DETAILED ACTION
The present application, filed on 1/5/2026 is being examined under the AIA first inventor to file provisions.
The following is a non-final First Office Action on the Merits. Claims 1-20 are pending and have been considered below.
Priority
This application is a CON of 18/075,381 12/05/2022 PAT 12,517,892 which claims benefit of 63/286,460 12/06/2021 and is a CIP of 17/576,861 01/14/2022 and is a CIP of 17/732,487 04/28/2022 and is a CIP of 17/566,876 12/31/2021 and is a CIP of 17/069,597 10/13/2020 PAT 12,346,987 and is a CIP of 16/380,771 04/10/2019 and is a CIP of 15/484,059 04/10/2017 PAT 12,039,585
The priority is acknowledged.
Information Disclosure Statement (IDS)
The information disclosure statement (IDS) submitted on 1/5/2026; 1/5/2026; 1/5/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, such IDS is being considered by Examiner.
Claim Rejections - 35 USC § 101
35 USC 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 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-13 are directed to a computer implemented method, claims 14-19 are directed to computer implemented method, and claims 20 are directed to a system.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 14, 20) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 (which is representative of independent claims 14, 20) recite an abstract idea, shown in bold below:
[A] A method, comprising:
[B] determining a plurality of optimized weight values for claim data stored in at least one memory,
[C] wherein the claim data comprises item data, geolocation data, liability data, user data, or combinations thereof;
[D] receiving search input data provided to a search engine via a user device,
[E] wherein the search engine is configured to search the at least one memory;
[F] determining claim event data based on the plurality of optimized weight values, the claim data, and the search input data,
[G] wherein the determined claim event data corresponds to one or more legal claims associated with the search input data; and
[H] transmitting the determined claim event data to the user device,
[I] wherein the user device is configured to display information relating to the determined claim event data.
Independent claim 1 (which is representative of independent claims 14, 20) recites: optimized weights are determined ([B]); search input data is received ([D]); claim event data re determined and transmitted ([F], [H]) which, based on the claim language and in view of the application disclosure, represents a process aimed at: making a determination about the type of data.
This is a combination that, under its broadest reasonable interpretation, covers reasonable performance of limitations expressing observation, evaluation in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. For example, the step “determining a plurality of optimized weight values for claim data stored in at least one memory”, as drafted in the context of this claim, encompasses the user manually or mentally determining values, without physical aid. Further, the step “receiving search input data provided to a search engine via a user device”, as drafted in the context of this claim, encompasses the user manually or mentally receiving data, without physical aid. Further, the step “determining claim event data based on the plurality of optimized weight values, the claim data, and the search input data”, as drafted in the context of this claim, encompasses the user manually or mentally determining event data, without physical aid. Further, the step “transmitting the determined claim event data to the user device”, as drafted in the context of this claim, encompasses the user manually or mentally transmitting the received claim event data, without physical aid. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)). The use of a physical aid would not negate the mental nature of this limitation (see MPEP 2106.04(a)(2) iii B)
Accordingly, it is concluded that independent claim 1 (which is representative of independent claims 14, 20) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “computing system,” “processors,” and “memory” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the additional elements “wherein the claim data comprises item data, geolocation data, liability data, user data, or combinations thereof”; “wherein the search engine is configured to search the at least one memory”; “wherein the determined claim event data corresponds to one or more legal claims associated with the search input data”; “wherein the user device is configured to display information relating to the determined claim event data” as applied to the claim data, search engine, claim event data, and user device, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea making a determination about the type of data, and do not serve to integrate the identified abstract idea into a practical application.
Per Step 2B. Independent claim 1 (which is representative of claims independent 14, 20) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 14, 20 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2, which is representative of dependent claims 15), recites: wherein determining the plurality of optimized weight values for the claim data comprises
training one or more machine learning models based on the claim data to determine the plurality of optimized weight values using iterative feedback based on the claim data.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to “performing repetitive calculations”, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (making a determination about the type of data) into a practical application (see MPEP 2106.05(d) II)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 2 (which is representative of dependent claims 15) is deemed ineligible.
Dependent claim 3 recites: wherein training the one or more machine learning models to determine the plurality of optimized weight values using iterative feedback comprises:
determining a plurality of expected values for the claim data, wherein a respective expected value corresponds to an expected utility of a respective claim datum with respect to the one or more legal claims;
determining a plurality of standard deviation values for the claim data based on the plurality of expected values;
determining a plurality of ratios for the claim data based on the plurality of expected values and the plurality of standard deviation values; and
determining the plurality of optimized weight values for the claim data based on the plurality of ratios.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 3 is deemed ineligible.
Dependent claim 4 recites: wherein determining the plurality of ratios comprises:
determining a plurality of opportunity sets based on the claim data, wherein a respective opportunity set corresponds to a plurality of candidate weight values for a respective subset of claim data; and
determining the plurality of ratios based on the plurality of opportunity sets, the plurality of expected values, and the plurality of standard deviation values,
wherein a respective ratio corresponds to a ratio of a respective expected value and a respective standard deviation value for the respective opportunity set.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 4 is deemed ineligible.
Dependent claim 5 recites: wherein determining the plurality of optimized weight values based on the plurality of ratios comprises:
determining a ranking of the plurality of ratios;
determining an optimized ratio of the plurality of ratios based on the ranking;
determining an optimized opportunity set of the plurality of opportunity sets based on the optimized ratio; and
determining the plurality of optimized weight values for the claim data based on the optimized opportunity set,
wherein the plurality of optimized weight values corresponds to a respective subset of the plurality of candidate weight values for the optimized opportunity set.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 5 is deemed ineligible.
Dependent claim 6 recites: wherein determining the plurality of optimized weight values for the claim data based on the optimized opportunity set comprises
determining the plurality of optimized weight values using the iterative feedback based on the claim data and updated claim data stored in the at least one memory,
wherein the iterative feedback corresponds to neural network feedback.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 6is deemed ineligible.
Dependent claim 7, which is representative of dependent claims 16) recites: wherein determining the claim event data based on the plurality of optimized weight values, the claim data, and the search input data comprises:
using the one or more trained machine learning models to determine the claim event data based on the plurality of optimized weight values, the claim data, and the search input data.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable receiving/transmitting data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (making a determination about the type of data) into a practical application (see MPEP 2106.05(f)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 7 (which is representative of dependent claims 16) is deemed ineligible.
Dependent claim 8 recites: wherein the one or more machine learning models are configured to
use linear and non-linear optimization systems,
wherein the linear and non-linear optimization systems comprise one or more vector maximization and minimization equations;
the one or more machine learning models comprise one or more neural networks, one or more linear regression models, one or more logistic regression models, one or more linear discriminant analysis models, one or more classification or regression tree models, one or more naïve Bayes models, one or more learning vector quantization models, one or more posterior density function models, one or more independent stochastic regressor models, one or more general stochastic regression models, one or more general non-linear hypothesis models, or combinations thereof; or combinations thereof.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 8 is deemed ineligible.
Dependent claim 12, which is representative of dependent claims 19 recites: wherein receiving the search input data provided to the search engine via the user device comprises:
generating the search engine for the at least one memory;
providing a search interface for the search engine to the user device,
wherein the search interface is configured to be displayed by the user device; and
receiving the search input data provided to the search interface via the user device.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to receiving/transmitting data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (making a determination about the type of data) into a practical application (see MPEP 2106.05(f)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 12 (which is representative of dependent claims 19 is deemed ineligible.
Dependent claim 13 recites: wherein receiving the search input data provided to the search engine via the user device comprises:
display the information relating to the determined claim event data using a results interface of the user device.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: making a determination about the type of data. The elements in this dependent claim are comparable to receiving/transmitting data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (making a determination about the type of data) into a practical application (see MPEP 2106.05(f)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 12 is deemed ineligible.
Dependent claims 9-11, which are representative of dependent claims 17-18, respectively, recite:
wherein the claim data further comprises accident data, biomarker data, transaction data, chemical drug data, food data, implant data, scientific data, satellite data, supply chain data, cellular data, sensor data, court document data, Internet-of-Things data, device data, litigation cost data, litigation payout data, claim expected value data, legal recovery data, crime data, traffic data, navigation data, audio data, video data, photo data, weather data, health care records data, billing data, purchase history data, commodities data, time duration data, real-time data, emissions data, pollution data, or combinations thereof.
wherein the augmented data comprises data generated based on at least a portion of the item data, the geolocation data, the liability data, the user data, or combinations thereof; or combinations thereof.
wherein the one or more legal claims comprise one or more litigation claims, one or more false advertising claims, one or more Medicare secondary payer claims, one or more automobile injury claims, one or more opioid claims, one or more products liability claims, one or more personal injury claims, one or more patent infringement claims, one or more workers’ compensation claims, one or more commercial litigation claims, one or more breach of contract claims, one or more breach of warranty claims, one or more libel claims, one or more slander claims, one or more nuisance claims, one or more debt collection claims, one or more malpractice claims, one or more property damage claims, one or more class action claims, one or more tort claims, one or more negligence claims, or combinations thereof.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – claim data; augmented data; legal claims – and as such, cannot change the nature of the identified abstract idea (making a determination about the type of data), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 9-11 (which are representative of dependent claims 17-18, respectively) are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Examiner Remarks
No art rejection has been applied to the instant set of claims.
The independent claims of Applicant’s invention recite a method, a method, and a system for determining claim event data.
The identified most relevant prior art references are listed here below.
However, neither of the most relevant prior art references discloses: determining claim event data based on the plurality of optimized weight values, the claim data, and the search input data, wherein the determined claim event data corresponds to one or more legal claims associated with the search input data.
The identified pertinent prior art discloses elements of the claimed invention. However, Examiner has determined that it would be impermissible hind-sight reasoning for a person of ordinary skill in the art to combine the individual elements disclosed in the prior-art in order to achieve Applicant's claimed invention.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20170232300 A1 Tran; Bao et al. SMART DEVICE An Internet of Thing (IoT) device includes a camera coupled to a processor; and a wireless transceiver coupled to the processor. Blockchain smart contracts can be used with the device to facilitate secure operation.
US 20100280748 A1 Mundinger; Jochen et al. OPTIMIZED ROUTE PLANNING AND PERSONALIZED REAL-TIME LOCATION-BASED TRAVEL MANAGEMENT A method for optimized route planning for a user, including: (a) determining a departure point and a destination point for multimodal travel; (b) based on said departure point and destination point, computing and proposing criteria for restricting the number of candidate routes to consider, (c) proposing an updated list of candidate routes between said departure point and said destination point, said updated list being either: i) automatically displayed after a delay, and/or: ii) based on user selection of said criteria.
US 20210382924 A1 AALTONEN; Janne et al. METHOD AND SYSTEM TO PERFORM TEXT-BASED SEARCH AMONG PLURALITY OF DOCUMENTS - A method for training system to perform text-based search among plurality of documents. The method including receiving starting document, having at least one reference-identifier associated with the document; selecting reference-text from starting document; generating search-string by using a plurality of keywords using at least one first machine learning algorithm and at least one search-operator using at least one second machine learning algorithm; performing search among plurality of documents using search-string to fetch set of relevant documents; deriving reference-identifier distribution, corresponding to the at least one reference-identifier associated with document, for the set of relevant documents; and corelating at least one reference-identifier associated with starting document with reference-identifier distribution to determine performance-score for system. The steps of method are iteratively performed until performance-score exceeds a predetermined threshold, wherein at each iteration at least one of: different search-string is generated, different reference-text is obtained, different document is received.
US 20180188715 A1 Cella; Charles Howard et al. METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS - The system generally includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state. The local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. The local data collection system includes distributed complex programmable hardware device (CPLD) chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment.
US 20190180862 A1 Wisser; Jamie R. et al. CLOUD-BASED INTERACTIVE DIGITAL MEDICAL IMAGING AND PATIENT HEALTH INFORMATION EXCHANGE PLATFORM - The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.
US 20180165364 A1 Mehta; Jatin V. et al. Content Validation And Coding For Search Engine Optimization - A method and a content validation and coding system (CVCS) for validating and coding content of an electronic document for search engine optimization (SEO) are provided. The CVCS integrates with application programming interfaces (APIs) of search engines and/or media platforms. The CVCS creates a categorized repository of schema codes. The CVCS executes a web crawler for crawling and indexing an electronic document to be coded and content related thereto, from one or more search engines and/or media platforms via the APIs, and identifies structured data and content in the electronic document. The CVCS validates the identified content based on analytic criteria and determines optimal schema codes from the categorized repository and/or dynamically generated supplementary schema codes. The CVCS generates a structured data code map (SDCM) of the optimal schema codes and codes the validated content with the optimal schema codes within the identified structured data using the SDCM for SEO.
US 20190259008 A1 Lindsey; Stephen Boyd System and Method of Indexing Websites Using Contextually Unique Domain Extensions - An online index of websites that leverages generic top-level domain extension names as an intuitive nomenclature with which to index websites using a website template or plug-in. The website template or plug-in and associated software accesses the consolidated data of the indexed websites into a virtual database and synchronizes the data to a user's hardware wallet which operates an exchange software program forming a decentralized exchange enabling transactions to be settled off-line and peer-to-peer which are then recorded onto a distributed ledger or block-chain.
US 20190324989 A1 Borochoff; Adam et al. RESOURCE DEPENDENCY SYSTEM AND GRAPHICAL USER INTERFACE - A resource dependency system displays two dynamically interactive interfaces in a resource dependency user interface, a hierarchical resource repository and a dependency graph user interface. User interactions on each interface can dynamically update either interface. For example, a selection of a particular resource in the dependency graph user interface causes the system to update the dependency graph user interface to indicate the selection and also updates the hierarchical resource repository to navigate to the appropriate folder corresponding to the stored location of the selected resource. In another example, a selection of a particular resource in the hierarchical resource repository causes the system to update the hierarchical resource repository to indicate the selection and also updates the dependency graph user interface to display an updated graph, indicate the selection and, in some embodiments, focus on the selected resource by zooming into a portion of the graph.
US 11704219 B1 Lerner; Steven et al. Performance monitoring of distributed ledger nodes - Systems and methods for performance monitoring of distributed ledger nodes by data intake and query systems. An example method includes: receiving, by an application performance monitoring engine, from a distributed ledger node, values of a plurality of metrics reflecting operational parameters of one or more tasks performed by the distributed ledger node; determining, by analyzing a data set comprising the values of the plurality of metrics, a value of a performance parameter of the distributed ledger node; and generating an alert responsive to determining that the value of the performance parameter satisfies an alert triggering condition.
US 11722500 B2 Singh; Anurag Secure identity verification marketplace using hashed data and forward hashing search functions - There are provided systems and methods for a secure identity verification marketplace using hashed data and forward hashing search functions. A service provider, such as an electronic transaction processor or other entity that may provide an online platform, may provide an online marketplace accessible by identity verification data bureaus, as well as entities requiring identity verification or other entities. The bureaus may utilize an SDK provided by the service provider to has their identity data and upload to the marketplace with metadata explaining the underlying identity verification data. This may be done so the underlying data is not revealed to the service provider or other entities without purchase and/or request to the bureau. When an entity searches for the data, a forward hashing function may determine hashes of different variations of the search query, which may be used to locate matching data.
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/Radu Andrei/
Primary Examiner, AU 3697
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