Techniques for dynamically creating regulatory representations

Abstract

A system and method for dynamically generating an object model corresponding to a regulation. According to certain aspects, a server computer can access a regulation and automatically generate a summary of the regulation based on a particular set of sentences. The server computer can additionally determine a set of topic and named entity attributes for text in the regulation object model, as well as the probability that the topic or attribute is applicable to the regulation. Furthermore, the server computer can generate and enrich the object model according to various analyses and make the enriched object model available for review by entities and users of a regulatory compliance service.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Patent Application No. 62 / 923,306, filed October 18, 2019, the disclosure of which is incorporated herein in its entirety.

[0002] The present disclosure is directed to creating a standardized object model for regulations. More specifically, the present disclosure is directed to a platform and techniques for analyzing ingested regulations, creating an object model, and enabling versioning of the regulations or object models for regulations that indicate applicable topics and categories. [Background technology]

[0003] The quantity and range of consumer products available for sale on the market is constantly changing as new products are introduced and existing products are improved or modified. In particular, product manufacturers, distributors, etc. consistently release new products and update existing products to meet consumer demand and compete with other manufacturers, distributors, etc. Generally, products are prescribed according to protocols and specifications, where a protocol is a collection of compliance and / or voluntary performance testing requirements that a product must meet in order for a given customer to enter a given market, and a product specification or data sheet may describe the product, its features, brand claims, and / or other aspects. Both protocols and specifications may help describe the differentiators of a product.

[0004] Throughout the product's life cycle, from conception to disposal, the product is subject to governance in the form of regulations, laws, legislative documents, and standards. Typically, various jurisdictions (e.g., federations, unions, labor unions, states, counties, etc.) have different regulations for various products. For example, California may regulate lithium-ion batteries differently than Texas. However, in addition to defining various requirements, regulations are often inconsistent in terminology, scope, form, and applicability, among other inconsistencies. In addition, regulations may govern other aspects associated with the product, such as components and materials that are part of the product engineering or packaging of the final product, making the investigation of applicable regulations very complicated. Thus, entities associated with the product (e.g., retailers, manufacturers, suppliers, etc.) are unable to effectively ascertain the requirements of a particular regulation or determine which regulations may be applicable to a particular product, especially new or updated products.

[0005] Thus, there is an opportunity for a platform and technology that effectively and efficiently classifies and determines the applicability of regulations promulgated by various jurisdictions. Summary of the Invention

[0006] In one embodiment, a computer-implemented method of creating an object model for a regulation for a given market(s) is provided. The method may include: accessing, by a computer processor, a set of regulatory information corresponding to the regulation, segmenting, by the computer processor, the set of regulatory information into a set of structured text and a set of metadata, generating, by the computer processor, an object model for the regulation, where the object model includes the set of structured text and the set of metadata, performing, by the computer processor, a linguistic analysis on the object model to detect a set of sentences in the set of structured text, generating, by the computer processor, a regulatory summary based on the set of sentences, and augmenting, by the computer processor, the object model for the regulation with the regulatory summary.

[0007] In another embodiment, a system for dynamically creating an object model for a regulation is provided. The system may include a memory storing instructions and a processor interfaced with the memory. The processor may be configured to execute instructions to cause the processor to access a set of regulatory information corresponding to the regulation, segment the set of regulatory information into a set of structured text and a set of metadata, generate an object model for the regulation, the object model including the set of structured text and the set of metadata, perform a linguistic analysis on the object model to detect a set of sentences in the set of structured text, generate a regulatory summary based on the set of sentences, and augment the object model for the regulation with the regulatory summary. [Brief description of the drawings]

[0008] [Figure 1A] 1 illustrates an overview of components and entities associated with systems and methods according to some embodiments. [Figure 1B] 1 provides an overview of certain components configured to facilitate systems and methods according to some embodiments. [Diagram 2] 1 is an exemplary flowchart illustrating various functions associated with systems and methods according to some embodiments. [Diagram 3] 11 is another example flowchart associated with creating an object model for a regulation, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present embodiments may relate, inter alia, to platforms and techniques for dynamically analyzing regulations applicable to multiple products, or components, materials, chemicals, attributes or features that may be associated with products, across multiple jurisdictions. According to certain aspects, the systems and methods may receive or otherwise access regulations and segment them into a structured set of text, which may include headers, footers, titles, bodies, sections, subsections, paragraphs, lists, sublists, citations, references, or any other type of information block presented in the format of an original document. The systems and methods may further segment the regulations into a set of metadata that includes additional information related to the content and subject matter of the underlying regulations. The systems and methods may further analyze the regulations to determine a set of sentences, from which a summary of each regulation may be automatically generated.

[0010] The system and method may further classify the set of sentences to determine a set of topics for each of the sentences, each of the set of topics having an associated probability of being applicable to the underlying regulation. In this regard, the system and method may serve sentences having topics that meet a threshold probability that allows the user to gain additional insight into the regulation. The system and method may generate an object model for the regulation and enrich the object model with additional data determined as a result of various analyses. It should be understood that the system and method may operate using English or any other language and / or may translate information from one language(s) to another language(s).

[0011] Thus, the present system and method provide numerous benefits. In particular, the object model generated by the system and method may be in a standard format that reduces various inconsistencies and complexities that exist in the issued regulations governing products and their associated components, materials, chemicals, attributes, features, labeling, and packaging. In addition, various entities or individuals may access the object model to effectively and efficiently ascertain relevant information regarding the regulations and / or products, components, materials, chemicals, attributes, or features that may be affected by the regulations. In particular, an entity or individual may determine from the object model a set of topics and / or a set of conditions that may be particularly relevant to the underlying regulation. The system and method may also store the object model in a central database for effective retrieval by entities and individuals. It is to be appreciated that further advantages are envisioned.

[0012] As an example, specific regulatory changes to a product, such as those specific to a particular product structure or product function, may drive product design changes. In addition, if the new regulations limit the content of hazardous chemicals, producers may have to reformulate the product to ensure continued compliance with the new regulations. In addition, the systems and methods can utilize automated processes to access and manage specific product-related details and features, components, or materials (e.g., to link with the product design and development stages of the product lifecycle). The systems and methods can be used by product designers to receive automated alerts whenever regulatory changes require specific changes in the product's design, testing, inspection, certification, labeling, or packaging, and can directly integrate with product development applications to automatically reflect applicable regulatory changes and suggest or suggest protocol or specification / datasheet enhancements.

[0013] The systems and methods described herein address challenges unique to supply chain management. The challenges relate in particular to the difficulty of accurately and effectively assessing how regulations should be interpreted or applied and determining which regulations are applicable to a product or product category prior to introduction to the marketplace due to inconsistencies and complexities between product protocols and regulations. Traditionally, individuals have had to manually review regulations to determine scope and applicability for a particular product. However, these traditional methods are often time-consuming, ineffective, and / or expensive. In addition, regulations are inconsistent in scope, terminology, and format across different jurisdictions. Furthermore, regulations typically refer to components, materials, chemicals, attributes, or characteristics associated with many types of products or product categories, rather than to end products. The systems and methods provide the ability to solve these problems by dynamically analyzing regulations to determine relevant attributes, generating object models for regulations that are consistent in format and indicative of relevant product attributes, and providing efficient access to the object models. Furthermore, since the systems and methods employ communication between multiple devices and components, the systems and methods are necessarily rooted in computer technology to overcome the noted shortcomings that arise particularly in the area of ​​supply chain management.

[0014] 1A illustrates an overview of a system 100 of components configured to facilitate systems and methods. It should be understood that system 100 is merely an example and that alternative or additional components are contemplated.

[0015] 1A, the system 100 may include a set of electronic devices 101, 102. Each of the electronic devices 101, 102 may be any type of electronic device, such as a mobile device (e.g., a smartphone), a desktop computer, a notebook computer, a tablet, a phablet, a GPS (Global Positioning System) or GPS-enabled device, a smart watch, smart glasses, a smart bracelet, a wearable electronic device, a PDA (Personal Digital Assistant), a pager, a computing device configured for wireless communication, etc. In an embodiment, any of the electronic devices 101, 102 may be an electronic device associated with an entity (e.g., a server computer or machine), such as a company, enterprise, corporation, etc.

[0016] Each of the electronic devices 101, 102 may be used by any individual or person (generally, a user). According to an embodiment, the user may use the respective electronic device 101, 102 to input various information associated with the product(s) and / or regulation(s). The product(s) may be offered for sale or purchased or made available by a business, company, service provider, etc., and may be regulated by applicable regulation(s) in an applicable jurisdiction. Alternatively or additionally, the business, company, service provider, etc. may be considering offering a product for sale or purchase in a particular jurisdiction(s). In an embodiment, the information may represent an iteration, update, or new version of the product(s). The user may also use the electronic device 101, 102 to input queries associated with the product and / or regulation.

[0017] The electronic devices 101, 102 can communicate with a server computer 115 via one or more networks 110. The server computer 115 can be associated with an entity, such as a company, enterprise, corporation, etc., that accesses, aggregates, and analyzes existing and / or new regulations. Additionally or alternatively, the server computer 115 can be associated with or otherwise interface with an entity, such as a company, enterprise, corporation, etc., that markets, manufactures, or sells products. In an embodiment, the electronic devices 101, 102 can send or communicate information associated with or queries regarding products and / or regulations to the server computer 115 via the network(s) 110.

[0018] In embodiments, the network(s) 110 may support any type of data communication via any standard or technology, including various wide area or local area network protocols (e.g., GSM, CDMA, VoIP, TDMA, WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB, Internet, IEEE 802 including Ethernet, WiMAX, Wi-Fi, Bluetooth, etc.). Further, in embodiments, the network(s) 110 may be any telecommunications network capable of supporting telephony between the electronic devices 101, 102 and the server computer 115.

[0019] In some implementations, the server computer 115 can communicate with one or more product-related data sources 117. According to an embodiment, the product-related data source(s) 117 can alternatively or additionally receive, access, and / or store various product information, including data that may relate to product components, materials, chemicals, attributes, characteristics, intended use, labeling, packaging, or regulatory requirements. In addition, the product-related data source(s) 117 can be associated with businesses, companies, service providers, etc. that may have agreements, partnerships, or contracts with the entity associated with the server computer 115 and that offer or are considering offering various products. Generally, when a business, company, service provider, etc. issues new or updated product information, the corresponding product-related data source 117 can push or send the new or updated product protocol or specification / data sheet to the server computer 115, or the server computer 115 can pull or retrieve the new or updated product information from the corresponding product-related data source 117. Thus, the server computer 115 may store up-to-date product information published by participating businesses, companies, service providers, and the like.

[0020] The server computer 115 may additionally communicate with a regulatory related data source(s) 116. According to an embodiment, the regulatory related data source(s) 116 may be associated with various regulatory bodies or agencies that may set or enact regulations. For example, the regulatory related data source(s) 116 may be associated with the US Consumer Product Safety Commission (CPSC), the US Environmental Protection Agency (EPA), the US Federal Aviation Administration (FAA), the US Federal Communications Commission (FCC), the US Food and Drug Administration (FDA), the US Federal Trade Commission (FTC), the US National Highway Traffic Safety Administration (NHTSA), the US Nuclear Regulatory Commission (NRC). The regulatory bodies or agencies may be any combination of federal, state, municipal, local, foreign, digital, or other level regulatory bodies or agencies. Generally, as a regulatory body or agency issues new or updated regulations, the corresponding regulatory-related data source 116 may push or transmit the new or updated regulations to the server computer 115, or the server computer 115 may pull or retrieve the new or updated regulations from the corresponding regulatory-related data source 116. Thus, the server computer 115 may store the most recent regulations issued by participating regulatory bodies or agencies. According to an embodiment, the server computer 115 may also store historical versions of regulations, which may be linked or otherwise associated with respective current, latest, and / or consolidated versions of the regulations.

[0021] In general, the server computer 115 can generate and maintain machine learning models associated with regulations and / or products using various machine learning techniques, calculations, algorithms, and the like. The server computer 115 can use a set of training data to initially train the machine learning model(s), or can not initially train the machine learning model(s). According to an embodiment, the server computer 115 can analyze any information received from the regulatory related data source(s) 116, for example, using the machine learning models to analyze the regulatory information, generate information resulting therefrom, and generate corresponding object models. The server computer 115 can make the result(s) of the analysis available for review and further selection by a user of the server computer 115 (e.g., by presenting the result(s) in a user interface). These functions are further described with respect to FIG. 1B.

[0022] The server computer 115 may be configured to interface with or support memory or storage 113 capable of storing various data, such as in one or more databases or other forms of storage. According to an embodiment, the storage 113 may store data or information associated with any machine learning models and / or object models generated by the server computer 115, any regulatory information received from a regulatory related data source 116, or any product information received from the electronic devices 101, 102 and / or from the product related data source(s) 117.

[0023] 1A as a single server computer 115, it should be understood that the server computer 115 may be in the form of a distributed cluster of computers, servers, machines, etc. In this implementation, the entities may utilize the distributed server computer(s) 115 as part of an on-demand cloud computing platform. Thus, when the electronic devices 101, 102 and the data sources 116, 117 interface with the server computer 115, the electronic devices 101, 102 and the data sources 116, 117 may in fact interface with one or more of several distributed computers, servers, machines, etc. to facilitate the described functionality.

[0024] Additionally, while Figure 1A illustrates two electronic devices 101, 102 and one server computer 115, it should be understood that a greater or lesser amount is contemplated. For example, there may be multiple server computers, each associated with a different entity. Figure 1B illustrates more specific components associated with the system and method.

[0025] 1B illustrates an example environment 150 in which regulatory data 151 is processed into a set of regulatory object models 152 via a regulatory analyzer platform 155, according to an embodiment. The regulatory analyzer platform 155 may be implemented on any computing device, including the server computer 115 (or in some implementations, one or more of the electronic devices 101, 102), as discussed with respect to FIG. 1A. Components of the computing device may include, but are not limited to, a processing unit (e.g., processor(s) 156), a system memory (e.g., memory 157), and a system bus 158 that couples various system components, including the memory 157 to the processor(s) 156.

[0026] In some embodiments, the processor(s) 156 may include one or more parallel processing units capable of processing data in parallel with one another. The system bus 158 may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, or a local bus, and may use any suitable bus architecture. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus (also known as a Mezzanine bus).

[0027] The regulatory analyzer platform 155 may further include a user interface 153 configured to present content (e.g., information associated with the regulations and / or object models generated therefrom). Additionally, a user may select content via the user interface 153, such as navigating different information, selecting and reviewing a particular object model and information thereabout, and / or other actions. The user interface 153 may be embodied as a portion of a touch screen configured to sense touch operations and gestures by the user. Although not shown, other system components communicatively coupled to the system bus 158 may include input devices such as a cursor control device (e.g., a mouse, a trackball, a touch pad, etc.) and a keyboard (not shown). A monitor or other type of display device may also be connected to the system bus 158 via an interface, such as a video interface. In addition to a monitor, a computer may also include other peripheral output devices, such as a printer, which may be connected through an output peripheral interface (not shown).

[0028] Memory 157 may include a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by a computing device and may include both volatile and nonvolatile media, and both removable and non-removable media. By way of non-limiting example, computer-readable media may comprise computer storage media, which may include nonvolatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, routines, applications (e.g., regulatory analyzer application 160), data structures, program modules, or other data.

[0029] Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the processor 156 of a computing system.

[0030] Regulatory analyzer platform 155 operates in a networked environment and can communicate with one or more remote platforms, such as remote platform 165, via network(s) 162, such as a local area network (LAN), a wide area network (WAN), a communications network, or other suitable network. Platform 165 can be implemented on any computing device, including one or more of electronic devices 101, 102, or server computer 115, as discussed with respect to FIG. 1A, and can include many or all of the elements described above with respect to platform 155. In some embodiments, as described herein, regulatory analyzer application 160, described further herein, can be stored and executed by remote platform 165 instead of or in addition to platform 155.

[0031] Regulatory analyzer platform 155 can store any information associated with product protocols and regulations, such as received regulatory data 151, as regulatory and product data 164. Additionally, regulatory analyzer application 160 can use machine learning techniques, such as, for example, regression analysis (e.g., logistic regression, linear regression, or polynomial regression), k-nearest neighbors, decision trees, random forests, boosting, neural networks, support vector machines, deep learning, reinforcement learning, latent semantic analysis, Bayesian networks, graph analysis, word embeddings, etc. In general, regulatory analyzer platform 155 may support a variety of supervised and / or unsupervised machine learning techniques.

[0032] According to an embodiment, the regulatory analyzer application 160 can analyze the regulated data 151 and generate resultant object model data 163, which can be stored in memory 157. The regulatory analyzer application 160 can enrich the object model data 163 with information generated from the analysis of the regulated data 151 using various techniques as discussed herein. Additionally, the regulatory analyzer application 160 can output a set of regulated object models 152, which may include the ingested regulation(s) and metadata along with extracted information, topic classifications, and summaries generated by the regulatory analyzer application 160. The regulatory analyzer application 160 (or another component) can cause the regulated object model(s) 152 (and possibly the originally received data 151) to be displayed on a user interface 153 for review by a user of the regulatory analyzer platform 155. The user can choose to verify and / or modify the displayed data. FIG. 2 details the functionality associated with the analysis of the regulated data 151 and the generation of the set of regulated object models 152.

[0033] Generally, a computer program product according to embodiments may include a computer usable storage medium (e.g., standard random access memory (RAM), optical disk, Universal Serial Bus (USB) drive, big data processing engine, NoSQL repository, etc.) having computer readable program code embedded therein, which may be adapted to be executed by the processor 156 (e.g., to work in conjunction with an operating system) to facilitate the functions described herein. In this regard, the program code may be implemented in any desired language and may be implemented as machine code (e.g., via Golang, Python, Scala, C, C++, Java, Actionscript, Objective-C, Javascript, CSS, XML, JSON), assembly code, bytecode, interpretable source code, etc. In some embodiments, the computer program product may be part of a cloud network of resources. Generally, each of data 151 and data 152 may be embodied as any type of electronic document, file, template, object model, etc. that may include various textual content, images, figures, tables, footnotes, citations, appendices, or other reference material, and may be stored in memory as program data in a hard disk drive, magnetic disk, and / or optical disk drive within regulatory analyzer platform 155 and / or remote platform 165. For example, the set of regulatory object models 152 may be stored in JavaScript Object Notation (JSON) format.

[0034] 2 is an exemplary flowchart illustrating various functions associated with the system and method. A server computer (e.g., server computer 115 discussed with respect to FIG. 1A) can perform the various functions illustrated in FIG.

[0035] References 200 in FIG. 2 represent a corpus or set of data that may be extracted from a website or another source (such as regulation-related data source(s) 116 as discussed with respect to FIG. 1A). Set of data 200 may include a set of electronic documents each corresponding to a set of regulations, and each electronic document may have a defined structure, format (e.g., HTML, PDF, XML, JSON, etc.), etc. In an embodiment, set of data 200 may be retrieved from a source via an application programming interface (API) or via one or more other data sources (e.g., web crawl, RSS, etc.). According to an embodiment, set of data 200 may be annotated or labeled (e.g., may include metadata indicating topics corresponding to the regulation) or may be unlabeled.

[0036] The server computer can analyze or inspect (201) the data 200 to segment or parse the data 200 into various components or sections. In particular, depending on the format or structure of the electronic document, the server computer can identify, extract, organize, or segment various sections of the electronic document into a structured set of text including, for example, a title, a body or paragraph(s), a section or subsection, an itemized list or sublist, a citation, a bibliography, a header, a footer, and the like. In addition, the server computer can identify, extract, or segment various metadata associated with the electronic document. For example, the metadata can include a set of tags or topic labels that are included with the electronic document and may be descriptions of regulations that are set forth in the electronic document. In some embodiments, the server computer can automatically generate (e.g., using a data model) a set of topics or tags for the electronic document based on the content of the electronic document, such as if the electronic document does not have such topics or tags. The server computer may alternatively or additionally generate a set of topics or tags for the electronic document based on additional information from internal data sources, which may be product testing, inspection and certification requirements or engineering descriptions of the product and its associated components, materials, chemicals, attributes or characteristics.

[0037] The server computer can generate a regulatory object model (202) for each electronic document, which according to an embodiment can be a structured data object having a consistent format and structure throughout the object model. Subsequent processing of the regulatory object model and each electronic document can enrich the regulatory object model with additional data, as discussed with respect to 203, 204, 205, and 206. According to an embodiment, the regulatory object model can be in a variety of formats, such as, for example, JSON, XML, or RDF.

[0038] At 203, the server computer can tokenize and detect sentences contained in the electronic document. In particular, the server computer can detect words and phrases, represented as n-grams, in the electronic document and identify sentences or phrases as consecutive strings of n-grams. The n-grams, phrases, and sentences identified or determined by the server computer are represented as 204.

[0039] At 205, the server computer can generate a summary of the electronic document. In some embodiments, the server computer can generate the summary using relevant or representative sentence(s) from the electronic document, or can generate the summary based on learned representations of the regulatory text, and the summary can be an abstract summary. In general, the server computer can generate a regulatory summary or regulatory section using a machine learning algorithm that generates a summary model from a corpus of regulatory training data. In addition, the server computer can use various techniques to rank sets of sentences according to their relevance to the regulation, extract or identify portions of the set of sentences deemed most relevant to the regulatory requirements, and generate a summary using the portions of the set of sentences. In other embodiments, the server computer can generate summary content from any portion of the regulatory object model using trained language models, cosine similarity, natural language processing, or any other sentence or word similarity measure. The server computer can add summary content (represented as 206) to the regulatory object model, thereby enhancing the regulatory object model.

[0040] At 207, the server computer may train, update, modify, or extend the summary model(s) from various portions of the corresponding regulatory object models in (205) and / or including adding topic information (212) and / or extracted entities (215), each of which may have a probability assigned to it that it is applicable to the underlying regulation.

[0041] At 208, the classifier artifact updates one or more of the topic prediction model and the entity extraction model, the output of which can be fed back to the object model for further training (with or without SME / analyst review). In particular, the server computer can apply the classifier model to a portion of text in the electronic document and calculate the probability of applicability of a regulated topic to the sentence. Block 210 represents a sentence in the electronic document that is classified using the classifier model, and block 211 represents a sentence that is filtered based on the probability of applicability of the regulated topic. Thus, the server computer (or its user) can output a list of sentences in the electronic document, each sentence in the list of sentences listing at least one particular regulated topic that has a probability of at least meeting a specified threshold. For example, a user can query and / or the server can output a sentence in a particular regulation that has predicted regulated topics "plastics" and "food additives" with a probability of applicability of at least 85%, thereby meeting a set threshold for adding a topic assignment to the sentence in the object model. As a result, the server computer may understand what regulatory topics are discussed within the various sections (block 212) of the electronic document, which may be further reflected by the enhanced regulatory object model.

[0042] The server computer may also extract a set of entities recognized as significant terms or phrases from the regulated text(s). At 209, a named entity extraction (NER) artifact may identify and classify named entity entities mentioned in the unstructured text into predefined categories. In particular, the server computer may annotate the text (block 213). In addition, the server computer may identify significant terms and phrases based on blocks of text having topic labels above a prediction threshold (block 214) according to an entity recognition model trained to identify significant terms and phrases associated with predicted regulated topics. This generates a set of keywords and / or phrases of the text that are meaningful or significant to the electronic document and / or the regulation itself (block 215). It should be understood that the server computer may employ additional or alternative topic salience techniques or algorithms to extract the set of keywords or phrases. The server computer may add the keywords and / or phrases, along with the predicted entity names, to the regulated object model represented by block 202.

[0043] By enhancing the original regulatory object model 202, the server computer can output or utilize an underlying regulatory profile for storage within the regulatory database 216. When the server computer accesses a new, existing, or updated regulation, the server computer can identify the regulatory structure, segment and parse the document, assign topic labels, keywords, phrases, and add any or all of this information to the regulatory object model for that particular regulation. The database 216 can then store multiple regulatory object models representing multiple regulations, where the regulatory object models can be consistent in format and structure and cover multiple jurisdictions. Thus, instead of having to review the raw text of a given regulation, an entity or user can access a particular regulation stored within the database 216 to effectively review requirements, topics, keywords, and / or issues that may be included in the regulatory object model. According to an embodiment, the regulatory database 216 can store previous (i.e., historical) and / or consolidated versions of various regulations, where access to the previous versions can indicate differences between the previous and current versions of the various regulations.

[0044] As an example, a regulation may be issued in a particular jurisdiction restricting the amount of lead in surface coatings of a selected product category. The server computer may determine influential or prominent sentence(s) (e.g., sentence(s) identifying relevant chemical restrictions and target product types) based on frequency statistics across the corpus and generate a summary using the determined sentence(s). The server computer may apply a classification model to determine a set of topic labels from the summary: lead, coating, surface coating, hazardous, toy, child, lead-containing, paint, furniture, consumer product, along with a probability of applicability for each of the topic labels.

[0045] 3 illustrates a block diagram of an example method 300 for creating an object model for a regulation. The method 300 can be facilitated by an electronic device (such as the server computer 115 or components associated with the regulatory analyzer platform 115 discussed with respect to FIGS. 1A and 1B) that can communicate with additional devices and / or data sources.

[0046] The method 300 may begin when the electronic device accesses a set of regulatory information corresponding to a regulation (block 305). In an embodiment, the electronic device may access the set of regulatory information from one or more data sources. The electronic device may segment the set of regulatory information into a structured set of text (block 310) that may include, for example, a header, footer, title, body, section, subsection, paragraph, list, sublist, citation, bibliography, or other type of information block presented in the form of the original document, as well as a set of metadata that may include additional information regarding the content and subject matter of the regulation. In some embodiments, the set of metadata may include a set of topic labels that were originally included in the set of regulatory information. In other embodiments, the electronic device may examine the set of regulatory information to determine that a set of topic labels does not exist, generate a set of topic labels that are applicable to the set of regulatory information, and store the set of applicable topic labels with the set of metadata.

[0047] The electronic device may generate an object model for the regulation (block 315), where the object model may include a structured set of text and a set of metadata. According to an embodiment, the object model may be in a variety of formats, such as, for example, JSON, XML, RDF, or other formats.

[0048] The electronic device may perform a linguistic analysis on the object model (block 320) to detect a set of sentences within the set of structured text. In performing the analysis, the electronic device may use one or more various linguistic and / or statistical analyses to generate a set of token n-grams from the set of sentences, or conversely, to detect a set of sentences from the set of n-grams.

[0049] The electronic device may generate a summary of the regulation and / or a segment thereof based on the set of sentences (block 325). In an embodiment, the electronic device may rank the set of sentences in the text and extract a portion of the set of sentences based on the ranking of the set of sentences. Additionally, the electronic device may generate an abstract summary using the portion of the parsed text.

[0050] The electronic device may use the object model and the generated summary to train a classification and / or entity recognition model (block 330). The electronic device may use the classification model to further determine a set of topics for any portion of text in the object model (block 335). In addition, the electronic device may determine, for each topic in the set of topics, a probability that the topic is applicable to the regulation (block 340). In an embodiment, the electronic device may output at least a portion of the regulatory object model, where the data in the portion has a probability of meeting at least a specified threshold. It is to be appreciated that the specified threshold may be a default value and / or configurable.

[0051] The electronic device may enrich the object model for the regulation with the regulatory abstract, the set of topics, and the set of extracted attributes for each selected portion of the object model (block 345). Thus, with enrichment and storage of multiple object models corresponding to multiple regulations, the method may ensure consistent format and structure of regulatory object models across multiple jurisdictions.

[0052] Although the following text describes detailed descriptions of many different embodiments, it should be understood that the legal scope of the present invention may be defined by the language of the claims set forth at the end of this patent. The detailed description should be construed as merely exemplary and does not describe all possible embodiments, as describing all possible embodiments would be impractical, if not impossible. Many alternative embodiments may be implemented using either current technology or technology developed after the filing date of this patent, and still be within the scope of the claims.

[0053] Throughout this specification, multiple instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods have been illustrated and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations need not be performed in the order illustrated. Structures and functions presented as separate components in an example configuration may be implemented as a combined structure or component. Similarly, structures and functions presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements are included within the scope of the subject matter of this specification.

[0054] Further, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a non-transitory machine-readable medium) or hardware. In hardware, routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a particular way. In an exemplary embodiment, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware modules of a computer system (e.g., a processor or processors) may be configured as hardware modules that operate by software (e.g., an application or portion of an application) to perform certain operations described herein.

[0055] In various embodiments, a hardware module can be implemented mechanically or electronically. For example, a hardware module can comprise dedicated circuitry or logic that can be permanently configured to perform certain operations (e.g., as a special purpose processor such as a field programmable gate array (FPGA) or application specific integrated circuit (ASIC)) . A hardware module can also comprise programmable logic or circuitry that can be temporarily configured by software to perform certain operations (e.g., contained within a general purpose processor or other programmable processor). It will be appreciated that the decision as to whether to implement a hardware module mechanically, with dedicated and permanently configured circuitry, or with temporarily configured circuitry (e.g., configured by software) can be determined by cost and time considerations.

[0056] Thus, the term "hardware module" should be understood to encompass tangible entities that are physically constructed, permanently configured (e.g., physically embedded), or temporarily configured (e.g., programmed) to operate in a particular manner or perform particular operations as described herein. When considering embodiments in which the hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one time. For example, if the hardware modules include a general-purpose processor configured using software, the general-purpose processor can be configured as different hardware modules at different times. Thus, the software may, for example, configure the processor to configure a particular hardware module at one time and another hardware module at another time.

[0057] Hardware modules may provide information to and receive information from other hardware modules. Thus, the described hardware modules may be considered to be communicatively coupled. When such hardware modules are present simultaneously, communication may be achieved via signal transmission (e.g., through appropriate circuits and buses) connecting the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communication between such hardware modules may be achieved, for example, via storage and retrieval of information in a memory structure accessed by the multiple hardware modules. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which the hardware module is communicatively coupled. An additional hardware module may then later access the memory device to retrieve and process the stored output. Hardware modules may also initiate communication with input or output devices and operate on resources (e.g., collect information).

[0058] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily (e.g., by software) or permanently configured to perform the associated operations. Such processors, whether temporarily or permanently configured, may constitute processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example embodiments, include processor-implemented modules.

[0059] Similarly, methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. Certain performance of operations may reside not only in a single machine, but also be distributed among one or more processors deployed across several machines. In some embodiments, one or more processors may reside in a single location (e.g., in a home environment, in a work environment, or as a server farm), while in other embodiments, the processors may be distributed across multiple locations.

[0060] The reliable performance of an operation can be distributed among one or more processors that are not only present in a single machine, but also deployed across several machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be present in a single location (e.g., in a home environment, in a work environment, or in a server farm). In other exemplary embodiments, one or more processors or processor-implemented modules may be distributed across multiple locations.

[0061] Unless otherwise indicated, descriptions herein using words such as "processing," "calculating," "computing," "determining," "presenting," "displaying," and the like may refer to machine (e.g., computer) operations or processes that manipulate or transform data represented as physical (e.g., electronic, magnetic, or optical) quantities in one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0062] As used herein, any reference to "one embodiment" or "an embodiment" means that a particular element, feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

[0063] As used herein, the terms "comprises," "comprising," "may include," "including," "has," "having," or any other variation thereof, are intended to cover non-exclusive inclusions. For example, a process, method, article, or apparatus that includes a list of elements is not necessarily limited to only those elements and may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive or, not an exclusive or. For example, a condition A or B is satisfied by any one of A being true (or present) and B being false (or absent), A being false (or absent) and B being true (or present), and both A and B being true (or present).

[0064] In addition, the use of "a" or "an" is used to describe elements and components of the embodiments of this specification. This is done merely for convenience and to give a general sense of the description. This specification and the claims that follow should be read to include one or at least one, and the singular may include the plural unless it is clear that otherwise is meant.

[0065] This detailed description should be construed as merely exemplary and does not describe every possible embodiment, as doing so would be impractical.

Claims

1. 1. A computer-implemented method for creating an object model for a regulation, comprising: accessing, by a computer processor, a set of regulatory information corresponding to the regulation; segmenting, by the computer processor, the set of regulatory information into a set of structured text and a set of metadata; generating, by the computer processor, an object model for the regulation, the object model including the set of structured text and the set of metadata; performing, by the computer processor, a linguistic analysis on the object model to detect a set of sentences within the set of structured text; ranking, by the computer processor, the set of sentences according to their relevance to the regulation; extracting, by the computer processor, a portion of the set of sentences that are relevant to the regulation according to a ranking of the set of sentences; generating, by the computer processor, the regulatory summary from the portion of the set of extracted sentences; adding, by the computer processor, a summary of the regulation to the object model for the regulation.

2. training, by the computer processor, at least one of a classification model or an entity recognition model using the summary of the regulation; determining, by the computer processor, a set of topics for any portion of the object model for said regulation using the classification model; The computer-implemented method of claim 1 , further comprising adding, by the computer processor, the set of topics to the object model for the regulation.

3. 3. The computer-implemented method of claim 2, further comprising determining, by the computer processor, for each topic in the set of topics assigned to the portion of the object model, a probability that the topic is applicable to that portion.

4. The computer-implemented method of claim 3 , further comprising outputting at least a portion of the object model having at least one topic in the set of topics that has a probability that at least meets a specified threshold.

5. analyzing, by the computer processor, a portion of the set of structured text within the object model using entity recognition analysis to determine a set of entity names that are meaningful to the regulation; The computer-implemented method of claim 2 , further comprising adding, by the computer processor, the set of entity names to the object model.

6. outputting at least a portion of the set of structured text within the object model that corresponds to a set of keywords or phrases; The computer-implemented method of claim 5 , further comprising adding, by the computer processor, the set of keywords or phrases to the object model.

7. 1. A system for dynamically creating an object model for a regulation, comprising: A memory for storing instructions; a processor that interfaces with the memory and executes the instructions to cause the processor to: accessing a set of regulatory information corresponding to the regulation; Segmenting the set of regulatory information into a set of structured text and a set of metadata; generating an object model for the regulation, the object model including the set of structured text and the set of metadata; performing a linguistic analysis on the object model to detect a set of sentences within the set of structured text; ranking the set of sentences according to their relevance to the regulation; extracting a portion of the set of sentences that is relevant to the regulation according to a ranking of the set of sentences; generating a regulatory summary from the portion of the set of extracted sentences; adding a summary of the regulation to the object model for the regulation.

8. The processor, training at least one of a classification model or an entity recognition model using the summary of the regulation; using the classification model to determine a set of topics for any portion of the object model for the regulation; The system of claim 7 , further configured to add the set of topics to the object model for the regulation.

9. The processor, 9. The system of claim 8, further configured to: determine, for each topic in the set of topics assigned to the portion of the object model, a probability that the topic is applicable to that portion.

10. The processor, 10. The system of claim 9, further configured to output at least a portion of the object model having at least one topic of the set of topics that has a probability that at least meets a specified threshold.

11. The processor, analyzing a portion of the set of structured text within the object model using entity recognition analysis to determine a set of entity names that are meaningful to the regulation; The system of claim 8 , further configured to add the set of entity names to the object model.

12. The processor, outputting at least a portion of the set of structured text within the object model that corresponds to a set of keywords or phrases; The system of claim 11 , further configured to add the set of keywords or phrases to the object model.

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