DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 06/17/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC §101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
5. In the instant case, claims 1 and 11 are directed to an “apparatus and method for customization and utilization of target profiles”.
6. Claim 11 recites “presenting a user an insurance situation and recommendations for updating an insurance policy”. Specifically, claim recites “receiving … a dataset comprising a plurality of target data; determining … a validity status of the plurality of target data within the dataset; modifying … the dataset as a function of the validity status; determining … one or more protection gaps within the modified dataset …; generating … one or more target profiles as function of the modified dataset, the one or more protection gaps, and a user input; generating … a … report as a function of the one or more target profiles, wherein generating the … report comprises: receiving target training data comprising examples of target data correlated to examples of … report data; training … using the target training data; and generating the … report as a function of the one or more target profiles …; and displaying… the … report…”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., fundamental economic principles or practices) and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
7. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 11 such as “at least a processor”, “a gap finder module”, “a protection machine-learning model”, “a video report”, “video report data”, “a target machine-learning model”, and “a graphical user interface” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of presenting a user an insurance situation and recommendations for updating an insurance policy. With respect to “receiving, using at least a processor, a dataset comprising a plurality of target data” and “receiving target training data comprising examples of target data correlated to examples of video report data” is simply transmitting data “[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 computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more”, (MPEP 2106.05(f)(2)).
8. When analyzed under step 2B (MPEP 2106.04 II), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of presenting a user an insurance situation and recommendations for updating an insurance policy using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
9. Hence, claim 11 is not patent eligible.
10. Claim 1 also recites “presenting a user an insurance situation and recommendations for updating an insurance policy”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., fundamental economic principles or practices) and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
11. As in the case of claim 1, the judicial exception is not integrated into a practical application because when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of the claim 1 such as “an apparatus”, “a processor”, “a memory”, “a gap finder module”, “a protection machine-learning model”, “a video report”, “video report data”, “a target machine-learning model”, and “a graphical user interface” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of presenting a user an insurance situation and recommendations for updating an insurance policy. With respect to “receive a dataset comprising a plurality of target data” and “receiving target training data comprising examples of target data correlated to examples of video report data” is simply transmitting data “[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 computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more”, (MPEP 2106.05(f)(2)).
12. When analyzed under step 2B (MPEP 2106.04 II), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of presenting a user an insurance situation and recommendations for updating an insurance policy using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
13. Hence, claim 1 is not patent eligible.
14. The following dependent claims recent additional elements not addressed above:
claims 2 and 12 recite “a generative machine learning model” and “the script is configured to animate a digital avatar”;
claims 3 and 13 recite “converting the script to a voice output using a text-to-speech system”;
claims 7 and 17 recite “a unified dashboard” and “a predictive model”;
claims 8 and 18 recite “a summary generator” and “a large language model”; and
claims 9 and 19 recite “an application programming interface layer” and “a third party application”.
When considered individually, and as a whole, each of these additional elements amount to merely "apply it", as they are merely applying the abstract idea to the technical environment of the generative machine learning model, the animated digital avatar, the text-to-speech system, the unified dashboard, the predictive model, the summary generator, the large language model, the application programming interface layer, and the third party application.
Dependent claims 2-10 and 12-20 merely expand upon the abstract ideas of the independent claims, and are therefore rejected under the same rationale as claims 1 and 11 respectively.
Conclusion of 35 USC §101
15. The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
16. Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
17. 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 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.
18. 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.
19. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
20. Claims 1, 4-7, 9, 11, 14-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US11336669B2 to Bazalgette et al. in view of US11887367B1 to Baker et al.
21. As per claim 1:
Bazalgette et al. discloses the following limitations:
An apparatus for customization and utilization of target profiles, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to: (Col.25, lines 30-34; col.10, lines 6-11; col.25, lines 35-37, line 43, line 59; col.26, line 14; disclosed an apparatus with processor, memory, and non-transitory computer readable medium executing instructions for cyber threat analysis)
receive a dataset comprising a plurality of target data (Col.15, lines 7-8; col.23, lines 35-37; col.3, lines 46-50; discloses ingesting system data from multiple sources including metrics data for cyber threat analysis)
determine a validity status of the plurality of target data within the dataset (Col.23, lines 24-28; col.6, lines 40-44; col.24, lines 26-30; discloses using AI models to determine whether system behavior is normal or abnormal/suspicious, effectively validating each data point against a learned baseline of normal patterns)
modify the dataset as a function of the validity status (Col.24, lines 28-35; col.5, lines 37-40; discloses modifying the collected dataset by filtering and condensing data down to relevant/salient features based on the validity determination)
determine one or more protection gaps within the modified dataset using a gap finder module which includes a protection machine-learning model (Col.25, lines 49-58; col.5, lines 41-45; col.17, lines 8-13; discloses using an ML-based analyzer module to identify cyber threat hypotheses (protection gaps in the system’s security posture) within filtered system data. The analyzer module functions as a “gap finder module” using “AI models trained with machine learning” (protection ML model))
generate one or more target profiles as function of the modified dataset, the one or more protection gaps, and a user input (Col/line 24/58-25/3; col.6, lines 58-62; discloses generating formalized threat incident reports (target profiles) populated with data derived from identified gaps, ranked by severity, and configurable by each user through the UI – teaching all three inputs (modified data, gaps, user input))
display […] report using a graphical user interface (Col/line 23/63-24/4; col.18, lines 26-34; col.27, lines 23-32; discloses displaying formalized reports digitally on a user interface, including an interactive 3D visualization with graphs and threat data, which is the closest analog to displaying a generated report via GUI.)
Bazalgette et al. does not explicitly disclose, however, Baker et al., as shown, teaches the following limitations:
generate a video report as a function of the one or more target profiles, wherein generating the video report comprises (Col.2, lines 46-47)
receiving target training data comprising examples of target data correlated to examples of video report data (Col.20, lines 14-16)
training a target machine-learning model using the target training data (Col.19, lines 30-32, 49-51)
generating the video report as a function of the one or more target profiles using the trained target machine-learning model (Col.19, lines 33-38)
(Discloses the video + ML training pipeline: receiving labeled video data correlated with action data, generating predictions from the trained model, and its output is automated interface actions)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system training a machine learning model to label unlabeled data and/or perform automated actions of Baker et al. (‘367, abstract) with teaching of Bazalgette et al. for analyzing a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models (‘669, abstract) for receiving labeled video data correlated with action data and generating predictions from the trained model (‘672, col.20, lines 14-16; col.19, lines 33-38).
Claim 11 is rejected using the same rationale that was used for the rejection of claim 1.
22. As per claim 4:
Bazalgette et al. discloses the following limitations:
The apparatus of claim 1, wherein determining the one or more protection gaps comprises: (Col.25, lines 49-58)
sorting the modified dataset into one or more protection categorizations and determining the one or more protection gaps as a function of the sorting (Col.7, lines 21-26; col.7, lines 34-35; col.25, lines17-20; col.7, lines 26-33; discloses sorting and groupings threat hypotheses into incident types (protection categorizations for cyber security) and determining gaps from that groupings by analyzing which hypotheses are supported or refuted. The analyzer module maps groups to incident types and ranks by severity – directly teaching sorting into protection categorizations and determining gaps therefrom).
Claim 14 is rejected using the same rationale that was used for the rejection of claim 4.
23. As per claim 5:
Bazalgette et al. discloses the following limitations:
The apparatus of claim 1, wherein determining the validity status of the plurality of target data comprises comparing the plurality of target data to a validity threshold (Col.17, lines 50-52; col.6, lines 53-62; discloses computing a threat risk parameter and comparing it against one or more configurable thresholds to determine validity (severity) of threats. Users can configure threshold levels, and the system compares behavioral data against these thresholds – directly teaching threshold-based validity determination)
Claim 15 is rejected using the same rationale that was used for the rejection of claim 5.
24. As per claim 6:
Bazalgette et al. discloses the following limitations:
The apparatus of claim 1, wherein: the plurality of target data comprises at least a geographical datum (Col.10, lines 21-25; discloses “geographical datum” by any data with geographic/location component (i.e., San-Francisco office, U.K. office))
generating the one or more target profiles comprises: generating the one or more target profiles as a function of the at least a geographical datum (Col.10, lines 26-32; col.18, lines 31-34; discloses building behavioral profiles (pattern of life) as a function of the entity’s geographic network position. The network topology is projected geographically via the interactive 3D UI. The system uses geographic/locational context to build and differentiate entity profiles)
Claim 16 is rejected using the same rationale that was used for the rejection of claim 6.
25. As per claim 7:
Bazalgette et al. discloses the following limitations:
The apparatus of claim 1, wherein the apparatus comprises a unified dashboard, wherein the unified dashboard comprises a predictive model, wherein the memory contains instructions configuring the at least a processor to send the user a notification based on an output of the predictive model (Col.18, lines 26-31; col.12, lines 52-57; col.6, lines 58-62; col.14, lines 42-44; discloses single-pane visualization (dashboard) with Bayesian predictive model and configurable automatic responses (notifications) triggered by model output)
Claim 17 is rejected using the same rationale that was used for the rejection of claim 7.
26. As per claim 9:
Bazalgette et al. discloses the following limitations:
The apparatus of claim 1, wherein: the apparatus comprises an application programming interface layer, wherein the application programming interface layer is configured to integrate with a third-party application (Col.5, lines 65-67)
the memory contains instructions further configuring the at least a processor to: display a stewardship file using a graphical user interface (Col.19, lines 30-32)
update the display of the stewardship file as a function of an input from the application programming interface layer (Col/line 23/63 -24/2)
(Discloses receiving data via Open Source APIs (API layer + third-party), displaying formalized reports (stewardship files) via UI, updates from external inputs)
Claim 19 is rejected using the same rationale that was used for the rejection of claim 9.
27. Claims 2, 3, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US11336669B2 to Bazalgette et al. in view of US11887367B1 to Baker et al. and US11756251B2 to Kaushik et al.
28. As per claim 2:
Neither Bazalgette et al. nor Baker et al. disclose, however, Kaushik et al., as shown, teaches the following limitations:
The apparatus of claim 1, wherein the memory further instructs the processor to generate a script as a function of the of the one or more target profiles generated using a generative machine learning model of the target machine-learning model, wherein the script is configured to animate a digital avatar (Col.8, lines 27-30; col.8, lines 37-39; col.7, lines 61-62; discloses ML model receiving text (script) and generating facial action units to animate a computerized digital avatar)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system training a machine learning model to label unlabeled data and/or perform automated actions of Baker et al. (‘367, abstract) and an apparatus for processing text and speech from a computer simulation by a machine learning engine to animate the face of a computer avatar of Kaushik et al. (‘251, abstract) with teaching of Bazalgette et al. for analyzing a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models (‘669, abstract) for training ML model using a training set of animated faces speaking words and processing the text and speech using ML model (‘251, col.7, lines 61-62; col.8, lines 37-39).
Claim 12 is rejected using the same rationale that was used for the rejection of claim 2.
29. As per claim 3:
Neither Bazalgette et al. nor Baker et al. disclose, however, Kaushik et al., as shown, teaches the following limitations:
The apparatus of claim 2, wherein animating the digital avatar comprises converting the script to a voice output using a text-to-speech system (Fig.5, items 500, 502; col.6, lines 40-42; col.7, lines 43-45; discloses that the animated avatar speaking the text that converted the text to speech)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system training a machine learning model to label unlabeled data and/or perform automated actions of Baker et al. (‘367, abstract) and an apparatus for processing text and speech from a computer simulation by a machine learning engine to animate the face of a computer avatar of Kaushik et al. (‘251, abstract) with teaching of Bazalgette et al. for analyzing a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models (‘669, abstract) for generating an image of a first face to be animated to speak words in accordance with both text and speech (‘251, col.7, lines 43-45).
Claim 13 is rejected using the same rationale that was used for the rejection of claim 3.
30. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US11336669B2 to Bazalgette et al. in view of US11887367B1 to Baker et al. and US11769017B1 to Gray et al.
31. As per claim 8:
Neither Bazalgette et al. nor Baker et al. disclose, however, Gray et al., as shown, teaches the following limitations:
The apparatus of claim 1, wherein the apparatus comprises a summary generator, wherein the summary generator comprises a large language model configured to receive the plurality of target data as input and output a summary of the plurality of target data (Col.40, lines 10-13, 15-16; col.5, lines 62-64; col.10, lines 17-19; discloses using LLM to receive data and generating natural language summary)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system training a machine learning model to label unlabeled data and/or perform automated actions of Baker et al. (‘367, abstract) and a method for generating a natural language (NL) based summary additional content is processed using a large language model (LLM) of Gray et al. (‘017, abstract) with teaching of Bazalgette et al. for analyzing a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models (‘669, abstract) for generating NL based summary by utilizing the received data via LLM (‘017, col.10, lines 17-19).
Claim 18 is rejected using the same rationale that was used for the rejection of claim 8.
32. Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US11336669B2 to Bazalgette et al. in view of US11887367B1 to Baker et al. and US20150019263A1 to Kiper et al.
33. As per claim 10:
Neither Bazalgette et al. nor Baker et al. disclose, however, Kiper et al., as shown, teaches the following limitations:
The apparatus of claim 9, wherein the input comprises a home replacement datum ([claim 2], [0004], [claim 4], [0016]; discloses that the system receiving replacement cost data through cost estimation server interface – the “input” from this server includes home replacement data (replacement cost estimation for residential structure))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system training a machine learning model to label unlabeled data and/or perform automated actions of Baker et al. (‘367, abstract) and a method for receiving a cost estimate from the cost estimation server, the cost estimate being generated based on customer interaction with the cost estimation interface of Kiper et al. (‘263, [0004]) with teaching of Bazalgette et al. for analyzing a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models (‘669, abstract) for receiving replacement cost data through server interface, where the cost estimation server output includes residential replacement cost estimation data (‘263, [0016]).
Claim 20 is rejected using the same rationale that was used for the rejection of claim 10.
Conclusion
34. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US9135348B2 – Wu et al. – Discloses a method and system for profiling a user based upon a user's previous on-line actions, wherein the profile provides a characterization of the user's preferences based upon a received user event.
US20220005121A1 – Hayward. – Discloses a system for detecting an emerging trend in insurance claims, wherein each insurance claim may include a customer profile of a customer and claim data associated with the customer.
35. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANULLA ABDULLAEV whose telephone number is (571)272-4367. The examiner can normally be reached Monday-Friday 9:30AM -4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan D Donlon can be reached at 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMANULLA ABDULLAEV/ Examiner, Art Unit 3692
/RYAN D DONLON/ Supervisory Patent Examiner, Art Unit 3692
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