Response to an Amendment
This office action is a response to a communication made on 11/06/2025.
Claims 1, 5, 10, 14 and 19 are currently amended.
Claims 4, 13 and 21-22 are canceled.
Claims 1-3, 5-12, and 14-20 are pending for this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/07/2025 and 12/03/2025 were filed before the mailing date of the final action on 01/27/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant: Applicant’s arguments, see remarks on page 7-8, filed 11/06/2025, applicant argues that, “Fieteria does not specify or suggest any process in which a chatbot is selected or identified from among a plurality of different ML chatbots on the basis of the type of loss indicated in the FNOL information.” recited in claims 1, 10 and 19.
Examiner: Applicant's arguments filed 11/06/2025 have been fully considered but they are not persuasive. Examiner respectfully disagrees.
Fieteria teaches wherein identifying the ML chatbot from a plurality of ML chatbots is based upon a type of loss indicated in the FNOL information because Col-5, II. 17-48, Col-18, II. 41-45 and Col-20, II. 37-51, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots (i.e. identifying ML chatbot of plurality of ML chatbots), through machine learning or execution of artificial intelligence… provide users with a self-service mobile computing based application and/or Internet based portal for providing a notice of loss (i.e. FNOL information) associated with a claim in the event that the user suffers damage (e.g., “type of a loss”) to his/her real property, personal/business property, loss of use of real or business/personal property, loss of income from business operations, and/or incurs expenses associated with a loss… the FNOL engine 225 may determine whether the set of current claim evaluation data meets pre-defined claim data criteria by comparing the predictive impact assessment scores with a set of impact assessment thresholds…The criteria may vary based on the type of loss, property involved, and loss attributes identified previously. Example defined criteria applied to physical structure of related claims may include room or location indicators, size of rooms or locations involved, types of materials contained within the rooms, quantity of affected materials in the rooms, and/or the like. Criteria applied to business/personal property related claims may include make, model, manufacturer, quantity of items, and others).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-12, and 14-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feiteira et al. (US11250515), hereinafter “Feiteira” in view of Patt et al. (US 2023/0116639), hereinafter “Patt”, and further in view of Harding et al. (US 11038821), hereinafter “Harding”.
With respect to claim 1, Feiteira discloses a computer-implemented method for receiving a first notice of loss (FNOL) using a machine learning (ML) chatbot, the method comprising:
initiating, by one or more processors (Col-9, II. 5-8, teaches the claims processing system 120 may include or be in communication with one or more processing elements 205 ( also referred to as processors), an FNOL session with a user device (Col-11, II. 20-26, teaches the FNOL engine may receive notice of loss and / or a claim via direct report from users, for example via user computing entity 105);
obtaining, by the one or more processors, FNOL information (Col-15, II. 27-28, teaches receiving the notice of loss associated with the claim associated with the user, Col-20, II. 43-44, teaches the criteria may vary based on the type of loss (i.e. FNOL information) , property involved , and loss attributes identified previously);
identifying, by the one or more processors, the ML chatbot from plurality of ML chatbots based upon the FNOL information (Col-5, II. 17-48, Col-9, II. 5-8, Col-23, II. 10-13, and Col-26, II. 1-4, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots (i.e. identifying ML chatbot of plurality of ML chatbots), through machine learning or execution of artificial intelligence, in various embodiments, are created as technical solutions to help the human-machine interface, or the online software applications, to connect to the legal computer system and its processes of an insurer, and facilitate speedier execution to quicken the claims process…a notice of loss (FNOL) associated with a claim may be provided from a direct report from the customer or user… the claims processing system 120 may include or be in communication with one or more processing elements 205 (also referred to as processors)… all data captured and generated by the camera 10 potential , a referral is autonomously generated to internal or tool may be transmitted to the claims processing system 120 along with a claim identifier identifying the claim where the data is associated with .…intelligent virtual agents, such as IVAs (i.e. plurality of chatbots), can also be programed to complete telephone calls to customers to provide the claim details), wherein identifying the ML chatbot from a plurality of ML chatbots is based upon a type of loss indicated in the FNOL information (Col-5, II. 17-48, Col-18, II. 41-45 and Col-20, II. 37-51, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots (i.e. identifying ML chatbot of plurality of ML chatbots), through machine learning or execution of artificial intelligence… provide users with a self-service mobile computing based application and/or Internet based portal for providing a notice of loss (i.e. FNOL information) associated with a claim in the event that the user suffers damage (e.g., “type of a loss”) to his/her real property, personal/business property, loss of use of real or business/personal property, loss of income from business operations, and/or incurs expenses associated with a loss… the FNOL engine 225 may determine whether the set of current claim evaluation data meets pre-defined claim data criteria by comparing the predictive impact assessment scores with a set of impact assessment thresholds…The criteria may vary based on the type of loss, property involved, and loss attributes identified previously. Example defined criteria applied to physical structure of related claims may include room or location indicators, size of rooms or locations involved, types of materials contained within the rooms, quantity of affected materials in the rooms, and/or the like. Criteria applied to business/personal property related claims may include make, model, manufacturer, quantity of items, and others);
generating, by the one or more processors via the ML chatbot (Col-5, II. 17-28, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots, through machine learning or execution of artificial intelligence, in various embodiments, are created as technical solutions to help the human-machine interface, or the online software applications, to connect to the legal computer system and its processes of an insurer, and facilitate speedier execution to quicken the claims process), one or more requests for claim information based upon the FNOL information (Col-5, II. 29-48, teaches a notice of loss (FNOL) associated with a claim may be provided from a direct report from the customer or user, a home of industrial Internet of Things (“IoT” alert), or an alert from a third party, e.g., independent agent, catastrophe event monitoring service, facility manager, or other proxy authorized by the customer, Col-14, II. 60-67, the claims processing system 120 is configured to automatically process a claim provided by a user via a user computing entity 105… claim information may be automatically provided/collected, Col-19, II. 11-15, teaches the claims processing system 120 may be configured to initiate such a service via an established protocol between the claims processing system 120 and an external computing entity 115 operated by the third party configured to process such a service request, Col-20, II. 43-44, teaches the criteria may vary based on the type of loss (i.e. FNOL information), property involved, and loss attributes identified previously, Col-26, II. 1-4, teaches intelligent virtual agents, such as IVAs, can also be programed to complete telephone calls to customers to provide the claim details described above if the customer requests (i.e. one or more request));
providing, by the one or more processors via the ML chatbot, the one or more requests for the claim information to the user device during the FNOL session ((Col-14, II. 60-67, the claims processing system 120 is configured to automatically process a claim provided by a user via a user computing entity (i.e. user device) 105… claim information may be automatically provided/collected, Col-19, II. 11-15, teaches the claims processing system 120 may be configured to initiate such a service via an established protocol between the claims processing system 120 and an external computing entity 115 operated by the third party configured to process such a service request, Col-20, II. 43-44, teaches the criteria may vary based on the type of loss (i.e. FNOL information) , property involved , and loss attributes identified previously, Col-26, II. 1-4, teaches intelligent virtual agents, such as IVAs, can also be programed to complete telephone calls to customers to provide the claim details described above if the customer requests (i.e. one or more request));
receiving, by the one or more processors via the ML chatbot (Col-5, II. 17-28, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots, through machine learning or execution of artificial intelligence, in various embodiments, are created as technical solutions to help the human-machine interface, or the online software applications, to connect to the legal computer system and its processes of an insurer, and facilitate speedier execution to quicken the claims process), the claim information based upon the one or more requests for the claim information from a user via the user device during the FNOL session (Col-14, II. 60-67, the claims processing system 120 is configured to automatically process a claim provided by a user via a user computing entity 105… claim information may be automatically provided/collected, Col-19, II. 11-15, teaches the claims processing system 120 may be configured to initiate such a service via an established protocol between the claims processing system 120 and an external computing entity 115 operated by the third party configured to process such a service request, Col-20, II. 43-44, teaches the criteria may vary based on the type of loss (i.e. FNOL information) , property involved , and loss attributes identified previously);
analyzing, by the one or more processors (Col-9, II. 5-8), information from the FNOL session to determine one or more session actions (Col-25, II. 25-40, teaches the summary report (i.e. action) may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts. These key facts would include the policy forms involved in the loss, customer and interested party's identifying information, coverage types involved, locations involved, limits involved, deductibles involved, coinsurance requirements, and other general information which identifies the subjects of the claim and general policy overviews. Additionally or alternatively, the summary report may restate the identified cause of loss and any ensuing incident. The summary report may also include a coverage disposition summary which indicates whether coverage is available in full, in part, or unavailable for the loss); and
implementing, by the one or more processors (Col-9, II. 5-8), the one or more session actions (Col-18, II. 64-67, teaches the claims processing system 120 may perform an autonomous needs assessment to determine whether immediate mitigation services need to be dispatched to the user, Col-25, II. 25-40, teaches the summary report (i.e. action) may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts. These key facts would include the policy forms involved in the loss, customer and interested party's identifying information, coverage types involved, locations involved, limits involved, deductibles involved, coinsurance requirements, and other general information which identifies the subjects of the claim and general policy overviews).
Feiteria teaches Col-11, II. 20-26, teaches the FNOL engine may receive notice of loss and / or a claim via direct report from users, However, Feiteira remain silent on a session with a user device,
Patt discloses ¶0073, teaches during any interactive session described herein, the live engagement monitor 140 can execute machine learning and/or artificial intelligence techniques to determine responsiveness factors for each individual to which a content flow is provided, ¶0088, teaches the system 100 may then initiate customized, interactive sessions with the users 197 to provide loss mitigation content based on each user's unique property characteristics (460), ¶0099 teaches the computing system 100 can receive contextual FNOL information from the user 197 based on an interactive session with the user 197 via the FNOL interface (810).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Feiteria’s FNOL engine with a session of Patt, in order to quickly gather essential information about an incident or loss (Patt, see ¶0034).
Feiteria teaches Col-11, II. 20-26, teaches the FNOL engine may receive notice of loss and / or a claim via direct report from users, Patt ¶0057, teaches in order to determine, for example, the user's character for truth-telling and accuracy reliability in making an insurance claim ¶0097, teaches the FNOL interface 750 can include a prompt 752 that requests that the user 197 record a video that shows the damage, ¶0099 teaches the computing system 100 can receive contextual FNOL information from the user 197 based on an interactive session with the user 197 via the FNOL interface (810), ¶0104, teaches the feasibility levels may indicate a high level of feasibility that the claims made by the user 197 are accurate and truthful, ¶0129, teaches enable the user 197 to precisely indicate the location and severity of the injury. However, Feiteria in view of Patt remain silent on the one or more session actions including generating one or more additional requests for the claim information, determining accuracy of the claim information received from the user, personalizing the ML chatbot to the user, storing the information from the FNOL session in a user profile, and/or retraining the ML chatbot using the information from the FNOL session.
Harding discloses the one or more session actions including generating one or more additional requests for the claim information, determining accuracy of the claim information received from the user, personalizing the ML chatbot to the user, storing the information from the FNOL session in a user profile, and/or retraining the ML chatbot using the information from the FNOL session (Col-4, II. 13-18, teaches an event triggers a chat session. An event is a textual action or occurrence detected by a task engine 104 served by the primary node 210. A chat session refers to a series of requests (i.e. Additional requests), responses, and replies that comprise an entire conversation (e.g., the real time exchange that occurs on the computer) in a chat application, Col-6, II. 14-18, teaches when one or more conversational assistant (i.e. chatbot) pod 204 requests attempt to identify the captured input as a request for on-line insurance servicing (i.e. claim), Col-7, II. 63-65, teaches the conversational systems may have permissions to access user data and tailor the user's experience based on access to stored user profiles, Col-11, II. 60-63, teaches the machine learning algorithm and grammar/natural language - based recognition engines automatically recognize the requests as related to an insurance quote, Col-11, II. 64-67 and Col-12, II. 1, teaches a detection system accurately detects misunderstanding in chat-based exchanges, and in response requests supplemental information by transmitting replies for clarifications during the chat session through textual responses).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Feiteria’s FNOL engine may receive notice of loss and / or a claim via direct report from users in view of Patt’s accuracy reliability in making an insurance claim with generating one or more additional requests for the claim information, determining accuracy of the claim information received from the user, personalizing the ML chatbot to the user, storing the information from the FNOL session in a user profile, and/or retraining the ML chatbot using the information from the FNOL session of Harding, in order to ensure that claim information is correct, optimize the claim process, minimize errors, and improve user satisfaction (Harding, Col-6, II. 35-43, and Col-9, II. 29-34).
For claim 10, it is a system claim corresponding to the method of claim 1. Therefore claim 10 is rejected under the same ground as claim 1.
For claim 19, it is a non-transitory computer readable medium claim corresponding to the method of claim 1. Therefore claim 19 is rejected under the same ground as claim 1.
With respect to claims 2, 11 and 20, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the FNOL information includes one or more of: (i) a type of claim, (ii) a user profile, and/or (iii) state requirements (Feiteria, Col-15, II. 27-28, teaches receiving the notice of loss associated with the claim associated with the user, Col-20, II. 43-44, teaches the criteria may vary based on the type of loss (i.e. FNOL information) , property involved , and loss attributes identified previously, Col-9, II58-59, teaches a customer profile database, Patt, ¶0164, teaches traffic light state information)
With respect to claims 3 and 12, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein analyzing the information from the FNOL session indicates one or more of: (1) a duration of the FNOL session, (i1) a quantity of the one or more requests for claim information, (iii) a quality of the claim information, (iv) a sentiment of the user, and/or (v) an ML chatbot confidence level (Feiteria, Col-20, II. 45-51, teaches quantity of affected materials in the rooms and / or the like. Criteria applied to business / personal property related claims may include make , model , manufacturer, quantity of items , and others, Col-19, II. 66-67-Col-20, II. 1-3, teaches audial tools that provide voice analysis include speech patterns , voice pitch and volume , sentiment , a attributes may be applied to verbal statements provided by the customer and recorded at intake may be utilized to provide, Col-22, 53-55, teaches If the AI based image recognition tool provide determines that there is a low confidence in the recognition of the item or failed to identify the item, Patt, ¶0033, teaches the determination of engagement level of a user by the computing system may be based on a confidence threshold or probability of the user exiting the service application within a given time frame (e.g., the next five seconds).
With respect to claims 5 and 14, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the FNOL session includes one or more of: (i) audio, (11) a text message, (ii1) an instant message, (iv) a video, (v) a virtual reality, (vi) an augmented reality, (vii) a blockchain, and/or (viii) a metaverse (Feiteria, Col-11, II. 39-45, teaches the loss scoping module 230 may use data from a variety of sources to scope loss associated with the claim received by the FNOL engine 225. For example, the loss scoping module 230 may utilize user reported data such as digital images, digital renderings of documents such as receipts or other identifying materials, digital video, digital audio recordings, Col-22, II. 20-23, teaches Voice to text conversion tools populate the description fields in an augmented reality ( “ AR ” ) display in the user computing device where the digital tools are provided, Patt, ¶0054, teaches the FNOL interface includes content recording functions that enable the user 197 to record images, video, and/or audio to provide added contextual information regarding an insurance claim..
With respect to claims 6 and 15, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the one or more session actions comprise one or more of:
transferring, by the one or more processors, the FNOL session to a customer service device; initiating, by the one or more processors, a second FNOL session between the user device and the customer service device (Patt, ¶0057, teaches the content flows provided to these additional individuals may be adaptive over a single session or over multiple sessions to ultimately gather as much contextual information from the individuals as necessary to provide a policy provider with a robust claim and recommendation, ¶0088, teaches the system 100 may then initiate customized, interactive sessions with the users 197 to provide loss mitigation content based on each user's unique property characteristics (460), ¶0099 teaches the computing system 100 can receive contextual FNOL information from the user 197 based on an interactive session with the user 197 via the FNOL interface (810)).
With respect to claims 7 and 16, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the one or more session actions comprise terminating, by the one or more processors, the FNOL session (Feiteria, Col-4, II. 48-50, teaches the human-machine interface includes online software applications that allow customers to start and finish claim completely online, Col-11, II. 20-26, teaches the FNOL engine may receive notice of loss and / or a claim via direct report from users, for example via user computing entity 105, Col-26, II. 1-4, teaches intelligent virtual agents, such as IVAs, can also be programed to complete (i.e. terminate) telephone calls to customers to provide the claim details described above if the customer requests).
With respect to claims 8 and 17, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the one or more session actions comprise:
generating, by the one or more processors via the ML chatbot (Feiteria, Col-5, II. 17-28), a summary based upon the information from the FNOL session (Feiteria, Col-25, II. 25-40, teaches the summary report (i.e. action) may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts. These key facts would include the policy forms involved in the loss, customer and interested party's identifying information, coverage types involved, locations involved, limits involved, deductibles involved, coinsurance requirements, and other general information which identifies the subjects of the claim and general policy overviews. Additionally or alternatively, the summary report may restate the identified cause of loss and any ensuing incident. The summary report may also include a coverage disposition summary which indicates whether coverage is available in full, in part, or unavailable for the loss); and
providing, by the one or more processors via the ML chatbot (Feiteria, Col-5, II. 17-28), the summary to an enterprise device (Feiteria, Col-14, II. 63-66, teaches a user may access an application, website, portal, and/or the like (e.g., via the user computing entity 105) to provide a claim to the claims processing system 120, Col-25, II. 25-40, teaches the summary report (i.e. action) may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts. These key facts would include the policy forms involved in the loss, customer and interested party's identifying information, coverage types involved, locations involved, limits involved, deductibles involved, coinsurance requirements, and other general information which identifies the subjects of the claim and general policy overviews. Additionally or alternatively, the summary report may restate the identified cause of loss and any ensuing incident. The summary report may also include a coverage disposition summary which indicates whether coverage is available in full, in part, or unavailable for the loss), wherein one or more of:
the summary is provided to the enterprise device associated with a customer service agent communicating with the user of the user device (Col-5, II. 37-42, Col-25, II. 25-40, teaches the a notice of loss associated with a claim may be provided from a direct report from the customer or user, a home of industrial Internet of Things (“IoT” alert), or an alert from a third party, e.g., independent agent, catastrophe event monitoring service, facility manager, or other proxy authorized by the customer……, the summary report may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts... the summary report may restate the identified cause of loss and any ensuing incident. The summary report may also include a coverage disposition summary which indicates whether coverage is available in full, in part, or unavailable for the loss); or
the ML chatbot is retrained based upon the information from the FNOL session indicated in the summary (Feiteria, Col-5, II. 17-48, Col-9, II. 5-8, and Col-25, II. 25-40, teaches the automation is engineered to assess coverage and settle a claim speedily and in an effective manner. Smart chat bots (i.e. identifying ML chatbot of plurality of ML chatbots), through machine learning or execution of artificial intelligence, in various embodiments, are created as technical solutions to help the human-machine interface, or the online software applications, to connect to the legal computer system and its processes of an insurer, and facilitate speedier execution to quicken the claims process…a notice of loss (FNOL) associated with a claim may be provided from a direct report from the customer or user… the claims processing system 120 may include or be in communication with one or more processing elements 205 (also referred to as processors…, the summary report may be generated by aggregate data produced from the previously described operational phases (FNOL and Scope) to provide a summary of key facts... the summary report may restate (i.e. retrained) the identified cause of loss and any ensuing incident. The summary report may also include a coverage disposition summary which indicates whether coverage is available in full, in part, or unavailable for the loss).
With respect to claims 9 and 18, Feiteria in view of Patt, and further in view of Harding discloses the computer-implemented method of claim 1, wherein the one or more session actions comprise:
generating, by the one or more processors via the ML chatbot (Feiteria, Col-5, II. 17-28), an insurance claim based upon the information from the FNOL session (Feiteria, Col-5, II36-38, teaches the a notice of loss associated with a claim may be provided from a direct report from the customer or user, Patt, ¶0054, teaches The FNOL interface can enable each user 197 to submit insurance claims for damaged or lost property and/or personal injuries); and
providing, by the one or more processors via the ML chatbot (Feiteria, Col-5, II. 17-28), the insurance claim to an enterprise device (Feiteria, Col-14, II. 63-66, teaches a user may access an application, website, portal, and/or the like (e.g., via the user computing entity 105) to provide a claim to the claims processing system 120).
Conclusion
THIS ACTION IS MADE FINAL. 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 GOLAM MAHMUD whose telephone number is (571)270-0385. The examiner can normally be reached Mon-Fri 8.00-5.00pm.
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, Umar Cheema can be reached on 5712703037. 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.
/G.M/ Examiner, Art Unit 2458/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458