Patent ID: 12254275

DETAILED DESCRIPTION

While principles of the present disclosure are described herein with reference to illustrative embodiments for particular applications, it should be understood that the disclosure is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications, embodiments, and substitution of equivalents all fall within the scope of the embodiments described herein. Accordingly, the invention is not to be considered as limited by the foregoing description.

Various non-limiting embodiments of the present disclosure will now be described to provide an overall understanding of the principles of the structure, function, and use of systems and methods disclosed herein for processing forms to automatically adjudicate religious exemptions.

Centers for Disease Control and Prevention (CDC) offers religious exemptions from vaccinations for various infectious diseases, however, rules and regulations on religious exemptions change frequently with every exposure occurrence. Furthermore, the federal government, state government, and private companies each have their own rules and regulations regarding religious exemptions. Such varying requirements of religious exemptions make the adjudication process challenging.

The majority of state governments now allow religious exemptions to decline vaccinations, hence requests for religious exemptions have grown substantially. With the influx of exemption requests, manpower shortages are evident, and adjudication of religious exemptions is becoming more difficult. As an example, user A is employed with an institutional special needs plans (ISNP) business and is required to visit the members' homes to administer care. The state government requires user A to get vaccinated, however, due to specific religious beliefs, user A has applied for exemption under religious grounds. Since user A cannot return to work without approval of the exemption request, the adjudication process needs to be expedited. In general, the adjudication process is time-consuming because the regulations are fairly new and the explanation of the religious beliefs involves a human element that puts pressure on the reviewing team to fairly determine the eligibility of the participants, e.g., people may fabricate a story to seek exemptions from vaccination requirements. Furthermore, the reviewing team should be skillful to understand and adapt to the changing private, state, and federal regulations. Due to these circumstances, only a few religious exemption requests may get approved timely amongst thousands of requests. This demonstrates the complexity of the adjudication process and the time it takes for fair decision making. It is important to efficiently adjudicate religious exemptions with care and precision, and without any bias to protect the patients and contain exposure risks.

The existing health system does not utilize the ubiquitous modern technological infrastructure to address the backlog, the delays in processing, and the unconscious bias. The existing solution involves (i) ramping up resources and workforce to manually collect completed paper forms and manually process them, (ii) encouraging users to submit digital versions of the forms, or (iii) implementing independent reviews of the forms by outlining processes and protocols to ensure a bias-free assessment. However, information submitted through paper forms becomes stale with frequently changing rules and regulations, and implementing independent reviews is time-consuming, costly, and does not guarantee an absence of biases resulting from the manual review process.

As an example of the existing health system's shortcomings, user A, a physician in the state of Texas, has filed a request for a religious exemption offered by the state, in which user A provides employment information, location information, state information, and an explanation on the reason he is requesting an exemption under religious grounds. Despite the shortage of healthcare workers, user A has to wait for approval of his requests before he can continue to examine patients. Moreover, service providers, e.g., reviewing group that adjudicates religious exemptions, need to train someone on an urgent basis to understand the request under the religious grounds and whether the request qualifies under the religious exemption offered by the state government. It is challenging for the reviewer to assess, without any bias, the authenticity of religious requests and determine that the request satisfies all the legal requirements.

The present disclosure may solve one or more of the problems set forth above and/or other problems. The present disclosure teaches a method that adopts NLP and text analytics to analyze and interpret data in forms, e.g., vaccination exemption forms, to automatically adjudicate religious exemptions. The present disclosure teaches monitoring, in real-time or near real-time, the state or federal rules, and automatically updating the forms, in real-time or near real-time, based on the changing state or federal rules. The embodiments of the present disclosure remove manual bias, e.g., cultural bias, cognitive bias, information bias, etc., while adjudicating religious exemptions.

System100ofFIG.1introduces the capability to employ NLP and text analytics tools to analyze the text on exemption forms or applications to automatically adjudicate exemption requests. Since religious exemption requests may include personal and faith-based arguments and statements, employing NLP algorithms and offering training based on several different inputs removes manual bias, e.g., cultural, cognitive, or information bias, during the adjudication of exemption requests.

FIG.1introduces a capability to implement modern communication and data processing capabilities into methods and systems for processing, in real-time or near real-time, one or more forms to automatically adjudicate religious exemptions, according to one example embodiment.FIG.1, an example architecture of one or more example embodiments of the present invention, includes system100that comprises user101a-101n(collectively referred to as user101), user102a-102n(collectively referred to as user102), user equipment (UE)103a-103n(collectively referred to as UE103) that includes application105a-105n(collectively referred to as application105) and sensor107a-107n(collectively referred to as sensor107), form109, communication network111, analysis platform113, and database115.

In one embodiment, user101may be a person or a group of people interacting with a user interface or a web interface of UE103to access a service for vaccination exemptions. In one example embodiment, user101may include a registered patient, a potential patient, a returning patient, a visiting patient, an authorized user, a visiting user, etc., that provides contextual information, e.g., religious information, job description, location information, health-related information, etc., to exemption applications to request an exemption from vaccination requirements. It is understood that user101may provide contextual information for any other health-related services.

In one embodiment, user102may be a service provider, e.g., physicians, nurses, healthcare professionals, medical staff, etc., or an employee of a private or government entity, that may review medical forms associated with user101to provide a set of rules, e.g., specialized religious indicators or health indicators. The set of rules may be utilized by analysis platform113while adjudicating religious exemptions. In one example embodiment, analysis platform113may select at least one form109, e.g., rejected form109or flagged form109, for special review by user102. Analysis platform113may communicate with user102for their feedback, whereupon user102may review at least one form109to provide a recommendation, e.g., an assessment regarding exemptions from vaccinations.

In one embodiment, UE103may include, but is not restricted to, any type of a mobile terminal, wireless terminal, fixed terminal, or portable terminal. Examples of the UE103, may include, but are not restricted to, a mobile handset, a wireless communication device, a station, a unit, a device, a multimedia computer, a multimedia tablet, an Internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a Personal Communication System (PCS) device, a personal navigation device, a Personal Digital Assistant (PDA), a digital camera/camcorder, an infotainment system, a dashboard computer, a television device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. In addition, the UE103may facilitate various input means for receiving and generating information, including, but not restricted to, a touch screen capability, a keyboard, and keypad data entry, a voice-based input mechanism, and the like. Any known and future implementations of the UE103may also be applicable.

In one embodiment, application105may include various types of applications such as, but not restricted to, content provisioning applications, networking applications, software applications, imaging applications, multimedia applications, and the like. In one embodiment, application105at UE103may act as a client for analysis platform113and may perform one or more functions associated with the functions of analysis platform113by interacting with analysis platform113over communication network111.

By way of example, sensor107may be any type of sensor. In one embodiment, sensor107may include, for example, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC), etc.), a global positioning sensor for gathering location data, a camera/imaging sensor for gathering image data, an audio/video recorder for gathering audio/video data, and the like.

In one embodiment, form109may be an electronic representation of a religious exemption form or a more general version of a form. Form109may include a plurality of data fields, and user101may input, via a user interface of UE103, religious information, health-related information, etc., into the plurality of data fields to request exemptions from vaccination requirements. Examples of electronic representation may include portable document format (PDF), word document, rich text format (RTF), fillable web form (e.g., a webpage with fillable data fields), or any other suitable electronic format as would be understood in the art. There may be multiple ways of entering information into form109, including typing text in a word document, filling in a data field of a PDF form, filling in a data field of a web form, or simply signing a pre-filled form. User101may also print form109, enter the information by hand, and submit a scanned copy for further processing.

In one embodiment, various elements of system100may communicate with each other through communication network111. Communication network111may support a variety of different communication protocols and communication techniques. In one embodiment, communication network111allows analysis platform113to communicate with UE103and database115. The communication network111of system100includes one or more networks such as a data network, a 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), a public data network (e.g., the Internet), short range wireless network, 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, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular communication network and may employ various technologies including 5G (5th Generation), 4G, 3G, 2G, Long Term Evolution (LTE), wireless fidelity (Wi-Fi), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), vehicle controller area network (CAN bus), and the like, or any combination thereof.

In one embodiment, analysis platform113may be a platform with multiple interconnected components. Analysis platform113may include one or more servers, intelligent networking devices, computing devices, components, and corresponding software for employing NLP and text analytics tools to analyze the text on exemption forms or applications to automatically adjudicate exemption requests. In addition, it is noted that analysis platform113may be a separate entity of system100.

Analysis platform113may calculate the authenticity of the expressed religious beliefs without any bias. In one embodiment, analysis platform113may create a library of religious exemption forms in available formats and templates. The religious exemption forms are categorized based on various criteria, e.g., state government, the federal government, public health agencies, employers or private companies, etc. Analysis platform113may establish real-time polling to read the changes or updates to the forms and keep the religious exemption forms up-to-date. In one embodiment, analysis platform113may analyze and assign a score for each completed question and employs a method to ensure all required fields are complete. Analysis platform113may identify religious statements/reasoning, demographic details, job code, nature of work, health-related information, and/or location information, associated with user101. In one embodiment, analysis platform113may employ NLP algorithms to evaluate the sentiment reflected in religious statement/reasoning. Analysis platform113may assign an NLP score for the specific religious description that was entered by the user. Analysis platform113may then define a threshold score range as per specific requirement and establish acceptance criteria for auto adjudication. Further details of analysis platform113are provided below.

In one embodiment, database115may be any type of database, such as relational, hierarchical, object-oriented, and/or the like, wherein data are organized in any suitable manner, including as data tables or lookup tables. In one embodiment, database115may store and manage multiple types of information that can provide means for aiding in the content provisioning and sharing process, e.g., database115includes information pertaining to available religious exemptions that belong to various state governments, the federal government, public health institutions, employers or private companies, etc., including laws and regulations associated with religious exemptions. Database115may also store information provided by users to apply for religious exemptions, such as completed application forms stored in one or more data formats (e.g., a picture, a word processing document, a PDF document, etc.). In an embodiment, database115may include a machine-learning based training database with pre-defined mapping defining a relationship between various input parameters and output parameters based on various statistical methods. In an embodiment, the training database may include machine-learning algorithms to learn mappings between input parameters related to the user such as religious information, employment information, location information, user's health records, etc., and inputs provided by the health or regulation experts, e.g., user102. In an embodiment, the training database may include a dataset that may include data collections that are not subject-specific, e.g., data collections based on population-wide observations, local, regional or super-regional observations, and the like. Exemplary datasets include religious information, health-related information, geographic data, location information, occupational information, environmental information, scientific and medical-related periodicals and journals, research studies data, and the like. In an embodiment, the training database is routinely updated and/or supplemented based on machine learning methods.

By way of example, UE103, analysis platform113, and database115may communicate with each other and other components of the communication network111using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network111interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model.

Communications between the network nodes are typically effected by exchanging discrete packets of data. Each packet typically comprises (1) header information associated with a particular protocol, and (2) payload information that follows the header information and contains information that may be processed independently of that particular protocol. In some protocols, the packet includes (3) trailer information following the payload and indicating the end of the payload information. The header includes information such as the source of the packet, its destination, the length of the payload, and other properties used by the protocol. Often, the data in the payload for the particular protocol includes a header and payload for a different protocol associated with a different, higher layer of the OSI Reference Model. The header for a particular protocol typically indicates a type for the next protocol contained in its payload. The higher layer protocol is said to be encapsulated in the lower layer protocol. The headers included in a packet traversing multiple heterogeneous networks, such as the Internet, typically include a physical (layer 1) header, a data-link (layer 2) header, an internetwork (layer 3) header and a transport (layer 4) header, and various application (layer 5, layer 6 and layer 7) headers as defined by the OSI Reference Model.

FIG.2is a diagram of the components of analysis platform113, according to one example embodiment. As used herein, terms such as “component” or “module” generally encompass hardware and/or software, e.g., that a processor or the like may use to implement associated functionality. By way of example, analysis platform113includes one or more components for processing, in real-time or near real-time, one or more forms to automatically adjudicate religious exemptions. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In one embodiment, analysis platform113comprises data collection module201, data processing module203, data extraction module205, language module207, classification module209, training module211, machine learning model213, user interface module215, or any combination thereof.

In one embodiment, data collection module201may collect relevant data, e.g., religion-related information, location data, employment data, health data, etc., associated with user101through various data collection techniques. In one embodiment, the data collection module201may use a web-crawling and/or other types of data collection mechanism to access various databases, e.g., database115, or other information sources, e.g., state government websites, federal government websites, public health agencies websites, etc. (e.g., information provided by users to fill in data fields of web application forms or other types of electronic forms for religious exemptions), to collect relevant data associated with user101and form109. In one embodiment, data collection module201may include various software applications, e.g., data mining applications in Extended Meta Language (XML), that automatically search for and return relevant data regarding user101. In one embodiment, data collection module201may parse and arrange the data into a common format that can be easily processed by other modules and platforms. In one example embodiment, data collection module201may collect, via form109, religion-related information and/or employment data associated with user101. In another example embodiment, data collection module201may collect, in real-time or real-time, location data of user101via sensor107, e.g., a global positioning system (GPS). In a further example embodiment, data collection module201may collect health data associated with user101via a variety of UE103, e.g., monitoring devices that measures the physiological parameters, e.g., heart rate, blood oxygen saturation levels, respiratory rate, glucose level, blood pressure, weight, etc., of user101.

In one embodiment, data processing module203may process data collected by data collection module201. The data processing techniques may include, but are not limited to, an optical character recognition (OCR) technique, an NLP technique, or a data cleansing technique. In one example embodiment, OCR may be applied to a scanned form109, a picture of form109that includes text, and/or the like, to produce electronic data, e.g., text data. Converting printed text to electronic data allows the information represented by the printed text to be electronically edited, searched, stored more compactly, displayed online, and/or used in machine processes such as cognitive computing, machine translation, (extracted) text-to-speech, key data and text mining, and/or the like. Implementations of OCR may employ pattern recognition, artificial intelligence, computer vision, and/or the like. In one example embodiment, the NLP technique may be applied to analyze, understand, and derive meaning from the texts written by humans. Rather than treating text like a mere sequence of symbols, NLP considers a hierarchical structure of language, e.g., several words may be treated as a phrase, several phrases may be treated as a sentence, and the words, phrases, and/or sentences convey ideas that can be interpreted. NLP can be applied to analyze text, allowing machines to understand how humans speak/write, enabling real world applications such as automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech/text tagging, relationship extraction, stemming, and/or the like. In one example embodiment, data cleansing technique may include detecting corrupt or inaccurate data, e.g., records from a database, and then replacing, modifying, or deleting the corrupt or inaccurate data. The data cleansing technique may detect and correct inconsistencies originally caused by user entry errors, corruption during transmission or storage, or by utilization of different definitions for similar data in different data stores or form109. The data cleansing technique may include removing typographical errors or validating and correcting values against a known list of entities. For example, validation may be strict, e.g., rejecting any address that does not have a valid postal code, or fuzzy, e.g., correcting records that partially match existing, known records. The data cleansing technique may also include cleaning data by cross-checking the data with a validated data set, standardizing the data by changing a reference data set to a new standard, e.g., use of standard codes, and/or the like. Additionally, the data cleansing technique may include data enhancement, where data is made more complete by adding related information.

In one embodiment, data extraction module205receives the processed data from data processing module203or directly from data collection module201. The data extraction module205may extract relevant information, e.g., fields in the religious exemption form (including text), job code, demographic details, location information, etc., from the received data. In one embodiment, data extraction module205may extract data based, at least in part, on a notification that a record has been changed, detecting a record has been modified, per system requirements, per schedule, or a combination thereof. In one example embodiment, data extraction module205may utilize NLP techniques to process form109to identify relevant information and phrases, e.g., noun phrases or predicate phrases. Data extraction module205may extract the relevant information and phrases via various extraction techniques, e.g., a full extraction mechanism or an incremental extraction mechanism. Data extraction module205may generate a data object such as a new document, e.g., a training document, from the extracted information to build, train, or be processed by a language model via language module207. The new document has a structure that combines all the fields in the religious exemption form which is provided to language module207, e.g., religious exemption language model (RELM).

In one embodiment, language module207may receive the new document from data extraction module205, and may review all features of the new document as one single text document to build a semantic relationship between them. The language module207may use the features, e.g., texts, fields, etc., in form109as a language, and may utilize one or more language modeling techniques, e.g., statistical models, neural-network models, transformers models, etc., to represent each feature in form109. Features that appear together in form109have latent relations, but language module207may represent the relationship between different features found in form109. For example, language module207may look at all features in form109and may represent the relationship between different features as they occur together, rather than looking at the features separately. To emphasize the notion of using the features in form109as a language, consider the following example:“The brown fox jumped over the lazy dog”

This sentence (e.g., natural language text) may be transmitted to an NLP in natural language, i.e., “The”, “brown”, “fox”, “jumped”, “over”, “the”, “lazy”, “dog”. Similarly, features in form109may contain the following in Table 1:

TABLE 1SiteCertifiedtheySpecialfromworkJobLocationStatestateatCodecodeTextTXLAClinicRNTX078My religion . . .

In one embodiment, language module207may transform the input from text format to numerical representations, i.e., embeddings, using one or more language modelling techniques discussed above. These embeddings may project the features in form109, e.g., job codes, text, etc., into a geometric hyperspace where semantically similar features are close to each other. In one example embodiment, language module207may add ‘#’ to features that are not natural texts, e.g., #TX, #LA, #Clinic, #RN, #TX078, to differentiate the features from natural texts. The language module207may input tokens to the language modeling technique as “#TX”, “#LA”, “#Clinic”, “#RN”, “#TX078”, “my”, “religion”, etc. This representation is then used with a classification model to assign a score to each form.

In one embodiment, classification module209may, using a classification model (e.g., a machine learning model/algorithm for classification), analyze texts and then assign a set of predefined tags or categories based on their context. In one embodiment, classification module209may implement a rule-based approach to categorize texts into an organized group using a set of pre-defined linguistic rules. These rules may instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. In another embodiment, classification module209may implement a machine learning text classification to make classifications based on past observations. By using pre-labeled examples as training data, machine learning models/algorithms, e.g., Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Tree, deep learning, etc., can learn the different associations between pieces of texts, and that particular output, i.e., tags, is expected for a particular input, i.e., text. A “tag” is the pre-determined classification or category that any given text could fall into. In a further embodiment, classification module209may implement a hybrid system to combine a machine learning-trained base classifier with a rule-based system to further improve the results.

In one embodiment, classification module209may perform a variety of calculations to assign scores, e.g., NLP scores, to the embeddings. In one example embodiment, classification module209may perform a weighted threshold per specific requirements, e.g., special state, certified from state, site they work at, job code, location code, texts, etc., to assign scores. Classification module209may also employ text analytics to evaluate the sentiment reflected in the texts of form109to calculate an NLP score. As illustrated in the table ofFIG.4A, varying NLP scores are generated for the corresponding embeddings (i.e., for the corresponding exemption requests represented by the embeddings). In one embodiment, a higher NLP score, e.g.,65, may indicate strong semantic relationships between the language components of form109.

In another example embodiment, classification module209may define a decision threshold establishing a range of acceptable NLP scores (e.g., a decision threshold score range). In one embodiment, analysis platform109may determine a decision threshold based, at least in part, on the location information and the type of service provided by the service provider. In one example embodiment, the service provider is a primary care facility that treats life-threatening health conditions and is located in the state of Florida. Though the state regulations in Florida are lenient, analysis platform109may establish a stringent decision threshold for the service provider based on the service provided. In one embodiment, analysis platform109may tune, in real-time or near real-time, the decision threshold with changing information, e.g., changing location information, change in the service offered, etc. It should be understood that decision threshold may be calculated based on other parameters of form109, e.g., health condition of user101, employment information of user101, religious information of user101, etc. Classification module209may compare the NLP score with the decision threshold. If the NLP score is within the range established by the decision threshold, the corresponding form or exemption request may be determined to be authentic. On the other hand, if the NLP score is not within the range established by the decision threshold, the corresponding form or exemption request may be determined to be inauthentic or non-authentic.

Classification module209may thus validate the NLP score against the decision threshold to establish acceptance criteria for auto adjudication. For example, if the NLP scores fall within the pre-defined range of the decision threshold, the religious exemption request may be approved. User101may be presented with an approval notification in the user interface of UE103upon determining the religious request is authentic and has met the confirmation criteria. On the other hand, if the exemption request has failed to meet the confirmation criteria, it is flagged for a special review. User101may be presented with a denial notification in the user interface of UE103upon determining the religious request is fraudulent (i.e., inauthentic or non-authentic).

In one embodiment, training module211may provide learning, or training, to machine learning model213by providing training data, e.g., data from other modules, that contains input and correct output, to allow machine learning model213to learn over time. The training may be performed based on the deviation of a processed result from a documented result when the inputs are fed into machine learning model213, e.g., an algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. Training module211may conduct the training in any suitable manner, e.g., in batches, and may include any suitable training methodology. Training may be performed periodically, and/or continuously, e.g., in real-time or near real-time. Further details of training a machine learning model are provided below.

In one embodiment, machine learning model213may receive the training data from training module211to learn patterns of various exemption requests to detect an unusual request pattern, e.g., an anomaly, that could influence the NLP score or decision score. The ordering of the training data may be randomized during training. Machine learning model213may visualize the training data to identify relevant relationships between different variables and identify any data imbalances. The training data may be split into two parts where one part is for training the model and the other part is for validating the trained model, de-duplicating, normalizing, correcting errors in the training data, and so on. Machine learning model213may implement various machine learning techniques (e.g., language modelling techniques, classification techniques, etc.) discussed in the present disclosure.

In one embodiment, user interface module215may enable a presentation of a graphical user interface (GUI) in UE103. User interface module215may employ various application programming interfaces (APIs) or other function calls corresponding to application105on UE103, thus enabling the display of graphics primitives such as icons, menus, buttons, data entry fields, etc., associated with form109. In another embodiment, user interface module215may cause interfacing of guidance information with user101to include, at least in part, one or more annotations, audio messages, video messages, or a combination thereof while completing form109. In one embodiment, user interface module215may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. Still further, user interface module215may be configured to operate in connection with augmented reality (AR) processing techniques, wherein various applications, graphic elements, and features may interact.

The above presented modules and components of analysis platform113may be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity inFIG.2, it is contemplated that analysis platform113may be implemented for direct operation by respective UE103. As such, analysis platform113may generate direct signal inputs by way of the operating system of UE103. In another embodiment, one or more of the modules201-215may be implemented for operation by respective UEs, as analysis platform113, or a combination thereof. The various executions presented herein contemplate any and all arrangements and models.

FIG.3is a flowchart of a process for utilizing NLP and text analytics to analyze, in real-time or near real-time, texts of forms to automatically adjudicate religious exemptions, according to one example embodiment. In various embodiments, analysis platform113and/or any of modules201-215may perform one or more portions of process300and may be implemented in, for instance, a chip set including a processor and a memory as shown inFIG.6. As such, analysis platform113and/or any of modules201-215may provide means for accomplishing various parts of process300, as well as means for accomplishing embodiments of other processes described herein in conjunction with other components of system100. Although process300is illustrated and described as a sequence of steps, it is contemplated that various embodiments of process300may be performed in any order or combination and need not include all of the illustrated steps.

In step301, analysis platform113may receive, via a user interface of UE103, an input from user101to the data fields of form109associated with a religious exemption request. The input is in a first data format, e.g., textual format, and includes location information, religious information, employment information, and/or demographic information associated with user101. In one embodiment, analysis platform113may monitor, in real-time or near real-time, the input from user101to determine the completion of the data fields of form109. Analysis platform113may generate a notification in the user interface of UE103to alert the user regarding incomplete data fields and request the user for timely completion to initiate the exemption request process. In another embodiment, analysis platform113may collect and store form109in a database, e.g., database115, in different formats and/or templates. The stored form109may be categorized based on the entity associated with the form, e.g., a service provider, a state government, and/or the federal government. Analysis platform113may monitor, in real-time or near real-time, changes in private, state, or federal laws or regulations pertaining to form109. Analysis platform113may update, in real-time or near real-time, form109upon determining changes in state or federal laws or regulations to ensure the forms are up-to-date.

In step303, analysis platform113may determine exemption-relevant features from the input. In one example embodiment, analysis platform113may process the input from user101to the data fields of form109to determine relevant information, e.g., religious statement or reasoning, job code, demographic details, location information, etc.

In step305, analysis platform113may generate a data object (e.g., a document or a training document) including the exemption-relevant features. In one embodiment, analysis platform113may extract, via data extraction module205, the exemption-relevant features from the data fields of form109to generate a data object. In one example embodiment, a data object is a training document that clusters the extracted exemption-relevant features, and may train language module207to build semantic relations between the extracted information.

In step307, analysis platform113may transform, via language module207(i.e., using a language model), the exemption-relevant features into corresponding embeddings in a second data format. The embeddings represent semantic relations between the exemption-relevant features. In one embodiment, the one or more embeddings in the second data format are numerical representations that project the one or more exemption-relevant features in a geometric hyperspace based, at least in part, on semantic similarities relative to each other. In one embodiment, the language model applies a statistical model (e.g., Global Vectors for Word Representation (GloVe), etc.), a neural-network based model (e.g., word2vec, GPT, etc.), and/or a transformer model (Bidirectional Encoder Representations from Transformers (BERT), etc.) to transform the exemption-relevant features in the first data format into embeddings in the second data format. In one example embodiment, the statistical model(s) factorize a feature co-occurrence matrix built from a dataset of form109. In one example embodiment, the neural-network based models predict a pair of features co-occurring together within a time threshold of each other.

In step309, analysis platform113may determine, via classification module209(i.e., using a classification model), authenticity of the data object based on the embeddings. The classification model is trained using a plurality of embeddings representative of a plurality of exemption-relevant features. In one embodiment, analysis platform113may classify, via classification module209, the data object based on the embeddings in the second data format. Analysis platform113may generate an NLP score based on the semantic relations and/or authenticity definitions between the embeddings. Analysis platform113may then compare the NLP score to a decision threshold to determine the authenticity of the data object and adjudicate religious exemptions. Analysis platform113may process, using a machine learning model213, the data fields of form109to determine patterns pertaining to the religious exemption request. Analysis platform113may determine an unusual pattern in the religious exemption request that influences the NLP score, and may recalibrate the NLP score based on the unusual pattern. In one embodiment, analysis platform113, via machine learning model213, may detect unusual patterns that do not conform to regular arrangements and are suspicious. The unusual patterns may indicate values that do not correspond to expected values based on observation of similar data points in the same context. The unusual patterns may include errors in texts of form109, an inadequate semantic relationship between the texts of form109, inconsistent data, etc. In one example embodiment, it is standard for user101to elaborate on how complying with vaccination requirements substantially burdens his religious exercise or conflicts with his sincerely held religious beliefs. However, if user101states that he has previously received multiple vaccines or has held such religious beliefs for a short duration, e.g., a week or a few days, it indicates that user101is dishonest and the request is not authentic. Such unusual patterns may negatively impact the NLP score, thereby resulting in a lower NLP score.

In step311, analysis platform113may transmit a notification in a user interface of UE103associated with user101. The notification indicates an approval or a disapproval of the religious exemption request. In one embodiment, the disapproved religious exemption request is automatically queued in a review database based on temporal information, contextual information, or a combination thereof for additional review.

FIG.4Adepicts table400with a plurality of exemption requests and NLP scores assigned to the exemption requests, according to one example embodiment. In one embodiment, analysis platform113, via user interface module215, may generate a display of form109in UE103. The data entry fields for form109may include, but are not limited to, special state403, certified from state405, site they work at407, job code409, and/or location code411. User101may interact with the various user interface elements, e.g., virtual keyboard, icons, menus, buttons, etc., to input requested information in these data entry fields. Analysis platform113may process the input to generate NLP score413in accordance with the methods and processes discussed herein. Analysis platform113may then compare NLP score413with decision threshold415to determine the authenticity of the request. For example, analysis platform113may determine exemption requests, e.g., rows402and406, to be authentic since NLP score413is within the range established by decision threshold415. On the other hand, analysis platform113may determine exemption requests, e.g., rows404and408, to be inauthentic or non-authentic because NLP score413is outside the range established by decision threshold415. Analysis platform113may approve the authentic religious exemption requests, whilst the inauthentic or non-authentic religious exemption requests may be further reviewed or rejected.

FIG.4Bis an architectural diagram that represents an exemplary NLP pipeline417to determine the authenticity of religious exemption requests, according to one example embodiment. In step419, data extraction module205may receive contextual information, e.g., religious statement/reasoning, employment information, location information, and/or demographic information, associated with user101from various data sources, e.g., form109and database115. The contextual information may be received in a textual format, and data extraction module205may extract relevant information, e.g., texts from religious statements, job code, state rules and regulation, federal rules and regulations, current location of the user, etc., from the contextual information. In one embodiment, the selection of relevant information for extraction may be based, at least in part, on predefined criteria. The extracted information may be used to build/train language module207.

In step421, data extraction module205may create a new data object or document that includes or clusters the extracted information, and the new document may be inputted to language module207to determine semantic relations between the extracted information. In one embodiment, language module207may utilize the new document to build a language model (LM) by transforming the extracted information in a textual format into numerical representations, i.e., embeddings. These embeddings may project the relevant features into a geometric hyperspace where semantically similar features are close to each other. In one example embodiment, different language modeling techniques may be utilized to generate embeddings from codes, for example, (a) statistical models such as GloVe may factorize a feature co-occurrence matrix built from religious exemption forms dataset; (b) neural-network-based models such as word2vec may predict whether a pair of features co-occur together within a short window of each other; or (c) transformers models such as BERT, where embeddings are context-sensitive due to the self-attention mechanism in transformers. In one embodiment, language modelling techniques may be selected based on pre-defined requirements.

In step423, language module207may transmit the embeddings to classification module209to classify the documents. In one embodiment, classification module209may generate NLP scores against a pre-defined decision threshold, and may validate these scores against an established threshold, and may signal user101with the outcome. For example, if the religious exemption request has met the positive confirmation criteria, user101may receive a notification in UE103outlining the specific details pertaining to the approval of the religious exemption request and the type of accommodation that has been provided. On the other hand, if the religious exemption request has failed the criteria, then the request is flagged and is automatically pushed into a queue for a special review by an independent review team, e.g., user102. Automated notifications of denials are sent when fraudulent information is detected.

One or more implementations disclosed herein include and/or may be implemented using machine learning model. For example, one or more of the modules of analysis platform113, e.g., language module207, classification module209, training module211, machine learning model213, etc., may be implemented using a machine learning model and/or may be used to train the machine learning model. A given machine learning model may be trained using the data flow500ofFIG.5. Training data512may include one or more of stage inputs514and known outcomes518related to the machine learning model to be trained. The stage inputs514may be from any applicable source including text, visual representations, data, values, comparisons, stage outputs, e.g., one or more outputs from one or more steps fromFIG.3. The known outcomes518may be included for the machine learning models generated based on supervised or semi-supervised training. An unsupervised machine learning model may not be trained using known outcomes518. Known outcomes518may include known or desired outputs for future inputs similar to or in the same category as stage inputs514that do not have corresponding known outputs.

The training data512and a training algorithm520, e.g., one or more of the modules implemented using the machine learning model and/or may be used to train the machine learning model, may be provided to a training component530that may apply the training data512to the training algorithm520to generate the machine learning model. According to an implementation, the training component530may be provided comparison results516that compare a previous output of the corresponding machine learning model to apply the previous result to re-train the machine learning model. The comparison results516may be used by training component530to update the corresponding machine learning model. The training algorithm520may utilize machine learning networks and/or models including, but not limited to a deep learning network such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN) and Recurrent Neural Networks (RCN), probabilistic models such as Bayesian Networks and Graphical Models, classifiers such as K-Nearest Neighbors, and/or discriminative models such as Decision Forests and maximum margin methods, models specifically discussed in the present disclosure, or the like.

The machine learning model used herein may be trained and/or used by adjusting one or more weights and/or one or more layers of the machine learning model. For example, during training, a given weight may be adjusted (e.g., increased, decreased, removed) based on training data or input data. Similarly, a layer may be updated, added, or removed based on training data/and or input data. The resulting outputs may be adjusted based on the adjusted weights and/or layers.

In general, any process or operation discussed in this disclosure that is understood to be computer-implementable, such as the process illustrated inFIG.3may be performed by one or more processors of a computer system as described herein. A process or process step performed by one or more processors may also be referred to as an operation. The one or more processors may be configured to perform such processes by having access to instructions (e.g., software or computer-readable code) that, when executed by the one or more processors, cause the one or more processors to perform the processes. The instructions may be stored in a memory of the computer system. A processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable types of processing unit.

A computer system, such as a system or device implementing a process or operation in the examples above, may include one or more computing devices. One or more processors of a computer system may be included in a single computing device or distributed among a plurality of computing devices. One or more processors of a computer system may be connected to a data storage device. A memory of the computer system may include the respective memory of each computing device of the plurality of computing devices.

FIG.6illustrates an implementation of a general computer system that may execute techniques presented herein. The computer system600can include a set of instructions that can be executed to cause the computer system600to perform any one or more of the methods or computer based functions disclosed herein. The computer system600may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices.

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining”, analyzing” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data, e.g., from registers and/or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and/or memory. A “computer,” a “computing machine,” a “computing platform,” a “computing device,” or a “server” may include one or more processors.

In a networked deployment, the computer system600may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system600can also be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular implementation, the computer system600can be implemented using electronic devices that provide voice, video, or data communication. Further, while a computer system600is illustrated as a single system, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

As illustrated inFIG.6, the computer system600may include a processor602, e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor602may be a component in a variety of systems. For example, the processor602may be part of a standard personal computer or a workstation. The processor602may be one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor602may implement a software program, such as code generated manually (i.e., programmed).

The computer system600may include a memory604that can communicate via a bus608. The memory604may be a main memory, a static memory, or a dynamic memory. The memory604may include, but is not limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one implementation, the memory604includes a cache or random-access memory for the processor602. In alternative implementations, the memory604is separate from the processor602, such as a cache memory of a processor, the system memory, or other memory. The memory604may be an external storage device or database for storing data. Examples include a hard drive, compact disc (“CD”), digital video disc (“DVD”), memory card, memory stick, floppy disc, universal serial bus (“USB”) memory device, or any other device operative to store data. The memory604is operable to store instructions executable by the processor602. The functions, acts or tasks illustrated in the figures or described herein may be performed by the processor602executing the instructions stored in the memory604. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like.

As shown, the computer system600may further include a display610, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display610may act as an interface for the user to see the functioning of the processor602, or specifically as an interface with the software stored in the memory604or in the drive unit606.

Additionally or alternatively, the computer system600may include an input/output device612configured to allow a user to interact with any of the components of computer system600. The input/output device612may be a number pad, a keyboard, or a cursor control device, such as a mouse, or a joystick, touch screen display, remote control, or any other device operative to interact with the computer system600.

The computer system600may also or alternatively include drive unit606implemented as a disk or optical drive. The drive unit606may include a computer-readable medium622in which one or more sets of instructions624, e.g. software, can be embedded. Further, instructions624may embody one or more of the methods or logic as described herein. The instructions624may reside completely or partially within the memory604and/or within the processor602during execution by the computer system600. The memory604and the processor602also may include computer-readable media as discussed above.

In some systems, a computer-readable medium622includes instructions624or receives and executes instructions624responsive to a propagated signal so that a device connected to a network630can communicate voice, video, audio, images, or any other data over the network630. Further, the instructions624may be transmitted or received over the network630via a communication port or interface620, and/or using a bus608. The communication port or interface620may be a part of the processor602or may be a separate component. The communication port or interface620may be created in software or may be a physical connection in hardware. The communication port or interface620may be configured to connect with a network630, external media, the display610, or any other components in computer system600, or combinations thereof. The connection with the network630may be a physical connection, such as a wired Ethernet connection or may be established wirelessly as discussed below. Likewise, the additional connections with other components of the computer system600may be physical connections or may be established wirelessly. The network630may alternatively be directly connected to a bus608.

While the computer-readable medium622is shown to be a single medium, the term “computer-readable medium” may include a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” may also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein. The computer-readable medium622may be non-transitory, and may be tangible.

The computer-readable medium622can include a solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. The computer-readable medium622can be a random-access memory or other volatile re-writable memory. Additionally or alternatively, the computer-readable medium622can include a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals such as a signal communicated over a transmission medium. A digital file attachment to an e-mail or other self-contained information archive or set of archives may be considered a distribution medium that is a tangible storage medium. Accordingly, the disclosure is considered to include any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions may be stored.

In an alternative implementation, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, can be constructed to implement one or more of the methods described herein. Applications that may include the apparatus and systems of various implementations can broadly include a variety of electronic and computer systems. One or more implementations described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.

The computer system600may be connected to a network630. The network630may define one or more networks including wired or wireless networks. The wireless network may be a cellular telephone network, an 802.11, 802.16, 802.20, or WiMAX network. Further, such networks may include a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols. The network630may include wide area networks (WAN), such as the Internet, local area networks (LAN), campus area networks, metropolitan area networks, a direct connection such as through a Universal Serial Bus (USB) port, or any other networks that may allow for data communication. The network630may be configured to couple one computing device to another computing device to enable communication of data between the devices. The network630may generally be enabled to employ any form of machine-readable media for communicating information from one device to another. The network630may include communication methods by which information may travel between computing devices. The network630may be divided into sub-networks. The sub-networks may allow access to all of the other components connected thereto or the sub-networks may restrict access between the components. The network630may be regarded as a public or private network connection and may include, for example, a virtual private network or an encryption or other security mechanism employed over the public Internet, or the like.

In accordance with various implementations of the present disclosure, the methods described herein may be implemented by software programs executable by a computer system. Further, in an exemplary, non-limited implementation, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

Although the present specification describes components and functions that may be implemented in particular implementations with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. For example, standards for Internet and other packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed herein are considered equivalents thereof.

It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the disclosure is not limited to any particular implementation or programming technique and that the disclosure may be implemented using any appropriate techniques for implementing the functionality described herein. The disclosure is not limited to any particular programming language or operating system.

It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a computer system or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as falling within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other implementations, which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. While various implementations of the disclosure have been described, it will be apparent to those of ordinary skill in the art that many more implementations and implementations are possible within the scope of the disclosure. Accordingly, the disclosure is not to be restricted except in light of the attached claims and their equivalents.