CTFR 18/823,314 CTFR 92609 DETAILED ACTION Claims 1 – 10, 12 – 17 and 19 – 20 have been amended. Claims 1 – 20 have been examined and are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process i.e. observation) without significantly more. Each of the independent claims recites steps such as: • receiving, by communications hardware, first data indicating a device type and a remaining life expectancy associated with an IoT device; • determining, by an analytics engine using the first data, a communications protocol; • receiving, by the communications hardware, second data indicating a usage associated with the IoT device; • generating, by the analytics engine using the second data, third data indicating a maintenance recommendation or a suitability recommendation; and • outputting, by the communications hardware, a graphical user interface representing the maintenance recommendation or the suitability recommendation. The limitation of generating, by the analytics engine, a maintenance recommendation or a suitability recommendation based on device usage data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by the analytics engine,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by the analytics engine” language, “generating a maintenance recommendation” in the context of this claim encompasses a person mentally deciding whether a device should be repaired or replaced based on observed usage and life expectancy. Similarly, “generating a suitability recommendation” encompasses a person thinking about whether a different model would be more appropriate based on usage patterns and preferences. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally, the use of a generative machine learning model to determine communications protocols or generate recommendations constitutes a mathematical concept, which is also a recognized abstract idea. Accordingly, the claims recite an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements such as communications hardware, a display device, and transmitting control instructions to an IoT device. These elements are recited at a high level of generality (i.e., as generic computer and network components performing conventional functions of receiving, processing, displaying, and transmitting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims 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 elements of communications hardware, display devices, and transmitting control instructions amount to no more than mere instructions to apply the exception using generic computer and network components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1 – 2, 4 – 9, 11 – 16 and 18 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2025/0117756 to Cardona et al. (hereinafter Cardona) and in view of US Patent Application Publication No. 2024/0147266 to Goebel et al. (hereinafter Goebel) . Regarding Claim 1 , Cardona (¶ 2) describes using an artificial intelligence model and sensor data to predict an end-of-life and/or suggested maintenance for home appliances and other machines or components of a home , which further includes: method for monitoring usage of an IoT device within an IoT network (Cardona discloses (Fig. 1, ¶51 and ¶54) system 100 illustrates monitoring devices and other sensor devices configured to receive, analyze, and report the data collected about a home 105, and includes manufacturer server 130 that collects usage data about IoT device 110) , the method comprising: receiving, by communications hardware, first data indicating a device type (Cardona discloses (¶77) various aspects of data collection from IoT devices 110 (e.g., by device type, by type of data collected, by data use) and a remaining life expectancy associated with the IoT device (Cardona discloses (¶171 and Fig. 8: 812) computing, using an artificial intelligence model, a predicted remaining lifetime of the appliance based upon the received appliance data) determining, by an analytics engine using the first data, a communications protocol (Cardona discloses (¶75 and ¶148) the home 105 and IoT devices 110 are communicatively connected with the appliance monitoring system (AMS) computing device 150 through the communications protocols such as the Internet, a local area network (LAN), a wide area network (WAN), or an integrated services digital network (ISDN), a dial-up-connection, a digital subscriber line (DSL), a cellular phone connection, a wireless network (e.g., Wi-Fi network), a wireless mobile network such as a 3G, 4G, or 5G network, a satellite connection, and a cable modem) receiving, by the communications hardware, second data indicating a usage associated with the IoT device (Cardona discloses (¶54) collect data observed by IoT device 110, including, but not limited to, usage data about IoT device 110, e.g., hours of operation, number of loads, error codes, etc. Cardona discloses (¶99) as another example, data may include the appliance's total electricity consumption or usage rate over a period of time) generating, by the analytics engine using the second data, third data indicating a maintenance recommendation or a suitability recommendation (Cardona discloses (¶5-¶6) a machine learning and/or AI model computes a predicted remaining lifetime for an appliance and create recommendations of maintenance actions or determine one or more replacements for the appliance, and the output of the AI model is generated on a user interface (e.g., within a mobile application and/or web page) to present the predicted remaining lifetime associated with a user's registered appliances, the determined one or more replacements for the appliance and any corresponding recommendations. For example, Cardona discloses (¶99) the refrigerator's changing electricity consumption over time may indicate that the refrigerator is in need of repair and/or maintenance and/or may be nearing an end of its life) Cardona does not explicitly disclose outputting, by the communications hardware, a graphical user interface representing the maintenance recommendation or the suitability recommendation using a display device within the IoT network. However, in an analogous art, Goebel teaches: and outputting, by the communications hardware, a graphical user interface representing the maintenance recommendation or the suitability recommendation using a display device within the IoT network ( Goebel discloses (Fig. 3 and ¶47-¶49) agent desktop UI 300 on the agent desktop 104 can provide a visual representation of any service alerts 302 that provide information detailing any area where the network is down as it relates to the location of the IoT device 102, and it allows the agent to perform particular actions and functions related to the network and IoT device 102 by interacting with the graphical user interface (GUI). The selection is based on the identified problem with the device and/or the identified problem (or lack of identified problem) with the network. These may include a Disconnect Device Interface 310 to disconnect a particular IoT device 102 currently on the network. A Change Network Interface 312 allows the agent to manually change the network servicing the IoT device 102. An Increase/Decrease Data Limit Interface 314 allows the agent to change the data limit associated with a particular IoT device 102. These functions may be selected from a plurality of functions that may include other functions determined to be related to possible solutions an agent would perform in addressing problems related to the IoT device 102.) It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the method for monitoring usage of an IoT device within an IoT network, the method comprising: receiving, by communications hardware, first data indicating a device type and a remaining life expectancy associated with the IoT device, determining, by an analytics engine using the first data, a communications protocol, receiving, by the communications hardware, second data indicating a usage associated with the IoT device, generating, by the analytics engine using the second data, third data indicating a maintenance recommendation or a suitability recommendation, as disclosed by Cardona, and outputting, by the communications hardware, a graphical user interface representing the maintenance recommendation or the suitability recommendation using a display device within the IoT network, as taught by Goebel , for the purpose of implementing (¶1) methods, systems, and machine-readable storage media for programmatically accessing and/or configuring Internet of Things (IoT) devices . Claim 2 , Cardona and Goebel disclose all the elements of claim 1. Further they disclose: wherein the maintenance recommendation indicates (i) a replacement part ( Cardona discloses (¶41) the mobile application and/or web page provide additional information relating to the appliance, such as, for example, replacement parts and links to purchase the replacement parts) and (ii) a replacement part cost in relation to an IoT device replacement cost ( Cardona discloses (¶38) the AI model may also determine an estimated value of how much it will cost to repair or replace the appliance) , wherein the suitability recommendation indicates a more suitable upgrade or downgrade model for a user based on the second data (Cardona discloses (¶198) the AMS computing device 150 may also provide the user with equivalents to the official replacement parts, this may be for when the official replacement parts are no longer available. Further, Cardona discloses (¶218) appliance monitoring system (AMS) computing device 150 transmits comparison data comparing the cost to perform the recommendation and a cost of each of the one or more replacements) wherein the second data indicates current usage patterns, past usage patterns, environmental factors, user preferences, and IoT device settings ( Cardona discloses (¶99 and ¶182) historical data indicating the appliance's past operation usage patterns or trends.) The motivation to combine the references is similar to the reasons in Claim 1. Claim 4 , Cardona and Goebel disclose all the elements of claim 1. Further they disclose: outputting, by the communications hardware, the usage of the IoT device to an IoT device user interface, wherein the IoT device user interface is associated with any IoT device hosted by the IoT network ( Cardona discloses (¶85) the AMS computing device 150 may use the different attributes and/or conditions of the home and appliances installed in the home to build a digital property profile of the home 105, and the digital property profile may be displayed to the user, such as through a dashboard). The motivation to combine the references is similar to the reasons in Claim 1. Claim 5 , Cardona and Goebel disclose all the elements of claim 1. Further they disclose: generating, by the analytics engine and using a generative machine learning model, an IoT device recommendation, wherein the IoT device recommendation is generated (Cardona discloses (¶5) system may query a machine learning and/or AI model, such as a large language trained generative AI model, to compute a predicted remaining lifetime for an appliance and create, for example, recommendations of maintenance actions) based on an analysis of one or more of (i) a historical usage trend (Cardona discloses (¶6) wherein the artificial intelligence model is trained based upon historical appliance data) , (ii) an ongoing usage trend (Cardona discloses (¶54) usage data about IoT device 110, e.g., hours of operation, number of loads, error codes, etc.) , and (iii) a projected usage trend corresponding to the usage of the IoT device (Cardona discloses (¶87) configuration to monitor or detect abnormal usage or trends. Cardona discloses (¶113) AMS computing device 150 may use the occupant data to adjust the determined lifecycle of one or more appliances, where the expectation is that the appliance will be used more as the number of occupants ( ¶59) in the home 105 increases) and outputting, by the communications hardware, the IoT device recommendation ( Cardona discloses (¶85) dashboard is displayed to the user that shows installed appliances, predicted remaining lifetimes, maintenance recommendations, links to product providers and service providers relating to the recommendations or relating replacing one or more appliances and/or other information.) The motivation to combine the references is similar to the reasons in Claim 1. Claim 6 , Cardona and Goebel disclose all the elements of claim 5. Further they disclose: wherein generating the IoT device recommendation occurs in response to receipt of a user request, wherein the user request comprises a query associated with the IoT device (Cardona discloses (¶60) the AMS computing device 150 may receive a natural language query from the homeowner or user including at least one currently-installed appliances and inquiring for an expected remaining lifetime of the appliance. T he recommendations may be generated (¶63) in response to query by the homeowner/user (e.g., in conjunction with computing the estimated remaining lifetime of the appliance) and/or may be periodically generated automatically e.g., as a monthly report to the homeowner/user). The motivation to combine the references is similar to the reasons in Claim 1. Claim 7 , Cardona and Goebel disclose all the elements of claim 5. Further they disclose: generating, by the analytics engine and based on the IoT device recommendation, control instructions configured to modify a feature of the IoT device (Cardona discloses (¶183) the AMS computing device 150 may also provide instructions to the user on how to repair the first appliance 312) and transmitting, by the communications hardware, the control instructions to the IoT device ( Goebel teaches the OW interface surfaces information on the agent desktop 104 that informs the agent about the network data, the device data, and the customer data. The desktop UI 300 can present different functions to the agent in real-time (Fig. 3 and ¶48 - ¶49) and the agent can manually change the network servicing the IoT device, or the agent can increase/decrease data limit associated with a particular IoT device 102) The motivation to combine the references is similar to the reasons in Claim 1. Claim 8, do not teach or further define over the limitations in claim 1. Therefore, claim 8 is rejected for the same rationale of rejection as set forth in claim 1. Claim 9, do not teach or further define over the limitations in claim 2. Therefore, claim 9 is rejected for the same rationale of rejection as set forth in claim 2. Claim 11, do not teach or further define over the limitations in claim 4. Therefore, claim 11 is rejected for the same rationale of rejection as set forth in claim 4. Claim 12, do not teach or further define over the limitations in claim 5. Therefore, claim 12 is rejected for the same rationale of rejection as set forth in claim 5. Claim 13, do not teach or further define over the limitations in claim 6. Therefore, claim 13 is rejected for the same rationale of rejection as set forth in claim 6. Claim 14, do not teach or further define over the limitations in claim 7. Therefore, claim 14 is rejected for the same rationale of rejection as set forth in claim 7. Claim 15, do not teach or further define over the limitations in claim 1. Therefore, claim 15 is rejected for the same rationale of rejection as set forth in claim 1. Claim 16, do not teach or further define over the limitations in claim 2. Therefore, claim 16 is rejected for the same rationale of rejection as set forth in claim 2. Claim 18, do not teach or further define over the limitations in claim 4. Therefore, claim 18 is rejected for the same rationale of rejection as set forth in claim 4. Claim 19, do not teach or further define over the limitations in claim 5. Therefore, claim 19 is rejected for the same rationale of rejection as set forth in claim 5. Claim 20, do not teach or further define over the limitations in claim 7. Therefore, claim 20 is rejected for the same rationale of rejection as set forth in claim 7 . 07-21-aia AIA Claim s 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2025/0117756 to Cardona, in view of US Patent Application Publication No. 2024/0147266 to Goebel, and in view of US Patent Application Publication No. 2021/0037050 to Ree . Claim 3 , Cardona in view of Goebel discloses all the elements of claim 1. Cardona in view of Goebel does not explicitly disclose identifying, by the analytics engine and based on an IoT device type, a primary usage parameter and a secondary usage parameter associated with the IoT device, determining, by the analytics engine and based on the primary usage parameter and the secondary usage parameter, a usage type ratio, and determining, by the analytics engine and using a generative machine learning model, the communications protocol based on the usage type ratio. However, in an analogous art, Ree teaches: identifying, by the analytics engine (Ree teaches (¶97) processor 400 ) and based on an IoT device type (Ree teaches (¶75) the IoT type is a categorization of each IoT device 102a-n based on a function performed) , a primary usage parameter and a secondary usage parameter associated with the IoT device ( Ree teaches power data parameters related to power usage/consumption (¶45) such as voltage supplied, current supplied and a duration. Ree teaches (¶95) a set of first power usage parameters of the normal power utilization (i.e. primary usage parameters), and a set of second power measured by the power sensor (i.e. secondary usage parameters) associated with the IoT device) determining, by the analytics engine and based on the primary usage parameter and the secondary usage parameter, a usage type ratio (Ree teaches using machine learning (¶92-¶93) to detect excessive power consumption by using power utilization and usage frequency difference (i.e. usage type ratio). Ree compares the usage frequency (¶95) of the first power usage parameter (i.e. primary usage parameter of the normal power utilization) by the IoT device 102 a-n, with the second power measured by the power sensor (i.e. secondary usage parameter) to determine if a particular IoT device 102 a-n has been compromised or infected if the usage frequency is different from the normal usage frequency model) and determining, by the analytics engine (Ree teaches (¶97) the processor 400) and using a generative machine learning model (Ree teaches (¶101) selecting one or more parameter model e.g. the power model (i.e. first power usage parameter and a second power usage parameter) or the power-time model (i.e. power parameter and duration parameter), the protocol based on the usage type ratio (Ree selects (Fig. 4:212 and ¶101) disable transmitter by sending a disable command as a protocol (i.e. a set of predefined rules such as device shutdown, or transceiver shutdown or ) when it detects that a parameter measurement (i.e. data rate, power consumption or any other measured usage value of a IoT device) is greater than predetermined threshold.) It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the method for monitoring usage of an IoT device within an IoT network, the method comprising: receiving, by communications hardware, first data indicating a device type and a remaining life expectancy associated with the IoT device, determining, by an analytics engine using the first data, a communications protocol, receiving, by the communications hardware, second data indicating a usage associated with the IoT device, generating, by the analytics engine using the second data, third data indicating a maintenance recommendation or a suitability recommendation, and outputting, by the communications hardware, a graphical user interface representing the maintenance recommendation or the suitability recommendation using a display device within the IoT network, as disclosed by Cardona and Goebel , and identifying, by the analytics engine and based on an IoT device type, a primary usage parameter and a secondary usage parameter associated with the IoT device, determining, by the analytics engine and based on the primary usage parameter and the secondary usage parameter, a usage type ratio, and determining, by the analytics engine and using a generative machine learning model, the communications protocol based on the usage type ratio, as taught by Ree, for the purpose of implementing (¶8) a method of operating an Internet of Things device . Claim 10, do not teach or further define over the limitations in claim 3. Therefore, claim 10 is rejected for the same rationale of rejection as set forth in claim 3. Claim 17, do not teach or further define over the limitations in claim 3. Therefore, claim 17 is rejected for the same rationale of rejection as set forth in claim 3. Response to Arguments Claim Rejections - 35 USC § 101 Applicant’s arguments and amendments, filed on 03/02/2026 with respect to the Claims 1 – 20 hav e been fully considered and they are not persuasive. Hence, the 35 USC § 101 rejection is maintained. In response to applicant’s arguments (Page 11) stating the claims cannot be characterized as mental processes because a human cannot literally perform the claimed steps, the examiner respectfully disagrees. A “mental process” is defined as a concept performed in the human mind or using pen and paper, including observations, evaluations, judgments, and opinions. The core concept of the claims of analyzing IoT device data to generate maintenance/suitability recommendations is an evaluation and judgment that can be performed manually. Physical impossibility of human execution (e.g., “communicating with IoT devices”) does not remove abstraction. Lastly, determining a communications protocol, receiving usage data, and outputting recommendations are conventional computer functions applied to the abstract idea of information analysis and recommendation. The claims recite mental processes (evaluations/judgments) implemented on generic hardware. These are judicial exceptions under Step 2A, prong one. In response to applicant’s argument’s (Page 12), that the claims integrate any alleged abstract idea into a practical application by improving IoT network monitoring and providing tailored recommendations, the examiner respectfully disagrees. The claimed steps of receiving data, determining protocol, generating recommendation, outputting via GUI are generic computer/network operations that do not improve the underlying IoT hardware, communications protocols, or analytics engine. The alleged “improvement” is to the business process of maintenance planning and energy recommendations, not to the technical functioning of the IoT network. The additional elements are generic computer/network functions that do not impose meaningful limits on the abstract idea. The claims do not integrate the exception into a practical application. In response to applicant’s argument (Page 17) stating the claims recite combinations that go beyond well understood, routine, conventional activity, the examiner respectfully disagrees. The applicant provides no substantive Step 2B rebuttal, stating only conclusory statements without technical evidence. The claimed combination of receiving device data, analyzing via ML, generating recommendations, and displaying results is a well understood, routine, conventional sequence in IoT and analytics systems. The claims do not add significantly more than the abstract idea. The additional elements are conventional and routine. Claim Rejections - 35 USC § 103 Applicant’s arguments and amendments, filed on 03/02/2026 with respect to the Claims 1 – 20 hav e been fully considered and they are persuasive. Hence, the 35 USC § 103 rejection is withdrawn. However, based on the claim amendments and the newly introduced limitations, the search is updated and two new references ( US Patent Application Publication No. 2025/0117756 to Cardona et al. and in view of US Patent Application Publication No. 2024/0147266 to Goebel et al.) are being introduced for the 35 USC § 103 rejection. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASSAN ABDUR-RAHMAN KHAN whose telephone number is (313)446-6574. The examiner can normally be reached TEAPP - (M-Sa) 9/30/17-9/30/18, 6am-10pm IFP. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Parry can be reached at (571) 272-8328. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /H. A. K./ Examiner, Art Unit 2451 /Chris Parry/Supervisory Patent Examiner, Art Unit 2451 Application/Control Number: 18/823,314 Page 2 Art Unit: 2451 Application/Control Number: 18/823,314 Page 3 Art Unit: 2451 Application/Control Number: 18/823,314 Page 5 Art Unit: 2451 Application/Control Number: 18/823,314 Page 6 Art Unit: 2451 Application/Control Number: 18/823,314 Page 7 Art Unit: 2451 Application/Control Number: 18/823,314 Page 8 Art Unit: 2451 Application/Control Number: 18/823,314 Page 10 Art Unit: 2451 Application/Control Number: 18/823,314 Page 11 Art Unit: 2451 Application/Control Number: 18/823,314 Page 12 Art Unit: 2451 Application/Control Number: 18/823,314 Page 13 Art Unit: 2451 Application/Control Number: 18/823,314 Page 14 Art Unit: 2451
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