DETAILED ACTION
This Final Office Action is in response to the application filed on 09/27/2024 and the Amendment & Remark filed on 02/10/2026.
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 Claims
Claims 6-7 and 16-17 are canceled.
Claims 1 and 11 are amended.
Claims 1-5, 8-15 and 18-20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 8-15 and 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 1 and 11 that “training a policy machine-learning model using a policy training data, wherein the policy training data comprises a plurality of entity data as input correlated to a plurality of protocol metrics as output; adjusting one or more parameter values of the policy machine-learning model during training to minimize a loss function based on predicted protocol metrics and corresponding protocol objects; and iteratively retraining the policy machine-learning model using newly added entity data to generate an updated policy machine-learning model, wherein determinations of protocol metrics are updated across successive training iterations; outputting, by the updated policy machine-learning model, the at least a protocol metric for each protocol object of the plurality of protocol objects as a function of the composite entity criterion and the adjusted parameter values of the policy machine-learning model”, there is no written content as to how or what specific training process are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to train a policy machine-learning model, to adjust parameter values (what parameter values to be adjusted and what adjustment methodology) of the model to minimize a loss function based on predicted protocol metrics and corresponding protocol objects and to iteratively train the model that generates output to be used to output protocol metric for each protocol object. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 1-5, 8-15 and 18-20 are rejected as failing the written description requirement.
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 1-5, 8-15 and 18-20 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.
As an initial matter, the claims as a whole are to an apparatus and a process, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
An apparatus for determining and recommending transaction protocols, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive entity data associated with an entity;
identify one or more entity matches for the entity from a plurality of entity data as a function of the entity data;
determine at least a protocol metric for each protocol object of a plurality of protocol objects as a function of the entity data and the one or more entity matches, wherein determining the at least a protocol metric comprises:
identifying a plurality of protocol objects associated with the one or more entity matches;
normalizing and weighing a plurality of entity-specific measures to generate a composite entity criterion, wherein the composite entity criterion is provided as an input to a policy machine-learning model to constrain determination of the protocol metric;
training the policy machine-learning model using a policy training data, wherein the policy training data comprises a plurality of entity data as input correlated to a plurality of protocol metrics as output;
adjusting one or more parameter values of the policy machine-learning model during training to minimize a loss function based on predicted protocol metrics and corresponding protocol objects;
iteratively retraining the policy machine-learning model using newly added entity data to generate an updated policy machine-learning model, wherein determinations of protocol metrics are updated across successive training iterations;
outputting, by the updated policy machine-learning model, the at least a protocol metric for each protocol object of the plurality of protocol objects as a function of the composite entity criterion and the adjusted parameter values of the policy machine-learning model;
select at least one protocol object of the plurality of protocol objects as a function of the at least a protocol metric;
generate at least one policy agreement as a function of the at least one protocol object; and
transmit the at least one policy agreement to at least a remote device.
Claim 11 recites:
A method for determining and recommending transaction protocols, wherein the method comprises:
receiving, by the at least a processor, entity data associated with an entity;
identifying, by the at least a processor, one or more entity matches for the entity from a plurality of entity data as a function of the entity data;
determining, by the at least a processor, at least a protocol metric for each protocol object of a plurality of protocol objects as a function of the entity data and the one or more entity matches, wherein determining the at least a protocol metric comprises:
identifying a plurality of protocol objects associated with the one or more entity matches;
normalizing and weighing a plurality of entity-specific measures to generate a composite entity criterion, wherein the composite entity criterion is provided as an input to a policy machine-learning model to constrain determination of the protocol metrics;
training a policy machine-learning model using a policy training data, wherein the policy training data comprises a plurality of entity data as input correlated to a plurality of protocol metrics as output; and
adjusting one or more parameter values of the policy machine-learning model during training to minimize a loss function based on predicted protocol metrics and corresponding protocol objects;
iteratively retraining the policy machine-learning model using newly added entity data to generate an updated policy machine-learning model, wherein determinations of protocol metrics are updated across successive training iterations;
outputting, by the updated policy machine-learning model, the at least a protocol metric for each protocol object of the plurality of protocol objects as a function of the composite entity criterion and the adjusted parameter values of the policy machine-learning model;
selecting, by the at least a processor, at least one protocol object of the plurality of protocol objects as a function of the at least a protocol metric;
generating, by the at least a processor, at least one policy agreement as a function of the at least one protocol object; and
transmitting, by the at least a processor, the at least one policy agreement to at least a remote device.
Claims 2 and 12 recites:
wherein the entity data comprises historical claim data.
Claims 3 and 13 recites:
wherein generating the at least one policy agreement as a function of the at least one protocol object comprises:
receiving a policy template from a data store; and populating the at least one policy template using the entity data and the at least one protocol object.
Claims 4 and 14 recites:
wherein receiving the entity data associated with the entity comprises:
receiving an initial input from the entity through the remote device;
utilizing a web crawler to retrieve information associated with the entity as a function of the initial input; and
generating entity data as a function of the initial input and the web crawler.
Claims 5 and 15 recites:
wherein the entity data comprises financial information.
Claims 6 and 16 recites:
wherein training the policy machine-learning model using the policy training data comprises, iteratively retraining the policy machine-learning model as a function of a newly added entity data within the plurality of entity data.
Claims 7 and 17 recites:
wherein the newly added entity data comprises the entity data associated with the entity.
Claims 8 and 18 recites:
wherein identifying the one or more entity matches for the entity from the plurality of entity data as a function of the entity data comprises:
classifying the entity data and the plurality of entity data to one or more entity categorizations; and
identifying one or more entity matches as a function of the one or more entity categorizations.
Claims 9 and 19 recites:
wherein receiving the entity data comprises: receiving entity data from a data store, wherein the data store comprises a dealer management system (DMS).
Claims 10 and 20 recites:
wherein the plurality of protocol objects is contained in an immutable sequential listing in a decentralized platform.
Based on the limitations above and Specification paragraph 0048 disclosure defining “protocol object” as “may include Finance & Insurance products,”, the claims describe a process that covers generating insurance policy. Generating insurance policy is considered to be a fundamental economic practice / commercial interaction, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “a processor” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “receiving, by the at least a processor, entity data associated with an entity” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving entity data associated with an entity;
the limitation “identifying, by the at least a processor, one or more entity matches for the entity from a plurality of entity data as a function of the entity data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of identifying one or more entity matches as a function of the entity data;
the limitation “determining, by the at least a processor, at least a protocol metric for each protocol object of a plurality of protocol objects as a function of the entity data and the one or more entity matches” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining metric for each financial & insurance product as a function of the entity data and entity matches;
the limitation “identifying a plurality of protocol objects associated with the one or more entity matches” encompasses no more than generically invoking a processor to apply the Judicial Exception step of identifying a plurality of financial & insurance products associated with the one or more entity matches;
the limitation “normalizing and weighing a plurality of entity-specific measures to generate a composite entity criterion, wherein the composite entity criterion is provided as an input to a policy machine-learning model to constrain determination of the protocol metrics” encompasses no more than generically invoking a processor to apply the Judicial Exception step of normalizing and weighing a plurality of entity-specific measures to generate a composite entity criterion;
the limitation “training a policy machine-learning model using a policy training data, wherein the policy training data comprises a plurality of entity data as input correlated to a plurality of protocol metrics as output; adjusting one or more parameter values of the policy machine-learning model during training to minimize a loss function based on predicted protocol metrics and corresponding protocol objects; iteratively retraining the policy machine-learning model using newly added entity data to generate an updated policy machine-learning model, wherein determinations of protocol metrics are updated across successive training iterations” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training policy model using training data, adjusting model parameter values and iteratively training the policy model to generate an updated policy model;
the limitation “outputting, by the updated policy machine-learning model, the at least a protocol metric for each protocol object of the plurality of protocol objects as a function of the composite entity criterion and the adjusted parameter values of the policy machine-learning model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining product metric for each financial & insurance product based on the updated policy model;
the limitation “selecting, by the at least a processor, at least one protocol object of the plurality of protocol objects as a function of the at least a protocol metric” encompasses no more than generically invoking a processor to apply the Judicial Exception step of selecting at least one financial & insurance product as a function of the product metric;
the limitation “generating, by the at least a processor, at least one policy agreement as a function of the at least one protocol object” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating at least one policy agreement as a function of the at least one financial & insurance product;
the limitation “transmitting, by the at least a processor, the at least one policy agreement to at least a remote device” encompasses no more than generically invoking a processor to apply the Judicial Exception step of sending the policy agreement to a remote entity;
the limitation “receiving a policy template from a data store; and populating the at least one policy template using the entity data and the at least one protocol object” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving a policy template and populating the policy template based on the entity data and the financial & insurance product;
the limitation “receiving an initial input from the entity through the remote device; utilizing a web crawler to retrieve information associated with the entity as a function of the initial input; and generating entity data as a function of the initial input and the web crawler” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving the initial input from the entity, retrieving information associated with the entity as a function of the initial input and generating entity data based on the input and retrieved information;
the limitation “wherein identifying the one or more entity matches for the entity from the plurality of entity data as a function of the entity data comprises:
classifying the entity data and the plurality of entity data to one or more entity categorizations; and identifying one or more entity matches as a function of the one or more entity categorizations” encompasses no more than generically invoking a processor to apply the Judicial Exception step of classifying the entity data into one or more entity categorizations and identifying one or more entity matches as a function of the categorizations;
the limitation “wherein receiving the entity data comprises: receiving entity data from a data store, wherein the data store comprises a dealer management system (DMS)” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving entity data from a dealer management entity.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory”, “data store”, “dealer management system (DMS)”, and/or “an immutable sequential listing in a decentralized platform” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “transmitting … to at least a remote device” and/or “receiving … through the remote device” are generically recited to perform communication steps such as receiving and transmitting.
The additional element(s) of “training a … machine-learning model”, “determining … as a function of the trained … machine-learning model”, “adjusting one or more parameter values of the … machine learning model during training to minimize a loss function” and “iteratively retraining the … machine-learning model” are generically recited to perform steps of determining protocol metric described only by a result-oriented solution with insufficient detail for how the steps are technologically accomplished.
The examiner noted that the mere inclusion of machine learning model usage / training / nominal parameter adjustment / iterative retraining do not render an otherwise abstract claim patent eligible under 101. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit held that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101." 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025). The court specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. As the patentee in Recentive conceded, "'using a machine learning technique … necessarily includes [an] iterative training step.'" Id. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id. Accordingly, the claimed invention here, which similarly applies conventional machine learning technique[s] to determine product metric of financial & insurance product, fails to recite patent-eligible subject matter under 101.
The additional element(s) of “web crawler” are generically recited to perform steps retrieving information described only by a result-oriented solution with insufficient detail for how the step is technologically accomplished.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and other generic computing elements such as ML model and web crawler to generate insurance policy agreement amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Dependent claim 2, 5, 12 and 15 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1-5, 8-15 and 18-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The previous rejection under 35 USC 102 is withdrawn in view of the Amendment filed on 02/10/2026.
Claim Rejections - 35 USC § 103
The previous rejection under 35 USC 103 is withdrawn in view of the Amendment filed on 02/10/2026.
Response to Arguments
Applicant's arguments filed 02/10/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the Specification provides adequate written description for the claim limitations rejected under 35 USC 112(a), the examiner respectfully disagrees. However, the cited paragraph 0075 of the Specification only discloses that “parameter values may be adjusted during training or pretraining in order to minimize a loss function”, “the loss function may adjust parameter values of the machine learning model”, “during training and/or pretraining of the machine learning model parameter values of the machine learning model may be adjusted as a function of entity data and corresponding protocol objects.”, “processor 104 may be configured to minimize a loss function by adjusting parameter values of policy machine-learning model based on predicted protocol metrics and actual protocol objects in a test set. In one or more embodiments”. The examine noted that the disclosure encompasses only that parameter “may be adjusted”, but not what the parameter values are or how the parameter values “may be adjusted”. The methodology of adjusting parameter values of the model to achieve the desired result of minimizing a loss function is absent from the disclosure. As such, the disclosure does not objectively demonstrate the possession of the claimed limitation of “training the policy machine-learning model using a policy training data … adjusting one or more parameter values of the policy machine-learning model during training to minimize a loss function based on predicted protocol metrics and corresponding protocol object”.
Regarding the applicant’s argument that claims do not recite a certain method of organizing human activity, the examiner respectfully disagrees. As explained in the rejection, Specification paragraph 0048 disclosure defines “protocol object” as “may include Finance & Insurance products” but not any other “protocol object” other finance and insurance products. Thus, the claims’ recitation on determining protocol metric of protocol object, selecting at least one protocol object and generating at least one policy agreement to be transmitted is a recitation of generating insurance policy. The machine learning language are considered as additional elements to be considered in the Step 2A prong two, but such additional elements does not negate the recitation of the Judicial Exception. As such, the argument is not persuasive.
Regarding the applicant’s argument that claims recite additional element integrating the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant alleged that claims provide improvements to computer functionality and machine-learning technology. Contrary to that of Enfish and McRO, the normalizing and weighing of entity-specific measures to generate a composite entity criterion … provided as an input to a policy machine-learning model is no more than a training data preparation step similar to the “discretizing, by the computer, the continuous training data to generate input data” step of ineligible claim 2 of Example 47. Both normalizing and weighing of values are mathematical concept similar to discretizing values. RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”). Thus, when added to the Judicial Exception of generating insurance policy, the mathematical concept of normalizing and weighing entity specific value does not result in eligibility. As to the training model, adjusting model parameter to minimize loss function and iteratively retraining limitations, the rejection articulated based on Recentive Analytics, Inc. v. Fox Corp.,that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101." 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025). The court specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. As the patentee in Recentive conceded, "'using a machine learning technique … necessarily includes [an] iterative training step.'" Id. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id. Accordingly, the claimed invention here, which similarly applies conventional machine learning technique[s] of loss function minimization and iteratively training to determine finance and insurance related product metric for the F&I product, fails to recite patent-eligible subject matter under 101. As such, the argument is not persuasive.
Regarding the applicant’s argument that claims recite additional elements amount to significantly more than the cited Judicial Exception, the examiner respectively disagrees. As explained above, the addition of normalizing and weighing metric (mathematical concept) to the generating of insurance product (Certain Methods of Organizing Human Activity) does not result inventive concept. Also as explained above, the mere usage of machine learning to the Judicial Exception is not an improvement to technology or computer functionality because "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." As such, the argument is not persuasive.
Conclusion
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 CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHO YIU KWONG/Primary Examiner, Art Unit 3693
Prosecution Insights