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 .
Claim Rejections - 35 USC § 101
Claims 1-10 are rejected under U.S.C 101 as being directed to an abstract idea without adding significantly more
Regarding Claim 1
Step 1: “A method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: defining an intervention for a target unit and selecting an intervention target unit is a mental process that can be done with the aid of pen and paper, a person can define and select a target unit
flattening the pre-intervention data and the post-intervention data from the donor units into sequences is a mental process that can be done with the aid of pen and paper, a person can flatten data into sequences
linearly embedding the sequences to form linearly embedded sequences is the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
forming a sequence of vectors by injecting temporal embeddings, spatial embeddings, and target embeddings to the linearly embedded sequences is the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
…the Transformer-based encoder-decoder model configured to use a causal map and enables spatial bidirectionality to autoregressively generate a synthetic control of the target unit is a mental process that can be done with the aid of pen and paper
Step 2A Prong 2: The additional limitations collecting pre-intervention data and post-intervention data from donor units that underwent the intervention is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: collecting pre and post intervention data amounts to nothing more than mere data gathering
and passing the sequence of vectors to a Transformer-based encoder-decoder model… are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of passing vectors through an encoder-decoder to generate a synthetic target unit
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation collecting pre-intervention data and post-intervention data from donor units that underwent the intervention is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: collecting pre and post intervention data amounts to nothing more than mere data gathering
and passing the sequence of vectors to a Transformer-based encoder-decoder model… are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of passing vectors through an encoder-decoder to generate a synthetic target unit
Regarding Claim 2
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation …includes an encoder configured to compute a hidden representation for the pre-intervention data and send the hidden representation to a decoder is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). – examiners note: computing hidden representation here for the pre-intervention data amounts to nothing more than insignificant extra solution
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation …includes an encoder configured to compute a hidden representation for the pre-intervention data and send the hidden representation to a decoder is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to performing repetitive calculations, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II) – examiners note: computing hidden representation here for the pre-intervention data amounts to nothing more than insignificant extra solution
Regarding Claim 3
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation wherein the decoder is configured to use the hidden representation and the post-intervention data to autoregressively generate the synthetic control is a post-solution step of data which is adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). – examiners note: configuring a decoder to autoregressively generate data amounts to nothing more than insignificant extra solution activity
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the decoder is configured to use the hidden representation and the post-intervention data to autoregressively generate the synthetic control is a post-solution step of data which is adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed at performing repetitive calculations, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). – examiners note: configuring a decoder to autoregressively generate data amounts to nothing more than insignificant extra solution activity
Regarding Claim 4
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Comprising making a decision based on the synthetic control is a mental process that can be done with the aid of pen and paper, a person can observe a synthetic control and make a decision
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 5
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: further comprising performing an action to execute the decision is a mental process that can be done with the aid of pen and paper, a person can perform an action based off of a decision
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 6
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: further comprising using the synthetic control to plan a medical treatment is a mental process that can be done w the aid of pen and paper, a person can observe data to plan out medical treatment
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 7
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: further comprising using the synthetic control to increase a speed of A/B testing is a mental process that can be done with the aid of pen and paper, a person can observe data to increase speed of testing
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 8
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: further comprising using the synthetic control to predict advertisement outcomes is a mental process that can be done with the aid of pen and paper, a person can observe data to predict outcomes of an advertisement
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 9
Step 1: “A system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 10
Step 1: “A non-transitory computer readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Conclusion
The prior art made of record and relied upon is considered to applicant’s disclosure
Amjad et al NPL: mRSC: Multi-dimensional Robust Synthetic Control (09-23-2019) ([Abstract] “When evaluating the impact of a policy (e.g., gun control) on a metric of interest (e.g., crime-rate), it may not be possible or feasible to conduct a randomized control trial. In such settings where only observational data is available, Synthetic Control (SC) methods Abadie et al. (2010); Abadie et al. (2011); Abadie and Gardeazabal (2003) provide a popular data-driven approach to estimate a “synthetic” or “virtual” control by combining measurements of “similar” alternatives or units (called “donors”). Recently, Robust Synthetic Control (RSC) Amjad et al. (2018) was proposed as a generalization of SC to overcome the challenges of missing data and high levels of noise, while removing the reliance on expert domain knowledge for selecting donors. However, both SC and RSC (and its variants) suffer from poor estimation when the pre-intervention period is too short. As the main contribution of this work, we propose a generalization of unidimensional RSC to multi-dimensional Robust Synthetic Control, mRSC. Our proposed mechanism, mRSC, incorporates multiple types of measurements (or metrics) in addition to the measurement of interest for estimating a synthetic control, thus overcoming the challenge of poor inference due to limited amounts of pre-intervention data. We show that the mRSC algorithm, when using K relevant metrics, leads to a consistent estimator of the synthetic control for the target unit of interest under any metric. Our finite-sample analysis suggests that the post-intervention (testing) mean-squared error (MSE) of our predictions vanishes faster than the RSC algorithm by a factor of K. Additionally, we propose a principled scheme to combine multiple metrics of interest via a diagnostic test that evaluates if adding a metric can be expected to result in improved inference. Our mechanism for validating mRSC performance is also an important and related contribution of this work: time series prediction. We propose a method to predict the future evolution of a time series based on limited data when the notion of time is relative and not absolute, i.e., where we have access to a donor pool that has already undergone the desired future evolution. We conduct extensive experimentation to establish the efficacy of mRSC in three different scenarios: predicting the evolution of a metric of interest using synthetically generated data from a known factor model, and forecasting weekly sales and score trajectories of a Walmart store and Cricket game, respectively.
Song et al NPL: TSTNet: A Sequence to Sequence Transformer Network for Spatial-Temporal Traffic Prediction (09-07-2021) ([Abstract] “Making accurate traffic forecasting is of great importance in smart city-related researches. However, as the traffic features like traffic speed have a complex spatial-temporal characteristics, how to build an accurate traffic prediction model is still an open challenge. In this work, we propose TSTNet, a Sequence to Sequence (Seq2Seq) spatial-temporal traffic prediction model. TSTNet adopts Graph Attention Network (GAT), which can learn the spatial feature aggregation, to build spatial dependency. For temporal dependency, TSTNet applies a Seq2Seq Transformer structure to establish temporal dependency. As a GAT layer’s operation only aggregate the attribute information for neighbor nodes, it does not involve any spatial positional information. Similarly, if we apply the Transformer model on sequence learning tasks, the Transformer model also does not involve any temporal positional information as it does not know the exact time slot of different inputs. To solve the above problems, TSTNet implements a spatial-temporal embedding method to obtain the spatial-temporal positional representation for each input data. We evaluate TSTNet on traffic speed prediction tasks with other baselines upon two real-world datasets, the results show that TSTNet outperforms all the baseline models.”
Allowable Subject Matter
Claims 1-10 are allowed. But the 101 rejections still maintain
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/URIAH VENDELL MOORE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
