DETAILED ACTION
This action is responsive to the Application filed 10/17/24.
Accordingly, claims 1-20 are submitted for prosecution on merits.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1 is/are directed to an Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the 2-step analysis as follows.
Step 1
This claim belongs to a method/process category.
Step 2A
Prong one:
The recited steps of “comparing the image delta to a threshold value” is a concept that can be performed in the human mind or via pen/paper (e.g. observing two conditions and evaluating whether a threshold is met) – MPEP 2106.04(a)(2)(III) – whereas the step recited as “processing the image data to determine a delta value … the image delta represents a change in the image delta over a period of time” amounts to calculating a mathematical difference between data points. MPEP 2106.04(a)(2)(I)
The claim recites limitations that fall within enumerated groupings of an abstract idea: Mental Processes and Mathematical Concepts of data processing.
Prong two:
The element recited as “capturing image data inside the air conditioning system” merely amounts to well-understood, routing, conventional data gathering prior to performing the abstract calculations. MPEP 2106.04(d) - Pre-solution data gathering does not integrate an abstract Idea into a practical application.
The action element recited as “transmitting a alert signal over a computer network …” constitutes a generic post-solution activity using standard computer networking components to output results of the comparison/calculation.
The element recited as “method for maintaining an air conditioning system” as a preamble recites rather a technological field, yet the body of the claim is silent about any physical maintenance step (adjusting valve, changing a filter, altering a compressor operation …). In other words, simply sending alert does not transform the claim into an improvement to air conditioning technology itself.
The claim does not integrate the abstract Idea into a practical application because the elements recited as set forth above do not impose a meaningful limitation on the judicial exception.
Step 2B
The step of “capturing image data” amounts to a pre-activity of no significance toward rendering the abstract idea significantly more than itself- MPEP.2106.05(g)
The additional element recited as “transmitting over a computer network” relies rather on a off-the-shelf, conventional technology functioning in its ordinary manner, instead of specialized components tailored for a particular improvement to the technology in this NW communication field, and use of generic computer components fall under the deficiency identified per MPEP 2106.05(d) therefore, the “transmitting” cannot transform the abstract Idea into a inventive concept – MPEP 2106.05(c )
The claim considered as an ordered combination includes capturing image data, use a generic NW component to transmit a alert: i.e. no technical improvement is being perceived
The claim does not improve computer functionality or improve the technical operation of the air conditioning unit itself. Instead, it merely uses standard hardware to collect data, compare values and send a message. MPEP 2106.05(a)
The additional elements amount to nothing more than generic computer enforcement of an abstract Idea, thus is not eligible under the 35 USC § 101 statute.
Claims 19 and 20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 19 and 20 is/are directed to an Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the 2-step analysis as follows.
A. Eligibility of claim 19
Step 1
This claim is directed to a apparatus/system category
Step 2A
Prong one:
The recited steps of “comparing the image delta to a threshold value” is a concept that can be performed in the human mind or via pen/paper (e.g. observing two conditions and evaluating whether a threshold is met) – MPEP 2106.04(a)(2)(III) – whereas the step recited as “processing the image data to determine a delta value … the image delta represents a change in the image delta over a period of time” amounts to calculating a mathematical difference between data points. MPEP 2106.04(a)(2)(I)
Prong two:
The “capturing image data inside the air conditioning system” merely amounts to well-understood, routing, conventional data gathering prior to performing the abstract calculations. MPEP 2106.04(d) - Pre-solution data gathering does not integrate an abstract Idea into a practical application.
The “transmitting a alert signal over a computer network …” constitutes a generic post-solution activity using standard computer networking components to output results of the comparison/calculation.
The element recited as “method for maintaining an air conditioning system” as a preamble recites rather a technological field, yet the body of the claim is silent about any physical maintenance step over equipment of this HVAC field (adjusting valve, changing a filter, altering a compressor operation …). That is, simply sending alert does not transform the claim into an improvement to air conditioning technology itself.
The claim does not integrate the abstract Idea into a practical application
Step 2B
The additional element of “capturing image data” amounts to a pre-activity of no significance toward rendering the abstract idea significantly more than itself- MPEP.2106.05(g)
The additional element recited as “transmitting over a computer network” relies on an off-the-shelf, conventional technology functioning in its ordinary manner, instead of non-conventional, specialized components tailored for a particular improvement to the technology in this NW communication field, and use of generic computer components fall under the deficiency identified per MPEP 2106.05(d) therefore, the “transmitting” cannot transform the abstract Idea into an inventive concept – MPEP 2106.05(c )
The claim considered as an ordered combination includes capturing image data, use a generic NW component to transmit a alert: i.e. no technical improvement is being perceived
Claim 19 does not improve computer functionality or improve the technical operation of the air conditioning unit itself. Instead, it merely uses standard hardware to collect data, compare values and send a message. MPEP 2106.05(a) - thus is not eligible under the 35 USC § 101 statute
B. Eligibility of claim 20
Step 1
This claim is directed to medium/product category.
Step 2A
Prong one:
The recited steps of “comparing the image delta to a threshold value” is a concept that can be performed in the human mind or via pen/paper (e.g. observing two conditions and evaluating whether a threshold is met) – MPEP 2106.04(a)(2)(III) – whereas the step recited as “processing the image data to determine a delta value … the image delta represents a change in the image delta over a period of time” amounts to calculating a mathematical difference between data points. MPEP 2106.04(a)(2)(I)
Prong two:
The “capturing image data inside the air conditioning system” merely amounts to well-understood, routing, conventional data gathering prior to performing the abstract calculations. MPEP 2106.04(d) - Pre-solution data gathering does not integrate an abstract Idea into a practical application.
The “transmitting an alert signal over a computer network …” constitutes a generic post-solution activity using standard computer networking components to output results of the comparison/calculation.
The element recited as “method for maintaining an air conditioning system” as a preamble recites rather a technological field, yet the body of the claim is silent about any physical maintenance step (adjusting valve, changing a filter, altering a compressor operation). Simply sending alert does not transform the claim into an improvement to air conditioning technology itself.
The claim does not integrate the abstract Idea into a practical application
Step 2B
The additional element of “capturing image data” amounts to a pre-activity of no significance toward rendering the abstract idea significantly more than itself- MPEP.2106.05(g)
The additional element recited as “transmitting over a computer network” relies on conventional technology functioning in its ordinary manner, as opposed to specialized components tailored for a particular improvement to the technology in this NW communication field, and use of generic computer components fall under the deficiency identified per MPEP 2106.05(d) therefore, the “transmitting” cannot transform the abstract Idea into a inventive concept – MPEP 2106.05(c )
Claim 20 is deemed non-eligible under the 35 USC § 101 statute.
Step 2B analysis of dependent claims
Claim 2 recites that an alert signifies a degradation for which a maintenance is to be performed; and this well-understood characterization does not add a significant limitation that would actually transform the abstract Idea into a inventive and patentable application.
Claims 3-4, recites the image comparing in terms of dust level value respective to a threshold, and comparing pixel value to that of a baseline to determine image difference; but use of computed level or values as part of comparing a target data to a baseline or reference threshold amount to well-understood routines that fail to significantly transform the abstract Idea into an inventive and patentable implementation that effectively improves the air conditioning technical field
Claim 5 recites what a baseline is and how a reference object is obtained as part of forming a baseline; but this generic description of a reference source fails to demonstrate how any technical operational details actually enhance the image processing in direction of an improvement to the air conditioning technical field
Claim 6 recites calculating formula for a delta value and this mathematical formula falls into the Mathematical Concepts subgroup defined under a Judicial Exception.
Claim 7 recites a camera or a wireless-based server to support capture or transmission of the image near a unit of the HVAC system; but generic enumerating of a wireless transmission or a digital camera expressed in broad/nominal form cannot demonstrate that the abstract Idea has a limitation that functionally converts it into a technical implementation that effectively improves units or components of this air conditioning field.
Claim 8 recites use of Neural Network to determine the threshold, with an interface to help generating a difference between predicted delta value(s) and actual delta value(s) as part of the a predictive analysis that anticipates a time at which a service is required. But action of a predictive model (NN) to help anticipate a maintenance schedule via assessment from a UI entails use of mathematical support with an assessment UI in which a decision is carried out by a mental process. The claim merely recites a mental process operating via well-understood computer tools such as mathematical means or numerical black-box to come up with a desired outcome (values delta). Claim 8 amounts to applying a computer tool to the Abstract idea – MPEP 2106.05(f) - thus cannot be viewed as adding significantly more to the Judicial Exception defined above as a combination of Mental Processes and Mathematical concepts.
Claim 9 recites determination of a risk level from capturing temperature and humidity data, so that a contamination alert is transmitted if the contamination level based thereon exceeds a threshold based on setting by the NN and monitoring the HVAC. Transmitting a alert based on capture of information and use of mathematical means (a intelligent model expressed as a black box) to determine whether a value exceeds a threshold fall into flow of activities that belong to the subgroup of Mental process and Mathematical concepts of a Judicial Exception.
Claim 10 recites use of historical values into a NN to predict contamination risk and whether/when the HVAC system is at risk under some conditions; and as mentioned in claim 9, these generically recited activities fall into flow of actions that belong to the subgroup of Mental process and Mathematical concepts of a Judicial Exception, and therefore fails step 2B
Claim 11 recites capturing of air pressure differential, determining status of a component based thereon and transmitting a alert if the differential exceeds a threshold; the acquiring of information, comparing it against a reference, and transmitting of an alert fall smack into the scenario that belongs to the Abstract Idea identified in step 1A, prong one.
Claim 12 recites use of historical values (air pressure differential) into a NN to predict whether/when a compromise status of a HVAC component is set on basis of the differential data referred to a threshold, where the differential pressure relates to component filter, a system coil component in terms of enthalpy data associated with this component so that the enthalpy data is transmitted for remote analysis based on the differential pressure. Regardless of the nature of data or component from which the data is captured from, the acquiring of information, comparing it against a reference, and transmitting of acquired/preprocessed data are activities that fall smack into the flow scenario that belongs to the Abstract Idea identified in step 1A, prong one
Claim 13 recites NN predictive analysis using historical air pressure and enthalpy data corresponding to a HVAC component to determine compromised status and time thereof for the component. This enumeration of NN recited with a desired outcome ( e.g. compromised status) fails to show details of operation by which the desired outcome is realized (as via concrete and non-conventional flow of technical transformations) and this enumeration fails to significantly add more to the Abstract Idea, therefore fails step 2B.
Claim 14 recites capture of air quality data (particulate count, CO2, CO …) indicative of intake of the HVAC, determining air quality status, and transmitting a alert if the status exceeds a threshold; these activities constitute acquiring of information, comparing it against a reference, and transmitting of acquired/preprocessed data thus fall smack into the activity scenario that belongs to the Abstract Idea identified in step 1A, prong one
Claim 15 recites use of NN as a predictive model trained on air quality and status thereof to identify whether air quality is compromised or likely to enter that status. No details are provided to describe how the NN operates to makes the air quality identification particularly a novel implementation that improves operation of components or equipment in the field of air quality processing; thus the claim fails to pass step 2B.
Claim 16 recites monitoring sanitation equipment in treating air of the HVAC via UV so status of the sanitation equipment indicative of a malfunction is transmitted, the status based on light intensity value based on image data. No part in the “air” treating and UV analysis is described with operational details showing how status of sanitation equipment as determined improves the operation components of the HVAC or equipment thereof. Claim 16 fails the step 2B analysis.
Claim 17 recites determining whether maintenance is needed based on accessed data and determination of maintenance status via a UI, the determination based on sensor time whereby maintenance determination is based on status that indicates improvement to the HVAC. No details are provided to describe how the maintenance status determination operates to makes the HVAC maintenance particularly novel in operating of components or equipment in the field of air quality processing; thus the claim fails to pass step 2B.
Further, as recited in claim 17, the features about accessing the HVAC to initiate an alert (provided to a remote location) as indicated via the UI for a anomalous type being access outside a expected maintenance period, a period of time less or greater than an expected time, or an access having a corresponding performance metric fail to show operational limitations are included to improve the functioning of the HVAC using said UI and anomaly determination; hence claim 17 cannot be seen as significantly transform the Abstract Idea into a eligible practical application.
Claim 18 recites the HVAC being a heating, cooling, heating or ventilation system and that image data is transmitted multiple times a day. These additional and mostly extra-solution characterization fail to convey technical and functional mechanisms that add significantly more to the Abstract Idea.
Based on the above, claims 1-20 are deemed non-eligible under the 35 USC § 101 statute.
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, 7, 18-20 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone)
As per claim 1, Enohara discloses a computer-implemented method for maintaining an air conditioning system (air conditioning in a work area – pg. 2; air conditioning control – pg. 3), comprising:
capturing image data (image sensor – pg. 2) inside of the air conditioning system (ambient control … divided area set … target floor, lightning, air conditioning - pg. 2; air conditioning controller – pg. 3-4);
processing the image data (receives image … from the image sensor – pg. 3) to determine an image delta value (acquires difference image information, whether … the acquired difference exceeds a predetermined threshold value – pg. 4; calorific value calculation unit … cumulative difference image information – pg. 9), wherein the image delta value represents a change in the image data over a period of time (image information acquired by … image sensors … based on substantially the same time data – pg. 3);
comparing the image delta value to a threshold value (whether … the acquired difference image information exceeds a predetermined threshold value – pg. 5)
Enohara does not explicitly disclose
transmitting an alert signal over a computer network when the image delta value exceeds
the threshold value.
Carone discloses tracking of occupancy motion within a building and comparing of captured image (difference image …between consecutive images, changed pixels that represent change in lighting – para 0115) from various sensors (sensor 340 – Figure 3-4; motion, light, temperature sensor – Fig. 6; para 0085) for determining if a difference (color difference – para 0115; para 0128; difference image of a color … between consecutive images – para 0101-0102) satisfies a threshold (predetermined threshold value – para 0107) indicative of a maintenance situation or need, thereby generating an alert or a notification to be directed to maintenance/repair personnel/staff (para 0118, para 0129); so that for a maintenance/cleaning operation can be performed. Hence, comparing images or pixels to determine a change or delta evaluated to a threshold for a notification to issue a repair action is recognized.
Abibrojo discloses normalization of sensor data frames (para 0093-0094) into a steady-state analytic module of a HVAC system (Fig. 5A) configured for evaluating difference between sensed air flow (difference between return air temperature and supply air temperature – para 0135), current draw (para 0179-0183; Fig. 6; para 0140-0141) based on comparison (para 0244; claims 8, 15 - pg. 23) respective to a baseline value or a threshold (para 0184-0185; para 0190-0191) by which to generate a alert indicative of performance reduction (para 0054), a fault – dirty state of HVAC filter (Fig. 9) - that needs a component change or replacement (para 0186-0187, 0192; claim 15 pg. 23; claim 19 pg. 24) a performance being. That is, normalization of HVAC sensor data communicated via remote monitoring (para 0111) into a analytic environment that compares values to a baseline or threshold to determine a fault state of a HVAC component and send out alert or notifications via a network (para 0051) or a networked capability (para 0070) to cause a corrective action (filter replacement) to be taken is recognized.
Therefore, based on a management scheme in Enohara (management server connected to the communication network that performs ambient control – pg. 12) where a service can be rendered to correct temperature to control the air conditioner bed on information acquisition unit (pg. 10), it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement the evaluation of difference in captured image from the HVAC sensor so that a difference considered exceeding or not satisfying a predetermined threshold will trigger a maintenance notification – as in Carone; or alert transmission across a network – as in Abiprojo – for a corrective action or maintenance operation to be taken; because
a remote service provisioning in communication with an acquisition and image processing module attached to the HVAC system as in Enohara, to acquire therefrom, image diagnose result in form of notification or alert indicative of image difference exceeding a threshold or not satisfying a baseline as set forth above, would enable the remote service management to consider proper maintenance action and assign a repair service or dispatch a corrective action to a given HVAC system or cluster thereof under coverage by the remote server, using NW and equipment resources whose availability and usage is being tracked and managed by the remote service so that service support of a geographical region in regard to sustaining comfort of household occupant and responding to (air conditioning) operational fault/degradation can be carried out more adequately and efficiently.
As per claim 2, Enohara does not explicitly disclose method of claim 1, wherein the alert signal indicates that maintenance is to be performed on the air conditioning system based on a degradation of a condition at the air conditioning system, wherein the alert is transmitted to a maintenance staff computer system.
Monitoring operational values of a HVAC in the effect of tracking performance degradation or a component faulty state is shown in Abiprojo (para 0054, para 0186-0187, 0192), so that an alert would be sent out to a remote server by virtue of rationale A in claim 1; and alert signal indicative of a need for a maintenance is to be performed on the air conditioning system based on a degradation would equally rendered the above limitation obvious for the same reasons set forth in rationale A of claim 1.
As per claim 4, Enohara discloses method of claim 1, wherein the image delta value is determined by comparing a pixel value of the image data to a pixel value of a baseline image, wherein the image delta value represents a difference (position serving a base point corresponding to x=0 and y =0 of … area E01 that appears in the … difference image information … number of pixels in the x and y directions … of the accumulated difference – top pg. 5) in pixel value from the baseline image.
As per claim 7, Enohara discloses method of claim 1, wherein the image data is captured using a digital camera (CCD camera – pg. 4) at or near an air handling unit (electrical device control … includes an image sensor – bottom pg. 11) or duct of the air conditioning system, wherein the image data is wirelessly transmitted to a remote server (server 51 captures information …transmitted from the image sensor – pg. 3).
As per claim 18, Enohara discloses method of claim 1, wherein (i) the air conditioning system is a heating system (total heat generation – pg. 9), a cooling system or a heating, ventilation, and air conditioning (HVAC) system (ambient air conditioning – pg 11), or (ii) image data is captured (pg. 4) and transmitted to the remote locations (refer to claim 7) multiple times each day.
As per claim 19, Enohara discloses a computer-implemented system for maintaining an air conditioning system, comprising: one or more data processors; a non-transitory computer-readable medium encoded with instructions for commanding a system to execute steps that include:
capturing image data inside of the air conditioning system;
processing the image data to determine an image delta value, wherein the image delta value represents a change in the image data over a period of time;
comparing the image delta value to a threshold value;
transmitting an alert signal over a computer network when the image delta value
exceeds the threshold value.
( All of which having been addressed in claim 1)
As per claim 20, Enohara discloses a non-transitory computer-readable medium encoded with instructions for commanding a system to execute steps that include, comprising:
capturing image data inside of the air conditioning system;
processing the image data to determine an image delta value, wherein the image delta
value represents a change in the image data over a period of time;
comparing the image delta value to a threshold value;
transmitting an alert signal over a computer network when the image delta value exceeds
the threshold value.
( All of which having been addressed in claim 1)
Claims 3 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone) further in view of Aultman et al, USPubN: 2016/0116181 (herein Aultman)
As per claim 3, Enohara does not explicitly disclose method of claim 1, wherein comparing the image delta value to a threshold value comprises converting the image delta value to a dust level value and comparing the dust level value to the threshold value.
Abiprojo discloses indoor air quality (IAQ) devices to track states associated with improving comfort and operating parameters of a HVAC (para 0145) via tracking of air handling unit, air flow and duct, air flow with temperature (para 0138-0140) in terms of measurements and operating parameters to inter-correlate parametric behavior and monitored values as basis for the diagnostic (para 0141-0142) and generating a maintenance alert (para 0143) in the course of aggregating data per steady-state analytic operation, which operates by comparing measured data against a threshold (para 0151, 0157)
Aultman discloses measure as to whether the sensed IAQ parameters is greater than a predetermined threshold (para 0070-0071) that corresponds to the level of unhealthy gas, particulates within the air as part of adjusting control setting of a HVAC system (para 0069-0070; Fig. 2) using a IAQ control hub (para 0032) operating with parameters and monitored data from sensor modules (para 0055-0059) including dust sensor (para 0019) to track proximity of health hazards to occupants of the home and monitoring climate/seasonal differences observed from the HVAC behavior, to generate warning or adjust HVAC settings (Fig. 4) by the hub to improve comfort of users and energy efficiency on HVAC controls. Hence dust sensor data converted into IAQ parameter for determination that dust level can be correlated to a threshold value indicative of quality of air setting or warning is recognized
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement Enohara HVAC and sensor-based monitoring of indoor conditions associated with occupant and tenant comfort so that sensor devices can be used to track air quality and gas contaminants as set forth monitoring air flow, ducts in Abiprojo’s HVAC so that difference evaluation of various raw data associated with the HVAC component monitoring includes translating dust level (from dust sensors) into parametric values to be evaluated or compared against a predetermined threshold – as in Aultman; because
dust particle from real-world sensor converted into digital information such image data that can be processed by computer means or intelligent methods with which to determine metrics deviation against norms and quantization of contaminants or related motion behavior deemed hazardous to the occupants for which a HVAC system is purported to provide comfort; in that the intelligent correlation techniques by the computer can easily interpret the image data or digits translated from raw dust information to establish variations or calculate differential type changes with respect to a predetermined health baseline or known good historical references, which in turn will help the HVAC control system to determine criticality of a contaminant level (e.g. in the air ducts) or degradation to the ambient air quality, so that mitigation actions can be dispatched to meet/address this level of hazard before negative effects to the humans (occupants of a workspace, household) would aggravate or become intolerable.
Claims 5 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone), further of Kovesi et al, USPubN: 2014/0072211 (herein Kovesi) and Jin et al, CN 105376462 (translation) 03-02-2016, 7 pgs (herein Jin)
As per claim 5, Enohara does not explicitly disclose method of claim 4, wherein:
a) the pixel values are pixel color values; or
b) the image data comprises an image of a reference object in the air conditioning system,
wherein the baseline image is acquired at installation of the reference object or upon a cleaning
of the air conditioning system that includes a cleaning of the reference object, wherein especially
the reference object is a flat object comprising sections of different objects, wherein optionally
the reference object is a sticker or decal; [or]
Kovesi discloses color difference (para 0006-0008) in processing plurality of images from building sensors in relation to conditions and control of lighting and HVAC components (Fig. 2) with focus of comparing centroid elements of the images to detect color difference indicative of a motion change (Figure 5) or occupancy change (Figure 8) respective to a threshold, using arithmetic means to aggregate or averaging of pixel values and changes thereof within a occupancy region within a specified time( para 0049-0051); hence pixel values for color as part of detecting color differences to establish motion change in occupancy is recognized.
Jin discloses finetuning of ambient air via video content analysis to determine variation of particulate in video frames of captured image to evaluating pollution state of a environment and minimizing its expansion/variation as part of compensating unequalled distribution of noise and filtering information on the extent of a pollution (pg. 1-2), the compensative method including frames detection combining calibrating of pixel values and establishing of calibration matrix (pg. 4) serving as reference to perform the compensating (pg. 3) centered over a pollution area
Hence, use of a baseline image installed as a reference object (or upon a cleaning
of the air conditioning system) that includes a cleaning of the reference object (calibration structure), wherein especially the reference object is a flat object comprising sections of different objects (row, columns of a matrix) is recognized.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement image processing and image comparing in Enohara system so that control over conditions of occupants comfort and HVAC operations include processing of sensor information is based on evaluation of pixel values for color differences as set forth in Kovesi, using a reference object provided as baseline image previously installed as reference object in association with air quality tracking in a air conditioning system or cleaning of its component, wherein the reference object is a flat object comprising sections of different objects – as per the calibration matrix in Jin provided as a calibration sticker or flat structure; because
use of a 2D structure or matrix serving as calibrated reference to evaluate difference between pixel elements of a image and that counterparts in the 2D calibration reference would facilitate computing of difference from the context of pixel values thereby algorithmic evaluation of local color difference at the pixel levels corresponding to each of the compared images can be aggregated into a final difference value which when correlated to standard for a threshold comfort and steady-state baseline of operation in a HVAC system can trigger appropriate maintenance alert and notifications for a responsible repair or service entity to be deployed with the level engagement commensurate with the degree of urgency suggested or induced by the aggregated difference quantity.
Claims 6 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone), further in view of Williams et al, USPubN: 2019/0130535 (herein Williams)
As per claim 6, Enohara does not explicitly disclose method of claim 4, wherein the image delta value is calculated as a contamination weight according to:
Weight = m * average_value + q
where Weight is the contamination weight, m is a constant value, average_value is a pixel
value associated with a the image data, and q is a normalization value, wherein the q normalization
value is set based on a pixel value associated with an initial image according to:
q = m * average_value_min.
Williams image motion analysis with use of active pixel map (para 0099) in conjunction with a amplitude filter (Fig. 18) in association with thermal sensor information/image analysis for pattern noise mitigation (para 0037-0038) where weight of a image amplitude that form pixels of filter is computed to meet certain criteria for a filtering exclusion, using a formula that establishes the average pixel value being greater than a multiplicative average pixel value multiplied by a predetermined factor(para 0098) so that pixels identified by these filters are excluded to form a active pixel map.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement air quality assessment in Enohara HVAC system so that pixel amplitude considered in their average value expressed with a weight factor are considered in a filter implementation by which certain pixels of significance can be retained so to reinforce Enohara’s image-based difference analysis inside a HVAC system, the weighted pixel average value to be considered respective to a multiplicative average pixel value multiplied by a predetermined factor to realize a post-filter active pixel map – as in Williams; because
the use of a multiplicative formula enhancing a pixel value with a weight can clearly rid of unsignificant pixels that behave like noise so that a more definite amount of steady-state pixels can be separated so to form a reliable pool by which difference analysis applied thereon would help deriving a more accurate quantitative expression of difference respective to a image baseline, as set forth in the rationale A of claim 1, facilitating thereby pinpoint whether an abnormal deviation from a standard value would trigger an alert for a maintenance action to be issued; e.g. to address severity of a given fault in the HVAC control system via observation and analytics of sensor data.
Claims 8-9, 14 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone), further in view of Xu et al, CN 104636823 (translation) 02-16-2018, 12 pgs (herein Xu) and Blackley, USPubN: 2016/0367926 (herein Blackley)
As per claim 8, Enohara discloses method of claim 1,
wherein the threshold value is determined using a neural network (refer to rationale of claim 9 using Xu as following), wherein the neural network is trained using the historic images and collected data (refer to obviousness of claim 9) wherein
a) the threshold value is further determined by the neural network using corresponding qualitative assessments of air conditioning systems (refer to rationale of claim 9 and the teachings by Xu), or
b) the method further comprises providing a user interface that identifies a current air conditioning system status based on the image delta value and a predictive analysis status based on the image delta value, wherein the predictive analysis status identifies an anticipated time until degradation of the air conditioning system that will require service using the neural network.
As per claim 9, Enohara discloses method of claim 1, further comprising: capturing building properties like air to a remote location (refer to claim 7)
Enohara does not explicitly disclose
capturing temperature data and humidity data that is transmitted to the remote location;
determining a microbiological contamination risk level based on the image data, the temperature data, and the humidity data;
wherein a microbiological alert is transmitted when the microbiological contamination
risk level exceeds a microbiological threshold, wherein especially the microbiological risk level
threshold is set using historic data and a neural network or a characteristic of an air conditioning
system being monitored.
Use of a neural network to predict parameter values and HVAC control setpoints or bounded threshold therefor based on collected building characteristics is shown in Turney; that is, Turney discloses a building control system where input to a constraint generator as part of a predictive controller (para 0122-0124) includes air temperature (para 0111-0112) and air properties of a building zone forming a thermal transfer model whose parameters can be applied to the constraint generator (Fig. 6 ) configured to forecast appropriate values (para 0098) associated with setpoints for operating the HVAC cooling, where one embodiment of constraint generator includes a neural network model (Fig. 7) where output of each NN model layer comprise a boundary value or range (para 0093) around a set point in direct relevance to temperature inputs from the building zone set to be processed by the NN constraint generator (threshold value - para 0119)
Air particulates as negative data affecting air draw and operation of filter within a HVAC system is shown in Abiprojo as particulate data collected and normalized for a model processing based on a preestablished threshold for the purpose to readjust setting of the HVAC (para 0198) or to initiate a HVAC filter replacement (para 0199-0200) via a generated alert (para 0201) in response to determining a dirty filter (para 0139). Thus, contaminant particulates in the air that negatively affects proper functioning of a HVAC component and use of server-side neural network algorithm to refine learning over collected HVAC data respective to a baseline to derive a malfunction (para 0065 ) is recognized.
Xu discloses a pollution prevention system using historical data/sample as factors on temperature, humidity, pressure, wind speed all subjected to a normalization process and initiated as input into a neural network predictive model to provide quality convergence over the input factors, relative error weight for the training and a threshold value of each layer and predicted value of each sample (pg. 9). Hence, training a neural network model in a pollution analytic service using captured temperature and humidity data as input for the model to perform convergence analysis of micro-pollution or contamination risk based and generate one or more threshold for the risk being analyzed at each layer of a neural network is recognized.
Blackley discloses periodically analyzing and checking air to determine its constituent respective to a predetermined threshold of density and in response to air constituent exceeding the threshold, triggering a server to activate augmentation to a filtering mechanism (para 0154), the threshold being predetermined in association with measurement of air and vaporized materials therein per an air treatment protocol/processor (para 0124; para 0050-0051) or air contaminants screening (para 0054) implemented with use of vaper filtration component (para 0113); e.g. to account for negative effect and density of pollen count (Fig. 10) in the air inhaled by user. Thus, triggering a remote service to activate corrective filtering to account for event caused by level of air contaminants exceeding a threshold based on measuring air constituents associated with treatment protocol to improve user’s inhaled air quality is recognized.
Based on the monitoring and analyzing image difference in Enohara so that a notification can be sent out (or a alert be transmitted when delta value exceeds a threshold per rationale A in claim 1) so to elicit corrective actions from a remote server, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement Enohara’s risk/hazard evaluation and diagnostic in regard to improving tenant comfort so that conditions and parametric setting of the HVAC relevant to tenant comfort would include collection of air contaminant and characteristics associated with microbiological effect – as per air particulates in Abroprojo - within a ambient/indoor environment according to which alert can be sent out(per rationale A in claim 1) and/or a predictive model (as in Xu) can be applied to forecast range of operational parameters or HVAC control settings by which building controller would be able minimize risks to the building occupant, the contaminant predictive model implemented by
determining a microbiological contamination risk level – as per Abiprojo - based on the sensor/image data, the temperature data, and the humidity data – as set forth in Xu - according to which, a predictive model is configured so that the microbiological risk level threshold can set - using a predictive neural network as in Xu - or generated by respective layers of the neural network – as in Turney constraint generator – the NN coupled with historic data and air properties collected from the an air conditioning system - e.g. temperature or humidity factors as in Xu – and
transmitting a alert when microbiological contaminant level exceeds a pollution or contamination threshold – as in Blackley; because
air contaminants (as in Abiprojo) or level of pollution by particulates in the air inhaled a building occupants are integral to the factors to consider in regard to safety and meeting health-related standards by which a HVAC system control should be governed to provide the most conformant level of comfort and safety to tenants of the building or indoor within which Enohara’s HVAC system is servicing; and implementing a intelligent predictive model (NN model as set forth above) that is capable of predicting parametric setting, values range or threshold value thereof associated with how to parameterize, impart HVAC control setpoints - based on historical values or collected characteristics of the ambient air, temperature, humidity factors as in Xu - as desired operational target would enable the HVAC system to identify the shortest path in obtaining the best setting for HVAC components, which result in a most efficient realization of the intended air quality function while meeting a certain satisfaction level from the building tenants.
As per claim 14, Enohara discloses method of claim 1, further comprising:
capturing air quality data indicating a particulate matter count (refer to teaching by Xu and air contaminant per Abiprojo) or concentration of a substance at an intake of the air condition system or inside the air conditioning system (refer to rationale of claim 9 using Abiprojo and Xu) that is transmitted to the remote location (refer to claim 7);
determining an internal air quality status (as in Xu) based on the air quality data;
wherein an air quality alert is transmitted when the air quality status surpasses a
threshold, wherein optionally the air quality data comprises a particulate matter count (as in Abiprojo), a carbon dioxide concentration, a carbon monoxide concentration, or a volatile matter count.
Claims 10, 15 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone), further in view of Xu et al, CN 104636823 (translation) 02-16-2018, 12 pgs (herein Xu) and KR 101666820 (translation) 10-17-2016, 9 pgs (herein ‘820)
As per claim 10, Enohara discloses method of claim 1, further comprising:
providing a predictive analysis for microbiological contamination risk level using a
neural network trained using historical temperature data, humidity data, image data, and
corresponding microbiological contamination risk level data;
( All of which having been addressed in the teaching of Xu in claim 9)
Enohara does not explicitly disclose wherein the predictive analysis for microbiological identifies
whether the air conditioning system currently is at risk for microbiological contamination and a time until likely microbiological contamination under current conditions.
‘820 discloses a predictive air quality modeling and management system that detect threat constituents in air to prevent environment disaster, via provision of information displayed that include time-space and time-series informative of atmospheric quality as part of the early detection (pg. 9) on basis of degree of air pollutant source according to a time-dependency determination (pg. 8) for a given area with reference to one such early detection condition, the display and early detection reflective of air quality exceeding a threshold where SMS message can be dispatched as early forecast of the disaster prospect (pg. 5).
Hence, interface displaying time-based (time space, time series) information on a air contamination/pollution prospect provided as terms of numerical time information forecasting of a likely disaster entails a predictive analysis for early detection of microbiological hazard in the air (of a HVAC) and likely time at which one such contamination disaster might occur.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement air quality assessment in Enohara and ambient air content analytic and anomaly detection based thereon so that a predictive model is imparted with this air quality assessment capability to provide early detection microbiological effects on the quality of air, per effect of informing the users on whether the air conditioning system is currently is at risk for air contamination as well as forecasting a time until likely microbiological contamination event (or air quality disaster/hazard exceeding a threshold) is to occur – as set forth above in ‘820; because
use of predictive model to derive likely condition of a contamination hazard, a time-based forecast of hazard or predicted arrival of air contamination disaster as set forth above would provide significant time-based warning to (a) responsible entities (staff, remote service) that manage air flow and conditioning equipment so that timely engagement or deployment of resources thereby can be made toward maintaining steady-state quality or restoring air quality (in case of degradation event) to the users of the air flow and conditioning system; (b) to users or tenants in building or indoors covered by the HVAC components that are slated to maintain the air quality, so that users response to avert a impending hazard to be timely put into effect and/or materialized into disaster averting actions.
As per claim 15, Enohara discloses method of claim 1, wherein the method further comprises:
providing a predictive analysis for air quality status within the air conditioning system using a neural network trained using historical air quality data (refer to rationale of claim 10) and corresponding air quality status data (refer to rationale of claim 10),
wherein the predictive analysis for air quality status identifies whether air quality is
currently compromised and a time until the air quality is likely to enter a compromised status.
(refer to obviousness of a predictive analysis identifying a time until entering a compromised state in claim 10)
Claims 11 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Enohara et al, JP 2014167796 (translation), 9-11-2014, 15 pgs (herein Enohara) in view of Abiprojo et al, USPubN: 2015/0330650 (herein Abiprojo) and Carone, USPubN: 2021/0334918 (herein Carone), further in view of Freen, USPubN: 2011/0185895 (herein Freen )
As per claim 11, Enohara does not explicitly disclose method of claim 1, further comprising:
capturing differential air pressure data indicating a difference in air pressure before and
after a component in the air conditioning system that is transmitted to the remote location;
determining a status of the component based on the differential air pressure data;
wherein a component status alert is transmitted when the differential air pressure exceeds
a differential air pressure threshold.
Freen discloses a HVAC environment (Fig. 1-2) that involves monitoring of air (para 0024-0026) and filtering conditions for the air, including monitoring pressure differential between air upstream and air downstream for determining if a pressure differential exceeds a baseline/threshold value (para 0008; Fig. 5) based on which to set an alarm (step 40 - Fig. 6; para 0035)
Therefore, based on use of remote server (refer to claim 7) to acquire HVAC information of analytic purpose in Enohara, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement air quality assessment in Enohara and ambient air content analytic and anomaly detection based thereon so that this air quality module would detect conditions for alert on basis of
providing capture air pressure information to a remote service responsible for differential metrics analysis (refer to claim 7) and based on the transmitting
capturing differential air pressure data indicating a difference in air pressure before and
after a air conducting component within the air conditioning system – as in Freen; and determining a status of the component based on the differential air pressure data; such that a status alert is transmitted when the differential air pressure exceeds a differential air pressure threshold – as set forth in Freen; because
air pressure difference at specific locations with the air movement conduits of a HVAC system can be indicative of abnormality in the air content or air flow components, and use of pressure analysis module to capture this specific pressure differential as set forth above, can derive likely conditions affecting the quality of air or degradation in the supply of expected quantity/quality of air current/flow at designated locations of this system, in that early warnings or indication of pre-hazard grace period afforded by such this differential assessment can be provided (a) to HVAC control level entities so that timely engagement or deployment of resources thereby can be issued/made toward maintaining steady-state quality or restoring air quality (in case of degradation event) to the users of the air flow and conditioning system; (b) to users or tenants in building or indoors covered by the service of HVAC that is slated to maintain the air quality and proper ambient conditioning service, so that users response to avert a impending hazard to be timely put into effect and/or materialized into disaster averting actions.
Claim Objections
Claim 5 is objected to because of the following informalities: the ending “; or” (last line) as a orthographic redundancy needs to be corrected. Appropriate correction is recommended.
Claim 10 is objected to because of the impropriety in the phrase construction of lines 6-7: “identifies whether the air conditioning system is currently is at risk … and a time until likely microbiological contamination under current conditions”, which appears to be subordinate phrase ( underlined) devoid of a verb.
Allowable Subject Matter
Claims 12-13 as an indivisible set is/are objected to as being dependent upon a rejected base claim, but would be allowable (pending resolution of any outstanding form of rejection) if rewritten in independent form including all of the limitations of the base claim and any intervening claims, the subject matter objected to including
(claims 12 and 13) method of claim 11, wherein:
(i) the method further comprises providing a predictive analysis for status of the component using a neural network trained using historical differential air pressure data and corresponding component status data, wherein the predictive analysis for status of the component
identifies whether the component is currently compromised and a time until the component is
likely to enter a compromised status,
(ii) the differential air pressure threshold is set using historic data and a neural network,
(iii) the component is a filter in the air conditioning system, or
(iv) the component is an air condition system coil, wherein the method further comprises:
capturing enthalpy data associated with the component that is transmitted to the remote location,
wherein the status of the coil is determined based on the differential air pressure and the enthalpy
data; the method of alternative (iv) further comprising:
providing a predictive analysis for status of the coil using a neural network trained using
historical differential air pressure data, historical enthalpy data, and corresponding coil status
data;
wherein the predictive analysis for status of the coil identifies whether the coil is currently compromised and a time until the coil is likely to enter a compromised status.
Claims 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable (pending resolution of any outstanding form of rejection) if rewritten in independent form including all of the limitations of the base claim and any intervening claims, the subject matter objected to including
(claim 16-17), method of claim 1, wherein the method further comprises:
monitoring status of sanitation equipment in the air conditioning system, wherein the sanitation equipment introduces a substance into the air conditioning system or treats air in the air conditioning system using ultraviolet light,
wherein the status of the sanitation equipment is monitored based on the image data,
wherein a sanitation equipment alert is transmitted when the sanitation equipment is determined to be malfunctioning or functioning sub-optimally based on the image data,
wherein especially the sanitation equipment is an ultraviolet light, wherein status of the sanitation equipment is determined based on a light intensity value determined based on the image data.
wherein the method further comprises:
capturing air conditioning system access data that is transmitted to the remote location,
determining whether maintenance was performed on the air conditioning system based on the system access data,
wherein maintenance status data is provided on a user interface based on the determination of whether maintenance was performed, wherein
(i) the determination of whether maintenance was performed is based on an amount of time that the air conditioning system was accessed based on a sensor responsive to an access portal of the air conditioning system, wherein optionally the determination of whether maintenance was performed is additionally based on a change in a system status parameter that indicates improved performance of the air conditioning system after the air conditioning system was accessed, or
(ii) the method further comprises determining whether access to the air conditioning system corresponds with scheduled maintenance or maintenance initiated by an alert signal, wherein the access corresponding with the scheduled maintenance of the maintenance initiated by the alert signal is indicated on the user interface, or
(iii) the method further comprises: determining whether an access to the air conditioning system was an anomalous access based on one or more of:
the access being outside of an expected maintenance period;
the access being for a period of time less than or greater than an amount of expected time to perform the maintenance;
the access not having a corresponding improvement in a performance metric of the air conditioning system; and
providing an alert to the remote location when the access is determined to be an anomalous access.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan A Vu whose telephone number is (571) 272-3735. The examiner can normally be reached on 8AM-4:30PM/Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Chat Do can be reached on (571)272-3721.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3735 ( for non-official correspondence - please consult Examiner before using) or 571-273-8300 ( for official correspondence) or redirected to customer service at 571-272-3609.
Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 Group receptionist: 571-272-2100.
/Tuan A Vu/
Primary Examiner, Art Unit 2193
Octobre 01, 2026
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