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
2. The request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for Continued Examination under 37 CFR 1.114, the fee set forth in 37 CFR 1.17(e) has been paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed 3/10/2026 has been entered. An action on the RCE follows.
Summary of claims
3. Claims 1-26 are pending,
Claims 1, 22 are amended,
Claim 25-26 are newly added,
Claims 1, 22 are independent claims,
Claims 1-26 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 3/10/2025, are carefully reviewed, with respect to the rejection(s) of claim(s) 1-26 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
Applicant argued on pages 12-14 that the cited references including Bain and Sanders did not teach meeting localized needs and a set of local demand requirements as required by claim 1. Examiner respectfully disagrees and submits that Bain discloses specifying the location parameter such as local geographic region, a community region, and an administrative district (Bain: [0027], [0041], [0275]), optimizing energy consumption by accounting renewable energy credits at local level, coordinating use of storage resources with local distributed resource (Bain: [0330], [0335], [0389]). Further, Sanders also discloses coordinating one or more energy resources in one or more local events each associated with a distributed energy resource energy storage apparatus (Sanders: [0174], [0183]-[0186]), optimizing performance based on real-time local conditions (Sanders: [0218], [0222]). Accordingly, Bain and Sanders disclose meeting localized need and a set of local demand requirements.
Applicant argued on pages 13-14 that Bain and Sanders did not teach relocating an energy source to meet a specific energy demand. Examiner respectfully disagrees and submits that Bain discloses the energy sources including a battery source, a stored energy source (Bain: [0080], [0102], [0106]), Bain also discloses managing electronic vehicle including gathering and analyzing information about consume energy for the home from the EV battery and deliver energy to the grid from the EV (Bain: [0261]), that is, at least the battery is an energy source which can be transported and relocated.
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 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 of this title, 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.
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.
5. Claims 1-9, 12-19, 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Nicholas Jordan Bain et al (US Publication 20190372345 A1, hereinafter Bain), and in view of Dean Sanders et al (US Publication 20170005515 A1, hereinafter Sanders).
As for independent claim 1, Bain discloses: An artificial intelligence-based (AI-based) system for enabling intelligent orchestration and management (Bain: [0406], Embodiments of a machine learning engine 104 or artificial intelligence may include a wide variety of systems, including expert systems, model-based systems, deep learning systems, rule-based systems, and systems using various kinds of neural networks, as well as hybrids of the foregoing) of power and energy (Bain: Abstract, A platform and components for an automated consumer retail utility marketplace are provided, including components for machine learning, components for gamification, and components for supporting a related consumer mobile application that enables improved visibility and control by a consumer over its interaction with energy markets), the AI-based system comprising: memory hardware configured to store instructions; and processor hardware configured to execute the instructions, wherein the instructions include, controlling a set of energy resources that is modular and distributed (Bain: [0106], the raw energy sources are among at least two of a fossil fuel source, a coal source, an oil source, a natural gas source, a nuclear source, a renewable source, a wind source, a solar source, a hydropower source, a stored energy source, a battery source, and a gravity power source; please note at least a battery source may be transportable), and implementing an AI-based coordination system configured to, select a subset of resources of the set of energy resources (Bain: [0254], the platform includes an energy consumption and generation optimization algorithm for determining at any given time period of time which sources of generation to use and which sources of consumption to fulfill) to meet a set of local demand requirements of at least one energy load (Bain: [0066], platform for a consumer energy marketplace including a service organization interface through which the consumer energy marketplace receives at least one of energy demand information and real-time pricing information for energy delivered over an energy distribution network that supplies an energy consumer and a host interface through which the consumer energy marketplace processes delivery-related cost information for multiple consumer energy delivery offerings; [0465], the marketplace platform may enable managing production based on the forecast demand for energy from particular types of energy sources. The marketplace platform may collect and optionally aggregate demand estimates for each of the raw sources of energy from a collection of consumers, such as indicated by consumers in a mobile application or other interfaces of the platform. An energy producer load manager may control or signal for energy flow from the raw energy sources at least in part based on the demand. Thus, in embodiments, an energy supply network for a consumer is provided, the network including multiple producers of a type of energy suitable for distribution over a residential energy distribution network, wherein a set of the multiple producers each uses different raw sources of energy to produce the type of energy; a consumer energy marketplace platform that calculates aggregated demand estimates for each of the raw sources of energy based on aggregated consumer energy consumption estimates allocated to each of the raw sources of energy as specified by the consumers); [0328], may match demand and supply; [0466], a platform is provided for controlling or signaling for energy producer output onto the grid based on feedback from consumer selection of raw energy source; with respect to “meet a set of local demand, Bain discloses specifying the location parameter such as local geographic region, a community region, and an administrative district (Bain: [0027], [0041], [0275]), optimizing energy consumption by accounting renewable energy credits at local level, coordinating use of storage resources with local distributed resource (Bain: [0330], [0335], [0389])), wherein a first resource of the subset of resources is at least one of mobile or transportable (Bain: Bain: [0080], [0102], [0106], the energy sources including a battery source, a stored energy source; [0261], managing electronic vehicle including gathering and analyzing information about consume energy for the home from the EV battery and deliver energy to the grid from the EV), select a location for at least one resource of the subset of resources, wherein the location is selected to meet a location requirement of the set of local demand requirements of the at least one energy load (Bain: [0118], the leaderboard calculates a position for the consumer based on a filter that displays the consumer's position relative to similar consumers. In embodiments, the filter is at least one filter selected from the group consisting of an age filter, a property size filter, a property pride filter, a gender filter, an income bracket filter, a grid location filter, a geographic location filter, and a demographic filter), select a time for the at least one resource to be relocated to the selected location, wherein the time is selected to meet a timing requirement of the set of local demand requirements of the at least one energy load, and cause, at the selected time, the first resource to be relocated to the selected location (Bain: [0392]-[0393], adjusting the timing and/or operation of the home's air conditioning system to leverage the renewable energy)
Bain does not expressly disclose intelligent orchestration, in an analogous art of AI-based platform for energy management, Sanders expressly discloses: An artificial intelligence-based (AI-based) system (Sanders: [0132], updating the expected output using in certain aspects an artificial intelligence component ) for enabling intelligent orchestration and management of power and energy (Sanders: Abstract, methods of virtual power plant and orchestration; [0206], the orchestrated virtual power plant modifies one or more of the rule sets to automatically adapt via an artificial intelligence component);
Bain and Sanders are analogous arts because they are in the same field of endeavor, AI-based platform for energy management. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bain using the teachings of Sanders to expressly include intelligent orchestration of power management. It would provide Bain’s platform with enhanced capabilities of optimizing the power and energy management system.
As for claim 2, Bain-Sanders discloses: wherein the set of local demand requirements is forecast by demand forecasting algorithm operating on a set of edge networking devices that is linked to a set of systems that consume energy (Bain: [0058], The network also includes an energy producer load manager that controls energy flow to the energy supply network from the multiple producers based on a demand forecast received from the consumer energy marketplace platform for each of the different raw sources of energy; [0332], The utility marketplace platform 100 may use data sources, including energy usage data 146, consumer behavior data, gamification data (such as from a gamification engine 138 or gamification UI), energy mix data 172, weather zone usage data 188, pricing data 148, and other data sources 174 to predict various factors, including energy usage, prices, demand, production, and the like. Predicting various factors may include using data science and machine learning, such as provided by a prediction and machine learning engine 104, prediction engine 106 and the like).
As for claim 3, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to be located in proximity to a location demand (Bain: [0420], variation of the parameters of a location-based game (such as the locations involved, the size of locations involved (including geographic locations, geo-fence locations, grid locations, weather regions, and the like), the number of consumers, the type and allocation of rewards, type of challenge conducted, timing of the game, duration of time intervals involved in a game and the like) may be managed using machine learning 104, including based on feedback, such as based on the effect of the game on behavior, the effect of different rewards on behavior, the effect of a game for consumers in a given location (such as contrasting with playing the same game in another location) and the like; [0438], time-shift consumption, location of energy consumption.
As for claim 4, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to be located based on a location and type of a local demand requirement (Bain: [0027], the location parameter specifies one of a local geographic region, a multi-state region, a community region, and an administrative district).
As for claim 5, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to generate energy at a point of local demand (Sanders: [0155], During an outage, based upon current demand, current available renewable energy from local generation sources, and the amount of energy available in the renewable energy storage devices of the one or more SIS/DER-ES units, a consumer is given an estimate of how much backup power is available at a given time).
As for claim 6, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to deliver a modular generation system to a location of demand (Bain: [0438], energy demand, location of energy consumption; Sanders: [0055], Load and generation profiles are each uniquely associated to a specific location. Understanding of the dynamics of these two profiles, combined with the energy tariff at the site (e.g., the variable rates of energy and demand charges levied by the energy provider)).
As for claim 7, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to route a delivery of energy by a set of energy delivery facilities to a location of demand (Bain: [0248], the platform includes a device management system to control the storage, consumption, generation, and/or delivery of energy by devices).
As for claim 8, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured by the AI-based coordination system to store energy in proximity to a location and time of demand (Bain: [0248], the platform includes a device management system to control the storage, consumption, generation, and/or delivery of energy by devices).
As for claim 9, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured to adapt a transport of data over at least one of a network communication system, and the adapting is based on at least one of, a congestion condition (Bain: [0334], transmission and distribution grid congestion management), a delay condition, a latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition (Bain: [0022], The method includes gathering consumer energy usage measurements for the energy consumed by the consumer across a plurality of energy consumption devices over discrete time intervals), a market factor condition (Bain: Abstract, A platform and components for an automated consumer retail utility marketplace are provided, including components for machine learning, components for gamification, and components for supporting a related consumer mobile application that enables improved visibility and control by a consumer over its interaction with energy markets), or a user configuration condition.
As for claim 12, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured to perform at least one of, extracting energy-related data, detecting errors in energy-related data, correcting errors in energy-related data, transforming energy-related data, converting energy-related data, normalizing energy-related data, cleansing energy-related data, parsing energy-related data, detecting patterns in energy-related data (Bain: [0158], The system includes a machine learning engine configured to receive and analyze the production data and the consumption data to detect patterns, including patterns of consumer and producer behavior), detecting content in energy-related data, detecting objects in energy-related data, compressing energy-related data (Bain: [0257], receive and process compressed and encrypted energy usage information), streaming energy-related data, filtering energy-related data, loading energy-related data, storing energy-related data, routing energy-related data, transporting energy-related data (Bain: [0248], the platform includes a device management system to control the storage, consumption, generation, and/or delivery of energy by devices), or maintaining security of energy-related data.
As for claim 13, Bain-Sanders discloses: wherein the set of local demand requirements is based on at least one of a set of public data resources, that includes at least one of, a weather data resource, a satellite data resource, a census data resource, population data resource, demographic data resource, and/or psychographic data resource, a market data resource, or a set of enterprise data resources, the one or more enterprise data resources that includes at least one of, resource planning data, sales and/or marketing data, financial planning data, demand planning data, supply chain data,
procurement data, pricing data, customer data, product data, or operating data (Bain: Abstract, A platform and components for an automated consumer retail utility marketplace are provided, including components for machine learning, components for gamification, and components for supporting a related consumer mobile application that enables improved visibility and control by a consumer over its interaction with energy markets).
As for claim 14, Bain-Sanders discloses: comprising at least one of an AI-based model or an algorithm, that is trained based on a training data set, wherein the training data set is based on at least one of, one or more human tags, one or more labels, one or more human interactions with a hardware system, one or more human interactions with a software system, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process,
a semi-supervised learning training process, or a deep learning training process (Bain: [0408], In embodiments of the present disclosure, including ones involving expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes).
As for claim 15, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes at least one of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy (Bain: [0248], the platform includes a device management system to control the storage, consumption, generation, and/or delivery of energy by devices).
As for claim 16, Bain-Sanders discloses: wherein a first resource of the set of energy resources is configured to communicate with a second resource of the set of energy resources to orchestrate the delivery of energy to the one or more points of consumption by adjusting at least one of, an energy generation by at least one or the first resource or the second resource, an energy storage by at least one of the first resource or the second resource, an energy delivery by at least one of the first resource or the second resource or an energy consumption by at least one of the first resource or the second resource (Bain: [0183], In embodiments, the machine learning feedback engine feeds an output of machine learning of consumer responses to the presented scenarios into a pricing module of the platform that adjusts a pricing aspect of the criteria so that a measure of meeting the energy usage objective increases).
As for claim 17, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured to adjust the delivery of energy to the one or more points of consumption based on at least one of, a carbon generation policy, or a carbon emissions policy (Bain: [0330], renewable energy credits, carbon credits).
As for claim 18, Bain-Sanders discloses: wherein at least one of the set of energy resources is configured to record, in a distributed ledger, one or more energy-related events, and the one or more energy-related events include at least one of, an energy purchase, an energy sale event, a service charge associated with an energy purchase, a service charge associated with an energy 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 (Bain: [0330], renewable energy credits, carbon credits).
As for claim 19, Bain-Sanders discloses: wherein at least one of the set of energy resources 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 (Bain: [0226], the machine learning of consumer response facilitates influencing user consumption behavior of off-grid energy consumption; [0227], the off-grid energy consumption includes operating an electric car).
As for claim 21, Bain-Sanders discloses: wherein, the selecting the subset of resources includes matching each respective resource of the subset of resources with the set of local demand requirements, and the matching the respective resource with the set of local demand requirements is based on a correspondence between, at least one energy production criterion of the respective resource, and the set of local demand requirements (Bain: [0066], platform for a consumer energy marketplace including a service organization interface through which the consumer energy marketplace receives at least one of energy demand information and real-time pricing information for energy delivered over an energy distribution network that supplies an energy consumer and a host interface through which the consumer energy marketplace processes delivery-related cost information for multiple consumer energy delivery offerings; [0465], the marketplace platform may enable managing production based on the forecast demand for energy from particular types of energy sources. The marketplace platform may collect and optionally aggregate demand estimates for each of the raw sources of energy from a collection of consumers, such as indicated by consumers in a mobile application or other interfaces of the platform. An energy producer load manager may control or signal for energy flow from the raw energy sources at least in part based on the demand. Thus, in embodiments, an energy supply network for a consumer is provided, the network including multiple producers of a type of energy suitable for distribution over a residential energy distribution network, wherein a set of the multiple producers each uses different raw sources of energy to produce the type of energy; a consumer energy marketplace platform that calculates aggregated demand estimates for each of the raw sources of energy based on aggregated consumer energy consumption estimates allocated to each of the raw sources of energy as specified by the consumers); [0328], may match demand and supply).
As per claim 22, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As per claim 23, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As per claim 24, it recites features that are substantially same as those features claimed by claim 21, thus the rationales for rejecting claim 21 are incorporated herein.
As for claim 25, Bain-Sanders discloses: wherein the set of local demand requirements is associated with at least one of: a set of mobile entities, a fleet of vehicles (Sanders: [0043], the distributed architecture described above can be so beneficial in managing energy at the local level), a set of individuals, a set of mobile factory units, or a short-term events (Bain: [0027], [0041], [0275], specifying the location parameter such as local geographic region, a community region, and an administrative district; [0330], [0335], [0389], optimizing energy consumption by accounting renewable energy credits at local level, coordinating use of storage resources with local distributed resource; Sanders: [0174], [0183]-[0186], coordinating one or more energy resources in one or more local events each associated with a distributed energy resource energy storage apparatus; Sanders: [0218], [0222], optimizing performance based on real-time local conditions).
As per claim 26, it recites features that are substantially same as those features claimed by claim 25, thus the rationales for rejecting claim 25 are incorporated herein.
6. Claims 10, 11, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bain and Sanders, and further in view of Youngehoon Park et al (US Publication 20190158309 A1, hereinafter Park).
As for claim 10, Bain-Sanders discloses: comprising … that represents one or more 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 (Bain: Abstract, A platform and components for an automated consumer retail utility marketplace are provided, including components for machine learning, components for gamification, and components for supporting a related consumer mobile application that enables improved visibility and control by a consumer over its interaction with energy markets); but Bain-Sanders does not disclose using digital twin, in another analogous art of AI-based platform for energy management, Park discloses: an adaptive energy digital twin (Park: [0177], the space graph can be a total representation, a digital twin, of an entire space since the space graph can represent the entities of the space, relationships between the entities, and data for the entities).
Bain and Park are analogous arts because they are in the same field of endeavor, AI-based platform for energy management. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bain using the teachings of Park to include digital twin. It would provide Bain’s platform with enhanced capabilities of optimizing the power and energy management system.
As for claim 11, Bain-Sanders-Park discloses: comprising an adaptive energy digital twin (Park: [0177], the space graph can be a total representation, a digital twin, of an entire space since the space graph can represent the entities of the space, relationships between the entities, and data for the entities) that is configured to perform at least one of, providing at least one of a visual indicator or an analytic indicator of energy consumption by one or more energy consumers, filtering energy data, highlighting energy data, adjusting energy data, or generating at least one of a visual or an analytic indicator of energy consumption by at least one of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet (Bain: [0428], the platform 100 can make available a variety of inputs and information to an operator of the platform 100 or other user, such as dashboards, reports, alerts, and the like, so that the operator or other user can respond to and manage individual, group, or aggregate behavior, such as to recognize one or more problems or opportunities that may be evident from the track information, to recognize a pattern in behavior that can be influenced by or considered in connection with managing the platform 100, to predict some aspect of future behavior or outcome related to the platform 100 or some activity managed by the platform, or the like).
As for claim 20, Bain-Sanders-Park discloses: wherein at least one resource of the set of energy resources is associated with a digital twin (Park: [0177], the space graph can be a total representation, a digital twin, of an entire space since the space graph can represent the entities of the space, relationships between the entities, and data for the entities) that is configured to at least one of model at least one of one or more properties or operations of the at least one resource of the set of energy resources, or predict at least one of one or more properties operations of the at least one resource of the set of energy resources (Bain: [0005], The platform may have engines for demand management, pricing and billing, including ones that take advantage of machine learning capabilities, prediction capabilities, and the like).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
Prosecution Insights