Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
The following is a FIRST, NON-FINAL OFFICE ACTION for Application #18/337,006, filed on 06/18/2023. This application is a Continuation-in-Part of PCT/US2022/050932, and PCT/US2022050924, filed on 11/23/2022 and claims benefit to multiple Provisional Applications, the earliest filed on 11/23/2021.
Claims 1-20 are pending and have been examined.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f): (FP 7.30.03)
(f) ELEMENT IN CLAIM FOR A COMBINATION.—An element in a claim for a combination
may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term “means” or “step” or a term used as a substitute for “means”
that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. (FP 7.30.05)
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
An AI-based platform for enabling… in claim 1
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. However, there is no corresponding structure in the filed specification that performs the said enabling. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph interpreted under35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph(e.g., by reciting sufficient structure to perform the claimed function);or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under35 U.S.C.112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.(FP7.30.06)
Claim Rejections–35 USC §112
The following is a quotation of35 U.S.C. 112(b): (FP 7.30.02)
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of pre-AIA 35 U.S.C. 112,second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under35 U.S.C. 112(b)or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.(FP7.34.01)
Claim 1 recites the limitation “An AI-based platform for enabling…” which invokes 35 U.S.C. 112(f) or pre- AIA 35 U.S.C.112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which mechanical structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 2-20 inherit the deficiency by virtue of their dependency on claim 1.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. (FP 7.34.23) .
The following is a quotation of 35 U.S.C. 112(a): (FP 7.30.01)
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and
exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. (FP 7.31.01). Claims 2-20 inherit the deficiencies of claim 1 by virtue of their dependency on claim 1.
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 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below.
Per Step 1 of the analysis, the claims are analyzed to determine if they are directed to statutory subject matter. Claim 1 claims a “platform.” However, there is no structural component at all in the claims, drawings, or filed specification that seems to correspond with the “platform” at all. Therefore the claim is interpreted as software per se. Software per se is not a statutory category for patentability.
Per Step 2A, Prong 1 of the analysis, the examiner must now determine if the claims recite an abstract idea or eligible subject matter. In the instant case, the independent claims are directed towards an abstract idea. Specifically, independent claim 1 recites “generate at least one recommendation and/or instruction with respect to optimization of at least one energy objective and at least one other objective.” Therefore, the claims recite an abstract idea, namely “certain methods of organizing human activity.” Specifically, the claims recite “commercial interactions, business relations.” The claims describe a professional evaluation and recommendation for management of energy objective optimization such as in a business setting. The claims simply automate these steps using a computer and a machine learning model. Therefore, the claims recite an abstract idea, namely “commercial interactions, business relations.” The claims secondarily recite a mental process. A professional could mentally evaluate the available data and other information and make a recommendation for energy objectives and other objectives. Therefore, the claims secondarily recite a mental process.
Per Step 2A, Prong 2 of the analysis, the examiner must now determine if the claims integrate the abstract idea into a practical application. The additional elements of the independent claims include “an AI-based platform” and “an energy provisioning system.” However, these additional elements are considered generic recitations of a technical element and are recited at a high level of generality. These additional elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)) and are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). Therefore, these additional elements are not considered to integrate the abstract idea into a practical application. The claims also recite “an intelligent agent trained on a data set of expert interactions with an energy provisioning system.” However, these additional elements are recited at a high level of generality and are considered a generic recitation of a technical element and the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea. The “intelligent agent trained…” has no detail whatsoever as how any AI model or other agent is trained or any steps on how it is used. Therefore, the use of the trained AI agent is not considered to integrate the abstract idea into a practical application.
Per Step 2B of the analysis, the examiner must now determine if the claims include limitations that are “significantly more” than the abstract idea by demonstrating an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements of the independent claims include “an AI-based platform” and “an energy provisioning system.” However, these additional elements are considered generic recitations of a technical element and are recited at a high level of generality. These additional elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)) and are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). Therefore, these additional elements are not considered significantly more than the abstract idea itself. The claims also recite “an intelligent agent trained on a data set of expert interactions with an energy provisioning system.” However, these additional elements are recited at a high level of generality and are considered a generic recitation of a technical element and the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea. The “intelligent agent trained…” has no detail whatsoever as how any AI model or other agent is trained or any steps on how it is used. Therefore, the use of the trained AI agent is not considered significantly more than the abstract idea itself.
When considered as an ordered combination, the claim is still considered to be directed to an abstract idea as the claim steps in the ordered combination simply recite the logical steps for generating at least one recommendation or instruction with respect to optimization of energy objectives and other objectives. Therefore, the ordered combination does not lead to a determination of significantly more.
When considering the dependent claims, claims is considered part of the abstract idea. Claim 2 is considered part of the abstract idea, as an operational objective does not change the analysis. Claims 3-7, 11, 14, 17, and 19 are recited at a high level of generality and are considered a generic recitation of a technical element and the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea. The type of data the intelligent agent operates on or what data the agent is trained on, absent further detail, does not change the analysis. Claims 8-10 and 15 are considered part of the abstract idea, as “further comprising an adaptive energy digital twin,” absent further detail, is considered a recitation of data associated with the platform. The generating and providing of a visual is considered “receiving and/or transmittal of data over a network,” cited in the MPEP 2106.05 (d) (II) (i-ii) as an example of conventional computer functioning- see Symantec, TLI Communications, and buySAFE v Google. Claims 12 and 13 are considered part of the abstract idea, as what the data set comprises does not change the analysis. Claim 16 is recited at a high level of generality and are considered a generic recitation of a technical element and the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea. The use of a blockchain to record data, absent further detail, does not change the analysis. Further, the examiner takes Official Notice that it is old and well known in the computer arts to use a blockchain to record data. Claim 18 is considered part of the abstract idea, as a description of where an AI agent is located, especially since an AI agent is not a physical component as recited, does not change the analysis. Claim 20 is recited at a high level of generality and is considered a generic recitation of a technical element and the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea. Reciting that an AI agent “governs” gives no technical detail whatsoever as to how this occurs.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. Vs. CLS Bank International et al., 2014 (please reference link to updated publicly available Alice memo at http://www.uspto.gov/patents/announce/alice_pec_25jun2014.pdf as well as the USPTO January 2019 Updated Patent Eligibility Guidance.)
Claim Rejections - 35 USC § 102
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 1-2, 4, 11-14, and 18-20 are rejected under 35 USC 102 (a) (2) as being anticipated by Goparaju, et al., Pre-Grant Publication No. 2015/0301548 A1.
Regarding Claim 1, Goparaju teaches:
An AI-based platform… comprising:
an intelligent agent trained on a data set of expert interactions with an energy provisioning system (see [0029], [0108], and [0114]-[0116] in which the AI cognitive decision maker is trained on a data set of historical interactions with the energy system)
wherein the intelligent agent is trained to generate at least one recommendation and/or instruction with respect to optimization of at least one energy objective and at least one other objective (see [0029], [0035], [0108], and [0114]-[0116] in which the AI cognitive decision maker is trained; see also Abstract, [0030], [0034]-[0035], [0089], [0100], [0117], and [0129]-[0130] in which the AI agent makes recommendations and outputs instructions on various energy objectives as well as on other objectives like environmental management, work flows, user management conservation options, and maintenance of the plant and machinery)
Regarding Claim 2, Goparaju teaches:
the AI-based platform of claim 1
wherein the other objective is an operational objective of an enterprise (see Abstract, [0089], [0100], [0117], and [0129]-[0130] in which the AI agent makes recommendations and outputs instructions on operational objectives of an enterprise like environmental management, work flows, user management conservation options, and maintenance of the plant and machinery)
Regarding Claim 4, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent operates on status data from a set of edge devices via which a set of energy consumption resources are controlled (see Figures 6-8 and [0124]-[0126] in which multiple energy equipment entities include multiple equipment controllers on the equipment side which gather and send information to the cognitive device system and receive data and instructions; the controllers are on the equipment side and therefore act as a network of edge devices that communicate with the AI agent; see also Abstract, [0028]-[0035], [0088], [0096], [0127], and [0129]-[0130] which teach a set of energy consumption resources)
Regarding Claim 11, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent is further configured to perform at least one of transforming, converting, normalizing, and/or cleansing energy-related data, detecting patterns, content, and/or objects in energy-related data, compressing energy-related data, filtering energy-related data, loading and/or storing energy-related data (see at least [0072], [0108]-[0111], [0132] which teach the intelligent agent at least normalizing, detecting patterns, and storing the energy-related data)
Regarding Claim 12, Goparaju teaches:
the AI-based platform of claim 1
wherein the data set is based on at least one public data resource
the public data resources including at least one of a weather data resource (see [0012], [0021], and [0109])
Regarding Claim 13, Goparaju teaches:
the AI-based platform of claim 1
wherein the data set is based on at least one enterprise data resource
the enterprise data resources including at least one of resource planning data, financial planning data, demand planning data, procurement data, pricing data, product data, or operating data (see Abstract, [0026]-[0028], [0091]-[0092], [0100]-[0103], [0110], and [0129]-[0130] in which cost, demand, resource planning, procurement, product, and operating data for the enterprise are all used)
Regarding Claim 14, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent is trained based on a training data set, and the training data set is based on at least one of at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process (see [0113]-[0116])
Regarding Claim 18, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent is located in proximity to at least one entity that generates, stores, delivers, and/or uses energy (see [0090] in which the energy management cognitive platform can be placed physically near the facility or plant that stores and uses energy)
Regarding Claim 19, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent provides information about an energy state and/or energy flow of at least one entity that generates, stores, delivers, and/or uses energy (see [0030]-[0034], [0089]-[0099], and [0127]-[0130])
Regarding Claim 20, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent governs at least one sensor of a set of sensors, the set of sensors is associated with a set of infrastructure assets that are configured to generate, store, deliver, and/or use energy (see at least [0040], [0045], and [0119]; while the citations do not recite a set of sensors, it is inherent based on Figures 6-8 that if there are many pieces of equipment and many cognitive energy devices there would be a “set of sensors” and not just one)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 3, 5-6, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju, et al., Pre-Grant Publication No. 2015/0301548 A1 in view of Thirumurthy, et al., Patent No. 11,399,065 B1.
Regarding Claim 3, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent operates on status data from a set of edge devices via which a set of energy consumption resources are controlled (see Figures 6-8 and [0124]-[0126] in which multiple energy equipment entities include multiple equipment controllers on the equipment side which gather and send information to the cognitive device system and receive data and instructions; the controllers are on the equipment side and therefore act as a network of edge devices that communicate with the AI agent see also Abstract, [0028]-[0035], [0088], [0096], [0127], and [0129]-[0130] which teach a set of energy consumption resources)
Goparaju, however, does not appear to specify:
a set of energy generation resources are controlled
Thirumurthy teaches:
a set of energy generation resources are controlled (see Column 9, lines 1-5, Column 11, lines27-33, and Column 16, lines 26-34
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Thirumurthy with Goparaju because Goparaju already teaches applying the same use of an intelligent agent in communication with edge devices in an energy management system for energy consumption, and applying the teachings to energy generation would allow for optimization and efficiency in energy generation as well using the advantages of an AI agent.
Regarding Claim 5, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent operates on status data from a set of edge devices via which a set of energy consumption resources are controlled (see Figures 6-8 and [0124]-[0126] in which multiple energy equipment entities include multiple equipment controllers on the equipment side which gather and send information to the cognitive device system and receive data and instructions; the controllers are on the equipment side and therefore act as a network of edge devices that communicate with the AI agent; see also Abstract, [0028]-[0035], [0088], [0096], [0127], and [0129]-[0130] which teach a set of energy consumption resources)
Goparaju, however, does not appear to specify:
a set of energy storage resources are controlled
Thirumurthy teaches:
a set of energy storage resources are controlled (see Abstract, Column 9, lines 1-5, and Column 11, lines 27-33 and 65-67)
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Thirumurthy with Goparaju because Goparaju already teaches applying the same use of an intelligent agent in communication with edge devices in an energy management system for energy consumption, and applying the teachings to energy storage would allow for optimization and efficiency in energy storage as well using the advantages of an AI agent.
Regarding Claim 6, Goparaju teaches:
the AI-based platform of claim 1
wherein the intelligent agent operates on status data from a set of edge devices via which a set of energy consumption resources are controlled (see Figures 6-8 and [0124]-[0126] in which multiple energy equipment entities include multiple equipment controllers on the equipment side which gather and send information to the cognitive device system and receive data and instructions; the controllers are on the equipment side and therefore act as a network of edge devices that communicate with the AI agent; see also Abstract, [0028]-[0035], [0088], [0096], [0127], and [0129]-[0130] which teach a set of energy consumption resources)
Goparaju, however, does not appear to specify:
a set of energy delivery resources are controlled
Thirumurthy teaches:
a set of energy delivery resources are controlled (see Abstract, Column 10, line 45-Column 11 line 33, and Column 16, lines 26-34
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Thirumurthy with Goparaju because Goparaju already teaches applying the same use of an intelligent agent in communication with edge devices in an energy management system for energy consumption, and applying the teachings to energy delivery/distribution would allow for optimization and efficiency in energy delivery as well using the advantages of an AI agent.
Regarding Claim 15, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
wherein the intelligent agent is further configured to orchestrate delivery of energy to at least one point of consumption
the delivery of the energy includes at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy
Thirumurthy teaches:
wherein the intelligent agent is further configured to orchestrate delivery of energy to at least one point of consumption and the delivery of the energy includes delivery of stored energy (see Abstract, Column 10, line 45-Column 11 line 33, and Column 16, lines 26-34
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Thirumurthy with Goparaju because Goparaju already teaches applying the same use of an intelligent agent in communication with edge devices in an energy management system for energy consumption, and applying the teachings to energy delivery/distribution would allow for optimization and efficiency in energy delivery as well using the advantages of an AI agent.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju, et al., Pre-Grant Publication No. 2015/0301548 A1 in view of Official Notice.
Regarding Claim 7, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
wherein the intelligent agent is further configured to adapt a transport of data over a network and/or communication system, wherein the adapting is based on at least one of a congestion condition, a delay and/or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition
The examiner, however, takes Official Notice that it is old and well known in the computer arts at the time of the effective filing date of the application to adapt transport of data over a network when there are delays and challenges due to congestion, latency, error rate conditions, and other conditions. Companies such as IBM, Verizon, Motorola, and others have done so for at least a decade prior to the effective filing date of this application.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine wherein the intelligent agent is further configured to adapt a transport of data over a network and/or communication system, wherein the adapting is based on at least one of a congestion condition, a delay and/or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition with Goparaju because Goparaju already teaches communication over a network and the AI agent using the data received to optimize the energy system, and allowing for adaptation when data communication is compromised would allow for a quick solution and for the data to be received and the energy objectives to be optimized in a timely fashion.
Regarding Claim 17, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
wherein the intelligent agent is deployed in an off-grid environment, and the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system
The examiner, however, takes Official Notice that it is old and well known in the computer arts at the time of the effective filing date of the application to deploy an intelligent agent in an off-grid environment. Companies such as Rockwell, Exxon Mobil, Peco, and many utility and energy companies have done so for at least a year prior to the effective filing date of this application.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine wherein the intelligent agent is deployed in an off-grid environment, and the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system with Goparaju because Goparaju already teaches communication over a network and the AI agent using the data received to optimize the energy system, and allowing for deployment in an off-grid environment would allow for deployment to all types of energy systems including off-grid, making the system more universally applicable.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Goparaju, et al., Pre-Grant Publication No. 2015/0301548 A1 in view of Brooks, et al., Pre-Grant Publication No. 2022/0083027 A1.
Regarding Claim 8, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
further comprising an adaptive energy digital twin that represents at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition
Brooks teaches:
further comprising an adaptive energy digital twin that represents at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition (see Abstract, Figures 2 and 13-14, [0035]-[0041], [0044], [0055]-[0056], [0070], and [0089]-[0096] in which a digital twin of the industrial or other facility is taught, the facility in such as [0079] used for various production, distribution, and industrial purposes and measured for such as fluid distribution and energy consumption and is therefore an energy-dependent production facility; the examiner notes that Goparaju already teaches such as an energy stakeholder entity, and energy distribution resource, a production facility, etc so Brooks is being relied upon to teach the other aspects of the invention)
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Brooks with Goparaju because Goparaju already teaches applying an intelligent agent and large-scale analysis of energy and industrial management systems, and using a digital twin allows for online/digital modeling rather than only brick and mortar applications, allowing for more flexibility and digital application and exploring of options without needing to conduct the analysis using only the physical building.
Regarding Claim 9, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
an adaptive energy digital twin that is configured to perform at least one of providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data
Brooks teaches:
an adaptive energy digital twin that is configured to perform at least one of providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data (see Abstract, Figures 2 and 13-14, [0035]-[0041], [0044], [0055]-[0056], [0070], and [0089]-[0096] in which a digital twin of the industrial or other facility is taught, the facility in such as [0079] used for various production, distribution, and industrial purposes and measured for such as fluid distribution and energy consumption and is therefore an energy-dependent production facility; the examiner notes that Goparaju already teaches such as an energy stakeholder entity, and energy distribution resource, a production facility, etc so Brooks is being relied upon to teach the other aspects of the invention)
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Brooks with Goparaju because Goparaju already teaches applying an intelligent agent and large-scale analysis of energy and industrial management systems, and using a digital twin allows for online/digital modeling rather than only brick and mortar applications, allowing for more flexibility and digital application and exploring of options without needing to conduct the analysis using only the physical building.
Regarding Claim 10, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of at least one machine or at least one factory
Brooks teaches:
an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of at least one machine or at least one factory (see Abstract, Figures 2 and 13-14, [0035]-[0041], [0044], [0055]-[0056], [0070], and [0089]-[0096] in which a digital twin of the industrial or other facility is taught, the facility in such as [0079] used for various production, distribution, and industrial purposes and measured for such as fluid distribution and energy consumption and is therefore an energy-dependent production facility; see also [0047]-[0052], [0062]-[0066], and [0076]-[0079] which teach a visual display interface for the digital twin and accompanying data points)
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Brooks with Goparaju because Goparaju already teaches applying an intelligent agent and large-scale analysis of energy and industrial management systems, and using a digital twin allows for online/digital modeling rather than only brick and mortar applications, allowing for more flexibility and digital application and exploring of options without needing to conduct the analysis using only the physical building.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Goparaju, et al., Pre-Grant Publication No. 2015/0301548 A1 in view of Cella, et al., Pre-Grant Publication No. 2018/0188715 A1.
Regarding Claim 16, Goparaju teaches:
the AI-based platform of claim 1
Goparaju, however, does not appear to specify:
wherein the intelligent agent is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event
the at least one energy-related event including at least one of an energy purchase and/or sale event, a service charge associated with an energy purchase and/or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event
Cella teaches:
wherein the intelligent agent is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event and the at least one energy-related event including at least one of an energy purchase and/or sale event, a service charge associated with an energy purchase and/or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event (see at least [0209] in which the energy related events are recorded on a distributed ledger)
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine Cella with Goparaju because Goparaju already teaches applying an intelligent agent for large-scale analysis of energy and industrial management systems and storage of data and analysis, and storing energy-related events to a distributed ledger would allow for secure and immutable records, leading to better trust and further learning based on the recorded data.
Conclusion
The following prior art references were not relied upon in this office action but are considered pertinent to this application:
Janous, et al., Pre-Grant Publication No. 2016/0011618 A1- teaches a trained AI algorithm for analyzing electrical grid conditions and making recommendations for energy optimization. A network of input nodes is used. The energy management system can also be for fossil fuels.
Yu, et al., Pre-Grant Publication No. 2020/0074570 A1
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/LUIS A BROWN/Primary Examiner, Art Unit 3626