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 .
Specification
The Specification filed on 01/22/2024 is objected because the number [000.] and/or [00.] is assigned to each paragraph starting from pages 1 to 17.
A correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 7, 8, 10, 12 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 1 recites the broad recitation "a portable ozone gas generating devices" in lines 5-6, and the claim also recites “a plurality of ozone gas generating devices that constitute the non-homogeneous cohort…” in lines 3-5, which is the narrower statement of the range or limitation and the plural generating devices in a single portable ozone gas. The claim(s) are considered unclear and indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
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, 3, 5-8, 10, 11, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Horstman [US 4,742,761] in view of Seidman [US 6,416,479]
Claim 1. A method of controlling a non-homogeneous cohort of ozone gas generating devices (the non-homogeneous distribution of carbon dioxide CO2 in the seating area 12, see Fig. 1, col. 4, lines 49-51), the method comprising:
identifying, at a computing device, a plurality of ozone gas generating devices that constitute the non-homogeneous cohort (read upon the microprocessor automatically controls the non-homogeneous CO2 in a space, see Figs. 1, 3, col. 2, lines 44-54, col. 6, lines 38-68), the plurality including at least a ceiling-mounted, an in-duct mounted (the ceiling mounted sensors 40 and 41 and any such sensors, see Fig. 1, col. 4, lines 63-68); and
continuously detecting, via at least one remote ozone gas sensor device located in a spatial area associated with the non-homogeneous cohort, in conjunction with one or more processors of the computing device (the microprocessor controls remote sensors 41 and 42 and any such sensors detect CO2 gas within an aircraft cabin 11, see Figs. 1, 3, col. 4, lines 46-68, col. 6, lines 38-51),
a concentration of ozone gas constituent within ozonated air of the spatial area (the concentration of CO2 in the space, see col. 2, lines 18-27); and
instructing, by the one or more processors responsive to continuously detecting the concentration of the ozone gas constituent as being one of above and below a predetermined threshold concentration (the microprocessor controls or instructs to detect CO2 level is higher than or does not exceed the desired limit, Fig. 3, col. 6, lines 38-54),
at least one ozone gas generating device of the non-homogeneous cohort to perform one of increasing and decreasing a rate of ozone gas generation associated therewith (read upon once the cabin pressure is within the predetermined limits, program logic ascertains if the concentration of carbon dioxide is within predetermined upper and lower limits, in blocks 107 and 109. If the CO.sub.2 level is too high, the control causes air pack 37 to increase the flow of pressurized fresh air into the cabin, in block 108, or conversely, if the CO.sub.2 level is too low, to decrease the flow of pressurized fresh air into the cabin, in block 110. Following either action, the control recycles to start, as it also does if the level of carbon dioxide concentration is within limits, in block 111, see Figs. 3, 4, col. 6, lines 38-60, col. 7, lines 39-49). But
Horstman fails to disclose a portable ozone gas generating devices. However, Horstman discloses the non-homogeneous distribution of carbon dioxide CO2 in the seating area 12, and ceiling mounted sensors 40, 41 and any such sensors positioned within cabin 11 (see Fig. 1, col. 4, lines 49-51).
Seidman suggests that the End-tidal carbon monoxide ("ETCO") concentrations, sometimes referred to as alveolar concentrations, can be measured in a variety of ways. For example, a breath sample is continuously drawn from the patient's breath stream and directed to a fast-responding carbon dioxide (CO.sub.2) sensor and a slower responding CO sensor. The signals from the CO.sub.2 sensor, the CO sensor and a measurement of inhaled CO concentration are used to calculate the end-tidal CO concentration (see col. 4, lines 17-27).
ETCOc measurements were obtained using a portable automated CO analyzer (see col. 7, lines 17-18).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or substitute the portable automated CO measurement and analyzer of Seidman for the CO2 sensors or any such sensors of Horstman for providing convenience and flexibility of using a portable CO/CO2 gas sensor to a particular situation or a special person to be monitored of CO/CO2 concentration levels in any environments.
Claim 3. The method of claim 1 wherein the at least one ceiling mounted ozone gas generating device comprises an optical lamp module including a plurality of optical lamps (the IR light, see col. 5, lines 19-26), and a fan apparatus arranged for forcibly dispersing ozonated air in a downward direction relative to a ceiling portion of an at least partially enclosed building wherein the ceiling mounted ozone gas generating device is housed (the fans 27 for forcing the pressurized fresh air into the cabin 11 when the CO2 level is too high, see Figs. 1, 3, col. 6, lines 50-57, col. 7, lines 42-44).
Claim 5. The method of claim 3 wherein the optical lamp module is disposed at one of upstream and downstream of the fan apparatus (as cited in respect to claims 1 and 3 above, wherein the system controls the exhaust air and return air passing through a carbon dioxide sensor 40 and/or air vented overboard, see Figs. 1-3, col. 3, lines 39-67, col. 4, lines 1-39, col. 6, lines 38-60).
Claim 6. The method of claim 5 wherein, in the upstream disposition, the optical lamp module is at least partially obscured by the fan apparatus from a view relative to an observer distally situated below the ceiling portion (as cited in respect to claims 1, 3 and 5 above, such as the IR light, see Fig. 1, col. 5, lines 19-26).
Claim 7. The method of claim 1 wherein the in-duct mounted ozone generating device comprises an optical lamp module including a plurality of optical lamps and a fan apparatus arranged for forcibly dispersing ozonated air along at least a portion of an air supply duct that houses the in-duct mounted ozone generating device (as cited in respect to claim 1 above, and including the air pack 37, see Fig. 1, col. 4, lines 1-15).
Claim 8. The method of claim 1 wherein the in-duct mounted ozone generating device comprises an optical lamp module including a plurality of optical lamps and a fluid flow sensor device that detects passage of air along the duct (as cited in respect to claim 1 above, and including the fluid communication, see Fig. 1, col. 4, lines 6-9).
Claim 10. The method of claim 1 wherein the at least one remote ozone gas sensor device comprises a plurality of remote ozone gas sensor devices located within the spatial area associated with the non-homogeneous cohort (as cited in respect to claim 1 above). But
Horstman fails to disclose the spatial area including an at least partially enclosed building. However, Horstman teaches that the control and method for maintaining the concentration of carbon dioxide in the cabin 11 of an aircraft at a desired level. A carbon dioxide sensor 40, 41 is disposed to monitor the concentration of carbon dioxide CO2 in the cabin of the aircraft (see Fig. 1, abstract).
Seidman suggests that the method for early detection of pathological conditions in pregnancy by measuring breath CO or ETCOc and can be applied in a hospital, clinic, or physician's office (see col. 5, lines 6-9).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to applying or using the controlling of CO2 inside the enclosed cabin of an aircraft of Horstman to the controlling of CO in a hospital building, clinic building or physician office of Seidman as a design choice to expanding applications and uses of the monitoring and controlling of carbon and/or gaseous within an enclosure, building, house, office, premises, school campus and/or warehouse without changing the results to save a life.
Claim 11. The method of claim 10 wherein the spatial area associated with the non-homogeneous cohort includes multiple buildings within a campus infrastructure that includes the at least partially enclosed building (as discussed in respect to claims 1 and 10 above).
Claim 16. The method of claim 1 wherein the at least one ozone gas generating device of the non-homogeneous cohort performs ozone gas generation in accordance with applying ultraviolet (UV) irradiation provided in a wavelength of 185 nanometer (nm) to at least a portion of the gaseous oxygen constituted in an incoming stream of air to produce ozonated air, the ozonated air having a higher concentration of ozone gas than the incoming stream of air.
Claim 17. A computing device comprising: a processor (the microprocessor, see col. 6, lines 15-60); and a non-transitory memory including instructions, the instructions when executed by the processor causing the processor to perform operations (read upon the programmed microprocessor and memory ROM, see Figs. 2-4, col. 6, lines 15-60, col. 7, lines 13-30), comprising: identifying a plurality of ozone gas generating devices that constitute a non-homogenous cohort, the plurality including at least a ceiling-mounted, an in-duct mounted and a portable ozone gas generating devices; continuously detecting, via at least one remote ozone gas sensor device located in a spatial area associated with the non-homogeneous cohort, in conjunction with one or more processors of the computing device, a concentration of ozone gas constituent within ozonated air of the spatial area; and instructing, by the one or more processors responsive to continuously detecting the concentration of the ozone gas constituent as being one of above and below a predetermined threshold concentration, at least one ozone gas generating device of the non-homogeneous cohort to perform one of increasing and decreasing a rate of ozone gas generation associated therewith (as cited in respect to claim 1 above).
Claim 18. A non-transitory computer-readable memory storing instructions, the instructions being executable in one or more processor devices to cause the one or more processor to perform operations comprising: identifying, at a computing device, a plurality of ozone gas generating devices that constitute a non-homogeneous cohort, the plurality including at least a ceiling- mounted, an in-duct mounted and a portable ozone gas generating devices; continuously detecting, via at least one remote ozone gas sensor device located in a spatial area associated with the non-homogeneous cohort, in conjunction with one or more processors of the computing device, a concentration of ozone gas constituent within ozonated air of the spatial area; and instructing, by the one or more processors responsive to continuously detecting the concentration of the ozone gas constituent as being one of above and below a predetermined threshold concentration, at least one ozone gas generating device of the non-homogeneous cohort to perform one of increasing and decreasing a rate of ozone gas generation associated therewith (as discussed in respect to claims 1 and 17 above).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Horstman [US 4,742,761] and Seidman [US 6,416,479] and further in view of Cooner [US 2020/0027096]
Claim 2. Horstman fails to disclose wherein the non-homogeneous cohort of ozone gas generating devices are communicatively coupled to the computing device within a cloud communication network. However, Horstman teaches that the non-homogeneous distribution of carbon dioxide CO2 in the seating area 12, and ceiling mounted sensors 40, 41 and any such sensors positioned within cabin 11 (see Fig. 1, col. 4, lines 49-51).
Cooner suggests that the “Edge” is the “Internet of Things” (IoT for short) front-line of where technology intersects with business and people, capturing raw data used by the rest of the IoT system. Data is captured by embedding sensors in consumer devices (i.e. fitness trackers, thermostats) appliances or industrial systems (i.e. heating & cooling systems, factory automation) or more specialized applications such as remotely monitoring food temperature and humidity. Such devices can be referred to in this discussion as “Sensor Devices”. Data can then be passed to a “Router” and/or “Gateway” or other “Aggregation Points” that can provide some basic data analytics (parsing raw data) before being sent to the IoT Platform via an Internet connection and beyond. “Routers” can be thought of as local grid or mesh networks whereby implementations such as Bluetooth, Zigbee, WiFi, ANT, OpenWare, LoRa, Sigfox, or other short to mid-range wireless transmissions are used to communicate between Sensor Devices and Gateways. Gateways can be thought of as Internet-enabled hardware devices (usually through a wireless WiFi, cellular based such as GSM, CDMA, or other mobile phone carrier network, or landline connection) that communicate either directly to sensors, to sensors through Routers, or a hybrid of both Routers and sensors directly to allow for data to be passed bi-directionally to an Internet platform such as a cloud computing environment or computer network. Also, IoT is not just about capturing data but can also alter the operation of a device with an actuator or other configurable components (see Figs. 1, 2, para [0320]).
The IoT sensor market is divided into two broad categories. Original Device Manufacturers (ODMs) and Original Equipment Manufacturers (OEMs). ODMs design manufacture the core sensor technology (pressure, temperature, accelerometers, light, chemical, etc.) with over 100,000 types of sensors currently available for IoT solutions. These sensors typically do not include any of the communication or intelligence capabilities needed for IoT solutions so OEMs embed ODM sensors into their IoT devices while adding the communications, analytics and other potential capabilities needed for their specified markets. For example, an OEM who builds a Building Automation IoT application may include various sensor types such as light (IR or visual), temperature, chemical (CO2) (see Figs. 1, 2, para -0341]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to add or implement the Internet platform such as a cloud computer network for communicating data information of the LoT sensors and CO sensor of Cooner to the non-homogeneous distribution of carbon dioxide CO2 and any such sensors of Horstman and Seidman for eliminating of communication cables/wires and to providing a faster data transmissions therebetween.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Horstman [US 4,742,761] and Seidman [US 6,416,479] and further in view of Forbis et al [US 2017/0257925]
Claim 4. Horstman fails to disclose wherein the ceiling portion of the at least partially enclosed building comprises a ceiling tile of a predetermined size, and the at least one ceiling mounted ozone gas generating device further comprises a mounted configuration that is generally coincident with a footprint of the ceiling tile. However, Horstman teaches that the non-homogeneous distribution of carbon dioxide CO2 in the seating area 12, and ceiling mounted sensors 40, 41 and any such sensors positioned within cabin 11 (see Fig. 1, col. 4, lines 49-51).
Forbis et al suggests that the artificial lighting system 100 or a light engine 2004 may be configured in a form factor adapted to fit into the space of a standard ceiling tile and to create the appearance of a skylight. A configuration may be a two-foot-by-two-foot configuration, a two-foot-by-four-foot configuration, a four-foot-by-four-foot configuration and the like. The form factor may be a standard configurable form factor. A standard configurable form factor may be a skylight feature form factor or a design form factor. A standard configurable form factor may be sized. A standard configurable form factor may be sized for ceiling tile replacement, sized for standard window replacement and sized to match standard features (see Figs. 5-8, 13, 18, para [0245, 0266]). When the space is replaced with another medium that is non-homogenous, like the atmosphere, it may be absorbed by atmospheric gases or altered by spatial variations in refractive index (see Fig. 34, para [0316]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or implement the non-homogenous artificial lighting system fixed to a standard ceiling tile of Forbis et al to the non-homogenous gas sensors mounted into the ceiling of Horstman and Seidman for easily installation and reducing cost by the ceiling tiles.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Horstman [US 4,742,761] and Seidman [US 6,416,479] and further in view of Wang et al [US 2017/0261357]
Claim 9. Horstman fails to disclose wherein the optical lamp module including the plurality of optical lamps is operationally switched on for generation of ozonated air responsive to detecting passage of air along the duct that exceeds a predetermined flow rate. However, Horstman teaches that the microprocessor control checks to determine if cabin pressure is below a desired setting. If the pressure is too low, the microprocessor causes the air pack to increase its flow of pressurized fresh air into cabin 11, as indicated in block 62. Thereafter (or if the cabin pressure is not too low), in block 63, the microprocessor control checks to determine if cabin pressure exceeds an upper limit. If the cabin pressure is too high, in block 64, the control causes air pack 37 to decrease the flow of pressurized fresh air into mixing manifold 23. In block 65, the control determines if the carbon dioxide level sensed by sensor 40 (41) is higher than a desirable limit. If the carbon dioxide level does exceed the desired limit, in block 66, the control checks to determine if the cabin pressure is less than a minimum set point. If the pressure is less than the set point, in block 69, the control decreases the flow of air vented overboard by causing three-way valve 29 to recirculate back into cabin 11 more of the air drawn out through exhaust ducts 24 and 25. Otherwise, in block 67, three-way valve 29 is caused to increase the amount of air vented overboard (see Figs. 1, 3, col. 6, lines 38-60).
Wang et al suggests that for a vertical gas-oil-water three-phase (water-continuous) flow, an EIT technique with dual-plane sensors is used to extract local volume fraction distribution, local flow velocity and flow rate of the dispersed phases, e.g. gas and oil, see para [0182]). By observing the trend of measured oil flow rates, it can be seen that the level of deviation is more pronounced than that of the measured water flow rates and that of the measured gas flow rates; the deviation grows more with the increase of oil flow rate. On the other hand, the comparison results of gas flow rate between the measured and reference values suggest that higher deviation in the oil flow rate is associated with higher gas flow rates (see Fig. 19, 19c, para [0188]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or implement the EIT technique with dual-plane sensors is used to extract local volume fraction distribution, local flow velocity and flow rate of the dispersed phases, e.g. gas and oil of Wang et al to the microprocessor controls to increasing/decreasing of air flow overboard of Horstman and Seidman for maintaining the CO1 gas levels and air pressure levels within a safety desired level as well as to prevent of dangerous situations to people inside a cabin, building and/ or office.
Claims 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Horstman [US 4,742,761] and Seidman [US 6,416,479] and further in view of Borshch et al [US 2019/0103182]
Claim 12. Horstman fails to disclose continuously detecting the concentration of ozone gas constituent of ozonated air at least partly using a trained machine learning model in conjunction with the plurality of remote ozone gas sensor devices. However, Horstman teaches that the apparatus includes means for sensing the non-homogenous distribution of carbon dioxide and producing a signal indicative of the concentration thereof in the enclosed space. The programmed microprocessor operates to automatically maintain the enclosed space at a desired air pressure while at the same time maintaining the concentration of carbon dioxide in the space at an acceptable level. The present invention accomplishes this object by venting only air that is relatively higher in carbon dioxide, while recirculating air that is lower in carbon dioxide concentration. Thus, the environmental quality of the air in the enclosed space is maintained at a desirable level, but only the minimum air necessary to accomplish this object is vented from the space (see Figs. 1-4, col. 2, lines 18-20, 44-54, col. 6, lines 15-37).
Borshch et al suggests that at each environment, any or all environmental characteristic information may be sensed by device 100 from any or all features of the environment, e.g., directly via sensor assembly 114 of device 100 and/or via any suitable auxiliary environment subsystem(s) 200 of the environment (see Fig. 1).
As another example, as shown, at environment E1 during time T1, vehicle(s) V may provide one or more types of vehicle effects VE that may be sensed by sensor assembly 114 of device 100 for determining one or more environmental characteristics of environment E1 during time T1, including, but not limited to, a noise environmental characteristic of environment E1 that may be at least partially detected from a sensed noise vehicle effect VE generated by vehicle(s) V, a harmful gas level environmental characteristic of environment E1, such as in an office, laboratory, etc. (see Fig. 2, para [0052, 0062]).
A neural network may use any suitable machine learning techniques to optimize a training process. The neural network may be used to estimate or approximate functions that can depend on a large number of inputs and that may be generally unknown. The neural network may generally be a system of interconnected “neurons” that may exchange messages between each other, where the connections may have numeric weights (e.g., initially configured with initial weight values) that can be tuned based on experience, making the neural network adaptive to inputs and capable of learning (e.g., learning pattern recognition (see Figs. 1, 2, para [0037]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or implement of any suitable machine learning of Borshch et al to the programmed microprocessor for automatically control of CO gas in an enclosed cabin, building of Horstman and Seidman for providing on-the-spot instant results vital for rapid treatment of such serious conditions of a person, since the system is a programmed microprocessor to operate automatically as a self-learning, processing and executing operation functions.
Claim 13. The method of claim 12 further comprising producing the trained machine learning model via a training process comprising: receiving a plurality of input datasets at respective ones of a plurality of input layers of a neural network, the neural network being instantiated in the one or more processors and having an output layer interconnected to the plurality of input layers via a set of intermediate layers, each of the plurality of input datasets comprising an input attribute associated with ones of the plurality of ozone gas generating devices, ones of the set of intermediate layers being configured in accordance with an initial matrix of weights; and training the neural network in accordance with the respective ones of the plurality of input layers based at least in part upon recursively adjusting the initial matrix of weights by back propagation in generating, at the output layer, an output attribute in accordance with diminishment of an error matrix computed at the output layer of the neural network (as the combination of any suitable machine learning techniques to optimize a training process between Horstman, Seidman and Borschch et al in respect to claim 12 above, wherein Borschch et al teaches that the neural network or neuronal network or artificial neural network may be hardware-based, software-based, or any combination thereof, such as any suitable model (e.g., an analytical model, a computational model, etc.), which, in some embodiments, may include one or more sets or matrices of weights (e.g., adaptive weights, which may be numerical parameters that may be tuned by one or more learning algorithms or training methods or other suitable processes) and/or may be capable of approximating one or more functions (e.g., non-linear functions or transfer functions) of its inputs. The weights may be connection strengths between neurons of the network, which may be activated during training and/or prediction. A neural network
may generally be a system of interconnected neurons that can compute values from inputs and/or that may be capable of machine learning and/or pattern recognition (e.g., due to an adaptive nature). A neural network may use any suitable machine learning techniques to optimize a training process. The neural network may be used to estimate or approximate functions that can depend on a large number of inputs and that may be generally unknown. The neural network may generally be a system of interconnected “neurons” that may exchange messages between each other, where the connections may have numeric weights (e.g., initially configured with initial weight values) that can be tuned based on experience, making the neural network adaptive to inputs and capable of learning (e.g., learning pattern recognition). A suitable optimization or training process may be operative to modify a set of initially configured weights assigned to the output of one, some, or all neurons from the input(s) and/or hidden layer(s). A non-linear transfer function may be used to couple any two portions of any two layers of neurons, including an input layer, one or more hidden layers, and an output, e.g., an input to a hidden layer, a hidden layer to an output, etc. (see Figs. 1-3, para [0037]).
Claim 14. The method of claim 13 wherein the plurality of input datasets comprises one or more of: ozone gas generation capacity in regular mode of operation, location coordinates defining external boundaries or perimeter of a given spatial area, coordinate location of ozone gas generating device within the spatial area, model identification of ozone gas generating device, device operational reliability metrics (as discussed between Horstman and Seidman in respect to claim 1 above, see Figs. 1-3). But
Horstman fails to disclose device wireless communication reliability metrics and device historical, cumulative ozone gas generating metrics (as the combination of any suitable machine learning techniques to optimize a training process between Horstman, Seidman and Borschch et al in respect to claims 12, 13 above, and furthermore, Borshch et al teaches that the electronic device 100 may include a processor assembly 102, a memory assembly 104, a communications assembly 106, a power supply assembly 108, an input assembly 110, an output assembly 112, and a sensor assembly 114. Electronic device 100 may also include a bus 116 that may provide one or more wired or wireless communication links or paths for transferring data and/or power to, from, or between various assemblies of electronic device 100 (see Fig. 1, para [0015, 0016]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or implement the wireless communication assembly of Borshch et al to the system and programmed microprocessor of Horstman and Seidman for eliminating of cables/wires running throughout the cabin, building and/or office while provides a higher speed and communication therebetween.
Claim 15. The method of claim 13 wherein the output attribute comprises a desired concentration of ozone gas as constituted in ozonated air of the spatial area (as cited in respect to claim 1 above).
Claim 16. Horstman fails to disclose wherein the at least one ozone gas generating device of the non-homogeneous cohort performs ozone gas generation in accordance with applying ultraviolet (UV) irradiation provided in a wavelength of 185 nanometer (nm) to at least a portion of the gaseous oxygen constituted in an incoming stream of air to produce ozonated air, the ozonated air having a higher concentration of ozone gas than the incoming stream of air.
Borshch et al suggests that the sensor assembly 114 may include any suitable sensor components or subassemblies for detecting any suitable characteristics of any suitable condition of the lighting of the environment of device 100. For example, sensor assembly 114 may include any suitable light sensor that may include, but is not limited to, one or more ambient visible light color sensors, illuminance ambient light level sensors, ultraviolet (“UV”) index and/or UV radiation ambient light sensors, and/or the like. Any suitable light sensor or combination of light sensors may be provided for determining the illuminance or light level of ambient light in the environment of device 100 (e.g., in lux or lumens per square meter, etc.) and/or for determining the ambient color or white point chromaticity of ambient light in the environment of device 100 (e.g., in hue and colorfulness or in x/y parameters with respect to an x-y chromaticity space, etc.) and/or for determining the UV index or UV radiation in the environment of device 100 (e.g., in UV index units, etc.). A suitable light sensor may include, for example, a photodiode, a phototransistor, an integrated photodiode and amplifier, or any other suitable photo-sensitive device. For example, a plot of a chromaticity curve from the Commission International de l'Eclairage (“CIE”) may be accessible to system 1 (e.g., as a portion of data stored by memory assembly 104), wherein the circumference of the chromaticity curve may represent a range of wavelengths in nanometers of visible light. Different input neurons of the neural network may be associated with respective different types of environment categories and may be activated by environment category data of the respective environment categories (e.g., each possible category of environmental characteristic information (e.g., temperature, illuminance/light level, ambient color/white point chromaticity, UV index, noise level, oxygen level, air velocity, humidity, various gas levels (e.g., various VOC levels, pollen level, dust level, etc.), geo-location, location type, time of day, day of week, week of month, week of year, month of year, season, holiday, time zone, and/or the like (see Fig. 1, para [0026, 0038, 0052, 0063]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or supplement the ultraviolet UV wavelength of Borshch et al to the IR light of Horstman and Seidman for greater accuracy and reliable results to provide a comfortable and safety to people inside the cabin, building and/or office in any environmental conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from examiner should be directed to primary examiner craft is Van Trieu whose telephone number is (571) 2722972. The examiner can normally be reached on Mon-Fri from 8:00 AM to 3:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Wang Quan-Zhen can be reached on (571) 272-3114.
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/VAN T TRIEU/
Primary Examiner, Art Unit 2685
12/19/2025
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