DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 07/25/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
[0082] of the specification details “At step 202, method 200 includes measuring a first dataset comprising time series data for a plurality of parameters including at least a first surrogate parameter, a second surrogate parameter, a third surrogate parameter, and the target parameter. In some examples, the target parameter is a water quality parameter, and the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll respectively. In some examples, more than three surrogate parameters are used.” The paragraph should state: “At step 202, method 200 includes measuring a first dataset comprising time series data for a plurality of parameters including at least a first surrogate parameter, a second surrogate parameter, a third surrogate parameter, and the target parameter. In some examples, the target parameter is a water quality parameter, and the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll concentration respectively. In some examples, more than three surrogate parameters are used.” This is so that the specification is clear and concise that the parameter is chlorophyll concentration, and is consistent as [0046]-[0047], [0053]-[0054], [0057], [0072], and [0140] detail chlorophyll concentration as the parameter.
[0132] details “A7. The method of paragraph A1, wherein the target parameter is a water quality parameter, and the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll respectively.” The paragraph should state “A7. The method of paragraph A1, wherein the target parameter is a water quality parameter, and the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll concentration respectively.” This is so that the specification is clear and concise that the parameter is chlorophyll concentration, and is consistent as [0046]-[0047], [0053]-[0054], [0057], [0072], and [0140] detail chlorophyll concentration as the parameter.
Appropriate correction is required.
Claim Objections
Claim 7 is objected to because of the following informalities:
Claim 7 details the limitation “the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll respectively.” Examiner notes Chlorophyll is not a parameter, and as Claim 15 and [0046]-[0047] in the specification details, the parameter should be “chlorophyll concentration”, thus Claim 7 should read “the first, second, and third surrogate parameters are discharge rate, turbidity, and chlorophyll concentration respectively.”
Appropriate correction is required.
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. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A method for predicting a value of a target parameter based on measurements of surrogate parameters, the method comprising:
measuring a first dataset comprising time series data for a plurality of parameters including at least a first surrogate parameter, a second surrogate parameter, a third surrogate parameter, and the target parameter;
identifying a plurality of regions in a multidimensional space corresponding to clusters of points of the first dataset in the multidimensional space, wherein the multidimensional space has dimensions corresponding to at least a subset of the plurality of parameters;
determining a nonlinear manifold comprising a plurality of modeling functions multiplied by corresponding domain indicator functions, wherein each modeling function models the cluster of points of the first dataset corresponding to a respective one of the regions, and each domain indicator function is defined to be close to one in the region associated with the corresponding modeling function, and close to zero in all of the other regions;
measuring a second dataset comprising at least one value of the first surrogate parameter, at least one value of the second surrogate parameter, and at least one value of the third surrogate parameter; and
predicting a value of the target parameter corresponding to the second dataset by determining a value of the nonlinear manifold corresponding to the at least one value of the first surrogate parameter, the at least one value of the second surrogate parameter, and the at least one value of the third surrogate parameter.
8. A method of estimating a target parameter, the method comprising:
obtaining values of a plurality of surrogate parameters;
determining a nonlinear surface in a phase space defined by the plurality of surrogate parameters, wherein the nonlinear surface defines values of the target parameter; and
predicting a first value of the target parameter by identifying a value of the nonlinear surface at a point in the phase space corresponding to the obtained values of the plurality of surrogate parameters.
20. A data processing system, comprising:
one or more processors;
a memory; and
a plurality of instructions stored in the memory and executable by the one or more processors to:
receive values of a plurality of surrogate parameters;
determine a nonlinear surface in a phase space defined by the plurality of surrogate parameters, wherein the nonlinear surface defines values of a target parameter; and
predicting a first value of the target parameter by identifying a value of the nonlinear surface at a point in the phase space corresponding to the received values of the plurality of surrogate parameters.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 8, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of measuring/receiving/obtaining datasets/values is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. measuring, obtaining, receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The processors, memory, and stored instructions of Claim 20 are interpreted under broadest reasonable interpretation to be a generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-7 and 9-19 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 8 is are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (CN106227697A).
In regards to Claim 8, Liu teaches “obtaining values of a plurality of surrogate parameters (This invention pertains to the problem of high-precision estimation of the state of space targets, and relates to a method for determining the motion state of nonlinear space targets based on manifolds. Specifically, by utilizing measurement elements such as the distance, azimuth, pitch angle, or rate of change of distance (sum/difference) of a space target, a high-precision estimate of the target's position and velocity is obtained through a trajectory calculation process on a manifold. This method is applicable to the technical field of high-precision post-processing of measurement data in aerospace telemetry and control systems - [0002]; Where X(t<sub>1</sub>),…,X(t<sub>N</sub>) are the position and velocity parameters of the target at N time points, C<sub>1</sub>,…,C<sub>M</sub> are multi-scale representation coefficients, M<<N, that is, the sparse representation of the target state parameters is completed - [0017]);
determining a nonlinear surface in a phase space defined by the plurality of surrogate parameters, wherein the nonlinear surface defines values of the target parameter (The purpose of this invention is to overcome the difficulties in selecting spline function nodes and the large computational load in current high-precision estimation methods for space target states. Utilizing the characteristic that a spacecraft trajectory is essentially a one-dimensional manifold in three-dimensional space, this invention introduces nonlinear estimation theory and methods on differential manifolds, providing a manifold-based nonlinear motion state determination method. This method leverages the curvilinear manifold characteristics of the spatial trajectory in space, employing a manifold-based trajectory representation method to improve upon traditional spline representation methods. This improves the accuracy of trajectory calculation while avoiding complex processes such as spline node optimization. It transforms traditional spline representation methods into manifold-based trajectory representation methods, thereby further saving representation parameters, improving state estimation accuracy, avoiding node optimization problems, and reducing the complexity of the solution process - [0008]; Step 2) Express the target state parameters to be estimated using the multi-scale transformation operator F(·) on the manifold – [0015]); and
predicting a first value of the target parameter by identifying a value of the nonlinear surface at a point in the phase space corresponding to the obtained values of the plurality of surrogate parameters (Where Y(t) is the trajectory of the spatial target; X(t<sub>i</sub>) is the target state parameter to be estimated; H is the observation equation; where the target state parameter to be estimated, which is the position and velocity in three-dimensional space - [0014]).”
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, 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.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Holiday (Alexander Holiday, et. al., “Manifold learning for parameter reduction”, 11/13/2018, Arxiv.org, https://arxiv.org/abs/1807.08338).
In regards to Claim 20, Liu teaches “receive values of a plurality of surrogate parameters (This invention pertains to the problem of high-precision estimation of the state of space targets, and relates to a method for determining the motion state of nonlinear space targets based on manifolds. Specifically, by utilizing measurement elements such as the distance, azimuth, pitch angle, or rate of change of distance (sum/difference) of a space target, a high-precision estimate of the target's position and velocity is obtained through a trajectory calculation process on a manifold. This method is applicable to the technical field of high-precision post-processing of measurement data in aerospace telemetry and control systems - [0002]; Where X(t<sub>1</sub>),…,X(t<sub>N</sub>) are the position and velocity parameters of the target at N time points, C<sub>1</sub>,…,C<sub>M</sub> are multi-scale representation coefficients, M<<N, that is, the sparse representation of the target state parameters is completed - [0017]);
determine a nonlinear surface in a phase space defined by the plurality of surrogate parameters, wherein the nonlinear surface defines values of a target parameter (The purpose of this invention is to overcome the difficulties in selecting spline function nodes and the large computational load in current high-precision estimation methods for space target states. Utilizing the characteristic that a spacecraft trajectory is essentially a one-dimensional manifold in three-dimensional space, this invention introduces nonlinear estimation theory and methods on differential manifolds, providing a manifold-based nonlinear motion state determination method. This method leverages the curvilinear manifold characteristics of the spatial trajectory in space, employing a manifold-based trajectory representation method to improve upon traditional spline representation methods. This improves the accuracy of trajectory calculation while avoiding complex processes such as spline node optimization. It transforms traditional spline representation methods into manifold-based trajectory representation methods, thereby further saving representation parameters, improving state estimation accuracy, avoiding node optimization problems, and reducing the complexity of the solution process - [0008]; Step 2) Express the target state parameters to be estimated using the multi-scale transformation operator F(·) on the manifold – [0015]); and
predicting a first value of the target parameter by identifying a value of the nonlinear surface at a point in the phase space corresponding to the received values of the plurality of surrogate parameters (Where Y(t) is the trajectory of the spatial target; X(t<sub>i</sub>) is the target state parameter to be estimated; H is the observation equation; where the target state parameter to be estimated, which is the position and velocity in three-dimensional space - [0014]).”
Liu is silent with regards to the language of “one or more processors; a memory; and a plurality of instructions stored in the memory and executable by the one or more processors.”
Holiday teaches “one or more processors; a memory; and a plurality of instructions stored in the memory and executable by the one or more processors (High model dimensionality and complexity makes symbolic, pen–and–paper model reduction tedious and impractical, a difficulty addressed by recently developed frameworks that computerize reduction.” – Abstract).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu to incorporate the teaching of Holiday to computerize the method. By computerizing the method, this is an improvement that yields predictable results in reducing the tedious and impractical nature of doing the process with pen and paper.
Examiner’s Note
The following prior art is considered to be of interest to the art:
Macauslan (US20070038420) teaches a technique for determining the number of constraints on a set of input data, or equivalently the topological dimension, especially when such data are produced by a nonlinear system, such as a pathological vocal system or econometric data and the like. The technique characterizes the tangent space about a predetermined base point by identifying a maximal set of non-redundant nonlinear fits to the data.
Sinfield (US20150198534) details relation between nitrate concentrations and turbidity.
Peterson (W. Peterson, et. al., “Ferrybox – Real-time monitoring of water Quality by Ferryboats”, 06/17/2004, IEEE, https://ieeexplore.ieee.org/document/7296842) details a sensor tool to measure multiple parameters including temperature, salinity, turbidity, oxygen, pH, chlorophyll fluorescence, nutrients including nitrates, algal groups.
Kermorvant (Claire Kermorvant, et. al., “Understanding links between water quality variables and nitrate concentrations in freshwater streams using high-frequency sensor data”, 06/03/2021, arxiv, https://arxiv.org/abs/2106.01719) details determining the relations between nitrate concentrations and other sensor data including turbidity and chlorophylls.
Pao (US20230260662) teaches in [0059] a manifold network constructed around a given target variable where selected variables together with the target variable define the manifold that is to be used to predict the target variable using the Simplex projection method.
Claims 1-7 and 9-19 are not rejected under 35 U.S.C. 102 or 35 U.S.C. 103.
In regards to Claim 1, Liu teaches “measuring a first dataset comprising time series data for a plurality of parameters including at least a first surrogate parameter, a second surrogate parameter, a third surrogate parameter, and the target parameter (This invention pertains to the problem of high-precision estimation of the state of space targets, and relates to a method for determining the motion state of nonlinear space targets based on manifolds. Specifically, by utilizing measurement elements such as the distance, azimuth, pitch angle, or rate of change of distance (sum/difference) of a space target, a high-precision estimate of the target's position and velocity is obtained through a trajectory calculation process on a manifold. This method is applicable to the technical field of high-precision post-processing of measurement data in aerospace telemetry and control systems - [0002]);
determining a nonlinear manifold (The purpose of this invention is to overcome the difficulties in selecting spline function nodes and the large computational load in current high-precision estimation methods for space target states. Utilizing the characteristic that a spacecraft trajectory is essentially a one-dimensional manifold in three-dimensional space, this invention introduces nonlinear estimation theory and methods on differential manifolds, providing a manifold-based nonlinear motion state determination method. This method leverages the curvilinear manifold characteristics of the spatial trajectory in space, employing a manifold-based trajectory representation method to improve upon traditional spline representation methods. This improves the accuracy of trajectory calculation while avoiding complex processes such as spline node optimization. It transforms traditional spline representation methods into manifold-based trajectory representation methods, thereby further saving representation parameters, improving state estimation accuracy, avoiding node optimization problems, and reducing the complexity of the solution process. - [0008]);
predicting a value of the target parameter corresponding to the dataset by determining a value of the nonlinear manifold corresponding to the at least one value of the first surrogate parameter, the at least one value of the second surrogate parameter, and the at least one value of the third surrogate parameter (Where Y(t) is the trajectory of the spatial target; X(t<sub>i</sub>) is the target state parameter to be estimated; H is the observation equation; where the target state parameter to be estimated, which is the position and velocity in three-dimensional space - [0014]; Step 2) Express the target state parameters to be estimated using the multi-scale transformation operator F(·) on the manifold - [0015]; Where X(t<sub>1</sub>),…,X(t<sub>N</sub>) are the position and velocity parameters of the target at N time points, C<sub>1</sub>,…,C<sub>M</sub> are multi-scale representation coefficients, M<<N, that is, the sparse representation of the target state parameters is completed – [0017]; Step 3) Establish the partial derivative matrix of the measurement information with respect to the target state parameters, and at the same time establish the partial derivative matrix of the target state parameters with respect to the multi-scale representation coefficients. Multiply the two matrices to obtain the composite partial derivative matrix of the measurement information with respect to the multi-scale representation coefficients; use a nonlinear iterative method to obtain the estimated values of the multi-scale representation coefficients C<sub>1</sub>,…,C<sub>M</sub>. – [0018]).”
Liu is silent with regards to the language of “identifying a plurality of regions in a multidimensional space corresponding to clusters of points of the first dataset in the multidimensional space, wherein the multidimensional space has dimensions corresponding to at least a subset of the plurality of parameters;
determining a nonlinear manifold comprising a plurality of modeling functions multiplied by corresponding domain indicator functions, wherein each modeling function models the cluster of points of the first dataset corresponding to a respective one of the regions, and each domain indicator function is defined to be close to one in the region associated with the corresponding modeling function, and close to zero in all of the other regions;
measuring a second dataset comprising at least one value of the first surrogate parameter, at least one value of the second surrogate parameter, and at least one value of the third surrogate parameter; and
predicting a value of the target parameter corresponding to the second dataset by determining a value of the nonlinear manifold corresponding to the at least one value of the first surrogate parameter, the at least one value of the second surrogate parameter, and the at least one value of the third surrogate parameter.”
Claims 2-7 are dependent on Claim 1.
In regards to Claim 9, Liu is silent with regards to the language of “wherein determining the nonlinear surface comprises:
determining a plurality of local surfaces each configured to model the target parameter in a respective region of the phase space; and
joining the plurality of local surfaces together using a plurality of joining functions configured to have a nonzero value at a respective one of the regions and a zero value outside the respective one of the regions.”
Claims 10-13 are dependent on Claim 9.
In regards to Claim 14, Liu teaches the method of claim 8, but it would not be obvious to one of ordinary skill in the art to modify Liu to teach “wherein the target parameter and the surrogate parameters each correspond to water quality parameters” as Liu is focused on the process with motion of state of nonlinear state targets, not that of values associated with water quality parameters.
Claim 15 is dependent on Claim 14.
In regards to Claim 16, Liu is silent with regards to the language of “obtaining second values of the plurality of surrogate parameters, wherein the obtained second values of the plurality of surrogate parameters correspond to a first region of the phase space;
determining a set of values at the first region by subtracting the nonlinear surface at the first region from a mean value of the target parameter with respect to a time parameter and a first one of the surrogate parameters;
determining a second surface using a plurality of radial basis functions, such that the second surface approximates the determined set of values in a phase space defined by the time parameter and the first one of the surrogate parameters; and
predicting a second value of the target parameter based on a value of the second surface corresponding to the obtained second values of the plurality of surrogate parameters.”
Claims 17-18 are dependent on claim 16.
In regards to Claim 19, Liu teaches the method of claim 8, but it would not be obvious to one of ordinary skill in the art to modify Liu to teach “wherein obtaining the values of the plurality of surrogate parameters comprises measuring the values using one or more sensors disposed in situ at a body of water” as Liu is focused on the process with motion of state of nonlinear state targets, not that of values associated with sensor results from a body of water.
Conclusion
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/YOSSEF KORANG-BEHESHTI/Primary Examiner, Art Unit 2857
