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 .
Response to Amendment
Amendments filed 05/26/2026 have been entered. Claims 1-12, & 14-22 remain pending. Claims 1, 5, 7-8, 14, 16, & 19 have been amended. Claim 13 has been cancelled.
Applicant’s amendments & arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to “Objections to the Claims” have been fully considered and are persuasive. The objection of claims 7 & 8 has been withdrawn.
Applicant’s amendments & arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to “Objections to the ” have been fully considered and are persuasive. The objection of the Drawings has been withdrawn.
Response to Arguments
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to Rejections under 35 have been fully considered but they are not persuasive.
Applicant argues that (page 11 lines 15-16):
“Applicant respectfully traverses this rejection, and submits that specific types of sensors do not need to be recited.”
& (page 11 lines 18-19):
“One of ordinary skill in the art of HVAC systems can readily identify sensors measuring operating conditions of an air conditioning unit.”
Examiner respectfully responds:
That one of ordinary skill in the art could conceive of “sensors measuring operating conditions” does not mean that there has been sufficient disclosure such that such a one of ordinary skill in the art would know what is intended to be the claimed invention; leaving this information undisclosed would then require experimentation and leave part of the inventive process to the practitioner in the field.
Note: at such a level of generality, “sensors” is not significantly more than a judicial exception which requires measurement data (since measurement data requires sensors).
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to Rejections under 35 U.S.C. 112(b) claims 1-15 and 19 have been fully considered but they are not persuasive.
Applicant argues that (page 11 line 21 to page 12 line 2):
“The specification at paragraph [0061] provides an explicit definition of “similar models.” Because the specification provides this clear definition, one of ordinary skill in the art would understand the scope of “similar models” with reasonable certainty.”
Examiner respectfully responds:
The instant application at para 0061 states: “As used herein, similar models are models with the same product classification, but have some differences in the subsystems or major components, and in the same model those subsystems and major components are of the same type.”
This definition includes “have some differences” and includes within the proviso “and in the same model”, so this definition only limits “similar models” to devices with the same “product classification, but have some differences”. This definition (when used to interpret the claim limitation) also renders the claim indefinite
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to Rejections under 35 U.S.C. 1 have been fully considered but they are not persuasive.
Applicant argues that (page 12 line 9-14):
“Applicant submits that claims 1 and 16 as amended integrate any such exception into a practical application under Step 2A Prong Two of the eligibility analysis. As set forth in the MPEP 2106.04(d)(1) and 2106.05(a), one way to demonstrate integration into a practical application is when the claim improves the functioning of a computer or improves another technology or technical field.”
Examiner respectfully responds:
Rule:
See MPEP 2106(I): “Because abstract ideas, laws of nature, and natural phenomenon "are the basic tools of scientific and technological work", the Supreme Court has expressed concern that monopolizing these tools by granting patent rights may impede innovation rather than promote it.”
See MPEP 2106.04(d)(1): “The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not "directed to" the recited judicial exception.”
See MPEP 2106.05(h): “For claim limitations that generally link the use of the judicial exception to a particular technological environment or field of use, examiners should explain in an eligibility rejection why they do not meaningfully limit the claim. For example, an examiner could explain that employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more,”
Analysis:
That an inventive concept which is within a judicial exception grouping is useful does not remove that judicial exception from the judicial exception grouping. As stated by the MPEP above, judicial exceptions are useful and allowing them to be patented would “impede innovation rather than promote it”. Within the instant application any recitations directed towards “functioning of a computer or improves another technology or technical field” is not significantly more than generic computer functions to execute a judicial exception.
Conclusion:
The limitations are not significantly more than a judicial exception(s) and instructions to apply that judicial exception within a computer environment.
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to Rejections under 35 U.S.C. 1 have been fully considered but they are not persuasive.
Applicant argues that (page 14 lines 6-14):
“This is a fundamentally different approach from the prior art … This represents a concrete improvement in AI training technology applied to HVAC systems.”
& (page 15 line 21 to page 16 line 1):
“Claim 1 as amended reflects this technical improvement by reciting the generation and transmission of predictive maintenance information, which is a concrete technical output that enables preventative action before equipment failure. This is not merely applying generic data analysis to HVAC data or indicating a “field of use” as alleged in the Office Action.”
Examiner respectfully responds:
Issues relating to whether or not the disclosure is “a fundamentally different approach from the prior art” are addressed under prior art rejections (i.e. 35 U.S.C. §102 or 35 U.S.C. §103). The 35 U.S.C. §101 analysis is a separate analysis which, (if it considers prior art at all) considers prior art at step 2B in order to establish whether or not claimed subject matter is conventional and therefore not able to incorporate a judicial exception into a practical application. The claimed subject matter of “generation and transmission of predictive maintenance information” is within the judicial exception groupings of either mathematical concepts or mental processes. To integrate such judicial exception(s) into a practical application at step 2A prong two there would have to be additional subject matter which is significantly more than the judicial exception (see MPEP 2106.04(d)(I) for “relevant considerations …”).
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page , filed 05/26/2026, with respect to Rejections under 35 U.S.C. 1 have been fully considered but they are not persuasive.
Applicant argues that (page 17 lines 7-8):
“The Office Action alleges that Persaud teaches most elements previously recited in claim 1, … Even accepting the proposed combination, Applicant submits that neither Persaud nor Ba teaches amended claim 1. ”
& (page 18 lines 3-4):
“It does not determine an expected output using a physically-based thermodynamic model of expected performance as recited by claim 1 as amended. ”
Examiner respectfully responds:
At least under the broadest reasonable interpretation, that the system of Persaud acts upon data such as that from a thermistor and CO sensor and air flow sensor which are parts of an HVAC system (see Persaud Fig. 4) to make diagnoses necessarily means that Persaud makes use of a thermodynamic model of expected performance (see Persaud Fig. 2-312: Performance Degradation Estimator). Additionally, Ba teaches “thermodynamic models” (see Ba Fig. 2-212).
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 17 line 9 to page 18 line 2, filed 05/26/2026, with respect to Rejections under 35 U.S.C. 103 claims 1-15 and 19 have been fully considered but they are not persuasive.
Applicant argues that (page 17 lines 14-15):
“Even accepting the proposed combination, Applicant submits that neither Persaud nor Ba teaches amended claim 1.”
& (page 18 lines 2-4):
“This is a signal processing module that converts analog signals to digital data. It does not determine an expected output using a physically-based thermodynamic model of expected performance as recited by claim 1 as amended.”
Examiner respectfully responds:
“Detecting and Diagnosing Faults” (Persaud) requires comparing the output against a model to determine if there is a fault.
“Monitoring and Optimizing HVAC System” (Ba) requires evaluating performance of a HVAC system or it would not be possible to optimize (i.e. ‘optimizing’ requires analyzing performance (compare model against output) so that parameters can be adjusted to make improvements).
Applicant’s arguments, see "Applicant Arguments/Remarks Made in an Amendment" page 18 line 5 to page 20 line 14, filed 05/26/2026, with respect to Rejections under 35 U.S.C. 103 claims 1-15 and 19 have been fully considered but they are not persuasive.
Applicant argues that(page 18 lines 12-16)
“However, Ba uses these thermodynamic models for a fundamentally different purpose. Ba’s thermodynamic models are used to model zone temperature behavior and detect when the model does not balance in real-time monitoring. They are not used as a static filter to determine expected output for labeling training data before training an AI model.”
Examiner respectfully responds:
At least under the broadest reasonable interpretation, any determination of rules for filtering done before or established before the collection of the actual data is static filtering (i.e. filtering not dependent on the data as it is being collected). Persaud Fig. 2-304: “Filtering and Converting Module” & para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”, model is established before data collection step). The “Creating Ideal Model” of Fig. 3-304 requires that data and filtering rules be determined before the data is collected.
Claim Objections
Claims 5 objected to because of the following informalities:
Claims 1 in lines 20-22 recites the amended limitation "labeling the potential anomaly as an anomaly when the dynamic filter identifies that the potential anomaly is not due to a variation in the operating conditions
Claims 5 in lines 1-2 recites the limitation "(original) The method of claim 3, wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning, and ...". The claim has been amended (underlined section) and so should not be preceded by “(original)”, but rather by “amended”.
Appropriate correction is required.
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 1-12, 14-15, & 19 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.
Regarding “Failure to particularly point out & distinctly claim [indefinite]”:
Claim 1 in line 5-6 recites the limitation "receiving operating data from a plurality of sensors located in an air conditioning unit". It is unclear what sort of sensors “a plurality of sensors located in an air conditioning unit” are and unclear as to what type of data is being collected.
Claims 1 & 19 in lines 20-22 & lines 18-20 (respectively) recites the limitation "labeling the potential anomaly as an anomaly when the dynamic filter identifies that the potential anomaly is not due to a variation in the [operating conditions][measured input data]". It is unclear how an anomaly can be determined based on data if that anomaly is not due to a variation in the measured input data; it would seem to require another data source. What additional data is required to determine if the variation was due to an “anomaly”?
Regarding “Lack of antecedent basis in the claims”:
Claim 1 in line 26 recites the limitation "receiving, from a plurality of sensors located in an operational air conditioning unit". There is previous recitation of “a plurality of sensors” & “an operational air conditioner”; if this is the same sensors as previously recited then it should be “the plurality of sensors” and if it is a different “plurality of sensors” then the terminology should be distinct. Similarly regarding the “operational air conditioner”.
Note: the first instance of an element should be in the form “a [unique descriptive terminology]” and successive references to that element should be in the form “the [unique descriptive terminology]” where [unique descriptive terminology] is the same throughout the claims. This is necessary because similarly phrased elements can be patentably distinct. Otherwise, the claim would likely raise 35 U.S.C § 112(b) antecedent basis issues.
Regarding “Indefinite Language”:
Claim 7 in lines 1-2, the phrase "are similar models" renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "similar models"), thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d). It is not clear how the elements are similar or what this would imply about how to determine if two such elements qualify as “similar”.
Regarding ‘rejected for inherited limitations’:
Claims 2-12, & 14-15, are rejected for inheriting the rejected limitation(s) of a parent claim without rectifying the issue(s) for which the parent claim was rejected.
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.
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Flow diagrams from MPEP 2106(III) & 2106.04(II)(A), respectively.
Claims 1-22 rejected under 35 U.S.C. 101 because:
Claim 1:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
“labeling anomalies within the operating data to generate labeled operating data by:”
“applying a static filter to the operating data, the static filter (i) determining an expected output based on the measured input data using a physically-based thermodynamic model of expected performance and (ii) identifying a potential anomaly when a difference between the expected output and the measured output is greater than a predetermined amount;”
“applying a dynamic filter to the operating data, the dynamic filter applying a time series forecasting model to the measured input data to identify if the potential anomaly is due to temporal variations in environmental conditions;”
“and labeling the potential anomaly as an anomaly when the dynamic filter identifies that the potential anomaly is not due to a variation in the operating conditions;”
“and training an artificial-intelligence-based model using the labeled operating data to generate the anomaly detection model.”
“using the anomaly detection model to analyze the operational data of the operational air conditioning unit and identify an operational anomaly;”
“generating and transmitting predictive maintenance information corresponding to the identified operational anomaly to a communicatively coupled device.”
Explanation:
Rule:
See MPEP 2106.04(a)(2)(III)(C): “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept.”
See MPEP 2106.04(a)(2): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Analysis:
At least under the broadest reasonable interpretation:
“labeling anomalies within the operating data” is a mental process and in this case is done in a computer environment.
‘applying filters to data’ is a mathematical concept.
“training an artificial-intelligence-based model” is a mathematical concept. Such a limitation amounts to using statistics on data to determine weights in a mathematical model.
Conclusion:
Therefore, the claim is directed towards the abstract idea groupings of either mental processes or mathematical concepts.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The additional elements are:
“an air conditioning system”
“a plurality of sensors”
“an air conditioning unit”
“communicatively coupled device”
Explanation:
Rule:
See MPEP 2106.05(h): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.”
Analysis:
The elements listed above amount to no more than indicating a field of use corresponding to at least CPC symbol F24F11/38 “Failure Diagnosis” which inherits scope of F24F11/00 “Control or safety arrangements” and which further inherits the scope of F24F “Air-Conditioning; Air humidification; Ventilation; Use of air currents for screening”. These elements in combination with the judicial exceptions listed in the Revised Step 2A Prong One analysis would monopolize the judicial exception(s) over the field of use.
Conclusion:
Therefore the additional elements do not integrate the judicial exception into a practical application.
Claim 1 recites the additional limitation of:
“receiving operating data from a plurality of sensors located in an air conditioning unit, the plurality of sensors measuring operating conditions of the air conditioning unit to generate the operating data, the operating data including measured input data and measured output data corresponding to the input data”
Explanation:
Rule:
see MPEP 2106.05(g): “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”
Analysis:
This limitation is not significantly more than insignificant extra solution activity (pre solution) of data gathering. Additionally, all computations and data analysis require collecting data to be used as input to the computations or algorithms.
Conclusion:
Therefore, this limitation is not significantly more than the judicial exception.
Neither the additional elements nor the additional limitation integrate the judicial exception into a practical application
The additional amended limitation of:
“receiving, from a plurality of sensors located in an operational air conditioning unit, operational data, the plurality of sensors measuring operating conditions of the operational air conditioning unit to generate the operational data;”
This limitation is not significantly more than extra solution activity of data gathering (see MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process”)
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
There are no additional elements other than those listed in revised step 2A prong Two, but those are field of use limitations and so are not considered at step 2B.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Note: the content of the claims (filed 11/30/2023) is not significantly more than general recitation of an artificial-intelligence-based model to generate data regarding health of an air conditioning system, which would not be within one of the four patentable categories.
Claim 2:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 2 additionally recites:
“wherein the artificial-intelligence-based model is a machine-learning-based model.”
Explanation:
Rule:
See MPEP 2106.04(a)(2): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Analysis:
At least under the broadest reasonable interpretation: “artificial-intelligence-based model” and “machine-learning-based model” are mathematical relations applied to data.
Conclusion:
Therefore, the claim recites judicial exceptions.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim does not recite any additional elements.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 3:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 3 additionally recites:
“wherein the air conditioning unit is one air conditioning unit of a plurality of air conditioning units and the operating data includes measurements from a plurality of sensors located on each air conditioning unit.”
Explanation:
This limitation does not amount to significantly more than either applying the judicial exception(s) multiple times or restating field of use limitations.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 4:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 3 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 4 additionally recites:
“wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit older than the first air conditioning unit.”
Explanation:
This limitation and elements does not amount to significantly more than either applying the judicial exception(s) multiple times or restating field of use limitations.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 5:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 3 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 5 additionally recites:
“wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit older than the first air conditioning unit.”
“and the first air conditioning unit and the second air conditioning unit each operate at the same geographical location.”
Explanation:
This limitation does not amount to significantly more than either applying the judicial exception(s) multiple times or restating field of use limitations.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 6:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 3 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 6 additionally recites:
“wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit operating at a different geographical location than the first air conditioning unit.”
The claim does not recite any additional elements
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 7:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 3 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 7 additionally recites:
“wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit
“and the first air conditioning unit and the second air conditioning unit are similar models.”
Explanation:
This limitation and elements does not amount to significantly more than either applying the judicial exception(s) multiple times or restating field of use limitations.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 8:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 3 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 8 additionally recites:
“wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit,”
“and the first air conditioning unit and the second air conditioning unit are the same model.”
Explanation:
This limitation and elements does not amount to significantly more than either applying the judicial exception(s) multiple times or restating field of use limitations.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 9:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 9 additionally recites:
“wherein the air conditioning unit is a dehumidifier.”
The additional element of “a dehumidifier” is no more than a field or art limitation corresponding to at least the CPC symbol F24F “Air-Conditioning; Air humidification; Ventilation; Use of air currents for screening”
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 10:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 9 and thereby from claim 1.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
Claim 10 additionally recites:
“wherein the dehumidifier includes a rotary desiccant wheel.”
Explanation:
Rule:
See MPEP 2106.05(g): “When determining whether an additional element is insignificant extra-solution activity, examiners may consider the following: (1) Whether the extra-solution limitation is well known.”
Analysis:
The additional element of “includes a rotary desiccant wheel” is insignificant extra-solution activity. The claim(s) are already directed towards use of a dehumidifier and the use of a rotary desiccant wheel is not significantly more than one means of dehumidifying and is stated with no further specificity.
Conclusion:
Inclusion of “a rotary desiccant wheel.” Does not integrate the judicial exception into a practical application
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The additional element of “a rotary desiccant wheel” is insignificant extra solution activity.
Explanation:
Rule:
See MPEP 2106.05(II): “Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP §2106.05(d).”
See MPEP 2106.05(d)(I): “2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).”
Analysis:
The prior art of at least:
US 20230349566 A1 “Air Dehumidifier” (Moffitt) see Fig. 3-1014: “Dehumidifying, via a first end of a first desiccant wheel”
US 11738301 B2 “Continuous Processes And Systems To Reduce Energy Requirements Of Using Zeolites For Carbon Capture Under Humid Conditions” (Holman) see Fig. 1-119: “Desiccant Wheel”.
The instant application also acknowledges that the desiccant wheel is well-understood, routine, conventional. The initially filed specification in para 0028: “As shown in FIG. 2, the desiccant rotor 220 is rotating a Honeycombe® wheel, such as the desiccant wheel produced by Munters Corp. of Amesbury, Massachusetts, USA.”
Conclusion:
Therefore, the additional elements and limitations do not amount to significantly more than the judicial exception(s).
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 11:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 11 additionally recites:
“further comprising provoking a failure in the air conditioning unit to produce an anomaly associated with the failure.”
Explanation:
This limitation is not significantly more than inputting test data into an algorithm or mathematical concept and analyzing the output data, and as such is not significantly more than a judicial exception of the mathematical concepts grouping.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim does not recite any additional elements
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 12:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 12 additionally recites:
“further comprising provoking, at different times, a plurality of failures in the air conditioning unit to produce an anomaly associated with each failure.”
Explanation:
This limitation is not significantly more than inputting test data into an algorithm or mathematical concept and analyzing the output data, and as such is not significantly more than a judicial exception of the mathematical concepts grouping.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim does not recite any additional elements
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 14:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 1.
Claim 14 additionally recites:
“further comprising: evaluating the operational anomaly to identify if the anomaly is an actual anomaly or a false anomaly;”
“and labeling the operating data corresponding to the operational anomaly with the outcome of the evaluation and updating the labeled operating data.”
Explanation:
At least under the broadest reasonable interpretation these limitations are directed towards the abstract idea category of mental processes (evaluating and labeling of data and/or anomalies can be done in the mind).
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim does not recite any additional elements.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Claim 15:
Step
Analysis
Step 1:
Is the claim to a process, machine, manufacture or composition of matter?
Yes;
The claim is directed towards a method, which is a process and one of the four statutory categories.
Revised Step 2A Prong One:
Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes;
The claim recites:
The judicial exception(s) as inherited from claim 14 and thereby from claim 1.
Claim 15 additionally recites:
“further comprising periodically retraining the artificial-intelligence-based model using the updated labeled operating data.”
Explanation:
This limitation and elements does not amount to significantly more than either applying the judicial exception(s) multiple times.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim does not recite any additional elements.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception?
No;
The claim does not recite any additional elements.
Conclusion:
Therefore, the claim is not eligible subject matter under 35 U.S.C. §101.
Regarding claims 16-22, these claims are rejected under 35 U.S.C. §101 for analogous reasons as claims 1-15.
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.
Claim(s) 1-8, & 11-12, & 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20120072029 A1 (cited in IDS filed 09/23/2024, henceforth Persaud) in view of US 20220146136 A1 ( cited in IDS 07/21/2025, henceforth Ba).
Regarding claim 1, Persaud teaches a method of detecting an anomaly in an air conditioning system including an operational air conditioning unit (Title: “… diagnosing faults in heating, ventilating, and air conditioning…”, anomaly/(“faults”)), the method comprising: generating an anomaly detection model for an air conditioning system (Fig. 2: “HVAC” & Fig. 2-312: “Performance Degradation Estimator”), the method comprising: receiving operating data from a plurality of sensors (Fig. 2-302: “Sensors”) located in an air conditioning unit (Fig. 2: “HVAC”), the plurality of sensors measuring operating conditions of the air conditioning unit to generate the operating data, the operating data including measured input data and measured output data (Fig. 2: “
v
⃑
”, para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”); labeling anomalies within the operating data (Fig. 2-316: “Fault Conditions”, anomalies/(“fault conditions”)) to generate labeled operating data by: applying a static filter to the operating data (Fig. 2-304: “Filtering and Converting Module”), the static filter (i) determining an expected output based on the measured input data … and (ii) identifying a potential anomaly when a difference between the expected output and the measured output is greater than a predetermined amount (Fig. 2-316: “Fault Conditions”, a difference between an expected amount and a measured amount is a fault); applying a dynamic filter to the operating data, the dynamic filter applying a time series forecasting model to the measured input data to identify if the potential anomaly is due to temporal variations in environmental conditions (para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”); and labeling the potential anomaly as an anomaly when the dynamic filter identifies that the potential anomaly is not due to a variation in the operating conditions (Fig. 2-316: Fault Conditions & Fig. 3-308: “Fault Monitoring”, anomaly/(“fault”) are found and are in the system including in a remote server 320); … receiving, from a plurality of sensors located in an operational air conditioning unit, operational data, the plurality of sensors measuring operating conditions of the operational air conditioning unit to generate the operational data (Fig. 2-302: “Sensors”); … generating and transmitting predictive maintenance information corresponding to the identified operational anomaly to a communication coupled device (Fig. 2-316: “Fault Conditions”, Fig. 2-320: “remote server”, predictive maintenance information/(“Fault Conditions”) to a communication coupled device/(“Remote server”)).
Persaud does not as explicitly teach … using a physically-based thermodynamic model of expected performance … and training an artificial-intelligence-based model using the labeled operating data to generate the anomaly detection model (see Persaud para 0044) … using the anomaly detection model to analyze the operational data of the operational air conditioning unit and identify an operational anomaly.
Ba teaches … using a physically-based thermodynamic model of expected performance (Abstract: “The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system”) … and training an artificial-intelligence-based model using the labeled operating data to generate the anomaly detection model (para 0018: “In other embodiments of the present invention, the recorder component can incorporate extra capabilities (i.e., edge computing) to do the scoring of the AI models (i.e., trained in the cloud) in order to deliver the prediction and the anomaly detection results.”) … using the anomaly detection model to analyze the operational data of the operational air conditioning unit and identify an operational anomaly (para 0018: “AI models (i.e., trained in the cloud) in order to deliver the prediction and the anomaly detection results.”);.
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the method taught by Persaud with the teachings of Ba. One would have added to the “Intelligent System and Method for Detecting and Diagnosing Fault in Heating, Ventilating And Air Conditioning (HVAC) Equipment” of Persaud the “Monitoring and Optimizing HVAC System” which uses trained AI models of Ba. The motivation to combine would have been that the use of AI models would improve the performance and detection of anomalies in an HVAC system (See Ba para 0012: “online analytics with adaptive tuning parameters to monitor HVACs in buildings, and perform real-time detection and localization of anomalies in HVACs. Tuning parameters are parameters that belongs to an algorithm that can be adjusted to improve the performance of the AI (artificial intelligence) algorithm and models.”).
Regarding claim 2, Persaud in view of Ba teaches the method of claim 1,
Ba further teaches wherein the artificial-intelligence-based model is a machine-learning-based model (Abstract: “The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system”).
Regarding claim 3, Persaud in view of Ba teaches the method of claim 1,
Persaud further teaches wherein the air conditioning unit is one air conditioning unit of a plurality of air conditioning units and the operating data includes measurements from a plurality of sensors located on each air conditioning unit (Fig. 2-320: “Remote Server”, para 0051: “The administrator at the remote server 320 may perform further analysis before notifying the homeowner of the anomaly in the HVAC equipment.”, administer at a remote server is in order to handle a plurality of air conditioning units).
Regarding claim 4, Persaud in view of Ba teaches the method of claim 3,
Persaud further teaches wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit older than the first air conditioning unit (at least under the broadest reasonable interpretation of the claim it is necessarily the case that if there are more than one units then one of the units will be older than the others).
Regarding claim 5, Persaud in view of Ba teaches the method of claim 3,
Persaud further teaches wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit (para 0041: “the homeowner's local internet access device 152”, system includes multiple homeowners and so an air conditioning unit for each homeowner), and the first air conditioning unit and the second air conditioning unit each operate at the same geographical location (para 0041: “Such external conditions may also be provided by the thermostat 114 or the local weather stations … In one embodiment, communication is established wirelessly by a wireless adapter 150 that is plugged into the homeowner's local internet access device 152”, system can be applied to the homes of multiple users covered by same geographical location/(“a local weather station”)).
Regarding claim 6, Persaud in view of Ba teaches the method of claim 3,
Persaud further teaches wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit operating at a different geographical location than the first air conditioning unit (para 0041: “In other embodiments, additional sensors may be installed to measure other environmental conditions, for example but not limited to, external temperature, external humidity. Such external conditions may also be provided by the thermostat 114 or the local weather stations”, system can take into the account weather conditions at locations which have different local weather stations and are thus at different geological locations (including greater than 150km)).
Regarding claim 7, Persaud in view of Ba teaches the method of claim 3,
Persaud further teaches wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit (para 0041: “the homeowner's local internet access device 152”, system includes multiple homeowners and so an air conditioning unit for each homeowner), and the first air conditioning unit and the second air conditioning are similar models (Fig. 5: “Training Phase” vs “Monitoring Phase”, para 0015: “The system comprises a sensor, a classifier modelling a normal behaviour of the HVAC equipment in situ in the installed operation environment, and a decision module for comparing the classifier score to a decision threshold, the decision threshold being set during the training phase.”, AI algorithms are trained on similar systems (in this case models of HVAC systems) as the systems that are monitored).
Regarding claim 8, Persaud in view of Ba teaches the method of claim 3,
Persaud further teaches wherein the plurality of air conditioning units includes a first air conditioning unit and a second air conditioning unit (para 0041: “the homeowner's local internet access device 152”, system includes multiple homeowners and so an air conditioning unit for each homeowner), and the first air conditioning unit and the second air conditioning are the same model (Fig. 5: “Training Phase” vs “Monitoring Phase”, para 0015: “The system comprises a sensor, a classifier modelling a normal behaviour of the HVAC equipment in situ in the installed operation environment, similar models necessarily includes same model, ideally AI algorithms are trained on systems as similar as possible to the systems that they are used to monitor).
Regarding claim 11, Persaud in view of Ba teaches the method of claim 1,
Persaud further teaches further comprising provoking a failure in the air conditioning unit to produce an anomaly associated with the failure (Fig. 5-308: “Classifier”, para 0050: “The classifier 3080 is trained to operate on normal conditions, while classifiers 3081, . . . 308N are trained and operate independently based on specific fault conditions.”, a classifier is trained to recognize failures/(“faults”) as well as normal behaviour).
Regarding claim 12, Persaud in view of Ba teaches the method of claim 1,
Persaud further teaches further comprising provoking, at different times, a plurality of failures in the air conditioning unit to produce an anomaly associated with each failure (para 0050: “The classifier 3080 is trained to operate on normal conditions, while classifiers 3081, . . . 308N are trained and operate independently based on specific fault conditions.”, a classifier is trained to recognize failures/(“faults”) as well as normal behaviour and there is a plurality of faults/(“classifiers…trained and operate independently”)).
Regarding claim 14, Persaud in view of Ba teaches the method of claim 1,
Persaud teaches further comprising: evaluating the operational anomaly to identify if the anomaly is an actual anomaly or a false anomaly(Fig. 2-316: Fault Conditions & Fig. 3-308: “Fault Monitoring”, anomaly/(“fault”) are found and are in the system including in a remote server 320); and labeling the operating data corresponding to the operational anomaly with the outcome of the evaluation and updating the labeled operating data (Fig. 2-316: “Fault Conditions”, anomalies/(“fault conditions”))
Regarding claim 15, Persaud in view of Ba teaches the method of claim 14,
Persaud teaches further comprising periodically retraining the artificial-intelligence-based model using the updated labeled operating data (Fig. 5-512: “Set Threshold”, para 0061: “With the classifier parameters refined during the on-line learning process, the decision threshold T is set at step 512. The decision threshold T may be set using, for example, statistical property of the classifier, confidence limits, and a cost analysis for different types of errors.”, system monitors and collects data on-line with the purpose of retraining in order to account for updated cost analysis of different types of errors and to then set new thresholds).
Regarding claim 16, Persaud teaches an air conditioning system (Fig. 2: “HVAC” & Fig. 2-312: “Performance Degradation Estimator”) comprising: an operational air conditioning unit (Fig. 2: “HVAC”), a plurality of sensors located in the operational air conditioning unit (Fig. 2-302: “Sensors”), the plurality of sensors measuring operating conditions of the operational air conditioning unit to generate operating data(Fig. 2: “
v
⃑
”, para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”); and a computing device (Fig. 2-300: “FDD system”, para 0041-0042: “Fault Detection and Diagnosis (FDD) System …Referring to FIGS. 1 and 2, a block level diagram of the FDD system 300 in accordance with an embodiment of the present invention is shown.”, The FDD system includes processing and calculations and is therefore a computing device) … the anomaly detection model utilizing a physically-based thermodynamic model of expected performance and a time series forecasting model to distinguish true anomalies from temporal variations in environmental conditions (para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”) wherein the computing device is further configured to generate and transmit predictive maintenance information corresponding to the detected anomaly to a communicatively coupled device (Fig. 2-316: “Fault Conditions”, Fig. 2-320: “remote server”, predictive maintenance information/(“Fault Conditions”) to a communication coupled device/(“Remote server”)).
Persaud does not as explicitly teach coupled to the operational air conditioning unit to receive the operating data and being configured to execute an anomaly detection model to detect an anomaly in the operational air conditioning unit.
Ba teaches coupled to the operational air conditioning unit to receive the operating data and being configured to execute an anomaly detection model to detect an anomaly in the operational air conditioning unit (para 0018: “In other embodiments of the present invention, the recorder component can incorporate extra capabilities (i.e., edge computing) to do the scoring of the AI models (i.e., trained in the cloud) in order to deliver the prediction and the anomaly detection results.”).
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the system taught by Persaud with the teachings of Ba. One would have added to the “Intelligent System and Method for Detecting and Diagnosing Fault in Heating, Ventilating And Air Conditioning (HVAC) Equipment” of Persaud the “Monitoring and Optimizing HVAC System” which uses trained AI models of Ba. The motivation to combine would have been that the use of AI models would improve the performance and detection of anomalies in an HVAC system (See Ba para 0012: “online analytics with adaptive tuning parameters to monitor HVACs in buildings, and perform real-time detection and localization of anomalies in HVACs. Tuning parameters are parameters that belongs to an algorithm that can be adjusted to improve the performance of the AI (artificial intelligence) algorithm and models.”).
Regarding claim 17, Persaud in view of Ba teaches the air conditioning system of claim 16,
Ba further teaches wherein the anomaly detection model is an artificial-intelligence-based model (Abstract: “The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system”, machine learning is an type of artificial intelligence model).
Regarding claim 18, Persaud in view of Ba teaches the air conditioning system of claim 17,
Ba further teaches wherein the artificial-intelligence-based model is a machine-learning-based model (Abstract: “The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system”).
Regarding claim 19, Persaud in view of Ba teaches the air conditioning system of claim 17,
Persaud teaches … , the labeled operating data having been generated by labeling anomalies within training operating data from a plurality of sensors located on a training air conditioning unit (Fig. 2-316: “Fault Conditions”, anomalies/(“fault conditions”)), the plurality of sensors measuring operating conditions of the training air conditioning unit to generate the training operating data (Fig. 2: “
v
⃑
”, para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”), the training operating data including measured input data and measured output data (Fig. 2: “
v
⃑
”, para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”), and wherein labeling anomalies within the training operating data (Fig. 2-316: “Fault Conditions”, anomalies/(“fault conditions”)) include: applying a static filter to the training operating data (Fig. 2-304: “Filtering and Converting Module”), the static filter (i) determining expected output based on the measured input data … and (ii) identifying a potential anomaly when a difference between the expected output and the measured output data corresponding to the measured input data is greater than a predetermined amount (Fig. 2-316: “Fault Conditions”, a difference between an expected amount and a measured amount is a fault); applying a dynamic filter to the operating data, the dynamic filter applying a time series forecasting model to the measured input data to identify if the potential anomaly is due to temporal variations in environmental conditions (para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”); and labeling the potential anomaly as an anomaly when the dynamic filter identifies that the potential anomaly is not due to a variation in the measured input data and/or measured output data (Fig. 2-316: Fault Conditions & Fig. 3-308: “Fault Monitoring”, anomaly/(“fault”) are found and are in the system including in a remote server 320).
Ba teaches wherein the artificial-intelligence-based model has been trained using a training database including labeled operating data (para 0018: “In other embodiments of the present invention, the recorder component can incorporate extra capabilities (i.e., edge computing) to do the scoring of the AI models (i.e., trained in the cloud) in order to deliver the prediction and the anomaly detection results.”) … using a physically-based thermodynamic model of expected performance (Abstract: “The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system”)
Claim(s) 9-10, & 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20120072029 A1 (cited in IDS filed 09/23/2024, henceforth Persaud) in view of US 20220146136 A1 ( cited in IDS 07/21/2025, henceforth Ba) in further view of US 20220260262 A1 (cited in IDS filed 09/23/2024, henceforth Pandit).
Regarding claim 9, Persaud in view of Ba teaches the method of claim 1,
Neither Persaud nor Ba as explicitly teaches wherein the air conditioning unit is a dehumidifier.
Pandit teaches wherein the air conditioning unit is a dehumidifier (Fig. 1-1 : “HVACR system”, para 0013: “The HVACR system 1 is configured to condition (e.g., heat, cool, dehumidify, and the like) a conditioned space 3”).
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the method taught by Persaud in view of Ba with the teachings of Pandit. One would have added to the “Intelligent System and Method for Detecting and Diagnosing Fault in Heating, Ventilating And Air Conditioning (HVAC) Equipment” with “Monitoring and Optimizing” of Persaud in view of Ba the additional teachings of “Dehumidifying Air Handling Unit and Dessicant Wheel Therefor” of Pandit. The motivation would have been that the system of Persuad already has a humidifier (see Persuad Fig. 4-450) and inclusion of a dehumidifier would allow for better HVAC control (see Persuad para 0025: “an outside humidity level sensor, a room temperature sensor, a room humidity level sensor and a combination thereof.” & para 0040: “During operation of an exemplary HVAC equipment, a user sets the desired parameters, for example but not limited to, humidity or temperature on the thermostat 114,”).
Regarding claim 10, Persaud in view of Ba in further view of Pandit teaches the method of claim 9,
Pandit further teaches wherein the dehumidifier includes a rotary desiccant wheel (Fig. 1, para 0007: “FIG. 1 is a schematic diagram of an embodiment of an HVACR system that includes an air handling unit with a desiccant wheel.”).
Regarding claim 20, Persaud in view of Ba teaches the air conditioning system of claim 16,
Persaud teaches and the plurality of sensors located on the operational air conditioning unit being positioned to measure values of the process air and the reactivation air (para 0041: “various sensors are installed within the HVAC equipment to sample the performance of the system. In the exemplary embodiment as shown in FIG. 1, temperature 120 and humidity 122 sensors, are installed in the return air path. Temperature 124, carbon monoxide 126 and air flow 128 sensors are installed in the supply air path 132, and temperature sensor 130 is installed in the flue gas exit path. In other embodiments, additional sensors may be installed to measure other environmental conditions, for example but not limited to, external temperature, external humidity.”, system has a plurality of sensors which measure air before and after HVAC conditioning).
Neither Persaud nor Ba as explicitly teaches wherein the operational air conditioning unit is a dehumidifier for dehumidifying process air, the dehumidifier including a desiccant wheel moveable between a process zone and a reactivation zone, in operation, the process air flowing through the desiccant in the process zone and the desiccant absorbing or adsorbing moisture from the process air, reactivation air flowing through the desiccant in the reactivation zone and absorbing or adsorbing moisture from the desiccant.
Pandit teaches wherein the operational air conditioning unit is a dehumidifier (Fig. 1-1 : “HVACR system”, para 0013: “The HVACR system 1 is configured to condition (e.g., heat, cool, dehumidify, and the like) a conditioned space 3”) for dehumidifying process air, the dehumidifier including a desiccant wheel (Fig. 1, para 0007: “FIG. 1 is a schematic diagram of an embodiment of an HVACR system that includes an air handling unit with a desiccant wheel.”) moveable between a process zone and a reactivation zone, in operation, the process air flowing through the desiccant in the process zone and the desiccant absorbing or adsorbing moisture from the process air, reactivation air flowing through the desiccant in the reactivation zone and absorbing or adsorbing moisture from the desiccant (Fig. 1-16: “air inlet” & Fig. 1-14: “air discharge outlet”, para 0017: “The AHU 10 includes a cooling heat exchanger 30 and a desiccant wheel 40 that are disposed within the housing 12. The air flows through the desiccant wheel 40 and the cooling heat exchanger 30 as the air flows from the air inlet 16 to the air discharge outlet 14 within the housing 12”),
It would have been obvious to one of ordinary skill in the relevant art before the effective filing date of the claimed invention to have modified the system taught by Persaud in view of Ba with the teachings of Pandit. One would have added to the “Intelligent System and Method for Detecting and Diagnosing Fault in Heating, Ventilating And Air Conditioning (HVAC) Equipment” with “Monitoring and Optimizing” of Persaud in view of Ba the additional teachings of “Dehumidifying Air Handling Unit and Dessicant Wheel Therefor” of Pandit. The motivation would have been that the system of Persuad already has a humidifier (see Persuad Fig. 4-450) and inclusion of a dehumidifier would allow for better HVAC control (see Persuad para 0025: “an outside humidity level sensor, a room temperature sensor, a room humidity level sensor and a combination thereof.” & para 0040: “During operation of an exemplary HVAC equipment, a user sets the desired parameters, for example but not limited to, humidity or temperature on the thermostat 114,”).
Regarding claim 21, Persaud in view of Ba in further view of Pandit teaches the air conditioning system of claim 20,
Persaud further teaches wherein the anomaly detection model, when executed by the computing device, includes: determining a moisture mass balance between the process air and the reactivation air based on the measured values from the plurality of sensors (para 0042: “home may be installed with a plurality of sensors to measure flue gas temperature (e.g. sensor 130), return air temperature (e.g. sensor 120), return air humidity levels (e.g. sensor 122), supply air temperature (e.g. sensor 124), supply air carbon monoxide levels (e.g. sensor 126) or supply air flow (e.g. sensor 128). There may also be sensors that measure the external driving conditions of the HVAC equipment. For example, internal room temperature and humidity level of the house may be measured and taken into consideration.”); determining, using an outlier detection method, if the moisture mass balance is an outlier(Fig. 2: “
v
⃑
”, para 0043: “The raw data {right arrow over (x)}, {right arrow over (y)}, {right arrow over (v)}, from the sensors 302 are then received by the filtering and converting module 304 for signal conditioning and conversion into digital data.”); and identifying the anomaly when the moisture mass balance is an outlier (para 0042: “the home may be installed with a plurality of sensors to measure flue gas temperature (e.g. sensor 130), return air temperature (e.g. sensor 120), return air humidity levels (e.g. sensor 122), … There may also be sensors that measure the external driving conditions of the HVAC equipment. For example, internal room temperature and humidity level of the house may be measured and taken into consideration. Measurement of the external driving conditions may be done by separate sensors installed throughout the home (outside and inside) or by existing hardware, such as the thermostat 114.”, the “Fault Detection and Diagnosis (FDD) System” detects anomalies regarding humidity and controls the HVAC accordingly).
Regarding claim 22, Persaud in view of Ba in further view of Pandit teaches the air conditioning system of claim 21,
Persaud further teaches wherein the operating data includes system factors, and wherein the anomaly detection model, when executed by the computing device, further includes, when the moisture mass balance is an outlier: determining a plurality of anomaly scores for the air conditioning unit, each anomaly score of the plurality of anomaly scores being based on a comparison between of a plurality of the system factors (para 0042: “the sensors may be installed throughout the HVAC equipment, and optionally throughout the home to measure conditions external to the HVAC equipment as mentioned above.”, The “Fault Detection and Diagnosis (FDD) System” determines a score based on data from multiple sensors including humidity and then determines how to control the HVAC); ranking the system factors based on the plurality of anomaly scores (for the system to use the data from the sensors to control the HVAC there must be ranking and scoring); and selecting one or more of the system factors with the highest anomaly scores as the anomaly detected by the anomaly detection model (Fig. 2-310: “Decision Module”, para 0045: “The output of the classifier 308 is a classifier score S(A[1: L]) or S({right arrow over (v)}) that is used by the decision module 310 to assess the operating conditions and fault conditions of the HVAC system.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240044539 A1 "Data Processing Apparatus and Data Processing Method" (Hashikawa) is relevant to the Applicant's disclosure, see Fig. 1 & Fig. 4.
US 20250155149 A1 "System and Method for HVAC (Heating, Ventilating, and Air Conditioning) Optimization" (Kochar) is relevant to the Applicant's disclosure, see Fig. 1 & Fig. 2.
US 10948209 B2 "Monitoring System For Residential HVAC Systems" (Song) is relevant to the Applicant's disclosure, see Fig. 1 & Fig. 3.
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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 MARTIN WALTER BRAUNLICH whose telephone number is (571)272-3178. The examiner can normally be reached Monday-Friday 7:30 am-5:00 pm.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Phan can be reached at (571) 272-7924. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN WALTER BRAUNLICH/Examiner, Art Unit 2858
/HUY Q PHAN/Supervisory Patent Examiner, Art Unit 2858
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