CTNF 18/452,714 CTNF 102039 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 in response to the application filled 08/21/2023. Claims 1-23 are pending and have been examined. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim 1: Step 1: Claim 1 recites a method which falls into the statutory category of process. Step 2A Prong 1: Claim 1 recites multiple mathematical concepts: diagonally initializing a state matrix to produce a discretized state convolutional kernel, computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep, computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep, and performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Step 2A Prong 2: Claim 1 does not integrate the abstract idea into a practical application since the additional elements of: A computer-implemented method, is merely an instruction to apply the abstract ideas using a generic computer. Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of implementing the method using a computer is nothing more than mere instructions to apply the abstract idea using a generic computer. Claim 1 is not patent eligible. Regarding claim 2, the rejection of claim 1 is incorporated, further the claim recites further comprising computing a subsequent multidimensional prediction using the extended spatiotemporal sequence as the input spatiotemporal sequence; and performing the non-linear function using the subsequent multidimensional prediction to generate a subsequent multidimensional output. which amounts to applying the method of claim 1 using a prediction as the input. This merely recites the same abstract ideas as in claim 1 but with a different input and nothing more. Claim 2 is not patent eligible. Regarding claim 3, the rejection of claim 1 is incorporated, further the claim recites wherein the multidimensional prediction is computed using an encoded version of the input spatiotemporal sequence. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 3 is not patent eligible. Regarding claim 4, the rejection of claim 1 is incorporated, further the claim recites wherein the non-linear function generates an intermediate output and the intermediate output is decoded to generate the multidimensional output. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 4 is not patent eligible. Regarding claim 5, the rejection of claim 1 is incorporated, further the claim recites wherein the at least one dimension is time. This limitation amounts to more specifics of the output produced by the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 5 is not patent eligible. Regarding claim 6, the rejection of claim 1 is incorporated, further the claim recites wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 6 is not patent eligible. Regarding claim 7, the rejection of claim 1 is incorporated, further the claim recites wherein the input spatiotemporal sequence comprises data for at least one of weather forecasting, traffic modeling, video prediction, video generation, and physics simulation. This limitation amounts to linking the use of the judicial exception to a particular technological environment or field of us (MPEP 2109.05(h)). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 7 is not patent eligible. Regarding claim 8, the rejection of claim 1 is incorporated, further the claim recites wherein the input spatiotemporal sequence comprises biomedical or robotics data. This limitation amounts to linking the use of the judicial exception to a particular technological environment or field of us (MPEP 2109.05(h)). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 8 is not patent eligible. Regarding claim 9, the rejection of claim 1 is incorporated, further the claim recites wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed on a server or in a data center and the multidimensional output is streamed to a user device. This limitation amounts to merely the use of a generic computer (server) to apply the judicial exception and insignificant extra-solution activity (streaming to a user) given it’s merely the transmitting of data and is therefore considered well-known, routine, conventional as evidenced by MPEP §2106.05(d)(II)(I). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 9 is not patent eligible. Regarding claim 10, the rejection of claim 1 is incorporated, further the claim recites wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed within a cloud computing environment. This limitation amounts to applying the judicial exception with or by a generic machine (cloud computing). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 10 is not patent eligible. Regarding claim 11, the rejection of claim 1 is incorporated, further the claim recites wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle. This limitation amounts to applying the judicial exception with or by a generic machine. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 11 is not patent eligible. Regarding claim 12, the rejection of claim 1 is incorporated, further the claim recites wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed on a virtual machine comprising a portion of a graphics processing unit. This limitation amounts to applying the judicial exception with or by a generic machine (virtual machine). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 12 is not patent eligible. Regarding claim 13: Step 1: Claim 13 recites a system which falls into the statutory category of machine. Step 2A Prong 1: Claim 13 recites multiple mathematical concepts: diagonally initializing a state matrix to produce a discretized state convolutional kernel, computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep, computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep, and performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Step 2A Prong 2: Claim 13 does not integrate the abstract idea into a practical application since the additional elements of: a memory that stores the input spatiotemporal sequence, is insignificant extra-solution activity. a processor that is connected to the memory, wherein the processor is configured to extend the input spatiotemporal sequence, is merely an instruction to apply the abstract ideas using a generic computer. Step 2B: Claim 13 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of implementing the method using a processor is nothing more than mere instructions to apply the abstract idea using a generic computer and the additional element of storing data in memory Is considered insignificant extra-solution activity because it is well-understood, routine, conventional activity as evidenced by MPEP §2106.05(d)(II)(Iv). Claim 13 is not patent eligible. Regarding claim 14, the rejection of claim 13 is incorporated, further the claim recites wherein the multidimensional prediction is computed using an encoded version of the input spatiotemporal sequence. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 14 is not patent eligible. Regarding claim 15, the rejection of claim 13 is incorporated, further the claim recites wherein the non-linear function generates an intermediate output and the intermediate output is decoded to generate the multidimensional output. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 15 is not patent eligible. Regarding claim 16, the rejection of claim 13 is incorporated, further the claim recites wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 16 is not patent eligible. Regarding claim 17, the rejection of claim 13 is incorporated, further the claim recites wherein the input spatiotemporal sequence comprises data for at least one of weather forecasting, traffic modeling, video prediction, video generation, and physics simulation. This limitation amounts to linking the use of the judicial exception to a particular technological environment or field of us (MPEP 2109.05(h)). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 17 is not patent eligible. Regarding claim 18, the rejection of claim 13 is incorporated, further the claim recites wherein the input spatiotemporal sequence comprises biomedical or robotics data. This limitation amounts to linking the use of the judicial exception to a particular technological environment or field of us (MPEP 2109.05(h)). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 18 is not patent eligible. Regarding claim 19: Step 1: Claim 19 recites a non-transitory computer-readable media which falls into the statutory category of manufacture. Step 2A Prong 1: Claim 19 recites multiple mathematical concepts: diagonally initializing a state matrix to produce a discretized state convolutional kernel, computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep, computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep, and performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Step 2A Prong 2: Claim 19 does not integrate the abstract idea into a practical application since the additional elements of: when executed by one or more processors, cause the one or more processors to perform the steps of, is merely an instruction to apply the abstract ideas using a generic computer. Step 2B: Claim 19 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of implementing the method using a processor is nothing more than mere instructions to apply the abstract idea using a generic. Claim 19 is not patent eligible. Regarding claim 20, the rejection of claim 19 is incorporated, further the claim recites wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence. This limitation amounts to more specifics of the judicial exception. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. Claim 20 is not patent eligible. Regarding claim 21: Step 1: Claim 21 recites a method which falls into the statutory category of process. Step 2A Prong 1: Claim 21 recites multiple abstract ideas : reshaping a diagonal state matrix to produce a discretized state convolutional kernel, computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep, computing the next element in the input spatiotemporal sequence using the input spatiotemporal sequence and the multidimensional state at the current timestep. These limitations amount to mental processes (reshaping) or mathematical concepts (computing a multidimensional state and the next element). Step 2A Prong 2: Claim 21 does not integrate the abstract idea into a practical application since the additional elements of: A computer-implemented method, is merely an instruction to apply the abstract ideas using a generic computer. Receiving an input spatiotemporal sequence of data for weather forecasting, traffic modeling, video generation, or physics simulation, is insignificant extra-solution activity. Step 2B: Claim 21 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of implementing the method using a computer is nothing more than mere instructions to apply the abstract idea using a generic computer and the additional element of receiving data Is considered insignificant extra-solution activity because it is well-understood, routine, conventional activity as evidenced by MPEP §2106.05(d)(II)(I). Claim 21 is not patent eligible. Regarding claim 22, the rejection of claim 21 is incorporated, further the claim recites wherein computing the next element comprises performing a non-linear function on the multidimensional prediction. This limitation amounts to a mathematical concept. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical exception, nor to significantly more than the judicial exception. Claim 22 is not patent eligible. Regarding claim 23, the rejection of claim 21 is incorporated, further the claim recites further comprising computing a subsequent element using the input spatiotemporal sequence and the next element which amounts to applying the method of claim 21 using a prediction alongside the sequence as the input. This merely recites the same abstract ideas as in claim 21 but with a different input and nothing more. Claim 23 is not patent eligible. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim (s) 21 and 23 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Gu (Efficiently Modeling Long Sequences with Structured State Spaces) . Regarding claim 21, Gu teaches receiving an input spatiotemporal sequence of data for weather forecasting, traffic modeling, video generation, or physics simulation (Table 9, Notably S4 is better on the longest setting in each task, e.g. reducing MSE by 37% when forecasting 30 days of weather data); reshaping a diagonal state matrix to produce a discretized state convolutional kernel (Section 2.3, Notationally, throughout this paper we use Ā ,... to denote discretized SSM matrices defined by (3)); computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state ; Pg. 10, S4 requires constant memory and computation per time step) ; and computing the next element in the input spatiotemporal sequence using the input spatiotemporal sequence and the multidimensional state at the current timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state, y is the multidimensional prediction, and u is the input sequence ). Regarding claim 23, Gu teaches the method of claim 21 and Gu teaches further comprising computing a subsequent element using the input spatiotemporal sequence and the next element (Title, Efficiently Modeling Long Sequences with Structured State Spaces; Table 9, Univariate long sequence time-series forecasting results, SSMs are used to generate long sequence therefore a sequence of predictions each depending on the previous prediction are generated ) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-7,9,13-17,19,20,22 are rejected under 35 U.S.C. 103 as being unpatentable over Gu in view of Nazir (US10996374B1) . Regarding claim 1, Gu teaches method of extending an input spatiotemporal sequence in at least one dimension (Title, Efficiently Modeling Long Sequences, Section 4.3, The tasks we focus on (generative modeling, image classification, time-series forecasting) are considered as LRD tasks in the literature, and serve as additional validation that S4 handles LRDs efficiently) comprising: diagonally initializing a state matrix (Section 1, special state matrices A, Abs, Our technique involves conditioning A with a low-rank correction, allowing it to be diagonalized stably) to produce a discretized state convolutional kernel (Section 2.3, Notationally, throughout this paper we use Ā ,... to denote discretized SSM matrices defined by (3)); computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state ); computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state, y is the multidimensional prediction, and u is the input sequence ; Pg. 10, S4 requires constant memory and computation per time step), but fails to teach performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Nazir teaches performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence (Fig 7, As illustrated at 704 , a forecast of local weather indicators can also be received and processed. As illustrated 706 , a difference between the forecast and the measured local weather indicators can be determined. Such difference is determined particularly for historical data that can be used in generating a correction module, as illustrated at 708 . In an example, the correction module can take the form of a neural network model, such as a LSTM module, the correction module, a neural network containing non-linear layers, is the non-linear function ). Gu and Nazir are analogous to the claimed invention because they are both in the field of endeavor of predicting or forecasting sequences of data such as weather. Furthermore, because Nazir states that the weather forecast engine can be a state space model (Col. 5 lines 20-25, Weather forecast engine 210 can utilize heuristics or algorithms to form regression models, Markov chains, time series models, state space models), it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in the prediction produced by the state space model and runs it through a correction module in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Regarding claim 2, Gu in view of Nazir teaches the method of claim 1; furthermore, Gu teaches further comprising computing a subsequent multidimensional prediction using the extended spatiotemporal sequence as the input spatiotemporal sequence (Title, Efficiently Modeling Long Sequences with Structured State Spaces; Table 9, Univariate long sequence time-series forecasting results, SSMs are used to generate long sequence therefore a sequence of predictions each depending on the previous prediction are generated ) and Nazir teaches performing the non-linear function using the subsequent multidimensional prediction to generate a subsequent multidimensional output (Fig 7, As illustrated at 704 , a forecast of local weather indicators can also be received and processed. As illustrated 706 , a difference between the forecast and the measured local weather indicators can be determined. Such difference is determined particularly for historical data that can be used in generating a correction module, as illustrated at 708 . In an example, the correction module can take the form of a neural network model, such as a LSTM module, the correction module, a neural network containing non-linear layers, is the non-linear function ). Gu and Nazir are analogous to the claimed invention because they are both in the field of endeavor of predicting or forecasting sequences of data such as weather. Furthermore, because Nazir states that the weather forecast engine can be a state space model (Col. 5 lines 20-25, Weather forecast engine 210 can utilize heuristics or algorithms to form regression models, Markov chains, time series models, state space models), it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in the prediction produced by the state space model and runs it through a correction module in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Regarding claim 3, Gu in view of Nazir teaches the method of claim 1 and Gu also teaches the multidimensional prediction is computed using an encoded version of the input spatiotemporal sequence (Fig 1, Fast Discrete Representations, y is produced using a version of U encoded through the use of B) . Regarding claim 4, Gu in view of Nazir teaches the method of claim 1 and Nazir also teaches wherein the non-linear function generates an intermediate output and the intermediate output is decoded to generate the multidimensional output (Col. 7, lines 52-58, In an example, the correction module can take the form of a neural network model, such as a LSTM module… Utilizing the generated module, future forecasts of local weather indicators can then be used as an input to forecast corrected weather indicators, here the non-linear function is the correction module, a neural network such as a LSTM, which can encode inputs creating some latent or intermediate output, which is later decoded into the output, the corrected forecast). Gu and Nazir are analogous to the claimed invention as described above and it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in a prediction and outputs another prediction in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Regarding claim 5, Gu in view of Nazir teaches the method of claim 1 and Gu teaches wherein the at least one dimension is time (Table 9, Univariate long sequence time-series forecasting results). Regarding claim 6, Gu in view of Nazir teaches the method of claim 1 and Gu teaches wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence (Fig 1, Fast Discrete Representations, where x is the multidimensional state, u is the input, and Ā is the kernel ). Regarding claim 7, Gu in view of Nazir teaches the method of claim 1 and Gu teaches wherein the input spatiotemporal sequence comprises data for at least one of weather forecasting, traffic modeling, video prediction, video generation, and physics simulation (Table 9, Notably S4 is better on the longest setting in each task, e.g. reducing MSE by 37% when forecasting 30 days of weather data). Regarding claim 9, Gu in view of Nazir teaches the method of claim 1, additionally Gu fails to teach but Nazir teaches wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed on a server or in a data center and the multidimensional output is streamed to a user device (Abs, A system includes at least one server implementing a weather forecast engine, Col. 2 lines 50-52, In addition, a user device 110 can access the one or more servers 102 to interact with the weather forecasting engine 104). Gu and Nazir are analogous to the claimed invention as described above and it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a server to perform the forecasting and stream it to a user device because using servers allows the forecast system to draw in more complex data (Col. 2 lines 28-37) and streaming to users improves the forecasting system by allowing users to display and interact with forecasts. Regarding claim 13, Gu teaches diagonally initializing a state matrix (Section 1, special state matrices A, Abs, Our technique involves conditioning A with a low-rank correction, allowing it to be diagonalized stably) to produce a discretized state convolutional kernel (Section 2.3, Notationally, throughout this paper we use Ā ,... to denote discretized SSM matrices defined by (3)); computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state ); computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state, y is the multidimensional prediction, and u is the input sequence; Pg. 10, S4 requires constant memory and computation per time step), but fails to teach performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Gu fails to teach but Nazir teaches performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence (Fig 7, As illustrated at 704 , a forecast of local weather indicators can also be received and processed. As illustrated 706 , a difference between the forecast and the measured local weather indicators can be determined. Such difference is determined particularly for historical data that can be used in generating a correction module, as illustrated at 708 . In an example, the correction module can take the form of a neural network model, such as a LSTM module, the correction module, a neural network containing non-linear layers, is the non-linear function ) and A system for extending an input spatiotemporal sequence in at least one dimension, comprising: a memory that stores the input spatiotemporal sequence; and a processor that is connected to the memory, wherein the processor is configured to extend the input spatiotemporal sequence (Abs, A system includes at least one server implementing a weather forecast engine, Col. 4 lines 1-3, Further, the computational system 200 can include storage 208 in communication with the processor 202 . The storage can be implemented as random access memory (RAM), read-only memory (ROM), or long-term storage, or any combination thereof). Gu and Nazir are analogous to the claimed invention because they are both in the field of endeavor of predicting or forecasting sequences of data such as weather. Furthermore, because Nazir states that the weather forecast engine can be a state space model (Col. 5 lines 20-25, Weather forecast engine 210 can utilize heuristics or algorithms to form regression models, Markov chains, time series models, state space models), it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in the prediction produced by the state space model and runs it through a correction module in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to use the memory and processor system used by Nazir to store instructions and data to implement the forecasting system (Col. 4 lines 5-7, The storage 308 can include instructions and data to implement the functionality of the computational system). Regarding claim 14, Gu in view of Nazir teaches the system of claim 13 and Gu also teaches the multidimensional prediction is computed using an encoded version of the input spatiotemporal sequence (Fig 1, Fast Discrete Representations, y is produced using a version of U encoded through the use of B) . Regarding claim 15, Gu in view of Nazir teaches the system of claim 13 and Nazir also teaches wherein the non-linear function generates an intermediate output and the intermediate output is decoded to generate the multidimensional output (Col. 7, lines 52-58, In an example, the correction module can take the form of a neural network model, such as a LSTM module… Utilizing the generated module, future forecasts of local weather indicators can then be used as an input to forecast corrected weather indicators, here the non-linear function is the correction module, a neural network such as a LSTM, which can encode inputs creating some latent or intermediate output, which is later decoded into the output, the corrected forecast). Gu and Nazir are analogous to the claimed invention as described above and it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in a prediction and outputs another prediction in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Regarding claim 16, Gu in view of Nazir teaches the system of claim 13 and Gu teaches wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence (Fig 1, Fast Discrete Representations, where x is the multidimensional state, u is the input, and Ā is the kernel ). Regarding claim 17, Gu in view of Nazir teaches the system of claim 13 and Gu teaches wherein the input spatiotemporal sequence comprises data for at least one of weather forecasting, traffic modeling, video prediction, video generation, and physics simulation (Table 9, Notably S4 is better on the longest setting in each task, e.g. reducing MSE by 37% when forecasting 30 days of weather data). Regarding claim 19, Gu teaches diagonally initializing a state matrix (Section 1, special state matrices A, Abs, Our technique involves conditioning A with a low-rank correction, allowing it to be diagonalized stably) to produce a discretized state convolutional kernel (Section 2.3, Notationally, throughout this paper we use Ā ,... to denote discretized SSM matrices defined by (3)); computing a multidimensional state at a current timestep by applying the discretized state convolutional kernel to the multidimensional state at a previous timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state ); computing a multidimensional prediction using the input spatiotemporal sequence and the multidimensional state at the current timestep (Fig 1, Fast Discrete Representations, where x is the multidimensional state, y is the multidimensional prediction, and u is the input sequence ; Pg. 10, S4 requires constant memory and computation per time step), but fails to teach performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence. Gu fails to teach but Nazir teaches performing a non-linear function using the multidimensional prediction to generate a multidimensional output that extends the input spatiotemporal sequence in the at least one dimension, producing an extended spatiotemporal sequence (Fig 7, As illustrated at 704 , a forecast of local weather indicators can also be received and processed. As illustrated 706 , a difference between the forecast and the measured local weather indicators can be determined. Such difference is determined particularly for historical data that can be used in generating a correction module, as illustrated at 708 . In an example, the correction module can take the form of a neural network model, such as a LSTM module, the correction module, a neural network containing non-linear layers, is the non-linear function ) and A non-transitory computer-readable media storing computer instructions for extending an input spatiotemporal sequence in at least one dimension that, when executed by one or more processors, cause the one or more processors to perform (Col. 4 lines 1-7, Further, the computational system 200 can include storage 208 in communication with the processor 202 . The storage can be implemented as random access memory (RAM), read-only memory (ROM), or long-term storage, or any combination thereof. The long-term storage can incorporate optical storage, magnetic storage, or solid-state storage, or any combination thereof. The storage 308 can include instructions and data to implement the functionality of the computational system). Gu and Nazir are analogous to the claimed invention because they are both in the field of endeavor of predicting or forecasting sequences of data such as weather. Furthermore, because Nazir states that the weather forecast engine can be a state space model (Col. 5 lines 20-25, Weather forecast engine 210 can utilize heuristics or algorithms to form regression models, Markov chains, time series models, state space models), it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in the prediction produced by the state space model and runs it through a correction module in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to use the memory and processor system used by Nazir to store instructions and data to implement the forecasting system (Col. 4 lines 5-7, The storage 308 can include instructions and data to implement the functionality of the computational system). Regarding claim 20, Gu in view of Nazir teaches the non-transitory computer-readable media of claim 19 and Gu teaches wherein computing the multidimensional state at the current timestep further comprises applying a discretized input convolutional kernel to the input spatiotemporal sequence (Fig 1, Fast Discrete Representations, where x is the multidimensional state, u is the input, and Ā is the kernel ). Regarding claim 22, Gu teaches the method of claim 21 as described in the 102 rejection above. Furthermore, Gu fails to teach, but Nazir teaches, wherein computing the next element comprises performing a non-linear function on the multidimensional prediction (Fig 7, As illustrated at 704 , a forecast of local weather indicators can also be received and processed. As illustrated 706 , a difference between the forecast and the measured local weather indicators can be determined. Such difference is determined particularly for historical data that can be used in generating a correction module, as illustrated at 708 . In an example, the correction module can take the form of a neural network model, such as a LSTM module, the correction module, a neural network containing non-linear layers, is the non-linear function ). Gu and Nazir are analogous to the claimed invention because they are both in the field of endeavor of predicting or forecasting sequences of data such as weather. Furthermore, because Nazir states that the weather forecast engine can be a state space model (Col. 5 lines 20-25, Weather forecast engine 210 can utilize heuristics or algorithms to form regression models, Markov chains, time series models, state space models), it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Gu to incorporate the use of a correction module that takes in the prediction produced by the state space model and runs it through a correction module in order to reduce error and improve forecasts (Col. 7 line 55-65, Table 1 indicates that the root mean square error is reduced using such a correction module which is further illustrated in the figures. For example, FIG. 20 shows an improvement in the six-hour forecast of windspeed) . 07-22-aia AIA Claim (s) 8, 10-12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gu in view of Nazir as applied to claim 1 above, and further in view of Rangapuram (US11281969B1) . Regarding claim 8, Gu in view of Nazir teaches the method of claim 1 but fails to teach wherein the input spatiotemporal sequence comprises biomedical or robotics data. Rangapuram teaches wherein the input spatiotemporal sequence comprises biomedical or robotics data (Col. 3 lines 60-65, Other types of domain specific metadata may be used for time series pertaining to autonomous vehicles, robotics, image/speech processing applications and the like). Ranagapuram is analogous to the claimed invention because it is in the field of endeavor of predicting or forecasting sequences of data using machine learning models such as state space models. It would have been obvious for one of ordinary skill in the art before the filling date of the claimed invention to have used robotics data alongside the invention in Gu because Ranagapuram states that state space models can be used to forecast time series data that can be robotics data (Abs, A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained, Col. 9 lines 13-15, In another embodiment, the forecasts may for example be used to cause a robotic system and/or an autonomous vehicle system…) therefore the weather time series data can be substituted with robotics data. Regarding claim 10, Gu in view of Nazir teaches the method of claim 1 but fails to teach wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed within a cloud computing environment. Rangapuram teaches wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed within a cloud computing environment (Col. 21 lines 15-20, FIG. 10 illustrates an example provider network environment in which a forecasting service may be implemented…..networks set up by an entity such as a company or a public sector organization to provide one or more network-accessible services (such as various types of cloud-based computing, storage or analytics services)). Rangapuram is analogous to the claimed invention as stated above, therefore it would have been obvious for one of ordinary skill in the art before the filling date of the claimed invention to have used a network environment including cloud-based computing to implement the forecasting to eliminate the need for heavy on-premises infrastructure. Regarding claim 11, Gu in view of Nazir teaches the method of claim 1 but fails to teach wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle. Rangapuram teaches wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle. (Col. 9 lines 13-17, In another embodiment, the forecasts may for example be used to cause a robotic system and/or an autonomous vehicle system, e.g., within a factory or warehouse, to relocate items from one location to another, or to reposition the robotic system or autonomous vehicle system in anticipation of one or more events). Ranagapuram is analogous to the claimed invention because it is in the field of endeavor of predicting or forecasting sequences of data using machine learning models such as state space models. It would have been obvious for one of ordinary skill in the art before the filling date of the claimed invention to have used the state space model in Gu in robotics or autonomous vehicles because Ranagapuram states that forecast systems using state space models can be used in robotic/autonomous vehicle systems (Col. 23 lines 43-50, Similarly, for other problem domains such as human resources planning, automated data center resource provisioning/planning, traffic modeling/planning, autonomous vehicle or robot trajectory planning and the like, the forecasting techniques described may be able to generate high-quality forecasts using relatively short time series…). Regarding claim 12, Gu in view of Nazir teaches the method of claim 1 but fails to teach wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed on a virtual machine comprising a portion of a graphics processing unit. Rangapuram teaches wherein at least one of the steps of diagonally initializing, computing the multidimensional state, computing the multidimensional prediction, and performing is performed on a virtual machine comprising a portion of a graphics processing unit (Col. 21 lines 41-45, for example, the forecasting service 1043 , and/or the machine learning service 1071 may utilize virtual machines instantiated at the virtual computing), Col. 22 lines 1-4, In some embodiments, special execution servers 1076 (e.g., servers comprising graphics processing units (GPUs) or other processors optimized specifically for machine learning) may be available at the MLS 1071 , and may be employed for some of the algorithms/models trained and executed by the forecasting service 1043 ). Rangapuram is analogous to the claimed invention as stated above, therefore it would have been obvious for one of ordinary skill in the art before the filling date of the claimed invention to have used a network environment including a virtual machine to implement the forecasting to enable multiple environments to run on shared hardware, furthermore, it would have been obvious before the effective filling date of the claimed invention to use a GPU in this network environment because they can handle large scale computations, such as those needed during machine learning, efficiently. Regarding claim 18, Gu in view of Nazir teaches the system of claim 13 but fails to teach wherein the input spatiotemporal sequence comprises biomedical or robotics data. Rangapuram teaches wherein the input spatiotemporal sequence comprises biomedical or robotics data (Col. 3 lines 60-65, Other types of domain specific metadata may be used for time series pertaining to autonomous vehicles, robotics, image/speech processing applications and the like). Ranagapuram is analogous to the claimed invention because it is in the field of endeavor of predicting or forecasting sequences of data using machine learning models such as state space models. It would have been obvious for one of ordinary skill in the art before the filling date of the claimed invention to have used robotics data alongside the invention in Gu because Ranagapuram states that state space models can be used to forecast time series data that can be robotics data (Abs, A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained, Col. 9 lines 13-15, In another embodiment, the forecasts may for example be used to cause a robotic system and/or an autonomous vehicle system…) therefore the weather time series data can be substituted with robotics data. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATNAEL A ASEGDEW whose telephone number is (571)270-0407. The examiner can normally be reached 7:30-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NATNAEL A ASEGDEW/Examiner, Art Unit 2122 /KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122 Application/Control Number: 18/452,714 Page 2 Art Unit: 2122 Application/Control Number: 18/452,714 Page 3 Art Unit: 2122 Application/Control Number: 18/452,714 Page 4 Art Unit: 2122 Application/Control Number: 18/452,714 Page 5 Art Unit: 2122 Application/Control Number: 18/452,714 Page 6 Art Unit: 2122 Application/Control Number: 18/452,714 Page 7 Art Unit: 2122 Application/Control Number: 18/452,714 Page 8 Art Unit: 2122 Application/Control Number: 18/452,714 Page 9 Art Unit: 2122 Application/Control Number: 18/452,714 Page 10 Art Unit: 2122 Application/Control Number: 18/452,714 Page 12 Art Unit: 2122 Application/Control Number: 18/452,714 Page 13 Art Unit: 2122 Application/Control Number: 18/452,714 Page 14 Art Unit: 2122 Application/Control Number: 18/452,714 Page 15 Art Unit: 2122 Application/Control Number: 18/452,714 Page 16 Art Unit: 2122 Application/Control Number: 18/452,714 Page 17 Art Unit: 2122 Application/Control Number: 18/452,714 Page 18 Art Unit: 2122 Application/Control Number: 18/452,714 Page 19 Art Unit: 2122 Application/Control Number: 18/452,714 Page 20 Art Unit: 2122 Application/Control Number: 18/452,714 Page 21 Art Unit: 2122 Application/Control Number: 18/452,714 Page 22 Art Unit: 2122 Application/Control Number: 18/452,714 Page 23 Art Unit: 2122 Application/Control Number: 18/452,714 Page 24 Art Unit: 2122 Application/Control Number: 18/452,714 Page 25 Art Unit: 2122 Application/Control Number: 18/452,714 Page 26 Art Unit: 2122
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