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 . The amendment filed 03/02/2026 has been received and considered. Claims 1 and 3-23 are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 or 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/120,685, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, the provisional application does not appear to mention:
Claim 14: a pulley system; a motor; an autonomous vehicle; a silicon foundry; a medical device; a genetic sequencing device; an X-ray crystallography device; a cryo-electron microscopy device; a cryo-tomography device; a microscope device; a mass spectrometry device; and a microfluidic device.
Claim 15: biological, living organism, medical, pharmacokinetic, pharmacodynamic, or metabolite handling.
If Applicants wish to rebut this conclusion, they are requested to explicitly map the claims of the instant application in view of the specification of the earlier filed application. Thus, priority is not granted for said claims.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1, 3, 9-12, 14, 16-20, 22, and 23 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sean Javad Kamkar, (Kamkar hereinafter), U.S. Patent 11893466, taken in view of Seung Wook Kim, (Kim hereinafter), U.S. Pre–Grant publication 20220269937.
As to claim 1, Kamkar discloses an apparatus comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the apparatus to: (see “system can be a local (e.g., on-premises) system, a cloud-based system, or any combination of local and cloud-based systems. The system can be a single-tenant system, a multi-tenant system, or a combination of single-tenant and multi-tenant components” in col. 3, lines 61-65): determine a plurality of differentiable models (see “generating the initial model includes defining the model (e.g., by using Python, R, a model development system, a text editor, a workflow tool, a web application, etc… the initial model 111a is an alternative model (F)… that is an alternative to a pre-existing model (M), and F is trained based on input variables… M and F are both differentiable models” in col. 7, line 59 to col. 8, line 54), each of the differentiable models representing a component of a physical system; combine (see “new model can be a version of the initial model with new model parameters, a new model constructed by combining the initial model with one or more additional models in an ensemble, a new model constructed by adding one or more transformations to an output of the initial model, a new model having a different model type from the initial model, or any other suitable new model having a new construction and/or model parameters” in col. 11, lines 41-49) the plurality of differentiable models using an integration layer such that the integration layer and the combined plurality of differentiable models form a differentiable machine (see “integration layer“ as “neural network“ – see claim 17, “preparing an initial model F (e.g., at S210), a fair alternative model to the pre-existing model (M), and training F (e.g., S262) based on input variables x and an adversary A (e.g., the adversarial classifier 112), wherein A is a model that predicts the sensitive attribute based on model F's score, and wherein the alternative model F includes one or more of a linear model, neural network, or any other differentiable model, and wherein the model F is a replacement to the pre-existing model (M)” in col. 19, line 61 to col. 20, line 1; “new model (e.g., 111e) is an ensemble of a pre-existing model (M) and a modified version (F) (e.g., 111b-d) of the initial model (e.g., 111a). In some variations, M and F are both differentiable models” in col. 12, lines 29-32) representing the physical system (see “new model… is a compound model in which the outputs of the initial model and one or more submodels are ensembled together” in col. 12, lines 6-9).
Kamkar does not disclose, but Kim discloses deploy (see “[0316]… a highly-parallel general-purpose graphics processing unit ("GPGPU") 1830 suitable for deployment on a multi-chip module") the differentiable machine to a computing device in communication with a physical instance of the physical system; determine one or more measured outputs of the physical instance of the physical system by receiving sensor data from one or more sensors (see “[0077]… driving neural simulator 310 may be a fully differentiable simulator that allows for re-simulation of a given video sequence by offering an agent to drive through a recorded scene again while taking different or additional actions than the actions performed in the original video… a differentiable simulator may be a type of simulator that provides an improvement of the simulation to real gap by enabling a use of gradient-based optimization algorithms to find simulation parameters that best fit observed sensor readings") physically coupled to the physical instance of the physical system (see “[0201]… vehicle 1000 may include GPU(s) 1020 (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to SoC(s) 1004 via a high-speed interconnect (e.g., NVIDIA's NVLINK channel)… GPU(s) 1020 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based at least in part on input (e.g., sensor data) from sensors of a vehicle 1000"; "[0217]… vehicle 1000 may further include vibration sensor(s) 1042. In at least one embodiment, vibration sensor(s) 1042 may measure vibrations of components of vehicle 1000"); generate one or more predicted outputs of the physical system using the differentiable machine; and adjust one or more parameters of the differentiable machine based on the one or more measured outputs by reducing a discrepancy between the one or more predicted outputs and the one or more measured outputs (see “[0106]… a neural simulator can first optimize for an underlying sequence of inputs that can reproduce a real video and then replay a same scenario with modified content or scene condition… a neural simulator can create an editable simulation environment from a real video using differentiable simulation aspects. Differentiable simulation may refer to an ability for recovering a scene and scenario by discovering underlying factors of variations that comprise a video, while also recovering actions that an agent took, if actions are not provided. When said factors are discovered, an agent can use neural simulator to re-simulate a scene, while taking different actions… a neural simulator further allows sampling and modification of various components of a scene, thus enabling testing of an agent such as one or more neural networks of an autonomous vehicle in a same scenario under different weather conditions and/or with different objects”).
Kamkar and Kim are analogous art because they are related to differentiable modeling.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, because Kim discloses that “[0077]… a differentiable simulator may be a type of simulator that provides an improvement of the simulation to real gap by enabling a use of gradient-based optimization algorithms to find simulation parameters that best fit observed sensor readings", and as a result, Kim reports that “[0106]… a neural simulator can create an editable simulation environment from a real video using differentiable simulation… an agent can use neural simulator to re-simulate a scene, while taking different actions… a neural simulator further allows sampling and modification of various components of a scene, thus enabling testing of an agent such as one or more neural networks of an autonomous vehicle in a same scenario under different weather conditions and/or with different objects”.
As to claim 3, Kamkar does not disclose, but Kim discloses wherein the one or more parameters of the differentiable machine comprise weights for the differentiable models (see “[0137]… inference and/or training logic 115 may be used in system FIG. 9 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
As to claim 9, Kamkar discloses determine a plurality of differentiable machines, each of the plurality of differentiable machines representing different physical systems (see “new model (e.g., 111e) is an ensemble of a pre-existing model (M) and a modified version (F) (e.g., 111b-d) of the initial model (e.g., 111a). In some variations, M and F are both differentiable models” in col. 12, lines 29-32); and combine the plurality of differentiable machines using a group integration layer such that the group integration layer and the combined plurality of differentiable machines form a differentiable group (see “preparing an initial model F (e.g., at S210), a fair alternative model to the pre-existing model (M), and training F (e.g., S262) based on input variables x and an adversary A (e.g., the adversarial classifier 112), wherein A is a model that predicts the sensitive attribute based on model F's score, and wherein the alternative model F includes one or more of a linear model, neural network, or any other differentiable model, and wherein the model F is a replacement to the pre-existing model (M)” in col. 19, line 61 to col. 20, line 1).
As to claim 10, Kamkar discloses determine the plurality of differentiable models based on user input selecting one or more of the plurality of differentiable models from a library of components selectable using a user interface (see “model development system provides a graphical user interface (e.g., 115) which allows an operator (e.g., via 120) to access a programming environment and tools such as R or python, and contains libraries and tools which allow the operator to prepare, build, explain, verify, publish, and monitor machine learning models… a graphical user interface (e.g., 115) which allows an operator (e.g., via 120) to access a model development workflow… creating and analyzing a predictive model” in col. 4, lines 3-15).
As to claim 11, Kamkar discloses combine the plurality of differentiable models linearly combining outputs of the differentiable models using the integration layer (see “new model… is a compound model in which the outputs of the initial model and one or more submodels are ensembled together (e.g., using a simple linear stacking function)” in col. 12, lines 6-9).
As to claim 12, Kamkar does not disclose, but Kim discloses combine the plurality of differentiable models in a pairwise configuration with communication between the differentiable models (see “[0510]… for different machine learning models used by deployment system 3606, different training pipelines 3704 may be used… training pipeline 3704 similar to a first example described with respect to FIG. 36 may be used for a first machine learning model, training pipeline 3704 similar to a second example described with respect to FIG. 36 may be used for a second machine learning model, and training pipeline 3704 similar to a third example described with respect to FIG. 36 may be used for a third machine learning model… any combination of tasks within training system 3604 may be used depending on what is required for each respective machine learning model… one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system 3604, and may be implemented by deployment system 3606").
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
As to claim 14, Kamkar does not disclose, but Kim discloses wherein the physical system comprises one or more of: (see “[0106]… an agent can use neural simulator to re-simulate a scene, while taking different actions… a neural simulator further allows sampling and modification of various components of a scene, thus enabling testing of an agent such as one or more neural networks of an autonomous vehicle in a same scenario under different weather conditions and/or with different objects”)
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
As to claim 16, Kamkar does not disclose, but Kim discloses wherein at least one of the differentiable models comprises an approximator model configured to approximate one or more attributes of at least one of the components of the physical system based on one or instance of the physical system (see “[0111]… frame interpolation using differentiable simulation to produce a subsequent frame given a previous frame… frame interpolation can be utilized as an application of a differentiable simulation aspect of a neural simulator. Frame interpolation discovers in-between frames given a reference and a future frame”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
As to claim 17, Kamkar discloses wherein the integration layer comprises a neural net configured to determine an arrangement for the combined plurality of differentiable models based on one or more outputs of the differentiable models and the instance of the physical system (see “preparing an initial model F (e.g., at S210), a fair alternative model to the pre-existing model (M), and training F (e.g., S262) based on input variables x and an adversary A (e.g., the adversarial classifier 112), wherein A is a model that predicts the sensitive attribute based on model F's score, and wherein the alternative model F includes one or more of a linear model, neural network, or any other differentiable model, and wherein the model F is a replacement to the pre-existing model (M)” in col. 19, line 61 to col. 20, line 1; “new model (e.g., 111e) is an ensemble of a pre-existing model (M) and a modified version (F) (e.g., 111b-d) of the initial model (e.g., 111a). In some variations, M and F are both differentiable models” in col. 12, lines 29-32).
As to claim 18, Kamkar does not disclose, but Kim discloses wherein at least one of the differentiable models approximates (see “[0111]… frame interpolation using differentiable simulation to produce a subsequent frame given a previous frame… frame interpolation can be utilized as an application of a differentiable simulation aspect of a neural simulator. Frame interpolation discovers in-between frames given a reference and a future frame”) a physical property of at least one of the components of the physical system (see “[0106]… an agent can use neural simulator to re-simulate a scene, while taking different actions… a neural simulator further allows sampling and modification of various components of a scene, thus enabling testing of an agent such as one or more neural networks of an autonomous vehicle in a same scenario under different weather conditions and/or with different objects”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
Regarding claims 19 and 22, their features correspond to features of claim 1. Therefore, claims 19 and 22 and are rejected for the same reasons given above.
As to claim 20, Kamkar does not disclose, but Kim discloses determining one or more outputs of the instance of the physical system; and adjusting one or more parameters of the differentiable machine based on the one or more outputs (see “[0077]… driving neural simulator 310 may be a fully differentiable simulator that allows for re-simulation of a given video sequence by offering an agent to drive through a recorded scene again while taking different or additional actions than the actions performed in the original video… a differentiable simulator may be a type of simulator that provides an improvement of the simulation to real gap by enabling a use of gradient-based optimization algorithms to find simulation parameters that best fit observed sensor readings").
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kim with Kamkar, (see supra).
Regarding claim 23, its features correspond to features of claim 20. Therefore, claim 23 is rejected for the same reasons given above.
Claims 4-7, 13, 15, and 21 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kamkar taken in view of Kim as applied to claim 1 above, and further in view of Yuanming et al., (Yuanming(1) hereinafter), Chainqueen: A real-time differentiable physical simulator for soft robotics.
As to claim 4, Kamkar and Kim do not disclose, but Yuanming(1) discloses a plurality of different hardware computing devices in communication with a plurality of different instances of the physical system, wherein: the at least one processor is configured to cause the apparatus to deploy the differentiable machine to each of the different hardware computing devices (see “ChainQueen… MPM) is a hybrid Lagrangian-Eulerian method that uses both particles and grid nodes for simulation… In ChainQueen, we introduce the first fully differentiable MLS-MPM simulator with respect to both state and model parameters, with both forward simulation and backpropagation running efficiently on GPUs” in page 1, col. 1, last paragraph to col. 2, 1st paragraph; “our CUDA implementation is tailored for MLS-MPM and explicitly optimized for parallelism and locality” in page 3, col. 1, last paragraph to col. 2, 1st paragraph); and the different hardware computing devices are configured to: determine different outputs of the different instances of the physical system (see “Gradients of state at the end of a time step with respect to states at the starting of the time step can be computed using the chain rule. With the single-step gradients computed, applying the chain rule at a higher level from the final state all-the-way to the initial state yields gradients of the final state with respect to the initial state, as well as to the controller parameters that are used in each state” in page 3, col. 1, next to last paragraph); and separately adjust one or more parameters of the differentiable machines for each of the different instances of the physical system based on the different outputs (see “optimize regression-based controllers for soft robots and efficiently discover stable gaits. The controller takes as input the state vector z, which includes target position, the center of mass position, and velocity of each composed soft component. In our examples, the actuation vector a for up to 16 actuators is generated by the controller in each time step. During optimization, we perform gradient descent on variables W and b, where a = tanh (Wz + b) is the actuation-generating controller” in page 5, col. 1, last paragraph to col. 2, 1st paragraph).
Kamkar, Kim, and Yuanming(1) are analogous art because they are related to differentiable modeling.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, because Yuanming(1) discloses that “ChainQueen… MPM) is a hybrid Lagrangian-Eulerian method that uses both particles and grid nodes for simulation… In ChainQueen, we introduce the first fully differentiable MLS-MPM simulator with respect to both state and model parameters, with both forward simulation and backpropagation running efficiently on GPUs” (see page 1, col. 1, last paragraph to col. 2, 1st paragraph, and as a result, Yuanming(1) reports that “our CUDA implementation is tailored for MLS-MPM and explicitly optimized for parallelism and locality, thus delivering high-performance” (see page 3, col. 1, last paragraph to col. 2, 1st paragraph).
As to claim 5, Kamkar and Kim do not disclose, but Yuanming(1) discloses manage the different instances of the physical system over time based on outputs of the differentiable machines for each of the different instances one or more outputs of the instance of the physical system (see “ChainQueen… MPM) is a hybrid Lagrangian-Eulerian method that uses both particles and grid nodes for simulation… In ChainQueen, we introduce the first fully differentiable MLS-MPM simulator with respect to both state and model parameters, with both forward simulation and backpropagation running efficiently on GPUs” in page 1, col. 1, last paragraph to col. 2, 1st paragraph; “our CUDA implementation is tailored for MLS-MPM and explicitly optimized for parallelism and locality” in page 3, col. 1, last paragraph to col. 2, 1st paragraph).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, (see supra).
As to claim 6, Kamkar and Kim do not disclose, but Yuanming(1) discloses prototype versions of the physical system by iteratively: updating one or more parameters of the differentiable machine based on one or more outputs of the instance of the physical system; and updating one or more aspects of the instance of the physical system based on one or more outputs of the differentiable machine (see “Gradients of state at the end of a time step with respect to states at the starting of the time step can be computed using the chain rule. With the single-step gradients computed, applying the chain rule at a higher level from the final state all-the-way to the initial state yields gradients of the final state with respect to the initial state, as well as to the controller parameters that are used in each state” in page 3, col. 1, next to last paragraph).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, (see supra).
As to claim 7, Kamkar and Kim do not disclose, but Yuanming(1) discloses wherein updating the one or more aspects of the instance of the physical system comprises one or more of changing a property of the instance of the physical system and replacing the instance of the physical system with a new version of the instance of the physical system, the new version comprising a different property than the replaced instance of the physical system (see “3) Grid-to-particle transfer (G2P). Particles gather updated velocity vn+1p, local velocity field gradients Cn+1p and position xn+1p. The constitutive model properties (e.g. deformation gradients Fn+1p) are updated” in page 2, last paragraph).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, (see supra).
As to claim 13, Kamkar and Kim do not disclose, but Yuanming(1) discloses combine the plurality of differentiable models in a graph configuration with communication connections between the differentiable models determined based on a physical arrangement of the components of the physical system (see ‘Our high-performance implementation takes advantage of the computational power of modern GPUs through CUDA. We also implemented a reference implementation in TensorFlow… programming physical simulation as a “computation graph”’ in page 3, col. 1, last paragraph).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, (see supra).
As to claim 15, Kamkar and Kim do not disclose, but Yuanming(1) discloses wherein the physical system comprises one or more of, a biological system, one or more biological cells, a living organism (see “As a particle-grid-based hybrid simulator, MPM simulates objects of various states, such as… elastoplastic materials (e.g… human tissue” in page 1, col. 2, 2nd paragraph)(see “adversarial training techniques… applied to models used to make predictions in which fairness is a factor for deciding whether to permit the model for use in production… applied to predictive models for use in decisions related to… drug testing… medical results analysis” in col. 3, lines 4-13).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming(1) with Kamkar and Kim, (see supra).
Regarding claim 21, its features correspond to features of claim 6. Therefore, claim 21 is rejected for the same reasons given above.
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over Kamkar taken in view of Kim as applied to claim 1 above, and further in view of Yuanming et al., (Yuanming hereinafter), Difftaichi: Differentiable programming for physical simulation.
As to claim 8, Kamkar and Kim do not disclose, but Yuanming discloses wherein each differentiable model comprises a domain and a derivative exists at each point in the domains for the differentiable models (see “using SCT to differentiate a whole simulator with thousands of time steps” in page 3, last paragraph; “performance… on an NVIDIA GTX 1080 Ti GPU” in page 6, next to last paragraph; “program takes 10 seconds to run in DiffTaichi on a GPU” in page 7, 3rd paragraph) such that the differentiable machine is differentiable end-to-end (see “4.3 DIFFERENTIABLE RIGID BODY SIMULATORS… an impulse-based (Catto, 2009) differentiable rigid body simulator (Fig. 1, rigid_body) for optimizing robot controllers… The simulation is end-to-end differentiable… Gravity is ignored” in page 7).
Kamkar, Kim, and Yuanming are analogous art because they are related to differentiable modeling.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Yuanming with Kamkar and Kim, because Yuanming presents "DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. Based on an imperative programming language, DiffTaichi generates gradients of simulation steps using source code transformations that preserve arithmetic intensity and parallelism" (see page 1, ABSTRACT), and as a result, Yuanming reports that ‘[o]ur language uses a “megakernel” approach, allowing the programmer to naturally fuse multiple stages of computation into a single kernel, which is later differentiated using source code transformations and just-in-time compilation… DiffTaichi kernels… are therefore more efficient for physical simulation tasks’ (see page 2, 2nd paragraph).
Response to Arguments
Regarding the Claim of Priority, Applicant's arguments have been considered, but they are not persuasive. Applicant argues, (see page 12, 3rd paragraph to page 15, 2nd paragraph):
‘… [0005] Under Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336 (Fed. Cir. 2010) (en banc), the written description requirement is satisfied when the disclosure reasonably conveys to a person of ordinary skill in the art that the inventor had possession of the claimed subject matter as of the filing date. The test is not whether every species is expressly listed, but whether the disclosure demonstrates possession of the claimed genus, either through representative species or structural/functional commonalities. The Federal Circuit has repeatedly held that disclosure of a genus does not require recitation of every possible species. See Ariad, 598 F.3d at 1350; MPEP § 2163.
[0006] Claim 14 recites that the physical system may comprise various example devices (e.g., laser device, plasma generation device, pulley system, lithography machine, autonomous vehicle, silicon foundry equipment, medical devices, genetic sequencing devices, X-ray crystallography devices, cryo-electron microscopy devices, cryo-tomography devices, microscopes, mass spectrometers, and microfluidic devices).
[0007] The provisional application does not merely disclose a single narrow embodiment. Rather, it discloses:
• A general architecture for constructing differentiable machines representing components of physical systems;
• Express examples including laser systems, mirror systems, and plasma-generation/Xe-based physical devices;
• A framework expressly applicable to arbitrary physical systems governed by physical laws.
[0008] The provisional repeatedly emphasizes that differentiable models may be constructed for physical systems governed by physics-based relationships and that the differentiable machine architecture is system-agnostic. The disclosed methodology is not limited to a particular physical domain, but instead teaches modeling components, combining differentiable representations via an integration layer, and deploying such models to instances of physical systems.
[0009] The devices listed in Claim 14 are not unrelated, conceptually distinct inventions. They are representative examples of machines or instruments composed of physical components governed by physical laws - precisely the class of systems the provisional teaches how to model using differentiable components. The written description requirement does not demand that each enumerated device be expressly recited in the provisional, where the disclosure conveys possession of a generalizable differentiable-machine framework applicable to physical systems broadly. See Ariad, 598 F.3d at 1351. Accordingly, the provisional provides adequate written description support for the genus of physical systems recited in Claim 14.
[0010] Claim 15 recites that the physical system may comprise a biological system, biological cells, a living organism, or that at least one differentiable model comprises a pharmacokinetic model, pharmacodynamic model, or metabolite handling model.
[0011] The provisional expressly acknowledges applicability of the differentiable machine architecture to biological and medical systems and discusses modeling of biological and biochemical processes as governed by measurable physical and chemical interactions. The framework described in the provisional is directed to constructing differentiable representations of system components where governing relationships can be expressed mathematically and optimized using gradient-based techniques.
[0012] Pharmacokinetic and pharmacodynamic models are canonical examples of dynamical systems governed by differential equations and measurable parameters - precisely the type of systems to which the disclosed differentiable machine methodology applies. Similarly, metabolite handling models represent biochemical transformation systems governed by physical and chemical laws.
[0013] The provisional's disclosure of modeling physical and biologically governed systems using differentiable representations reasonably conveys possession of applying the same architecture to pharmacokinetic, pharmacodynamic, and metabolite systems. The claims do not introduce a fundamentally different technological field; rather, they identify additional examples within the disclosed modeling framework.
[0014] Under Ariad and MPEP § 2163, disclosure of a generalizable modeling architecture applicable to systems governed by physics, chemistry, and biology is sufficient to support the claimed genus.
[0015] The Office Action relies on MPEP § 2163.03(V) regarding broad genus claims allegedly supported only by narrow species. However, this is not a case where the disclosure provides a single isolated embodiment and the claims attempt to capture an unrelated genus.
[0016] Here, the provisional discloses:
• A generalized differentiable modeling architecture;
• A component-based physical system framework;
• Deployment to physical instances;
• System-agnostic integration of differentiable components.
[0017] The enumerated systems in Claims 14 and 15 are species falling within the expressly described class of systems composed of components governed by physical, chemical, or biological laws. The disclosure therefore conveys possession of the claimed subject matter as required by 35 U.S.C. § 112(a)…’
As pointed out by Applicant ('See Ariad Pharm., Inc. v. Eli Lilly'), the MPEP reads (underline emphasis added):
'2163 Guidelines for the Examination of Patent Applications Under the 35 U.S.C. 112{a) or Pre-AIA 35 U.S.C. 112, first paragraph, "Written Description" Requirement [R-01.2024]…
II. METHODOLOGY FOR DETERMINING ADEQUACY OF WRITTEN DESCRIPTION
A. Read and Analyze the Specification for Compliance with 35 U.S.C. 112(a) or Pre-AIA 35 U.S.C. 112, first paragraph…
3. Determine Whether There is Sufficient Written Description to Inform a Skilled Artisan That Inventor was in Possession of the Claimed Invention as a Whole at the Time the Application Was Filed…
ii) For each claim drawn to a genus:
The written description requirement for a claimed genus may be satisfied through sufficient description of a representative number of species by actual reduction to practice (see i)(A) above), reduction to drawings (see i)(B) above), or by disclosure of relevant, identifying characteristics, i.e., structure or other physical and/or chemical properties, by functional characteristics coupled with a known or disclosed correlation between function and structure, or by a combination of such identifying characteristics, sufficient to show the inventor was in possession of the claimed genus (see i)(C) above). See Eli Lilly…
A "representative number of species" means that the species which are adequately described are representative of the entire genus. Thus, when there is substantial variation within the genus, one must describe a sufficient variety of species to reflect the variation within the genus…
Satisfactory disclosure of a "representative number" depends on whether one of skill in the art would recognize that the inventor was in possession of the necessary common attributes or features possessed by the members of the genus in view of the species disclosed. For inventions in an unpredictable art, adequate written description of a genus which embraces widely variant species cannot be achieved by disclosing only one species within the genus. See, e.g., Eli Lilly… Instead, the disclosure must adequately reflect the structural diversity of the claimed genus, either through the disclosure of sufficient species that are "representative of the full variety or scope of the genus," or by the establishment of "a reasonable structure-function correlation."
Such correlations may be established "by the inventor as described in the specification," or they may be "known in the art at the time of the filing date."… If a representative number of adequately described species are not disclosed for a genus, the claim to that genus must be rejected as lacking adequate written description under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph.
2163.03 Typical Circumstances Where Adequate Written Description Issue Arises [R-01.2024]…
V. ORIGINAL CLAIM NOT SUFFICIENTLY DESCRIBED
While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed. In re Wertheim… a question as to whether a specification provides an adequate written description may arise in the context of an original claim. An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly…'.
The Specification merely reads (underline emphasis added):
'2. Background Information: Modern physical devices are complex entities, with many interacting parts and varied purposes. While all industrial machines operate under known physics, they do so in complex combinations which may not be feasible to model effectively. For example, a device may make use of mechanical gears, electrochemical batteries, magnetic fields, optical amplification, biological enzymes and more to achieve its stated purpose. Mathematical models of the device by necessity will often elide much of the underlying complexity and are often of limited use for modelling tile real-world behavior of a device. For example, a simple circuit model of a battery might suffice for back-of-tile-envelope modelling but will not suffice to model the behavior of a live battery. Simulation techniques can serve as powerful stopgaps allowing more complex models of devices to be built, but these simulations are often limited by computational cost and approximation error…' (see page 1, 2nd paragraph).
Examiner's response: Applicant's argument is not persuasive, because Applicants failed to explicitly map the claims of the instant application in view of the specification of the earlier filed application. As indicated in the previous Office Action, "If Applicants wish to rebut this conclusion, they are requested to explicitly map the claims of the instant application in view of the specification of the earlier filed application". As argued, the Specification is silent regarding "A general architecture for constructing differentiable machines representing components of physical systems… A framework expressly applicable to arbitrary physical systems governed by physical laws", biological, medical, pharmacokinetic, pharmacodynamic, metabolite, handling, alive, living, organism, architecture, system-agnostic, chemical interactions, physical laws, chemical laws, or biological laws.
The disclosure only describes narrow species with no evidence that the genera are contemplated. (See MPEP 2163.03 and Specification, page 1, 2nd paragraph supra). As indicated in the previous Office Action, "The disclosure of the prior-filed application, Application No. 63/120,685, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application".
Regarding the Claim Interpretations: for claims 16 and 17, the amendment corrected all issues and the Claim Interpretations are withdrawn. For claims 22 and 23, Applicant does intend to have the limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Regarding the arguments with respect to the rejection under 103, Applicant’s arguments with respect to the independent claims have been fully considered, but they are not persuasive. Applicant argues that the prior art disclosures in the previous rejection fail to teach the newly added limitations. These features of Applicants' claims and arguments were newly added. The previous Office Action could not have pointed out disclosures of a limitation that was not claimed before. Independent claims are rejected over Kamkar in view of Kim instead of Kamkar in view of Yuanming(1) and further in view of Gomes. As to Applicant's arguments, '[0026]… Kamkar does not deploy a differentiable machine "to a hardware computing device in communication with a physical instance of the physical system." Nor does Kamkar disclose receiving "sensor data from one or more sensors physically coupled" to a physical system instance', see rejection supra.
Applicant argues, (see page 17, 2nd paragraph):
‘[0027]… Yuanming 1 does not deploy a differentiable machine to hardware controlling a real-world physical instance, does not receive sensor data from sensors physically coupled to a physical system, and does not reduce a discrepancy between predicted outputs and measured outputs of a real device. Yuanming 1optimizes controllers within a simulation environment; it does not perform hardware-in-the-loop digital twin calibration’
Examiner's response: Applicant's argument is not persuasive, because Applicant's arguments are more specific than the claims language and are therefore not persuasive. Examiner does not see these argued features expressed in the claims: "hardware controlling a real-world physical instance… perform hardware-in-the-loop digital twin calibration". Examiner is not allowed to bring limitations set forth in the description into the claims. Although a claim should be interpreted in light of the Specification disclosure, it is generally considered improper to read limitations contained in the Specification into the claims. See In re Van Geuns1, In re Prater2, and In re Winkhaus3, which discuss the premise that one cannot rely on the Specification to impart limitations to the claim that are not recited in the claim.
Conclusion
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 8:00 PM.
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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 7 Fed. Cir. 1993
2 In re Prater, 415 F.2d 1393, 162 USPQ 541 (CCPA 1969)
3 In re Winkhaus, 527 F.2d 637, 188 USPQ 129 (CCPA 1975)
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