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 .
Status of Claims
This action is responsive to Applicant’s claims filed 06/06/2024.
Claims 1-20 are currently pending and have been examined here.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101. The claims are drawn to ineligible patent subject matter, because the claims are directed to a recited judicial exception to patentability (an abstract idea), without claiming something significantly more than the judicial exception itself.
Claims are ineligible for patent protection if they are drawn to subject matter which is not within one of the four statutory categories, or, if the subject matter claimed does fall into one of the four statutory categories, the claims are ineligible if they recite a judicial exception, are directed to that judicial exception, and do not recite additional elements which amount to significantly more than the judicial exception itself. Alice Corp. v. CLS Bank Int'l, 375 U.S. ___ (2014). Accordingly, claims are first analyzed to determine whether they fall into one of the four statutory categories of patent eligible subject matter. Then, if the claims fall within one of the four statutory categories, it must be determined whether the claims are directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea). In determining whether a claim is directed to a judicial exception, the claim is first analyzed to determine whether the claim recites a judicial exception. If the claim does not recite one of these exceptions, the claim is directed to patent eligible subject matter under 35 U.S.C. 101. If the claim recites one of these exceptions, the claim is then analyzed to determine whether the claim recites additional elements that integrate the exception into a practical application of that exception. Claims which integrate the exception into a practical application of that exception are directed to patent eligible subject matter under 35 U.S.C. 101. If the claim fails to integrate the exception into a practical application of that exception, the claim is directed to an abstract idea. Finally, if the claims are directed to a judicial exception to patentability, the claims are then analyzed determine whether the claims are directed to patent eligible subject matter by reciting meaningful limitations which transform the judicial exception into something significantly more than the judicial exception itself. If they do not, the claims are not directed towards eligible subject matter under 35 U.S.C. § 101.
Regarding independent claims 1, 12, and 19 the claims are directed to one of the four statutory categories (a process, a machine, and an article of manufacture, respectively.) The claimed invention of independent claims 1, 12, and 19 is directed to a judicial exception to patentability, an abstract idea. The claims include limitations which recite elements which can be properly characterized under at least one of the following groupings of subject matter recognized as abstract ideas by MPEP 2106.04(a):
Mathematical Concepts: mathematical relationships, mathematical formulas or equations, and mathematical calculations;
Certain methods of organizing human activity: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
Claims 1, 12, and 19, as a whole, recite the following limitations:
using a resource intensive algorithm to answer a first question of a question type; (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could use a resource intensive algorithm to answer a first question of a first type)
generating at least one training example from the normal operation of the resource intensive algorithm; (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate a training example from normal operation of an algorithm)
. . . produce answers to questions of the question type; (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could produce answers to questions of a question type)
. . . produce an answer to a second question of the question type. (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could produce answers to questions of a type)
The above elements, as a whole, recite mental processes since, but for the requirement to implement the above steps on a set of generic computer components, the entirety of the above set of steps could be performed by a human using their mind, pen and paper, and simple observation, evaluation, and judgment.
Moving forward, the above recited abstract idea is not integrated into a practical application.
The added limitations do not represent an integration of the abstract idea into a practical application because:
the claims represent mere instructions to implement an abstract idea on a computer, and merely use a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
the claims merely add insignificant extra-solution activity to the judicial exception (activity which can be characterized as incidental to the primary purpose or product that is merely a nominal or tangential addition to the claim). See MPEP 2106.05(g) and/or
the claims represent mere general linking of the use of the judicial exception to a particular technological environment or field of use. See MPEP 2016.05(h)
Beyond those limitations which recite the abstract idea, the following limitations are added:
training a lightweight machine learning model based on the at least one training example to (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation represents the mere requirement to “apply” the abstract idea by training and using a lightweight machine learning model since the model is recited at a high level of generality, merely serves in its ordinary capacity to perform the existing function of answering a question, and since the limitation recites the outcome or solution of training and using the model without indicating how the solution or outcome of using the model is brought forth)
using the lightweight machine learning model to (claims 1, 12, and 19; the broadest reasonable interpretation of this limitation represents the mere requirement to “apply” the abstract idea by training and using a lightweight machine learning model since the model is recited at a high level of generality, merely serves in its ordinary capacity to perform the existing function of answering a question, and since the limitation recites the outcome or solution of training and using the model without indicating how the solution or outcome of using the model is brought forth)
A controller that utilizes a simulation in controlling a real world system, the controller comprising a memory and a processer, wherein the processor is in communication with the memory configured to: (claim 12; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
A non-transitory machine-readable medium encoded with instructions for execution by a processor, the non-transitory machine-readable medium comprising: (claim 19; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
The claims, as a whole, are directed to the abstract idea(s) which they recite. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims, as a whole, are directed to the judicial exception.
Turning to the final prong of the test (Step 2B), independent claims 1, 12, and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because there are no meaningful limitations which transform the exception into a patent eligible application.
As outlined above, the claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
Furthermore, no specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Besides performing the abstract idea itself, the generic computer components only serve to perform the court-recognized well-understood computer functions of receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory. See MPEP 2106.05(d). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation. The specification details any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation and because the Alice decision noted that generic structures that merely apply the abstract ideas are not significantly more than the abstract ideas. Therefore, independent claims 1, 12, and 19 are rejected under 35 U.S.C. §101 as being directed to ineligible subject matter.
Claims 2-11, 13-18, and 20, recite the same abstract idea as their respective independent claims.
The following additional features are added in the dependent claims:
Claims 2 and 14:
wherein the resource intensive algorithm answers the first question by tuning at least one or more input of a simulator until an output of the simulator sufficiently meets a criteria of the question.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could tune a simulator in this fashion.
Claims 3 and 15:
wherein the at least one training example includes: output of the simulation as input, and input to the simulation as output.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate training examples comprising inputs and outputs.
Claims 4 and 16:
wherein the resource intensive algorithm is a multi-agent optimizer.
The broadest reasonable interpretation of this limitation represents the mere requirement to “apply” the abstract idea by using a multi-agent optimizer since the model is recited at a high level of generality, merely serves in its ordinary capacity to perform the existing function of answering a question, and since the limitation recites the outcome or solution of training and using the model without indicating how the solution or outcome of using the model is brought forth.
Claims 5 and 17:
further comprising: using the answer to the second question as a starting position of at least one agent of the multi-agent optimizer; and
using the multi-agent optimizer to produce a refined answer to the second question.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could use an answer as a starting position and produce a refined answer to a question. Regarding the use of the multi-agent optimizer, the broadest reasonable interpretation of this limitation represents the mere requirement to “apply” the abstract idea by using a multi-agent optimizer since the model is recited at a high level of generality, merely serves in its ordinary capacity to perform the existing function of answering a question, and since the limitation recites the outcome or solution of training and using the model without indicating how the solution or outcome of using the model is brought forth.
Claims 6 and 18:
wherein generating at least one training example comprises generating a training example from the location of an agent after each optimization iteration of the multi-agent optimizer.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate a training example from locations of agents after iterations of an optimizer.
Claim 7:
further comprising: using the answer to the second question as input to a simulator to produce a simulated result, and using the simulated result to verify acceptability of the answer to the second question.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could use an answer as an input into a simulator and use a simulated result to verify acceptability.
Claim 8:
further comprising: using the answer to the second question to perform at least one control action in a real world system.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could use an answer to perform a control action. Furthermore, regarding the performance of a control action, the broadest reasonable interpretation of this limitation recites the mere addition of insignificant extra-solution activity in the form of post-solution activity, the broadest reasonable interpretation of which comprises the well-understood routine and conventional computer function of transmitting and receiving information over a network.
Claims 9 and 20:
wherein the resource intensive algorithm utilizes a digital twin of a real world system to answer the first question.
Regarding the use of the digital twin, the broadest reasonable interpretation of this limitation represents the mere requirement to “apply” the abstract idea by using a digital twin, since the digital twin is recited at a high level of generality, merely serves in its ordinary capacity to perform the existing function of answering a question, and since the limitation recites the outcome or solution of using the digital twin without indicating how the solution or outcome of using the model is brought forth.
Claim 10:
wherein using a resource intensive algorithm to answer a first question of a question type comprises computing a cost function from the digital twin.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could compute a cost function of a digital twin.
Claim 11:
further comprising: using the resource intensive algorithm to answer a third question of a different question type;
generating at least one additional training example from the normal operation of the resource intensive algorithm;
training an additional lightweight machine learning model based on the at least one additional training example to produce answers to questions of the different question type; and
using the additional lightweight machine learning model to produce an answer to a fourth question of the different question type.
These limitations merely repeat steps recited in the claims above, please see the claims above regarding the ineligibility of these steps.
The above limitations do not represent a practical application of the recited abstract idea. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims are also directed to the judicial exception.
Furthermore, the added limitations do not direct the claim to significantly more than the abstract idea. No specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Accordingly, none of the dependent claims 2-11, 13-18, and 20, individually, or as an ordered combination, are directed to patent eligible subject matter under 35 U.S.C. 101.
Please see MPEP §2106.05(d)(II) for a discussion of elements that the Courts have recognized as well-understood, routine, conventional, activity in particular fields.
Please see MPEP §2106 for examination guidelines regarding patent subject matter eligibility.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7, 11-13, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Leary et al. (U.S. PG Pub. No. 20230342670; hereinafter "Leary").
As per claim 1, Leary teaches:
A method for improving simulation-based question answering, the method comprising:
Leary teaches a system and method for improving query/responses using first and second models. (Leary: abstract)
using a resource intensive algorithm to answer a first question of a question type;
Leary teaches a first model which may receive a first query, and provide a response to the first query. (Leary: paragraphs [0042-43], Fig. 6) Leary further teaches that the first model may comprise a more resource intensive, general model. (Leary: paragraphs [0026-27])
generating at least one training example from the normal operation of the resource intensive algorithm;
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6)
training a lightweight machine learning model based on the at least one training example to produce answers to questions of the question type;
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 34, 42-44])
and using the lightweight machine learning model to produce an answer to a second question of the question type.
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary further teaches that the second model may thereafter be used to respond to queries for such classes. (Leary: paragraph [0026-28, 34, 42-46], Figs. 6A-6B) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 42-44])
As per claim 7, Leary teaches all of the limitations of claim 1, as outlined above, and further teaches:
further comprising: using the answer to the second question as input to a simulator to produce a simulated result, and using the simulated result to verify acceptability of the answer to the second question.
Leary further teaches that the answer provided by the second answer may be run through a simulator which determines whether the second model can replace the first based on acceptability of the answer provided to the second question. (Leary: paragraph [0034-36, 46])
As per claim 11, Leary teaches all of the limitations of claim 1, as outlined above, and further teaches:
further comprising: using the resource intensive algorithm to answer a third question of a different question type;
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary further teaches that the second model may thereafter be used to respond to queries for such classes. (Leary: paragraph [0026-28, 34, 42-46], Figs. 6A-6B) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 42-44])
generating at least one additional training example from the normal operation of the resource intensive algorithm;
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary further teaches that the second model may thereafter be used to respond to queries for such classes. (Leary: paragraph [0026-28, 34, 42-46], Figs. 6A-6B) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 42-44])
training an additional lightweight machine learning model based on the at least one additional training example to produce answers to questions of the different question type; and
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second models. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary further teaches that the second model may thereafter be used to respond to queries for such classes. (Leary: paragraph [0026-28, 34, 42-46], Figs. 6A-6B) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 42-44])
using the additional lightweight machine learning model to produce an answer to a fourth question of the different question type.
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second models. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary further teaches that the second model may thereafter be used to respond to queries for such classes. (Leary: paragraph [0026-28, 34, 42-46], Figs. 6A-6B) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 42-44])
As per claim 12, Leary teaches the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A controller that utilizes a simulation in controlling a real world system, the controller comprising a memory and a processer, wherein the processor is in communication with the memory configured to:
Leary teaches a system and method for improving query/responses using first and second models. (Leary: abstract) Leary further teaches the implementation of the system and method using a computer comprising a processor which executes code stored in a physical memory in order to perform the functions of the system. (Leary: paragraphs [0060-68], Fig. 8)
As per claim 13, Leary teaches the limitations of this claim which are substantially identical to those of claim 11, as outlined above, and claim 13 is rejected for the same reasons as claim 11, as outlined above.
As per claim 19, Leary teaches the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A non-transitory machine-readable medium encoded with instructions for execution by a processor, the non-transitory machine-readable medium comprising:
Leary teaches a system and method for improving query/responses using first and second models. (Leary: abstract) Leary further teaches the implementation of the system and method using a computer comprising a processor which executes code stored in a physical memory in order to perform the functions of the system. (Leary: paragraphs [0060-68], Fig. 8)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-6, 8-10, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leary in view of Gavin et al. (U.S. PG Pub. No. 20240401833; hereinafter "Gavin").
As per claim 2, Leary taches all of the limitations of claim 1, as outlined above, but does not appear to explicitly teach:
wherein the resource intensive algorithm answers the first question by tuning at least one or more input of a simulator until an output of the simulator sufficiently meets a criteria of the question.
Gavin, however, teaches that a machine learning model may comprise a building simulation model whose parameters are tuned until its outputs meet sufficient criteria. (Gavin: paragraphs [0064-67, 98, 119, 122]) Gavin teaches combining the above elements with the teachings of Leary for the benefit of improving precision and accuracy of models and feedback data and helping to fix devices and provide corrective actions in building management. (Gavin: paragraph [0145, 187]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gavin with the teachings of Leary to achieve the aforementioned benefits.
As per claim 3, Leary in view of Gavin teaches all of the limitations of claim 2, as outlined above, and further teaches:
wherein the at least one training example includes: output of the simulation as input, and input to the simulation as output.
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) In teaching that the output of the first model is used to train the second, Leary teaches outputs used as inputs. In teaching training the first model, Leary teaches inputs into the simulation as outputs.
As per claim 4, Leary taches all of the limitations of claim 1, as outlined above, but does not appear to explicitly teach:
wherein the resource intensive algorithm is a multi-agent optimizer.
Gavin, however, teaches that a machine learning model which is tuned using a second model may comprise an LLM (a multi-agent optimizer). (Gavin: paragraphs [0042-43]) Gavin teaches combining the above elements with the teachings of Leary for the benefit of improving precision and accuracy of models and feedback data and helping to fix devices and provide corrective actions in building management. (Gavin: paragraph [0145, 187]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gavin with the teachings of Leary to achieve the aforementioned benefits.
As per claim 5, Leary in view of Gavin teaches all of the limitations of claim 4, as outlined above, and further teaches:
further comprising: using the answer to the second question as a starting position of at least one agent of the multi-agent optimizer; and
Gavin, however, teaches that a machine learning model which is tuned using a second model may comprise an LLM (a multi-agent optimizer). (Gavin: paragraphs [0042-43]) Gavin further teaches that the output of a second model may be placed back into the first model in order to produce a refined answer at the first model. (Gavin: paragraph [0169]) The motivation to combine Gavin persists.
using the multi-agent optimizer to produce a refined answer to the second question.
Gavin, however, teaches that a machine learning model which is tuned using a second model may comprise an LLM (a multi-agent optimizer). (Gavin: paragraphs [0042-43]) Gavin further teaches that the output of a second model may be placed back into the first model in order to produce a refined answer at the first model. (Gavin: paragraph [0169]) The motivation to combine Gavin persists.
As per claim 6, Leary in view of Gavin teaches all of the limitations of claim 4, as outlined above, and further teaches:
wherein generating at least one training example comprises generating a training example from the location of an agent after each optimization iteration of the multi-agent optimizer.
Leary teaches that the responses from the first models may be generated and used as training examples for a more specific, less general, and less resource intensive second model. (Leary: paragraphs [0026-28, 42-44], Fig. 6) Leary teaches question types in the form of domains or classes associated with queries and responses. (Leary: paragraph [0017, 28, 34, 42-44]) Gavin, as outlined above, teaches that a machine learning model which is tuned using a second model may comprise an LLM (a multi-agent optimizer). (Gavin: paragraphs [0042-43, 64-67]) The motivation to combine Gavin persists.
As per claim 8, Leary taches all of the limitations of claim 1, as outlined above, but does not appear to explicitly teach:
further comprising: using the answer to the second question to perform at least one control action in a real world system.
Gavin, however, teaches that a machine learning model may comprise a building simulation model, the outputs of which may be used to perform control actions regarding a building. (Gavin: paragraphs [0064-67, 107, 187]) Gavin teaches combining the above elements with the teachings of Leary for the benefit of improving precision and accuracy of models and feedback data and helping to fix devices and provide corrective actions in building management. (Gavin: paragraph [0145, 187]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gavin with the teachings of Leary to achieve the aforementioned benefits.
As per claim 9, Leary taches all of the limitations of claim 1, as outlined above, but does not appear to explicitly teach:
wherein the resource intensive algorithm utilizes a digital twin of a real world system to answer the first question.
Gavin, however, teaches that a machine learning model may comprise a building simulation model in the form of a digital twin of the building and digital twins of individual spaces. (Gavin: paragraphs [0064-67, 187, 189]) Gavin teaches combining the above elements with the teachings of Leary for the benefit of improving precision and accuracy of models and feedback data and helping to fix devices and provide corrective actions in building management. (Gavin: paragraph [0145, 187]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gavin with the teachings of Leary to achieve the aforementioned benefits.
As per claim 10, Leary in view of Gavin teaches all of the limitations of claim 9, as outlined above, and further teaches:
wherein using a resource intensive algorithm to answer a first question of a question type comprises computing a cost function from the digital twin.
Gavin, however, teaches that a machine learning model may comprise a building simulation model in the form of a digital twin of the building and digital twins of individual spaces which uses a cost function. (Gavin: paragraphs [0081, 179, 187, 189]) The motivation to combine Gavin persists.
As per claims 14-18 and 20, Leary in view of Gavin teaches the limitations of these claims which are substantially identical to those of claims 2-6 and 9, and claims 14-18 and 20 are rejected for the same reason as claims 2-6 and 9, as outlined above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMETT K WALSH whose telephone number is (571)272-2624. The examiner can normally be reached Mon.-Fri. 6 a.m. - 4:45 p.m..
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/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
