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 .
DETAILED ACTION
This non-final office action is responsive to the U.S. patent application no. 18/775,568 filed on July 17, 2024.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
The application claims priority under 35 U.S.C. 120 to U.S. non-provisional application No. 18/231,562 filed on August 8, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 17, 2024 is compliant with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 5 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6 and 14 of U.S. Patent No. 12,061,622. Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Application No. 18/775,568
Patent No. 12,061,622
1. An apparatus for communication associated with one or more data sets, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
retrieve a plurality of allies from a database;
determine an eligibility status of each ally of the plurality of allies, comprising: receiving a data set comprising one or more sets of target data;
(Claim 5. The apparatus of claim 1,
wherein receiving the data set further comprises:
determining a validity status of the one or more sets of target data;
modifying the data set as a function of the validity status;
determining one or more protection gaps within the modified data set using a gap finder module; and
determining the eligibility status as a function of the data set.) (End of claim 5)
(claim 1 continues below)
calculate, using an optimization module, a score for each ally of the plurality of allies;
compare, using the optimization module, the score for each ally of the plurality of allies;
store the score on an immutable sequential listing;
select at least one ally of the plurality of allies as a function of the eligibility status;
generate an optimized communication protocol as a function of the selection; and
modify a graphical user interface as a function of the selection and the optimized communication protocol, wherein modification of the graphical user interface comprises, visually presenting a step associated with the optimized communication protocol.
1. An apparatus for communication associated with one or more data sets, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
retrieve a plurality of allies from a database;
determine an eligibility status of each ally of the plurality of allies, comprising: receiving a data set comprising one or more sets of target data,
wherein receiving the data set further comprises:
determining a validity status of the one or more sets of target data;
modifying the data set as a function of the validity status;
determining one or more protection gaps within the modified data set using a gap finder module; and
determining an eligibility status as a function of the data set;
(4. The apparatus of claim 3, wherein receiving at least one ally from the end user comprises receiving a quantitative datum from the end user. - Examiner’s Note: the quantitative datum corresponds to the “score” in the left column
5. The apparatus of claim 4, wherein determining an eligibility status of each ally of the plurality of allies further comprises comparing the quantitative datum to one or more eligibility thresholds.
6. The apparatus of claim 4, wherein the quantitative datum is representative of a plurality of numerical elements, wherein each numerical element is associated with one of the one or more sets of target data.) (end of claim 6)
(claim 1 continues here)
select at least one ally of the plurality of allies as a function of the eligibility status;
generate an optimized communication protocol as a function of the selection; and
modify a graphical user interface as a function of the selection and the optimized communication protocol, wherein modification of the graphical user interface comprises, visually presenting a step associated with the optimized communication protocol.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Lombard et al. (U.S. 2024/0362735).
Regarding claim 1, Lombard disclosed an apparatus for communication associated with one or more data sets, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
retrieve a plurality of allies from a database (Lombard, [0024], “extract a user profile 108 from a user” and “a user profile may be part of a single user view for the user. A “single user view” as used herein is defined as a collection of information about a user gathered from different sources.” Said user anticipates “ally”);
calculate, using an optimization module, a score for each ally of the plurality of allies (Lombard, Fig. 10, step 1020 and [0072], “processor 104 is configured to generate a profile evaluation 128 as a function of at least one evaluation factor 120.” And “a profile evaluation may include data regarding risk categories, types of risks, driving records, risks associated with driving records, a factor score, criteria for risk evaluation, data associated with a driving record or user profile 108 associated to a level of risk, and the like”);
compare, using the optimization module, the score for each ally of the plurality of allies (Lombard, [0068], “each piece of information associated with a verified user profile 116 may be compared to one or more examples of an evaluation factor 120,”);
store the score on an immutable sequential listing (Lombard, [0024], “A user profile may be placed through an encryption process for security purposes. This may additionally include storing a user profile 108 on an immutable sequential listing or blockchain as described herein below. ”);
determine an eligibility status of each ally of the plurality of allies, comprising:
receiving a data set comprising one or more sets of target data (Lombard, [0062, 0073], “a verified user profile 116 may include a trustworthiness score. … a trustworthiness score may include an estimate of the degree to which a user is trustworthy. A trustworthiness score may be evaluated on a numerical scale.” said “trustworthiness score” is an embodiment of the “eligibility status” in the claim);
select at least one ally of the plurality of allies as a function of the eligibility status (Lombard, [0085], “the processor 104 may be configured to modify the informational contents of the user profile database 300 based on at least one of the user datum and the user profile 108. … For example, processor 104 may modify the informational contents of user profile database 300 to reflect that a claim should be filed for user Bill Jones with account number 55375914 and store a task datum indicating that an insurance adjustor should be scheduled to travel to Bill Jones' residence to assess damages” and [0086], “modifying informational contents of user profile database 300 may include modifying user identifying information (for example updating a user weight, updating a user disability, creating a user profile when onboarding a new user, deleting a user profile for a user who leaves an insurance company operating apparatus 100, and the like), modifying policy coverage for a user (for example adding additional coverage for a new car, changing a policy rate for a user home following the construction of an addition, deleting coverage for a boat based on a user selling the boat, adding a new policy for a newborn child”);
generate an optimized communication protocol as a function of the selection (Lombardi, [0102], “processor 104 may be further configured to adapt a natural language format based on one or more language elements according to a determined entity communication style in the user profile 108); and
modify a graphical user interface as a function of the selection and the optimized communication protocol, wherein modification of the graphical user interface comprises, visually presenting a step associated with the optimized communication protocol (Lombard, [0102], “As an additional or alternative example, a user named Joseph may have a casual writing style and may say something like “y do u talk like a robot, bro?” Processor 104 may input this phrase into a dynamic response machine learning model and receive an output better reflecting Joseph's implied preference for a more casual writing style such as “my b broseph i aim 2 please” instead of “My apologies Joseph, I aim to please.”” This example shows that what is output on the graphical user interface is modified in response to the determination of a user’s communication style preference).
Claim 11 lists substantially the same elements as claim 1, in method form rather than apparatus form. Therefore, the rejection rationale for claim 1 applies equally as well to claim 11.
Regarding claims 2 and 12, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein retrieving the plurality of allies from the database further comprises:
receiving at least one ally from the user (Lombard, [0024], “A user profile 108 may be received by process 104 via user input”); and
storing the at least one ally in the database (Lombard, [0085], “processor 104 may store the entirety of user profile 108 on user profile database 300”).
Regarding claims 3 and 13, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed a machine learning model, wherein the machine learning model is configured to generate an optimized communication protocol as a function of the selection (Lombard, [0105], “training a machine learning model…”; [0106], “dynamic response machine learning model ” and [0107], “processor 104 may utilize one or more trained machine learning models in combination to interpret communications from user and initiate or perform a corresponding action related to one or more functions of an operator of digital assistant apparatus 100”) by:
receiving ally training data comprising a plurality of allies correlated to a plurality of optimized communication protocols; training an ally machine learning model as a function of the ally training data; and generating an optimized communication protocol as a function of the ally machine learning model (Lombard, [0105], “processor 104 may adapt the natural language format by receiving training data correlating exemplary language elements with exemplary communication styles, training a machine learning model using the received training data, and generating one or more natural language outputs corresponding to the determined entity communication style by inputting, to the trained machine learning model, at least one of the first information and the single user view and receiving the one or more natural language outputs from the trained machine learning model. ”).
Regarding claims 4 and 14, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein the apparatus comprises a summary generator, wherein the summary generator comprises a large language model configured to receive the one or more sets of target data as input and output a summary of the one or more sets of target data (Lombard, [0088], “processor 104 may be further configured to create a single user view by agglomerating user information from one or more sources including the user profile 108.” Lombard further disclosed in [0038, 0047, 0084] that the processing of user information in user profile is done by digital assistant 112 using language processing modules / machine learning models).
Regarding claims 5 and 15, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein receiving the data set further comprises:
determining a validity status of the one or more sets of target data (Lombard disclosed in [0060] that “a verification process may include a verification of the user profile 108.” And in [0062] that “A machine learning model is configured to analyze user profile 108 concerning whether the user is truthful. A verification machine learning model may be configured to determine the trustworthiness score”);
modifying the data set as a function of the validity status (Lombard disclosed in [0061] that “processor 104 may be configured to generate a verified user profile 116 using a verification machine learning model” and “Inputs to the machine learning model may include user profile 108, vehicle profiles, secondary sources, examples of verified user profiles 116, digital fingerprint, and the like. The outputs of the verification machine learning model may include a verified user profile 116.” This process of generating a verified user profile 116 is a process to modify the user profile 108);
determining one or more protection gaps within the modified data set using a gap finder module (Lombard disclosed in [0086] that “modifying informational contents of user profile database 300 may include … modifying policy coverage for a user (for example adding additional coverage for a new car, changing a policy rate for a user home following the construction of an addition, deleting coverage for a boat based on a user selling the boat, adding a new policy for a newborn child… ” Said “modifying policy coverage” inherent involves determining one or more protection gaps using a gap finder); and
determining the eligibility status as a function of the data set (Lombard disclosed in [0062] that “a verified user profile 116 may include a trustworthiness score” and that “…a trustworthiness score may include an estimate of the degree to which a user is trustworthy.” The “trustworthiness score” is an embodiment of the “eligibility status” in the claim).
Regarding claims 6 and 16, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed a predictive model configured to: receive an input comprising external information; determine an ally trend as a function of the external information; and utilize a summary generator, comprising a large language model, to generate an ally trend summary as a function of the ally trend (Lombard, [0076], “a fuzzy inferencing system may be used to generate a profile evaluation 128. The current fuzzy inferencing system may be the same or substantially similar to any fuzzy inferencing system described herein. Input vectors into the fuzzy inferencing system may include an evaluation factor 120 and an example of a profile evaluation 128. The output to the fuzzy inferencing system may be a profile evaluation 128” and “The machine learning model may output a fuzzy rule that is tailored to the current user.” The fuzzy inference system disclosed by Lombard is a possible embodiment of the “predictive model” in the claim).
Regarding claims 7 and 17, Lombard disclosed the subject matter of claims 6 and 16, respectively.
Lombard further disclosed wherein the predictive model is further configured to retrieve the external information from the immutable sequential listing (Lombard disclosed in [0024] that the user profile 108 is stored in an immutable sequential listing, implying that the fuzzy inference system is configured to retrieve the user profile from the immutable sequential listing).
Regarding claims 8 and 18, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein the score for each ally of the plurality of allies is determined based on a quantitative datum received from one or more end users (Lombard disclosed in [0024] that “User profile 108 can be retrieved from multiple sources” and in [0062] that “A machine learning model is configured to analyze user profile 108 concerning whether the user is truthful. A verification machine learning model may be configured to determine the trustworthiness score.”).
Regarding claims 9 and 19, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein the apparatus is further configured to utilize a role-based access control system, wherein the role-based access control system is configured to:
assign ally access permissions to a database; verify the ally access permissions; and conditionally grant the ally access to the database as a function of a verification of the ally access permissions (Lombard disclosed in [0084] that “Processor 104 may extract at least one user datum by generating the following prompt: “Ok, I'll get that claim process started for you. What's your account number?” User may then generate an additional portion of user profile 108, such as: “My account number is 55375914.” Processor 104 may then analyze the replied portion of user profile 108 generated by user by parsing the reply and searching for a number consisting of a predetermined number of digits, such as eight. The processor 104 may then compare the account number to a database associating account numbers and user identifying information and determine that account number 55375914 belongs to user Bill Jones and is therefore a valid user datum” Said account number is an example of the “ally access permission” in the claim).
Regarding claims 10 and 20, Lombard disclosed the subject matter of claims 1 and 11, respectively.
Lombard further disclosed wherein the processor is further configured to modify the graphical user interface as a function of the ally selection by populating user interface data structure with data of the ally and visually presenting the data to the ally through modification of the graphical user interface (Lombard, [0078], “processor 104 may be configured to display the profile evaluation 128 using a display device 132”).
Related Prior Art
Agrawal et al. (US 2019/0312879) disclosed methods and systems for creating a verified customer profile that can be used to determine insurance policy prices.
Lombard et al. (US 11,922,515) disclosed methods and apparatuses for AI digital assistants that can be sued by insurance companies to interact with customers and users.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY X ZHANG whose telephone number is (571)270-5012. The examiner can normally be reached 8:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H Hwang can be reached at 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIRLEY X ZHANG/Primary Examiner, Art Unit 2447