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 Office Action is in response to the application filed on 06/06/2024.
Claims 1-21 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/04/2024 has been considered (see form-1449, MPEP 609).
Drawings
The drawings filed on 06/06/2024 are accepted.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Qu & Co (WO 2023/012375, hereinafter Qu & Co), in view of Nigel John Conrad Greenwood (US PGPUB 2017/0147722, hereinafter Greenwood).
As per as claim 1, Qu & Co discloses:
(Currently Amended) A method comprising:
accessing, using a computing system, data comprising a plurality of variables, each variable having one or more elements (Qu&Co, e.g., [page 18, lines 16-30], “…Access to the PDF, allows computation of the probabilities of certain outcomes by integrating that PDF over some variable(s)… correspond the actual underlying stochastic process described by the SDE…”) and ;
determining stochastic partial differences between elements of respective variables of the plurality of variables, wherein determining the stochastic partial differences is performed via direct stochastic processing that employs a weighted binary value to represent a count of stochastic bits over a sliding window along a bitstream of the data without requiring data conversion (Qu&Co, e.g., [page 6, lines 6-23], “… receiving by the classical computer a partial differential equation, PDE, the PDE describing dynamics of a quantile function QF associated a stochastic differential equation SDE, preferably the partial differential equation defining a quantilized Fokker-Planck QFP equation, the SDE defining a stochastic process as a function of time and one or more further variables and the QF defining a modelled distribution of the stochastic process; executing by the classical computer a first training process for training one or more neural networks to model an initial quantile function…” and [page 6, lines 20-32], “… receiving by the classical computer a partial differential equation, PDE, the PDE describing dynamics of a quantile function QF associated a stochastic differential equation SDE, preferably the partial differential equation defining a quantilized Fokker-Planck QFP equation, the SDE defining a stochastic process as a function of time and one or more further variables and the QF defining a modelled distribution of the stochastic process; executing by the classical computer a first training process for training one or more neural networks to model an initial quantile function …The samples may be generated based on quantile functions (QFs) and derivatives thereof that is associated with the SDE. To that end, the SDE may be rewritten as a set of differential equations for the quantile function…”);
combining respective stochastic partial differences into groups comprising one or more stochastic partial difference equations (SPDEs) (Qu&Co, e.g., [page 6, lines 24-33], “…generate sets of samples that form solutions to a time-evolution of a stochastic differential equation, SDE. The samples may be generated based on quantile functions (QFs) and derivatives thereof that is associated with the SDE. To that end, the SDE may be rewritten as a set of differential equations for the quantile function. Further, a neural network representation of the QF and its derivatives may be determined, which can be used to generate samples that form solutions of the SDE…”;
evaluating, using a fitness measure criterion, the one or more SPDEs in relation to an objective function (Qu&Co, e.g., [page 7, lines 24-33], “…classical computer system, based on the quantum hardware measurement data and a loss function, if the quantum hardware measurement data forms a solution to the PDE…”); and
determining, by the computing system, based on the evaluating, a prediction related to at least one data input to an application executable by one of the computing system or a second computing system communicatively coupled to the computing system, wherein the at least one data input relies, at least in part, on one or more of the plurality of variables (Qu&Co, e.g., [page 7, lines 24-33], “…execution of the quantum circuits quantum, hardware measurement data…” and [page 27, lines 18-33], “…input/dependent variable x 502 may be inserted in an UFA, and the resultant output value ƒ 508 may be retrieved. By repeatedly checking the value of ƒ for each x, one may represent or plot the functional dependence 510 of ƒ on x. Sufficiently intricate UFA designs 504/506 may represent arbitrary functions 508. This way, UFAs may be used to fit data (a regular regression task), or it can be used to represent solutions to (partial/stochastic) differential equations…”) and further see figs. 10A and B associating with texts description, (Page, 37, lines 25-31], [pages 55-56, lines 28-5], “…First a set of points {X} (a regular or a randomly- drawn grid) may be specified for each equation variable x 1006. The variational parameters 0 are set to initial values (e.g. as randomly drawn grid). Then, an expectation value over variational quantum state for the cost function may be estimated 1010, using the quantum hardware, for the chosen point Xj. Then, a potential solution at this point may be constructed taking into account the boundary conditions…”)
To make records clearer regarding to the language of “prediction related to data input to an application” (although as stated above, Qu&Co functional disclose the features of prediction related to data input to an application” (Qu&Co, e.g., [page 1, lines 20-24] and [page 55, lines 233-25]).
However Greenwood, in an analogous art, discloses “prediction related to data input to an application” (Greenwood, e.g., [0365], [0375], [0470], [0558], “… predict observed data, while Texture is based on other additional considerations such as trajectory interaction with Texture Sets. These sets may themselves be built from other mathematical formulations of the interactions between trajectory elements and observed data, or from other considerations. However it should be apparent that this relationship can be “flipped”: Fitness being constructed from how well the trajectories interact with the Texture sets and this Fitness being textured based on how well the trajectories track or predict the observed data…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Greenwood and Qu&Co to used for analysis, modelling and prediction of the epidemiology of a disease, including estimating the dynamics of its propagation and computing candidate counter-strategies and/or quantitative analysis and optimisation of tissue culture, growth, dynamical behaviour and functional performance, transplantation and post-transplant function, including transplants of mature cells, cultured cells and/or stem-cell transplants, bone-marrow, pancreatic beta-cells and/or liver tissue or including construction and transplantation of wholly artificially-constructed tissues and/or organs (Greenwood, e.g., [0616-0617]).
As per as claim 2, the combination of Greenwood and Qu&Co discloses:
(Currently Amended) The method of claim 1, further comprising:
determining the weighted binary value for a plurality of stochastic bits of the data, wherein determining the stochastic partial difference between the elements of the respective variables employs the weighted binary value (Qu&Co, e.g., [Page 41, lines 10-28], “…applying measurement laser pulses, and then observing the brightness using a camera to spot which atomic qubit is turned ‘on’ or ‘off’, 1 or 0. This bit information across the array…”, and see (page 49, lines 1-8], “…the three time slices of Euler-Maruyama trajectories are shown, built with N.sub.s = 100,000 samples to see distributions in full. The counts are binned and normalized by N.sub.s, and naturally show excellent correspondence with analytical results. The sampling from trained quantile may be performed by drawing random z ~ uniform(-1 , 1 ) for the same number of samples…”); and
adjusting a value of the at least one data input based on a result of evaluating the one or more SPDEs (Qu&Co, e.g., [page 1, lines 15-23]).
As per as claim 3, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 2, further comprising determining that the result of evaluating the one or more SPDEs is in a linear response and within a threshold percentage of the objective function before adjusting the value of the at least one data input (Qu&Co, e.g., [page 25, lines 5-14], “… the use of a loss function and iteratively training the neural networks based on a trial function (representing the quantile function) in a feedback loop until the difference between the input data and output data is sufficiently small (smaller than a threshold value)…”).
As per as claim 4, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 1, wherein determining the stochastic partial differences between the elements of respective variables comprises determining the partial differences in one of dependence form with respect to space or in finite difference form with respect to time (Qu&Co, e.g., [page 6, lines 6-22] and [page 7, lines 15-27], “…receiving by the classical computer a partial differential equation, PDE, the PDE describing dynamics of a quantile function QF associated a stochastic differential equation SDE, preferably the partial differential equation defining a quantilized Fokker-Planck QFP equation, the SDE defining a stochastic process as a function of time and one or more further variables and the QF defining a modelled distribution of the stochastic process…”).
As per as claim 5, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 1, further comprising grouping a subset of the plurality of variables based on the subset having related attributes, wherein determining the stochastic partial differences is between elements of the respective variables of the subset (Qu&Co, e.g., [page 6, lines 6-22] and [page 7, lines 15-33], “…differential equation, PDE, the PDE describing dynamics of a quantile function QF associated a stochastic differential equation SDE, preferably the partial differential equation defining a quantilized Fokker-Planck QFP equation, the SDE defining a stochastic process as a function of time and one or more further variables and the QF defining a modelled distribution of the stochastic process…”).
As per as 6, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 5, wherein the related attributes comprise at least one of a data type, a time period, an information distance in time, an information distance in space, or information of an element in another variable related within a stochastic partial difference (Qu&Co, e.g., [page 6, lines 6-33], “…defining a quantilized Fokker-Planck QFP equation, the SDE defining a stochastic process as a function of time and one or more further variables and the QF defining a modelled distribution of the stochastic process; executing by the classical computer a first training process for training one or more neural networks to model an initial quantile function, the one or more neural networks being trained by the special purpose processor based on training data, the training data including measurements of the stochastic process…”).
As per as claim 7, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 5, further comprising employing at least one of the subset of the plurality of variables, the one or more elements, or the related attributes as key values useable to index the data within a storage device (Qu&Co, e.g., [Page 35, lines 1-9], “…a quantum feature map generated by strings of Pauli matrices or any involutory matrix, the parameter shift rule may be used such that a function derivative may be expressed as a sum of expectations…defined through the parameter shifting, and index j runs through individual quantum operations used in the feature map encoding…”).
As per as claim 8, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 1, wherein at least one variable of the plurality of
variables has multiple dimensions and each value of the multidimensional variable is an element of the one or more elements device (Qu&Co, e.g., [page 6, lines 6-33] and [page, 18, page 20-27], “…sampling process including drawing x from a complicated multidimensional distribution p(x, t), at different time instances t…”).
As per as claim 9, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 1, wherein the fitness measure criterion is one of a
handwriting feature prediction in handwriting analysis, a price direction prediction in financial trading, or an information efficiency level modification in computing (Qu&Co, e.g., [page 45, lines 25-33], “…physical sciences and financial analysis…”).
As per as claim 10, the combination of Greenwood and Qu&Co discloses:
(Original) The method of claim 1, wherein the fitness measure criterion is based on a fitness function executable by the computing system (Qu&Co, e.g., [page 7, lines 24-27, “…hardware measurement data…classical computer system…quantum hardware…”).
Claims 11-20 are essentially the same as claims 1-10 except that they set forth the claimed invention as a system rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 11-20.
Claim 21 is essentially the same as claim 1 except that it set forth the claimed invention as a non-transitory computer readable storage medium rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claim 1.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to machine learning to stochastic learning of computing input, which is determining stochastic partial differences between elements of respective variables of the plurality of variables and combining respective stochastic partial differences into groups including one or more stochastic partial difference equations (SPDEs). The method includes evaluating, using a fitness measure criterion, the one or more SPDEs in relation to an objective function.
a. Nash et al. (US PGPUB 2016/0364803, hereafter Nash); “System and Method for Residential Estate Risk Transference Via Asset backed Contract” disclose residential real estate risk transference via asset-backed index swap and/or investment contract which transferred via a contract associated with a real estate property. Such a contract may be an asset-backed index swap or an investment contract in which an owner entity of the real estate property grants to an investor entity an economic right to a portion of future appreciation of the real estate property in exchange for consideration”.
Nash also teaches a stochastic-partial-differential-equation-contract-valuation or stochastic-PDE-contract-valuation module, and/or other modules [0016].
Nash further teaches determined via stochastic partial differential equation, also resulting in a Return Test of “Pass” [0091].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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