System and method for anonymous transactions

Abstract

A system and method for anonymous transactions includes at least one processor coupled to at least memory device, the at least one processor programmed to: generate an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format; store the anonymous consumer profile; store one or more advertising preferences associated with the consumer profile; encrypt the anonymous consumer profile; generate at least one virtual credit; store at least one proof of purchase associated with the anonymous consumer profile; encrypt the at least one proof of purchase; generate and encrypt a seller profile; store a seller profile; and associate the at least one virtual credit with the seller profile or the anonymous consumer profile.

Description

47384-3PCTSYSTEM AND METHOD FOR ANONYMOUS TRANSACTIONSCROSS-REFERENCE TO RELATED APPLICATIONS:

[0001] The current patent application claims the benefit of U.S. Provisional Application Serial No. 63 / 690,117, filed September 3, 2024, and entitled “SYSTEM AND METHOD FOR ANONYMOUS TRANSACTIONS,” the disclosure of which is hereby incorporated herein by reference in entirety.TECHNICAL FIELD

[0002] The field of the disclosure relates generally to data privacy. More specifically, the present invention is a system and method for conducting anonymous transactions, enabling anonymized targeted advertising, and providing a purchasing marketplace.BACKGROUND OF THE INVENTION

[0003] A challenge for modem consumer markets is a lack of negotiation power by consumers. Consumers must often do business on the terms of large corporations or companies, without having the power to negotiate or otherwise bargain with companies.

[0004] As a result, consumers must often sacrifice personal information privacy when doing business. When conducting transactions, companies often require consumers to submit personal information, including names, addresses, ages, product information, and other demographic or otherwise identifying information associated with the consumer. Existing platforms may also track consumer activity, such as with website cookies or other activity or behavior tracking. With no way to refuse, consumers are forced to submit personal information as a cost of doing business with companies and corporations. Existing commerce platforms and other marketplaces may offer pricing improvements, but value gained in these venues still goes to stockholders, and not towards further reducing prices.

[0005] Companies may use the obtained consumer information to target ads to consumers in the future, sell consumer data to other entities, or otherwise profit off of the obtained consumer information.

[0006] There exists a need for a centralized transaction system capable of anonymizing consumer data while permitting transactions between consumers and business to occur47384-3PCT seamlessly. There exists a need for a system capable of incentivizing both consumers and businesses to benefit from anonymizing consumer data in both advertising and purchasing transactions.SUMMARY OF THE INVENTION

[0007] One aspect includes a system for anonymous transactions including at least one processor coupled to at least memory device, the processor configured to: generate an anonymous consumer profile based on answers provided to questionnaires of nonidentifying information in a non-text format; store the anonymous consumer profile, store one or more advertising preferences associated with the consumer profile, encrypt the anonymous consumer profile, generate at least one virtual credit, store at least one proof of purchase associated with the anonymous consumer profile, encrypt the at least one proof of purchase, generate a seller profile, store a seller profile, and associate the at least one virtual credit with the seller profile or the anonymous consumer profile.

[0008] One aspect includes a system for anonymous transactions. The system also includes a consumer device; a seller device; and a logistics device. The consumer device may include at least one consumer processor coupled to at least one consumer memory device, the at least one consumer processor programmed to: generate an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format; store the anonymous consumer profile; store one or more advertising preferences associated with the consumer profile; encrypt the anonymous consumer profile; store at least one virtual credit associated with the anonymous consumer profile; store at least one proof of purchase associated with the anonymous consumer profile; generate at least one limited identity information; transmit the at least one limited identity information to the logistics device. The system also includes the seller device, the seller device may include at least one seller processor coupled to at least one seller memory device, the at least one seller processor programmed to: generate a seller profile; store the seller profile; where the seller profile includes at least one of a company name, company address, company contact information, product categories, product model listing, and links to product model videos; generate at least one transaction information; transmit the at least one transaction information to the logistics device; the logistics device may include at least one logistics processor coupled to at least one logistics memory device, the at least one47384-3PCT logistics processor programmed to: store, using a first encrypted data structure, the at least one limited identity information; store, using a second encrypted data structure, the at least one transaction information; associate a transaction number of the at least one transaction information with an identity of the at least one limited identity information to produce a shipping identifier; associate the shipping identifier with a shipping destination; and generate one or more shipping instructions based on the associating.BRIEF DESCRIPTION OF DRAWINGS

[0009] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0010] FIG. 1 A is a block diagram of an embodiment of a system for anonymous transactions;

[0011] FIG. IB is a block diagram of an alternative embodiment of FIG. 1A of an embodiment of a system for anonymous transactions;

[0012] FIG. 1C is a block diagram of an alternative embodiment of FIG. IB showing a system for anonymous transactions;

[0013] FIG. 2 is a flowchart of a method for anonymous transactions; and

[0014] FIG. 3 is a block diagram of an example computing system.

[0015] Corresponding reference characters indicate corresponding parts throughout the several views of the drawings. Although specific features of various examples may be shown in some drawings and not in others, this is for convenience only. Any feature of any drawing may be referenced or claimed in combination with any feature of any other drawing. The drawings are not to scale unless otherwise noted.47384-3PCTDETAILED DESCRIPTION

[0016] Described herein are system and methods for facilitating anonymous transactions between multiple parties. The anonymous transactions may include advertising, bidding, sales, transfer of information, shipping, or other business and information transactions.

[0017] The system may include an anonymous consumer profile and a seller profile. The anonymous consumer profile may be encrypted using one or more different forms of encryption, such as zero knowledge proofs. The anonymous consumer profile may store at least information relating to a consumer’s demographic information, such as gender, age, income, education, work industry, residence type, zip code, children, race / ethnicity, religion, and more. The anonymous consumer profile may transmit information, such as responses to targeted advertising, requests for participation in surveys and focus groups, proof of purchase information, and other requests received from the seller profile. The seller profile may store seller’s information including company name, address, email, telephone number, primary contact name, address, email, cell phone number, banking or other financial transaction or payment information, product categories, models available for sale or advertising, a link to an advertisement video for each model, a rebate amount offered as a percent of a purchase price of each product, one or more desired profiling data, and more.

[0018] To incentivize participation by consumers, all consumer data is anonymized and encrypted in the anonymous consumer profile. When a seller registers on the system, the seller may be prompted to offer one or more rebates exclusively on the system. Seller may be required to have rebates have a minimum value, for example, 3% of the purchase price of the item. Consumers may transmit anonymous and encrypted proof of purchases to sellers using the system that prove the consumer purchased the seller’s product, receiving rebate credit from the seller. Rebate credit may be provided as currency or as one or more virtual credits associated with the system. The existence of data in the anonymous consumer profile may be verifiable by a third-party while the content of the data remains anonymized and encrypted, for example, by using a zero-knowledge proof.

[0019] Sellers may leverage the system by submitting one or more requests to the system to obtain limited anonymized data from the anonymous consumer profiles stored on the system. Sellers may query anonymous consumer profiles for anonymized demographic47384-3PCT information, send survey requests, send requests to participate in focus groups, send questionnaires, and otherwise interact with consumers while the consumers’ personal identifying information remains anonymized. For example, if a seller wants to advertise a product exclusively to men over the age of 50 making at least $60,000 a year, the seller may submit a request with those parameters via the system. The number of anonymous consumer profiles matching those parameters is returned to the seller, at which point the seller can decide if they would like to transmit a targeted advertisement to those consumer profiles. The number of anonymous consumer profiles both matching the parameters and having matching advertising preferences may also be returned to the seller, or having at least one or more matching advertising preference or parameter. For example, out of 1000 profiles matching the parameters, the seller might be informed that 300 of those profiles have at least one matching advertising preference, 250 profiles have at least two matching advertising preferences, and 100 profiles have all matching advertising preferences. In some embodiments, the seller will not receive any personal identifying information from any consumer profile. Sellers may further transmit requests for consumer participation in surveys, focus groups, or other requests for consumer participation, the requests being filtered such that the request is shown or distributed to anonymous consumer profiles having matching advertising preferences, where the showing or distributing is based on one or more parameters, as described above.

[0020] Upon receiving a targeted advertisement, the consumer of the anonymous consumer profile can watch the advertisement and submit a proof of viewing of the advertisement, for example, by completing a questionnaire about the advertisement. Upon submission and verification of the proof of viewing, the anonymous consumer profile may generate and / or receive one or more virtual credits or one or more net revenue points. Virtual credits may be used as payment for transactions on the system. Net revenue points may be paid out as virtual credits or currency based on the net revenue of the system over a period of time. Virtual credits and net revenue points may also be awarded to consumer profiles based on measured participation using the system, such as for participation in focus groups, surveys, watching ads, submission of proof of purchases, referrals of other consumers, or participating in other transactions through the system.

[0021] A marketplace Al model and a personal Al agent are provided by the system. The Al models may retrieve and utilize data from system sources and / or external sources. The47384-3PCT personal Al agent may generate recommendations to assist consumers in leveraging the system, such as by providing tutorials on using the system or generating purchasing recommendations that align with consumer interests and reduce consumer costs. The marketplace Al model may generate recommendations for sellers, including tutorials on using the system or generating selling recommendations to help sellers leverage the available anonymous consumer data. The marketplace Al model may be trained or altered based upon feedback from users, such as through a voting process. The personal Al agent may be trained in collaboration with the marketplace Al model, such as by having the training of personal Al agent be augmented by the marketplace Al model.

[0022] The system may include a computing device including a processor and a memory device, the processor configured to execute one or more computer executable instructions stored on the memory. One or more computing devices may be used, for example, a consumer device, a seller device, and a logistics device may be communicatively coupled to work in concert. Data and consumer information may be separated between devices to further improve anonymity, for example, by ensuring that only the logistics device has access to the consumer shipping address.

[0023] FIG. 1 A is a block diagram of an example anonymous transaction system 100, including at least one consumer device 110, at least one seller device 130, and at least one logistics device 150. Consumer device 110 may generate and / or store at least one anonymous consumer profile 112, anonymous consumer profile 112 being configured to store at least one of a proof of purchase 114, an encrypted consumer information 116, and a virtual credit 118. Consumer device 110 may be in operable communication with the seller device 130 and the logistics device 150. Seller device 130 may generate and / or store at least one seller profile 132, seller profile 132 being configured to store at least one of a rebate offer 134, product listing 136, and participation opportunity 138. Seller device 130 may be in operable communication with consumer device 110 and logistics device 150. Logistics device 150 may generate and / or store at least one limited consumer shipping information 152. Logistics device 150 may be in operable communication with consumer device 110 and seller device 130. Consumer device 110, seller device 130, and logistics device 150 may be embodied by an example computing device, described further herein. Consumer device 110, seller device 130, and logistics device 150 may be in operable communication with each other by, for example, connecting over a network, a server, cloud47384-3PCT system, the Internet, or any other means to send and receive data. In some embodiments where a plurality of consumer devices 110 are used, each consumer device 110 may be associated with only one anonymous consumer profile 112 at once. If a new user wishes to generate a new anonymous consumer profile on the consumer device 110, the old profile must first be deleted.

[0024] Anonymous consumer profile 112 may store proof of purchase 114, encrypted consumer information 116, and at least one virtual credit 118. Anonymous consumer profile 112 may be generated by consumer device 110 and the generation may be based on one or more information provided by an associated consumer. Initial information associated with anonymous consumer profile 112 may be generated based upon an account creation associated with system 100. For example, system 100 may receive basic login information, such as a user name, password, and / or other information necessary to set up a website account within system 100. Each account may be associated with a single anonymous consumer profile 112. Anonymous consumer profile 112 may be generated for an account based on answers provided by a consumer to questionnaires of non-identifying information in a non-text format, for example, in response to a questionnaire containing only multiple-choice questions. The non-text format ensures that information provided to create the anonymous consumer profile 112 will not contain personally identifying information. Information received from the questionnaire may be encrypted and stored as encrypted consumer information 116 in the anonymous consumer profile 112. In some embodiments, all information contained in anonymous consumer profile 112 is encrypted by consumer device 110. Anonymous consumer profile 112 may be encrypted using a zero-knowledge proof, such that the presence or existence of one or more information of the anonymous consumer profile 112 is verifiable by a third-party using the zeroknowledge proof. Other encryption methods may be used, including symmetric encryption and asymmetric encryption, such as Triple Data Encryption Standard, Rivest-Shamir- Adleman algorithms, Advanced Encryption Standard, Format-Preserving Encryption, and / or Elliptic Curve Cryptography. In some embodiments, anonymous consumer profile 112 may further store reviews, images, and / or videos of products owned by a consumer. A consumer may upload videos, images, and / or reviews of products owned as owned product information, and choose whether the owned product information should be publicly available. Owned product information that is public may be queried by Al models, sellers,47384-3PCT and / or other consumers as described further herein. Encryption using a zero-knowledge proof may include using a zero-knowledge succinct non-interactive argument of knowledge or a zero-knowledge scalable transparent argument of knowledge, such that data of anonymous consumer profile 112 is verifiable by a third-party without revealing the contents of the data.

[0025] Encrypted consumer information 116 may further include one or more advertising preferences. Advertising preferences may be determined based on user input. Advertising preferences may include an indication of the type of products, type of ads, or other filter based on consumer preference for what ads or offers the consumer would like to receive.

[0026] Consumer device 110 may generate one or more virtual credits 118 and / or one or more net revenue points 119 upon completion of one or more actions by anonymous consumer profile 112, and associate the virtual credits 118 and / or net revenue points 119 with anonymous consumer profile 112, such that the virtual credits 118 and / or net revenue points 119 are “owned” by the anonymous consumer profile 112. For example, if anonymous consumer profile 112 submits a verified proof of purchase 114, consumer device 110 may generate and associate one or more virtual credits 118 and / or net revenue points 119 for anonymous consumer profile 112. Other actions that may generate virtual credits 118 and / or net revenue points 119 include but are not limited to participation in focus groups, an earned referral fee for inviting new members, participation in surveys, submission of verified proof of purchases 114, updating and / or completing submission of information of the anonymous consumer profile 112, verification that a user has been a member of an external website for a threshold period of time, and verification of viewing of an advertisement.

[0027] Virtual credit 118 may include any virtual or digital currency. Virtual credit 118 may be stored in blockchain format as an element of a blockchain. Virtual credit 118 may be valued in fiat and / or correspond 1 : 1 with any unit of legal tender, for example, the U.S. Dollar and / or a U.S. Dollar Denominated Coin (USDC). Virtual credit 118 may include net revenue points 119. In some embodiments, net revenue points 119 may be stored as separate entities from virtual credit 118. System may associate virtual credit 118 and / or net revenue points 119 with consumer profile 112 and / or seller profile 132. In some embodiments, virtual credit 118 and / or net revenue points 119 may be associated with only47384-3PCT one profile at a time, such that a credit is associated with either consumer profile 112 or seller profile 132, and thus “owned” by one profile. System may distribute currency to a party using the system, for example, an anonymous consumer profile 112, a marketing partner, or a seller, when a predetermined value of time or virtual credits 118 is reached. The amount of the distribution may be based on the number of virtual credits 118 and / or net revenue points 119 associated with or owned by the party. At periodic intervals or over a predetermined amount of time, the system may distribute net revenue credit. Net revenue credit may include legal tender, cryptocurrency, and / or other currency. The distributing of net revenue credit may be based on the amount of net revenue of the system, such that a party receives a portion of the net revenue based on the number of net revenue points 119 owned by or associated with the party. The portion of net revenue received by a party may be calculated based on a formula to determine the percent of the party’s net revenue points 119 in relation to the total net revenue points 119 earned by all parties during a period of time, such that a party receives a percent of a total net revenue credit available during the period of time. In some embodiments, virtual credit 118 may be usable as part of one or more transactions in the system. For example, seller profile 132 may transfer one or more virtual credits 118 to an anonymous consumer profile 112 in response to an action taken by anonymous consumer profile 112, including submitting a verified proof of purchase 114, proof of survey completion, proof of focus group completion, or any other action or transaction. Likewise, anonymous consumer profile 112 may transfer virtual credit 118 to seller profile 132, for example, in response to anonymous consumer profile 112 purchasing a product from seller profile 132 or any other transaction. Transferring of virtual credit 118 may include changing ownership or association of virtual credit 118. In some embodiments, virtual credit 118 may be used for transaction with any third-party companies associated with the system for anonymous transactions. For example, one or more companies may agree to accept virtual credit 118 from consumers in exchange for goods or services offered by the company. In the same manner described above, net revenue points 119 may be generated, stored, and / or associated with a party using the system, including a seller or logistics company. In some embodiments, virtual credits 118 may be tracked by system 100 using double-entry accounting. For example, for a transfer of virtual credit 118 to be completed, the virtual credit 118 must be sent from a first source and received by a second source, such that the transaction represents a debit of virtual credit 118 from the first source and a credit of virtual credit 118 to the second source.47384-3PCT

[0028] Proof of purchase 114 may include information verifiable by seller profile 132 that a consumer has purchased one or more seller’s products, including the product origin. The product origin may include information related to the manufacturer, model number, SKU, serial number, retailer, and / or other identifying information or information about parties responsible for the creation and distribution of the product. Proof of purchase 114 may be stored as a non-fungible token. For example, proof of purchase 114 may be a receipt, transaction number, serial number, purchase location, or other information capable of identifying a location of the purchase and / or the origin of the product. Consumer device 110 may encrypt proof of purchase 114. For example, proof of purchase 114 may be encrypted using a zero-knowledge proof, such that the product origin is verifiable by a third-party using the zero-knowledge proof, while any consumer-identifying information on the proof of purchase 114 remains encrypted and anonymous. Consumer device 110 may transmit verifiable information from proof of purchase 114 to seller device 130. In some embodiments, verifiable information may include one or more piece of consumer information 116. The inclusion of verifiable information may be based upon a request or requirement from the seller profile 132 associated with the creation of the rebate offer 134. Upon verification by system 100 or seller device 130 of the product origin from the proof of purchase 114, consumer device 110 may generate one or more virtual credits 118 which are then associated with the anonymous consumer profile 112 that submitted the proof of purchase 114. In some embodiments, seller device 130 may generate virtual credits 118 and transmit and / or associate virtual credits 118 with consumer profile 112. In some embodiments, system 100 or seller device 130 may transfer the one or more virtual credits 118 from the seller profile 132 associated with the rebate offer 134 to anonymous consumer profile 112 submitting the proof of purchase 114. In some embodiments, system 100 may generate and store a preview of any rebate information associated with a purchase upon submission of a proof of purchase 114 by a user, where the preview of rebate information may include rebate amount information (e.g., the rebate percentage being offered) and the direct-to-consumer custom price.

[0029] In some embodiments, a user of anonymous consumer profile 112 may upload information regarding any consumer products owned by the user. Uploaded information may include product information such as model numbers and serial numbers, purchase date, reviews, photos of the product, and / or other information about the product. The user47384-3PCT may elect to transmit this information to marketplace device 140 and / or a manufacturer associated with a seller profile 132, where the manufacturer may transmit virtual credit to the user of anonymous consumer profile 112 in response to receiving the uploaded information. The uploading process may be managed by personal Al agent 160 and / or marketplace Al model 162 to prompt a user with what information the manufacture would want to receive.

[0030] Anonymous consumer profile 112 may further generate and / or store a personal referral link associated with the anonymous consumer profile 112. Upon creation of a new anonymous consumer profile 112 using the referral link, consumer device 110 may generate at least one virtual credit 118 and / or net revenue point and associate the virtual credit 118 and / or net revenue point with the referring anonymous consumer profile 112. Each anonymous consumer profile 112 may have a limited number of referral links, for example, a profile may have a capacity of ten referral links.

[0031] In some embodiments, a marketing partner may be registered in the system with a marketing partner profile. Marketing partners may include alliance partners and individual members (e.g., consumers). Marketing partners may have access to a limited subset of features of the system, for example, alliance partner profiles may only be able refer new members and receive a percentage of the subscription revenue from new members that they have referred. Marketing partners may be stored as a type of consumer profile 112 on consumer device 110, or, in some embodiments, marketing partner profiles may be stored on seller device 130 and / or on a separate marketing partner device that is structurally and functionally similar to the consumer device 110. Marketing partners may generate referral links as described above with respect to anonymous consumer profile 112, such that marketing partners may distribute referral links and receive virtual credits 118 when a new user signs up using the referral link.

[0032] Anonymous consumer profile 112 may generate and / or transmit at least one limited identity information to logistics device 150 and / or seller device 130. Limited identity information may include, for example, data necessary for conducting a shipping transaction to an address associated with anonymous consumer profile 112. Limited identity information may include, for example, a full name, a transaction number, a transaction code, a full zip code, and / or the first three digits of a ZIP code associated with the47384-3PCT consumer’s address. Anonymous consumer profile 112 may transmit the limited identity information upon response to receiving a shipping request, upon generating a shipping request, and / or based on some other criteria or value. In some embodiments, limited identity information may include all digits of a ZIP code associated with the consumer’s address, with the first three digits of the ZIP code being transmitted to seller device 130. In some embodiments, consumer device 110 may store a portion of the limited identity information, for example, the first three digits of the ZIP code, and may transmit the portion of the limited identity information to the seller device 130. In an example embodiment, anonymous consumer profile may generate and / or transmit a first limited identity information to the seller device 130, and a second limited identity information including a full name and an address to logistics device 150. The first limited identity information may include a three digit zip code.

[0033] Seller profile 132 may store rebate offer 134, product listing 136, and participation opportunities 138. Seller profile 132 may include seller information, such as company name, address, email, telephone number, primary contact name, address, email, cell phone number, product categories, banking or other financial transaction information, product model listings, and / or a link to an advertisement video for product models. Product listing 136 may include various information about one or more products offered by the seller, and may be stored and / or presented as an information catalogue. For example, product listing 136 may include a product’s categorization information, any associated rebate information including a rebate value as a percent of the purchase price, focus group opportunities, survey opportunities, advertisements, availability information, and / or other information associated with a product. Product listing 136 may include the link to an advertisement video for the product model, alongside the video length and an estimated time to complete any response associated with the video or associated surveys or focus group opportunities.

[0034] Seller device 130 may generate and / or store one or more advertisements, surveys, questionnaires, or focus group requests associated with the seller profile 132, for example, generating and storing a video advertisement and associated video hyperlink for a seller product model. A user of the seller profile 132 may select one or more marketing filters to be associated with advertisement. For example, a marketing filter may include restricting the ad to a specific income bracket, age demographic, or may be based on other consumer information. Once marketing filters are selected and confirmed, seller profile 132 may47384-3PCT query one or more consumer devices 110 to receive the number of anonymous consumer profiles 112 matching the marketing filters. For example, a seller may wish to submit an advertisement for televisions to only people over the age of 40 having more than $65,000 of yearly income. When consumer device 110 receives the query, consumer device 110 may return the number of consumer profiles having the matching information. In some embodiments, consumer device 110 may return the number of consumer profiles having both matching information to the marketing filter and matching advertising preferences. The number of consumer profiles matching the query may be displayed to the seller using seller device 130. The seller may then approve or disapprove of the advertisement. If the seller elects to transmit the advertisement, the advertisement may be transmitted to the consumer device 110 and distributed or otherwise made available to any anonymous consumer profile 112 matching the marketing filter and having advertising preferences that match the advertisement content. In some embodiments, the advertisement may be transmitted to the consumer device 110 and distributed or otherwise made available to any anonymous consumer profile 112 matching the marketing filter and having at least one advertising preference that matches the advertisement content. In some embodiments, the advertisement may be transmitted to marketplace device 140 and only displayed to a user consumer device 110 upon the user’s request to view advertisements.

[0035] Seller device 130 may generate one or more virtual credits 118 and associate the one or more virtual credits 118 with a seller profile 132 or consumer profile 112. Seller profile 132 may be encouraged to keep sufficient virtual credits 118 on hand to cover the costs of outstanding or anticipated rebate offers 134, focus group offers, advertising campaigns, and / or other expenses associated with the system. Seller device 130 may store and monitor a virtual credit 118 balance associated with seller profile 132. If the virtual credit 118 balance drops below a threshold value, seller device 130 locks or otherwise restricts access to one or more functions or features of the seller account. The threshold value may be calculated based on a predicted aggregated value of proof of purchases 114, focus groups, and surveys. For example, if a seller profile 132 is detected to not have a sufficient balance to cover outstanding proof of purchases 114, the seller profile 132 may be restricted from distributing advertisements until the threshold virtual credit 118 balance is met or exceeded.47384-3PCT

[0036] Seller profile 132 may generate and / or transmit a request to participate in a participation opportunity 138 to anonymous consumer profile 112. Participation opportunities may include, for example, focus groups, surveys, or advertisement videos. For example, a seller may transmit a request for a consumer to participate in a focus group for a new computer. The participation opportunity 138 may be transmitted to anonymous consumer profiles 112 having matching advertising preferences. Seller profile 132 may apply marketing filters as described above to the participation opportunity 138, such that the participation opportunity 138 request is transmitted only to anonymous consumer profiles 112 matching the applied marketing filters. In response to receiving proof of completion of the participation opportunity 138, seller profile 132 may transmit, or system may generate, one or more virtual credits 118 associated with the anonymous consumer profile 112 completing the participation opportunity 138. In some embodiments, requests associated with a participation opportunity 138 may be transmitted to marketplace device 140. The requests associated with a participation opportunity 138 may be presented to consumer profile 112 when a user of consumer profile 112 interacts with marketplace device, as described in greater detail below with respect to FIG. IB.

[0037] Seller profile 132 may generate and / or transmit at least one transaction information. The at least one transaction information may be generated, for example, in response to a purchase request or product request received from an anonymous consumer profile 112. The at least one transaction information may include a transaction number, shipping information and destinations, limited consumer distribution information (e.g. the first three digits of consumer’s ZIP code), distribution information such as distribution center locations, and / or a current location of the product to be shipped.

[0038] Logistics device 150 may store limited consumer shipping information 152 that may include the necessary information to ship an item to the address associated with an anonymous consumer profile 112. Limited consumer shipping information 152 may be encrypted, for example, using a zero-knowledge proof.

[0039] Logistics device 150 may receive and / or store limited identity information. Limited identity information may be stored using an encrypted data structure, or may be stored and encrypted using, for example, a zero-knowledge proof.47384-3PCT

[0040] Logistics device 150 may receive and / or store at least one transaction information. The at least one transaction information may be stored using an encrypted data structured, or may be stored and encrypted using, for example, a zero-knowledge proof. Logistics device 150 may associate the transaction number of the at least one transaction information with the limited identity information received from an anonymous consumer profile 112 to produce a shipping identifier. By associating the transaction information with the limited identity information, logistics device 150 may associate the shipping identifier with a shipping destination - for example, the shipping address associated with the anonymous consumer profile 112 involved in the transaction. Logistics device 150 may generate one or more shipping instructions based on the associating, such that a product of the transaction may be shipped to the associated anonymous consumer profile 112.

[0041] In some embodiments, an owner of anonymous consumer profile 112 may submit a request for a bid or an offer to buy a product to seller device 130. The request for a bid may include, for example, a request to purchase a specific product. The request for a bid may be distributed to one or more seller profiles 132 having product listings matching a product listed in the request for a bid. Sellers may bid by lowering prices in a reverse auction format, such that the lowest price product will be sold to the consumer at the close of the request for a bid. Once the bid is closed, seller device 130 may transmit transaction information including a transaction number identifying the product, the product’s location, and the anonymous consumer profile 112 to logistics device 150, while anonymous consumer profile 112 transmits limited identity information to the logistics device.Logistics device 150 may generate one or more shipping instructions based on the receive information, as described above.

[0042] Referring now to FIG. IB, in some embodiments, the system for anonymous transactions may include an marketplace device 140. Marketplace device 140 may be in operable communication with consumer device 110 and seller device 130, such that marketplace device 140 acts as a screen between consumer device 110 and seller device 130, reducing direct interaction between consumer device 110 and seller device 130.

[0043] As shown in FIG. IB, rebate offers 134, product listings 136, participation opportunities 138, and anonymous consumer data may be stored on marketplace device 140. Consumer device 110 and seller device 130 may transmit or otherwise make available47384-3PCT proof of purchase 114, rebate offers 134, product listings 136, participation opportunities 138, and anonymous consumer data to marketplace device 140 for storage and future use. When a seller wishes to obtain data from anonymous consumer profiles 112 as described above, seller device 130 may query marketplace device 140 for anonymous consumer data. Anonymous consumer data may include one or more components of the anonymous consumer profiles 112 stored on consumer device 110, for example, advertising preferences, demographic data, psychographic data (e.g. special interests of consumers) and more. In one example, a seller may query marketplace device 140 to receive anonymous consumer data to determine a number of profiles having information matching only people over the age of 40 having more than $65,000 of yearly income who are interested in purchasing a television. Seller may transmit the participation opportunities 138 related to a product to marketplace device 140 instead of to consumer profile 112. Data stored on marketplace device 140 may be encrypted such that only the intended viewer or recipient of the data may decrypt and view the data (e.g., only a seller or a consumer participating in a focus group may decrypt the focus group information stored on the marketplace device 140).

[0044] In some embodiments, a user of anonymous consumer profile 112 may interact with the system through a user interface. For example, user interface may display information from consumer device 110, personal Al agent 160, marketplace Al model 162, marketplace device 140, and / or seller device 130. User interface may include one or more discrete sections. For example, the one or more discrete sections may include a social section, a profile section, a shopping section, and an offers section. Information, offers, and interactions from seller profiles 132 may be kept separated from the user’s personal tabs, such that seller information is only displayed on dedicated sections such as the shopping section and / or the offers section. Rather than receiving offers directly from a seller, user may access product listings 136 and participation opportunities 138 using a discrete section of the user interface. For example, marketplace device 140 may store product information from sellers in a listing format, such that each product may be associated with at least one of a category, sub-category, brand, brands in each category, products of each brand, available rebate offers, available advertisements, product tags, a total number of “preferred” products marked according to anonymous consumer profile 112 preferences, and / or any other information associated with a product. Users may then search, using the47384-3PCT interface, the listings of marketplace device 140 as needed through the associated discrete section of the user interface. For example, if a user wants to view microwaves having prices below $100, an instant rebate value of greater than $10, and at least one available advertisement, the user may filter the product listing view of the user interface to display only product listings matching those settings.

[0045] Sellers may also interact with the system using the user interface. Sellers may view and enter seller profile 132 information, product listings, product information, surveys, focus groups, queries, query results, and conduct any other interaction with the system through the user interface.

[0046] In some embodiments, system 100 may include bartering functionality between consumers of a plurality of consumer devices 110. Consumer devices 110 may be in communication with each other directly or through an external device, such as marketplace device 140. Consumer devices 110 may post offers to sell and / or trade items or services to other consumers. System 100 may manage the transactions between consumers, including handling any transfer of virtual credits between profiles in association with the transactions and management of shipping details in conjunction with logistics device.

[0047] In some embodiments, system 100 may include an area of interest network. The area of interest network may permit consumers (e.g., users of consumer profiles 110) to interact with other consumers based upon areas of interest designated by consumers. For example, if five users all indicate an interest in “deep water fishing” system 100 may identify the common area of interest and generate a communication channel for the users based upon the identification. The communication channel may include a forum, direct message thread, and / or other similar digital communication channel to permit transmission and receipt of messages between the users having the common interest. Areas of interest may include at least politics, religion, sports, technologies, geographic-based events, activities, and more. In some embodiments, system 100 may further include a dating website that may match users based upon indicated areas of interest. The area of interest network may be implemented on any device including a processor and memory, including at least one of consumer device 110, marketplace device 140, seller device 130, and / or another device.47384-3PCT

[0048] In the example embodiment, as shown in FIG. IB, system 100 may include one or more Al models. For example, system may include a marketplace Al model 162 and a personal Al agent 160. Al models are configured to receive input from elements of system 100 and external sources 164 (e.g., websites associated with sellers, news websites, social media, research sites, etc.). Al models may be restricted in the information the Al models can view by privacy settings, encryption, and / or anonymized data. For example, Al models may only be able to view data from system 100 that is publicly accessible and cannot view private encrypted consumer information 116, but can view consumer preferences that are public to sellers in system 100. Al models may be trained on historical information associated with system 100 and / or based on data obtained from external sources 164.

[0049] Al models may query or otherwise retrieve information from external sources 164 and elements of system 100 for use in both training and generating responses and / or recommendations to users of system 100. Al models may generate recommendations in response to a query submitted by a user and / or may generate recommendations upon an analysis value reaching a threshold value. The analysis value may be based on or associated with the interests, preferences, products, and / or history of a user. Al models may periodically monitor conditions of system 100 and external sources 164, including consumer preferences and market prices. When an event occurs with respect to an item (e.g., a price change, new model is released), Al models may increase the analysis value associated with the item. When the analysis value meets or exceeds a threshold value, Al models may transmit a notification to a user and / or seller who has listed the item as a preference. For example, for a user who has listed toasters as items of interest in their preferences, Al models may periodically monitor news and prices relating to toasters, and may alert user when a toaster model matching the user preferences goes on sale or when a new model matching the user preferences is released.

[0050] Al models may further provide walkthroughs, onboarding processes, and / or tutorials regarding system 100. For example, Al models may help users set up accounts, list items for sale, fill out consumer information and / or describe how to submit rebate offers. In some embodiments, Al models may generate video tutorials for users. The tutorials generated by Al models may be based on user experience with the system. For example, new users may receive a detailed tutorial, while a more experienced user may receive a tutorial of only new or advanced features. In another example, Al models may provide47384-3PCT information on monetization methods for personal information through system 100, advising users on ways to leverage the system without releasing private information. Al models may also provide information on security, such as by recommending security actions to be taken by users, such as implementing two-factor authentication, recommending and / or implementing a Virtual Private Network (VPN), providing walkthroughs on how to anonymize information within system 100, and / or taking other security measures to protect personal information.

[0051] Al models may output recommendations as videos, AR / VR overlays, graphic displays, user interfaces, prompts, and / or other digital and / or physical outputs viewable by a user. In some embodiments, Al models may be implemented as a chatbots to generate responses based on natural language processing, where a user is prompted to enter a phrase and the Al models parse the phrase and generate a response based on the parsed phrase.

[0052] In the example embodiments, Al models may be trained on an initial set of training data (e.g., a curated set of training data and / or existing data from system 100 and external sources 164). Al models may further be re-trained on an updated set of training data. The updated set of training data may be retrieved from system 100 and / or external sources 164, and may undergo one or more transformations and / or alterations, such as weighting data, transforming data, filtering data, altering data, and / or masking portions of the data.

[0053] In the example embodiment, marketplace Al model 162 is configured to receive queries and / or input from users and generate responses, marketplace Al model 162 may be in communication with consumer device 110, marketplace device 140, and seller device 130. In some embodiments, marketplace Al model 162 may generate notifications, alerts, and / or other information to transmit to users of system 100, including, for example, alerts about changing market conditions, notification of sales, and / or identifying issues within system 100. In some embodiments, marketplace Al model 162 may perform data analysis to analyze and synthesize data to generate one or more analysis outputs, synthesis outputs, charts, graphs, trendlines, recommendations, overviews, and / or other data analysis. The data analysis may be generated based on a query submitted by a user of system 100. For example, a seller may ask marketplace Al model 162 for advice regarding the best time to sell unused stock of smart phones, marketplace Al model 162 may query external sources 164 for news regarding smart phone developments and sales numbers, may query47384-3PCT seller device 132 and / or marketplace device 140 for information on current smartphone listings and may query consumer device 110 for information on users currently interested in purchasing a smart phone. Based on the retrieved information and historical data, marketplace Al model 162 may generate a recommendation including, for example, a plurality of dates recommended for a sale, a recommended pricing plan for the smartphones, a suggested rebate, and / or marketing strategies to sell the unused stock of smart phones.

[0054] In the example embodiment, marketplace Al model 162 162may further be configured to perform one or more security procedures and / or deploy one or more security measures to protect system 100. Marketplace Al model 162, may, for example, be trained to identify one or more security anomalies, such as a security breach, fraudulent transaction, and / or one or more other security anomalies that indicate an abnormality with system 100. Upon identifying a security anomaly, marketplace Al model 162 may take one or more remedial measures to rectify the security anomaly, including, for example, sending an alert to one or more users devices, instructing personal Al agent 160 to notify users of consumer device 110 and / or seller device 130 of the security anomaly, deploying one or more security programs to protect system 100 from the security anomaly, and / or taking another remedial measure to protect system 100 or to remedy the security anomaly. Marketplace Al model 162 may also be configured to perform the security procedures described above.

[0055] In some embodiments, as shown in, for example, FIG. 1C, marketplace Al model 162 may be in communication with personal Al agent 160 and may be updated and / or trained based upon information received from personal Al agent 160. For example, users of personal Al agent 160 may elect to have interactions with the personal Al agent 160 be made available for training, such as by a privacy setting in anonymous consumer profile 112 or on a case-by-case basis through interactions with personal Al agent 160. Personal Al agent 160 may transmit permissible data (e.g., non-private data approved for sending by a user) to marketplace Al model 162. Marketplace Al model 162 may aggregate and use the received data for training, allowing marketplace Al model 162 to be updated in realtime to better reflect changing consumer needs. Marketplace Al model 162 may store and maintain a consumer data set based upon the received data from personal Al agent 160. The consumer data set may be used to offer updates or recommendation to train personal47384-3PCTAl agent 160 to produce more accurate and relevant results that are personalized for specific users, allowing personal Al agent 160 to stay updated on trends in the community. A user associated with personal Al agent 160 may elect to accept or decline the recommendation and / or updates to train personal Al agent 160. In some embodiments, system 100 may provide one or more periodic voting system to determine training parameters for marketplace Al model 162 and / or governance issues related to system 100. For example, users may vote upon and / or veto one or more proposed features of marketplace Al model 162. Voting may be implemented as a multiple choice questionnaire that is transmitted to one or more consumer devices 110, where users of system 100, such as users of consumer devices 110 and / or seller devices 130, may respond to the questionnaire. The responses to the questionnaire may be used by system 100 to further define training parameters and training resources for marketplace Al model 162.

[0056] In the example embodiment, personal Al agent 160 may be configured to provide recommendations to consumer device 110. The personal Al agent 160 receives queries and / or other input from consumer device 110. In response, personal Al agent 160 generates a response to the query. Personal Al agent 160 may act as an advisor to empower consumer interests. For example, personal Al agent 160 may monitor a portion of consumer data from anonymous consumer profile 112, such as demographic information, consumer preferences, and / or consumer history. Personal Al agent 160 may synthesize and / or analyze data from system 100 and / or external sources 164 to generate one or more recommendations based on user queries and / or user interests. For example, if a user is looking to buy a computer, the user may ask personal Al agent 160 what the best time to buy a computer would be. Personal Al agent 160 may, for example, analyze historical sales data, current sales data, and / or computer development news to generate one or more recommendations for dates to purchase a computer at a reduced price. In some embodiments, personal Al agent 160 may be implemented as a chatbot to generate responses based on natural language processing. For example, a consumer user may submit a phrase such as “I want to build a canoe” to personal Al agent 160, and personal Al agent 160 may parse the phrase and generate recommendations based on the phrase, such as a materials lists, products in system 100 to assist in building a canoe, tutorials on woodworking, and / or other recommendations to assist the user in accomplishing the goal. In some embodiments, personal Al agent 160 may proactively provide suggestions to users47384-3PCT based on user interests, such as notifying users of sales on items or changes to the market for items that are listed in the users’ preferences.

[0057] In some embodiments, a user of consumer device 110 may have a personalized consumer Al model 160 that is trained and updated according to one or more user preferences, enabling the trained personal Al agent 160 to produce more accurate and relevant results for the user as more interactions between the user and the trained personal Al agent 160 occur. In some embodiments, personal Al agent 160 may proactively transmit recommendations to a user based upon user preferences and / or user history. For example, a user may select a frequency with which to receive updates (e.g., frequent, infrequent, never) and / or categories of interest, and personal Al agent 160 may generate and transmit one or more recommendations based upon the frequency and categories of interest to produce recommendations and / or updates that are relevant to the interests of the user. In some embodiments, personal Al agent 160 may be trained based upon data associated with marketplace Al model 162.

[0058] In another example embodiment, as shown in FIG. 1C, system 100 further includes a marketplace Al model 162. Marketplace Al model 162 is configured to receive queries and / or input from users of seller device 130 and generate responses and / or recommendations. Marketplace Al model 162 may be in communication with seller device 130, external sources 164, consumer device 110, and / or marketplace device 140. Marketplace Al model 162 may be trained and configured to generate and / or present an onboarding process to new users of the seller device 130. For example, marketplace Al model 162 may impart onboarding information, such as by tracking and presenting, in realtime, the number of users of system 100, a demographic breakdown of users of the platform based upon the non-confidential information of anonymous consumer profiles 112 and / or seller profiles 132, and / or tutorials on how to best use system 100.

[0059] In some embodiments, marketplace Al model 162 may generate recommendations, notifications, alerts, and / or other information to transmit to users of system 100, including, for example, alerts about changing market conditions, notification of sales, and / or identifying issues within system 100. In some embodiments, marketplace Al model 162 may perform data analysis to analyze and synthesize data to generate one or more analysis outputs, synthesis outputs, charts, graphs, trendlines, recommendations, overviews, and / or47384-3PCT other data analysis. The data analysis may be generated based on a query submitted by a user of system 100. For example, a seller may ask marketplace Al model 162 for advice regarding the best time to sell unused stock of smart phones. Marketplace Al model 162 may query external sources 164 for news regarding smart phone developments and sales numbers, may query seller device 130 and / or marketplace device 140 for information on current smartphone listings and / or may query consumer device 110 for information on users currently interested in purchasing a smart phone. Based on the retrieved information and historical data, marketplace Al model 162 may generate a recommendation including, for example, a plurality of dates recommended for a sale, a recommended pricing plan for the smartphones, a suggested rebate, and / or marketing strategies to sell the unused stock of smart phones.

[0060] In other embodiments, a user may query marketplace Al model 162 regarding publicly available demographic information of anonymous consumer profiles 112. For example, a user may query consumers based upon owned and purchased products, and marketplace Al model 162 may respond by transmitting or otherwise providing a summary and / or analysis of which products are popular, which features of products are popular, what price ranges and / or features are typically purchased by specific demographics, user satisfaction based upon public reviews, and / or other information available from external sources 164 and / or anonymous consumer profiles 112.

[0061] In some embodiments, system 100 may restrict access of marketplace Al model 162 to certain information from seller device 130. For example, non-public information stored in seller profiles 132 may be inaccessible to marketplace Al model 162. This restriction prohibits marketplace Al model 162 from using sensitive or confidential seller information for training the model and responding to user queries. This improves security and confidentiality by restricting sellers from accessing confidential data of competitors through marketplace Al model 162, while still enabling the marketplace Al model 162 to access otherwise public data for training and queries.

[0062] In some embodiments, marketplace Al model 162 may be implemented in communication with seller device 132, similar to the relationship between marketplace Al model 162 and personal Al agent 160, described above. One or more seller Al agents may be implemented similarly to personal Al agents 160 described above, receiving queries47384-3PCT from users of seller device 132 and generating recommendations in response. Marketplace Al model 162 may be updated and / or trained based upon information received from one or more seller Al agents. For example, users of the seller Al agents may elect to have interactions with the seller Al agents be made available for training, such as by a privacy setting in seller profile 132 or on a case-by-case basis through interactions with the seller Al agents. Seller Al agents may transmit permissible data (e.g., non-private data approved for sending by a user) to marketplace Al model 162. Marketplace Al model 162 may use the received data for training, allowing marketplace Al model 162 to be updated in realtime to better reflect changing consumer needs. Marketplace Al model 162 may store and maintain a seller data set based upon the received data from seller Al agents. The seller data set may be used to update and / or train seller Al agents to produce more accurate and relevant results that are personalized for specific users, allowing seller Al agents to stay updated on trends in the community.

[0063] Referring now to FIG. 2, a method 200 of conducting anonymous transactions may include generating 202 an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format. The method further includes storing 204 the anonymous consumer profile and storing 206 one or more advertising preferences associated with the anonymous consumer profile. The method may further include encrypting 208 the anonymous consumer profile, for example, using an encryption method such as a zero-knowledge proof, such that the existence of data in the anonymous consumer profile is verifiable by a third party without revealing the contents of the information. The method may include generating 210 at least one virtual credit, where the virtual credit may include at least one net revenue point. In some embodiments, the at least one virtual credit and the at least one net revenue point may be separate entities, such that the method further includes generating at least one net revenue point. The method may include storing 212 at least one proof of purchase associated with the anonymous consumer profile and encrypting 214 the at least one proof of purchase, for example, using a zeroknowledge proof, such that the existence of data in the proof of purchase is verifiable by a third party without revealing the contents of the information. The method may include generating 216 and storing 218 a seller profile. The method may include associating 220 at least one virtual credit with the seller profile or the anonymous consumer profile.47384-3PCT

[0064] FIG. 3 is a block diagram of an example computing device 300. In some embodiments, consumer device 110, seller device 130, marketplace device 140, and logistics device 150 may be embodied by computing device 300. In some embodiments, consumer device 110, seller device 130, and logistics device 150 may be embodied by an example computing device further including a database, server, mobile device, personal computer, or other known computing and / or storage device. Computing device 300 includes a processor 302 and a memory device 304. The processor 302 is coupled to the memory device 304 with a system bus 308. The term “processor” refers generally to any programmable system including systems and microcontrollers, reduced instruction set computers (RISC), complex instruction set computers (CISC), application specific integrated circuits (ASIC), programmable logic circuits (PLC), and any other circuit or processor capable of executing the functions described herein. The above examples are example only, and thus are not intended to limit in any way the definition or meaning of the term “processor.”

[0065] In the example embodiment, the memory device 304 includes one or more devices that enable information, such as executable instructions or other data (e.g., sensor data), to be stored and retrieved. Moreover, the memory device 304 includes one or more computer readable media, such as, without limitation, dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, or a hard disk. In the example embodiment, the memory device 304 stores, without limitation, application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, or any other type of data. The computing device 300, in the example embodiment, may also include a communication interface 306 that is coupled to the processor 302 via system bus 308. Moreover, the communication interface 306 is communicatively coupled to data acquisition devices.

[0066] In the example embodiment, processor 302 may be programmed by encoding an operation using one or more executable instructions and providing the executable instructions in the memory device 304. In the example embodiment, the processor 302 is programmed to select a plurality of measurements that are received from data acquisition devices.47384-3PCT

[0067] In operation, a computer executes computer-executable instructions embodied in one or more computer-executable components stored on one or more computer-readable media to implement aspects of the disclosure described or illustrated herein. The order of execution or performance of the operations in embodiments of the disclosure illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the disclosure may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the disclosure.

[0068] Some embodiments involve the use of one or more electronic processing or computing devices. As used herein, the terms “processor” and “computer” and related terms, e.g., “processing device,” and “computing device” are not limited to just those integrated circuits referred to in the art as a computer, but broadly refers to a processor, a processing device or system, a general purpose central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a microcomputer, a programmable logic controller (PLC), a reduced instruction set computer (RISC) processor, a field programmable gate array (FPGA), a digital signal processor (DSP), an application specific integrated circuit (ASIC), and other programmable circuits or processing devices capable of executing the functions described herein, and these terms are used interchangeably herein. These processing devices are generally “configured” to execute functions by programming or being programmed, or by the provisioning of instructions for execution. The above examples are not intended to limit in any way the definition or meaning of the terms processor, processing device, and related terms.

[0069] The various aspects illustrated by logical blocks, modules, circuits, processes, algorithms, and algorithm steps described above may be implemented as electronic hardware, software, or combinations of both. Certain disclosed components, blocks, modules, circuits, and steps are described in terms of their functionality, illustrating the interchangeability of their implementation in electronic hardware or software. The implementation of such functionality varies among different applications given varying system architectures and design constraints. Although such implementations may vary47384-3PCT from application to application, they do not constitute a departure from the scope of this disclosure.

[0070] Aspects of embodiments implemented in software may be implemented in program code, application software, application programming interfaces (APIs), firmware, middleware, microcode, hardware description languages (HDLs), or any combination thereof. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to, or integrated with, another code segment or an electronic hardware by passing or receiving information, data, arguments, parameters, memory contents, or memory locations. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0071] The actual software code or specialized control hardware used to implement these systems and methods is not limiting of the claimed features or this disclosure. Thus, the operation and behavior of the systems and methods were described without reference to the specific software code being understood that software and control hardware can be designed to implement the systems and methods based on the description herein.

[0072] When implemented in software, the disclosed functions may be embodied, or stored, as one or more instructions or code on or in memory. In the embodiments described herein, memory includes non-transitory computer-readable media, which may include, but is not limited to, media such as flash memory, a random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as a firmware, physical and virtual storage, CD-ROM, DVD, and any other digital source such as a network, a server, cloud system, or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory propagating signal. The methods described herein may be47384-3PCT embodied as executable instructions, e.g., “software” and “firmware,” in a non-transitory computer-readable medium. As used herein, the terms “software” and “firmware” are interchangeable and include any computer program stored in memory for execution by personal computers, workstations, clients, and servers. Such instructions, when executed by a processor, configure the processor to perform at least a portion of the disclosed methods.

[0073] The computer-implemented methods discussed herein may include additional, less, or alternate actions, including those discussed elsewhere herein. The methods may be implemented via one or more local or remote processors, transceivers, servers, and / or sensors (such as processors, transceivers, servers, and / or sensors mounted on vehicles or mobile devices, or associated with smart infrastructure or remote servers), and / or via computer-executable instructions stored on non-transitory computer-readable media or medium.EXEMPLARY COMPUTER SYSTEM

[0074] Figure 4 is a schematic diagram of a portion of exemplary anonymous transaction system 100 (shown in Figure 1). Anonymous transaction system 100 may communicate with other components or systems, such as third-party servers, client computer devices, user computer devices, server computer and / or server computer devices.

[0075] Anonymous transaction system 100 may include and / or be in communication with a database 402 that stores data 404, such as database 104 (shown in Figure 1), stored records generated by anonymous transaction system 100, and / or any other relevant data described herein. Data 404 received from network 400 may be stored in database 402. Anonymous transaction system 100 may be configured to use data 404 to generate an operational predictive model module 406 to operate as a chatbot and / or generational Al, the operational Al model being configured to, for example, receive requests, parse requests, generate responses to requests, generate recommendations, and transmit recommendations and / or responses to another device.

[0076] In exemplary embodiments, anonymous transaction system 100 may include a training set builder module 408 configured to submit one or more queries 410 to database 402 to retrieve subsets 412 of data 404, and to use those subsets 412 to build training data sets 414 for generating Operational predictive model module 406. For example, query 41047384-3PCT may be configured to retrieve certain fields from data 404 for a specific feature, a specific category, and / or any other division of factors desired by the user.

[0077] In various embodiments, training set builder module 408 may be configured to derive training data sets 414 from retrieved subsets 412. Each training data set 414 corresponds to a historical data 404 (“historical” in this context means completed in the past, as opposed to completed in real-time with respect to the time of retrieval). Each training data set 414 may include “model input” data fields along with at least one “result” data field representing a historical outcome associated with the model input. The model input data fields represent factors that may be expected to, or unexpectedly be found during model training to, have some correlation.

[0078] In exemplary embodiments, the model input data fields in training data sets 414 may be generated from data fields in subset 412 corresponding to historical data 404. In other words, a trained machine learning model 416 produced by a model trainer module 418 for use by an Al model, also known as Operational Predictive Model 406, is trained to make predictions based upon input values that can be generated from the data fields in data 404. Values in the model input data fields may include values copied directly from values in a corresponding data field in the retrieved subset 412, and / or values generated by modifying, combining, and / or otherwise operating upon values in one or more data fields in the retrieved subset 412. The use of such data fields as model input data fields facilitates the machine learning model in weighing these factors directly.

[0079] After training set builder module 408 generates training data sets 414, training set builder module 408 passes the training data sets 414 to model trainer module 418. In certain embodiments, model trainer module 418 may be configured to apply the model input data fields of each training data set 414 as inputs to one or more machine learning models. Each of the one or more machine learning models may be programmed to produce, for each training data set 414, at least one output intended to correspond to, or “predict,” a value of the at least one result data field of the training data set 414. “Machine learning” refers broadly to various algorithms that may be used to train the model to identify and recognize patterns in existing data in order to facilitate making predictions for subsequent new input data.47384-3PCT

[0080] Model trainer module 418 may be configured to compare, for each training data set 414, the at least one output of the model to the at least one result data field of the training data set 414, and apply a machine learning algorithm to adjust parameters of the model in order to reduce the difference or “error” between the at least one output and the corresponding at least one result data field. In this way, model trainer module 418 trains the machine learning model to accurately predict the value of the at least one result data field.

[0081] In other words, model trainer module 418 cycles the one or more machine learning models through the training data sets 414, causing adjustments in the model parameters, until the error between the at least one output and the at least one result data field falls below a suitable threshold, and then uploads at least one trained machine learning model 416 to Operational predictive model module 406 for application to data analysis 420. In exemplary embodiments, model trainer module 418 may be configured to simultaneously train multiple candidate machine learning models and to select the best performing candidate for each result data field, as measured by the “error” between the at least one output and the corresponding result data field, to upload to operational predictive model module 406.

[0082] In certain embodiments, the one or more machine learning models may include one or more neural networks, such as a convolutional neural network, a deep learning neural network, or the like. The neural network may have one or more layers of nodes, and the model parameters adjusted during training may be respective weight values applied to one or more inputs to each node to produce a node output. In other words, the nodes in each layer may receive one or more inputs and apply a weight to each input to generate a node output. The node inputs to the first layer may correspond to the model input data fields, and the node outputs of the final layer may correspond to the at least one output of the model, intended to predict the at least one result data field. One or more intermediate layers of nodes may be connected between the nodes of the first layer and the nodes of the final layer.

[0083] As model trainer module 418 cycles through the training data sets 414, model trainer module 418 applies a suitable backpropagation algorithm to adjust the weights in each node layer to minimize the error between the at least one output and the47384-3PCT corresponding result data field. In this fashion, the machine learning model is trained to produce output that reliably predicts the corresponding result data field. Alternatively, the machine learning model may have any suitable structure.

[0084] In some embodiments, model trainer module 418 may provide an advantage by automatically discovering and properly weighting complex, second- or third-order, and / or otherwise nonlinear interconnections between the model input data fields and the at least one output. Absent the machine learning model, such connections are unexpected and / or undiscoverable by human analysts.

[0085] The anonymous transaction system 100 of the present disclosure may be configured to operate on input data related to pricing models, image features, and complete images, including to receive, review, and respond to inquiries and submissions from a user device. In one exemplary embodiment, anonymous transaction system 100 executes the operational Al module 406 programmed to learn, without limitation, the veracity of data based upon varying events and details, relevant data sources, and queries used to prompt a user to provide relevant information.

[0086] To facilitate this learning, anonymous transaction system 100 may include one or more databases 402 at which the data, including data as well as responses, evidence, outcomes, etc., is stored. This data becomes one or more input training sets used by the training set builder 408. Model outputs can be formatted for presentation or review as visual representations of recommendations, as text-based or natural language recommendations, and the like. In exemplary embodiments, Operational predictive model module 406 may compare feedback, and may route a comparison result 422 generated by comparing data analysis 420 to the feedback to a model updater module 424 of AIA computer device 102. Model updater module 424 is configured to derive a correction signal 426 from comparison results 422 received for one or more analyses, and to provide correction signal 426 to model trainer module 418 to enable updating or “re-training” of the at least one machine learning model to improve performance. The retrained at least one machine learning model 416 may be periodically re-uploaded to operational predictive model module 406.MACHINE LEARNING AND OTHER MATTERS47384-3PCT

[0087] In some embodiments, anonymous transaction system 100 is configured to implement machine learning, such that anonymous transaction system 100 “learns” to analyze, organize, and / or process data without being explicitly programmed. Machine learning may be implemented through machine learning methods and algorithms (“ML methods and algorithms”). In an exemplary embodiment, a machine learning module (“ML module”) is configured to implement ML methods and algorithms. In some embodiments, ML methods and algorithms are applied to data inputs and generate machine learning outputs (“ML outputs”). Data inputs may include but are not limited to images, text data, and / or other types of data. ML outputs may include, but are not limited to identified objects, items classifications, textual product, and / or other data extracted from the images or textual data. In some embodiments, data inputs may include certain ML outputs.

[0088] In some embodiments, at least one of a plurality of ML methods and algorithms may be applied, which may include but are not limited to: linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, combined learning, reinforced learning, dimensionality reduction, and support vector machines. In various embodiments, the implemented ML methods and algorithms are directed toward at least one of a plurality of categorizations of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.

[0089] In one embodiment, the ML module employs supervised learning, which involves identifying patterns in existing data to make predictions about subsequently received data. Specifically, the ML module is “trained” using training data, which includes example inputs and associated example outputs. Based upon the training data, the ML module may generate a predictive function which maps outputs to inputs and may utilize the predictive function to generate ML outputs based upon data inputs. The example inputs and example outputs of the training data may include any of the data inputs or ML outputs described above. In the exemplary embodiment, a processing element may be trained by providing it with a large sample of text with known characteristics or features. Such information may include, for example, information associated with a plurality of text of a plurality of different questions, responses, objections, items, and / or information.47384-3PCT

[0090] In another embodiment, a ML module may employ unsupervised learning, which involves finding meaningful relationships in unorganized data. Unlike supervised learning, unsupervised learning does not involve user-initiated training based upon example inputs with associated outputs. Rather, in unsupervised learning, the ML module may organize unlabeled data according to a relationship determined by at least one ML method / algorithm employed by the ML module. Unorganized data may include any combination of data inputs and / or ML outputs as described above.

[0091] In yet another embodiment, a ML module may employ reinforcement learning, which involves optimizing outputs based upon feedback from a reward signal. Specifically, the ML module may receive a user-defined reward signal definition, receive a data input, utilize a decision-making model to generate a ML output based upon the data input, receive a reward signal based upon the reward signal definition and the ML output, and alter the decision-making model so as to receive a stronger reward signal for subsequently generated ML outputs. Other types of machine learning may also be employed, including deep or combined learning techniques.

[0092] In some embodiments, generative artificial intelligence (Al) models (also referred to as generative machine learning (ML) models) may be utilized with the present embodiments and may utilize voice bots or chatbots configured to utilize artificial intelligence and / or machine learning techniques as described herein. For instance, the voice or chatbot may be a ChatGPT chatbot, and may be configured to help generate a response document as described herein. The voice or chatbot may employ supervised or unsupervised machine learning techniques, which may be followed by, and / or used in conjunction with, reinforced or reinforcement learning techniques. The voice or chatbot may employ the techniques utilized for ChatGPT. The voice bot, chatbot, ChatGPT-based bot, ChatGPT bot, and / or other bots may generate audible or verbal output, text or textual output, visual or graphical output, output for use with speakers and / or display screens, and / or other types of output for user and / or other computer or bot consumption.47384-3PCT

[0093] Based upon these analyses, the processing element may learn how to identify characteristics and patterns that may then be applied to analyzing and classifying objects. The processing element may also learn how to identify attributes of different objects in different lighting. This information may be used to determine which classification models to use and which classifications to provide.

[0094] As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps unless such exclusion is explicitly recited. Furthermore, references to “one embodiment” of the disclosure or an “exemplary” or “example” embodiment are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Likewise, limitations associated with “one embodiment” or “an embodiment” should not be interpreted as limiting to all embodiments unless explicitly recited.

[0095] Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is generally intended, within the context presented, to disclose that an item, term, etc. may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Likewise, conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, is generally intended, within the context presented, to disclose at least one of X, at least one of Y, and at least one of Z.

[0096] The disclosed systems and methods are not limited to the specific embodiments described herein. Rather, components of the systems or steps of the methods may be utilized independently and separately from other described components or steps.

[0097] This written description uses examples to disclose various embodiments, which include the best mode, to enable any person skilled in the art to practice those embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences form the literal language of the claims.

Claims

47384-3PCTWhat is claimed is:

1. A system for anonymous transactions comprising at least one processor coupled to at least one memory device, the at least one processor programmed to: generate an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format; store the anonymous consumer profile; store one or more advertising preferences associated with the anonymous consumer profile; encrypt the anonymous consumer profile; generate at least one virtual credit; store at least one proof of purchase associated with the anonymous consumer profile; encrypt the at least one proof of purchase; generate a seller profile; store the seller profile; and associate the at least one virtual credit with the seller profile or the anonymous consumer profile.

2. The system of claim 1, wherein the non-text format of the questionnaire comprises one or more multiple choice questions.

3. The system of claim 1, wherein the at least one virtual credit comprises a blockchain block.

4. The system of claim 1, wherein the anonymous consumer profile is encrypted, the at least one proof of purchase comprises a non-fungible token including a product origin, and the at least one proof of purchase is encrypted.

5. The system of claim 4, wherein the anonymous consumer profile is encrypted using a first zero-knowledge proof and an existence of one or more information of the anonymous consumer profile is verifiable by a third-party using the first zeroknowledge proof.

6. The system of claim 4, further comprising: wherein the proof of purchase is encrypted using a second zero-knowledge proof and the product origin is verifiable by a third-party using the second zeroknowledge proof; and the at least one processor of the system is further configured to generate, upon verification of the product origin by a third-party, at least one virtual credit associated with the anonymous consumer profile.

7. The system of claim 1, wherein the at least one processor of the system is further configured to: generate one or more advertisements associated with the seller profile, wherein the advertisement includes one or more marketing filters; determine a number of anonymous consumer profiles having advertising preferences that match the marketing filter of the advertisement; display, using the seller profile, the number of anonymous consumer profiles that have advertising preferences that match the marketing filter of the advertisement; and transmit the one or more advertisements to the anonymous consumer profile when the marketing filter of the advertisement matches the advertising preferences of the anonymous consumer profile.

8. The system of claim 1, wherein the at least one processor of the system is further configured to: store a virtual credit balance associated with the seller profile; and lock one or more features of the seller profile when the virtual credit balance drops below a threshold value, wherein the threshold value is calculated based on a predicted aggregated value of proof of purchases, focus groups, and surveys.

9. The system of claim 1, wherein the system is further configured to: generate at least one referral link associated with the anonymous consumer profile;generate, upon creation of a new anonymous consumer profile using the referral link, at least one virtual credit associated with the anonymous consumer profile; and distribute, based on an amount of virtual credits stored, currency to an owner of the anonymous consumer profile when a predetermined value of time or virtual credits is reached.

10. The system of claim 1, wherein the at least one processor of the system is further configured to: store a net revenue point; and distribute, based on an amount of net revenue points stored, net revenue credit to an owner of the anonymous consumer profile when a predetermined value of time or net revenue points is reached.

11. The system of claim 1, wherein the at least one processor of the system is further configured to: transmit a request to participate in a focus group to the anonymous consumer profile; transmit a request to participate in a survey to the anonymous consumer profile; generate, in response to receiving a proof of focus group attendance, at least one virtual credit associated with the anonymous consumer profile; and generate, in response to receiving a proof of survey completion, at least one virtual credit associated with the anonymous consumer profile.

12. The system of claim 4, wherein the processor of the system is further configured to: transmit, in response to receiving a shipping request, one or more limited identity information; and wherein the limited identity information comprises a full-name and a first three digits of a ZIP code.

13. The system of claim 1, wherein the seller profile includes a product model listing and link to product model videos.

14. The system of claim 13, wherein the at least one processor of the system is further configured to: generate, in response to the anonymous consumer profile providing verification of viewing a product model video, one or more virtual credits associated with the anonymous consumer profile.

15. A system for anonymous transactions comprising: a consumer device; a seller device; a logistics device; the consumer device comprising at least one consumer processor coupled to at least one consumer memory device, the at least one consumer processor programmed to: generate an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format; store the anonymous consumer profile; store one or more advertising preferences associated with the anonymous consumer profile; encrypt the anonymous consumer profile; store at least one virtual credit associated with the anonymous consumer profile; store at least one proof of purchase associated with the anonymous consumer profile; generate at least one limited identity information; transmit the at least one limited identity information to the logistics device; the seller device comprising at least one seller processor coupled to at least one seller memory device, the at least one seller processor programmed to: generate a seller profile; store the seller profile;wherein the seller profile includes at least one of a company name, company address, company contact information, product categories, product model listing, and links to product model videos; generate at least one transaction information; transmit the at least one transaction information to the logistics device; the logistics device comprising at least one logistics processor coupled to at least one logistics memory device, the at least one logistics processor programmed to: store, using a first encrypted data structure, the at least one limited identity information; store, using a second encrypted data structure, the at least one transaction information; associate a transaction number of the at least one transaction information with an identity of the at least one limited identity information to produce a shipping identifier; associate the shipping identifier with a shipping destination; and generate one or more shipping instructions based on the associating.

16. The system of claim 15, wherein the non-text format of the questionnaire comprises one or more multiple choice questions.

17. The system of claim 15, wherein the at least one virtual credit stored by the consumer device comprises a blockchain block.

18. The system of claim 15, wherein the anonymous consumer profile of the consumer device is encrypted using at least one of a symmetric encryption method or an asymmetric encryption method. .

19. The system of claim 18, wherein the anonymous consumer profile of the consumer device is encrypted using a first zero-knowledge proof, the at least one proof of purchase comprises a non-fungible token including a product origin, and the at least one proof of purchase is encrypted using a second zero-knowledge proof,and wherein an existence of one or more information of the anonymous consumer profile is verifiable by a third-party using the first zero-knowledge proof.

20. The system of claim 18, wherein the product origin is verifiable by a third-party using the second zero-knowledge proof and the at least one processor of the system is further configured to: generate, upon verification of the product origin by a third-party, at least one virtual credit associated with the anonymous consumer profile.

21. The system of claim 15, wherein the at least one seller processor of the seller device is further configured to: generate one or more advertisements associated with the seller profile, wherein the advertisement includes one or more marketing filters; determine a number of anonymous consumer profiles having advertising preferences that match the marketing filter of the advertisement; display, using the seller profile, the number of anonymous consumer profiles that have advertising preferences that match the marketing filter of the advertisement; and transmit the one or more advertisements to the anonymous consumer profile when the marketing filter of the advertisement matches the advertising preferences of the anonymous consumer profile.

22. The system of claim 15, wherein the at least one seller processor of the seller device is further configured to: store a virtual credit balance associated with the seller profile; and lock one or more features of the seller profile when the virtual credit balance drops below a threshold value, wherein the threshold value is calculated based on a predicted aggregated value of proof of purchases, focus groups, and surveys.

23. The system of claim 15, wherein the at least one consumer processor of the consumer device is further configured to: generate a referral link associated with the anonymous consumer profile;generate, upon creation of a new anonymous consumer profile using the referral link, at least one virtual credit associated with the anonymous consumer profile; and distribute, based on an amount of virtual credits stored, currency to an owner of the anonymous consumer profile when a predetermined value of time or virtual credits is reached.

24. The system of claim 23, wherein the at least one consumer processor of the consumer device is further configured to: store a net revenue point; and distribute, based on an amount of net revenue points stored, net revenue credit to an owner of the anonymous consumer profile25. The system of claim 15, wherein the at least one seller processor of the seller device is further configured to: transmit a request to participate in a focus group to the anonymous consumer profile; transmit a request to participate in a survey to the anonymous consumer profile; generate, in response to receiving a proof of focus group attendance, at least one virtual credit associated with the anonymous consumer profile; and generate, in response to receiving a proof of survey completion, at least one virtual credit associated with the anonymous consumer profile.

26. The system of claim 15, wherein the consumer processor of the consumer device is further configured to: store a portion of the limited identity information; and transmit the portion of the limited identity information to the seller device, where the portion of the limited identity information includes the first three digits of a ZIP code.

27. A system for anonymous transactions comprising at least one processor coupled to at least memory device, the at least one processor programmed to:generate an anonymous consumer profile based on answers provided to questionnaires of non-identifying information in a non-text format; store the anonymous consumer profile; store one or more advertising preferences associated with the anonymous consumer profile; encrypt the anonymous consumer profile; generate at least one virtual credit; store at least one proof of purchase associated with the anonymous consumer profile; encrypt the at least one proof of purchase; generate a seller profile; store the seller profile; generate, using one or more Al models, a recommendation; transmit the recommendation to at least one of the seller profile or the anonymous consumer profile; and associate the at least one virtual credit with the seller profile or the anonymous consumer profile.

28. The system of Claim 27, wherein the one or more Al models are configured to generate the recommendation based on an analysis value reaching a threshold value.

29. The system of Claim 27, wherein the at least one processor of the system is further configured to train the one or more Al models at least based on an initial training set including data from one or more external sources.

30. The system of Claim 29, wherein the at least one processor of the system is further configured to train the one or more Al models based on an updated training set including transformed data from one or more external sources.

31. The system of Claim 27, wherein the recommendation includes a video tutorial.

32. The system of Claim 27, wherein the one or more Al models include a personal Al agent and a marketplace Al model.

33. The system of Claim 32, wherein the at least one processor is further configured to: access the marketplace Al model to generate one or more seller recommendations; and transmit the one or more seller recommendations to the seller profile.

34. The system of Claim 32, wherein the at least one processor is further configured to: access the personal Al agent to generate one or more consumer recommendations; and transmit the one or more consumer recommendations to the anonymous consumer profile.

35. The system of Claim 32, wherein the marketplace Al model is in communication with the personal Al agent to receive data from the personal Al agent, and wherein the at least one processor is further configured to: aggregate, using the marketplace Al model, data received from the personal Al agent; and transmit a recommendation to train the personal Al agent based on the aggregated data.

36. The system of Claim 27, wherein the one or more Al models include a marketplace Al model, wherein the at least one processor is further configured to: access the marketplace Al model to generate one or more seller recommendations; and transmit the one or more seller recommendations to the seller profile.

37. The system of claim 32, wherein the at least one processor is further programmed to detect, using the marketplace Al model, one or more security anomalies.

38. The system of claim 37, where in the at least one processor is further programmed to, upon detecting one or more security anomalies, deploy, using the marketplace Al model, one or more remedial measures.

39. The system of claim 15, further comprising: a marketplace device comprising at least one marketplace processor coupled to at least one marketplace memory device, the at least one marketplace processor programmed to: receive at least one request from the seller device; and transmit the at least one request to the consumer device.

40. The system of claim 39, wherein the at least one marketplace processor programmed to: transmit the at least one request to the consumer device upon receiving a query from the consumer device.

41. The system of claim 15, further comprising: a virtual credit associated with at least one of the consumer device or the seller device, wherein the virtual credit may be transmitted between devices in association with a transaction between the devices.

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