DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 1. Claims 1-20 are presented for examination. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter Claim 1: An apparatus for generating time series predictions from a first dataset comprising a first plurality of data entries and having a first feature space, comprising: a memory storing instructions; and one or more processors coupled to the memory, the one or more processors being configured to execute the instructions to: encode the first dataset to generate a latent vector having a latent space smaller than the first feature space; for a first time period (n=1) in a plurality of time periods: process the latent vector using an attention mechanism to generate a first attention vector; process the first attention vector using an LSTM (Long Short-Term Memory) neural network model to generate a first latent prediction vector; decode the first latent prediction vector to generate a first prediction vector of a plurality of time series prediction vectors having a second feature space larger than the latent space; for each successive nth time period (n> 1)in the plurality of time periods: process the latent vector and an (n- 1) th latent prediction vector using the attention mechanism to generate an nth attention vector; process the nth attention vector using the LSTM (Long Short-Term Memory) neural network model to generate an nth latent prediction vector; decode the nth latent prediction vector to generate an nth prediction vector of the plurality of time series prediction vectors in the second feature space; classify the first dataset using an XGBoost (Extreme Gradient Boosting) classifier to generate a classified dataset, the classified dataset comprising a set of probability weights from 0 to 1; scale each of the plurality of time series prediction vectors based on the classified dataset to generate a weighted plurality of time series prediction vectors, each of the weighted plurality of time series prediction vectors having a plurality of prediction values corresponding to the first plurality of data entries. The claim limitations in the abstract idea have been highlighted in bold above. The remaining limitations are “additional elements”. Similar limitations comprise the abstract ideas of claims 11 and 20. MPEP 2106 III provides a flowchart for the subject matter eligibility test for product and processes. The claim analysis following the flowchart is as follows: Step 1 : Is the claim to a process, machine, manufacture or composition of matter? Yes. Claim 1 recites an apparatus , which is a machine . Claim s 1 1 and 20 recite a method , which is a process . Step 2A, Prong One : Does the claim recite an abstract idea, law of nature, or nature phenomenon? Yes. The highlighted claim limitations constitute an abstract idea because the broadest reasonable interpretation of these steps fall within the mathematical concepts and mental process groupings of abstract ideas . Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The additional elements claim limitations recite generic computer components. Here, the computer is used as a tool to perform mathematical concepts and mental process. It amounts to no more than mere instructions to apply the exception using a generic computer. Even when viewed in combination, there additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. (Step 2A: Yes). Step 2B : Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As explained with respected to Step 2A, Prong Two, there are two additional elements. The additional elements of “ a memory storing instructions ” and “ one or more processors …” cannot provide an inventive concept because they are generic computer components to perform mere instructions of mathematical concepts and mental process . Therefore, claim 1 do es not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding claim s 11 and 20 , all claimed limitations are set forth and rejected as per discussion for claim 1 . Claims 2-6, 8 -10, 12-16 and 18- 19 constitute an abstract idea because the broadest reasonable interpretation of these claim limitations fall s within the mathematical concepts and mental process groupings of abstract ideas. Claims 7 and 17 recite generic computer components. Here, the computer is used as a tool to perform mathematical concepts and mental process. It amounts to no more than mere instructions to apply the exception using a generic computer. Thus , claim s 1-20 are not eligible subject matter under 35 USC 101. 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 of this title, 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. 1 . Claim s 1, 7-8, 11, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shailabh et al. (US Publication No. 2023/ 0169271 ) in view of Filar et al. (US Publication No . 2022/0100857 ). Regarding claim 1 , Shailabh et al. meets the claim limitations, as follows: An apparatus for generating time series predictions from a first dataset comprising a first plurality of data entries and having a first feature space, comprising: a memory storing instructions ( Fig. 1, para[ 0030], i.e. a memory subsystem of Topic modeling apparatus 110 ) ; and one or more processors coupled to the memory ( Fig. 1, para[ 0030], i.e. one or more processors of Topic modeling apparatus 110) , the one or more processors being configured to execute the instructions to: encode the first dataset to generate a latent vector having a latent space smaller than the first feature space ( para[ 0007], i.e. words of a document are encoded using an embedding matrix to obtain word embeddings for the document. ) ; for a first time period (n=1) in a plurality of time periods: process the latent vector using an attention mechanism to generate a first attention vector ( para[ 0024], i.e. The topic attention network can also compute a topic context matrix based on the topic embedding matrix and the word embeddings for a document. ) ; process the first attention vector using an LSTM (Long Short-Term Memory) neural network model to generate a first latent prediction vector ( para[ 0069]-[0070], i.e. the embedded sequence is processed by one or more long short-term memory neural networks to obtain hidden representations corresponding to the word embeddings . ) ; decode the first latent prediction vector to generate a first prediction vector of a plurality of time series prediction vectors having a second feature space larger than the latent space ( Fig. 6, para[ 0056] , [0091] , i.e. auto-encoder 600 decodes the latent vector 615 to obtain a predicted word vector 625. Note, the data after LSTM process and decoding process is a time series data. ) ; for each successive nth time period (n> 1)in the plurality of time periods: process the latent vector and an (n- 1) th latent prediction vector using the attention mechanism to generate an nth attention vector (para[0024], i.e. The topic attention network can also compute a topic context matrix based on the topic embedding matrix and the word embeddings for a document.); process the nth attention vector using the LSTM (Long Short-Term Memory) neural network model to generate an nth latent prediction vector ( para[ 0069]-[0070], i.e. the embedded sequence is processed by one or more long short-term memory neural networks to obtain hidden representations corresponding to the word embeddings . ) ; decode the nth latent prediction vector to generate an nth prediction vector of the plurality of time series prediction vectors in the second feature space ( Fig. 6, para[ 0056] , [0091] , i.e. auto-encoder 600 decodes the latent vector 615 to obtain a predicted word vector 625.) ; Shailabh et al. does not explicitly disclose the following claim limitations: classify the first dataset using an XGBoost (Extreme Gradient Boosting) classifier to generate a classified dataset, the classified dataset comprising a set of probability weights from 0 to 1; scale each of the plurality of time series prediction vectors based on the classified dataset to generate a weighted plurality of time series prediction vectors, each of the weighted plurality of time series prediction vectors having a plurality of prediction values corresponding to the first plurality of data entries. However, in the same field of endeavor Filar et al. discloses the deficient claim limitations, as follows: classify the first dataset using an XGBoost (Extreme Gradient Boosting) classifier to generate a classified dataset, the classified dataset comprising a set of probability weights from 0 to 1 ( para[ 0042], i.e. a XGBoost model predict a class probability between 0.0-1.0 for a malicious label ) ; scale each of the plurality of time series prediction vectors based on the classified dataset to generate a weighted plurality of time series prediction vectors, each of the weighted plurality of time series prediction vectors having a plurality of prediction values corresponding to the first plurality of data entries ( para[ 0042], i.e. a predicted class probability is used as the weight for a given edge ) . Therefore , it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teachings of Shailabh to classify words of a document using a XGBoost model into a probability between 0.0-1.0 as taught by Filar , and then apply the probability as weights on word vector to determine topics , the motivation being to improve prediction efficiency and accuracy . Regarding claim s 11 and 20 , all claimed limitations are set forth and rejected as per discussion for claim 1 . Here, CRM is equivalent to the memory recited in claim 1. Regarding claim 7 , the rejection of claim 1 is incorporated herein. Filar et al. meets the claim limitations, as follows: The apparatus of claim 1, wherein the one or more processors comprises a central processing unit (CPU) and a graphical processing unit (GPU) ( para[ 0070], i.e. a central processing unit (CPU), a graphics processing unit (GPU) ) . Regarding claim 8 , the rejection of claim 1 is incorporated herein. Filar et al. meets the claim limitations, as follows: The apparatus of claim 1, wherein the first plurality of data entries comprises a plurality of user accounts and data associated with each one of the plurality of user accounts (Fig. 1, i.e. user device 105 ) . Regarding claim 17 , all claimed limitations are set forth and rejected as per discussion for claim 7 . Regarding claim 1 8 , all claimed limitations are set forth and rejected as per discussion for claim 8 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KATE H LUO whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-5635 . The examiner can normally be reached on FILLIN "Work Schedule?" \* MERGEFORMAT 8:00-5:00PM . 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