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 .
Status of the Application
This is a Final Action in response to the claim amendments submitted on 07/07/2026.
Claims 1, 5-7, 9, 13-15, and 17 are amended
Claims 1-20 are pending and examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/14/2026 is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claims are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Certain Methods Of Organizing Human Activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Claims 1, 9 and 17 recites the abstract idea of claim processing in the event of an accident (see paragraph 031). This idea is described by the following claim steps:
receiving, information from first source relating to a claim event, the information identifying multiple individuals, other than the first source, to provide additional information for the claim event;
implement an information gathering process for each of the multiple individuals by:
initiating first contact through communications, with each of the multiple individuals to receive additional information pertaining to the claim event;
for at least some of the multiple individuals, after initiating first contact, authenticate the individual;
implement, a customized content flow that is customized for the individual based at least in part on the information that the individual has previously provided about the claim event;
determining a responsiveness of each of the multiple individuals with the respective customized content flow;
communicating a set of reminders to each of the multiple individuals, in accordance with the corresponding optimized reminder strategy for each of the multiple individuals;
wherein the corresponding reminder strategy for each of the multiple individuals being dynamically adapted for a communication type and a communication cadence that is determined and specifically adapted to each individual, according to one or more demographic characteristics of the individual, to increase engagement form the individual, so as to expedite the information gathering process for each of the multiple individuals and the overall claim process;
refining the reminder strategy for one or more of the multiple individuals according to a responsiveness of the corresponding individual; and
executing one or more corroborative process on information gathered during the information gathering process corresponding to the claim event from a set of the multiple individuals to determine a correct narrative of the facts of the claim event.
This idea falls within the certain methods of organizing human activity grouping of abstract ideas because it is directed towards managing interactions between people such as that required during communications when filling a claim for an event such as an accident.
Because the above-noted limitations recite steps falling within the Certain Methods Of Organizing Human Activity abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Certain Methods Of Organizing Human Activity abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Claim 12 and 19 recites similar limitations as claim 1 and is therefore determined to recite the same abstract idea.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
a network communication interface, communicatively coupled to one or more networks;
one or more processors, communicatively coupled to the network communication interface;
a memory, communicatively coupled to the one or more processors and storing instructions;
transmitting a selectable link;
implementing a set of user interface features provided on the corresponding computing device;
execution of a machine-learning model;
a computing device of a user;
a computing device of the one or more individuals;
a non-transitory computer readable medium.
However, using a computer environment such as a network and other recited computer elements amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Filling a claim for an event such as an accident and generating a reminder strategy can reasonably be performed by pencil and paper until limited to a computerized environment by requiring the user of the computing devices.
These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
Regarding the use of machine-learning, the examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 12 and 19 fail to recite additional elements that amount to an inventive concept. For example, 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 commercial or legal interaction or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-8 and 10-16, and 18-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Regarding claims 7-8, and 15-16 reciting the limitations “determine when an overall threshold of information gathering is met by the information gathering process being implemented for each individual, when the overall threshold of information gathering is met for the claim event, generate an AI prompt corresponding to the claim event; transmit, over the one or more networks, the AI prompt to a remote large language model (LLM) engine; and receive, over the one or more networks, an LLM summary of the claim event; and receive, of the one or more networks, an LLM summary of the claim event. and generate a claim view interface to provide details of the claim process and the LLM summary.” the examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claim(s) 1-4, 9-12 and 17-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Patt (US 2023/0115771).
Regarding claims 1, 9, 17, Patt discloses a computing system (See Figure 1) comprising:
a network communication interface, communicatively coupled to one or more networks (Fig.1, [0042] FIG. 1 is a block diagram illustrating an example computing system 100 implementing targeted event monitoring, alert, loss mitigation, and fraud detection techniques, in accordance with examples described herein. The computing system 100 can include a communication interface 115 that enables communications, over one or more networks 170, with computing devices 190 of users 197 of the various services described throughout the present disclosure.);
one or more processors, communicatively coupled to the network communication interface (Fig. 2, processor 240.); and
a memory, communicatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors ([037] one These instructions can be stored in one or more memory resources of the computing device. A programmatically performed step may or may not be automatic. See also Fig.2)
a non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing device cause the computing system to (See Figure 1 and claim 8);
a computer-implemented method of generating optimized reminder strategies, the method being performed by one or more processors and comprising (abstract):
receive, over the one or more networks, information from first source relating to a claim event, (See Fig. 1 and network 170 receiving information from users 197 by means of devices 190 and service aps 196. [0107] In various implementations, the computing system 100 can perform corroboration techniques for insurance claims automatically. In doing so, the computing system 100 can connect with a plurality of data sources 175 to receive additional contextual information corresponding to the claim event (840).), the information identifying multiple individuals, other than the first source, to provide additional information for the claim event ([0108 In further implementations, the computing system 100 can identify one or more individuals that have additional contextual information corresponding to the claimant user 197 and/or the claim event (845). Such individuals may comprise witnesses to the claim event or witnesses to the damage to the user's home (846), neighbors of the user 197 that may have relevant knowledge of the user's character or who may have witnessed the damage to the user's property (847), or passengers or other victims in a vehicle incident (848).);
implement an information gathering process for each of the multiple individuals (See Fig. 8B [0109] In various implementations, the computing system 100 can generate an interactive user interface for each of the identified individuals to acquire the additional contextual information (850)) by:
initiating first contact, over the one or more networks, with a corresponding computing device of each of the multiple individuals to receive additional information pertaining to the claim event (See Fig. 8B [0109] In various implementations, the computing system 100 can generate an interactive user interface for each of the identified individuals to acquire the additional contextual information (850). As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852). The content flow can include a question flow that may ask the individual a series of questions regarding the claimant user 197 and/or the damage to the user's property or injuries sustained by the claimant user 197. For example, the question flow may ask whether the individual witnessed a vehicle incident involving the claimant user 197, and if so, may ask further questions regarding the nature of the incident and seek to corroborate, validate, or invalidate certain claims made by the claimant user 197. As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854). );
for at least some of the multiple individuals, after initiating first contact, transmitting a selectable link to the individual, each link being selectable on the corresponding computing device of the individual to authenticate the individual and to link the individual to one or more user interfaces (See [0141] wherein it is disclosed that the system, via the user interfaces (selectable links) requests the user identifiers in order to identify and authenticate the user by performing a lookup in a policy database to determine information about the user. );
in response to any one of the individuals selecting the link on the corresponding computing device and being authenticated, implementing, via a set of user interface features provided on the corresponding computing device a content flow that is customized for the individual based at least in part on information that the individual has previously been provided about the claim event ([0109] For example, the question flow may ask whether the individual witnessed a vehicle incident involving the claimant user 197, and if so, may ask further questions regarding the nature of the incident and seek to corroborate, validate, or invalidate certain claims made by the claimant user 197. As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854). [0142] The damage assessment interface can further present the claimant user 197 with a contextual content flow based on the claimant user's vehicle and initial inputs regarding the vehicle incident (1014). For example, the content flow (e.g., see FIG. 9A through FIG. 9L, and FIG. 9M and FIG. 9N) can ask the claimant user 197 a series of questions regarding the vehicle (e.g., whether the vehicle still runs), whether any injuries occurred in the incident (FIG. 9O through FIG. 9Q), and more specific information based on the damage inputs the claimant user 197 provides on the virtual representation of the vehicle. As a further example, if the claimant user 197 indicates damage to the front of the vehicle, the content flow can present one or more queries relevant to the front of the vehicle, such as whether the radiator is leaking, whether the headlight lenses are cracked or destroyed, whether the hood of the vehicle is still intact, whether the windshield is cracked, and the like.);
determining a responsiveness of each of the multiple individuals with the respective customized content flow ( [0073] 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. The responsiveness factors may be generalized for users based on effective engagement techniques performed for a population of users 197, or may be individually determined based on the individual engagement of the users 197 and other parties relevant to a claim event. As such, the live engagement monitor 140 can determine the various methods of content presentation that provoke response and engagement with the content flows, and create a response profile of each individual or like subsets of individuals that the content generator 130 can utilize to tailor content flows to each individual. [0110] As additional contextual information is gathered, one or more issues may arise regarding contextual information initially provided by the claimant user 197, such as an inconsistency with regard to vehicle damage, property damage, lost property, or injury. In such a scenario, the computing system 100 may perform follow-up operations with the identified individuals to parse out the inconsistency, and either flag the inconsistency as potentially fraudulent or resolve the inconsistency through further investigation. In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers).);
based on the determined responsiveness of each of the multiple individuals, executing a machine learning model to generate a corresponding reminder strategy for providing reminders to each of the multiple individuals to complete the respective customized content flow (See Fig. 8B [0073] 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. The responsiveness factors may be generalized for users based on effective engagement techniques performed for a population of users 197, or may be individually determined based on the individual engagement of the users 197 and other parties relevant to a claim event. As such, the live engagement monitor 140 can determine the various methods of content presentation that provoke response and engagement with the content flows, and create a response profile of each individual or like subsets of individuals that the content generator 130 can utilize to tailor content flows to each individual. [0109] In various implementations, the computing system 100 can generate an interactive user interface for each of the identified individuals to acquire the additional contextual information (850). As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852). The content flow can include a question flow that may ask the individual a series of questions regarding the claimant user 197 and/or the damage to the user's property or injuries sustained by the claimant user 197.);
transmitting, over the one or more networks, a set of reminders to the corresponding computing device of each of the multiple individuals, in accordance with the corresponding optimized reminder strategy for each of the multiple individuals ([0110] In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers).); and
wherein the corresponding reminder strategy for each of the multiple individuals is dynamically adapted for a communication type ([0074] Furthermore, the interactive content generator 130 can leverage the various engagement triggers—corresponding to response factors indicating whether individuals engaged with the content flows and the extent of engagement with the content flows—to alter presentations, the timing of notifications, the types of notifications (e.g., text reminders, app notifications, emails, etc.) in order to maximize contextual information received from the individuals with regard to a particular claim event.) and a communication cadence that is determined and specifically adapted to each individual, by executing the machine-learning model according to one or more demographic characteristics of the individual, to increase engagement from the individual, so as to expedite the information gathering process for each of the multiple individuals and the overall claim process ([0072] In further implementations, the fraud detection engine 160 can receive historical user data from the computing device 190 of a claimant user 197, which can comprise sensor data (e.g., from an accelerometer of the computing device 190), and/or location data indicating where the user 197 was located during a claim event. Additionally, or alternatively, telematic information received from sensors of the user's vehicle can indicate a claim event, such as a vehicle collision. In some aspects, the telematics information can trigger the system 100 to proactively transmit content data to the computing device 190 of the user 197. If an incident has occurred, the content generator 130 can automatically initiate content flows corresponding to the FNOL interface. As an example, the sensor data or telematics information may indicate a large acceleration in accelerometer data at the moment of the claim event, indicating that the user 197 experienced a car accident. The accelerometer data and location data can be utilized by the fraud detection engine 160 to corroborate the user's location at the claim event and even the severity of the claim event itself. [0092] FIG. 6 is a flow chart describing an example method of dynamically interacting with a user during an event, according to various examples. Referring to FIG. 6, the computing system 100 can generate a real-time, customized event dashboard for each user 197 affected by an event (600). As provided herein, the event dashboard can provide localized contextual data corresponding to the event for the user 197, such as updates corresponding to the user's home location (602). In further implementations, the event dashboard can provide the user 197 with interactive safety content specifically tailored to the user 197 and the user's property based on the event severity at the user's location or home location (604). [0110] In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers).);
refining the reminder strategy for one or more of the multiple individuals according to a responsiveness of the corresponding individual ( [0073] 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. The responsiveness factors may be generalized for users based on effective engagement techniques performed for a population of users 197, or may be individually determined based on the individual engagement of the users 197 and other parties relevant to a claim event. As such, the live engagement monitor 140 can determine the various methods of content presentation that provoke response and engagement with the content flows, and create a response profile of each individual or like subsets of individuals that the content generator 130 can utilize to tailor content flows to each individual.); and
executing one or more corroborative processes on information gathered during the information gathering process corresponding to the claim event from a set of the multiple individuals to determine a correct narrative of the facts of the claim event ( [0062] The content generator 130 can further transmit content flows to any witnesses or other relevant parties to the incident to receive additional contextual information regarding the claim event, such as statements (e.g., statements of fault), photographs, video, etc. In one example, the content generator 130 can provide the map interface to the witnesses and/or parties and prompt them to indicate an estimated speed, direction of travel, and/or right-of-way of each involved vehicle or person to corroborate and/or dispute the claimant's statements and/or evidence. [0066] According to examples described herein, the computing system 100 can include a simulation engine 150 that receives the input data from the claimant user 197 and the additional individuals (e.g., map interface inputs and statements) to generate a simulation of a vehicle incident. In some examples, the simulation engine 150 can generate multiple simulations of the incident, such as one based solely on the claimant's statements and evidence, and another based on only corroborated information in the scenario which the claimant's statements and map inputs are inconsistent with those of the other individuals. Accordingly, in certain implementations, the simulation engine 150 can reject inconsistent inputs, statements and evidence or ignore certain information that is uncorroborated by other parties or witnesses. In some examples, the simulation engine 150 can prioritized corroborated information in generating the simulation, and in some examples, deprioritize unreliable information from interested parties (e.g., the owner(s) of the vehicle(s) involved in the incident). See also [068]).
Regarding claims 2, 10, 18, Patt discloses
wherein implementing the content flow includes transmitting over the one or more networks, content data to the corresponding computing device of each individual of the multiple individuals, the content data being customized, based on the determined responsiveness of the individual, to induce the individual in providing information about the claim event (See Fig. 8B [0109] In various implementations, the computing system 100 can generate an interactive user interface for each of the identified individuals to acquire the additional contextual information (850). As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852)… As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854). ).
Regarding claims 3, 11, 19, Patt discloses:
wherein determining the responsiveness of each of the multiple individuals includes executing an engagement monitoring model to determine a set of response data for each of the multiple individuals (See Fig. 8B and [0109] As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852). The content flow can include a question flow that may ask the individual a series of questions regarding the claimant user 197 and/or the damage to the user's property or injuries sustained by the claimant user 197. For example, the question flow may ask whether the individual witnessed a vehicle incident involving the claimant user 197, and if so, may ask further questions regarding the nature of the incident and seek to corroborate, validate, or invalidate certain claims made by the claimant user 197. As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854). [0110] As additional contextual information is gathered, one or more issues may arise regarding contextual information initially provided by the claimant user 197, such as an inconsistency with regard to vehicle damage, property damage, lost property, or injury. In such a scenario, the computing system 100 may perform follow-up operations with the identified individuals to parse out the inconsistency, and either flag the inconsistency as potentially fraudulent or resolve the inconsistency through further investigation. In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers).).
Regarding claims 4, 12, 20 Patt discloses:
wherein the executed instructions cause the computing system to adapt the customized content flow, implemented via the set of user interface features on the corresponding computing device of each of the multiple individuals, based on each of the set of response data, information received form the first source, and a type of the claim event (See Fig. 8B and [0109] As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852). The content flow can include a question flow that may ask the individual a series of questions regarding the claimant user 197 and/or the damage to the user's property or injuries sustained by the claimant user 197. For example, the question flow may ask whether the individual witnessed a vehicle incident involving the claimant user 197, and if so, may ask further questions regarding the nature of the incident and seek to corroborate, validate, or invalidate certain claims made by the claimant user 197. As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854).).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5-8, 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patt (US 2023/0115771) in view of Henry (US 2025/0193132).
Regarding claims 5, 13, Patt discloses
wherein the executed instructions further cause the computing system to:
implement the content flow including transmitting, over the one or more networks, content data to a computing device of each of the multiple individuals (See Fig. 8B and [0109] As provided herein, the interactive user interface presented to the individuals may be similar to the information gathering features described with respect to the FNOL interface above, and may include a content flow based on the nature of the event and damage claimed by the claimant user 197 (852). The content flow can include a question flow that may ask the individual a series of questions regarding the claimant user 197 and/or the damage to the user's property or injuries sustained by the claimant user 197. For example, the question flow may ask whether the individual witnessed a vehicle incident involving the claimant user 197, and if so, may ask further questions regarding the nature of the incident and seek to corroborate, validate, or invalidate certain claims made by the claimant user 197. As such, the computing system 100 can dynamically adjust the content flow based on the information provided by the individual and/or the engagement of the individual with the content flow (854). [0110] As additional contextual information is gathered, one or more issues may arise regarding contextual information initially provided by the claimant user 197, such as an inconsistency with regard to vehicle damage, property damage, lost property, or injury. In such a scenario, the computing system 100 may perform follow-up operations with the identified individuals to parse out the inconsistency, and either flag the inconsistency as potentially fraudulent or resolve the inconsistency through further investigation. In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers).).
Patt does not explicitly disclose:
providing a chatbot with the customized user interface features to further facilitate the individual in responding the content flow.
However, Henry which similarly teaches a system for facilitating interactions between users to file a claim further teaches:
providing a chatbot with the customized user interface features to further facilitate the individual in responding the content flow (abstract: “The UMC session can involve multiple participants, including human users and software agents (e.g., conversational bots, virtual agents, digital assistants, and other dialog interfaces). The UMC platform can facilitate creating and interacting with a digital assistant providing unified multichannel communication.”, [0034] “One example of a communication channel includes a chat communication channel. A chat refers to the process of exchanging messages between two or more users in real-time (or near real-time) over the Internet or other network. The users interacting during a chat can include human users and software agents (e.g., bots). A bot can be a web application that has a conversational interface. Users connect to a bot through one of the communication channels. Examples of bots include, but are not limited to, conversational bots, virtual agents, digital assistants, and other dialog interfaces.” ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include a chatbot with the customized user interface features to further facilitate the individual in responding the content flow, since such modification is a known improvement in the art that provides the known benefit of analyzing the data received with a chatbot and training a digital assistant using the generated AI prompt, including a type of generative AI that can understand and generate human-like text and inform the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt as disclosed by Henry on [005] and [0119].
Regarding claims 6, 14, Patt discloses
wherein the computing system initiates first contact with each of the multiple individuals to achieve a network effect of cascading information gathering for the claim event ([0032] In further implementations, upon receiving a claim trigger, the system can implement an investigative and/or corroborative process to compile a complete contextual record of the claim event and the resultant loss, damage, and/or injury. In doing so, the system can determine other parties to the claim event or parties that may have relevant information related to the claimant (e.g., other victims, witnesses, passengers of a vehicle, neighbors, family members, coworkers, etc.). Upon identifying each of the relevant individuals, the system can utilize various contact methods to remotely engage with the individuals, including text messaging, email, social media messaging, snail mail, etc. In one aspect, the engagement method can include a link to a query interface corresponding to the claim event, which can enable the individual to interact with a question flow that provides a series of interactive questions that seek additional contextual information regarding the claim event. [0110] As additional contextual information is gathered, one or more issues may arise regarding contextual information initially provided by the claimant user 197, such as an inconsistency with regard to vehicle damage, property damage, lost property, or injury. In such a scenario, the computing system 100 may perform follow-up operations with the identified individuals to parse out the inconsistency, and either flag the inconsistency as potentially fraudulent or resolve the inconsistency through further investigation. In various implementations, the computing system 100 can further perform engagement monitoring techniques on the additional individuals to dynamically adjust the content flow presentations to either maximize engagement or maximize information gathering until the individual completes the content flow(s) (855). For example, the individual may be busy or disinterested in getting involved in the claim. In such an example, the individual may be provided with reminder notifications to complete the content flow(s) and/or incentives for completing a content flow (e.g., discounted insurance offers). [0165] Based on the input data provided by each party (e.g., via the accident reconstruction interface and contextual content flows), the computing system 100 can generate a simulation of the vehicle accident (1215). ).
Regarding claim 7, Patt does not explicitly disclose:
wherein the executed instructions further cause the computing system to:
determine when an overall threshold of information gathering is met by the information gathering process being implemented for each individual;
when the overall threshold of information gathering is met for the claim event, generate an AI prompt corresponding to the claim event;
transmit, over the one or more networks, the AI prompt to a remote large language model (LLM) engine; and receive, over the one or more networks, an LLM summary of the claim event.
However, Henry which similarly teaches a system for facilitating interactions between users to file a claim further teaches:
determine when an overall threshold of information gathering is met by the information gathering process being implemented for each individual; when the overall threshold of information gathering is met for the claim event, generate an AI prompt corresponding to the claim event ([0119] The AI enrichment service 460 can be a generative AI service and include a large language model (LLM). An LLM is a type of generative AI that can understand and generate human-like text, while multi-model generative AI extends this capability to generate a variety of media types, including text, images, audio, video, etc., allowing for more diverse and versatile content creation. In generative AI, such as LLMs, a prompt serves as an input or instruction that informs the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt. [0321] The agent service can (1640) generate the digital assistant by processing the artifact and the information associated with the set of parameters. During the generation of the digital assistant, the agent service can communicate (1642) with an AI service to analyze the artifact and the information associated with the set of parameters. The agent service can generate (1644) an AI prompt based on the analysis of the artifact and the information associated with the set of parameters; and train (1646) the digital assistant using the generated AI prompt. );
transmit, over the one or more networks, the AI prompt to a remote large language model (LLM) engine; and receive, of the one or more networks, an LLM summary of the claim event ([0118] The AI enrichment service 460 can include, for example, cloud-based AI services and can provide AI enrichments to a UMC thread. The AI enrichment service 460 can provide machine learning capabilities for analyzing text for emotional sentiment, providing summarization and insights, or analyzing images to recognize objects or faces. [0119] The AI enrichment service 460 can be a generative AI service and include a large language model (LLM). An LLM is a type of generative AI that can understand and generate human-like text, while multi-model generative AI extends this capability to generate a variety of media types, including text, images, audio, video, etc., allowing for more diverse and versatile content creation. In generative AI, such as LLMs, a prompt serves as an input or instruction that informs the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include determine when an overall threshold of information gathering is met by the information gathering process being implemented for each individual; when the overall threshold of information gathering is met for the claim event, generate an AI prompt corresponding to the claim event; transmit, over the one or more networks, the AI prompt to a remote large language model (LLM) engine; and receive, of the one or more networks, an LLM summary of the claim event., since such modification is a known improvement in the art that provides the known benefit of analyzing the data entered with AI and training a digital assistant using the generated AI prompt, including a type of generative AI that can understand and generate human-like text and inform the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt as disclosed by Henry on [005] and [0119].
Regarding claim 8, Henry further teaches:
wherein the executed instructions further cause the computing system to: generate a claim view interface to provide details of the claim process and the LLM summary ([0119] The AI enrichment service 460 can be a generative AI service and include a large language model (LLM). An LLM is a type of generative AI that can understand and generate human-like text, while multi-model generative AI extends this capability to generate a variety of media types, including text, images, audio, video, etc., allowing for more diverse and versatile content creation. In generative AI, such as LLMs, a prompt serves as an input or instruction that informs the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt. [0120] In any of the examples herein, an LLM can take the form of an AI model that is designed to understand and generate human language. Such models typically leverage deep learning techniques such as transformer-based architectures to process language with a very large number (e.g., billions) of parameters. Examples include the Generative Pre-trained Transformer (GPT) developed by OpenAI, Bidirectional Encoder Representations from Transforms (BERT) by Google, A Robustly Optimized BERT Pretraining Approach developed by Facebook AI, Megatron-LM of NVIDIA, or the like. Pretrained models are available from a variety of sources. [0121] In any of the examples herein, prompts can be provided to LLMs to generate responses. Prompts in LLMs can be initial input instructions that guide model behavior. Prompts can be textual cues, questions, or statements that users provide to elicit desired responses from the LLMs. Prompts can act as primers for the model's generative process. Sources of prompts can include user-generated queries, predefined templates, or system-generated suggestions. Technically, prompts are tokenized and embedded into the model's input sequence, serving as conditioning signals for subsequent text generation. Users can experiment with prompt variations to manipulate output, using techniques like prefixing, temperature control, top-K sampling, etc. These prompts, sourced from diverse inputs and tailored strategies, enable users to influence LLM-generated content by shaping the underlying context and guiding the neural network's language generation. For example, prompts can include instructions and/or examples to encourage the LLMs to provide results in a desired style and/or format. ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include generate a claim view interface to provide details of the claim process and the LLM summary, since such modification is a known improvement in the art that provides the known benefit of analyzing the data entered with AI and training a digital assistant using the generated AI prompt, including a type of generative AI that can understand and generate human-like text and inform the AI of the desired content, context, or task, allowing users to guide the AI's text generation to produce tailored responses, explanations, or creative content based on the provided prompt as disclosed by Henry on [005] and [0119].
Response to Arguments
Applicant's arguments filed 07/07/2026 have been fully considered but they are not persuasive.
Applicant argues on page 13 “Under the first prong of Step 2A, amended Claim 1 is not directed to an abstract idea. It is directed to a specific technical solution to a technical problem identified in the Specification. The Specification identifies the technical solution as follows: Examples described herein achieve a technical solution of optimizing information gathering processes, particularly for insurance claims and claim processing for insurance policy providers, in furtherance of a practical application of reducing time from an initial incident to the final step in the claim process (e.g., a settlement or payout). The technical solutions achieved by the various embodiments described herein also involve significantly reduced computing time using machine-learning techniques and LLM summarization that also significantly reduce claim processing time, automating previously time- consuming manual procedures that have been observed to cause frustration in policy holders and inefficient delays for policy providers. Specification, [0038].” Examiner respectfully disagrees. The claims are directed to the abstract idea of processing insurance claims, including evaluating claim information and determining an appropriate processing action or workflow. The claimed use of machine learning and large language model does not alter the character of the claimed subject matter because the recited machine learning and large language model are used as tools to automate the underlying insurance claim processing activity. Applicant identified the problems as reducing processing time, automating a manual process, reducing delays and frustration and improving efficiency of an insurance claim workflow, however such identified problems are business process efficiency problems rather than technological problems. The claims do not identify an improvement to the operation of the computer itself, an improvement to an underlying computer technology or a particular technological mechanism that necessarily produces the asserted reduction in processing time. It is noted that the claimed computer components are invoked to perform the otherwise abstract activity more efficiently. Merely automating a manual process and obtaining the resulting benefit of increased speed or efficiency does not integrate the abstract idea into a practical application. Therefore, it is concluded that the recitation of LLM and machine-learning techniques does not, standing alone, provide a technological improvement or transform the abstract idea into patent eligible subject matter under 35 USC 101.
Applicant argues on page 14 “The Office Action treats the recited machine-learning operations as "results- oriented steps" for which "there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result," equating them to "apply it." (Office Action, p. 5.) The amendments foreclose that characterization. In Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), the Federal Circuit held machine-learning claims ineligible because they failed to "delineate steps through which the machine learning technology achieves an improvement." Amended Claim 1 delineates those steps. The claim specifies the inputs to the model-"the determined responsiveness of each of the multiple individuals" and "one or more demographic characteristics of the individual"; the output-a "corresponding reminder strategy" that is "dynamically adapted for a communication type and a communication cadence"; the adaptation-"by executing the machine-learning model ... to increase engagement from the individual, so as to expedite the information gathering process"; and the refinement-"refining the reminder strategy ... according to a responsiveness of the corresponding individual." Under Recentive, Claim 1 is patent eligible because the recited features delineate how the machine-learning model achieves its improvement. The recited execution of a machine-learning model is not a process that can be carried out in the human mind or with pen and paper.” Examiner respectfully disagrees. The argued limitations of the claim recite the the abstract idea of selecting and adapting communications to an individual based on responsiveness and demographic characteristics, with the objective of increasing engagement. Although the claims recite the use of machine-learning techniques, the claim does not recite a particular model architecture, model update procedure or other technological mechanism that achieves the asserted result. The limitations identify the information provided to the model and the result produced by the model, but do not define how the model technically generates or adapts the reminder strategy. Therefore, the machine learning limitation amounts to a generic implementation of the underlying activity rather than a technological improvement. It is noted that the additional elements does not integrate the abstract idea into a practical application. The claimed improvement is related to the response or behavior of an individual, not to the functioning of a computer, machine learning model or communication network. Therefore, it is concluded that the claimed machine-learning tetchiness or the recited LLM merely perform their ordinary functions in applying the abstract idea process.
Applicant argues on page 14 “Under the second prong of Step 2A, amended Claim 1 recites "additional elements" that integrate any alleged abstract idea into a practical application. See MPEP § 2106.05(e). Additional elements include elements that provide "an improvement to other technology or technical field," and elements that apply the exception "in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment."… These additional elements are not extra-solution activity. They improve the efficiency of the recited computer-implemented information gathering process- expediting the process and reducing claim processing time and computing resources-and therefore integrate any alleged abstract idea into a practical application.” Examiner respectfully disagrees. The argued limitations of the claim recite the abstract idea of refining communications strategy of an individual based on responsiveness and demographic characteristics, with the objective of increasing engagement and corroborating received information in order to determine a correct narrative of the claim events. As presented above, although the claims recite the use of a machine-learning model, the claim does not recite a particular model architecture, model update procedure or other technological mechanism that achieves the asserted result. The limitations identify the information provided to the model and the result produced by the model, but do not define how the model technically generates or adapts the reminder strategy. Therefore, the machine learning limitation amounts to a generic implementation of the underlying activity rather than a technological improvement. Applicant argues that the additional elements improve the efficiency of the information gathering process and reduction on claim processing time and computing resources. Examiner disagrees; the claim does not recite an improvement to information gathering or claim processing operation. Claim 1 does not require gathering information more efficiently, reducing processing time, reducing processor usage, reducing memory usage, reducing network traffic or changing the operation of the computer. Instead, the claim uses responsiveness and demographic information to select and adapt a reminder strategy for the purpose of increasing individual engagement. Any alleged reduction in processing time or computing resources is not required by the claim or tied to a specific computer operation. Additionally, merely performing a conventional information processing activity more quickly with a computer does not establish a technological improvement. To rely on such an improvement, Applicant would need to identify the claimed technical mechanism that produces the alleged efficiency, for example a specific data reduction technique, processing architecture, model training method, or resource allocation procedure, and demonstrate that the mechanism is recited in the claim. Therefore, since the claims do not recite any specific mechanism and the Applicant failed to provide an articulated reasoning as to how by generically reciting a machine learning model the invention provides improvements in the technology is not persuasive.
Applicant argues on page 15 “As in Example 40, amended Claim 1 recites a specific manner of carrying out the recited operations-executing and refining a machine-learning- generated reminder strategy adapted to each individual "to increase engagement from the individual, so as to expedite the information gathering process"-that yields a technological improvement, and therefore integrates any alleged abstract idea into a practical application.” Examiner respectfully disagrees. Applicant’s reliance on Example 40 is not persuasive because the claims in Example 40 recited a specific improvement to network technology. In Example 40 the claims were directed to reduce network traffic and improve network performance, claim 1 of the instant application does not recite a comparable technical mechanism for reducing data collection, processing, transmission, or computing resources. Instead, it uses responsiveness of a user and demographic information to select and adapt a reminder strategy for the purpose of increasing individual engagement. Although both claims involve processing information and dynamically adapting an operation, similarity at that level of generality is insufficient. Example 40 was determined to be eligible due to the claimed improvement to the operation of the network itself. Claim 1 does not require reduced information gathering, reduced claim processing time, reduced processor or memory usage, reduced network traffic or any other improvement to technology. Therefore, it is concluded that Example 40 is not analogous to claim 1 of the instant application.
In regard to the 35 USC 102 and 35 USC 103, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patt, US 2023/0116840, AUTOMATED CONTEXTUAL FLOW DISPATCH FOR CLAIM CORROBORATION. A computing system can receive input about a claim event from a first party. The system may then initiate a process for obtaining information about the claim event by providing a series of prompts to the first party to obtain information about the claim event from the first party, identifying a second party to provide information about the claim event based on the information provided by the first party, providing a series of prompts to the second party to obtain information about the claim event from the second party, and determining one or more actions for completing the process based on the information provided by the first party and the second party.
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 MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/MARIA C SANTOS-DIAZ/Primary Examiner, Art Unit 3629
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