DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the first inventor to file provisions of the AIA .
Claims 1 and 9 were amended in the Amendment filed on August 29, 2025 and claims 3, 4, 11, and 12 were cancelled.
Claims 1, 2, 5-10, and 13-16 are currently pending and under examination, of which claims 1 and 9 are independent claims.
Response to Amendment
The Amendment to Non-Final Office Action, filed on August 29, 2025 is fully responsive.
Response to Arguments
On page 7 of the Amendment, the following is argued:
Applicant submits that the reference in Bistany to "correlating" is a generic reference that fails to disclose the details of claim 1, as amended. While Bistany describes the use of machine learning to analyze air quality data, generate thresholds, and determine mitigation actions (see, e.g., [0023], [0035]), Bistany does not disclose increasing a confidence interval for the indoor air quality based on the correlation between patterns identified from multiple sensors. Bistany discusses generating and using training data sets, determining thresholds, and adjusting building systems accordingly ([0020]-[0032]). Bistany mentions "correlating" data in the context of generating training data sets (e.g., [0035]: "the machine-learning engine is configured to generate one or more substance threshold training data sets related to a specific substance of interest from the sets of historical air quality data by sorting, labeling, correlating, relating, or any combination thereof..."). However, Bistany does not describe determining a correlation between patterns from multiple sensors and then using that correlation to increase a confidence interval for the assessment of indoor air quality. As such, even if Wenger and Bistany are combined, the elements of claim 1 do not result, as neither reference discloses the use of correlation to increase a confidence level.
The Office respectfully disagrees and maintains that Bistany teaches “determine a correlation between each of the plurality of patterns; and increase a confidence interval for the indoor air quality based on the correlation”. In addition to the referred portions of Bistany included in the Non-Final Office Action, Bistany provides in paragraph [0011], that in response to the particle count of one or more substances, one or more building systems being adjusted so that hazards presented by the substances of interest are mitigated, would increase or improve a confidence interval based on a correlation performed as described in paragraph [0035]. Paragraph [0029] of Bistany provides that the machine-learning engine 218 is configured to generate one or more baseline value training data sets relating to a substance of interest from historical air quality data by correlating and relating at least a portion of the data in the historical air quality data. According to embodiments, to generate such baseline value training data sets, machine-learning engine 218 includes, runs, and trains, for example, a classifier configured to sort, label, correlate, and relate historical air quality data. See also Paragraphs [0030] and [0037]. Therefore, the Office submits, based on various portions the specification of Bistany, that using the set of historical air quality data by correlating using machine learning engine for ongoing improvement of the machine learning model results in a confidence interval, which teaches “increase a confidence interval based on the correlation” as recited in amended independent claims 1 and 9.
Constantly training with new data including the historical air quality data, the machine learning model is updated and, therefore, improving confidence of the output of the machine learning model. Bistany teaches “increase a confidence interval based on the correlation” as recited in amended independent claims 1 and 9.
Dependent claims 2, 5-10, and 13-16 depend directly, or indirectly, from independent claims 1 and 9. The rejections to these claims are maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5-7, 9, 10, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication No. 2022/0136717 A1 to Wenger et al. (“Wenger”) in view of US Patent Publication No. 2023/0167997 A1 to Bistany et al. (“Bistany”).
Regarding independent claim 1, Wenger teaches:
A system for identifying patterns of indoor air quality, the system comprising: Wenger: Paragraph [0004] (“…an indoor air quality (IAQ) control system for a heating, ventilation, air conditioning, and Refrigeration (HVACR) system, includes an IAQ monitor that is configured to collect air quality data from an air quality sensor of an air quality-controlled space, a controller that is configured to manage a remediation device of the air quality-controlled space… Additionally, a control method includes collecting air quality data from an air quality sensor of an air quality-controlled space using an IAQ monitor…”)
a plurality of sensors, wherein the plurality of sensors comprises indoor air quality (IAQ) sensors; Wenger: Paragraph [0050] (“The sensors 130 in FIG. 1 are air quality sensors that generate air quality data. The air quality data can include air quantity parameters measured by sensors 130 such as quantities of carbon dioxide, carbon monoxide, nitrogen dioxide, sulfur dioxide, or the like. The air quality data can also include other parameters such as quantities of one or more volatile organic compounds, quantities of particulate matter, temperature, humidity, location data, or the like. In an embodiment, the quantities that can be quantities that are correlated with a quantity of a biological pollutant such as a pathogen.”)
a controller communicatively coupled to the plurality of sensors, Wenger: Paragraph [0060] (“The sensors 230 collect air quality data and transmit the collected air quality … to the controller 240. The controller 240 can process the air quality data according to an algorithm that further transmits the air quality data to the IAQ management server 280 for further processing, instruct the remediation device 250 to execute a remediation action, or both. The sensors 230 can be, for example, the sensors 130 shown in FIG. 1 and described above.”) [The controller and/or the server processing air quality data from the sensors reads on “a controller communicatively coupled to the plurality of sensors”.]
the controller is configured to:
receive sensor data from a sensor of the plurality of sensors to determine indoor air quality; Wenger: Paragraph [0060] [As described above.] Wenger: Paragraph [0014] (“…collecting air quality data from an air quality sensor of an air quality-controlled space using an IAQ monitor, managing a remediation device of the air quality-controlled space using a controller, generating a biological pollutant estimate based on the air quality data using an algorithm that correlates the air quality data to the biological pollutant estimate, and generating a remediation recommendation based on the biological pollutant estimate.”) Wenger: Paragraph [0098] (“an IAQ monitor that is configured to collect air quality data from an air quality sensor of an air quality-controlled space;”) [One of the sensors 130 or 230 read on “a sensor of the plurality of sensors”.)
identify a pattern of the sensor data for a period of time; Wenger: Paragraph [0065] (“In one embodiment, the remediation device can be triggered proactively before an air quality parameter reaches a predetermined threshold. The proactive triggering can be determined by a trend of air quality data recorded over time. The proactive triggering can be determined by factors or patterns recognized and/or updated by a machine learning algorithm or artificial intelligence (AI). The remediation device 250 can be, for example, the remediation device 150 shown in FIG. 1 and described above.”) Wenger: Paragraph [0067] (“The server 280 includes an algorithm that uses the air quality data from the sensors 230 as inputs, and uses a prediction model generated from experimental or simulated data to estimate a biological pollutant load in an air quality controlled space where the system is deployed.”) Wenger: Paragraph [0080] (“…an algorithm 481 is used by a server 480 to estimate biological pollutant load from air quality data collected by sensors. The algorithm 481 receives air quality data from the sensors of an air quality-controlled space as inputs.”) Wenger: Paragraph [0088] (“In an embodiment, the air pollutant can be a biological pollutant, and the reduction of the biological pollutant can be estimated from the air quality data before and after the remediation action using the algorithm 481. The server 480 can generate an efficacy report of the air pollutant remediation action and transmit the report to the user device 445, the controller 440, or both. In another embodiment, a change of a pollutant load can be estimated from the air quality data before and after a remediation action. The pollutant can be any type of pollutant measured economically and/or continuously by one or more air quality sensors. The change in pollutant load can be included in an efficacy report for the air pollutant remediation action. The efficacy report can be transmitted to the user device 445, the controller 440, or both. In an embodiment, the efficacy report can include an equivalent ventilation time by correlating the change of the pollutant load to an equivalent time period of ventilation with ambient air.”) [The trend of air quality or estimated biological pollutant load reads on “a pattern of the sensor data”.]
compare current sensor data to the identified pattern of the sensor data; and Wenger: Paragraphs [0067] and [0088] [As described above.] Wenger: Paragraph[0082] (“The algorithm 481 includes a prediction model constructed from experimental or simulated data that correlates air quality data as inputs with a biological pollutant load as an output. The input data of the prediction model can also include ambient air quality data as inputs.”) Wenger: Paragraph [0087] (“The server 480 can also calculate efficacy of a remediation action by comparing the air quality data of the air quality-controlled space over time. The efficacy can be a difference between a reduction of an air pollutant monitored by the air quality sensor after a remediation action, compared to a predicted reduction of the air pollutant without the remediation action. The predicted reduction of the air pollutant can be based on experimental or simulated data.”)[The comparing of the air pollutant from the air quality sensor after the remediation action with the predicted reduction of the air pollutant reads on “compare current sensor data to the identified pattern of the sensor data”.]
transmit a message to a user device based at least in part on the comparison to indicate the indoor air quality and the pattern of the sensor data; Wenger: Paragraph [0046] (“The IAQ monitor 105 can provide a remediation recommendation to a user to take appropriate remediation action. The remediation recommendation can be provided through a display attached to the IAQ monitor 105.”) Wenger: Paragraph [0083] (“The remediation recommendation from the server 480 is transmitted over a network to a controller 440, a user device 445, or both. The remediation recommendation can be transmitted as a control signal to directly instruct a remediation device 450 to perform a remediation action.”) Wenger: Paragraph [0084] (“… the controller 440, the user device 445, or both can include a screen to display the remediation recommendation from the server 480.”)
wherein the controller is further configured to:
receive sensor data from the plurality of sensors; Wenger: Paragraphs [0060] and [0080] [As described in claim 1.]
identify a plurality of patterns for each of the plurality of sensors, respectively;… Wenger: Paragraphs [0065] and [0067] [As described in claim 1.] Wenger: Paragraph [0048] and FIG. 1 (“The air quality-controlled space 110 can include one or more enclosed spaces or areas with one or more occupancies including, for example, a conference room, a cubicle area, a breakroom, an office suite, a floor of a building, a building, a production floor, or the like.”) Wenger: Paragraph [0097] (“The system can guide the user to enter location information into the system through a user interface... The location information can include the location of the air quality monitor, the sensors, the user device, and the remediation devices in relation to the air quality controlled space, such as a first conference room, a second conference room, a living room, a front desk area, a breakroom area, a floor level, a cubicle area, or the like. The location information can be used to generate and update the algorithm 481. The location information can further be used in the efficacy demonstration to generate and update the correlation between the pathogen and the reagent as a proxy feasibly measurable in a HVACR system in a general purpose building. For example, the correlation can be tailored to the air quality controlled space based on the location of the remediation devices. Further, the efficacy of the remediation can be estimated based on the location of the sensor that is positioned in a particular room. For example, the remediation device is located in the living room, while the bedroom is adjacent to the living room. The pathogen remediation efficacy of the living room, because it is closer to the remediation device, can be higher than the remediation efficacy of the bedroom. The correlation can incorporate the location information of the remediation device, the living room, and the bedroom to predict an efficacy of the remediation for the living room, and another efficacy of the remediation for the bedroom incorporating their relative locations with the remediation device.”) [The trend of air quality or estimated biological pollutant load of each of the air quality controlled space based on the location of the sensor reads on “identify a plurality of patterns for each of the plurality of sensors, respectively”.]
Wenger does not expressly teach determining a correlation between each of the plurality of patterns; and increase a confidence interval based on the correlation. However, Bistany describes an air quality monitoring system integrated with the building management system observes and records air quality data and building system data. Bistany teaches:
… determine a correlation between each of the plurality of patterns; and increase a confidence interval based on the correlation. Bistany: Paragraph [0023] (“For example, HVAC system 226 includes systems, sensors, and computing devices to control the filtration of odors, smoke, chemicals, contagions, bacteria, gasses, and/or any substance of interest discussed above with reference to FIG. 1 in one or more portions of the environment. As another example, HVAC system 226 includes one or more systems, sensors, or computing devices configured to filter substances from at least a portion of an environment by activating or modifying the operation of one or more fans, ultraviolet light filters, high efficiency particulate air (HEPA) filters, electrostatic filters, washable filters, pleated filters, spun glass filters, media filters, forced-air systems, exhaust systems, or any combination thereof.”) Bistany: Paragraph [0026] (“In embodiments, one or more servers 210 are configured to receive one or more detection signals, detected particle counts of one or more substances of interest, detected particle densities of one or more substances of interest, or any combination thereof, associated with one or more portions of an environment from sensor units 104 of air quality monitoring system 102. As discussed above with reference to FIG. 1, each sensor unit detects particle counts and particle densities for one or more substances of interest in one or more portions of the environment and generates one or more detection signals based on the detected particle counts and particle densities.”) Bistany: Paragraph [0030] (“Machine-learning engine 218 is configured to generate one or more substance threshold training data sets relating to a substance of interest from historical air quality data, data associated with the creation of the sets of historical air quality data (e.g., time, environment, location), or both by sorting, labeling, correlating, and relating at least a portion of the data in the historical air quality data. For example, machine-learning engine 218 labels historical air quality data relating to a specific substance of interest to form a substance threshold training data set. As another example, machine-learning engine 218 is configured to filter data from one or more sets of historical air quality data, data associated with the creation of the sets of historical air quality data, or both based on one or more labels generated by machine-learning engine 218, stored in building management system 200, or both to from a substance threshold training data set. As an additional example, machine-learning engine 218 is configured to filter out any data from one or more sets of historical air quality data, data associated with the creation of the sets of historical air quality data, or both not associated with a harmful label to generate a substance threshold training data set. According to embodiments, machine-learning engine 218 runs and trains a classifier to sort, label, correlate, and relate historical air quality data.”) Bistany: Paragraph [0035] (“…the machine-learning engine is configured to generate one or more substance threshold training data sets related to a specific substance of interest from the sets of historical air quality data by sorting, labeling, correlating, relating, or any combination thereof, at least a portion of the data in the historical air quality data related to the specific substance of interest.”) Bistany: Paragraph [0036] (“In response to the air quality exceeding the threshold values, the system moves to block 330. At block 330, the system determines a mitigation action based on the monitored air quality. That is to say, the system determines a mitigation action based on the particle count and particle density of the substance of interest in one or more portions of the environment. Based on the mitigation action, the system adjusts, modifies, or activates one or more building systems, the same as or similar to building systems 220.”) [Using the set of historical air quality data by correlating using machine learning engine for ongoing improvement of the machine learning model results in a confidence interval, which teaches “increase a confidence interval based on the correlation”.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Wenger and Bistany before them, to determine a correlation between each of the plurality of patterns; and increase a confidence interval based on the correlation because the references are in the same field of endeavor as the claimed invention and they are focused on analyzing air quality parameters.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would improve mitigation of hazard substances presented in various portions of a building system. Bistany Paragraphs [0011]-[0012]
Regarding claim 2, Wenger and Bistany teach all the claimed features of claim 1, from which claim 2 depends. Wenger further teaches:
The system of claim 1, wherein identifying the pattern comprises a controller that is configured to use a machine learning algorithm to identify the pattern. Wenger: Paragraphs [0065], [0067], [0080], and [0088] [As described in claim 1.] Wenger: Paragraph [0090] (“The prediction model can be generated and updated by artificial intelligence (AI), machine learning, or the like. The AI prediction model can be trained with one or more data sets of air quality parameters that can be continuously and/or economically measured by one or more air quality sensors measuring one or more of the air quality parameters.”)
Regarding claim 5, Wenger and Bistany teach all the claimed features of claim 1, from which claim 5 depends. Wenger further teaches:
The system of claim 1, wherein the IAQ sensors are configured to detect one or more of: a CO2 level, a particulate matter level, and a humidity level. Wenger: Paragraph [0050] [As described in claim 1.]
Regarding claim 6, Wenger and Bistany teach all the claimed features of claim 1, from which claim 6 depends. Wenger further teaches:
The system of claim 1, wherein the controller is further configured to control a heating, ventilation, and air conditioning (HVAC) system responsive to the comparison. Wenger: Paragraph [0004] [As described in claim 1.]
Regarding claim 7, Wenger and Bistany teach all the claimed features of claim 6, from which claim 7 depends. Wenger further teaches:
The system of claim 6, wherein controlling the HVAC system comprises the controller configured to automatically operate the HVAC system if the current reading is within the identified pattern. Wenger: Paragraphs [0065], [0067], [0080], and [0088] [As described in claim 1.] Wenger: Paragraph [0054] (“The remediation device 150 reduces air pollutants from the air quality controlled space. The reduction can be achieved by adhesion, filtration, neutralization through physical, electrical, or chemical methods, replacement of more polluted air with less polluted air, or the like. For example, remediation device 150 can include one or more of a smart air filter, an add-on filter monitoring device, a fan, a bipolar ionization air cleaning device, a photocatalytic air cleaning device, a stand-alone air filter unit, an aqueous or gas-phase hydrogen peroxide generator, an UV, UV-C or far UV wavelength photo source, an air quality control attachment or accessory to the HVACR system, or ventilation such as a ventilation system, a door or a window of the air quality controlled space. The reduction in pollutants can include reduction in biological pollutants such as pathogens.”) Wenger: Paragraph [0060] (“The controller 240 can process the air quality data according to an algorithm that further transmits the air quality data to the IAQ management server 280 for further processing, instruct the remediation device 250 to execute a remediation action, or both. The sensors 230 can be, for example, the sensors 130 shown in FIG. 1 and described above.”) Wenger: Paragraph [0061] (“The remediation device 250 can receive the air quality data from the sensors 230, and an internal controller of the remediation device 250 can trigger one or more remediation actions according to an algorithm of the internal controller and the air quality data received.”)
Regarding independent claim 9, this claim recites similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 10, this claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale.
Regarding claim 13, this claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale.
Regarding claim 14, this claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale.
Regarding claim 15, this claim recites similar limitations as corresponding claim 7 and is rejected using the same teachings and rationale.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wenger and Bistany, in view of US Patent Publication No. 2020/0378641 A1 to Uriarte et al. (“Uriarte”).
Regarding claim 8, Wenger and Bistany teach all the claimed features of claim 1, from which claim 2 depends. Wenger and Bistany do not expressly teach the features of claim 8. However, Uriarte describes techniques for detecting habitat air quality. Uriarte teaches:
The system of claim 6, wherein controlling the HVAC system comprises the controller configured to: inhibiting operating the HVAC system if the current reading falls outside of the identified pattern; and Uriarte: Paragraph [0033] (“For example, system controlling component 126 can instruct the controlling device 140 to start ventilation (e.g., to purge air of pollutants for easier breathing quality) or stop ventilation (e.g., to prevent from spreading or further activating pollutants) of the habitat system 142 (e.g., which may also depend on detected room occupancy), open a window or other egress, start or stop a humidifier or dehumidifier in the zone (e.g., based on detected humidity), etc. where the controlling device 140 allow for receiving such commands and accordingly operating the habitat system 142.”) provide an indication of the current reading that falls outside of the identified pattern to the user device. Uriarte: Paragraph [0022] (“Where condition determining component 122 detects an air quality condition, alerting component 124 can trigger an alert of the condition and/or of values of the one or more parameters, an indication of the zone(s) within which the condition is detected, etc., to a controlling device 140. For example, the controlling device 140 may include a mobile device, an application executing thereon, a centralized home management device, etc. Alerting component 124 can send the alert via transceiver 116. The controlling device 140 may present the alert and/or a prompt to take a remedial action on a habitat system 142, such as shutting off ventilation in an occupied room,…”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Wenger, Bistany, and Uriarte before them, to inhibit operating the HVAC system if the current reading falls outside of the identified pattern; and provide an indication of the current reading that falls outside of the identified pattern to the user device because the references are in the same field of endeavor as the claimed invention and they are focused on analyzing air quality parameters.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would prevent pollutants from spreading while notifying an occupant of pollutant levels. Uriarte Paragraphs [0032] and [0033].
Regarding claim 16, this claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication No. 2024/0283675 A1 to Pelski et al. describes in paragraph [0298] sustainability models 3602 executed to generate predictions 3604 relating to the energy usage and/or sustainability of the building. For example, these predictions may indicate the total expected energy usage of the building over time or at a specific time, the expected emissions from the building, the expected resource (e.g., water, gas) consumption, etc. As another example, the orientation of a building can be used to determine an impact of sunlight on comfort and energy usage at different times of day (e.g., more incident sunlight on a side of building means more air conditioning is needed to keep it comfortable). Thus, predictions 3604 may indicate these types of energy and sustainability related insights. Advantageously, the accuracy of sustainability models 3602 may be correlated with the accuracy of BIM data and/or digital twin data, which may be continuously updated to account for asset movement, construction changes, etc.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M. CHOI whose telephone number is (571)272-1473. The examiner can normally be reached on Monday - Friday 7:30 am to 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached on 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117