DETAILED CORRESPONDENCE
This non-final office action is in response to the Amendments filed on 17 December 2025, regarding application number 16/417,540.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 17 December 2025 has been entered.
Response to Amendment
Claims 1-21 and 23-26 remain pending in the application. Claim 22 is cancelled. Claims 1, 4-5, 7, 11, 14 and 19-20 were amended in the Amendments to the claims.
Applicant's amendments to the claims have overcome the 35 U.S.C. 112(a) rejections previously set forth in the final office action mailed 17 September, 2025. Therefore, the rejections have been withdrawn.
Response to Arguments
Applicant’s arguments, see Pages , filed 17 December 2025, with respect to the rejections of claims 1-21 and 23-26 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant has made the following argument on Page 10 with respect to independent claim 1:
“The above recitations bear certain similarities to the claims at issue in Ex Parte Desjardins, Appeal No. 2024-000567, Dec. on Req. for Rehearing (Sep. 26, 2025) (precedential) (hereinafter "Desjardins"). In Desjardins, the Appeals Review Panel vacated the P.T.A.B.'s new ground of rejection under 35 U.S.C. § 101 of a claim directed to "computing ..., an approximation of a posterior distribution over possible values of the plurality of parameters" on the basis of the Step 2A, Prong Two, relying in part on the reasoning set forth in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 118 USPQ2d 1684 (Fed. Cir. 2016):"Enfish recognized that '[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes."' Desjardins, at 8. As further stated by M.P.E.P. § 2106.04(d)(1), "[o]ne way to demonstrate ... integration [of a judicial exception into a practical application] is when the claimed invention improves the functioning of a computer or improves another technology or technical field." Likewise, even if amended claim 1 was shown to recite a judicial exception, claim 1 as reproduced above is directed to an improvement in the functioning of a computer, or other technology or technical field, and does not amount to a judicial exception under Prong Two of Step 2A. Specifically, in amended claim 1, any alleged judicial exception is integrated into the practical application of improving the functioning of a specific computing environment, e.g., a controller that is to cause a robotic device to perform a task, in a specific way, e.g., by causing information, that is to be used by the controller, to be generated. Accordingly, amended claim 1 recites a combination of additional elements that direct the claim as a whole to a specific improvement over prior art systems. This is done in a specific and novel way, as recited by the claim.”
Examiner respectfully disagrees. Claim 1 states that the information is “to be used” by the controller and that the controller is “to cause” the robotic device to perform the task; however, the scope of the claim does not encompass the controller or the robotic device. Instead, the information is only generically linked to the controller and is not necessarily required to be used by the controller. Additionally, there are no claimed particulars of how the robotic device uses the information nor what task is being performed. The robotic task could be additional mathematical calculations, for example. Additional recitation of the controller and robotic device amounts to mere indication of a field of use or technological environment. It does not amount to significantly more than the abstraction exception itself and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h). Employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment such as a robotic device, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. The “cause information … to be generated…” step is recited at a high level of generality (i.e. as a general means of gathering data) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The instant claims do not bear certain similarities to the claims at issue in Ex Parte Desjardins. Desjardins claims a particular way of training a machine learning model, see “A computer-implemented method of training a machine learning model…”. Desjardins, at Pages 8-9, states “Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ¶ 21. The Specification also recites with particulars that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”. However, the instant claims do not claim a machine learning model nor a training process and additionally do not provide any particulars of the simulation besides generally reciting “cause a simulation … to be performed…”. The claim does not positively recite any simulation step. The language of the claim indicates “casue a simulation … by selecting one or more simulation parameters...”. This means that by selecting parameters one will cause simulation. The claim does not contain a simulating step.
Examiner recommends to amend the claim to require explicit direct control of the robotic device, to demonstrate explicit improvement to the functioning of a computer, and/or to integrate an explicit robot task being performed, and to provide support from the disclosure.
Applicant has made the following argument on Pages 10-11 with respect to claim 1:
“Analogous considerations are applicable under the eligibility analysis of Step 2B ... This combination of elements, in view of claim 1 as a whole, results in subject matter that is both novel and results in a benefit, e.g., improving autonomous control of a robotic device, that arises because of the specific combination of elements recited by the claim. As such, claim 1 as a whole amounts to more than any alleged judicial exception.”
Examiner respectfully disagrees at least for the same reasons discussed above with respect to Step 2A. As for the additional elements in the claims in which the judicial exception is generally linked to a particular technological environment, the same analysis applies here as above. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot integrate a judicial exception into a practical application or provide an inventive concept. See 2106.05(h).
Applicant has made the following argument on Pages 11-12 with respect to independent claims 7 and 14:
“Applicant respectfully submits that claims 7 and 14 are allowable under 35 U.S.C. § 101 at least for reasons including some of those discussed above in connection with claim 1…”
Examiner respectfully disagrees for at least for the same reasons discussed above with respect to claim 1.
Applicant has made the following argument on Page 12 with respect to dependent Claims 2-6, 8-13, 15-21, and 23-26 :
“Dependent claims 2-6, 8-13, 15-21, and 23-26 each depend from one of claims 1, 7, and 14 described above. Accordingly, Applicant respectfully submits that claims 2-6, 8-13, 15-21, and 23-26 are allowable under 35 U.S.C. § 101 at least for depending from an independent claim allowable under 35 U.S.C. § 101...”
Examiner respectfully disagrees for at least for the same reasons discussed above with respect to claim 1 because the independent claims are not allowable under 35 U.S.C. § 101.
For at least the reasons stated above, the claim rejections under 35 U.S.C. 101 have been maintained. See full details below.
Applicant’s arguments, see Pages 14-18, with respect to the rejections of claims 1-21 and 23-26 under 35 U.S.C. 103, 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. See full details below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 7 and 14 - The claim language is written in passive voice and intended use language. This renders the scope of the claim combination unclear and therefore not distinct/indefinite. It is not certain that the step of “cause a simulation” is actually a simulating step. The claim recites that “by selecting …parameters based ... on one or more Fourier-transformed representation” causes a simulation. It is not clear how selecting input parameters would cause simulation. The claimed scope is indefinite. For purpose of compact prosecution, the claim is interpreted as having no simulating step.
It not known from the claim language which simulation results the claims are referring since the claims contain no simulating step.
The term “Fourier-transformed representations” renders the term unclear and indistinct. The claim scope of the term cannot be ascertained because it is not known exactly what other mathematical transformations would represent a Fourier-transform. The claims are also not distinct whether a Fourier transform step is being performed in the claims.
It is not clear and indistinct whether a multimodal probability distribution is created from the claim language. The claims only require information based at least in part on multimodal probability distribution be generated. Hence, it is not clear whether the generated information is the multimodal probability distribution.
Claims dependent upon rejected independent claims are also rejected via 112(b).
Claims 2, 8 and 15 - It is not known which additional simulations the claims are referring. The language of claims 1, 7 and 14 contain only “a simulation”. The claims are indefinite.
Claims 5, 23 and 24 - The claim language appears to be contradictory in functionality. It is not clear from the support of the specification as to how the multimodal probability distribution improves accuracy of the simulator/simulation when claims 1, 7 and 14 indicate that the results of the simulation are used to generate the multimodal distribution. The claims are indefinite.
Since the claims are repleted with 112(b) issues as identified above, it is strongly recommended that all claims be carefully reviewed by Applicant for any additional potential 112(b) issues.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 and 23-26 are rejected under 35 U.S.C. 101.
Regarding Claims 1-6, 21 and 25-26
Claims 1-6, 21 and 25-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mathematical concepts without significantly more.
Claim analysis via 2019 PEG
Step 1: Statutory Category – Yes
Regarding claims 1-6, 21 and 25-26, the claims recite “one or more processors”. Thus, the claims fall within one of the four statutory categories because the claims are to a manufacture.
Step 2A Prong One Evaluation: Judicial Exception – Yes – Mathematical Concepts
Claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
Regarding claim 1, the claim recites “cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task; generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation;”. The limitations as drafted, are a process that, under their broadest reasonable interpretation, cover a mathematical calculation. For example, a person can mentally select one more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of a robotic device performing the task for a simulation via the aid of pen and paper. The claim only requires a positive step of “selecting” the simulation parameters which can be used to cause a simulation and does not require a positive step for “simulating” a robotic device performing a task. The person can then mentally generate a multimodal probability distribution based, at least in part, on one or more results of the simulation via the aid of pen and paper. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim limitation recites mental processes and mathematical concept.
Regarding claim 2, the claim recites “wherein the one or more Fourier-transformed representations model a density function based, at least in part, on results of one or more additional simulations.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers metal processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 3, the claim recites “wherein the density function is parameterized as a set of Fourier features”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 4, the claim recites “wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a predicted prior distribution of parameters.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 26, the claim recites “wherein the one or more multimodal probability distributions comprise one or more mixtures of Gaussian distributions.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Accordingly, the claims are directed to an abstract idea.
Step 2A Prong Two Evaluation: Practical Application - No
The claim is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The judicial exceptions are not integrated into a practical application.
Claim 1 recites the additional limitation “cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions”. The claim states that the information is “to be used” by the controller; however, the scope of the claim does not encompass the controller or the robotic device. Instead, the information is only generically linked to the controller and is not necessarily required to be used by the controller. Additionally, there are no claimed particulars of how the robot uses the information nor what task is being performed. The robotic task could be additional mathematical calculations, for example. Additional recitation of the controller and robotic device amounts to mere indication of a field of use or technological environment. It does not amount to significantly more than the abstraction exception itself and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h). Employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment such as a robotic device, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. The information generating step is recited at a high level of generality (i.e. as a general means of gathering data) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Examiner recommends to amend the claim to require direct control of the robotic device, to demonstrate explicit improvement to the functioning of a computer, and/or to integrate an explicit robot task being performed, and to provide support from the disclosure.
Claims 1-6, 21 and 25-26 recite additional elements “one or more processors…”. The processor, does not integrate the abstract idea into a practical application because it is described at high level of generality and is merely a computer being used as a tool to perform/apply the abstract idea. See MPEP 2106.04(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 5-6, 21 and 25 recite additional elements “wherein the one or more multimodal probability distributions, as a result of being applied to a simulator, cause the simulator to approximate the one or more measured results with greater accuracy.”, “wherein the simulator is to perform the simulation”, “wherein applying the one or more multimodal probability distributions to the simulator includes randomizing over the one or more simulation parameters via the simulator based, at least in part, on the one or more multimodal probability distributions. ” and “cause the one or more multimodal probability distributions to be updated to predict a result of the robotic device performing the task.”, respectfully. They additionally do not integrate the abstract idea into a practical application because they generally link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). For example, claims 5-6 generally describe simulating of a task without describing how the robot simulation is executed and without including a step of controlling the robot. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea.
Step 2B Evaluation: Inventive Concept - No
The claim is evaluated whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As for the additional element in the claims in which the processor is merely a tool being used to perform the abstract idea, the same analysis applies here as above. Merely using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application or provide an inventive concept. See MPEP 2106.05(f).
As for the additional elements in the claims in which the judicial exception is generally linked to a particular technological environment, the same analysis applies here as above. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot integrate a judicial exception into a practical application or provide an inventive concept. See 2106.05(h).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the data gathering steps were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification recites that the processor may be a conventional CPU. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claims 1-6, 21 and 25-26 are not patent eligible.
Regarding Claims 7-13 and 23
Claims 7-13 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mathematical concepts without significantly more.
Claim analysis via 2019 PEG
Step 1: Statutory Category – Yes
Regarding claims 7-13 and 23, the claims recite “one or more processors”. Thus, the claims fall within one of the four statutory categories because the claims are to a manufacture.
Step 2A Prong One Evaluation: Judicial Exception – Yes – Mathematical Concepts
Claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
Regarding claim 7, the claim recites “cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task; generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation;”. The limitations as drafted, are a process that, under their broadest reasonable interpretation, cover mental processes and mathematical calculation. For example, a person can mentally select one more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of a robotic device performing the task for a simulation via the aid of pen and paper. The claim only requires a positive step of “selecting” the simulation parameters which can be used to cause a simulation and does not require a positive step for “simulating” a robotic device performing a task. The person can then generate a multimodal probability distribution based, at least in part, on one or more results of the simulation via the aid of pen and paper. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 8, the claim recites “wherein the one or more Fourier-transformed representations model a density function based, at least in part on results of one or more additional simulations.”. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim limitation recites mental processes and mathematical concept. Thus, the claim is patent ineligible.
Regarding claims 9-10, the claims recite “the density function is modeled as a set of Fourier features; and the set of Fourier features is selected using Halton sequences, wherein the density function is modeled as a set of randomly selected Fourier features”. This falls into one of the three identified judicial exceptions per 2106.04(a)(1). Thus, the claim limitation recites mental processes and mathematical concept. Thus, the claims are patent ineligible.
Regarding claim 11, the claim recites “wherein the one or more multimodal probability distributions are to be used to estimate a second distribution of parameter values, and wherein the one or more multimodal probability distributions are to be further generated based, at least in part, on one or more additional simulations performed by a simulator using sets of parameters chosen in accordance with a first distribution of parameter values, the first distribution of parameter values generated prior to the second distribution of parameter values.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 13, the claim recites “wherein the one or more multimodal probability distributions are to model non-Gaussian distribution that indicates a plurality of parameter solutions.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim limitation recites mental processes and mathematical concept. Thus, the claim is patent ineligible.
Accordingly, the claims are directed to an abstract idea.
Step 2A Prong Two Evaluation: Practical Application - No
The claim is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The judicial exceptions are not integrated into a practical application.
Claim 7 recites the additional limitation “cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions”. The claim states that the information is “to be used” by the controller; however, the scope of the claim does not encompass the controller or the robotic device. Instead, the information is only generically linked to the controller and is not necessarily required to be used by the controller. Additionally, there are no claimed particulars of how the robot uses the information nor what task is being performed. The robotic task could be additional mathematical calculations, for example. Additional recitation of the controller and robotic device amounts to mere indication of a field of use or technological environment. It does not amount to significantly more than the abstraction exception itself and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h). Employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment such as a robotic device, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. The information generating step is recited at a high level of generality (i.e. as a general means of gathering data) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Examiner recommends to amend the claim to require direct control of the robotic device, to demonstrate explicit improvement to the functioning of a computer, and/or to integrate an explicit robot task being performed, and to provide support from the disclosure.
Claims 7-13 and 23 recite additional elements “one or more processors…”. The processor, does not integrate the abstract idea into a practical application because it is described at high level of generality and is merely a computer being used as a tool to perform/apply the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 12 and 23 recite additional elements “wherein the simulator is to produce results for individual parameter sets in the sets of parameters” and “wherein the one or more multimodal probability distributions are to be applied to a simulator to improve an accuracy thereof.”, respectfully. They additionally do not integrate the abstract idea into a practical application because they generally link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). For example, claim 12 generally describes applying the simulator to produce results for individual parameter sets in the sets of parameters. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea.
Step 2B Evaluation: Inventive Concept - No
The claim is evaluated whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As for the additional element in the claims in which the processor is merely a tool being used to perform the abstract idea, the same analysis applies here as above. Merely using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application or provide an inventive concept. See MPEP 2106.05(f).
As for the additional elements in the claims in which the judicial exception is generally linked to a particular technological environment, the same analysis applies here as above. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot integrate a judicial exception into a practical application or provide an inventive concept. See 2106.05(h).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the data gathering steps were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification recites that the processor may be a conventional CPU. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claims 7-13 and 23 are not patent eligible.
Regarding Claims 14-20 and 24
Claims 14-20 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mathematical concepts without significantly more.
Claim analysis via 2019 PEG
Step 1: Statutory Category – Yes
Regarding claims 14-20 and 24, the claims recite “a non-transitory computer-readable storage medium”. Thus, the claims fall within one of the four statutory categories because the claims are to a manufacture.
Step 2A Prong One Evaluation: Judicial Exception – Yes – Mathematical Concepts
Claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
Regarding claim 14, the claim recites “cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task; generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation;”. The limitations as drafted, are a process that, under their broadest reasonable interpretation, cover mental processes and mathematical calculation. For example, a person can mentally select one more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of a robotic device performing the task for a simulation via the aid of pen and paper. The claim only requires a positive step of “selecting” the simulation parameters which can be used to cause a simulation and does not require a positive step for “simulating” a robotic device performing a task. The person can then generate a multimodal probability distribution based, at least in part, on one or more results of the simulation via the aid of pen and paper. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 15, the claim recites “wherein the one or more Fourier-transformed representations model a density based at least in part on parameter-result pairs produced by the one or more additional simulations.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical calculation, but for the recitation of a generic computer component “the processor”. That is, other than reciting “the processor”, nothing in the claim element precludes the step from practically being calculated by a human. For example, but for the processor language, the limitation encompass a human performing the calculation as discussed above, wherein the Fourier-transformed representation models a density function based on the simulations. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claims 16-17, the claims recite “wherein the density function is modeled as a set of Fourier features, wherein the set of Fourier features is determined in accordance with a quasi Monte Carlo strategy”. The limitations as drafted, are processes that, under their broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claims are patent ineligible.
Regarding claim 18, the claim recites “use additional simulations selected in accordance with the one or more multimodal probability distributions to produce a refined distribution of the one or more simulation parameters.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental process and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 19, the claim recites “wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a bounded uniform prior.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Regarding claim 20, the claim recites “wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a Gaussian prior.”. The limitation as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes and mathematical calculation. This falls into one of the three identified judicial exceptions per MPEP 2106.04(a)(1). Thus, the claim is patent ineligible.
Accordingly, the claims are directed to an abstract idea.
Step 2A Prong Two Evaluation: Practical Application - No
The claim is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The judicial exceptions are not integrated into a practical application.
Claim 14 recites the additional limitation “cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions”. The claim states that the information is “to be used” by the controller; however, the scope of the claim does not encompass the controller or the robotic device. Instead, the information is only generically linked to the controller and is not necessarily required to be used by the controller. Additionally, there are no claimed particulars of how the robot uses the information nor what task is being performed. The robotic task could be additional mathematical calculations, for example. Additional recitation of the controller and robotic device amounts to mere indication of a field of use or technological environment. It does not amount to significantly more than the abstraction exception itself and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h). Employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment such as a robotic device, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. The information generating step is recited at a high level of generality (i.e. as a general means of gathering data) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Examiner recommends to amend the claim to require direct control of the robotic device, to demonstrate explicit improvement to the functioning of a computer, and/or to integrate an explicit robot task being performed, and to provide support from the disclosure.
Claims 14-20 and 24 recite additional elements “a non-transitory computer-readable storage medium…”. The non-transitory computer-readable storage medium, does not integrate the abstract idea into a practical application because it is described at high level of generality and is merely a computer component being used as a tool to perform/apply the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 24 recites additional element “wherein the one or more multimodal probability distributions are to improve an accuracy of a simulator as a result of being applied to the simulator.”. The additional element does not integrate the abstract idea into a practical application because it generally links the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). For example, claim 24 generally describes applying a multimodal probability distribution to a simulator without describing the simulator or how the multimodal probability distribution is applied. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea.
Step 2B Evaluation: Inventive Concept - No
The claim is evaluated whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As for the additional element in the claims in which the non-transitory computer-readable storage medium is merely a tool being used to perform the abstract idea, the same analysis applies here as above. Merely using a computer component as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application or provide an inventive concept. See MPEP 2106.05(f)
As for the additional elements in the claims in which the judicial exception is generally linked to a particular technological environment, the same analysis applies here as above. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot integrate a judicial exception into a practical application or provide an inventive concept. See 2106.05(h).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the data gathering steps were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification recites that the processor may be a conventional CPU. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claims 14-20 and 24 are not patent eligible.
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.
Claims 1, 4-7, 11-14, 18-21 and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over NPL - McCalman et al. "Multi-modal estimation..." (McCalman hereinafter), in view of Mori et al. (US 20170262572 A1 and Mori hereinafter).
Regarding Claim 1
McCalman teaches one or more processors (see all Figs.; Abstract, all; Section V. A. Multi-modal Tracking Simulation "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc."), comprising circuitry to:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters (see Algorithm 1 on Page 2849; Fig. 1, KBR-GM; Abstract all, especially "We demonstrate our algorithm with two filtering experiments and one regression experiment; a multi-modal tracking simulation, a real tracking problem involving a miniature slot-car with an attached inertial measurement unit, and a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning."; Section IV. all, especially E. Algorithm Overview; Section V. A. Multi-modal Tracking Simulation, all);
generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation (see Fig. 1, KBR-GM; Fig, 2(b), all; Abstract, all, especially "The algorithm has wide application in robotics as no assumptions are made about the underlying distributions, it can represent non-Gaussian multi-modal posteriors, and learn arbitrary non-linear models from noisy data. Our method is a generalisation of the Kernel Bayes' Rule that produces multi-modal posterior estimates represented as Gaussian mixtures."; Section I. Introduction "Overall, the KBR-GM algorithm relaxes many of the restrictions of other estimators: it requires no transition or observation models, being able to learn these directly from noisy training data, the models learned can be nonlinear, it can represent non-Gaussian, multi-modal posteriors, and is scalable to high-dimensional problems, both in the observation space and the state space." Section V. A. Multi-modal Tracking Simulation, all; Section V. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions ... On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data."; Section VI, Conclusion, all); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions (see Fig. 3(a), all; Abstract "We demonstrate our algorithm with two filtering experiments and one regression experiment; ... a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning"; Section V. all, especially Section V. C. Pedestrian Velocity Field, "To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
McCalman is silent regarding selecting the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task.
Mori teaches one or more processors (see all Figs.; [0001], [0013] and [0208]), comprising circuitry to:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task (see [0013], [0016]-[0017], [0065 "Besides, in the present embodiment, a measured object is actually measured to obtain a frequency response function, and inverse Fourier transform is performed on a frequency transfer function obtained according to the frequency response function and the control parameters to compute the impulse response, which is used to execute simulation."] and [0135]-[0136]); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on one or more results of the simulation (see [0014], [0062]-[0063 "That is, the user 5 uses the adjusting software 50 on the setting device 1 to set and adjust the control parameters of the servo driver 2, such that the response state of the servo driver 2 is optimal. In other words, an actual measuring result and simulation result are used to confirm the response state and adjust the control parameters."], [0066] and [0074]-[0075]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the one or more processors of McCalman to include instructions to select the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task, as taught by Mori, in order to reduce error and therefore perform a more accurate simulation.
Regarding Claim 4
Modified McCalman teaches the one or more processors of claim 1 (as discussed above in claim 1),
McCalman further teaches wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a predicted prior distribution of parameters (see Fig. 1, KBR-GM; Section III. B. RKHS and Bayes' Theorem, "The Kernel Bayes' Rule algorithm uses approximate mean mappings of the prior μ[P(X)] and the likelihood μ[P(Y|X)] derived from samples {ui}Mi=1 from X and {(xj,yj)}Nj=1 from (X,Y) to estimate the posterior embedding μ[P(X|Y=y)] for an observation y .... Critically, the posterior embedding can be used as the prior embedding for a subsequent observation."; Section IV. C. Parameter Learning, "To learn these parameters for regression, we perform k fold cross-validation in two stages. First on σx, σy, ϵ and δ whilst holding Σ and λ constant, and then on Σ and λ, holding the other parameters constant. These two procedures are then alternated for a fixed number of iterations, or until convergence." Section V. A. Multi-modal Tracking Simulation, all, especially "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc. Mixture centres corresponded to the locations of these points.").
Regarding Claim 5
Modified McCalman teaches the one or more processors of claim 1 (as discussed above in claim 1),
McCalman further teaches wherein the one or more multimodal probability distributions, as a result of being applied to a simulator, are to cause the simulator to approximate the one or more measured results with greater accuracy (see Fig. 3(a), all; Abstract, all; Section V. all, especially Section V. C. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions. The corridor at the top of the image in Figure 2 a) had people walking in both directions to reach the same destination. As the GP is only able to learn a uni-modal posterior, the result is a weighted average of the two directions, which in this case points straight down. On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data. To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 6
Modified McCalman teaches the one or more processors of claim 5 (as discussed above in claim 5),
McCalman further teaches wherein the simulator is to perform the simulation (see Fig. 3(a), all; Abstract, all; Section V. all, especially Section V. C. Pedestrian Velocity Field, "To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 7
McCalman teaches a system (see all Figs.; Abstract, all; Section V. A. Multi-modal Tracking Simulation "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc."), comprising one or more processors to:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters (see Algorithm 1 on Page 2849; Fig. 1, KBR-GM; Abstract all, especially "We demonstrate our algorithm with two filtering experiments and one regression experiment; a multi-modal tracking simulation, a real tracking problem involving a miniature slot-car with an attached inertial measurement unit, and a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning."; Section IV. all, especially E. Algorithm Overview; Section V. A. Multi-modal Tracking Simulation, all);
generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation (see Fig. 1, KBR-GM; Fig, 2(b), all; Abstract, all, especially "The algorithm has wide application in robotics as no assumptions are made about the underlying distributions, it can represent non-Gaussian multi-modal posteriors, and learn arbitrary non-linear models from noisy data. Our method is a generalisation of the Kernel Bayes' Rule that produces multi-modal posterior estimates represented as Gaussian mixtures."; Section I. Introduction "Overall, the KBR-GM algorithm relaxes many of the restrictions of other estimators: it requires no transition or observation models, being able to learn these directly from noisy training data, the models learned can be nonlinear, it can represent non-Gaussian, multi-modal posteriors, and is scalable to high-dimensional problems, both in the observation space and the state space." Section V. A. Multi-modal Tracking Simulation, all; Section V. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions ... On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data."; Section VI, Conclusion, all); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions (see Fig. 3(a), all; Abstract "We demonstrate our algorithm with two filtering experiments and one regression experiment; ... a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning"; Section V. all, especially Section V. C. Pedestrian Velocity Field, "To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
McCalman is silent regarding selecting the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task.
Mori teaches a system (see all Figs.; [0001], [0013] and [0208]), comprising one or more processors to:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task (see [0013], [0016]-[0017], [0065 "Besides, in the present embodiment, a measured object is actually measured to obtain a frequency response function, and inverse Fourier transform is performed on a frequency transfer function obtained according to the frequency response function and the control parameters to compute the impulse response, which is used to execute simulation."] and [0135]-[0136]); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on one or more results of the simulation (see [0014], [0062]-[0063 "That is, the user 5 uses the adjusting software 50 on the setting device 1 to set and adjust the control parameters of the servo driver 2, such that the response state of the servo driver 2 is optimal. In other words, an actual measuring result and simulation result are used to confirm the response state and adjust the control parameters."], [0066] and [0074]-[0075]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of McCalman to include instructions to select the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task, as taught by Mori, in order to reduce error and therefore perform a more accurate simulation.
Regarding Claim 11
Modified McCalman teaches the system of claim 7 (as discussed above in claim 7),
wherein the one or more multimodal probability distributions are to be used to estimate a second distribution of parameter values (see Fig. 1, KBR-GM; Section IV Posterior Recovery "Recovering an estimate of the posterior distribution after (possible repeated) application of the KBR requires determining the inverse kernel mapping..."; Section IV. C. Parameter Learning, "To learn these parameters for regression, we perform k fold cross-validation in two stages. First on σx, σy, ϵ and δ whilst holding Σ and λ constant, and then on Σ and λ, holding the other parameters constant. These two procedures are then alternated for a fixed number of iterations, or until convergence." Section V. A. Multi-modal Tracking Simulation, all, especially "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc. Mixture centres corresponded to the locations of these points."), and wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed by a simulator using sets of parameters chosen in accordance with a first distribution of parameter values, the first distribution of parameter values generated prior to the second distribution of parameter values (see Fig. 1, KBR-GM; Section III. B. RKHS and Bayes' Theorem, "The Kernel Bayes' Rule algorithm uses approximate mean mappings of the prior μ[P(X)] and the likelihood μ[P(Y|X)] derived from samples {ui}Mi=1 from X and {(xj,yj)}Nj=1 from (X,Y) to estimate the posterior embedding μ[P(X|Y=y)] for an observation y ... Critically, the posterior embedding can be used as the prior embedding for a subsequent observation."; Section IV. C. Parameter Learning, "To learn these parameters for regression, we perform k fold cross-validation in two stages. First on σx, σy, ϵ and δ whilst holding Σ and λ constant, and then on Σ and λ, holding the other parameters constant. These two procedures are then alternated for a fixed number of iterations, or until convergence." Section V. A. Multi-modal Tracking Simulation, all, especially "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc. Mixture centres corresponded to the locations of these points.").
Regarding Claim 12
Modified McCalman teaches the system of claim 11 (as discussed above in claim 11),
McCalman further teaches wherein the simulator is to produce results for individual parameter sets in the sets of parameters (see Fig. 2(b), all; Fig. 3(a), all; Section V. all, especially Section V. C. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions. The corridor at the top of the image in Figure 2 a) had people walking in both directions to reach the same destination. As the GP is only able to learn a uni-modal posterior, the result is a weighted average of the two directions, which in this case points straight down. On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data. To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 13
Modified McCalman teaches the system of claim 7 (as discussed above in claim 7),
McCalman further teaches wherein the one or more multimodal probability distributions are to model a non-Gaussian distribution that indicates a plurality of parameter solutions (see Fig. 2(b), all; Fig. 3(a), all; Abstract, all, especially "The algorithm has wide application in robotics as no assumptions are made about the underlying distributions, it can represent non-Gaussian multi-modal posteriors, and learn arbitrary non-linear models from noisy data. Our method is a generalisation of the Kernel Bayes' Rule that produces multi-modal posterior estimates represented as Gaussian mixtures."; Section V. all, especially Section V. C. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions. The corridor at the top of the image in Figure 2 a) had people walking in both directions to reach the same destination. As the GP is only able to learn a uni-modal posterior, the result is a weighted average of the two directions, which in this case points straight down. On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data. To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 14
McCalman teaches a non-transitory computer-readable storage medium having stored thereon a set of instructions that, as a result of being performed by one or more processors (see all Figs.; Abstract, all; Section V. A. Multi-modal Tracking Simulation "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc."), cause the one or more processors to at least:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters (see Algorithm 1 on Page 2849; Fig. 1, KBR-GM; Abstract all, especially "We demonstrate our algorithm with two filtering experiments and one regression experiment; a multi-modal tracking simulation, a real tracking problem involving a miniature slot-car with an attached inertial measurement unit, and a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning."; Section IV. all, especially E. Algorithm Overview; Section V. A. Multi-modal Tracking Simulation, all);
generate one or more multimodal probability distributions based, at least in part, on one or more results of the simulation (see Fig. 1, KBR-GM; Fig, 2(b), all; Abstract, all, especially "The algorithm has wide application in robotics as no assumptions are made about the underlying distributions, it can represent non-Gaussian multi-modal posteriors, and learn arbitrary non-linear models from noisy data. Our method is a generalisation of the Kernel Bayes' Rule that produces multi-modal posterior estimates represented as Gaussian mixtures."; Section I. Introduction "Overall, the KBR-GM algorithm relaxes many of the restrictions of other estimators: it requires no transition or observation models, being able to learn these directly from noisy training data, the models learned can be nonlinear, it can represent non-Gaussian, multi-modal posteriors, and is scalable to high-dimensional problems, both in the observation space and the state space." Section V. A. Multi-modal Tracking Simulation, all; Section V. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions ... On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data."; Section VI, Conclusion, all); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the one or more multimodal probability distributions (see Fig. 3(a), all; Abstract "We demonstrate our algorithm with two filtering experiments and one regression experiment; ... a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning"; Section V. all, especially Section V. C. Pedestrian Velocity Field, "To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
McCalman is silent regarding selecting the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task.
Mori teaches a non-transitory computer-readable storage medium having stored thereon a set of instructions that, as a result of being performed by one or more processors (see all Figs.; [0001], [0013] and [0208]), cause the one or more processors to at least:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task (see [0013], [0016]-[0017], [0065 "Besides, in the present embodiment, a measured object is actually measured to obtain a frequency response function, and inverse Fourier transform is performed on a frequency transfer function obtained according to the frequency response function and the control parameters to compute the impulse response, which is used to execute simulation."] and [0135]-[0136]); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on one or more results of the simulation (see [0014], [0062]-[0063 "That is, the user 5 uses the adjusting software 50 on the setting device 1 to set and adjust the control parameters of the servo driver 2, such that the response state of the servo driver 2 is optimal. In other words, an actual measuring result and simulation result are used to confirm the response state and adjust the control parameters."], [0066] and [0074]-[0075]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the non-transitory computer-readable storage medium of McCalman to include instructions to select the one or more simulation parameters based, at least in part, on one or more Fourier-transformed representations of one or more measured results of the robotic device performing the task, as taught by Mori, in order to reduce error and therefore perform a more accurate simulation.
Regarding Claim 18
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 14 (as discussed above in claim 14),
McCalman further teaches wherein the instructions, as a result of being performed by the one or more processors, further cause the one or more processors to use additional simulations selected in accordance with the one or more multimodal probability distributions to produce a refined distribution of the one or more simulation parameters (see Fig. 1, KBR-GM; Section III. B. RKHS and Bayes' Theorem, "The Kernel Bayes' Rule algorithm uses approximate mean mappings of the prior μ[P(X)] and the likelihood μ[P(Y|X)] derived from samples {ui}Mi=1 from X and {(xj,yj)}Nj=1 from (X,Y) to estimate the posterior embedding μ[P(X|Y=y)] for an observation y ... Critically, the posterior embedding can be used as the prior embedding for a subsequent observation."; Section IV Posterior Recovery "Recovering an estimate of the posterior distribution after (possible repeated) application of the KBR requires determining the inverse kernel mapping..."; Section IV. C. Parameter Learning, "To learn these parameters for regression, we perform k fold cross-validation in two stages. First on σx, σy, ϵ and δ whilst holding Σ and λ constant, and then on Σ and λ, holding the other parameters constant. These two procedures are then alternated for a fixed number of iterations, or until convergence." Section V. A. Multi-modal Tracking Simulation, all, especially "Our filter was given 600 training points, a number chosen to keep running times fairly short on a desktop pc. Mixture centres corresponded to the locations of these points.").
Regarding Claim 19
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 14 (as discussed above in claim 14),
McCalman further teaches wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a bounded uniform prior (see Section II, Previous Work "The kernel Bayes' rule algorithm (KBR), used as the basis for our approach to regression and filtering [5], provides a converging estimate to full Bayesian inference. It learns non-linear models from training data, has no restrictions on the shape of prior or posterior distributions, and has demonstrated scalability to high dimension." Section III. B. RKHS and Bayes' Theorem, "The Kernel Bayes' Rule algorithm uses approximate mean mappings of the prior μ[P(X)] and the likelihood μ[P(Y|X)] derived from samples {ui}Mi=1 from X and {(xj,yj)}Nj=1 from (X,Y) to estimate the posterior embedding μ[P(X|Y=y)] for an observation y. ").
Regarding Claim 20
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 14 (as discussed above in claim 14),
McCalman further teaches wherein the one or more multimodal probability distributions are to be generated further based, at least in part, on one or more additional simulations performed with a set of parameters chosen in accordance with a Gaussian prior (see Section II, Previous Work "The kernel Bayes' rule algorithm (KBR), used as the basis for our approach to regression and filtering [5], provides a converging estimate to full Bayesian inference. It learns non-linear models from training data, has no restrictions on the shape of prior or posterior distributions, and has demonstrated scalability to high dimension." Section III. B. RKHS and Bayes' Theorem, "The Kernel Bayes' Rule algorithm uses approximate mean mappings of the prior μ[P(X)] and the likelihood μ[P(Y|X)] derived from samples {ui}Mi=1 from X and {(xj,yj)}Nj=1 from (X,Y) to estimate the posterior embedding μ[P(X|Y=y)] for an observation y. "; Section V. C. Pedestrian Velocity Field, "As a result, the prior velocity field pointed straight down everywhere in the test area. For the GP, this involved setting the prior mean to −π/2, and for the KBR-GM, we created an embedded prior distribution from Gaussian samples centred around −π/2.").
Regarding Claim 21
Modified McCalman teaches the one or more processors of claim 5 (as discussed above in claim 5),
McCalman further teaches wherein applying the one or more multimodal probability distributions to the simulator includes randomizing, by the simulator, over the one or more simulation parameters based, at least in part, on the one or more multimodal probability distributions (see Section V. Multi-modal Tracking Simulation, "Noisy observations of the body were taken every five time steps. The equations of motion of the particle were x=(cos(2π/20t),(0.5+1.5η)sin(2π/20t))+Zt, where η is a boolean-valued random variable that was re-drawn at t=0,10,20,… and Zt is zero-mean Gaussian process noise with σp=0.05. Observation noise is similarly distributed with σo=0.02."; Section V. C. Pedestrian Velocity Field, "600 training points were given to both algorithms, which were randomly sampled from all available tracks which ended in the area of interest.").
Regarding Claim 23
Modified McCalman teaches the system of claim 7 (as discussed above in claim 7),
McCalman further teaches wherein the one or more multimodal probability distributions are to be applied to a simulator to improve an accuracy thereof (see Fig. 3(a), all; Abstract, all; Section V. all, especially Section V. C. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions. The corridor at the top of the image in Figure 2 a) had people walking in both directions to reach the same destination. As the GP is only able to learn a uni-modal posterior, the result is a weighted average of the two directions, which in this case points straight down. On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data. To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 24
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 14 (as discussed above in claim 14),
McCalman further teaches wherein the one or more multimodal probability distributions are to improve an accuracy of a simulator as a result of being applied to the simulator (see Fig. 3(a), all; Abstract, all; Section V. all, especially Section V. C. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions. The corridor at the top of the image in Figure 2 a) had people walking in both directions to reach the same destination. As the GP is only able to learn a uni-modal posterior, the result is a weighted average of the two directions, which in this case points straight down. On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data. To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 25
Modified McCalman teaches the one or more processors of claim 1 (as discussed above in claim 1),
McCalman further teaches wherein the circuitry is to cause the one or more multimodal probability distributions to be updated to predict a result of the robotic device performing the task (see Fig. 3(a), all; Abstract "We demonstrate our algorithm with two filtering experiments and one regression experiment; ... a regression problem of estimating the velocity field of a set of pedestrian paths for robot path-planning"; Section V. all, especially Section V. C. Pedestrian Velocity Field, "To illustrate how this result would be useful to a real robot, we performed a simulation of a simple indoor robot using the posterior direction field learned by the algorithms to navigate from a starting position to the goal area. The robot first evaluated the posterior at its location. The robot then moved in the resulting direction for about 0.2 metres, and re-evaluated the posterior. In the case of a multi-modal posterior, the robot used the mode which was within π of its current orientation.").
Regarding Claim 26
Modified McCalman teaches the one or more processors of claim 1 (as discussed above in claim 1),
McCalman further teaches wherein the one or more multimodal probability distributions comprise one or more mixtures of Gaussian distributions (see Fig. 1, KBR-GM; Fig, 2(b), all; Abstract, all, especially "The algorithm has wide application in robotics as no assumptions are made about the underlying distributions, it can represent non-Gaussian multi-modal posteriors, and learn arbitrary non-linear models from noisy data. Our method is a generalisation of the Kernel Bayes' Rule that produces multi-modal posterior estimates represented as Gaussian mixtures."; Section I. Introduction "Overall, the KBR-GM algorithm relaxes many of the restrictions of other estimators: it requires no transition or observation models, being able to learn these directly from noisy training data, the models learned can be nonlinear, it can represent non-Gaussian, multi-modal posteriors, and is scalable to high-dimensional problems, both in the observation space and the state space." Section V. A. Multi-modal Tracking Simulation, all; Section V. Pedestrian Velocity Field, "The KBR-GM algorithm outperforms the GP, primarily because of its ability to represent multi-modal distributions ... On the other hand, the KBR-GM is able to learn a bimodal distribution which points both left and right. See Figure 2 b) for close ups of this behaviour on the testing data."; Section VI, Conclusion, all).
Claims 2-3, 8-10 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over McCalman (as modified by Mori) as applied to claims 1, 7 and 14 above, and further in view of NPL - Milton et al. "Spatial Analysis Made Easy…" (Milton hereinafter).
Regarding Claim 2
Modified McCalman teaches the one or more processors of claim 1 (as discussed above in claim 1),
McCalman is silent regarding wherein the one or more Fourier-transformed representations model a density function based, at least in part, on results of one or more additional simulations.
Milton teaches one or more processors (see Abstract, all; Section "Random Fourier Features", all; Section "Quasi-Monte-Carlo Features (QMC RFF)", all), comprising circuitry to:
cause a simulation to be performed based, at least in part on one or more Fourier-transformed representations (see Equations (29)-(32); Section "Random Fourier Features", all));
generate one or more probability distributions based, at least in part, on one or more results of the simulation (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all); and
cause information to be generated based, at least in part, on the one or more probability distributions (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all);
wherein the one or more Fourier-transformed representations model a density function based, at least in part, on results of one or more additional simulations (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the one or more processors of modified McCalman to include instructions to model a density function with the one or more Fourier-transformed representations, based on results of one or more additional simulations, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 3
Modified McCalman teaches the one or more processors of claim 2 (as discussed above in claim 2),
McCalman is silent regarding wherein the density function is parameterized as a set of Fourier features.
Milton teaches wherein the density function is parameterized as a set of Fourier features (see Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the one or more processors of modified McCalman to include instructions to model a density function parameterized as a set of Fourier features, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 8
Modified McCalman teaches the system of claim 7 (as discussed above in claim 7),
McCalman is silent regarding wherein the one or more Fourier-transformed representations model a density function based, at least in part, on results of one or more additional simulations.
Milton teaches a system (see Abstract, all; Section "Random Fourier Features", all; Section "Quasi-Monte-Carlo Features (QMC RFF)", all), comprising one or more processors to:
cause a simulation to be performed based, at least in part on one or more Fourier-transformed representations (see Equations (29)-(32); Section "Random Fourier Features", all));
generate one or more probability distributions based, at least in part, on one or more results of the simulation (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all); and
cause information to be generated based, at least in part, on the one or more probability distributions (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all);
wherein the one or more Fourier-transformed representations model a density function based, at least in part, on results of one or more additional simulations (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the system of modified McCalman to include instructions to model a density function with the one or more Fourier-transformed representations, based on results of one or more additional simulations, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 9
Modified McCalman teaches the system of claim 8 (as discussed above in claim 8), wherein:
McCalman is silent regarding the density function is modeled as a set of Fourier features; and the set of Fourier features is selected using Halton sequences.
Milton teaches the density function is modeled as a set of Fourier features; and the set of Fourier features is selected using Halton sequences (see Section "Quasi-Monte-Carlo Features (QMC RFF)", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the system of modified McCalman to include instructions to select the set of Fourier features using Halton sequences, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 10
Modified McCalman teaches the system of claim 8 (as discussed above in claim 8),
McCalman is silent regarding wherein the density function is modeled as a set of randomly selected Fourier features.
Milton teaches wherein the density function is modeled as a set of randomly selected Fourier features (see Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the system of modified McCalman to include instructions to model a density function parameterized as a set of randomly selected Fourier features, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 15
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 14 (as discussed above in claim 14),
McCalman is silent regarding wherein the one or more Fourier-transformed representations model a density based, at least in part, on parameter-result pairs produced by one or more additional simulations.
Milton teaches a non-transitory computer-readable storage medium having stored thereon a set of instructions that, as a result of being performed by one or more processors (see Abstract, all; Section "Random Fourier Features", all; Section "Quasi-Monte-Carlo Features (QMC RFF)", all), cause the one or more processors to at least:
cause a simulation to be performed based, at least in part on one or more Fourier-transformed representations (see Equations (29)-(32); Section "Random Fourier Features", all));
generate one or more probability distributions based, at least in part, on one or more results of the simulation (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all); and
cause information to be generated based, at least in part, on the one or more probability distributions (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all);
wherein the one or more Fourier-transformed representations model a density based, at least in part, on parameter-result pairs produced by one or more additional simulations (see Figure 3, all, especially Figure 3(I); Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the non-transitory computer-readable storage medium of modified McCalman to include instructions to model a density function with the one or more Fourier-transformed representations, based on parameter-result pairs produced by one or more additional simulations, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 16
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 15 (as discussed above in claim 15),
McCalman is silent regarding wherein the density is modeled as a set of Fourier features.
Milton teaches wherein the density is modeled as a set of Fourier features (see Section "Random Fourier Features", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the non-transitory computer-readable storage medium of modified McCalman to include instructions to model a density function parameterized as a set of Fourier features, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Regarding Claim 17
Modified McCalman teaches the non-transitory computer-readable storage medium of claim 16 (as discussed above in claim 16),
McCalman is silent regarding wherein the set of Fourier features is determined in accordance with a quasi Monte Carlo strategy.
Milton teaches wherein the set of Fourier features is determined in accordance with a quasi Monte Carlo strategy (see Section "Quasi-Monte-Carlo Features (QMC RFF)", all).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the non-transitory computer-readable storage medium of modified McCalman to include instructions to determine the set of Fourier features in accordance with a quasi Monte Carlo strategy, as taught by Milton, in order to provide substantial improvements in the accuracy of the approximation of the kernel matrix for the same computational complexity.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Bischoff et al. (US 20210122038 A1 and Bischoff hereinafter).
Bischoff teaches at least one or more processors (see all Figs; [0006]-[0014]), comprising circuitry to:
cause a simulation, of a robotic device performing a task, to be performed by selecting one or more simulation parameters based, at least in part, on one or more measured results of the robotic device performing the task (see [0009]-[0011], [0018] and [0058]-[0066], especially [0011 "simulating various combinations of action steps of the technical system by means of the physical simulation model…"], [0061 "For example, it is conceivable that a simulation of the rigid-body mechanics is used in order to simulate the gripping of a fixed object by a robot."] and [0065 "For example, an autonomous robot may turn a movable robot arm about various axes and thereby reach a target position on various paths. The various combinations of action steps are simulated by means of the physical simulation, control parameters being correspondingly varied for controlling the various action steps."]); and
cause information, that is to be used by a controller that is to cause the robotic device to perform the task, to be generated based, at least in part, on the simulation (see [0006]-[0014], [0026]-[0027], [0058], [0070] and [0078], especially [0011 "...control parameters of the technical system for carrying out the action steps being varied…"], [0014 "outputting the control parameters of the optimized combination of action steps for controlling the technical system."] and [0026 "In a further advantageous embodiment of the method, the control parameters for an optimized combination of action steps may be transferred to the technical system."]-[0027 "The control parameters may be transferred to the technical system for controlling the technical system, so that the technical system can carry out the combination of action steps. Only control parameters that are assigned a favorable evaluation may be transferred to the technical system for controlling a combination of action steps."]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TANNER LUKE CULLEN whose telephone number is (303)297-4384. The examiner can normally be reached on Monday-Friday 7:30-4:30 MT.
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/TANNER L CULLEN/Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656