METHOD AND APPARATUS FOR PROCESSING DATA USING ARTIFICIAL INTELLIGENCE TO DETERMINE GOALS

An approach is provided for determining financial/business goals from multiple data sources by applying artificial intelligence models. The approach involves, for example, collecting unstructured data relating to at least one user from one or more data sources. The approach also involves processing the unstructured data to determine the at least one goal and contextual data related to the at least one goal. The approach further involves extracting the contextual data related to the at least one goal from the unstructured data. The approach also involves classifying the contextual data related to the at least one goal into a first confidence data or a second confidence data, wherein the first confidence data has a higher confidence level than the second confidence data. The approach further involves integrating the second confidence data with a structured data. The approach also involves causing presentation of the integrated data via a graphical user interface.

BACKGROUND

Services professionals, e.g., wealth managers and advisors, face the daunting task of delivering high performance for all clients, while providing highly personalized service and attention. In the financial services sector, the advisor must dedicate significant time to research, form strategies, and administer portfolio allocations in alignment with the clients' financial goals; these activities ironically detract from engaging with their clients to learn and understand each of the clients' evolving goals. Although customer relationship management (CRM) systems help with retaining useful, relevant information about clients, these systems can generate voluminous data about such clients, particularly in the case of a wealth manager who has a large book. Despite the large repository of personalized information, it can be impractical for the advisor to analyze such information for all but the top clients.

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for an approach for determining goals (e.g., financial, business, etc.) from multiple data sources by applying artificial intelligence models.

According to one embodiment, a method for determining at least one goal by applying artificial intelligence, comprising collecting unstructured data relating to at least one user from one or more data sources. The method also comprises processing the unstructured data to determine the at least one goal and contextual data related to the at least one goal. The method further comprises extracting the contextual data related to the at least one goal from the unstructured data. The method also comprises classifying the contextual data related to the at least one goal into a first confidence data or a second confidence data, wherein the first confidence data has a higher confidence level than the second confidence data. The method further comprises integrating the second confidence data with a structured data. The method also comprises causing a presentation of the integrated data via a graphical user interface.

According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to collect unstructured data relating to at least one user from one or more data sources. The apparatus is further caused to process the unstructured data to determine the at least one goal and contextual data related to the at least one goal, to extract the contextual data related to the at least one goal from the unstructured data, and to classify the contextual data related to the at least one goal into a first confidence data or a second confidence data, wherein the first confidence data has a higher confidence level than the second confidence data. The apparatus is further caused to integrate the second confidence data with a structured data and to initiate presentation of the integrated data via a graphical user interface.

According to one embodiment, a system determining at least one goal by applying artificial intelligence, comprising a data processing module configured to aggregate data relating to at least one user from one or more data sources. The system also comprises a database configured to store the aggregated data. The system further comprises an artificial intelligence platform configured to process the aggregated data using a natural language processing (NLP) mechanism, to modify the aggregated data with metadata, and to apply an artificial intelligence (AI)/machine learning (ML) process on the aggregated data to generate a recommendation. The system further comprises causing a presentation module configured to cause a presentation of the recommendation via a graphical user interface.

In various example embodiments, the methods (or processes) can be accomplished on the service provider side or on the mobile device side or in any shared way between a service provider and mobile device with actions being performed on both sides.

For various example embodiments, the following is applicable: An apparatus comprising means for performing a method of any of the claims.

DESCRIPTION OF SOME EMBODIMENTS

FIG. 1is a diagram of a system capable of determining goals from multiple data sources by applying artificial intelligence models, according to one embodiment. Although various embodiments of system100are described with respect to financial and/or business goals, it is contemplated that any objective or goal relating to any other activities of a user can be determined. With respect to wealth management, for example, traditionally, investors have relied on investment advisors to manage their investment portfolios; however, investment advisors are expensive, and the investors who use such advisors often have complex financial portfolios. Furthermore, some investors have difficulty in accessing investment advisors because of minimum balances or investment funds requirements, geographic distance limitations, and conventional structuring or practices of financial institutions. It is recognized that investors are increasingly reliant on computer-based financial analysis tools that provide them with a summary of their financial health. Such tools typically calculate life expectancy and future assets from minimal data, e.g., age, income, expenses, marital status, asset information, and loan amount. Such predictions provide little accuracy in that these financial analysis tools do not account for other relevant information, e.g., the investors' health-related information, genetic background, lifestyle, medical status, etc. Predictions of financial futures require much more information to be reasonably accurate and to provide a more realistic summary of the financial future. However, collecting and analyzing such information poses significant technical challenges due, in part, from volume and from the fact that such information is largely unstructured and exists across multiple systems.

To address this problem, system100ofFIG. 1introduces the capability to retrieve data from a myriad of public and/or private data sources to develop a financial/business goal. System100applies artificial intelligence (AI) models to process the retrieved data and identify relevant goals and related contextual information pertaining to at least one investor, e.g., a user. Thereafter, system100combines the relevant goals and related contextual information with structured client profile data. The structured client profile data comprises of portfolio and holdings information. Subsequently, system100generates signal across one or more clients to dynamically surface goal-based insight. In one example embodiment, system100identifies retirement goals, e.g., planning and discussions around retirement savings and contributions. In another example embodiment, system100identifies other useful goals, e.g., planning for future education-related expenses, on-going expenses, and lifestyle choices, future purchases, cash outlays, and accumulation goals, etc.

As shown inFIG. 1, the system100comprises user equipment (UE)101a-101n(collectively referred to as UE101) that may include or be associated with applications103a-103n(collectively referred to as applications103) and sensors105a-105n(collectively referred to as sensors105). In one embodiment, the UE101has connectivity to a data analysis platform109via a communication network107, e.g., a wireless communication network. In one embodiment, the data analysis platform109performs one or more functions associated with determining financial/business goals from multiple data sources by applying artificial intelligence models.

As shown inFIG. 1, the system100comprises of UE101. In one embodiment, the UE101may include, but is not restricted to, any type of a mobile terminal, wireless terminal, fixed terminal, or portable terminal. Examples of the UE101, may include, but are not restricted to, a mobile handset, a wireless communication device, a station, a unit, a device, a multimedia computer, a multimedia tablet, an Internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a Personal Communication System (PCS) device, a personal navigation device, a Personal Digital Assistant (PDA), a digital camera/camcorder, an infotainment system, a dashboard computer, a television device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. In addition, the UE101may facilitate various input means for receiving and generating information, including, but not restricted to, a touch screen capability, a keyboard, and keypad data entry, a voice-based input mechanism, and the like. Any known and future implementations of the UE101may also be applicable.

The UE101includes applications103. Further, the applications103may include various applications such as, but not restricted to, content provisioning application, networking application, calendar applications, camera/imaging application, multimedia application, location-based application, and the like. In one example embodiment, the application103enables the data analysis platform109to process content information, communication information, contextual information, and/or sensor information to determine relevant goals and related contextual information for at least one user.

The system100also includes one or more sensors105, which can be implemented, embedded or connected to the UE101. The sensors105may be any type of sensor, e.g., a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, Near Field Communication (NFC), etc.), temporal information sensors, and the like.

In one embodiment, the data analysis platform109may be a platform with multiple interconnected components. The data analysis109may include one or more servers, intelligent networking devices, computing devices, components and corresponding software for determining financial/business goals from multiple data sources by applying artificial intelligence models. In addition, it is noted that the data analysis109may be a separate entity of the system100, a part of the one or more services113a-113n(collectively referred to as services113) of the services platform111, or the UE101.

In one embodiment, the data analysis platform109extracts financial planning goals from unstructured notes and correspondence logged in a relationship management system, e.g., customer relationship management (CRM), using machine-learning and Natural Language Processing (NLP). The data analysis platform109builds a classifier from anonymized notes data set; and then labels, tests, and refines NLP. Thereafter, the data analysis platform109identifies additional data elements associated with goals from unstructured data. The data analysis platform109refines the scope of related data for extraction, and provides leverage to advice insight model building to deliver client-centric goal-based opportunities and strategies, publish client goal tracking online, and help drive foundational advice discussions. Subsequently, the data analysis platform109combines unstructured data elements with known structured client data to strengthen client profile and identify sales opportunities and unimplemented recommendations. Furthermore, when combined with structured data, the accuracy of the models is improved, and new and useful information can be surfaced and delivered into CRM.

In one embodiment, the data analysis platform109trains artificial intelligence models to find goals, e.g., retirement, in unstructured data, e.g., CRM notes, documents, and emails. Thereafter, the data analysis platform109identifies key data related to the goal, e.g., the age to retire, expected age to retire, amount saving, employer match amount, etc., and extract these goal-related contextual data. The data analysis platform109then extract fields, e.g., cash amount, savings amount, home value, home equity line amount, life insurance amount and years remaining, disability insurance, etc., from unstructured data, and use these extracted data to add context around goals for client conversations. Subsequently, the data analysis platform109combines the fields surfaced from unstructured data with structured data to improve the accuracy of artificial intelligence models, validate data, and leverage for advisors and client engagement.

The services platform111may include any type of service. By way of example, the services platform111may include content (e.g., audio, video, images, etc.) provisioning services/application, application services/application, contextual information determination services/application, notification services/application, storage services/application, social networking services/application, etc. In one embodiment, the services platform111may interact with the UE101, the data analysis platform109and the content provider115to supplement or aid in the processing of the content information. In one embodiment, the services platform111may be implemented or embedded in the data analysis platform109or in its functions.

By way of example, the services113may be an online service that reflects the interests and/or activities of users. The services113allow users to share contact information, location information, activities information, contextual information, historical user information and interests within their individual networks, and provides for data portability. The services113may additionally assist in providing the data analysis platform109with activity information of at least one user, user profile information, and a variety of additional information.

The content providers115a-115n(collectively referred to as content provider115) may provide content to the UE101, the data analysis platform109, and the services113of the services platform111. The content provided may be any type of content, such as, image content, textual content, audio content, video content, etc. In one embodiment, the content provider115may provide content that may supplement the content of the applications103, the sensors105, or a combination thereof. In one embodiment, the content provider115may provide or supplement the notification services/application, social networking services/application, content (e.g., audio, video, images, etc.) provisioning services/application, application services/application, storage services/application, contextual information determination services/application, location based services/application, or any combination thereof. In one embodiment, the content provider115may also store content associated with the UE101, the data analysis platform109, and the services113of the services platform111. In another embodiment, the content provider115may manage access to a central repository of data, and offer a consistent, standard interface to data. Any known or still developing methods, techniques or processes for determining financial/business goals may be employed by the data analysis platform109.

FIG. 2is a diagram of the components of a data analysis platform109, according to one example embodiment. By way of example, the data analysis platform109includes one or more components for determining financial/business goals from multiple data sources by applying artificial intelligence models. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In one embodiment, the data analysis platform109comprises a data collection module201, a data processing module203, an extraction module205, a training module209, the machine learning module211, a user interface module213, recommendation module215, business rule engine217, or any combination thereof.

In one embodiment, the data collection module201collects user data from one or more data sources and then stores the data in database215. In one embodiment, the user data collected by the data collection module201are unstructured data, e.g., CRM notes, documents, and emails. In one example embodiment, the data collection module201may use a web-crawling component to access various websites and databases on the internet and collect data associated with the users. In another example embodiment, one or more users, e.g., members, may provide personal data, e.g., age information, education information, employment information, financial information, etc., that forms the “core data.” More subjective data, such as contextual information, activity information, preference information, may form “supplemental data.” Data such as purchase intentions may form “intention data,” that indicate intention or desire of a user to make a purchase.

In one embodiment, the data processing module203processes the user data collected by the data collection module201to find goals, e.g., the age to retire, expected year to retire, amount saving, employer match amount, etc., for one or more users. In another embodiment, the data processing module203processes the user data to identify key data, e.g., descriptive data, supplemental data, etc., related to the goals. In a further embodiment, the data processing module203processes the user data to identify any additional data elements associated with the one or more goals.

In one embodiment, the extraction module205extracts goal-related contextual data form the unstructured user data collected by the data collection module201. In another embodiment, the extraction module205extracts one or more fields associated with the one or more goals form the unstructured user data and then use the extracted data to add context around the one or more goals for client interactions.

In one embodiment, the training module209trains a machine learning module211using various inputs to enable the machine learning module211to automatically find one or more goals from unstructured data. In another embodiment, the training module209trains a machine learning module211using various inputs to identify key data, e.g., descriptive data, supplemental data, etc., related to the goals. In a further embodiment, the training module209trains a machine learning module211using various inputs to enable the machine learning module211to combine the fields surfaced from unstructured data with structured data to improve the accuracy of artificial intelligence models, validate data, and leverage for advisors and client engagement.

In one embodiment, the user interface module213may generate a user interface element in response to detection of an input for a presentation of one or more data types. In one embodiment, the user interface module213employs various application programming interfaces (APIs) or other function calls corresponding to the application103of UE101; thus enabling the display of graphics primitives such as menus, data entry fields, etc., for generating the user interface elements. Still further, the user interface module213may be configured to operate in connection with augmented reality (AR) processing techniques, wherein various different applications, graphic elements, and features may interact.

In one embodiment, recommendation module215may generate a recommendation that acts as a rule in business rule engine217. In one embodiment, the recommendation module215implements a deep learning-based recommendation system which aims to provide adaptive user representations which can process many forms of user interest/signal by assessing interests over the short-term vs. long-term. In another embodiment, recommendation module205may present a ranking of the service providers in a user interface of UE101for selection by the user. The ranking of the service providers is based on service attribute information and/or user attribute information.

The above presented modules and components of the data analysis platform109can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity inFIG. 1, it is contemplated that the data analysis platform109may be implemented for direct operation by respective UE101. As such, the data analysis platform109may generate direct signal inputs by way of the operating system of the UE101for interacting with the applications103. In another embodiment, one or more of the modules201-211may be implemented for operation by respective UEs, as the data analysis platform109, or combination thereof. Still further, the data analysis platform109may be integrated for direct operation with the services115, such as in the form of a widget or applet, in accordance with an information and/or subscriber sharing arrangement. The various executions presented herein contemplate any and all arrangements and models.

FIG. 3is a flowchart of a process for determining financial/business goals from multiple data sources by applying artificial intelligence models, according to one example embodiment. In step301, the data analysis platform109identifies relevant goals from unstructured data pertaining to at least one user via machine learning models. Thereafter, in step303, the data analysis platform109finds and extracts related contextual information pertaining to the identified goals from the unstructured data. Subsequently, the processed data is classified into high confidence data and low confidence data. In step305, the high confidence data is added to the data table. In step307, the data analysis platform109evaluates the low confidence data to determine whether to add it back to the pool of unstructured data or add it to the business scheme or recommendations.

FIG. 4is a diagram that represents a reference architecture for determining financial/business goals from multiple data sources by applying artificial intelligence models, according to one embodiment. In one embodiment, the reference architecture400comprises a data processing unit401, a data storage unit403, an AI platform405, and a presentation module407. In one embodiment, the data processing pipeline begins with the data analysis platform109aggregating various content and profile data associated with a user via a data processing unit401. The data processing unit401comprises content aggregator, e.g., web scraper, various document collector mechanisms, to aggregate various content. The data processing unit401comprises a profile data aggregator that collects data relating to at least one user, e.g., CRM, transactions, web analytics, etc. In another embodiment, the aggregated data comprises, but is not limited to, external data, market data, premium news, external web sources, enterprise data, CRM notes, internal content, transaction, portfolio holdings, sub-holdings, etc.

In one embodiment, the data analysis platform109ingests the aggregated data to a data storage unit403via a data storage mechanism. In one embodiment, the data storage mechanism incorporates an extract, transform, and load (ETL) process. In one embodiment, the database storage mechanism, e.g., database215, stores, but is not limited to, documents, profile data associated with at least one user, NLP metadata, ranking scores, etc.

In one embodiment, the NLP pipeline manager, a sub-component of the data processing module401, processes the ingested aggregated data via a natural language processing (NLP) mechanism and enriches the ingested aggregated data with metadata. Thereafter, the data analysis platform109applies artificial intelligence (AI)/ machine learning (ML) processes, i.e., AI platform405, on the ingested aggregated data to generate a recommendation. In one embodiment, the data analysis platform109may diagnose and optimize AWL models by: (i) diagnose and understand ambiguities, training data shortages, data deficiencies; and (ii) optimize models and apply to larger datasets of research news and financial content. In another embodiment, the AI platform405designates the ingested aggregated data according to one of a plurality of classification criteria using the artificial intelligence (AI)/ machine learning (ML) processes. Thereafter, the AI platform405performs an AI-based ranking based on categories, e.g., content, client, and product. In a further embodiment, an AI platform403performs the AI/ML processes that run signals to recommend content, product, and actions. This is then delivered to the presentation module407to generate a presentation in the user interface. In this embodiment, the presentation module407comprises of several components, e.g., personalized content, client prioritization, product matching, user feedback APIs. The presentation module407collaborates with an AI recommendation engine, a sub-component of the AI platform107to generate a recommendation.

FIG. 5is a diagram that represents a common reference data model, according to one embodiment. In one embodiment, the data analysis platform109processes the ingested aggregated data via an NLP mechanism and enriches the ingested aggregated data with metadata from the common reference data model500. As depicted inFIG. 5, the common reference data model500comprises reference data501, entity data503, corporate actions505, listed market pricing507, valuation pricing services509, and historical pricing511. In one embodiment, the reference data501comprises of descriptive data, industry classification data, symbol/cross reference data, historical data, etc. In one embodiment, entity data503comprises information on global instrument-issuer linkages, parent IDs and full corporate hierarchies, audited data, etc. As discussed, entity resolution is important for AI-powered insights engines, and the classified data needs to be connected to real, live reference data. In one embodiment, corporate actions505comprises information on intraday activities, e.g., reports on securities that trade on the markets during regular business hours, global coverage, historical information, etc. In one embodiment, listed market pricing507comprises information on intraday snapshots, validated end-of-day, equity analytics fund, etc. In one embodiment, valuation pricing services509comprises evaluated pricing, cash flows, performance data, model analytics, etc. In one embodiment, historical pricing511comprises information on evaluated pricing, end-of-day pricing, etc.

FIG. 6Ais a flowchart of a process for integrating contextual data with a lower confidence level with structured data for data accuracy, according to one embodiment. In one embodiment, a data analysis platform109performs the process600and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step601, the data analysis platform109collects unstructured data relating to at least one user from one or more data sources. In one embodiment, unstructured data includes information that either does not have a data structure or has one that is not easily usable by a computer program. In another embodiment, unstructured data may not provide any explicit data structure and instead provides dimensions or identification attributes, such as tags or metadata that may describe the unstructured data. In a further embodiment, unstructured data may have an explicit structure, but it not compatible with any applications. In addition, unstructured data may not provide a schema or other data descriptor that may be interpreted by current systems.

In step603, the data analysis platform109processes the unstructured data to determine the at least one goal and contextual data related to the at least one goal. In one embodiment, the data analysis platform109determines the at least one goal, the contextual data related to the at least one goal, or a combination thereof based on a filtering mechanism. In one example embodiment, the filtering mechanism comprises a collaborative filtering process, a memory attention-aware recommender system (MARS), a neural collaborative filtering (NCF) framework, or a combination thereof.

In step605, the data analysis platform109extracts the contextual data related to the at least one goal from the unstructured data. In one embodiment, the data analysis platform109extracts the contextual data related to the at least one goal from the unstructured data using a machine learning model, and the machine learning model includes a deep learning neural network.

In step607, the data analysis platform109classifies the contextual data related to the at least one goal into a first confidence data or a second confidence data. In one embodiment, the first confidence data has a higher confidence level than the second confidence data.

In step609, the data analysis platform109integrates the second confidence data with structured data. In one embodiment, structured data are data that have been organized into a formatted repository, typically a database, so that its elements can be made addressable for more effective processing and analysis. In one example embodiment, integrating second confidence data, i.e., data with a lower confidence level, with structured data improves the confidence level regarding data accuracy.

In step611, the data analysis platform109causes a presentation of the integrated data via a graphical user interface. In one embodiment, a graphical user interface is a form of user interface that allows users to interact with electronic devices through graphical icons and audio indicators such as primary notation. In another embodiment, a graphical user interface is a system of interactive visual components for computer software that displays objects that convey information and represent actions that can be taken by the user.

FIG. 6Bis a flowchart of a process for generating recommendations based on configured rule and the rating information, according to one embodiment. In one embodiment, data analysis platform109performs the process602and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step613, the data analysis platform109configures a rule for a business rule engine based on at least one goal. In one embodiment, the data analysis platform109is a modular heuristics engine that combines qualitative best practices in the form of business rules/logic, as well as quantitative signals from transactions, market data, portfolios, and other numerical data. In one embodiment, the data analysis platform109coordinates with recommendation module215to generate a recommendation that acts as a rule in the business rule engine. Accordingly, the recommendation module215may add multiple, different, recommenders to the business rule engine, e.g., collaborative filtering (matrix factorization), Memory attention-aware recommender system (MARS), Neural Collaborative Filtering (NCF) framework, etc. In another embodiment, the data analysis platform109determines an important feature or a least important feature based on user inputs and preferences. In one example embodiment, the data analysis platform109may predict a user's response by using all the business rules as inputs.

In step615, the data analysis platform109determines rating information associated with the unstructured data. In one embodiment, the data analysis platform109takes the star rating given to contents, e.g., rating of 1 to 5 stars, as input and provide recommendations.

In step617, the data analysis platform109generates recommendation information based on the configured rule and the rating information. In one embodiment, the data analysis platform109is an integrated hybrid recommendation engine which provides full data auditing capabilities and reasoning. In one example embodiment, the data analysis platform109may use hundreds of signals with conditionality and criteria from semi-structured and unstructured data, and go beyond matrix-factorization and capture complex business patterns and correlations in behaviors that may exist across many data sources such as click-stream data from web activity, business transactions, notes from CRM and client meetings, and many other interactions and activities.

FIG. 6Cis a flowchart of a process for entering business rules and predicting a user response, according to one embodiment. In one embodiment, data analysis platform109performs the process604and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step619, the data analysis platform109inputs a plurality of business rules that include the configured business rule. In one example embodiment, the data analysis platform109may execute a newly configured business rule by selecting the desired business rule from configurable business rule list, for example, as enumerated in Table 1:

TABLE 1Signal CategoryDescription of Signal1Holdings/TransactionClient has recently transacted in productPatternthat has no previous transaction activity2Holdings/TransactionHalt in transactions of a product usuallyPatternpurchased3Holdings/TransactionSignificant Redemption - Recent clientPatternredemptions in security are above a certainthreshold4Holdings/TransactionSignificant Purchase - Recent client pur-Patternchases in security are above a certainpercentage threshold5Holdings/TransactionBuying Pattern Change - YTD Sales Differ-Patternence from previous year6Holdings/TransactionSub-holding of top X funds involved inPatternrecent transactions has experienced recentlarge move*7CRM/RelationshipOngoing discussions - Product mentionedpatternmultiple times in recent CRM notes8CRM/RelationshipSales opportunity - Product discussedpatternduring recent client interaction not inclient's recent transactions9CRM/RelationshipClient colleague has significant relation-patternship with Firm10CRM/RelationshipClient has not been contacted in past Xpatterndays

In step621, the data analysis platform109predicts response of the at least one user to a financial scenario. In one embodiment, the data analysis platform109implements a predictive analytics model that leverages historical data with goals to predict actions to take, products to sell, etc.

FIG. 6Dis a flowchart of a process for extracting contextual data, according to one embodiment. In one embodiment, data analysis platform109performs the process606and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step623, the data analysis platform109specifies an entity type for the extraction of the contextual data. In one example embodiment, entity resolution is important for AI-powered insights engines, it is not enough to correctly classify data but it has to be connected to real, live reference data. In one embodiment, the data analysis platform109collaborates with database215, e.g., a comprehensive master data reference, that is seamlessly integrated with the NLP services for superior entity resolution and identification.

FIG. 6Eis a flowchart of a process for managing notes corresponding to a user, according to one embodiment. In one embodiment, data analysis platform109performs the process608and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step625, the data analysis platform109establishes a data connection to a relationship management system that stores a plurality of notes corresponding to a plurality of users including at least one user. In one embodiment, one or more data sources include the relationship management system. In one embodiment, the relationship management system provides feedback on the system that automates personalized recommendations for a user based on their business, sector, transactions, and other data.

In step627, the data analysis platform109retrieves one of the notes corresponding to the one user. In one embodiment, the data analysis platform109builds a classifier from the anonymized notes data set. In one embodiment, the data analysis platform109extracts financial planning goals from CRM notes including emails, advisor notes, and other textual content. In one example embodiment, useful information on user goals and their interests is buried in CRM notes which, if surfaced, can be used for client outreach and calls that can accelerate financial planning discussions. In addition, positive and negative experiences identified in the CRM notes compare negative notes history to the current Retention Report. Furthermore, surface a user's financial goals from unstructured notes and emails logged in CRM.

FIG. 6Fis a flowchart of a process for designating at least one goal and unstructured data, according to one embodiment. In one embodiment, a data analysis platform109performs the process610and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step629, the data analysis platform109designates at least one goal according to a plurality of classifications using a machine learning model. In one embodiment, at least one goal is a primary goal or a secondary goal. In one example embodiment, the primary goal pertains to retirement information. In one example embodiment, the secondary goal pertains to education information, purchase information, and lifestyle information.

In step631, the data analysis platform109designates the unstructured data according to one of a plurality of sector classifications using a machine learning model. In one embodiment, the plurality of sector classifications includes retail, technology, media, telecommunications, and food.

FIG. 7Ais a flowchart of a process for generating a recommendation on the ingested aggregated data relating to a user, according to one embodiment. In one embodiment, a data analysis platform109performs the process700and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step701, the data analysis platform109aggregates data relating to at least one user from one or more data sources via a data processing module. The data processing pipeline begins with the data analysis platform109aggregating various content and profile data associated with a user. In one embodiment, a content aggregator collects contents via web scraper, document collector, etc. In another embodiment, the profile data aggregator collects data relating to at least one user. In one embodiment, the aggregated data comprises, but is not limited to, external data, market data, premium news, external web sources, enterprise data, CRM notes, internal content, transaction, portfolio holdings, sub-holdings, etc.

In step703, the data analysis platform109ingests the aggregated data to a database via a data storage mechanism. In one embodiment, the data storage mechanism incorporates an extract, transform, and load (ETL) process. In one embodiment, the database, i.e., database215, stores, but is not limited to, documents, profile data associated with at least one user, NLP metadata, ranking scores, etc.

In step705, the data analysis platform109processes the ingested aggregated data via a natural language processing (NLP) mechanism and enriches the ingested aggregated data with metadata. Thereafter, the ingested aggregated data is linked to the common data model. In one embodiment, features of a common data model comprise, but is not limited to, (i) access to broad, rich dataset over 70 million globally-sourced instruments automatically updated in a scheduled duration, e.g., every 15 minutes. In one embodiment, the data processing module is further configured to pipeline the aggregated data for the NLP mechanism, and the data processing module is further configured to perform entity extraction, sentiment analysis, topic modeling, summarization, or a combination thereof of the aggregated data.

In step707, the data analysis platform109applies artificial intelligence (AI) processes, e.g., a machine learning or deep learning, on the ingested aggregated data to generate a recommendation. In one embodiment, the data analysis platform109may diagnose and optimize AI models by: (i) diagnose and understand ambiguities, training data shortages, data deficiencies; and (ii) optimize models and apply to larger datasets of research news and financial content. In another embodiment, an AI platform403performs the AI processes that run signals to recommend content, product, and actions.

In step709, the data analysis platform109causes a presentation of the recommendation via a graphical user interface (e.g., UE101) via a presentation module.

FIG. 7Bis a flowchart of a process for designating the ingested aggregated data according to classification criteria, according to one embodiment. In one embodiment, a data analysis platform109performs the process702and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG. 9.

In step711, the data analysis platform109designates the ingested aggregated data according to one of a plurality of classification criteria using the artificial intelligence (AI) processes, e.g., a machine learning or deep learning. In one embodiment, the plurality of classification criteria includes content, client, and product.

The processes described herein for determining financial/business goals from multiple data sources by applying artificial intelligence models may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below.

A bus810includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus810. One or more processors802for processing information are coupled with the bus810.

Computer system800also includes a memory804coupled to bus810. The memory804, such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for determining financial/business goals from multiple data sources by applying artificial intelligence models. Dynamic memory allows information stored therein to be changed by the computer system800. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory804is also used by the processor802to store temporary values during execution of processor instructions. The computer system800also includes a read only memory (ROM)806or other static storage device coupled to the bus810for storing static information, including instructions, that is not changed by the computer system800. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus810is a non-volatile (persistent) storage device808, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system800is turned off or otherwise loses power.

Information, including instructions for determining financial/business goals from multiple data sources by applying artificial intelligence models, is provided to the bus810for use by the processor from an external input device812, such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system800. Other external devices coupled to bus810, used primarily for interacting with humans, include a display device814, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device816, such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display814and issuing commands associated with graphical elements presented on the display814, and one or more camera sensors894for capturing, recording and causing to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings. In some embodiments, for example, in embodiments in which the computer system800performs all functions automatically without human input, one or more of external input device812, display device814and pointing device816is omitted.

Network link878typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link878may provide a connection through local network880to a host computer882or to equipment884operated by an Internet Service Provider (ISP). ISP equipment884in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet890.

A computer called a server host892connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host892hosts a process that provides information representing video data for presentation at display814. It is contemplated that the components of system can be deployed in various configurations within other computer systems, e.g., host882and server892.

At least some embodiments of the invention are related to the use of computer system800for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system800in response to processor802executing one or more sequences of one or more processor instructions contained in memory804. Such instructions, also called computer instructions, software and program code, may be read into memory804from another computer-readable medium such as storage device808or network link878. Execution of the sequences of instructions contained in memory804causes processor802to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC820, may be used in place of or in combination with software to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware and software, unless otherwise explicitly stated herein.

The signals transmitted over network link878and other networks through communications interface870, carry information to and from computer system800. Computer system800can send and receive information, including program code, through the networks880,890among others, through network link878and communications interface870. In an example using the Internet890, a server host892transmits program code for a particular application, requested by a message sent from computer800, through Internet890, ISP equipment884, local network880and communications interface870. The received code may be executed by processor802as it is received, or may be stored in memory804or in storage device808or any other non-volatile storage for later execution, or both. In this manner, computer system800may obtain application program code in the form of signals on a carrier wave.

Various forms of computer readable media may be involved in carrying one or more sequence of instructions or data or both to processor802for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host882. The remote computer loads the instructions and data into its dynamic memory and sends the instructions and data over a telephone line using a modem. A modem local to the computer system800receives the instructions and data on a telephone line and uses an infra-red transmitter to convert the instructions and data to a signal on an infra-red carrier wave serving as the network link878. An infrared detector serving as communications interface870receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus810. Bus810carries the information to memory804from which processor802retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory804may optionally be stored on storage device808, either before or after execution by the processor802.

FIG. 9illustrates a chip set900upon which an embodiment of the invention may be implemented. Chip set900is programmed to determine financial/business goals from multiple data sources by applying artificial intelligence models as described herein and includes, for instance, the processor and memory components described with respect toFIG. 8incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip. It is further contemplated that in certain embodiments the chip set or chip900can be implemented as a single “system on a chip.” It is further contemplated that in certain embodiments a separate ASIC would not be used, for example, and that all relevant functions as disclosed herein would be performed by a processor or processors. Chip set or chip900, or a portion thereof, constitutes a means for performing one or more steps of providing user interface navigation information associated with the availability of functions.

In one embodiment, the chip set or chip900includes merely one or more processors and some software and/or firmware supporting and/or relating to and/or for the one or more processors.

The processor903and accompanying components have connectivity to the memory905via the bus901. The memory905includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to determine financial/business goals from multiple data sources by applying artificial intelligence models. The memory905also stores the data associated with or generated by the execution of the inventive steps.

Pertinent internal components of the telephone include a Main Control Unit (MCU)1003, a Digital Signal Processor (DSP)1005, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit1007provides a display to the user in support of various applications and mobile station functions that offer automatic contact matching. The display1007includes display circuitry configured to display at least a portion of a user interface of the mobile terminal (e.g., mobile telephone). Additionally, the display1007and display circuitry are configured to facilitate user control of at least some functions of the mobile terminal. An audio function circuitry1009includes a microphone1011and microphone amplifier that amplifies the speech signal output from the microphone1011. The amplified speech signal output from the microphone1011is fed to a coder/decoder (CODEC)1013.

A radio section1015amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna1017. The power amplifier (PA)1019and the transmitter/modulation circuitry are operationally responsive to the MCU1003, with an output from the PA1019coupled to the duplexer1021or circulator or antenna switch, as known in the art. The PA1019also couples to a battery interface and power control unit1020.

The encoded signals are then routed to an equalizer1025for compensation of any frequency-dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator1027combines the signal with a RF signal generated in the RF interface1029. The modulator1027generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter1031combines the sine wave output from the modulator1027with another sine wave generated by a synthesizer1033to achieve the desired frequency of transmission. The signal is then sent through a PA1019to increase the signal to an appropriate power level. In practical systems, the PA1019acts as a variable gain amplifier whose gain is controlled by the DSP1005from information received from a network base station. The signal is then filtered within the duplexer1021and optionally sent to an antenna coupler1035to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna1017to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile station1001are received via antenna1017and immediately amplified by a low noise amplifier (LNA)1037. A down-converter1039lowers the carrier frequency while the demodulator1041strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer1025and is processed by the DSP1005. A Digital to Analog Converter (DAC)1043converts the signal and the resulting output is transmitted to the user through the speaker1045, all under control of a Main Control Unit (MCU)1003—which can be implemented as a Central Processing Unit (CPU) (not shown).

The MCU1003receives various signals including input signals from the keyboard1047. The keyboard1047and/or the MCU1003in combination with other user input components (e.g., the microphone1011) comprise a user interface circuitry for managing user input. The MCU1003runs a user interface software to facilitate user control of at least some functions of the mobile station1001to determine financial/business goals from multiple data sources by applying artificial intelligence models. The MCU1003also delivers a display command and a switch command to the display1007and to the speech output switching controller, respectively. Further, the MCU1003exchanges information with the DSP1005and can access an optionally incorporated SIM card1049and a memory1051. In addition, the MCU1003executes various control functions required of the station. The DSP1005may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP1005determines the background noise level of the local environment from the signals detected by microphone1011and sets the gain of microphone1011to a level selected to compensate for the natural tendency of the user of the mobile station1001.

An optionally incorporated SIM card1049carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card1049serves primarily to identify the mobile station1001on a radio network. The card1049also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile station settings.

Further, one or more camera sensors1053may be incorporated onto the mobile station1001wherein the one or more camera sensors may be placed at one or more locations on the mobile station. Generally, the camera sensors may be utilized to capture, record, and cause to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings.