DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
Claims 1-20 details the term “severe air quality percentile”. The specification provides a standard for ascertaining the requisite degree of “severe” as being that of 85 percentile air quality index or higher in [0053]. Thus one of ordinary skill in the art would ascertain that the severe air quality percentile is at least 85th percentile or higher.
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 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A computer-implemented method for evaluating air quality, the method comprising:
receiving air quality information for a plurality of locations for a plurality of days over a time period;
for each location of the plurality of locations:
determining a severe air quality percentile for the air quality information for each location of the plurality of locations for the time period; and
determining a variance of the air quality information for each location of the plurality of locations for the time period; and
evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information.
11. A non-transitory computer readable storage medium having computer readable program code stored thereon for causing a computer system to perform a method for evaluating air quality, the method comprising:
receiving air quality information for a plurality of locations for a plurality of days over a time period;
for each location of the plurality of locations:
determining a severe air quality percentile for the air quality information for each location of the plurality of locations for the time period;
determining a variance of the air quality information for each location of the plurality of locations for the time period; and
determining a logarithm of the variance of the air quality information for each location of the plurality of locations for the time period; and
evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information, the evaluating comprising:
performing a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations; and
performing a clustering operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations.
17. A system for transformation of evaluating air quality, the system comprising:
a memory device; and
a hardware processor coupled with memory device, the hardware processor configured to:
receive air quality information for a plurality of locations for a plurality of days over a time period, wherein the air quality information received is a daily average air quality for each location, wherein the time period is at least one year,
and wherein the air quality information for at least half of the days of the time period is received for each location of the plurality of locations;
for each location of the plurality of locations:
determine a severe air quality percentile for the air quality information for each location of the plurality of locations for the time period;
determine a variance of the air quality information for each location of the plurality of locations for the time period; and
determine a logarithm of the variance of the air quality information for each location of the plurality of locations for the time period; and
perform a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations; and
perform a clustering operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 11, and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of receiving air quality information is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The additional limitation of Claim 11 of a computer system with non-transitory computer readable storage medium and of Claim 17 of the memory device and hardware process are interpreted under broadest reasonable interpretation to be a generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-3, 5-9, 13-16, and 19-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
The additional limitation of Claims 4, 10, 12, and 18 for displaying a visualization is considered to be an insignificant extra solution activity. Displaying a visualization is a well known activity in the art as evidenced by Hyde (US20210372650) in [0064] and Kim (KR20210032808A) in [0037], [0050], and [0054]. Furthermore, displaying a visualization is not impose a meaningful limit on the claim that is not nominally or tangentially related to the invention, as the invention is directed towards the evaluation of air quality data.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde (US20210372650) in view of Kim (KR20210032808A) and Hu (CN106651100A).
In regards to Claim 1, Hyde teaches “receiving air quality information for a plurality of locations for a plurality of days over a time period (ambient air quality monitoring system determines air quality information and determines the air quality data at multiple locations – [0033]; the system computes the air quality data that has been accumulated over a period of time including day, week, and month – [0050]);
for each location of the plurality of locations:
determining a severe air quality for the air quality information for each location of the plurality of locations for the time period (“In an embodiment, the system facilitates providing support of dissemination of air quality results to smart citizens to improve their health. For the same, the system enables creating a real-time conversion service to generate categories of Good, Acceptable, Poor and Very Poor according to comparison of the air quality with the AQI. The proposed system enables helping the entities to be aware of the air quality around them, and by using the air quality data on the map the entities can make informed decisions while planning their activities. The database created for the determined ambient air quality can be used by related entities for creating public awareness of the air pollution around them in real time” – [0053]; system monitors and records exposure to the polluted air during a given day/week/month and take a mean percentage of the polluted air – [0054]; “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) [i.e. severe air quality].” – [0060]); and
determining a variance of the air quality information for each location of the plurality of locations for the time period (“The determined air quality data level is compared against a benchmark as established by the WHO. In response to an upward deviation [i.e. variance] between the air quality data level and the benchmark, the monitor broadcasts a notification to an entity to take remedial action to abate the upward deviation of the air pollution. The deviation may be represented as spikes (e.g., both positive and negative) representing the level of deviation between the determined air quality and the established benchmark.” – [0059]); and
evaluating the plurality of locations according to the severe air quality for the air quality information and the variance of the air quality information (“The determined air quality data level [i.e. severe air quality] is compared against a benchmark as established by the WHO. In response to an upward deviation [i.e. variance] between the air quality data level and the benchmark, the monitor broadcasts a notification to an entity to take remedial action to abate the upward deviation of the air pollution. The deviation may be represented as spikes (e.g., both positive and negative) representing the level of deviation between the determined air quality and the established benchmark. Further, the deviation may be broadcasted by the monitor through use of a short message service (SMS), a multimedia messaging service (MMS), a paging service, an e-mail, or telephonically. Further, in response to an upward deviation between the air quality data level and the benchmark, the server delivers a notification to the air quality monitor to activate an audio/visual warning indicator.” – [0059]; “In an embodiment, the entities can upload the determined air quality records as determined by their air quality measuring devices (e.g., one or more sensors) to the air quality monitor. The air quality monitor may further maintain records related to the entities such as a vehicle type used, a determined route, a home size, a heating source, and so forth for determining pollution trends at multiple locations the entity has visited and various sources of the same. This may be used to instruct the entities to avoid the determined sources and locations so as to improve their health conditions” – [0062]).
Hyde is silent with regards to the language of “determining a air quality percentile for the air quality information”
Kim teaches “determining a air quality percentile for the air quality information (the mean, minimum, maximum, percentile, and standard deviation for the air quality measurements are analyzed – [0040]-[0041]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Hyde of the six categories of air quality with a range to be based on a percentile as taught by Kim. By using percentiles instead of a range of 0-500, this is an improvement that yields predictable results.
Hyde in view of Kim is silent with regards to the language of “determining a severe air quality percentile for the air quality information.”
Hu teaches “determining a severe air quality percentile for the air quality information (the percentiles are calculated up to the 90th percentile – [0123]-[0124]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Hyde in view of Kim to incorporate the teaching of Hu to utilize the 90th percentile. By utilizing the percentiles as detailed by Hu in [0123]-[0124] with the ranges including the hazardous range as taught by Hyde in [0060], this is an improvement that yields predictable results in the evaluation of the air quality data.
In regards to Claim 2, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde further teaches “wherein the evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information comprises:
performing a clustering operation on the severe air quality percentile for the air quality information and the variance of the air quality information for each location of the plurality of locations (“In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories [i.e. clustering] 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). These categories may be increased with increasing effect on human health and may be assigned standard colors for easier identification and reporting.” – [0060]).”
In regards to Claim 4, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde further teaches “displaying a visualization of output of the clustering operation (the system with the air quality evaluation includes a graphical display to display the results – [0051]).”
In regards to Claim 5, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde further teaches “wherein the clustering operation generates five clusters of locations of the plurality of locations (“In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories [i.e. clustering] 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). These categories may be increased with increasing effect on human health and may be assigned standard colors for easier identification and reporting.” – [0060]).”
Hyde in view of Kim and Hu is silent with regards to the language of “five clusters”, but it would be obvious to one of ordinary skill in the art to utilize five categories rather than the six categories as taught by Hyde in view of Kim and Hu. Combining the categories of “unhealthy for sensitive groups” and “unhealthy” to a single category, this yields predictable results for having only five clusters rather than six.
In regards to Claim 6, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde in view of Kim is silent with regards to the language of “wherein the severe air quality percentile is a 90th percentile air quality index (AQI).”
Hu further teaches “wherein the severe air quality percentile is a 90th percentile air quality index (AQI) (the percentiles are calculated up to the 90th percentile – [0123]-[0124]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Hyde in view of Kim to incorporate the teaching of Hu to utilize the 90th percentile. By utilizing the percentiles as detailed by Hu in [0123]-[0124] with the ranges including the hazardous range as taught by Hyde in [0060], this is an improvement that yields predictable results in the evaluation of the air quality data.
In regards to Claim 8, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde is silent with regards to the language of “wherein the air quality information received is a daily average air quality for a location.”
Kim further teaches “wherein the air quality information received is a daily average air quality for a location (“The present invention relates to a method for automatically analyzing daily variation patterns of air quality measurement data, which enables quicker and more convenient analysis of daily variations in air quality for a wide range of air measurement data, thereby identifying the cause of pollution generation and characteristics of emission sources.” – [0001]; the present invention can be applied to various air quality items including fine dust, allowing daily changes to be identified at a glance because it takes into account the average [i.e. daily average air quality], percentile, and standard deviation so that it is easy to see the changes in air quality over time – [0015]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim and Hu to incorporate the further teaching of Kim to evaluate the average air quality. By monitoring the average air quality this is an improvement in analyzing the daily variations in the air quality.
In regards to Claim 10, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde further teaches “displaying a visualization based at least in part on the evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information (the system with the air quality evaluation includes a graphical display to display the results – [0051]).”
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Kim and Hu as applied to claim 2 above, and further in view of Liu (CN114240000A).
In regards to Claim 3, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde in view of Kim and Hu is silent with regards to the language of “wherein the evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information further comprises:
performing a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the variance of the air quality information for each location of the plurality of locations.”
Liu teaches wherein the evaluating the plurality of locations according to the severe air quality percentile for the air quality information and the variance of the air quality information further comprises:
performing a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the variance of the air quality information for each location of the plurality of locations (Data normalization with a Min-Max normalization algorithm to standard the data – [n0016]-[n0018]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim and Hu to incorporate the teaching of Liu to perform a Min-Max normalization algorithm on the data. By utilizing the normalization operation with the data this yields predictable results in the evaluation of the air quality with spatial data.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Kim and Hu as applied to claim 8 above, and further in view of Lou (CN111814964A).
In regards to Claim 9, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde in view of Kim is silent with regards to the language of “wherein the time period is at least one year, and wherein the air quality information for at least half of the days of the time period is received for each location of the plurality of locations.”
Lou teaches “wherein the time period is at least one year, and wherein the air quality information for at least half of the days of the time period is received for each location of the plurality of locations (air quality level and geographical location information are collected – [0043]; collect air quality observation data over a period of time including a year – [0073]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim and Hu to incorporate the teaching of Lou to monitor the air quality data for a period of time of a year. By monitoring the air quality over a period of a year this yields predictable results by improving the quantity of the data that is evaluated.
Examiner’s Note
Claims 7 and 11-20 are not rejected under a prior art rejection (35 U.S.C. 102 or 35 U.S.C. 103).
In regards to Claim 7, Hyde in view of Kim and Hu discloses the claimed invention as detailed above. Hyde in view of Kim and Hu is silent with regards to the language of “for each location of the plurality of locations: determining a logarithm of the variance of the air quality information for each location of the plurality of locations for the time period.”
In regards to Claim 11, Hyde teaches “receiving air quality information for a plurality of locations for a plurality of days over a time period (ambient air quality monitoring system determines air quality information and determines the air quality data at multiple locations – [0033]; the system computes the air quality data that has been accumulated over a period of time including day, week, and month – [0050]);
for each location of the plurality of locations:
determining a severe air quality for the air quality information for each location of the plurality of locations for the time period (“In an embodiment, the system facilitates providing support of dissemination of air quality results to smart citizens to improve their health. For the same, the system enables creating a real-time conversion service to generate categories of Good, Acceptable, Poor and Very Poor according to comparison of the air quality with the AQI. The proposed system enables helping the entities to be aware of the air quality around them, and by using the air quality data on the map the entities can make informed decisions while planning their activities. The database created for the determined ambient air quality can be used by related entities for creating public awareness of the air pollution around them in real time” – [0053]; system monitors and records exposure to the polluted air during a given day/week/month and take a mean percentage of the polluted air – [0054]; “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).” – [0060]);
determining a variance of the air quality information for each location of the plurality of locations for the time period (“The determined air quality data level is compared against a benchmark as established by the WHO. In response to an upward deviation [i.e. variance] between the air quality data level and the benchmark, the monitor broadcasts a notification to an entity to take remedial action to abate the upward deviation of the air pollution. The deviation may be represented as spikes (e.g., both positive and negative) representing the level of deviation between the determined air quality and the established benchmark.” – [0059]); and
evaluating the plurality of locations according to the severe air quality for the air quality information and the variance of the air quality information (“The determined air quality data level [i.e. severe air quality] is compared against a benchmark as established by the WHO. In response to an upward deviation [i.e. variance] between the air quality data level and the benchmark, the monitor broadcasts a notification to an entity to take remedial action to abate the upward deviation of the air pollution. The deviation may be represented as spikes (e.g., both positive and negative) representing the level of deviation between the determined air quality and the established benchmark. Further, the deviation may be broadcasted by the monitor through use of a short message service (SMS), a multimedia messaging service (MMS), a paging service, an e-mail, or telephonically. Further, in response to an upward deviation between the air quality data level and the benchmark, the server delivers a notification to the air quality monitor to activate an audio/visual warning indicator.” – [0059]; “In an embodiment, the entities can upload the determined air quality records as determined by their air quality measuring devices (e.g., one or more sensors) to the air quality monitor. The air quality monitor may further maintain records related to the entities such as a vehicle type used, a determined route, a home size, a heating source, and so forth for determining pollution trends at multiple locations the entity has visited and various sources of the same. This may be used to instruct the entities to avoid the determined sources and locations so as to improve their health conditions” – [0062]), the evaluating comprising:
performing a clustering operation on the severe air quality percentile for the air quality information and the variance of the air quality information for each location of the plurality of locations (“In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories [i.e. clustering] 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). These categories may be increased with increasing effect on human health and may be assigned standard colors for easier identification and reporting.” – [0060]).”
Hyde is silent with regards to the language of “determining a air quality percentile for the air quality information”
Kim teaches “determining a air quality percentile for the air quality information (the mean, minimum, maximum, percentile, and standard deviation for the air quality measurements are analyzed – [0040]-[0041]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Hyde of the six categories of air quality with a range to be based on a percentile as taught by Kim. By using percentiles instead of a range of 0-500, this is an improvement that yields predictable results.
Hyde in view of Kim is silent with regards to the language of “performing a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations.”
Liu teaches “performing a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations (Data normalization with a Min-Max normalization algorithm to standard the data – [n0016]-[n0018]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim to incorporate the teaching of Liu to perform a Min-Max normalization algorithm on the data. By utilizing the normalization operation with the data this yields predictable results in the evaluation of the air quality with spatial data.
Hyde in view of Kim and Liu is silent with regards to the language of “determining a logarithm of the variance of the air quality information for each location of the plurality of locations for the time period; and performing a clustering operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations
Claims 12-16 are dependent on Claim 11.
In regards to claim 17, Hyde teaches “a memory device (computer with memory – [0068]); and
a hardware processor coupled with memory device (computer with memory – [0068]), the hardware processor configured to:
receive air quality information for a plurality of locations for a plurality of days over a time period (ambient air quality monitoring system determines air quality information and determines the air quality data at multiple locations – [0033]; the system computes the air quality data that has been accumulated over a period of time including day, week, and month – [0050]);
for each location of the plurality of locations:
determine a severe air quality for the air quality information for each location of the plurality of locations for the time period (“In an embodiment, the system facilitates providing support of dissemination of air quality results to smart citizens to improve their health. For the same, the system enables creating a real-time conversion service to generate categories of Good, Acceptable, Poor and Very Poor according to comparison of the air quality with the AQI. The proposed system enables helping the entities to be aware of the air quality around them, and by using the air quality data on the map the entities can make informed decisions while planning their activities. The database created for the determined ambient air quality can be used by related entities for creating public awareness of the air pollution around them in real time” – [0053]; system monitors and records exposure to the polluted air during a given day/week/month and take a mean percentage of the polluted air – [0054]; “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).” – [0060]);
determine a variance of the air quality information for each location of the plurality of locations for the time period (“The determined air quality data level is compared against a benchmark as established by the WHO. In response to an upward deviation [i.e. variance] between the air quality data level and the benchmark, the monitor broadcasts a notification to an entity to take remedial action to abate the upward deviation of the air pollution. The deviation may be represented as spikes (e.g., both positive and negative) representing the level of deviation between the determined air quality and the established benchmark.” – [0059]); and
perform a clustering operation on the severe air quality for the air quality information and the variance of the air quality information for each location of the plurality of locations (“In an embodiment, the determined air quality data accumulated in the database present on the server may be categorized into six categories [i.e. clustering] 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). These categories may be increased with increasing effect on human health and may be assigned standard colors for easier identification and reporting.” – [0060]).”
Hyde is silent with regards to the language of “wherein the air quality information received is a daily average air quality for a location; determining a air quality percentile for the air quality information”
Kim teaches “wherein the air quality information received is a daily average air quality for a location (“The present invention relates to a method for automatically analyzing daily variation patterns of air quality measurement data, which enables quicker and more convenient analysis of daily variations in air quality for a wide range of air measurement data, thereby identifying the cause of pollution generation and characteristics of emission sources.” – [0001]; the present invention can be applied to various air quality items including fine dust, allowing daily changes to be identified at a glance because it takes into account the average [i.e. daily average air quality], percentile, and standard deviation so that it is easy to see the changes in air quality over time – [0015]); determine a air quality percentile for the air quality information (the mean, minimum, maximum, percentile, and standard deviation for the air quality measurements are analyzed – [0040]-[0041]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Hyde of the six categories of air quality with a range to be based on a percentile as taught by Kim. By using percentiles instead of a range of 0-500, this is an improvement that yields predictable results.
Hyde in view of Kim is silent with regards to the language of “perform a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations.”
Liu teaches “perform a maximum/minimum normalization operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations (Data normalization with a Min-Max normalization algorithm to standard the data – [n0016]-[n0018]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim to incorporate the teaching of Liu to perform a Min-Max normalization algorithm on the data. By utilizing the normalization operation with the data this yields predictable results in the evaluation of the air quality with spatial data.
Hyde in view of Kim and Liu are silent with regards to the language of “wherein the time period is at least one year, and wherein the air quality information for at least half of the days of the time period is received for each location of the plurality of locations.”
Lou teaches “wherein the time period is at least one year, and wherein the air quality information for at least half of the days of the time period is received for each location of the plurality of locations (air quality level and geographical location information are collected – [0043]; collect air quality observation data over a period of time including a year – [0073]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hyde in view of Kim and Liu to incorporate the teaching of Lou to monitor the air quality data for a period of time of a year. By monitoring the air quality over a period of a year this yields predictable results by improving the quantity of the data that is evaluated.
Hyde in view of Kim, Liu and Lou is silent with regards to the language of “determine a logarithm of the variance of the air quality information for each location of the plurality of locations for the time period; and perform a clustering operation on the severe air quality percentile for the air quality information and the logarithm of the variance of the air quality information for each location of the plurality of locations”
Claims 18-20 are dependent on claim 17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
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/YOSSEF KORANG-BEHESHTI/ Examiner, Art Unit 2863