阅读说明：本技术 交互式数据管理的方法和系统 (Method and system for interactive data management ) 是由 埃尔迪·阿塔·布莱达 哈利勒·特恩 卡里姆·理查德·德拉尔 于 2019-10-24 设计创作，主要内容包括：所述处理器可以配置为以电子方式处理计算机可读的用户数据记录集以生成媒体消费数据。所述处理器可以配置为以电子方式处理计算机可读的用户数据记录集以生成社交媒体交互数据。在一些实施方式中,所述处理器可以配置为以电子方式处理计算机可读的用户数据记录集以生成游戏交互数据。在另外一些实施方式中,所述处理器可以配置为利用量子推荐引擎/模块以电子方式处理媒体消费数据、社交媒体交互数据和游戏交互数据。在一些实施方式中,所述处理器可以配置为生成与至少一个用户数据记录相关联的计算机可读用户配置文件向量。(The processor may be configured to electronically process a computer-readable set of user data records to generate media consumption data. The processor may be configured to electronically process a computer-readable set of user data records to generate social media interaction data. In some embodiments, the processor may be configured to electronically process a computer-readable set of user data records to generate game interaction data. In still other embodiments, the processor may be configured to electronically process media consumption data, social media interaction data, and game interaction data using a quantum recommendation engine/module. In some embodiments, the processor may be configured to generate a computer-readable user profile vector associated with at least one user data record.)

1. A computer-implemented data processing method, comprising:

electronically processing a computer-readable set of user data records to generate media consumption data;

electronically processing a computer-readable set of user data records to generate social interaction data;

electronically processing a computer-readable set of user data records to generate game interaction data;

electronically processing the media consumption data, the media consumption data using the quantum recommendation module, and the game interaction data; and

a computer-readable user profile vector associated with at least one user data record is generated.

2. The method of claim 1, further comprising electronically matching the at least one user data record with media content.

3. The method of claim 1, wherein the quantum recommendation module comprises a quantum-based wave function.

4. The method of claim 1, wherein the quantum recommendation module comprises a quantum center trapping potential.

5. The method of claim 1, wherein the quantum recommendation module comprises a machine learning module.

6. The method of claim 1, wherein the game interaction data comprises user league attribute data.

7. The method of claim 1, wherein the game interaction data comprises focus list attribute data.

8. The method of claim 1, wherein the media consumption data comprises a media feed.

9. The method of claim 1, further comprising electronically providing instructions for transmitting a computer-readable mobile alert to a communication device.

10. A system configured for data processing, the system comprising:

one or more hardware processors configured by machine-readable instructions to:

electronically processing a computer-readable set of user data records to generate media consumption data;

electronically processing a computer-readable set of user data records to generate social interaction data;

electronically processing a computer-readable set of user data records to generate game interaction data;

electronically processing the media consumption data, the media consumption data using the quantum recommendation module, and the game interaction data; and

a computer-readable user profile vector associated with at least one user data record is generated.

11. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to electronically match the user profile vector of at least one of the user data records to media content.

12. The system of claim 10, wherein the quantum recommendation module utilizes a quantum-based wave function.

13. The system of claim 10, wherein the quantum recommendation module utilizes a quantum center trapping potential.

14. The system of claim 10, wherein the quantum recommendation module utilizes machine learning.

15. The system of claim 10, wherein the game interaction data comprises user league attribute data.

16. The system of claim 10, wherein the game interaction data comprises focus list attribute data.

17. The system of claim 10, wherein the media consumption data comprises a media feed.

18. A computing platform configured for data processing, the computing platform comprising:

a non-transitory computer-readable storage medium having executable instructions embodied thereon; and

one or more hardware processors configured to execute instructions to: electronically processing a computer-readable set of user data records to generate media consumption data;

electronically processing a computer-readable set of user data records to generate social interaction data;

electronically processing a computer-readable set of user data records to generate game interaction data;

electronically processing the media consumption data, the media consumption data using the quantum recommendation module, and the game interaction data; and

a computer-readable quantum profile vector associated with at least one user data record is generated.

19. The computing platform of claim 18, wherein the one or more hardware processors are further configured by the instructions to electronically match the quantum profile vector of at least one of the user data records to media content.

20. The computing platform of claim 18, in which the quantum recommendation module utilizes a quantum-based wave function.

Technical Field

The present disclosure relates to methods, systems, and computing platforms for data interaction management using quantum mechanical methods.

Background

We are facing a big data age. In the age of internet of things, a plurality of digital products can be networked. Online gaming may be provided over a computer network. The world contains a large amount of digital information, and this information is becoming increasingly voluminous at a faster rate. This effect is ubiquitous from business to science, government to art. Allen Glossopan states that: "the first problem of today's generation and economy is the lack of financial knowledge". In this environment hundreds of millions of people worldwide have not encouraged learning how to invest. Investing is a process of deploying savings in such a way that they can actually produce more consumption in the future than they can enjoy today's spending of these savings. A situation where public engagement is relatively poor has been seen as a problem. Unfortunately, we neglect our savings and investments. Three things discourage individuals from investing in confidence, knowledge, and funds that we are aware of. In this new computing age we need to improve this technical process.

Disclosure of Invention

In view of the foregoing background, the following presents a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Before providing a more detailed description, some concepts of the present disclosure may be presented below in a simplified form only.

Aspects of the present disclosure relate to a system and method configured for data processing that can aggregate one or more of gaming functions, social functions, content management functions, and asset instruction execution functions. The system and method are supported by a plurality of components, such as engines or modules.

Aspects of the present disclosure relate to a system and method that can provide a rich big data user experience in a technical platform environment. Aspects of the present disclosure relate to a system and method that can provide a rich large data set derived from a user experience and utilize output from a profiling process to provide rich content.

The system may include one or more hardware processors configured by machine-readable instructions. The processor may be configured to electronically process a computer-readable set of user data records to generate media consumption data. The processor may be configured to electronically process a computer-readable set of user data records to generate social interaction data. In some embodiments, the processor may be configured to electronically process a computer-readable set of user data records to generate game interaction data. In still other embodiments, the processor may be configured to electronically process the media consumption data, the media interaction data, and the game interaction data using a quantum recommendation engine/module. In some embodiments, the processor may be configured to generate a computer-readable user profile vector associated with at least one user data record.

In some embodiments of the systems and methods, a gamification engine provides simulated trading activity within a portfolio management game. The gamification engine may provide real-time branding across global instruments and all major asset classes to a market of user account simulation trades and portfolios. In some embodiments of the gamification engine, a live real-time fantasy league game leaderboard is provided. In some embodiments of the gamification engine, the ability to track other users' simulated transactions, view their simulated portfolios, and in-depth analysis of the assets they hold is provided. In some embodiments of the gamification engine, user members are provided with the ability to create and manage their own private leagues and invite friends from within the user community.

In some embodiments of the systems and methods, interest profile creation and periodic updates of personality vectors are provided via a quantum recommendation engine/module that creates and maintains this dynamic profile through a variety of analysis algorithms, processing actions and behaviors of users "within applications" in near real-time.

These and other features and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise.

Drawings

FIG. 1 is a schematic diagram of a digital computing environment in which certain aspects of the present disclosure may be implemented.

FIG. 2 is an illustrative block diagram of workstations and servers that may be used to implement the processes and functions of certain embodiments of the present disclosure.

FIG. 3 represents a system configured for data processing in accordance with one or more embodiments.

FIG. 4 illustrates a method for data processing in accordance with one or more embodiments.

FIG. 5 is an illustrative functional block diagram of a neural network that may be used to implement the described processes and functions in accordance with one or more embodiments.

FIG. 6 is an example block diagram of illustrative user data storage data in accordance with one or more embodiments.

FIG. 7 is an example block diagram of an illustrative user media feed environment in accordance with one or more embodiments.

FIG. 8 is an example block diagram of an illustrative set of social interaction environments in accordance with one or more embodiments.

FIG. 9 is an example block diagram of an illustrative gaming portfolio environment in accordance with one or more implementations.

FIG. 10 is an example block diagram of an illustrative system federation environment in accordance with one or more embodiments.

FIG. 11 is an example block diagram of an illustrative watch list environment in accordance with one or more embodiments.

FIG. 12 is an example process flow of an illustrative operational data process in accordance with one or more embodiments.

FIG. 13 is an example process flow of an illustrative operational data process in accordance with one or more embodiments.

FIG. 14 is an example diagram of an illustrative profile simulation using quantum mechanics in accordance with one or more embodiments.

FIG. 15 is an example diagram of an illustrative profile simulation using quantum mechanics in accordance with one or more embodiments.

FIG. 16 is an example block diagram of an illustrative API architecture in accordance with one or more embodiments.

FIG. 17 is an example block diagram of a data streaming environment in accordance with one or more embodiments.

FIG. 18 is an example block diagram of an illustrative cache structure in accordance with one or more embodiments.

FIG. 19 is an example block diagram of an illustrative data warehouse environment in accordance with one or more embodiments.

FIG. 20 is an example block diagram of an illustrative PPAD engine in accordance with one or more embodiments.

FIG. 21 is a schematic diagram of a digital computing environment in which certain aspects of the present disclosure may be implemented.

FIG. 22 is an example block diagram of visualizing illustrative profile vector data in accordance with one or more embodiments.

Detailed Description

In the following description of the various embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration various embodiments in which the disclosure may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made.

FIG. 1 shows a block diagram of one particular type of programmed computing device 101 (e.g., a computer server) that may be used in accordance with an illustrative embodiment of the present disclosure. The computer server 101 may have a processor 103 for controlling the overall operation of the server and its associated components, including RAM 105, ROM 107, input/output module 109 and memory 115.

Input/output (I/O)109 may include a microphone, keyboard, touch screen, camera, and/or stylus through which a user of device 101 may provide input, and may also include one or more of a speaker (for providing audio output) and a video display device (for providing textual, audiovisual, and/or graphical output). Other I/O devices used by a user and/or other devices to provide input to device 101 may also be included. Software may be stored in memory 115 and/or storage to provide computer readable instructions to processor 103 to enable server 101 to perform various technical functions. For example, memory 115 may store software used by server 101, such as an operating system 117, application programs 119, and an associated database 121. Alternatively, some or all of the instructions executable by the server 101 computer may be embodied in hardware or firmware (not shown). As described in detail below, database 121 may provide centralized storage of features associated with suppliers and customers, allowing functional interoperation between different elements located in multiple physical locations.

The server 101 may operate in a networked environment using connections to one or more remote computers, such as terminals 141 and 151. Terminals 141 and 151 may be personal computers or servers that include many or all of the elements described above in connection with server 101. The network connections depicted in FIG. 1 include a Local Area Network (LAN)125 and a Wide Area Network (WAN)129, but may also include other networks. When used in a LAN networking environment, the computer 101 is connected to the LAN 125 through a network interface or adapter 123. When used in a WAN networking environment, the server 101 may include a modem 127 or other means for establishing communications over the WAN 129, such as the Internet 131. It will be appreciated that the network connections shown are for illustration only, and other means of establishing a communications link between the computers may be used. The existence of various protocols is assumed, such as TCP/IP, Ethernet, FTP, HTTP, and the like.

Computing device 101 and/or terminals 141 or 151 may also be mobile terminals including various other components, such as a battery, speaker, and antenna (not shown).

The disclosure is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of computing systems, environments, and/or configurations that may be suitable for use with the disclosure include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, cloud-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile computing devices such as smartphones, wearable computing devices, tablets, distributed computing environments that include any of the above systems or devices, and the like.

The present disclosure may be described in the context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular computer data types. The present disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

Referring to FIG. 2, one illustrative system 200 for implementing methods in accordance with the present disclosure is shown. As shown, the system 200 may include one or more workstations 201. The workstation 201 may be local or remote and is connected to a computer network 203, 210 by one or more communication links 202, the computer network 203, 210 being linked to a server 204 by a communication link 205. In system 200, server 204 may be any suitable server, processor, computer, or data processing device, or combination thereof. Computer network 203 may be any suitable computer network including the internet, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a wireless network, a Digital Subscriber Line (DSL) network, a frame relay network, an Asynchronous Transfer Mode (ATM) network, a Virtual Private Network (VPN), or any combination of any of these networks. The communication links 202 and 205 may be any communication links suitable for communication between the workstation 201 and the server 204, such as network links, dial-up links, wireless links, hard-wired links, and the like.

FIG. 3 shows a system 300 configured for data processing in accordance with one or more embodiments. The present disclosure may be described in the context of a cloud-based computing architecture employing Amazon Web Services (AWS). However, other commercially available Cloud-based services, such as Microsoft Azure and Google Cloud, may also be used. The system 300API component may be provided in an AWS cloud, having been constructed to expand in a flexible manner without any legacy dependencies by using selected techniques. In some embodiments of the system 300 and method, the primary persistent data store is Amazon dynamo db, a fully hosted proprietary NoSQL database service that supports key-value and document data structures, in which content, interactions, configuration files, and other non-financial information may be stored. In some embodiments of the system 300 and method, social graph data (i.e., relationships between users) is stored in Amazon Neptune, a fully hosted graph database. In some embodiments of the system 300 and method, extensibility is supported by multiple Redis (remote dictionary servers of Redis Labs) clusters acting as read-only memory databases. In some embodiments of the system 300 and method, data is stored in Amazon Redshift, a cloud data warehouse, and reporting capabilities are built using the Tableau BI toolset. In some embodiments of the system 300 and method, API components (including daemons and engines) are encoded in node. In some embodiments, clustering algorithms (almost any clustering algorithm can be applied once the profile vectors are obtained) and machine learning can be implemented. In some embodiments of the system 300 and method, some API components execute on AWS Lambda (serverless computing), allowing highly extensible capability to respond to user database interactions and system failures/alerts.

In some implementations, the system 300 may include one or more computing platforms 302. Computing platform 302 may be configured to communicate with one or more remote platforms 304 according to a client/server architecture, a peer-to-peer architecture, and/or other architectures. Remote platform 304 may be configured to communicate with other remote platforms via computing platform 302 and/or according to a client/server architecture, peer-to-peer architecture, and/or other architectures. A user may access system 300 through remote platform 304.

In some embodiments of the system 300 and method, user registration, profile creation, and maintenance are provided. In some embodiments of the system 300 and method, a secure database, discovery mechanism, and instrument watch list maintenance are provided to a user. In some embodiments of the system 300 and method, the present technology enables synchronization of the tool database with multiple brokerage/hosting systems. In some embodiments of the system 300 and method, the present technology enables social graph functionality by allowing discovery and focus on other users in the system. In some embodiments of the system 300 and method, social functionality allows posts to be posted, commented on, and shared on the media feed 700 — via a social graph database that allows relationship maintenance. In some embodiments of the system 300 and method, the delivery of event notifications to client devices is accomplished by a mobile event management component with "over the air" infrastructure technology. In some implementations of the system 300 and method, two-way external social network interactions may be used to share resources from the media feed 700 to other social networks and to share external content to the media feed 700.

Some embodiments of the system 300 and method enable the use of the PPAD engine 2000 (see FIG. 20) to deliver market data for real-time price data to a customer and to deliver notification of price and game position profit-loss alerts to the customer. In some implementations, delivering charts and historical market data of technical analytics to mobile clients (e.g., smartphones, wearable computing devices, tablets) may be provided.

In some embodiments of the system 300 and method, media content such as news, reviews, calendars, basic data, research and community mood and tailored news, reviews and research content are delivered individually to each user's feed 700 using a profile and recommendation engine (block 308). The user may also search all historical news articles and community posts. With the system 300, some embodiments provide a "clear at a glance" tool score computed from the underlying tool data. In addition, real-time user community emotions and the accuracy or inaccuracy of transactions may be provided to users on a per-instrument basis.

Computing platform 302 may be configured by machine-readable instructions 306. The machine-readable instructions 306 may include one or more instruction modules or engines. The instruction modules may include computer program modules. The instruction modules may include one or more of a quantum recommendation engine/module 308, a media consumption module 310, a social media interaction module 312 and gameplay module 314, a matching module 316, and/or other instruction modules.

The modules 308, 310, 312, 314, 316 and other modules implement APIs containing functions/subroutines that may be performed by another software system, such as email and internet access control. An API represents an application programming interface. The systems and methods described in this disclosure may be implemented in various technical computing environments, including Simple Object Access Protocol (SOAP) or in representational state transfer (REST). REST is a software architecture style of the world wide web. The REST API is a networking API that may be published to allow various clients (e.g., mobile applications) to integrate with the organization's software services and content. As understood by those skilled in the art, many common applications work using REST APIs.

Referring to fig. 3, the quantum recommendation engine/module 308 receives media consumption attribute data from the media consumption module 312, media interaction attribute data from the social media interaction module 314, and game interaction attribute data from the gamification module 314 to generate at least one user profile vector for each user of the system 300. "attribute data" includes ASCII characters in computer-readable form or binary compiled data, such as biometric data. The ASCII characters or binary data may operate in the software of system 300.

Referring to fig. 3, 6 and 7, the media consumption module 310 implements attribute data regarding media consumption by a user. The attribute data 320 is associated with a unique user ID 322. Media consumption analysis may include storage of media attribute records 324 instructing the user to read news articles, view financial instrument prices, history charts, technical charts, financial calendars, research reports, and the like. The media consumption module 310 may be a software system implementing an API containing functions/subroutines.

Referring to fig. 3, 6 and 8, the social media interaction module 312 implements social attribute data 326 about a user's social media interactions 800 within the system 300 and external networks. Social media interaction analysis may include social attribute record storage, such as who the user is interested in; who is paying attention to the user; posts, praise, comment, internal and external share issued by a user; the private federation the user is in and who is among the other members of these private federations are in the system. The social media interaction module 312 may be a software system implementing an API containing functions/subroutines.

Referring to fig. 3, 6, 9, 10, and 11, the gamification module 314 implements game attribute data 328 regarding game play by the user. The gameplay analysis can include the tools that the user owns in their watchlist environment 1100 and their portfolio 900, and what tools the user purchases or sells. In the gamification module 314, a virtual portfolio management game is implemented using a watchlist environment 1100 with watchlist attribute data, securities and individuals compete in the global digital virtual fantasy league environment 1000 using user league attribute data. In this manner, users of the system 300 can organically understand that investment is a matter of producing consistent return on capital over time and employing a diverse concept without excessive trading. In some embodiments, the system 300 enables users to create and manage their own private leagues and invite their friends, colleagues and classmates to compete with them. In some embodiments, the group chat functionality enables members of the private federation to communicate between them. They may further collaborate with users communicating with the group chat in these private unions. In this way, the user can learn how to invest in a risk-free manner. In some implementations, the module 314 includes a digital trophy — awarded by the technology platform to highlight the user's progress or achievement in various potential interactions. The gamification module 314 may be a software system that implements an API containing functions/subroutines.

Referring to fig. 3 and 6, the matching module 316 implements attribute data 332 for matching each user's profile with automatically indexed content. Content may be indexed using machine learning techniques according to the present disclosure. The user is then provided with the content having the strongest match through various publishing techniques including notifications and the user's media feed 700. In some implementations, the matching module 316 implements attribute data for matching users with other users. Users with similar interests in the profile may be "introduced" to each other as suggested attention. This is done to encourage participation and point-to-point learning. In some implementations, the matching module 316 implements the attribute data for matching to include a product, such as a financial product. Some or all of the components of a user profile, including their interests, financial performance, risk and behavioral characteristics, may be used to match users with financial products having similar characteristics. The matching module 316 may be a software system implementing an API containing functions/subroutines.

With continued reference to fig. 3, in one embodiment of the present disclosure, the system 300 represents the user (their interest) with a quantum mechanical wave function in the quantum recommendation engine/module 308. The wave function is then propagated over time according to a time-dependent potential resulting from the user's interaction with the recommended content. At the time of recommendation, a profile vector is generated, and the system recommends content close to the user profile vector. In one embodiment, the system 300 may represent content with a wave function. In this case, the calculation of the overlap integral is sufficient to find the best match for the content recommendation. System 300 may generate a profile vector for user interaction assuming that the user is in a quantum environment. In another embodiment, the quantum recommendation engine 308 provides a recommendation engine based on the assumption that the user's data interaction is in a quantum environment. Applications of quantum mechanics can be used to create profiles/recommendations.

In one operation, when a user engages in media content within the system 300, the associated quantum mechanical wave functions may be disturbed. Such interference can cause fluctuations in the observables (e.g., user interests). These may be interpreted as mood swings between multiple personalities that cause slight changes in user interest even without interaction. One point in recommending media content (e.g., financial content) is that the relevance of the content to the consumer's interests is time sensitive, i.e., when more recent media content is available, the content that is likely to be relevant at one time can easily become irrelevant. The available media content at any point in time may also take into account the change in consumer/user interest that occurs from one small time period to another.

Some implementations of module 308 may include a Data Acquisition Component (DAC). Js, which may be written in node, is responsible for extracting data from the AWS dynamdb or any other relevant data repository and creating user data to be used in any component of the module 308. The component is also responsible for resolving the transformation of profile data from the engine's analyzer component (PC) into useful Personnel Vectors (PV) for use not only in the engine's Recommendation Component (RC), but also in any other module of the system 300.

Some implementations of module 308 may include an analyzer component (PC). Multiple computing tasks may occur in the component. It is responsible for analyzing users in a multi-metric environment. In one embodiment, it can be a stand-alone unit in the server-side ecosystem, allowing any server-side unit to directly obtain the profile tensor. PC implements various line prediction models as well as Quantum Mechanics (QM). In one embodiment, the PC is written in Python, but the compute intensive part is written in C/F95. The PC generated profile tensor is then sent back to the DAC to generate a Profile Vector (PV) by slicing the profile tensor.

Some embodiments of system 300 may include a Content Generation Component (CGC): the purpose is to analyze the incoming content and bind them with the relevant tool symbols and/or departments. The component also analyzes the data and extracts emotions.

Some embodiments of the system 300 may include a Recommender Component (RC): the component may be written in Python programming language and is where the content data is analyzed along with the configuration file data. The RC is also responsible for sending the recommended content to the user.

In fig. 12, operations 1200 in module 308 implement profiling based on the assumption that the USER is in a quantum environment to create USER _ QPROFILE. Referring to fig. 12, for each USER on the recommendation list of system 300, the system can load the existing USER _ qprefile that is otherwise built on initial quantum data based on USER preferences. The process flow of operation 1200 is provided in fig. 12, USER _ QPROFILE is updated over a predetermined time interval based on the propagated wave function and the capture potential center of a particular financial instrument in the USER portfolio. In accordance with one or more embodiments, operations 1200 may be performed by one or more hardware processors configured by machine-readable instructions including modules that are the same or similar to modules 308.

Referring to fig. 13, operations 1300 in block 308 implement quantum mechanical functions. Referring to fig. 13, the operations 1300 process quantum data (wave function per tool ψ) of a user_sAnd capture potential center q_cs). In the flow, if there is USER qpupdate, CONTENT CONTAINER contains normalized CONTENT vector and number of CONTENTs N to be recommended to the USER. The resulting output includes N content IDs to be recommended to each user. In accordance with one or more embodiments, operations 1300 may be performed by one or more hardware processors configured by machine-readable instructions including modules that are the same or similar to modules 308.

The framework of the present disclosure for implementing quantum mechanics is discussed below with block 308. User interest in a particular financial instrument may be represented by a non-relative quantum particle of mass m constrained to move in a one-dimensional infinite-depth potential well of length L, the boundaries of which are given by the following equations

Where ζ is a generalized coordinate representing the user's interest in the financial instrument. The operator ζ ^ measures the user's interest in the financial instrument (or other product), corresponding to the location operator x ^ in quantum mechanics, so the operator ζ ^ is the hermitian operator. Similarly, the operator Λ, which measures the rate of change of user interest, corresponds to the momentum operator in quantum mechanics, which is also the Hermite operator, and is derived from the following equation

Zeta and ζ + d ζ are of interest to a user at a given timeWhere Ψ (ζ, t) is a normalized wave function representing the user's interest. To distinguish between negative and positive user interest in a financial instrument,the potential well is subdivided into two equal sized regions. Positive area is formed by<ζ_cThe negative area is represented by ζ>ζ_cIs represented by, wherein_cIs L/2.

The user's interest in a financial instrument at a given time t is defined by a number between-1 and 1, and is given by

Where < ζ ^ (t) > is the expected value of the user's interest at time t, the calculation is as follows

The percentage of the user's interest in the financial instrument can then be easily calculated from equation (3) and formulated as

The time dependence of user interest is modeled by a displacement potential function in the form of

Wherein U is₀And a are parameters relating to the depth and width of the displacement potential, respectively. The time dependent function d (t) in equation (5) plays an important role in driving user interest through a given feedback. The displacement potential may also serve as a means to localize the user's interest to a smaller region of space if selected in a manner that the system supports at least one constrained state. By first setting d (0) to a certain coordinate ζ₀This coordinate corresponds to the user's desired initial interest in the financial instrument, which is formulated as equation (2), so that the initial wave function is derived entirely in one bound state or a linear combination of these bound states, since t ═ 0, i.e. in the case of the ground state, with respect to ζ₀Symmetry provided that only ζ₀Sufficiently far from the boundary. Selecting a suitable parameter a, setting ζ₀＝ζ_cZero initial interest or ζ₀＝ζ_c(1-x/100) is the user's desired x initial interest in the financial instrument.

Characteristic state psi of user interest when t is 0_nZeta and characteristic energy E_nObtained by solving the time-independent schrodinger equation,

the Hamiltonian H in equation (6) is given by:

where V ^ con (ζ) ═ Vcon (ζ), V ^ drift (ζ, 0) ═ Vdrift (ζ, 0), h is a reduced planck constant, and m is the "mass" of interest to the user. Equation (6) can be solved numerically by dispersing the hamiltonian in equation (7) over a uniformly spaced spatial grid consisting of N points. If the grid spacing is δ ζ, where δ ζ<<1, then the coordinate ζ_jIs equal to j δ ζ, where j ═ 0.., N-1, and ζ₀0 and ζ_N-1L. The discretized Hamiltonian applied to ψ (ζ) can then be written as using the second-order central derivative formula

Where the index j represents the function value at the spatial coordinate zetj. Equation (8) can be rewritten in the form of a tri-diagonal symmetric matrix according to the following boundary conditions:

ψ (0) ═ 0, and ψ (L) ═ N-1 ═ 0.

Equation 9

Is rewritten as

Wherein

And is

The eigenvalues (eigenenergies) of the coefficient matrix in equation (10) can be easily found by calling a linear algebraic package, such as the commercially available LAPACK (http:// www.netlib.org/LAPACK). In order to find the eigenvectors (eigenstates) of the hamiltonian given in equation (10), the targeting method or the relaxation method [2] can be deployed according to the boundary conditions given in equation (9). The initial wave function of the user's interest in the financial instrument can be written as a superposition of all these eigenstates, since they form a complete set in the hilbert space:

wherein psi_n(ζ) is the nth characteristic state corresponding to the nth characteristic energy, | a_n|²Is the probability of finding a system in the nth eigenstate, which can be calculated from the following equation

Once the initial wave function of the user's interest in the financial instrument is obtained, it propagates from the time of the last recommendation session at ti-1 to t_iThe next recommendation session in time. If feedback is given at t ═ t' within the time interval Δ t of the two successive recommendation sessions, the time-dependent potential V_driftThe center d (t) of (a) moves in the direction of the given feedback, with the formula,

d(t)←d(t)-δf₊，

equation 15

If the feedback is positive, and

d(t)←d(t)+δf_-，

equation 16

If the feedback is negative. Parameter δ f₊And δ f_-Much smaller than the size of the infinite depth well L. The time propagation of the wave function can be achieved by applying a time evolution operator, which has the formula

Applied to the wave function Ψ (ζ, t)_i-1) And

wherein

If the time interval Δ t is subdivided into a finite number of time steps with δ t intervals, the time evolution operator in equation (17) can be written as

Wherein, for a sufficiently small time step δ t, the operator U ^ can be approximated as

To propagate the wave function numerically, the time evolution operator in equation (21) is further approximated by a Pade' approximation. Pade' approximation yields formula

Applying equation (22) to the wave function Ψ (ζ, t) yields the formula

Then

Discretizing equation (24) using a central derivative formula of second derivatives to obtain a formula

Wherein

The upper and lower indices k and j in the above equation represent function values at the time t ═ k δ t and the coordinate ζ ═ j δ ζ, respectively. Equation (25) gives a linear system of equations where Ψ^k+1Represents Ψ (ζ, t + δ t) and is an unknown to the system of equations. However, equation (25) can be solved by rewriting it into a form of a tri-diagonal symmetric matrix, i.e.

Fang Cheng(29) The system of linear equations given in (1) is written according to the boundary conditions given in equation (9), and needs to be written at the time from t_i-1From the last recommended time of day to t_iThe solution is performed in each time step of the next recommended time of time.

At the start of the next recommendation session, a profile vector is generated for the user from the wave function representing the financial instrument (or other product) using equation (2), and then a similarity match is performed between the profile vector and all financial or other content vectors. The content vector that is most similar to the profile vector is recommended to the user by using the matching module 316.

Configuration File simulation example

Referring to fig. 14 and 15, the initial interest is based on the order of financial instruments in the user's watch list 1100 on the system 300. The spatial distribution of the capture potentials localizes the "interest" to a certain region in the space of interest. Over time, the user's interest in the financial instruments may slowly diminish. At the moment the feedback is given, the wave function representing the user's interest in a particular financial instrument is disturbed, resulting in fluctuations in the user's interest in that instrument.

In this example, the profile vector is generated by the algorithm of the application module 308 to compare with the content vector.

In this example, the following constants are used:

-a₀＝5.29177

-L＝30a₀

-m₀＝1.129

-δu＝-0.3

-δq＝1

-δλ＝0.0055

the number of spatial grid points and temporal grid points is 1000 and 1440, respectively.

Assume that the dimension of the tool space (common stock) is 5, with the following tools and their respective index numbers FORD (0), TESLA (1), INTEL (2), NVIDIA (3), APPLE (4).

Assume that the user has TESLA and INTEL in his/her watch list and user data records in system 300. In this example, the wave function representing the user's interest in each tool is selected as the ground state of the system. For tools already in the user's watch list, the initial interest is chosen to be 10% (q)_c＝ζ₀71.4389, as described above in operations 1200 and 1300), and for other tools, zero (q) is selected (q)_c＝ζ₀L/2 79.3766, as described in operations 1200 and 1300).

	FB1	FB2	FB3	FB4	Interest(％)
						FORD	-	-	-	-	0.0
TESLA(on watch list)	0.6hr	3.6hr	-	-	9.869
						INTEL(on watch list)	4.2hr	-	-	-	9.324
NVIDL4	-	-	-	-	0.0
						APPLE	-	-	-	-	0.0

TABLE 1

Table 1 shows the schedule of Feedback (FB) actions between two consecutive recommendation sessions and their respective interest levels at the start of the second recommendation session.

The list of feedback actions provided by the user between two consecutive recommendation sessions is given in table 1. The profile vector consists of 5 components, corresponding to each tool in the tool space, defined asWherein the summation index i runs { FORD, TESLA, INTEL, NVIDIA, APPLE }, such thatThe premise of the method is thatAre orthogonal vectors. In this case, the profile vector at the beginning of the second recommendation session is

Content vectors are also defined in a similar manner. For each content vectorThe similarity is checked by simply using the cosine theorem. And recommending the content with the content vector closest to the configuration file vector to the user. An example of a profile vector visualization is provided in FIG. 22.

Some aspects of various exemplary configurations are described by reference to and/or using neural networks. The quantum recommendation engine/module 308 may be configured to electronically process with a machine deep learning controller. The various structural elements of the neural network include layers (input layer, output layer, and hidden layer), nodes (or cells) of each layer, and connections between nodes. Each node is connected to other nodes and has a node value (or weight), while each connection may also have a weight. The inode values and connections may be random or uniform. After training using the training data set, the node values/weights may be negative, positive, small, large, or zero. The computer networks 203, 201 may incorporate the functionality of various Machine Intelligence (MI) neutral networks 500 (see FIG. 5) of available Tensorflow (https:// www.tensorflow.org) or Neuroph software development platforms (incorporated herein by reference). Referring to fig. 5, the neural network 500 is typically arranged in a "layer" of node processing units that act as analog neutrons, such that there is an input layer 508, representing the input field into the network. To provide an automated machine learning process, one or more hidden layers 509 having a set of machine learning rules process the input data. An output layer 511 provides the results of the processing of the network data.

In some other configurations, the quantum recommendation engine/module 308 implements deep learning machine learning techniques, implementing descriptions of learning methods that allow the machine to be given the raw data and determine the descriptions needed for data classification. Deep learning is a subset of machine learning that uses a set of algorithms to model high-level abstractions in data using a depth map with multiple processing layers (including linear and non-linear transformations). While many machine learning systems have initial features and/or network weights to modify through learning and updating of the machine learning network, deep learning networks train themselves to recognize "good" features for analysis. Using a multi-layered architecture, machines using deep learning techniques can process raw data better than machines using traditional machine learning techniques. The use of different evaluation or abstraction layers facilitates the examination of data for highly correlated sets of values or unique subject sets.

Deep learning determines structures in a data set using a back-propagation algorithm that is used to change internal parameters (e.g., node weights) of the deep learning machine. Deep learning machines can utilize various multi-layer architectures and algorithms. For example, machine learning involves identifying features used to train a network, while deep learning processes raw data to identify features of interest without external identification.

In some implementations, the machine learning controller processing module 308, deep learning in a neural network environment includes numerous interconnected nodes called neurons. Input neurons activated from external sources activate other neurons according to connections with those other neurons controlled by machine parameters. Neural networks react in some way based on their own parameters. Learning improves machine parameters and, in turn, the connections between neurons in the network, causing the neural network to react in a desired manner.

One embodiment of the machine learning controller processing module 308 includes deep learning techniques that can use convolution filters to segment data using a convolutional neural network to locate and identify learned, observable features in the data. Each filter or layer of the CNN architecture transforms the input data to increase the selectivity and invariance of the data. This abstraction of the data enables the machine to focus on features in the data it is trying to classify, and ignore irrelevant background information.

Deep learning operates based on the understanding that many datasets include high-level features, including low-level features. For example, when examining an image, it is more efficient to find edges that form a decorative pattern, forming part of, and forming the object being sought, than to find the object. These feature hierarchies can be found in many different forms of data, such as speech and text.

The learned observable features include objects and quantifiable rules learned by the machine during supervised learning. Machines equipped with a large well-sorted dataset are better able to distinguish and extract features associated with successfully sorting new data. Deep learning machines utilizing transfer learning can correctly link data features to certain classes confirmed by human experts. Conversely, the same machine may update the classification parameters when the human expert informs of an incorrect classification. For example, settings and/or other configuration information may be guided by learning the use of settings and/or other configuration information, and as the system is used more (e.g., repeated and/or used by multiple users), many variations and/or other possibilities of settings and/or other configuration information may be reduced for a given example training data set.

For example, an example deep learning neural network may be trained on a set of expert-classified data. This set of data builds the first parameters for the neural network, which will enter the supervised learning phase. During the supervised learning phase, it can be tested whether the neural network achieves the desired behavior.

Once the desired neural network behavior has been achieved (e.g., module 308 has been trained to operate according to specified thresholds, etc.), module 308 may be deployed for use (e.g., testing the machine with "real" data, etc.). During operation, neural network classification may be confirmed or rejected (e.g., by expert users, expert systems, reference databases, etc.) to continue to improve neural network behavior. The example neural network is then in a transition learning state, as the classification parameters that determine the behavior of the neural network are updated according to the ongoing interaction. In some examples, the neural network may provide direct feedback to another process. In some examples, the neural network outputs the data that is buffered (e.g., through a cloud, etc.) and validated before the data can be provided to another process.

API architecture

Referring to fig. 16, in some embodiments, the system 300 employs an AWS Elastic beans load balancing cluster with "auto-scaling" functionality, housed in a Virtual Private Cloud (VPC) for robust security. When users of an existing server exceed a certain percentage of their capacity, the cluster will automatically "add" a new server. Communications with system 300 are SSL encrypted and require verification using a time-limited client token. The amazon infrastructure provides network intrusion and network attack protection through a Route 53 DNS; this function can also be achieved using VPC and a load balancer.

Data streaming techniques

Referring to fig. 17, in some embodiments, the system 300 and associated client framework can work with multiple streaming data providers (on the cloud or locally) depending on implementation requirements, such as PubNub, Lightstreamer, or Kaazing. PubNub is used to distribute real-time and delayed market data to local mobile clients in a "throttled" manner, thereby optimizing data costs. In some embodiments, the custom index data feed and the portfolio game valuation are part of a real-time data stream.

Cache structure

Referring to FIG. 18, in some embodiments, the system 300 uses multiple Redis memory database clusters to improve the performance of the system (which also allows for elimination of single points of failure). The described embodiments allow for fast object storage, which can be "well" extended for user access ("token") management. The described embodiments may enable high write throughput for survey and feed items. The described embodiments enable up-to-date pricing of real-time game positions and tools. The embodiments may implement atomic operations on the subject members, such as game prices and positions, and real-time feed structures. The described embodiments may implement custom index computations. In another embodiment, the API, content daemon, PPAD engine 2000 (see fig. 20), and market data distribution use separate AWS Redis instances to avoid single points of failure and perform better load distribution. The system 300 also implements a distributed job queue. Whenever an API call pushes a task to the queue, the "task" will be popped up and executed by exactly one worker. For example, the real-time feed 700 of the system 300 works with such a job queue.

Data warehouse

Referring to fig. 19, in some embodiments, the system 300 is implemented on an AWS Redshift cluster and consists of three or more data groups (e.g., daily data snapshots, user events and logs, reports). Of the daily Data snapshots, the daily production Data table snapshot is copied to the Data warehouse by the AWS Data pipeline. Real-time and near real-time user events and logs include all server HTTPS communication logs, client intra-application event logs, and chat and real-time market data logs. In the aggregation of daily, weekly, and monthly reports, aggregation is performed on the AWS Data profiles for users, sessions, games, tools, purchases, and other important interactions.

Referring to fig. 20, in some embodiments, the system 300 with the gamification module 314 employs a price and portfolio position profit alert engine 2000, the task of which is to send alerts (in the form of mobile device notifications) to the user regarding significant changes in the user's predicted safe prices (in the game with the gamification module 314) and large changes in the profit position. In some implementations, if the latest price of a tool exceeds 52 weeks of new low/high, the system 300 draws a chart and publishes it as a tweet to the media feed 700 (e.g., # invstream).

In some implementations, computing platform 302, remote platform 304, and/or external resources 340 may be operably linked via one or more electronic communication links. Such electronic communication links may be established, for example, at least in part over a network such as the internet and/or other networks. It should be understood that this is not intended to be limiting, and that the scope of the present disclosure includes embodiments in which computing platform 302, remote platform 304, and/or external resources 340 are operably linked via some other communications medium.

A given remote platform 304 may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with a given remote platform 304 to interface with system 300 and/or external resources 340 and/or to provide other functionality attributed herein to remote platform 304. In a non-limiting example, given remote platform 304 and/or given computing platform 302 may include one or more of a server, a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a netbook, a smartphone, a gaming console, and/or other computing platform.

External resources 340 may include information sources external to system 300, entities external to participating system 300, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources 340 may be provided by resources included in system 300.

Computing platform 302 may include electronic memory 330, one or more processors 318, and/or other components. Computing platform 302 may include communication lines or ports to enable the exchange of information with a network and/or other computing platforms. The display of computing platform 302 in FIG. 3 is not intended to be limiting. Computing platform 302 may include a number of hardware, software, and/or firmware components that operate together to provide the functionality attributed herein to computing platform 302. For example, computing platform 302 may be implemented by a computing platform cloud operating together as computing platform 302.

Electronic storage 330 may include non-transitory storage media that electronically store information. The electronic storage media of electronic storage 330 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with computing platform 302 and/or removable storage that is removably connectable to computing platform 302 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 330 may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage 330 may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and/or other virtual storage resources). Electronic storage 330 may store software algorithms, information determined by processor 318, information received from computing platform 302, information received from remote platform 304, and/or other information that enables computing platform 302 to function as described herein.

Processor 318 may be configured to provide information processing capabilities in computing platform 302. Thus, processor 318 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although the processor 318 is shown as a single entity in fig. 3, this is for illustration only. In some implementations, the processor 318 may include multiple processing units. These processing units may be physically located within the same device, or processor 318 may represent processing functionality of a plurality of devices operating in coordination. Processor 318 may be configured to execute modules 308, 310, 312, 314, 316, and/or other modules. Processor 318 may be configured to execute modules 308, 310, 312, 314, and/or 316 and/or other modules via software, hardware, firmware, or some combination of software, hardware, and/or firmware, and/or other mechanisms for configuring processing capabilities on processor 318. As used herein, the term "module" may refer to any component or collection of components that perform the function attributed to that module. This may include one or more physical processors executing during execution of processor-readable instructions, circuitry, hardware, storage media, or any other component.

It should be appreciated that although modules 308, 310, 312, 314, and 316 are illustrated in fig. 3 as being implemented within a single processing unit, in implementations in which processor 318 includes multiple processing units, one or more of modules 308, 310, 312, 314, and/or 316 may be implemented remotely from the other modules. The description of the functionality provided by the different modules 308, 310, 312, 314, and/or 316 described below is for illustrative purposes, and is not intended to be limiting, as modules 308, 310, 312, 314, and/or 316 may provide more or less functionality than is described. For example, one or more of modules 308, 310, 312, 314, and/or 316 may be eliminated, and some or all of the functionality may be provided by other ones of modules 308, 310, 312, 314, and/or 316. As another example, processor 318 may be configured to execute one or more other modules that may perform some or all of the functionality attributed below to one of modules 308, 310, 312, 314, and/or 316.

FIG. 4 illustrates a method 400 of data processing in accordance with one or more embodiments. The operations of method 400 presented below are intended to be illustrative. In some implementations, the method 400 may be accomplished with one or more other operations not described and/or without one or more of the operations discussed. Further, the order in which the method 400 operates is shown in FIG. 4 and described below is not intended to be limiting.

In some implementations, method 400 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices operable to perform one or all of the operations of method 400 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for performing one or more operations of the method 400.

FIG. 4 illustrates a method 400 in accordance with one or more embodiments. Operation 402 may include generating media consumption data from a set of computer-readable user data records. In accordance with one or more embodiments, operation 402 may be performed by one or more hardware processors configured by machine-readable instructions, including modules that are the same or similar to modules 310.

Operation 404 may include electronically processing the set of computer-readable user data records to generate social interaction data. In accordance with one or more embodiments, operation 404 may be performed by one or more hardware processors configured with machine-readable instructions, including modules that are the same or similar to modules 312.

Operation 406 may include electronically processing the set of computer-readable user data records to generate game interaction data. In accordance with one or more embodiments, operation 406 may be performed by one or more hardware processors configured with machine-readable instructions, including modules that are the same or similar to modules 314.

Operation 408 may include electronically processing the media consumption data, the media interaction data, and the game interaction data with a quantum recommendation module. In accordance with one or more embodiments, operation 408 may be performed by one or more hardware processors configured with machine-readable instructions, including modules that are the same or similar to modules 308.

Operation 410 may comprise generating a computer-readable user profile vector or quantum-based profile vector associated with the or each user data record. In accordance with one or more embodiments, operation 410 may be performed by one or more hardware processors configured with machine-readable instructions, including modules that are the same or similar to modules 308.

Operation 412 may include electronically processing the user profile vector to generate matching parameters. In accordance with one or more embodiments, operation 412 may be performed by one or more hardware processors configured with machine-readable instructions, including modules that are the same or similar to modules 316.

FIG. 21 shows a schematic diagram of a digital computing environment 300' in which certain aspects of the present disclosure may be implemented. In some embodiments, a portfolio page is provided that displays a customer's portfolio and a historical representation of the portfolio. In some embodiments, a watch list is provided that displays financial instruments of interest to a customer. In some embodiments, a portion is provided where a customer can discover new tools to pay attention to or invest in. In some embodiments, a tool center is provided in which customers can view the underlying data, community mood, history, comparison and technical charts for each financial tool, as well as specialized news feeds including news articles, research reports and event calendars for each financial tool. In some embodiments, a "trading screen" is provided on which a customer may perform a trade. In some implementations, a leaderboard page is provided in which the customer can find the best performing person within the community. In some embodiments, tracking record functions are provided, such as analysis of the client's portfolio to describe its performance, stealth investment authorizations, investment style based on financial factor analysis, behavioral analysis of the user's investment transaction history, and a measure of the user's success in determining their investment decision entry and exit opportunities. In some embodiments, users are provided with the ability to open bank and brokerage accounts and pay for their funds using a connected debit card or invest her funds in a wide range of financial assets and cryptocurrency. In some embodiments, a transaction history and filter is provided, i.e., the ability of a user to view her bank or investment transactions, filter them, and drill down into specific transaction details.

Aspects of the present disclosure provide a rich user experience by integrating one or more of personalized content, gaming for financial markets, social features, and e-commerce capabilities in a single user experience. The system 300, 300' facilitates customer participation and helps customers build confidence, knowledge and wealth in a financial investment environment. It overcomes the problems that have been found in terms of pervasive investment. While the technology has been described for illustrative purposes based on what is currently considered to be the most practical and preferred embodiment, aspects of the disclosure may be applied to many other vertical industries where a technology platform or service provider seeks to create maximum customer participation, personalization, and convenience.

Although the technology has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the technology is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present technology contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.