SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE INVESTMENT AND ARTICLE RECOMMENDATIONS

Various examples are directed to computer-implemented systems and methods for artificial intelligence investment and article recommendations. A method includes receiving a user input indicating values or interests of a user, analyzing the user input to locate and extract preference data from the user input, and tracking activities of the user. Using machine learning, differences between the preference data and the tracked activities are determined to illustrate contradictions by the user, and a recommendation is composed for the user based on the determined differences. The recommendation is displayed on a graphical user interface to assist the user in aligning the activities of the user with the values or interests of the user.

TECHNICAL FIELD

This document relates generally to computer systems and more particularly to systems and methods for artificial intelligence investment and article recommendations.

BACKGROUND

Individuals may have values or interests that could factor into their investment decisions. It is sometimes difficult to understand if investment choices are aligned with these values and interests without performing time-consuming research, which may lead to investments being made that inadvertently conflict with values and interests. Improved systems and methods for investment and article recommendations that better support an individual's values or interests are needed.

DETAILED DESCRIPTION

Individuals may have values or interests that could factor into their investment decisions. It is sometimes difficult to understand if investment choices are aligned with these values and interests without performing time-consuming research, which may lead to investments being made that inadvertently conflict with values and interests. Improved systems and methods for investment and article recommendations that better support an individual's values or interests are needed.

The present subject matter provides systems and methods for

investment and article recommendations. Various embodiments include receiving a user input indicating values or interests of a user, analyzing the user input to locate and extract investment preference data from the user input, and tracking investment activities of the user. Using machine learning, differences between the preference data and the tracked investment activities are determined to illustrate contradictions between the user-stated/model-determined and actual user values (as shown by existing investment choices), and then an investment, article, or other recommendation is composed for the user based on the differences. The recommendation is displayed on a graphical user interface to assist the user in aligning the investment activities of the user with the values or interests of the user.

The present subject matter provides systems and methods for investment and article recommendations. Various embodiments include receiving a user input indicating values or interests of a user, analyzing the user input to locate and extract preference data from the user input, and tracking activities of the user. Using machine learning, differences between the preference data and the tracked activities are determined to illustrate contradictions by the user, and a recommendation is composed for the user based on the determined differences. The recommendation is displayed on a graphical user interface to assist the user in aligning the activities of the user with the values or interests of the user.

The present system for investment and article recommendations may include a specialized computer system for providing users with an interface to access data within the systems, providing the users with an interface to monitor the system, and may further include customized or dedicated computer storage or memory for the users, in various embodiments.

FIG.1Aillustrates an example embodiment of a method for investment and article recommendations, according to various embodiments. The method100includes receiving a user input indicating values or interests of a user, at step102. In some examples, the user input is related to user priorities such as sustainability ratings of an exchange-traded fund (ETF).

At step104, the method100includes analyzing the user input to locate and extract preference data from the user input. The preference data may include investment preference data, in various embodiments. The method100further includes tracking activities of the user, at step106. The activities of the user may be related to user priorities and may include investment activities of the user, in some examples. In some examples, the method further includes automatically adjusting a user investment portfolio based on the recommendation. In various embodiments, the user is prompted to approve the automatic adjustment. In some embodiments, the user may have pre-approved the automatic adjustment if certain thresholds are met, such as fees for the automatic adjustment being less than a threshold amount. The automatic adjustment may include using machine learning, in an embodiment.

The method100also include determining, using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user, at step108. In various embodiments, the present system determines differences between the preference data and tracked investment activities to illustrate contradictions between the user-stated/model-determined values and actual user values (as shown by existing investment choices). Using the machine learning includes using a model that is opaque to the user, in various embodiments. In various examples, the method further includes providing an alert to the user based on the determined differences.

At step110, the method100includes composing, using the machine learning, a recommendation for the user based on the determined differences. The recommendation may include an investment recommendation, an article recommendation, and/or other recommendation, in various embodiments. In various examples, using machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.

The method further includes displaying, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user, at step112. In some examples, displaying the recommendation includes displaying a list of differences between the preference data and investment activities of the user to illustrate contradictions by the user. Displaying the recommendation includes displaying an article recommendation for a news article related to the activities of the user or the user priorities, in some embodiments. In some examples, displaying the article recommendation includes displaying the news article. Displaying the news article includes composing, by the computer system using the machine learning, the news article, in some examples.

FIG.1Billustrates an example embodiment of a method for investment and article recommendations, according to various embodiments. According to various embodiments, the method150may include receiving user inputs and extracting preference data, such as investment preference data, at step152. In some examples, the user inputs are related to user priorities such as sustainability ratings of an exchange-traded fund (ETF). Other user priorities may be used without departing from the scope of the present subject matter.

The method150may also include tracking activities of the user, at step154. The activities of the user include investment activities of the user, in some examples. In some examples, the method further includes automatically adjusting, using machine learning, a user investment portfolio based on the recommendation. In various embodiments, the user is prompted to approve the automatic adjustment. In some embodiments, the user may have pre-approved the automatic adjustment if certain thresholds are met, such as fees for the automatic adjustment being less than a threshold amount. In various examples, using machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.

The method150continues at step156, where predictive analysis is performed to determine differences between the preference data and the tracked activities, in various examples. In various embodiments, the present system determines differences between the preference data and tracked investment activities to illustrate contradictions between the user-stated/model-determined values and actual user values (as shown by existing investment choices). The predictive analysis may include using machine learning using a model that is opaque to the user, in various embodiments. In various examples, the method further includes providing an alert to the user based on the determined differences.

At step158, the method150may include composing a recommendation using machine learning based on the differences, in various embodiments. The recommendation may include an investment recommendation, an article recommendation, and/or other recommendation, in various embodiments. At step160, the method may include automatically displaying the recommendation on a graphical user interface, in various embodiments. In some examples, displaying the recommendation includes displaying a list of differences between the preference data and the tracked activities to illustrate contradictions by the user. Displaying the recommendation includes displaying an article recommendation for a news article related to the activities of the user or the user priorities, in some embodiments. In some examples, displaying the article recommendation includes displaying the news article. Displaying the news article includes composing, by the computer system using the machine learning, the news article, in some examples.

In various embodiments, the present subject matter provides for artificial intelligence (AI) investment and article recommendations where the investor (or user) can specify interests and receive recommended investments or articles to read. The AI can learn investor interests over time and apply them automatically, in various examples.

In some embodiments, the present system may make recommendations based on sustainability ratings for ETFs, which may be based on for example, supply chains, worker conditions, safe for LGBTQ+, etc. The present system may make determinations such as “existing sustainability ratings are corrupt” (green washing), and provide feedback that independent ratings are needed. For example, the system may determine that activities of a company that is part of an investment are not as sustainable as their sustainability ratings indicate, such that an independent sustainability rating should be generated to reflect a more accurate rating. The system may prompt the user to generate an independent rating, or may automatically generate the rating, in various embodiments. In various examples, a user's personal values may be used as part of a selection or recommendation. Input to the system may include priority ratings by the user, in various examples, which may include fees associated with an investment, return on investment (ROI), personal values, etc. In various embodiments, the AI may learn that the user is actually valuing ROI over what they claim to want (e.g., sustainability) based on the user input. A user interface (UI) may be provided to show what the user prioritizes and also show the investment's ROI to provide the user with options with respect to economic and non-economic user priorities.

Another aspect of the present subject matter may include an AI system that provides investment advice to the user, and may also draft articles or retrieve articles based on predictive analysis. In various examples, the system may search available databases to gather information and create text from the gathered information for article generation. The present system may include machine learning or AI that can write recommendation text, in various embodiments. For example, the system may use a large language model (LLM) such as Chat GPT to generate the articles, in various embodiments.

The present subject matter may include showing a value-agnostic suggestion to compare a current user investment portfolio with the historical record of other non-value based selections of investment options. For example, the system may provide investment suggestions that are not related to a user's stated priorities for comparison with other investors. In some embodiments, the present system may provide a display for a financial advisor of the user, so they can recommend appropriate investments for the user. In one embodiment, a value-agnostic recommendation may be presented with a value-based recommendation on a display to illustrate to the user a difference in return that the user's value-based choice would cost, which may be used justify a values-based investment purchase if the difference is not that large.

In various embodiments, the present system provides for machine learning to determine sustainability of investment options in a manner that is opaque to the user. In some embodiments, the present system may consider whether companies that are subjects of potential investing participate in self-reporting of factors that may be important to a user, such as diversity, equity and inclusion (DEI) lists, etc. In various examples, the present system automates sustainability ratings considering these additional factors related to self-reporting. When a user sets up an investment account, the user may prioritize wants for investing. The present system may then analyze user investment activity and use AI to evaluate and compare a user's stated values with a user's actual choices, and may provide feedback to the user to show how their investment choices align with their stated values.

According to various examples, the present system provides AI-based output to show a user current and projected portfolio alignment with stated values-based priorities of the user. For example, if a user always selects high ROI over a stated value of a sustainability factor, then the user is investing differently than they claim they want to invest. The present system may provide the user feedback based on selections, or may automatically redirect investments based on the analysis, in various embodiments.

Various embodiments of the present subject matter provide AI article recommendations and/or article writing. The present subject matter may provide sustainability ratings for ETFs based on an AI-generated value statement for a user based on user data (not based on active user selection), that is opaque to the user, in various embodiments. In various embodiments, the present subject matter may provide an AI presentation of a user's stated values compared to what a user's choices indicate. The present system may provide AI that tracks what a user is actually doing (e.g., in daily purchases, stock/ETF purchases, etc.) compared to what the user intends to do or says they are doing, in various embodiments. For example, a user may indicate that they support sustainability, but constantly order takeout that creates extra trash. The user may not be aware of this behavior, and the AI may suggest that the user offset this by investing in green companies. In another example, a user may not know that the user's ETF includes a defense manufacturer when the user has identified a value statement as being against war. The present system may include an AI/ML model that is opaque to the user, does not use a user's explicit statements, and/or relies only on background data, for example.

The present AI recommendation system may be provided generally to the public, or may be restricted to new investors or new potential customers, in various examples. According to various embodiments, the present system may make investment recommendations within a particular sector, or provide article generation, doing research based on user input or risk tolerance, and bringing research to the user. The present system may use natural language processing, in various examples, to provide or replicate a normal conversation of a financial advisor with a client or user, in various examples. User input may include, but is not limited to, a user's goals, risk, values, kinds of companies the user wants to support with investment, kinds of companies the user wants to avoid supporting (no fossil fuels, no exploiting child labor, etc.), and the like.

According to various embodiments, the present subject matter may provide a user display to offer options, risk ratings, sustainability index scores and prospectus-based historical returns of different funds available to a user. If the user continues to purchase shares in particular types of funds, the AI may track user activity across their portfolio to show more of types of funds the user actually selects, in an embodiment. In various embodiments, the present system may be used by a user that prioritizes environmental, social, and governance (ESG) investing. ESG investing is a way of investing in companies based on their commitment to one or more ESG factors, such as sustainable investing or socially responsible investing. The present system may compare a user's investments based on growth or return-driven investments and compare with ESG ratings to allow a user to align investments with goals, in various embodiments.

In some examples, the present system may provide a user with educational materials and articles based on user investment activity and stated priorities. The system may recommend investment vehicles and strategies, and deliver articles of interest based on what a user indicates they value and based on the user's actual behavior and investments. In various embodiments, the system may provide performance-related information and/or sector-related information, to allow the user to adjust (or to automatically adjust) the user's portfolio on an ongoing basis to change investments, provide educational packages, or present additional recommendations. In various embodiments, the user is prompted to approve the automatic adjustment. In some embodiments, the user may have pre-approved the automatic adjustment if certain thresholds are met, such as fees for the automatic adjustment being less than a threshold amount.

In some examples, the present system provides balance or hybridization between AI and self-directed investment choices and strategies to consider. For example, the system may provide suggestions for investments or articles that are generated by machine learning, as well as providing suggestions that are based on a user's previous pattern of self-directed investment choices for comparison. The system may provide investment advice in a format similar to that provide by a financial advisor, in various embodiments. In some embodiments, the present subject matter provides a chat-bot for the user to ask questions and obtain feedback for investment decisions. Thus, a user may benefit by obtaining financial advisor-type feedback without paying financial advisor fees, and by being able to track how their investments align with their stated values.

The present system provides additional benefits to the user by examining investment choices and identifying companies or investments that have stated values that do not match their activities, so-called green-washing. The present system uses AI to research and determine if such contradictions exist, and provide notice to the user. For example, a company may indicate that it is environmentally friendly, but may have recently purchased a subsidiary that is not environmentally friendly. The present system may train a learning model to look activity by a company or investment vehicle that is out of step with stated purposes, such as donations made to certain causes or employment of child labor, for example. This activity then may be flagged and the user may be alerted, in various embodiments.

In various embodiments, inputs to the system may include a user's stated investment objectives or types of investments, such as stocks, mutual funds, EFTs, and the like. The present system provides not only ratings of companies or investments, but the system also goes beyond the ratings and looks at what standards a company adheres to in its activities and supply chains. The system may compose and deliver articles that educate the user about how sustainability ratings are accurate or not for various investments. The system may make recommendations of investments or articles to read for the user, in various embodiments. The present system may provide its own definitions of ESG, such as an AI-created rating of funds, in various embodiments. The system may also examine company activity and flag a company if its publicly available rating does not align with company activity.

In some examples, the present system may determine how other users are investing based on similar information, and look at historical choices of a plurality of users, to help to determine recommendations for a current user, similar to crowdsourcing. For example, the system may inform the user how other investors have acted when presented with a similar situation. Calculations and predictions used herein may include using a blockchain, smart contracts, or machine learning, in various embodiments.

Various embodiments include a computing system with one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to execute the steps of the methods ofFIGS.1A-1B. In some examples, the machine learning may include a machine learning model including a neural network. The machine learning model may include one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree, in various examples. Other types of machine learning models may be used without departing from the scope of the present subject matter. In some examples, the present platform may use a blockchain and/or smart contracts to implement the investment and article recommendations.

Various embodiments include a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by computers, cause the computers to perform operations including the methods ofFIGS.1A-1B. In various embodiments, the present system runs simulations to train the machine learning models, and to identify process improvements and optimization for investment and article recommendations. Training of the models may be accomplished online or offline, in various embodiments. According to various embodiments, the method may include using artificial intelligence.

FIG.2illustrates an exemplary infrastructure for providing a system of the present subject matter. The infrastructure may comprise a distributed system200including a computing system that may include a client-server architecture or cloud computing system. Distributed system200may have one or more end users210. An end user210may have various computing devices212, which may be a machine500as described below. The end-user computing devices212may comprise applications214that are either designed to execute in a stand-alone manner, or interact with other applications214located on the device212or accessible via the network205. These devices212may also comprise a data store216that holds data locally, the data being potentially accessible by the local applications214or by remote applications.

The system200may also include one or more data centers220. A data center220may be a server222or the like associated with a business entity that an end user210may interact with. The server222or other portions of the distributed system may create and manage the system for investment and article recommendations, such as by performing operations including the methods ofFIGS.1A-1B, in various embodiments. The business entity may be a computer service provider, as may be the case for a cloud services provider, or it may be a consumer product or service provider, such as a financial institution. The data center220may comprise one or more applications224and databases226that are designed to interface with the applications214and databases216of end-user devices212. Data centers220may represent facilities in different geographic locations where the servers222may be located. Each of the servers222may be in the form of a machine(s)500.

The system200may also include publicly available systems230that comprise various systems or services232, including applications234and their respective databases236. Such applications234may include news and other information feeds, search engines, social media applications, and the like. The systems or services232may be provided as comprising a machine(s)500.

The end-user devices212, data center servers222, and public systems or services232may be configured to connect with each other via the network205, and access to the network by machines may be made via a common connection point or different connection points, e.g., a wireless connection point and a wired connection. Any combination of common or different connections points may be present, and any combination of wired and wireless connection points may be present as well. The network205, end users210, data centers220, and public systems230may include network hardware such as routers, switches, load balancers and/or other network devices.

Other implementations of the system200are also possible. For example, devices other than the client devices212and servers222shown may be included in the system200. In an implementation, one or more additional servers may operate as a cloud infrastructure control, from which servers and/or clients of the cloud infrastructure are monitored, controlled and/or configured. For example, some or all of the techniques described herein may operate on these cloud infrastructure control servers. Alternatively, or in addition, some or all of the techniques described herein may operate on the servers222.

FIG.3shows an example machine learning module300according to some examples of the present disclosure. The machine learning module300may be implemented in whole or in part by one or more computing devices. In some examples, the training module310may be implemented by a different device than the prediction module320. In these examples, the model120may be created on a first machine and then sent to a second machine.

Machine learning module300utilizes a training module310and a prediction module320. Training module310inputs training feature data330into feature determination module350. The training feature data330may include data determined to be predictive of user-specific investment and article recommendations. Categories of training feature data may include tracked user data, input user data, news articles, social media data, other third-party data, or the like. Specific training feature data and prediction feature data390may include, for example one or more of: current tracked user data, past tracked user data, and the like.

Feature determination module350selects training vector360from the training feature data330. The selected data may fill training vector360and comprises a set of the training feature data that is determined to be predictive of user-specific investment and article recommendations. In some examples, the tasks performed by the feature determination module350may be performed by the machine learning algorithm370as part of the learning process. Feature determination module350may remove one or more features that are not predictive of user-specific investment and article recommendations to train the model120. This may produce a more accurate model that may converge faster. Information chosen for inclusion in the training vector360may be all the training feature data330or in some examples, may be a subset of all the training feature data330.

In other examples, the feature determination module350may perform one or more data standardization, cleanup, or other tasks such as encoding non numerical features. For example, for categorical feature data, the feature determination module350may convert these features to numbers. In some examples, encodings such as “One Hot Encoding” may be used to convert the categorical feature data to numbers. This enables a representation of the categorical variables as binary vectors and provided a “probability-like” number for each label value to give the model more expressive power. One hot encoding represents a category as a vector whereby each possible category value is represented by one element in the vector. When the data is equal to that category value, the value of the vector is a ‘1’ and all other elements are zero (or vice versa).

The training vector360may be utilized (along with any applicable labels) by the machine learning algorithm370to produce a model120. In some examples, other data structures other than vectors may be used. The machine learning algorithm370may learn one or more layers of a model. Example layers may include convolutional layers, dropout layers, pooling/up sampling layers, SoftMax layers, and the like. Example models may be a neural network, where each layer is comprised of a plurality of neurons that take a plurality of inputs, weight the inputs, input the weighted inputs into an activation function to produce an output which may then be sent to another layer. Example activation functions may include a Rectified Linear Unit (ReLu), and the like. Layers of the model may be fully or partially connected. In other examples, machine learning algorithm may be a gradient boosted tree and the model may be one or more data structures that describe the resultant nodes, leaves, edges, and the like of the tree.

In the prediction module320, prediction feature data390may be input to the feature determination module395. The prediction feature data390may include the data described above for the training feature data, but for a specific items such as sustainability of funds in a user investment portfolio. In some examples, the prediction module320may be run sequentially for one or more items. Feature determination module395may operate the same, or differently than feature determination module350. In some examples, feature determination modules350and395are the same modules or different instances of the same module. Feature determination module395produces vector397, which is input into the model120to produce predictions399. For example, the weightings and/or network structure learned by the training module310may be executed on the vector397by applying vector397to a first layer of the model120to produce inputs to a second layer of the model120, and so on until the prediction399is output. As previously noted, other data structures may be used other than a vector (e.g., a matrix).

The training module310may operate in an offline manner to train the model120. The prediction module320, however, may be designed to operate in an online manner. It should be noted that the model120may be periodically updated via additional training and/or user feedback. For example, additional training feature data330may be collected. The feedback, along with the prediction feature data390corresponding to that feedback, may be used to refine the model by the training module310.

In some example embodiments, results obtained by the model120during operation (e.g., outputs produced by the model in response to inputs) are used to improve the training data, which is then used to generate a newer version of the model. Thus, a feedback loop is formed to use the results obtained by the model to improve the model.

The machine learning algorithm370may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of learning algorithms include artificial neural networks, convolutional neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like), random forests, gradient boosted tree, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, logistic regression, a region based CNN, a full CNN (for semantic segmentation), a mask R-CNN algorithm for instance segmentation, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. In various embodiments, smart contracts or blockchain may be used to calculate and/or implement user-specific investment and article recommendations.

FIG.4illustrates a flowchart of a method400of training a model for investment and article recommendations, according to various embodiments. At operation410the training module (e.g., training module310as implemented by a model system) may request training feature data, from one or more systems. At operation415the training module may receive the training feature data. The training feature data may be processed using more data standardization, cleanup, or other tasks such as encoding non numerical features (e.g., one hot encoding). At operation420, the training model may use the training feature data to train the model. For example, by creating a gradient boosted tree, neural network, or the like. At operation425the model may be stored in a storage device. In some examples in which the training operations and predictions are done on separate computing devices, the model may be transmitted to a computing device doing predictions.

Machine (e.g., computer system)500may include a hardware processor502(e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory504and a static memory506, some or all of which may communicate with each other via an interlink (e.g., bus)508. The machine500may further include a display unit510, an alphanumeric input device512(e.g., a keyboard), and a user interface (UI) navigation device514(e.g., a mouse). In an example, the display unit510, input device512and UI navigation device514may be a touch screen display. The machine500may additionally include a storage device (e.g., drive unit)516, a signal generation device518(e.g., a speaker), a network interface device520, and one or more sensors521, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine500may include an output controller528, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

The storage device516may include a machine readable medium522on which is stored one or more sets of data structures or instructions524(e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions524may also reside, completely or at least partially, within the main memory504, within static memory506, or within the hardware processor502during execution thereof by the machine500. In an example, one or any combination of the hardware processor502, the main memory504, the static memory506, or the storage device516may constitute machine readable media.

The instructions524may further be transmitted or received over a communications network526using a transmission medium via the network interface device520. The Machine500may communicate with one or more other machines utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers

(IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, a Long Term Evolution (LTE) family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device520may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network526. In an example, the network interface device520may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. In some examples, the network interface device520may wirelessly communicate using Multiple User MIMO techniques.

OTHER NOTES AND EXAMPLES

Example 1 is a computer-implemented method including receiving, by a computer system, a user input indicating values or interests of a user, analyzing, by the computer system, the user input to locate and extract preference data from the user input, tracking, by the computer system, activities of the user, determining, by the computer system using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user, composing, by the computer system using the machine learning, a recommendation for the user based on the determined differences, and displaying, on a graphical user interface in communication with the computer system, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.

In Example 2, the subject matter of Example 1 optionally includes wherein the user input is related to sustainability ratings of an exchange-traded fund (ETF).

In Example 3, the subject matter of Example 1 optionally includes wherein the activities of the user include investment activities of the user.

In Example 4, the subject matter of Example 3 optionally further includes automatically adjusting, by the computer system using the machine learning, a user investment portfolio based on the recommendation.

In Example 5, the subject matter of Example 1 optionally includes wherein displaying the recommendation includes displaying a list of differences between the preference data and the tracked activities to illustrate contradictions by the user.

In Example 6, the subject matter of Example 1 optionally includes wherein using the machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.

In Example 7, the subject matter of Example 1 optionally includes wherein displaying the recommendation includes displaying an article recommendation for a news article related to the activities of the user.

In Example 8, the subject matter of Example 7 optionally includes wherein displaying the article recommendation includes displaying the news article.

In Example 9, the subject matter of Example 8 optionally includes wherein displaying the news article includes composing, by the computer system using the machine learning, the news article.

Example 10 is a system including: a computing system comprising one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to: receive a user input indicating values or interests of a user, analyze the user input to locate and extract preference data from the user input, track activities of the user, determine, using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user, compose, using the machine learning, a recommendation for the user based on the determined differences, and display, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.

In Example 11, the subject matter of Example 10 optionally includes

wherein the machine learning includes a machine learning model including a neural network.

In Example 12, the subject matter of Example 11 optionally includes wherein the neural network includes a long short-term memory (LSTM) network.

In Example 13, the subject matter of Example 10 optionally includes wherein the machine learning includes bidirectional encoder representations from transformers (BERT).

In Example 14, the subject matter of Example 10 optionally includes wherein the machine learning includes natural language processing (NLP).

In Example 15, the subject matter of Example 10 optionally includes wherein the machine learning includes an artificial intelligence (AI)-based knowledge tree.

Example 16 is a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by computers, cause the computers to perform operations of: receiving a user input indicating values or interests of a user, analyzing the user input to locate and extract preference data from the user input, tracking activities of the user, determining, using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user, composing, using the machine learning, a recommendation for the user based on the determined differences, and displaying, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.

In Example 17, the subject matter of Example 16 optionally includes wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of providing an alert to the user based on the determined differences.

In Example 18, the subject matter of Example 16 optionally includes wherein using machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.

In Example 19, the subject matter of Example 16 optionally includes wherein the activities of the user include investment activities of the user.

In Example 20, the subject matter of Example 16 optionally includes wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of automatically adjusting, using the machine learning, a user investment portfolio based on the recommendation.

Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. However, the claims may not set forth every feature disclosed herein as embodiments may feature a subset of said features. Further, embodiments may include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with a claim standing on its own as a separate embodiment. The scope of the embodiments disclosed herein is to be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.