DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the application filed on February 24 th , 2023 . Claims 1-2 1 are pending in the case. Claims 1, 20 and 21 are independent claims. The information disclosure statement (IDS) submitted on April 30 th , 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character not mentioned in the description: 512 in Figure 5. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “ Replacement Sheet ” or “ New Sheet ” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim SEQ claimNum 1 : Step 1 : Claim 1 is directed to A method , therefore it falls under the statuary category of a process. Step 2A Prong 1 : The claim recites, in part: “ generating data representing a plurality of historical episodes, wherein each historical episode is divided into a sequence of time units, wherein historical information is associated with each time unit ” this encompasses the mental creation of data representing observed historical episodes divided into time units containing historical information. “ generating…for each historical episode of the plurality of episodes, a respective training action sequence comprising a respective sequence of actions that corresponds to the sequence of time units for the historical episode ” this encompasses the mental creation of a respective training sequence for observed historical episodes. “ generating a training data set comprising a plurality of training data points wherein each of the plurality of training data points comprises an action extracted from a training action sequence ” this encompasses the mental creation of a training data set containing data points extracted from an observed training action sequence. “ generating…a future action for a current or future time unit ” this encompasses the mental creation of a future action for a current or future time unit. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ using an evolutionary algorithm ” , “ generated by the evolutionary algorithm ” , “ using the trained deep learning model ” , “ training a deep learning model using the training data set to generate future actions to be executed at current or future time units ” the limitation is an additional element that amounts to adding the words “ apply it ” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 2 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ dividing the historical information into a first information subset associated with a first set of time units and a second information subset associated with a second set of time units, wherein the first set of time units and the second set of time units are consecutive ” this encompasses the mental division of observed historical information into different subsets. “ determining a scale factor based on the first information subset ” this encompasses the mental determination of a scale factor based on an observed subset. “ scaling one or more values in the second information subset by the scale factor ” this limitation is a mathematical concept. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ receiving the historical information ” , “ outputting the second set of time units and the scaled second information subset as the historical episode ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “ receiving the historical information ” , “ outputting the second set of time units and the scaled second information subset as the historical episode ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claim SEQ claimNum 3 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ randomly generating a set of candidate action sequences corresponding to the sequence of time units for the historical episode ” this encompasses the mental creation of random candidate action sequences. “ determining a set of fitness values, wherein each fitness value in the set of fitness values corresponds to a candidate action sequence in the set of candidate action sequences ” this encompasses the mental determination of a set of fitness values based on an observed candidate action sequence. “ identifying, based on the set of fitness values, a fittest subset of the set of candidate action sequences ” this encompasses the mental identification of a fittest subset of the set of candidate action sequences based on an observed set of fitness values. “ generating an updated set of candidate action sequences by modifying candidate action sequences in the fittest subset ” this encompasses the mental creation of an updated set of candidate action sequences by modifying candidate action sequences in an observed fittest subset. “ iteratively repeating the steps of determining a set of fitness values, identifying a fittest subset, and generating an updated set of candidate action sequences ” this encompasses the mental repetition of previous mental processes. “ identifying, based on the iterative repeating process, a fittest candidate action sequence ” this encompasses the mental identification of an observed fittest candidate after repeating previous mental processes. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 4 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the training action sequence for each historical episode is the fittest candidate action sequence corresponding to said historical episode that is identified ” this encompasses the mental identification of the observed fittest candidate action sequence. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ by the evolutionary algorithm ” the limitation is an additional element that amounts to adding the words “ apply it ” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 5 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the plurality of cessation conditions comprise: a total number of iterations exceeds a threshold number of iterations, and one or more fitness values in the set of fitness values exceeds a threshold fitness value ” this encompasses the mental repetition of mental processes until a set number of repetitions is achieves, or an observed fitness value exceeds a threshold value. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 6 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ modifying candidate actions sequences in the fittest subset comprises switching one or more actions in each action sequence of the fittest subset of from a first action type to a second action type ” this encompasses the mental modification of an observed candidate actions sequence by switching observed actions. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 7 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ modifying candidate action sequences in the fittest subset of candidate action sequences comprises: selecting a first set of actions from a first action sequence of the fittest subset; selecting a second set of actions from a second action sequence of the fittest subset; and combining the first set of actions and the second set of actions to form a third action sequence ” this encompasses the mental modification of an observed candidate action sequence by combining a first and second action selected based on an observed fittest subset. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 8 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the historical information associated with each time unit comprises a numerical value ” this encompasses the mental association of numerical values with observed historical information. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 9 , the rejection of claim 8 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ each training data point of the plurality of training data points in the training data set further comprises an average value of the numerical value over a set of time units preceding a time unit in a historical episode of the plurality of historical episodes that corresponds to an action sequence from which the action in the training data point was extracted ” this limitation is a mathematical concept. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 10 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ generating a predicted action sequence ” this encompasses the mental creation of a predicted action sequence. “ comparing the predicted action sequence to the training action sequence that corresponds to the historical episode ” this encompasses the mental comparison of a predicted action sequence with an observed training action sequence. “ adjusting one or more parameters…based on the comparison between the predicted action sequence and the training action sequence in the training data ” this encompasses the mental adjusting of parameters based on a comparison between a predicted action sequence and an observed training action sequence Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ of the deep learning model ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 11 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the future action…is configured to maximize a reward for an entity for the current or future time unit ” this limitation is a mathematical concept. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ generated by the trained deep learning model ” The limitation is an additional element that amounts to adding the words “ apply it ” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 12 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the historical information comprises market performance information ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 13 , the rejection of claim 12 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ each training action sequence…comprises, for each time unit in the historical episode associated with the training action sequence, an indication of whether to execute a purchase of an ETF at that time unit ” this encompasses the mental decision of whether to execute a purchase of the ETF at a time point. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ generated by the evolutionary algorithm ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 14 , the rejection of claim 13 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the future action for the current or future time unit…comprises an indication of whether to execute a purchase of the ETF at said time unit ” this encompasses the mental decision of whether to execute a purchase of the ETF at a current or future time point. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ that is generated by the deep learning model ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 15 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the evolutionary algorithm is a genetic algorithm ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 16 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the deep learning model is a neural network ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 17 , the rejection of claim 16 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the deep learning model is a feed-forward neural network ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 18 , the rejection of claim 17 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the feed-forward neural network comprises at least six layers ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 19 , the rejection of claim 17 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ utilizes a rectified linear unit (ReLU) activation function at one or more layers ” this limitation is a mathematical concept. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ the feed-forward neural network ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 20 : Step 1 : Claim 20 is directed to A system , therefore it falls under the statuary category of a machine. Step 2A Prong 1 : The claim recites, in part: “ generate data representing a plurality of historical episodes, wherein each historical episode is divided into a sequence of time units, wherein historical information is associated with each time unit ” this encompasses the mental creation of data representing observed historical episodes divided into time units containing historical information. “ generate…for each historical episode of the plurality of episodes, a respective training action sequence comprising a respective sequence of actions that corresponds to the sequence of time units for the historical episode ” this encompasses the mental creation of a respective training sequence for observed historical episodes. “ generate a training data set comprising a plurality of training data points wherein each of the plurality of training data points comprises an action extracted from a training action sequence ” this encompasses the mental creation of a training data set containing data points extracted from an observed training action sequence. “ generate…a future action for a current or future time unit ” this encompasses the mental creation of a future action for a current or future time unit. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ using an evolutionary algorithm ” , “ generated by the evolutionary algorithm ” , “ using the trained deep learning model ” , “ training a deep learning model using the training data set to generate future actions to be executed at current or future time units ” , “ output, using the user interface, the future action to a user ” the limitation is an additional element that amounts to adding the words “ apply it ” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 21 : Step 1 : Claim 21 is directed to A non-transitory computer readable storage medium , therefore it falls under the statuary category of a manufacture. Step 2A Prong 1 : The claim recites, in part: “ generate data representing a plurality of historical episodes, wherein each historical episode is divided into a sequence of time units, wherein historical information is associated with each time unit ” this encompasses the mental creation of data representing observed historical episodes divided into time units containing historical information. “ generate…for each historical episode of the plurality of episodes, a respective training action sequence comprising a respective sequence of actions that corresponds to the sequence of time units for the historical episode ” this encompasses the mental creation of a respective training sequence for observed historical episodes. “ generate a training data set comprising a plurality of training data points wherein each of the plurality of training data points comprises an action extracted from a training action sequence ” this encompasses the mental creation of a training data set containing data points extracted from an observed training action sequence. “ generate…a future action for a current or future time unit ” this encompasses the mental creation of a future action for a current or future time unit. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ using an evolutionary algorithm ” , “ generated by the evolutionary algorithm ” , “ using the trained deep learning model ” , “ training a deep learning model using the training data set to generate future actions to be executed at current or future time units ” the limitation is an additional element that amounts to adding the words “ apply it ” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 1, 3-4 and 6-21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. ( “ An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm ” , Chen et al., January 23, 2019), as cited in the IDS, hereinafter Chen in view of Karathanasopoulos et al. ( “ Ensemble Models in Forecasting Financial Markets ” , March 18, 2019 ) hereinafter Karathanasopoulos in further view of Xie et al. (“Research on Gold ETF forecasting based on LSTM”, Xie et al., March 26, 2020) hereinafter Xie. Regarding claim SEQ claimMapNum 1 : Chen teaches A method for training a deep learning model comprising: generating data representing a plurality of historical episodes, wherein each historical episode is divided into a sequence of time units (Chen, page 10, col 1-2, section C, ¶2 “ From Table 11, we can observe that irrespective of whether one-, two- or three-year training datasets are used for training, TABLE 13. The optimized GTSPs on the sideways trend dataset. TABLE 14. The optimized GTSPs on the downtrend dataset. the proposed approach is better than the BHS in terms of returns. ” Here, the years can be considered the time units ) , wherein historical information is associated with each time unit (Chen, page 3, col 2, ¶5 “ In Fig. 1, through the given stock price series and technical indicators, it shows that three steps are used in the data preprocessing procedure to generate the m processed TSs. ” Here, the stock price series can be considered the historical episodes and the stock price can be considered the historical information. ) ; generating, using an evolutionary algorithm (Chen, page 3, col 2, ¶3 “ The main goal of the GTSPO problem is to optimize a GTSP in accordance with objective and subjective criteria given by users using evolutionary algorithms ” ) , for each historical episode of the plurality of episodes, a respective training action sequence comprising a respective sequence of actions that corresponds to the sequence of time units for the historical episode (Chen, page 6, col 2, ¶1 “ The m selected strategies are utilized to find buy and sell signals and form the initial population (Lines 3 to 4). Every chromosome is then evaluated using the fitness function, which is composed of the four factors of the portfolio return, risk, group balance and weight balance (Lines 6 to 13). The crossover, mutation, inversion and selection operations are executed on the population to generate new offspring and the next population ” Here, sell and buy signals can be considered a respective training action sequence comprising a respective sequence of actions in light of the specification , ¶20 “ In some embodiments of the method, each training action sequence generated by the evolutionary algorithm comprises, for each time unit in the historical episode associated with the training action sequence, an indication of whether to execute a purchase of an ETF at that time unit. ” ) ; generating a training data set comprising a plurality of training data points wherein each of the plurality of training data points comprises an action extracted from a training action sequence generated by the evolutionary algorithm (Chen, page 10, col 2, ¶1 “ In addition, since the returns of the GTSPs trained from the two-year training periods are almost positive, we can also conclude that using the two-year training period to obtain GTSPs for making trading plans is appropriate when the market trend is a downtrend. ” ) ; Chen does not teach “ training a deep learning model using the training data set to generate future actions to be executed; and generating, using the trained deep learning model, a future action ” However, Karathanasopoulos teaches training a deep learning model using the training data set ( Karathanasopoulos , page 7, ¶1 “ The network parameters are then estimated by fitting the training data using the above mentioned iterative procedure (backpropagation of errors). The iteration length is optimized by maximizing a fitness function in the test dataset . ” ) to generate future actions to be executed (Karathanasopoulos, page 11, section 4.2, ¶1 “ In this section, we present the results of the proposed methodology applied to trade the S&P 500 and Nasdaq 100 exchange trade funds in the relevant out-of-sample period. ” Here, the trading of the exchange trade funds can be considered a future action ) ; and generating, using the trained deep learning model, a future action (Karathanasopoulos, page 11, section 4.2, ¶1 “ In this section, we present the results of the proposed methodology applied to trade the S&P 500 and Nasdaq 100 exchange trade funds in the relevant out-of-sample period. ” Here, the trading of the exchange trade funds can be considered a future action for a current or future time unit ) . Chen and Karathanasopoulos are analogous art because both references concern learning methods for trading financial assets and portfolios. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen’s trading strategy genetic algorithm to incorporate the neural network ensemble taught by Karathanasopoulos . The motivation for doing so would have been to improve final performance, as stated in Karathanasopoulos, page 13, ¶1 “ All nine ensemble NN-evolutionary models have been managed to trade successfully the two ETFs. The RBF hybrid combination models present the best performance, the second performance its coming from the RNN combinations while the MLP architectures provide the third lowest performance. All the combinations show a remarkable performance proving that hybrid combinations are improving the final performance of the model and in continuation, they are making good profit returns. ” . Chen in view of Karathanasopoulos does not teach “ at current or future time units ; for a current or future time unit ” However, Xie teaches at current or future time units (Xie, page 4, col 2-1, section IV, ¶4 “ The data for each consecutive m days is input, and the output is k days after m days. After processing, the input is a matrix of m rows and n columns, and the result is the vector of one k row and one column. n represents the characteristic information of each day. k represents the predicted field of view, and the predicted Adj Close price for the next k days. ” Here, the next k days can be considered future time units ) . for a current or future time unit (Xie, page 4, col 2-1, section IV, ¶4 “ The data for each consecutive m days is input, and the output is k days after m days. After processing, the input is a matrix of m rows and n columns, and the result is the vector of one k row and one column. n represents the characteristic information of each day. k represents the predicted field of view, and the predicted Adj Close price for the next k days. ” Here, the next k days can be considered future time units ) Chen in view of Karathanasopoulos and Xie are analogous art because both references concern methods for forecasting financial markets . Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen/Karathanasopoulos’s forecasting neural network to incorporate the future time units taught by Xie . The motivation for doing so would have been to predict prices more accurately, as stated in Xie, page 5, col 2, section V, ¶1 “ The model is mainly researched on the accuracy of prediction, so that the price of gold ETF can be predicted more accurately. ” Regarding claim SEQ claimMapNum 3 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein generating for each historical episode of the plurality of historical episodes, a respective training action sequence comprises: randomly generating a set of candidate action sequences corresponding to the sequence of time units for the historical episode (Chen, page 5, col 2, section C, ¶1 “ In the grouping part, it first picks two individuals randomly, where one is used as the base chromosome and the other one is the insertion chromosome. Then, parts of groups in the insertion chromosome are selected and put into the base chromosome. After insertion, groups in the base chromosome are removed if they are redundant. At last, groups are merged or split until the number of groups in a chromosome is correct. To execute the crossover operation on the weight part, the two-point crossover operator is employed to generate new chromosomes in which two points will first be determined and their sub sequences will be exchanged. Note that the appropriate arrangement should be made to ensure that the numbers of ‘1’s and ‘0’s in a chromosome are correct. ” Here, the “ grouping part ” procedure can be considered randomly generating a set of candidate action sequences ) ; determining a set of fitness values, wherein each fitness value in the set of fitness values corresponds to a candidate action sequence in the set of candidate action sequences (Chen, page 7, col 3, Step 2 “ The fitness value of each chromosome is calculated using the following sub-steps. ” ) ; identifying, based on the set of fitness values, a fittest subset of the set of candidate action sequences (Chen, page 8, col 1, Step 3 “ The ten chromosomes derived by the previous step are selected to form the next population using the elitist selection strategy ” here, the next population formed using the elitist selection strategy can be considered a fittest subset of the set of candidate action sequences ; generating an updated set of candidate action sequences by modifying candidate action sequences in the fittest subset (Chen, page 8, col 1, Step 4 “ The crossover operation is utilized in this step to generate new offspring. ” Here, the new offspring can be considered an updated set of candidate action ) ; iteratively repeating the steps of determining a set of fitness values, identifying a fittest subset, and generating an updated set of candidate action sequences; and identifying, based on the iterative repeating process, a fittest candidate action sequence (Chen, page 4, col 1, ¶2 “ The evolution is repeated until the stop conditions are reached. Finally, the best GTSP that has the highest fitness value will be delivered to users for making trading plans. ” Here, the repetition of the evolution can be considered iteratively repeating the steps ) . Regarding claim SEQ claimMapNum 4 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 3, wherein the training action sequence for each historical episode is the fittest candidate action sequence corresponding to said historical episode that is identified by the evolutionary algorithm (Karathanasopoulos, pages 12-13, section 5, ¶2 “ Find ing the best data optimised inputs from the backtest we start feeding and testing our ensemble models . ” here, the best data optimised inputs can be considered the fittest training action sequence ) . Chen / Karathanasopoulos /Xie are analogous art because both references concern learning methods for trading financial assets and portfolios. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen /Xie ’s trading strategy genetic algorithm to incorporate the fittest candidate action sequence taught by Karathanasopoulos . The motivation for doing so would have been to improve final performance, as stated in Karathanasopoulos, pages 12-13, section 5, ¶2 “ Find ing the best data optimised inputs from the backtest we start feeding and testing our ensemble models. All nine ensemble NN-evolutionary models have been managed to trade successfully the two ETFs. The RBF hybrid combination models present the best performance, the second performance its coming from the RNN combinations while the MLP architectures provide the third lowest performance. All the combinations show a remarkable performance proving that hybrid combinations are improving the final performance of the model and in continuation, they are making good profit returns. ” . Regarding claim SEQ claimMapNum 6 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 3, wherein modifying candidate actions sequences in the fittest subset comprises switching one or more actions in each action sequence of the fittest subset of from a first action type to a second action type (Chen, page 6, col 1, ¶2 “ For the mutation operations, they are performed on the trading strategy and the weight parts. To execute the mutation operation on the trading strategy part, it first chooses two groups, Gi and Gj , that both contain more than one trading strategy. Then, a trading strategy in group Gi is picked and reassigned randomly to the group Gj . With respect to the mutation operator on the weight part, two genes are selected and exchanged if they have different values. Finally, the inversion operation is executed only on the grouping part. Since the purpose of inversion operation is to increase the diversity of chromosomes, this operation exchanges two groups from the two selected groups. ” ) . Regarding claim SEQ claimMapNum 7 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 3, wherein modifying candidate action sequences in the fittest subset of candidate action sequences comprises: selecting a first set of actions from a first action sequence of the fittest subset; selecting a second set of actions from a second action sequence of the fittest subset (Chen, Chen, page 8, col 1, Step 3 “ The ten chromosomes derived by the previous step are selected to form the next population using the elitist selection strategy. ” ) ; and combining the first set of actions and the second set of actions to form a third action sequence (Chen, page 8, col 1, Step 4 “ Step 4: The crossover operation is utilized in this step to generate new offspring ” here the crossover can be considered a combination from Chen, page 5, col 2, section C, ¶1 “ To execute the crossover operation on the weight part, the two-point crossover operator is employed to generate new chromosomes in which two points will first be determined and their sub sequences will be exchanged. ” ) . Regarding claim SEQ claimMapNum 8 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein the historical information associated with each time unit comprises a numerical value (Chen, page 3, col 2, ¶5 “ In Fig. 1, through the given stock price series and technical indicators, it shows that three steps are used in the data preprocessing procedure to generate the m processed TSs. ” Here, the stock price is a numerical value. ) . Regarding claim SEQ claimMapNum 9 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 8, wherein each training data point of the plurality of training data points in the training data set further comprises an average value of the numerical value over a set of time units preceding a time unit in a historical episode of the plurality of historical episodes that corresponds to an action sequence from which the action in the training data point was extracted (Chen, page 9, col 2, ¶2 “ The ten technical indicators are the Moving Average (MA)… ” Here, the moving average can be considered an average value of the numerical value over a set of time units preceding a time unit ) . Regarding claim SEQ claimMapNum 10 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein training the deep learning model comprises, for each historical episode: generating a predicted action sequence; comparing the predicted action sequence to the training action sequence that corresponds to the historical episode (Karathanasopoulos, page 7, ¶1 “ Finally, the predictive value of the model is evaluated by applying it to the validation dataset (out-of-sample dataset) ” here, the predictive value can be considered a predicted action sequence and the evaluation of a predictive value by applying it to the validation dataset can be considered a comparison of a predicted action with a corresponding historical episode ) ; adjusting one or more parameters of the deep learning model based on the comparison between the predicted action sequence and the training action sequence in the training data (Karathanasopoulos, page 6-7, section 3.2.2, ¶3 “ The training of the network (which is the adjustment of its weights in the way that the network maps the input value of the training data to the corresponding output value) starts with randomly initialized weights and proceeds by applying a learning algorithm called backpropagation of errors1 (Shapiro, 2000). ” ) . Chen / Karathanasopoulos /Xie are analogous art because both references concern learning methods for trading financial assets and portfolios. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen /Xie ’s trading strategy genetic algorithm to incorporate the neural network training methods taught by Karathanasopoulos . The motivation for doing so would have been to improve final performance, as stated in Karathanasopoulos, page 13, ¶1 “ All nine ensemble NN-evolutionary models have been managed to trade successfully the two ETFs. The RBF hybrid combination models present the best performance, the second performance its coming from the RNN combinations while the MLP architectures provide the third lowest performance. All the combinations show a remarkable performance proving that hybrid combinations are improving the final performance of the model and in continuation, they are making good profit returns. ” . Regarding claim SEQ claimMapNum 11 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein the future action generated by the trained deep learning model is configured to maximize a reward for an entity for the current or future time unit (Chen, page 3, col 2, ¶3 “ For instance, the criteria could be minimizing the risk while maximizing the profit based on the allocated capitals of groups. ” Here, maximizing the profit can be considered the maximizing a reward ) . Regarding claim SEQ claimMapNum 12 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein the historical information comprises market performance information (Chen, page 3, col 2, ¶5 “ In Fig. 1, through the given stock price series and technical indicators, it shows that three steps are used in the data preprocessing procedure to generate the m processed TSs. ” Here, the stock price series can be considered market performance information. ) . Regarding claim SEQ claimMapNum 13 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 12, wherein each training action sequence generated by the evolutionary algorithm comprises, for each time unit in the historical episode associated with the training action sequence, an indication of whether to execute a purchase of an ETF at that time unit (Chen, page 6, col 2, ¶1 “ The m selected strategies are utilized to find buy and sell signals and form the initial population (Lines 3 to 4). Every chromosome is then evaluated using the fitness function, which is composed of the four factors of the portfolio return, risk, group balance and weight balance (Lines 6 to 13). The crossover, mutation, inversion and selection operations are executed on the population to generate new offspring and the next population ” here, the buy and sell signals can be considered the indication of whether to execute a purchase ) . Regarding claim SEQ claimMapNum 14 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 13, wherein the future action for the current or future time unit that is generated by the deep learning model comprises an indication of whether to execute a purchase of the ETF at said time unit (Chen, page 6, col 2, ¶1 “ The m selected strategies are utilized to find buy and sell signals and form the initial population (Lines 3 to 4). Every chromosome is then evaluated using the fitness function, which is composed of the four factors of the portfolio return, risk, group balance and weight balance (Lines 6 to 13). The crossover, mutation, inversion and selection operations are executed on the population to generate new offspring and the next population ” here, the buy and sell signals can be considered the indication of whether to execute a purchase ) . Regarding claim SEQ claimMapNum 15 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein the evolutionary algorithm is a genetic algorithm (Chen, page 1, abstract “ Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem ” ) . Regarding claim SEQ claimMapNum 16 : Chen in view of Karathanasopoulos in further view of Xie teaches The method of claim 1, wherein the deep learning model is a neural network (Karathanasopoulos, page 6, section 3.2.2, ¶3 “ The most popular and simple NN is the Multi-Layer Perceptron (MLP). ” ) . Chen / Karathanasopoulos /Xie are analogous art because both references concern learning methods for trading financial assets and portfolios. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen /Xie ’s trading strategy genetic algorithm to incorporate the neural network taught by Karathanasopoulos . The motivation for doing so would have been to improve final performance, as stated in Karathanasopoulos, page 13, ¶1 “ All nine ensemble NN-evolutionary models have been managed to trade successfully the two ETFs. The RBF hybrid combination models present the best performance, the second performance its coming from the RNN combinations while the MLP architectures provide the third lowest performance. All the combinations show a remarkable performance proving that hybrid combinations are