DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 47-66 are pending.
Claims 1-46 are cancelled.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 07/26/2023 have been considered by the examiner.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 52, line 2, replace “.” with “,”
Appropriate correction is respectfully requested.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 47 recites the claim limitation “a data processing module”. For purposes of examination, as described in paragraph [0190] of the published specification, the “data processing module” will be construed as any computing system with memory and artificial intelligence engine.
Claim 51 recites the claim limitation “a control module”. For purposes of examination, as described in paragraphs [0210] of the published specification, each of the “control module” will be construed as a controller with central processing unit.
Claim 56 recites the claim limitation “a power management module”. The published specification does not describe the structure of the power management module. For purposes of examination, the “power management module” will be construed as software.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) 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.
Claim 56 is 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.
Regarding Claim 56 recites “a power management module”, which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. In particular, the specification describes in paragraphs [0173] that the power management module is used for managing power usage in the sensor module. There is no disclosure of any particular structure, either explicitly or inherently, to perform the power usage management. The use of the term “power management module” is not adequate structure for performing the power usage management because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 56 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function of power usage management. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 63 and 65-66 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 2A, Prong One) Independent claim 63 recites, “computing an air quality index from the air quality data via a data processing module; deriving an air quality rating corresponding to the air quality index”
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0144], [0304]-[0338], for instance, the limitations of the computing and deriving, as claimed, is a process that entails purely mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Accordingly, the claim recites an abstract idea.
(Step 2A, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim recites the additional limitations of, “sensing one or more air pollutants via a sensor module at a geographic location; generating air quality data; receiving the air quality data from the sensor module by an application program on a user device via a communication module; transmitting the air quality data to a server via the communication module; transmitting the air quality rating to the application program on the user device; and wherein the air quality data comprises one or more air quality parameters, the geographic location corresponding to the one or more air quality parameters and a time stamp; and wherein the method is operable to implement a crowdsourced sensor system for determining air quality.”
The additional limitation “sensing one or more air pollutants via a sensor module at a geographic location; generating air quality data; … and wherein the air quality data comprises one or more air quality parameters, the geographic location corresponding to the one or more air quality parameters and a time stamp” and “wherein the method is operable to implement a crowdsourced sensor system for determining air quality” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to identify and group as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. see MPEP 2106.05(f)
The additional limitations of “receiving the air quality data from the sensor module by an application program on a user device via a communication module”, “transmitting the air quality data to a server via the communication module”, and “transmitting the air quality rating to the application program on the user device” are insignificant extra-solution activities under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output).
The claim does not recite an improvement in a technology as set forth in MPEP 2106.04(d) and MPEP 2106.05(a). Accordingly, the additional limitations recited in the claim do not integrate the abstract idea into a practical application.
In view of the foregoing, the additional limitations are not sufficient to demonstrate integration of a judicial exception into a practical application.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional features including “sensing one or more air pollutants via a sensor module at a geographic location; generating air quality data; … and wherein the air quality data comprises one or more air quality parameters, the geographic location corresponding to the one or more air quality parameters and a time stamp” and “wherein the method is operable to implement a crowdsourced sensor system for determining air quality”, as recited in the claim that are configured to carry out the additional and abstract idea limitation may be tools that are used for the functions recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more.
The receiving the air quality data represents a function that is recognized as well-understood, routine, and conventional. For instance, PARISEAU (US 2018/0247526 A1) describes in paragraph [0034] (“The Cloud Connected Device 251 would typically be an off-the-shelf though it could be custom device like a srnartphone, a tablet, or a laptop, etc. An application 255 running either locally on the Controller 252 or partially from the web, in the case of a web-based application would gather aft quality and status information from the AQI 201 as well as position and date/time information from the position/clock sensor 256 on the Cloud Connected device 251 and would send this information using the Cloud Communications Controller 254 through the Cloud 281 to Server 291. The application 255 would also receive aggregated data or visual representations of the data like maps over the same link and could display such with its local data on its user interface 257. The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”), Hyde (US 2021/0372650 A1) describes in paragraph [0049] (“In an embodiment, the one or more sensors may be in immediate proximity of the entity, such as on a push chair, a cot, or a buggy. The one or more sensors may be fixed or portable (handheld) or wearable. The wearable sensor may collect air pollution, biometric, activity, and/or location information directly from the entity's body. Further, a stationary sensor may collect air pollution and activity information at a fixed location, such as a home wall or building roof (e.g., NEST indoor sensors, Netatmo sensors and so forth). In yet another embodiment, the sensors may be selectively installed in vehicles such as motorbikes, bicycles, or the entity's portable paraphernalia, such as a helmet worn by a motorbike rider. While the vehicles or the entities keep moving around a vast place the air pollution information of various geographic locations over a vast area is readily obtained.”), and Douglas et al. (US 2024/0044538 A1) describes in Abstract (“Systems, methods, and computer-readable storage media for building air quality assessment. One system includes a one or more processors configured to receive air quality measurements of at least a plurality of air quality sensors of a plurality of spaces of the building. The one or more processors further configured to generate a plurality of air quality metrics of the plurality of spaces based on the air quality measurements and at least one IAQ performance metric, and wherein the at least one IAQ performance metric contextualizes the air quality measurements. The one or more processors further configured to generate a graphical interface including a plurality of interface objects corresponding to the plurality of air quality metrics of the plurality of spaces of the building and cause a display device of a user device to display the graphical interface.”).
The transmitting the air quality data and transmitting the air quality rating represent functions that is recognized as well-understood, routine, and conventional. For instance, PARISEAU (US 2018/0247526 A1) describes in paragraph [0034] (“The Cloud Connected Device 251 would typically be an off-the-shelf though it could be custom device like a srnartphone, a tablet, or a laptop, etc. An application 255 running either locally on the Controller 252 or partially from the web, in the case of a web-based application would gather aft quality and status information from the AQI 201 as well as position and date/time information from the position/clock sensor 256 on the Cloud Connected device 251 and would send this information using the Cloud Communications Controller 254 through the Cloud 281 to Server 291. The application 255 would also receive aggregated data or visual representations of the data like maps over the same link and could display such with its local data on its user interface 257. The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”), Hyde (US 2021/0372650 A1) describes in paragraph [0051] (“In an embodiment, the system by using the air quality transmitting unit 214 may use the sensed and determined air quality data to be stored in the database along with providing the air quality data to the entities using a graphical display device. The graphical display device may be one of a personal computer, a laptop, a smartphone, a table computer, a wearable computer device, etc.”), and Douglas et al. (US 2024/0044538 A1) describes in Abstract (“Systems, methods, and computer-readable storage media for building air quality assessment. One system includes a one or more processors configured to receive air quality measurements of at least a plurality of air quality sensors of a plurality of spaces of the building. The one or more processors further configured to generate a plurality of air quality metrics of the plurality of spaces based on the air quality measurements and at least one IAQ performance metric, and wherein the at least one IAQ performance metric contextualizes the air quality measurements. The one or more processors further configured to generate a graphical interface including a plurality of interface objects corresponding to the plurality of air quality metrics of the plurality of spaces of the building and cause a display device of a user device to display the graphical interface.”).
Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
For similar reasons as discussed above for independent claim 63, independent claim 66 is not patent eligible.
The recitations of claim 65 simply add more detail to or are cumulative to the abstract idea of claim 63.
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 47-48, 50-51, 53-55, 57-59 and 62 are rejected under 35 U.S.C. 103 as being unpatentable over PARISEAU (US 2018/0247526 A1) (“Pariseau”), in view of Douglas et al. (US 2024/0044538 A1) (“Douglas”).
Regarding independent claim 47, Pariseau teaches:
A system comprising: (Pariseau: [0001] “The present invention is related to instruments within systems and, more specifically to aft quality monitoring instruments within systems.”)
a sensor module operable to detect and measure one or more air pollutants at a geographic location via one or more sensors and generate air quality data; (Pariseau: [0011] “A crowdsourced aft quality monitoring system is disclosed in accordance with the various aspects and embodiments of the invention. The system monitors aft quality in a given geographical area and provides information about the air quality for the given geographical area to a user. The system includes at least one aft quality monitoring instrument and a server that is communication with the instrument. The instrument Includes at least one aft quality sensor and connects to the server through a network. The system monitors aft quality and aft quality information, including at least one of a current date, a time, and a position, is communicated from the instrument through the network to the server.”) (Pariseau: [0022] “There is a distinction between the types of factors one is most concerned with for indoor air quality vs. outdoor aft quality monitoring, for example for indoor aft quality CO2 is of larger concern though CO2 is seldom an issue in outdoor air quality. Aft quality monitoring instruments measure various parameters in proximity to the instrument. These parameters might include, but are not limited to particulates and/or various gases to create a picture of the local environment. In order to map out a larger area Ike a building for indoor aft quality, or a city for outdoor aft quality current systems if they exist rely on networks of fixed instruments. One or more computers collect the data from these instruments in order to determine the conditions in proximity to each and then attempt to represent the area from these. As noted, these systems are largely comprised of fixed systems and typically purchased and deployed by the system administrator.”) [The air quality monitoring instrument reads on “a sensor module”. Measuring of the particulates in the specific, represented area by the air quality monitoring instrument reads on “to detect and measure one or more air pollutants at a geographic location”. The at least one sensor reads on “one or more sensors”. The air quality monitoring instrument providing air quality and air quality information reads on “generate air quality data”.]
a cloud based application comprising a data processing module operable to receive, via a communication module, the air quality data and derive a first air quality index; (Pariseau: FIGS. 2-3) (Pariseau: [0029] “The aft quality, date/time and position information sent from all the aft quality instruments 101-103, through the Cloud 125 would be collected by one or more Servers 151 and aggregated to create an aggregate data set of the various aft quality parameters across a geographic area. One or more servers 151 would make that aggregated data available to applications 105. These applications 105 could run on separate devices like personal computers, tablets, smart phones, etc. or within the aft quality instruments themselves 101-103.”) (Pariseau: [0065] “The Server 151,291,391 would receive data from all connected aft quality sensors 411. It would store this data and then periodically aggregate data into a current geographic dataset that would be used by the applications 105,255,355 to represent this aggregated data locally for the user, either in map form or in some other form.”) [The server reads on “a cloud based application”. The server collecting data from the instruments reads on “configured to receive … the air quality data”. The server function performing the aggregating of data reads on “a data processing module”. Communication through the CloudComm 254 or 354, as illustrated in FIGS. 2-3, reads on “a communication module”. The server creating the aggregate data set for the specific, represented area reads on “a first air quality index”.]
a user device comprising an application program operable to receive, via the communication module, the first air quality index; and (Pariseau: [0034] “The Cloud Connected Device 251 would typically be an off-the-shelf though it could be custom device like a srnartphone, a tablet, or a laptop, etc. An application 255 running either locally on the Controller 252 or partially from the web, in the case of a web-based application would gather aft quality and status information from the AQI 201 as well as position and date/time information from the position/clock sensor 256 on the Cloud Connected device 251 and would send this information using the Cloud Communications Controller 254 through the Cloud 281 to Server 291. The application 255 would also receive aggregated data or visual representations of the data like maps over the same link and could display such with its local data on its user interface 257. The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”) [The cloud connected device 251 or 351 with the application reads on “a user device comprising an application program”. The cloud connected device receiving the aggregated data or the visual representation of the data like maps reads on “to receive … the first air quality index”.]
… an alternate location having a second air quality index, wherein the alternate location has better air quality than the geographic location; and (Pariseau: [0059] “In addition to allowing the aggregate display of the air quality over some user selectable geographic area, the application 105,255,355 could also display results from local Air Quality Instrument 101,102,103,201 or Sensor Network 301. This would allow the instrument to operate in local mode in the absence of a network for example on a plane.”) (Pariseau: [0062] “The maps themselves might become quite elaborate, allowing all air quality sensors to be displayed concurrently, or allowing only selected air qualify parameters to be displayed. As noted, the sensor data could be aged through an area, perhaps delimiting an area of travel through an environment, like a comet trace with the head being the current position of the sensor and it being the most intense and the tail trailing along behind the head showing the path of travel and with the intensity declining to behind the head eventually to nothing. Perhaps the tail would vary in length and location based on factors like speed of travel, velocity of prevailing winds, etc. This would be useful in sparely sensed areas, but in more densely sensed areas the display could significantly reduce the area for individual sensors and provide a more heavily aggregated view.”) [Any one of the areas or selected areas and its aggregated data that has a better air quality than another area or selected area reads on “an alternate location having a second air quality index”.]
wherein the air quality data comprises one or more air quality parameters, the geographic location corresponding to the one or more air quality parameters and a time stamp; and (Pariseau: [0029] and [0034] as discussed above) (Pariseau: [0040] “A small number of Sensor Receivers 311, with precise clocks and position sensors 356 would timestamp these received messages and communicate such within a local network using theft integrated local communications controllers 353. From this information, a local controller, in this case the Cloud Connected device 351, could determine the position of the sensors and attach that position information along with the current date/time to the aft quality information received from the Sensor 302. The position information would be determined by triangulation with 3 Sensor Receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 Sensor Receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each Sensor Receiver 311 would also have accurate position information due to theft local position sensor 356 the relative position of the sensors could be converted to an absolute position.”) [The air quality information from the air quality instruments read on “one or more air quality parameters”, the position information read on “the geographic location”, and the date/time or timestamp information read on “a time stamp”.]
wherein the system is operable to provide real-time data of an air quality of the geographic location and the alternate location. (Pariseau: [0034] as discussed above) (Pariseau: [0043] “A key differentiator of this invention is that the air quality information is closely coupled with current date, time, and position information for that instrument. The position information is deemed to be dynamic and is recorded if not with every record then at a sufficient rate to record changes in position. This information is used to create a dynamic aggregate map of air quality conditions across a geographic area. And, though in some cases, gross location information is included in some monitoring systems, it is not intended as geographic coordinates but rather as informational location markers like “Gowning Room Vent” or “Lithography door” or “Room 103 doorway” and such are typically stationary designators even if the instrument is sometimes moved and the location changed manually by a user.”) [The cloud connected device displaying the aggregated data or the visual representation of the data like maps reads on “to provide real-time data of an air quality …”.]
Pariseau does not expressly teach: the application program operable to suggest an alternate location having a second air quality index, wherein the alternate location has better air quality than the geographic location.
Douglas teaches:
the application program operable to suggest an alternate location having a second air quality index, wherein the alternate location has better air quality than the geographic location. (Douglas: [0078] “In some embodiments, the recommendation generator 307 could recommend that persons with allergies be assigned to areas of a building with low VOC, TVOC, PM2.5, and/or PM10 levels. This may allow the allergenic persons to avoid having an asthma attack or other breathing problems. In some embodiments, class scheduling can be set up and/or recommended by the analysis system 304 such that students or teachers are not assigned spaces with high VOC, TVOC, PM2.5 levels for a long duration.”) (Douglas: [0135] “Additionally, air quality metrics, as shown in FIGS. 12 and 13, are used to generate graphical interfaces that display CO2 levels and the ventilation-occupancy ratio within rooms. In FIG. 12, the interface shows how CO2 levels change over time in correlation with occupancy. This data is displayed on a scale similar to the ranges in FIG. 7. FIG. 13 uses air quality metrics to present ventilation-occupancy data points. The ventilation to occupancy (Voa) ratio metric helps determine if ventilation is sufficient for the number of people in a room, aiding in energy management and air quality control. In FIGS. 14, 15, and 16, air quality metrics are employed in different ways to monitor air quality and building performance. FIG. 14 shows how metrics can be used to recommend actions based on the current ventilation status of different spaces and potential savings from schedule changes or compliance with ASHRAE standards. FIG. 15 uses metrics to create a performance score table for different buildings or campuses across various parameters, allowing for easy comparisons across different locations. Lastly, FIG. 16 uses air quality metrics to create a geographical map with data points representing the air quality of specific locations. Users can interact with these points to get detailed air quality data, demonstrating the practical use of these metrics in air quality evaluation and management.”)
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 Pariseau and Douglas before them, to modify monitoring and analyzing air qualities of geographical areas, to incorporate recommending an area to the user based on the air quality evaluations of the geographical areas.
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 allow for the user to avoid areas with high levels of pollutants. (Douglas: [0078] and [0135])
Regarding claim 48, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the system is operable to generate the first air quality index and the second air quality index by crowdsourcing via cloud computing. (Pariseau: [0025] “Accordingly, a system of air quality instruments which are aggregated using crowdsourcing to produce an aggregate picture of aft quality for use for indoor and/or outdoor aft quality monitoring/reporting. Referring now to FIG. 1, a crowdsourced aft quality system 100 is shown in accordance with the various aspects and embodiments of the invention.”)
Regarding 50, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the system is operable to monitor air quality of an indoor space, wherein the indoor space comprises one or more of an office space, a house, a restaurant, a cafe, a movie theater, a shopping mall, an office buildings, indoor spaces of a school, indoor spaces of a university, an indoor sports facility, an inside space of a public transportation vehicle, an indoor space of a private transport vehicle, an indoor space of a hospital and healthcare facility, an underground parking lot, and an indoor concert venues. (Pariseau: [0022] as discussed in claim 47) (Pariseau: [0051] “In accordance with the various aspects and embodiment of the invention, the system could also be implemented as a private system. It would allow, for example, for an entity to offer such a system distributed across a local area of concern and while making it accessible only to its members. For example, a university could implement such a system on a campus and make such a system available only to faculty and students. As such, annotations could also be used as public service announcements for the campus. The information stream could also be added to existing system to enhance it with aft quality information. Likewise, a corporation could do the same for theft campus, or even within one or more buildings”) [The indoor air quality of the building reads on “air quality of an indoor space”.]
Regarding claim 51, Pariseau and Douglas teach all the claimed features of claims 47 and 50. Pariseau further teaches:
wherein the system further comprises a control module operable to control a sterilization and circulation unit operable to remedy the air quality of the indoor space. (Pariseau: [0052] “In accordance with the various aspects and embodiment of the invention, the system can also be integrated within a facility management system with the data being used as an additional input for aft handling and filtration systems. Thus, the filtration systems could respond to issues that might need to be addressed, but to which the system might otherwise be unaware. For example, alarm levels could be raised if personal air quality instruments worn by staff in a facility exceeded some aggregate alarm level for an area. This might precipitate a response by staff to the areas in question, or it might even be integrated into an automated system to have an automatic response like increasing filtration or aft changes to the areas in question.”)
Regarding claim 53, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the sensor module comprises a sensor array with a plurality of air quality sensors, comprising a gas sensor, a particle sensor, and an environmental sensor. (Pariseau: FIG. 4) (Pariseau: [0026] “FIG. 1 shows a hybrid system showing instruments and users in both an outdoor environment 121 and indoor environment, for example within a building 122. It also shows a mixture of aft quality instruments, such as: Personal instruments 101, Portable instruments 102 and Fixed aft quality instruments (AQI) 103. Each instrument 101-103 might measure one or more aft quality parameters which might include, but is not limited to: particulates, Temperature, Relative Humidity, CO2, CO, NO, NO2, SO2, O3, aggregate or specific VOCs. They would report this information along with current sensor date/time and current sensor position information to at least one server in a network. The data would be aggregated by the Server 151. The aggregated data would be available to applications (App) 105 allowing them to represent current or historical aggregated views of the system's geographical area.”) [The integrated sensors of the instrument, as illustrated in FIG. 4, reads on “a sensor array with a plurality of air quality sensors”.]
Regarding claim 54, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the one or more air quality parameters comprise CO2, volatile organic compounds, particulate matter, pathogens, temperature, humidity, and airflow. (Pariseau: [0026] “FIG. 1 shows a hybrid system showing instruments and users in both an outdoor environment 121 and indoor environment, for example within a building 122. It also shows a mixture of aft quality instruments, such as: Personal instruments 101, Portable instruments 102 and Fixed aft quality instruments (AQI) 103. Each instrument 101-103 might measure one or more aft quality parameters which might include, but is not limited to: particulates, Temperature, Relative Humidity, CO2, CO, NO, NO2, SO2, O3, aggregate or specific VOCs. They would report this information along with current sensor date/time and current sensor position information to at least one server in a network. The data would be aggregated by the Server 151. The aggregated data would be available to applications (App) 105 allowing them to represent current or historical aggregated views of the system's geographical area.”)
Regarding claim 55, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the sensor module comprises a global positioning system (GPS) sensor operable to generate the geographic location comprising longitude and latitude data. (Pariseau: [0040] “A small number of Sensor Receivers 311, with precise clocks and position sensors 356 would timestamp these received messages and communicate such within a local network using theft integrated local communications controllers 353. From this information, a local controller, in this case the Cloud Connected device 351, could determine the position of the sensors and attach that position information along with the current date/time to the aft quality information received from the Sensor 302. The position information would be determined by triangulation with 3 Sensor Receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 Sensor Receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each Sensor Receiver 311 would also have accurate position information due to theft local position sensor 356 the relative position of the sensors could be converted to an absolute position.”)
Regarding claim 57, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the air quality data is sent to the cloud based application by a smartphone which collects the air quality data from the sensor module via a Bluetooth connection, wherein a location data from the smartphone is also sent to the cloud based application. (Pariseau: [0032] “The power management circuitry 204 implements these features in the system, it might include voltage regulators, voltage references, current sources for some of the sensors battery management and/or charging, etc. The local communications controller 203 implements a local communication link, for example Bluetooth, or Zigbee, etc. to a Cloud Connected Device 251. If the Local communications controller 203 has enough onboard resources it might even also be the local controller 202 for the instrument. The bidirectional interface between the Personal AQI 201 and the Cloud Connected device 251 is concerned with gathering air quality information from the device as well as any status or error conditions, although the Cloud Connected Device 251 might also control the operation of the aft quality instrument 201, perhaps to switch between sleep/active modes or performing diagnostics or calibration.”) (Pariseau: [0039] “Referring now to FIG. 3, such a network is shown. A number of aft quality sensors 302 would be released and move about the environment or be strewn throughout the environment perhaps dropped by air using a drone. These sensors 302 would have one or more onboard aft quality sensors and at least one short-range communications transmitter though they might also have receivers, discussed later. These sensors 302 would comprise a sensor network 301. Each sensor 302 would periodically report air quality information from theft sensors using theft integrated transmitters”) (Pariseau: [0040] “A small number of Sensor Receivers 311, with precise clocks and position sensors 356 would timestamp these received messages and communicate such within a local network using theft integrated local communications controllers 353. From this information, a local controller, in this case the Cloud Connected device 351, could determine the position of the sensors and attach that position information along with the current date/time to the aft quality information received from the Sensor 302. The position information would be determined by triangulation with 3 Sensor Receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 Sensor Receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each Sensor Receiver 311 would also have accurate position information due to theft local position sensor 356 the relative position of the sensors could be converted to an absolute position.”) (Pariseau: [0041] “Once position information is derived, that information can be attached to the sensor air quality information and then communicated via a Cloud connected device 351 via its Cloud Communications controller 354 through a network cloud 381 to at least one Server 391. This would allow for a large number of low-cost air quality sensors 302 to be deployed with a minimum of electronics.”)
Regarding claim 58, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the application program generates an alert and notifies a user via one or more of an audio cue, a visual cue, a tactile cue, and a text message. (Pariseau: Abstract “A crowdsourced air quality monitoring system. Such a system could provide information beyond the user's local instrument on air quality over a much larger area. This information could be used by a user to make decisions about frequenting particular areas based on the results, or to alert them to changing conditions in the area so that the user might act before local conditions change.”) (Pariseau: [0045] “In addition to position information, one or more annotation fields night also be included with the data. These fields might be used to provide additional location or status information to the air quality records. For example, an annotation field might be used to provide additional location information like “Starbucks” so that if annotations were included on a map the user would see then enhanced location information, in this way, a merchant had installed filtration equipment and was touting enhanced aft quality might note the name of theft establishment on a map.”) (Pariseau: [0046] “Annotations might also enable users to add observations to the data attempting in order to provide context to the reported values. For example, a user night note that “Burning leaves” to provide some explanation for the increased particulate counts in a neighborhood. “Vehicle on fire on shoulder” might be provide context for elevated particulates on a stretch of freeway. Applications like Waze allow users to make comments about traffic issues they witness which provides context for users of the system in understanding the underlying causes of issues in the traffic and gauge how this might impact the data over time. So, similarly allowing users to make comments about leaf-blowers, pressure washers, dirty truck exhaust, traffic jams, etc. when aft quality is compromised might add context to the raw information.”) (Pariseau: [0049] “The tags could also be media such as images or video. In that way, a picture, audio cup, or video of the current area or the user describing such could be attached to a sample and viewed by other system users.”) (Pariseau: [0052] “… For example, alarm levels could be raised if personal air quality instruments worn by staff in a facility exceeded some aggregate alarm level for an area. This might precipitate a response by staff to the areas in question, or it might even be integrated into an automated system to have an automatic response like increasing filtration or aft changes to the areas in question.”) (Pariseau: [0058] “FIG. 4 includes a sample of an Air Quality Instrument data record 411. That record has information on the sample in question 412 which includes the instrument type described above, a unique identifier for this instrument within the system, the date/time of the sample, and the position of the sample. To that is added the air quality instrument sensor information 413 which might include a list of the sensors, their status for that sample and their values for that sample. Finally, an annotation section 414 would avow annotations to be added to that sample, as noted these might include simple Text notation, special tags, or media notations.”)
Regarding claim 59, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the user device comprises a display operable to display the first air quality index. (Pariseau: [0059] and [0062] as discussed in claim 47) [Displaying the aggregate air quality in the user selected geographic area reads on “to display the first air quality index”.]
Regarding claim 62, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the communication module is enabled for communication using one of a wired connection and a wireless connection, wherein the communication module comprises one or more of a Wi-Fi, a Bluetooth, and a cellular connectivity for data reception and data transmission. (Pariseau: [0034] “… The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”) (Pariseau: [0041] “Once position information is derived, that information can be attached to the sensor air quality information and then communicated via a Cloud connected device 351 via its Cloud Communications controller 354 through a network cloud 381 to at least one Server 391. This would allow for a large number of low-cost air quality sensors 302 to be deployed with a minimum of electronics.”) (Pariseau: [0070] “The wireless device 10 includes an antenna 12 (or multiple antennae) in operable connection or communication with a transmitter 14 and a receiver 16 in accordance with one aspect of the invention. In accordance with other aspects of the present invention, the transmitter 14 and the receiver 16 may be part of a transceiver 15. The wireless device 10 may further include an apparatus, such as a controller 20 or other processing element, which provides signals to and receives audio segments from the transmitter 14 and receiver 16, respectively. The signals include signaling information in accordance with the air interface standard of the applicable cellular system, and also user speech, received data and/or user generated data. In this regard, the wireless device 10 is capable of operating with one or more air interface standards, communication protocols, modulation types, and access types.”)
Claim 49 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Douglas, further in view of PARISEAU (US 2020/0378940 A1) (“Pariseau-940”).
Regarding claim 49, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau and Douglas do not expressly teach the recitations of claim 49.
Pariseau-940 teaches:
wherein the system is operable to generate an air quality index for a future period based on current air quality information and a history of air quality information. (Pariseau-940: [0158] “Sensors within the site can be a mixture of fixed and mobile (with geographic data) versions, data from these sensors can be collected and recorded. A dynamic aggregate picture of air quality for the site can be constructed from the data. Models can be used to interpolate intermediate conditions within the map to produce a more continuous map. The aggregate (as well as individual) data can be stored for later analysis or reporting. The collected data can also be used to project air quality into the future based on changes in conditions over time (an application use of machine learning or other model based analysis). During an air quality event, sensors with fixed patrol routes can be assigned modified routes to provide more visibility into areas near and around the source of the issue or controlled dynamically to help identify the actual source.”) [The projected air quality into the future reads on “an air quality index for a future period”. The collected and recorded data read on “current air quality information and a history of air quality information”.]
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 Pariseau, Douglas and Pariseau-940 before them, to modify monitoring and analyzing air qualities of geographical areas, to incorporate using the monitored or collected data to project air quality of the geographical areas.
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 allow for the user to be informed of the future air quality of the area. (Pariseau-940: [0158])
Claim 52 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Douglas, further in view of Hyde (US 2021/0372650 A1) (“Hyde”).
Regarding claim 52, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the system is operable to monitor air quality of an outdoor space. (Pariseau: [0022] as discussed in claim 1) [The outdoor air quality of the city reads on “air quality of an outdoor space”.]
Pariseau and Douglas do not expressly teach: wherein the outdoor space comprises one or more of a park, a garden, a beach, a waterfront area, a playground, a sports field, an outdoor sports stadium, a hiking trail, a nature reserves, a public square, a plaza, an open-air market, an outdoor dining area, a patio, a campground, and a rooftop terrace.
Hyde teaches:
wherein the outdoor space comprises one or more of a park, a garden, a beach, a waterfront area, a playground, a sports field, an outdoor sports stadium, a hiking trail, a nature reserves, a public square, a plaza, an open-air market, an outdoor dining area, a patio, a campground, and a rooftop terrace. (Hyde: [0050] “In an embodiment, the air quality sensing and comparison unit 212 may enable maintaining temporally indexed location information of the entity based on current location, previous visited locations and/or planned locations to be visited of the entity. Determined spatiotemporally indexed air quality data is obtained from the one or more sensors. The one or more sensors may be indexed both in terms of location (e.g., GPS coordinates) and time. The system 102 may compute the air quality data for the entity that are accumulated over a time period (e.g., day, week, month, etc.). The air quality data determined over the time period may be communicated to the entity and its corresponding entities. For example, the entity may determine his/her total air pollution exposure during sleep time in the bedroom, during his/her visit to a park, and during time spent at a workplace. The system 102 may enable the entity to observe that the entity has little air pollution exposure at the workplace, while higher exposure to air pollution, during his/her visit to the park.”)
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 Pariseau, Douglas and Hyde before them, to modify monitoring and analyzing air qualities of geographical areas, to incorporate monitoring and analyzing air qualities of outdoor areas.
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 allow for determining and warning the user of exposure to air pollution during outdoor activities. (Hyde: [0051])
Claim 56 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Douglas, further in view of Thomas et al. (US 2013/0317659 A1) (“Thomas”).
Regarding claim 56, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau further teaches:
wherein the sensor module further comprises a power management module comprising a sleep mode … (Pariseau: [0032] “The power management circuitry 204 implements these features in the system, it might include voltage regulators, voltage references, current sources for some of the sensors battery management and/or charging, etc. The local communications controller 203 implements a local communication link, for example Bluetooth, or Zigbee, etc. to a Cloud Connected Device 251. If the Local communications controller 203 has enough onboard resources it might even also be the local controller 202 for the instrument. The bidirectional interface between the Personal AQI 201 and the Cloud Connected device 251 is concerned with gathering air quality information from the device as well as any status or error conditions, although the Cloud Connected Device 251 might also control the operation of the aft quality instrument 201, perhaps to switch between sleep/active modes or performing diagnostics or calibration.”)
Pariseau does not expressly teach: wherein the sensor module further comprises a power management module comprising a sleep mode to conserve battery life when the sensor module is not in use.
Thomas teaches:
wherein the sensor module further comprises a power management module comprising a sleep mode to conserve battery life when the sensor module is not in use. (Thomas: [0031]-[0039] “[0031] A key advantage of embodiments of the invention is its various features that allow it to operate with very low power consumption over extended periods of time without sacrificing the quality of data collection. This is achieved through several design features, including the following: [0032] The microcomputer 524 is a very low power design. [0033] Several standard on-board sensors 508 (e.g., air and water quality sensors) are integrated into the DAP, sharing the same power supply, logger and radio. [0034] In one embodiment, the DAP can be deployed with a battery charging solar panel. [0035] The unit operates in two modes: a dynamic or active mode, and a sleep or low power mode. Most of the time, the unit is in sleep mode and no data is logged or transmitted. In this mode, all on-chip and off-chip peripherals use little or no current, and the current consumption of the DAP is approximately 300 microamps. Upon detection of a change in the sensor parameter beyond a configurable threshold, the DAP wakes up from sleep mode and switches into dynamic mode to perform data logging. [0036] Wireless data reporting (i.e., transmitting to the cloud server) is performed periodically according to a user-configured schedule, e.g., periodically once every 5 minutes up to once per month. Alternatively, or in addition, wireless data reporting is performed intermittently, e.g., reporting is triggered when a predetermined amount of data has been logged or an alarm condition (e.g., low battery capacity) occurs. [0037] Prior to wireless data transmission to the cloud server, sensor data is compressed to reduce the amount of time needed for transmission, reducing the time the cell module 528 needs to be powered on. Preferably, a run-length encoding (RLE) or other lossless compression algorithm is used. [0038] If the communications protocol is disrupted during wireless transmission (e.g., due to connectivity issues such as maintenance on a cellular network tower), the unit makes several (e.g., at least three) re-connection attempts. If connection fails after re-connection attempts, the units switches into sleep mode until the next scheduled or triggered reporting time. [0039] The DAP battery status and consumption information is reported to the cloud server when data is uploaded so that it can be monitored and trended by algorithms on the server. The server can send the DAP adjusted configuration parameters, such as measurement and reporting intervals, to manage the power consumption. For example, if the battery level is very low, scheduled reporting intervals can be made less frequent.”)
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 Pariseau, Douglas and Thomas before them, to modify the power management of the air quality monitoring instrument, to incorporate the sleep mode with little or no power consumption by the sensors of the air quality monitoring instrument.
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 allow for extending the operation time of the air quality monitoring instrument on a given charge amount of the battery. (Thomas: [0031])
Claim 60 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Douglas, further in view of Vuu et al. (US 2022/0157408 A1) (“Vuu”).
Regarding claim 60, Pariseau and Douglas teach all the claimed features of claim 47. Pariseau and Douglas do not expressly teach the recitations of claim 60.
Vuu teaches:
wherein the data processing module comprises an artificial intelligence engine comprising a machine learning algorithm for identifying and classifying the one or more air pollutants. (Vuu: [0004] The pollution sensing system may perform a multifactor analysis of a combination of the locally measured data and the remotely sourced data to generate more specific classifications of measured pollutants. The multifactor analysis may use processes such as machine learning, regressions calculations, and other means to generate classification models or tables of pollutants. For example, an AI or machine learning process may identify and distinguish data patterns that correspond to different classifications or types of pollutants, and the pollution sensing system may provide a measurement of pollutant levels and an identification of the type or types of pollutants identified.”) (Vuu: [0017] “Pollution sensing systems and methods as disclosed herein may use a multifactor analysis to identify pollutant types from measurements that local sensors in a monitored area produce and from context data that may be downloaded from the Cloud or from nearby devices. (Pollutant type as used herein refers to the source, structure, or nature of a particulate pollutant and not merely to the particle size for the pollutant, although the particle sizes, as described further below, may provide indicators for identifying a pollutant type.) The local measurements generally include measurement of particulate matter suspended in air in the monitored area and one or more other measurements of other properties of the monitored area. The multifactor analysis may employ tables or models that Artificial Intelligence (AI) or machine learning processes produce to characterize specific types of pollutants based on data patterns of local measurements and contextual data.”)
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 Pariseau, Douglas and Vuu before them, to modify the use of machine learning in optimizing air quality, to incorporate using machine learning to analyze air quality data to classify different types of pollutants.
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 allow for providing specificity identifying the particle pollutant type for the optimization process. (Vuu: [0002])
Claim 61 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Douglas, further in view of Vuu, further in view of Pariseau-940, further in view of Mecikalski (US 2021/0356625 A1) (“Mecikalski”).
Regarding claim 61, Pariseau, Douglas and Vuu teach all the claimed features of claims 47 and 60. Pariseau, Douglas and Vuu do not expressly teach the recitations of claim 61.
Pariseau-940 teaches:
wherein the machine learning algorithm is operable to analyze the air quality data collected from the sensor module, air quality monitoring stations … , to predict a pollution level and forecast air quality in the geographic location. (Pariseau-940: [0026] “Though the data can be stored locally in the sensors, the geographic location and air quality information (and environmental information if present) is transmitted to one or more servers either in the cloud or via the cloud or cellular network, for example. And, that the system (i.e. the server(s) connected to the fleet of sensors via the cloud) can direct the motion of at least some these sensors in real-time to optimize the mission in question (or perhaps change it altogether). The cloud-based system can function using fixed pre-defined algorithms but it can also implemented using artificial intelligence processing system whereby a computational method is used to interactively compute a metric based on the measured data provided at one or more selected temporal intervals. The computational method can be structured to minimize a selected metric, which can be a particle size and/or mass measurement error metric, for example.”) (Pariseau-940: [0027] “A further application of machine learning methods can be applied to generate improved geographic mapping of air quality information by utilizing real time weather data including wind velocity and precipitation data over the particle detection spatial region. Weather radar data can be used, for example, to improve the particle data distribution and/or machine learning to deriver or refine algorithms over time and thus optimize sensor motion. In this way the data collection can be tailored to provide the best coverage given the current conditions (air velocity/direction, location, and air quality).”) (Pariseau-940: [0158] “Sensors within the site can be a mixture of fixed and mobile (with geographic data) versions, data from these sensors can be collected and recorded. A dynamic aggregate picture of air quality for the site can be constructed from the data. Models can be used to interpolate intermediate conditions within the map to produce a more continuous map. The aggregate (as well as individual) data can be stored for later analysis or reporting. The collected data can also be used to project air quality into the future based on changes in conditions over time (an application use of machine learning or other model based analysis). During an air quality event, sensors with fixed patrol routes can be assigned modified routes to provide more visibility into areas near and around the source of the issue or controlled dynamically to help identify the actual source.”)
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 Pariseau, Douglas, Vuu and Pariseau-940 before them, to modify monitoring and analyzing air qualities of geographical areas, to incorporate using the monitored or collected data to project air quality of the geographical areas.
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 allow for the user to be informed of the future air quality of the area. (Pariseau-940: [0158])
Pariseau, Douglas, Vuu and Pariseau-940 do not expressly teach: wherein the machine learning algorithm is operable to analyze the air quality data collected from the sensor module, air quality monitoring stations and satellite imagery to identify patterns, to predict a pollution level and forecast air quality in the geographic location.
Mecikalski teaches:
wherein the machine learning algorithm is operable to analyze the air quality data collected from the sensor module, air quality monitoring stations and satellite imagery to identify patterns, to predict a pollution level and forecast air quality in the geographic location. (Mecikalski: [0023] “Referring to FIG. 3, the weather forecasting logic 50 is configured to compare the NWP model data 110, the satellite image data 17, and/or the weather prediction data 112 to the topographical data 113 to time trends in interest fields to determine one or more interest fields that are indicative of how likely precipitation or cloud formation is in one or more sub-regions. As an example, one interest field may be a temperature differential field indicating the extent to which temperature gradients lead to low-level airflow convergence is predicted to occur in one or more sub-regions during a particular time period. …”)
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 Pariseau, Douglas, Vuu, Pariseau-940 and Mecikalski before them, to modify predicting the air quality of the geographical area, to incorporate using the satellite image data of the geographical area.
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 allow for providing weather prediction of sub-regions of the geographical area using the satellite image data. (Mecikalski: [0023])
Claims 63-64 and 66 are rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Hyde.
Regarding independent claim 63, Pariseau teaches:
A method, comprising: (Pariseau: Abstract “A crowdsourced air quality monitoring system. Such a system could provide information beyond the user's local instrument on air quality over a much larger area. This information could be used by a user to make decisions about frequenting particular areas based on the results, or to alert them to changing conditions in the area so that the user might act before local conditions change.”) (Pariseau: [0001] “The present invention is related to instruments within systems and, more specifically to aft quality monitoring instruments within systems.”)
sensing one or more air pollutants via a sensor module at a geographic location; generating air quality data; (Pariseau: [0011] “A crowdsourced aft quality monitoring system is disclosed in accordance with the various aspects and embodiments of the invention. The system monitors aft quality in a given geographical area and provides information about the air quality for the given geographical area to a user. The system includes at least one aft quality monitoring instrument and a server that is communication with the instrument. The instrument Includes at least one aft quality sensor and connects to the server through a network. The system monitors aft quality and aft quality information, including at least one of a current date, a time, and a position, is communicated from the instrument through the network to the server.”) (Pariseau: [0022] “There is a distinction between the types of factors one is most concerned with for indoor air quality vs. outdoor aft quality monitoring, for example for indoor aft quality CO2 is of larger concern though CO2 is seldom an issue in outdoor air quality. Aft quality monitoring instruments measure various parameters in proximity to the instrument. These parameters might include, but are not limited to particulates and/or various gases to create a picture of the local environment. In order to map out a larger area Ike a building for indoor aft quality, or a city for outdoor aft quality current systems if they exist rely on networks of fixed instruments. One or more computers collect the data from these instruments in order to determine the conditions in proximity to each and then attempt to represent the area from these. As noted, these systems are largely comprised of fixed systems and typically purchased and deployed by the system administrator.”) [The air quality monitoring instrument reads on “a sensor module”. Measuring of the particulates in the specific, represented area by the air quality monitoring instrument reads on “sensing one or more air pollutants … at a geographic location”. The at least one sensor reads on “one or more sensors”. The air quality monitoring instrument providing air quality and air quality information reads on “generating air quality data”.]
receiving the air quality data from the sensor module by an application program on a user device via a communication module; transmitting the air quality data to a server via the communication module; (Pariseau: [0034] “The Cloud Connected Device 251 would typically be an off-the-shelf though it could be custom device like a srnartphone, a tablet, or a laptop, etc. An application 255 running either locally on the Controller 252 or partially from the web, in the case of a web-based application would gather aft quality and status information from the AQI 201 as well as position and date/time information from the position/clock sensor 256 on the Cloud Connected device 251 and would send this information using the Cloud Communications Controller 254 through the Cloud 281 to Server 291. The application 255 would also receive aggregated data or visual representations of the data like maps over the same link and could display such with its local data on its user interface 257. The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”) [The cloud connected device 251 or 351 with the application reads on “an application program on a user device”. The cloud connected device 251 gathering the air quality and status information data from the air quality instruments 201 reads on “receiving the air quality data. The cloud connected device 251 sending the air quality and status information to the server 291 reads on “transmitting the air quality data to a server …”. The combination of all communication modules 253 and 254, as illustrated in FIG. 2, read on “a communication module”.]
computing an air quality index from the air quality data via a data processing module; (Pariseau: FIGS. 2-3) (Pariseau: [0029] “The aft quality, date/time and position information sent from all the aft quality instruments 101-103, through the Cloud 125 would be collected by one or more Servers 151 and aggregated to create an aggregate data set of the various aft quality parameters across a geographic area. One or more servers 151 would make that aggregated data available to applications 105. These applications 105 could run on separate devices like personal computers, tablets, smart phones, etc. or within the aft quality instruments themselves 101-103.”) (Pariseau: [0065] “The Server 151,291,391 would receive data from all connected aft quality sensors 411. It would store this data and then periodically aggregate data into a current geographic dataset that would be used by the applications 105,255,355 to represent this aggregated data locally for the user, either in map form or in some other form.”) [The server function performing the aggregating of data reads on “a data processing module”. The server creating the aggregate data set for the specific, represented area reads on “an air quality index”.]
wherein the air quality data comprises one or more air quality parameters, the geographic location corresponding to the one or more air quality parameters and a time stamp; and (Pariseau: [0029] and [0034] as discussed above) (Pariseau: [0040] “A small number of Sensor Receivers 311, with precise clocks and position sensors 356 would timestamp these received messages and communicate such within a local network using theft integrated local communications controllers 353. From this information, a local controller, in this case the Cloud Connected device 351, could determine the position of the sensors and attach that position information along with the current date/time to the aft quality information received from the Sensor 302. The position information would be determined by triangulation with 3 Sensor Receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 Sensor Receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each Sensor Receiver 311 would also have accurate position information due to theft local position sensor 356 the relative position of the sensors could be converted to an absolute position.”) [The air quality information from the air quality instruments read on “one or more air quality parameters”, the position information read on “the geographic location”, and the date/time or timestamp information read on “a time stamp”.]
wherein the method is operable to implement a crowdsourced sensor system for determining air quality. (Pariseau: [0025] “Accordingly, a system of air quality instruments which are aggregated using crowdsourcing to produce an aggregate picture of aft quality for use for indoor and/or outdoor aft quality monitoring/reporting. Referring now to FIG. 1, a crowdsourced aft quality system 100 is shown in accordance with the various aspects and embodiments of the invention.”)
Pariseau does not expressly teach: deriving an air quality rating corresponding to the air quality index; and transmitting the air quality rating to the application program on the user device.
Hyde teaches:
deriving an air quality rating corresponding to the air quality index; and (Hyde: [0035] “In an embodiment, the air quality data mapped to the geographical map data may be used to plan activities for the entities 108 by providing suggestions related to taking precautionary measures for the entities' health. The determined air quality data may be used to determine level of air pollution in real-time. The system 102 may use the air quality data to offer the air pollution free routes and zones to the entities 108.”) (Hyde: [0036] “In an embodiment, the system 102 may facilitate comparing the air quality data with a predefined standard air quality data, e.g., as prescribed by World Health Organization (WHO). Upon the comparison when the air quality data is determined to be in a severe category a warning notification for the entities 108 is generated. The system 102 may generate and issue a warning notification based on different levels of severity of the air quality data. When the abnormality of the air quality data is high a warning notification suggesting the air quality data as abnormal is generated. The warning notification may be sent to the entity device 106 through, for example, a push notification service. The generated warning notification may be for example in any form of visual cues, auditory cues or vibration touch, so as to alert the entity that the air quality is abnormal and not good for heath of the entity.”) (Hyde: [0060] “In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories of air quality where the air quality is categorized into parameters such as good with range (0-50), moderate with range (51-100), unhealthy for sensitive groups with range (101-150), unhealthy with range (151-200), very unhealthy with range (201-300) and hazardous with range (301-500).”) [Determining the level of air pollution reads based on the determined air quality data reads on “deriving an air quality rating …”.]
transmitting the air quality rating to the application program on the user device. (Hyde: [0036] as discussed above) (Hyde: [0030] “FIG. 1 illustrates a network architecture 100 in which or with which an ambient air quality monitoring system 102 may be implemented for determining outdoor and indoor air quality at a present location of an entity and the various other locations the entity has visited or plans to visit. The determined air quality may be used to determine impact of the air quality on health of the entity in accordance with an embodiment of the present disclosure. In context of the present exemplary architecture 100, the ambient air quality monitoring system 102 (also referred to as the system 102, hereinafter) is described. The ambient air quality monitoring system 102 can be implemented for determining air quality of the air present in surroundings (both indoor and outdoor air) of the entities. The system 102 can be implemented in any computing device and can be configured/operatively connected with a server 110. As illustrated, entities 108-1, 108-2, . . . , 108N (individually referred to as the entity 108 and collectively referred to as the entities 108, hereinafter) can interact with the system 102 using respective entity devices 106-1, 106-2, . . . , 106-N (individually referred to as the entity device 106 and collectively referred to as the entity devices 106, hereinafter), which can be communicatively coupled with the system 102 through a network 104. The entity devices 106 can include a variety of computing systems, including but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a portable computer, a personal digital assistant, a handheld device and a mobile device.”) [Any one of the devices 106, as illustrated in FIG. 1, reads on “the user device”. Sending the levels of severity of the air quality data to the entity device 106 reads on “transmitting …”.]
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 Pariseau and Hyde before them, to modify monitoring and displaying the visual representation of the air qualities in geographical areas, to incorporate providing a warning notification to the user regarding severity category of the air quality at the geographical areas.
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 allow for the user to avoid a geographical area that may have harmful air quality conditions. (Hyde: [0062])
Regarding claim 64, Pariseau and Hyde teach all the claimed features of claim 63. Pariseau further teaches:
wherein the method further comprises controlling an air purification system via a control module based on the air quality rating to improve the air quality. (Pariseau: [0052] “In accordance with the various aspects and embodiment of the invention, the system can also be integrated within a facility management system with the data being used as an additional input for aft handling and filtration systems. Thus, the filtration systems could respond to issues that might need to be addressed, but to which the system might otherwise be unaware. For example, alarm levels could be raised if personal air quality instruments worn by staff in a facility exceeded some aggregate alarm level for an area. This might precipitate a response by staff to the areas in question, or it might even be integrated into an automated system to have an automatic response like increasing filtration or aft changes to the areas in question.”)
Regarding independent claim 66, Pariseau teaches:
A non-transitory computer-readable medium having stored thereon instructions executable by a computer system to perform operations comprising: (Pariseau: Abstract “A crowdsourced air quality monitoring system. Such a system could provide information beyond the user's local instrument on air quality over a much larger area. This information could be used by a user to make decisions about frequenting particular areas based on the results, or to alert them to changing conditions in the area so that the user might act before local conditions change.”) (Pariseau: [0081] “The article of manufacture (e.g., computer or computing device) includes a non-transitory computer readable medium or storage that may include a series of instructions, such as computer readable program steps or code encoded therein. In certain aspects of the invention, the non-transitory computer readable medium includes one or more data repositories. Thus, in certain embodiments that are in accordance with any aspect of the invention, computer readable program code (or code) is encoded in a non-transitory computer readable medium of the computing device. The processor or a module, in turn, executes the computer readable program code to create or amend an existing computer-aided design using a tool. The term “module” as used herein may refer to one or more circuits, components, registers, processors, software subroutines, or any combination thereof. In other aspects of the embodiments, the creation or amendment of the computer-aided design is implemented as a web-based software application in which portions of the data related to the computer-aided design or the tool or the computer readable program code are received or transmitted to a computing device of a host.”)
receiving an air quality data on a server from an application program on a user device via a communication module; (Pariseau: [0011] “A crowdsourced aft quality monitoring system is disclosed in accordance with the various aspects and embodiments of the invention. The system monitors aft quality in a given geographical area and provides information about the air quality for the given geographical area to a user. The system includes at least one aft quality monitoring instrument and a server that is communication with the instrument. The instrument Includes at least one aft quality sensor and connects to the server through a network. The system monitors aft quality and aft quality information, including at least one of a current date, a time, and a position, is communicated from the instrument through the network to the server.”) (Pariseau: [0022] “There is a distinction between the types of factors one is most concerned with for indoor air quality vs. outdoor aft quality monitoring, for example for indoor aft quality CO2 is of larger concern though CO2 is seldom an issue in outdoor air quality. Aft quality monitoring instruments measure various parameters in proximity to the instrument. These parameters might include, but are not limited to particulates and/or various gases to create a picture of the local environment. In order to map out a larger area Ike a building for indoor aft quality, or a city for outdoor aft quality current systems if they exist rely on networks of fixed instruments. One or more computers collect the data from these instruments in order to determine the conditions in proximity to each and then attempt to represent the area from these. As noted, these systems are largely comprised of fixed systems and typically purchased and deployed by the system administrator.”) (Pariseau: [0034] “The Cloud Connected Device 251 would typically be an off-the-shelf though it could be custom device like a srnartphone, a tablet, or a laptop, etc. An application 255 running either locally on the Controller 252 or partially from the web, in the case of a web-based application would gather aft quality and status information from the AQI 201 as well as position and date/time information from the position/clock sensor 256 on the Cloud Connected device 251 and would send this information using the Cloud Communications Controller 254 through the Cloud 281 to Server 291. The application 255 would also receive aggregated data or visual representations of the data like maps over the same link and could display such with its local data on its user interface 257. The Cloud 281 could be any network, wireless or wired, and the Server 291 any type of computer or instrument that could be used to collect and store the incoming data, and serve aggregate data back to the client applications 255.”) [The cloud connected device 251 or 351 with the application reads on “an application program on a user device”. The cloud connected device 251 sending the air quality and status information from the air quality instruments 201 to the server 291 reads on the server “receiving an air quality data from an application program on a user device”. The combination of all communication modules 253 and 254, as illustrated in FIG. 2, read on “a communication module”.]
computing an air quality index from the air quality data via a data processing module; (Pariseau: FIGS. 2-3) (Pariseau: [0029] “The aft quality, date/time and position information sent from all the aft quality instruments 101-103, through the Cloud 125 would be collected by one or more Servers 151 and aggregated to create an aggregate data set of the various aft quality parameters across a geographic area. One or more servers 151 would make that aggregated data available to applications 105. These applications 105 could run on separate devices like personal computers, tablets, smart phones, etc. or within the aft quality instruments themselves 101-103.”) (Pariseau: [0065] “The Server 151,291,391 would receive data from all connected aft quality sensors 411. It would store this data and then periodically aggregate data into a current geographic dataset that would be used by the applications 105,255,355 to represent this aggregated data locally for the user, either in map form or in some other form.”) [The server function performing the aggregating of data reads on “a data processing module”. The server creating the aggregate data set for the specific, represented area reads on “an air quality index”.]
wherein the air quality data comprises one or more air quality parameters, a geographic location corresponding to the one or more air quality parameters and a time stamp; and (Pariseau: [0029] and [0034] as discussed above) (Pariseau: [0040] “A small number of Sensor Receivers 311, with precise clocks and position sensors 356 would timestamp these received messages and communicate such within a local network using theft integrated local communications controllers 353. From this information, a local controller, in this case the Cloud Connected device 351, could determine the position of the sensors and attach that position information along with the current date/time to the aft quality information received from the Sensor 302. The position information would be determined by triangulation with 3 Sensor Receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 Sensor Receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each Sensor Receiver 311 would also have accurate position information due to theft local position sensor 356 the relative position of the sensors could be converted to an absolute position.”) [The air quality information from the air quality instruments read on “one or more air quality parameters”, the position information read on “the geographic location”, and the date/time or timestamp information read on “a time stamp”.]
wherein the operations are operable to implement a crowdsourced system for determining air quality. (Pariseau: [0025] “Accordingly, a system of air quality instruments which are aggregated using crowdsourcing to produce an aggregate picture of aft quality for use for indoor and/or outdoor aft quality monitoring/reporting. Referring now to FIG. 1, a crowdsourced aft quality system 100 is shown in accordance with the various aspects and embodiments of the invention.”)
Pariseau does not expressly teach: deriving an air quality rating corresponding to the air quality index; and transmitting the air quality rating to the application program on the user device.
Hyde teaches:
deriving an air quality rating corresponding to the air quality index; and (Hyde: [0035] “In an embodiment, the air quality data mapped to the geographical map data may be used to plan activities for the entities 108 by providing suggestions related to taking precautionary measures for the entities' health. The determined air quality data may be used to determine level of air pollution in real-time. The system 102 may use the air quality data to offer the air pollution free routes and zones to the entities 108.”) (Hyde: [0036] “In an embodiment, the system 102 may facilitate comparing the air quality data with a predefined standard air quality data, e.g., as prescribed by World Health Organization (WHO). Upon the comparison when the air quality data is determined to be in a severe category a warning notification for the entities 108 is generated. The system 102 may generate and issue a warning notification based on different levels of severity of the air quality data. When the abnormality of the air quality data is high a warning notification suggesting the air quality data as abnormal is generated. The warning notification may be sent to the entity device 106 through, for example, a push notification service. The generated warning notification may be for example in any form of visual cues, auditory cues or vibration touch, so as to alert the entity that the air quality is abnormal and not good for heath of the entity.”) (Hyde: [0060] “In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories of air quality where the air quality is categorized into parameters such as good with range (0-50), moderate with range (51-100), unhealthy for sensitive groups with range (101-150), unhealthy with range (151-200), very unhealthy with range (201-300) and hazardous with range (301-500).”) [Determining the level of air pollution reads based on the determined air quality data reads on “deriving an air quality rating …”.]
transmitting the air quality rating to the application program on the user device. (Hyde: [0036] as discussed above) (Hyde: [0030] “FIG. 1 illustrates a network architecture 100 in which or with which an ambient air quality monitoring system 102 may be implemented for determining outdoor and indoor air quality at a present location of an entity and the various other locations the entity has visited or plans to visit. The determined air quality may be used to determine impact of the air quality on health of the entity in accordance with an embodiment of the present disclosure. In context of the present exemplary architecture 100, the ambient air quality monitoring system 102 (also referred to as the system 102, hereinafter) is described. The ambient air quality monitoring system 102 can be implemented for determining air quality of the air present in surroundings (both indoor and outdoor air) of the entities. The system 102 can be implemented in any computing device and can be configured/operatively connected with a server 110. As illustrated, entities 108-1, 108-2, . . . , 108N (individually referred to as the entity 108 and collectively referred to as the entities 108, hereinafter) can interact with the system 102 using respective entity devices 106-1, 106-2, . . . , 106-N (individually referred to as the entity device 106 and collectively referred to as the entity devices 106, hereinafter), which can be communicatively coupled with the system 102 through a network 104. The entity devices 106 can include a variety of computing systems, including but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a portable computer, a personal digital assistant, a handheld device and a mobile device.”) [Any one of the devices 106, as illustrated in FIG. 1, reads on “the user device”. Sending the levels of severity of the air quality data to the entity device 106 reads on “transmitting …”.]
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 Pariseau and Hyde before them, to modify monitoring and displaying the visual representation of the air qualities in geographical areas, to incorporate providing a warning notification to the user regarding severity category of the air quality at the geographical areas.
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 allow for the user to avoid a geographical area that may have harmful air quality conditions. (Hyde: [0062])
Claim 65 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, in view of Hyde, further in view of Pariseau-940.
Regarding claim 65, Pariseau and Hyde teach all the claimed features of claim 63. Pariseau and Hyde do not expressly teach the recitations of claim 65.
Pariseau-940 teaches:
wherein the method is configured for predicting the air quality rating in a region comprising: collecting historical air quality data from one or more sources of the sensor module; using a machine learning algorithm to identify a pattern and a trend in the historical air quality data; generating a predictive model for air quality for a future period; updating the predictive model with real-time air quality data; disseminating the air quality for the future period to public; and issuing alerts and recommendations based on a predicted air quality. (Pariseau-940: [0026] “Though the data can be stored locally in the sensors, the geographic location and air quality information (and environmental information if present) is transmitted to one or more servers either in the cloud or via the cloud or cellular network, for example. And, that the system (i.e. the server(s) connected to the fleet of sensors via the cloud) can direct the motion of at least some these sensors in real-time to optimize the mission in question (or perhaps change it altogether). The cloud-based system can function using fixed pre-defined algorithms but it can also implemented using artificial intelligence processing system whereby a computational method is used to interactively compute a metric based on the measured data provided at one or more selected temporal intervals. The computational method can be structured to minimize a selected metric, which can be a particle size and/or mass measurement error metric, for example.”) (Pariseau-940: [0027] “A further application of machine learning methods can be applied to generate improved geographic mapping of air quality information by utilizing real time weather data including wind velocity and precipitation data over the particle detection spatial region. Weather radar data can be used, for example, to improve the particle data distribution and/or machine learning to deriver or refine algorithms over time and thus optimize sensor motion. In this way the data collection can be tailored to provide the best coverage given the current conditions (air velocity/direction, location, and air quality).”) (Pariseau-940: [0086] “In accordance with the various aspects and embodiment of the invention, number of different systems can be configured from the basic premise. For each of these, a number of business cases are possible. In accordance with the various aspects and embodiment of the invention, the system can be implemented as a public system, much like a Waze, providing public access to the system to all enrollees, which might be paid or free subscribers.”) (Pariseau-940: [0087] “In accordance with the various aspects and embodiment of the invention, the system could also be implemented as a private system. It would allow, for example, for an entity to offer such a system distributed across a local area of concern and while making it accessible only to its members. For example, a university could implement such a system on a campus and make such a system available only to faculty and students. As such, annotations could also be used as public service announcements for the campus.”) (Pariseau-940: [0158] “Sensors within the site can be a mixture of fixed and mobile (with geographic data) versions, data from these sensors can be collected and recorded. A dynamic aggregate picture of air quality for the site can be constructed from the data. Models can be used to interpolate intermediate conditions within the map to produce a more continuous map. The aggregate (as well as individual) data can be stored for later analysis or reporting. The collected data can also be used to project air quality into the future based on changes in conditions over time (an application use of machine learning or other model based analysis). During an air quality event, sensors with fixed patrol routes can be assigned modified routes to provide more visibility into areas near and around the source of the issue or controlled dynamically to help identify the actual source.”) (Pariseau-940: [0158] “Sensors within the site can be a mixture of fixed and mobile (with geographic data) versions, data from these sensors can be collected and recorded. A dynamic aggregate picture of air quality for the site can be constructed from the data. Models can be used to interpolate intermediate conditions within the map to produce a more continuous map. The aggregate (as well as individual) data can be stored for later analysis or reporting. The collected data can also be used to project air quality into the future based on changes in conditions over time (an application use of machine learning or other model based analysis). During an air quality event, sensors with fixed patrol routes can be assigned modified routes to provide more visibility into areas near and around the source of the issue or controlled dynamically to help identify the actual source.”) [The projected air quality of the geographic area into the future reads on “predicting the air quality rating in a region”. The collecting and recording data read on “collecting historical air quality data”. Refining algorithm with the machine learning using real time weather data reads on “updating …”. The public service announcement reads on “disseminating… to public” and “issuing alerts …”.]
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 Pariseau, Hyde and Pariseau-940 before them, to modify monitoring and analyzing air qualities of geographical areas, to incorporate using the monitored or collected data to project air quality of the geographical areas.
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 allow for the user to be informed of the future air quality of the area. (Pariseau-940: [0158])
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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MICHAEL W CHOI/Primary Examiner, Art Unit 2116
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