Patent Grant
12307528
This disclosure generally relates to software systems and methods and, more particularly, to software systems and methods to automatically correlate subject matter items and provider data across multiple platforms.
Since 1959, when IBM's Arthur Samuel pioneered machine learning (ML), it has been used to perform data matching. The concept of information extraction became widespread in 1987 by the US Navy's MUC-1 Naval operations message system, with significant support by the US Defense Advanced Research Projects Agency throughout the 1990s. The proliferation of the World Wide Web after its introduction in 1990 by Tim Berners turned the internet into a series of interlocked documents, making it accessible to computer-based information extraction. There have been many tools created to extract text-based information: naïve Bayes classifiers, support vector machines, multinomial logistic regression, recurrent neural networks, and maximum-entropy Markov models, to name a few. These conventional extraction techniques use a regression analysis and/or low-dimensional classification schemes. Although these models have had success with smaller datasets, they require supervised training of the dataset. The amount of data needed to train a system to represent accurate natural language processing is very large and, thus, the amount of training time required makes the effort very costly.
In 2018, Jacob Devlin created a new technique called the Bidirectional Encoder Representations from Transformers (BERT) model. BERT and its successors, Generative Pre-trained Transformer (GPT, GPT2, and GPT3), Transformer XL (XLNet), Robustly Optimized BERT (RoBERTa), etc., use high-dimensional classification schemes like embedded transformers.
Training for BERT and its successors is unsupervised and highly parallelizable, greatly reducing the training time. With training time no longer acting as the gating item, advanced linguistic techniques, like masked language models and next sentence prediction, can be used to increase the accuracy of extracted meaning and include such concepts as automatic keyword extraction, statement focus and meaning determination, and the writer's sentiment. The writer's sentiment can be given as strongly negative, negative, neutral, positive, and strongly positive for each keyword and statement derived from a given corpus of text.
Social media and search engines allow individuals to search for knowledge and interact globally with others, making it possible to perform online consulting, which from 2015 through 2020 generated $383 billion in value. In the modern world, with the vast amount of information available from multiple sources combined with the effect of influencers on popular opinion, it is very difficult for consultants to track the expanding data available across platforms as well as the frequently changing preferences of clients.
As such, improvements and innovations are needed for an online automated consultancy assistance system, using data scraping technology combined with modern natural language processing techniques.
The present invention provides embodiments configured to automatically correlate subject matter items and provider data across multiple platforms. These platforms can include newsfeeds, websites, social websites, apps and networks, internet and social network posts, online reviews, online queries, and the like.
In various embodiments, a Subject Matter Item Assistance System (SMIAS) and method co-joins providers of subject matter items with displayers of subject matter items, which can provide access to certain information, goods, or services that are within a subject matter area. Novice actors, such as subject matter item users, are able to describe their preferences to subject matter experts, such as subject matter item displayers, who use the SMIAS to automatically sift through the internet, or other network environments, to find out how reviewers feel about certain subject matter items while taking into consideration the novice user's preferences to help guide them to their desired goal. Similarly, providers of subject matter items can get very granular information not only of what the novice actors are selecting but, using the preferences, why they are selecting them. This is accomplished with their access to information provided by the SMIAS system operator. This system allows a subject matter displayer to determine a novice user's subject matter literacy, through tracking the webpage access, and how their preferences change over time.
In various embodiments, a SMIAS as a Consultancy Assistance System (CAS) and method co-joins providers of subject matter items with consultants as displayers of subject matter items, which can provide access to certain information, goods, or services that are within a subject matter area. Novice actors, such as clients, are able to describe their preferences to subject matter experts, such as consultants, who use the CAS to automatically sift through the internet to find out how reviewers feel about certain subject matter items while taking into consideration the client's preferences to help guide them to their desired goal. Similarly, providers of subject matter items can get very granular information. This is accomplished with their access to information provided by the CAS system operator. This system allows a consultant to determine a client's subject matter literacy, through tracking the webpage access, and how their preferences change over time.
Consulting services provide expertise and advice specific to a client's goals and preferences for consideration. The present invention presents systems and methods as tools for a consultancy organization and can benefit the consultant, the client, and subject matter item providers. The system of the present invention automatically generates a targeted list of relevant subject matter items, associated with consultant-provided workflow steps, to be matched with enhanced client preferences, thereby generating a list of options to be presented to the client. Subject matter items can be listed in order based on third-party reviews, if any, and the best fit for client preferences, with or without associated providers. All providers are analyzed for the value of their offered items and reputation, based on online third-party reviews. Subject matter item providers who interact with the system operator can ensure that their goods and services are included within the system.
Since the meaning, focus, and sentiment can be directly obtained from text data (and even image data), the present invention can automatically correlate specific subject matter items and provider data gleaned from webpages across multiple platforms with a client's preference data compiled from all relevant data accessed by that client. In an online consultancy setting, the client is appropriately presented with a list of acceptable items with associated providers within that subject matter, which can be sorted by relevancy, from which they can select an option. Unlike online searches which have no context and thus depend on the efficacy of a given set of queries, the present invention derives context from the workflow of the consultant, and subject matter results that are presented are associated with both the context and the current preferences of the client. Tracking selection preferences and their associated sentiment, by subject, per client over time is analogous to updating changes in client preferences over time.
Using platform-independent information and natural language processing to construct both client preferences and the most relevant best-fitting options of subject matter items with their associated providers can be complex. Client preferences can encompass not only the traditional goods and services (subject matter items) but also the perceived value of the items from third-party evaluators, any item-associated provider corporate and corporate leadership behavior identified in third-party reviews, and such diverse concepts as a place of origin for goods or services, past-present-future business ties, and the provider's service or philanthropic philosophy.
By using data gathering bots and modern natural language processing to automatically capture both client preferences and platform-independent subject matter items with associated providers, the present invention can better match relevant subject matter items found by online consulting services with the needs of their clients. The CAS of the present invention has three categories of users: system operators, consultants, and clients.
A system operator provides a set of keywords and seed URLs to the CAS on a per subject matter basis. Subject matter is defined herein as the area of expertise related to a class of consultants. For example, a furnishing consultant's subject matter might contain information on various kinds of furniture and home and office accessories with associated vendors and manufacturers. The CAS generates the subject matter items and their associated providers with semantic embedding to match those items to the preferences of the consultant's clients.
Consultants construct workflows to ensure that the options presented to clients are ones that can be offered by the consultant and all required work for a client is completed in the necessary order. A workflow consists of a number of workflow steps, each containing a list of subject matter keywords which are a subset of the keywords used by the system operator to locate subject matter items for the purpose of matching to client preferences. For example, for a financial consultant, workflow steps could include gathering information on investments, qualifying a client for a set of funds, determining investment types, and qualifying particular potential investments. Each consultancy has its own workflow, even those using the same subject matter. The workflow steps define the context needed for matching items to client preferences.
Clients go to consultants for expertise on a subject matter. They expect to be presented with choices that they find acceptable and help them achieve some set of goals. To define acceptable, the client usually creates a profile that is used by the system as the starting point for their preferences. The CAS generates the client preferences with semantic embedding so that subject matter items can be matched to the preferences of the consultant's clients in the context of the consultant's workflow steps. Changes in the subject matter area or in the client's preferences require different options to be presented; the present invention automatically and continuously tracks both.
The above and other aspects of the embodiments are described below with reference to the accompanying drawings.
The accompanying drawings, which are incorporated herein and form part of the specification, illustrate various embodiments of the present disclosure and, together with the description, further explain the principles of the disclosure and enable a person skilled in the pertinent art to make and use the embodiments disclosed herein. In the drawings, like reference numbers indicate identical or functionally similar elements.
Referring generally to
Novice actors, such as the subject matter item users 104, are able to describe or provide their preferences to the subject matter experts, such as subject matter item displayers 106, who use the SMIAS 102 to automatically sift through the internet, or other network environments, to find out how reviewers feel about certain subject matter items while taking into consideration user 104 preferences to help guide them to their desired goal. Similarly, providers 110 of subject matter items can get very granular information, not only of what the users 104 are selecting, but using the preferences of why they are selecting them. This is accomplished with access to information provided by the SMIAS system operator 108. This system 100 allows the subject matter displayer 106 to determine a novice user's 104 subject matter literacy, through tracking the webpage access, and how their preferences change over time.
The following list details various features, processing methods and steps, and system aspects in accordance with embodiments of the present invention.
Referring generally to
The system 300 automatically determines the preferences of consulting-service clients 304 for specific subject matter items by using semantic analysis 322 of the online platform-independent text read by each client 304 along with their initial profile. The system 300 automatically matches relevant internet-obtained, platform-independent, annotated subject matter data that has been selected by the consultant 306 via provided workflow steps with generated and managed client preferences. The system operator 308 can interact with subject matter item providers 310 for inclusion in the set of system-known subject matter items and providers. Client 304 and consultant 306 preference data, as well as data for providers 310 that interact with the system operator 308. are automatically updated. Selection trends for individual and grouped clients are automatically tracked for future projections.
The circled alphabetic references (e.g., a, b, c, d . . . n) in
The present invention comprises software systems and methods 300 as tools for consultancy organizations that can benefit the consultant 306, the client 304, and subject matter item providers 310. The CAS 302 of the present invention automatically generates a targeted list of relevant subject matter items, associated with consultant-provided workflow steps, to be matched with enhanced client preferences, generating a list of options to be presented to the client 304. Subject matter items can be listed in order based on third-party reviews, if any, and the best fit for client preferences, with or without associated providers 310. All providers 310 are analyzed for the value of their offered items and reputation, based on online third-party reviews. Subject matter item providers 310 who interact with the system operator 308 can ensure that their goods and services are included within the system.
Client preferences can encompass not only traditional goods and services (subject matter items) but also the perceived value of the items from third-party evaluators, any item-associated provider corporate and corporate leadership behavior identified in third-party reviews, and such diverse concepts as a place of origin for goods or services, past-present-future business ties, and the provider's service or philanthropic philosophy.
By using data-gathering bots or a bot engine 320 and modern natural language processing or an NPL engine 322 to automatically capture both client preferences and platform-independent subject matter items with associated providers 310, the present invention can better match relevant subject matter items found by online consulting services with the needs of their clients 304.
The system operator 308 provides a set of keywords and seed URLs to the CAS 302 on a per subject matter basis. Subject matter is defined herein as the area of expertise related to a class of consultants 306, identified as expert entities or subject matter item displayers in a SMIAS not configured to support consultants. For example, a furnishing consultant's subject matter might contain information on various kinds of furniture and home and office accessories with associated vendors and manufacturers. The CAS 302 generates the subject matter items and their associated providers 310 with semantic embedding 322 to match those items to the preferences of the consultant's clients 304.
Consultants 306 construct workflows to ensure that the options presented to clients 304 are ones that can be offered by the consultant 306 and all required work for a client 304 is completed in the necessary order. A workflow consists of a number of workflow steps, each containing a list of subject matter keywords which are a subset of the keywords used by the system operator 308 to locate subject matter items for the purpose of matching to client preferences. For example, for a financial consultant, workflow steps could include gathering information on investments, qualifying a client for a set of funds, determining investment types, and qualifying particular potential investments. Each consultancy has its own workflow, even those using the same subject matter. The workflow steps define the context needed for matching items to client preferences.
To define what is acceptable, the client 304 can create a profile that is used by the system 302 as the starting point for their preferences. The CAS 302 generates the client preferences with semantic embedding 322 so that subject matter items can be matched to the preferences of the consultant's clients 304 in the context of the consultant's workflow steps. Changes in the subject matter area or in the client's preferences require different options to be presented; the present invention automatically and continuously tracks both.
Referring to
Embodiments of the present invention can include a preferred method of natural language processing 322 using the Bidirectional Encoder Representations from Transformer (BERT) model. This model has already been trained against a large English language database and comes complete with masked language models (MLM) and next sentence prediction (NSP). All that is required is training for specialty words and phrases, after which the system 302 is ready to accept text for analysis. A “BertTokenizer” is the tool used by the data-gathering bot 320 when the BERT model is used. It takes text strings from webpages, texts, queries, posts, etc., and converts those text strings into a list of tokens 321. As shown in
Referring to
Referring generally to
Referring to
Referring to
Referring to
Referring generally to
Referring to the grouping feature 400 of
If a member of a group changes their profile and/or preferences such that they no longer fit their existing group, then group membership changes occur, and new groups can be created at least in one of the five ways 410 shown in
Referring to
Referring to graph 430 of
The probability of a group member selecting an item that has also been selected by the group leader is determined by the system 302. The average time from the group leader's selection of an item from a set of options and the final selection of an item by a group member from the same set options is calculated and split into the number of time bins given by the system operator 308. The probability of a group member selecting the same item as a group leader within a particular time bin is calculated. This allows the system 302 to estimate the number of members who are likely to select the same item as the leader at each time bin. Graphing the expected number of members selecting the same item as the group leader per time bin allows the system to predict for any new selection.
Referring to Table 1 below, because there can be overlap between multiple groups, there can be overlap between some or all group members. An indicator is assigned to the account of each group leader for each simultaneous group that leader represents. The number of groups, the number of members in each group, and the number of members per group who make the same selection as the leader, gives the predictive strength of the group leader.
Referring to the global trend line graph 470 of
Referring to the influencer strength graph 480 of
Referring to the influence effect graph 490 of
The influencer group illustration 495 of
The following examples of use cases as they apply to the online consultancy industry represent how various system actors interact with the system and methods depicted herein:
Use case UC0001:
Use case 0001 can be converted to a specific application as shown in use case UC0001a.
Use case UC0002:
Use case 0002 can be converted to a specific application as shown in use case UC0002a.
Use case UC0003:
Use case 0003 can be converted to a specific application as shown in use case UC0003a.
Use case UC0004:
Use case 0004 can be converted to a specific application as shown in use case UC0004a.
Use case UC0005:
Use case 0005 can be converted to a specific application as shown in use case UC0005a.
Various devices or computing systems can be included and adapted to process and carry out the aspects, computations, and algorithmic processing of the software systems and methods of the present invention. Computing systems, devices, or appliances of the present invention may include a computer system, which may include one or more microprocessors, one or more processing cores, and/or one or more circuits, such as an application-specific integrated circuit (ASIC), field-programmable gate arrays (FPGAs), graphics processing units (GPU), general purpose graphics processing units (GPGPU), etc. Any such device or computing system is defined as a processing element herein. A server processing system for use by or connected with the systems of the present invention may include a processor, which may include one or more processing elements. Further, the devices can include a network interface or a bus system in cases where the processing elements are within the same chip. The network interface is configured to enable communication with the internet, communication networks, other devices and systems, and servers, using a wired and/or wireless connection.
The devices or computing systems may include memory, such as non-transitive, which may include one or more non-volatile storage devices and/or one or more volatile storage devices (e.g., random access memory (RAM)). In instances where the devices include a microprocessor, computer-readable program code may be stored in a computer-readable medium or memory, such as but not limited to magnetic media (e.g., a hard disk), optical media (e.g., an OVO), memory devices (e.g., random access memory, flash memory), etc. The computer program or software code can be stored on a tangible, or non-transitive, machine-readable medium or memory. In some embodiments, computer-readable program code is configured such that when executed by a processing element, the code causes the device to perform the steps described above and herein. In other embodiments, the device is configured to perform steps described herein without the need for code.
It will be recognized by one skilled in the art that these operations, algorithms, logic, method steps, routines, sub-routines, and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof without deviating from the spirit and scope of the present invention as recited within the claims attached hereto.
The devices, appliances, or computing devices may include an input device. The input device is configured to receive an input from either a user (e.g., admin, user, etc.) or a hardware or software component as disclosed herein in connection with the various user interface or automatic data inputs. Examples of an input device include data ports, keyboards, a mouse, a microphone, scanners, sensors, touch screens, game controllers, and software enabling interaction with a touch screen, etc. The devices can also include an output device. Examples of output devices include monitors, televisions, mobile device screens, tablet screens, speakers, remote screens, screen less 3D displays, data ports, HUDs, etc. An output device can be configured to display images, media files, text, or video, or play audio to a user through speaker output.
The term communication network includes one or more networks such as a data network, wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), the internet, cloud computing platform, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including global system for mobile communications (GSM), internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wireless fidelity (WIFI), satellite, mobile ad-hoc network (MANET), and the like.
While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present disclosure should not be limited by any on the above-described embodiments or examples. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.
It is understood that any specific order or hierarchy of steps in any disclosed process is an example of a sample approach. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged while remaining within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order and are not meant to be limited to the specific order or hierarchy presented.
While the present invention has been described in connection with various aspects and examples, it will be understood that the present invention is capable of further modifications. This application is intended to cover any variations, uses or adaptation of the invention following, in general, the principles of the invention, and including such departures from the present disclosure as come within the known and customary practice within the art to which the invention pertains.
It will be readily apparent to those of ordinary skill in the art that many modifications and equivalent arrangements can be made thereof without departing from the spirit and scope of the present disclosure, such scope to be accorded the broadest interpretation of the appended claims so as to encompass all equivalent structures and products.
For purposes of interpreting the claims for the present invention, it is expressly intended that the provisions of 35 U.S.C. § 112 (f) are not to be invoked unless the specific terms “means for” or “step for” are recited in a claim
| Number | Name | Date | Kind |
|---|---|---|---|
| 5812987 | Uskin et al. | Sep 1998 | A |
| 6857004 | Howard et al. | Feb 2005 | B1 |
| 7133950 | Olukotun | Nov 2006 | B2 |
| 7418470 | Howard et al. | Aug 2008 | B2 |
| 7426488 | Gompers et al. | Sep 2008 | B1 |
| 7730121 | Howard et al. | Jun 2010 | B2 |
| 7941479 | Howard et al. | May 2011 | B2 |
| 7958194 | Howard | Jun 2011 | B2 |
| 8108512 | Howard et al. | Jan 2012 | B2 |
| 8325761 | Howard et al. | Dec 2012 | B2 |
| 8386356 | Saccone | Feb 2013 | B2 |
| 8499025 | Howard | Jul 2013 | B2 |
| 8762946 | Howard | Jun 2014 | B2 |
| 8843879 | Howard | Sep 2014 | B2 |
| 8949796 | Howard | Feb 2015 | B2 |
| 8959494 | Howard | Feb 2015 | B2 |
| 9098638 | Howard | Aug 2015 | B2 |
| 9146709 | Howard | Sep 2015 | B2 |
| 9158502 | Howard | Oct 2015 | B2 |
| 9170909 | Howard | Oct 2015 | B2 |
| 9229688 | Howard | Jan 2016 | B2 |
| 9280320 | Howard | Mar 2016 | B2 |
| 9292263 | Howard | Mar 2016 | B2 |
| 9324126 | Howard | Apr 2016 | B2 |
| 9335974 | Howard | May 2016 | B2 |
| 9395954 | Howard | Jul 2016 | B2 |
| 9424168 | Howard | Aug 2016 | B2 |
| 9626329 | Howard | Apr 2017 | B2 |
| 9747080 | Howard | Aug 2017 | B2 |
| 9851949 | Howard | Dec 2017 | B2 |
| 9977655 | Howard | May 2018 | B2 |
| 10009168 | Howard | Jun 2018 | B2 |
| 10148425 | Howard | Dec 2018 | B2 |
| 10216692 | Howard | Feb 2019 | B2 |
| 10496514 | Howard | Dec 2019 | B2 |
| 10943299 | Byun | Mar 2021 | B2 |
| 20050256793 | Hamilton et al. | Nov 2005 | A1 |
| 20070100743 | Barge et al. | May 2007 | A1 |
| 20070168270 | De Diego Arozamena et al. | Jul 2007 | A1 |
| 20080228663 | Smith | Sep 2008 | A1 |
| 20100251259 | Howard | Sep 2010 | A1 |
| 20110029457 | Axelrad et al. | Feb 2011 | A1 |
| 20120036399 | Howard | Feb 2012 | A1 |
| 20120101929 | Howard | Apr 2012 | A1 |
| 20130325750 | Howard | Dec 2013 | A1 |
| 20130325860 | Howard | Dec 2013 | A1 |
| 20130332903 | Howard | Dec 2013 | A1 |
| 20140025430 | Howard | Jan 2014 | A1 |
| 20140068464 | Howard et al. | Mar 2014 | A1 |
| 20140198196 | Howard et al. | Jul 2014 | A1 |
| 20140298286 | Howard | Oct 2014 | A1 |
| 20140310678 | Howard | Oct 2014 | A1 |
| 20140310680 | Howard | Oct 2014 | A1 |
| 20150149535 | Howard | May 2015 | A1 |
| 20160148264 | Winstanley | May 2016 | A1 |
| 20160232241 | Stoyanov | Aug 2016 | A1 |
| 20160241505 | Hermsdorff | Aug 2016 | A1 |
| 20180101910 | Smith et al. | Apr 2018 | A1 |
| 20190370308 | Riggs et al. | Dec 2019 | A1 |
| 20200097951 | Abramson et al. | Mar 2020 | A1 |
| 20200151682 | Hurry et al. | May 2020 | A1 |
| 20200210162 | Howard | Jul 2020 | A1 |
| 20210019158 | Howard | Jan 2021 | A1 |
| 20210142143 | Howard | May 2021 | A1 |
| 20220076346 | Smith et al. | Mar 2022 | A1 |
| Number | Date | Country |
|---|---|---|
| 2001001219 | Jan 2001 | WO |
| 2003060748 | Jul 2003 | WO |
| 2005111843 | Nov 2005 | WO |
| 2012021523 | Feb 2012 | WO |
| 2012024435 | Feb 2012 | WO |
| 2013036824 | Mar 2013 | WO |
| 2013184952 | Dec 2013 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20250029182 A1 | Jan 2025 | US |
