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 .
This is a response to U.S. Patent Application No. 18/664,834 filed on 05/15/2024 in which Claims 1 – 8 were filed for examination.
Status of the Claims
Claims 1 – 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph and Claims 1 – 4 and 6 – 8 are rejected under 35 U.S.C. 103.
Examiner Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/14/2025, 06/26/2025 and 03/17/2026 have been entered and considered by the examiner.
Specification
The use of the trademarks (e.g. OPEN AI, AZURE, GEMINI, AWS and IBM) has been noted in this application. They should be capitalized wherever they appear and/or be accompanied by the generic terminology.
Although the use of trademarks is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as trademarks.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “big” in claim 1is a relative term which renders the claim indefinite. The term “big” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For examination purposes, the term “big” will be given no weight, and the claim is interpretated as a storing the information in a server.
Claim 1 recites “an application software, inputting the information data of the indoor air cleaning system and transmitting the information data of the indoor air cleaning system through the Internet of Things (IoT) to be stored in the storage center”. This Claim language is indefinite because it is unclear where the information is inputted into. It appears that the claim is intending to claim the use the application software to input the information into the application software, however, the claim as claimed is unclear.
For examination purposes, the claim is interpreted as inputting the information into a software application.
Claim 1 also recites “…a control command is intelligently selected…”. The term “Intelligently” in claim 1 is a relative term which renders the claim indefinite. The term “intelligently” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For examination purposes, the examiner is interpreting the term as selecting a control command to control the fan associated with the filtration element.
Claim 1 further recites “thereby gas state in the indoor field is cleaned to reach a clean room requirement”. This claim language is indefinite, because it is unclear what the clean room requirement is, and at which point the space is considered clean.
For examination purposes, the examiner is interpreting the claim as operating a fan to control the air circulation until reaching air quality threshold.
Claim 3 recites “… and required software and hardware specifications of the air cleaning system”. This claim language is indefinite because it is unclear which software and hardware is required. After reviewing the disclosure, the examiner cannot determine which software and hardware is required by the air cleaning system.
For purpose of examination, the examiner interprets the claim as any software and hardware.
Claim 4 recites “…experimental measurement...” The use of this term render the claim language indefinite, because it is unclear what is considered experimental measurement. After despite reviewing of the disclosure, the examiner cannot determine what is considered experimental measurement.
For examination purposes, the examiner is interpreting this claim language as the measurements obtained from the sensors within a space.
Claim 6 contains the trademark/trade name (OPEN AI, AZURE, GEMINI, AWS and IBM). Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name.
In the present case, the trademark/trade names are used to identify/describe specific artificial intelligence models and, accordingly, the identification/description is indefinite.
Claim 7 recites “wherein the generative artificial intelligence (GAI) model includes an autoregressive correction analysis mechanism, which uses the automatically-generated data for prediction, and generate new and real deep learning processing data corrected to an optimization, so that the generative artificial intelligence model is led to quickly converge in a correct and applicable direction”. This claim language is indefinite, because it is unclear how the model is generating new and real deep learning processing data corrected to an optimization. IT is unclear what is considered “real deep learning processing data” and it is unclear how this generated data is being corrected to an optimization.
Furthermore, the term “quickly” in claim 7 is a relative term which renders the claim indefinite. The term “quickly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Furthermore, it is unclear what is considered “correct and applicable direction”.
For examination purposes, the examiner is interpreting the claims as using artificial intelligence to analyze sensor data and control an air purifier system.
Claim 8 recites “wherein the clean room grade standard data includes a cleanliness of ZAPClean Room 1-9”. This Claim language is indefinite, because it is unclear which standard is associated with ZAPClean Room 1 – 9. After despite reviewing of the disclosure, the disclosure is silent regarding any description of the claimed standard or any description specifically associated with ZAPClean Room 1 – 9.
For purposes of examination, the examiner interprets the claim as the use of any standard associated with the requirements of a clean room.
Due to at least their dependency upon Claim 1, Claims 2 – 8 are also indefinite.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over OH et al. (US 2021/0063036) (hereinafter, Oh) (cited in IDS dated 06/26/2025) in view of Fletcher (US 2025/0216099) (hereinafter, Fletcher).
Regarding Claim 1, Oh teaches a generative artificial intelligence indoor air cleaning system (See Oh’s Abstract and par 0061), comprising:
a storage center, collecting information data of an indoor air cleaning system to form a big data database including professional generated data and user generated data (Oh in par 002 and Fig.1, teaches that the sensor information from the external sensor devices 120, 130, 140 and 150 may be transmitted to the smart home server 190. Oh in par 0120 and Fig. 2, further teaches that the controller may receive information from one or more sensors units from tone or more external sensor devices. When the air pollution degree of the indoor environment is higher than a reference value the controller 210 may generate a control signal. Oh in par 0127, further teaches that the controller 210 may include any type of processor implemented in hardware having a structured circuit for performing functions represented by codes or instructions included in a memory and a program stored in the memory);
at least one air quality detector, detecting air pollution in an outdoor field and an indoor field of buildings to output air pollution data, and transmitting the air pollution data to the storage center through an Internet of Things (IoT) to form the user generated data (Oh in par 0114 and Fig. 2, teaches that the air pollution sensor 232 measures a pollution degree of air in the indoor environment around the air purifier 100 to generate pollution data and provides the generated pollution data to the controller 210. Oh in par 0117, teaches that the network interface 250 may receive information regarding an outdoor air pollution degree from an external server via the Internet. Oh in par 0135 and Fig. 2, further teaches that the sensor information received by the controller 210 may include an air pollution degree measured by the air pollution sensor 232);
an application software, inputting the information data of the indoor air cleaning system and transmitting the information data of the indoor air cleaning system through the Internet of Things (IoT) to be stored in the storage center (Oh in par 0115 and Fig(s). 2 – 3, further teaches that the user interface 240 includes a display panel 370 for displaying information related to the operation of the air purifier 100 and buttons 380 or a touch screen for receiving input from the user. Oh in par 0119, further teaches that the controller 210 controls the operation of the first airflow generator 220 and/or the second airflow generator 260 by manipulation of the user through the user interface 240. Oh in par 0122, further teaches the environment configuration engine 212 may allow the user to set environment configurations through the user interface 240);
a calculation center, comprising a generative artificial intelligence (GAI) model capturing the professional generated and the user generated data stored in the storage center through the Internet of Things (IoT), processing through deep learning and analyzing data to form automatically-generated data (Oh in par 0038, teaches that an Artificial Neural Network (ANN) may include deep neural network. Oh in par 0120, further teaches that the controller 210 may receive sensor information from one or more sensor units 230 or from one or more external sensor devices 120, 130, 140, and 150 via the network interface 250, and control the operation of the first airflow generator 220 and/or the second airflow generator 260 based on the received sensor information. For example, when the air pollution degree of the indoor environment is higher than a reference value, the controller 210 may generate a control signal for driving the first airflow generator 220. Oh in par 0129 and claim 4, further teaches that the ANN 420 may have been trained in advance to identify that the user is cooking, the user is cleaning, the user is smoking, or the user is opening the window or the door. In addition, for example, the ANN 420 may have been trained in advance to identify that smoke is generated in the indoor environment or that the window or the door is open); and
at least one gas purification device hardware disposed in the indoor field of the buildings and comprising at least one fan at least one filtration element (Oh in par 0112, further teaches that the first airflow generator 220 generates an airflow such that air is introduced into the air purifier 100 through an inlet 320 formed in a housing 310 of the air purifier 100, the introduced air passes through a filter 330, and the filtered air is discharged to the outside of the air purifier 100 through an outlet 360. The first airflow generator 220 includes a first fan 340 disposed inside the housing 310 of the air purifier 100. The first fan 340 may be located downstream of the filter 330 in a direction of the airflow),
However, Oh does not specifically disclose wherein the at least one gas purification device hardware receives the automatically-generated data generated by the calculation center through the Internet of Things (IoT), and a control command is intelligently selected and issued to regulate an activation operation of the at least one fan of the at least one gas purification device hardware, whereby the air pollution in the indoor field is guided to pass through the filtration element for circulation and filtration, thereby gas state in the indoor field is cleaned to reach a clean room requirement.
Fletcher teaches the use of one or more air quality sensors and an air purifier to detect and adjust air quality within a structure (See Fletcher’s Abstract).
Fletcher in par 0032, further teaches that the manufacturer deploys a machine learning system that automatically and optimally adjusts thresholds based on building usage, measured effectiveness, and user preferences and behavior. As such, the air quality within a structure or building or home can be constantly or continuously or periodically improved by adding or integrating an air purifier to the existing HVAC system, where the air purifier takes action when detecting an adverse air quality event by dynamically and automatically adjusting the speed (or input power to the motor) of the first and second fans of the intake and exhaust channels, to control fresh air flow within the structure or building or home.
Fletcher in par 0106 and Fig. 8, further teaches that abnormal gas levels detected in real-time are provided and abnormal gas level adjustments are made to an app downloaded on an electronic device or computing device operated or handled by a user. As such, the user is provided with the air improvement data in real-time. For example, if the CO2 within a room of the house increased to 1250 ppm (exceeding the threshold of, e.g., 600 ppm), the user will be notified on the app with a time of the abnormal detection, a time when the first fan 212 was activated in response to that abnormal detection, a new speed of the first fan 212, a time it takes to adjust the CO2 to less than 600 μm, and a progress report showing how the CO2 is being reduced in real-time as more fresh air is entering the affected room. Fletcher in par 0114, further teaches that the sensors can be artificial intelligence (AI) sensors. The AI sensors can employ machine learning (ML) techniques to collect data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Fletcher with the teachings as in Oh to automatically control the filtration system of Oh as disclosed in Fletcher. The motivation for doing so would have been to effectively provide real time monitoring data and automatically control a filtration system, thus improving air quality (See Fletcher’s par 0002 and 0032).
Regarding Claim 2, Oh in view of Fletcher teaches the limitations contained in parent Claim 1. Oh further teaches:
wherein the gas purification device hardware comprises one selected from the group consisting of a fresh air blower, a full heat exchanger, a fan-filter unit (FFU), a range hood, a bathroom exhaust fan, a negative pressure exhaust fan and a combination thereof (Oh in par 0098, teaches that the stove area sensor 120 may be installed on the kitchen stove 110, installed on a wall near the kitchen stove 110, or installed on a kitchen hood disposed above the kitchen stove 110).
Regarding Claim 4, Oh in view of Fletcher teaches the limitations contained in parent Claim 1. Oh further teaches:
wherein the user generated data includes indoor field air pollution data of a user's building (Oh in par 0153, teaches that the controller 210 measures the indoor air pollution degree after ventilation using the air pollution sensor 232 and determines whether the indoor air pollution degree is lower than a reference value), indoor field experimental measurement air pollution data of the user's building (Oh in par 0154, further teaches that a criterion for restarting the air purifying function is preferably different from a criterion for suspending the air purifying function. When these criteria are the same, the air purifier 100 may repeat suspension and restart of the air purifying function as the air pollution degree slightly varies in the vicinity of the criterion), and
However, Oh does not specifically disclose that the generated data includes air exchange rate data of heating, ventilation and air conditioning (HVAC) in the user's building.
Fletcher in pr 0036, teaches that one or more sensors can be connected to multiple locations on the HVAC ducts and occupied spaces.
Fletcher in par 0046, teaches the evaporator coils of the HVAC system 130 may be used to condition a flow of air entering a building from an ambient environment, such as the atmosphere. For example, in cases when the HVAC system 130 is operating in a cooling mode, the supply duct 140 may direct outdoor air across a heat exchange area of the evaporator, such that the refrigerant within the evaporator absorbs thermal energy from the outdoor air. Fletcher in par 0068, further teaches that airflow is a reading of the current air exchange rate, which will be partly dependent on the AQI at the time of the ACH reading.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Fletcher with the teachings as in Oh to automatically control the filtration system of Oh as disclosed in Fletcher. The motivation for doing so would have been to effectively provide real time monitoring data and automatically control a filtration system, thus improving air quality (See Fletcher’s par 0002 and 0032).
Regarding Claim 6, Oh in view of Fletcher teaches the limitations contained in parent Claim 1. Oh further teaches:
wherein the generative artificial intelligence (GAI) model includes one selected from the group consisting of the OpenAI API artificial intelligence model, Azure artificial intelligence model, Gemini artificial intelligence model, AWS artificial intelligence model, IBM Watson artificial intelligence model and a combination thereof (Oh teaches operating an air purifier by executing an artificial intelligence (AI) algorithm and/or machine learning algorithm (See Oh’s abstract). Oh in par 0061 further teaches that examples of artificial neural networks using unsupervised learning include, but are not limited to, a generative adversarial network (GAN) and an autoencoder (AE)).
Regarding Claim 7, Oh in view of Fletcher teaches the limitations contained in parent Claim 1. Oh further teaches:
wherein the generative artificial intelligence (GAI) model includes an autoregressive correction analysis mechanism, which uses the automatically-generated data for prediction, and generate new and real deep learning processing data corrected to an optimization, so that the generative artificial intelligence model is led to quickly converge in a correct and applicable direction (Oh in par 0044, further teaches that the Artificial Neural Network can be trained by using training data. The training may refer to the process of determining parameters of the artificial neural network by using the training data, to perform tasks such as classification, regression analysis, and clustering of inputted data. Such parameters of the artificial neural network may include synaptic weights and biases applied to neurons. Oh in par 0050, further teaches that among the functions that may be thus derived, a function that outputs a continuous range of values may be referred to as a regressor, and a function that predicts and outputs the class of an input vector may be referred to as a classifier. Oh in par 0083, further teaches that in machine learning or deep learning, learning optimization algorithms may be deployed to minimize a cost function. Oh in par 0120, further teaches that the controller 210 may receive sensor information from one or more sensor units 230 or from one or more external sensor devices 120, 130, 140, and 150 via the network interface 250, and control the operation of the first airflow generator 220 and/or the second airflow generator 260 based on the received sensor information. For example, when the air pollution degree of the indoor environment is higher than a reference value, the controller 210 may generate a control signal for driving the first airflow generator 220).
Claims 3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Oh in view of Fletcher and in further view of Willman (US 12,146,674) (hereinafter, Willman).
Regarding Claim 3, Oh in view of Fletcher teaches the limitations contained in parent Claim 1. Oh further teaches:
wherein the professional generated data includes […], indoor field space data of the buildings (Oh in par 0095, teaches an “indoor environment” refers to an indoor space where an air purifier 100 is disposed and where an operation of the air purifier 100 may affect. For example, the indoor environment may be an indoor environment of a home. The indoor environment may be surrounded by a wall having a window 175 and a door 185 that may open to an exterior space. Oh in par 0100, further teaches that for example, the smoke detector 140 is a device for detecting smoke generated in the indoor environment by being attached to a wall or ceiling of the room. Oh in par 0120, further teaches that when the air pollution degree of the indoor environment is higher than a reference value, the controller 210 may generate a control signal for driving the first airflow generator 220), […], and required software and hardware specifications of the air cleaning system (Oh in par 0113 and Fig. 3, further teaches that the sensor unit may be disposed at positions 352, 354, and 356 of an air purifier body exposed through a through-hole 350 of the housing 310. Oh in par 0127The controller 210 may include any type of processor implemented in hardware having a structured circuit for performing functions represented by codes or instructions included in a memory and a program stored in the memory).
However, Oh in view of Fletcher does not specifically disclose that the professional generated data includes outdoor and indoor air pollution standard data of the buildings and clean room grade standard data.
Willman teaches a computer and software enabled system for real-time and ongoing assessment and adjustment of current air quality and airflow within and exiting an operation room (See Willman’s Abstract).
Willman teaches outdoor and indoor air pollution standard data of the buildings (Willman in Col. 2 lines 50 – 67, teaches that conventional HVAC systems maintaining the room air within operating rooms follow guidelines of the CDC which has adopted the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard for operating room ventilation. This, as noted, requires complete air changes of the positively pressured air within the operating room to be changed fifteen times per hour with a minimum of three outdoor air changes per hour. Additionally, the air temperature within the operating room should be maintained at between 68-73° F. with a relative humidity between 30-60 percent. Conventional HVAC systems maintaining the room air within operating rooms follow guidelines of the CDC which has adopted the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard for operating room ventilation. This, as noted, requires complete air changes of the positively pressured air within the operating room to be changed fifteen times per hour with a minimum of three outdoor air changes per hour. Additionally, the air temperature within the operating room should be maintained at between 68-73° F. with a relative humidity between 30-60 percent).
clean room grade standard data (Willman in Col. 3 lines 19 – 22, further teaches that the optimal CFD model can also include the current ISO standard 14644-1 for a concentration of particles which OSHA cites as required for infection control in operating rooms and clean rooms. Willman in Col. 11 lines 54 – 60, further teaches that the system will communicate commands 30 over the network to the HVAC system of the identified operating room 12, which have been determined by the CFD analysis 28 and the comparison 29 to be required for the air entering the identified operation room to reach that of the optimal CFD model and thereby meet any of the regulating agency standards, such as ISO standard 14644-1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Willman with the teachings as in Oh and Fletcher, to use ISO standard 14644-1 as the reference value in Oh as disclosed in Willman. The motivation for doing so would have been to maintain the room air quality within a clean room standard following guidelines of the CDC (See Willman’s Col. 2 lines 50 – 54 and Col. 3 lines 19 – 22).
Regarding Claim 8, Oh in view of Fletcher and in further view Willman teaches the limitations contained in parent Claim 1. Willman further teaches:
wherein the clean room grade standard data includes a cleanliness of ZAPClean Room 1-9 (Willman in Col. 3 lines 19 – 22, further teaches that the optimal CFD model can also include the current ISO standard 14644-1 for a concentration of particles which OSHA cites as required for infection control in operating rooms and clean rooms. Willman in Col. 11 lines 54 – 60, further teaches that the system will communicate commands 30 over the network to the HVAC system of the identified operating room 12, which have been determined by the CFD analysis 28 and the comparison 29 to be required for the air entering the identified operation room to reach that of the optimal CFD model and thereby meet any of the regulating agency standards, such as ISO standard 14644-1).
Allowable Subject Matter
Claim 5 is 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIEL MERCADO VARGAS whose telephone number is (571)270-1701. The examiner can normally be reached M-F 8:00am - 4:00pm.
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/ARIEL MERCADO-VARGAS/ Primary Examiner, Art Unit 2118
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