DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims Claim s 1-17 and 19-20 are pending . Claim 18 is missing from the claim set as originally filed. Applicant is required to clarify whether the omission of claim 18 is intentional and if so, to submit a renumbered claim set. 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. Claims 1, 1 2 , and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method) or a machine (claim 1 2 , an system ) or a manufacture (claim 20 , a non-transitory computer readable medium), which are statutory categories. However, evaluating claim 1 , under Step 2A , Prong One , the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship /mental process . The limitations include: detecting, by the one or more processors via the ML chatbot, a request to identify the optimal placement location of the one or more water sensors proximate the structure; in response to detecting the request, providing, by the one or more processors, the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: claims data, the trained machine learning model is trained using historical water damage , t he optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model; detecting, by the one or more processors via the ML chatbot, an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure; and providing, by the one or more processors, the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device. Next , Step 2A , Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application. This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application Therefore, the claims are directed to an abstract idea. At Step 2B , consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B , there are no additional elements that make the claim significantly more than the abstract idea. The additional element s of “ obtaining, by one or more processors, structure information for a structure at which the one or more water sensors are to be placed ” are considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The act of data gathering by the sensors is considered insufficient to elevate the claim to a practical application. The examiner notes that the additional element “ providing, by the one or more processors, the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: claims data, the trained machine learning model is trained using historical water damage ” is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself. Dependent claim s 2- 11 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above. Claims 12 and 20 are rejected 35 USC § 101 for the same rational as in claim 1. The examiner notes that the additional element “ providing, by the one or more processors, the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: claims data, the trained machine learning model is trained using historical water damage ” is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). Dependent claim s 12-17 and 19 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above. Examiner’s Notes Claims 1, 12 and 20 distinguish over the prior art of record. Regarding claim 1 , the closest prior art of record Allen et al. (Patent No. US 11,847,666) discloses a computer device and method for processing data to detect modifications to insured property. Received is specification data regarding modifications to be made to insured property in connection with an insurance claim. Also received is data from one or more sensor devices associated with the insured property indicative of a status regarding modifications made to the insured property. Analysis is performed on the received specification and data to determine whether one or more predefined conditions for the insurance claim is satisfied regarding the modifications to be made to the insured property . However, Allen et al. fails to teach a computer-implemented method for determining an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot , the method including the steps of: detecting, by the one or more processors via the ML chatbot, a request to identify the optimal placement location of the one or more water sensors proximate the structure; in response to detecting the request, providing, by the one or more processors, the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: the trained machine learning model is trained using historical water damage claims data, the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model; detecting, by the one or more processors via the ML chatbot, an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure , in combination with the rest of the claim limitations. Regarding claim 1 2 , the closest prior art of record Allen et al. (Patent No. US 11,847,666) discloses a computer device and method for processing data to detect modifications to insured property. Received is specification data regarding modifications to be made to insured property in connection with an insurance claim. Also received is data from one or more sensor devices associated with the insured property indicative of a status regarding modifications made to the insured property. Analysis is performed on the received specification and data to determine whether one or more predefined conditions for the insurance claim is satisfied regarding the modifications to be made to the insured property . However, Allen et al. fails to teach a computer system configured to determine an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot, the computer system comprising: one or more processors; and one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to: detect a request to identify the optimal placement location of the one or more water sensors proximate the structure; in response to detecting the request, provide the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: the trained machine learning model is trained using historical water damage claims data, the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model; detect an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure , in combination with the rest of the claim limitations. Regarding claim 20 , the closest prior art of record Allen et al. (Patent No. US 11,847,666) discloses a computer device and method for processing data to detect modifications to insured property. Received is specification data regarding modifications to be made to insured property in connection with an insurance claim. Also received is data from one or more sensor devices associated with the insured property indicative of a status regarding modifications made to the insured property. Analysis is performed on the received specification and data to determine whether one or more predefined conditions for the insurance claim is satisfied regarding the modifications to be made to the insured property . However, Allen et al. fails to teach a non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to: detect a request to identify the optimal placement location of the one or more water sensors proximate the structure; in response to detecting the request, provide the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein: the trained machine learning model is trained using historical water damage claims data, the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model; detect an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure , in combination with the rest of the claim limitations. Prior art The prior art made record and not relied upon is considered pertinent to applicant’s disclosure: Jacob [‘342] discloses a building monitoring computer system for monitoring building integrity may be provided. Various types of sensors may be embedded throughout or within certain portions of different types of building or construction material making up the building, such as within roofing, foundation, or structural materials. The sensors may be in wireless communication with a home controller. The sensors may be water, moisture, temperature, vibration, or other types of sensors, and may detect unexpected or abnormal conditions within the home. The sensors and/or home controller may transmit alerts to a mobile device of the home owner associated with the unexpected condition, and/or that remedial actions may be required to repair the home or mitigate further damage to the home. The sensor data may also be communicated to an insurance provider remote server to facilitate the insurance provider communicating insurance-related recommendations, updating insurance policies, or preparing insurance claims for review for home owners. Conway et al. [‘705] discloses m ethods, computer-readable media, systems and apparatuses for home services and for completing various home services. Based on received information, the system may generate a services list for the home, provide products and service providers for completion of the services, and provide clickable links to experts for advice to complete the services. Data related to services performed on the home may be received and, based on the received services data, a determination may be made as to whether one or more services on the services list have been completed. Contact information Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT MOHAMED CHARIOUI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2213 . The examiner can normally be reached Monday through Friday, from 9 am to 6 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Mohamed Charioui /MOHAMED CHARIOUI/ Primary Examiner, Art Unit 2857