DETAILED ACTION
Response to Amendment
This Final Office action is in response to Applicant’s amendment filed 7/25/2026. Claims 1-24 have been amended and 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 .
The previously pending objection to the claims has been withdrawn.
Applicant's arguments filed 7/25/2026 have been fully considered but they are not persuasive. Additionally, Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in INDIA on 09/26/2023. It is noted, however, that applicant has not filed a certified copy of the 202321064636 application as required by 37 CFR 1.55.
Drawings
The drawings received on 7/25/2026 are accepted.
Claim Objections
Claims 1 and 13 are objected to because of the following informalities: The claims recite “AI powered”, without defining the acronym “AI”. Appropriate correction is required.
Regarding the previously pending 35 USC 101 rejection, the claims as a whole, recite additional elements that integrate the judicial exception into a practical application, under Prong Two of Step 2A of the Alice analysis.
Specifically, independent claim 1 recites, inter alia, “an edge platform associated with each building of the at least one building, the edge platform configured to integrate with a plurality of sensors and to analyze and process, in real-time, data received from the plurality of sensors, wherein the edge platform moves a portion of storage and computing resources out of a central data center and closer to a source of the data such that a result of the processing at the edge platform is transmitted to the central data center, the one or more executable components of the processor comprising: a data acquisition module configured to acquire data in real-time from the plurality of sensors, via the edge platform, and one or more external sources, wherein the plurality of sensors is configured to monitor data pertaining to one or more assets associated with each building of the at least one building, and wherein the data acquired from the plurality of sensors comprises the result of the processing at the edge platform.”
Additionally, independent claim 13 recites, inter alia, “processing, by an edge platform associated with each building of the at least one building, data received from a plurality of sensors in real-time, wherein the edge platform moves a portion of storage and computing resources out of a central data center and closer to a source of the data such that a result of the processing at the edge platform is transmitted to the central data center; and acquiring, via the edge platform, the data in real-time from the plurality of sensors and one or more external sources, wherein the acquiring comprises monitoring, by the plurality of sensors, data pertaining to one or more assets associated with each building of the at least one building, and wherein the data acquired from the plurality of sensors comprises the result of the processing at the edge platform.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Jones et al (US 20240037471 A1), in view of Chakrabarti et al (US 20230401518 A1), in further view of McDade (US 20210374297 A1).
As per claim 1, Jones et al disclose a system for calculating real-time sustainability score of an entity 102, the entity 102 having at least one building in at least one geographical location (i.e., obtaining building operational data from a plurality of building management systems, providing scores for a plurality of potential sustainable infrastructure projects, ¶ 0018), the system comprising:
a memory configured to store one or more executable components; a processor operatively coupled to the memory, the processor configured to execute the one or more executable components (i.e., Referring now to FIG. 2, a block diagram of a building automation system (BAS) 200 is shown, according to an exemplary embodiment, ¶ 0026), and the one or more executable components of the processor comprising:
a data acquisition module configured to acquire data in real-time from the plurality of sensors and one or more external sources, wherein the plurality of sensors is configured to monitor data pertaining to one or more assets associated with each building of the at least one building (i.e., building subsystem integration layer 220 can receive sensor data and input signals from building subsystems 228 and provide output data and control signals to building subsystems 228, ¶ 0035);
a KPI evaluation module configured to assess key performance indicators (KPIs) pertaining to the one or more assets based on the data acquired from the plurality of sensors and the one or more external sources (i.e., analyzer 408 can identify an energy consumption baseline for the building, identify benchmarking for the building (e.g., compare the baseline of the building to other peer buildings or an industry to determine a benchmark index), determine facility key performance indicators (KPIs), ¶ 0057);
a sustainability score evaluation module configured to evaluate a sustainability score of each building (i.e., Scoring in step 1106 can provide a value (e.g., number, letter grade) for each potential sustainable infrastructure project that represents an overall assessment of the feasibility and benefits of the potential sustainable infrastruction project, ¶ 0119), wherein the sustainability score evaluation module is configured to compare KPIs pertaining to the one or more assets against at least one of a compliance standard, organization energy goals, water goals, emission goals, waste goals, and digital transformation scale (i.e., sustainability tracker 808 can track the progress of the building towards meeting various sustainability goals. The sustainability tracker 808 can, in some embodiments, retrieve operational building data from the data storage 324, energy bills from the data storage 324, receipts of REC purchases from the data storage 324, etc. The sustainability tracker 808 can identify carbon emissions levels for a building at various times in the past and/or at the present, ¶ 0071);
an aggregation module configured to aggregate the sustainability scores corresponding to each building to estimate a global sustainability score (i.e., At step 1104, various data relating to the sustainable infrastructure projects is aggregated, ¶ 0118. Projects which provide the highest benefits at the lowest costs and at the highest rates of success may have the best (e.g., highest) scores, whereas projects with lower benefits, higher costs, and/or more risks may have worse (e.g., lower) scores. Scoring in step 1106 is based on some or all of the data aggregated in step 1104, ¶ 0119); and
a reporting module configured to generate a report indicating the sustainability score of each building, and global sustainability score of the entity (i.e., The user portal 802 includes a sustainability reporter 810. The sustainability report generator 804 can generate various reports indicating sustainability information for the building, ¶ 0072. The GUI elements may summarize relative energy use and intensity across various plants, subplants, or the like. Other GUI elements or reports may be generated and shown based on available data that allow users to assess the results of the simulation. The user interface or report (or underlying data engine) may be configured to aggregate and categorize resource allocation and the costs associated therewith and provide the results to a user via a GUI, ¶ 0092),
wherein the report further comprises corrective action recommendations, and wherein the processor generates one or more real-time corrective actions based on the report to optimize at least one of heating, venting, and air conditioning (HVAC) or lighting systems of the at least one building through Al powered smart maintenance (i.e., The information displayed in the user portal 802 can be based on the results of the optimizations run by the optimization system 322. The portal 802 can provide various reports and/or recommendations to the user (e.g., recommended facility improvement measures (FIMs), recommendations to purchase renewable energy credits (RECs), recommendations to adopt updated control strategies, etc.) for planning the construction, retrofit, and/or operation of a building to meet one or more sustainability goals, ¶ 0068, wherein a neural network or other machine-learning algorithm can be trained to classify (or otherwise rate) sets of environmental conditions, building characteristics, and/or other data as being feasible or unfeasible for certain types of projects, ¶ 0123).
Jones et al does not disclose an edge platform associated with each building of the at least one building, the edge platform configured to integrate with a plurality of sensors and to analyze and process, in real-time, data received from the plurality of sensors, wherein the edge platform moves a portion of storage and computing resources out of a central data center and closer to a source of the data such that a result of the processing at the edge platform is transmitted to the central data center, and a data acquisition module configured to acquire data in real-time from the plurality of sensors, via the edge platform, and one or more external sources, wherein the plurality of sensors is configured to monitor data pertaining to one or more assets associated with each building of the at least one building, and wherein the data acquired from the plurality of sensors comprises the result of the processing at the edge platform.
Chakrabarti et al disclose edge and gateway systems may be located in warehouses (i.e., on-site) as embedded or fixed systems and/or other user devices such as tablet PCs and mobile phones. Each edge system may be coupled to a warehouse system from which warehouse operations data may be collected, and in communication with other edge systems and gateway systems (¶ 0027).
The edge systems and gateway systems each identify source(s) of relevant data, and request that data be provided dynamically (as needed) or statically (all the time) from the identified source(s), such as from other edge systems coupled to warehouse systems in the warehouse or other warehouses, gateway systems in the warehouse or other warehouses, decentralized system(s) such as cloud computing center(s), and centralized system(s) such as dedicated server farms (¶ 0028). Each edge system in a warehouse may be coupled to a sensor of a corresponding warehouse system in the same warehouse, enabling data captured by the sensor to be provided directly to the edge system (¶ 0029).
The edge software may enable each edge system or gateway system to perform various functions listed below (non-exhaustive) to enable data analysis and data exchange among the various systems illustrated herein (e.g., edge systems, gateway systems, warehouse operations centers, remote systems): Filter and analyze real-time and stored data collected from other edge systems, warehouse systems, gateway systems, and/or operations center(s), and generate events based on the analysis (¶¶ 0032-0033).
Additionally, Jones et al does not disclose wherein the global sustainability score is estimated in real-time based on one or more weights assigned to the KPIs pertaining to the one or more assets of each building.
McDade discloses a system value may be assigned to each building characteristic and each KPI stored at the building database 110. The value associated with each KPI, may be based on the units for a given building attribute, or may be scaled to a unitless system value. The impact of each system value on the overall building quality index score may be weighted (¶ 0029).
Jones et al, Chakrabarti et al and McDade are concerned with effective building management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include an edge platform associated with each building of the at least one building, the edge platform configured to integrate with a plurality of sensors and to analyze and process, in real-time, data received from the plurality of sensors, wherein the edge platform moves a portion of storage and computing resources out of a central data center and closer to a source of the data such that a result of the processing at the edge platform is transmitted to the central data center, and a data acquisition module configured to acquire data in real-time from the plurality of sensors, via the edge platform, and one or more external sources, wherein the plurality of sensors is configured to monitor data pertaining to one or more assets associated with each building of the at least one building, and wherein the data acquired from the plurality of sensors comprises the result of the processing at the edge platform, and wherein the global sustainability score is estimated in real-time based on one or more weights assigned to the KPIs pertaining to the one or more assets of each building in Jones et al, as seen in McDade, since 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.
As per claim 2, Jones et al disclose the entity is related to a plurality of categories such as, the plurality of categories comprising at least one of a commercial category an industrial category, a residential category, and a retail-mixed developments category (i.e., The facility data 312 can describe a building facility, e.g., provide a name of the facility or campus, identify a number of buildings in the facility or campus, identify a use of each building, include a name of each building, indicate campus layout, indicate building size, indicate building square footage, indicate campus square footage, indicate geographic location, ¶ 0053).
As per claim 3, Jones et al disclose the one or more assets comprise at least one waste treatment plants, Uninterrupted Power Supply (UPS), generators, security systems, transformers, air conditioners, water resources, chiller systems, emission systems, roof top solar systems, Heating, ventilation, and Air Conditioning (HVAC), and exhaust systems (i.e., The BMS that serves building 10 includes an HVAC system 100. HVAC system 100 can include HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building 10, ¶ 0022).
As per claim 4, Jones et al disclose the plurality of sensors comprises at least one of temperature sensors, relative humidity (RH) sensors, pressure sensors, flow sensors, power sensors, occupancy sensors, motion sensors, water quality sensors, gas sensors, plant growth sensors, internal air quality sensors, and external air quality sensors (i.e., AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow, ¶ 0025).
As per claim 5, Jones et al disclose the one or more external sources can be at least one of manual logs, utility bills, government compliances, regulatory policies, government policies, health and safety compliances, meteorological data, air quality data, weather data, any other related applications (i.e., Data can be aggregated from public (e.g., government) sources (e.g., U.S. Energy Information Administration, Department of Energy, National Oceanic and Atmospheric Administration, National Weather Service, EnergyPlus), utility companies, client relationship management software/databases, ¶ 0118).
As per claim 6, Jones et al disclose the KPIs are assessed based on one or more sustainability factors, the one or more sustainability factors comprising at least one of building performance index (BPI), energy efficiency index (EEI), energy savings, carbon footprint, green power quotient – onsite offsite renewables, water balance – consumption and recycled, waste management and reduce-reuse-recycle practices, air quality, water quality, emission and effluent control, and organization Corporate Socio Responsibility (CSR) plantations health and respective carbon sequestration (i.e., The sustainability goals 314 can be customer goals for their building with respect to energy reduction, carbon creation, carbon footprint, water usage reduction, switching to renewable energy, purchasing a certain number of renewable energy credits, etc., ¶ 0054).
As per claim 7, Jones et al does not disclose a compliance standard is selected from one of IGBC (Indian Green Building Council), ECBC (Energy Conservation Building Code under Bureau of Energy Efficiency), USGBC (United States Green Building Council), BREAM (Building Research Establishment Environmental Assessment Methodology), SGBC (Singapore Green Building Council), and BRSR (Business Responsibility and Sustainability Reporting).
McDade discloses Information stored at the building database 110 may also come from third parties such as contractors, designers, permit offices, utilities, as well as other building standards such as U.S. Green Building Council's Leadership in Energy and Environmental Design (LEED®), National Association of Home Builders' ICC 700 National Green Building Standard (NGBS), Energy Star, etc. (¶ 0028).
Jones et al and McDade are concerned with effective building management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a compliance standard is selected from one of IGBC (Indian Green Building Council), ECBC (Energy Conservation Building Code under Bureau of Energy Efficiency), USGBC (United States Green Building Council), BREAM (Building Research Establishment Environmental Assessment Methodology), SGBC (Singapore Green Building Council), and BRSR (Business Responsibility and Sustainability Reporting) in Jones et al, as seen in McDade, since 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.
As per claim 8, Jones et al does not disclose the weights assigned to the KPIs pertaining to the one or more assets of each building is based on building design efficiency, Site Planning, Water Conservation, Energy Efficiency, Emissions, Waste Management, Indoor Environmental Quality, and Innovations.
McDade discloses key performance indicators may be numeric metrics associated with observed building characteristics such as construction type, operational cost, natural disaster risk, safety equipment, energy efficiency, etc. A system value may be assigned to each building characteristic and each KPI stored at the building database 110. The value associated with each KPI, may be based on the units for a given building attribute, or may be scaled to a unitless system value. The impact of each system value on the overall building quality index score may be weighted (¶ 0029).
Jones et al and McDade are concerned with effective building management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the weights assigned to the KPIs pertaining to the one or more assets of each building is based on building design efficiency, Site Planning, Water Conservation, Energy Efficiency, Emissions, Waste Management, Indoor Environmental Quality, and Innovations in Jones et al, as seen in McDade, since 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.
As per claim 9, Jones et al disclose an insights module configured to derive insights and provide corrective action recommendations, wherein the insights module leverages one or more machine learning (ML) models to derive the insights and optimize the operations (i.e., A neural network or other machine learning classifier can be used to group/classify projects based on such inputs, for example. Historical project data can be used to identify expected savings in energy usage, emissions, etc. and/or expected project costs, ¶ 0125).
As per claim 10, Jones et al disclose the reporting module further comprises a centralized dashboard configured to display sustainability insights and corrective action recommendations from at least one of a ground level of the entity, a building level of the entity, and a holistic organization level (i.e., Reporting applications 932 may include a web-based reporting application with several graphical user interface (GUI) elements (e.g., widgets, dashboard controls, windows, etc.) for displaying key performance indicators (KPI) or other information to users of a GUI, ¶ 0092).
As per claim 11, Jones et al disclose an audit generation module configured to generate audit reports in real-time based on selected compliance standard(s) for single building, or buildings grouped under a particular region (City, State, Country) or Global Organization Report in a single click (i.e., the analyzer 408 could identify invoice data, perform an audit on utility bill data, and/or perform an analysis on energy rates and/or tariffs for the energy (e.g., environmental penalties for various forms of energy). The analyzer 408 can identify an energy consumption baseline for the building, identify benchmarking for the building (e.g., compare the baseline of the building to other peer buildings or an industry to determine a benchmark index), ¶ 0057).
As per claim 12, Jones et al disclose a forecast module configured to forecast sustainability score of each building and global sustainability score of the entity, wherein the forecast module forecasts sustainability score by utilizing the one or more machine learning (ML) models that are trained on sustainability score pattern, historical trends of energy consumption and savings, water consumption and savings, emissions, waste generation and recycled, indoor environment quality, building design, and operational efficiency (i.e., step 1106 includes performing an advanced assessment of compatibility between environmental conditions, utility costs/programs/etc., and building characteristics to influence the scoring. In some embodiments, a neural network or other machine-learning algorithm can be trained to classify (or otherwise rate) sets of environmental conditions, building characteristics, and/or other data as being feasible or unfeasible for certain types of projects, ¶ 0123).
Claims 13-24 are rejected based upon the same rationale as the rejection of claims 1-12, respectively, since they are the method claims corresponding to the system claims.
Response to Arguments
In the Remarks, Applicant argues that the combination of Jones and McDade does not teach, suggest, or render obvious at least, for example, the newly presented features of "an edge platform associated with each building of the at least one building, the edge platform configured to integrate with a plurality of sensors and to analyze and process, in real-time, data received from the plurality of sensors, wherein the edge platform moves a portion of storage and computing resources out of a central data center and closer to a source of the data such that a result of the processing at the edge platform is transmitted to the central data center" and "the data acquired from the plurality of sensors comprises the result of the processing at the edge platform transmitted to the central data center," as recited in amended independent claim 1.
The Examiner respectfully submits that Jones et al, in view of Chakrabarti et al, in further view of McDade, indeed disclose Applicant’s amended claim language, as described in the updated rejection.
The Applicant further submits that Jones also does not describe the aggregation limitation recited by claim 1, namely "an aggregation module configured to aggregate the sustainability scores corresponding to each building to estimate a global sustainability score." The aggregation step in Jones aggregates input data - e.g., utility information, climate data, building characteristics, and historical project results (See Jones, paragraph [0118]) - rather than aggregating per-building sustainability scores. Jones also does not describe estimating a "global sustainability score" of the entity from the sustainability scores of the individual buildings. McDade does not cure this deficiency, as McDade was cited only for the proposition that KPI values may be weighted when calculating a building quality index score.
The Applicant further submits that Jones also fails to describe the reporting limitation recited by claim 1, namely a "reporting module 222 configured to generate a report indicating the sustainability score of each building [and] global sustainability score of the entity 102." Jones's report indicates sustainability information and construction, retrofit, or operational plans for a building (See Jones, paragraph [0072]), but does not indicate a global sustainability score of the entity. McDade does not cure this deficiency.
The Examiner respectfully disagrees. Contrary to Applicant’s assertion, Jones et al indeed disclose Applicant’s claimed limitations.
Specifically, Jones et al disclose scoring in step 1106 can provide a value (e.g., number, letter grade) for each potential sustainable infrastructure project that represents an overall assessment of the feasibility and benefits of the potential sustainable infrastruction project. Scoring in step 1106 is based on some or all of the data aggregated in step 1104, ¶ 0119).
Additionally, McDade discloses a system value may be assigned to each building characteristic and each KPI stored at the building database 110. The value associated with each KPI, may be based on the units for a given building attribute, or may be scaled to a unitless system value. The impact of each system value on the overall building quality index score may be weighted (¶ 0029). Moreover, the claim language merely describes the entity having at least one building.
Further, Jones et al disclose the user portal 802 includes a sustainability reporter 810. The sustainability report generator 804 can generate various reports indicating sustainability information for the building, ¶ 0072. The GUI elements may summarize relative energy use and intensity across various plants, subplants, or the like. Other GUI elements or reports may be generated and shown based on available data that allow users to assess the results of the simulation. The user interface or report (or underlying data engine) may be configured to aggregate and categorize resource allocation and the costs associated therewith and provide the results to a user via a GUI (¶ 0092), wherein at step 1106, the potential sustainable infrastructure projects are scored (e.g., valued, rated, graded, awarded points). Scoring in step 1106 can provide a value (e.g., number, letter grade) for each potential sustainable infrastructure project that represents an overall assessment of the feasibility and benefits of the potential sustainable infrastruction project.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623
October 2, 2026
