Patent Application 18289850 - METHODS AND ELECTRONIC DEVICE FOR HANDLING

Title: METHODS AND ELECTRONIC DEVICE FOR HANDLING SUSTAINABILITY GOAL SETTING IN PHYSICAL INFRASTRUCTURE
Application Information

Invention Title: METHODS AND ELECTRONIC DEVICE FOR HANDLING SUSTAINABILITY GOAL SETTING IN PHYSICAL INFRASTRUCTURE
Application Number: 18289850
Submission Date: 2025-05-15T00:00:00.000Z
Effective Filing Date: 2023-11-07T00:00:00.000Z
Filing Date: 2023-11-07T00:00:00.000Z
National Class: 705
National Sub-Class: 007370
Examiner Employee Number: 90574
Art Unit: 3623
Tech Center: 3600
Rejection Summary

102 Rejections: 1
103 Rejections: 1
Cited Patents

The following patents were cited in the rejection:
Office Action Text



    DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-14 are pending.  The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Priority
Applicant is claiming Foreign Priority to Foreign Applications IN202141020792 filed on 05/07/2021. 

Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(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 35 U.S.C. 112 (pre-AIA ), 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 4 & 11 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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.
Claims 4 & 11  recite the machine learning controller.  There is insufficient antecedent basis for these limitations. Examiner interprets that these limitations as a machine learning controller.

Claim Objections
Claims 6 and 13 are objected to because of the following informalities:
(a)	In claims 6 and 13, it requires a period at the end of the limitation instead of comma.
Appropriate correction is required.

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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.  Specifically, claims 1-14 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1 and 8 recite an abstract idea.  Claims 1 and 8 include “acquire one or more details of the physical infrastructure; acquire a third party information associated with the physical infrastructure; identify a number of goals based on a type of a project and a geography associated with the project, wherein the project is associated with the physical infrastructure; and determine an achievable action based on the one or more acquired details of the physical infrastructure and the third party information associated with the physical infrastructure”.
The limitations above recite an abstract idea under Step 2A Prong One.  More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for managing soil moisture.  As a result, claims 1 and 8 recite an abstract idea under Step 2A Prong One.
Claims 2-7 and 9-14 further describe the process for managing soil moisture.  As a result, claims 2-7 and 9-14 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1 and 8.
With respect to Step 2A Prong Two of the framework, claims 1 and 8 do not include additional elements that integrate the abstract idea into a practical application.  Claims 1 and 8 include additional elements that do not recite an abstract idea under Step 2A Prong One.  The additional elements of claims 1 and 8 include an electronic device, a processor, a memory, and setting controller.  When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea.  As a result, claims 1 and 8 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-3, 5-6, 9-10, and 12-13 do not include any additional elements beyond those recited with respect to claims 1 and 8.  As a result, claims 2-3, 5-6, 9-10, and 12-13 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1 and 8.
Claims 4, 7, 11, and 14 include additional elements that do not recite an abstract idea under Step 2A Prong One.  The additional elements of claims 4, 7, 11, and 14  include a machine learning controller.  When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment.  As a result, claims 4, 7, 11, and 14 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. 
With respect to Step 2B of the framework, claims 1 and 8 do not include additional elements amounting to significantly more than the abstract idea.  As noted above, claims 1 and 8 include additional elements that do not recite an abstract idea under Step 2A Prong One.  The additional elements of claims 1 and 8 include an electronic device, a processor, a memory, and setting controller.  The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea.  Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually.  As a result, independent claims 1 and 8 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-3, 5-6, 9-10, and 12-13 do not include any additional elements beyond those recited with respect to claims 1 and 8.  As a result, claims 2-3, 5-6, 9-10, and 12-13 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1 and 8. 
Claims 4, 7, 11, and 14 include additional elements that do not recite an abstract idea under Step 2A Prong One.  The additional elements of claims 4, 7, 11, and 14  include a machine learning controller.  The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment.  Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually.  As a result, claims 4, 7, 11, and 14 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.  Accordingly, claims 1-14 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.

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 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 1-3, 5-10, and 12-14 are rejected under 35 U.S.C. 102(a)(2)  as being anticipated by Cruickshank et al. (US Pub No. 2022/0385064) (hereinafter Cruickshank et al.), hereinafter Cruickshank.
Regarding claims 1 & 8, Cruickshank a method for handling sustainability goal setting for a physical infrastructure, comprising:
acquiring, by an electronic device (100), one or more details of the physical infrastructure (para [0041], wherein FIG. 1, a load sharing apparatus may comprise a computing device 115, such as a computer, having a processor 116 to run a computer readable medium 117; para [0120], wherein observations included total per residence electricity usage at the service entrance along with up to 25 individually measured loads per home that were acquired and reported separately, including various types of HVAC systems, appliances, lighting, entertainment, home office, and plug loads; and para [0040], wherein infrastructure, including power distribution lines 103, such as high-tension lines, power lines or so-called transmission lines, transformers and substations (step up substations 107/step down substations 120 which may include a step down transformer 105) (i.e., physical infrastructure)); 
acquiring, by the electronic device (100), a third party information associated with the physical infrastructure (para [0121], wherein data anomalies brought into question potential gaps or errors in acquisition, along with the possibility that some appliances, such as a secondary refrigerator or freezer, were intentionally turned off for days, weeks, or months; and para [0403], wherein generated by the distributor, a third party such as a supplier of software or power grid infrastructure, or locally at the residence or building, and even by the energy consuming device itself. In that case, the optimum load shape may be created from forecast data, model data, actual data, or estimates from previous calculations of the optimum load shape); 
identifying, by the electronic device (100), a number of goals based on a type of a project and a geography associated with the project, wherein the project is associated with the physical infrastructure (paras [0087] and [0104]-[0106], wherein …..the goals in Phase 1 were to apply statistical methods to quantify the diversity observed in real-world empirical appliance measurements at varying spatiotemporal scales in order to inform simulation….Phase 2 were to develop physical models that reflect the energy usage diversity observed in Phase 1, e.g., for the DHW heater, and then apply these models in the context of MPC to calculate instantaneous load add and shed opportunities at the electrical feeder level across all U.S…..The goals in Phase 3 were to: 1) Create realistic models of utility generation, distributed generation, distribution grids, and load across the state of Texas…..a model predictive control framework was proposed to determine optimal operating strategies in consideration of energy use, energy expense, peak demand, economic DR revenue, and frequency regulation revenue……; and paras [0039] & [0084], wherein predictive control solutions reduce the energy usage of buildings, improve occupant comfort, and reduce peak electricity demand. The focus was on the automated optimal control of blinds, electric lighting, heating, cooling, and ventilation in individual building zones. Project results were: software, models, and data sets for the simulation based assessment of building control; new algorithms for improved weather predictions at a building's location; analysis of energy-saving potentials related to control; novel control algorithms; and preparation of a demonstration project in a representative office building); and 
determining, by the electronic device (100), an achievable action based on the one or more acquired details of the physical infrastructure and the third party information associated with the physical infrastructure (para [0087], wherein a user-centric home energy management system could help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee was built on a multi-objective model predictive control framework, wherein the objectives consisted of minimizing energy cost and carbon emissions while maximizing thermal comfort and user convenience. Foresee learned user preferences on different objectives and acted on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage).
Regarding claims 2 & 9, Cruickshank the method as claimed in claim 1, comprises: 
determining, by the electronic device (100), a maximum possible value for the number of goals based on the determined achievable action (paras [0087] and [0104]-[0106], wherein …..the goals in Phase 1 were to apply statistical methods to quantify the diversity observed in real-world empirical appliance measurements at varying spatiotemporal scales in order to inform simulation….Phase 2 were to develop physical models that reflect the energy usage diversity observed in Phase 1, e.g., for the DHW heater, and then apply these models in the context of MPC to calculate instantaneous load add and shed opportunities at the electrical feeder level across all U.S…..The goals in Phase 3 were to: 1) Create realistic models of utility generation, distributed generation, distribution grids, and load across the state of Texas. 2) Jointly optimize supply and demand by calculating and broadcasting optimum load shapes to appliances managed by MPC. 3) Calculate the maximum possible impact in variable generation costs and CO2 emissions and the subset thereof (if any) attributable to ARLS.……); and 
allowing, by the electronic device (100), a user of the electronic device (100) to modify the achievable action based on the determined maximum possible value for the goal (para [0087], wherein a user-centric home energy management system could help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee was built on a multi-objective model predictive control framework, wherein the objectives consisted of minimizing energy cost and carbon emissions while maximizing thermal comfort and user convenience; and para [0195], wherein when operating as an Internet-connected smart thermostat, in the context of MPC, GridMPC adjusted setpoints in increments of 0.25 K, which is a typical precision of residential thermostats).
Regarding claims 3 & 10, Cruickshank the method as claimed in claim 1, comprises: 
identifying, by the electronic device (100), resource challenges at the physical infrastructure (para [0064], wherein an OPF model was used that determined power schedules for controllable devices in a power network, such as generators, storage, and curtailable loads, which minimized expected short-term operating costs under various device and network constraints…..); and 
determining, by the electronic device (100), another achievable action based on the one or more acquired details of the physical infrastructure, the third party information associated with the physical infrastructure and the identified resource challenges at the physical infrastructure (para [0265], wherein optimization of electricity supply and demand, one objective of ARLS was flattening the net load met by thermal generators in order raise the overall heat rate efficiency across the generation fleet and thus minimize variable production costs. Another objective of ARLS was reducing the curtailment of low-cost RES by modulating loads to match in-time the forecast availability of RES. The overarching goal of this work was to advance current trends to modernize generation of electricity by introducing ARLS which created load flexibility and elasticity thereby allowing for higher RES utilization, more efficient operation of existing thermal generation, and more effective management of distributed energy resources (DERs) including thermal and battery storage; and para [0403], wherein generated by the distributor, a third party such as a supplier of software or power grid infrastructure, or locally at the residence or building, and even by the energy consuming device itself. In that case, the optimum load shape may be created from forecast data, model data, actual data, or estimates from previous calculations of the optimum load shape).
Regarding claims 5 & 12, Cruickshank the method as claimed in claim 1, wherein the achievable action corresponds to control a usage of at least one of energy, water, carbon, waste and gas at the physical infrastructure, wherein the sustainability goal setting in the physical infrastructure is handled during climate change condition (para [0039], wherein total electricity usage in the U.S., with about 50%
of that consumption occurring in residential buildings; para [0417], wherein thermostats and controllers for storage-capable end-uses were able to use model predictive control to automatically optimize their own on and off setpoints; and para [0429], wherein the annual load was unchanged across all cases and scenarios. A next step is modeling the expected growth in load due to combinations of reasons such as climate change and increased electrification of buildings).
Regarding claims 6 & 13, Cruickshank the method as claimed in claim 1, wherein the one or more details of the physical infrastructure comprises a location of the physical infrastructure, size of a land, a total built area, a type of a building, a number of dwelling, a number of rooms, a number of offices, a number of floors in the office, a number of floors in the building, a size of a dwellings, total rooftop, total landscape area, bathroom fittings, electrical fittings, equipment’s used in the physical infrastructure temperature details associated with the physical infrastructure (para [0407], wherein a location of the subset of the total load or entire grid such as a local region, micro-grid (including a single building, residence), or appliance/energy consuming device may be detected or determined. The location information may be determined on the demand side by the local region, micro-grid (including a single building, residence), or appliance/energy consuming device, or on the supply side (power utility or utility, infrastructure or IT supplier, or distributor, for example, noting that a utility is a broader definition of power utility).
Regarding claims 7 & 14, Cruickshank the method as claimed in claim 1, wherein the third party information is determined based on at least one of a location information of the physical infrastructure and a data source, wherein the third party information comprises a local policy information, a local resource availability information, and usage patterns of resources from consumers, wherein the third party information is determined based on a detail associated with the physical infrastructure using a machine learning controller (150) (para [0407], wherein a location of the subset of the total load or entire grid such as a local region, micro-grid (including a single building, residence), or appliance/energy consuming device may be detected or determined. The location information may be determined on the demand side by the local region, micro-grid (including a single building, residence), or appliance/energy consuming device, or on the supply side (power utility or utility, infrastructure or IT supplier, or distributor, for example, noting that a utility is a broader definition of power utility; para [0261], wherein Initiatives have resulted in a series of policies and mandates……; para [0002], wherein the management of energy resources, and more particularly to a power grid, from the supply side, to the demand side and/or the distributor side; and para [0087], wherein a user-centric home energy management system could help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee was built on a multi-objective model predictive control framework, wherein the objectives consisted of minimizing energy cost and carbon emissions while maximizing thermal comfort and user convenience. Foresee learned user preferences on different objectives and acted on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage; and Data driven appliance models and usage patterns to predict the home's future energy consumption).
Regarding claim 8 rejected based upon the same rationale as the rejection of claim 1, respectively, since it is the system claim corresponding to the method claim. Claim 8 discloses additional feature a processor (140), a memory ( 130), and setting controller (110) (paras [0417] & [0479]-[0481]).

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.

Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Cruickshank et al. (US Pub No. 2022/0385064) (hereinafter Cruickshank et al.) in view of Sinha et al. (US Pub No. 2019/0017719) (hereinafter Sinha et al.). 
Regarding claims 4 and 11, Cruickshank the method as claimed in claim 1, comprises:
monitoring, by the electronic device (100), the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure over a period of time using the machine learning controller (150) (see Cruickshank, para [0407], wherein demand/load may be detected locally by meters, including grid meters that are provided by, for example, cable operators. Such grid meters are capable of connecting to the internet and providing grid metrics for a localized area on the grid, and can be used to detect voltages/load of that area; para [0402], wherein the percentage load for the optimum load shape per hour is the amount of load the respective grid should be drawing, and the energy consuming devices should draw energy at that percentage for that period of time; and para [0087], wherein machine-learning algorithms were used to derive data driven appliance models and usage patterns to predict the home's future energy consumption).
Cruickshank et al. fails to explicitly disclose monitoring, by the electronic device (100), a feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure; and determining, by the electronic device (100), the achievable action based on the monitored feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure.
Analogous art Sinha discloses monitoring, by the electronic device (100), a feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure (see Sinha, para [0070], wherein external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444, applications residing on client devices 448, etc.) for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428…..BMS interface 409 can facilitate communications between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.); and para [0132], wherein provided to the learning engine 512, which may process the feedback to help learn from previously generated agents. For example, a function-based agent 910, such as an energy management agent, may suggest running chillers in parallel to decrease the time to regulate temperature in a portion of a facility); and 
Analogous art Sinha determining, by the electronic device (100), the achievable action based on the monitored feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure (see Sinha, para [0093], wherein the agents are generated having a desired goal, and allowed to determine how to meet the desired goal. In some examples, a generalized framework can be provided to a generated agent to provide constraints as to how the goal may be achieved; para [0070], wherein external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444, applications residing on client devices 448, etc.) for allowing user control, monitoring, and adjustment to BMS controller 366 and/or subsystems 428…..BMS interface 409 can facilitate communications between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.); and para [0132], wherein provided to the learning engine 512, which may process the feedback to help learn from previously generated agents. For example, a function-based agent 910, such as an energy management agent, may suggest running chillers in parallel to decrease the time to regulate temperature in a portion of a facility).
Cruickshank directed to a system for optimizing production and consumption of energy. Sinha directed to providing building simulation for optimal control.  It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Cruickshank, regarding the Optimized Load Shaping System, Method & Apparatus, to have included monitoring, by the electronic device (100), a feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure; and determining, by the electronic device (100), the achievable action based on the monitored feedback corresponding to the one or more details of the physical infrastructure and the third party information associated with the physical infrastructure because both inventions teach improving energy efficiency.  Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. 

Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2022/0344934; US Pat No. 8,694,163; US Pub No. 2023/0085641; US Pub No. 2011/0251807; US Pub No. 2012/0173444; US Pub No. 2016/0127875; US Pub No. 2011/0060612; US Pub No. 2012/0232701; US Pub No. 2021/0287072; US Pub No. 2013/0073488; US Pub No. 2019/0171987; US Pub No. 2019/0362445; US Pub No. 2018/0254632; US Pub No. 2021/0055417; US Pat No. 10,373,085; US Pub No. 2019/0228115; and PL Donti, JZ Kolter (Machine learning for sustainable energy systems) … Review of Environment and Resources, 2021 - annualreviews.org. 
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ KASSIM whose telephone number is (571)272-8534.  The examiner can normally be reached on Mon - Fri (8am - 5pm) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045.  The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623                                                                                                                                                                                                        5/12/2025