DETAILED ACTION
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
This Non-Final Office Action is in reply to the application filed on 28 November 2023.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statements filed on 11/28/2023 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
smart engine configured to in claims 9, 11, 12, 13, 15, 16
communications hardware configured to in claims 9, 13, 14
user detection circuitry configured to in claims 10
authentication engine in claim 14
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 claimed invention is directed to an abstract without significantly more.
Under Step 1 the claims are analyzed to determine whether they are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03.
Claims 1-8 are directed to a method, claims 9-16 are directed to an apparatus and claims 17-20 are directed to a computer program product. Therefore, the claims fall within the statutory categories of invention.
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes). See MPEP §2106.04.
Independent claims 1, 9 and 17 recite a mental process as they include the limitations, determining, … a user attribute set based on the extracted user data, wherein (i) the user attribute set comprises one or more user attributes and (ii) each user attribute is associated with a user data type; generating, …an aggregated veteran likelihood score, wherein the aggregated veteran likelihood score is based on an analysis of the one or more user attributes from the user attribute set; and in an instance in which the aggregated veteran likelihood score satisfies a predefined veteran likelihood threshold: assigning, … a veteran status classification to a user based on the aggregated veteran likelihood score based on human observation, evaluation, judgement and opinion, either mentally or with the aid of pen/paper. The Courts generally treat collecting information as well as analyzing information by steps people go through in their minds and/or by pen & paper as essentially mental processes within the abstract-idea category. See MPEP §2106.04(a) Part III, C.
Dependent claims 3 and 11 recite in part, analyzing, …a plurality of veteran status indicators from the extracted user data, wherein the one or more veteran status indicators comprise one or more of (i) financial data, (ii) address history, (iii) medical history, or (iv) familial relationships.
Dependent claims 4, 12 and 18 recite in part, wherein generating the aggregated veteran likelihood score comprises: determining, …a plurality of per-veteran status indicator veteran likelihood scores, wherein (i) each per-veteran status indicator veteran likelihood score is determined based on an analysis of one or more veteran status indicators associated with a corresponding user data type, and (ii) the aggregated veteran likelihood score is generated based on the plurality of per-veteran status indicator veteran likelihood scores.
Dependent claims 5, 13 and 19 recite in part, determining, whether the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment
Dependent claims 6, 14 and 20 recite in part, determining, an authenticity score for the verification evidence.
Dependent claims 7 and 15 recite in part, in an instance in which the verification evidence satisfies the predefined authenticity score threshold for an affiliate user, further comprising: determining, … a familial veteran status classification for the affiliate user, wherein the familial veteran status classification is indicative of a relationship type between the affiliate user and the user.
Dependent claims 4-7, 11-15, 18-20 further recite a mental process as they include steps based on human observation, evaluation, judgement and opinion, either mentally or with the aid of pen/paper.
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. See MPEP §2106.04(d).
Claims 1, 9 and 17 recite the additional elements, a smart engine, communications hardware, computer program comprising at least one non-transitory computer readable storage medium storing software instructions. The additional elements are recited at a high-level of generality and perform generic computing functions such as extracting, determining, generating, assigning, and providing. In this case, the claims merely involve automated steps executed by generic computer components recited above at a high-level of generality with no technical improvement to the functioning of the smart engine, communications hardware, or computer program since the additional element is no more than mere instructions to apply the abstract idea using a generic computing components.
The claims recite the additional limitations “ extracting, by a smart engine and using a veteran status predictive classification model, user data from a data environment” and “providing, by communications hardware and based on the assigned veteran status classification, a verification prompt, wherein the verification prompt requests the user to verify the assigned veteran status classification”. These limitations are recited at a high level of generality and amount to mere data gathering and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The judicial exception of “determining, a user attribute set based on the extracted user data, wherein (i) the user attribute set comprises one or more user attributes and (ii) each user attribute is associated with a user data type,” is performed by “using the veteran status predictive classification model.” The predictive classification model is used to generally apply the abstract idea without placing any limits on how the predictive classification model functions or how it is applied. Rather, these limitations only recite the outcome of determining, a user attribute set based on the extracted user data and do not include any details about how the “determining” is accomplished. See MPEP 2106.05(f).
Dependent claims 3, 4, 11, 12, 18 merely reiterate the same abstract ideas using the same additional elements as recited above for determining, generating and analyzing without imposing any meaningful limits or any further practical application.
Dependent claims 5, 13, and 19 recite in part, “in an instance in which the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment, the communications hardware is further configured to: provide, a verification prompt, wherein the verification prompt indicates that the user qualifies for the at least one veteran status opportunity offered by the establishment.” These limitations are recited at a high level of generality and amount to mere data gathering and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Dependent claims 6, 14 and 20 recite in part, receiving, by the communications hardware, a verification response to the verification prompt, wherein the verification response confirms or refutes the assigned veteran status classification; and in an instance in which the veteran status classification is confirmed, requesting, by communications hardware, verification evidence for the assigned veteran status classification;…and in an instance in which the authenticity score satisfies a predefined authenticity score threshold, updating, by the smart engine, a veteran status of the user to the assigned veteran status classification. These limitations are recited at a high level of generality and amount to mere data gathering and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Dependent claims 7 and 15 recite in part, and in an instance in which the familial veteran status classification for the affiliate user corresponds to a qualifying veteran status classification of the user, updating, by the smart engine, a veteran status of the affiliate user to the familial veteran status classification. These limitations are recited at a high level of generality and amount to mere data gathering and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Dependent claims 8 and 16 recite in part, training, by the smart engine and using a veteran status predictive classification model, the veteran status classification model based on (i) the one or more veteran status indicators from the user attribute set and predictive classification accuracy, wherein said predictive classification accuracy is determined by a historical confirmed or refuted response to the assigned veteran status classification for a plurality of users. The limitation recites ‘training” the veteran status classification model but fails include technical details about how the “training” is accomplished. The limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
Considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. The sequence of the claimed limitations is equally generic and otherwise held to be abstract since the combination of these additional elements is no more than mere instructions to apply the judicial exception using generic computer components. Therefore, the additional elements recited in the claimed invention individually, and even in combination, fail to integrate the recited judicial exception into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea.
Under Step 2B the claims are analyzed to determine whether the claims recite additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
Claims 1-20 as a whole do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
For the extracting, receiving, providing, prompting, requesting and updating steps that were considered extra-solution activity in Step 2A, Prong Two, this has been re-evaluated in Step 2B and determined to be well understood, routine, and conventional in the field. The Ultramercial, Symantec, TLI, and OIP Techs. court decisions indicate that mere data collection, and transmission is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. See MPEP 2106.05(d), subsection II.
Considered as an ordered combination, the additional elements of the claim do not add anything further than when they are considered separately. Thus, under Step 2B, the claims are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
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.
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.
Claim(s) 1, 3, 8-9, 11-12, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mallard (US 2018/0225366 A1) in view of Qu et al (US Patent No. 8,655,695 B1)
Claims 1, 9 and 17: Robinson discloses method of automatic identification of veteran status, apparatus for automatic identification of veteran status and a computer program product for automatic identification of veteran status, the computer program comprising at least one non-transitory computer readable storage medium storing software instructions that, when executed, cause an apparatus to (See [0018-0023]: Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments may take the form of a program product embodied in one or more computer readable storage devices storing machine readable code. [0080]: For example, the processor 405 may predict 515 that the client is a veteran from the client profile 271. In addition, the processor 405 may predict 515 that the client was honorably discharged from the military service based on the client profile 271.):
providing, by communications hardware and based on the assigned veteran status classification, a verification prompt, wherein the verification prompt requests the user to verify the assigned veteran status classification (see [0081]: For example, the processor 405 may contact the other server 120 of the Veterans Administration to confirm that the client was honorably discharged from military service to verify 520 the actionable entry 200).
Mallard does not expressly disclose the following limitations but Qu which discloses and system and method of determining a user is associated with a feature segment, teaches,
extracting, by a smart engine and using a veteran status predictive classification model, user data from a data environment (see col. 10 lines 55-64: Given one or more specified features 306, one or more user feature extractors 304 may be used for extracting users that contain the specified features 306, together with the respective feature values (i.e., "feature value pairs" that include a unique feature ID and its feature value, where the "feature value" can be categorical or numeric). User feature extractors 304 may be software modules configured to process and extract data from user event data 302.);
determining, by the smart engine and using the veteran status predictive classification model, a user attribute set based on the extracted user data, wherein (i) the user attribute set comprises one or more user attributes and (ii) each user attribute is associated with a user data type (see col. 12 lines 40-53: In one embodiment, model builder 312 may be provided with model specifications 314 for building models. Model builder 312 may then receive aggregated user feature profiles from user feature profile merger 308, and produce a training data set based on model specifications 314. The model specifications 314 may include one or more files or sets of data that specify various parameters for building a model. Generally, model specifications 314 may describe the target classes (i.e., specified features 306), a list of features used as independent features or predictors, sampling rates for preparing training instances of different class values, the methods used for modeling, and/or the rates of samples reserved for training and evaluation.).
generating, by the smart engine and using the veteran status predictive classification model, an aggregated veteran likelihood score, wherein the aggregated veteran likelihood score is based on an analysis of the one or more user attributes from the user attribute set; and in an instance in which the aggregated veteran likelihood score satisfies a predefined veteran likelihood threshold: assigning, by the smart engine and using the veteran status predictive classification model, a veteran status classification to a user based on the aggregated veteran likelihood score (see col. 16 lines, 31-38: The scores produced by user scoring module 320 may be utilized for grouping users into different segments. For example, numerical thresholds can be set for determining whether a user belongs to a certain segment of a class. If, for a given user and a given class, the predicted score is above a given threshold, the user can be considered as a member of the segment).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the system and method of predicting that a client is a veteran of Mallard, extracting, by a smart engine and using a veteran status predictive classification model, user data from a data environment; determining, by the smart engine and using the veteran status predictive classification model, a user attribute set based on the extracted user data, wherein (i) the user attribute set comprises one or more user attributes and (ii) each user attribute is associated with a user data type; generating, by the smart engine and using the veteran status predictive classification model, an aggregated veteran likelihood score, wherein the aggregated veteran likelihood score is based on an analysis of the one or more user attributes from the user attribute set; and in an instance in which the aggregated veteran likelihood score satisfies a predefined veteran likelihood threshold: assigning, by the smart engine and using the veteran status predictive classification model, a veteran status classification to a user based on the aggregated veteran likelihood score as taught by Qu so that “users and/or user segments may be targeted and optimized based on profile information, thereby improving ad serving performance” (Qu, col. 20 lines 1-2).
Claims 3 and 11: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1 and 9 above. Qu further teaches, wherein determining the user attribute set comprises: analyzing, by the smart engine and using the veteran status predictive classification model, a plurality of veteran status indicators from the extracted user data, wherein the one or more veteran status indicators comprise one or more of (i) financial data, (ii) address history, (iii) medical history, or (iv) familial relationships (see col. 9 lines 18-26: In addition, in instances when a cookie is unavailable, a virtual cookie may be used. A virtual cookie may be derived from available information such as the user's IP address, browser type, geographic location, connection speed, or any other accessible and appropriate session level data. In certain embodiments, data may be obtained for each user, which represents the user's tendency to return to a specific web site in a given period of time).
Claims 4, 12 and 18: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1, 9 and 17 above. Qu further teaches, wherein generating the aggregated veteran likelihood score comprises: determining, by the smart engine and using the veteran status predictive classification model, a plurality of per-veteran status indicator veteran likelihood scores, wherein (i) each per-veteran status indicator veteran likelihood score is determined based on an analysis of one or more veteran status indicators associated with a corresponding user data type, and (ii) the aggregated veteran likelihood score is generated based on the plurality of per-veteran status indicator veteran likelihood scores (See col. 15 lines 51-60: Given suitable models 316, a user scoring module 320 may be a software-implemented module that receives one or more models 316, and optionally the modeling specifications 314, for application to one or more user records provided by user profile merger 308. An exemplary user scoring/segmentation module 218 is described above with reference to FIG. 2. In one embodiment, the features in the model may be applied to the user record and only features present in the model are maintained for scoring the user. Each user may be given a score computed based on the specified classification method and the parameters of the method from the model 316).
Claims 8 and 16: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1, and 9 above. Qu further teaches, training, by the smart engine and using a veteran status predictive classification model, the veteran status classification model based on (i) the one or more veteran status indicators from the user attribute set and predictive classification accuracy, wherein said predictive classification accuracy is determined by a historical confirmed or refuted response to the assigned veteran status classification for a plurality of users (See col. 15 lines 40-50: In one embodiment, the models 316 may be further evaluated by evaluation module 318. For instance the models 316 and/or user segments generated by user segmentation 322 may be evaluated by evaluation module 318 for accuracy and fitting error. Evaluation statistics may be generated, including confusion matrix, accuracy, precision, recall, false positive, and false negative. The model parameters and the evaluation results of the respective models can be returned back to the feature selection module as additional statistics for feature selection).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the system and method of predicting that a client is a veteran of Mallard as modified by Qu, training, by the smart engine and using a veteran status predictive classification model, the veteran status classification model based on (i) the one or more veteran status indicators from the user attribute set and predictive classification accuracy, wherein said predictive classification accuracy is determined by a historical confirmed or refuted response to the assigned veteran status classification for a plurality of users as taught by Qu so that “users and/or user segments may be targeted and optimized based on profile information, thereby improving ad serving performance” (Qu, col. 20 lines 1-2).
Claim(s) 2, 5-7, 10, 13-15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mallard and Qu as applied to claims 1, 9 and 17 above, and further in view of Higgins et al (US 20160078468 A1).
Claims 2 and 10: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1 and 9. Mallard and Qu do not expressly disclose wherein extracting user data occurs in response to: detecting, by user detection circuitry, a user visit event at an establishment, wherein the establishment is associated with at least one veteran status opportunity but Higgins which also discloses a system and method of military service verification teaches, wherein extracting user data occurs in response to: detecting, by user detection circuitry, a user visit event at an establishment, wherein the establishment is associated with at least one veteran status opportunity (see [0046]: a member may instead display a smartphone image to a merchant as a form of identification. In other cases, the member might present a physical identification card to a merchant (e.g., when or she is making a purchase at a retail store. [0025]: After checking the verified member database 120, the verification and confirmation server 150 may transmit an API confirmation to response to the partner server 180 indicating the member is, in fact, verified. The partner enterprise may then arrange for the veteran to receive his or her benefit.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Mallard and Qu with the system and method of extracting user data occurs in response to: detecting, by user detection circuitry, a user visit event at an establishment, wherein the establishment is associated with at least one veteran status opportunity as taught by Higgins because it would “facilitate verification of military service in an efficient and accurate manner” (Higgins, [0019]).
Claims 5, 13 and 19: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1, 9 and 17 above. Mallard and Qu do not expressly disclose the following limitations but Higgins teaches, wherein providing a verification prompt occurs in response to: determining, by the smart engine and using the veteran status predictive classification model, whether the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment; and in an instance in which the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment, the communications hardware is further configured to: provide, a verification prompt, wherein the verification prompt indicates that the user qualifies for the at least one veteran status opportunity offered by the establishment (see [0044] Similarly, the network diagram 700 may be used to a partner to verify whether someone (associated with a travel discount code) is, or has been, a service member. According to some embodiments, this may be performed via a member identifier ping API. The API might comprise a simple URL call with parameters (e.g., without needing HTTP POST or headers). The API might be associated with 256 bit Secure Socket Layer (“SSL”) securing to secure the URL call with a username and password. Moreover, an HTTP GET protocol may be used to implement the API. The partner may then call a URL via code and check for a 1 (for valid results) or a 0 (for an invalid result—that is the member has not been verified as having served in the military). [0045] Once a party obtains a ping API username and password, a travel discount code entered by a customer (e.g., in a “promo code” box) can be passed via an HTTPS web call. The party may then read the output from the URL. The API may, according to some embodiments return a 1 if the specified travel discount code is a valid veteran or military member. The API will return a 0 if the travel discount code cannot be found in the first database 740 or the member is no longer active in the program).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Mallard and Qu with the system and method wherein providing a verification prompt occurs in response to: determining, by the smart engine and using the veteran status predictive classification model, whether the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment; and in an instance in which the veteran status classification assigned to the user qualifies for the at least one veteran status opportunity offered by the establishment, the communications hardware is further configured to: provide, a verification prompt, wherein the verification prompt indicates that the user qualifies for the at least one veteran status opportunity offered by the establishment as taught by Higgins because it would “facilitate verification of military service in an efficient and accurate manner” (Higgins, [0019]).
Claims 6, 14 and 20: The combination of Mallard and Qu discloses the claimed invention as applied to claims 1, 9 and 17 above. Mallard and Qu do not expressly disclose the following limitations but Higgins teaches, receiving, by the communications hardware, a verification response to the verification prompt, wherein the verification response confirms or refutes the assigned veteran status classification; and in an instance in which the veteran status classification is confirmed, requesting, by communications hardware, verification evidence for the assigned veteran status classification; determining, by an authentication engine, an authenticity score for the verification evidence; and in an instance in which the authenticity score satisfies a predefined authenticity score threshold, updating, by the smart engine, a veteran status of the user to the assigned veteran status classification (See Fig. 5, [0030] At S210, the system may receive, from a remote potential member device, an electronic message comprising a request for military service verification. For example, a potential member might access a web page and seek to sign-up with a third-party service verification platform or program. [0031] At S220, the system may receive, from the remote potential member device, an indication of an affirmative attestation that the potential member, or a relative of the potential member, has served or is currently serving in the military. Providing this type of attestation may cause people to hesitate before providing untruthful information. [0034] At S250, the system may automatically perform a validation process on the request for military service verification. For example, business rules or logic may be applied to determine that the supplement information is consistent (e.g., that a particular unit exists). [0052]: The table includes entries associated with members who have been verified as being service members, such as by a KBA process. The table also defines fields 1002, 1004, 1006, 1008 for each of the entries. The fields specify: a verified member identifier 1002, a name 1004, service details 1006, and a unique code 1008. The information in the database 1000 may be periodically created and updated based on information generated by a verification and confirmation server. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Mallard and Qu with the system and method of receiving, by the communications hardware, a verification response to the verification prompt, wherein the verification response confirms or refutes the assigned veteran status classification; and in an instance in which the veteran status classification is confirmed, requesting, by communications hardware, verification evidence for the assigned veteran status classification; determining, by an authentication engine, an authenticity score for the verification evidence; and in an instance in which the authenticity score satisfies a predefined authenticity score threshold, updating, by the smart engine, a veteran status of the user to the assigned veteran status classification as taught by Higgins because it would “facilitate verification of military service in an efficient and accurate manner” (Higgins, [0019]).
Claims 7 and 15: The combination of Mallard, Qu and Higgins discloses the claimed invention as applied to claims 6 and 14 above. Higgins further teaches in an instance in which the verification evidence satisfies the predefined authenticity score threshold for an affiliate user, further comprising: determining, by the smart engine and using a veteran status predictive classification model, a familial veteran status classification for the affiliate user, wherein the familial veteran status classification is indicative of a relationship type between the affiliate user and the user; and in an instance in which the familial veteran status classification for the affiliate user corresponds to a qualifying veteran status classification of the user, updating, by the smart engine, a veteran status of the affiliate user to the familial veteran status classification (see Fig. 4, [0038]: Similarly, the user may select a service period when the user (or a family member of the user) served in the military. After attesting that he or she is providing truthful information via indication 460, he or she may submit the information via activation of a button icon 470. [0052] FIG. 10 is a tabular view of a portion of a verified service member database 1000 in accordance with some embodiments of the present invention. The table includes entries associated with members who have been verified as being service members, such as by a KBA process. The table also defines fields 1002, 1004, 1006, 1008 for each of the entries. The fields specify: a verified member identifier 1002, a name 1004, service details 1006, and a unique code 1008. The information in the database 1000 may be periodically created and updated based on information generated by a verification and confirmation server.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Mallard as modified by Qu and Higgins with the system and method of in an instance in which the verification evidence satisfies the predefined authenticity score threshold for an affiliate user, further comprising: determining, by the smart engine and using a veteran status predictive classification model, a familial veteran status classification for the affiliate user, wherein the familial veteran status classification is indicative of a relationship type between the affiliate user and the user; and in an instance in which the familial veteran status classification for the affiliate user corresponds to a qualifying veteran status classification of the user, updating, by the smart engine, a veteran status of the affiliate user to the familial veteran status classification as taught by Higgins because it would “facilitate verification of military service in an efficient and accurate manner” (Higgins, [0019]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Robinson et al (US 2024/0020648 A1): [0076] In response, the machine learning model 630 may identify respective attributes in each of the transaction records. The machine learning model may output transaction attributes 631 identified by the machine learning model 630 from the transaction record 610 and transaction attributes 632 identified by the machine learning model 630 from the transaction record 611. Transaction attributes may include one or more of a payment amount, a payment date, a counterparty entity, a geographical location, and the like. In some cases, no attributes may be identified. [0078] When determining whether a user is eligible for a benefit, such as a benefit offered by a basic income benefits program, the host platform may perform one or more of an identity verification, an income verification, a fraud detection, and the like, which are described herein as part of the eligibility verification of a user. The host may also retrieve criteria/qualifications of the benefits program that the user wishes to be certified with and determine whether or not the user qualifies for the benefits program based on the retrieved criteria and user-specific data, such as income data and other data of the user, which may be primarily obtained from authorized accounts of the user.
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MAAME BALLOU
Examiner
Art Unit 3629
/MAAME BALLOU/ Examiner, Art Unit 3629
/NATHAN C UBER/ Supervisory Patent Examiner, Art Unit 3626