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. The amendment filed on 03/02/2026 has been received and considered. Claims 1-20 are presented for examination.
Information Disclosure Statement
2. The listing of references in the specification (NPL listed in the paragraph [0013] of PGPUG such as “by Roger Brockett, “Notes on the Control of the Liouville Equation,” pages 101-129, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.”, “ Bartsch et al., “A theoretical investigation of Brockett's ensemble optimal control problems,” ”, In Efstathios Bakolas, “Dynamic output feedback control of the Liouville equation for discrete-time siso linear systems,”” and so on). is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
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 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.
3. Claims 1-7, 10-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matei et al. (“Micro-scale 2D chiplet position control: a formal approach to policy design”), and further in view of Coufal (“Kernel density estimates in particle filter”).
As per Claim 1 and 11, Matei et al. teaches a system/method for micro-object density distribution control with the aid of a digital computer (section II, Figure 1, “The system has three hardware devices and four software modules.”, “design actuation algorithms and control strategies for manipulating chiplets”), 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 system for positioning a plurality of micro-objects (section II., Fig. 1 “an imaging module and a high speed camera for tracking the chiplet locations”), the system comprising a plurality of electrodes, the electrodes configured to induce a movement of the 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, Fig. 1 “The projected images activate or deactivate electrodes…The system uses an array of electrodes to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces.”);
define using the parameters a model describing a change of positions of the micro-objects due to capacitance-based interactions of the micro-objects with the electrodes (section III “Dynamical model”, “describe a 2D model for the chiplet motion under the effect of the potential field induced by the electrode array.”, “. We compute the potential energy by using a capacitive-based electrical circuit that lumps the interaction between the electrodes and the chiplet”, “Fig. 2. Capacitive-based model”);
estimate a density distribution of the micro-objects… and at least one sensor (section II, Fig. 1 “an imaging module and a high speed camera for tracking the chiplet locations”, section IV-A “The discrete probability distribution can be seen as a discretization of a continuous probability distribution,
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where yi is the position of the ith electrode and δ is half of the electrode pitch. The parameterization of the capacitance function in terms of the error functions tells us that the conditional probability density function (pdf) is a mixture of Gaussian functions.”);
obtain a target density distribution of the micro-objects (section IV-A “the chiplet potential can be expressed as v ̅(x) = E[V(Y)|X = x], where Y is a continuous random variable with a Gaussian distribution, i.e., Y ∼ N(X,σ), and where V(y) is a function that reflects the potential at each point y.”);
solve an optimal control problem to derive based on the model, the target density distribution, and the estimated density distribution a sequence of the electric potentials for moving at least some of the micro-objects to minimize an error between the estimated density distribution and the target density distribution (section IV-B “Optimization based control design”, “To solve (3) we run Adam gradient based optimization algorithm [6], for 1500 iteration with a step size α = 0.001. We solved the optimization problem for x = 0, and a sequence of the number of sample points”, Equation (3)); and
actuate at least some of the electrodes to generate the sequence of the electric potentials, wherein at least some of the micro-objects are moved upon the generation of the electric potentials (Fig. 5).
Matei et al. fails to teach explicitly using kernel density estimation.
Coufal teaches using kernel density estimation (Abstraction “kernel density estimates of filtering densities in the particle filter.”; Section 3 “Kernel methods are widely used for nonparametric estimation of densities of probability distributions”; section 4 “Particle filter and kernel methods”).
Matei et al. and Coufal are analogous art because they are both related to a method for simulating particular with density estimation.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Coufal into Matei et al.’s invention for purpose of designing control policy for micro-objects to efficiently compute integral characteristics (moments) of distributions of interest (Coufal: Introduction).
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 Claim 5 and 15, Matei et al. fails to teach explicitly wherein the error between the estimated density distribution and the target density distribution is expressed using an L2 norm metric.
Coufal teaches wherein the error between the estimated density distribution and the target density distribution is expressed using an L2 norm metric (section 4.1 - section 4.2).
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 control the video projector to project the one or more images to the photo-transistors, wherein the phototransistors control the electrodes to generate the sequence 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.”).
4. Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Matei et al. (“Micro-scale 2D chiplet position control: a formal approach to policy design”), in view of Coufal (“Kernel density estimates in particle filter”), and further in view of Matei2 et al. (“Towards printing as an electronics manufacturing method: micro-scale chiplet position control”).
Matei et al. as modified by Coufal teaches most all the instant invention as applied to claims 1-7, 10-17, and 20 above.
As per Claim 8 and 18, Matei et al. as modified by Coufal fails to teach explicitly wherein the one or more processors comprise at least one of one or more processing units (GPUs) and one or more tensor processing units (TPUs).
Matei2 et al. teaches wherein the one or more processors comprise at least one of one or more 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 et al., Coufal, and Matei2 et al. are analogous art because they are all related to a method for simulating particular with density estimation.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Matei2 et al into Matei et al. as modified by Coufal’s invention for purpose of designing control policy for micro-objects to efficiently compute integral characteristics (moments) of distributions of interest (Coufal: Introduction) and to improve scalability with parallel computation capabilities (Matei2 et al.: section VII).
As per Claim 9 and 19, Matei et al. as modified by Coufal fails to teach explicitly wherein two or more of the processors work in parallel to solve the control optimization problem using at least one first order optimization algorithm.
Matei2 et al. teaches wherein two or more of the processors work in parallel to solve the control optimization problem using at least one first order optimization algorithm (section IV, section V-A, “All algorithms may be implemented in parallel on the GPU.)
Response to Arguments
5. Applicant's arguments filed on 03/02/2026 have been fully considered but they are not persuasive.
Examiner respectfully withdraws Claim Objections in view of the amendment and/or applicant’s arguments.
As per 103 rejection, applicants have argued that:
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Examiner acknowledges that “Matei teaches a system that compares desired positions of individual chiplets and their current positions and generates input signals for the electrodes that minimize the error between the current and desired positions”. However, Examiner relies on the teaching in section (IV-A) of Matei to teach the limitation of “a density distribution of the micro-objects”. In particular, see section (IV-A) where it states: “The discrete probability distribution can be seen as a discretization of a continuous probability distribution,
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where yi is the position of the ith electrode and δ is half of the electrode pitch. The parameterization of the capacitance function in terms of the error functions tells us that the conditional probability density function (pdf) is a mixture of Gaussian functions.”
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Specifically, applicants have not addressed Examiner’s finding that Matei section IV-A explicitly derives a probability distribution function associated with the micro-object; applicants fail to explain why a probability density function associated with a micro-object of Matei does not constitutes the claimed limitation. Examiner maintains that this continuous probability distribution” constitutes the claimed limitation “a density distribution of the micro-objects” under broadest reasonable interpretation because it is a probability density function that is defined by and associated with the micro-object. Furthermore, it is noted that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims.
Applicants have argued that:
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Examiner relies on the teaching in Matei to teach the limitation of estimate a density distribution of the micro-objects… and at least one sensor " while Coufal is relied upon for a teaching of "using kernel density estimation". Also applicant's argument is more specific than the claim language. The claim recites “wherein the error between the estimated density distribution and the target density distribution is expressed using an L2 norm metric”. The claim does not limit the technique of kernel density estimation to any particular application domain. As rejected above, Coufal teaches the kernel density estimation methodology (section 3.1, Equation (1)) which is the same domain-independent mathematical technique. It would have obvious to one having ordinary skill in the art would have recognized that the KDE methodology taught by Coufal could be applied to the density estimation performed in Matei’s continuous model (section IV-A) because both perform the same mathematical operation such as smoothing discrete data with a kernel function to produce a continuous density estimate.
Applicants have argued that:
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In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). It is noted that Examiner relies on Coufal section 4.1 - section 4.2 (pages 18-22) as rejected above. Specifically, Theorem 6 (equation 28) and Theorem 7 (equation 35) which prove convergence of the kernel density estimates to the true filtering densities in the mean integrated squared error (MISE). The MISE is defined as the expected value of the integrated squared difference between the estimated density and the true density, i. e. the L2 norm of the error between the estimated density distribution and target density distribution. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicants have argued that:
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In response to applicant's argument that Matei and Coufal is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, both references are related to a method involving density estimation using kernel techniques. Further “the particular problem with which the inventor was concerned” here is estimating a continuous density function from discrete observations which is the precise problem both Matei (estimating continuous potential from discrete electrode data) and Coufal (estimating continuous filtering density from discrete particle samples) address. Further, KDE is a well-known, domain-agnostic statistical technique, and a person of ordinary skill in Matei's field (control engineering with mathematical modeling) would routinely encounter KDE as a standard tool.
Thus 103 rejection maintains. Examiner finds that the argument is not persuasive and does not place the application in condition for allowance for the reason set forth the above.
As per applicant’s argument regarding “Matei et al. (“2D Density Control of Micro-Particles using Kernel Density Estimation”)”, it is not used in the rejection.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zheng T, Han Q, Lin H. PDE-based dynamic density estimation for large-scale agent systems. IEEE Control Systems Letters. 2020 Jun 23;5(2):541-6. (Year: 2020)
Sinigaglia C, Manzoni A, Braghin F. Density control of large-scale particles swarm through PDE-constrained optimization. IEEE Transactions on Robotics. 2022 Jun 2;38(6):3530-49. (Year: 2022)
7. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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, 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
Prosecution Insights