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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/23/2024 & 07/21/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because:
Figs. 6-7, graphs should have both x and y axes labeled with both what is being plotted on the respective axes as well as the units of that which is being plotted on the axes.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 7-8 objected to because of the following informalities:
Claims 7-8 in lines 1-2 (both claims) recites the limitation "the second air conditioning". There is insufficient antecedent basis for this limitation in the claim. Other claims (but not parent claims of 7 or 8) recite “second air conditioning unit”. For the purposes of examination, wherever the claims state “second air conditioning” it is assumed that “second air conditioning unit” is intended.
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 1-15, & 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 3 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 9-11 & lines 10-12 (respectively) recites the limitation "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". It is unclear how there can be a difference between the expected output data and the measured input data; if a data point passes the filter then it should be in the “expected output data” and the “measured input data”, in which case there should be no difference between the two.
Claims 1 & 19 in lines 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 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.
Claims 1 & 19 in lines 5-6 & lines 6-7 (respectively) recites the limitation "operating data including measured input data and measured output data corresponding to the measured input data". It is unclear how “measured output data” is different from “measured input data”. It is also unclear how “measured output data” “corresponds” to “measured input data”. Possible interpretations include 1) that the output data is the result of processing on the input data or 2) that the output data is collected by a different sensor(s), or etc..
Claim 14 in lines 2-3 recites the limitation "evaluating the operational anomaly to identify if the anomaly is an actual anomaly or a false anomaly". It is not clear how an “actual anomaly” or a “false anomaly” is identified.
Regarding “Lack of antecedent basis in the claims” (multiple potential antecedents):
Claims 1-12 in line 1 (each claim) recites the limitation "The method of claim 1[3][9]". There is insufficient antecedent basis for this limitation in the claim. There are multiple methods within claim 1 which could be “the method of claim 1[3][9]”. The Examiner recommends ‘The method of generating an anomaly detection model for an air conditioning system of claim 1[3][9]’.
Claims 13 in line 6 recites the limitation "The method of claim 1". There is insufficient antecedent basis for this limitation in the claim. There are multiple methods within claim 1 which could be “the method of claim 1”. The Examiner recommends ‘The method of generating an anomaly detection model for an air conditioning system of claim 1’.
Claims 14-15 in line 1 (each claim) recites the limitation "The method of claim 13[14]". There is insufficient antecedent basis for this limitation in the claim. There are multiple methods within claim 13 & 14 (as well as parent claim 1) which could be “the method of claim 13[14]”. The Examiner recommends ‘The method of detecting an anomaly in an air conditioning system of claim 13[14]’.
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 “Lack of antecedent basis in the claims” (lack of antecedent):
Claims 5-8 in lines 1-2 recites the limitation "the first air conditioning unit". There is insufficient antecedent basis for this limitation in the claim. Neither the parent Claim 3 nor its parent claim 1 recites ‘a first air conditioning unit’ or ‘a second air conditioning unit’.
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 2-15, 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 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;”
“applying a dynamic filter to the operating data, the dynamic filter applying a forecasting model to the measured input data to identify if the potential anomaly is due to a variation in the measured input data and/or the measured output 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 training an artificial-intelligence-based model using the labeled operating data to generate the anomaly detection model.”
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”
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 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
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.
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.
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 first air conditioning unit and the second air conditioning unit each operate at the same geographical location.”
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 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.
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.
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 first air conditioning unit and the second air conditioning are similar models.”
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 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.
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 first air conditioning unit and the second air conditioning are the same model.”
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 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 than is conventional.
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 13:
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 13 additionally recites:
“and using the anomaly detection model generated using the method of claim 1 to analyze the operating data of the operational air conditioning unit and identify an operational anomaly.”
Explanation:
This additional limitation is not significantly more than applying a judicial exception of the abstract idea grouping mathematical concepts to data.
Revised Step 2A Prong Two:
Does the claim recite additional elements that integrate the judicial exception into a practical application?
No;
The claim recites:
“the method comprising: receiving operating data from a plurality of sensors located in the operational air conditioning unit, the plurality of sensors measuring operating conditions of the operational air conditioning unit to generate the operating 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
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.
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 13 and thereby 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 13 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, & 11-19 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 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 corresponding to the measured input 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 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 forecasting model to the measured input data to identify if the potential anomaly is due to a variation in the measured input data and/or the measured output data (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 (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);
Persaud does not as explicitly teach and training an artificial-intelligence-based model using the labeled operating data to generate the anomaly detection model (see Persaud para 0044).
Ba teaches 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.”).
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 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 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 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 13, Persaud in view of Ba teaches … generated using the method of claim 1…
Persaud teaches a method of detecting an anomaly in an air conditioning system (Fig. 2: “HVAC” & Fig. 2-312: “Performance Degradation Estimator”) including an operational air conditioning unit (Fig. 2: “HVAC”), the method comprising: receiving operating data from 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 the 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.”); … to analyze the operating data of the operational air conditioning unit and identify an operational anomaly (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).
Ba teaches and using 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.”)
Regarding claim 14, Persaud in view of Ba teaches the method of claim 13,
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).
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 corresponding to the measured input 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 forecasting model to the measured input data to identify if the potential anomaly is due to a variation in the measured input data and/or measured output data (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.”)
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 20240198313 A1 "Microwave-Assisted, Silica-Based Composite Desiccant Method and System" (Akhtar) is relevant to the Applicant's disclosure, see Fig. 5 & Fig 14 A.
US 12228303 B2 "HVAC System Using Interconnected Neural Networks And Online Learning And Operation Method Thereof" (Jang) is relevant to the Applicant's disclosure, see Fig.2 & Fig. 6.
US 20210071889 A1 "HVAC Analytics" (Picardi) is relevant to the Applicant's disclosure, see Fig. 1 & Fig. 6.
US 20210018209 A1 "Building Management System with Triggered Feedback Set-Point Signal for Persistent Excitation" (Ellis) is relevant to the Applicant's disclosure, see Fig. 4 & Fig. 26.
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.
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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