1. An intelligent legislation analysis method comprising: receiving a legislative text; identifying key terms and phrases in the legislative text; identifying relationships between the key terms and phrases; analyzing the legislative text using natural language processing techniques to identify legal concepts; generating a legal ontology based on the identified legal concepts; storing the legal ontology in the memory; comparing the legal ontology with existing legal ontologies to identify similarities and differences; providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies; identifying potential inconsistencies or conflicts in the legislative text; providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and providing a visual representation of the legal ontology and the relationships between legal concepts.

2. The method of claim 1, further comprising step of receiving user input regarding the legal concepts identified in the legislative text, Updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface.

3. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

4. The method of claim 1, wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

5. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

6. An intelligent legislation analysis system comprising: a processor; a memory; a software application stored in the memory, the software application being executable by the processor to perform the following steps: receiving a legislative text; identifying key terms and phrases in the legislative text; identifying relationships between the key terms and phrases; analyzing the legislative text using natural language processing techniques to identify legal concepts; generating a legal ontology based on the identified legal concepts; storing the legal ontology in the memory; comparing the legal ontology with existing legal ontologies to identify similarities and differences; providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies; identifying potential inconsistencies or conflicts in the legislative text; providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and providing a visual representation of the legal ontology and the relationships between legal concepts.

7. The method of claim 1, further comprising a user interface for receiving user input, wherein the software application is executable to perform the following additional steps: Receiving user input regarding the legal concepts identified in the legislative text; Updating the legal ontology based on the user input; Displaying the updated legal ontology in the user interface.

8. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

9. The method of claim 1,wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

10. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts. INTELLIGENT LEGISLATION ANALYSIS FRAMEWORK Abstract In certain implementations, a legislative text is received and processed by an intelligent legislation analysis approach. Certain embodiments may require the determination of legislatively significant terminology and phrases. The embodiment process may also involve establishing connections between the core concepts. The legislative text may be analysed with NLP tools in order to pick out legal notions in some embodiments. As a further step, embodiments may create a legal ontology from the gathered legal ideas. In other embodiments, the legal ontology is likewise kept in the brain. In certain manifestations, the legal ontology is compared to other ontologies in the same field in order to highlight areas of overlap and differentiation. In certain manifestations, you may also be asked to compile a report detailing the legal ontology's parallels and differences with preexisting legal ontologies. Identifying any contradictions or conflicts in the legislation language may also be part of embodiments. Fig. 1 , Claims:Claims :

1. An intelligent legislation analysis method comprising: receiving a legislative text; identifying key terms and phrases in the legislative text; identifying relationships between the key terms and phrases; analyzing the legislative text using natural language processing techniques to identify legal concepts; generating a legal ontology based on the identified legal concepts; storing the legal ontology in the memory; comparing the legal ontology with existing legal ontologies to identify similarities and differences; providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies; identifying potential inconsistencies or conflicts in the legislative text; providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and providing a visual representation of the legal ontology and the relationships between legal concepts.

2. The method of claim 1, further comprising step of receiving user input regarding the legal concepts identified in the legislative text, Updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface.

3. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

4. The method of claim 1, wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

5. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

6. An intelligent legislation analysis system comprising: a processor; a memory; a software application stored in the memory, the software application being executable by the processor to perform the following steps: receiving a legislative text; identifying key terms and phrases in the legislative text; identifying relationships between the key terms and phrases; analyzing the legislative text using natural language processing techniques to identify legal concepts; generating a legal ontology based on the identified legal concepts; storing the legal ontology in the memory; comparing the legal ontology with existing legal ontologies to identify similarities and differences; providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies; identifying potential inconsistencies or conflicts in the legislative text; providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and providing a visual representation of the legal ontology and the relationships between legal concepts.

7. The method of claim 1, further comprising a user interface for receiving user input, wherein the software application is executable to perform the following additional steps: Receiving user input regarding the legal concepts identified in the legislative text; Updating the legal ontology based on the user input; Displaying the updated legal ontology in the user interface.

8. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

9. The method of claim 1,wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

10. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

Description:INTELLIGENT LEGISLATION ANALYSIS FRAMEWORK
Field of the Invention
[0001] The present invention relates generally to public policy research. More particularly, the system and method for intelligent legislation analysis.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Legislation analysis is a critical area of public policy research that seeks to understand the legal and regulatory framework that governs various aspects of society. This analysis is essential for policymakers, stakeholders, and the general public to make informed decisions and develop effective policies and regulations that promote the public interest. Legislation analysis is an essential component of public policy research that seeks to understand the legal and regulatory framework that governs various aspects of society. This analysis involves examining the laws and regulations that have been put in place by governments at different levels, including federal, state, and local, and assessing their impact on the public and private sectors. The importance of legislation analysis is derived from the fact that laws and regulations have a significant impact on various aspects of society, including economic activity, public health and safety, environmental protection, and social justice. Therefore, understanding the legal and regulatory framework that governs these areas is crucial for policymakers, stakeholders, and the general public to make informed decisions.
[0004] The study of legislation analysis is interdisciplinary, drawing on various fields such as law, political science, economics, sociology, and public administration. Researchers in these fields analyze legislation using various methods, including legal research, quantitative analysis, and qualitative research. One important area of legislation analysis is regulatory impact analysis (RIA), which is the systematic assessment of the potential benefits, costs, and impacts of a proposed regulation. RIA provides policymakers with information about the potential effects of a proposed regulation, including its impact on the economy, public health and safety, and the environment. Another important area of legislation analysis is comparative analysis, which involves examining the laws and regulations of different jurisdictions to identify best practices, areas of convergence, and divergence. Comparative analysis is essential for policymakers seeking to learn from the experiences of other jurisdictions and develop effective and efficient regulations.
[0005] In patent literature disclosed multiple documents related to this domain. Few of them are discussed below.
[0006] The US20220067844 (by BORDIER NANCY) relates to computer-implemented system and methods for electoral and legislative consensus building via a social network provide decision support to assist network users define their legislative priorities and set common legislative agendas. Users include individuals intending to vote (“voters”), lawmakers, electoral candidates, political parties, and others. Decision-assisting Artificial Intelligence, machine learning technology, a corpus of data in the domain of elections and legislation, and database of user stories, generate legislative priorities for user fact-checking, evaluating, debating, and voting to include in common agendas. The network connects voters within and across election districts and national boundaries to build consensus around legislative agendas with cross national scope. The network assists voters form online voting blocs, political parties, and electoral coalitions to elect lawmakers to enact their agendas, by attracting electoral support from voters across partisan lines. Users can provide legislative mandates to lawmakers by conducting petition drives, referendums, initiatives, and informal recall votes.
[0007] The RU2565525 (by FEDERAL NOE G BJUDZHETNOE UCHREZHDENIE NAUKI INST SISTEMNOGO ANALIZA ROSSIJSKOJ AKADEMII NAUK) relates to a system that comprises: a unit for receiving requests of legislative process participants, a unit for identifying reference addresses of draft bill records in a system server database, a unit for identifying the record address of a draft bill of a specified type in the system server database, a unit for controlling sampling of draft bill records, a unit for generating database entry reading signals, a unit for identifying draft bill records of a specified type, a unit for determining the data sampling depth, a unit for cumulative summation of analytical data and a data output unit
[0008] The US20120310841 (by PAYNE BRINTON) relates to legislative tracker, a method of representing status of a legislative matter and an apparatus are disclosed herein. In one embodiment, the apparatus includes: (1) a data interface configured to receive legislative data associated with a legislative issue and (2) a computer processor configured to generate a progress marker from the legislative data that represents activity associated with the legislative issue during a legislative session.
[0009] However, known techniques are associated with several limitations. Thus, there is need of advance technological solutions in domain of legislation analysis.

Summary
[00010] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] The present invention relates generally to public policy research. More particularly, the system and method for intelligent legislation analysis.
[00013] Embodiments of the present disclosure may include an intelligent legislation analysis method including receiving a legislative text. Embodiments may also include identifying key terms and phrases in the legislative text. Embodiments may also include identifying relationships between the key terms and phrases. Embodiments may also include analyzing the legislative text using natural language processing techniques to identify legal concepts.
[00014] Embodiments may also include generating a legal ontology based on the identified legal concepts. Embodiments may also include storing the legal ontology in the memory. Embodiments may also include comparing the legal ontology with existing legal ontologies to identify similarities and differences. Embodiments may also include providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies.
[00015] Embodiments may also include identifying potential inconsistencies or conflicts in the legislative text. Embodiments may also include providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts. Embodiments may also include providing a visual representation of the legal ontology and the relationships between legal concepts.
[00016] In some embodiments, the method may include step of receiving user input regarding the legal concepts identified in the legislative text, Updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface. In some embodiments, the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.
[00017] In some embodiments, the software application may be executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts. In some embodiments, the software application may be executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.
[00018] In some embodiments, the method may include a user interface for receiving user input. In some embodiments, the software application may be executable to perform the following additional steps receiving user input regarding the legal concepts identified in the legislative text. Embodiments may also include updating the legal ontology based on the user input. Embodiments may also include displaying the updated legal ontology in the user interface.
[00019] In some embodiments, the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging. In some embodiments, the software application may be executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts. In some embodiments, the software application may be executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.
[00020] Embodiments of the present disclosure may also include an intelligent legislation analysis system including a processor and a memory. Embodiments may also include a software application stored in the memory, the software application being executable by the processor to perform the following steps receiving a legislative text.
[00021] Embodiments may also include identifying key terms and phrases in the legislative text. Embodiments may also include identifying relationships between the key terms and phrases. Embodiments may also include analyzing the legislative text using natural language processing techniques to identify legal concepts. Embodiments may also include generating a legal ontology based on the identified legal concepts.
[00022] Embodiments may also include storing the legal ontology in the memory. Embodiments may also include comparing the legal ontology with existing legal ontologies to identify similarities and differences. Embodiments may also include providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies.
[00023] Embodiments may also include identifying potential inconsistencies or conflicts in the legislative text. Embodiments may also include providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts. Embodiments may also include providing a visual representation of the legal ontology and the relationships between legal concepts.
Brief Description of the Drawings
[00024] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00025] FIG. 1A is a flowchart illustrating an intelligent legislation analysis method, according to some embodiments of the present disclosure.
[00026] Figure 1B is a flowchart extending from figure 1A and further illustrating the intelligent legislation analysis method, according to some embodiments of the present disclosure.
[00027] FIG. 2 is a flowchart further illustrating the intelligent legislation analysis method from FIG. 1A, according to some embodiments of the present disclosure.
[00028] FIG. 3 is a block diagram illustrating an intelligent legislation analysis system, according to some embodiments of the present disclosure.
Detailed Description
[00029] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00030] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00031] The present invention relates generally to public policy research. More particularly, the system and method for intelligent legislation analysis. A method for conducting an insightful analysis of statutory provisions is depicted in FIGS. 1A and 1B, which are flowcharts of the technique. This method is compatible with certain variations of the implementations described in the present disclosure. Step 102 of the intelligent legislation analysis method includes an optional step for some implementations of the method called "receiving a legislative text. " The method of intelligent legislation analysis that is described at step 104 has a number of potential components, one of which is the identification of key words and phrases found within the legislative text. The method of intelligent legislation analysis, which can be found at step 106, includes a number of potential components, one of which is the identification of linkages between the significant terms and phrases. The method of intelligent analysis of legislation that is discussed in step 108 may include carrying out an analysis of the legislative text by employing natural language processing methods in order to recognise legal conceptions.
[00032] Step 110 of the method for intelligent analysis of legislation may, depending on the particular implementation, entail the production of a legal ontology that is founded on the recognised legal concepts. The process of performing an intelligent analysis of existing laws may include the step of storing the legal ontology in the memory, as demonstrated at step 112. At step 114, the method of intelligent legislation analysis may include conducting comparisons between the legal ontology and other legal ontologies that currently exist in order to ascertain the degree to which they are comparable to one another or dissimilar from one another.
[00033] The intelligent legislation analysis method may, in some implementations, include, at step 116, the provision of a report that identifies the similarities and differences between the legal ontology being analysed and any existing legal ontologies. The method of intelligent legislation analysis, which is explained on line 118, may include a number of potential components, one of which is the identification of possible contradictions or conflicts in the language of the legislation. At position 120, the process of intelligent analysis of legislation may include the provision of recommendations for alterations or amendments to the legislative text in order to remedy the inconsistencies or conflicts that have been detected. This is done in order to remedy the inconsistencies or conflicts that have been detected. As a component of the method of intelligent legislation analysis mentioned in paragraph 122, a graphical representation of the legal ontology and the connections between different legal concepts may be offered.
[00034] The method may, in some implementations, include the steps of receiving user input regarding the legal concepts that are identified in the legislative text, updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface. This is possible because the method may, in some implementations, include the steps of receiving user input regarding the legal concepts that are identified in the legislative text. An analysis of the legislative text may be carried out using a variety of implementations of natural language processing techniques, some of which include entity identification, sentiment analysis, and the tagging of parts of speech.
[00035] It is possible, in some implementations, to improve the precision with which legal ideas and the links that can be identified between legal concepts can be identified by making the software programme executable so that it can carry out procedures related to machine learning. The software programme might, in some implementations, be capable of being executed to carry out statistical analysis on the legislative text in order to discover patterns and trends in the application of legal principles. An analysis of the legislative text may be carried out using a variety of implementations of natural language processing techniques, some of which include entity identification, sentiment analysis, and the tagging of parts of speech. These techniques are only some of the many that can be used. It's possible that the software programme is executable, which would make it possible to use techniques for machine learning to improve the accuracy of recognising legal ideas and the connections between legal concepts. It is possible to make use of the programme in order to carry out statistical analysis on the legislative text in order to detect patterns and trends in the application of legal principles.
[00036] The method of intelligent legislation analysis indicated in Figure 1A is shown as a flowchart in Figure 2, according to various implementations of this disclosure. More information on the method may be found in the flowchart here. In step 210 of the approach, it is possible that certain implementations will include the optional step of receiving comment from the user on the legal concepts that are specified in the legislative language; however, the inclusion of this step is not required. The method may entail updating the legal ontology in response to the input from the user at step 220, if that is something that is desired. The most up-to-date version of the legal ontology may be displayed in the user interface at the point 230 of the method. A user interface designed specifically for the aim of gathering information from end users. The software application may be able to be run in order to carry out the subsequent steps, which might range from 210 to 230 in the case of the intelligent legislation analysis method.
[00037] In Figure 3, a block diagram of an intelligent legislation analysis system 300 is depicted for your viewing pleasure. This system will be discussed in accordance with the numerous illustrative examples included in the current disclosure. Depending on the specific implementation, the intelligent legislation analysis system 300 could have a memory 320 and a central processing unit (CPU) 310. The intelligent legislation analysis system 300 may also include a software application 330 that is saved in the memory 320, with the software application 330 being able to be run by the processor 310 in order to carry out the operations The process of locating important words and phrases inside the main body of the statute.
[00038] It is possible, depending on the implementation, to determine the connections that exist between the significant words and sentences. The process of recognising legal concepts by way of an investigation into the legislative text carried out with the assistance of natural language processing techniques. Creating a legal ontology by grounding it in the numerous legal notions that have been acknowledged as existing in the world. At this moment, memory 320 is being used to store the legal ontology that has been constructed. Comparing the legal ontology to other existing legal ontologies in order to identify the areas in which it is similar to and distinct from those other legal ontologies. Providing a report that analyses the similarities and differences between the legal ontology that is being developed and other legal ontologies that are already in use is one of the deliverables of this project. finding out if the wording of the legislation contains any potential contradictions or conflicts and deciding whether or not it does. Providing recommendations for the modifications or alterations to the legislative text that have to be made in order to rectify the contradictions or conflicts that have been uncovered in the legislation. It would be helpful if you could provide a graphical representation of the legal ontology as well as the relationships that exist between the various legal concepts.
[00039] A method for performing intelligent analysis of legislation may be included in certain embodiments of the present disclosure. This method may involve receiving a legislative text. Furthermore, embodiments can involve determining which terms and phrases in the legislative text are the most important ones. Identifying the links between the important terms and phrases is another possible aspect of embodiments. In other embodiments, identifying legal ideas may also involve doing an analysis of the legislative text using natural language processing techniques.
[00040] In some embodiments, there is also the possibility of producing a legal ontology by basing it on the previously defined legal ideas. A further possibility for embodiments is the archiving of the legal ontology within the memory. Comparing the legal ontology being embodied with other, already-existing legal ontologies in order to uncover similarities and differences is another possible embodiment. Providing a report that identifies the similarities and differences between the legal ontology being embodied and any pre-existing legal ontologies is another possible aspect of embodiments.
[00041] Identifying any contradictions or conflicts in the legislative language is another aspect of embodiments that may be included. The provision of recommendations for adjustments or alterations to the legislative text in order to remedy the contradictions or conflicts that have been detected is another possible aspect of embodiments. The provision of a visual representation of the legal ontology and the links between different legal ideas may likewise be considered to be embodiments.
[00042] The method may, in some implementations, include the steps of receiving user input regarding the legal concepts that are identified in the legislative text, updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface. Entity recognition, sentiment analysis, and part-of-speech tagging are some examples of the natural language processing techniques that can be utilised in various implementations to perform an analysis of the legislative text.
[00043] The precision with which legal ideas and the links between legal concepts may be identified can, in some implementations, be improved by making the software programme executable so that it can carry out procedures related to machine learning. The software programme may, in certain implementations, be capable of being executed to carry out statistical analysis on the legislative text in order to determine patterns and trends in the application of legal principles.
[00044] A user interface that may take input from users is sometimes included as part of the process in various implementations. Receiving user input about the legal notions that are specified in the legislative text may be an executable capability of the software programme in certain instances. In other embodiments, it may also be possible to update the legal ontology depending on the information provided by the user. Displaying the most recent version of the legal ontology in the user interface is another possibility for embodiments.
[00045] Entity recognition, sentiment analysis, and part-of-speech tagging are some examples of the natural language processing techniques that can be utilised in various implementations to perform an analysis of the legislative text. The precision with which legal ideas and the links between legal concepts may be identified can, in some implementations, be improved by making the software programme executable so that it can carry out procedures related to machine learning. The software programme may, in certain implementations, be capable of being executed to carry out statistical analysis on the legislative text in order to determine patterns and trends in the application of legal principles.
[00046] Moreover, embodiments of the present disclosure may incorporate a system for the intelligent analysis of legislation that include a processor. A memory can sometimes be included in embodiments. Moreover, embodiments may comprise a software programme that is stored in the memory and that is able to be executed by the processor.
[00047] Furthermore, embodiments can involve determining which terms and phrases in the legislative text are the most important ones. Identifying the links between the important terms and phrases is another possible aspect of embodiments. In other embodiments, identifying legal ideas may also involve doing an analysis of the legislative text using natural language processing techniques. In some embodiments, there is also the possibility of producing a legal ontology by basing it on the previously defined legal ideas.
[00048] A further possibility for embodiments is the archiving of the legal ontology within the memory. Comparing the legal ontology being embodied with other, already-existing legal ontologies in order to uncover similarities and differences is another possible embodiment. Providing a report that identifies the similarities and differences between the legal ontology being embodied and any pre-existing legal ontologies is another possible aspect of embodiments.
[00049] Identifying any contradictions or conflicts in the legislative language is another aspect of embodiments that may be included. The provision of recommendations for adjustments or alterations to the legislative text in order to remedy the contradictions or conflicts that have been detected is another possible aspect of embodiments. The provision of a visual representation of the legal ontology and the links between different legal ideas may likewise be considered to be embodiments.
[00050] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00051] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00052] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00053] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00054] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. An intelligent legislation analysis method comprising:
receiving a legislative text;
identifying key terms and phrases in the legislative text;
identifying relationships between the key terms and phrases;
analyzing the legislative text using natural language processing techniques to identify legal concepts;
generating a legal ontology based on the identified legal concepts;
storing the legal ontology in the memory;
comparing the legal ontology with existing legal ontologies to identify similarities and differences;
providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies;
identifying potential inconsistencies or conflicts in the legislative text;
providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and
providing a visual representation of the legal ontology and the relationships between legal concepts.

2. The method of claim 1, further comprising step of receiving user input regarding the legal concepts identified in the legislative text, Updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface.

3. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

4. The method of claim 1, wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

5. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

6. An intelligent legislation analysis system comprising:

a processor;

a memory;

a software application stored in the memory, the software application being executable by the processor to perform the following steps:

receiving a legislative text;

identifying key terms and phrases in the legislative text;

identifying relationships between the key terms and phrases;

analyzing the legislative text using natural language processing techniques to identify legal concepts;

generating a legal ontology based on the identified legal concepts;

storing the legal ontology in the memory;

comparing the legal ontology with existing legal ontologies to identify similarities and differences;

providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies;

identifying potential inconsistencies or conflicts in the legislative text;

providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and

providing a visual representation of the legal ontology and the relationships between legal concepts.

7. The method of claim 1, further comprising a user interface for receiving user input, wherein the software application is executable to perform the following additional steps:

Receiving user input regarding the legal concepts identified in the legislative text;

Updating the legal ontology based on the user input;

Displaying the updated legal ontology in the user interface.

8. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

9. The method of claim 1,wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

10. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

INTELLIGENT LEGISLATION ANALYSIS FRAMEWORK
Abstract
In certain implementations, a legislative text is received and processed by an intelligent legislation analysis approach. Certain embodiments may require the determination of legislatively significant terminology and phrases. The embodiment process may also involve establishing connections between the core concepts. The legislative text may be analysed with NLP tools in order to pick out legal notions in some embodiments. As a further step, embodiments may create a legal ontology from the gathered legal ideas. In other embodiments, the legal ontology is likewise kept in the brain. In certain manifestations, the legal ontology is compared to other ontologies in the same field in order to highlight areas of overlap and differentiation. In certain manifestations, you may also be asked to compile a report detailing the legal ontology's parallels and differences with preexisting legal ontologies. Identifying any contradictions or conflicts in the legislation language may also be part of embodiments.

Fig. 1 , Claims:Claims
I/We Claim:
1. An intelligent legislation analysis method comprising:
receiving a legislative text;
identifying key terms and phrases in the legislative text;
identifying relationships between the key terms and phrases;
analyzing the legislative text using natural language processing techniques to identify legal concepts;
generating a legal ontology based on the identified legal concepts;
storing the legal ontology in the memory;
comparing the legal ontology with existing legal ontologies to identify similarities and differences;
providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies;
identifying potential inconsistencies or conflicts in the legislative text;
providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and
providing a visual representation of the legal ontology and the relationships between legal concepts.

2. The method of claim 1, further comprising step of receiving user input regarding the legal concepts identified in the legislative text, Updating the legal ontology based on the user input, and displaying the updated legal ontology in the user interface.

3. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

4. The method of claim 1, wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

5. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.

6. An intelligent legislation analysis system comprising:

a processor;

a memory;

a software application stored in the memory, the software application being executable by the processor to perform the following steps:

receiving a legislative text;

identifying key terms and phrases in the legislative text;

identifying relationships between the key terms and phrases;

analyzing the legislative text using natural language processing techniques to identify legal concepts;

generating a legal ontology based on the identified legal concepts;

storing the legal ontology in the memory;

comparing the legal ontology with existing legal ontologies to identify similarities and differences;

providing a report identifying the similarities and differences between the legal ontology and existing legal ontologies;

identifying potential inconsistencies or conflicts in the legislative text;

providing recommendations for revisions or amendments to the legislative text to address the identified inconsistencies or conflicts; and

providing a visual representation of the legal ontology and the relationships between legal concepts.

7. The method of claim 1, further comprising a user interface for receiving user input, wherein the software application is executable to perform the following additional steps:

Receiving user input regarding the legal concepts identified in the legislative text;

Updating the legal ontology based on the user input;

Displaying the updated legal ontology in the user interface.

8. The method of claim 1, wherein the natural language processing techniques used to analyze the legislative text include entity recognition, sentiment analysis, and part-of-speech tagging.

9. The method of claim 1,wherein the software application is executable to perform machine learning techniques to improve the accuracy of identifying legal concepts and relationships between legal concepts.

10. The method of claim 1, wherein the software application is executable to perform statistical analysis on the legislative text to identify trends and patterns in the use of legal concepts.