System for automated account, call and sample reconciliation in the CRM area of ​​life sciences using agent-based AI, relational data warehousing and MDM synchronization for commercial operations

Abstract

A system (100) for automated account, call and sample reconciliation in commercial life sciences operations, the system comprising: an agent-based AI orchestration engine (1) configured to independently initiate, sequence, and manage voting workflows across multiple data sources; a relational data warehousing and lineage layer (2) configured to ingest, normalize, correlate and store structured operational data associated with accounts, calls and samples; an MDM synchronization and golden record management module (3) configured to synchronize master data entities and generate consistent golden records across different platforms; an engine for reconciling accounts, calls and samples (4) configured to compare related records across the synchronized records and to identify mismatches, missing links, duplicate records and transaction inconsistencies; a module for compliance rules, exception notifications and policy validation (5) that is configured to validate reconciled data against predefined business, regulatory and sample responsibility rules; a human-in-the-loop workflow, recommendation, and resolution console (6) configured to present detected exceptions along with corrective suggestions and to enable user-assisted or automated resolution; and a module (7) for audit analysis, continuous learning and reporting, configured to generate audit trails, reconciliation summaries, performance analyses and adaptive learning updates based on historical exception resolution results.

Description

INVENTION AREA

[0001] The present invention relates generally to the fields of commercial data management, customer relationship management, master data management, and intelligent enterprise automation. In particular, the present invention relates to a system (100) for the automated reconciliation of account data, field service call data, and sample transaction data used in commercial life science operations, wherein the reconciliation is achieved through the coordinated use of agent-based artificial intelligence, relational data warehousing, and master data management (MDM) synchronization.

[0002] The invention further relates to a platform for supporting commercial operations that improves data integrity, operational efficiency, regulatory compliance and traceability across customer retention platforms, sample responsibility systems, commercial reporting environments and downstream governance systems in the life sciences. BACKGROUND OF THE INVENTION

[0003] The subject matter discussed in the "Background" section should not be considered prior art solely because it is mentioned in that section. Likewise, a problem mentioned in the "Background" section or related to the subject matter of the "Background" section should not be considered prior art. The subject matter in the "Background" section merely presents various approaches, which could themselves also be inventions.

[0004] Life science companies, including pharmaceutical, biotechnology, medical device, and specialty healthcare companies, rely on complex commercial ecosystems to manage healthcare professional accounts, healthcare organization hierarchies, field sales activities, and regulated product sample transactions. These ecosystems typically encompass multiple software platforms, such as CRM applications, MDM environments, enterprise resource planning systems, reporting databases, territory management tools, consent platforms, and sample inventory systems. Because the same commercial entity or transaction can be represented differently across such systems, discrepancies, duplicate records, missing links, timing inconsistencies, and compliance exceptions are common.

[0005] Traditional reconciliation methods are typically manual, semi-manual, or rule-bound. Commercial operations teams often rely on spreadsheets, isolated dashboards, point-to-point scripts, or isolated validation tasks to identify discrepancies between account data, visit records, and pattern records. These traditional methods are slow, error-prone, resource-intensive, and unable to adapt to evolving data structures, business rules, and operational contexts. In particular, existing systems often fail to consistently and promptly reconcile data across account hierarchy changes, agent reassignments, territory shifts, sample payout events, and post-call updates.

[0006] In the life sciences, reconciliation errors can have significant repercussions for downstream processes. An incorrect link between a call record and an account record can distort representative performance data, campaign attributions, and customer interaction history. A discrepancy between call activity and sample distribution records can lead to compliance risks, gaps in sample accountability, inventory inaccuracies, and delayed investigations. Furthermore, inconsistent master records for healthcare professionals, healthcare organizations, and product identifiers can propagate across multiple systems, impacting analytics, territory building, incentive calculation, and audit readiness.

[0007] Existing rule engines and static ETL workflows are also inadequate for handling complex matching logics where the correct solution requires contextual reasoning, evidence aggregation, workflow interpretation, and adaptive decision-making. For example, a pattern record might appear invalid when viewed in isolation but be valid when interpreted in light of a subsequent MDM update, a late call report submission, or a corrected account merge. In such cases, a simple deterministic match cannot reliably deliver the correct operational outcome.

[0008] Therefore, there is a need for an intelligent and integrated reconciliation platform capable of capturing distributed commercial data, synchronizing master data records, analyzing origin, identifying discrepancies, deriving likely causes, applying compliance rules, and recommending or implementing corrective actions. Furthermore, there is a need for such a platform to operate in a traceable, scalable, verifiable, and continuously improving manner, suitable for commercial activities in the life sciences sector.

[0009] The present invention meets these requirements by providing a system (100) that combines agent-based AI orchestration, relational data storage and MDM synchronization with automated reconciliation, exception handling, compliance validation, manual review and continuous learning.

[0010] The use of any examples or illustrative phrases (e.g., "as") relating to specific embodiments serves only to better explain the invention and does not constitute a limitation of the otherwise claimed scope of the invention. No phrase in the description should be interpreted as referring to an unclaimed element that is essential for the exercise of the invention.

[0011] The information disclosed above in this "Background" section is provided solely for a better understanding of the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY

[0012] Before describing the systems and methods presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly presented in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application.

[0013] The present invention relates to a system (100) for automated account, call, and sample reconciliation in CRM environments in the life sciences. The system (100) comprises an agent-based AI orchestration engine (1), a relational data storage and provenance layer (2), an MDM synchronization and golden record management module (3), an account, call, and sample reconciliation engine (4), a compliance rule, exception notification, and policy validation module (5), a human-in-the-loop workflow, recommendation, and resolution console (6), and a module for audit analytics, continuous learning, and reporting (7).

[0014] The agent-based AI orchestration engine (1) is configured to autonomously coordinate reconciliation workflows, task allocation, evidence collection, discrepancy classification, and action sequences. The relational data storage and provenance layer (2) is configured to ingest and normalize data from CRM, MDM, sample management, commercial reporting, and enterprise systems, preserving the provenance from the source to the target system. The MDM synchronization and golden record management module (3) is configured to synchronize master entities and maintain consistent, trusted golden records for healthcare professionals, healthcare organizations, products, territories, and field service personnel.

[0015] The account, call, and pattern reconciliation engine (4) compares related records to detect duplicate entries, missing links, ownership inconsistencies, sequence anomalies, product mismatches, timing conflicts, and orphaned transactions. The compliance rules, exception reporting, and policy validation module (5) validates the reconciled results against commercial and regulatory rules. The human-in-the-loop workflow, recommendation, and resolution console (6) supports guided or assisted resolution of exceptions requiring manual approval or contextual assessment. The audit analytics, continuous learning, and reporting module (7) generates traceable audit logs, exception metrics, and adaptive updates to improve future reconciliation cycles.

[0016] In one embodiment, the system (100) performs near real-time reconciliation as soon as new CRM events, sample transactions, or MDM updates are detected. In another embodiment, the system performs batch reconciliation over scheduled time windows for regular operational reviews, compliance certification, or month-end reporting. BRIEF DESCRIPTION OF THE DRAWING

[0017] To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments illustrated in the accompanying drawing. It is understood that these drawings represent only illustrated embodiments of the invention and are therefore not to be considered as limiting its scope. The invention is described and explained with additional specificity and detail using the accompanying drawing.

[0018] To make the advantages of the present invention easily understandable, a detailed description of the invention is given below in conjunction with the accompanying drawing, which, however, should not be regarded as limiting the scope of the invention to the accompanying drawings, in which: Fig. shows a block diagram of the system (100) for automated account, call and sample reconciliation in the CRM for life sciences using agent-based AI, relational data warehousing and MDM synchronization for commercial operations. DETAILED DESCRIPTION

[0019] The present invention relates to the system (100) for automated account, call and sample reconciliation in the CRM field of life sciences using Agentic AI, relational data warehousing and MDM synchronization for commercial operations.

[0020] Fig. shows a detailed block diagram representation of the system (100) for automated account, call and sample reconciliation in the CRM for life sciences using Agentic AI, relational data warehousing and MDM synchronization for commercial operations.

[0021] The following description contains exemplary embodiments of the invention and is not intended to limit the scope of the invention. A person skilled in the art will recognize that various modifications, substitutions, and implementation options can be adopted without departing from the spirit of the invention.

[0022] The present invention provides a system (100) configured to automate the reconciliation of records of commercial transactions used in CRM environments in the life sciences sector. The system (100) is implemented using hardware, software, databases, network services, or a combination thereof, and can be deployed in cloud, on-premises, hybrid, or multi-tenant environments.

[0023] The agent-based AI orchestration engine (1) acts as the decision and workflow coordinator of the system (100). It is configured to receive a reconciliation target, determine the relevant data records, identify the type of discrepancy to be investigated, and orchestrate a sequence of machine-executable tasks required to perform the reconciliation process. These tasks may include retrieving account data, comparing account hierarchies, validating representative alignment, retrieving related call events, evaluating sample transaction sequences, applying policy checks, generating recommendations, escalating the issue, and logging completion.

[0024] In one embodiment, the agent-based AI orchestration engine (1) creates an execution plan for each reconciliation case based on metadata such as source system, record type, confidence score, exception class, materiality, and compliance risk. In another embodiment, the engine dynamically changes the workflow path depending on new evidence retrieved during processing. Thus, if a discrepancy initially classified as a duplicate call entry is later identified as the result of a delayed account merge, the engine can trigger a different sequence of tasks without manual intervention.

[0025] The agent-based AI orchestration engine (1) also enables multi-stage reasoning, context-related prioritization and action selection across the modules of the system (100).

[0026] The relational data warehousing and origin layer (2) is configured to ingest, normalize, store, and relate operational data received from multiple internal and external systems. This data may include account profiles, medical professional records, health organization records, call logs, visit timestamps, field service activity data, sample inventory events, sample issue confirmations, representative identifiers, territory assignments, product master data, and downstream reporting attributes.

[0027] In one embodiment, the relational data storage and provenance layer (2) transforms source-specific schemas into a common relational structure. In another embodiment, the layer maintains provenance metadata for each field, record, and transformation path, so that each reconciled result can be traced back to its original source record or event. This provenance function is particularly useful for audits, root cause analysis, exception document generation, and regulatory reviews.

[0028] The relational data warehousing and source layer (2) can further support incremental data acquisition, the processing of late-arriving data, historical snapshots, and time-related comparisons of the data record status. This allows the system not only to assess a discrepancy in the current state, but also whether the discrepancy existed at a relevant point in time during operation.

[0029] The MDM synchronization and golden record management module (3) is configured to synchronize master records and generate unified entity views across fragmented source systems. It manages identity unification for customer accounts, medical professionals, healthcare organizations, products, territories, field service employees, and related reference objects used in business operations.

[0030] In one embodiment, the module applies survival logic to determine a preferred attribute value from multiple candidate source records. In another embodiment, the module performs trust-based entity resolution by evaluating multiple matching attributes such as name, identifier, domain, organizational affiliation, address, territory code, product code, and historical linking behavior. The module can also detect potential merges, splits, obsolete relationships, and master record discrepancies.

[0031] The Golden Records generated by the MDM Synchronization and Golden Record Management module (3) provide a trusted identity layer for downstream reconciliation performed by the Account, Call and Sample Reconciliation Engine (4).

[0032] The account, call, and sample reconciliation engine (4) is configured to analyze the relationships between account, call, and sample records and determine whether these records are logically, temporally, and operationally consistent. This module forms the main reconciliation core of the system (100).

[0033] In one embodiment, the engine compares account ownership and hierarchy status with the account referenced in a call entry. In another embodiment, it verifies that a sample transaction is properly associated with a valid account, a valid call event, a valid agent, and a valid product entry. The engine can identify several classes of discrepancies, including, but not limited to, duplicate call entries, unlinked sample transactions, orphaned records, invalid product references, repeated payout patterns, inconsistent timestamps, territory conflicts, or improper account-call mappings.

[0034] The account, call, and sample matching engine (4) can calculate a matching confidence score based on the source confidence level, master data consistency, event chronology, identity matching strength, business rule compliance, historical resolution patterns, and evidence completeness. This score can be used either for automated resolution or to escalate the discrepancy for manual review.

[0035] The compliance rules, exception detection, and policy validation module (5) is configured to evaluate voting results against predefined business, governance, and regulatory policies relevant to commercial life sciences operations. These rules may include sampling eligibility criteria, maximum sample sizes, authorized representative conditions, approved product-account combinations, call frequency rules, account ownership restrictions, consent or confirmation requirements, and policies for controlled sampling.

[0036] In one embodiment, the module uses deterministic policy rules. In another embodiment, the module employs a hybrid mechanism that combines deterministic logic with risk-weighted, context-aware interpretation. This allows the system (100) to identify both direct violations and suspicious patterns that require further investigation.

[0037] The module can assign severity levels to exceptions and also specify whether an exception can be closed automatically, depends on an auditor, or must be escalated. The results of the rules are transferred to the Human-in-the-Loop Workflow, Recommendation and Resolution Console (6) and to the module for audit analytics, continuous learning, and reporting (7).

[0038] The Human-in-the-Loop Workflow, Recommendation and Resolution Console (6) is configured to display exceptions to authorized users when the matching trust value falls below a defined threshold, when the policy risk is high, or when manual approval is required according to the governance policies.

[0039] The console displays an exception package that includes the type of discrepancy, the likely cause, the source records, the golden record context, the timeline of events, the rules applied, the confidence level, and one or more recommended corrective actions. These actions may include reassigning accounts, approving record merges, correcting call linkages, validating sample orders, approving agent exceptions, escalating to the compliance department, or rejecting a proposed automated resolution.

[0040] In one embodiment, the console captures the reviewers' decisions and comments. In another embodiment, the console provides ranked recommendations generated from previous resolution histories, similar exceptions, and organizational policy preferences. This feature reduces manual investigation effort while ensuring governance control.

[0041] The module for audit analytics, continuous learning, and reporting (7) is configured to maintain a complete and traceable audit trail of reconciliation actions, source evidence, rule assessments, user decisions, timestamps, and final results. The module can generate dashboards, operational KPIs, exception aging reports, representative-level error metrics, territory-level quality insights, and compliance certification summaries.

[0042] In one embodiment, the module stores the results of the solutions and uses them as learning signals for subsequent voting cycles. In another embodiment, it updates the confidence thresholds, the ranking logic, the sensitivity of duplicate detection, and the prioritization of recommendations based on accepted or rejected solution patterns. Accordingly, the overall performance of the system (100) improves with repeated use.

[0043] The audit capabilities of module (7) make the system (100) particularly suitable for regulated commercial environments in the life sciences sector where traceability and defense capability are required. HOW THE INVENTION WORKS

[0044] During operation, the system (100) receives or retrieves data from several commercial operating systems. The relational data warehousing and origin layer (2) ingests and normalizes the incoming data records. The MDM synchronization and golden record management module (3) updates or validates the master data records associated with the ingested data.

[0045] The agent-based AI orchestration engine (1) then initiates a matching workflow and calls the account, call, and sample matching engine (4) to evaluate record relationships, link integrity, event chronology, and logical consistency. If a discrepancy is detected, the compliance rules, exception detection, and policy validation module (5) assesses whether the discrepancy represents a business or regulatory exception.

[0046] If the discrepancy poses a low risk and the confidence score is sufficiently high, the system (100) can automatically apply a corrective action and close the case. If the discrepancy is ambiguous, high-risk, or policy-relevant, the case is escalated to the Human-in-the-Loop Workflow, Recommendation, and Resolution Console (6) for user review. Once resolved, the outcome is recorded by the Audit Analytics, Continuous Learning, and Reporting module (7) for traceability and future learning purposes. EXAMPLE USE CASE

[0047] A pharmaceutical representative submits a visit report to a CRM platform and records a sample issue event for a product. Due to a synchronization delay, the account specified in the visit report is linked to an outdated healthcare organization identifier, while the sample platform has already adopted a merged primary account. The system (100) detects that the visit and sample do not match. The MDM synchronization and golden record management module (3) determines the most recent valid golden record, and the account, visit, and sample matching engine (4) determines that the mismatch is due to a valid account transition after merge, not an unauthorized sample transaction.The compliance rules, exception detection, and policy validation module (5) confirms policy compliance, and the agent-based AI orchestration engine (1) automatically closes the exception (). The full explanation is stored in the audit analytics, continuous learning, and reporting module (7).

[0048] The present invention offers numerous advantages over conventional reconciliation systems. It reduces manual reconciliation effort and improves the turnaround time for identifying and closing exceptions. It creates a unified reconciliation framework for accounts, calls, and samples, instead of relying on disparate validations. It improves the trustworthiness of master data through synchronized golden record logic. It strengthens governance by combining policy validation with a traceable resolution history. It supports intelligent automation through agent-based workflow orchestration and trust-based decision-making. It improves the accuracy of operational reporting and reduces downstream data quality errors. Furthermore, it supports continuous improvement through learning from past cases and auditor decisions.

[0049] The invention is industrially applicable in pharmaceutical companies, biotechnology companies, specialty healthcare companies, medical device sales departments, contract distribution organizations, commercial departments, CRM governance teams, sample compliance teams, and enterprise data quality environments. The invention can be used in organizations that utilize CRM systems for life sciences, MDM systems, sample accountability platforms, inventory-based reporting systems, and compliance monitoring infrastructures.

[0050] The foregoing description illustrates preferred embodiments of the invention and is not intended to limit its scope. Variations and modifications such as alternative deployment topologies, additional runtime enforcement points, alternative telemetry sources, and alternative prediction model types can be implemented without departing from the spirit and scope of the present invention as defined by the claims.

Claims

[1] A system (100) for automated account, call and sample reconciliation in commercial life sciences operations, the system comprising: an agent-based AI orchestration engine (1) configured to independently initiate, sequence, and manage voting workflows across multiple data sources; a relational data warehousing and lineage layer (2) configured to ingest, normalize, correlate and store structured operational data associated with accounts, calls and samples; an MDM synchronization and golden record management module (3) configured to synchronize master data entities and generate consistent golden records across different platforms; an engine for reconciling accounts, calls and samples (4) configured to compare related records across the synchronized records and to identify mismatches, missing links, duplicate records and transaction inconsistencies; a module for compliance rules, exception notifications and policy validation (5) that is configured to validate reconciled data against predefined business, regulatory and sample responsibility rules; a human-in-the-loop workflow, recommendation, and resolution console (6) configured to present detected exceptions along with corrective suggestions and to enable user-assisted or automated resolution; and a module (7) for audit analysis, continuous learning and reporting, configured to generate audit trails, reconciliation summaries, performance analyses and adaptive learning updates based on historical exception resolution results. [2] System (100) according to claim 1, wherein the agent-based AI orchestration engine (1) is configured to decompose a voting target into several machine-executable subtasks that include data retrieval, candidate matching, exception classification, root cause analysis, corrective action recommendation, and workflow escalation. [3] System (100) according to claim 1, wherein the relational data storage and origin layer (2) is configured to maintain the source-to-destination origin for each account record, call record, and sample transaction record, so that each reconciled output can be traced back to one or more original source entries. [4] System (100) according to claim 1, wherein the MDM synchronization and golden record management module (3) is configured to merge fragmented identities corresponding to medical professionals, health organizations, products, territories and field service personnel by applying survival logic, cross-referencing mapping and trust-based entity resolution. [5] System (100) according to claim 1, wherein the module (5) for compliance rules, exception notification and policy validation is configured to detect one or more of the following situations: unauthorized sample issuance, excessive sample distribution, unlinked call activity, duplicate call reports, invalid account and call assignment, incomplete acknowledgment capture and territory rule violations. [6] System (100) according to claim 1, wherein the human-in-the-loop workflow, recommendation and resolution console (6) is configured to display an exception-specific resolution package that includes a discrepancy assessment, a likely cause, supporting evidence documents, recommended corrective actions and an approval or rejection interface for an authorized reviewer. [7] System (100) according to claim 1, wherein the module (7) for audit analysis, continuous learning and reporting is configured to learn from previous votes and decisions of auditors in order to dynamically update the agreement thresholds, the logic for prioritizing exceptions and the recommendation ranking for subsequent voting cycles. [8] System (100) according to claim 1, wherein the agent-based AI orchestration engine (1) and the account, call and sample matching engine (4) are jointly configured to automatically close an exception when a matching confidence score exceeds a predefined threshold, and to forward the exception to the human-in-the-loop workflow, recommendation and resolution console (6) when the confidence score is below the predefined threshold. [9] System (100) according to claim 1, wherein the system (100) is configured to operate in near real time by continuously receiving CRM updates, MDM updates and sample management events, synchronizing the received updates via the MDM synchronization and golden record management module (3), performing reconciliation via the account, call and sample reconciliation engine (4), validating compliance via the compliance rules, exception detection and policy validation module (5) and storing a tamper-proof audit history via the audit analysis, continuous learning and reporting module (7).

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