Contextual comparison of semantics in conditions of different strategies

Abstract

A system, a computer-implemented method, and a computer program product are provided for facilitating a contextual comparison of the semantics of conditions in different policies. According to an embodiment, the system may include a processor that executes computer-executable components stored in a memory. The computer-executable components may include a comparison component that contextually compares the semantics of conditions in policy data of different policies based on characteristics of at least one entity. The computer-executable components further include a contextualization component that employs a model to provide a contextual interpretation of how a first condition in first policy data of a first policy semantically differs from a second condition in second policy data of a second policy based on characteristics of at least one entity.

Description

Background Art

[0001] The present subject matter disclosure relates to the comparison of conditions in different strategies, and more particularly, to the contextual comparison of semantics in the conditions of different strategies. Summary of the Invention

[0002] The following presents an overview to provide a basic understanding of one or more embodiments of the present invention. This overview is not intended to identify key or important elements, or to depict any scope of a particular embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, computer-implemented methods, and / or computer program products are described that facilitate the contextual comparison of semantics in the conditions of different strategies.

[0003] According to an embodiment, a system may include a processor that executes computer-executable components stored in a memory. The computer-executable components may include a comparison component that contextually compares the semantics of conditions in policy data of different strategies based on characteristics of at least one entity. The computer-executable components further include a contextualization component that employs a model to provide a contextual interpretation of how a first condition in first policy data of a first strategy semantically differs from a second condition in second policy data of a second strategy based on characteristics of at least one entity.

[0004] According to another embodiment, a computer-implemented method may include contextually comparing, by a system operatively coupled to a processor, the semantics of conditions in policy data of different strategies based on characteristics of at least one entity. The computer-implemented method may further include the system employing a model to provide a contextual interpretation of how a first condition in first policy data of a first strategy semantically differs from a second condition in second policy data of a second strategy based on characteristics of at least one entity.

[0005] According to another embodiment, a computer program product includes a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to contextually compare the semantics of conditions in policy data of different strategies based on characteristics of at least one entity. The program instructions are further executable by the processor to cause the processor to employ a model to provide a contextual interpretation of how a first condition in first policy data of a first strategy semantically differs from a second condition in second policy data of a second strategy based on characteristics of at least one entity. Brief Description of the Drawings

[0006] Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 andFigure 5 A block diagram of an example non-limiting system that can facilitate contextual comparison of semantics in conditions of different policies in accordance with one or more embodiments described herein is shown.

[0007] Figure 6 An example non-limiting diagram is shown that may facilitate contextual comparison of semantics in conditions of different policies in accordance with one or more embodiments described herein.

[0008] Figure 7 A flow chart is shown of an example, non-limiting computer-implemented method that can facilitate contextual comparison of semantics in conditions of different policies in accordance with one or more embodiments described herein.

[0009] Figure 8 A block diagram illustrating an example, non-limiting operating environment that can facilitate one or more embodiments described herein.

[0010] Figure 9 A block diagram of an exemplary non-limiting cloud computing environment is shown in accordance with one or more embodiments of the subject disclosure.

[0011] Figure 10 A block diagram illustrating example, non-limiting abstract model layers in accordance with one or more embodiments of the subject disclosure is shown. DETAILED DESCRIPTION

[0012] The following detailed description is illustrative only and is not intended to limit the embodiments and / or the application or uses of the embodiments. In addition, it is not intended to be bound by any express or implied information presented in the previous background or summary or detailed description.

[0013] One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are used to refer to the same elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, in various cases, it is apparent that one or more embodiments may be practiced without these specific details.

[0014] As used herein, an "entity" may include a person, a client, a user, a computing device, a software application, an agent, a machine learning (ML) model, an artificial intelligence (AI) model, and / or another entity. As used herein, a "policy" may include a text document that describes principles that guide decision-making and achieve results, where such principles may be described in the form of conditions or rules. Examples of policies include, but are not limited to, auto insurance policies, home insurance policies, private health policies, public and / or state health policies, financial compliance regulations, and / or another policy. It should be understood that when an element is referred to herein as being "coupled" to another element, it may describe one or more different types of couplings, including but not limited to chemical coupling, communication coupling, electrical coupling, electromagnetic coupling, operational coupling, optical coupling, physical coupling, thermal coupling, and / or another type of coupling.

[0015] It should be understood from the following description that various embodiments of the present disclosure enable a contextual comparison of semantically similar conditions in different policies and / or an interpretation of the differences between semantically similar conditions. For example, it should be understood from the following description that various embodiments of the present disclosure enable a contextual comparison of different policies in a given context, where such a context may be defined by one or more characteristics of a particular entity and / or a particular group.

[0016] Figure 1 、 Figure 2 and Figure 3 respectively show block diagrams of example non-limiting systems 100, 200, and 300 according to one or more embodiments described herein, each of which may facilitate a contextual comparison of the semantics in the conditions of different policies. Systems 100, 200, and 300 may each include a policy comparison system 102. Figure 1 The policy comparison system 102 of the system 100 depicted in may include a memory 104, a processor 106, a comparison component 108, a contextualization component 110, and / or a bus 112. Figure 2 The policy comparison system 102 of the system 200 depicted in may further include an extraction component 202. Figure 3 The policy comparison system 102 of the system 300 depicted in may further include a similarity component 302.

[0017] It should be understood that the embodiments of the present disclosure depicted in the various figures herein are for illustrative purposes only, and thus, the architectures of these embodiments are not limited to the systems, devices, and / or components depicted herein. For example, in some embodiments, systems 100, system 200, system 300, and / or the policy comparison system 102 may further include various computers and / or computing-based elements described herein with reference to the operating environment 800 and Figure 8 described, and in several embodiments, such computers and / or computing-based elements may be combined to implement in combinationFigure 1 , Figure 2 , Figure 3 and / or used in one or more of the systems, devices, components, and / or computer-implemented operations shown and described in other figures disclosed herein.

[0018] Memory 104 may store one or more computer and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by processor 106 (e.g., a classical processor, a quantum processor, and / or another type of processor), may facilitate the execution of operations defined by the executable components and / or instructions. For example, memory 104 may store computer and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by processor 106, may facilitate the execution of various functions related to policy comparison system 102, comparison component 108, contextualization component 110, extraction component 202, similarity component 302, and / or another component associated with policy comparison system 102, as described herein with or without reference to the various figures of the present disclosure.

[0019] Memory 104 may include volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), and / or another type of volatile memory) and / or non-volatile memory that may employ one or more memory architectures (e.g., read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), and / or another type of non-volatile memory). Further examples of memory 104 are described below with reference to system memory 816 and Figure 8 described, and such examples of memory 104 may be employed to implement any embodiment of the present subject matter disclosure.

[0020] Processor 106 may include one or more types of processors and / or electronic circuits (e.g., classical processors, quantum processors, and / or another type of processor and / or electronic circuit), which may implement one or more computer and / or machine-readable, writable, and / or executable components and / or instructions storable on memory 104. For example, processor 106 may perform various operations that may be specified by such computer and / or machine-readable, writable, and / or executable components and / or instructions, including but not limited to logical, control, input / output (I / O), arithmetic, and / or similar operations. In some embodiments, processor 106 may include one or more central processing units, multi-core processors, microprocessors, dual microprocessors, microcontrollers, system-on-a-chip (SOC), array processors, vector processors, quantum processors, and / or another type of processor. Further examples of processor 106 are described below with reference to processing unit 814 and Figure 8 These examples of processor 106 may be used to implement any embodiment of the present disclosure.

[0021] The policy comparison system 102, memory 104, processor 106, comparison component 108, contextualization component 110, extraction component 202, similarity component 302, and / or another component of the policy comparison system 102 as described herein may be communicatively, electrically, operatively, and / or optically coupled to each other via bus 112 to perform the functions of system 100, system 200, system 300, policy comparison system 102, and / or any component coupled thereto. Bus 112 may include one or more memory buses, memory controllers, peripheral buses, external buses, local buses, quantum buses, and / or another type of bus that may employ various bus architectures. Other examples of bus 112 are described below with reference to system bus 818 and Figure 8 These examples of bus 112 may be used to implement any embodiment of the present invention.

[0022] The policy comparison system 102 may include any type of component, machine, device, facility, apparatus, and / or instrument that includes a processor and / or that may be capable of effective and / or operable communication with a wired and / or wireless network. All of these embodiments are foreseeable. For example, the policy comparison system 102 may include server devices, computing devices, general-purpose computers, special-purpose computers, quantum computing devices (e.g., quantum computers), tablet computing devices, handheld devices, server-class computing machines, and / or databases, laptop computers, notebook computers, desktop computers, cellular phones, smart phones, consumer appliances and / or instruments, industrial and / or commercial devices, digital assistants, multimedia Internet-enabled telephones, multimedia players, and / or another type of device.

[0023] The policy comparison system 102 can be coupled (e.g., communicatively, electrically, operationally, optically, and / or via another type of coupling) to one or more external systems, sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or another type of external system, source, and / or device) using wires and / or cables. For example, the policy comparison system 102 can be coupled (e.g., communicatively, electrically, operationally, optically, and / or via another type of coupling) to one or more external systems, sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or another type of external system, source, and / or device) using a data cable, including but not limited to a high-definition multimedia interface (HDMI) cable, a recommended standard (RS) 232 cable, an Ethernet cable, and / or another data cable.

[0024] In some embodiments, the policy comparison system 102 may be coupled (e.g., communicatively, electrically, operationally, optically, and / or via another type of coupling) via a network to one or more external systems, sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or another type of external system, source, and / or device). For example, such a network may include wired and / or wireless networks, including but not limited to cellular networks, wide area networks (WANs) (e.g., the Internet), or local area networks (LANs). The policy comparison system 102 may communicate with one or more external systems, sources, and / or devices, such as computing devices, using virtually any desired wired and / or wireless technology, including but not limited to: Wi-Fi (Wireless Fidelity), GSM (Global System for Mobile Communications), UMTS (Universal Mobile Telecommunications System), WiMAX (Worldwide Interoperability for Microwave Access), enhanced GPRS (General Packet Radio Service), 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution), 3GPP2 (3rd Generation Partnership Project 2), UMB (Ultra Mobile Broadband), HSPA (High Speed Packet Access), Zigbee, and other 802.XX wireless technologies and / or traditional telecommunication technologies, Bluetooth, SIP (Session Initiation Protocol), ZIGBEE, RF4CE protocol, WirelessHARTTM protocol, 6LoWPAN (IPv6 over Low Power Wireless Personal Area Network), Z-Wave, ANT, Ultra Wideband (UWB) standard protocol, and / or other proprietary and non-proprietary communication protocols. Thus, in some embodiments, the policy comparison system 102 may include hardware (e.g., a central processing unit (CPU), transceiver, decoder, quantum hardware, quantum processor, and / or other hardware), software (e.g., a collection of threads, a collection of processes, software in execution, quantum pulse scheduling, quantum circuits, quantum gates, and / or other software), or a combination of hardware and software that facilitates the transfer of information between the policy comparison system 102 and external systems, sources, and / or devices (e.g., computing devices, communication devices, and / or another type of external system, source, and / or device).

[0025] The policy comparison system 102 can include one or more computers and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by a processor 106 (e.g., a classical processor, a quantum processor, and / or another type of processor), can facilitate the execution of operations defined by these components and / or instructions. Additionally, in many embodiments, as described herein with or without reference to the various figures of the present disclosure, any component associated with the policy comparison system 102 can include one or more computers and / or machine-readable, writable, and / or executable components and / or instructions that, when executed by the processor 106, can facilitate the execution of operations defined by these components and / or instructions. For example, the comparison component 108, the contextualization component 110, the extraction component 202, the similarity component 302, and / or any other component associated with the policy comparison system 102 as disclosed herein (e.g., communicatively, electronically, operatively, and / or optically coupled to and / or employed by the policy comparison system 102) can include such computers and / or machine-readable, writable, and / or executable components and / or instructions. Thus, according to many embodiments, the policy comparison system 102 as disclosed herein and / or any component associated therewith can employ the processor 106 to execute such computers and / or machine-readable, writable, and / or executable components and / or instructions to facilitate the execution of one or more operations described herein with reference to the policy comparison system 102 and / or any such component associated therewith.

[0026] The policy comparison system 102 can facilitate (e.g., via the processor 106) the execution of and / or operations associated with the comparison component 108, the contextualization component 110, the extraction component 202, the similarity component 302, and / or another component associated with the policy comparison system 102 as disclosed herein. For example, as described in detail below, the policy comparison system 102 can facilitate (e.g., via the processor 106): semantically comparing conditions in the policy data of different policies contextually based on characteristics of at least one entity; and / or employing a model to provide a contextual interpretation of how a first condition in first policy data of a first policy semantically differs from a second condition in second policy data of a second policy based on characteristics of at least one entity.

[0027] In the above example, as described in detail below, the policy comparison system 102 may also facilitate (e.g., via the processor 106): extracting first data from a first policy data and second data from a second policy data, where the first data and the second data correspond to characteristics of at least one entity; identifying first data in the first policy data, the first data having a first semantic similarity score that is within a defined range of a second semantic similarity score of second data in the second policy data, where the first semantic similarity score and the second semantic similarity score are calculated based on characteristics of at least one entity; comparing the semantics of conditions in the policy data contextually based on domain knowledge corresponding to the first policy data or the second policy data, the first data in the first policy data having a first semantic similarity score within the defined range of the second semantic similarity score of the second data in the second policy data, and / or the first data extracted from the first policy data and the second data extracted from the second policy data; employing a model to provide a contextual interpretation of how a first condition in the first policy data semantically differs from a second condition in the second policy data based on contextual data corresponding to at least one entity, and reducing the workload and / or execution time of the model and / or the processor when comparing the first policy data and the second policy data; employing a model to provide one or more first conditions in the first policy data that are semantically different from one or more second conditions in the second policy data based on characteristics of at least one entity; and / or employing a model to rank a first condition in the first policy data based on its relevance to at least one entity and / or characteristics of at least one entity, the first condition being semantically different from a second condition in the second policy data.

[0028] The comparison component 108 can contextually compare the semantics of conditions in the policy data of different policies based on the characteristics of at least one entity. For example, the comparison component 108 can contextually compare the semantics of a condition (e.g., an eligibility criterion) in the first policy data of a first policy with the semantics of a condition in the second policy data of a second policy based on the characteristics (e.g., features and / or attributes) of an entity (e.g., a target entity) as defined herein and / or the characteristics of a group (e.g., a target group including a group of entities having at least one common feature and / or attribute). In various embodiments of the present disclosure, each of such first and / or second policies can include, but is not limited to, an insurance policy, a government policy (e.g., a federal policy, a state policy, and / or another government policy), a corporate policy, an organizational policy, a program policy, a service provider policy (e.g., a payer-provider policy), a policy in the social services domain, and / or another type of policy. In various embodiments of the present subject matter disclosure, each of such first and / or second policy data can include, but is not limited to, sections, paragraphs, sentences, tables, charts, graphs, glossaries, appendices, and / or other policy data.

[0029] In multiple embodiments described herein, the comparison component 108 can employ one or more machine learning (ML) and / or artificial intelligence (AI) models and / or techniques to contextually compare the semantics of a condition (e.g., an eligibility criterion) in the first policy data of a first policy with the semantics of a condition in the second policy data of a second policy. For example, to contextually compare the semantics of a condition (e.g., an eligibility criterion) in the first policy data of a first policy with the semantics of a condition in the second policy data of a second policy, the comparison component 108 can employ one or more ML and / or AI models and / or techniques, including but not limited to sequence models (e.g., word sequence models), neural networks (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), and / or variants of CNNs and / or RNNs), n-gram models, classification models, support vector machines (SVMs), logistic regression models, natural language processing (NLP), deep learning, and / or another ML and / or AI model and / or technique.

[0030] To contextually compare the semantics of conditions in policy data based on the characteristics of at least one entity, the comparison component 108 can identify similarities and / or differences between similar policy data of different policies (e.g., similar policy sections, paragraphs, sentences, and / or other similar policy data). In one embodiment, the comparison component 108 can compare the rules of semantically similar conditions in a first policy with those in a second policy and can also express the differences (e.g., in a computer-readable format, a human-readable format, and / or in another type of format). For example, the comparison component 108 can use knowledge graph comparison techniques to identify similarities and differences between the condition rules in the first policy data of the first policy and the second policy data of the second policy. In another example, the comparison component 108 can semantically compare the rules extracted from the first policy data of the first policy (e.g., the text in a paragraph) with the rules extracted from the second policy data of the second policy (e.g., the text in a paragraph) to identify similarities and differences between the condition rules in the first policy and the second policy. In this example, as follows, these rules can be extracted from the first and / or second policy data by the extraction component 202.

[0031] In some embodiments, the comparison component 108 can contextually compare the semantics of conditions in the first policy data of a first policy with the semantics of conditions in the second policy data of a second policy based on, for example, a domain. To contextually compare the semantics of conditions in the first policy data of a first policy with the semantics of conditions in the second policy data of a second policy based on a domain, the comparison component 108 can employ one or more of the ML and / or AI models and / or techniques defined above. In various embodiments of the present disclosure, such domain knowledge can correspond to the first policy data, the second policy data, the first policy, and / or the second policy. In these embodiments, such domain knowledge can include, but is not limited to, ontologies (e.g., ontology information providing definitions used in the domain), knowledge bases (e.g., knowledge graph information defining one or more knowledge graphs used in the domain), terms (e.g., dictionary and / or term information defining terms used in the domain and associated definitions), the cost of services covered by the policy, census data, classifications, data models defining relationships between data objects associated with the domain, tables, business rule patterns, and / or other domain knowledge. For example, regarding the medical industry, the domain knowledge can include information defining process codes, medical terms, information relating data objects associated with a particular medical service to each other, and / or other domain knowledge in the medical industry.

[0032] In some embodiments, the comparison component 108 may contextually compare the semantics of the conditions in the first policy data of the first policy with the semantics of the conditions in the second policy data of the second policy based on, for example, the first data in the first policy data, where the first data has a first semantic similarity score that is within the defined range of the second semantic similarity score of the second data in the second policy data. To contextually compare the semantics of the conditions in the first policy data of the first policy with the semantics of the conditions in the second policy data based on the first data in the first policy data, the comparison component 108 may employ one or more of the ML and / or AI models and / or techniques defined above, where the first data in the first policy data has a first semantic similarity score that is within the defined range of the second semantic similarity score of the second data in the second policy data. In various embodiments of the present subject matter disclosure, such first and / or second data may include, but is not limited to, structured data, unstructured data, text data, alphanumeric data, token data, character data, object data, graphic data, rule data, tabular data, and / or other data.

[0033] In some embodiments, the comparison component 108 may contextually compare the semantics of the conditions in the first policy data of the first policy with the semantics of the conditions in the second policy data of the second policy based on, for example, the first data extracted from the first policy data and the second data extracted from the second policy data. To contextually compare the semantics of the conditions in the first policy data of the first policy with the semantics of the conditions in the second policy data based on the first data extracted from the first policy data and the second data extracted from the second policy data, the comparison component 108 may employ one or more of the ML and / or AI models and / or techniques defined above.

[0034] The contextualization component 110 may employ a model to provide a contextual interpretation of how a first condition (e.g., a first eligibility criterion) in first policy data (e.g., sections and / or paragraphs) of a first policy semantically differs from a second condition in second policy data of a second policy, based on characteristics (e.g., traits and / or properties) of at least one entity (e.g., a single entity and / or a group). To provide such a contextual interpretation, the contextualization component 110 may employ a model to provide one or more first conditions in the first policy data of the first policy, based on characteristics of at least one entity (e.g., a single entity and / or a group), where the one or more first conditions semantically differ from one or more second conditions in the second policy data of the second policy. For example, to provide one or more first conditions in the first policy data based on characteristics of at least one entity, where the one or more first conditions semantically differ from one or more second conditions in the second policy data, the contextualization component 110 may determine which of the characteristics of the at least one entity (e.g., a single entity and / or a group) and / or which of the conditions in the first and / or second policy data of the first and / or second policies are relevant to the at least one entity. To determine such (one or more) characteristics and / or (one or more) conditions, the contextualization component 110 may use one or more preferences of the at least one entity (e.g., (one or more) preferences of a single entity and / or a group). In some embodiments, an entity as defined herein may define such preferences (e.g., relevant characteristics and / or conditions) using an interface component (not shown in the figure) of, for example, the policy comparison system 102 (e.g., a graphical user interface (GUI), an application programming interface (API), a representational state transfer (REST) API, and / or another type of interface).

[0035] In some embodiments, the contextualization component 110 may implement a preference learning process to infer such preferences (e.g., relevant (one or more) features and / or (one or more) conditions) corresponding to at least one entity (e.g., corresponding to a single entity and / or a group). In these embodiments, the contextualization component 110 may implement such a preference learning process to infer such preferences using, for example, a recommender system (e.g., a recommender model such as a regression model and / or a classification model). In these embodiments, such a recommender system may be trained to learn the preferences of at least one entity (e.g., learn the relevant features and / or conditions corresponding to a single entity and / or a group) using historical usage pattern data corresponding to the at least one entity. For example, such a recommendation system may be trained to learn such preferences of at least one entity using historical usage pattern data, which includes but is not limited to historical operation data (e.g., policy provider statements), historical policy holder data, historical policy record data, contextual data (e.g., entity profile data, activities of daily living (ADL) data, census data, and / or other contextual data), and / or other historical usage pattern data corresponding to the at least one entity (e.g., corresponding to a single entity and / or a group).

[0036] In some embodiments, to determine such preferences of at least one entity and / or to provide the contextual explanations described above, the contextualization component 110 may employ ML and / or AI models and / or techniques (e.g., neural networks, recommender systems, interpretive models, predictive models, classifiers, and / or another ML and / or AI model and / or technique). In another example, the contextualization component 110 may employ one or more preference inference models and / or techniques to determine such preferences of at least one entity and / or to provide such contextual explanations. For example, the contextualization component 110 may employ one or more preference inference models and / or techniques, including but not limited to a lexicon model, a Pareto chart, and / or another preference inference model and / or technique that can be used to determine such preferences of at least one entity and / or to provide the above-described contextual explanations.

[0037] The contextualization component 110 may employ a model to provide a contextual interpretation of how a first condition in first policy data of a first policy differs semantically from a second condition in second policy data of a second policy, based on contextual data corresponding to at least one entity (e.g., a single entity and / or a group). For example, the contextualization component 110 may employ one or more of the models and / or techniques defined above (e.g., a preference inference model and / or technique, a lexicon model, a Pareto chart, a neural network, a recommender system, an interpretation model, a prediction model, a classifier, and / or another model and / or technique) to determine which contextual data is relevant to at least one entity and / or to provide such a contextual interpretation based on the contextual data. In various embodiments of the present disclosure, such contextual data may include, but is not limited to: operational data (e.g., policy provider statements); statistical data (e.g., census data and / or other statistical data describing, for example, a city, town, state, or country); entity profile data; entity preference data; ADL data; and / or another type of contextual data related to at least one entity (e.g., a single entity and / or a group).

[0038] The contextualization component 110 may employ a model to rank a first condition in first policy data of a first policy based on its relevance to at least one entity (e.g., a target entity and / or a target group) and / or at least one feature of the at least one entity (e.g., a characteristic and / or an attribute), where the first condition is semantically different from a second condition in second policy data of a second policy. For example, to rank a first condition in first policy data that is semantically different from a second condition in second policy data based on its relevance to at least one entity (e.g., a single entity and / or a group) and / or at least one feature of the at least one entity (e.g., a characteristic and / or an attribute), the contextualization component 110 may employ one or more of the models and / or techniques defined above (e.g., a preference inference model and / or technique, a lexicographic model, a Pareto chart, a neural network, a recommender system, an explanation model, a prediction model, a classifier, and / or another model and / or technique). For example, the contextualization component 110 may implement a preference learning process to infer and further rank a first condition in first policy data based on its relevance to at least one entity (e.g., a target entity and / or a target group) and / or at least one feature of the at least one entity (e.g., a characteristic and / or an attribute), where the first condition is semantically different from a second condition in second policy data. In this example, the contextualization component 110 may use, for example, a recommender system (e.g., a recommender model such as a regression model and / or a classification model) to implement the preference learning process to rank a first condition in first policy data that is semantically different from a second condition in second policy data based on its relevance to at least one entity (e.g., a single entity and / or a group) and / or at least one feature of the at least one entity (e.g., a characteristic and / or an attribute). In this example, such a recommender system may be trained to use historical usage pattern data corresponding to the at least one entity as described above to learn such a first condition in first policy data (e.g., learn a condition in policy data related to the at least one entity).

[0039] In accordance with the above description, it should be understood that the contextualization component 110 may extract relevant context data (e.g., a set of condition values (e.g., conditions) related to a target entity and / or a target group) based on the contextual data and / or domain knowledge described above to identify and further rank relevant and / or areas of interest of the target entity and / or the target group. The relevant context that the contextualization component 110 may extract may include, but is not limited to, services, different eligibility criteria across services, the eligibility criteria most relevant to the target entity and / or the target group, and / or other relevant contextual data.

[0040] It should also be understood from the above description that, in some embodiments, the contextualization component 110 may employ one or more preference inference models and / or techniques (e.g., a lexicon model and / or a Pareto chart) to infer and / or rank the preferences (e.g., relevant features and / or conditions) of entities and / or groups. In these embodiments, the contextualization component 110 may use, for example, the following to determine the usage patterns of entities and / or groups: operational data corresponding to a particular entity and / or group (e.g., a policy provider statement); historical data (e.g., a policy holder and / or policy record database); contextual data (e.g., entity profile data, ADL data, census, and / or other contextual data); and / or any combination thereof. For example, services for an older group are not relevant to a younger group (e.g., a group in a state), and differences in eligibility criteria for services not present in the operational data are not relevant.

[0041] It should be further appreciated from the above description that, in some embodiments, the contextualization component 110 may apply contextual data (e.g., the most relevant contextual data) to one or more policy portions that may be identified by the extraction component 202 and / or the similarity component 302 as follows, to support the identification of relevant portions and / or paragraphs in different policies, and may also rank the differences in these policy portions based on the relevance to entities and / or groups. It should also be understood from the above description that, in some embodiments, the contextualization component 110 may utilize relevant (e.g., preferred) contextual data (e.g., by calculating the cost of a particular service for a particular entity and / or group) to enhance and / or enrich the data in the policy portions (e.g., data that may be extracted by the extraction component 202, as described below). In these embodiments, as described above, the relevant contextual data may be explicitly defined by the entity implementing the policy comparison system 102 and / or inferred by the contextualization component 110 using historical usage pattern data (e.g., historical policy holder data, historical policy record data, historical operational data, historical contextual data, combinations thereof, and / or other available historical data sources). It should also be understood from the above description that, in some embodiments, the contextualization component 110 may present (e.g., via the GUI, API, REST API, and / or another interface component of the policy comparison system 102) a ranked list with rich evidence (e.g., supporting details and / or data) and / or explanations that may be generated by the policy comparison system 102. It should also be understood from the above description that, by contextually comparing the semantics of conditions in the policy data of different policies based on the characteristics of entities and / or groups and also providing a contextual explanation of how the conditions in the policies are semantically different based on such characteristics, the policy comparison system 102 may thereby reduce the workload (e.g., processing workload) and / or execution time of the models and / or processors employed to separately compare the first policy data and / or the first policy with the second policy data and / or the second policy.

[0042] The extraction component 202 can extract first data from the first policy data in the first policy and second data from the second policy data in the second policy, where the first data and the second data correspond to characteristics (e.g., features and / or attributes) of at least one entity (e.g., a target entity and / or a target group). As described above, in various embodiments of the present invention, such first and / or second data may include, but are not limited to, structured data, unstructured data, text data, alphanumeric data, token data, character data, object data, graphic data, rule data, table data, and / or other data.

[0043] To extract such first data and / or such second data, the extraction component 202 can employ one or more models (e.g., ML and / or AI models) and / or techniques, including but not limited to: natural language processing (NLP), deep NLP parsing (e.g., using one or more neural networks), portable document format (PDF) parsing, text segment classifier, entity extraction, supervised frequent pattern learning, unsupervised frequent pattern learning, semantic filtering, and / or another technique. In some embodiments, the extraction component 202 can employ different information extraction techniques and / or different combinations of information technologies to extract such first data and / or second data. In this regard, the extracted first data and / or second data can reflect the use of different extraction techniques and / or different combinations of extraction techniques, which may have different advantages and disadvantages depending on the type of data being extracted (e.g., unstructured data, structured data, and / or another type of data).

[0044] In some embodiments, the extraction component 202 can extract semantic knowledge from the first data and / or the second data cited above and can also generate a formal representation such as structured data. For example, the extraction component 202 can employ one or more of the models and / or techniques defined above to extract unstructured data from one or more parts of different policies and can further structure the unstructured data by generating semantic annotations, knowledge graphs, and / or rules that provide structure to the unstructured data.

[0045] In some embodiments, the extraction component 202 may use domain knowledge to facilitate the extraction of first data from first policy data in a first policy and / or second data from second policy data in a second policy. For example, the extraction component 202 may use domain knowledge to extract one or more structured representations of policy conditions and / or policy rules included in the first policy and / or the second policy. In some embodiments, such domain knowledge may correspond to the first data, the second data, the first policy data, the second policy data, the first policy, and / or the second policy. In these embodiments, such domain knowledge may include, but is not limited to, ontologies (e.g., ontology information providing definitions used in the domain), knowledge bases (e.g., knowledge graph information defining one or more knowledge graphs used in the domain), glossaries (e.g., dictionary and / or glossary information defining terms used in the domain and associated definitions), costs of services covered by the policy, census data, classifications, data models defining relationships between data objects associated with the domain, tables, business rule patterns, and / or other domain knowledge. For example, with respect to the medical industry, domain knowledge may include information defining procedure codes, medical terms, information relating data objects associated with a particular medical service to each other, and / or other domain knowledge in the medical industry.

[0046] The similarity component 302 may identify first data in the first policy data of the first policy that has a first semantic similarity score within a defined range of a second semantic similarity score of second data in the second policy data of the second policy, where the first semantic similarity score and the second semantic similarity score are calculated based on characteristics (e.g., features and / or attributes) of at least one entity (e.g., a target entity and / or a target group). For example, the similarity component 302 may identify characteristics of a target entity and / or a target group in a portion (e.g., a paragraph) of the first policy that has a first semantic similarity score within a defined range of a second semantic similarity score of characteristics of a target entity and / or a target group in a portion (e.g., a paragraph) of the second policy, where the first semantic similarity score and the second semantic similarity score are calculated based on the characteristics. To perform this identification operation, the similarity component 302 may employ one or more semantic similarity models and / or techniques, including but not limited to topic modeling, Jaccard similarity, and / or another semantic similarity model and / or technique that can be used to identify semantically similar data with respect to specific characteristics of an entity and / or a group.

[0047] In some embodiments, the similarity component 302 may identify data in a first policy (e.g., paragraphs, sections, and / or other data) that is missing in a second policy. In these embodiments, the similarity component 302 may employ an anomaly detection (e.g., outlier detection) model and / or technique to identify such data that is present in the first policy and missing in the second policy.

[0048] Figure 4 FIG. shows a block diagram of an example non-limiting system 400 that may facilitate context comparison of semantics under different policies in accordance with one or more embodiments described herein. For brevity, repeated descriptions of the same elements and / or processes employed in the various embodiments are omitted.

[0049] As Figure 4 shown in the example embodiments depicted, the extraction component 202 and the similarity component 302 may receive a policy 402 (e.g., via a GUI, API, REST API, and / or another interface component of the policy comparison system 102). The policy 402 may include, but is not limited to, insurance policies, government policies (e.g., federal policies, state policies, and / or another government policy), corporate policies, organizational policies, program policies, service provider policies (e.g., payer-provider policies), policies in the social service domain, and / or another type of policy.

[0050] In Figure 4 the example embodiments shown, the extraction component 202 may extract the extracted knowledge 404 from one or more of the policies 402. In this example embodiment, the extracted knowledge 404 may include semantic knowledge corresponding to features (e.g., characteristics and / or attributes) of a target entity and / or group 412. In this example embodiment, the extraction component 202 may use, for example, one or more ML and / or AI models and / or techniques such as those described in the example embodiments depicted with reference to Figure 1 and 2 and 3 to extract the extracted knowledge 404.

[0051] In Figure 4In the example embodiment shown, the similarity component 302 may identify semantically similar data 406 in different policies of the policy 402. For example, the similarity component 302 may identify first data in a first policy of the policy 402 that has a first semantic similarity score within the defined range of a second semantic similarity score of second data in a second policy of the policy 402, where the first semantic similarity score and the second semantic similarity score are calculated based on the characteristics (e.g., features and / or attributes) of the target entity and / or group 412. For example, the similarity component 302 may identify semantically similar data 406 that includes, for example, the characteristics of the target entity and / or group 412 in a portion (e.g., paragraph) of the first policy of the policy 402 that has a first semantic similarity score within the defined range of a second semantic similarity score of the characteristics of the target entity and / or group 412 in a portion (e.g., paragraph) of the second policy of the policy 402, where the first semantic similarity score and the second semantic similarity score are calculated based on the characteristics. To perform this identification operation, the similarity component 302 may employ one or more semantic similarity models and / or techniques, including but not limited to topic modeling, Jaccard similarity, and / or another semantic similarity model and / or technique that can be used to identify semantically similar data regarding specific characteristics of the target entity and / or group 412.

[0052] As Figure 4 As depicted in the example embodiment shown, the extraction component 202 and the similarity component 302 may respectively provide the extracted knowledge 404 and the semantically similar data 406 to the comparison component 108. In this example embodiment, the comparison component 108 may also receive (e.g., via the GUI, API, REST API, and / or another interface component of the policy comparison system 102) domain knowledge 408. In this example embodiment, the domain knowledge 408 may include but is not limited to ontologies (e.g., ontology information providing definitions used in the domain), knowledge bases (e.g., knowledge graph information defining one or more knowledge graphs used in the domain), terms (e.g., dictionary and / or term information defining terms used in the domain and associated definitions), costs of services covered by the policy, census data, classifications, data models defining relationships between data objects associated with the domain, tables, business rule patterns, and / or other domain knowledge. For example, regarding the medical industry, the domain knowledge 408 may include information defining procedure codes, medical terms, information relating data objects associated with a particular medical service to each other, and / or other domain knowledge in the medical industry.

[0053] In Figure 4In the example embodiment shown, based on the received extracted knowledge 404, semantically similar data 406, and domain knowledge 408, the comparison component 108 can contextually compare the semantics of conditions in the policy data of different policies in the policy 402 based on the characteristics (e.g., features and / or attributes) of the target entity and / or group 412 to generate a formalized difference and supporting evidence 410. For example, to generate the formalized difference and supporting evidence 410, the comparison component 108 can use the extracted knowledge 404, semantically similar data 406, and / or domain knowledge 408 to contextually compare the semantics of a condition (e.g., eligibility criteria) in the first policy data in the policy 402 with the semantics of a condition in the second policy data in the policy 402 based on the characteristics (e.g., features and / or attributes) of the target entity and / or group 412. In this example embodiment, each of such first and / or second policy data may include, but is not limited to, sections, paragraphs, sentences, tables, charts, graphs, glossaries, appendices, and / or other policy data.

[0054] In Figure 4 the example embodiment depicted, to perform the above-described contextual comparison and generate the formalized difference and supporting evidence 410 using the extracted knowledge 404, semantically similar data 406, and / or domain knowledge 408, the comparison component 108 can employ one or more ML and / or AI models and / or techniques as described in the example embodiments depicted above with reference to Figure 1 , 2 and 3.

[0055] In Figure 4In the example embodiment shown, to contextually compare the semantics of conditions in different policies of policy 402 based on the characteristics of the target entity and / or group 412, the comparison component 108 may identify similarities and / or differences between similar policy data (e.g., similar policy sections, paragraphs, sentences, and / or other similar policy data) in different policies of policy 402. For example, using the extracted knowledge 404, semantically similar data 406, and / or domain knowledge 408, the comparison component 108 may compare the rules of semantically similar conditions in a first policy of policy 402 with the rules of semantically similar conditions in a second policy of policy 402, and may further express the differences (e.g., expressed as formalized differences in a computer-readable format, human-readable format, and / or another type of format). For example, using the extracted knowledge 404, semantically similar data 406, and / or domain knowledge 408, the comparison component 108 may employ knowledge graph comparison techniques to identify similarities and differences between the condition rules in the first policy data in policy 402 and the condition rules in the second policy data in policy 402. In some embodiments, the comparison component 108 of system 400 may use the extracted knowledge 404, semantically similar data 406, and / or domain knowledge 408 to semantically compare the rules extracted from the first policy data (e.g., the text in a paragraph) in policy 402 with the rules extracted from the second policy data (e.g., the text in a paragraph) in policy 402 to identify similarities and differences between the condition rules in the first and second policies. In this example, such rules may be extracted by the extraction component 202 as described above.

[0056] As Figure 4 shown in the example embodiment depicted, the comparison component 108 may provide the formalized differences and supporting evidence 410 to the contextualization component 110. In this example embodiment, the contextualization component 110 may also receive (e.g., via a GUI, API, REST API, and / or another interface component of the policy comparison system 102) the domain knowledge 408, data 412 identifying and / or defining the target entity and / or group, and / or contextual data 414. In various embodiments of the present disclosure, the contextual data 414 may include, but is not limited to: operational data (e.g., policy provider statements); statistical data (e.g., census data and / or other statistical data describing, for example, cities, towns, states, or countries); entity profile data; entity preference data; ADL data, and / or another type of contextual data related to the target entity and / or group 412. In Figure 4In the example embodiment depicted, the contextualization component 110 can use domain knowledge 408, formalized differences and supporting evidence 410, target entity and / or group 412 to identify data and / or context data 414 to provide a list 416 of the identified differences explained as described below. For clarity, in Figure 6 a list 416 of the identified differences illustrated is shown.

[0057] In Figure 4 the example embodiment shown, to generate a list 416 of the identified differences explained using domain knowledge 408, formalized differences and supporting evidence 410, target entity and / or group 412 to identify data and / or context data 414, the contextualization component 110 can employ a model to provide a contextual interpretation of how a first condition (e.g., a first eligibility criterion) in a first policy data (e.g., a portion and / or a paragraph) in policy 402 semantically differs from a second condition in a second policy data in policy 402 based on characteristics (e.g., features and / or attributes) of the target entity and / or group 412. To generate a list 416 of the identified differences that can include such a contextual interpretation, the contextualization component 110 can employ a model to provide one or more first conditions in a first policy data in policy 402 that are semantically different from one or more second conditions in a second policy data in policy 402 based on characteristics of the target entity and / or group 412. For example, to provide one or more first conditions in a first policy data that are semantically different from one or more second conditions in a second policy data based on characteristics of the target entity and / or group 412, the contextualization component 110 can determine which of the characteristics of the target entity and / or group 412 and / or which of the conditions in the first and / or second policy data in policy 402 are relevant to the target entity and / or group 412. To determine such (one or more) characteristics and / or (one or more) conditions, the contextualization component 110 can use one or more preferences of the target entity and / or group 412. In some embodiments, an entity as defined herein can define such preferences (e.g., relevant characteristics and / or conditions) using, for example, a GUI, an API, a REST API, and / or another type of interface of the policy comparison system 102.

[0058] In some embodiments, the contextualization component 110 may implement a preference learning process to infer such preferences (e.g., (one or more) relevant features and / or (one or more) conditions) corresponding to the target entity and / or group 412. In these embodiments, the contextualization component 110 may implement such a preference learning process to infer such preferences using, for example, a recommender system (e.g., a recommender model such as a regression model and / or a classification model). In these embodiments, such a recommender system may be trained to learn the preferences of the target entity and / or group 412 (e.g., learn the relevant features and / or conditions corresponding to the target entity and / or group 412) using historical usage pattern data corresponding to the target entity and / or group 412. For example, such a recommendation system may be trained to use historical usage pattern data to learn such preferences of the target entity and / or group 412, where the historical usage pattern data includes, but is not limited to, historical operation data (e.g., policy provider statements), historical policyholder data, historical policy record data, contextual data (e.g., entity profile data, ADL data, census data, and / or other contextual data), and / or other historical usage pattern data corresponding to the target entity and / or group 412.

[0059] In some embodiments, to determine such preferences of the target entity and / or group 412 and / or to provide a list of the identified differences 416 including the above-described contextual explanations, the contextualization component 110 may employ ML and / or AI models and / or techniques (e.g., neural networks, recommender systems, interpretive models, predictive models, classifiers, and / or another ML and / or AI model and / or technique). In another example, the contextualization component 110 may employ one or more preference inference models and / or techniques to determine such preferences of the target entity and / or group 412 and / or to provide a list of the identified differences including such contextually explained differences 416. For example, the contextualization component 110 may employ one or more preference inference models and / or techniques, including but not limited to a lexicographic model, a Pareto chart, and / or another preference inference model and / or technique, which may be used to determine such preferences of the target entity and / or group 412 and / or to provide a list of the identified differences 416 including the above-described contextually explained differences.

[0060] In Figure 4In the example embodiment shown, the contextualization component 110 may employ a model to provide a list 416 of the identified differences that are explained, which includes a contextual interpretation of how a first condition in a first policy data in the policy 402 semantically differs from a second condition in a second policy data in the policy 402 based on contextual data 414 corresponding to the target entity and / or group 412. For example, the contextualization component 110 may employ one or more of the models and / or techniques defined above (e.g., a preference inference model and / or technique, a lexicographic model, a Pareto chart, a neural network, a recommender system, an explanation model, a prediction model, a classifier, and / or another model and / or technique) to determine which contextual data is relevant to the target entity and / or group 412 and / or to provide a list of the identified differences 416 that are explained as including such a contextual interpretation based on the contextual data 414.

[0061] In Figure 4In the example embodiment shown, the contextualization component 110 may employ a model to rank a first condition in the first policy data of the policy 402 based on its relevance to the features (e.g., characteristics and / or attributes) of the target entity and / or group 412, where the first condition is semantically different from a second condition in the second policy data of the policy 402. For example, to rank a first condition in the first policy data of the policy 402 that is semantically different from a second condition in the second policy data of the policy 402 based on its relevance to the features (e.g., characteristics and / or attributes) of the target entity and / or group 412, the contextualization component 110 may employ one or more of the models and / or techniques defined above (e.g., preference inference models and / or techniques, lexicographic models, Pareto charts, neural networks, recommendation systems, explanation models, prediction models, classifiers, and / or another model and / or technique). For example, the contextualization component 110 may implement a preference learning process to infer and further rank a first condition in the first policy data of the policy 402 that is semantically different from a second condition in the second policy data of the policy 402 based on its relevance to the features (e.g., characteristics and / or attributes) of the target entity and / or group 412. In this example, the contextualization component 110 may use, for example, a recommender system (e.g., a recommender model such as a regression model and / or a classification model) to implement the preference learning process to rank a first condition in the first policy data of the policy 402 that is semantically different from a second condition in the second policy data of the policy 402 based on its relevance to the features (e.g., characteristics and / or attributes) of the target entity and / or group 412. In this example, such a recommender system may be trained to use historical usage pattern data corresponding to the target entity and / or group 412 as described above to learn such a first condition in the first policy data of the policy 402 (e.g., learn the condition in the policy data of the policy 402 that is related to the target entity and / or group 412). In some embodiments, the contextualization component 110 may provide the above ranking of such conditions in the list 416 of the identified differences that are explained.

[0062] As Figure 4As shown in the example embodiments depicted, the contextualization component 110 may provide the formal differences and supporting evidence 410, the target entity and / or group 412 identification data, the contextual data 414, and / or the list 416 of the interpreted identified differences to the extraction component 202 and / or the similarity component 302. In some embodiments, the contextualization component 110 may provide the formal differences and supporting evidence 410, the target entity and / or group 412 identification data, the contextual data 414, and / or the list 416 of the interpreted identified differences to the extraction component 202 and / or the similarity component 302 to facilitate an active learning process. In some embodiments, such an active learning process may facilitate, for example: the extraction of the extracted knowledge 404 from the policy 402 by the extraction component 202; and / or the identification of the semantically similar data 406 from the policy 402 by the similarity component 302.

[0063] Figure 5 FIG. shows a block diagram of an example non-limiting system 500 that may facilitate a contextual comparison of semantics under different policies, in accordance with one or more embodiments described herein. For the sake of brevity, repeated descriptions of the same elements and / or processes employed in the various embodiments are omitted.

[0064] As Figure 5 As shown in the example embodiments depicted, the policy comparison system 102 may receive (e.g., via a GUI, API, REST API, and / or another interface component of the policy comparison system 102) the policy 402, the domain knowledge 408, and / or the provider statement 502. As Figure 5 As noted in the exemplary embodiments shown, the policy 402 of the system 500 may include policies in various states (represented as "Policy A1", "Policy B1", "Policy B2", "Policy C1", and "Policy D1" in Figure 5 ), and the provider statement 502 may include statements submitted by the policy holder from state A (represented as "Claim 1", "Claim 2", and "Claim N" in Figure 5 ), where N represents the total number of statements.

[0065] In Figure 5 the example embodiments shown, based on receiving the policy 402, the domain knowledge 408, and / or the provider statement 502, the policy comparison system 102 may perform one or more preprocessing operations 504 on one or more of these inputs. For example, the policy comparison system 102 may perform preprocessing operations 504 including, for example, extraction, transformation, and loading (ETL) operations on the policy 402, the domain knowledge 408, and / or the provider statement 502.

[0066] In Figure 5In the example embodiments shown, based on performing one or more such preprocessing operations 504 on the policy 402, domain knowledge 408, and / or provider statement 502, the policy comparison system 102 may employ the extraction component 202 to perform policy data extraction 506. For example, the extraction component 202 may extract knowledge from the policy 402 and / or provider statement 502 of the system 500 in the same manner as the extraction component 202 may extract the extracted knowledge 404 from the policy 402 of the system 400, as described in the example embodiments shown in Figure 1 , 2 , 3, and 4 above.

[0067] In Figure 5 the example embodiments shown, based on the extraction component 202 that performs policy data extraction 506 as described above, the policy comparison system 102 may employ the similarity component 302 to perform semantically similar data identification 508. For example, the similarity component 302 may identify semantically similar data 406 in different policies of the policy 402 of the system 500 and / or in the provider claims 502 in the same manner as the similarity component 302 may identify semantically similar data 406 in different policies of the policy 402 of the system 500 and / or in the provider claims 502 as described in the example embodiments shown in Figure 1 , 2 , 3, and 4 above.

[0068] In Figure 5 the example embodiments shown, based on performing policy data extraction 506 and semantically similar data identification 508, the policy comparison system 102 may employ the comparison component 108 to perform knowledge comparison 510. In this example embodiment, based on performing policy data extraction 506 and semantically similar data identification 508, the extraction component 202 and the similarity component 302 may respectively provide the semantic knowledge extracted by the extraction component 202 and the semantically similar data identified by the similarity component 302 to the comparison component 108. In this example embodiment, based on receiving such extracted semantic knowledge and identified semantically similar data, the comparison component 108 may compare the semantics of the conditions in the policy data of different policies in the policy 402 of the system 400 in context in the same manner as the comparison component 108 may compare the semantics of the conditions in the policy data of different policies in the policy 402 of the system 500 in context, as described in the example embodiments shown in Figure 1 , 2 , 3, and 4 above.

[0069] In Figure 5In the example embodiment shown, based on performing knowledge comparison 510, the policy comparison system 102 may employ the contextualization component 110 to provide a contextual explanation 512. For example, the contextualization component 110 may provide the contextual explanation 512 in the same manner as the contextual explanation that may be included in the list 416 of identified differences described in the example embodiment depicted above with reference to Figure 4 The contextual explanation 512 can be provided in the same way as the above-described contextual explanations in the list 416 of identified differences described in the example embodiment depicted in. For example, the contextualization component 110 may employ one or more of the models and / or techniques defined above with reference to system 400 to provide a contextual explanation of how one or more first conditions (e.g., first eligibility criteria) in the first policy data (e.g., a portion and / or a paragraph) in policy 402 semantically differ from one or more second conditions in the second policy data in policy 402 based on the characteristics (e.g., features and / or attributes) of the target entity and / or group 412 and / or domain knowledge 408.

[0070] In Figure 5 In the example embodiment shown, based on providing the contextual explanation 512, the contextualization component 110 may further generate a list 416 of the explained identified differences, which may include the contextual explanation 512. In this example embodiment, the contextualization component 110 may generate the list 416 of the explained identified differences in the same manner as described in the example embodiment depicted above with reference to Figure 4 The contextualization component 110 may generate the list 416 of the explained identified differences in the same way as described in the example embodiment depicted in. For example, the contextualization component 110 may employ one or more of the models and / or techniques defined above with reference to system 400 to rank a first condition in the first policy data in policy 402 that semantically differs from a second condition in the second policy data in policy 402 based on its relevance to the characteristics (e.g., features and / or attributes) of the target entity and / or group 412. In Figure 5 In the example embodiment depicted, the contextualization component 110 may provide the above ranking of such conditions in the list 416 of the explained identified differences. In this example embodiment, the policy comparison system 102 and / or the contextualization component 110 may provide (e.g., via a GUI, an API, a REST API, and / or another interface component of the policy comparison system 102) the list 416 of the explained identified differences to an entity implementing the policy comparison system 102 as defined herein.

[0071] Figure 6 FIG. 600 shows an example non-limiting diagram that may facilitate a contextual comparison of semantics under the conditions of different policies according to one or more embodiments described herein. For the sake of brevity, repeated descriptions of the same elements and / or processes employed in the various embodiments are omitted.

[0072] FIG. 600 may include the above-referenced Figure 4 and5 The example non - limiting embodiments of the list 416 of identified differences explained by the example embodiments described in Figure 6 As shown in the example embodiments depicted in Figure 1 , 2 , 3, 4 and 5, the list of the most relevant conditions (e.g., by the contextualization component 110) can be ranked as described in the example embodiments shown above. As Figure 6 shown in the example embodiments depicted in

[0073] Figure 7 FIG. shows a flowchart of an example, non - limiting computer - implemented method 700 that can facilitate semantic contextual comparison under the conditions of different policies according to one or more embodiments described herein. For brevity, repeated descriptions of the same elements and / or processes employed in the various embodiments are omitted.

[0074] At 702, the computer - implemented method 700 can include contextually comparing the semantics of conditions in the policy data of different policies based on the characteristics of at least one entity by a system (e.g., via the policy comparison system 102 and / or the comparison component 108) operatively coupled to a processor (e.g., processor 106).

[0075] At 704, the computer - implemented method 700 can include the system (e.g., via the policy comparison system 102 and / or the contextualization component 110) employing a model to provide a contextual interpretation of how a first condition in the first policy data of a first policy differs semantically from a second condition in the second policy data of a second policy based on the characteristics of at least one entity.

[0076] The policy comparison system 102 can be associated with various technologies. For example, the policy comparison system 102 can be associated with data comparison technologies, policy comparison technologies, ML and / or AI model technologies, cloud computing technologies, and / or other technologies.

[0077] The policy comparison system 102 can provide technical improvements to systems, devices, components, operation steps, and / or processing steps associated with the various technologies identified above. For example, the policy comparison system 102 can, based on the characteristics of at least one entity, compare the semantics of conditions in the policy data of different policies in context; and / or employ a model to provide a contextual interpretation of how a first condition in the first policy data of a first policy semantically differs from a second condition in the second policy data of a second policy based on the characteristics of at least one entity. In this example, by contextually comparing the semantics of conditions in the policy data of different policies based on the characteristics of the entity and / or group and further providing such a contextual interpretation of how the conditions in the policies semantically differ based on such characteristics, the policy comparison system 102 can thereby reduce the workload (e.g., processing workload) and / or execution time of the model and / or processor employed to separately compare the first policy data and / or the first policy with the second policy data and / or the second policy.

[0078] The policy comparison system 102 can provide technical improvements to the processing unit associated with the policy comparison system 102. For example, as described above, by contextually comparing the semantics of conditions in the policy data of different policies based on the characteristics of the entity and / or group and further providing a contextual interpretation of how the conditions in the policies are semantically different based on such characteristics, the policy comparison system 102 can thereby reduce the workload (e.g., processing workload) and / or execution time of the model and / or processor employed to separately compare the first policy data and / or the first policy with the second policy data and / or the second policy. In this example, by reducing the workload (e.g., processing workload) and / or execution time of the processor (e.g., processor 106) employed to separately compare the first policy data and / or the first policy with the second policy data and / or the second policy, the policy comparison system 102 can thereby improve the performance and / or efficiency of such a processor (e.g., processor 106) and / or reduce the computational cost of the processor.

[0079] A practical application of the policy comparison system 102 is that it can be implemented in one or more domains to enable contextual comparison of semantically similar data in different policies based on one or more characteristics (e.g., features and / or attributes) of a certain entity and / or a certain group. For example, a practical application of the insurance policy comparison system 102 is that it can be implemented in, for example, the medical insurance domain to enable contextual comparison of semantically similar eligibility conditions (e.g., criteria) in different medical insurance policies based on one or more characteristics (e.g., age, residential location, pre-existing conditions, and / or another characteristic) of a certain entity and / or a certain group.

[0080] It should be understood that the policy comparison system 102 provides a new approach driven by relatively new data comparison techniques. For example, the policy comparison system 102 provides a new method for automatically contextually comparing the semantics of conditions in the policy data of different policies based on the characteristics of entities and / or groups, and also providing a contextual interpretation of how the conditions in the policies are semantically different based on such characteristics.

[0081] The policy comparison system 102 can employ hardware or software to solve problems that are inherently highly technical, non-abstract, and cannot be performed as a set of mental acts of a human. In some embodiments, one or more of the processes described herein can be performed by one or more dedicated computers (e.g., dedicated processing units, dedicated classical computers, dedicated quantum computers, and / or another type of dedicated computer) to perform defined tasks related to the various technologies identified above. The policy comparison system 102 and / or its components can be used to solve new problems that arise through the advancement of the above technologies, the use of quantum computing systems, cloud computing systems, computer architectures, and / or another technology.

[0082] It should be understood that the policy comparison system 102 can utilize various combinations of electrical components, mechanical components, and circuits that cannot be replicated or performed by a human in the mind, because the various operations that can be performed by the policy comparison system 102 and / or its components are operations that are beyond the capabilities of a human mind. For example, the amount of data processed within a certain period of time, the speed of processing such data, or the type of data processed by the policy comparison system 102 can be greater than, faster than, or different from the amount, speed, or type of data that can be processed by a human mind within the same period of time.

[0083] According to several embodiments, the policy comparison system 102 can also be fully operable to perform one or more other functions (e.g., fully powered on, fully executed, and / or another function), while also performing the various operations described herein. It should be understood that this simultaneous multi-operation execution is beyond the capabilities of a human mind. It should also be understood that the policy comparison system 102 can include information that cannot be manually obtained by an entity such as a human user. For example, the type, amount, and / or variety of information included in the policy comparison system 102, the comparison component 108, the contextualization component 110, the extraction component 202, and / or the similarity component 302 can be more complex than the information that can be manually obtained by an entity such as a human user.

[0084] In some embodiments, the policy comparison system 102 can be associated with a cloud computing environment. For example, the policy comparison system 102 can be associated with the cloud computing environment 950 described in the following reference Figure 9 and / or the following reference Figure 10associated with one or more functional abstraction layers described (e.g., hardware and software layer 1060, virtualization layer 1070, management layer 1080, and / or workload layer 1090).

[0085] The policy comparison system 102 and / or its components (e.g., comparison component 108, contextualization component 110, extraction component 202, similarity component 302, and / or another component) may employ the following references Figure 9 one or more computing resources of the cloud computing environment 950 described and / or the following references Figure 10 one or more functional abstraction layers described to perform one or more operations in accordance with one or more embodiments disclosed in the subject matter described herein. For example, the cloud computing environment 950 and / or such one or more functional abstraction layers may include one or more classical computing devices (e.g., classical computers, classical processors, virtual machines, servers, and / or another classical computing device), quantum hardware, and / or quantum software (e.g., quantum computing devices, quantum computers, quantum processors, quantum circuit simulation software, superconducting circuits, and / or other quantum hardware and / or quantum software), which may be used by the policy comparison system 102 and / or its components to perform one or more operations in accordance with one or more embodiments disclosed in the subject matter described herein. For example, the policy comparison system 102 and / or its components may employ such one or more classical and / or quantum computing resources to perform one or more classical and / or quantum: mathematical functions, computations, and / or equations; computational and / or processing scripts, processing threads, and / or instructions; algorithms; models (e.g., AI models, ML models, and / or another type of model); and / or another operation in accordance with one or more embodiments disclosed in the subject matter described herein.

[0086] It should be understood that although this disclosure includes a detailed description of cloud computing, the implementation of the teachings presented herein is not limited to cloud computing environments. Instead, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

[0087] Cloud computing is a service delivery model for enabling convenient on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services), which can be rapidly provisioned and released with minimal management effort or interaction with the provider of the service. The cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[0088] The characteristics are as follows:

[0089] On-demand self-service: Cloud consumers can unilaterally and automatically provision computing capabilities, such as server time and network storage, as needed, without the need for human interaction with the service provider.

[0090] Wide area network access: The capabilities are available over a network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0091] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically assigned and reassigned according to demand. There is a location-independent aspect, as consumers generally do not control or know the exact location of the provided resources but can specify location at a higher level of abstraction (e.g., country, state, or data center).

[0092] Rapid elasticity: In some cases, the ability to rapidly scale out and rapidly scale in can be provided quickly and elastically. To the consumer, the available capabilities for provisioning generally appear unlimited and can be purchased in any quantity at any time.

[0093] Measured service: The cloud system automatically controls and optimizes resource use by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource use can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

[0094] The service models are as follows:

[0095] Software as a Service (SaaS): The capabilities provided to the consumer are to use the provider's applications running on the cloud infrastructure. The applications can be accessed from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0096] Platform as a Service (PaaS): The capabilities provided to the consumer are to deploy onto the cloud infrastructure the applications created or acquired by the consumer, which are created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but has control over the deployed applications and possibly the application hosting environment configuration.

[0097] Infrastructure as a Service (IaaS): The capabilities provided to consumers are processing, storage, networking, and other basic computing resources on which consumers can deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0098] The deployment model is as follows:

[0099] Private Cloud: The cloud infrastructure is operated only for the organization. It can be managed by the organization or a third party and can exist inside or outside the building.

[0100] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0101] Public cloud: Cloud infrastructure is available to the general public or large industrial groups and is owned by an organization that sells cloud services.

[0102] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community or public) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0103] The cloud computing environment is service-oriented, with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the core of cloud computing is an infrastructure consisting of a network of interconnected nodes.

[0104] For the sake of simplicity of explanation, the computer-implemented method is depicted and described as a series of actions. It is understood and appreciated that the present invention is not limited by the actions and / or order of actions shown, for example, the actions can occur in various orders and / or concurrently, and can occur together with other actions not presented and described herein. In addition, not all the actions shown are necessary for realizing the computer-implemented method according to the disclosed subject matter. In addition, it will be understood and appreciated by those skilled in the art that the computer-implemented method can be represented as a series of interrelated states via state diagrams or events alternatively. In addition, it should also be understood that the computer-implemented method disclosed hereinafter and throughout this specification can be stored on a product, so that these computer-implemented methods are transmitted and transferred to a computer. The term product as used herein is intended to cover a computer program accessible from any computer-readable device or storage medium.

[0105] To provide context for various aspects of the disclosed subject matter, Figure 8The following discussion is intended to provide a general description of a suitable environment in which aspects of the disclosed subject matter may be implemented. Figure 8 A block diagram of an example, non-limiting operating environment in which one or more embodiments described herein may be facilitated is shown. For simplicity, repetitive descriptions of like elements employed in other embodiments described herein are omitted.

[0106] Referring Figure 8 to, a suitable operating environment 800 for implementing aspects of the present disclosure may also include a computer 812. The computer 812 may also include a processing unit 814, a system memory 816, and a system bus 818. The system bus 818 couples system components including, but not limited to, the system memory 816 to the processing unit 814. The processing unit 814 may be any of a variety of available processors. Dual microprocessors and other multiprocessor architectures may also be used as the processing unit 814. The system bus 818 may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus or external bus, and / or a local bus using any of a variety of available bus architectures, including, but not limited to, Industry Standard Architecture (ISA), Micro Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), FireWire (IEEE 1394), and Small Computer System Interface (SCSI).

[0107] The system memory 816 may also include volatile memory 820 and non-volatile memory 822. The basic input / output system (BIOS), containing basic routines that transfer information between elements within the computer 812 at startup, is stored in the non-volatile memory 822. The computer 812 may also include removable / non-removable, volatile / non-volatile computer storage media. For example, Figure 8 a magnetic disk storage 824 is shown. The magnetic disk storage 824 may also include, but not limited to, devices such as a magnetic disk drive, a floppy disk drive, a tape drive, a Jaz drive, a Zip drive, an LS-100 drive, a flash card, or a memory stick. The magnetic disk storage 824 may also include separate storage media or storage media combined with other storage media. To facilitate connection of the magnetic disk storage 824 to the system bus 818, a removable or non-removable interface, such as interface 826, is typically used. Figure 8Software that acts as an intermediary between the user and the basic computer resources depicted in a suitable operating environment 800 is also depicted. Such software can also include, for example, an operating system 828. The operating system 828 can be stored on disk storage 824 for controlling and allocating the resources of computer 812.

[0108] System applications 830 utilize the operating system 828 for the management of resources through, for example, program modules 832 and program data 834 stored in system memory 816 or disk storage 824. It should be understood that the present disclosure can be implemented with various operating systems or combinations of operating systems. The user inputs commands or information into computer 812 through input device 836. Input device 836 includes, but is not limited to, pointing devices such as a mouse, trackball, stylus, touchpad, etc., keyboard, microphone, joystick, gamepad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices are connected to processing unit 814 via interface port 838 through system bus 818. Interface port 838 includes, for example, serial ports, parallel ports, game ports, and universal serial bus (USB). Some of the (one or more) output devices 840 use the same type of ports as some of the (one or more) input devices 836. Thus, for example, a USB port can be used to provide input to computer 812 and output information from computer 812 to output device 840. Output adapter 842 is provided to account for the presence of some output devices 840 among other output devices 840 that require a special adapter, such as monitors, speakers, and printers. By way of example and not limitation, output adapter 842 includes video cards and sound cards that provide means of connection between output device 840 and system bus 818. It should be noted that other devices and / or systems of devices provide input and output capabilities, such as remote computer 844.

[0109] The computer 812 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer 844. The remote computer 844 can be a computer, server, router, network PC, workstation, microprocessor-based appliance, peer device, or other common network node, etc., and generally can also include many or all of the elements described with respect to computer 812. For simplicity, only the memory storage device 846 is shown together with the remote computer 844. The remote computer 844 is logically connected to the computer 812 through the network interface 848 and then physically connected through the communication connection 850. The network interface 848 includes wired and / or wireless communication networks, such as local area networks (LANs), wide area networks (WANs), cellular networks, and / or other wired and / or wireless communication networks. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring, etc. WAN technologies include, but are not limited to, point-to-point links, circuit-switched networks like Integrated Services Digital Network (ISDN) and its variants, packet-switched networks, and Digital Subscriber Line (DSL). The communication connection 850 refers to the hardware / software for connecting the network interface 848 to the system bus 818. Although shown inside the computer 812 for clarity, it can also be outside the computer 812. For illustrative purposes only, the hardware / software for connecting to the network interface 848 can also include internal and external technologies, such as modems including conventional telephone-grade modems, cable modems, and DSL modems, ISDN adapters, and Ethernet cards.

[0110] Now referring to Figure 9 , an illustrative cloud computing environment 950 is depicted. As shown, the cloud computing environment 950 includes one or more cloud computing nodes 910 with which local computing devices used by cloud consumers can communicate, such as personal digital assistants (PDAs) or cellular phones 954A, desktop computers 954B, laptop computers 954C, and / or in-vehicle computer systems 954N. Although not shown in Figure 9 , the cloud computing nodes 910 can also include a quantum platform (e.g., a quantum computer, quantum hardware, quantum software, and / or another quantum platform) with which local computing devices used by cloud consumers can communicate. The nodes 910 can communicate with each other. They can be physically or virtually grouped (not shown) in one or more networks, such as a private cloud, community cloud, public cloud, or hybrid cloud or a combination thereof as described above. This allows the cloud computing environment 950 to provide infrastructure, platform, and / or software as a service for which cloud consumers do not need to maintain resources on local computing devices. It should be understood that Figure 9The type of computing device 954A-N shown is for illustrative purposes only, and the computing nodes 910 and the cloud computing environment 950 can communicate with any type of computerized device via any type of network and / or network addressable connection (e.g., using a web browser).

[0111] Now referring to Figure 10 , a set of functional abstraction layers provided by the cloud computing environment 950 ( Figure 9 ) is shown. It should be understood in advance that Figure 10 the components, layers, and functions shown are for illustrative purposes only, and embodiments of the present invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0112] The hardware and software layer 1060 includes hardware and software components. Examples of hardware components include: host 1061; servers 1062 based on RISC (Reduced Instruction Set Computer) architecture; server 1063; blade server 1064; storage device 1065; and network and networking components 1066. In some embodiments, software components include web application server software 1067, database software 1068, quantum platform routing software ( Figure 10 not shown in Figure 10 ) and / or quantum software ( Figure 10 not shown in ).

[0113] The virtualization layer 1070 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 1071; virtual memory 1072; virtual network 1073, including virtual private networks; virtual applications and operating systems 1074; and virtual clients 1075.

[0114] In one example, the management layer 1080 can provide the functions described below. Resource provisioning 1081 provides dynamic procurement of computing resources and other resources used to perform tasks within the cloud computing environment. Metering and pricing 1082 provides cost tracking when resources are utilized in the cloud computing environment, as well as accounting or invoicing for the consumption of these resources. In one example, these resources can include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. The user portal 1083 provides access to the cloud computing environment for consumers and system administrators. Service level management 1084 provides cloud computing resource allocation and management such that the required service levels are met. Service level agreement (SLA) planning and fulfillment 1085 provides pre-arrangement and procurement of cloud computing resources, where future demands are anticipated according to the SLA.

[0115] The workload layer 1090 provides examples of workloads that can utilize the capabilities of a cloud computing environment. Non-limiting examples of workloads and functions that can be provided from this layer include: mapping and navigation 1091; software development and lifecycle management 1092; virtual classroom education delivery 1093; data analysis processing 1094; transaction processing 1095; and policy comparison software 1096.

[0116] The present invention can be a system, method, apparatus, and / or computer program product at any possible level of integration of technical details. The computer program product can include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the present invention. The computer-readable storage medium can be a tangible device that is capable of storing and retaining instructions for use by an instruction execution device. The computer-readable storage medium can be, by way of example and not limitation, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium can further include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punched card or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0117] The computer-readable program instructions described herein can be downloaded to a respective computing / processing device from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device. The computer-readable program instructions for carrying out operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network connection, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, in order to carry out aspects of the present invention, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), can execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

[0118] Aspects of the present invention are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed via the processor of the computer or other programmable data processing apparatus create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium in which the instructions are stored comprises an article of manufacture including instructions for implementing aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram. The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other devices implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0119] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a special-purpose hardware-based system that performs the specified functions or acts or a combination of special-purpose hardware and computer instructions.

[0120] Although the subject matter has been described above in the general context of computer-executable instructions of a computer program product running on one and / or more computers, those skilled in the art will recognize that the present disclosure may also be implemented in conjunction with other program modules or may be implemented in conjunction with other program modules. In general, program modules include routines, programs, components, data structures, and / or other program modules that perform particular tasks and / or implement particular abstract data types. Additionally, those skilled in the art will appreciate that the computer-implemented methods of the present invention may be practiced with other computer system configurations, including single-processor or multi-processor computer systems, small computing devices, mainframe computers, and computers, handheld computing devices (e.g., PDAs, telephones), microprocessor-based or programmable consumer or industrial electronic products, etc. The aspects shown may also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through a communications network. However, some aspects of the present disclosure, if not all aspects, may be practiced on a stand-alone computer. In a distributed computing environment, program modules may be located in local and remote memory storage devices. For example, in one or more embodiments, computer-executable components may be executed from a memory that may include one or more distributed memory units or may be composed of one or more distributed memory units. As used herein, the terms "memory" and "memory unit" may be used interchangeably. Additionally, the code of computer-executable components described herein for one or more embodiments may be executed in a distributed manner, e.g., multiple processors may combine or cooperate to execute code from one or more distributed memory units. As used herein, the term "memory" may include a single memory or memory unit at one location or multiple memories or memory units at one or more locations.

[0121] As used in this application, the terms "component", "system", "platform", "interface", etc. can refer to and / or can include computer-related entities or entities related to an operating machine with one or more specific functions. The entities disclosed herein can be hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. By way of illustration, an application running on a server and the server can both be components. One or more components can reside within a process and / or an execution thread, and a component can be located on one computer and / or distributed between two or more computers. In another example, the corresponding components can execute from various computer-readable media on which various data structures are stored. These components can communicate via local and / or remote processes, such as in accordance with a signal having one or more data packets (e.g., data from one component, which interacts with another component in a local system, a distributed system, and / or interacts with other systems via a network such as the Internet) via the signal. As another example, a component can be a device having a specific function provided by a mechanical part operated by an electrical or electronic circuit, which electrical or electronic circuit is operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the device and can execute at least a portion of the software or firmware application. As yet another example, a component can be a device that provides a specific function by an electronic component rather than a mechanical part, where the electronic component can include a processor or other device to execute software or firmware that at least partially imparts the function of the electronic component. In one aspect, a component can emulate an electronic component via a virtual machine, such as within a cloud computing system.

[0122] In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any natural inclusive permutation. That is, if X uses A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied in any of the foregoing instances. Further, unless otherwise specified or clear from the context to refer to the singular form, the articles "a" and "an" as used in this specification and the drawings are generally to be construed to mean "one or more". As used herein, the terms "example" and / or "exemplary" are used to mean serving as an example, instance, or illustration. To avoid doubt, the subject matter disclosed herein is not limited by these examples. Further, any aspect or design described herein as "example" and / or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor does it mean excluding equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0123] As used in this specification, the term "processor" can refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single-processor with software multithreading execution capabilities; a multi-core processor; a multi-core processor with software multithreading execution capabilities; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof that is designed to perform the functions described herein. Further, a processor can employ nanoscale architectures, such as but not limited to transistors, switches, and gates based on molecules and quantum dots, in order to optimize space usage or enhance the performance of a user device. A processor can also be implemented as a combination of computing processing units. In the present disclosure, terms such as "storage", "database", and substantially any other information storage component related to the operation and functionality of a component are used to refer to "memory components", "entities embodied in" memory ", or components that include memory. It should be understood that the memory and / or memory components described herein can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. By way of illustration and not limitation, non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)), and volatile memory can include RAM, which can be used as an external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM).

[0124] The above description includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components or computer-implemented methods for the purpose of describing the present disclosure, but one of ordinary skill in the art will recognize that many further combinations and permutations of the present disclosure are possible. Additionally, with respect to the use of the terms "comprising", "having", "owning", etc. in the detailed description, the claims, the appendices, and the drawings, these terms are intended to be inclusive in a manner similar to the way the term "including" is interpreted when used as a transitional word in the claims.

[0125] The description of the various embodiments has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen to best explain the principles of the embodiments, the practical application, or technical improvements made to the technologies that are available on the market, or to enable other ordinary skilled artisans in the art to understand the embodiments disclosed herein.

Claims

1. A computer-implemented method for context comparison of semantics in conditions of different policies, comprising: contextually comparing, by a system operatively coupled to a processor, semantics of conditions in policy data of different policies based on characteristics of at least one entity; and employing, by the system, a model to provide a contextual interpretation of how a first condition in first policy data of a first policy semantically differs from a second condition in second policy data of a second policy based on the characteristics of the at least one entity; The method further comprises: extracting, by the system, first data from the first policy data and second data from the second policy data, wherein the first data and the second data correspond to the characteristics of the at least one entity, and the first data and / or the second data can include structured data, unstructured data, text data, alphanumeric data, token data, character data, object data, graphic data, rule data, tabular data; wherein the model includes a recommendation system that is trained to learn preferences of the at least one entity using historical usage pattern data, and the historical usage pattern data includes historical operation data, historical policy holder data, historical policy record data, or context data.

2. The computer-implemented method according to claim 1, further comprising: identifying, by the system, first data in the first policy data that has a first semantic similarity score within a defined range of a second semantic similarity score of second data in the second policy data, wherein the first semantic similarity score and the second semantic similarity score are calculated based on the characteristics of the at least one entity.

3. The computer-implemented method according to claim 1, further comprising: contextually comparing, by the system, the semantics of the conditions in the policy data based on at least one of: domain knowledge corresponding to the first policy data or the second policy data; first data in the first policy data that has a first semantic similarity score within a defined range of a second semantic similarity score of second data in the second policy data; or first data extracted from the first policy data and second data extracted from the second policy data.

4. The computer-implemented method according to claim 1, further comprising: employing, by the system, the model to provide the contextual interpretation of how the first condition in the first policy data semantically differs from the second condition in the second policy data based on context data corresponding to the at least one entity, and reducing at least one of: the workload or execution time of the model or the processor when comparing the first policy data with the second policy data.

5. The computer-implemented method according to claim 1, further comprising: The system uses the model to provide one or more first conditions in the first policy data based on the features of the at least one entity, and the one or more first conditions are semantically different from one or more second conditions in the second policy data.

6. The computer-implemented method according to claim 1, further comprising: The system uses the model to rank a first condition in the first policy data based on a correlation with at least one of the following: the features of the at least one entity, or the at least one entity, and the first condition is semantically different from a second condition in the second policy data.

7. A system for context comparison of semantics in conditions of different policies, comprising: A processor that executes computer-executable components for performing the steps of the method according to any one of claims 1-6.

8. A computer program product comprising program instructions executable by a processor to cause the processor to perform the method according to any one of claims 1-6.

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