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 .
Status of Application
Claim(s) 1-20 were previously pending and were rejected in the previous office action. Claim(s) 1, 12-14, and 20 were amended. Claim(s) 2-11 and 15-19 were left as originally/previously presented. Claim(s) 1-20 are currently pending and have been examined.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 16, 2026, has been entered.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments, see page(s) 8-19 Applicant’s Response, filed March 16, 2026, with respect to 35 USC § 101 rejection of Claim(s) 1-20 have been fully considered but they are not persuasive.
First, Applicant argues, on page(s) 12-14, that the amended Independent Claim(s) 1, 14, and 20, do not fall within the revised Step 2A Prong 1 framework under the grouping of “Certain Methods of Organizing Human Activity.” Examiner, respectfully, disagrees.
As an initial matter, Courts have provided various sub groupings within organizing human activity grouping encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is also noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings, see MPEP 2106.04(a)(2)(II).
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea. Independent Claim(s) 1, 14, and 20, recite(s) “receive a plurality of sensor information for a first location,” “analyze the plurality of sensor information to identify sensor data indicating a presence of a first item location,” “execute the identified sensor data to determine (i) an initial maintenance schedule of the first item, and (ii) one or more recurring maintenance tasks included as part of the initial maintenance schedule for the first item,” “receive a plurality of new electronic data about the first item, including usage data of the first item during a designated time period,” “execute on the received plurality of new electronic data to determine a condition of the first item including whether an amount of usage of the first item during the designated time period is consistent with a usage pattern associated with the determined initial maintenance schedule,” “based upon a determination that the amount of usage is not consistent with the usage pattern, adjust the maintenance schedule of the first item and generate at least one electronic notification indicating that performance of the one or more recurring maintenance tasks is recommended in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule,” and “transmit the at least one electronic notification to a user associated with the fist location for display of information corresponding to the earlier time,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic principles or practices, and/or commercial or legal interactions (e.g., marketing or sales activities or behaviors and/or business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including following rules or instructions).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found that that processing a credit application between a customer and dealer, where the business relation is the relationship between the customer and the dealer during the vehicle purchase was merely a commercial transaction, which, is a form of certain methods of organizing human activity. In this case, the claim(s) are similar to a business relationship between an entity and a user. The entity can determine maintenance information based on analyzing sensor data, which the entity can then provide a maintenance schedule to the user based on analyzing the data and usage data, which is merely a business relation. Thus, applicant’s claims fall within at least the enumerated grouping of certain methods of organizing human activity.
Furthermore, as an initial matter, the courts do not distinguish between mental processes that are performed by humans and claims that recite mental processes performed on a computer, see MPEP 2106.04(a)(2)(III). As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).
Similar to, Electric Power Group v. Alstom, S.A., when the court provided that a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps, which, were recited at a high level of generality such that they could practically be performed in the human mind.
Here, applicant’s claim limitations are recited at a high level of generality that can be performed in the human mind when the limitations recite receiving a plurality of sensor information for a first location and receiving a plurality of new electrotonic data about the first item including usage data of the first item during a designated time period (i.e., collecting). The system can then analyze the plurality of sensor information to identify sensor data indicating a presence of a first item at the first location (i.e., analyzing). The system can execute the identified sensor data (i) an initial maintenance schedule of the first item and (ii) one or more recurring maintenance tasks included as part of the initial maintenance schedule (i.e., analyzing). The system can determine, based upon the received plurality of new electronic data, a condition of the first item including whether an amount of usage of the first item during the designated time period is consistent with a usage pattern associated with the determined maintenance schedule (i.e., analyzing). Based upon determination that the amount of usage is not consistent with the usage pattern, adjust the maintenance schedule of the first item and generate at least one electronic notification indicating that performance of the one or more maintenance tasks is recommended in accordance with the adjusted maintenance schedule at an earlier time than is recommended in the initial maintenance schedule (i.e., analyzing). The system can then transmit an electronic notification to a user associated with the first location of information corresponding to the earlier time (i.e., displaying), thus collecting information, analyzing that information, and then displaying the maintenance schedule information is merely related to a mental processes. Also, see "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. Therefore, the claim(s) recite at least an abstract idea of mental processes. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mentally. The claims would still fall under certain methods of organizing human activity, see above analysis.
Second, applicant argues, on page(s) 9-12 and 14-17 in applicant’s arguments, that the application is now integrated into a practical application. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, it is important to note that first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"), see MPEP 2106.04(d)(1). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Here, in this case the specification discloses the system improves the likelihood of damages to homeowners homes by performing certain mitigating actions, see applicants arguments on page 10 and applicants specification paragraph(s) 0004-0006. This is at best an improvement to the business process (e.g., abstract idea) itself rather than a technological improvement.
First, the step(s) of accomplishing this desired improvement in the specification is made in blanket conclusory manner by merely making a bare assertion of the improvement without any details of how the home maintenance system is able to help reduce the inconveniences using non-conventional and non-generic arrangement of components, see applicant’s specification paragraph(s) 0003-0006, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
While applicant argues that the claims improve the ability to detect a condition of an item that may otherwise be missed by a human or by another conventional monitoring system, see applicant’s arguments on page 10-11 and applicant’s specification paragraph(s) 0004-0006. However, again this is at best an improvement to the business process (e.g., abstract idea) itself rather than a technological improvement.
Also, another important consideration in determining whether a claim improves
technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP §2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, analyzing, executing, receiving, executing, determining, adjusting, and transmitting, respectively, maintenance schedule information using computer components that operate in their ordinary capacity (e.g., a machine learning (ML) tool, a smart home maintenance computer system, one or more processors, a user computer device, one or more memory devices, a non-transitory computer-readable media, and a smart home computing device), which are no more than “applying,” the judicial exception.
Also, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when mere gathering and analyzing information using conventional techniques and displaying the result. Here, in this case the system will receive a plurality of sensor information for a first location and receiving a plurality of new electrotonic data about the first item including usage data of the first item during a designated time period (i.e., gathering). The system can then analyze the plurality of sensor information to identify sensor data indicating a presence of a first item at the first location (i.e., analyzing). The system can determine, based upon the received plurality of new electronic data, a condition of the first item including whether an amount of usage of the first item during the designated time period is consistent with a usage pattern associated with the determined maintenance schedule (i.e., analyzing). Based upon determination that the amount of usage is not consistent with the usage pattern, adjust the maintenance schedule of the first item and generate at least one electronic notification indicating that performance of the one or more maintenance tasks is recommended in accordance with the adjusted maintenance schedule (i.e., analyzing). The system can then transmit an electronic notification to a user associated with the first location (i.e., displaying), thus merely gathering information then determining and analyzing sensor and other data, and based on that adjust a maintenance schedule that is then provide in a notification for display to a user are not sufficient to show an improvement in computers or technology of determining and adjusting home maintenance schedules.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, the claims lack the details as to how the system uses sensors to detect various usage patterns. And how the system can then use the sensors raw data to make certain determinations or adjustments sensors
Also, unlike Thales Visionix, Inc. v. United States, when the court found an improvement based on a particular configuration of inertial sensors and a particular method of using the raw data from the sensors. Here, in this case applicant makes no mention as to how the current process is so fundamentally different from prior processes in a manner similar to that of Thales Visionix, Inc v. United States. Nor does applicant indicate any differences that would bring about an improvement similar to the claims in Thales Visionix, Inc. v. United States. In fact the sensors are no different from any other temperature, vibration, flow, leak, pressure, and/or humidity sensor(s) that can output and/or detect current data since the sensors here are merely detecting information and collecting information, which, the sensors are merely being used in their ordinary capacity similar to the additional elements in Affinity Labs v. DirecTv, the claim(s) do nothing to improve how the home maintenance system and/or sensors function.
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. In this case, there is no improvement to the machine learning tool, merely providing an ML tool to be used in a determining maintenance schedules for household appliances is not enough to be considered significantly more. Therefore, applicant’s arguments are not persuasive.
Third, Applicant argues on page(s) 9-12 and 17, that the Claims are significantly more since it recites eligible subject matter in view of Ex Parte Desjardins. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO). The ARP also found the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
However, as an initial matter it should be noted that applicant’s limitations were not analyzed under the abstract idea of a mathematical concept(s). Furthermore, applicant’s claims are not as narrowly claimed as Ex Parte Desjardins. In fact, applicant doesn’t recite how the machine learning tool works together in an unconventional way with other sensor components to improve the function of the computer for instance automating specific maintenance task based on using the raw data from the sensors and how those sensors are configured within the smart home environment to determine if maintenance task need to be fixed or replaced. Furthermore, the claims recite the functional results (e.g., using machine learning models to merely diagnose problems) to be achieved rather than implementation details. Thus “these claims in substance [are] directed to nothing more than the performance of an abstract business practice ... using a conventional computer. Such claims are not patent- eligible." See, the above analysis; also, see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014).
Fourth, Applicant argues, on page(s) 15-16, that the invention provides that the application is now integrated into a practical application thus sufficient to amount to significantly more than the abstract idea based on the ordered combinations similar to Example 47 since the limitations do not recite a mathematical concept. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, claim 3 of Example 47 is eligible because it recites an improvement in the technical field of network intrusion detection by taking proactive measures to remediate the danger by detecting the source address of potentially malicious packet in step (d), automatically dropping the malicious network packets in step (e), and blocking future traffic from the source address in step (f) to offer a specific computer security solution, see July 2024 Subject Matter Eligibility Examples. Thus, claim 3 of Example 47 recited steps that were determined to be additional elements rather than steps/features of the abstract idea recited in the claim, July 2024 Subject Matter Eligibility Examples. In fact, step (a) recited the use of specific mathematical calculations and steps (b) and (c) fall in the mental process groupings of abstract ideas, while steps (d)–(f) were found to be the non-abstract ideas.
In this case, applicant’s limitations are not as narrowly claimed as Claim 3 of Example 47. The limitations recited in Independent Claim(s) 1, 14, and 20 are part of the abstract idea, which fall into one or more of the enumerated groupings (e.g., certain methods of organizing human activity and/or mental processes), see the above analysis.
Also, unlike claim 3 of Example 47, applicant’s limitations are more similar to claim 2 of Example 47. The machine learning tool here at best adjust maintenance schedules and displaying information about the maintenance, which helps with an homeowner to expand the health and/or lifespan of their home, which does not improve computer(s), sensor(s), and/or a machine learning tool but at best merely improves the business process (e.g., determining and transmitting maintenance task). Thus, not enough for integrating the underlying abstract ideas into a patent-eligible practical application. Therefore, applicants argument is not persuasive.
Fifth, Applicant argues on page(s) 12-13 of applicants’ arguments, that the Claims are not well-understood, routine, or conventional activity and amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: (1) Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; (2) Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): (3) Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re- evaluation finds that the element is unconventional or otherwise more than what is well- understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) 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 2106.5(B)(II).
Examiner respectfully notes that in the Final Office Action mailed 12/16/2025 on page(s) 6-14 and 20, the Step 2B prong was used to analysis the previous Step 2A Prong Two additional elements that merely amounted to describing how to generally “apply,” the abstract idea in a computer environment thus Examiner carried over the identification of the additional elements and conclusions of the additional elements that were analyzed under Step 2A Prong Two, which the analysis also explained how the
limitations were not an improvement to the technology. As stated above, any claim
elements that were identified as insignificant extra-solution activity should be
reevaluated under Step 2B for determining if they are well-understood, routine, and
conventional.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer
or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive,
store, or transmit data) or simply adding a general purpose computer or computer
components after the fact to an abstract idea (e.g., a fundamental economic practice
or mathematical equation) does not integrate a judicial exception into a practical
application or provide significantly more. Here, in this case applicant’s limitations merely receiving, analyzing, executing, receiving, executing, determining, receiving, adjusting, and transmitting, respectively, maintenance schedule information using computer components that operate in their ordinary capacity (e.g., a ML tool, a smart home maintenance computer system, one or more processors, a user computer device, one or more memory devices, a non-transitory computer-readable media, and a smart home computing device), which are no more than “applying,” the judicial exception
It should also be noted that when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity, see MPEP 2106.05(d). In this case, examiner provided why these limitations are not sufficient to show an improvement (e.g., Affinity Labs v. DirecTv., TLI Communications, and Recentive Analytics, Inc. v. Fox Corp.) and how the limitations amount to mere instructions to apply an exception, see the above analysis in the argument section(s). Thus, the claims do not provide an improvement to the home maintenance schedule system.
Furthermore, on page(s) 18-19 of applicant’s arguments, applicant argues that the additional elements are not so widely prevalent as to render them conventional, routine, or well-understood since the prior art does not teach nor suggest the additional elements as evidenced by the Independent claim(s) due to possibly overcoming the prior art for 35 USC 103. Examiner respectfully disagrees.
In, Intellectual Ventures I v. Symantec Corp, found that the “novelty’’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the 35 USC §101 categories of possibly patentable subject matter. Furthermore, the court in, Synopsys, Inc. v. Mentor Graphics Corp, stated that a claim for a new abstract idea is still an abstract idea the search for a 35 USC § 101 inventive concept is distinct from demonstrating 35 USC § 102 novelty rejection. And similar to the court in, BASCOM Global Internet v. AT&T Mobility LLC, the court stated that the search for a 35 USC § 101 inventive concept is also different from an obviousness analysis under 35 USC § 103. The lack of novelty under 35 USC § 102 or obviousness under 35 USC § 103 of a claimed invention does not necessarily indicate that additional elements are well- understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 USC § 102 and 35 USC § 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 USC § 101. Examiner, respectfully, suggest that Applicant refer back to MPEP § 2106.05(d). Therefore, Applicant’s arguments are found to be unpersuasive.
Sixth, Applicant argues, on page 17, that the claims are patent eligible under 35 USC 101 since the claims meet the standard that it is more likely than not (i.e., preponderance of the evidence) that the claim(s) are patent eligible. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, when evaluating a claimed invention for compliance with the substantive law on eligibility, examiners should review the record as a whole (e.g., the specification, claims, the prosecution history, and any relevant case law precedent or prior art) before reaching a conclusion with regard to whether the claimed invention sets forth patent eligible subject matter. The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application. For example, even if an independent claim is determined to be ineligible, the dependent claims may be eligible because they add limitations that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception recited in the independent claim. And conversely, even if an independent claim is determined to be eligible, a dependent claim may be ineligible because it adds a judicial exception without also adding limitations that integrate the judicial exception or provide significantly more. Thus, each claim in an application should be considered separately based on the particular elements recited therein. If the evaluation of the claimed invention results in a conclusion that it is more likely than not that the claim as a whole does not satisfy both criteria for eligibility (Step 1: NO and/or Step 2B: NO), then examiners should formulate an appropriate rejection of that claim under Step 1 and/or Step 2B. The rejection should set forth a prima facie case of ineligibility under the substantive law. The concept of the prima facie case is a procedural tool of patent examination, which allocates the burdens going forward between the examiner and applicant. In particular, the initial burden is on the examiner to explain why a claim or claims are ineligible for patenting clearly and specifically, so that applicant has sufficient notice and is able to effectively respond. When an examiner determines a claim does not fall within a statutory category (Step 1: NO), the rejection should provide an explanation of why the claim does not fall within one of the four statutory categories of invention. See MPEP § 2106.03 for a discussion of Step 1 and the statutory categories of invention. When an examiner determines that a claim is directed to a judicial exception (Step 2A: YES) and does not provide an inventive concept (Step 2B: NO), the rejection should provide an explanation for each part of the Step 2 analysis. For example, the rejection should identify the judicial exception by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an exception, identify any additional elements (specifically point to claim features/limitations/steps) recited in the claim beyond the identified judicial exception, and explain the reason(s) that the additional elements taken individually, and also taken as a combination, 1) do not integrate the judicial exception into a practical application and 2) do not result in the claim as a whole amounting to significantly more than the judicial exception. See MPEP § 2106.04 et seq. for a discussion of Step 2A and the judicial exceptions, MPEP § 2106.05 et seq. for a discussion of Step 2B and the search for an inventive concept, and MPEP § 2106.07(a) for more information on formulating an ineligibility rejection. See, MPEP 2106.07.
Examiner, has provided a detailed analysis on how applicant’s limitations recite an abstract idea and are not integrated into a practical application, see the Final Office action mailed on 12/16/2025, on page(s) 2-25. Examiner has provided why applicant’s limitations are more likely than not unpatentable as being directed to patent- ineligible subject matter under 35 U.S.C. § 101. Examiner, has provided arguments, with specific evidence from applicants current claims and specification, along with relevant court cases. Thus, showing applicants current claims are more likely than not directed to patent-ineligible subject matter under 35 USC 101. Therefore, applicants arguments are not persuasive.
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, see page(s) 11-14, filed March 17, 2026, with respect to the 35 U.S.C. 103 have been fully considered and are persuasive. The 35 U.S.C. 103 has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Independent Claim(s) 1, 14, and 20, recites an entity that is able to receive information from a home, which the entity can determine maintenance task and a schedule for the maintenance task. The entity can then receive other types of information, which the entity can then adjust the maintenance schedule based on a determination that the usage is not consistent with a usage pattern. The system can then provide a notification to user of about a maintenance task. Independent Claim(s) 1, 14, and 20 as a whole recites limitation(s) that are directed to the abstract idea(s) of certain methods of organizing human activity: fundamental economic principles or practices (e.g., insurance) and/or certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions) and/or mental processes (e.g., observation, evaluation, and/or judgment).
Independent Claim(s) 1, 14, and 20 recite(s) “receive a plurality of sensor information for a first location,” “analyze the plurality of sensor information to identify sensor data indicating a presence of a first item location,” “execute the identified sensor data to determine (i) an initial maintenance schedule of the first item, and (ii) one or more recurring maintenance tasks included as part of the initial maintenance schedule for the first item,” “receive a plurality of new electronic data about the first item, including usage data of the first item during a designated time period,” “execute on the received plurality of new electronic data to determine a condition of the first item including whether an amount of usage of the first item during the designated time period is consistent with a usage pattern associated with the determined initial maintenance schedule,” “based upon a determination that the amount of usage is not consistent with the usage pattern, adjust the maintenance schedule of the first item and generate at least one electronic notification indicating that performance of the one or more recurring maintenance tasks is recommended in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule,” and “transmit the at least one electronic notification to a user associated with the fist location for display of information corresponding to the earlier time,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic principles or practices, and/or commercial or legal interactions (e.g., marketing or sales activities or behaviors and/or business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including following rules or instructions) and/or mental processes (e.g., observation, evaluation, and/or judgment). Furthermore, as, explained in the MPEP and the October 2019 update, where a series of step(s) recite judicial exceptions, examiners should combine all recited judicial exceptions and treat the claim as containing a single judicial exception for purposes of further eligibility analysis. (See, MPEP 2106.04, 2016.05(II) and October 2019 Update at Section I. B.). For instance, in this case, Independent Claim(s) 1, 14, and 20, are similar to an entity determining maintenance task and schedules for the one or more maintenance task, which the entity will notify a user of the maintenance task. The mere recitation of generic computer components (Claim 1: one or more processor, one or more memory devices, a ML tool, and user computer device; Claim 14: a computer device, one or more processors, one or more memory devices, a ML tool, and a user computer device; and Claim 20: at least one non-transitory computer-readable media, a computing device, at least one processor, a ML tool, and a user computing device) do not take the claims out of the enumerated grouping certain methods of organizing human activity and/or mental processes. Therefore, Independent Claim(s) 1, 14, and 20, recites the above abstract idea(s).
Step 2A Prong 2: This judicial exception is not integrated into a practical
application because the claims as a whole describes how to generally “apply,” the
concept(s) of “receiving,” “analyzing,” “executing,” “receiving,” “executing,” determining,” “adjusting,” and “transmitting,” respectively. The limitations that amount to “apply it,” are as follows (Claim 1: one or more processor, one or more memory devices, a ML tool, and user computer device; Claim 14: a computer device, one or more processors, one or more memory devices, a ML tool, and a user computer device; and Claim 20: at least one non-transitory computer-readable media, a computing device, at least one processor, a ML tool, and a user computing device). Examiner, notes that the one or more processor, one or more memory devices, user computer device, a computer device, ML tool, and at least one non-transitory computer-readable media, are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, analyzing, executing, determining, executing, receiving, adjusting, and transmitting, respectively, maintenance schedule information using computer components that operate in their ordinary capacity (e.g., a ML tool, a smart home maintenance computer system, one or more processors, a user computer device, one or more memory devices, a non-transitory computer-readable media, and a smart home computing device), which are no more than “applying,” the judicial exception.
Also, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when mere gathering and analyzing information using conventional techniques and displaying the result. Here, in this case the system will receive a plurality of sensor information for a first location and receiving a plurality of new electrotonic data about the first item including usage data of the first item during a designated time period (i.e., gathering). The system can then analyze the plurality of sensor information to identify sensor data indicating a presence of a first item at the first location (i.e., analyzing). The system can determine, based upon the received plurality of new electronic data, a condition of the first item including whether an amount of usage of the first item during the designated time period is consistent with a usage pattern associated with the determined maintenance schedule (i.e., analyzing). Based upon determination that the amount of usage is not consistent with the usage pattern, adjust the maintenance schedule of the first item and generate at least one electronic notification indicating that performance of the one or more maintenance tasks is recommended in accordance with the adjusted maintenance schedule (i.e., analyzing). The system can then transmit an electronic notification to a user associated with the first location (i.e., displaying), thus merely gathering information then determining and analyzing sensor and other data, and based on that adjust a maintenance schedule that is then provide in a notification for display to a user are not sufficient to show an improvement in computers or technology of determining and adjusting home maintenance schedules.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). Here, the limitations fail to provide how the results are being accomplished. The claims lack the details as to how the system uses sensors to detect various usage patterns. And how the system can then use the sensors raw data to make certain determinations or adjustments sensors. Therefore, the limitations lack the details as to how the step(s)/function(s) are being accomplished, thus merely “applying,” the judicial exception.
Also, unlike Thales Visionix, Inc. v. United States, when the court found an improvement based on a particular configuration of inertial sensors and a particular method of using the raw data from the sensors. Here, in this case applicant makes no mention as to how the current process is so fundamentally different from prior processes in a manner similar to that of Thales Visionix, Inc v. United States. Nor does applicant indicate any differences that would bring about an improvement similar to the claims in Thales Visionix, Inc. v. United States. In fact the sensors are no different from any other temperature, vibration, flow, leak, pressure, and/or humidity sensor(s) that can output and/or detect current data since the sensors here are merely detecting information and collecting information, which, the sensors are merely being used in their ordinary capacity similar to the additional elements in Affinity Labs v. DirecTv, the claim(s) do nothing to improve how the home maintenance system and/or sensors function.
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. In this case, there is no improvement to the machine learning tool, merely providing an ML tool to be used in a determining maintenance schedules for household appliances is not enough to be considered significantly more Each of the above limitations simply implement an abstract idea that is no more than mere instructions to apply the exception using a generic computer component, which, not practical application(s) of the abstract idea. Therefore, when viewed in combination these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the above abstract idea(s).
Step 2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted previously, the claims as a whole merely describe how to generally “apply it,” to the abstract idea in a computer environment. Thus, even when viewed as a whole, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible.
Claim(s) 3-4, 8-11, 16-17, and 19: The various metrics of Dependent Claim(s) 4, 8-11, 17, and 19, merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Independent Claim(s) 1 and 14, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 2 and 15 : The additional limitation of “receiving,” is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim(s) 1 and 14. The microphone is recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “wherein the plurality of sensor information includes noise information received at the first location and related to one or more items,” function(s)/step(s) falls within the enumerated grouping certain methods of organizing human activity and mental processes. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely receiving, noise information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim(s) 2 and 15 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 5 and 18 : The additional limitation is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim(s) 1 and 14. The one or more intelligent devices and home wireless network are recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Therefore, for the reasons described above with respect to Claim(s) 5 and 18 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 6 and 18 : The additional limitation is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim(s) 1 and 14. The one or more intelligent devices are recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “wherein the plurality of sensor information includes about one or more items connected,” function(s)/step(s) falls within the enumerated grouping certain methods of organizing human activity and mental processes. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Therefore, for the reasons described above with respect to Claim(s) 6 and 18 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 7 and 18 : The additional limitation of “identify,” is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim(s) 1 and 14. The one or more processors is recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “identify a plurality of items at or near a home located at the first location, wherein the plurality of sensor information includes operational data of the one or more items,” function(s)/step(s) falls within the enumerated grouping certain methods of organizing human activity and mental processes. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely identifying, item information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim(s) 7 and 18 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 12 : The additional limitation of “generating,” and “training,” is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim 1. The one or more processors, a trained model, and a ML tool, are recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “generate a model using a plurality of historical data, a plurality of operational data of a plurality of related items, a plurality of historical maintenance data of the plurality of related items, and a plurality of historical performance data of the plurality of related items,” and “train the model to identify operational trends for items,” function(s)/step(s) falls within the enumerated grouping certain methods of organizing human activity and mental processes. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely identifying and training, item information which is no more than “applying,” the judicial exception. Also, "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). Therefore, for the reasons described above with respect to Claim 12 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 13 : The additional limitation of “executing,” and “adjusting,” is further directed to a certain method of organizing human activity and/or mental processes, as described in Claim 1. The one or more processors and a ML tool are recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “wherein the received plurality of new electronic data includes first operational data for the first item,” “programmed to execute the model with the first operational data for the first item to determine the condition of the first item and to adjust the maintenance schedule for the one or more maintenance tasks,” function(s)/step(s) falls within the enumerated grouping certain methods of organizing human activity and mental processes. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely executing and adjusting, item maintenance schedules which is no more than “applying,” the judicial exception. Also, see "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). Therefore, for the reasons described above with respect to Claim 13 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2-13 and 15-19, above do not include additional
elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical
application, the additional element(s) in the dependent claim(s) above are no more than
mere instructions to apply the exception using generic computer component(s), which,
do not provide an inventive concept. Therefore, Claim(s) 1-20 are not patent eligible.
Novelty/Non-Obviousness
For the reasons outlined below, Independent Claim(s) 1, 14, and 20, are
distinguished from the art.
Aspro et al. (US 11,232,873 B1). Aspro et al. teaches a server that can obtain sensor readings from a plurality of sensors associated with a plurality of homes. Aspro et al., also, teaches a server can determine available maintenance items for the first home based on sensor readings from sensors associated with the home. The server can receive sensor readings from a water flow sensor that is included in a sewage line and if the weather data for the geographic region associated with the home indicates that a rainstorm is affecting or impending for the region and the water flow sensor readings suggest an unusual flow consistent with a possible drain blockage, the server can determine that maintenance of the drainage system is required for the home. Aspro et al., also, teaches that other maintenance tasks can include changing filters in a furnace, repairing doors and windows, cleaning grills of wall furnace, checking for leaks in a washing machine, inspecting caulking, etc. Aspro et al., further, teaches a server can obtain weather data and utility usage data for a first home. The weather data is obtained via a weather application, which can be based on a geographic area. The utility usage data can be obtained based on sensor and/or meter data associated with the home. The server can receive the sensor readings from a water flow sensor that is within a sewage line and if the water flow sensor readings suggest an unusual flow consistent with a possible drain blockage. The sever can then suggest that maintenance is required for the home. Aspro et al., further, teaches that the sever determine threshold values for the sensor readings, which can trigger conditions for maintenance tasks associated with the first home such as a value of water pressure, flow rate, etc. is detected to exceed the threshold and the server can then determine that a maintenance event is required. After the server generates the maintenance recommendation for the first home then a graphical user interface can display the recommendation on the user device. The server can send a message and/or notifications to the client device, which the client device is associated with the owner or entity of the first home. However, Aspro e al., doesn’t explicitly teach a machine learning tool/algorithm. Aspro et al., also, doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Aspro et al., also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Lyman et al. (US 11,488,077 B1). Lyman et al. teaches an HAVC filter and/or other appliances, which a system can determine airflow or other usage data for the appliances. The system can predict a future conditions of the appliances. Lyman et al., further, teaches that the system can predict a future change in condition associated with the parameter based on the monitoring. The prediction component can retrieve historical usage data associated with appliance. The system can compare the retrieved historical usage data and with the real-time usage data to compute a usage model for predicting if the usage data will change in the future. The system can predict a future changes in conditions of the appliances. Lyman et al., also, teaches that based on the determination of the future changes of the usage data then the system can automatically schedule a service personnel to visit the structure and perform an action associated with the service based on the schedule data of the individual associated with the smart home structure. The system can automatically schedule the service personnel, which the system will also transmit a message. Lyman et al., also, teaches the control panel that can predict a future condition of an HVAC filter. The panel can apply a machine learning technique to predict the future condition of the HVAC filter using current sensor data, a training set of sensor data, and historical sensor data associated with a HVAC filter. While Lyman et al., is the closest prior art in determining inconsistent usage patterns for appliances. However, Lyman et al., doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Lyman et al., also, doesn’t, explicitly teach If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Kreiner et al. (US 2025/0191422 A1)(with a filing date of December 12, 2023). Kreiner et al. teaches a processing system may train or retrain at least one forecasting model, which will forecast/predict air filter functional condition at a future time. The model can consider historical air filter utilization data, historical and/or forecast environmental data, air filtration system operational data, and/or user calendar/schedule data, or the like. Kreiner et al., further, teaches the forecasting model may comprise a time series prediction model that may predict/forecast an air filter functional condition at a future time period based at least in part upon past functional condition and environmental data. Kreiner et al., further, teaches the forecasting model(s) may be particularized to an air filtration system type and a building. Kreiner et al., also, teaches that the system can determine at least one filter change based on the operational data of the air filter or the operational data of the air filtration system. The system can determine one filter changing action based on the utilization data, the environmental data, operational data, and calendar/schedule data. Kreiner et al., teaches an air filter can be predicted to have two months of usable life. If the system determines there is a forecast of smoke, smog, dust storm, etc., in the area then a probability within the two month time window then the remaining usable life may be reduced and the system can adjust the two month time period up or down. The filter changing action can include replacing or cleaning the air filter at a selected time, which is dependent upon forecast environmental data, forecast usage, and/or forecast travel calendar data of the user. Kreiner et al., also, teaches the system can have a user replace or change the air filter based on the use’s calendar/schedule, such that when the user is leaving town for the next three weeks the system can have the user replace the filter. The system can also provide a filter changing action notification to the user mobile device. However, Kreiner et al., doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Kreiner et al., also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Gillette et al. (US 2018/0031256 A1). Gillette et al. teaches a system can determine if preventative maintenance is required for an HVAC system. If preventative maintenance is required then the user is informed of the maintenance, which the user can indicate that they would like to perform the preventative maintenance. Gillette et al., further, teaches that the user interface can be provided with preventative maintenance instructions, which the instructions can include a video tutorial. However, Gillette, doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Gillette, also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Getting (WO 2014/151445 A1). Getting teaches a monitoring device includes a sensor subsystem can include one or more sensor devices, which the sensor devices can include a camera or video recorder and/or one or more microphones. Getting, further, teaches an environmental condition can include an acoustic signal that detects the presence of a leaking faucet or pipe, which a maintenance notification can be provided for the environmental condition. Getting, also, teaches that acoustic signals can detect the presence of certain weather conditions. Getting, also, teaches that the monitoring device can be a computer, smartphone, tablet, etc., which the monitoring devices can be incorporated within a building. However, Getting, doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Getting, also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Lee et al. (US 2019/0196893 A1). Lee et al. teaches a server that can predict a failure of an appliance. The server can receive historic information such as failure repair history. The failure repair history information contains information about the time that a home visit repair service was used and information about an item that was repaired or replaced through the home visit repair service. Lee et al., further, teaches that the server can calculate a degree of similarity between the failure repair history, and the operation history. When the degree of similarity exceeds a threshold then the system can determine an optimized diagnosis treatment solution for an appliance based on the failure repair history and operation history. The system can then provide the diagnosis treatment solution to the user device. Lee et al., also, teaches appliance refers to smart appliances installed in homes or offices. However, Lee e al., doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Lee et al., also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Milz et al. (US 2024/0150953 A1)(filed on November 9, 2022). While Milz et al. is the closest prior art with teaching a ML tool that identifies sensor data to determine an initial maintenance schedule and recurring maintenance tasks. When Milz et al. teaches the system can generate, using one or more machine learning models, a predictive maintenance data object, wherein (a) the one or more machine learning model have been trained with training operating data from a plurality of appliances, and (b) the predictive maintenance data object describes a predicted outcome of the appliance based at least in part on the operating data. The predictive maintenance data object comprises at least one of a cleaning schedule or maintenance program. The predictive maintenance data object may include textual instructions intended for a user, operator, or repair person. The predictive maintenance data object may include codes or other indicators of predefined recommendations relating to cleaning, maintenance, and/or repairs. The predictive maintenance data object includes codes or other indicators associated computer-executed instructions that automatically trigger a cleaning, maintenance and/or repair task to be performed at or by the appliance. The operation data can includes sensor data. However, Milz et al., doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule. Thus, it be obvious to combine Milz et al. with the other references.
Kale (US 2022/0026879 A1). Kale teaches a machine learning model that identifies a component to be replaced or repaired based on sensor data. However, Kale, doesn’t explicitly teach executing using a machine learning tool on identified sensor data to determine an initial maintenance schedule of the first item and one or more recurring maintenance tasks included as a part of the initial maintenance schedule for the first item. Kale, also, doesn’t, explicitly teach using the machine learning model to determine if the amount of usage of the first item during a designated time period is consistent with a usage pattern associated with an initial maintenance schedule. If its determined that the amount of usage is not consistent with the usage pattern, adjusting the initial maintenance schedule and generating a notification of the recurring maintenance task in accordance with the adjusted maintenance schedule at an earlier time than is recommended in accordance with the initial maintenance schedule.
Conclusion
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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