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 .
DETAILED ACTION
This action is in response to the claims filed 1/19/2024. Claims 1-20 are pending 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8 and 15 recite the abstract idea of “calculating spot sensitivity data of derivative element based on volatility surface deformation data and conduct transaction with respect to the derivative instrument”, which is grouped under “Certain Methods of Organizing Human Activity” such as “fundamental economic principles or practices” (hedging, mitigating risk; managing transactions). (MPEP 2016.04(a)). Specifically, claims 1, 8 and 15 recite “… stores a plurality of historical data and … data corresponding to a derivative instrument”, “…. model with the historical data and the … data corresponding to the derivative instrument for time-series data prediction”, “learning, in response to …., volatility surface deformation data over time corresponding to the derivative instrument”, “calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data ……model”, “displaying the spot sensitivity data onto ….” and “receiving… to conduct a transaction with respect to the derivative instrument”. Claims 2-7, 9-14 and 16-20 are dependent on claims 1, 8 and 15 and include all the limitations of claims 1, 8 and 15. Therefore, claims 2-7, 9-14 and 16-20 recite the same abstract idea of “calculating spot sensitivity data of derivative element based on volatility surface deformation data and conduct transaction with respect to the derivative instrument”. The limitations recited in the depending claims (For example, the capturing and storing steps and the specific types of service, data and model used) are further details of the abstract idea and not significantly more. As such, the description in claims 1-20 of calculating spot sensitivity data of derivative element based on volatility surface deformation data and conduct transaction with respect to the derivative instrument is an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04II), the additional elements of claims 1, 8 and 15 such as “accessing a database”, “receiving user input”, “output”, displaying and receiving via “user interface”, “processor (claims 8 and 15), “memory operatively connected to the processor via communication interface (claim 8), “non-transitory computer readable medium configured to store instructions” (claim 15) represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular technological environment or field of use. With respect to the claimed “implementing an artificial intelligence deep learning (model)” and “training the artificial intelligence deep learning (model)”, the claims are directed to the abstract idea of using generic machine learning technique in a particular environment and lack detail regarding how the “implementing” and “training” are performed (MPEP 2106.05(f)(1)). Patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 35 U.S.C. 101. The additional elements of claims 2-7, 9-14 and 16-20, such as “implementing an algorithm” and “applying bidirectional gate recurrent unit neural network algorithm” also lack detail regarding how the “implementing” and “applying” are performed (MPEP 2106.05(f)(1)). Therefore, the limitations do not integrate the abstract idea into a practical application as they are no more than “apply it” (MPEP 2106.05(f)(1)).
When analyzed under step 2B (MPEP 2106.04II), because the additional elements do no more than represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use, they do not provide an improvement to computer functionality, or an improvement to another technology or technical field and, therefore, do not amount to significantly more than the judicial exception itself (MPEP 2106.05(I)(A)(f)&(h)).
Hence, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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 1-2, 5-6, 8-9, 12-13, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hadi et al. (US 2010/0042550 A1) in view of BUEHLER et al. (US 2021/0224911 A1).
As per Claims 1, 8, 15
Hadi (‘550) discloses
a processor; and a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to *claim 8 only, see at least paragraph 0016 (computer device includes a processor; interface units and drives), paragraph 0007 (computer-readable medium; storing computer-executable instructions), paragraph 0021 (computer device may include computer-executable instructions for receiving…and displaying
a non-transitory computer readable medium configured to store instructions for data processing, the instructions, when executed, cause a processor to perform * claim 15 only, see at least paragraph 0007 (computer-readable medium; storing computer-executable instructions), paragraph 0021 (computer device may include computer-executable instructions for receiving…and displaying)
accessing a database that stores a plurality of historical data and input data corresponding to a derivative instrument, paragraph 0015 (a trade database may be included to store information identifying trades; identifying the time that a trade took place and the contract price), paragraph 0029 (volatility is determined from historical and forecast…data; price volatility…for derivative contracts), paragraph 0020 (providing bid and offer prices for a derivative), paragraph 0016 (interact with the computer with…input device)
implementing model, see at least paragraph 0035 (model could also be used to find the volatility) and paragraph 0049 (models)
historical data and the input data corresponding to the derivative instrument for time-series data prediction, paragraph 0015 (a trade database may be included to store information identifying trades; identifying the time that a trade took place and the contract price), paragraph 0029 (volatility is determined from historical and forecast…data; price volatility…for derivative contracts) and paragraph 0035 (strike/current price; time series could be used to forecast forward a future volitivity level), paragraph 0020 (providing bid and offer prices for a derivative), paragraph 0016 (interact with the computer with…input device)
receiving user input via the user interface to conduct a transaction with respect to the derivative instrument, see at least paragraph 0021 (computer device…receiving order information from a user), paragraph 0017 (user of computer device may then transmit the trader or other information), paragraph 0016 (computer device may also include a variety of interface units; user… input device), paragraph 0003 derivative contracts), paragraph 0015 (orders and trades), paragraph 0020 (derivative or security to exchange computer system; trade)
Hadi (‘550) discloses implementing model and determining volatility surface deformation data over time corresponding to the derivative instrument, see at least paragraph 0035 (model could also be used to find the volatility) and paragraph 0049 (models) and paragraph 0035 (model could also be used to find the volatility…. volatility surface where by the volatility of the strike or strike/current price for puts or current price/strike for calls) and paragraph 0052 (volatility surface for a collection of …derivative option contracts) and Fig 2 (implied volatility surface; days to maturity), paragraph 0023 (spikes in the volatility of weather derivatives having relatively far away maturity dates), paragraph 0006 (data may be reduced to three dimensional surface), but fails to explicitly disclose implementing an artificial intelligence deep learning model, training the artificial intelligence deep learning model with data and learning, in response to training, data over time and calculating based on output data from the artificial intelligence deep learning model. BUEHLER (‘911) teaches implementing an artificial intelligence deep learning model, training the artificial intelligence deep learning model with data and learning, in response to training, data over time and calculating based on output data from the artificial intelligence deep learning model, see at least paragraph 0078 (implements machine learning problem that may be address via deep learning), paragraph 0095 (volatility dynamics; the modeling may be performed via machine learning techniques such as deep learning), paragraph 0008 (training the market model simulation function based on the obtained historical market data), paragraph 0077 (data may then be used to learn what are the expected….current volatility surface), paragraph 0095 (volatility dynamics), alparagraph 0091 (time t; machine learning…given timeline). Both Hadi and BUEHLER are directed toward managing derivatives and determining volatility surface data. Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include implementing an artificial intelligence deep learning model, training the artificial intelligence deep learning model with data and learning, in response to training, data over time and calculating based on output data from the artificial intelligence deep learning model. One would have been motivated to do so for the benefit of increasing accuracy.
Hadi (‘550) discloses displaying data onto user interface, see at least paragraph 0021 (computer devices; displaying that information to a user) and paragraph 0016 (each computer device may also include a variety of interface…for reading…data), but fails to explicitly disclose calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data. BUEHLER (‘911) teaches calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data, see at least paragraph 0077 (data may then be used to learn what are expected carry and statistical risk of any portfolio given the market conditions i.e. current volatility surface), paragraph 0074 (the risks may include …portfolio sensitivities e.g. delta), paragraph 0093 (using the implied volatility surface to characterize the market environment), paragraph 0095 (volatility dynamics), paragraph 0029 (assess risk…derivatives portfolio). Both Hadi and BUEHLER are directed toward managing derivatives and determining volatility surface data. Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data. One would have been motivated to do so for the benefit of reducing risk.
As per Claims 2, 9, 16
Hadi (‘550) discloses implementing an algorithm to capture volatility surface dynamics data corresponding to the derivative instrument, see at least paragraph 0035 (volatility surface; algorithm could be used such that the … volatility for that strike or stike/current price for puts or current price/strike for calls), paragraph 0025 (volatility levels for weather derivative….range of strike prices and time to maturity) and Fig 2 (implied volatility surface; days to maturity), but fails to explicitly disclose implementing algorithm in calculating spot sensitivity data. BUEHLER (‘911) teaches implementing algorithm in calculating spot sensitivity data, see at least paragraph 0077 (data may then be used to learn what are expected carry and statistical risk of any portfolio), paragraph 0074 (the risks may include …portfolio sensitivities e.g. delta), paragraph 0029 (assess risk…derivatives portfolio), paragraph 0073 (electronic execution with trading algorithms). Both Hadi and BUEHLER are directed toward managing derivatives and determining volatility surface data. Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include implementing algorithm in calculating spot sensitivity data. One would have been motivated to do so for the benefit of improving accuracy.
As per Claims 5, 12
Hadi (‘550) discloses wherein the database is a position service that stores position service data corresponding to the derivative instrument, see at least paragraph 0020 (maintain a market by providing bid and offer prices for a derivative), paragraph 0021 (receiving market data from exchange computer system), paragraph 0015 (trade database may store information identifying the time that a trade took place and the contract price) and paragraph 0024 (calls/puts)
As per Claims 6, 13
Hadi (‘550) discloses wherein the database is a market data service that stores market data corresponding to the derivative instrument, see at least paragraph 0020 (maintain a market by providing bid and offer prices for a derivative), paragraph 0021 (receiving market data from exchange computer system), paragraph 0024 (calls/puts)
As per Claim 19
Hadi (‘550) discloses wherein the database includes a position service that stores position service data corresponding to the derivative instrument and a market data service that stores market data corresponding to the derivative instrument, see at least paragraph 0020 (maintain a market by providing bid and offer prices for a derivative), paragraph 0021 (receiving market data from exchange computer system), paragraph 0015 (trade database may store information identifying the time that a trade took place and the contract price) and paragraph 0024 (calls/puts)
Claims 3, 7, 10, 14, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hadi et al. (US 2010/0042550 A1) in view of BUEHLER et al. (US 2021/0224911 A1), as applied to claims 1, 8 and 15 above, and further in view of MAGDELINIC (US 2020/0167869 A1).
As per Claims 3, 10, 17
Hadi (‘550) fails to explicitly disclose wherein the artificial intelligence deep learning model is a recurrent neural network model. MAGDELINIC (‘869) teaches recurrent neural network model, see at least paragraph 0150 (recurrent neural network capable of learning long-term dependencies) and claim 21 of MAGDELINIC. Both Hadi and MAGDELINIC are directed toward using data models to generate predictions associated with derivatives. Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include wherein the artificial intelligence deep learning model is a recurrent neural network model. One would have been motivated to do so for the benefit of increasing accuracy.
As per Claims 7, 14, 20
Hadi (‘550) discloses outputting implied volatility dynamics data corresponding to the derivative instrument, see at least paragraph 0023 (implied volatility surface for a collection of … derivative option contracts), paragraph 0025 (volatility levels for weather derivative….range of strike prices and time to maturity) and Fig 2 (implied volatility surface; days to maturity), but fails to explicitly disclose applying bidirectional gate recurrent unit neural network algorithm and outputting data in response to applying the bidirectional gate recurrent unit neural network algorithm. MAGDELINIC (‘869) teaches applying bidirectional gate recurrent unit neural network algorithm and outputting data in response to applying the bidirectional gate recurrent unit neural network algorithm, see at least paragraph 0150 (bidirectional long short-term memory neural network, a recurrent neural network capable of learning long-term dependencies) and claim 21 of MAGDELINIC, paragraph 0110 (perform algorithms), paragraph 0136 (machine learning algorithm as training data to generate several models to calculate the output). Both Hadi and MAGDELINIC are directed toward using data models to generate predictions associated with derivatives. Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include applying bidirectional gate recurrent unit neural network algorithm and outputting data in response to applying the bidirectional gate recurrent unit neural network algorithm. One would have been motivated to do so for the benefit of increasing accuracy.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hadi et al. (US 2010/0042550 A1) in view of BUEHLER et al. (US 2021/0224911 A1) and MAGDELINIC (US 2020/0167869 A1), as applied to claims 3, 10 and 17, above, and further in view of Official Notice.
As per Claims 4, 11, 18
Hadi (‘550) discloses implementing feedback loop to allow strike-wise dynamic corresponding to the derivative instrument, see at least paragraph 0047 (options contract volatility level; loop is repeated until there are no volatility levels that deviate), paragraph 0048 (repeated for volatiles across time with constant strike prices), but fails to explicitly disclose inputting variable length sequences as the input data. Official Notice is taken that it was old and well known in the art to input variable length sequences as input data (For example, the inherent design of Recurrent Neural Networks involves a feedback loop and can handle variable-length inputs due to their sequential processing). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hadi’s invention to include inputting variable length sequences as the input data. One would have been motivated to do so for the benefit of allowing computation to be performed more efficiently.
Related But Not Relied Upon
Relevant prior art cited but not applied: Koziol et al. (US 2022/0230242 A1), directed to determining sensitivity to volatility.
Conclusion
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/CHIA-YI LIU/Primary Examiner, Art Unit 3692