Method for calculating process parameter

Abstract

To provide a method for calculating process parameters to achieve a given process result.SOLUTION: A method for calculating process parameters includes the following steps of: providing a trained prediction model, obtained by machine learning of a dataset by a machine learning method, the dataset including a plurality of samples, each of the samples including a plurality of sample parameters, and the trained prediction model being used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting a predicted result of the trained prediction model and providing at least one confirmed input parameter of the input parameters; and comparing the expected result, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset by a reverse derivation algorithm to determine at least one non-confirmed input parameter of the input parameters.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for calculating process parameters, and in particular to a method for calculating process parameters that can be applied to a cell culture process to obtain corresponding process steps based on desired process effects. [Background technology]

[0002] With the progress of medical engineering technology, the application of regenerative medicine in clinical treatment of diseases is becoming more and more diverse. Regenerative medicine is a medical technology that mainly uses the regenerative ability of cells to repair damaged tissues and organs, and has a very wide range of applications. In addition, by combining medical technologies such as tissue engineering and molecular biology, it is expected that diseases that were once difficult to treat, such as diabetes, neurological diseases, cardiovascular diseases, and cancer, can be improved and treated. Currently, regenerative medicine is mainly applied to organ repair, immune cell therapy, and stem cell therapy. In addition, research and applications of cell therapy in regenerative medicine are attracting more and more attention from various fields. Cell therapy involves the transplantation of human body cells into an individual's body after a procedure of culturing or processing the cells outside the body.

[0003] In recent years, as many governments have gradually lifted the ban on the application of cell therapy, many scholars at home and abroad have been entering the research of cell therapy one after another. As a result, cell therapy has made remarkable progress in the treatment of many diseases, such as the treatment of skin defects with autologous fibroblasts, the treatment of knee joint cartilage defects with autologous chondrocytes, and the treatment of spinal cord injuries with autologous bone marrow mesenchymal stem cells. According to previous research, each cell in an individual's body has its own unique characteristics, so the types of cell preparations applied to each case are different according to the difference in disease and symptoms. Therefore, it is necessary to customize cells suitable for treatment according to each individual situation, which increases the complexity and difficulty of cells in the process. In addition, the quality of cell therapy products has a direct impact on the safety and efficacy of the treatment. Therefore, in the cell culture process, it is necessary to strictly control the cell growth state and monitor the culture parameters and environmental parameters of cell growth in real time to avoid cell contamination or deterioration of quality during the culture process. However, since the variability of cells is extremely high between different cases, the optimal culture parameters and environmental parameters of cell preparations applied to different cases are not completely the same. In addition, each cell preparation cannot be produced with fixed process parameters because each process parameter needs to be adjusted in the process to achieve the desired result for each cell preparation. In the prior art, a prediction model is obtained by training on a large number of sample data sets using machine learning. This prediction model can input various process parameters to generate predicted results of cell culture, so that the user can simulate the effect of the cell culture process to be designed in advance. However, the above prediction model trained by machine learning cannot reverse-estimate the process parameters of the cell culture process from the user's desired results. In other words, when designing a cell culture process, the user needs to try a large number of different process parameters, so a large amount of effort and resources are spent on designing and improving the cell culture process.

[0004] Therefore, there is a need to develop methods to further optimize cellular processes by enabling inverse estimation of process parameters that match desired outcomes. Summary of the Invention [Problem to be solved by the invention]

[0005] In view of the above, the present invention provides a method for calculating process parameters to solve the above-mentioned problems of the prior art. [Means for solving the problem]

[0006] The present invention provides a trained predictive model, the trained predictive model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, each sample including a plurality of sample parameters, the trained predictive model being used to input the plurality of input parameters and generate a predicted result corresponding to the input parameters; setting a predicted result of the trained predictive model as a target result and providing at least one confirmed input parameter among the input parameters; and a method for calculating process parameters, the method including the steps of: matching the target result, the at least one confirmed input parameter and the sample parameters of the samples in the dataset by an inverse inference algorithm to identify at least one unconfirmed input parameter among the input parameters.

[0007] The data set further includes a process data set, the process data set including a plurality of process samples, each process sample including a plurality of process sample parameters, each process sample parameter including a source parameter and an incubation parameter.

[0008] The source parameters for each process sample further include attribute data for the source of each process sample.

[0009] The incubation parameters for each process sample further include human, equipment, material, method and environment parameters for each process sample.

[0010] Each sample parameter includes at least one corresponding sample parameter and at least one unconfirmed reference parameter. In addition, the step of identifying at least one unconfirmed input parameter of the input parameters by the inverse inference algorithm by matching the target outcome, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset further includes the steps of: calculating a first vector of the at least one confirmed input parameter; obtaining at least one corresponding sample parameter from the sample parameters of the samples according to the at least one confirmed input parameter; calculating a second vector of the at least one corresponding sample parameter; matching the first vector with the second vector of each sample to select at least one candidate sample; combining the at least one unconfirmed reference parameter and the confirmed input parameters of the at least one candidate sample and inputting them into the trained prediction model to obtain a reference sample from the samples, where the second vector of the reference sample is close or consistent with the first vector, and the predicted outcome of the reference sample is close or consistent with the target outcome; and taking the at least one unconfirmed reference parameter in the sample parameters of the reference sample as the at least one unconfirmed input parameter.

[0011] In the process parameter calculation method of the present invention, the step of matching the first vector with the second vector of each sample to select at least one candidate sample further includes the step of calculating an included angle between the first vector and the second vector of each sample, and selecting the sample corresponding to the second vector having the smallest included angle with the first vector as the candidate sample.

[0012] In the process parameter calculation method of the present invention, the step of matching the first vector with the second vector of each sample to select at least one candidate sample further includes a step of calculating a distance between the coordinate of the end point of the first vector and the coordinate of the end point of the second vector of each sample using a distance function, and selecting the sample corresponding to the second vector having the smallest distance from the first vector as the candidate sample.

[0013] The trained predictive models include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Decision Trees, Support Vector Machines (SVM), Random Forests, K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), Linear Regression, Logistic Regression, Gradient Boosting Machines, Deep Belief Networks (DBN), Recursive Neural Networks (RecNN), Reinforcement Learning, Autoencoders, and Gaussian Processes. It is obtained by machine learning a dataset using either Neural Networks (NNNNs) or Complex Neural Networks (CNNs).

[0014] In the process parameter calculation method of the present invention, the step of matching the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset by the inverse inference algorithm to identify at least one unconfirmed input parameter among the input parameters further includes the steps of matching the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset by the inverse inference algorithm to identify the sample parameters of a first reference sample among the samples as reference input parameters, where the reference input parameters include at least one unconfirmed reference parameter, inputting the at least one confirmed input parameter and the at least one unconfirmed reference parameter into a trained predictive model to generate a reference prediction result, matching the reference prediction result with the target result, and if the reference prediction result matches the target result, identifying the at least one unconfirmed reference parameter as the at least one unconfirmed input parameter.

[0015] The method for calculating process parameters in the present invention further includes the step of excluding the first reference sample when the reference prediction result does not match the target result, and again using the inverse inference algorithm to match the target result, at least one confirmed input parameter and the sample parameters of the samples in the dataset, to obtain the sample parameters of the second reference sample among the samples as reference input parameters.

[0016] The method for calculating process parameters in the present invention further includes the steps of: matching at least one corresponding sample parameter of the samples in the dataset, and inputting the at least one confirmed input parameter and at least one unconfirmed reference parameter corresponding to each sample into the trained prediction model to generate corresponding reference prediction results; and matching the reference prediction results corresponding to each sample with the target result to find reference samples whose reference prediction results are consistent with the target result, and further forming a reference sample set.

[0017] The method for calculating process parameters in the present invention further includes the step of linearly combining the first K data in the reference sample set, and finding the optimal combination of unconfirmed input parameters through an optimization search method.

[0018] The present invention provides a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, each sample including a plurality of sample parameters, the trained prediction model being used to input the plurality of input parameters and generate a prediction result corresponding to the input parameters; setting a prediction result of the trained prediction model as a target result, and providing at least one confirmed input parameter among the input parameters, each sample parameter including at least one corresponding sample parameter corresponding to the at least one confirmed input parameter and at least one unconfirmed reference parameter; inputting the sample parameters of the at least one sample into the trained prediction model to obtain at least one candidate sample, the prediction result of the at least one candidate sample being approximate or consistent with the target result; calculating a first vector of the at least one confirmed input parameter and a second vector of the at least one corresponding sample parameter in the at least one candidate sample; matching the first vector with the second vector to select a reference sample, the second vector of the reference sample being approximate or consistent with the first vector; and taking the at least one unconfirmed reference parameter of the reference sample as the at least one unconfirmed input parameter.

[0019] The present invention provides a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, each sample including a plurality of sample parameters, the trained prediction model being used to input the plurality of input parameters and generate a prediction result corresponding to the input parameters; setting a prediction result of the trained prediction model as a target result, and providing at least one confirmed input parameter of the input parameters, each sample parameter including at least one corresponding sample parameter corresponding to the at least one confirmed input parameter and at least one unconfirmed reference parameter; calculating a first vector of the at least one confirmed input parameter; According to another aspect of the present invention, there is provided a method for calculating process parameters, the method comprising the steps of: obtaining at least one corresponding sample parameter from sample parameters of the samples based on one confirmed input parameter respectively; calculating a second vector of the at least one corresponding sample parameter; matching the first vector with the second vector of each sample to select at least one candidate sample, the second vector of the at least one candidate sample being close to or consistent with the first vector; inputting the sample parameters of the at least one candidate sample into the trained prediction model to obtain a reference sample, and the predicted result of the reference sample being close to or consistent with a target result; and taking the at least one unconfirmed reference parameter of the reference sample as at least one unconfirmed input parameter.

[0020] The present invention provides a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, each sample including a plurality of sample parameters, each sample parameter including at least one corresponding sample parameter and at least one unconfirmed reference parameter, the trained prediction model being used to input the plurality of input parameters and generate a prediction result corresponding to the input parameters; setting a prediction result of the trained prediction model as a target result and providing at least one confirmed input parameter among the input parameters; inputting a combination of the at least one confirmed input parameter and at least one unconfirmed reference parameter of each sample parameter into the trained prediction model to obtain a reference sample from the sample, the prediction result of the reference sample being close to or consistent with the target result; and setting the at least one unconfirmed reference parameter in the sample parameters of the reference sample as at least one unconfirmed input parameter. Effect of the Invention

[0021] In summary, the present invention provides a method for calculating process parameters by inputting a desired target result and confirmed input parameters and using a data set of a trained predictive model to inversely estimate unconfirmed input parameters, thereby effectively optimizing the process method and achieving the target result of the process. In addition to being applied to the field of cellular processes, the process parameter calculation method of the present invention can also be applied to any other trained machine learning model to inversely estimate process parameters and unconfirmed input parameters. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 shows a flow chart of the steps of a method for calculating process parameters according to an embodiment of the present invention. [Diagram 2]FIG. 2 shows a block diagram of a data set and a sample in a method for calculating process parameters according to a specific embodiment of the present invention. [Diagram 3] FIG. 3 shows a flow chart of the steps of a method for calculating process parameters according to an embodiment of the present invention. [Figure 4] FIG. 4 shows a flow chart of the steps of a method for calculating process parameters according to another embodiment of the present invention. [Diagram 5] FIG. 5 shows a flow chart of the steps of a method for calculating process parameters according to another embodiment of the present invention. [Figure 6] FIG. 6 shows a flow chart of steps of a method for calculating process parameters according to another embodiment of the present invention. [Figure 7] FIG. 7 shows a block diagram of a data set and a sample in a method for calculating a process parameter according to another specific embodiment of the present invention. [Figure 8] FIG. 8 shows a flow chart of steps of a method for calculating process parameters according to another embodiment of the present invention. [Figure 9] FIG. 9 shows a flow chart of steps of a method for calculating process parameters according to another embodiment of the present invention. [Figure 10] FIG. 10 shows a flow chart of steps of a method for calculating process parameters according to another embodiment of the present invention. [Figure 11] FIG. 11 shows a flow chart of steps of a method for calculating process parameters according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] In order to make the advantages, spirits and features of the present invention easier and clearer to understand, the present invention will be described and discussed in detail with reference to the drawings, using specific examples. It should be noted that these specific examples are merely representative specific examples of the present invention, and the specific methods, devices, conditions, materials, etc. illustrated do not limit the present invention or the corresponding specific examples. In addition, each component in the drawings is used only to represent their relative positions, and is not described based on actual ratios. In addition, the step numbers of the present invention are only used to distinguish different steps, and do not represent the order of steps. The above will be explained in advance.

[0024] Please refer to Figure 1 and Figure 2. Figure 1 shows a flowchart of the steps of the process parameter calculation method according to a specific embodiment of the present invention. And Figure 2 shows a block diagram of the data set and sample in the process parameter calculation method according to a specific embodiment of the present invention. As shown in Figure 1, in this specific embodiment, the process parameter calculation method includes:

[0025] Step S1: Provide a trained prediction model. The trained prediction model is obtained by machine learning a dataset using a machine learning method. The dataset includes a plurality of samples (including sample 1 to sample n). In addition, each sample includes a plurality of sample parameters. The trained prediction model is used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters.

[0026] Step S2: Set the predicted result of the trained predictive model as the target result, and provide the confirmed input parameters 10 among the input parameters.

[0027] Step S3: Using an inverse inference algorithm, the target result, the confirmed input parameters and the sample parameters of the samples in the data set are matched to identify unconfirmed input parameters 12 among the input parameters.

[0028] The trained prediction model in step S1 in this specific embodiment may be any model that has completed machine learning training and is available on an open platform, or may be a model that has been trained by a user. In addition, the dataset in this specific embodiment may be a set of any data that can be used for training, testing, and validation of machine learning. In practical applications, the method for calculating process parameters in this specific embodiment may be applied to any other trained machine learning model, and the confirmed input parameters are used to match data in a database, and the confirmed input parameters are combined with unconfirmed reference parameters to inversely estimate the unconfirmed input parameters.In addition, the trained prediction models in this specific embodiment may be Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Decision Trees, Support Vector Machines (SVM), Random Forests, K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), Linear Regression, Logistic Regression, Gradient Boosting Machines, Deep Belief Networks (DBN), Recursive Neural Networks (RecNN), Reinforcement Learning, Autoencoders, Gaussian Processes, and the like. The data sets are obtained by machine learning using the Machine Learning Processes and Complex Neural Networks, or some other machine learning or neural network algorithm. The choice of machine learning or neural network algorithm depends on the needs of the user.

[0029] The data set in this specific embodiment may further include a process data set. The process data set includes a plurality of process samples. Each process sample includes a plurality of process sample parameters, and each process sample parameter includes a source parameter and a culture parameter. When the process parameter calculation method in this specific embodiment is applied to the field of cell culture process, the process data set may be a cell data set. In practice, the cell data set may be any data obtained from an open platform, or may be data collected by a user himself. In addition, the type of cell sample may be immune cells (e.g., dendritic cells (DC cells), cytokine-induced killer cells (CIK), tumor-infiltrating lymphocytes (TIL), natural killer cells (NK cells) and CAR-T cells), stem cells (e.g., peripheral blood stem cells, adipose stem cells, bone marrow mesenchymal stem cells), chondrocytes, fibroblasts, etc., but is not limited to these in actual applications and may be determined according to the type of cell culture that the user wants to perform. In addition, in this specific embodiment, the source parameters of each cell sample may further include attribute data of the source of each cell sample. Each cell sample in the cell dataset may include a cell source corresponding to the cell and attribute data of the source, which may be physiological data of the source or other data related to the source, such as the source's gender, age, medical history, living environment, residential area, etc. However, in practical applications, the source parameters of the cell sample may further include other parameters related to the cell source that may affect cellular processes.

[0030] In this specific embodiment, the culture parameters of each cell sample further include human, equipment, material, method, and environment parameters for each cell sample. The cell culture process includes many steps, and each step is related to many culture parameters. The culture parameters include human-related parameters such as the gender and age of the cell source, the experience and stability of the cell culture operator, the personnel performing the operation, the location, environment, transportation, etc., equipment-related parameters such as the type and grade of the cell operation platform, the stability and accuracy of the temperature control and humidity control of the cell culture device, material-related parameters such as the material of the cell culture dish, the components and ratios and mixtures of the cell culture medium, method-related parameters such as the technique of the cell culture operator, the method of the cell culture process, and environment-related parameters such as the environmental temperature, humidity, carbon dioxide, and organic molecule concentration of the cell culture.

[0031] In this specific embodiment, the method for calculating process parameters uses an inverse inference algorithm to find unconfirmed process parameters in a process waiting for confirmation by comparing with a data set for generating a trained prediction model. In detail, if the trained model used by the user has a good prediction effect, the input process parameters cannot be inversely estimated based on the trained model from the prediction effect alone, but the trained model is obtained by machine learning the data set, so the process parameter data in the data set can be used as the basis for inverse estimation of the process parameters.

[0032] Please refer to Figure 1, Figure 2 and Figure 3 together. Figure 3 shows a flow chart of steps of a method for calculating process parameters according to a specific embodiment of the present invention. The specific embodiment will be further described below with reference to Figure 3. In this specific embodiment, each sample parameter includes at least one corresponding sample parameter and at least one unconfirmed reference parameter. As shown in Figure 3, step S3 of the method for calculating process parameters in this specific embodiment further includes steps S31, S32, S33, S34, S35 and S36, which are sequentially performed after step S2.

[0033] Step S31: Calculate a first vector of verified input parameters 10.

[0034] Step S32: Based on the confirmed input parameters 10, corresponding sample parameters C1 to Cn are obtained from the sample parameters of each sample.

[0035] Step S33: A second vector of each of the corresponding sample parameters C1 to Cn is calculated.

[0036] Step S34: Match the first vector with the second vector of each sample to select a candidate sample i (ie, sample i).

[0037] Step S35: obtain a reference sample i from the sample by combining the unconfirmed reference parameters Pi of the candidate sample i and the confirmed input parameters 10 and inputting them into the trained prediction model, the second vector of the reference sample i is close to or consistent with the first vector, and the prediction result of the reference sample i is close to or consistent with the target result.

[0038] Step S36: The unconfirmed reference parameter Pi in the sample parameters of the reference sample i is set as the unconfirmed input parameter 12.

[0039] As shown in Figures 1, 2 and 3, the process awaiting confirmation may include an already-cultured stage and an awaiting-culture stage (not shown). In addition, the process awaiting confirmation further includes cultured process parameters (i.e., confirmed input parameters 10) and awaiting-culture process parameters (i.e., unconfirmed input parameters 12) corresponding to the already-cultured stage and the awaiting-culture stage. In this specific embodiment, the data set may include process parameter data having an incubation time and flow related to or similar to the process awaiting confirmation as a basis for inversely estimating the unconfirmed input parameters 12. The data set includes samples 1 to n. Each sample includes sample parameters (not shown), which further include corresponding sample parameters C1 to Cn and unconfirmed reference parameters P1 to Pn. For example, a user may be attempting to implement a 21-day process and have already implemented the process for 7 days (i.e., the user already has confirmed input parameters 10), but may not have confirmed how to implement or adjust the process parameters (i.e., unconfirmed input parameters 12) for the subsequent 8th through 21st days in order to achieve the target result the user desires on the pre-defined 21st day.

[0040] In the inverse inference algorithm of step S3 in this specific embodiment, first, the confirmed input parameters 10 when the user performed for 7 days are compared with the corresponding sample parameters C1 to Cn of the first 7 days of all samples in the data set (i.e., sample 1 to sample n) to select a candidate sample from the data set. Then, the unconfirmed reference parameters of the candidate sample are combined with the confirmed input parameters 10 of the process waiting for confirmation to form a reference sample. Next, the reference sample is input to the trained prediction model to confirm whether the reference sample matches the desired target result. If it matches, the unconfirmed reference parameters of this reference sample can be used as the unconfirmed input parameters 12 of the process waiting for confirmation. For example, if the confirmed input parameters 10 are compared with all the corresponding sample parameters C1 to Cn in samples 1 to n and it is found that the corresponding sample parameters Ci of sample i are most similar or identical to the confirmed input parameters 10, the unconfirmed reference parameters Pi of sample i and the confirmed input parameters 10 are used to form a reference sample i, and all parameters of the reference sample i are input to the trained prediction model. Then, if the predicted result is equal to or better than the target result, the unconfirmed reference parameter Pi can be the unconfirmed input parameter 12 of the process waiting for confirmation. That is, it can be the process parameter for the 8th to 21st days. In this specific embodiment, the selection of the candidate sample may further include calculating the included angle between the first vector and the second vector of each sample, and the sample corresponding to the second vector having the smallest included angle with the first vector may be the candidate sample. In addition, in practice, the process waiting for confirmation may have only a waiting-to-culture stage without a cultured stage, and may include only the corresponding waiting-to-culture process parameters (i.e., the unconfirmed input parameters 12), and the first vector in this embodiment may be an empty vector.

[0041] In the above specific embodiment, in step S3, the confirmed input parameters are matched with the sample parameters of the samples in the data set to obtain a suitable sample, and the process parameters (unconfirmed input parameters 12) for the 8th to 21st days are identified from the sample parameters. Therefore, hereinafter, how to obtain a suitable sample by matching using the confirmed input parameters will be described. Here, FIG. 2 and FIG. 3 are referred to together. As shown in FIG. 3, in this specific embodiment, the first vector of the confirmed input parameters 10 in the process waiting for confirmation is used to match with the second vector of the corresponding sample parameters C1 to Cn in the sample parameters of all samples (samples 1 to n) in the data set. Then, if the second vector of sample i is close to or coincides with the first vector of the confirmed input parameters 10 in the process waiting for confirmation (i.e., the included angle between the two vectors is close to or is 0 degrees), and if the sample i is input into the trained prediction model and a prediction result equal to or better than the target result is obtained, the sample i can be used as the reference sample i. Next, the user can set sample parameters other than the corresponding sample parameters in this reference sample i (ie, unconfirmed reference parameters Pi) as unconfirmed input parameters 12.

[0042] In addition, in this specific embodiment, when step S34 is performed to compare the first vector with the second vector of each sample, if the second vector of sample 1 is not close to or does not match the first vector of the confirmed input parameter 10 in the confirmation waiting process (i.e., the included angle between the two vectors is not close to 0 degrees), or if a prediction result that does not meet the target result is obtained after inputting sample 1 into the trained prediction model, sample 1 is excluded. Then, other samples in the data set are taken and steps S32 to S35 are performed again until a close or matching sample is found. Since all of these parameters have been verified to have excellent predictive effects in the trained model, the user can expect to finally obtain the target result of the user by continuing to perform the subsequent process steps using the unconfirmed input parameters obtained by the above method after completing the steps of the confirmed process. In practice, the user may adjust and set the indicators of closeness and matching according to different situations and needs. It should be noted that the other steps of the process parameter calculation method in this specific embodiment are almost the same as the corresponding steps in the above specific embodiment, and will not be described in detail again here.

[0043] In addition to the above specific embodiment, the present invention also provides another method for selecting candidate samples. Now, refer to Fig. 4. Fig. 4 shows a flow chart of steps of a method for calculating process parameters according to another specific embodiment of the present invention. This specific embodiment may further include step S341, which is sequentially performed after step S33.

[0044] Step S341: Using a distance function, the distance between the coordinates of the end point of the first vector and the coordinates of the end point of the second vector of each sample is calculated, and the sample corresponding to the second vector that has the smallest distance from the first vector is determined as a candidate sample.

[0045] In practical applications, the distance functions commonly used in the field of machine learning include Euclidean Distance, Manhattan Distance, Cosine Similarity, Jaccard Similarity and Mahalanobis Distance. In practice, the calculation method of the minimum distance and the application scope of the minimum distance can be set according to the needs of users. It should be noted that the other steps of the calculation method of the process parameters in this specific embodiment are almost the same as the corresponding steps in the above specific embodiment, and will not be described in detail here.

[0046] As shown in the above specific embodiment, the parameters in the data set are vectorized to provide a trained prediction model for more efficient calculation and processing. However, in practical applications, in addition to the above-mentioned method of calculating the included angle between the first vector and the second vector and the method of calculating the distance between the first vector and the second vector, other vector calculation methods or other matching methods may be selected to identify the reference sample according to the needs of users, application scenarios, and types of data sets.

[0047] In the above specific embodiment, the parameters in the data set are vectorized, and the included angle and distance between the parameters are used to obtain a reference sample that matches or fits the confirmed input parameter by matching, thereby identifying the unconfirmed input parameter. However, if the included angle and distance between the parameters cannot obtain a matching reference sample by matching, or if the obtained included angle and distance are beyond the application range of the minimum included angle and minimum distance set in advance, other matching conditions may be used to obtain a more suitable reference sample. Now, please refer to FIG. 5. FIG. 5 shows a flowchart of steps of a process parameter calculation method according to another specific embodiment of the present invention. As shown in FIG. 5, the difference between this specific embodiment and the above specific embodiment is as follows. That is, the process parameter calculation method in this specific embodiment further includes steps S31', S32', S33', S341', and S342', which are sequentially performed after step S2.

[0048] Step S31': By using the inverse inference algorithm, the target result, the confirmed input parameters 10 and the sample parameters of the samples in the data set are matched to confirm the sample parameters of the first reference sample among the samples as reference input parameters, where the reference input parameters include unconfirmed reference parameters.

[0049] Step S32': The confirmed input parameters 10 and the unconfirmed reference parameters of the reference input parameters are input into the trained prediction model to generate a reference prediction result.

[0050] Step S33': The reference prediction result is compared with the target result.

[0051] Step S341': If the reference prediction result coincides with the target result, the unconfirmed reference parameter P1 is identified as the unconfirmed input parameter 12.

[0052] Step S342′: If the reference prediction result does not match the target result, the first reference sample is excluded, and the inverse inference algorithm is again used to match the target result, the confirmed input parameters and the sample parameters of the samples in the dataset to obtain the sample parameters of the second reference sample among the samples as the reference input parameters 12.

[0053] In this specific embodiment, the reference prediction results of the complete parameter vector formed by the first vector of the confirmed input parameters 10 and the unconfirmed reference parameters P1 to Pn of each sample (sample 1 to sample n) are evaluated in order to obtain a more suitable reference sample. For example, when a user performs a 21-day culture process, after completing the first 7 days of the process (i.e., the confirmed input parameters 10), the user may want to achieve a target result (95% cell viability) on the 21st day by confirming the process parameters (i.e., the unconfirmed input parameters 12) for the 8th to 21st days thereafter. However, if the user is unable to obtain a matching or compatible reference sample by comparison from the included angle and distance between the parameters to obtain the unconfirmed input parameters 12 that can achieve the desired target result, the following method may also be used to achieve the desired result. That is, in practice, the target result, the confirmed input parameters, and the sample parameters of the samples in the data set are first compared by the inverse inference algorithm, and the samples are arranged in an order in which the first vector and the second vector are close to each other. Then, the complete parameters formed by combining the confirmed input parameters 10 and the unconfirmed reference parameters of each sample are input into the trained prediction model in the above order to generate a reference prediction result and further compare the reference prediction result with the target result. At this time, if the reference prediction result of the first reference sample in the first order matches the target result (95% cell viability), the unconfirmed reference parameters of the first reference sample can be identified as unconfirmed input parameters 12. Also, if the reference prediction result of the first reference sample does not match the target result (95% cell viability), the first reference sample is excluded, and the unconfirmed reference parameters of the other samples are combined with the confirmed input parameters 10 in order and input into the trained prediction model until it is determined by comparison that the reference prediction result of the reference sample (i.e., the second reference sample) matches the target result (95% cell viability).

[0054] In addition, in practical applications, if the comparison does not finally result in the reference prediction result of the cell reference sample being consistent with the target result (95% cell viability), the comparison process may be stopped. In practice, the user may further adjust and set the matching index according to different situations and needs. That is, if the comparison using the inverse inference algorithm does not result in the reference prediction result of the cell reference sample being consistent with the target result (95% cell viability), the comparison process may be stopped. Alternatively, the target result may be adjusted (for example, the original 95% cell viability may be adjusted to 93% cell viability), or the goal may be changed to obtaining a process reference sample whose reference prediction result is closest to the target result by comparison. In this specific embodiment, the inverse inference algorithm automatically compares the sample parameters in the data set to inversely estimate the process parameters required to obtain the desired target result. This effectively optimizes the process method and achieves the target result of the process. It should be noted that the other steps of the process parameter calculation method in this specific embodiment are almost the same as the corresponding steps in the above specific embodiment, and will not be described in detail here.

[0055] In addition, the present invention may find a process reference sample whose reference prediction result is consistent with the target result (95% cell viability) by matching the sample parameters of all process samples in the process data set at once, and further select an optimal combination of unconfirmed input parameters to form a process reference sample set. Here, FIG. 6 and FIG. 7 are referred to together. FIG. 6 shows a flowchart of steps of a process parameter calculation method according to another specific embodiment of the present invention. FIG. 7 shows a block diagram of a data set and samples in the process parameter calculation method according to another specific embodiment of the present invention. As shown in FIG. 6, the process parameter calculation method in this specific embodiment is different from the above-mentioned specific embodiment as follows. That is, this specific embodiment may further include steps S32'' to S33'', which are sequentially performed after step S31'.

[0056] Step S32: Match the corresponding sample parameters C1 to Cn of all samples (including sample 1 to sample n) in the dataset, and input the confirmed input parameters 10 and the unconfirmed reference parameters (including P1 to Pn) corresponding to each sample into the trained prediction model to generate corresponding reference prediction results.

[0057] Step S33): Match the reference prediction result corresponding to each sample with the target result to find reference samples whose reference prediction results match the target result, and further form a reference sample set.

[0058] In this specific embodiment, the sample parameters of all process samples in the process data set are collated at once to obtain one or more process reference samples whose reference prediction results are consistent with the target results (95% cell viability), and further form a cell reference sample set. In addition, based on the conditions and constraints set by the user, the unconfirmed input parameters 12 are further selected for the process reference samples in the process reference sample set. In practice, the user may set the conditions and constraints in consideration of his / her own consumable inventory and costs, and further select the process parameters from the reference sample set that meet the target results and the needs of the user. It should be noted that the other steps of the process parameter calculation method in this specific embodiment are almost the same as the corresponding steps in the above-mentioned specific embodiment, and will not be described in detail here.

[0059] In addition, the method for calculating process parameters in the present invention may be in other forms than the above-mentioned forms. Now, please refer to Fig. 7 and Fig. 8. Fig. 8 shows a flowchart of steps of the method for calculating process parameters according to another specific embodiment of the present invention. As shown in Fig. 8, the difference between the method for calculating process parameters in this specific embodiment and the above-mentioned specific embodiment is as follows. That is, this specific embodiment may further include step S34'' performed after step S33''.

[0060] Step S34): Linearly combine the first K data in the reference sample set, and find the optimal combination of unconfirmed input parameters by an optimization search method.

[0061] In this specific embodiment, the user may use the process reference sample set obtained by collating the process data set at one time, and further arrange the process reference samples in the process reference sample set using the conditions and constraints set by the user. Then, the first K data in the process reference sample set are linearly combined, and the optimal combination of unconfirmed input parameters is found by the optimization search method. In practice, the user may set the conditions and constraints in consideration of the consumable inventory and cost owned by the user, and further arrange the process reference sample set, and select the reference sample that best meets the user's needs and target results by the optimization search method. In practical application, the optimization search method may be selected as the finite-difference method, gradient descent method, Newton's method, penalty function method, barrier function method, Lagrange multiplier method, or other calculation methods based on the user's needs. It should be noted that the other steps of the method for calculating the process parameters in this specific embodiment are substantially the same as the corresponding steps in the above specific embodiment, and therefore will not be described in detail again here.

[0062] The process parameter calculation method of the present invention may be in other embodiments other than the above. It will be further described below. Now, refer to FIG. 9. FIG. 9 shows a flowchart of steps of a process parameter calculation method according to another specific embodiment of the present invention. As shown in FIG. 9, in this specific embodiment, the process parameter calculation method includes:

[0063] Step S1': Provide a trained prediction model. The trained prediction model is obtained by machine learning a dataset using a machine learning method. The dataset includes a number of samples, and each sample includes a number of sample parameters. The trained prediction model is used to input a number of input parameters and generate a prediction result corresponding to the input parameters.

[0064] Step S2': Set the predicted result of the trained predictive model as the target result, and provide the confirmed input parameters of the input parameters. Each sample parameter includes a corresponding sample parameter corresponding to the confirmed input parameter and an unconfirmed reference parameter.

[0065] Step S3': Input the sample parameters of at least one sample into the trained prediction model to obtain at least one candidate sample, and the predicted result of the at least one candidate sample is close to or coincides with the target result.

[0066] Step S4': Calculate a first vector of at least one confirmed input parameter and a second vector of at least one corresponding sample parameter for at least one candidate sample.

[0067] Step S5': Match the first vector with the second vector to select a reference sample, where the second vector of the reference sample is close to or coincides with the first vector.

[0068] Step S6': At least one unconfirmed reference parameter of the reference sample is taken as at least one unconfirmed input parameter.

[0069] The difference between the process parameter calculation method in this specific embodiment and the above specific embodiment is as follows. That is, in this specific embodiment, the combination of the corresponding sample parameters and the unconfirmed reference parameters of each sample in the data set is directly input into the trained prediction model to obtain a prediction result. Then, if the prediction result of the sample in the data set matches the target result, the sample can be further regarded as a candidate sample. Next, the first vector of the confirmed input parameters and the second vector of the corresponding sample parameters in the candidate sample are calculated and compared, and a sample whose second vector is close to or matches the first vector is found from the candidate samples and used as a reference sample. Then, the corresponding unconfirmed reference parameters can be used as the unconfirmed input parameters of the current process. In practice, the process parameter calculation method in this specific embodiment is suitable for processes in which the data of the sample data set is huge. First, a reference sample whose prediction result is close to or matches the target result and whose second vector is close to or matches the first vector is found from the sample data set. In this case, the step of combining the confirmed input parameters with the unconfirmed reference parameters and inputting them into the trained prediction model is not required, so that the unconfirmed input parameters of the process can be obtained effectively and quickly. It should be noted that the other steps of the method for calculating the process parameters in this specific embodiment are substantially the same as the corresponding steps in the above specific embodiment, and therefore will not be described in detail again here.

[0070] In addition, the method for calculating process parameters in the present invention may also be in other forms. Now, refer to Fig. 10. Fig. 10 shows a flowchart of steps of a method for calculating process parameters according to another specific embodiment of the present invention. As shown in Fig. 10, in this specific embodiment, the method for calculating process parameters includes:

[0071] Step S1″: Provide a trained predictive model. The trained predictive model is obtained by machine learning a dataset using a machine learning method. The dataset includes a number of samples. Each sample includes a number of sample parameters. The trained predictive model is used to input a number of input parameters and generate a predictive result corresponding to the input parameters.

[0072] Step S2″'': Set the predicted result of the trained predictive model as the target result, and provide the confirmed input parameters among the input parameters. Each sample parameter includes a corresponding sample parameter corresponding to the confirmed input parameter and an unconfirmed reference parameter.

[0073] Step S3''): Calculate a first vector of confirmed input parameters.

[0074] Step S4'''': Based on the confirmed input parameters, corresponding corresponding sample parameters are obtained from the sample parameters of each sample respectively.

[0075] Step S5''): Calculate a second vector of parameters for each corresponding sample.

[0076] Step S6'': Match the first vector with the second vector of each sample to select a candidate sample, whose second vector is close to or coincides with the first vector.

[0077] Step S7'''': Input the sample parameters of the candidate sample into the trained prediction model to obtain a reference sample, whose prediction result is close to or coincides with the target result.

[0078] Step S8″): Unconfirmed reference parameters in the sample parameters of the reference sample are treated as unconfirmed input parameters.

[0079] The difference between this specific embodiment and the above-mentioned specific embodiment is as follows: first, the first vector is compared with the second vector of each sample, and then the predicted result is compared with the target result. In this case, the unconfirmed input parameters of the process can be obtained more accurately than in the above-mentioned specific embodiment. It should be noted that the other steps of the method for calculating the process parameters in this specific embodiment are almost the same as the corresponding steps in the above-mentioned specific embodiment, and therefore will not be described in detail here.

[0080] In addition, the method for calculating process parameters in the present invention may also be in other forms. Now, refer to FIG. 11. FIG. 11 shows a flowchart of steps of a method for calculating process parameters according to another specific embodiment of the present invention. As shown in FIG. 11, in this specific embodiment, the method for calculating process parameters includes:

[0081] Step S1''''': Provide a trained predictive model. The trained predictive model is obtained by machine learning a dataset using a machine learning method. The dataset includes a number of samples, and each sample includes a number of sample parameters. The trained predictive model is used to input a number of input parameters and generate a predictive result corresponding to the input parameters.

[0082] Step S2''''': Set the predicted result of the trained predictive model as the target result, and provide the confirmed input parameters among the input parameters.

[0083] Step S3''''': Obtain a reference sample from these samples by combining the confirmed input parameters and the unconfirmed reference parameters of each sample parameter and inputting them into the trained prediction model. The predicted result of the reference sample is close to or coincides with the target result.

[0084] Step S4''': Unconfirmed reference parameters in the sample parameters of the reference sample are treated as unconfirmed input parameters.

[0085] This specific embodiment is different from the above-mentioned specific embodiment in the following points. That is, in this specific embodiment, the confirmed input parameters and the unconfirmed reference parameters of each sample parameter are directly combined and input into the trained prediction model to obtain a prediction result without the need to match the first vector with the second vector of each sample. Then, the reference sample is obtained based on whether the prediction result corresponding to each sample is close to or coincides with the target result. Compared with the above-mentioned specific embodiment in which the reference sample is obtained in two steps, this specific embodiment can obtain the reference sample in one step. This is for the purpose of performing comprehensive matching to find the optimal solution. It should be noted that the other steps of the calculation method of the process parameters in this specific embodiment are almost the same as the corresponding steps in the above-mentioned specific embodiment, and will not be described in detail here.

[0086] In summary, the present invention provides a method for calculating process parameters by inputting a desired target result and confirmed input parameters and using a data set of a trained predictive model to inversely estimate unconfirmed input parameters, thereby effectively optimizing the process method and achieving the target result of the process. In addition to being applied to the field of cellular processes, the process parameter calculation method of the present invention can also be applied to any other trained machine learning model to inversely estimate process parameters and unconfirmed input parameters.

[0087] The detailed description of the preferred specific embodiments above is intended to more clearly describe the features and spirit of the present invention, and is not intended to limit the scope of the present invention to the preferred specific embodiments disclosed above. Rather, it is intended that various modifications and equivalent configurations be included within the scope of the claims that are to be claimed in the present invention. Therefore, the scope of the claims that are to be claimed in the present invention should be interpreted in the broadest possible manner based on the above description so as to include all possible modifications and equivalent configurations. [Explanation of symbols]

[0088] S1~S3, S31~S35, S341 steps S31'~S33', S341', S342' steps S32''~S34'',S1''~S6'' steps S1''''~S8'',S1''''''~S4'''' steps 10 Verified Input Parameters 12 Unvalidated Input Parameters C1, C2, C3, Ci...Cn Corresponding sample parameters P1,P2,P3,Pi...Pn Unconfirmed reference parameters

Claims

1. providing a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, and each of the samples including a plurality of sample parameters, the trained prediction model being used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting the predicted result of the trained predictive model as a target result and providing at least one verified input parameter of the input parameters; and matching the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the data set by an inverse inference algorithm to identify at least one unconfirmed input parameter among the input parameters.

2. 2. The method of claim 1, wherein the data set further comprises a process data set, the process data set comprising a plurality of process samples, each of the process samples comprising a plurality of process sample parameters, each of the process sample parameters comprising a source parameter and an incubation parameter.

3. 3. The method of claim 2, wherein the source parameters of each of the process samples further include attribute data of a source of each of the process samples.

4. The method for calculating process parameters according to claim 2 , wherein the culture parameters for each of the process samples further include human, equipment, material, method and environment parameters for each of the process samples.

5. Each of the sample parameters includes at least one corresponding sample parameter and at least one unconfirmed reference parameter, and the step of identifying the at least one unconfirmed input parameter among the input parameters by the reverse inference algorithm by matching the target outcome, the at least one confirmed input parameter, and the sample parameters of the samples in the data set further comprises: calculating a first vector of said at least one verified input parameter; respectively deriving the at least one corresponding sample parameter from the sample parameters of the samples based on the at least one confirmed input parameter; calculating a second vector of said at least one corresponding sample parameter; matching the first vector with the second vector for each of the samples to select at least one candidate sample; obtaining a reference sample from the at least one candidate sample by combining the at least one unconfirmed reference parameter and the confirmed input parameters of the at least one candidate sample and inputting them into the trained prediction model, the second vector of the reference sample being close to or consistent with the first vector, and the prediction result of the reference sample being close to or consistent with the target result; 2. The method of claim 1, further comprising the step of: determining the at least one unconfirmed reference parameter in the sample parameters of the reference sample as the at least one unconfirmed input parameter.

6. The step of matching the first vector with the second vector of each of the samples to select the at least one candidate sample further comprises:

6. The method for calculating a process parameter according to claim 5, further comprising the steps of: calculating an included angle between the first vector and the second vector of each of the samples; and determining the sample corresponding to the second vector having the smallest included angle with the first vector as the candidate sample.

7. The step of matching the first vector with the second vector of each of the samples to select the at least one candidate sample further comprises:

6. The method for calculating a process parameter according to claim 5, further comprising the step of calculating a distance between a coordinate of an end point of the first vector and a coordinate of an end point of the second vector of each of the samples by a distance function, and determining the sample corresponding to the second vector having the smallest distance from the first vector as the candidate sample.

8. The trained prediction models include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Decision Trees, Support Vector Machines (SVM), Random Forests, K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), Linear Regression (RL), and others.

2. The method of claim 1, wherein the dataset is obtained by machine learning using any one of Deep Belief Networks (DBN), Recursive Neural Networks (RecNN), Reinforcement Learning, Autoencoders, Gaussian Processes, and Complex Neural Networks.

9. The step of identifying the at least one unconfirmed input parameter among the input parameters by the inverse inference algorithm, by matching the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the data set, further comprises: verifying, by the inverse inference algorithm, the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset to verify the sample parameters of a first reference sample of the samples as reference input parameters, the reference input parameters including at least one unconfirmed reference parameter; inputting the at least one confirmed input parameter and the at least one unconfirmed reference parameter into the trained predictive model to generate a reference prediction result; matching the reference prediction result with the target result; 2. The method of claim 1, further comprising identifying the at least one unconfirmed reference parameter as the at least one unconfirmed input parameter if the reference predicted result matches the target result.

10. Furthermore, 10. The method for calculating process parameters according to claim 9, further comprising the step of: excluding the first reference sample when the reference prediction result does not match the target result, and again using the inverse inference algorithm to compare the target result, the at least one confirmed input parameter, and the sample parameters of the samples in the dataset to obtain the sample parameters of a second reference sample among the samples as the reference input parameters.

11. Furthermore, matching the at least one corresponding sample parameter of the samples in the dataset and inputting at least one confirmed input parameter and the at least one unconfirmed reference parameter corresponding to each of the samples into the trained predictive model to generate the corresponding reference prediction result; 11. The method for calculating process parameters according to claim 10, further comprising: finding the reference samples whose reference prediction results match the target results by matching the reference prediction results and the target results corresponding to each of the samples; and forming a reference sample set.

12. Furthermore, 12. The method for calculating process parameters according to claim 11, further comprising the step of linearly combining the first K data in the reference sample set and finding an optimal combination of unconfirmed input parameters by an optimization search method.

13. providing a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, and each of the samples including a plurality of sample parameters, the trained prediction model being used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting the prediction result of the trained predictive model as a target result and providing at least one validated input parameter of the input parameters, each of the sample parameters including at least one corresponding sample parameter corresponding to the at least one validated input parameter and at least one unvalidated reference parameter; inputting the sample parameters of at least one of the samples into the trained predictive model to obtain at least one candidate sample, wherein the predicted outcome of the at least one candidate sample approximates or coincides with the target outcome; calculating a first vector of said at least one confirmed input parameter and a second vector of said at least one corresponding sample parameter for said at least one candidate sample; selecting a reference sample by matching the first vector with the second vector, the second vector of the reference sample being close to or identical to the first vector; determining said at least one unconfirmed reference parameter in said sample parameters of said reference sample as at least one unconfirmed input parameter.

14. providing a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, and each of the samples including a plurality of sample parameters, the trained prediction model being used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting the prediction result of the trained predictive model as a target result and providing at least one validated input parameter of the input parameters, each of the sample parameters including at least one corresponding sample parameter corresponding to the at least one validated input parameter and at least one unvalidated reference parameter; calculating a first vector of said at least one verified input parameter; respectively deriving the at least one corresponding sample parameter from the sample parameters of the samples based on the at least one confirmed input parameter; calculating a second vector of said at least one corresponding sample parameter; selecting at least one candidate sample by matching the first vector with the second vector of each of the samples, the second vector of the at least one candidate sample being close to or identical to the first vector; inputting the sample parameters of the at least one candidate sample into the trained prediction model to obtain the reference sample, such that the predicted result of the reference sample approximates or coincides with the target result; determining said at least one unconfirmed reference parameter in said sample parameters of said reference sample as at least one unconfirmed input parameter.

15. providing a trained prediction model, the trained prediction model being obtained by machine learning a dataset by a machine learning method, the dataset including a plurality of samples, each of the samples including a plurality of sample parameters, each of the sample parameters including at least one corresponding sample parameter and at least one unconfirmed reference parameter, the trained prediction model being used to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting the predicted result of the trained predictive model as a target result and providing at least one verified input parameter of the input parameters; obtaining a reference sample from the sample by inputting a combination of the at least one confirmed input parameter and at least one unconfirmed reference parameter for each of the sample parameters into the trained predictive model, such that the predicted result of the reference sample approximates or coincides with the target result; determining said at least one unconfirmed reference parameter in said sample parameters of said reference sample as at least one unconfirmed input parameter.

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