DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the
first inventor to file provisions of the AIA .
Response to Arguments
2. The Amendment filed on March 3, 2026, has been entered. The examiner acknowledges the amendments to claims 1, 5, 14, 15, and 19, the cancellation of claims 12, and 13, and the addition of claims 20-21.
Rejections under 35 U.S.C. § 101: Applicant’s amendments and arguments present a compelling case that the invention integrates the abstract ideas into a practical application. The combination of sensor data collection, processing, analysis of environmental conditions leading to the application of interventions as needed, with analysis of the intervention and applying feedback to further refine the rules and modifiers to make the system more effective and enable anticipating future adverse risk conditions for proactive mitigation is a practical application of the abstract idea. Rejections under 35 U.S.C. § 101 are withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s arguments in favor of claims 1 and 19 includes points concerning libraries of historic interventions, modifiers, and other database equivalents, along with analysis and validation of the interventions, and a library of modifiers. Prior art describes equivalent processes of analysis, developing intervention plans and modifier equivalents. Independent claims 1 and 19 remain rejected, along with new claim 20. New claim 21 was not rejected by prior art.
Claim Rejections – 35 U.S.C. § 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 1-11, 14-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-11, 14-21 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-11, 14-21 are directed to a process (method), machine (system), which are statutory categories of invention.
Step 2A
Claims 1-11, 14-21 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of mitigating risk in an enclosed space based on data taken in the area and comparing it with a set of rules to determine if mitigation is required, selecting an intervention if mitigation action is required, and generating instructions to implement the intervention.
Claim 19 discloses a method of mitigating air quality risk in an enclosed space
comprising:
processing, environmental data indicating a risk factor of the enclosed space against a set of rules to determine if the risk is elevated to an elevated risk level where mitigating action is required; (following rules or instructions, observation, evaluation, judgment, opinion, mitigating risk),
selecting one or more available interventions which can be enacted when the risk is elevated to the elevated risk level where intervention is required; (following rules or instructions, observation, evaluation, judgment, opinion, mitigating risk),
modifying the intervention based on an energy efficiency modifier in order to modify the intervention that is required if it is known that one intervention is more energy efficient than the others: (following rules or instructions, observation, evaluation, judgment, opinion, mitigating risk),
when an intervention has been selected, sending a signal to instruct the operation of the selected intervention: and
analyzing the effect of an intervention on the risk factors of the space and recording the result storing historic interventions which have taken place, together with the (i) verification and/or (ii) validation of those interventions; (following rules or instructions, observation, evaluation, judgment, opinion, mitigating risk),
using the contents of the library when selecting one or more mitigation actions; and
modifying at least one of the rules and the modifiers for future use when selecting an intervention or when modifying the intervention, (following rules or instructions, observation, evaluation, judgment, opinion).
Additional limitations employ the method to generate a healthiness rating based on risk factors, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk - claim 2), indicating the healthiness rating, (following rules or instructions, observation, evaluation, judgement, opinion – claim 3), collecting a set of standards for the enclosed space, (following rules or instructions, observation, evaluation, judgement, opinion – claim 4), including a set of modifiers including risk, rating, and workflow to enable the intervention to be adjusted, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 5),
modifying the elevated risk level based on the modifiers in order to modify the level at which mitigation is required, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk - claim 6), modifying the intervention based upon the workflow or energy efficiency modifiers in order to modify the intervention that is required, (following rules or instructions, observation, evaluation, judgement, opinion - claim 7), a means for sourcing external environmental information and applying it to the process, (following rules or instructions, observation, evaluation, judgement, opinion – claim 8), maintaining collected data over a period of time, (following rules or instructions, observation, evaluation, judgement, opinion – claim 9), ensuring verification that an intervention has been implemented, (following rules or instructions, observation, evaluation, judgement, opinion – claim 10), analyzing the effect of the intervention on the risk factors of the enclosed space, and if further action is required, selecting a next intervention, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 11), where additional expertise is applied to the analysis of the intervention impacts on the risk factors, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 14), where the rules can be edited to change the intervention actions, (following rules or instructions, observation, evaluation, judgement, opinion – claim 15), where editing the rules can lead to changes in the risk, workflow, rating, or energy efficiency modifiers, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 16), conducting analysis on environmental data to identify repeat instances and pre-empt the elevation of risks with implementation of an intervention, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 17), and the process is connected to all the cognizant offices to carry out the interventions, (following rules or instructions, observation, evaluation, judgement, opinion - claim 18), where the workflow instruction signal controls an operating setting, (following rules or instructions, observation, evaluation, judgement, opinion – claim 20) and where the energy efficiency modifier restricts an intervention when an outdoor temperature is more than 15°C below a desired indoor temperature, (following rules or instructions, observation, evaluation, judgement, opinion – claim 21).
Claim 1 recites similar abstract ideas as those identified with respect to claim 19.
Thus, the concepts set forth in claims 1-11, 14-21 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-11, 14-21 recite additional limitations which are hardware or software elements such as a rules engine, environmental sensors, an HVAC system, an infrastructure library, that workflow instruction signal, a workflow interface, a workflow instruction library, in a workflow response library which
a processor unit, a workflow engine, an indicator, a standards library, a library of modifiers, a means for sourcing external environmental information, a machine learning or AI engine, and a plurality of different systems connected to the workflow interface,
these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination discloses an environmental monitoring and control system for enclosed spaces, where a workflow engine responds to sensed environmental conditions, using an HVAC system to mitigate air quality risks. The system collects and processes data, implements available interventions, and applies machine learning or artificial intelligence to the analysis of the effect of the intervention on the sensed risk factors. The system edits the rules to change its mitigation responses and modifiers, storing responses for future intervention, and analyzes stored sensor data and external environmental information to identify repeat instances where elevated risk is detected to anticipate and enable pre-empting future risk elevation. This recites a practical application.
Therefore, since the limitations in the claims 1-11, 14-21 transform the exception into a patent eligible application and the claims are directed to statutory subject matter, the claims are not rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form
the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on
sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 8-11, 14-16 18-20 are rejected under 35 U.S.C. § 102(a)(2) as being taught
by Nesler, (US 20210010701 A1), “Air Quality Control and Disinfection System.”
Regarding Claim 1, Nesler teaches, A system for mitigating air quality risk in an enclosed space comprising:
a processor unit having a rules engine and a workflow engine;
an infrastructure library accessible by the processor unit and holding a plurality of
available interventions using an HV AC system which can be enacted by the system to
mitigate risk; (reducing health risks with respect to an infectious disease in buildings, [Abstract], a building management system (BMS) controller may include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.), [0124], business rules engine [0146], and demand response layer 414 includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer 418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner, [0138]),
Demand response layer 414 may further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.), [0140]),
a plurality of environmental sensors, each arranged to supply the processor unit
with sensor signals indicating a sensed risk factor of the enclosed space; (Input devices may also include sensors such as air quality sensors, lighting sensors, humidity sensors, air flow sensors, and other types of sensors that can obtain data about building 10. Input devices may also include user devices such as smartphone 558 and wearable device 559. This data can be leveraged to facilitate more effective and efficient control of building 10. [0167], Heat maps and other similar visualizations can be generated for infectious disease prevention and disinfection system control. These visualizations can be generated based on occupancy data, health risk data (e.g. from a health authority source), and other types of data, and can provide a user with an efficient and straightforward view of health risks within a building, [0158]);
a workflow interface arranged to instruct the operation of an intervention; (Client device 368 may include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 100, its subsystems, and/or devices, [0126] and directing targeted actions to minimize health risks, [0269]),
a library of modifiers including energy efficiency modifiers; (Demand response layer 414 may further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc., [0140], Demand response layer 414 may be configured to optimize resource usage (e.g., electricity use…) [0137] and determine optimal control actions for building subsystems, [0134]), and
a workflow response library which stores historic interventions which have taken
place, together with the (i) verification and/or (ii) validation of those interventions;[0144], 0134, (database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described, [0132], Automated measurement and validation (AM&V) layer 412 may be configured to verify that control strategies [ ] are working properly, using data aggregated by the AM&V layer, [0144], to generate a model that is accurately reflects the space and equipment, representative training data should be gathered. To gather representative training data, the training data generator can perform various tests to gather the training data. The tests can include providing experimental setpoints to the HVAC and disinfection system to determine how the space responds to various setpoints, [0501], Integrated control layer 418 may be configured to provide feedback to demand response layer 414 so that demand response layer 414 checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained [0143]).
wherein the rules engine is configured to process the sensor signals from the
plurality of sensors against a set of rules to determine if the risk is elevated to an
elevated risk level where mitigating action is required; (Process 1100 (FIG. 11) is also shown to include applying control logic (step 1106). A variety of different approaches are contemplated to evaluate the map and/or associated data generated in step 1104. For example, a rules-based approach can be implemented to trigger certain actions in response to parameters exceeding predetermined thresholds, 0169]),
wherein the workflow engine is configured to select one or more of the available
interventions from the infrastructure library when the risk is elevated to the elevated
risk level where mitigating action is required; (database 2524 is a memory bank of memory 2516 configured to store healthcare data collected from HAIS 2526, disinfection technique data collected from the disinfectant mechanisms 2502 (e.g., process duration, number of disinfection techniques conducted over a given period, etc.), and any other type of data useful for the operation and/or monitoring of the disinfection subsystem 450, [0288], (a rules-based approach can be implemented to trigger certain actions in response to parameters exceeding predetermined thresholds, [0169]).
wherein the workflow engine is configured to modify the intervention based on
the energy efficiency modifier in order to modify the intervention that is required if it
is known that one intervention is more energy efficient than the others; (For example, control variable optimizer 4117 may run optimization operations in order to determine and implement the most energy (and accordingly, cost) efficient means of achieving a level of occupant comfort by affecting one or more of the control variables. Further to the previous example, this may involve increasing fan speed in order to achieve a cooling effect and thus increase occupant comfort over time, which may be more energy efficient that implementing a chiller to affect air temperature within an area, [0398], Additionally, data from both comfort calculator 4114 and energy usage calculator 4116 can be communicated to control variable optimizer 4117, which can then accordingly incorporate both comfort and energy cost/usage considerations in various optimization processes as described previously, [0399]).
wherein the processor unit is configured to generate a workflow instruction signal
when an intervention has been selected and to send that workflow instruction signal
to the workflow interface; (Disinfection subsystem 450 may be implemented in a building (e.g., building 100) to automatically monitor and/or control various disinfectant mechanisms.
Disinfection subsystem 450 is shown to include a disinfection system controller 2500 and a plurality of disinfectant mechanisms 2502 configured to perform one or more disinfection techniques,[0270], disinfection system controller 2500 is shown to communicate with a device 2510, [0275], device 2510 may receive disinfection data from disinfection system controller 2500 through communications interface 2508 for viewing/analysis by a user, [0277]).
wherein the processor is configured to analyze the effect of an intervention[0167] on the sensed risk factors of the space from the environmental sensors, [0144] and to record the result in the workflow response library; (controller 4102 may analyze the environmental for each of the micro-climate locations in order to determine proper control action to be taken. Data compiler 4112 may be configured to sort or otherwise organize environmental data in various ways, such as by location, collection mechanism (e.g., IR camera 4122, mobile sensors 4124, thermostat 4126) and/or prioritize said environmental data, [0394], Process 1100 is also shown to include applying control logic (step 1106). A variety of different approaches are contemplated to evaluate the map and/or associated data generated in step 1104. For example, a rules-based approach can be implemented to trigger certain actions in response to
parameters exceeding predetermined thresholds, [0169], memory 408 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to an exemplary embodiment, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein.[0132]
wherein the processor unit is also configured to use the contents of the workflow
response library when selecting one or more mitigation actions; [system manager 3502 automatically creates equipment models for connected devices that do not contain an equipment model [0359], a building management system (BMS) 400 is shown in FIG. 4, implemented in building 10 to automatically monitor and control various building functions. 0129], In some embodiments, demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer 414 uses equipment models to determine an optimal set of control actions, [0139],
wherein the processor unit is arranged to modify at least one of the rules and the
modifiers for future use by the workflow engine when selecting an intervention or
when modifying the intervention, (model generator 1714 can generate/update a setpoint adjustment model to be able to more precisely model occupant comfort given various conditions of zone 1506, [0208].
Regarding claim 2, Nesler teaches, A system according to claim 1, and further teaches wherein the rules engine is arranged to generate a technical healthiness rating based on its determination of risk, the technical healthiness rating being an objective technical measure of the risk factors in the enclosed space. (Disinfection subsystem 450, as well as BMS 400 more generally, can be configured to model the probability of infectious disease spread within building 10. For example, probability of infection spread within a building can be determined based on a number of factors, including occupancy data, the quanta generation rate of the infectious disease, a clean air ventilation rate, and other factors. The Wells-Riley equation can be used to model probability of infection spread.)
Regarding claim 3, Nesler teaches, A system according to claim 1, and further teaches, further comprising an indicator for generating an indication of the technical healthiness rating. Nesler teaches, (The Wells-Riley equation can be used to model probability of infection spread, for example, and is denoted as follows:
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[0267]).
Regarding claim 5, Nesler teaches, A system according to claim 1, and further teaches, wherein the library of modifiers further includes at least one of: risk modifiers; rating modifiers; and workflow modifiers, (referring to FIG. 17, memory 1706 is shown to include a data collector 1712. In some embodiments, data collector 1712 is configured to receive data from various sources (e.g., temperature sensor 1518, thermostat 1522, supervisory controller 1526, a user device 1710 etc.) and communicate said data between components of memory 1706. For example, data collector 1712 is shown to communicate occupant comfort data to a comfortable range identifier 1718, a zone group setpoint to setpoint adjustment manager 1716, and training data to a model generator 1714, [0198]).
Regarding claim 8, Nesler teaches, A system according to claim 1, and further teaches, further comprising means for sourcing external environmental information and supplying that information to the processor unit. (The collected data provided to disinfection system controller 2500 by data 2540 is shown to include outdoor air quality data 2542, indoor air quality data 2544, and occupancy data 2546, according to some embodiments. [ ] Each of the data may be collected by sensors included in the disinfectant mechanisms 2502, BMS 400, and/or external, standalone devices, according to some embodiments. In some embodiments, the data is collected from an external source. For example, outdoor air quality data 2542 may be provided by a weather agency, [0265]).
Regarding claim 9, Nesler teaches, A system according to claim 1, and further teaches, further comprising a sensor data storage for storing sensor data corresponding to the sensor signals from the sensors over a period of time. (Memory 2516 is also shown to include a database 2524 configured to store data (e.g., data received from disinfectant mechanisms 2502, device
2510, BMS 400, HAIS 2526, etc.), according to some embodiments. In some embodiments, database 2524 is a memory bank of memory 2516 configured to store healthcare data collected from HAIS 2526, disinfection technique data collected from the disinfectant mechanisms 2502 (e.g., process duration, number of disinfection techniques conducted over a given period, etc.), and any other type of data useful for the operation and/or monitoring of the disinfection subsystem 450. In some embodiments, a user can access the data stored in database 2524 via device 2510, [0288]).
Regarding claim 10, A system according to claim 1, and further teaches, wherein the workflow interface is arranged to source verification that an intervention has been implemented. Nesler teaches, (Automated measurement and validation (AM&V) layer 412 may be configured to verify that control strategies commanded by integrated control layer 418 or demand response layer 414 are working properly (e.g., using data aggregated by AM&V layer 412, integrated control layer 418, building subsystem integration layer 420, FDD layer 416, or otherwise). The calculations made by AM&V layer 412 may be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model, [0144]).
Regarding claim 11, Nesler teaches, a system according to claim 1, and further teaches, wherein the processor unit is configured to analyze the effect of the intervention on the sensed risk factors of the enclosed space from the environmental sensors, and if further intervention is required, to select a further intervention. (Based on the presence detection and the environmental condition measurements, the comfort controller can determine if additional airflow is needed to be provided in the space. [ ] Based on what and where environmental conditions are determined to require adjustment, the comfort controller can generate control signals and operate an AHU based on said control signals, [0429]).
Regarding claim 14, Nesler teaches, A system according to claim 1, and further teaches, wherein the processor unit includes a machine learning or AI engine arranged to apply machine learning or AI to the analysis of the effect of an intervention on the sensed risk factors of the space (Process 1100 is also shown to include applying control logic (step 1106). A variety of different approaches are contemplated to evaluate the map and/or associated data generated in step 1104. For example, a rules-based approach can be implemented to trigger certain actions in response to parameters exceeding predetermined thresholds. Machine learning and artificial intelligence models (e.g., neural networks, random forests, logistic regression, support vector machines) can also be trained and implemented to analyze various types of maps and data from the input devices, [0169]).
Regarding claim 15, Nesler teaches, A system according to claim 1, and further teaches, wherein the processor unit or machine learning or AI engine are arranged to edit the set of rules in the rules engine and/or the workflow engine to change their responses. (Machine learning and artificial intelligence models can also be trained and implemented to analyze various types of maps and data from the input devices. Further, any of the control algorithms and strategies described above (e.g., ESC, PI, PID, MPC) can be implemented. The control logic applied in step 1106 may be applied in a variety of places within BMS 400 such as BMS controller 366, a more local controller such as AHU controller 330, VAV boxes, and other cloud-based or on-premises servers or controllers, [0169]).
Regarding claim 16, Nesler teaches, system according to claim 15, and further teaches, wherein the editing of the set of rules in the rules engine and/or the workflow engine changes the risk modifiers, workflow modifiers, rating modifiers and/or energy efficiency modifiers, (Demand response layer 414 may further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions may be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs may be tailored for the user's application, desired comfort level, particular building equipment, or based on other concerns, [0140]). These responses can include communicating with the control algorithms in integrated control layer 418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner, [0138]. Demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). Demand response layer 414 uses equipment models to determine an optimal set of control actions, [ 139], the demand response policy definitions can specify which equipment may be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, [0140].
Regarding claim 18, Nesler teaches, A system according to claim 1, and further teaches, wherein the workflow interface is connected to a plurality of different systems for carrying out different interventions. (FIG. 4, a block diagram of a building management system (BMS) 400 is shown, according to an exemplary embodiment. BMS 400 may be implemented in building 10 to automatically monitor and control various building functions. BMS 400 is shown to include BMS controller 366 and a plurality of building subsystems 428. Building subsystems 428 are shown to include a building electrical subsystem 434, an information communication technology (ICT) subsystem 436, a security subsystem 438, a HVAC subsystem 440, a lighting subsystem 442, a lift/escalators subsystem 432, and a fire safety subsystem 430. [ ] Building subsystems 428 may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building 10.
Claim 19 is rejected for reasons corresponding to those provided for Claim 1. In this claim, the absence of hardware and software elements does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Nesler teaches methods for reducing health risks in buildings [Abstract]).
Regarding claim 20, Nesler teaches, the system according to claim 1, and further teaches, wherein the workflow instruction signal comprises an instruction to control the HV AC system by selecting and setting an operating setting of the HVAC system, (the comfort controller can generate control decisions to provide to the HVAC and disinfection system in order to maintain conditions in the space at safe levels, [0523]).
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim 4 is rejected under 35 U.S.C. § 103 as being taught by Nesler, (US 20210010701
A1), “Air Quality Control and Disinfection System, in view of Boisvert, (US 20200227159 A1), “Methods and Systems for Improving Infection Control in a Building.”
Regarding claim 4, Nesler teaches a system according to claim 1, but Nesler does not teach a standards library; however Boisvert teaches, further comprising a standards library which holds a set of pre-programmed standards for the enclosed space to meet (the storage system 116 can be provided for reading from and writing to a non-removable, non-volatile magnetic media, [0032], and While the terms “high” and “low” are relative terms, it should be understood that as used herein, high is to be interpreted as exceeding or above a predetermined threshold while low is to be interpreted as under or below a predetermined threshold. The predetermined threshold may be user defined, defined by one or more programmable risk compliance parameters, or a combination thereof, [0045]).
It would have been obvious before the earliest effective filing date of this application to modify Nesler’s system for air quality control and disinfection with the teachings of Boisvert for programmable risk compliance parameters with the motivation to ensure rules stored in a rules database are improved through periodic updating [0044]. The claimed invention is a combination of existing elements and one of ordinary skill in the art would recognize that elements would continue to perform the same functions as they did separately and produce predictable results, in this case ensuring current and effective risk compliance [Abstract].
Claims 6-7 are rejected under 35 U.S.C. § 103 as being taught by Nesler, (US
20210010701 A1), hereafter Nesler, “Air Quality Control and Disinfection System, in view of Yan, (US 20220083932 A1), “Location-Based Risk Evaluation.”
Regarding claim 6, Nesler teaches, a system according to claim 5, but Nesler does not teach risk mitigation policies, however, Yan teaches, wherein the rules engine is configured to modify the elevated risk level of the set of rules based on the risk modifier in order to modify the risk level at which mitigation action is required. Nesler does not teach, Yan teaches, (in addition to calculating risk and implementing risk mitigation policies, systems and methods consistent with the present disclosure may also enable monitoring overall trends and making higher-level adjustments and recommendations, FIG. 5, and method 500 may enable an entity such as a business to monitor risk metrics over time, advantageously enabling analysis of impact of various risk mitigation policies. Additionally (or alternatively), method 500 may enable the business to set various risk constraints, receiving an alert whenever a constraint is violated, [0045]).
It would have been obvious before the earliest effective filing date of this application to modify Nesler’s system for air quality control and disinfection with the teachings of Yan on trend monitoring and higher-level adjustments with the motivation to adjust mitigation policies for unknown threats, [0015]. The claimed invention is a combination of existing elements and one of ordinary skill in the art would recognize that elements would continue to perform the same functions as they did separately and produce predictable results, in this case to enable proactive analysis and mitigation of health risks in various locations, [0015].
Regarding claim 7, Nesler teaches, A system according to claim 5, but does not teach the modifier changes, however Yan teaches, wherein the workflow engine is configured to modify the intervention based on the workflow modifier and/or the energy efficiency modifier in order to modify the intervention that is
required. (in addition to calculating risk and implementing risk mitigation policies, systems and methods consistent with the present disclosure may also enable monitoring overall trends and making higher-level adjustments and recommendations, FIG. 5, and method 500 may enable an entity such as a business to monitor risk metrics over time, advantageously enabling analysis of impact of various risk mitigation policies. Additionally (or alternatively), method 500 may enable the business to set various risk constraints, receiving an alert whenever a constraint is violated, [0045]).
It would have been obvious before the earliest effective filing date of this application to modify Nesler’s system for air quality control and disinfection with the teachings of Yan for long term analysis with the motivation of determining the impact of various risk mitigation policies, [0045]. The claimed invention is a combination of existing elements and one of ordinary skill in the art would recognize that elements would continue to perform the same functions as they did separately and produce predictable results, in this case maintaining risk mitigation, setting constraints and monitoring for any violations of the constraints, [0045].
Claim 17 is rejected under 35 U.S.C. § 103 as being taught by Nesler, (US 20210010701
A1), “Air Quality Control and Disinfection System, in view of Luckay (US 20210192373 A1), “Determining and Executing Proactive Delivery Actions using Artificial Intelligence.”
Regarding claim 17, Nesler teaches, a system according to claim 14, but does not teach proactive mitigation, but Luckay does teach, wherein the machine learning or AI engine is arranged to analyze the sensor data stored in the sensor data storage and to identify repeat instances where elevated risk is detected so as to cause the system to pre-empt the elevation of risks with the implementation of an intervention. (Systems and/or methods, described herein, may utilize advanced analytics systems henceforth summarized in the term “artificial intelligence,” leveraging historical data stored [ ], and/or rules engines to automatically generate and execute mitigating actions, [0014], the proactive mitigation system 140 may monitor [ ] data, and automatically generate a proactive mitigating action when it detects or determines the occurrence of a triggering event. [ ] The proactive mitigation system 140 may determine the appropriate mitigating action to be action that is the same or similar to an action that has been taken in the past by the user while in the same or a similar situation, as indicated or connoted in the user's user profile information, Luckay, [0023]).
It would have been obvious before the earliest effective filing date of this application to modify Nesler’s system for air quality control and disinfection with the teachings of Luckay in AI identification of recurring scenarios with the motivation of generating and executing mitigation actions proactively, [0023]. The claimed invention is a combination of existing elements and one of ordinary skill in the art would recognize that elements would continue to perform the same functions as they did separately and produce predictable results, in this case applying advanced analytics systems and generating and executing mitigation actions proactively, [0023].
Conclusion
THIS ACTION IS MADE FINAL. 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.
Claim 21 is not rejected by prior art under 35 U.S.C. § 103. The closest prior art
to the invention includes Nesler (US 20220284458 A1), “Risk-constrained Optimization Of Virtual Power Plants In Pool And Future Markets” and Przybylski, (US 20160313023 A1), “Systems and Methods for Retraining Outlier Detection Limits in a Building Management System,” and Granger, (WO2019046580 A1), “Systems, Methods, and Articles for Assessing and/or Improving Health and Well-Being.”
Regarding claim 21, Nesler teaches, the system according to claim 1, but only teaches an intervention in the form of a treatment, not restricting due to ambient temperatures, [0546].
Przybylski teaches part of the claim, fresh air ventilation through the HVAC system, (AHU 26 is shown as an economizer-type air handling unit. Economizer-type air handling units vary the amount of outside air and return air used by the air handling unit for heating or cooling, [0045], but this does not account for interventions or temperature limits, but this is general air mixing and not an intervention or treatment.
Granger teaches an intervention system, an intervention assessment system and methods include one or more sensors for measuring aspects related to the built environment, a personal user device, and a control circuit configured to receive one or more measurements form the sensor(s), identify a problem with the built environment, and identify potential interventions based on the indicators associated with the problem. By one approach, a plurality of potential interventions may be ranked based on, for example, the ability to reduce the prevalence of the problem or indicator in the built environment, feasibility, cost, and timeliness. In some approaches, the system and method also select and may implement one or more interventions, [Abstract].
These individually or in combination did not teach the complete scope of the claim.
No prior art was found that combined energy efficiency with environmental system intervention planning or application for health risk control or that specified a specific threshold for limiting interventions for energy efficiency reasons, wherein the energy efficiency modifiers include a modifier that deprioritizes and restricts an intervention to increase fresh air ventilation through the HVAC system where an outdoor temperature is more than 15°C below a desired indoor temperature.
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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