes an acquisition unit 11 , an extraction unit 12 , an estimation unit 13 , and an output control unit 16 .

[0077] The output control unit 16 causes the output unit 17 to output the estimation result output by the estimation unit 13. The output mode of the output unit 17 is not particularly limited, and may be, for example, a display output, a print output, or an audio output.

[0078] With the above configuration, the estimation device 10b is integrated with the user terminal 50, so the time lag until an estimation result is obtained is reduced.

[0079] [Software implementation example] In the estimation devices 10, 10a, and 10b, the control blocks (particularly the control units 1 and 1a) may be realized by a logic circuit (hardware) formed on an integrated circuit (IC chip) or the like, or may be realized by software.

[0080] In the latter case, the estimation devices 10, 10a, and 10b each include a computer that executes instructions from a program, which is software that realizes each function. The computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. The object of the present invention is achieved when the processor in the computer reads and executes the program from the recording medium. The processor may be, for example, a central processing unit (CPU). The recording medium may be a "non-transitory tangible medium," such as a read-only memory (ROM), a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The device may also include a random access memory (RAM) for loading the program. The program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast waves). Note that one aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0081] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0082] 〔summary〕 The information processing system according to a first aspect of the present invention comprises an acquisition unit that acquires a measurement signal from an odor measuring device that measures an odor derived from target tea leaves and outputs the measurement signal; and an estimation unit that inputs the feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves into a trained model obtained by machine learning using training data that includes explanatory variables including feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves, and a target variable including evaluation information indicating the results of a sensory evaluation test regarding the flavor characteristics derived from the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics derived from the target tea leaves, wherein the odor measuring device comprises a plurality of odor sensor elements, and the measurement signal is output from each of the plurality of odor sensor elements.

[0083] An information processing system according to aspect 2 of the present invention is the information system of aspect 1, wherein the feature may be a value related to at least one of the signal intensity and the change over time of the measurement signal output from each of the plurality of odor sensor elements.

[0084] An information processing system according to a third aspect of the present invention is the information processing system according to the first or second aspect, wherein the target tea leaves and the sample tea leaves may be either fresh tea leaves or dried tea leaves.

[0085] The information processing system according to aspect 4 of the present invention may further include an identification unit that, in any of aspects 1 to 3 above, identifies tea leaves from among the plurality of types of target tea leaves that match the subject's preferences in terms of tea leaf-derived flavor characteristics, based on the estimation results for the plurality of types of target tea leaves.

[0086] An information processing method according to aspect 5 of the present invention includes an acquisition step in which a computer acquires a measurement signal from an odor measuring device that measures the odor derived from target tea leaves; and an estimation step in which the computer inputs the feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the target tea leaves into a trained model obtained by machine learning using training data including explanatory variables including feature amounts based on the measurement signal output from the odor measuring device that measured the odor derived from the sample tea leaves, and a target variable including evaluation information indicating the results of a sensory evaluation test regarding the flavor characteristics derived from the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics derived from the target tea leaves, wherein the odor measuring device has a plurality of odor sensor elements, and the measurement signal is output from each of the plurality of odor sensor elements.

[0087] A control program according to aspect 6 of the present invention is a control program for causing a computer to function as an information processing system according to any one of aspects 1 to 4, and is a control program for causing a computer to function as the acquisition unit and the estimation unit.

[0088] A recording medium according to a seventh aspect of the present disclosure is a computer-readable recording medium on which the control program according to the sixth aspect is recorded. [Explanation of symbols]

[0089] 10, 10a, 10b Estimation device 30 Odor measuring device 31 Odor sensor element 40 Wide Area Communication Network 50 User terminal 100, 100a, 100b Information Processing Systems 311 Substrate 313A 1st metal wiring 313B 2nd metal wiring 315 Odorant receptor layer W lead wire

Claims

1. an acquisition unit that acquires a measurement signal from an odor measurement device that measures an odor derived from target tea leaves and outputs the measurement signal; an estimation unit that inputs the feature quantities based on the measurement signals output from the odor measuring device that measured the odor of the target tea leaves into a trained model obtained by machine learning using training data that includes explanatory variables including feature quantities based on the measurement signals output from the odor measuring device that measured the odor of the sample tea leaves, and a target variable including evaluation information that indicates the results of a sensory evaluation test regarding the flavor characteristics of the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics of the target tea leaves; Equipped with The odor measuring device includes a plurality of odor sensor elements, The measurement signal is output from each of the plurality of odor sensor elements. Information processing system.

2. The feature amount is a value related to at least one of the signal intensity and the change over time of the measurement signal output from each of the plurality of odor sensor elements; The information processing system according to claim 1 .

3. The target tea leaves and the sample tea leaves are either fresh tea leaves or dried tea leaves. The information processing system according to claim 1 .

4. The apparatus further includes an identification unit that identifies, from among the plurality of types of target tea leaves, tea leaves that match the subject's preferences regarding flavor characteristics derived from the tea leaves, based on the estimation results for the plurality of types of target tea leaves. The information processing system according to claim 1 .

5. An acquisition step in which a computer acquires a measurement signal from an odor measuring device that measures the odor derived from the target tea leaves; an estimation step in which the computer inputs the feature quantities based on the measurement signals output from the odor measuring device that measured the odor of the target tea leaves into a trained model obtained by machine learning using training data including explanatory variables including feature quantities based on the measurement signals output from the odor measuring device that measured the odor of the sample tea leaves, and objective variables including evaluation information that indicates the results of a sensory evaluation test regarding the flavor characteristics of the sample tea leaves, and outputs an estimation result that estimates the flavor characteristics of the target tea leaves; Including, The odor measuring device includes a plurality of odor sensor elements, The measurement signal is output from each of the plurality of odor sensor elements. Information processing methods.

6. A control program for causing a computer to function as the information processing system according to claim 1 , the control program causing the computer to function as the acquisition unit and the estimation unit.

7. A computer-readable recording medium on which the control program according to claim 6 is recorded.

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