DETAILED ACTION
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 .
Specification
The specification has been checked, but not to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 19 February 2026 and 24 September 2024, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
Claim Objections
Claim 25 is objected to because of the following informalities: “of an sequence” appears as though it should be “of a sequence.” Appropriate correction is required.
Claim 26 depends upon claim 25, and thus includes the aforementioned limitation(s).
Claim 28 is objected to because of the following informalities: “of an sequence” appears as though it should be “of a sequence,” there is an extra period in “and./or LiDAR images” that should be removed, and “and/or or to receive” appears as though it should be “and/or Appropriate correction is required.
Claim 29 depends upon claim 28, and thus includes the aforementioned limitation(s).
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.
Claim(s) 16-30 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical concepts and/or mental processes. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 16-26 recite a method, claims 27-29 recite a device, and claim 30 recites a manufacture. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
providing a first Gaussian process distribution for a first function for modelling a kinematic behavior of an agent independently of other agents – determining a Gaussian process distribution of a function modelling kinematic behavior (a mathematical formula) of an agent is a mathematical calculation.
and a second Gaussian process distribution for a second function for modelling an interaction between agents – determining a Gaussian process distribution of a function modelling interactions (a mathematical formula) is a mathematical calculation.
wherein the first function is configured to map a latent state of one agent to a contribution to a change of its latent state – this is describing a mathematical function/relationship.
wherein the second function is configured to map the latent states of two agents to a contribution to a change of a latent state of one of the two agents – this is describing a mathematical function/relationship.
and changing the latent state of the first agent depending on a first contribution that results from mapping of the latent state of the first agent with the first function to the first contribution, and a second contribution that results from mapping the latent state of the first agent and the latent state of the second agent with the second function to the second contribution – this is describing a mathematical function/relationship between a new latent state and multiple contributing functions/variables. Additionally/alternatively – a data scientist changes the latent state of the agent based upon the contributing factors.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer-implemented method for continuous-time interaction modeling of agents, the method comprising the following steps: – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
providing a latent state of a first agent and a latent state of a second agent, in characterizing a position or a velocity of the first and second agents, respectively – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
sampling the first function from the first Gaussian process distribution and the second function from the second Gaussian process distribution – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A computer-implemented method for continuous-time interaction modeling of agents, the method comprising the following steps: – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
providing a latent state of a first agent and a latent state of a second agent, in characterizing a position or a velocity of the first and second agents, respectively – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
sampling the first function from the first Gaussian process distribution and the second function from the second Gaussian process distribution – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
The claim recites the following additional mathematical concept and/or mental process elements:
(i) changing the latent state of the first agent depending on second contributions that result from mapping pairs of the latent state of the first agent and different second agents of the plurality of agents with the second function – this is describing a mathematical function/relationship between a new latent state and contributing functions/variables. Additionally/alternatively – the data scientist changes the latent state of the agent depending on the contributing factors.
or (ii) selecting a subset of the plurality of agents, and changing the latent state of the first agent depending on second contributions that result from mapping pairs of the latent state of the first agent and different second agents of the subset with the second function – changing the latent state describes a mathematical function/relationship between a new latent state and contributing functions/variables. Additionally/alternatively – the data scientist selects a subset of the plurality of agents for mapping latent states between them.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
providing an initial latent state of a plurality of agents including the first agent and the first agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
providing an initial latent state of a plurality of agents including the first agent and the first agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 18:
The claim recites the following additional mathematical concept and/or mental process elements:
determining agents from the plurality of agents for the subset that are, according to a measure for a distance between agents, closer to the first agent than other agents of the plurality of agents – determining a subset of agents based upon comparing distances is a mathematical calculation. Additionally/alternatively – the data scientist selects a subset of the closest agents.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 19:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
providing a data sequence, and providing an initial latent state of the first agent and/or the second agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
including determining its initial latent state with an encoder that is configured to map the data sequence to its initial latent state – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
providing a data sequence, and providing an initial latent state of the first agent and/or the second agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
including determining its initial latent state with an encoder that is configured to map the data sequence to its initial latent state – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 20:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
determining an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
determining an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 21:
The claim recites the following additional mathematical concept elements:
the first Gaussian process distribution includes a posterior, and wherein the method includes learning a sparse approximation to the posterior for the first Gaussian process distribution that entails variational parameters, and providing the approximation for the first Gaussian process distribution as the first Gaussian process distribution – finding a sparse approximation of the posterior of the Gaussian process distribution using variational parameters is a mathematical calculation/function.
and/or the second Gaussian process distribution includes a posterior, and wherein the method includes learning a sparse approximation to the posterior for the second Gaussian process distribution that entails variational parameters, and providing the approximation for the second Gaussian process distribution as the second Gaussian process distribution – finding a sparse approximation of the posterior of the Gaussian process distribution using variational parameters is a mathematical calculation/function.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 22:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
determining the first Gaussian process distribution or the second Gaussian process distribution with an expected likelihood term that depends on the output and that decomposes between agents and between time points – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
determining the first Gaussian process distribution or the second Gaussian process distribution with an expected likelihood term that depends on the output and that decomposes between agents and between time points – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 23:
The claim recites the following additional mathematical concept and/or mental process elements:
and wherein the method comprises changing the latent state of the first component of the latent state of the first agent depending on the second component of the latent state of the first agent and a change to the second component of the latent state of the first agent – this is describing a mathematical function/relationship between a new latent state and multiple contributing functions/variables. Additionally/alternatively – a data scientist changes the latent state of the agent based upon the contributing factors.
wherein the second component of the latent state of the first agent is changed depending on the first contribution to the change of the latent state of the first agent and the second contribution to the change of the latent state of the first agent – this is describing a mathematical function/relationship between a new latent state and multiple contributing functions/variables. Additionally/alternatively – a data scientist changes the latent state of the agent based upon the contributing factors.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the latent state of the first agent includes a first component and a second component – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the latent state of the first agent includes a first component and a second component – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 24:
The claim recites the following additional mental process elements:
determining an action for the first agent depending on the output – the data scientist looks at the output and determines an action for the agent.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 25:
The claim recites the following additional mental process elements:
determining the latent state of the first agent and the latent state of the second agent depending on a measurement of an sequence of observable states of the agents – this is describing a mathematical relationship between the latent state and the observable states. Additionally/alternatively – the data scientist determines a latent state of the agents based on the observed states.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 26:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
(i) the first agent is an existing object in the physical world, wherein the latent state of the first agent is determined depending on a measurement of a property of the first agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or (ii) the second agent is an existing object in the physical world, wherein the latent state of the second agent is determined depending on a measurement of a property of the second agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the measurement includes position data from a satellite navigation system, and/or digital images, and/or video images, and/or radar images, and/or LiDAR images, and/or ultrasonic images, and/or motion images and/or thermal images, from information about a position or a velocity of the agents – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
(i) the first agent is an existing object in the physical world, wherein the latent state of the first agent is determined depending on a measurement of a property of the first agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or (ii) the second agent is an existing object in the physical world, wherein the latent state of the second agent is determined depending on a measurement of a property of the second agent – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the measurement includes position data from a satellite navigation system, and/or digital images, and/or video images, and/or radar images, and/or LiDAR images, and/or ultrasonic images, and/or motion images and/or thermal images, from information about a position or a velocity of the agents – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 27:
See the rejection of claim 16 above wherein, under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A device for continuous-time interaction modeling of agents, comprising:
at least one processor; and
at least one memory;
wherein the device is configured to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A device for continuous-time interaction modeling of agents, comprising:
at least one processor; and
at least one memory;
wherein the device is configured to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 28:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the device further comprises:
an interface that is adapted to observe the continuous-time interaction of the agents including capturing a measurement of an sequence of observable states, including digital images and/or video images and/or radar images and/or LiDAR images and/or ultrasonic images and/or motion images and/or thermal images, and/or or to receive information about the continuous-time interaction of the agents – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the device further comprises:
an interface that is adapted to observe the continuous-time interaction of the agents including capturing a measurement of an sequence of observable states, including digital images and/or video images and/or radar images and/or LiDAR images and/or ultrasonic images and/or motion images and/or thermal images, and/or or to receive information about the continuous-time interaction of the agents – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 29:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the device is configured to determine an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and wherein the interface is adapted to control an action of at least one of the agents depending on the output – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the device is configured to determine an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and wherein the interface is adapted to control an action of at least one of the agents depending on the output – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 30:
See the rejection of claim 16 above wherein, under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A non-transitory computer-readable medium on which is stored a computer program including computer readable instructions for continuous-time interaction modeling of agents, the instructions, when executed by a computer, causing the computer to perform [the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A non-transitory computer-readable medium on which is stored a computer program including computer readable instructions for continuous-time interaction modeling of agents, the instructions, when executed by a computer, causing the computer to perform [the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claim(s) 16, 17, 19, 20, 23-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scibior et al. (Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation, April 2021, pgs. 1-10 – cited in an IDS) in view of Li (US 2021/0287531).
As per claim 16, Scibior teaches a computer-implemented method for continuous-time interaction modeling of agents [a system for multi-agent trajectory modeling and prediction (abstract, etc.) using continuous time modeling (pg. 7, Appendix A, etc.)], the method comprising the following steps:
providing a latent state of a first agent and a latent state of a second agent, in characterizing a position or a velocity of the first and second agents, respectively [for N agents, where at time t each agent is described by a 4-dimensional state vector which includes the agents’ position, orientation, and speed in a fixed global frame of reference (pg. 1, section II.A; etc.); which is the latent state(s) of first and second agents characterizing each of their positions and velocities];
providing a first Gaussian process distribution for a first function for modelling a kinematic behavior of an agent independently of other agents and a second function for modelling an interaction between agents [a kinematic bicycle model is provided for each agent (pg. 2, fig. 2 and section III; etc.) and the conditional distribution of actions given the latent variables and observations is deterministic, the distribution of the latent variables is unit Gaussian, and the distribution of states is Gaussian with a mean dictated by the kinematic model and a fixed diagonal variance with a hyperparameter of the model (pg. 2, section III; etc.) where each agent chooses actions at each time step independently from each other using their own information and information about other agents and the environment from ego-centered birdview images (pg. 2, section III; pg. 3, fig. 3; etc.) which includes interactions between the agents (pg. 6, first paragraph; etc.); where the kinematic model is the function modelling kinematic behavior of the agent independently of other agents, the Gaussian distribution is the distribution of that model of the agent, and the interactions captured from the birdview images are the second function];
sampling the first function from the first Gaussian process distribution and the second function, wherein the first function is configured to map a latent state of one agent to a contribution to a change of its latent state, wherein the second function is configured to map the latent states of two agents to a contribution to a change of a latent state of one of the two agents [a kinematic bicycle model is provided for each agent (pg. 2, fig. 2 and section III; etc.) and the conditional distribution of actions given the latent variables and observations is deterministic, the distribution of the latent variables is unit Gaussian, and the distribution of states is Gaussian with a mean dictated by the kinematic model and a fixed diagonal variance with a hyperparameter of the model (pg. 2, section III; etc.) where each agent chooses actions at each time step independently from each other using their own information and information about other agents and the environment from ego-centered birdview images (pgs. 2-3, section III; pg. 3, fig. 3; etc.) which includes interactions between the agents (pg. 6, first paragraph; etc.); where the sampling from the Gaussian distribution of states of the agent for choosing actions at each time step is the sampling the first function from the first Gaussian process distribution and sampling the interactions from the birdview images is the sampling the second function configured to map latent states of the two agents to contributions to changes of the agent]; and
changing the latent state of the first agent depending on a first contribution that results from mapping of the latent state of the first agent with the first function to the first contribution, and a second contribution that results from mapping the latent state of the first agent and the latent state of the second agent with the second function to the second contribution [a kinematic bicycle model is provided for each agent (pg. 2, fig. 2 and section III; etc.) and the conditional distribution of actions given the latent variables and observations is deterministic, the distribution of the latent variables is unit Gaussian, and the distribution of states is Gaussian with a mean dictated by the kinematic model and a fixed diagonal variance with a hyperparameter of the model (pg. 2, section III; etc.) where each agent chooses actions at each time step independently from each other using their own information and information about other agents and the environment from ego-centered birdview images (pgs. 2-3, section III; pg. 3, fig. 3; etc.) which includes interactions between the agents (pg. 6, first paragraph; etc.) and a trajectory is predicted for the agent based on the past trajectory (pgs. 3-4, sections III.D and IV.A; etc.) and updating the images providing the (latent) states and interactions of the agents (pg. 5, section IV.B; etc.), where the agents actions are translated into new agent states using a kinematic model (pg. 3, fig. 3, etc.)].
While Scibior teaches providing a second function for modelling an interaction between agents (see above), it has not been relied upon for teaching a second Gaussian process distribution for a second function for modelling an interaction between agents and sampling the second function from the second Gaussian process distribution.
Li teaches a computer-implemented method for continuous-time interaction modeling of agents [a system and method for utilizing interactive Gaussian processes for crown navigation (abstract, etc.) using multi-agent multi-modal trajectory prediction (para. 0003, etc.)], the method comprising the following steps:
a second Gaussian process distribution for a second function for modelling an interaction between agents [the encoder extracts interaction patterns from observed trajectories from the sensor data and context info from which the recurrent module generates a Gaussian distribution (abstract; paras. 0003-5, 0047-49, 0054-57; etc.); where the interaction pattern/model is the second function modelling the interactions between agents and the Gaussian distribution produces is the second Gaussian process distribution];
sampling the second function from the second Gaussian process distribution, wherein the second function is configured to map the latent states of two agents to a contribution to a change of a latent state of one of the two agents [samples are taken from the Gaussian distributions generated from the interaction graph/models to produce the distribution of future states (paras. 0003-5, 0054-56, etc.), where the interaction model is the second function configured to map the latent states of two agents to a contribution to a change of a latent state of one of the two agents]; and
changing the latent state of the first agent depending on a first contribution that results from mapping of the latent state of the first agent with the first function to the first contribution, and a second contribution that results from mapping the latent state of the first agent and the latent state of the second agent with the second function to the second contribution [the distribution of time dependent static latent interaction graphs is generated based on an observation graph based on sensor data at a corresponding time step of the series of time steps and the encoding process is repeated every T time steps to obtain the latent interaction graph based on the latest observation graph, where a new interaction graph has dependence on previous ones, to approximate the distribution of future trajectories (paras. 0049-51, etc.), which includes change the state(s) of the agent(s)].
Scibior and Li are analogous art, as they are within the same field of endeavor, namely modelling multi-agent interactions and predicting multi-agent trajectories.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the use of the second function and second Gaussian process distribution modelling the interactions between agents, as taught by Li, alongside the first function and first Gaussian distribution modelling the kinematic behavior of an agent independently of other agents, for the second function including interactions between agents in the system/method taught by Scibior.
Li provides motivation as [since usually only the trajectories of individual entities are available without any knowledge of the underlying interaction patterns and there are usually multiple possible modalities for each agent, it may be challenging to model such dynamics and forecast their future behaviors (para. 0002, etc.), therefore it is advantageous to provide effective understanding of the environment and accurate trajectory prediction of interactive agents for downstream prediction/planning (paras. 0012-13, etc.)].
As per claim 17, Scibior/Li teaches further comprising:
providing an initial latent state of a plurality of agents including the first agent and the first agent [for N agents, where at time t each agent is described by a 4-dimensional state vector which includes the agents’ position, orientation, and speed in a fixed global frame of reference (Scibior: pg. 1, section II.A; etc.)], and:
(i) changing the latent state of the first agent depending on second contributions that result from mapping pairs of the latent state of the first agent and different second agents of the plurality of agents with the second function, or
(ii) selecting a subset of the plurality of agents, and changing the latent state of the first agent depending on second contributions that result from mapping pairs of the latent state of the first agent and different second agents of the subset with the second function [a kinematic bicycle model is provided for each agent (Scibior: pg. 2, fig. 2 and section III; etc.) and the conditional distribution of actions given the latent variables and observations is deterministic, the distribution of the latent variables is unit Gaussian, and the distribution of states is Gaussian with a mean dictated by the kinematic model and a fixed diagonal variance with a hyperparameter of the model (Scibior: pg. 2, section III; etc.) where each agent chooses actions at each time step independently from each other using their own information and information about other agents and the environment from ego-centered birdview images (Scibior: pgs. 2-3, section III; pg. 3, fig. 3; etc.) and the agents actions are translated into new agent states using a kinematic model (Scibior: pg. 3, fig. 3, etc.) and the distribution of time dependent static latent interaction graphs is generated based on an observation graph based on sensor data at a corresponding time step of the series of time steps and the encoding process is repeated every T time steps to obtain the latent interaction graph based on the latest observation graph, where a new interaction graph has dependence on previous ones, to approximate the distribution of future trajectories (Li: paras. 0049-51, etc.); which includes at least mapping pairs of the latent state of the first agent and different second agents of the plurality of agents with the second function].
As per claim 19, Scibior/Li teaches further comprising:
providing a data sequence, and providing an initial latent state of the first agent and/or the second agent including determining its initial latent state with an encoder that is configured to map the data sequence to its initial latent state [for N agents, where at time t each agent is described by a 4-dimensional state vector which includes the agents’ position, orientation, and speed in a fixed global frame of reference (Scibior: pg. 1, section II.A; etc.), where each agent is modelled with a two-layer recurrent neural network using the gated recurrent unit (GRU) with a convolutional neural network (CNN) encoder for processing the birdview image (Scibior: pg. 3, section III.B; etc.) and the encoder extracts interaction patterns from observed trajectories from the sensor data and context info from which the recurrent module generates a Gaussian distribution (Li: abstract; paras. 0003-5, 0047-49, 0054-57; etc.); where the models and agents have an initial state (Li: para. 0059; Scibior: pgs. 3-4, section III.D; etc.)].
As per claim 20, Scibior/Li teaches further comprising:
determining an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time [a trajectory is predicted for the agent based on the past trajectory (Scibior: pgs. 3-4, sections III.D and IV.A; etc.) and updating the images providing the (latent) states and interactions of the agents (Scibior: pg. 5, section IV.B; etc.), where the agents actions are translated into new agent states using a kinematic model (Scibior: pg. 3, fig. 3, etc.); and the encoding process is repeated every T time steps to obtain the latent interaction graph based on the latest observation graph, where a new interaction graph has dependence on previous ones, to approximate the distribution of future trajectories (Li: paras. 0049-51, etc.)].
As per claim 23, Scibior/Li taches wherein the latent state of the first agent includes a first component and a second component, and wherein the method comprises changing the latent state of the first component of the latent state of the first agent depending on the second component of the latent state of the first agent and a change to the second component of the latent state of the first agent, wherein the second component of the latent state of the first agent is changed depending on the first contribution to the change of the latent state of the first agent and the second contribution to the change of the latent state of the first agent [for N agents, where at time t each agent is described by a 4-dimensional state vector which includes the agents’ position, orientation, and speed in a fixed global frame of reference (Scibior: pg. 1, section II.A; etc.) and each agent has its own hidden state and a source of randomness which is a random variable that is used to generate the agents stochastic behavior (Scibior: pg. 2, section III; etc.) where changes to the orientation and speed (second component change contributions) change the positions (first component) in the predicted trajectory].
As per claim 24, Scibior/Li teaches further comprising:
determining an action for the first agent depending on the output [each agent chooses actions at each time step independently from each other using their own information and information about other agents and the environment from ego-centered birdview images (Scibior: pgs. 2-3, section III; pg. 3, fig. 3; etc.); where the decoder outputs weights for the multi-modal distribution of future states based on probabilities of a next action for each modality represented in the sensor data (Li: paras. 0056; claim 7; etc.)].
As per claim 25, Scibior/Li teaches further comprising determining the latent state of the first agent and the latent state of the second agent depending on a measurement of an sequence of observable states of the agents [a trajectory is predicted for the agent based on the past trajectory (Scibior: pgs. 3-4, sections III.D and IV.A; etc.) and updating the images providing the (latent) states and interactions of the agents (Scibior: pg. 5, section IV.B; etc.), where the agents actions are translated into new agent states using a kinematic model (Scibior: pg. 3, fig. 3, etc.); and the encoding process is repeated every T time steps to obtain the latent interaction graph based on the latest observation graph, where a new interaction graph has dependence on previous ones, to approximate the distribution of future trajectories (Li: paras. 0049-51, etc.); where the prior trajectories/interactions are measurements of sequences of observable states of the agents].
As per claim 26, Scibior/Li teaches wherein:
(i) the first agent is an existing object in the physical world, wherein the latent state of the first agent is determined depending on a measurement of a property of the first agent, and/or
(ii) the second agent is an existing object in the physical world, wherein the latent state of the second agent is determined depending on a measurement of a property of the second agent [the agents can be physical objects in a physical environment, such as vehicles on roads (Scibior: figs. 1-2; Li: fig. 2; etc.)];
wherein the measurement includes position data from a satellite navigation system, and/or digital images, and/or video images, and/or radar images, and/or LiDAR images, and/or ultrasonic images, and/or motion images and/or thermal images, from information about a position or a velocity of the agents [the agent data can include sensors on the agents that collect digital images (Li: paras. 0036-38; etc.) or video or infrared (Li: paras. 0045, 0073, etc.)].
As per claim 27, see the rejection of claim 16 above, wherein Scibior/Li also teaches a device for continuous-time interaction modeling of agents, comprising:
at least one processor; and
at least one memory;
wherein the device is configured to: [perform the method] [a computing device including a processor executing instructions from a computer-readable medium/memory (Li: paras. 0026-28, 0064; figs. 1, 5; etc.)].
As per claim 28, Scibior/Li teaches wherein the device further comprises:
an interface that is adapted to observe the continuous-time interaction of the agents including capturing a measurement of an sequence of observable states, including digital images and/or video images and/or radar images and/or LiDAR images and/or ultrasonic images and/or motion images and/or thermal images, and/or or to receive information about the continuous-time interaction of the agents [the agent data can include sensors on the agents that collect digital images (Li: paras. 0036-38; etc.) or video or infrared (Li: paras. 0045, 0073, etc.) communicated via a communication interface (Li: para. 0021, etc.)].
As per claim 29, Scibior/Li teaches wherein the device is configured to determine an output depending on the latent state of the first agent, including a trajectory of a position and/or velocity of the first agent over time [a trajectory is predicted for the agent based on the past trajectory (Scibior: pgs. 3-4, sections III.D and IV.A; etc.) and updating the images providing the (latent) states and interactions of the agents (Scibior: pg. 5, section IV.B; etc.), where the agents actions are translated into new agent states using a kinematic model (Scibior: pg. 3, fig. 3, etc.); and the encoding process is repeated every T time steps to obtain the latent interaction graph based on the latest observation graph, where a new interaction graph has dependence on previous ones, to approximate the distribution of future trajectories (Li: paras. 0049-51, etc.)], and wherein the interface is adapted to control an action of at least one of the agents depending on the output [the trajectory predictions of interactive agents can be used for downstream prediction/planning (Li: paras. 0012-13, etc.) such as path planning for the agent(s) (Li: para. 0040, etc.)].
As per claim 30, see the rejection of claim 16 above, wherein Scibior/Li also teaches a non-transitory computer-readable medium on which is stored a computer program including computer readable instructions for continuous-time interaction modeling of agents, the instructions, when executed by a computer, causing the computer to perform [the method] [a computing device including a processor executing instructions from a computer-readable medium/memory (Li: paras. 0026-28, 0064; figs. 1, 5; etc.)].
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scibior and Li as applied to claim 17 above, and further in view of Sarsilmaz (US 11,129,236).
As per claim 18, Scibior/Li teaches the method of claim 17, as described above.
While Scibior/Li teaches tracking interactions between agents within a specific area (see above), it has not been relied upon for teaching further comprising: determining agents from the plurality of agents for the subset that are, according to a measure for a distance between agents, closer to the first agent than other agents of the plurality of agents.
Sarsilmaz teaches a method comprising:
determining agents from the plurality of agents for the subset that are, according to a measure for a distance between agents, closer to the first agent than other agents of the plurality of agents [a subset of agents in a multiagent system can be tasked with a specific local objective (abstract, etc.) where the subset are close neighbors of an agent (col. 4, lines 6-17; claim 1; etc.)].
Scibior/Li and Sarsilmaz are analogous art, as they are within the same field of endeavor, namely modeling/optimizing multiagent systems.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to create subsets of neighboring agents, as taught by Sarsilmaz, in the modelling of agent interactions in the system/method taught by Scibior/Li.
Sarsilmaz provides motivation as [With the system-theoretic advancements in distributed control of multiagent systems, groups of agents are able to utilize local interactions for achieving a broad class of prescribed global objectives. On the other hand, several agents forming the multiagent system are still expected to perform their own local objectives while they simultaneously achieve a given global objective. As such, it is beneficial to have distributed control methods with analytic foundations that offer an effective remedy to this problem. The present disclosure presents several distributed control solutions with comparable advantages, where all these control solutions allow a subset of agents to perform their local objectives without deteriorating the overall multiagent system's global objective (col. 2, lines 29-43; etc.)].
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scibior and Li as applied to claim 20 above, and further in view of Bosse (US 2023/0251951).
As per claim 21, Scibior/Li teaches wherein:
the first Gaussian process distribution includes a posterior, and providing the approximation for the first Gaussian process distribution as the first Gaussian process distribution, and/or
the second Gaussian process distribution includes a posterior, and providing the approximation for the second Gaussian process distribution as the second Gaussian process distribution [the encoder 120 may infer the latent interaction graph from the observation graph, which is a multi-class edge classification task. For example, a softmax function with a continuous approximation of the discrete distribution on the last updated edge attributes may be utilized to obtain the probability of each edge type (Li: paras. 0048-49; etc.) and the recurrent decoding process is applied to the interaction graph and observation graph to approximate the distribution of future trajectories (Li: paras. 0050-52; etc.) where the desired Gaussian distribution is first sampled from the categorical distribution. Suppose the kth one is determined, which means the most likely posterior trajectory in this situation (Li: paras. 0054-55, etc.); which is the posterior of the second Gaussian process distribution and learning the approximation to provide the approximation for the second Gaussian process distribution].
While Scibior/Li teaches determining posterior trajectories from the sampled Gaussian distribution (see above), it has not been relied upon for teaching wherein the method includes learning and wherein the method includes learning a sparse approximation to the posterior for the first Gaussian process distribution that entails variational parameters and/or a sparse approximation to the posterior for the second Gaussian process distribution that entails variational parameters.
Bosse teaches wherein the method includes learning and wherein the method includes learning a sparse approximation to the posterior for the first Gaussian process distribution that entails variational parameters and/or a sparse approximation to the posterior for the second Gaussian process distribution that entails variational parameters [approximate inference may be used for a sparse approximation of the underlying posterior distribution of the Gaussian process, with learned parameters (paras. 0022-23, etc.); for the first and/or second Gaussian distribution of Scibior/Li, above].
Scibior/Li and Bosse are analogous art, as they are within the same field of endeavor, namely modeling agents within an environment.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use a learned sparse approximation of an underlying Gaussian distribution posterior, as taught by Bosse, to provide an approximation of the Gaussian distribution in the system/method taught by Scibior/Li.
Bosse provides motivation as [Effectively, a smoother may take information captured at a sequence of time steps and solve a Bayesian optimization problem to provide the most likely configuration of states over that sequence of time steps (in other words, the joint distribution of states having the highest likelihood). In an offline setting, parameters associated with the smoother (such as entries of the covariance matrices associated with the measurement noise and the state transition noise) may be optimized or learned from the data, either for individual tracks or globally for all tracks in a given time frame, resulting in smoothed estimates of the states which may be Bayes optimal with respect to the chosen filtering model (para. 0022, etc.)].
As per claim 22, Scibior/Li teaches further comprising:
determining the first Gaussian process distribution or the second Gaussian process distribution with an expected likelihood term that depends on the output and that decomposes between agents and between time points [approximate inference may be used, for example using sampling techniques or sparse approximations of the underlying Gaussian processes. In either case, hyperparameters of the Gaussian processes may be optimized using maximum a posteriori (MAP) estimation, maximum likelihood estimation, evidence maximization, or sampling (Bosse: paras. 0022-25, etc.) and a fixed variance is set and use a slightly modified loss function definition instead of a traditional negative log-likelihood function (Li: para. 0056, etc.) and train them entirely by maximum likelihood (Scibior: pg. 1, section I; etc.)].
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-15 are canceled; claims 16-30 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Breiner (US 2006/0167971) – discloses modelling agent behaviors including utilizing states of multiple agents to produce positions relative to a Gaussian distribution of other agents
Malla (US 2021/0129871) – discloses a system/method for forecasting trajectories of multiple agents within an environment.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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