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 .
Allowable Subject Matter
Claims 12 and 13 are objected to as being dependent upon a rejected base claim, but would be
allowable if rewritten in independent form including all of the limitations of the base claim and any
intervening claims.
Response to Remarks/Arguments
Applicant’s arguments regarding claims 1, 18, and 24 have been fully considered and are found to be persuasive. The Applicant’s arguments are directed towards newly added claim language which changed the scope of the claim and necessitated new grounds of rejection as set forth in the 35 USC 103 section below. Therefore, the Applicant’s arguments are now moot in view of new grounds of rejection.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or no obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 3, 4, 8, 14, 17, 18, 20, 21, 22, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), and in view of Hong et al. (US 20210278096 A1.).
As per claim 1, Lee teaches
A control method for purification equipment comprising (Lee, para 281, Artificial intelligence system 100, air cleaning system 700 and ventilation system 300 communicating together to work as purification equipment):
specifying a concentration distribution of particulate matter with different particle sizes (paras 285-289); and
controlling at least one equipment parameter of the purification equipment in accordance with the concentration distribution (para 354, “allow the ventilation system and the air cleaning system to operate cooperatively with each other based on the prediction of the dust concentration by the environmental factor prediction model,”; para 235 shows equipment parameter control such as fan motor, air flow control device, air volume, wind direction; para 285 air purifying system operates in connection with predicted internal environmental factor),
the specifying the concentration distribution of particulate matter with different particle sizes including acquiring at least one of temperature data and humidity data (Lee, paras 285-289, para 288 indicates temperature, also see Fig.8), and
predicting a plurality of concentration distributions based on the at least one of the temperature data and the humidity data, each of the plurality of concentration distributions corresponding to a different time among a plurality of times after a current time (Lee ,para 354 “allow the ventilation system and the air cleaning system to operate cooperatively with each other based on the prediction of the dust concentration by the environmental factor prediction model,”; para 334 “The predicted internal dust concentration may refer to a future internal dust concentration output by the internal dust concentration prediction model based on input data”).
However, Lee does not teach
at a plurality of different heights in an indoor space.
In the same field of endeavor Hong et al. teach
at a plurality of different heights in an indoor space (Hong et al. para 210, plurality of air purification modules 10 are stacked in vertical direction motion and dust information can be detected at each height)
the specifying the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space including acquiring at least one of temperature data and humidity data at each of the plurality of different heights in the indoor space (Hong et al. para 146, the sensor unit 500 includes dust sensor 520, and also include temperature sensor and humidity sensor, para 293-306, and Fig. 14, Fig. 15, dust concentration prediction method according to the controlling method air purifier 1, accumulation and floating position is predicted according to the size of the dust having a higher concentration)
predicting a plurality of concentration distributions based on the at least one of the temperature data and the humidity data, each of the plurality of concentration distributions corresponding to a different time among a plurality of times after a current time (Hong et al.,para 293-306, and Fig. 14, Fig. 15, dust concentration prediction method according to the controlling method air purifier, accumulation and floating position is predicted according to the size of the dust having a higher concentration, para 198 describes fan 100 speed is controlled with time such as running for long time with low speed or running for short time with high speed can improve the air quality)
From above citation, both Lee and Hong et al. teach specifying and predicting plurality of dust concentration in an indoor environment. However, Hong et al. measured it in different heights by arranging air purification (10) including various sensors in vertical direction (Hong et al. para 210, plurality of air purification modules 10 are stacked in vertical direction motion and dust information can be detected on each height).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the air purification module with various sensors arranged in vertical direction taught by Hong et al. This would have been obvious because both Lee and Hong et al. teach a sensor that detects temperature and indoor dust concentration. By adding the feature of arranging the sensors in different heights, the sensors can collect data from different heights of the room. By collecting dust information from different heights, the clean air discharged from the flow path can be configured variously and the air purifier can be controlled accordingly (Hong et al. para 210).
As per claim 2, the combination of Lee and Hong et al. teach
The control method according to claim 1, wherein the specifying the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space further includes
using a look-up table to obtain the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space based on the at least one of the temperature data and the humidity data (Lee, para 151, “the processor 180 may determine the optimal match for executing a specific function by using stored usage history information and prediction modeling”, “stored usage history information” teaches the database or look-up table, also see Fig. 6 and Fig. 8. Hong et al., para 188, the reference information calculated by the motion calculation module 630 is transmitted to the blowing fan control module. Here the calculation data used as reference data teaches the database. Hong et al. para 210, calculation is performed in different heights).
As per claim 3, the combination of Lee and Hong et al. teach
The control method according to claim 1, wherein the specifying the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space further includes
inputting the at least one of the temperature data and the humidity data and height information into a first neural network model to obtain the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space (Lee, para 312, a temperature prediction model as a neural network using time varying temperature as training data to predict future temperature, para 313,the environmental prediction model also using dust, volatile organic compound, and temperature as input or trained data for prediction model, para 315, temperature prediction model mounted into artificial intelligence device 100 to predict future temperature, para 323, artificial intelligence device 100 obtain environmental factor through the communication unit Hong et al. para 146, the sensor unit 500 includes dust sensor 520, and also include temperature sensor and humidity sensor, para 293-306, and Fig. 14, Fig. 15 dust concentration prediction method according to the controlling method air purifier 1, accumulation and floating position is predicted according to the size of the dust having a higher concentration, also see, Hong et al. para 210, plurality of air purification modules 10 are stacked in vertical direction motion and dust information can be detected on each height ).
As per claim 4, the combination of Lee and Hong et al. teach
The control method according to claim 1, wherein the specifying the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor in the indoor space further includes
acquiring a sensor value for particulate matter with different particle sizes in a room (Lee, Para 227, dust sensor 735 sensing dust concentration), and
fitting the concentration distribution of particulate matter with different particle sizes in the indoor space based on the sensor value for the particulate matter with different particle sizes in the room (Lee, para 289, various dust concentration such as 50 um or less, PM10, PM2.5, para 307, fitting data to a neural network, para 313, “In addition, the environmental factor prediction model may include an indoor environmental factor prediction model that is a neural network trained to predict a future indoor environmental factor using indoor environmental factors (indoor dust, suspended dust, fine dust, ultra-fine dust, gas, carbon dioxide, volatile organic compound, temperature, or the like) as training data.”).
As per claim 8, the combination of Lee and Hong et al. teach
The control method according to claim 1, wherein the controlling, the at least one equipment parameter of the purification equipment in accordance with the concentration distributions includes
adjusting a control command for the at least one equipment parameter in real time according to the plurality of concentration distributions (Lee, para 354, “allow the ventilation system and the air cleaning system to operate cooperatively with each other based on the prediction of the dust concentration by the environmental factor prediction model,”; para 235 shows equipment parameter control such as fan motor, air flow control device, air volume, wind direction ,para 368-370,Processor 180 compare the real time dust concentration with the predicted model and adjust the operating system by switching air cleaning system to ventilation system based on the real time dust concentration and the future dust concentration, switching the operating system teaches adjusting control command, Hong et al.,para 223, 237, first flow path portion and the second flow path portion can be adjusted to perform rapid purification, also see Fig. 8).
As per claim 14, the combination of Lee and Hong et al. teach
The control method according to claim 1, wherein the specifying the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space includes
acquiring an environmental parameter of the indoor space (Lee, para 8, 313), and
predicting, the plurality of concentration distribution concentration distributions includes
predicting the plurality of concentration distributions based on the environment parameter (Lee, para 354 ,“allow the ventilation system and the air cleaning system to operate cooperatively with each other based on the prediction of the dust concentration by the environmental factor prediction model,”; para 235 shows equipment parameter control such as fan motor, air flow control device, air volume, wind direction; para 307; also see Fig.8 and para 305, also see, Hong et al.,para 293-306, and Fig. 14, Fig. 15 dust concentration prediction method according to the controlling method air purifier 1, dust accumulation and floating position is predicted according to the size of the dust having a higher concentration., para 198 describes fan 100 speed is controlled with time such as running for long time with low speed or running for short time with high speed can improve the air quality. Also see, para 307, the air purification system is implemented to the area of the indoor space in various capacities and vertical direction, Also see, Paras 192-195, dust concentration detection by dust sensor).
As per claim 17, the combination of Lee and Hong et al. teach
The control method according to claim 14, wherein
the environmental parameter includes at least one of the temperature data, the humidity data, concentration of particulate matter with different particle sizes, VOC concentration, formaldehyde concentration, odor concentration, and carbon dioxide concentration (Lee, para 288, environmental factor includes dust concentration, gas concentration, and temperature, para 290, gas concentration may include carbon dioxide and volatile organic compound concentration, Hong et al., Fig 8, S110, sensor unit detects dust information, para 156, dust sensor 520 detects the concentration of dust around purification module 10).
As per claim 18, Lee teaches
A control method for purification equipment, comprising (Lee, para 281, Artificial intelligence system 100, air cleaning system 700 and ventilation system 300 communicating together to work as purification equipment):
acquiring an environmental parameter of an indoor space, the environmental parameter including at least one of temperature data and humidity data at each of a plurality of different heights in the indoor space (please refer to the analysis of claim 1 above);
predicting, based on the environmental parameter, a plurality of pollutant concentration distributions at a plurality of times after a current time; (please refer to the analysis of claim 1 above) and
controlling at least one equipment parameter of the purification equipment according to the plurality of pollutant concentration distributions at the plurality of times after the current time (please refer to the analysis of claim 1 above).
As per claim 20, the combination of Lee and Hong et al. teach
The control method according to claim 18, wherein the predicting, based on the environmental parameter, the plurality of pollutant concentration distributions at the plurality of
times after the current time includes
inputting at least one of an environmental parameter sequence, a state of indoor environment equipment (Lee, para 317, Fig. 9, Fig.10, Fig.11 showing inputting environmental parameter as a sequence to the prediction model. Para 105, air conditioner, para 106, a fixed or movable robot, teach indoor environment equipment) and user behavior into a prediction model (Lee, para 293-294 dust concentration may vary if the employee does not open window during winter cold or opens the window in spring when dust concentration is high, at home the dust concentration may vary by the cleanliness teaches user behavior) and
outputting the plurality of pollutant concentration distributions at a plurality of times after the current time (Lee, para 296, Fig.6 and Fig. 7, environmental factor prediction model use indoor factor data as trained data as an input data, and output the output value using neural network, para 307, dust concentration prediction model; also see Fig.8 and para 305, also see, Hong et al., para 293-304, Fig 14, Fig. 15, dust concentration information gathering).
As per claim 21, the combination of Lee and Hong et al. teach
The control method according to claim 18, wherein the controlling at least one equipment parameter of the purification equipment according to the plurality of pollutant concentration distributions at a plurality of times after the current time includes
specifying at least one control strategy and a time required for the purification equipment to implement the control strategy according to the plurality of pollutant concentration distributions at a plurality of times after the current time (Lee, para 354 “allow the ventilation system and the air cleaning system to operate cooperatively with each other based on the prediction of the dust concentration by the environmental factor prediction model,”; para 235 shows equipment parameter control such as fan motor, air flow control device, air volume, wind direction, para 307, using indoor dust concentration for a predetermined time as a training data and using neural network to predict future dust concentration using a time varying dust concentration data as training data).
As per claim 22, the combination of Lee and Hong et al. teach
The control method according to claim 21, wherein
the controlling at least one equipment parameter of the purification equipment according to the plurality of pollutant concentration distributions at a plurality of times after the current time further includes
specifying and providing at least one control strategy to a user, and
controlling the purification equipment according to a control strategy selected by the user (Lee, para 222, “the input unit 720 is for receiving information from the user, and the processor 780 may control operation of the air cleaner 700 so as to correspond to input information when the information is inputted through the input unit 720”. Also see Hong et al., paras 223, 237, first flow path portion and the second flow path portion can be adjusted to perform rapid purification, also see Fig. 8).
As per claim 23, the combination of Lee and Hong et al. teach
The control method according to claim 21, wherein the controlling at least one equipment parameter of the purification equipment according to the plurality of pollutant concentration distributions at the plurality of times after the current time further includes
automatically selecting a control strategy with a shortest required time from the at least one control strategy and controlling the purification equipment according to the control strategy automatically selected (Lee, Fig. 7 displays the shortest time required, also please see para 307-308, using indoor dust concentration for a predetermined time as a training data and using neural network to predict future dust concentration using a time varying dust concentration data as training data,. Also see Hong et al. para 113, the equipment of the housing 400 with air purification module 10 is used and maintained for passage of time, also see para 153, more dust accumulation is expected when there is no movement for long time).
As per claim 24,
A purification control device comprising (Lee, para 235, purification control device 755):
a processor configured to specify a concentration distribution of particulate matter with different particle sizes in an indoor space: and control at least one equipment parameter of the purification equipment according to the concentration distribution (please refer to the analysis of claim 1 above);
the processor being configured to specify the concentration distribution of particulate matter with different particle sizes at the plurality of different heights in the indoor space by acquiring at least one of temperature data and humidity data at each of the plurality of different heights in the indoor space (please refer to the analysis of claim 1 above), and
predicting a plurality of concentration distributions based on the at least one of the temperature data and the humidity data, each of the plurality of concentration distributions corresponding to a different time among a plurality of times after a current time (please refer to the analysis of claim 1 above).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), in view of Hong et al.( US 20210278096 A1.), and further in view of He et al. (US 20220042694 A1.).
As per claim 6, Lee teaches
The control method according to claim 1, wherein the
predicting, the plurality of concentration distributions based on the at least one of the temperature data and humidity data includes
inputting the at least one of the temperature data and the humidity data into a
neural network model to obtain the plurality of concentration distribution concentration distributions (Lee, para 312, para 315, temperature prediction model mounted into artificial intelligence device 100 to predict future temperature, para 323, artificial intelligence device 100 obtain environmental factor through the communication unit, para 298-299, recurrent neural network (RNN)).
However, Lee does not teach,
inputting temperature or humidity and height information.
In the same field of endeavor, He et al. teach inputting temperature or humidity and height information (He et al., para 221, He et al. teach a wall mounted array sensor set up where sensor can be installed in different heights and the temperature and dust concentration data can be collected from different heights. He et al., Para 182-83, teach a flow diagram where collected sensor information can be gone through by first threshold and second threshold before putting into the air quality system).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the wall mounted arrays of sensor taught by He et al. in the air cleaning system. This would have been obvious because the combination of Lee, Hong et al., and He et al. teach a sensor that detects temperature and indoor dust concentration. By adding the feature of arranging the sensors in different heights, the sensors can collect temperature or humidity from different heights of the room and add the data into the artificial network taught by Lee. This combination would yield better prediction analysis (He et al., paras 221,182-183).
As per Claim 16, Lee does not teach
The control method according to claim 14, wherein the environmental parameter is obtained by
a plurality of sensors arranged at different positions in a room or different heights
in a room, or at least one sensor movable in the room.
In the same field of endeavor, the combination of Hong et al and He et al. teach
The control method according to claim 14, wherein the environmental parameter is obtained by
a plurality of sensors arranged at different positions in a room or different heights in a room, or at least one sensor movable in the room (He et al. parto221, He et al. teach an arrangement of wall mounted sensor arrays where the sensors can be set up at different heights during summer and winter to avoid direct sunlight, open windows and air intakes or exhausts. The wall mounted arrays sensor can be installed about 4 to 5 feet above the floor on interior or walls. These also can be installed between 8 inches to 1 foot 8 inches above the floor. These sensors are movable. Hong et al., para 210, plurality of air purification modules 10 are stacked in vertical direction motion and dust information can be detected on each height, para 146, the sensor unit 500 includes dust sensor 520, and also include temperature sensor and humidity sensor, para 293-306, and Fig. 14, Fig. 15 dust concentration prediction method according to the controlling method air purifier 1 accumulation and floating position is predicted according to the size of the dust having a higher concentration).
Although both He et al. and Hong et al. teach the sensor arrangement at different heights of the room, He et al. teach the wall mounted array that can be arranged at different heights or direction whereas, Hong et al. only arranges in vertical direction.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the wall mounted arrays of sensors taught by He et al. in the air cleaning system. This would have been obvious because the combination of Lee, He et al., and Hong et al. teach a sensor that detects temperature and indoor dust concentration. By adding the feature of arranging the sensors at different heights, the sensors can collect data from different heights of the room and avoid the environmental factors such direct sunlight, open windows and air intakes or exhaust. The heights of the sensors can be adjusted accordingly. Therefore. the purification system will function more efficiently (He et al., paras 221,182-183).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), in view of Hong et al. (US 20210278096 A1.), in view of He et al. (US 20220042694 A1.), and further in view of Pilskin (US 20160041074 A1.).
As per claim 7, Lee does not teach
The control method according to claim 1, wherein the predicting the plurality of concentrations distributions based on the at least one of the temperature data and humidity data includes
inputting the at least one of the temperature data and humidity data and height information into a simulation model to obtain the plurality of concentration distributions.
In the same field of endeavor, He et al. teach
inputting temperature or humidity and height information.
(He et al., para 221, He et al teach a wall mounted array sensor set up where sensor can be installed in different heights and the temperature and dust concentration data can be collected from different heights. He et al., Para 182-83, teach a flow diagram where collected sensor information can be compared to a first threshold and second threshold before putting it through the air quality system).
However, He et al. does not teach
inputting the at least one of the temperature and humidity
data and height information into a simulation model to obtain the concentration distributions
of particulate matter with different particle sizes in the indoor space at a plurality
of times after the current time.
In the same field of endeavor Pilskin teaches,
inputting the at least one of the temperature and humidity
data and height information into a simulation model to obtain the concentration distributions
of particulate matter with different particle sizes in the indoor space at a plurality
of times after the current time (Pilskin, para 60, a predictive analysis where data is collected from an air monitoring system as a historical data and using the whole physical layout of the room to determine the concentration distribution. The collected data then input into a predictive model to determine future contamination and also compiled the sanitization process).
It would have been obvious to a person of ordinary skilled in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and include the wall mounted array of sensors taught by He et al. and the predictive analysis model taught by Pilskin into Lee’s air cleaning system. This would have been obvious because the combination of Lee, He et al. and Pilskin teach an air purification system comprising a predictive analysis for future concentration distribution. By adding the feature of arranging the sensors in different heights, taught by He et al, and predictive analysis model taught by Pilskin into Lee’s air purification system, the system can collect the environmental factor at different heights and layouts, and predict better contamination throughout the entire room (Pilskin , para 60).
Claims 9, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), in view of Hong et al. (US 20210278096 A1.), and further in view of Pilskin (US 20160041074 A1.).
As per claim 9, Lee and Hong at al. do not specifically teach, but Pilskin does teach
The control method according to claim 1, wherein the specifying the concentration distribution of particulate matter with different particle sizes in the indoor space includes acquiring an indoor layout diagram of a room (Pilskin, para 57 using floor plan to determine particulate counts and sanitation) and
specifying the concentration distribution of particulate matter with different particle sizes in the indoor space while combining the indoor layout diagrams (Pilskin, para 57, using floor plan to determine particulate counts and sanitation; see analysis in claim 7 above).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and Hong et al. and to include the predictive analysis of concentration particulate or concentration distribution considering the room layout or floor diagram taught by Pilskin. This would have been obvious because the combination of Lee, Hong and Pilskin teach a prediction model for indoor concentration distribution. This combination will make the predictive analysis more precise.
As per claim 15, the combination of Lee and Hong et al. do not specifically teach
The control method according to claim 14, further
comprising: acquiring an indoor layout diagram of a room,
the predicting the plurality of the concentration distributions including
predicting the plurality of concentration distributions based on the environmental parameter and the indoor layout diagram.
However, in the same field of endeavor, Pilskin et al. teach
The control method according to claim 14, further
comprising: acquiring an indoor layout diagram of a room (Pilskin, para 57, using floor plan to determine particulate counts.)
the predicting the plurality of the concentration distribution of including predicting the plurality of concentration distributions of particulate matter with different particle based on the environmental parameter and the indoor layout diagram (Pilskin, para 60, predictive analysis including physical layout of the space).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the predictive analysis of concentration particulate or concentration distribution considering the room layout or floor diagram taught by Pilskin. This would have been obvious because the combination of Lee and Pilskin teach a prediction model for indoor concentration distribution. This combination will make the predictive analysis more precise.
Claim 19 has the same limitations as claim 15. Please refer to the analysis above.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), further in view of Hong et al. (US 20210278096 A1), and in view of Saiki et al. (US 20160245543 A1.).
As per claim 10, Lee teaches
The control method according to claim 1, further
comprising: controlling at least one of opening and closing numbers,
opening and closing ranges, and
opening and closing angles
of a suction port and a blow-out port (Lee, para 214, Fig. 3, #740 Blowing device, para 236, blowing device may include a suction port and a discharge port)
However, Lee does not teach,
detection result of an obstacle around the purification.
In the same field of endeavor, Saiki et al. teach
detection result of an obstacle around the purification equipment (Saiki et al., para 33, “blow-out angle and a volume of the air blown out from the outlet 4 are controlled to provide an optimum state according to the room size and the obstacles”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the purification system taught by Saiki et al. into Lee’s teaching. This would have been obvious because both Lee and Saiki et al. teach an air purification system for an indoor layout. Saiki et al.’s system has a control device 12 that can control the blow out port and outlet 4 to acquire the optimum state (para 33, Saiki et al.). By adding the control device to control the blow out port, the system can perform at the optimum stage.
As per claim 11, the combination of Lee and Hong et al. do not teach
The control method according to claim 1, wherein
the controlling the at least one equipment parameter of the purification equipment in accordance with the concentration distribution includes
controlling opening ranges of a suction port and a blow-out port according to the concentration distribution, and
changing, after operation of the purification equipment has continued for one time period, the opening ranges of the suction port and the blow-out port based on a detection result or a prediction result of the particulate matter with different particle sizes.
In the same field of endeavor, Saiki et al. teach
controlling opening ranges of a suction port and a blow-out port according to the concentration distribution (Saiki et al. para 20, “outlet 4 having an elongated quadrangular opening portion and extends horizontally as viewed from front casing 2. Therefore, two long sides of an opening end of the outlet 4 having a rectangular shape face each other in a front-rear direction of the casing 2.”, para 27, using a detection device 11 to detect the pollutant and provide the information to the control device 10 and an outer device 11, para 33, blowing device 5), and
changing, after operation of the purification equipment has continued for one time period, the opening ranges of the suction port and the blow-out port based on a detection result or a prediction result of the particulate matter with different particle sizes (Saiki et al. para 27, using a detection device 11 to detect the pollutant and provide the information to the control device 10 and an outer device 11,Saiki et al., para 33, “As described above, the blow-out angle and the volume of air are controlled based on information relating to the inside of the room, which enables purifying air blown out from the outlet 4 while circulating the air throughout the room. Accordingly, the room air can efficiently be purified in a short period of time.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Lee and to include the purification system taught by Saiki et al. into Lee’s teaching. This would have been obvious because both Lee and Saiki et al. teach an air purification system for an indoor layout. Saiki et al.’s system has a control device 12 that can control the blow out port and outlet 4 to acquire the optimum state (para 33, Saiki et al.). By adding the control device to control the blow out port, the system can perform at the optimum stage.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20210190360 A1.), Hong et al.(US 20210278096 A1), and further in view of Bassa et al. (US 20200240668 A1.).
As per claim 25, the combination of Lee, and Hong et al. teach
Purification equipment including the purification control device according to claim 24, the purification equipment further comprising: an outer case having a suction port provided in a lower end portion and a bottom portion of a peripheral side edge (Lee, para 214, Fig. 3, #740 Blowing device, para 236, blowing device may include a suction port and a discharge port. Hong et al. Abstract, Figs. 1-4, blowing fan (100));
an inner case having a blow-out port provided on a peripheral side edge and an upper side (Lee, para 214, Fig. 3, #740 blowing device, para 236, blowing device may include a suction port and a discharge port. Hong. Figs. 1-4);
a filter body unit provided in the inner case (Hong et al., para 53, filter unit)
a fan (Lee, para 239, a fan motor 750) provided in the inner case or the outer case,
the inner case being fitted into the outer case and being adjusted upward or downward by an up-down adjuster (Lee, para 237, the air flow control device 755 may change the flow direction of air of the blowing device),
the processor being configured to control (Lee, para 168, control unit 160, Hong et al. para 165, controller 600) at least one of the suction ports, the blow-out port, the up-down adjuster (Lee, para 214, Fig. 3, #740 Blowing device, para 236, blowing device may include a suction port and a discharge port).
However, the combination Lee and Hong et al. do not teach
a first wind-guide plate and a second wind gate plate
the blow-out port being provided with a second wind-guide plate.
In the same field of endeavor, the combination of He et al. and Bassa et al. teach
a first wind-guide plate and a second wind gate plate (Bassa et al., para 189, Fig. 8, baffles 605 and 606),
the blow-out port being provided with a second wind-guide plate (Bassa et al., para 37, baffle and dumpers maintaining outdoor flow in different zone),
It would have been obvious to a person of ordinary skill in the art, before the effective filing
date of the claimed invention, to modify the teaching of Lee and to include the purification system taught by Bassa et al. into Lee’s air purification system. This would have been obvious because both Lee, and Bassa et al. teach an air purification system for an indoor layout. By adding Bassa et al.’s baffles and dumpers into Lee’s air purification system, the overall air circulation and purification will be enhanced.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to the form 892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rokeya Alam whose telephone number is (571) 272-0083. The examiner can normally be reached between 7:30am - 4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Scott Baderman can be reached at telephone number (571-272-3644). The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
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