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公开(公告)号：US11790412B2
公开(公告)日：2023-10-17
申请号：US16412213
申请日：2019-05-14
IPC分类号： G06Q30/02 , H04M3/51 , G06Q30/016 , G06N20/00 , G06Q10/0631 , G06Q10/0639 , G06Q20/40 , G06F9/451 , H04L9/40 , G10L15/26
CPC分类号： G06Q30/0281 , G06F9/451 , G06N20/00 , G06Q10/06316 , G06Q10/06393 , G06Q10/06398 , G06Q20/40 , G06Q30/016 , G10L15/26 , H04L63/08 , H04M3/5166 , H04M3/5175
摘要： An enhanced customer relationship management (CRM) system is provided. The enhanced CRM system performs activities automatically and with the assistance of artificial intelligence and machine learning based on historical information. The enhanced CRM system provides: a) scheduling assistance prior to a customer contact, b) assistance during a call to direct call focus and achieve a personal connection between a customer facing user of the CRM system and a target customer contact, and c) automated assistance to complete a contact and transition to a next target customer contact. Achievement goals for a customer facing user may be presented and monitored with respect to a goal achievement period. Schedules may be dynamically adjusted across multiple CRM system users and with respect to overall organizational goals to enhance achievement of goals.
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公开(公告)号：US11758071B1
公开(公告)日：2023-09-12
申请号：US17873954
申请日：2022-07-26
申请人： HighRadius Corp.
摘要： Novel tools and techniques are provided for implementing identification and removal of noise from documents, and, more particularly, to methods, systems, and apparatuses for implementing identification and removal of noise from financial documents using one or more machine learning algorithms. In various embodiments, computing system might receive a document. The computing system might detect, using one or more machine learning algorithms, that noise exists in the document. Based on the detection that noise exists in the document, the computing system might remove the noise from the document. Once the noise is removed from the document, the computing system might generate a copy of the document with the noise removed while retaining important or useful information contained in the document.
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3.发明公开公开(公告)号：US20240046029A1
公开(公告)日：2024-02-08
申请号：US17817680
申请日：2022-08-05
发明人： Aditya Ankur , Rajesh Krishnan , Aishwarya Kumar , Mohit Bansal
IPC分类号： G06F40/174 , G06N5/022 , G06Q40/12 , G06F40/205
CPC分类号： G06F40/174 , G06N5/022 , G06Q40/12 , G06F40/205
摘要： A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.
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公开(公告)号：US11410181B2
公开(公告)日：2022-08-09
申请号：US16411566
申请日：2019-05-14
发明人： Vishal Shah , Sonali Nanda , Srinivasa Jami
摘要： Provided techniques manage and predict future events. For example, in a payment implementation, a supplier, at any given point in time, has multiple customer debtors that may owe payments (e.g., have outstanding invoices). Utilizing historical attributes for a given customer debtor payment predictions may be determined. By analyzing outstanding debts associated with this debtor customer an amount owed may be calculated and a predicted payment (e.g., a payment that has not yet been indicated by that debtor customer) created. Events may be provided to a second system to correlate predictions across multiple debtor collectors. Correlated information may be used to predict cash flow needs of an organization. Alternatively, optimization of help desk systems may be provided based on predictions from analysis of multiple events in an Event-driven feed back system. Provided techniques may be generalized to other applications as well.
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公开(公告)号：US11080768B2
公开(公告)日：2021-08-03
申请号：US16412161
申请日：2019-05-14
IPC分类号： G06Q30/02 , G10L15/26 , H04M3/51 , G06Q20/40 , G06Q30/00 , G06F9/451 , G06N20/00 , G06Q10/06 , H04L29/06
摘要： An enhanced customer relationship management (CRM) system is provided. The enhanced CRM system performs activities automatically and with the assistance of artificial intelligence and machine learning based on historical information. The enhanced CRM system provides: a) scheduling assistance prior to a customer contact, b) assistance during a call to direct call focus and achieve a personal connection between a customer facing user of the CRM system and a target customer contact, and c) automated assistance to complete a contact and transition to a next target customer contact. Achievement goals for a customer facing user may be presented and monitored with respect to a goal achievement period. Schedules may be dynamically adjusted across multiple CRM system users and with respect to overall organizational goals to enhance achievement of goals.
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公开(公告)号：US20240070678A1
公开(公告)日：2024-02-29
申请号：US18089959
申请日：2022-12-28
发明人： Sunil Nandam , Harsh Vardhan Singhania , Rajesh Bhonagiri , Rahul Gudla , Sashi Narahari , Glen George
摘要： Disclosed embodiments provide tools and techniques for the automated prioritized ranking of collection customers for accounts receivable and collections management processes. In some embodiments, one or more computing systems may repetitively generate a prioritized list of collection customers requiring collection activity. The prioritized lists can be generated at selected time intervals, for example daily, weekly, or monthly. The generation of the prioritized list for a selected time interval can include several processing and monitoring steps implemented with machine learning or artificial intelligence. The prioritized list may then be provided to collections agents, collections managers, or downstream software modules.
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7.发明公开公开(公告)号：US20230342793A1
公开(公告)日：2023-10-26
申请号：US18306278
申请日：2023-04-25
IPC分类号： G06Q30/0201
CPC分类号： G06Q30/0201
摘要： A Machine Learning (ML)-based computing system and method for financial transaction based customer worklist generation is disclosed. A data determination module configured to obtain a credit sale amount, an account receivable as of a run date of the module (RD), a disputed invoice amount and a skipped invoice amount using an Machine Learning (ML) model. A DSO component calculation module configured to calculate the obtained DSO components for each entity corresponding to a grouping category at a given point of time period. A DSO impact score generation module configured to generate a DSO impact score based on the estimated open amount reduction, desired number of days in period and the credit sale amount. A Machine Learning insight module configured to calculate the generated DSO impact score based on historical customer information associated with one or more customers. A data output module configured to output the DSO impact score.
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8.发明公开公开(公告)号：US20230342738A1
公开(公告)日：2023-10-26
申请号：US18306281
申请日：2023-04-25
CPC分类号： G06Q20/102 , G06Q30/0204 , G06Q40/03 , G06Q30/04 , G06Q10/0635
摘要： A Machine Learning (ML)-based computing system and method for customer segmentation and collections worklist generation is disclosed. The method includes data receiver module configured to receive one or more open invoices from one or more electronic devices associated with a user. Further, the method includes data processing module configured to process the received one or more open invoices. Further, the method includes segment clustering module configured to determine a payment behaviour and customer risk. Further, the method includes worklist generation module configured to generate a customer worklist. Further, the method includes action recommendation module configured to recommend one or more collection strategies. Further, the method includes prioritization module configured to rank each of the one or more customers in the generated worklist. Furthermore, the method includes the data output module configured to output the one or more collection strategies and the ranked one or more customers.
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公开(公告)号：US11100409B2
公开(公告)日：2021-08-24
申请号：US16412076
申请日：2019-05-14
发明人： Vishal Shah , Sonali Nanda , Srinivasa Jami
摘要： A system generates trade deduction settlement rules and associated confidence scores independent of buyer specifications. A machine learning equipped rewards based method performed by the system analyzes historically matched deductions and promotions to understand patterns. Penalties are applied to outdated rules, and recent trends are promoted through rewards. All available deduction-promotion combinations may be analyzed in batches for a given time period at each pair level within an artificial intelligence model of the method. A rules selector selects the most recurring patterns along those combinations based upon definable thresholds. The system finds hidden patterns to provide suggestions for trade deduction settlement. The system further captures the rules and evolves the rules over time.
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10.发明公开公开(公告)号：US20240354786A1
公开(公告)日：2024-10-24
申请号：US18305483
申请日：2023-04-24
发明人： Anupam Kunwar , Apoorva Shrivastava , Sayanta Mukherjee , Rohit Haldar , Maitreya Mohapatra , Vineet Kumar Gupta
IPC分类号： G06Q30/0202 , G06N5/022
CPC分类号： G06Q30/0202 , G06N5/022
摘要： A system and method for predicting financial transaction patterns is disclosed. The method includes receiving invoice data of one or more customers. The method further includes receiving granularity levels, thereby generating a set granularity level instances based on various invoice data attributes. The method further computes payment frequency bucket features for all the generated set of granularity level instances and assigns invoices to clusters based on the set of payment frequency bucket features. The set of payment frequency bucket features assigns a customer to be one of: a weekly payer, alternative weekly payer, a monthly payer, a bi-monthly, a quarterly payer, a half yearly payer and an annual payer. The method further includes the generation of a set of payment pattern features based on the set of payment frequency bucket features of the cluster. Further, the method includes selection of an optimal pattern with highest probability of adherence.
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