09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
36 - Financial, insurance and real estate services
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for use in the field of business finance,
namely, accounting, online banking, bill payment, creating
invoices, tracking accounts receivable and accounts payable,
and tax management; computer software in the field of
payroll processing and payroll tax preparation and filing;
computer software for inventory and sales management,
estimating, employee time tracking, customer relationship
management; computer software for bookkeeping, financial and
business transaction processing management, financial and
business transaction management, tax preparation and tax
planning, business process management, and financial
planning; computer software for use in the field of personal
and business finance for project costing management;
computer software for use in the management of payroll;
computer software for administering employee payroll;
computer software for use in the management of benefit
plans, insurance plans, retirement plans, unemployment
insurance plans, and pre-paid health care plans; computer
software for customizing and managing invoices, recording
payments, and issuing receipts; computer software for use in
organizing, servicing and tracking sales, collections and
receivables data; computer software for tracking income,
expenses, sales, and profitability by business location,
department, type of business, or other user set field;
computer software for customer relationship management;
computer software to calculate and charge sales tax and to
create reports to pay sales tax to appropriate tax agencies;
computer software for credit card invoicing and credit card
payment processing; computer software for managing online
bank accounts; computer software for controlling access to
financial information via electronic permission settings;
computer software to create, customize, print, export, and
e-mail purchase orders; computer software to track time
worked by subcontractors; computer software to create and
manage budgets; computer software to create price quote
estimates and transfer price quote estimates to invoices;
computer software to automate creation of invoices; computer
software to create, customize, print, export, and e-mail
financial reports, business reports, balance sheets, profit
and loss statements, cash flow statements, and taxable sales
reports; computer software to create, print, and track
checks and purchase orders; computer software to track
sales, expenses, and payments; computer software to analyze
the financial status of businesses and industries; computer
software to manage customer lists, e-mail and print sales
forms, and track running balances; computer software to
import contacts and financial data from other electronic
services and software; computer software for synchronizing
data among computers and mobile devices; computer software
for database management, data aggregation, data reporting,
and data transmission; computer software for online backup
of electronic files; computer software and hardware for use
in transaction processing, accounting, receipt printing,
customer relationship management, inventory management and
operations management, all in the field of point of sale
transactions and retail management; cash drawers, namely,
cash registers; computer printers for printing documents;
point of sale printers and terminals; credit card and debit
card readers and scanners; bar code readers and scanners;
credit card and secure electronic transaction processing
terminals and computer hardware; computer software for use
in business finance management, printing invoices, financial
reporting, and tax calculation; computer software for use in
small business enterprise resource planning, job costing,
sales quotation and tracking, purchase order creation and
management, profit and loss calculation, budgeting, and
forecasting; computer software for accounting; computer
software for use in the field of personal and business
finance for accounting and tax management; computer software
for creating invoices; computer software to track time
worked by employees; computer software for inventory
management; computer software for use in accounting, online
banking, payment processing, bill payment, creating
invoices, tracking accounts receivable and accounts payable,
and tax management; computer software for use in payroll
processing and payroll tax preparation and filing; computer
software for use in inventory management, estimating, sales
management, employee time tracking and customer relationship
management. Online business management services in the field business
finance; online accounting and bookkeeping services;
providing payroll preparation, payroll tax assessment, and
payroll tax filing services; member benefits program,
namely, customer loyalty services for commercial,
promotional and/or advertising purposes that provides a
variety of amenities to member accounting professionals,
computer consultants, tax professionals, and business
consultants; association services, namely promoting the
commercial interests of accountants; membership club
services, namely, providing on-line information to members
in the fields of branding, business development, business
marketing, and marketing advertising; marketing
consultation; online marketing and promotional services;
online advertising of the goods and services of others;
providing search engine marketing information; providing
product and computer software discounts, namely,
administration of a program for enabling participants to
receive free product samples and enabling participants to
obtain discounts on products and discounts on computer
software. Online credit card transaction processing services; payroll
tax debiting services; online bill payment services. Business, accounting, and computer software training
services; providing educational courses to accountants in
the fields of accounting, business, and computer software;
providing training of accountants, bookkeepers, and business
managers for certification in the fields of accounting,
business and computer software. Providing temporary use of online non-downloadable software
for accounting, bookkeeping, online financial and business
transaction processing management, financial and business
transaction management, tax preparation and tax planning,
business process management, and financial planning;
providing temporary use of online non-downloadable software
for use in the field of personal and business finance for
accounting, project costing management and tax management;
providing temporary use of online non-downloadable software
for use in the management of payroll; providing temporary
use of online non-downloadable software for administering
employee payroll; providing temporary use of online
non-downloadable software for use in the management of
benefit plans, insurance plans, retirement plans,
unemployment insurance plans, and pre-paid health care
plans; providing temporary use of online non-downloadable
software for creating, customizing, and managing invoices,
recording payments, and issuing receipts; providing
temporary use of online non-downloadable software for use in
organizing, servicing and tracking sales, collections and
receivables data; providing temporary use of online
non-downloadable software for tracking income, expenses,
sales, and profitability by business location, department,
type of business, or other user set field; providing
temporary use of online non-downloadable software for
customer relationship management; providing temporary use of
online non-downloadable software to calculate and charge
sales tax and to create reports to pay sales tax to
appropriate tax agencies; providing temporary use of online
non-downloadable software for credit card invoicing and
credit card payment processing; providing temporary use of
online non-downloadable software for managing online bank
accounts; providing temporary use of online non-downloadable
software for controlling access to financial information via
electronic permission settings; providing temporary use of
online non-downloadable software to create, customize,
print, export, and e-mail purchase orders; providing
temporary use of online non-downloadable software to track
time worked by employees and subcontractors; providing
temporary use of online non-downloadable software to create
and manage budgets; providing temporary use of online
non-downloadable software to create price quote estimates
and transfer price quote estimates to invoices; providing
temporary use of online non-downloadable software to
automate creation of invoices; providing temporary use of
online non-downloadable software to create, customize,
print, export, and e-mail financial reports, business
reports, balance sheets, profit and loss statements, cash
flow statements, and taxable sales reports; providing
temporary use of online non-downloadable software to create,
print, and track checks and purchase orders; providing
temporary use of online non-downloadable software to track
sales, expenses, and payments; providing temporary use of
online non-downloadable software to analyze the financial
status of businesses and industries; providing temporary use
of online non-downloadable software to manage customer
lists, e-mail and print sales forms, and track running
balances; providing temporary use of online non-downloadable
software for inventory management; providing temporary use
of online non-downloadable software to import contacts and
financial data from other electronic services and software;
providing temporary use of online non-downloadable software
for synchronizing data among computers and mobile devices;
providing temporary use of online non-downloadable software
for database management, data aggregation, data reporting,
and data transmission; providing temporary use of online
non-downloadable software for online backup of electronic
files; providing temporary use of online non-downloadable
software for use in transaction processing, accounting,
receipt printing, customer relationship management,
inventory management and operations management, all in the
field of point of sale transactions and retail management;
technical support services, namely, troubleshooting of
computer software problems, web sites, online services, web
and online application problems, mobile application
problems, and network problems; technical support services,
namely, help desk services; computer services, namely,
synchronizing data among computers and mobile devices;
computer technology consultation services; data hosting
services; hosting software for use by others for use in
managing, organizing and sharing data on computer server on
a global computer network.
2.
MULTIPLE INPUT MACHINE LEARNING FRAMEWORK FOR ANOMALY DETECTION
A method that includes extracting image features of a document image, executing an optical character recognition (OCR) engine on the document image to obtain OCR output, and extracting OCR features from the OCR output. The method further includes executing an anomaly detection model using features including the OCR features and the image features to generate anomaly score, and presenting anomaly score.
G06V 30/416 - Extracting the logical structure, e.g. chapters, sections or page numbersIdentifying elements of the document, e.g. authors
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06N 3/088 - Non-supervised learning, e.g. competitive learning
Systems and methods that generate an enhanced search report, which provides a significant improvement over conventional searches are provided. The systems and methods leverage a combination of keyword-based searches using search engine APIs to return URLs that are hit by the searches, large language models to generate additional keywords based on contextual information, and web crawlers to search the returned URLs with the additional keywords. The enhanced search report is not confined to the conventional keyword-URL match, but instead provides a sophisticated capture and compilation of additional useful information.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
36 - Financial, insurance and real estate services
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer programs for use in accounting; computer software
in the field of human resource management, namely, for use
in the management of payroll, and user manuals sold together
as a unit; computer software and hardware for use in
transaction processing, accounting, receipt printing,
customer relationship management, inventory management and
operations management, all in the field of point of sale
transactions and retail management, and user manuals sold as
a unit therewith; cash drawer being part of cash registers;
computer printers; point of sale printers and terminals;
credit card and debit card readers and scanners; bar code
readers and scanners; credit card and transaction processing
terminals and computer hardware; downloadable computer
software for time tracking; downloadable computer software
for creating, submitting, approving and managing timesheets;
downloadable computer software for capturing timesheet and
project signatures; downloadable computer software for
tracking, managing and analyzing project labor, project time
and task time estimates; downloadable workforce scheduling
computer software; downloadable computer software for
creating, editing and publishing job and shift schedules;
downloadable computer software for tracking paid time off,
sick days, and holidays; downloadable workforce time
management and scheduling management computer software;
downloadable computer software for tracking labor estimates
against actuals. Business intermediary services relating to the matching of
lenders with borrowers in the fields of consumer lending and
commercial lending; providing information in the nature of
business and marketing advice, news, and opinions for
professionals in the fields of accounting, finance,
financial planning, small business management, tax
preparation, tax filing and tax planning; accounting and
bookkeeping services; association and membership services,
namely promoting the commercial and business interests of,
and providing business referral, marketing, and business
management services to member professionals in the fields of
accounting, finance, financial planning, small business
management, tax preparation, tax filing, and tax planning;
business information and accounting advisory services;
business management in the field of bookkeeping and
accounting; member benefits program, namely, customer
loyalty services for commercial, promotional and/or
advertising purposes that provides a variety of amenities to
member accounting professionals, computer consultants, tax
professionals, and business consultants; business management
services, namely, providing human resource department
services for others featuring payroll preparation, payroll
tax calculation, payroll tax filing, and employee benefit
planning; providing online employee and contractor time
tracking services for others; providing online employee and
contractor timesheet management services for others; online
tracking, managing and analyzing project labor, project
time, and task time estimates for others; providing online
workforce scheduling for others; online business and
personnel management services, namely, tracking paid time
off, sick days, and holidays for others; workforce
management services for others; online tracking of labor
estimates against actuals for others; online categorizing
and analyzing of time data by job, task, employee, group, or
project for others for job costing and resource allocation;
online integration of employee time tracking data with
accounting, payroll and invoicing services; online business
analytics for predicting time needs to prepare job cost
estimates, plan for payroll, and assess profitability. Insurance services, namely, insurance brokerage services. Educational services, namely, conducting classes, seminars,
conferences, workshops and webcasts in the fields of
computer software, computer hardware, software as a service
(SAAS) technology, cloud computing, and mobile computing,
and distributing course materials in connection therewith;
providing training of accountants, bookkeepers, and business
managers for certification in the fields of accounting,
business and computer software; educational services,
namely, conducting classes, seminars, conferences, workshops
and webcasts in the fields of accounting, tax, finance,
business, and productivity and distributing course materials
in connection therewith; providing training and educational
services in the fields of accounting, tax, finance,
business, and computer software; educational services,
namely, conducting classes, seminars, conferences, workshops
and webcasts in the fields of business development and
business management, and distributing course materials in
connection therewith. Providing temporary use of on-line non-downloadable software
in the field of human resource management, namely for use in
the management of payroll, benefit plans, insurance plans,
retirement plans, unemployment insurance plans, and pre-paid
health care plans; providing temporary use of online
non-downloadable software for time tracking; providing
temporary use of online non-downloadable software for
creating, submitting, approving and managing timesheets;
providing temporary use of online non-downloadable software
for capturing timesheet and project signatures; providing
temporary use of online non-downloadable computer software
for tracking, managing and analyzing project labor, project
time and task time estimates; providing temporary use of
online non-downloadable workforce scheduling software;
providing temporary use of online non-downloadable software
for creating, editing and publishing job and shift
schedules; providing temporary use of online
non-downloadable software for tracking paid time off, sick
days, and holidays; providing temporary use of online
non-downloadable workforce management and scheduling
management software; providing temporary use of online
non-downloadable software for tracking labor estimates
against actuals; providing temporary use of online
non-downloadable time clock software; providing temporary
use of online non-downloadable time tracking software
featuring GPS location tracking and geofencing, biometric
identification, and facial recognition; providing temporary
use of online non-downloadable software for creating
customized alerts and automated notifications; providing
temporary use of online non-downloadable software to manage
overtime and to preset work dates and pay rates in advance;
providing temporary use of online non-downloadable software
featuring facial recognition; providing temporary use of
online non-downloadable software to categorize and analyze
time data by job, task, employee, group, or project for job
costing and resource allocation; providing temporary use of
online non-downloadable software for use in preparing time
analysis reports; providing temporary use of online
non-downloadable software to manage, send and receive
alerts; providing temporary use of online non-downloadable
software for integrating employee time tracking data with
accounting, payroll and invoicing software; providing
temporary use of online non-downloadable software for
creating real-time interactive business insight reports;
providing use of online non-downloadable software for
predicting time needs to prepare job cost estimates, plan
for payroll and assess profitability; technical support
services, namely, troubleshooting of computer software and
web application problems; software as a service (SAAS)
services featuring software to calculate and charge sales
tax and to create reports to pay sales tax to appropriate
tax agencies; software as a service (SAAS) services
featuring software for credit card invoicing and credit card
payment processing; software as a service (SAAS) services
featuring software for online banking; software as a service
(SAAS) services featuring software for controlling access to
financial information via electronic permission settings;
software as a service (SAAS) services featuring software to
create, customize, print, export, and e-mail purchase
orders; software as a service (SAAS) services featuring
software for tracking income, expenses, sales, and
profitability by business location, department, type of
business, or other user set field; software as a service
(SAAS) services featuring software to track time worked by
employees and subcontractors; software as a service (SAAS)
services featuring software for accounting, bookkeeping,
transaction processing, transaction management, tax
preparation and planning, business process management, and
financial planning; software as a service (SAAS) services
featuring software to create, customize, and manage
invoices; software as a service (SAAS) services featuring
software to create and manage budgets; software as a service
(SAAS) services featuring software to create price quote
estimates and transfer price quote estimates to invoices;
software as a service (SAAS) services featuring software to
automate creation of invoices; software as a service (SAAS)
services featuring software to create, customize, print,
export, and e-mail financial reports, business reports,
balance sheets, profit and loss statements, cash flow
statements, and taxable sales reports; software as a service
(SAAS) services featuring software to create, print, and
track checks and purchase orders; software as a service
(SAAS) services featuring software to track sales, expenses,
and payments; software as a service (SAAS) services
featuring software to analyze the financial status of
businesses and industries; software as a service (SAAS)
services featuring software to manage customer lists, email
and print sales forms, and track running balances; software
as a service (SAAS) services featuring software for
inventory management and customer relationship management;
software as a service (SAAS) services featuring software for
administering employee payroll; software as a service (SAAS)
services featuring software to import contacts and financial
data from other electronic services and software; software
as a service (SAAS) services featuring software for database
management, data aggregation, data reporting, and data
transmission; software as a service (SAAS) services
featuring software for online backup of electronic files;
providing temporary use of online non-downloadable software
to calculate and charge sales tax and to create reports to
pay sales tax to appropriate tax agencies; providing
temporary use of online non-downloadable software for credit
card invoicing and credit card payment processing; software
as a service (SAAS) services featuring software for online
banking; providing temporary use of online non-downloadable
software for controlling access to financial information via
electronic permission settings; providing temporary use of
online non-downloadable software to create, customize,
print, export, and e-mail purchase orders; providing
temporary use of online non-downloadable software for
tracking income, expenses, sales, and profitability by
business location, department, type of business, or other
user set field; providing temporary use of online
non-downloadable software to track time worked by employees
and subcontractors; providing temporary use of online
non-downloadable computer software for accounting,
bookkeeping, transaction processing, transaction management,
tax preparation and planning, business process management,
and financial planning; providing temporary use of online
non-downloadable computer software to create, customize, and
manage invoices; providing temporary use of online
non-downloadable computer software to create and manage
budgets; providing temporary use of online non-downloadable
computer software to create price quote estimates and
transfer price quote estimates to invoices; providing
temporary use of online non-downloadable computer software
to automate creation of invoices; providing temporary use of
online non-downloadable computer software to create,
customize, print, export, and e-mail financial reports,
business reports, balance sheets, profit and loss
statements, cash flow statements, and taxable sales reports;
providing temporary use of online non-downloadable computer
software to create, print, and track checks and purchase
orders; providing temporary use of online non-downloadable
computer software to track sales, expenses, and payments;
providing temporary use of online non-downloadable computer
software to analyze the financial status of businesses and
industries; providing temporary use of online
non-downloadable computer software to manage customer lists,
email and print sales forms, and track running balances;
providing temporary use of online non-downloadable computer
software for inventory management and customer relationship
management; providing temporary use of online
non-downloadable computer software for administering
employee payroll; providing temporary use of online
non-downloadable computer software to import contacts and
financial data from other electronic services and software;
providing temporary use of online non-downloadable computer
software for database management, data aggregation, data
reporting, and data transmission; providing temporary use of
online non-downloadable computer software for online backup
of electronic files; technical support services, namely,
help desk services; technical support services, namely,
troubleshooting of computer software problems, and technical
support services relating to web sites and online software
applications; data hosting services; software as a service
(SAAS) services, namely, hosting software for use by others
for use in managing, organizing and sharing data on computer
server on a global computer network.
Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or more electronic data sources using a knowledge graph that maps relationships among electronic data sources. Embodiments include retrieving data related to the natural language query by transmitting requests to the one or more electronic data sources and the one or more additional electronic data sources and providing a response to the natural language query based on the data related to the natural language query.
Aspects of the present disclosure relate to detecting spam messages. Embodiments include creating condensed vector representations of messages; calculating similarity scores for each message relative to other messages using the vector representations; associating messages of the plurality of messages with a grouping based on the calculated similarity score for the messages within the grouping exceeding a threshold; determining that a grouping of messages are spam messages by comparing an amount of messages of the grouping of messages sent within a first time window to an amount of messages of the grouping of messages sent within a second time window, wherein the second time window comprises a time period preceding the first time window; providing the identified spam message to a machine learning model; and training the machine learning model by iteratively adjusting parameters of the model based on tracking the identified spam message through multiple layers of the model.
An API security system that implements techniques for learning sensitive information fields from a group of API requests. The API security system implementing security measures for protecting the identified sensitive information fields.
A method including applying a propensity model to a subject vector to generate a propensity value estimating a probability that a subject will perform an action. The subject vector has a data structure having features storing information regarding the subject. The method also includes applying a Shapley additive explanation tool to the propensity model to generate a subset of the features that contributed to the propensity value more than a remaining set of the features. The method also includes selecting an actionable feature from the subset of the features. The actionable feature includes a feature in the subset that an entity is able to influence. The method also includes applying a correlation model to labels for training data and the actionable feature to generate an output that describes a reason why the subject performs the action. The method also includes presenting the actionable feature and the output.
Methods and a computer system are provided for recommending digital products based on user state. An application state is received at an endpoint. The endpoint is a single endpoint that includes a plurality of models. Each model of the plurality is trained on a corresponding dataset including features of the application state extracted at different stages of a workflow. The application state is matched to a first stage of the workflow and the first model is selected from the plurality of models. The first model corresponds to a first stage of the workflow. The first model processes the application state to select a first digital product of a plurality of digital products, which is presented as a recommendation to the user at the first stage of the workflow.
Aspects of the invention provide a method, computer system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a multitude of domains. The method additionally includes retrieving an index of domain-specific vector embeddings corresponding to the domains. The method further includes prompting a large language model (LLM) with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the LLM as grounded with the most relevant index results. The method further includes forwarding the query response.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Aspects of the invention provide a method, computer system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a multitude of domains. The method additionally includes retrieving an index of domain-specific vector embeddings corresponding to the domains. The method further includes prompting a Large Language Model (LLM) with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the LLM as grounded with the most relevant index results. The method further includes forwarding the query response.
The method may include generating, by a vector embedding model, a vector embedding of multiple terms in an input document to obtain multiple term encodings. The method may also include generating, by a cascading classifier model, a classification of the input document. Generating the classification includes iteratively: traversing a directed acyclic graph ordering multiple class groups, and while traversing the directed acyclic graph, classifying the input document into a first class of a current class group of the class groups using the term encodings, where classifying the input document into the first class uses at least one second class of at least one parent class group of the class groups, and where the classification includes the first class and the at least one second class. The method may furthermore include obtaining a set of target fields corresponding to the classification. The method may in addition include extracting a set of values from the input document matching the set of target fields.
Certain aspects of the present disclosure provide techniques for preprocessing data prior to generating vector representations. The data is preprocessed to generate one or more natural language texts describing entities and relationships included in the data. Vector representations of the natural language format texts are stored in a vector database. The embeddings may be retrieved to augment a prompt for a generative model. The embeddings may be selected by performing a search of the vector database using an embedding for the prompt to determine relevant or similar embeddings. The augmented prompt can be input into a large language model. Although the model may be unaware of the data from which the embeddings of the embedding database were generated, the augmented prompt may enable the model to use the data to improve breadth and depth of responses. The preprocessing of the data improves response results of the model.
Certain aspects of the disclosure provide a method for generating training data and training a machine learning model. The method may include rotating each document image in a first set of document images by a plurality of rotation angles to obtain a first set of rotated document images and associating a rotation classification label to each rotated document image in the first set of rotated document images. The method may further include for each document image in a second set of document images: rotating the respective document image by a plurality of rotation angles, performing an optical character recognition analysis at each rotation angle of the plurality of rotation angles, generating a confidence score based on the optical character recognition analyses, assigning the confidence score to the respective document image, and associating a rotation classification to the respective document image based on the optical character recognition analyses.
At least one processor can obtain configuration instructions to direct operations of a natural language processing (NLP) machine learning (ML) pipeline. The configuration instructions can comprise at least one plain-language indicator of at least one NLP operation to be performed by the ML pipeline. The at least one processor can configure the ML pipeline using the configuration file. The at least one processor can perform NLP on text data using the configured ML pipeline.
A method including receiving a domain vector including a first data structure describing a first domain with which a subject interacts. A multilabel classification model is applied to the domain vector to generate a classification prediction including a classification vector. The classification vector has a second data structure describing a likelihood that a second domain, which is different than the first domain, is related to the subject. The classification prediction is based on the first domain. An uplift model is applied to the classification vector to generate an uplift value. The uplift value represents a probability that the subject is positively associated with the second domain. A vectorization algorithm is applied to the subject, the second domain, and the uplift value to generate an uplift vector including a third data structure describing a triplet of the subject, the second domain, and the uplift value. The uplift vector is returned.
A method including applying a large language model to a query to generate a query vector. The query vector has a query data structure storing a semantic meaning of the query. The method also includes applying a semantic matching algorithm to both the query vector and a lookup vector. The lookup vector has a lookup data structure storing semantic meanings of entries of a lookup table. The semantic matching algorithm compares the query vector to the lookup vector and returns, as a result of comparing, a found entry in the lookup table. The method also includes looking up, using the found entry in the lookup table, a target entry in the lookup table. The method also includes returning the target entry.
Systems and methods are disclosed for managing categorization problem solutions and identifying miscategorizations. The identification of a miscategorization of an object is based on the object's first embedding being different than the first embeddings of other objects in a cluster. The objects in the cluster are clustered together based on second embeddings of the objects, with the first embedding generated based on a first description associated with an object and the second embedding generated based on a second description associated with the object. As such, while the clustering of second embeddings may initially indicate that the objects in the cluster are similar, the comparison between first embeddings of the objects in the cluster (such as calculating a distance between a first embedding and a center of the cluster based on the first embeddings) can confirm whether an object in the cluster is different and thus is potentially miscategorized.
A method for training a machine learning model to automatically predict a classification for an uncategorized transaction includes: retrieving a plurality of historical transactions involving a plurality of different payors and a plurality of different payees; generating a first plurality of embeddings based, at least in part, on a subset of the historical transactions having a first type, wherein each of the first plurality of embeddings is representative of an industry name associated with one or more payors involved in the first set of historical transactions; generating training data for the machine learning model based, at least in part, on the first plurality of embeddings; and training the machine learning model through a supervised learning process using the training data.
Methods of graph centrality-based conversion path optimization disclosed herein include determining central nodes of graph representations. Central nodes are determined separately for graph representations including a conversion node and separately for graph representations not including a conversion node. The nodes that are determined to be key nodes based on being central nodes for the graph representations including the conversion nodes but not for graph representations not including the conversion node may be used as candidate nodes. A candidate is selected from the candidate nodes, and an action associated with the candidate is presented to the user. An updated graph representation may be generated using data collected while the candidate is presented as a basis to determine updated key node candidates in a feedback loop.
Aspects of the present disclosure relate to automatically updating a software application to ensure compliance with an updated data source. Embodiments include providing an embedding of a first version of a data source and an embedding of a second version of the data source to a comparison engine configured to compare the embedding of the first version of the data source and the embedding of the second version of the data source and generate a data source difference summary. Embodiments further include providing an embedding of a software application code module and the data source difference summary to a code update engine trained to generate an updated version of the software application code module based on the embedding of the software application code module and the data source difference summary. Embodiments further include updating code of the software application using the updated version of the software application code module.
Certain aspects of the present disclosure provide techniques for performing inferencing for entities in a relational network by determining graph features of a graph representation of the relational network. Random paths are generated using each node of the graph representation as a starting node and using relative outgoing edge weights to determine next nodes along the traversal path. Probability values that nodes will be reached as secondary nodes along a traversal path are aggregated to generate a score value or graph feature for each node of the graph representation. The graph features may be used as training data to train a machine learning model to infer risk values or graph features based on additional transaction data input.
Certain aspects of the disclosure provide techniques for translating a user request (e.g., posed in natural language) into a structured input using intermediate representations, to resolve the user request as a planning problem. A method generally includes generating a first intermediate representation of a first user request using a large language model (LLM), wherein the first intermediate representation comprises one or more first facts and/or one or more rules in a declarative language; generating a first materialized representation of the first user request based on the first intermediate representation and a domain description using a logic reasoner, wherein the first materialized representation comprises one or more second facts in the declarative language, and wherein the one or more second facts comprise a subset of the one or more first facts and one or more inferred facts; and generating a task description based on the first materialized representation.
Certain aspects of the disclosure provide a method for providing user support by generating recommended response for a customer verbatim with an ensemble of machine learning models. The method includes processing a customer verbatim with a topic model trained to identify a topic associated with the customer verbatim. The method further includes processing the customer verbatim with a sentiment model trained to determine a sentiment of the customer verbatim. The method further includes processing the customer verbatim with an actionability model trained to assign an actionability score to the customer verbatim. The method includes processing the topic, the sentiment, and the actionability score with a recommendation model to generate the recommended response to the customer verbatim.
A method includes receiving an untransformed transaction including unstructured data. An embedding model generates a vector from the unstructured data. A cluster model matches the vector to a vector cluster. A cluster ID is assigned to the vector. The unstructured data in the untransformed transaction is replaced with the cluster ID to obtain a transformed transaction. A query including the cluster ID and based on the transformed transaction is generated. The query is processed to generate a query result from features of prior transformed transactions. A fraud determination model processes the query result to generate a fraud score for the transformed transaction. The fraud score is presented to a user of a software application. The cluster model is updated to add or delete or modify vector clusters to generate cluster IDs, whereby generating the set of cluster IDs does not affect an input or output of the fraud determination model.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
A method implements brand engine for extracting and presenting brand data with user interfaces. The method includes receiving a blueprint with a set of structure blocks extracted from a selected content. A structure block of the set of structure blocks includes a set of style parameter requests for a section of the selected content. The method further includes processing the set of structure blocks with a first set of smart blocks to generate a set of scores. A smart block of the first set of smart blocks includes brand data with style parameter selections. The method further includes selecting a second set of smart blocks, for the set of structure blocks, from the first set of smart blocks, using the set of scores. The method further includes presenting the second set of smart blocks with the brand data.
A contrastive in-context learning protocol for large language models. The protocol includes inputting positive and negative examples to a large language model. Additionally, the large language model may be instructed to analyze the reasons behind the positive examples being positive and the negative examples being negative. The large language model with such contrastive in-context learning can generate specific responses/answers based on user preferences, generally not possible using conventional models.
Embodiments disclosed herein generate a strategy insight report for a user's business, leveraging generative artificial intelligence—particularly large language models—and pre-stored data associated with the user. The large language models are used to capture subjective information associated with different insight areas, e.g., strength, weakness, opportunity, and threat (SWOT) of a SWOT model. The captured subjective information is augmented, supplemented, and/or modified by the pre-stored data to generate the strategy insight report. In contrast to conventional results and reports, the disclosed strategy insight report provides a current state of the user's business as well as next steps and recommendations.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
29.
SYSTEMS AND METHODS FOR SOLVING MATHEMATICAL WORD PROBLEMS USING LARGE LANGUAGE MODELS
Output sentences of a primary large language model is provided to a criteria model including a second large language model. The criteria model compares the output to a reference source. As a result of comparing, the criteria model generates a first data structure including a first vector. The first vector stores, an evaluation of the output as being consistent or inconsistent with the reference source, and a corresponding reason for the evaluation. The criteria model identifies an inconsistent sentence, in the sentences, that is inconsistent with the reference source. The method also includes rewriting, by a reason improver model including a third large language model, the inconsistent sentence into a consistent sentence. The consistent sentence is consistent with the reference source. The output is modified by replacing the inconsistent sentence in the sentences with the consistent sentence. Modifying generates a modified output. The method also includes returning the modified output.
Providing an output of a primary large language model to a criteria model including a second large language model. The criteria model compares each of the sentences to a reference source and generates a first data structure including a first vector. The first vector stores, for each of the sentences, a corresponding evaluation of a given sentence as being consistent or inconsistent with the reference source, and a corresponding reason for the corresponding evaluation of the given sentence. The first data structure is provided to a converter model including a third large language model. The converter model converts the first data structure to a second data structure. The second data structure includes a second vector storing scores indicating a corresponding consistency value for each of the sentences. A metric, indicating an overall consistency of the output with respect to the reference source, is generated from the second data structure.
A method including receiving a digital image including text arranged in a layout. The method also includes generating, by an optical character recognition model, a layout text vector that encodes at least one word in the text of the digital image and a position of the at least one word in the layout of the digital image. The method also includes generating, by a visual encoder model, a visual representation vector embedding a content of the digital image. The method also includes converting both the layout text vector and the visual representation vector into a projected text vector including a digital format suitable for input to a large language model. The method also includes combining, into a prompt, the projected text vector, a system message, and a task instruction. The method also includes generating an output including a key-value pair.
Certain aspects of the disclosure pertain to workflow creation assistance with generative artificial intelligence. A problem statement can be received that is specified by a user in a natural language. At least one machine learning model can infer a workflow template that maps to the problem statement. Workflow template parameters can be determined, and parameter values generated based on the problem statement. Additional interaction with the user in the natural language can be performed to request and receive further data associated with the workflow template with the at least one machine-learning model. Subsequently, the workflow template can be populated with generated parameter values and provided to a workflow system that generates a workflow based on the workflow template.
Certain aspects of the disclosure provide techniques for translating a prompt into a structured input to resolve the natural langue query as a planning problem. A method generally includes identifying and classifying tokens in a prompt using a large language model (LLM); extracting from a domain description in a planning domain definition language (PDDL): object types used to categorize objects; and predicates identifying relationships between the objects that may be true or false; categorizing at least one token in the prompt as one or more of the objects, one or more of the object types, or one or more of the predicates based on the classification of the at least one token determined by the LLM; and generating a task description in the PDDL based on the categorization, the task description comprising a translation of the prompt into a structured input for a planner.
Certain aspects of the disclosure provide techniques for prompt processing. A method generally includes generating a representation of a prompt using a large language model (LLM), the representation comprising semantic features of the prompt, wherein the prompt requests a state change from an initial state to a desired goal state; generating a task description based on using the representation and a domain description; generating an execution plan for the task description, the execution plan comprising a sequence of steps used to transform the initial state to the desired goal state; executing the sequence of steps; and generating a natural language response to the prompt after completing the execution of the sequence of steps, wherein the natural language response is based on the information obtained or the desired goal state.
Embodiments disclosed herein provide automated account recommendations for a hierarchical account structure. For an incoming transaction data record, a first language model is used to generate a recommended account name that is agnostic to the existing list of accounts. The recommended account name is appended to the existing list of accounts, which is consolidated to remove synonymous accounts. Additionally, a hierarchical relationship between the different accounts in the consolidated list of accounts is determined. A second language model is used to select an account name from the list of accounts. The selected account name along with any hierarchically related account name may be displayed to the user for selection.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
Certain aspects of the disclosure provide systems and methods for generating meaningful insights from a data frame based on an insight score. An insight score may quantify the significance and confidence of a given insight. Aspects of the disclosure provide for optimizing the most meaningful insight based on a greedy binary search approach. Aspects of the disclosure further provide for obtaining the optimal insight based on a gradient search approach.
A method for providing certified reviews on a third-party review service includes obtaining one or more tokens associated with a transaction between a buyer and a seller to purchase an item from the seller. The method includes authenticating the buyer to provide a review of at least one of the seller or the item based on the one or more tokens. The method includes receiving a review generated by the buyer and, based on the authenticating, publishing the review so that the review is publicly accessible via the third-party-review service. The method includes providing a public indication that the published review is certified.
G06Q 30/0282 - Rating or review of business operators or products
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
39.
DYNAMICALLY GENERATING USER INTERFACES BASED ON MACHINE LEARNING MODELS
Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.
Systems and methods are described for batch materialization of an incremental change data capture (CDC) changeset with full row changes. The primary keys are extracted from the incremental CDC changeset and an indication of the extracted primary keys are broadcast to a plurality of executors. The primary keys may be added to Bloom filter or a plurality of Bloom filters that are broadcast to the executors. Each executor filters a baseline data table based on the extracted primary keys to generate a baseline match dataframe with all primary keys matching the extracted primary keys, and a baseline unmatched dataframe with all primary keys not matching the extracted primary keys. Each executor receives full row changes from a partitioned incremental CDC changeset and combines the changes with the baseline unmatched dataframe to produce a final changed baseline data table.
Certain aspects of the disclosure provide techniques for access control policy management. A method generally includes factorizing a user access co-occurrence data element to generate two data sub-elements, wherein: the user access co-occurrence data element represents co-occurrences between users of a system and resources of the system, a product of the two data sub-elements approximates the user access co-occurrence data element, and each of the two data sub-elements has reduced dimensionality compared to the user access co-occurrence data element; generating an approximated user access co-occurrence data element based on the product of the two data sub-elements; comparing the user access co-occurrence data element and the approximated user access co-occurrence data element to determine one or more anomalies, wherein each of the one or more anomalies relates to access for a user to a resource of the system; and taking one or more actions to rectify the one or more anomalies.
Systems and methods for adapting an onboarding session to a user are disclosed. An example method is performed by one or more processors of a system and includes receiving a transmission over a communications network from a computing device associated with a user of the onboarding system, the transmission including one or more files, extracting data from each of the one or more files using one or more parser plugins, transforming the extracted data into a set of arrays, feeding a prompt including the set of arrays to a large language model (LLM), inferring characteristics of the user based on a response to the prompt from the LLM, mapping the inferred characteristics to a predefined list of system features, and optimizing components of an onboarding session for the user based on the mapping.
Systems and methods for intelligently repairing data are disclosed. An example method is performed by one or more processors of a data quality management (DQM) system and includes receiving a transmission over a communications network from a computing device associated with the DQM system, the transmission including an indication that source data stored in a source database was ingested and stored as target data in a target database at a time of ingestion, comparing, using an advanced DQM algorithm, the target data with the source data, the advanced DQM algorithm including generating a first set of parity results based on changes occurring before the time of ingestion, generating a second set of parity results based on changes occurring after the time of ingestion, and generating differential results based on the first and the second set of parity results, and selectively repairing ones of the changes based on the differential results.
Systems and methods for detecting errors in a data transfer uses a machine learning model to identify potential anomalies in the data transfer based on metadata. Mismatches between input data from the data transfer and output data after importing the data transfer may additionally be identified. User review and correction of data errors and potential anomalies identified using the machine learning model may be proactively prompted to ensure any errors or discrepancies are addressed before finalizing the import of the data transfer. User corrections are further used to retrain the machine learning model to enable continuous improvement and learning from the data transfer process.
A method is provided for authenticating a user. A request to access a resource is received from a user agent. A cookie associated with the request is identified. The cookie includes a first subset of data that was previously used to authenticate the user. The cookie is validated based on the first subset of the data. Responsive to validating the cookie, a second subset of the data is retrieved from server-side storage. A risk decision is generated based on the first subset and the second subset. When the risk decision meets a threshold, the user is authenticated without presenting an authentication challenge, and access to the resources permitted.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
A method includes detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold, and processing, by a sentence transformer model responsive to the input sentence satisf)fing the readability metric threshold, the input sentence to output a suggested set of sentences. The method further includes evaluating the first suggested set of sentences along a set of acceptability criteria, and determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method further includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested set of sentences to obtain a modified written electronic communication, and storing the modified written electronic communication.
A method of blank detection involves receiving a document from a user, where the document includes derived text; applying a trained blank detection model to the document to make a first prediction, where the first prediction indicates whether at least one field in the document is blank; comparing the first prediction with a second prediction, where the second prediction is made by an extraction model; and extracting the at least one field using the extraction model.
The one or more embodiments provide for a method. The method includes receiving a digital image stored in an object notation data format. The method also includes converting the digital image into hypertext markup language (HTML) data format. The method also includes caching the HTML data format to generate cached HTML data. The method also includes receiving a first request to reload the digital image. The method also includes rendering, responsive to receiving the first request to reload, the digital image using the cached HTML data to generate a rendered digital image.
Certain aspects of the present disclosure provide techniques for providing smart content to a user of an application. Embodiments include receiving a request from a client for content. The request may include context data. Embodiments include identifying a content template for the content based on the request. Embodiments include identifying a rule associated with the content template. Embodiments include evaluating the rule based on the context data in order to determine a value of a variable. Embodiments include generating personalized content based on the content template and the value of the variable. Embodiments include providing the personalized content to the client.
Systems and methods are disclosed for converting natural language queries to a query instruction set for searching a data warehouse. To generate a query instruction set from a natural language query, a system iteratively uses a generative artificial intelligence (AI) model and database query tools to generate a query instruction set in a stepwise manner. The system and generative AI model do not require a priori knowledge of data table contents in the data warehouse, which may include sensitive information. In addition, the system does not require access to the data warehouse to generate the query instruction set. Instead, the system is implemented to use structure information from the data warehouse, including table lists (such as table names) and table format information (such as column names) of tables in the data warehouse, and the generative AI model is a generally trained model to generate the query instruction set.
Certain aspects of the present disclosure provide techniques for automatically healing a product flow for a mobile application. The techniques include an auto-healer capable of performing one or more actions, such as healing a product flow or generating an alert for a product flow, in response to determining an issue with the health status of the product flow. The health status can be determined from metrics included in a signal sent from mobile devices executing a mobile application including the product flow and hosted on a mobile application distribution platform. The metrics may be collected at flags or checkpoints in the mobile application and sent to a metrics server. In some cases, artificial intelligence may be used to analyze the metrics to determine health status issues or anomalies.
Certain aspects of the present disclosure provide techniques retrieval augmented generation of language model responses using an embedding database. Embeddings for data is stored in an embedding database. When a prompt related to the data is received, relevant embeddings may be retrieved from the database and used generate an augmented prompt based on the initial prompt and the retrieved embeddings from the database. The augmented prompt can be input into a machine learning model. Although the model may be unaware of the data from which the embeddings of the embedding database were generated, the augmented prompt enables the model to use the data to improve breadth and depth of responses.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
54.
DETECTION OF CYBER ATTACKS DRIVEN BY COMPROMISED LARGE LANGUAGE MODEL APPLICATIONS
A method including receiving, at a large language model, a prompt injection cyberattack. The method also includes executing the large language model. The large language model takes, as input, the prompt injection cyberattack and generates a first output. The method also includes receiving, by a guardian controller, the first output of the large language model. The guardian controller includes a machine learning model and a security application. The method also includes determining a probability that the first output of the large language model is poisoned by the prompt injection cyberattack. The method also includes determining whether the probability satisfies a threshold. The method also includes enforcing, by the guardian controller and responsive to the probability satisfying the threshold, a security scheme on use of the first output of the large language model by a control application. Enforcing the security scheme mitigates the prompt injection cyberattack.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/55 - Detecting local intrusion or implementing counter-measures
55.
SECURITY MARKER INJECTION FOR LARGE LANGUAGE MODELS
A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query, inserting an security marker instruction into the LLM query to trigger an injection of a security marker, and sending the LLM query to the LLM. The method further includes receiving, from the LLM, an LLM response to the LLM query, evaluating the LLM response to detect whether the security marker is present in the LLM response, and setting a prompt injection signal based on whether the security marker is present in the LLM response.
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
56.
PROMPT INJECTION DETECTION FOR LARGE LANGUAGE MODELS
A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query, inserting a system prohibited request into the LLM query to generate a revised LLM query, and sending the revised LLM query to the LLM. The method further includes receiving, from the LLM, a first LLM response to the LLM query, testing the first LLM response to detect whether a prohibited response to the system prohibited request is included in the first LLM response, and setting a prompt injection signal based on whether the prohibited response to the system prohibited request is included in the first LLM response.
The present disclosure relates to dynamic targeting of network invitations. Embodiments include clustering, based on network usage data, a plurality of in-network entities into active network users and passive network users. Embodiments include generating, for each active in-network entity, a vector representation based on connections between the active in-network entity and one or more other entities. Embodiments include generating, for each out-of-network entity, a corresponding vector representation based on connections between the out-of-network entity and one or more in-network entities. Embodiments include determining, for each out-of-network, a probability that the out-of-network entity will join the network based on comparing the corresponding vector representation of the out-of-network entity to a vector that is determined based on the vector representation of each active in-network entity. Embodiments include selecting an out-of-network entity to invite to the network based on the probability that the out-of-network entity will join the network.
Systems and methods for determining ownership of cloud computing resources are disclosed. An example method includes identifying a first active table whose ownership is not defined in a central repository, determining, based on a write log associated with the first active table, a first timestamp and a first internet address associated with a most recent write to the first active table, determining, based on the first internet address, whether or not the first timestamp is more recent than a creation time of a first cloud computing instance corresponding to the most recent write, and in response to the first timestamp being more recent than the creation time of the first cloud computing instance, identifying a first owner of the first active table based on a first cost allocation tag associated with the first cloud computing instance.
A method including generating a revised prompt from user customization data for customizing a user interface of an application, a pre-engineered prompt, and an application artifact from the application. The method also includes generating an output by executing a large language model on the revised prompt. The method also includes receiving a modified template generated from the user customization data and at least one of a set of templates. The method also includes transforming the output of the large language model and the modified template into both a consumable user interface component and a user interface artifact. The method also includes modifying a user interface of the application by applying the consumable user interface component and the user interface artifact to the application.
Systems and methods are disclosed for managing categorization problem solutions and identifying miscategorizations. The identification of a miscategorization of an object is based on the object's first embedding being different than the first embeddings of other objects in a cluster. The objects in the cluster are clustered together based on second embeddings of the objects, with the first embedding generated based on a first description associated with an object and the second embedding generated based on a second description associated with the object. As such, while the clustering of second embeddings may initially indicate that the objects in the cluster are similar, the comparison between first embeddings of the objects in the cluster (such as calculating a distance between a first embedding and a center of the cluster based on the first embeddings) can confirm whether an object in the cluster is different and thus is potentially miscategorized.
A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query and an application context, gathering confidential information from the LLM query, and sending the LLM query to the LLM. The method includes receiving, from the LLM, an LLM response to the LLM query, comparing the LLM response to the confidential information to generate comparison result, and setting a leakage detection signal based on comparison result.
Certain aspects of the disclosure provide systems and methods for resolving ambiguities encountered by a decision machine learning (ML) model during processing of input data. For example, a method may include identifying an ambiguity during decision processing of first input data by a decision ML model; conveying the ambiguity to an expert agent for evaluation; receiving, by an LLM, feedback regarding the ambiguity from the expert agent; determining, by the LLM, that the feedback, received from the expert agent, resolves the ambiguity; generating second input data by the LLM, the second input data having the first input data and the feedback determined to resolve the ambiguity; processing the second input data by the decision ML model to generate a decision based on processing of the second input data; and outputting, by the LLM, the decision received from the ML model.
A method for automatically generating content for a document for an individual includes providing input data to a trained artificial intelligence model. The input data includes a plurality of input features specific to the individual, and the trained artificial intelligence model is trained through a supervised learning process using training data that includes a plurality of input features for each of a plurality of individual other than the individual for whom the document is being created. The method includes receiving output data from the artificial intelligence model that is based, at least in part, on the input data and includes the content the artificial intelligence model automatically generated for the document for the individual. The method includes receiving user feedback on the content automatically generated by the artificial intelligence model and generating updated training data for the artificial intelligence model based, at least in part, on the user feedback.
09 - Scientific and electric apparatus and instruments
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for management of employee benefit plans, insurance plans, health care plans, workers’ compensation plans, and retirement plan; Downloadable business management computer software for health care practice management Providing information about health care insurance plans Providing temporary use of online, non-downloadable computer software for management of employee benefits, insurance plans, workers’ compensation plans, and retirement plans; Providing temporary use of online, non-downloadable business management computer software for health care practice management
A method including receiving an input including a number of texts from a source of text and a number of images from a source of images. The texts are separate from the images. The input is embedded into a first data structure that defines first relationships among the images from the source of images and the texts from the source of text. The first data structure is compared to an index including a second data structure that defines second relationships among a number of pre-determined texts and a number of pre-determined images. The pre-determined texts have known relationships to the pre-determined images. Each pre-determined image in the pre-determined images is related to one or more instances of the pre-determined texts. A subset of images, those images in the pre-determined images for which matches exist between the first relationships and the second relationships, is returned from the pre-determined images.
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A method includes generating a new user query embedding for a new user query received from a user, obtaining an indexed user query matching the new user query from a search engine index, a vector index corresponding to the indexed user query, and a relevancy score corresponding to the indexed user query. The method further includes selecting a vector structure corresponding to the vector index from a plurality of vector structures in a vector store, obtaining, from the vector structure, a result embedding matching the new user query embedding, transmitting, by a user query answer service to an answer generation model, the result embedding and receiving, by the user query answer service, an answer to the new user query from the answer generation model.
Certain aspects of the disclosure provide techniques for information retrieval for large datasets. A method comprises receiving input text files and a query for the files; obtaining an index associated with the input text files to process the query, wherein: the index comprises key-value mappings, each key of a respective mapping identifying a voronoi cell of the index, and each value of a respective mapping identifying vector embeddings associated with text files associated with a voronoi cell of the index; creating a query embedding based on the query; identifying a first key-value mapping having a first key associated with a first voronoi cell in the index and corresponding to the query embedding; obtaining a set of vector embeddings associated with the first value; comparing the query embedding to the set of vector embeddings to determine closest vector embeddings; and generating a textual output based on the closest vector embeddings.
A first large language model (LLM) instance may be instructed to request data while being prevented from performing calculations using the data. A second LLM instance may be instructed to provide a response to the request for data based on a known complete data set. The response may be translated into a machine-readable response in a format configured for processing by a calculation engine. The calculation engine may process the machine-readable response, thereby generating a calculation engine output. A mismatch between the calculation engine output and a known result obtained using the known complete data set may be identified, and the instruction to the first LLM may be modified in response.
Systems and methods are disclosed for detecting hallucinations in large language models (LLMs). An example method includes receiving a first prompt for submission to the first LLM, generating, using the first LLM, a plurality of semantically equivalent prompts to the first prompt, generating, using the first LLM, a first response to the first prompt and a plurality of second responses to the plurality of semantically equivalent prompts, generating, using a second LLM, a plurality of third responses to the semantically equivalent prompts, generating a semantic consistency score for the first response based at least in part on the first prompt, the plurality of semantically equivalent prompts, the plurality of second responses, and the plurality of third responses, and determining whether or not the first response is an accurate response to the first prompt based at least in part on the semantic consistency score.
The present disclosure provides techniques for fast record matching using machine learning. One example method includes receiving a request indicating one or more attributes, identifying, from a plurality of records using a first machine learning model, a set of records, wherein each record of the set of records indicates the one or more attributes, computing, for each record of the set of records using a second machine learning model, a first relevance score for the record, computing, for each record of the set of records using a third machine learning model, a second relevance score for the record, and identifying, based on the first relevance score for each record of the set of records and the second relevance score for each record of the set of records, a given record of the set of records best matching the request.
A method for generating supplemental content for an explanation for a particular result determined by a software application includes receiving data indicative of a user selecting a first modality of a plurality of different modalities for supplementing the explanation. In response to receiving the data, the method includes providing inputs to a generative artificial intelligence model. The inputs include data indicative of the explanation and data indicative of a first natural language prompt associated with the first modality. The method includes receiving an output from the generative artificial intelligence model. The output includes supplemental content for the explanation. The method includes displaying the supplemental content for viewing via a user interface.
Systems and methods for training an encoder-decoder model are disclosed. An example method includes receiving, over a communications network, a plurality of extraction model outputs from a corresponding plurality of extraction models, each extraction model output received from a corresponding extraction model and each extraction model output including a respective plurality of key-value pairs corresponding to extracted text from one or more training documents, receiving, over the communications network, character recognition data corresponding to the one or more training documents, receiving, over the communications network, ground truth key-value data corresponding to the one or more training documents, and training the encoder-decoder model based at least in part on the plurality of extraction model outputs, the character recognition data, and the ground truth key-value data, wherein the trained encoder-decoder model is configured to generate key-value pairs for subsequent outputs of the plurality of extraction models.
Certain aspects of the disclosure provide systems and methods for detecting hallucinations in machine learning models. A method generally includes generating a potential answer from an initial prompt received from a user. The method generally includes interrogating the machine learning model with a verification prompt formulated to elicit a positive or negative response from the machine learning model based on the potential answer and initial prompt. A negative response by the neural network model to the verification prompt is indicative of the potential answer being a hallucination. A positive response by the neural network model to the verification prompt is indicative of the potential answer being free from a hallucination. The method generally includes outputting to the user the potential answer as a final answer upon receiving a positive response to the verification prompt.
Certain aspects of the present disclosure provide techniques for selecting between a model output of a machine learning (ML) model and a generic output. A method generally includes processing user-specific data with the ML model to generate the model output and a model predicted score associated with the model output; calculating a Shapley Additive Explanations (SHAP) score based on the model output, the model predicted score, and the user-specific data; and providing the model output or the generic output as output from the ML model based on the SHAP score.
Certain aspects of the disclosure provide a method for detecting data collection errors by processing error data with a plurality of regression models to generate a plurality of predicted error rates over a plurality of time intervals. The method includes determining an error mode by applying a set of policy rules optimized for determining the error mode to the plurality of predicted error rates.
Aspects of the present disclosure provide techniques for providing a graphical user interface for customizable application navigation. Embodiments include displaying a list of pages associated with a software application in a navigation customization screen and receiving selections of two or more pages of the pages as bookmarks. Embodiments include receiving drag and drop input via the navigation customization screen that changes an ordering of the two or more pages within the list of the plurality of pages and receiving a search query comprising a text string. Embodiments include moving one or more pages matching the search query to a top of the list of the pages within the navigation customization screen and displaying an indication in the navigation customization screen that one of the two or more pages also matches the search query without changing the ordering of the two or more pages within the list of the pages.
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
A method for automatically populating a document being prepared via a software application based on extracted data from one or more of a plurality of different source documents may include displaying a graphical user interface associated with the software application and include a first area configured to display data associated with the document and a second area displaying a queue including at least a first graphical object descriptive of a first source document of the plurality of source documents. The method includes automatically populating one or more data fields of the document that are displayed within the first area of the graphical user interface with the extracted data from the first source document. In response to the automatically populating, the method includes automatically updating the second area of the graphical user interface to reflect the data fields have been auto populated with the extracted data from the first source document.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software; downloadable computer software for use in personal and business finance, financial planning and management, financial and business transaction processing, accounting, bookkeeping, tax preparation, planning, and filing; payment processing software; downloadable computer software for creating, customizing, and managing invoices, recording payments, and issuing receipts; downloadable computer software for electronic invoicing and payment processing; downloadable computer software for facilitating payments; downloadable computer software for processing electronic transactions; downloadable computer software for electronic funds transfer; downloadable computer software that enables communication between business and financial professionals and their clients; downloadable computer software to automate creation of invoices; downloadable computer software to create, customize, print, export, and e-mail purchase orders, invoices, receipts, documents, and reports; downloadable software for sending, receiving and recording monetary transactions; downloadable software for use in organizing, servicing and tracking sales, collections, and receivables data; downloadable software that provides financial data for use by small businesses; downloadable computer software for controlling access to financial and business information, data, and documents; downloadable computer software to track sales, expenses, and payments; downloadable computer software for enabling consumers to make, and merchants to accept, payments using cryptocurrency, digital currency, electronic cash, and virtual currency; downloadable computer software for managing online bank accounts; downloadable computer software for tracking income, expenses, sales, and profitability by business location, department, type of business, or other user set field; downloadable computer software for use in transaction processing, accounting, customer relationship management, inventory management, business operations and operations management; downloadable computer software that enables users to capture, upload, download, store, organize, view, create, edit, encrypt, send and share documents, information, data, images, photographs, and electronic messages; downloadable computer software to import contacts and business data from other electronic services and software. Business invoicing services; online accounting and bookkeeping services. Banking services; online banking services; bank account financial management services; bill payment services; electronic bill presentment for others; electronic commerce payment services; electronic money transfer services; electronic payment processing services; electronic payment services; electronic processing and transmission of bill payment data for others; financial management services via global computer networks; financial services; financial transaction services; payment processing services; providing electronic cash, credit card, and debit card transaction services via computer and communication networks; provision of financial information; transaction processing services for consumers and businesses; online bill payment services. Providing non-downloadable computer software; Providing non-downloadable financial management software; Providing non-downloadable software for personal and business finance, accounting, bookkeeping, financial and business transaction processing management, financial and business transaction management, tax preparation, tax planning, and tax filing, business process management, and financial planning; Providing non-downloadable payment processing software; Providing non-downloadable payment software; Providing non-downloadable electronic payment processing software; Providing non-downloadable computer software for facilitating payments; Providing non-downloadable software for creating, customizing, and managing invoices, recording payments, and issuing receipts; Providing non-downloadable software for electronic funds transfer; Providing non-downloadable software for electronic invoicing and payment processing; Providing non-downloadable software to automate creation of invoices; Providing non-downloadable software to create, customize, print, export, and e-mail purchase orders, invoices, receipts, documents, and reports; Providing non-downloadable software for executing, processing, and recording financial transactions; Providing non-downloadable software for sending, receiving and recording monetary transactions; Providing online non-downloadable computer software that enables communication between business and financial professionals and their clients; Providing non-downloadable business management software; Providing non-downloadable computer software for enabling consumers to make, and merchants to accept, payments using cryptocurrency, digital currency, electronic cash, and virtual currency; Providing non-downloadable computer software for managing online bank accounts; Providing non-downloadable software for controlling access to financial and business information, data, and documents; Providing non-downloadable software for tracking income, expenses, sales, and profitability by business location, department, type of business, or other user set fields; Providing non-downloadable software for use in organizing, servicing and tracking sales, collections, and receivables data; Providing non-downloadable software for use in transaction processing, accounting, customer relationship management, inventory management, and operations management; Providing non-downloadable software that enables users to capture, upload, download, store, organize, view, create, edit, encrypt, send and share documents, information, data, images, photographs, and electronic messages; Providing non-downloadable software that provides financial data for use by small businesses; Providing non-downloadable software to import contacts and business data from other electronic services and software; Providing non-downloadable software to track sales, expenses, and payments; software as a service (SaaS) featuring cloud computing capabilities for accounting, bookkeeping, financial and business transaction processing management, financial and business transaction management, tax preparation and tax planning, business process management, and financial planning; software as a service (SaaS) services.
82.
LARGE LANGUAGE MODEL AND DETERMINISTIC CALCULATOR SYSTEMS AND METHODS
At least one large language model (LLM) may be instructed to request data from a user while being prevented from performing calculations using the data. A user-generated response to the request for data, including at least a portion of the data, may be received. The user-generated response may be translated into a machine-readable response in a format configured for processing by a calculation engine. The calculation engine may process the machine-readable response, thereby generating a calculation engine output.
A method for detecting fraudulent financial transactions in information technology networks involves obtaining a multitude of features associated with a financial transaction conducted over an information technology network by an unknown transaction party. The multitude of features includes clickstream data obtained from the unknown transaction party. The clickstream data is associated with data of the financial transaction being entered by the unknown transaction party. The method further involves obtaining a first fraud indicator using a machine learning classifier operating on the multitude of features, obtaining a second fraud indicator using a rule-based classifier operating on the multitude of features, obtaining a fraud prediction for the financial transaction, using the first fraud indicator and the second fraud indicator, and taking an action, in response to the fraud prediction.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06F 18/243 - Classification techniques relating to the number of classes
G06F 21/52 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure
At least one processor may receive a query response generated by a query machine learning (ML) model, wherein the query response is generated in response to a query from a client device. The at least one processor may generate an evaluated likelihood of the query response being found in a training data set comprising known valid data, wherein the generating is performed using an evaluation ML model. The at least one processor may determine that the evaluated likelihood indicates the query response is likely to include valid data. In response to the determining, the at least one processor may return the query response to the client device.
A method includes obtaining matches between target records in a target dataset and a reference records in a reference dataset, each match of the matches comprising a corresponding confidence level of the match, categorizing the target records into review level categories according to the corresponding confidence level, and presenting a graphical user interface (GUI). The GUI includes a first section for a first review level category showing a first subset of the target records assigned to the first review level category, the first subset comprising target records related, in the GUI, to at least one matching reference record. The GUI includes a second section for a second review level category, wherein the second section shows a second subset of the target records assigned to the second review level category, the second subset comprising target records related, in the GUI, to at least one matching reference record.
Systems and methods for enriching raw user text with a database to identify relevant context, wherein generated prompts provide responses to user queries is provided. A method includes receiving a query, wherein the query comprises the raw text, creating a first embedding based on the query, retrieving a plurality of other embeddings, wherein the plurality of other embeddings are complementary to the first embedding, creating a contextual prompt including context from at least one of the plurality of other embeddings, processing the contextual prompt using a trained machine learning model, thereby generating a response to the query, and causing the response to be displayed by a display device.
Certain aspects of the disclosure provide a method of providing an interactive user support interface, the method comprising receiving a communication with a support request for an application. The method further comprising determining, based on the communication, an account associated with the application and determining, based on the account, that the user is an active session in to the application. The method further comprising determining support content responsive to the support request and causing the support content to be displayed within the application based on the determination that the user is an active session in to the application.
A method classifies feedback from transcripts. The method includes receiving an utterance from a transcript from a communication session and processing the utterance with a classifier model to identify a topic label for the utterance. The classifier model is trained to identify topic labels for training utterances. The topic labels correspond to topics of clusters of the training utterances. The training utterances are selected using attention values for the training utterances and clustered using encoder values for the utterances. The method further includes routing the communication session using the topic label for the utterance.
A Large Language Model (LLM) for classifying documents by identifying indicators within the documents. A smart caching mechanism stores document classifications and associated indicators output from the LLM. The database contains document details, classifications, and associated indicators. A classification module classifies a new document by analyzing it for indicators, checking the cache for a match, and querying the database for the indicators if no match is found. The module applies a majority vote based on the classifications associated with the indicators.
G06F 12/0875 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches with dedicated cache, e.g. instruction or stack
The present disclosure provides techniques for schema-based machine learning model monitoring. One example method includes receiving input data to and output data related to a machine learning model, wherein the input data and the output data conform to a data schema, retrieving, based on the data schema, a set of fields associated with the input data and the output data, performing statistical analysis for the machine learning model based on the set of fields retrieved, and predicting one or more attributes of the machine learning model based on the statistical analysis, wherein the one or more attributes of the machine learning model indicate a result of monitoring of the machine learning model, explainability information related to the machine learning model, or health of the machine learning model.
Certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. A voice enabled content tracking system receives natural language content corresponding to audio input from a user. A determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. In response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
A transaction model of a general model generates a target transaction vector for a target transaction record. The general model also generates account vectors for accounts. A match score is generated between the account vectors and the transaction vector. The general model selects a first account identifier of an account using the match score. The transaction model also generates historical transaction vectors for historical transaction records. Further, a comparison score is generated between the historical transaction vectors and the target transaction vector. A second account identifier of an historical transaction is selected according to the comparison score. One of the first account identifier and the second account identifier is selected as the account identifier for the transaction record, and the transaction record is stored with the account identifier.
Systems and methods for matching received product information with stored product information. Incoming product information has multiple attributes, which may be fuzzy matched with corresponding attributes of stored product information to generate corresponding fuzzy matching scores. Each of the fuzzy matching scores is associated with a weighting factor, which is used to indicate a contribution of the corresponding fuzzy matched attribute to a match between the entire product information. A matching coefficient is initialized and progressively updated by using the weighted fuzzy matching scores. When a desired number of fuzzy matchings between the corresponding attributes is reached and the matching coefficient is finalized, the matching coefficient is compared to a threshold. If the matching coefficient is above the threshold, a recommendation is generated indicating a match between the received product information and the stored product information.
A method including receiving a user input from a user device. The method also includes generating test inputs including the user input and modified inputs. The user input is processed with a rephrasing model to form the modified inputs. The method also includes executing a test model to generate test outputs, including an original test output and modified test outputs, from processing the test inputs. The method also includes generating similarity scores by performing similarity comparisons among the test outputs. The method also includes determining a model confidence from the similarity scores. The method also includes routing the user input responsive to the model confidence satisfying or failing to satisfy a confidence threshold.
Certain aspects of the disclosure provide a method for training a machine learning model to predict text containing sensitive information. The method includes extracting one or more features from a historical data set. The method further includes anonymizing the historical data set, including determining, for each feature of the extracted one or more features, tokens containing personally identifiable information (sensitive information); assigning a category placeholder to each of the tokens containing sensitive information; and generating a new data set where each token containing sensitive information is replaced with the assigned category placeholder. The method further includes determining a probability associated with each token containing sensitive information; and training a generalized model to predict anonymized text given the one or more features.
A computer-implemented method includes receiving data comprising a plurality of application programming interface (API) requests from a plurality of client devices. The method includes generating a plurality of API sessions based on the data, wherein each of the API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes determining one or more API sessions of the plurality of API sessions generated based on the data are abnormal. Finally, the method includes performing one or more actions based on determining the one or more API sessions are abnormal.
Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
