Information processing system and information processing method

Abstract

To immediately notify a user of the timing for obtaining an optimal amount of a product from cultured cells.SOLUTION: An information processing system (100) comprises: an odor measurement device (30) equipped with multiple odor sensor elements; and an estimation unit (13) which inputs feature values based on measurement signals output by the odor measurement device (30), information on cultivation conditions, and elapsed time since the start of cultivation into a pre-trained model, and outputs an estimation result from estimating the product yield in the ongoing cultivation.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 estimate the state of a specific object based on the odor of the object. For example, Patent Document 1 describes an odor identification device that uses coke odor and tar odor, which are odors collected from assumed odor sources in a steel plant, as reference odors and identifies the source and cause of unknown odors in a steel plant or the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2006-017467 Summary of the Invention [Problem to be solved by the invention]

[0004] When culturing cells that produce a product, the culture is stopped at the optimal timing that allows as much product as possible to be obtained. Conventionally, the timing to stop the culture can be determined in advance by collecting cells over time during the previous culture and measuring the change in the number of cells and the change in the amount of the product being produced. In addition, whether the timing at which the culture was stopped was appropriate can be verified by an analysis performed for each culture after the culture is completed. However, the growth of the cells during culture and the amount of the product produced can vary even under the same culture conditions, so the timing determined in advance is not always the optimal timing.

[0005] One aspect of the present invention is to provide an information processing system, an information processing method, etc., that can instantly inform a user of the timing when an optimal amount of product can be obtained from cells being cultured. [Means for solving the problem]

[0006] An information processing system according to one embodiment of the present invention comprises: an acquisition unit that acquires the measurement signal from an odor measuring device that measures the odor in a culture container during cell culture and outputs a measurement signal; and an estimation unit that inputs information about the culture conditions of a current culture, the elapsed time since the start of culture, and the feature quantities based on the measurement signal output from the odor measuring device into a trained model obtained by machine learning using teacher data including explanatory variables including a combination of information about the culture conditions, the time elapsed since the start of culture, and feature quantities based on the measurement signal when the cells were previously cultured using the culture container, and a target variable including yield information indicating the yield of the product produced from the cells at each of the elapsed times, and outputs an estimation result that estimates the yield of the product in the current culture, 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.

[0007] The method includes an acquisition step in which a computer acquires a measurement signal from an odor measuring device that measures the odor in a culture vessel while cells are being cultured; and an estimation step in which the computer inputs information about the culture conditions of a current culture, the time elapsed since the start of culture, and the feature amounts based on the measurement signal output from the odor measuring device into a trained model obtained by machine learning using teacher data including explanatory variables including a combination of information about the culture conditions, the time elapsed since the start of culture, and feature amounts based on the measurement signal when the cells were previously cultured using the culture vessel, and a target variable including yield information indicating at least one of the number of cells in the culture vessel and the amount of product produced from the cells at each of the elapsed times, and outputs an estimation result that estimates the yield of the product in the current culture, 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. Effect of the Invention

[0008] According to one aspect of the present invention, it is possible to provide an information processing system and an information processing method capable of instantly informing a user of the timing when an optimal amount of product can be obtained from cells being cultured. [Brief description of the drawings]

[0009] [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. [Diagram 2] 1 is a functional block diagram showing an example of an odor measuring device according to an embodiment of the present invention. [Diagram 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 a configuration of an estimation device according to an embodiment of the present invention. [Diagram 5] 1 is a flowchart showing an example of a process flow in which an information processing system according to an embodiment of the present invention estimates a yield of a product produced from a cell. [Figure 6] 1 is a functional block diagram showing an example of a 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 PREFERRED EMBODIMENTS

[0010] An embodiment of the present invention will be described below, but the present invention is not limited thereto. In addition, unless otherwise specified in this specification, "A to B" representing a numerical range means "A or more and B or less."

[0011] [Embodiment 1] (Information processing system 100) First, an overview of an information processing system 100 according to an embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a schematic diagram showing an example of the configuration of an information processing system 100 according to an embodiment of the present invention.

[0012] As shown in FIG. 1, the information processing system 100 includes an odor measuring device 30, an estimation device 10, and a user terminal 50. The odor measuring device 30 can output a measurement signal based on an odor in a culture vessel in which cells are being cultured. The user terminal 50 is a terminal device such as a personal computer used by a user of the information processing system 100, and transmits a detection signal to the estimation device 10 and receives the estimation result. The estimation device 10 can output an estimation result that estimates the yield of a product produced from the cells currently being cultured based on a feature based on the measurement signal, information on the culture conditions, and the elapsed time from the start of the culture. The estimation device 10 may perform estimation based on information on the culture conditions of the cells currently being cultured in addition to the feature and the elapsed time. Note that the present time refers to the timing at which the odor measuring device 30 measures the odor in the culture vessel in which cells are being cultured, which is the subject of estimation by the estimation device 10. As shown in FIG. 1, the information processing system 100 may include a user terminal 50 and an estimation device 10 connected via a wide area communication network 40. Furthermore, the user terminal 50 and the odor measurement device 30 may be connected by a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0013] 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.

[0014] According to the above configuration, the information processing system 100 inputs information on the culture conditions of the cells currently being cultured, the time elapsed since the start of culture, and features based on a measurement signal measuring an odor inside the culture vessel during culture into a trained model to estimate the yield of a product produced from the cells currently being cultured. Here, the trained model is machine-learned using teacher data including explanatory variables including a combination of information on the culture conditions, the time elapsed since the start of culture, and features based on a measurement signal when the cells were previously cultured using the culture vessel, and a response variable including yield information indicating the yield of a product produced from the cells over the elapsed time.

[0015] This enables the information processing system 100 to instantly inform the user of the optimal timing for obtaining a large amount of product from the cells being cultured.

[0016] In this specification, the term "cell" may be selected from at least one of microbial cells, animal cells, and plant cells. The microbial cells may include bacteria, fungi (e.g., Escherichia coli, Bacillus subtilis, Lactobacillus, etc.), archaea, and protists. The animal cells may include cells of vertebrates such as mammals including humans, fish, reptiles, amphibians, and birds, as well as cells of invertebrates such as insects. The cells may be any of cells such as cultured cells established as cell lines (e.g., cultured meat), induced pluripotent stem cells (iPS cells), unestablished normal cells obtained from biological tissues, transformed cells obtained by genetic engineering techniques, and pluripotent stem cells.

[0017] The culture vessel in which the cells are cultured is not particularly limited and may be a vessel of any shape. The culture vessel may be capable of containing at least one of a liquid medium and a solid medium for culturing the cells.

[0018] As used herein, the term "product" refers to any substance that can be produced by a cell. Examples of such products include, but are not limited to, proteins, amino acids, alcohols, and biodegradable plastics. The "product" may include, for example, the cells themselves that have proliferated by cell division. The term "optimum amount" refers to the amount that is considered optimal in the manufacture of any product when the product is used to manufacture the product. In other words, the optimal amount of the product may vary depending on the type of the product and the product manufactured using the product. In other words, the "timing at which the optimal amount of the product can be obtained" may be the timing at which the yield of the product is the highest, or the timing at which a predetermined yield of the product is obtained.

[0019] The number of odor measuring devices 30 included in the information processing system 100 according to one embodiment of the present invention is not particularly limited, and may be one or more, three or more, five or more, or ten or more. Furthermore, each of the odor measuring devices 30 may be installed in a different culture vessel, or some or all of the odor measuring devices 30 may be installed in the same culture vessel.

[0020] In the information processing system 100, the connection between the estimation device 10 and the odor measurement device 30 may be a wired connection or a wireless connection. The estimation device 10 and the odor measurement device 30 may be connected via a wide area communication network. The estimation device 10 may be configured on a server capable of communicating with the odor measurement device 30. In this case, the information processing system 100 may include a device such as a terminal device that outputs the estimation result by the estimation device 10 and allows the user to understand the estimation result.

[0021] In the information processing system 100, the odor measuring device 30 includes a plurality of sensor elements. The odor substances that the plurality of sensor elements can measure may be different from each other or may be the same. The measurement signal is output from each of the plurality of odor sensor elements. The sensor elements are described below. After that, the odor measuring device 30 to which the odor sensor element 31 is applied, and the information processing system 100 including the odor measuring device 30 and the estimation device 10 are described in detail.

[0022] (Odor measuring device 30) The outline and effects of an odor measurement device 30 to which an odor sensor element 31 is applied 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 including an odor measurement device 30 to which an odor sensor element 31 is applied. 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 odor measuring device 30 is disposed, for example, at a position where the gas in the culture vessel can reach. A filter may be provided on the path of the gas in the culture vessel until it reaches the position where the odor measuring device 30 is disposed. The filter may be, for example, an activated carbon filter. The position where the odor measuring device 30 is disposed may be, for example, a wall surface in the culture vessel where the gas in the culture vessel can reach. If necessary, a fan or the like capable of supplying air containing an odorant toward the odor sensor element 31 of the odor measuring device 30 may be provided.

[0024] The power supply 32 is a power supply 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 a lead wire to the odor sensor element 31. The voltage value supplied by the constant voltage power supply 32 is 0.5V to 10V, for example, 2.5V or 5.0V.

[0025] 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 in which the time is set by the user, or may be a radio-controlled clock.

[0026] The control unit 34 controls each part 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. The control unit 34 may also output information indicating the time when the odor was detected together with the measurement signal.

[0027] 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.

[0028] The odor measuring device 30 may further include a housing, although this is not a required component. The housing is a container capable of containing air containing an odor substance. When the odor measuring device 30 includes a housing, the odor sensor element 31 is disposed within the housing.

[0029] 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 identify various odor substances.

[0030] <Odor sensor element 31> 2 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-mentioned resin composition, a first metal wiring 313A, and a second metal wiring 313B. In the following description, 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.

[0031] The first metal wiring 313A and the second metal wiring 313B are 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. 2.

[0032] 2, 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 that is generally used in electronic circuits. Metal wiring 313 may be a 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.

[0033] 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.

[0034] When the electrical conductivity of the odorant receiving layer 315 (ie, 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 (eg, 500 μm) or less.

[0035] The odorant receiving layer may contain a resin composition. The resin composition may contain 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, the term "odorant receiving layer" refers to a layer that adsorbs an 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 a part of the odor sensor element 31. The odorant receiving layer 315 changes its electrical resistance value depending on the adsorption of an odorant, etc. In other words, the odor sensor element 31 is an odor detection device that includes such an odorant receiving layer 315, and the odor measurement method of the odor sensor element 31 may be a chemiresistor type. In addition, 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.

[0036] When the odor sensor element 31 is a chemiresistor type containing a resin composition, the change in electrical conductivity over time differs between when odor substance A is adsorbed and when odor substance B, which is different from odor substance A, is adsorbed, so that it is possible to detect and distinguish various odor substances. In the odor measurement device 30 described later, a plurality of odor sensor elements 31 are arranged, each of which includes a substrate 311 on which a configuration for detecting an odor substance (metal wiring 313 and an odor substance receiving layer 315) is provided. A plurality of sets including a plurality of odor substance receiving layers 315 are arranged on each substrate 311. Each of the plurality of odor sensor elements 31 may be equipped with a constant voltage power supply and a voltmeter. In the odor measurement device 30, one configuration for detecting an odor substance (metal wiring 313 and an odor substance receiving layer 315) may be arranged on each substrate 311. Alternatively, in the odor measurement device 30, a plurality of sets of configurations for detecting an odor substance (metal wiring 313 and an odor substance receiving layer 315) may be arranged on one 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.

[0037] 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. When the odorant receiving layers 315 included in the multiple odor sensor elements 31 have the same composition, the multiple odorant receiving layers 315 can detect the same odorant. When the multiple odor sensor elements 31 each include an odorant receiving layer 315 with a different composition, each of the multiple odorant receiving layers 315 responds differently to the odorant. In this way, by providing multiple sets of configurations for detecting odorants, the accuracy of odorant identification in the odor measurement device 30 can be improved.

[0038] The odor measuring device 30 described above can output the change over time in the electrical conductivity of the odor sensor element 31 for each odor substance when various odor substances are adsorbed to the odor sensor element 31. By applying this odor measuring device 30, it is possible to compare the change over time in the electrical conductivity of the odor sensor element 31 when odor substance A is adsorbed to the odor sensor element 31 with the change over time in the electrical conductivity of the odor sensor element 31 when odor substance B is adsorbed to the odor sensor element 31. Based on such a comparison result, it is possible to realize an estimation device 10 that can estimate the yield of a product in a currently ongoing culture, which will be described later, from the odor substances adsorbed to the odor sensor element 31.

[0039] (Estimation device 10) The outline and effects of the estimation device 10 are described below. The estimation device 10 is a device that estimates the current yield of a product produced by cells from a measurement signal output from the above-mentioned odor measuring device 30. The estimation device 10 uses a trained model obtained by machine learning, and is therefore capable of determining odor substances with high accuracy. The estimation device 10 may perform estimation based on a sensor signal obtained from a device other than the odor measuring device 30. The device other than the odor measuring device 30 is a device such as a sensor that can detect information related to the culture conditions of cells, for example, a carbon dioxide sensor (e.g., a CO 2 concentration meter), temperature sensor, pH sensor (e.g., pH meter), dissolved oxygen (DO) sensor (e.g., bioengineering The sensor may be a dissolved oxygen sensor (DO electrode) OX_Series, manufactured by Marubishi Corporation), a turbidity sensor (UV-1700, manufactured by Shimadzu Corporation), or a humidity sensor. The estimation device 10 may utilize a signal acquired from the odor measurement device 30 and a signal from a device other than the odor measurement device 30 described above.

[0040] 1 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, a communication unit 14, and an input unit 15 that inputs information regarding cell culture conditions, 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.

[0041] 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. Furthermore, the communication unit 14 may be another device that is capable of acquiring the estimation result from the estimation device 10 and has the function 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.

[0042] The input unit 15 is for accepting various input operations from a user, and may be, for example, a keyboard, a mouse, a touch panel, or the like.

[0043] <Control unit 1> First, the control unit 1 will be described. The control unit 1 includes an acquisition unit 11, an extraction unit 12, and an estimation unit 13. In addition, some of the blocks included in the control unit 1 may be omitted from the control unit 1 by giving the functions of the blocks to other devices capable of communicating with the estimation device 10.

[0044] The acquisition unit 11 acquires a measurement signal output from the odor measuring device 30. Specifically, the acquisition unit 11 acquires a detection signal from each of the multiple odor sensor elements 31. The acquisition unit 11 may acquire the measurement signal in real time, or at a predetermined time interval (e.g., 0.1 second intervals). The acquisition unit 11 preferably acquires the measurement signal every first hour. The acquisition unit 11 may store the acquired measurement signal data in the storage unit 2. The acquisition unit 11 may further acquire information indicating the elapsed time from the odor measuring device 30 in addition to the measurement signal.

[0045] The first time period may be, for example, but is not limited to, 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 8 hours, 10 hours, or more.

[0046] The extraction unit 12 extracts a feature quantity indicating a feature of a target odor from the measurement signal acquired by the measurement signal acquisition unit 11. The feature quantity extracted by the extraction unit 12 may be, for example, 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 multiple odor sensor elements included in the odor measurement device 30.

[0047] More specifically, the feature amount may be a signal intensity ratio of the measurement signals output by a plurality of odor sensor elements, or a difference in the signal intensity of the measurement signal output by a specific odor sensor element with respect to the signal intensity of the measurement signal output by another odor sensor element. Furthermore, the feature amount may be a waveform showing a change over time of the measurement signal, or a pattern of the change over time of the measurement signal.

[0048] The feature amount may be a difference or ratio between the signal intensity of a measurement signal measuring the odor of cells in culture and the signal intensity of a measurement signal output by the odor measuring device 30 based on a reference gas such as compressed air or dry nitrogen. Alternatively, the feature amount may be a difference or ratio between the signal intensity of a measurement signal measuring the odor of cells in culture and a preset threshold value. Alternatively, the feature amount may be a difference or ratio between the signal intensity of a measurement signal measuring the odor of cells in culture and the signal intensity of a measurement signal at a time point prior to the time to be estimated.

[0049] The estimation unit 13 inputs data indicating features based on the measurement signal, information on the culture conditions, and the elapsed time from the start of cell culture to the present into the trained model 21 to estimate the yield of the product in the current culture. The measurement signal may be measurement signal data stored in the memory unit 2. The estimation unit 13 may estimate the yield of the product by, for example, class classification. The estimation device 10 may store information indicating the time (date and time) when the cell culture was started. In this case, the estimation unit 13 may specify the elapsed time from the start of cell culture to the present based on the information and the current time. Alternatively, the estimation unit 13 may acquire information indicating the elapsed time from the odor measurement device 30.

[0050] The estimation unit 13 may estimate the yield of the product obtained by culture, such as the weight of the current product relative to the weight of the cells at the start of culture, or the weight of the current product relative to the weight of the product during past culture, as an estimate of the yield of the product. The estimation unit 13 may also estimate a value indicating the number of cells in the culture medium, for example, a numerical value indicating the current amount of cells in the culture medium when the amount of cells at the start of culture is set to 1. The estimation unit 13 may further estimate the progress of the cell culture based on the estimation results at multiple timings and the elapsed time. The estimation unit 13 may store the estimation result data in the storage unit 2.

[0051] The estimation device 10 may further include a judgment unit that judges whether the culture state is good or bad based on the estimation result by the estimation unit 13. The judgment unit acquires the estimation result indicating the culture state of the cells by the estimation unit 13, and judges whether the culture state of the cells is good or bad based on a preset threshold value. For example, when a value indicating the culture state of the cells estimated by the estimation unit 13 is within a threshold value range, the judgment unit judges that the culture state of the cells is good, and when the value is outside the threshold value range, the judgment unit judges that the culture state of the cells is bad. When the estimation device 10 includes a judgment unit, the communication unit 14 may transmit the judgment result by the judgment unit in addition to the estimation result by the estimation unit 13.

[0052] <Storage section 2> Next, the storage unit 2 will be described. The storage unit 2 may store a trained model 21 and user terminal data 22. Furthermore, measurement signal data and estimation result data may be further stored as necessary. Furthermore, the storage unit 2 may store cell culture condition data and data indicating the time point when the cell culture was started.

[0053] The measurement signal data is data of a measurement signal output from the odor measuring device 30 and acquired by the measurement signal acquiring unit 11. The measurement signal data may be labeled with information such as the measurement time, the measurement location, the identification number of the odor measuring device 30, and the type of measurement signal pattern.

[0054] 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 information about the culture conditions when cells were previously cultured using the culture vessel, the time elapsed since the start of culture, and a combination of features based on the measurement signal output from the odor measuring device 30 that measured the odor inside the culture vessel. The response variable includes yield information indicating the yield of a product produced from the cells at each time point elapsed since the start of cell culture.

[0055] The information on the culture conditions includes information on the cell culture conditions, such as the type of cells, the culture volume, the environment of the culture vessel in which the cells are cultured, etc. By including these pieces of information as explanatory variables in the trained model 21, it becomes possible to more accurately estimate the current yield of the product based on the information inputted from the input unit 15.

[0056] 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 machine, random forest, and neural network.

[0057] The estimation result data is a result of estimating the yield of the product in the currently performed culture, output from the estimation unit 13. The estimation result data may be labeled with the same information as the measurement signal data on which the estimation is based.

[0058] 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 a measurement signal transmitted from the user terminal 50. Examples of users of the information processing system 100 include an experimenter who cultures cells, a manufacturer of fermented foods, and a manufacturer who produces products from products obtained by cell culture.

[0059] (Processing performed by information processing system 100) An overview of an information processing method according to an embodiment of the present invention will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an overview of an information processing method by the information processing system 100.

[0060] In step S1, the odor measuring device 30 measures the odor in the culture vessel in which the cells are being cultured, and outputs the measured 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 the result every first hour as described above.

[0061] In step S2, the measurement signal acquiring unit 11 acquires the measurement signal output from the odor measuring device 30 (acquisition step). The measurement signal acquiring unit 11 may acquire the measurement signal in real time, or may acquire the measurement signal at a predetermined time interval (e.g., 0.1 second interval). The measurement signal acquiring unit 11 may store the acquired measurement signal in the storage unit 2.

[0062] In step S3, the extraction unit 12 extracts a feature from the measurement signal. The extraction unit 12 may extract, as the feature, for example, a ratio or difference of the measurement signals, a waveform indicating a change over time in the measurement signal, or a pattern of the change over time in the measurement signal.

[0063] In step S4, the estimation unit 13 inputs the feature amount extracted in step S3 into the trained model 21 to estimate the yield of the product in the currently performed culture (estimation step). At this time, the trained model 21 is obtained by machine learning using training data. The explanatory variables and the objective variables included in the training data are as described above. The estimation unit 13 may store the output estimation result in the storage unit 2.

[0064] In step S5, the communication unit 14 transmits (outputs) the estimation result estimated by the estimation unit 13 based on the user terminal data 22 to the user terminal 50 via the wide area communication network 40.

[0065] When cells are cultured in a medium, it is difficult to directly and time-dependently measure the yield of the product produced by the cells. Therefore, the yield of the product can only be quantified by electrophoresis or assay after the end of the culture. Another method is to measure the amount of cells in the medium by measuring the turbidity of the culture tank during cell culture, and estimate the yield of the product based on the measured amount. However, since the medium during cell culture is stirred, it is difficult to accurately measure the turbidity due to bubbles that form on the liquid surface.

[0066] There are methods for detecting gases generated during culture using gas chromatography or the like, but since the target gases vary depending on the type of cell being cultured and precise analysis using gas chromatography is required, it has been difficult to grasp the yield of the product over time.

[0067] When the information processing system 100 is used, the estimation unit 13 inputs information on the culture conditions of the current cell culture, the time elapsed since the start of the culture, and features based on a measurement signal measuring an odor inside the culture vessel during the culture into the trained model to estimate the yield of the product in the current culture. Here, the trained model is machine-trained using teacher data including explanatory variables including a combination of the culture conditions, the time elapsed since the start of the culture, and features based on the measurement signal when cells were cultured in the past, and a response variable including yield information indicating the yield of the product produced from the cells over the elapsed time.

[0068] This allows the information processing system 100 to accurately estimate the cell culture state and instantly inform the user of the timing at which an optimal amount of product can be obtained from the cells being cultured. In addition, in the information processing system 100, if the estimation device 10 performs estimation at predetermined time intervals and outputs the estimation result, the user can grasp the cell culture state in real time. The user can take appropriate measures by grasping the cell culture state and the timing at which the product yield becomes good. For example, an operation to stop the culture of the cells can be performed at the timing at which the product yield becomes good. In addition, the user can more appropriately manage the product yield by grasping the relationship between the elapsed time, the cell culture state, and the cell culture conditions.

[0069] [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 the description of matters that have already been described will be omitted.

[0070] (Configuration of Estimation Device 10a) 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.

[0071] The estimation device 10a includes a control unit 1a that controls each unit of the estimation device 10a, a storage unit 2a that stores various data used by the information processing device, a communication unit 14, and an input unit 15.

[0072] <Control unit 1a> The estimation unit 13a estimates the yield of the product produced by the cells in the current culture, and stores the estimation result data 23 in the storage unit 2a.

[0073] The identifying unit 18 identifies an operation on the culture medium for optimizing the yield of the product, based on the estimation result data 23 regarding the currently performed culture. The identifying unit 18 may identify the culture temperature, the amount of glucose or the like to be added, for obtaining an optimal amount of the product, based on, for example, information input from the input unit 15 and the estimation result data 23. The identifying unit 18 may also identify the time until the timing to end the culture, at which an optimal amount of the product can be obtained, based on the number of cells in the culture medium.

[0074] <Storage section 2a> The storage unit 2a may store estimation result data 23.

[0075] The estimation result data 23 is a result of estimating the yield of a product produced from cells, output from the estimation unit 14. The estimation result data 23 may be labeled with data such as the number of cells currently being cultured and the current yield of the product.

[0076] [Embodiment 3] Hereinafter, an overview of an information processing system 100a according to another embodiment of the present invention will be described 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.

[0077] 7, in the information processing system 100a, a measurement signal output by an odor measuring device 30 is transmitted to an estimation device 10 via a wide area communication network 40. In addition, a user terminal 50 receives, via the wide area communication network 40, an estimation result output from the estimation device 10 based on the measurement signal.

[0078] 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 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.

[0079] In the information processing system 100a, the user terminal data 22 may further include data in which the device number of the odor measuring device 30 and the terminal number of the user terminal 50 are linked together, in addition to the data described above.

[0080] With the above configuration, it is possible to transmit the estimation result to a user in an area distant from the odor measurement device 30, for example, a user terminal 50 used by an experimenter culturing cells. Furthermore, when there are multiple culture vessels used for culturing and the information processing system 100a includes multiple odor measurement devices 30 provided in each of the multiple culture vessels, it is possible to transmit the estimation result based on the measurement signal measured at multiple locations. Furthermore, when the information processing system 100a is used by users in multiple locations, the information processing system 100a includes multiple user terminals 50, and can transmit the estimation result based on the measurement signal to each of the user terminals 50 in the multiple locations.

[0081] [Embodiment 4] Hereinafter, an overview of an information processing system 100b according to another embodiment of the present invention will be described 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.

[0082] As shown in Fig. 8, in the information processing system 100b, the measurement signal output by the odor measuring device is directly transmitted to the estimation device 10a. The estimation device 10a outputs the result of estimating the yield of the product in the currently ongoing culture based on the acquired measurement signal. That is, it can be said that the estimation device 10a is integrated with the user terminal 50 in the information processing system 100b. In addition, in the information processing system 100b, the estimation device 10a and the odor measuring device 30 may be connected by a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0083] (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 to 3. The information processing system 100b includes an estimation device 10b and an odor measuring device 30. The information processing system 100b may include a wide area communication network 40 as necessary.

[0084] The estimation device 10b includes a control unit 1a that controls each unit 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 result.

[0085] <Control unit 1a> The control unit 1 a includes a measurement signal acquisition unit 11 , an extraction unit 12 , an estimation unit 13 , and an output control unit 16 .

[0086] 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.

[0087] With the above configuration, since the estimation device 10a is integrated with the user terminal 50, the time lag until an estimation result is obtained is reduced.

[0088] [Software implementation example] In the information processing systems 100, 100a, and 100b, the control block (particularly the control unit 1) may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software.

[0089] In the latter case, the information processing system 100, 100a, 100b includes a computer that executes instructions of 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 by the processor reading the program from the recording medium and executing it in the computer. The processor may be, for example, a CPU (Central Processing Unit). The recording medium may be a "non-transient tangible medium," such as a ROM (Read Only Memory), as well as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like. The information processing system may further include a RAM (Random Access Memory) that expands the program. The program may be supplied to the computer via any transmission medium (such as a communication network or a broadcast wave) that can transmit the program. 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.

[0090] 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.

[0091] 〔summary〕 The information processing system according to a first aspect of the present invention includes an acquisition unit that acquires the measurement signal from an odor measuring device that measures the odor in a culture container during cell culture and outputs a measurement signal, and an estimation unit that inputs information about the culture conditions of a current culture, the elapsed time since the start of culture, and the feature quantities based on the measurement signal output from the odor measuring device into a trained model obtained by machine learning using teacher data including explanatory variables including a combination of information about the culture conditions, the time elapsed since the start of culture, and feature quantities based on the measurement signal when the cells were previously cultured using the culture container, and a target variable including yield information indicating the yield of the product produced from the cells at each of the elapsed times, and outputs an estimation result that estimates the yield of the product in the current culture, and the odor measuring device includes a plurality of odor sensor elements, and the measurement signal is output from each of the plurality of odor sensor elements.

[0092] In the information processing system according to aspect 2 of the present invention, in the above aspect 1, 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 multiple odor sensor elements.

[0093] An information processing system according to aspect 3 of the present invention is, in aspect 1 or 2 above, wherein the odor measuring device is arranged at a position within the culture container that can be reached by gas, and a filter may be provided in the path that the gas takes to reach said position.

[0094] In the information processing system according to aspect 4 of the present invention, in any one of aspects 1 to 3, the cells may be at least one of microbial cells, animal cells, and plant cells, and the culture vessel may be capable of containing at least one of liquid medium and solid medium for culturing the cells.

[0095] 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 in a culture container while cells are being cultured, and an estimation step in which the computer inputs information about the culture conditions of a current culture, the elapsed time since the start of culture, and the feature quantities based on the measurement signal output from the odor measuring device into a learned model obtained by machine learning using teacher data including explanatory variables including a combination of information about the culture conditions, the elapsed time since the start of culture, and feature quantities based on the measurement signal when the cells were previously cultured using the culture container, and a target variable including yield information indicating at least one of the number of the cells in the culture container and the amount of the product produced from the cells at each of the elapsed times, and outputs an estimation result that estimates the yield of the product in the current culture, 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.

[0096] A sixth aspect of the present invention may be a control program for causing a computer to function as the information processing system of any one of the first to fourth aspects, and may be a control program for causing a computer to function as the acquisition unit, the estimation unit, and the determination unit.

[0097] A seventh aspect of the present invention may be a computer-readable recording medium having the control program according to the sixth aspect recorded thereon. [Explanation of symbols]

[0098] 10, 10a, 10b Estimation device 30 Odor measuring device 40 Wide Area Communication Network 50 User terminal 31 Odor sensor element 100, 100a, 100b Information Processing Systems 311 Substrate 313A 1st metal wiring 313B 2nd metal wiring 315 Odor receptor layer W Lead Wire

Claims

1. an acquisition unit that acquires a measurement signal from an odor measuring device that measures an odor in a culture container in which cells are being cultured and outputs the measurement signal; an estimation unit that inputs information about the culture conditions of a currently-running culture, the elapsed time from the start of the culture, and the feature quantities based on the measurement signal output from the odor measuring device into a trained model obtained by machine learning using training data including explanatory variables including a combination of information about the culture conditions, the elapsed time from the start of the culture, and feature quantities based on the measurement signal when the cells were previously cultured using the culture vessel, and a target variable including yield information indicating the yield of a product produced from the cells at each of the elapsed times, and outputs an estimation result that estimates the yield of the product in the currently-running culture; 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 odor measuring device is disposed at a position where the gas in the culture container can reach. The information processing system according to claim 1 .

4. The cell is at least one of a microbial cell, an animal cell, and a plant cell; The culture vessel is capable of accommodating at least one of a liquid medium and a solid medium for culturing the cells. 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 an odor in a culture vessel in which cells are being cultured; an estimation step in which the computer inputs information about the culture conditions of the currently-running culture, the elapsed time from the start of the culture, and the feature amounts based on the measurement signal output from the odor measuring device into a trained model obtained by machine learning using training data including explanatory variables including a combination of information about the culture conditions, the elapsed time from the start of the culture, and feature amounts based on the measurement signal when the cells were previously cultured using the culture vessel, and a target variable including yield information indicating at least one of the number of the cells in the culture vessel and the amount of the product produced from the cells at each of the elapsed times, and outputs an estimation result of estimating the yield of the product in the currently-running culture; 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 a computer to function as the acquisition unit and the estimation unit.

7. A computer-readable recording medium having the control program according to claim 6 recorded thereon.

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