Computer-implemented system for authenticating and processing product sample requests on social media

Abstract

A computer-implemented system and method for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data are disclosed Interaction data is received from one or more social-media platforms and normalized into a standardized interaction record. The method determines whether the interaction corresponds to a product sample request or a product-evaluation request, and extracts a plurality of partial identity attributes from the interaction record and associated account data. One or more stored customer data repositories are accessed to generate candidate customer records, and a confidence score is computed for respective candidate customer records based on a comparison between the partial identity attributes and stored customer attributes. The requester is authenticated without requesting full identity data when the confidence score satisfies a first threshold. Duplicate, suspicious, and abusive requests are suppressed, and a fulfillment-eligible request record is generated when all policy criteria are satisfied.

Description

[0001] COMPUTER-IMPLEMENTED SYSTEM FOR AUTHENTICATING AND PROCESSING PRODUCT SAMPLE REQUESTS ON SOCIAL MEDIA

[0002] TECHNICAL FIELD

[0003]

[0001] The invention relates generally to computer-implemented systems for digital interaction processing, and more particularly to a system and a method for identifying, authenticating and processing product sample requests originating from social-media interactions associated with sparse, incomplete, or noisy requester identity information, and for generating fulfillment-eligible request records based on confidence-based authentication and duplicate suppression.

[0004] BACKGROUND OF THE INVENTION

[0005]

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of it being mentioned in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.

[0006]

[0003] Luxury-product brands, including manufacturers and distributors of perfumes, cosmetics, skincare products, premium consumer goods, and other sensory-evaluation-dependent products, frequently distribute product samples as part of their customer acquisition and retention strategies. Identifying genuine prospective customers who express product interest through social-media interactions, and distinguishing them from casual, fraudulent, duplicate, or low-value requesters, presents a significant technical challenge for such brands.

[0007]

[0004] Social-media platforms generate large volumes of interactions, including comments, mentions, direct messages, posts, replies, and engagement events, that may contain implicit or explicit expressions of interest in product samples or product evaluation. Conventional social customer relationship management systems and social listening platforms are configured to monitor and aggregate such interactions. However, such systems are generally not configured to authenticate a sample request originating from a social-media interaction where the requester'sidentity information is sparse, incomplete, fragmented, or otherwise insufficient for direct verification.

[0008]

[0005] In particular, existing systems suffer from one or more of the following deficiencies. First, they are unable to determine whether a social-media requester corresponds to an existing customer without initially requesting full personal identity data from the requester, which is commercially undesirable in luxury brand contexts where the brand experience and customer privacy are paramount. Second, they lack the capability to suppress duplicate, spam, abusive, or bot-generated sample requests across multiple accounts and platforms in a scalable and automated manner. Third, they are not configured to selectively escalate identity verification only when a confidence threshold is not satisfied, thereby avoiding unnecessary friction for genuine requesters. Fourth, they do not provide a mechanism for threshold-based activation of sample production or product evaluation programs based on validated social demand.

[0009]

[0006] At a technical level, the above deficiencies arise from four distinct and interrelated computational problems. First, social-media platforms expose interaction data in heterogeneous, platform-specific schemas and formats, making it technically impossible to perform uniform downstream identity processing without a normalization layer that resolves schema incompatibilities across sources. Second, the identity data available from social-media interactions is inherently sparse, fragmented, and noisy (comprising partial signals such as display-name tokens, username patterns, geographic indicators, and engagement histories) none of which individually constitute a reliable identity record, and all of which must be computationally aggregated and weighted to produce a meaningful correspondence estimate. Third, identity resolution from these incomplete signals produces ambiguous results where multiple candidate customer records may score similarly, and a system that fails to detect and handle this ambiguity will produce false-positive authentications. Fourth, the open and anonymous nature of socialmedia platforms creates machine-scale duplicate, bot-generated, and linked-account abuse patterns that cannot be suppressed by manual inspection at the volumes generated by modern social campaigns. Existing systems lack a technical architecture capable of addressing all four of these computational problems in an integrated pipeline.

[0010]

[0007] Therefore, there exists a need in the art for a computer-implemented system and method for identifying, authenticating and processing product sample requests from social-mediainteractions associated with incomplete requester identity data, that does not suffer from the above-mentioned deficiencies, or at least provides a viable alternative thereto.

[0011] SUMMARY OF THE INVENTION

[0012]

[0008] According to one aspect of the invention, there is provided a computer-implemented method for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data. The method comprises receiving, by one or more processors, interaction data from one or more social-media platforms, the interaction data being associated with a product, a brand, or a campaign. The method further comprises normalizing the interaction data into a standardized interaction record comprising a common field structure, and determining, from the standardized interaction record, whether the interaction data corresponds to a product sample request or a product-evaluation request. The method additionally comprises extracting, from the standardized interaction record and from account data associated therewith, a plurality of partial identity attributes corresponding to a requester, the plurality of partial identity attributes excluding a full postal address and a government-issued identifier. Further, the method comprises accessing one or more stored customer data repositories and generating a plurality of candidate customer records corresponding to the requester, and computing, for respective ones of the plurality of candidate customer records, a confidence score representative of a likelihood that the requester corresponds to an existing customer, based on a comparison between the plurality of partial identity attributes and customer attributes associated with the respective candidate customer records. Here, computing the confidence score comprises generating a ranked candidate list based on an aggregate similarity value derived from a plurality of correspondence values computed between a multi-attribute feature representation of the requester and stored customer attributes. Also, the confidence score is assigned an ambiguity indicator when a difference between a highest-ranked candidate customer record and a next-ranked candidate customer record in the ranked candidate list is below a defined margin. The method further comprises authenticating the requester as corresponding to an existing customer when the confidence score satisfies a first threshold, without initially requesting full identity data from the requester, and, when the confidence score does not satisfy the first threshold but satisfies a second threshold lower than the first threshold, initiating a staged verification workflow comprising requesting a single minimally-identifying datum from the requester andupdating the confidence score upon receipt thereof. The method further comprises determining whether the product sample request or the product-evaluation request is duplicate, suspicious, or abusive based on at least one of similarity analysis or frequency analysis performed across a plurality of request records, and generating a fulfillment-eligible request record when the interaction data corresponds to the product sample request or the product-evaluation request, the confidence score satisfies a policy criterion, and the product sample request or the productevaluation request is not determined to be duplicate, suspicious, or abusive.

[0013]

[0009] The technical advantage offered by the method described above lies in its ability to resolve a requester's identity against internal customer records from sparse, heterogeneous, and incomplete social-media interaction data, without initiating a request for personal information from the requester. The ambiguity-aware candidate ranking mechanism detects cases where multiple candidate records score similarly and flags them for conditional escalation, while the state-conditioned minimal-datum verification architecture ensures that only a single, maximally discriminating piece of information is requested when confidence is insufficient, rather than defaulting to full personal data collection. The multi-layered duplicate and abuse suppression mechanism, operating on cross-account linkage, message-content similarity, and rolling-window frequency patterns, ensures that only verified, non-abusive requests advance to the fulfillment pipeline. Together, these features provide a specific technical improvement over conventional social CRM and social listening systems that are not configured to perform automated identity resolution from sparse social signals without direct user interrogation.

[0014]

[0010] In one embodiment, the multi-attribute feature representation of the requester is generated from two or more of, but not limited to, a username, a display name, profile metadata, geographic information, prior brand-interaction history, linked account information, profile-image-derived features, message-pattern features, and campaign-participation data. Additionally, the single minimally-identifying datum is selected from one or more of, but not limited to, an email address, a postal code, a telephone number, or an order identifier.

[0015] [Oil] In one embodiment, the staged verification workflow further comprises generating a provisional requester record associated with the requester upon initiation of the staged verification workflow, and updating the confidence score upon receipt of the single minimally-identifyingdatum, and authenticating the requester when the updated confidence score satisfies the first threshold.

[0016]

[0012] In one embodiment, determining whether the product sample request or the productevaluation request is duplicate, suspicious, or abusive comprises detecting one or more of, but not limited to, repeated request patterns originating from a same account handle or from a plurality of linked account handles across one or more social-media platforms; repeated message-content similarity above a defined similarity threshold within a configurable rolling time window; abnormal request frequency exceeding a defined frequency threshold within the configurable rolling time window; or a common destination clustering pattern across a plurality of request records associated with different requester accounts.

[0017]

[0013] In one embodiment, extracting the plurality of partial identity attributes comprises deriving one or more of, but not limited to, username similarity features, display-name tokens, inferred geographic indicators, prior brand-interaction history, linked account references, profile biography data, profile-image-derived feature vectors, lexical or writing-style cues, campaign-specific participation patterns, time-zone consistency indicators, or social graph relationship indicators.

[0018]

[0014] In one embodiment, the one or more stored customer data repositories comprise two or more of, but not limited to, a customer relationship management database, an order-history database, a loyalty-program database, a prior sample-shipment database, a customer-support database, or a product-evaluation program database.

[0019]

[0015] In one embodiment, normalizing the interaction data into the standardized interaction record comprises converting platform-specific interaction objects received from a plurality of heterogeneous social-media platforms into the standardized interaction record having a common field structure comprising one or more of, but not limited to, a platform identifier, an account handle, a display name, interaction content, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

[0020]

[0016] In one embodiment, the method further comprises aggregating a plurality of fulfillment-eligible request records associated with a product, a campaign, or a geographic region, and computing a demand readiness score based on a weighted composite of two or more of, but not limited to, a validated request volume, a geographic concentration of the plurality of fulfillment-eligible request records, a distribution of confidence scores across the plurality of fulfillment-eligible request records, and a fulfillment cost parameter.

[0021]

[0017] In one embodiment, the method further comprises triggering one or more of, but not limited to, a sample-production recommendation, a sample-packaging recommendation, a trialdistribution event, or a product-evaluation program activation, when the demand readiness score satisfies a demand threshold associated with the product, the campaign, or the geographic region. Additionally, an evaluation cohort is generated comprising a ranked selection of requesters from the plurality of fulfillment-eligible request records, the ranking being based on one or more of, but not limited to, the confidence score, prior brand-interaction history, a loyalty-tier designation of a matched customer record, or campaign-participation data.

[0022]

[0018] In one embodiment, the method further comprises analyzing interaction content associated with a product sample request or a product-evaluation request to extract one or more fragrance preference indicators comprising one or more of, but not limited to, references to scent families, fragrance note descriptors, or olfactory attribute terms derived from the interaction content. The extracted fragrance preference indicators are stored in the one or more stored customer data repositories as a longitudinal preference profile associated with the requester. One or more products whose fragrance composition aligns with the stored longitudinal preference profile are identified for targeted promotional sample distribution to the requester upon a new product launch or campaign activation.

[0023]

[0019] According to another aspect of the invention, there is provided a computer-implemented system for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data. The system comprises a processor and a memory storing machine-readable instructions that, when executed by the processor, configure the processor to receive interaction data from one or more social-media platforms, the interaction data being associated with a product, a brand, or a campaign; normalize the interaction data into a standardized interaction record comprising a common field structure; determine, from the standardized interaction record, whether the interaction data corresponds to a product sample request or a product- evaluation request; extract, from the standardized interaction record and from account data associated therewith, a plurality of partial identity attributes corresponding to a requester, the plurality of partial identity attributes excluding a full postal address and agovernment-issued identifier; access one or more stored customer data repositories and generate a plurality of candidate customer records corresponding to the requester; compute, for respective ones of the plurality of candidate customer records, a confidence score representative of a likelihood that the requester corresponds to an existing customer, based on a comparison between the plurality of partial identity attributes and customer attributes associated with the respective candidate customer records, wherein computing the confidence score comprises generating a ranked candidate list based on an aggregate similarity value derived from a plurality of correspondence values computed between a multi-attribute feature representation of the requester and stored customer attributes, and wherein the confidence score is assigned an ambiguity indicator when a difference between a highest-ranked candidate customer record and a next-ranked candidate customer record in the ranked candidate list is below a defined margin; authenticate the requester as corresponding to an existing customer when the confidence score satisfies a first threshold, without initially requesting full identity data from the requester, and, when the confidence score does not satisfy the first threshold but satisfies a second threshold lower than the first threshold, initiate a staged verification workflow comprising requesting a single minimally-identifying datum from the requester and updating the confidence score upon receipt thereof; determine whether the product sample request or the product-evaluation request is duplicate, suspicious, or abusive based on at least one of similarity analysis or frequency analysis performed across a plurality of request records; and generate a fulfillment-eligible request record when the interaction data corresponds to the product sample request or the product-evaluation request, the confidence score satisfies a policy criterion, and the product sample request or the productevaluation request is not determined to be duplicate, suspicious, or abusive.

[0024]

[0020] In one embodiment, to compute the confidence score, the processor is further configured to generate a multi-attribute feature representation of the requester from two or more of, but not limited to, a username, a display name, profile metadata, geographic information, prior brandinteraction history, linked account information, profile-image-derived features, message-pattern features, and campaign-participation data; and the single minimally-identifying datum is selected from one or more of, but not limited to, an email address, a postal code, a telephone number, or an order identifier.

[0021] In one embodiment, to execute the staged verification workflow, the processor is further configured to generate a provisional requester record associated with the requester upon initiation of the staged verification workflow, and update the confidence score upon receipt of the single minimally-identifying datum, and authenticate the requester when the updated confidence score satisfies the first threshold.

[0025]

[0022] In one embodiment, to determine whether the product sample request or the productevaluation request is duplicate, suspicious, or abusive, the processor is further configured to detect one or more of, but not limited to, repeated request patterns originating from a same account handle or from a plurality of linked account handles across one or more social-media platforms; repeated message-content similarity above a defined similarity threshold within a configurable rolling time window; abnormal request frequency exceeding a defined frequency threshold within the configurable rolling time window; or a common destination clustering pattern across a plurality of request records associated with different requester accounts.

[0026]

[0023] In one embodiment, to extract the plurality of partial identity attributes, the processor is further configured to derive one or more of, but not limited to, username similarity features, display-name tokens, inferred geographic indicators, prior brand-interaction history, linked account references, profile biography data, profile-image-derived feature vectors, lexical or writing-style cues, campaign-specific participation patterns, time-zone consistency indicators, or social graph relationship indicators.

[0027]

[0024] In one embodiment, the one or more stored customer data repositories comprise two or more of, but not limited to, a customer relationship management database, an order-history database, a loyalty-program database, a prior sample-shipment database, a customer-support database, or a product-evaluation program database.

[0028]

[0025] In one embodiment, to normalize the interaction data into the standardized interaction record, the processor is further configured to convert platform-specific interaction objects received from a plurality of heterogeneous social-media platforms into the standardized interaction record having a common field structure comprising one or more of, but not limited to, a platform identifier, an account handle, a display name, interaction content, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

[0026] In one embodiment, the processor is further configured to aggregate a plurality of fulfillment-eligible request records associated with a product, a campaign, or a geographic region, and compute a demand readiness score based on a weighted composite of two or more of, but not limited to, a validated request volume, a geographic concentration of the plurality of fulfillment-eligible request records, a distribution of confidence scores across the plurality of fulfillment-eligible request records, and a fulfillment cost parameter.

[0029]

[0027] In one embodiment, the processor is further configured to trigger one or more of, but not limited to, a sample-production recommendation, a sample-packaging recommendation, a trialdistribution event, or a product-evaluation program activation, when the demand readiness score satisfies a demand threshold associated with the product, the campaign, or the geographic region; and generate an evaluation cohort comprising a ranked selection of requesters from the plurality of fulfillment-eligible request records, the ranking being based on one or more of, but not limited to, the confidence score, prior brand-interaction history, a loyalty-tier designation of a matched customer record, or campaign-participation data.

[0030]

[0028] In one embodiment, the processor is further configured to analyze interaction content associated with a product sample request or a product-evaluation request to extract one or more fragrance preference indicators comprising one or more of, but not limited to, references to scent families, fragrance note descriptors, or olfactory attribute terms derived from the interaction content. The extracted fragrance preference indicators are stored in the one or more stored customer data repositories as a longitudinal preference profile associated with the requester. The processor is further configured to identify one or more products whose fragrance composition aligns with the stored longitudinal preference profile, for targeted promotional sample distribution to the requester upon a new product launch or campaign activation.

[0031] BRIEF DESCRIPTION OF THE DRAWINGS

[0032]

[0029] So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of thisinvention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.

[0033]

[0030] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:

[0034] FIG. 1 illustrates a system environment for identifying and authenticating product sample requests from social-media interactions associated with incomplete requester identity data, in accordance with an embodiment of the present invention;

[0035] FIG. 2 illustrates a method for identifying and authenticating product sample requests from social-media interactions associated with incomplete requester identity data, in accordance with an embodiment of the present invention;

[0036] FIG. 3A and FIG. 3B illustrate information flow diagrams depicting exemplary data flows during the implementation of the method of FIG. 2, in accordance with an embodiment of the present invention;

[0037] FIG. 4 illustrates a sub-process for computing a confidence score for respective candidate customer records, in accordance with an embodiment of the present invention; and FIG. 5 illustrates a sub-process for staged verification of a requester, in accordance with an embodiment of the present invention.

[0038] DETAILED DESCRIPTION OF THE DRAWINGS

[0039]

[0031] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and is not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed. Still, on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e., meaning having the potential to),rather than the mandatory sense, (i.e., meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed after that, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like is included in the specification solely to provide a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.

[0040]

[0032] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases “consisting of’, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.

[0041]

[0033] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims.

[0042]

[0034] The present invention addresses a practical and technically complex challenge faced by luxury-product brands and consumer goods companies that distribute product samples as part of their customer engagement strategies. When a social-media user expresses interest in a product sample, whether through a comment, a direct message, a mention, or a reaction on platforms suchas Instagram, Facebook, TikTok, X, YouTube, WhatsApp, or Telegram, the brand typically has access only to sparse, publicly visible, or platform-accessible information about that user. The user's full identity (say, name, shipping address, email) is not immediately known, and asking for it upfront creates friction, risks a negative brand experience, and may inadvertently invite spam and abuse at scale.

[0043]

[0035] The disclosed system and method solve this problem by transforming fragmented socialmedia interaction data into authenticated, fulfillment-eligible sample request records through a multi-stage pipeline that operates, to the greatest extent possible, without disturbing the requester for personal information. The pipeline identifies whether an interaction constitutes a product sample request, extracts whatever partial identity signals are available from the requester's public profile and interaction history, matches those signals against internal customer records to compute a confidence score, and authenticates the requester silently when confidence is sufficient. Only when confidence is ambiguous or insufficient does the system escalate — and even then, it requests only the single most discriminating piece of information needed to resolve the ambiguity. Duplicate, spam, and abusive requests are actively suppressed throughout. When enough validated requests accumulate, the system can trigger sample production or beta-program activation automatically.

[0044]

[0036] The present invention provides a specific technical improvement over conventional social CRM and social listening systems by introducing a computerized sparse-identity resolution pipeline that addresses the four technical problems identified in the background section. The pipeline achieves this through: a canonical normalization layer that resolves schema heterogeneity across social-media platforms into a common interaction record; a bounded multi-attribute feature extraction mechanism that derives a defined set of identity signals from sparse account-level data; an ambiguity-aware candidate ranking architecture that computes correspondence values across multiple repositories and assigns an ambiguity indicator when the margin between top-ranked candidates falls below a defined threshold; a state-conditioned minimal-datum escalation mechanism that selectively requests only a single additional discriminating datum when confidence is ambiguous, rather than defaulting to full personal data collection; and a multicriterion abuse suppression layer that operates on cross-account linkage, message-content similarity, and rolling- window frequency patterns. The technical effect of this pipeline is a materialreduction in unnecessary identity verification exchanges, a reduction in false-positive fulfillment events caused by duplicate or fraudulent requests, and the automated processing of heterogeneous social-platform interaction streams into authenticated, abuse-filtered fulfillment records. These outcomes are not achievable by any single prior-art component individually or by their routine combination.

[0045]

[0037] The invention will now be described in reference to the accompanying drawings:

[0046]

[0038] Referring now to FIG. 1, the system environment (100) of the present invention is illustrated. The computer system (102) is the central processing entity of the disclosed system and comprises a memory (1022) and a processor (1024). The memory (1022) is configured to store machine-readable instructions that, when executed by the processor (1024), configure the processor (1024) to perform the operations described herein. The processor (1024) is configured to execute all processing logic of the invention, including interaction ingestion, normalization, request identification, sparse identity attribute extraction, candidate customer generation, confidence scoring, authentication, duplicate suppression, and fulfillment record generation. It will be appreciated by a person skilled in the art that the memory (1022) may include, but is not limited to, volatile memory such as random-access memory (RAM), non-volatile memory such as read-only memory (ROM), flash memory, electrically erasable programmable read-only memory (EEPROM), or any combination thereof. Similarly, the terminology "a processor" as used herein is intended to cover single-processor, multi-processor, multi-core, and distributed computing architectures, as the functionality described may be implemented across one or more processing units without departing from the scope of the present invention.

[0047]

[0039] The computer system (102) is communicatively connected to a plurality of external entities via a communication network (110). The communication network (110) may include, but is not limited to, the Internet, a wide area network (WAN), a local area network (LAN), a mobile data network, or any combination of public and private communication infrastructure. All data exchanges between the computer system (102) and external entities, including social-media platforms (108), the data repository (106), the requester device (112), and the operator interface (114), are conducted through the communication network (110), unless otherwise described.

[0048]

[0040] The social-media platforms (108) represent the primary input source of the system. The social-media platforms (108) may include, but are not limited to, Instagram, Facebook, TikTok,X (formerly Twitter), YouTube, WhatsApp, Telegram, Snapchat, Messenger, and other digital communication and social networking platforms. The computer system (102) is configured to retrieve interaction data from the social-media platforms (108) through, but not limited to, platform application programming interfaces (APIs), webhooks, permitted data connectors, or import pipelines, as may be supported by the respective platforms from time to time. It will be appreciated that the term "interaction data" as used herein encompasses, but is not limited to, comments, posts, mentions, direct messages, replies, reactions, engagement events, and any other form of usergenerated content or activity associated with a product, brand, or campaign on a social-media platform.

[0049]

[0041] The data repository (106) is a persistent data store communicatively connected to the computer system (102). The data repository (106) is configured to store one or more of, but not limited to, customer relationship management (CRM) records, order history records, loyalty program records, prior sample shipment records, customer support records, product evaluation program records, subscriber records, and prior request records. The data repository (106) may be implemented as a relational database, a NoSQL database, a distributed data store, a cloud-based storage service, or any combination thereof. The terminology "one or more stored customer data repositories" as used in the claims is intended to cover implementations where the stored data may be distributed across multiple physical or logical data stores, whether hosted on-premises, in the cloud, or in a hybrid environment.

[0050]

[0042] The requester device (112) represents the personal computing device or devices associated with a requester i.e. a social-media user who has expressed interest in a product sample. The requester device (112) may include, but is not limited to, a smartphone, a tablet, a laptop computer, a desktop computer, or any other internet- connected device through which the requester may access social-media platforms or receive communications. The requester device (112) is an external entity with respect to the computer system (102) and is not a component of the invention itself. It is shown in FIG. 1 and described herein for the purposes of illustrating the communication flows that occur during the staged verification phase of the disclosed method, as further described with reference to FIG. 3B.

[0051]

[0043] The operator interface (114) represents the computing device or dashboard through which the brand operator, being the entity responsible for administering the sample program, receivesand reviews the fulfillment-eligible request records generated by the computer system (102). The brand operator may include, but is not limited to, a brand manager, a customer experience team, a logistics coordinator, a CRM administrator, a marketing operations team, or any authorized personnel or automated downstream system responsible for making or executing fulfillment decisions with respect to the sample program. The operator interface (114) may be implemented as a web-based dashboard, a mobile application, an integrated CRM front-end, an enterprise resource planning system interface, an order management system, or any other operator-facing interface through which fulfillment decisions may be reviewed and actioned. Like the requester device (112), the operator interface (114) is an external endpoint and is not a processing component of the disclosed invention.

[0052]

[0044] In accordance with an embodiment of the present invention, the computer system (102) may be implemented as a cloud-hosted server or cluster of servers, enabling the system to scale horizontally to accommodate high volumes of social-media interaction data across multiple concurrent campaigns, brands, or product lines. In accordance with another embodiment of the present invention, the computer system (102) may be implemented as an on-premises enterprise server integrated with an existing CRM or marketing automation platform.

[0053]

[0045] In accordance with yet another embodiment of the present invention, the components of the computer system (102) may be distributed across a microservices architecture, wherein each processing function, ingestion, normalization, confidence scoring, duplicate suppression, and fulfillment authorization, is implemented as an independent, independently scalable service. However, it will be appreciated by a skilled addressee that any other suitable computational architecture may also be used without departing from the scope of the present invention.

[0054]

[0046] Referring now to FIG. 2, a method (200) for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data is illustrated. The method (200) is described below with concurrent reference to FIGS.

[0055] 3A and 3B, which illustrate the data flows associated with the respective stages of the method (200).

[0056]

[0047] The method starts at step 202, when the processor (1024) is configured to receive interaction data from one or more social-media platforms (108) associated with a product, a brand, or a campaign. As shown in FIG. 3A, social-media interaction data flows from the social-mediaplatforms (108) into the computer system (102) via the communication network (110). The interaction data may be collected continuously, periodically, or in response to trigger events such as a new mention, comment, or message associated with a monitored product, brand keyword, or campaign hashtag. The interaction data may include, but is not limited to, a platform identifier, an account handle, a display name, interaction content text, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

[0057]

[0048] Then, at step 204, the processor (1024) is configured to normalize the interaction data into a standardized interaction record comprising a common field structure. As shown in FIG. 3 A, the normalization process transforms platform-specific interaction objects received from a plurality of heterogeneous social-media platforms (108) into a unified representation. This is technically necessary because each social-media platform exposes interaction data in a platform-specific schema and format. For example: what Instagram returns for a comment differs structurally from what X returns for a mention or what WhatsApp returns for a direct message. The standardized interaction record provides a common field structure that enables all downstream processing steps to operate uniformly regardless of the originating platform. The common field structure may comprise, but is not limited to, a platform identifier, an account handle, a display name, interaction content, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

[0058]

[0049] After that, at step 206, the processor (1024) is configured to determine whether the interaction data corresponds to a product sample request or a product-evaluation request. As shown in FIG. 3 A, this determination is made by the computer system (102) based on analysis of the standardized interaction record. The determination may be performed using one or more of, but not limited to, keyword-based matching against a configurable request vocabulary, semantic intent analysis using natural language processing techniques, sentiment analysis, product mention detection, contextual reference evaluation, and engagement pattern analysis. An interaction may be classified as an explicit product sample request where the requester directly requests a sample (as an interest before buying), or as a product-evaluation request where the requester expresses interest in trying, testing, or evaluating the product, including, but not limited to, beta-tester style or early-access evaluation requests. Interactions that do not correspond to either category are not processed further by the disclosed pipeline.

[0050] In some embodiments, the system 100 may support multi-language request identification, wherein the semantic intent analysis is performed across interactions in multiple languages, enabling the system to identify sample requests expressed in languages other than English. This embodiment is particularly relevant for global luxury brands operating across linguistically diverse social-media audiences.

[0059]

[0051] Next, at step 208, the processor (1024) is configured to extract a plurality of partial identity attributes from the standardized interaction record and from account data associated therewith, the plurality of partial identity attributes corresponding to a requester. As shown in FIG. 3 A, this extraction is performed entirely within the computer system (102) without any direct query to the requester. The partial identity attributes are derived from whatever information is publicly or accessibly available through the social-media platform's API or permitted data access mechanisms. The partial identity attributes may include, but are not limited to, username similarity features, display-name tokens, inferred geographic indicators, prior brand-interaction history, linked account references, profile biography data, profile-image-derived feature vectors, lexical or writing-style cues, campaign-specific participation patterns, time-zone consistency indicators, and social graph relationship indicators. In accordance with an embodiment of the present invention, the partial identity attribute extraction may include profile-image-derived features, wherein feature vectors are extracted from the requester's publicly available profile image using a pre-trained image embedding model and compared against profile images stored in the customer data repository to generate an additional correspondence value contributing to the confidence score. This embodiment is particularly useful where a returning customer has changed their username across platforms but retains the same profile photograph.

[0060]

[0052] In accordance with an embodiment of the present invention, a cross-platform identity graph may be constructed by linking social-media account handles, display names, and profile metadata associated with the same individual across multiple platforms, generating a unified requester node with edge weights reflecting linkage confidence. The cross-platform identity graph may be used to enrich the partial identity attributes and improve confidence scoring accuracy across campaigns running on multiple platforms simultaneously.

[0061]

[0053] It is to be noted that the plurality of partial identity attributes, as used herein, expressly excludes a full postal address and a government-issued identifier, reflecting the deliberate designof the system to operate under conditions of incomplete requester identity data without initially requesting sensitive personal information.

[0062]

[0054] Then at, step 210, the processor (1024) is configured to access one or more stored customer data repositories and generate a plurality of candidate customer records corresponding to the requester. As shown in the information flow (300) in FIG. 3A, the computer system (102) queries the data repository (106), sending a request to access stored customer records and receiving candidate customer records in return. The one or more stored customer data repositories may comprise, but are not limited to, two or more of a customer relationship management database, an order-history database, a loyalty-program database, a prior sample-shipment database, a customersupport database, and a product-evaluation program database. The candidate customer records are generated by querying the repositories using the extracted partial identity attributes as search parameters, thereby identifying existing customer records that may correspond to the requester. In some embodiments, the system 100 may apply loyalty -tier weighting to the candidate customer record generation process, wherein a matched customer record associated with a higher loyalty tier receives a positive weighting factor in the aggregate similarity computation, prioritizing verified high-value customers in the fulfillment pipeline.

[0063]

[0055] It will be appreciated that the terminology "one or more stored customer data repositories" is intended to cover both single-database and multi-database implementations, including those where customer data is distributed across multiple organizational systems.

[0064]

[0056] Thereafter, at step 212, the processor (1024) is configured to compute, for respective ones of the plurality of candidate customer records, a confidence score representative of a likelihood that the requester corresponds to an existing customer, based on a comparison between the plurality of partial identity attributes and customer attributes associated with the respective candidate customer records. As shown in FIG. 3A, the confidence score is computed entirely within the computer system (102).

[0065]

[0057] Referring additionally to FIG. 4, the sub-process (400) for computing the confidence score is illustrated in detail. At step 402, the processor (1024) generates a multi-attribute feature representation of the requester from two or more of, but not limited to, a username, a display name, profile metadata, geographic information, prior brand-interaction history, linked account information, profile- image-derived features, message- pattern features, and campaign-participationdata. At step 404, for each candidate customer record, the processor (1024) computes a plurality of correspondence values between the multi-attribute feature representation and stored customer attributes associated with the respective candidate customer record. At step 406, the processor (1024) generates a ranked candidate list based on an aggregate similarity value derived from the plurality of correspondence values. At step 408, the processor (1024) evaluates whether the difference between the highest-ranked candidate customer record and the next-ranked candidate customer record in the ranked candidate list falls below a defined margin. If so, at step 410, the processor (1024) assigns an ambiguity indicator to the confidence score, indicating that the identity match is not sufficiently resolved, before returning the confidence score to the main pipeline at step 214. If no ambiguity is detected, the confidence score is returned without an ambiguity indicator. It will be appreciated that the confidence score computation may be implemented using, but not limited to, weighted attribute matching, machine learning-based similarity scoring, vector embedding and cosine distance computation, or any combination thereof. The precise weighting of attributes, the definition of the aggregate similarity function, and the threshold values used in the comparison are configurable parameters of the system and may be adjusted based on the specific brand context, customer data quality, and operational requirements.

[0066]

[0058] In accordance with an embodiment of the present invention, the confidence score may be expressed as a normalized value ranging from 0 to 1, or equivalently as a percentage ranging from 0% to 100%, wherein a value approaching 1 or 100% indicates a high likelihood of identity correspondence. The first threshold may range from, but is not limited to, 0.70 to 0.95, representing a high-confidence match sufficient for silent authentication. The second threshold, applicable to the staged verification sub-process, may range from, but is not limited to, 0.40 to 0.69, representing an ambiguous match where a minimal datum request may resolve the identity. A confidence score below the second threshold may be treated as a low-confidence or unresolvable match, resulting in the generation of a provisional requester record without initiating contact with the requester. It will be appreciated that the precise threshold values are configurable parameters of the system and may be adjusted by the brand operator based on acceptable false-positive rates, campaign sensitivity, and the quality and completeness of the available customer data. The terminology "satisfies a threshold", is intended to cover both equal -to and greater-than comparisons with the threshold value.

[0059] Returning to the method 200, at step 214, the processor ( 1024) is configured to authenticate the requester as corresponding to an existing customer when the confidence score satisfies a first threshold, without initially requesting full identity data from the requester. As shown in the information flow (300) in FIG. 3 A, the authentication result is generated within the computer system (102) and may be communicated to the requester device (112). This is the core nonintrusive authentication mechanism of the disclosed invention, when the confidence score is sufficiently high, the system silently resolves the requester's identity against internal records and proceeds without asking the requester for any personal information.

[0067]

[0060] When the confidence score does not satisfy the first threshold, the processor (1024) invokes the staged verification sub-process illustrated in FIG. 5. Referring to FIG. 5, at step 502, the processor (1024) evaluates the confidence score against a second threshold lower than the first threshold. If the confidence score satisfies the second threshold, at step 506, the processor (1024) initiates a staged verification workflow, requesting from the requester a single minimally-identifying datum selected from one or more of, but not limited to, an email address, a postal code, a telephone number, or an order identifier, without requesting a full set of personal identity information from the requester. This minimal datum request is delivered to the requester device (112) via the communication network (110), as shown in FIG. 3B. At step 508, the processor (1024) receives the single minimally-identifying datum from the requester. At step 510, a provisional requester record is generated. At step 512, the processor (1024) updates the confidence score based on the received datum. At step 514, if the updated confidence score satisfies the first threshold, the requester is authenticated at step 516, and processing returns to the main pipeline. If the updated confidence score remains below the first threshold, at step 518, the provisional record is maintained in a deferred state for potential future resolution. If at step 502 the confidence score does not satisfy even the second threshold, the processor (1024) generates a provisional requester record and defers the request at step 504, without initiating any contact with the requester. The terminology "one or more" before the confidence thresholds has been specifically used to cover both scenarios where a single threshold is applied and where a tiered threshold hierarchy is employed.

[0068]

[0061] Then, back to the method 200, at step 216, the processor (1024) is configured to determine whether the product sample request or the product-evaluation request is duplicate, suspicious, orabusive, based on at least one of similarity analysis or frequency analysis performed across a plurality of request records. As shown in the information flow (350) in FIG. 3B, this determination involves querying the data repository (106) for prior request records, which are then retrieved and evaluated by the computer system (102). The determination may comprise detecting one or more of, but not limited to:

[0069] repeated request patterns originating from a same account handle or from a plurality of linked account handles across one or more social-media platforms;

[0070] repeated message-content similarity above a defined similarity threshold within a configurable rolling time window; abnormal request frequency exceeding a defined frequency threshold within the configurable rolling time window;

[0071] a common destination clustering pattern across a plurality of request records associated with different requester accounts; and

[0072] suspicious or spam account identity indicators such as, but not limited to, an abnormally low account age, an anomalous follower-to-following ratio, an incomplete or autogenerated profile, or behavioral patterns consistent with automated bot activity.

[0073]

[0062] A request determined to be duplicate, suspicious, or abusive is flagged and excluded from fulfillment consideration. The configurable rolling time window and the frequency threshold are operational parameters of the system and may be adjusted based on campaign requirements and abuse risk profiles. In accordance with an embodiment of the present invention, the configurable rolling time window may range from, but is not limited to, 7 days to 90 days, with a default value of 30 days, and the frequency threshold for abnormal request detection may be set to, but is not limited to, more than three requests from the same account handle or linked account cluster within the rolling time window.

[0074]

[0063] In accordance with an embodiment of the present invention, the system 100 may apply campaign-specific validation rules for the duplicate and spam suppression check, wherein the frequency threshold, rolling time window, and suppression criteria are configurable on a percampaign basis, allowing different abuse prevention parameters to be applied to different product launches, geographic markets, or customer segments simultaneously.

[0075]

[0064] After that, at step 218, the processor (1024) is configured to generate a fulfillment-eligible request record when: the interaction data corresponds to the product sample request or the product-evaluation request; the confidence score satisfies a policy criterion; and the product sample request or the product-evaluation request is not determined to be duplicate, suspicious, or abusive. As shown in FIG. 3B, the fulfillment-eligible request record is transmitted from the computer system (102) to the operator interface (114) via the communication network (110). The fulfillment-eligible request record constitutes a verified, authenticated, and abuse-filtered output of the pipeline, representing a validated expression of genuine product interest that the brand operator may act upon for sample dispatch, beta-program enrollment, or campaign analysis.

[0076]

[0065] As used herein, the term "policy criterion" refers to one or more configurable conditions that must be satisfied before a fulfillment-eligible request record is generated. The policy criterion is a technical parameter of the system and may be defined independently of the identity authentication and abuse suppression steps. A policy criterion may include, but is not limited to, an inventory availability condition, a campaign eligibility rule, a geographic targeting parameter, a loyalty-tier qualification threshold, a per-campaign fulfillment cap, or a time-bounded activation window, as defined by the brand operator.. The policy criterion operates as a state-gate in the processing pipeline, ensuring that a fulfillment-eligible request record is generated only when all upstream authentication and suppression conditions are satisfied and all downstream operational constraints are met.

[0077]

[0066] In further embodiments of the present invention, upon receipt of the fulfillment-eligible request record at the operator interface (114), the brand operator may initiate one or more of, but not limited to, physical sample dispatch to a verified shipping address, enrollment of the requester into a beta-tester or early-access product evaluation program, delivery of a digital product trial such as a voucher, promotional code, or virtual sample, or scheduling of a follow-up engagement interaction. In an embodiment where the requester has been authenticated as an existing customer with a verified shipping address in the data repository (106), the fulfillment-eligible request record may include the pre-verified shipping address, enabling the brand operator to dispatch the sample without requiring the requester to re-enter delivery details. In an embodiment where the requester has not been matched to an existing customer record, the fulfillment-eligible request record may include a flag indicating that a shipping address collection step is required prior to dispatch, which may be initiated by the brand operator through the operator interface (114).

[0078] 1

[0067] In one embodiment of the present invention, the processor (1024) may further aggregate a plurality of fulfillment-eligible request records associated with a product, a campaign, or a geographic region, and compute a demand readiness score based on a weighted composite of, but not limited to, a validated request volume, a geographic concentration of the plurality of fulfillment-eligible request records, a distribution of confidence scores across the records, and a fulfillment cost parameter. The demand readiness score may range from, but is not limited to, 0 to 100, wherein a score approaching 100 indicates that validated demand is sufficient to justify production or distribution activation. When the demand readiness score satisfies a configurable demand threshold, the processor (1024) may trigger one or more of a sample-production recommendation, a sample-packaging recommendation, a trial-distribution event, or a productevaluation program activation.

[0079]

[0068] This capability enables brands to defer physical sample production until sufficient verified demand has been established across a campaign or geographic region, thereby reducing overproduction, inventory risk, and fulfillment cost. In accordance with a further embodiment, the processor (1024) may generate an evaluation cohort comprising a ranked selection of requesters from the fulfillment-eligible request records, wherein the ranking is based on one or more of the confidence score, prior brand-interaction history, a loyalty-tier designation of a matched customer record, and campaign-participation data, for the purposes of selecting a curated group of high-confidence, high-engagement requesters for early-access or beta-style product evaluation.

[0080]

[0069] In accordance with an embodiment of the present invention, the processor may be further configured to analyze interaction content associated with a product sample request or a productevaluation request to extract one or more fragrance preference indicators. The fragrance preference indicators may include, but are not limited to, references to scent families such as floral, woody, oriental, aquatic, or fresh; specific fragrance note descriptors such as rose, oud, vanilla, citrus, musk, sandalwood, jasmine, or vetiver; or olfactory attribute terms derived from the interaction content such as descriptors of scent intensity, longevity, seasonality, or occasion. The extracted fragrance preference indicators may be stored in the data repository as a longitudinal preference profile associated with the requester's customer record, and updated with each subsequent interaction. When one or more new or forthcoming fragrance products are introduced, the processor may be further configured to compare the stored longitudinal preference profile againsta fragrance composition database to identify products whose note composition aligns with the requester's demonstrated olfactory preferences, thereby enabling targeted promotional sample distribution. This embodiment transforms the system from a purely reactive sample request processor into a proactive preference-driven distribution engine, generating ongoing relational value beyond the initial request interaction. It will be appreciated that while this embodiment is described with reference to fragrance products, the same preference profiling and targeted distribution mechanism is applicable to other sensory or experiential product categories where consumer preference can be inferred from interaction language, including but not limited to cosmetics, skincare, beverages, and food products.

[0081] Workin Q Example

[0082]

[0070] Consider a luxury fragrance brand headquartered in New York running an Instagram campaign for a new fragrance launch. The brand monitors mentions of the product hashtag and all comments on its campaign posts. A user with the handle @scentseeker_nyc comments "I would absolutely love to try this — how do I get a sample?" on one of the brand's posts. The computer system (102) ingests this interaction via the Instagram API, normalizes it into a standardized interaction record, and determines, through keyword and semantic analysis, that the comment constitutes a product sample request.

[0083]

[0071] The processor (1024) then extracts partial identity attributes from @scentseeker_nyc's public profile: the username, display name "Casey Brennan," profile biography referencing New York, prior engagement with the brand's posts over the past six months, and an account creation date of four years ago. These attributes are used to query the data repository (106), which returns two candidate customer records. One of these, associated with a customer named Casey M. Brennan in Manhattan who made an online purchase nine months ago, scores a high aggregate similarity value across username tokens, display name, geographic indicator, and engagement history, yielding a confidence score of approximately 0.87 for that candidate, exceeding the first threshold of 0.70. The requester is authenticated silently as an existing customer. No personal information is requested from @scentseeker_nyc.

[0084]

[0072] The processor (1024) then checks the request against the data repository (106) for prior request records. No duplicate request from @scentseeker_nyc or any linked account is foundwithin the rolling time window of 30 days. The account profile does not exhibit bot-like behavior i.e., the account age, follower ratio, and engagement patterns are all consistent with a genuine user. The request is cleared. A fulfillment-eligible request record is generated and transmitted to the operator interface (114), where the brand's sample management team reviews and approves dispatch of a sample unit to the verified shipping address on file.

[0085]

[0073] In a second scenario from the same campaign, a user with the handle @xxxfreesamples789 submits a similar comment. The processor (1024) extracts partial identity attributes and finds no matching customer record in the data repository (106), yielding a confidence score of approximately 0.22 — below both the first threshold of 0.70 and the second threshold of 0.40. A provisional record is generated and the request is deferred without contacting the requester. Separately, the duplicate suppression check identifies that fourteen similar comments have been submitted from accounts with comparable naming patterns within the past 72 hours from the same IP address cluster, all exhibiting account ages of less than two weeks and near-zero prior brand engagement. These requests are flagged as suspicious and excluded from the fulfillment pipeline.

[0086]

[0074] The present invention offers a number of technical and operational advantages over conventional social CRM and social listening systems:

[0087]

[0075] Firstly, the invention provides non-intrusive, confidence-based customer identification from sparse social-media data. By computing a confidence score from partial identity attributes derived entirely from publicly or accessibly available interaction and profile data, the system is capable of authenticating a requester as an existing customer without initially requesting any personal information from the requester. This preserves the brand experience and eliminates the friction and risk of negative response that is associated with upfront personal data requests in luxury brand contexts.

[0088]

[0076] Additionally, the invention provides a technically rigorous mechanism for suppressing duplicate, spam, abusive, and bot-generated sample requests at scale. The combination of crossplatform account pattern analysis, message-content similarity detection, request frequency monitoring, destination clustering, and account- identity spam scoring provides a multi-layered abuse prevention capability that is not available in conventional social listening systems.

[0077] Also, the invention enables controlled, policy-based sample distribution driven by authenticated social demand rather than unfiltered self-reported interest. The generation of fulfillment-eligible request records that have passed through identity authentication, abuse suppression, and policy validation provides brand operators with a curated, high-confidence queue of actionable requests, materially improving the conversion rate of social-media engagement into genuine customer interactions.

[0089]

[0078] Furthermore, the staged verification architecture ensures that the system escalates gracefully, requesting progressively more information only when necessary, and only requesting the minimum discriminating datum, thereby maintaining a low-friction requester experience even in cases where silent authentication is not achievable.

[0090]

[0079] Finally, the demand readiness scoring and threshold-triggered sample production embodiment decouples sample manufacturing from individual request events, enabling brands to produce or package trial units only after sufficient verified demand has been established across a campaign or geographic region. This represents a meaningful operational efficiency improvement for brands managing limited-run product launches or controlled trial distributions.

[0091]

[0080] In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprise connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processing modules. The modules described herein may be implemented as either software and / or hardware modules and may be stored in any type of computer-readable medium or other computer storage device.

[0092]

[0081] Further, while one or more operations have been described as being performed by or otherwise related to certain modules, devices or entities, the operations may be performed by or otherwise related to any module, device or entity. As such, any function or operation that has been described as being performed by a module could alternatively be performed by a different server, by the cloud computing platform, or a combination thereof. It is implied that the techniques of the present disclosure might be implemented using a variety of technologies. For example, the methods described herein may be implemented by a series of computer executable instructionsresiding on a suitable computer readable medium. Suitable computer readable media may include volatile (e.g., RAM) and / or non-volatile (e.g., ROM, disk) memory, carrier waves and transmission media. Exemplary carrier waves may take the form of electrical, electromagnetic or optical signals conveying digital data streams along a local network or a publicly accessible network such as the Internet.

[0093]

[0082] Further, the operations need not be performed in the disclosed order, although in some examples, an order may be preferred. Also, not all functions need to be performed to achieve the desired advantages of the disclosed system and method, and therefore not all functions are required.

[0094]

[0083] The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. Examples and limitations disclosed herein are intended to be not limiting in any manner, and modifications may be made without departing from the spirit of the present disclosure. Those skilled in the art will recognize that many variations are possible within the spirit and scope of the disclosure, and their equivalents, in which all terms are to be understood in their broadest possible sense unless otherwise indicated.

[0095]

[0084] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the embodiments shown along with the accompanying drawings but is to be providing broadest scope of consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and the appended claims.

Claims

CLAIMS:

1. A computer-implemented method for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data, the method comprising:receiving, by one or more processors, interaction data from one or more social-media platforms, the interaction data being associated with a product, a brand, or a campaign;normalizing the interaction data into a standardized interaction record comprising a common field structure;determining, from the standardized interaction record, whether the interaction data corresponds to a product sample request or a product-evaluation request;extracting, from the standardized interaction record and from account data associated therewith, a plurality of partial identity attributes corresponding to a requester, wherein the plurality of partial identity attributes excludes a full postal address and excludes a government-issued identifier;accessing one or more stored customer data repositories and generating a plurality of candidate customer records corresponding to the requester;computing, for respective ones of the plurality of candidate customer records, a confidence score representative of a likelihood that the requester corresponds to an existing customer, based on a comparison between the plurality of partial identity attributes and customer attributes associated with the respective candidate customer records, wherein:computing the confidence score comprises generating a ranked candidate list based on an aggregate similarity value derived from a plurality of correspondence values computed between a multi-attribute feature representation of the requester and stored customer attributes, andthe confidence score is assigned an ambiguity indicator when a difference between a highest-ranked candidate customer record and a next-ranked candidate customer record in the ranked candidate list is below a defined margin; authenticating the requester as corresponding to an existing customer when the confidence score satisfies a first threshold, without initially requesting full identity data from the requester, and, when the confidence score does not satisfy the first threshold butsatisfies a second threshold lower than the first threshold, initiating a staged verification workflow comprising requesting a single minimally-identifying datum from the requester and updating the confidence score upon receipt thereof;determining whether the product sample request or the product- evaluation request is duplicate, suspicious, or abusive based on at least one of similarity analysis or frequency analysis performed across a plurality of request records; andgenerating a fulfillment-eligible request record when:the interaction data corresponds to the product sample request or the productevaluation request;the confidence score satisfies a policy criterion; andthe product sample request or the product-evaluation request is not determined to be duplicate, suspicious, or abusive.

2. The method of claim 1, wherein the multi-attribute feature representation of the requester is generated from two or more of, but not limited to: a username, a display name, profile metadata, geographic information, prior brand-interaction history, linked account information, profile-image-derived features, message-pattern features, and campaignparticipation data; and wherein the single minimally-identifying datum is selected from one or more of: an email address, a postal code, a telephone number, or an order identifier.

3. The method of claim 1, wherein the staged verification workflow further comprises:generating a provisional requester record associated with the requester upon initiation of the staged verification workflow; andupdating the confidence score upon receipt of the single minimally-identifying datum, and authenticating the requester when the updated confidence score satisfies the first threshold.

4. The method of claim 1, wherein determining whether the product sample request or the product-evaluation request is duplicate, suspicious, or abusive comprises detecting one or more of:repeated request patterns originating from a same account handle or from a plurality of linked account handles across one or more social-media platforms;repeated message-content similarity above a defined similarity threshold within a configurable rolling time window;abnormal request frequency exceeding a defined frequency threshold within the configurable rolling time window; ora common destination clustering pattern across a plurality of request records associated with different requester accounts.

5. The method of claim 1, wherein extracting the plurality of partial identity attributes comprises deriving one or more of: username similarity features, display-name tokens, inferred geographic indicators, prior brand-interaction history, linked account references, profile biography data, profile-image-derived feature vectors, lexical or writing-style cues, campaign-specific participation patterns, time-zone consistency indicators, or social graph relationship indicators.

6. The method of claim 1 , wherein the one or more stored customer data repositories comprise two or more of: a customer relationship management database, an order-history database, a loyalty-program database, a prior sample-shipment database, a customer-support database, or a product-evaluation program database.

7. The method of claim 1, wherein normalizing the interaction data into the standardized interaction record comprises converting platform-specific interaction objects received from a plurality of heterogeneous social-media platforms into the standardized interaction record having a common field structure comprising one or more of: a platform identifier, an account handle, a display name, interaction content, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

8. The method of claim 1, further comprising:aggregating a plurality of fulfillment-eligible request records associated with a product, a campaign, or a geographic region; andcomputing a demand readiness score based on a weighted composite of two or more of: a validated request volume, a geographic concentration of the plurality of fulfillment- eligible request records, a distribution of confidence scores across the plurality of fulfillment-eligible request records, and a fulfillment cost parameter.

9. The method of claim 8, further comprising:triggering one or more of: a sample-production recommendation, a samplepackaging recommendation, a trial-distribution event, or a product-evaluation program activation, when the demand readiness score satisfies a demand threshold associated with the product, the campaign, or the geographic region; andgenerating an evaluation cohort comprising a ranked selection of requesters from the plurality of fulfillment-eligible request records, wherein the ranking is based on one or more of: the confidence score, prior brand-interaction history, a loyalty -tier designation of a matched customer record, or campaign-participation data.

10. The method of claim 1, further comprising analyzing interaction content associated with a product sample request or a product-evaluation request to extract one or more fragrance preference indicators comprising one or more of, but not limited to, references to scent families, fragrance note descriptors, or olfactory attribute terms derived from the interaction content; storing the extracted fragrance preference indicators in the one or more stored customer data repositories as a longitudinal preference profile associated with the requester; and identifying one or more products whose fragrance composition aligns with the stored longitudinal preference profile, for targeted promotional sample distribution to the requester upon a new product launch or campaign activation.

11. A computer- implemented system for identifying, authenticating, and processing product sample requests from social-media interactions associated with incomplete requester identity data, the system comprising:a processor; anda memory storing machine-readable instructions that, when executed by the processor, configures the processor to:receive interaction data from one or more social-media platforms, the interaction data being associated with a product, a brand, or a campaign;normalize the interaction data into a standardized interaction record comprising a common field structure;determine, from the standardized interaction record, whether the interaction data corresponds to a product sample request or a product-evaluation request;extract, from the standardized interaction record and from account data associated therewith, a plurality of partial identity attributes corresponding to a requester, wherein the plurality of partial identity attributes excludes a full postal address and excludes a government-issued identifier;access one or more stored customer data repositories and generate a plurality of candidate customer records corresponding to the requester;compute, for respective ones of the plurality of candidate customer records, a confidence score representative of a likelihood that the requester corresponds to an existing customer, based on a comparison between the plurality of partial identity attributes and customer attributes associated with the respective candidate customer records, wherein:computing the confidence score comprises generating a ranked candidate list based on an aggregate similarity value derived from a plurality of correspondence values computed between a multi-attribute feature representation of the requester and stored customer attributes, andthe confidence score is assigned an ambiguity indicator when a difference between a highest-ranked candidate customer record and a next-ranked candidate customer record in the ranked candidate list is below a defined margin;authenticate the requester as corresponding to an existing customer when the confidence score satisfies a first threshold, without initially requesting full identity data from the requester, and, when the confidence score does not satisfy the first threshold but satisfies a second threshold lower than the first threshold, initiate a staged verification workflow comprising requesting a single minimally-identifying datum from the requester and updating the confidence score upon receipt thereof;determine whether the product sample request or the product-evaluation request is duplicate, suspicious, or abusive based on at least one of similarity analysis or frequency analysis performed across a plurality of request records; and generate a fulfillment-eligible request record when: the interaction data corresponds to the product sample request or the product-evaluation request; theconfidence score satisfies a policy criterion; and the product sample request or the product-evaluation request is not determined to be duplicate, suspicious, or abusive.

12. The system of claim 11, wherein the multi-attribute feature representation of the requester is generated from two or more of, but not limited to: a username, a display name, profile metadata, geographic information, prior brand-interaction history, linked account information, profile-image-derived features, message-pattern features, and campaignparticipation data; and wherein the single minimally-identifying datum is selected from one or more of: an email address, a postal code, a telephone number, or an order identifier.

13. The system of claim 11, wherein to execute the staged verification workflow, the processor is further configured to:generate a provisional requester record associated with the requester upon initiation of the staged verification workflow; andupdate the confidence score upon receipt of the single minimally-identifying datum, and authenticate the requester when the updated confidence score satisfies the first threshold.

14. The system of claim 11, wherein to determine whether the product sample request or the product-evaluation request is duplicate, suspicious, or abusive, the processor is further configured to detect one or more of:repeated request patterns originating from a same account handle or from a plurality of linked account handles across one or more social-media platforms;repeated message-content similarity above a defined similarity threshold within a configurable rolling time window;abnormal request frequency exceeding a defined frequency threshold within the configurable rolling time window; ora common destination clustering pattern across a plurality of request records associated with different requester accounts.

15. The system of claim 11, wherein to extract the plurality of partial identity attributes, the processor is further configured to derive one or more of: username similarity features, display-name tokens, inferred geographic indicators, prior brand-interaction history, linkedaccount references, profile biography data, profile-image-derived feature vectors, lexical or writing-style cues, campaign-specific participation patterns, time-zone consistency indicators, or social graph relationship indicators.

16. The system of claim 11, wherein the one or more stored customer data repositories comprise two or more of: a customer relationship management database, an order-history database, a loyalty-program database, a prior sample-shipment database, a customersupport database, or a product-evaluation program database.

17. The system of claim 11, wherein to normalize the interaction data into the standardized interaction record, the processor is further configured to convert platform-specific interaction objects received from a plurality of heterogeneous social-media platforms into the standardized interaction record having a common field structure comprising one or more of: a platform identifier, an account handle, a display name, interaction content, an engagement type, a timestamp, profile metadata, media references, location indicators, an interaction context, and a campaign identifier.

18. The system of claim 11, wherein the processor is further configured to:aggregate a plurality of fulfillment-eligible request records associated with a product, a campaign, or a geographic region; andcompute a demand readiness score based on a weighted composite of two or more of: a validated request volume, a geographic concentration of the plurality of fulfillment- eligible request records, a distribution of confidence scores across the plurality of fulfillment-eligible request records, and a fulfillment cost parameter.

19. The system of claim 18, wherein the processor is further configured to:trigger one or more of: a sample-production recommendation, a sample-packaging recommendation, a trial-distribution event, or a product-evaluation program activation, when the demand readiness score satisfies a demand threshold associated with the product, the campaign, or the geographic region; andgenerate an evaluation cohort comprising a ranked selection of requesters from the plurality of fulfillment-eligible request records, wherein the ranking is based on one or more of: the confidence score, prior brand-interaction history, a loyalty -tier designation of a matched customer record, or campaign-participation data.

0. The system of claim 11, wherein the processor is further configured to analyze interaction content associated with a product sample request or a product-evaluation request to extract one or more fragrance preference indicators comprising one or more of, but not limited to, references to scent families, fragrance note descriptors, or olfactory attribute terms derived from the interaction content; store the extracted fragrance preference indicators in the one or more stored customer data repositories as a longitudinal preference profile associated with the requester; and identify one or more products whose fragrance composition aligns with the stored longitudinal preference profile, for targeted promotional sample distribution to the requester upon a new product launch or campaign activation.

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