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 .
Priority
As detailed on the Filing Receipt filed 10/15/2024, the instant application claims priority to as early as 8/19/2022. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statements filed on 2/5/2024 and 4/2/2025 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. Signed copies of the IDS are included with this Office Action.
Claim Status
Claims 1-20 are pending, and under examination.
Claim Rejections - 35 USC § 101
35 USC § 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 USC § 101 because the claimed invention is directed to judicial exceptions without significantly more (i.e., non-statutory subject matter).
"Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
Natural phenomena and laws of nature include principles, relations, and products that are naturally occurring or do not have markedly different characteristics compared to what occurs in nature (MPEP 2106.04(b)).
The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea and a natural phenomenon.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
The claims are directed to a method (claims 1-10), one or more non-transitory computer storage media (claims 11-19), and a system (claim 20), which fall under categories of statutory subject matter.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements which encompass mathematical concepts, at least under their broadest reasonable interpretation, including:
providing a value to, and obtaining a value from, a prediction system that comprises (i) an ocean simulator and (ii) a trained network model (claims 1, 11 and 20), i.e., evaluating an algorithm set for particular input, wherein:
the trained network model is trained to provide output using the output from the ocean simulator (claims 1, 11 and 20),
the prediction system is configured to generate predictions through multiple iterations (claims 5 and 15), and
the ocean simulator includes an advection-diffusion module to perform numerical solutions (claims 6 and 16);
updating one or more weights of an initial network model using backpropagation of errors through (i) the ocean simulator and (ii) the initial network model to generate the trained network model (claims 4 and 14);
determining an estimate using satellite data (claims 7 and 17), i.e., calculating a value from a particular type of input, wherein determining the estimate comprises:
providing the satellite data to a second model trained using water samples to generate at least a portion of the estimate (claims 8 and 18), i.e., providing the input to a model optimized using a particular type of training data; and
training the trained network model, wherein training comprises training an initial network model within a time step of the ocean simulator (claim 10).
The recited acts of algorithmic calculation constitute mathematical concepts.
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The claims recite elements encompassing the following process that is practicably performable in the human mind, at least under its broadest reasonable interpretation:
comparing the output to an output threshold (claims 1, 11 and 20).
The human mind is capable of comparing an output value to a threshold value. Thus, the above step encompasses a mental process.
Mathematical concepts and mental processes are enumerated categories of abstract ideas (MPEP 2106.04(a)(2) §§ I and III). Hence, the claims recite elements that, individually and in combination, constitute an abstract idea.
The claims further recite the following claim elements, which require that obtained data and results embody particular natural phenomena and/or laws of nature:
the obtained estimate represents a number of marine-life cells representative of growth of the marine life within a first region (claims 1, 11 and 20), wherein:
the first number of cells represents a number of algae cells that contribute to harmful algal bloom (claims 3 and 13);
the trained network model is trained to provide an indication of marine-life change (claims 1, 11 and 20);
the output obtained from the prediction system indicates a second number of cells representative of growth of the marine life within a second region (claims 1, 11 and 20), wherein:
the second number of cells represents a number of algae cells that contribute to harmful algal bloom (claims 3 and 13);
the prediction system is configured to generate cell number predictions (claims 5 and 15);
the ocean simulator is used to determine a water temperature in the first region and water current in the first region (claims 5 and 15);
the trained network model is used to determine a change in the first number of cells (claims 5 and 15); and
the ocean simulator is configured to forecast one or more of the following: temperature, salinity, or ocean currents (claims 9 and 19).
The above elements specify that analyzed data represents naturally occurring biological and environmental phenomena (e.g., algae growth and water temperature) having naturally occurring relationships (i.e., laws of nature) that the claimed invention allows a user of the claimed method, system and/or computer-readable media to implement.
The claims must therefore be examined further to determine whether they integrate these judicial exceptions into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite additional elements that gather data necessary for performance of claimed method steps, including:
obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region (claims 1, 11 and 20), wherein obtaining the estimate comprises:
obtaining satellite data (claims 7 and 17).
Necessary data gathering is considered to be insignificant pre-solution activity, and as such insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)).
The claims further recite additional elements that apply the output of claimed method steps, including:
in response to the output satisfying the output threshold, performing an action (claims 1, 11 and 20), wherein performing the action comprises:
harvesting one or more fish in a vicinity of the second region (claims 2 and 12).
The above elements do not alter or affect how the process steps of obtaining and comparing the output are performed, and are therefore not considered to meaningfully limit the judicial exceptions embodied in those process steps. They are instead considered as reciting insignificant post-solutional activity that merely links use of the recited judicial exceptions to the field of fish farming. Insignificant post-solutional activity and field-of-use limitations are insufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g-h)).
The claims further recite additional elements that constitute computer hardware for performing claimed functions, including:
One or more non-transitory computer media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising claimed functions (claim 11); and
A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising claimed functions (claim 20).
The claims do not describe any specific computational steps by which computer hardware performs or carries out functions drawn to the judicial exceptions, nor do they provide any details of how specific structures of computer hardware are used to implement these functions. The claims state nothing more than that generic computer hardware performs functions drawn to the judicial exceptions, and are therefore mere instructions to apply the judicial exceptions using computer hardware. As such, the claims do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d) § I and 2106.05(f)).
No further additional elements are recited.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the judicial exceptions to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the judicial exceptions with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the judicial exceptions in some other meaningful way beyond linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., fish farming; MPEP 2106.05(e) and 2106.05(h)).
Hence, the recited judicial exceptions are not integrated into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea and a natural phenomenon, and do not integrate those judicial exceptions into a practical application, the claims are directed to those judicial exceptions. Claims that are directed to judicial exceptions must be examined further to determine whether the additional elements besides the judicial exceptions render the claims significantly more than the judicial exceptions. Additional elements besides the judicial exceptions may constitute inventive concepts that are sufficient to render the claims significantly more (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, several recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited judicial exceptions, particularly when the activity is well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
Recited additional elements encompassing extra-solution activity include the following:
obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region (claims 1, 11 and 20), comprising:
obtaining satellite data (claims 7 and 17).
The reviewed prior art reference Anderson (Harmful Algae 59: 1-18, published 9/23/2016), in view of relevant case law, indicates that these limitations can be accomplished through performance of well-understood, routine and conventional activity using generic computer hardware. Anderson discusses the California Harmful Algae Risk Mapping (C-HARM) system which predicts Pseudo-nitzschia blooms, in terms of numbers of cells/mL-1, based on sea-surface temperature and salinity variables estimated from satellite data via empirical models (pg. 2, Table 1 and r. column; pg. 5, l. column). Anderson describes obtaining satellite data from an available online repository (pg. 3, r. column), and states that a prototype implementation of the prediction system is available online (pg. 3, l. column).
In this way, Anderson indicates that obtaining an estimate and obtaining satellite data as claimed can be accomplished by accessing recited online resources. The courts have held that using the Internet to gather data is a well-understood, routine and conventional function of general-purpose computer hardware. See, e.g., CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011)). Thus, reviewed prior art indicates that the claimed data gathering functions can be accomplished through performance of activity recognized by the courts as well-understood, routine and conventional.
Recited additional elements also encompass the following extra-solution activity:
in response to the output satisfying the output threshold, performing an action (claims 1, 11 and 20), comprising:
harvesting one or more fish in a vicinity of the second region (claims 2 and 12).
Reviewed prior art references APEC (APEC #201-MR-01.1, Asia Pacific Economic Program, Singapore, and Intergovernmental Oceanographic Commission Technical Series No. 59; published 2001), Kallee (‘Living with Harmful Algal Blooms’ [thesis], Carl von Ossietzky University of Oldenburg; published 1/17/2002), and Engehagen (Marine Policy 129: 104528, 12 pages; published 5/4/2021) indicate that these limitations encompass well-understood, routine and conventional activity.
APEC reviews monitoring and management strategies for HABs. APEC discusses prospective use of predictions regarding HAB timing and transport, by fish farmers, to take impact-minimizing actions such as selling fish before they are killed by the HAB (pg. 178, para. 3). One of ordinary skill in the art would understand that selling farmed fish would necessarily involve harvesting the fish. Anderson also describes pre-emptive harvesting, as a response to algae, as a worldwide practice among fish farmers (pg. 225).
Kallee reviews management of harmful algal blooms (HABs) in aquaculture and discusses premature harvesting of farmed fish, responsive to advance information about a threatening HAB, as an employable mitigation technique (pg. 21, para. 1; pg. 25, para. 1).
Engehagen studies optimal harvesting decisions of salmon farmers for management of harmful algal bloom risk, including the decision to perform an early harvest based on the current and forecasted spread and density of a harmful algal bloom (pg. 1, Abstract and l. column – pg. 2, l. column).
These references provide evidence that the process of harvesting fish, in response to a marine cell growth prediction, constitutes well-known, routine and conventional activity in the relevant field. The cited elements are therefore considered to simply append well-understood, routine, conventional activity, previously known to the industry, to judicial exceptions.
Well-understood, routine, and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than judicial exceptions (MPEP 2106.05(d)).
Mere instructions to implement judicial exceptions using a computer are similarly insufficient to constitute an inventive concept that would render the claims significantly more than said judicial exceptions (MPEP 2106.05(f)).
Mere instructions to apply a judicial exception in a particular field of use are similarly insufficient to constitute an inventive concept that would render the claims significantly more than said judicial exceptions (MPEP 2106.05(f) and 2106.05(h)).
When the claims are considered as a whole, they do not integrate the judicial exceptions into a practical application; they do not confine the use of the judicial exceptions to a particular technology; they do not solve a problem rooted in or arising from the use of a
particular technology; they do not improve a technology by allowing the technology to
perform a function that it previously was not capable of performing; and they do not
provide any limitations beyond generally linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., fish farming; MPEP 2106.05(e) and 2106.05(h)).
Hence, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. See MPEP 2106.05.
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to judicial exceptions and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the judicial exceptions, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 USC § 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 factual inquiries for establishing a background for determining obviousness under 35 USC § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 1, 3-11 and 13-20 are rejected under 35 USC § 103 as being unpatentable over Bhabra (Proceedings of ‘Global Oceans 2020: Singapore – U.S. Gulf Coast’, 10 pages, IEEE; published online 4/9/2021; on IDS filed 2/5/2024), in view of Rackauckas (arXiv:2001.04385v4 [cs.LG], 55 pages; published 11/2/2021) and Anderson (Harmful Algae 59: 1-18, published 9/23/2016).
Claim 1 is directed to a method for predicting growth of marine-life cells of a particular type, the method comprising: obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a trained network model, wherein the trained network model is trained to provide an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; comparing the output to an output threshold; and in response to the output satisfying the output threshold, performing an action.
With respect to claim 1, Bhabra discloses a modeling framework for optimal management and harvesting of algae fields (pg. 1, Abstract), involving: simulating environmental parameters in particular ocean regions of interest via the MSEAS modeling system; modeling algae growth and decay via a point model comprising a set of differential equations (DE), wherein simulated ocean parameters are provided as input fields to the growth model to predict algae concentrations; and planning an optimal harvesting path, via a set of DE including Hamilton-Jacobi equations, based on predicted algae growth (pg. 3, l. column – pg. 4, l. column). Bhabra also discusses harmful algal blooms (HABs); control strategies including harvesting; and application of their modeling framework for HAB control via optimal harvesting, wherein areas of likely HAB growth are elucidated and the optimal path planning delivers control strategies in an efficient and cost-effective manner to at-risk areas (pg. 6, l-r. columns).
Bhabra graphically depicts modeled algae concentrations as a spatial algae collection rate field, where the represented algae collection rate is equivalent to units of algae over time (pg. 6, Fig. 5 and r. column – pg. 7, l. column). One depicted graph shows the modeled algae field at Time = 0.00 (pg. 6, Fig. 5, left), indicating that initial algae concentrations are input as model parameters. The initialization of algae concentrations is reinforced by later reference to “growth of the initial high-concentration area” (pg. 7, l. column). Initialization of algae concentrations in a modeled spatial field necessarily requires obtaining an estimate of a first algae concentration representative of growth of the algae within a first region. Although Bhabra does not expressly disclose notation of algae concentrations in terms of numbers of cells, one of ordinary skill in the art would ‘at once envisage’ notation in this manner upon reading the disclosed notation of units of algae (see In re Petering, 301 F.2d 676, 681 (CCPA 1962)).
Bhabra particularly discusses modeling of dynamic algae fields that are advected spatially by ocean currents, wherein spatial variability of cloud coverage and nutrient concentrations throughout the modeled domain produces spatial variability in the algae growth rate (pg. 2, l. column; pg. 6, Fig. 5 and r. column – pg. 7, l. column). In other words, the modeling framework of Bhabra estimates an algae concentration within an initial spatial region (i.e., a nowcast) and predicts the growth and decay of the algae field at points beyond that initial region (i.e., forecasts). This is considered equivalent to estimation with respect to a first region and a second region as claimed.
In this way, Bhabra is considered to disclose obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a DE-based model, wherein the model provides an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; and in response to the output indicating a bloom, performing an action.
Bhabra contrasts their modeling framework with prior algae growth models built on constants and parameters taken from existing research or fitted to experimental data, and characterizes these as not being generalizable (pg. 1, r. column – pg. 2, l. column).
The requirement that model output indicates growth within a second region merely requires application of the modeling framework of Bhabra to simulate environmental parameters and predict algae growth within a region other than that on which
Bhabra does not disclose implementation of a trained network model as claimed; comparing the output to an output threshold; or performing an action in response to the output satisfying the output threshold.
Rackauckas presents SciML, a mathematical framework and software ecosystem for integrating differential equation models and data-driven machine learning approaches (pg. 1, Abstract – pg. 2, para. 2), and teaches approximation of differential equations as trainable ‘universal differential equations’ (UDEs) that are defined in full or part by a universal approximator such an embedded neural network (pg. 2, para. 3 – pg. 3, para. 2).
Rackauckas discusses the impracticality of solving high-dimensional PDEs with mesh-based techniques (i.e., point models) due to exponential scaling of the number of mesh points, and teaches that mesh-free methods based on transforming partial differential equations into alternative forms, which are then approximated by neural networks, have been shown to be highly computationally efficient with respect to important equations such as the Hamilton-Jacobi-Bellman equations (pg. 13, para. 1). Hamilton-Jacobi equations, utilized by Bhabra (see above), are a special case of Hamilton-Jacobi-Bellman equations.
Rackauckas teaches that their framework is applicable to a wide variety of differential equations (pg. 4, para. 3). Rackauckas particularly demonstrates validation of their UDE approach as providing a ~15,000x computational acceleration in modeling time-stepped geophysical fluid dynamics, via approximation of Boussinesq equations, over full mesh-based estimation (pg. 54, para. 2; see title of reference 126 at pg. 29).
Rackauckas does not disclose comparing the output to an output threshold; or performing an action in response to the output satisfying the output threshold.
Anderson discusses the California Harmful Algae Risk Mapping (C-HARM) system, a HAB modeling framework (pg. 1, Abstract), which implements steps of: estimating ocean environmental parameters (e.g., sea surface temperature, current and salinity) via the Regional Ocean Modeling System (ROMS); and predicting the spatial likelihood of Pseudo-nitzschia blooms, via empirical generalized linear models (GLMs), based on the environmental parameters (pg. 3, l. column – pg. 5, l. column; see Fig. 2).
Anderson teaches model prediction of blooms based on estimates exceeding a threshold of 104 cells/L-1 (pg. 5, l. column). In this way, Anderson discloses comparing predicted algal cell numbers to an output threshold, and predicting an algal bloom in response to the output satisfying the output threshold. Thus, the combination of Bhabra and Anderson teaches comparing the output to an output threshold; and performing an action in response to predicting an algal bloom, wherein an algal bloom is predicted in response to the output satisfying the output threshold.
With respect to claim 3, the framework of Bhabra pertains to prediction of algae growth as represented by units of algae over time, and monitoring of HABs (pg. 6, Fig. 5 and r. column – pg. 7, l. column).
Anderson teaches model prediction of algal blooms based on estimates exceeding a threshold of 104 cells/L-1 (pg. 5, l. column). In this way, Anderson teaches estimation of numbers of algae cells that contribute to harmful algal bloom.
With respect to claim 4, Rackauckas teaches that training a UDE amounts to calculating gradients of a cost/loss function defined on the current differential equation solution, such as a Euclidean distance between the current solution and data at discrete time points, and discusses software implementation of several adjoint methods that can be utilized to calculate these gradients (pg. 4, paras. 2-3). Rackauckas further describes operations of an implemented software library of adjoint methods, comprising treatment of the differential equation solve as a differentiable primitive during the backwards pass, backpropagation of the cost/loss gradients through the solve function, and backpropagation of the cost/loss gradients through embedded neural network(s), to obtain gradients with respect to the model parameters (pg. 30, para. 1 – pg. 31, para. 1; pg. 34, para. 2 – pg. 35, para. 2).
Thus, applying the UDE training framework of Rackauckas to efficiently approximate the DE-based models of Bhabra would provide for updating network weight(s) using backpropagation of error-derived gradients through the ocean simulator and initial network model.
With respect to claim 5, Bhabra discloses modeling of chemical uptake rates (UC, UN, UP) as dependent on environmental parameters, including temperature (T) and fluid velocity (V, i.e., current), and modeling of algae growth as dependent thereon (pg. 3, r. column – pg. 4, l. column). Bhabra further discloses that environmental inputs to the algae growth model are taken from the MSEAS simulations (pg. 4, l. column). In this way, Bhabra discloses determining, using the ocean simulator, a water temperature in the first region and water current in the first region; and determining, using (i) the algae growth model, (ii) the water temperature in the first region, and (iii) the current in the first region, a change in the first number of cells. As explained above with respect to claim 1, Rackauckas advantageously teaches implementation of a trained network model in place of DE-based mechanistic models.
Neither Bhabra nor Rackauckas discloses generating predictions through multiple iterations as claimed.
Anderson describes application of C-HARM for routine monitoring, which implements daily steps of: interpolating a temporal window of input satellite imagery, initially including one month of imagery and subsequently updated each day (time stepped) to include the most recent 180 days, via DINEOF; generating up-to-3 day forecasts of physical fields (e.g., currents, salinity and temperatures), via ROMS; and generating separate nowcasts and up-to-3-day forecasts of Pseudo-nitzschia bloom probabilities at each spatial grid point, via the empirical HAB models, advection and/or DINEOF, based on the gap-filled satellite data and physical field forecasts (pg. 3, r. column – pg. 5, l. column, see Fig. 4). In this way, Anderson teaches generation of bloom predictions through multiple iterations as claimed.
With respect to claim 9, Bhabra discloses modeling of chemical uptake rates (UC, UN, UP) as dependent on environmental parameters, including temperature (T) and fluid velocity (V, i.e., current), and modeling of algae growth as dependent thereon (pg. 3, r. column – pg. 4, l. column). Bhabra further discloses that environmental inputs to the algae growth model are taken from the MSEAS simulations (pg. 4, l. column). In other words, the MSEAS model is configured to forecast temperature and ocean currents.
Additionally, Anderson describes generating forecasts of currents, salinity and temperatures via ROMS (pg. 3, l-r. columns).
With respect to claim 6, Bhabra discloses modeling of algae field dynamics incorporating modeling of algae field advection-diffusion due to environmental flow via the modular finite volume framework of the MSEAS model (pg. 3, r. column).
With respect to claim 7, Anderson teaches prediction of Pseudo-nitzschia blooms, in terms of numbers of cells/mL-1, based on sea-surface temperature and salinity variables estimated from satellite data via empirical HAB models (pg. 2, Table 1 and r. column; pg. 5, l. column). Estimation using satellite data inherently requires obtaining satellite data.
With respect to claim 8, Anderson discusses comparison of daily model outputs to weekly light microscopy counts of Pseudo-nitzschia from surface phytoplankton bucket samples, and further teaches model calibration to establish regionally relevant prediction points based thereon (pg. 5, l-r. columns). Calibrating a model using surface sample count data is equivalent to training a model using water samples.
With respect to claim 10, Rackauckas teaches training the UDE to solve the high-dimensional (original) DE by using a fixed time-step method (pg. 3, para. 2; pg. 13, para. 3 – pg. 14, para. 1). In other words, training the UDE within a time step of the approximated DE model.
With respect to claim 11, the claim is directed to one or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising functional limitations of substantive similarity to the process limitations of claim 1.
Bhabra discloses implementation of the MSEAS model via software (pg. 3, l. column), while Rackauckas teaches implementation of their framework via a set of software packages written in the Julia (.jl) programming language (pg. 5, Fig. 1; pg. 30, para. 1). Anderson discusses online availability of their prediction system (pg. 3, l. column). Thus, all references indicate implementation of their methods in a computer software environment. Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to implement computerized methods on a computer-readable storage medium as claimed.
The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to further read on the functional limitations of the claim in the same way as outlined above with respect to the process limitations of claim 1.
With respect to claim 13, the unique limitations of the claim are substantively similar to those of claim 3. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 1.
With respect to claim 14, the unique limitations of the claim are substantively similar to those of claim 4. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 4.
With respect to claim 15, the unique limitations of the claim are substantively similar to those of claim 5. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 5.
With respect to claim 16, the unique limitations of the claim are substantively similar to those of claim 6. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 6.
With respect to claim 17, the unique limitations of the claim are substantively similar to those of claim 7. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 7.
With respect to claim 18, the unique limitations of the claim are substantively similar to those of claim 8. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 8.
With respect to claim 19, the unique limitations of the claim are substantively similar to those of claim 9. The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to read on the unique limitations of the claim in the same way as outlined above with respect to the unique limitations of claim 9.
With respect to claim 20, the claim is directed to a system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising functional limitations of substantive similarity to the process limitations of claim 1.
Bhabra, Rackauckas and Anderson each indicate implementation of their methods in a computer software environment, as discussed above with respect to claim 11. Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to implement computerized methods on a system comprising one or more computers as claimed.
The teachings of Bhabra, in view of Rackauckas and Anderson, are considered to further read on the functional limitations of the claim in the same way as outlined above with respect to the process limitations of claim 1.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have utilized the SciML/UDE framework, taught by Rackauckas, to approximate the DE-based algae growth model, disclosed by Bhabra, as an embedded trained network model, because Rackauckas teaches that UDE approximation of DE-based models as neural networks provides highly improved computational efficiency (pg. 13, para. 1). Said practitioner would have had a reasonable expectation of success because Bhabra utilizes DE-based models for estimation of ocean dynamics and algae growth therefrom, and Rackauckas specifically demonstrates advantageous application of their framework to approximate DE-based models of ocean dynamics (pg. 54, para. 2; see title of reference 126 at pg. 29).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented comparing the output to an output threshold and performing an action in response to the output satisfying the output threshold, as taught by Anderson, in combination with the modeling framework disclosed by Bhabra, because Bhabra discloses performing a control action responsive to predicting areas of likely HAB but does not further elaborate criteria for deciding that HAB is ‘likely’, while Anderson teaches affirmative prediction of an algal bloom in a given spatial region based on the estimated cell concentration exceeding a particular threshold (pg. 5, l. column).Thus, Anderson provides a particular quantitative means for implementing the control application disclosed by Bhabra. Said practitioner would have had a reasonable expectation of success because Bhabra and Anderson are directed to similar fields of endeavor, both concerning forecasting of dynamic algae field growth based on modeled ocean environmental parameters including temperatures and currents.
In this way the disclosure of Bhabra, in view of Rackauckas and Anderson, makes obvious the limitations of claims 1, 3-11 and 13-20. Thus, the claimed invention is prima facie obvious.
Claims 2 and 12 are rejected under 35 USC § 103 as being unpatentable over Bhabra, in view of Rackauckas and Anderson, as applied to claims 1 and 11 above, and further in view of Engehagen (Marine Policy 129: 104528, 12 pages; published 5/4/2021).
With respect to claim 2, Bhabra discloses application of their modeling framework for HAB control via optimal harvesting, wherein areas of likely HAB growth are elucidated and the optimal path planning delivers control strategies in an efficient and cost-effective manner to at-risk areas (pg. 6, l-r. columns). Bhabra does not disclose harvesting fish.
Rackauckas teaches that the UDE architecture has utility for optimal control applications (pg. 3, paras. 2-4). Rackauckas does not teach harvesting fish.
Anderson teaches that toxic and/or extremely high-biomass blooms are high impact events that affect aquaculture operations (pg. 1, l. column), and that C-HARM could provide a useful warning tool to aquaculture and recreational shellfish growers and assist business decisions (pg. 16, l. column; pg. 17, l. column). Anderson does not teach harvesting fish.
Engehagen studies the optimal harvesting decisions of a salmon farmer for management of harmful algal bloom risk, including potential risks and benefits of the decision to perform an early harvest based on the current and forecasted spread and density of a harmful algal bloom (pg. 1, Abstract and l. column – pg. 2, l. column). Engehagen discusses findings that responsive decisions to harvest early were made more frequently farmers who received relatively higher-quality information regarding the arrival of a harmful algal bloom, and also provided more economic value to those farmers than did harvesting decisions made by farmers who received lower-quality information (pg. 6, l. column; pg. 7, r. column; pg. 8, Table 8; pg. 9, Table 9 and Figs. 3-5).
Engehagen concludes that that the value of the early harvesting option is substantially higher for larger values of algal bloom arrival intensity (i.e., higher-quality information), according with high dependence of early harvesting value on the actual risk of a harmful algal bloom, and underscores the importance of accuracy and frequency of information signals to the aquaculture industry (pg. 9, r. column; pg. 11, l. column).
Engehagen also discusses related work on early harvesting of farmed salmon as a strategy to mitigate outbreaks of viral disease, wherein local virus levels are monitored and utilized to forecast an outbreak, and certain forecast thresholds trigger an early harvest to avoid losses due to disease (pg. 2, r. column – pg. 3, l. column).
With respect to claim 12, the unique limitations of the claim are substantively similar to those of claim 2. The teachings of Bhabra, in view of Rackauckas, Anderson and Engehagen, are considered to read on the limitations of the claim in the same way as outlined above with respect to the limitations of claim 11 and the unique limitations of claim 2.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would combined the responsive HAB management strategy of harvesting fish, as taught by Engehagen, with the HAB prediction framework disclosed by Bhabra, in view of Rackauckas and Anderson, because Engehagen teaches that harvesting fish can be an economically advantageous response to prediction of an algal bloom (pg. 6, l. column; pg. 7, r. column; pg. 8, Table 8; pg. 9, Table 9 and Figs. 3-5; pg. 11, l. column). Said practitioner would have had a reasonable expectation of success because Bhabra and Engehagen are directed to similar fields of endeavor, both discussing optimal management strategies based on algal bloom forecasts.
In this way the disclosure of Bhabra, in view of Rackauckas, Anderson and Engehagen, makes obvious the limitations of claims 2 and 12. Thus, the claimed invention is prima facie obvious.
Conclusion
At this point in prosecution, no claim is allowed.
The following prior art, made of record and not relied upon, is considered pertinent to applicant's disclosure:
Baek (Frontiers in Marine Science 8: 729954, 13 pages; published 10/12/2021) discloses a computational modeling method for simulating blooms of the algae Alexandrium catanella, comprising: enumerating A. catanella cells in collected water surface samples; simulating ocean data via an environmental fluid dynamics code model; and providing ocean data and derived A. catanella growth rates, to a convolutional neural network model, to generate output indicating occurrence and number of A. catanella cells (pg. 1, Abstract; pg. 2, r. column – pg. 4, l. column); and
Wen (Knowledge-Based Systems 245: 108569, 14 pages; published 3/18/2022) discloses a modeling framework for forecasting HABs (pg. 1, Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Theodore C. Striegel whose telephone number is (571)272-1860. The examiner can normally be reached Mon-Fri 12pm-8pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571)272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 September 18, 2026
Prosecution Insights