Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/14/2026 has been entered.
2. The amendment filed on 07/14/2026 has been received and considered. Claims 1-20 are presented for examination.
Claim Objections
3. Claims 9, 10, 19, and 20 are objected to because of the following informalities:
As per Clam 9 and 19, they recite the limitation “the control optimization problem” which would be better as “optimal control problem” because it seems referring to the “optimal control problem” recited in claims 1 an d11, respectively.
As per Claim 9, it recites the limitation “wherein two or more of the processors work…” which would be better as “further comprising: two or more of the processors work…”
As per Claim 19, it recites the limitation "wherein two or more of the at least one or more processors work in parallel " which would be better as “further comprising: two or more of the one or more processors work in parallel”.
As per Claims 10 and 20, they recite the limitation "the one or more images" which would be better as “one or more images” to avoid an antecedent basis issue.
As per Claims 10 and 20, they recite the limitation "a control scheme" which would be better as “one or more images” to avoid an antecedent basis issue.
Appropriate correction is required.
Claim Rejections - 35 USC section 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1-7, 10-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matei ("Micro-scale 2D chiplet position control: a formal approach to policy design") in view of Zheng ("PDE-based Dynamic Density Estimation for Large-scale Agent Systems"), further in view of Sinigaglia ("Density control of large-scale particles swarm through PDE-constrained optimization").
As per Claims 1 and 11, Matei teaches a system/non-transitory, computer-readable storage medium storing code for micro-object density distribution control (pg. 5519, section II, Figure 1, “The system has three hardware devices and four software modules”), comprising:
one or more processors configured to execute computer-executable code, one or more of the processors (section II, Figure 1, “The system has three hardware devices and four software modules.”) configured to:
obtain one or more parameters of a positioning system for positioning a plurality of micro-objects, the positioning system comprising a plurality of electrodes, the electrodes configured to induce a movement of at least some of the micro-objects in the plurality of micro-objects when the micro-objects are suspended in a fluid proximate to the electrodes upon a generation of one or more electric potentials by one or more of the electrodes (section II, pg. 5519 "an imaging module and a high speed camera for tracking the chiplet locations"; section II, pg. 5519 "The system uses dielectric fluids (e.g., Isopar-M) and supports both electrophoretic (EP) and dielectrophoretic (DEP) forces."; section II, pg. 5519 "The projected images activate or deactivate electrodes, as indicated by the control inputs.": chiplets suspended in a dielectric fluid are moved by electric potentials generated at the electrodes of the micro-assembly platform, i.e., the "positioning system" as claimed, whose parameters and chiplet locations are obtained through the camera-equipped imaging module);
define using the parameters a model describing a change of positions of the micro-objects in the fluid due to capacitance-based interactions of the micro-objects with the electrodes (section III, pg. 5520 "describe a 2D model for the chiplet motion under the effect of the potential field induced by the electrode array."; section III, pg. 5520 "We compute the potential energy by using a capacitive-based electrical circuit that lumps the interaction between the electrodes and the chiplet": the capacitance-based 2D dynamical model describes how the chiplet positions in the fluid change under the electrode-induced potential field); and
actuate at least some of the electrodes to generate the sequence of generations of electric potentials, wherein at least some of the micro-objects are moved in the fluid upon the generation of the electric potentials (section II, pg. 5519; Fig. 5, pg. 5522: the control inputs are converted into projected images that activate the electrodes, moving the chiplets in the fluid).
However, Matei fails to teach explicitly estimate a density distribution of the positions of the micro-objects in the fluid using kernel density estimation and at least one sensor;
obtain a target density distribution of the positions of the micro-objects in the fluid; and
solve an optimal control problem to derive, based on the model, the target density distribution of the positions in the fluid, and the estimated density distribution of the positions in the fluid, a sequence of generations of electric potentials for moving at least some of the micro-objects to decrease an error between the estimated density distribution of the positions in the fluid and the target density distribution of the positions in the fluid.
Zheng teaches estimate a density distribution of the positions of the micro-objects in the fluid using kernel density estimation and at least one sensor (Abstract, section IV, pg. 3 "we use KDE to construct a noisy measurement of the unknown density"; section III, pg. 3 "Given the density dynamics (2) and agent states … we want to estimate their density"; section I, pg. 1 "kernel density estimation (KDE) [2] has been widely used for estimating the global density in the study of swarm robotic systems": the density of the positions of a plurality of moving objects is estimated by kernel density estimation from the objects’ states observed in real time, which, applied to Matei’s platform, are the chiplet locations tracked by the camera, i.e., the "at least one sensor" as claimed). In particular, Zheng teaches a density filter for large-scale agent systems that estimates the dynamically varying probability density of the agents’ positions by combining kernel density estimation with infinite-dimensional Kalman filters, using the agents’ states observed in real time as the measurement.
Matei and Zheng are analogous art because they are both related to estimating the spatial density of a population of controlled moving objects from observed positions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Zheng into Matei’s invention for the purpose of micro-scale 2D chiplet position control to provide a density filter that takes advantage of the system dynamics to gradually improve its estimation and is scalable to the agents’ population (Zheng: Abstract; section VI, pg. 5).
However, Matei as modified by Zheng fails to teach explicitly obtain a target density distribution of the positions of the micro-objects in the fluid; and
solve an optimal control problem to derive, based on the model, the target density distribution of the positions in the fluid, and the estimated density distribution of the positions in the fluid, a sequence of generations of electric potentials for moving at least some of the micro-objects to decrease an error between the estimated density distribution of the positions in the fluid and the target density distribution of the positions in the fluid.
On the other hand, Sinigaglia teaches obtain a target density distribution of the positions of the micro-objects in the fluid (section III, pg. 4 "
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": a target density distribution qT(x) over the positions of the swarm particles is obtained as the control objective); and
solve an optimal control problem to derive, based on the model, the target density distribution of the positions in the fluid, and the estimated density distribution of the positions in the fluid, a sequence of generations of electric potentials for moving at least some of the micro-objects to decrease an error between the estimated density distribution of the positions in the fluid and the target density distribution of the positions in the fluid (section III, pg. 4
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"; section V, pg. 13, Fig. 5 "
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": the optimal control problem is solved over the density dynamics to derive the actuation that drives the evolved density toward the target density, decreasing the quadratic (L2) mismatch between them; incorporated into Matei’s platform, the derived actuation is the sequence of generations of electrode electric potentials). In particular, Sinigaglia teaches an optimal control strategy for shaping a large-scale swarm of particles in which an initial density is optimally steered towards a target density by minimizing a quadratic cost functional, the difference between the reached density and the target density being measured as an L2 distance.
Matei, Zheng, and Sinigaglia are analogous art because they are all related to density-based estimation and control of large collectives of small moving objects.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Sinigaglia into Matei as modified by Zheng’s invention for the purpose of micro-scale 2D chiplet position control to provide an optimal control strategy for shaping a large-scale swarm of particles, the system being large-scale and underactuated, making the control strategy at the microscopic particle level infeasible (Sinigaglia: Abstract).
As per Claim 2 and 12, Matei et al. teaches the one or more processors further configured to: define a discrete representation of the model based on the parameters (section IV-A “. The discrete probability distribution can be seen as a discretization of a continuous probability distribution”);
transform the discrete representation into a continuous representation of the model (section IV-A “. The discrete probability distribution can be seen as a discretization of a continuous probability distribution”); and
apply Gauss-Hermite quadrature to compute variables of the model (section IV-B “It follows that the expectation of a function of a random variable with a Gaussian distribution, can be accurately approximated using Gauss-Hermite quadrature.”).
As per Claim 3 and 13, Matei et al. teaches the one or more processors further configured to:
perform a plurality of simulations of capacitance between the electrodes and one or more of the micro-objects (section III “The COMSOL simulations”); and
define a function comprised in the model and describing the capacitance between the micro-object and each of the electrodes as a function of a distance between the micro-object and that electrode (section III “
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”).
As per Claim 4 and 14, Matei et al. teaches wherein the model describes the change of the positions in at least one of one dimension and two dimensions (section III “We can map the 1D model to a 2D model by using the transformation”).
As per Claims 5 and 15, Matei fails to teach explicitly wherein the error between the estimated density distribution of the positions in the fluid and the target density distribution of the positions in the fluid is expressed using an L2 norm metric.
Sinigaglia teaches wherein the error between the estimated density distribution of the positions in the fluid and the target density distribution of the positions in the fluid is expressed using an L2 norm metric (section III, pg. 4, Eq. (11); section V, pg. 13, Fig. 5: the optimal control cost integrates the squared difference between the evolved density and the target density qT(x) over the domain, and the reached-to-target difference is reported as an L2 distance, i.e., the error is expressed using an L2 norm metric as claimed).
As per Claim 6 and 16, Matei et al. teaches wherein solving the optimal control problem comprises performing automatic differentiation to compute a plurality of gradients (section IV-B “We use the automatic differentiation feature”).
As per Claim 7 and 17, Matei et al. teaches wherein solving the optimal control problem comprises evaluating at least one expectation using Gauss-Hermite quadrature (section IV-B “Optimization based control design”, “It follows that the expectation of a function of a random variable with a Gaussian distribution, can be accurately approximated using Gauss-Hermite quadrature.”).
As per Claim 10 and 20, Matei et al. teaches wherein each of the electrodes is controlled by a photo-transistor, the one or more processors further configured to control a video projector to project the one or more images to the photo-transistors, wherein the phototransistors control the electrodes to generate the sequence of generations of the electric potentials in the control scheme based on the projected images (section II. Fig. 1 “The video projector is used to address each photo-transistor controlled electrode.”).
5. Claims 8, 9, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Matei ("Micro-scale 2D chiplet position control: a formal approach to policy design") in view of Zheng ("PDE-based Dynamic Density Estimation for Large-scale Agent Systems") and Sinigaglia ("Density control of large-scale particles swarm through PDE-constrained optimization"), and further in view of Matei2 ("Towards printing as an electronics manufacturing method: micro-scale chiplet position control").
Matei as modified by Zheng and Sinigaglia teaches most all the instant invention as applied to claims 1-7, 10-17, and 20 above.
As per Claims 8 and 18, Matei as modified by Zheng and Sinigaglia fails to teach explicitly wherein the one or more processors comprise at least one of one or more graphics processing units (GPUs) and one or more tensor processing units (TPUs).
Matei2 teaches wherein the one or more processors comprise at least one of one or more graphics processing units (GPUs) and one or more tensor processing units (TPUs) (section IV “All algorithms may be implemented in parallel on the GPU. Specifically, the template matching can be run very quickly and we provide run times for images of varying sizes in Table I. The template matching was implemented using an NVIDIA GeForce GTX 750 Ti with ArrayFire [16] libraries wrapped over CUDA”)
Matei, Zheng, Sinigaglia, and Matei2 are analogous art because they are all related to controlled manipulation of micro-scale objects and density-based treatment of large object collectives.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Matei2 into Matei as modified by Zheng and Sinigaglia’s invention for the purpose of micro-scale 2D chiplet position control to provide a density filter that takes advantage of the system dynamics to gradually improve its estimation and is scalable to the agents’ population (Zheng: Abstract; section VI, pg. 5) and to provide an optimal control strategy for shaping a large-scale swarm of particles, the system being large-scale and underactuated, making the control strategy at the microscopic particle level infeasible (Sinigaglia: Abstract). Further the motivation is to improve scalability with parallel computation capabilities (Matei2 et al.: section VII).
As per Claims 9 and 19, Matei as modified by Zheng and Sinigaglia teaches to solve the control optimization problem using at least one first order optimization algorithm (Matei: section IV-B, pg. 5521 "we run Adam gradient based optimization algorithm": the Adam algorithm is a gradient-based, first order optimization algorithm).
However, Matei as modified by Zheng and Sinigaglia fails to teach explicitly wherein two or more of the processors work in parallel.
Matei2 teaches wherein two or more of the processors work in parallel (section IV, section V-A, “All algorithms may be implemented in parallel on the GPU”).
Response to Arguments
6. Applicant's arguments filed on 07/14/2026 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument – in view of Zheng and Sinigaglia.
As per Applicant’s argument that Matei et al., "2D Density Control of Micro-Particles using Kernel Density Estimation," arXiv:2209.03550, is not prior art to the present application under 35 U.S.C. 102(b)(1), the argument is acknowledged; that publication is not relied upon in any rejection set forth in this Office Action.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matei ’747 (US 11,079,747 B2) teaches capacitance-based real-time micro-object position control with the aid of a digital computer.
Matei ’204 (US 10,558,204 B2) teaches scalable real-time micro-object position control with the aid of a digital computer.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188
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