DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 2/9/2022 and the Remarks and Amendments filed on 10/9/2025. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/187,730 filed on 5/12/2021.
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 18-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subjected matter because the claimed invention is directed towards signals per se.
With respect to independent claim 18, the originally filed specification fails to disavow any transitory signals as part of the claimed one or more computer storage media. See originally filed specification at [0061]. Under a broadest reasonable interpretation of the claim language, the “[o]ne or more computer storage media” of claim 18 and its dependents may encompass transitory signals, and are thus directed towards signals per se. Examiner suggests amending claim 18 and its dependents to recite "One or more non-transitory computer storage media". Dependent claims 19-20 depend on rejected claim 18 and are also rejected under 35 USC § 101 by virtue of this dependency. Appropriate correction is required.
Claims 1-20 are further rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding Claim 1:
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”
The limitations “obtaining, for each….for the discrete point” and “identifying the optimal point…. the given point”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more computers”, nothing in the claim element precludes the step from practically being performed in the mind. A user could easily, with the use of pen and paper, obtain a utility score for each of the discrete points in continuous domain and identify an optimal point on the basis of predicted utility scores. This is considered Mental Process under Abstract Ideas. See MPEP 2106.04(a)(2)(III).
Step 2A (2): “Does the claim recite additional elements that integrate the judicial exception into a practical application?”
The judicial exceptions recited are not integrated into a practical application. In general, claim 1 only recites the additional elements of “receiving a request……. for a plurality of agents”, “training for each of the plurality… for the agent at the input point”, “in response to identifying the optimal point, generating and transmitting a control signal to a target system to configure the target system according to the optimal point”. These are mere data gathering, executing, transmitting, and training recited at a high level of generality and thus are insignificant extra-solution activity. See MPEP 2106.05(g). ”). The additional elements “one or more computers” and “training for each of the plurality… for the agent at the input point” are generically recited computing elements and training processes using generic models, and thus amount to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting, and training, and as such, these limitations do not impose any meaningful limits on the claim.
Step 2B: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
The claim limitations reciting the abstract idea do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving a request……. for a plurality of agents”, “training for each of the plurality… for the agent at the input point”, “in response to identifying the optimal point, generating and transmitting a control signal to a target system to configure the target system according to the optimal point” are mere data gathering, executing, transmitting, and training recited at a high level of generality and thus are insignificant extra-solution activity. See MPEP 2106.05(g). These additional elements are also well understood, routine, conventional additional elements. See MPEP 2106.05(d), (“Receiving or transmitting data over a network”). The additional elements “one or more computers” and “training for each of the plurality… for the agent at the input point” are generically recited computing elements and training processes, and thus amount to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea and insignificant extra-solution activity, which do not provide an inventive concept. See MPEP 2106.05.
Therefore, the claim limitations do not include elements that mount to significantly more.
Claim 1 is not patent eligible.
Regarding Claim 2:
Claim 2 recites
The method of claim 1, wherein:
the shared outcome function includes a sum of predicted utility-value functions of the respective plurality of agents, each utility-value function being defined by, for any given point in the continuous domain, the predicted utility score generated by the respective neural network of the respective agent.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 2, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. The shared outcome function which is a sum of the predicted utility score is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 2 is directed to an abstract idea, it does not add significantly more.
Claim 2 is not patent eligible.
Regarding Claim 3:
Claim 3 recites
The method of claim 1, wherein identifying the optimal point includes: locating one or more local maxima of the shared outcome function.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 3 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 3 is directed to an abstract idea, it does not add significantly more.
Claim 3 is not patent eligible.
Regarding Claim 4:
Claim 4 recites
The method of claim 3, wherein locating the one or more local maxima of the shared outcome function includes:
selecting an initial point in the continuous domain; and
performing gradient ascent on the approximation of the shared outcome function to locate the local maxima of the shared outcome function.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 3.
The additional limitations of dependent claim 4, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. Performing the gradient ascent method on the shared outcome function (See specification Para [0038]) is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 4 is directed to an abstract idea, it does not add significantly more.
Claim 4 is not patent eligible.
Regarding Claim 5:
Claim 5 recites
The method of claim 3, wherein:
the one or more local maxima of the shared outcome function includes a plurality of local maxima; and
the method further includes:
identifying a global maximum from the plurality of local maxima; and
identifying a location of the global maximum as the optimal point.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 3.
The additional limitations of dependent claim 5 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 5 is directed to an abstract idea, it does not add significantly more.
Claim 5 is not patent eligible.
Regarding Claim 6:
Claim 6 recites
The method of claim 1, further comprising:
calculating, using the respective neural networks for each of the agents, an agent- specific cost for each agent.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 6, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. Calculating agent-specific cost for each agent is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 6 is directed to an abstract idea, it does not add significantly more.
Claim 6 is not patent eligible.
Regarding Claim 7:
Claim 7 recites
The method of claim 6, wherein calculating the agent-specific cost for the agent includes:
Identifying, using the trained neural networks, an agent-specific reject location for the agent in the continuous domain that maximizes an agent-rejection outcome function with respect to locations in the continuous domain;
calculating a first sum of utility values of all other agents in the plurality of agents at the agent-specific rejection location;
calculating a second sum of utility values at the optimal location of all other agents in the plurality of agents; and
calculating the agent-specific cost for the agent according to the first sum of utility values and the second sum of utility values.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 6.
The additional limitations of dependent claim 7, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. Calculating sum of utility values is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. The additional element of using a neural network to identify a location is a mere instruction to apply the abstract idea of identifying a location using a generic model or computer. See MPEP §2106.05(f). Because claim 7 is directed to an abstract idea, it does not add significantly more.
Claim 7 is not patent eligible.
Regarding Claim 8:
Claim 8 recites
The method of claim 7, wherein the agent-rejection outcome function for the agent includes a sum of utility-value functions of all other agents in the plurality of agents, the utility-value function of an agent being defined by, for any given point in the continuous domain, the predicted utility score generated by the respective neural network of the agent.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 7.
The additional limitations of dependent claim 8, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. The utility-value function calculates the sum of predicted utility scores which is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 8 is directed to an abstract idea, it does not add significantly more.
Claim 8 is not patent eligible.
Regarding Claim 9:
Claim 9 recites
The method of claim 8, wherein identifying the agent-specific rejection location for the agent includes:
locating one or more local maxima of the agent-rejection outcome function for the agent.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
The additional limitations of dependent claim 9 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 9 is directed to an abstract idea, it does not add significantly more.
Claim 9 is not patent eligible.
Regarding Claim 10:
Claim 10 recites
The method of claim 9, wherein locating the one or more local maxima of the agent-rejection outcome function includes:
calculating, using the trained neural network, gradients of the agent- rejection outcome function; and
locating the local maxima of the agent-rejection outcome function using a gradient ascent algorithm.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 9.
The additional limitations of dependent claim 10, as drafted, is a process that, under broadest reasonable interpretation, recites Mathematical concepts which is also an abstract idea. Locating the local maxima with the gradient ascent method is a mathematical calculation. See MPEP 2106.04(2)(2)(I)(C). They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. The additional element of using a neural network to calculate gradients is a mere instruction to apply the abstract idea of identifying a location using a generic model or computer. See MPEP §2106.05(f). Because claim 10 is directed to an abstract idea, it does not add significantly more.
Claim 10 is not patent eligible.
Regarding Claim 11:
Claim 11 recites
The method of claim 1, wherein: the discrete points from the continuous domain include a plurality of randomly selected locations in the continuous domain.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 11 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 11 is directed to an abstract idea, it does not add significantly more.
Claim 11 is not patent eligible.
Regarding Claim 12:
Claim 12 recites
The method of claim 1, wherein:
the continuous domain is a two-dimensional (2D) domain.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 12 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 12 is directed to an abstract idea, it does not add significantly more.
Claim 12 is not patent eligible.
Regarding Claim 13:
Claim 13 recites
The method of claim 1, wherein:
the continuous domain is an N-dimensional domain with N ≥ 3.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
The additional limitations of dependent claim 13 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05.
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 13 is directed to an abstract idea, it does not add significantly more.
Claim 13 is not patent eligible.
Regarding Claims 14-17:
Claims 14-17 recite a system (step 1: a machine) using computer and storage device to perform the steps of claims 1-4, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-4, respectively.
Regarding Claims 18-20:
Claims 18-20 recite one or more computer storage media (step 1: a manufacture, if it was directed towards a statutory category) using a computer and instructions to perform the steps of claims 1-3, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-3, respectively.
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.
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.
Claims 1, 2, 14, 15, 18, and 19 are rejected under 35 USC § 103 as being obvious over Johnson et al. (US 20100262298 A1, hereinafter “Johnson”) in view of Liu et al. (Liu et al., “A neural network evaluation model for individual thermal comfort”, Dec. 12, 2006, Energy and Buildings 39 (2007), pp. 1115–1122, hereinafter “Liu”).
Regarding claim 1, Johnson discloses [a] method performed by one or more computers, the method comprising: ([0007]; “A system and method for calibrating a set-point for climate control includes a sensor network having a plurality of sensors configured to report a climate condition”; and [0019])
receiving a request to identify an optimal point in a continuous domain that maximizes a shared outcome function for a plurality of agents; ([0007]; “A controller is configured to receive information from the profiles to generate a set-point based upon an optimization program. The optimization program balances competing goals to generate the set-point for controlling climate control equipment in accordance with the set-point”, which discloses receiving a request to identify an optimal point or set point in a continuous domain that optimizes or maximizes an outcome functions for a plurality of agents or profiles associated with individuals; and [0032]; “2) Maximize the minimum set-point value imposes a “fairness” objective, and reduces set-point differences among different thermostats. This technique ensures all local temperature within a thermal comfort zone”, which discloses maximizing a shared outcome function for the agents or profiles associated with individuals; and [0052]; “In one embodiment, all occupants have their own sensor 404 to provide feedback to the thermostats 402”)
obtaining, for each of the plurality of agents, respective agent-specific training data that comprises a respective agent-specific utility score for each of a plurality of discrete points in the continuous domain, wherein the agent-specific utility score represents a preference of the agent for the discrete point; ([0007]; “A database is configured to receive reports from the sensors and generate one or more profiles reflecting at least one of historic climate control information and occupant preferences”, which discloses that each occupant or agent’s sensor data is segregated per occupant (agent-specific) and represents that occupant’s preference; and [0024]; “In one embodiment, the user data entered or selected on the sensors 116 is stored in a user feedback database 112. The database 112 stores historic user data such as preferred climate conditions (e.g., temperature, humidity, etc.), time of day and work schedules (time in, time out, lunch, etc.), etc. The database 112 is preferably employed to learn and store individual comfort profiles in block 110. … The profiles 110 may store differential information, e.g., which show where the individual comfort levels deviate from the comfort standards 114”, which discloses each occupant’s sensor data is segregated per-occupant (agent-specific) and represents that occupant’s preference (comfort level expressed as a deviation from the comfort standards) at a given climate condition (discrete point); and [0052]; “In one embodiment, all occupants have their own sensor 404 to provide feedback to the thermostats 402”)
for each of the plurality of agents ([0052]; “In one embodiment, all occupants have their own sensor 404 to provide feedback to the thermostats 402”, which discloses a multi-occupant or multi-agent based analysis)
identifying the optimal point by optimizing an approximation of the shared outcome function that is defined by, for any given point in the continuous domain, a combination of the predicted utility scores [[generated by the respective neural networks]] for each of the plurality of agents by processing an input comprising the given point; and ([0037]; “2) Minimize total suffering imposes a “social” objective: the total degree of suffering across all occupants is minimized. This can be stated mathematically as: minimize …where y is the measured parameter at instant or index j (up to m), subject to Hx+b=y where T−[y[T+ and x−[x[x+”, which discloses, under a broadest reasonable interpretation of the claim language, identifying an optimal point by optimizing an approximation of a function that is a combination of predicted utility scores, and this minimizing total suffering formula sums a per-occupant deviation term evaluated at a common candidate set point. The function further optimizes or minimizes the sum to select the set-point, which is a combination for each of the plurality of agents for any given point that is optimized as claimed)
in response to identifying the optimal point, generating and transmitting a control signal to a target system to configure the target system according to the optimal point ([0068]; “In block 608, climate conditions are adjusted at the given location in accordance with the set-point. The climate conditions may be affected in many ways. For example, vents may be opened/closed, units turned on/off, ventilation increased/decreased, etc. The profiles (block 604), set-points (block 606) and climate condition adjustments (block 608) are constantly or periodically updated with changing conditions”).
Johnson fails to explicitly disclose but Liu discloses training, [[for each of the plurality of agents and]] on the respective agent-specific training data for the agent, a respective neural network that is configured to receive an input comprising an input point in the continuous domain and to generate as output a predicted utility score that represents a preference of the agent at the input point … predicted utility scores generated by the respective neural networks (Abstract; “An evaluation model for individual thermal comfort is presented based on the BP neural network. The train data came from a thermal comfort survey. The evaluation results of the model showed a good match with the subject’s real thermal sensation, which indicated that the model can be used to evaluate individual thermal comfort rightly”, which discloses evaluating an individual’s thermal comfort/preference from environmental input conditions or a neural network trained on one agent’s own preference data, configured to receive an environmental (continuous-domain) input and output a predicted preference/comfort score for that agent; and §2.1; “There was one output parameter in the NNEM, indicating the thermal comfort level. This parameter had three different values: 0, 0.5, and 1, where 0 stands for cool, 1 for warm, and 0.5 for comfort”, which discloses the output parameter or predicted utility score generated by the neural network or NNEM; and Figures 1 and 2; the figures disclose the training process that uses agent-specific training data to train the neural network).
Johnson and Liu are analogous art because both are concerned with evaluation models for optimizing thermal comfort for occupants. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in model optimization to combine the neural network training of Liu with the thermal comfort models and multi-occupant optimization of Johnson to yield to the predictable result of training, for each of the plurality of agents and on the respective agent-specific training data for the agent, a respective neural network that is configured to receive an input comprising an input point in the continuous domain and to generate as output a predicted utility score that represents a preference of the agent at the input point … predicted utility scores generated by the respective neural networks. The motivation for doing so would be to evaluate individual thermal comfort using neural network (Liu; Abstract).
Regarding claim 14, it is a system claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claim 18, it is a computer storage media claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claims 2, 15, and 19, the rejection of claims 1, 14, and 18 are incorporated and Johnson further discloses the shared outcome function includes a sum of predicted utility-value functions of the respective plurality of agents, each utility-value function being defined by, for any given point in the continuous domain, the predicted utility score generated by the respective neural network of the respective agent ([0037]; “2) Minimize total suffering imposes a “social” objective: the total degree of suffering across all occupants is minimized. This can be stated mathematically as: minimize …where y is the measured parameter at instant or index j (up to m), subject to Hx+b=y where T−[y[T+ and x−[x[x+”, which discloses, under a broadest reasonable interpretation of the claim language, identifying an optimal point by optimizing an approximation of a function that is a combination of predicted utility scores, and this minimizing total suffering formula sums a per-occupant deviation term evaluated at a common candidate set point. The function further optimizes or minimizes the sum to select the set-point, which is a combination for each of the plurality of agents for any given point that is optimized as claimed).
Claims 3, 4, 9, 10, 16, 17 and 20 are rejected under 35 U.S.C. 103 as being obvious over Johnson in view of Liu and further in view of Hehn et al. (US 20240127576 A1, hereinafter “Hehn”).
Regarding claims 3, 16 and 20, the rejection of claims 1, 14, and 18 are incorporated and Johnson fails to explicitly disclose but Hehn discloses wherein identifying the optimal location includes: locating one or more local maxima of the shared outcome function. ([0112]; “the feature extraction module 104 identifies local maxima in pixel intensity in the filtered image. The position in the image of the local maxima is identified by the feature extraction module 104 as being the position of a feature within the image.”).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the shared outcome functionality by including the process of locating local maxima using gradient ascent method based on the teachings of Hehn. The motivation for doing so would have been to find provide for good optimization and decision-making qualities. (Hehn, [0114]).
Regarding claims 4 and 17, the rejection of claims 1, 3, 14, and 16 are incorporated and Johnson fails to explicitly disclose but Hehn discloses wherein locating the one or more local maxima of the shared outcome function includes: selecting an initial point in the continuous domain; and performing gradient ascent on the approximation of the shared outcome function to locate the local maxima of the shared outcome function ([0112-0114]; “the feature extraction module 104 identifies local maxima in pixel intensity in the filtered image. The position in the image of the local maxima is identified by the feature extraction module 104 as being the position of a feature within the image… local maxima in pixel intensity may for example be identified by a search using gradient ascent: Starting at every point in the image, move along the gradient until the gradient has zero magnitude, this point is either a local minimum or a local maximum”).
The motivation to combine Johnson, Liu, and Hehn is the same as discussed above with respect to claim 3.
Claim 5 is rejected under 35 U.S.C. 103 as being obvious over Johnson in view of Liu and Hehn and further in view of Toledano et al. (US 20180136994 A1, hereinafter “Toledano”).
Regarding claim 5, the rejection of claims 1 and 3 are incorporated and Johnson fails to explicitly disclose but Toledano discloses wherein: the one or more local maxima of the shared outcome function includes a plurality of local maxima; and the method further includes: identifying a global maximum from the plurality of local maxima; and identifying a location of the global maximum as the optimal point ([0078]; Local maxima extraction modules 324-1, 324-2 and 324-3 identify local maxima which is equivalent to plurality of local maxima. See Fig. 6. Para [0085] Line 1-6 Local maxima extraction module 324-1 identifies the local maxima. Para [0087] Line 8-11 the global maximum of each segment is a local maxima of the ACF 400 which is equivalent to identifying the global maxima and identifying the global maximum as the optimal location).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the process of identifying the optimal location by finding the global maximum from the plurality of local maxima based on teachings of Toledano. The motivation for doing so would have been to find the best optimal point with the global maximum method which can help to make better decisions by considering different perspectives and in more structured way. (Toledano, Para [0087]).
Claims 6-8 and 11-13 are rejected under 35 U.S.C. 103 as being obvious over Johnson in view of Liu and further in view of Munoz et al. (US 20210110323 A1, hereinafter “Munoz”).
Regarding claim 6, the rejection of claim 1 is incorporated and Johnson fails to explicitly disclose but Munoz discloses calculating, using the respective neural networks for each of the agents, an agent-specific cost for each agent. ([0097]; calculating the gradient of the cost function in the neural network is similar to calculating agent specific cost for each agent).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the shared outcome functionality by including the process of calculating the agent-specific cost of Munoz. The motivation for doing so would have been to train artificial neural networks that are used with an optimization method such as a stochastic gradient descent (SGD) method (Munoz; [0096]).
Regarding claim 7, the rejection of claims 1 and 6 are incorporated and Johnson fails to explicitly disclose but Munoz discloses identifying, using the trained neural networks, an agent-specific reject location for the agent in the continuous domain that maximizes an agent-rejection outcome function with respect to locations in the continuous domain; calculating a first sum of utility values of all other agents in the plurality of agents at the agent-specific rejection location; calculating a second sum of utility values at the optimal point of all other agents in the plurality of agents; and calculating the agent-specific cost for the agent according to the first sum of utility values and the second sum of utility values ([0096-0098]).
The motivation to combine Johnson, Liu, and Munoz is the same as discussed above with respect to claim 3.
Regarding claim 8, the rejection of claims 1, 6, and 7 are incorporated and Johnson fails to explicitly disclose but Liu discloses the predicted utility score generated by the respective neural network of the agent (Abstract; and Figures 1 and 2).
The motivation to combine Johnson and Liu is the same as discussed above with respect to claim 1.
Johnson fails to explicitly disclose but Munoz discloses wherein the agent-rejection outcome function for the agent includes a sum of utility-value functions of all other agents in the plurality of agents, the utility-value function of an agent ([0097]).
The motivation to combine Johnson, Liu, and Munoz is the same as discussed above with respect to claim 3.
Regarding claim 11, the rejection of claim 1 is incorporated and Johnson fails to explicitly disclose but Munoz discloses the discrete points from the continuous domain include a plurality of randomly selected locations in the continuous domain (Figure 8; and [0122])
The motivation to combine Johnson, Liu, and Munoz is the same as discussed above with respect to claim 3.
Regarding claim 12, the rejection of claim 1 is incorporated and Johnson fails to explicitly disclose but Munoz discloses wherein: the continuous domain is a two-dimensional (2D) domain (Figure 8; and [0122])
The motivation to combine Johnson, Liu, and Munoz is the same as discussed above with respect to claim 3.
Regarding claim 13, the rejection of claim 1 is incorporated and Johnson fails to explicitly disclose but Munoz discloses wherein: the continuous domain is an N-dimensional domain with N >3 (Figure 8; and [0122])
The motivation to combine Johnson, Liu, and Munoz is the same as discussed above with respect to claim 3.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being obvious over Johnson in view of Liu and Munoz and further in view of Hehn.
Regarding claim 9, the rejection of claims 1, 6, 7, and 8 are incorporated and Johnson fails to explicitly disclose but Hehn discloses wherein identifying the agent-specific rejection location for the agent includes: locating one or more local maxima of the agent-rejection outcome function for the agent ([0112]; the feature extraction module 104 identifying the local maxima).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the shared outcome functionality by including the process of locating local maxima using gradient ascent method based on teachings of Hehn. The motivation for doing so would have been to find the best optimal point with gradient ascent method’s good optimization and decision-making qualities. (Hehn, Para [0114]).
Regarding claim 10, the rejection of claims 1, 6, 7, 8, and 9 are incorporated and Johnson fails to explicitly disclose but Hehn discloses wherein locating the one or more local maxima of the agent-rejection outcome function includes: calculating, using the trained neural networks, gradients of the agent-rejection outcome function; and locating the local maxima of the agent-rejection outcome function using a gradient ascent algorithm ([0114]; Starting at every point in the image, moving along the gradient until the gradient has zero magnitude giving a local maximum is equivalent to calculating the gradients of the agent-outcome function. Line 1-2 the local maxima in pixel intensity may be identified by gradient ascent which is similar to using gradient ascent algorithm to locate the local maxima).
The motivation to combine Johnson, Liu, Munoz, and Hehn is the same as discussed above with respect to claim 9.
Response to Arguments
Applicant’s arguments and amendments, filed on 10/9/2025, with respect to the objection to the drawings and specification have been fully considered and are persuasive. The objection to the drawings and specification are withdrawn.
Applicant’s arguments and amendments, filed on 10/9/2025, with respect to the 35 USC § 112(b) rejection of claim 17 have been fully considered and are persuasive. The 35 USC § 112(b) rejection of claim 17 is withdrawn.
Applicant’s arguments and amendments, filed on 10/9/2025, with respect to the 35 USC § 101 rejection of the pending claims have been fully considered and are not persuasive.
Beginning on page 10 of the Remarks, Applicant argues that the claims as amended overcome the 35 USC § 101 rejection of the claims. Examiner respectfully disagrees. Applicant has failed to provide any arguments or evidence as to why the amended claims overcome the 35 USC § 101 rejection of the pending claims. As discussed in the rejection above, the amended claims are still directed towards abstract ideas without more. Further, claims 18-20 are directed towards non-statutory subject matter. Accordingly, Applicant’s arguments and amendments are not persuasive, and the 35 USC § 101 rejection of the pending claims is maintained.
Applicant’s arguments and amendments, filed on 10/9/2025, with respect to the 35 USC § 103 rejection of the pending claims have been fully considered but are moot because the arguments do not apply to the references used to reject the amended independent claims. Johnson and Liu are now used to render the independent claims obvious under 35 USC § 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kim et al., “Personal comfort models– A new paradigm in thermal comfort for occupant centric environmental control”, Jan. 12, 2018, Building and Environment, Volume 132, 15 March 2018, pp. 114-124.
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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