SYSTEMS AND METHODS FOR AUTOMATIC MATTER CLASSIFICATION AND EXTRACTION OF DATA

Systems and methods for automatic extraction of electronic data are disclosed. In one aspect, a method of automatically generating a matter form, the method includes receiving input data from one or more sources, the input data relating to a matter, classifying, using a trained model, a matter classification based at least in part on the input data, selecting one or more matter forms based on the matter classification, automatically extracting field data from the input data, and automatically populating fields of the one or more matter forms with the extracted field data.

BACKGROUND

In many industries, such as the legal industry, matter intake forms are used to describe and provide information about a new matter. In the legal context, a matter intake form may include fields for information such as client name, matter title, matter description, NAICS code, and the like. The type of matter may have specific information that is needed. For example, a litigation matter will have different informational requirements than a patent matter. A litigation matter may have a plaintiff field, a defendant field, a complaint type field, docket due dates, and the like. A patent matter may have a bar date field, an invention type field, inventor fields, and the like.

It can be cumbersome for matter intake personnel to both determine the appropriate matter intake form for the incoming matter, as well as to fill out the various fields of the form with all of the relevant information describing the matter. Completing these matter intake forms is laborious and takes significant time.

Accordingly, alternative systems and methods for completing matter intake forms are desired.

BRIEF SUMMARY

In one embodiment, a method of automatically generating a matter form includes receiving input data from one or more sources, the input data relating to a matter, classifying, using a trained model, a matter classification based at least in part on the input data, selecting one or more matter forms based on the matter classification, automatically extracting field data from the input data, and automatically populating fields of the one or more matter forms with the extracted field data.

In another embodiment, a computing apparatus includes a processor. The computing apparatus also includes a memory storing instructions that, when executed by the processor, configure the apparatus to receive input data from one or more sources, the input data relating to a matter, classify, using a trained model, a matter classification based at least in part on the input data, select one or more matter forms based on the matter classification, automatically extract field data from the input data, and automatically populate fields of the one or more matter forms with the extracted field data.

DETAILED DESCRIPTION

Embodiments of the present disclosure are directed to systems and methods for automatically classifying a matter type and automatically completing one or more matter intake forms based on the matter type upon matter intake by extracting field data from input data. Generally, embodiments include receiving input data regarding a new matter, and automatically classifying the new matter into a matter classification based on the input data. One or more matter intake forms are selected based on the matter classification. Field data is then automatically extracted from the input data and populated into relevant fields of the one or more matter intake forms. The matter intake forms can then be used for downstream processes.

By automatically classifying the matter and auto-populating the fields of the relevant matter intake form(s), significant time and resources are saved. Personnel can instead perform other tasks.

Various embodiments of systems and methods for automatically completing matter intake forms are described in more detail below.

Although embodiments are described herein in the context of legal matter intake forms, embodiments are not limited thereto. Embodiments may be utilized in any field where matter intake forms are used, such as medical, construction, interior design, web design, consulting, and the like.

Referring now toFIG.1, an example system102for automatically filling out matter intake forms116is schematically illustrated. It should be understood that embodiments are not limited to the configuration ofFIG.1and that more or fewer elements may be provided. Generally, the system102includes various input data sources104-110that provide input data to a matter intake form generator114. As described in more detail below, the matter intake form generator114includes one or more trained models to both detect the matter classification and automatically complete one or more matter intake forms116based on the input data.

The input data sources may take on a variety of different forms. In the illustrated example, the input data sources include a chat bot104, a text input106, an electronic file input108, and a spoken input110. These various input data sources provide input data relating to a new matter that has been received into an organization, such as a new legal matter. The input data is used to classify the matter and complete one more matter intake forms116relating to the matter. The user may use the one or more input data source to provide information relating to the matter. In some embodiments, the input data comprises user data, such as position title, historical matters handled, queries and the like. The user data can be used by the system to more accurately predict the type of matter based on past matters.

The input data sources104-110may be provided in a computer software application that is accessible to the user.FIG.2illustrates an example user interface202of a software application that is configured to complete matter intake forms116. The user interface202may be a component of a standalone software application product, or it may be a plug-in feature of a larger software application tailored to a particular field (e.g., a legal research software program).

The example user interface202includes a chat bot interface214that provides chat bot104functionality for the user to provide input data regarding the particular matter. The chat bot interface214may utilize any known or yet-to-be-developed chat bot model for prompting questions and obtaining information. The chat bot model may be trained on various different matters and to produce relevant questions and statements to elicit the requisite input data from the user. The chat bot104may be further trained on historic data, such as previous queries.

The user may ask the chat bot various queries pertaining to a matter, such as legal queries of a legal matter. In the legal context, if the user's query can have its resolution automated by referencing internal legal policy documentation, the user is provided with search results for the given query. When the chat bot identifies that the user is indicating a need to submit a legal request (i.e., user's intended result cannot be automated), it will respond and ask the user to describe the needed legal task.

For example, if the user asks a question regarding elements of submitting a contract request, for example, a non-disclosure agreement, the chat bot104may ask for description of what the user needs. The chat bot then supplies an appropriate matter intake form for the user. In some embodiments, the chat bot104asks questions of the user relating to information that is still needed to include in a matter intake form.

The user interface202also include a file import interface204for the user to import electronic files relevant to the new matter. Thus, the file import interface204provides an interface for the electronic file input108illustrated inFIG.1. The user may drag-and-drop relevant files into the file import interface204, or use another means to import the electronic files. Electronic files that are inputted are then listed in an imported files list206for review by the user. The user can take various actions regarding the files in the imported files list206, including opening files, deleting the files, prioritizing the files (e.g., moving them up and down in the list).

As a non-limiting example, one of the electronic files may be an email. The email may be from a client describing a new matter as one example. As another example, the email may be from an organization member to person or matter intake department that describes the new matter in natural language. For example, the email may include language such as “We received a new patent infringement law suit from Client X where they are being sued by Company Y for alleged infringement of Patent No. X0,135,126. The patent is for a dog chew toy . . . . ” As described in more detail below, the matter intake form generator114receives the email and uses its text as input data to classify the matter and complete one or more matter intake forms.

Other electronic documents may also be imported using the file import interface204, such as a legal complaint, other legal documents, company profiles, and the like. These electronic documents may also be used as input data by the matter intake form generator114.

The user interface202ofFIG.2also includes a text box210that is provided for a user to add free-form text regarding the new matter. The user may type in natural language sentences, fragments, words, and the like into the text box210. No particular formatting is needed. The text box210may be used in lieu of the chat bot104if the user is familiar with what type of information is needed for the matter intake form generator114. For example, the user may type in “client: company X; matter: wrongful death; plaintiff: company Y; new client: yes; field: nautical;” and the like into the text box210. The matter intake form generator114receives this data and may use it to classify the matter and complete one or more matter intake forms as described in more detail below.

A microphone button212is provided to give the user the ability to use spoken language to provide input data into the system. When the user selects the microphone button212, the computer software program accesses a microphone of the computer (e.g., desktop computer, laptop computer, smart phone, tablet, and the like). The user can then speak into the microphone (not shown) to describe the new matter that has come in. The user can speak in natural language sentences, or in fragments such as in the wrongful death example above, to describe the new matter. The system may include a speech-to-text algorithm to convert the spoken language into text that is then used by the matter intake form generator114to classify the new matter and complete the one or more matter intake forms116.

Referring again toFIG.1and as described in more detail below, the matter intake form generator114receives input data from one or more input data sources104-110and uses it to classify the matter and to complete one or more matter intake forms116. The matter intake form generator114accesses a matter data storage112for data relevant to performing its functions, such as historical data, matter data, and any other data needed.

Referring now toFIG.3, a portion of the system102for classifying the matter is illustrated. The matter intake form generator114may include a matter classifier304, which may be a trained classifier that is trained to receive the input data302and output a matter classification (e.g., merger, patent infringement, real estate closing, etc.). The matter classifier304may be a supervised classifier such as, without limitation, a support vector machine, K-nearest neighbors, Naive Bayes, and logistic regression. The trained classifier is trained to output a matter classification based on input data describing the matter. Embodiments are not limited by the type or number of classifications.

Using the wrongful death example from above, the user may have submitted an email from the client describing the nature of the law suit into the file import interface204, and also may have inputted various facts regarding the matter into the text box210, or provided spoken dialogue using the microphone button212. The trained classifier receives this input data302and outputs one or more matter classifications. In this example, the matter is “litigation.”

Sub-matters classifications may also be generated, such as “wrongful death,” or a specific matter classifications may be generated, such as “litigation—wrongful death.”

The one or more matter classifications that are outputted by the matter classifier304may be displayed in a suggested forms region208, as shown inFIG.2. In this way, the user can be confident that the system properly classified the matter. If any changes need to be made, the user may make a change to the matter classification in the suggested forms region208.

The matter classifier304outputs one or more blank matter intake form306as shown inFIG.3. The one or more blank matter intake forms306may be listed in the suggested forms region208. A user can select a listed matter intake form from the suggested forms region208to open it, save it, or otherwise interact with it. The user may opt to fill out the one or more matter intake forms manually, or in some embodiments automatically without human input. There may be a user interface element that the user may select to cause a selected matter intake form to be automatically filled out using field data extracted from the input data.

Referring now toFIG.4, a portion of the system for automatically filling in (i.e., completing) one or more blank matter intake forms306is illustrated. The matter intake form generator114includes a field data extractor402that is operable to extract field data from the input data and populate the extracted field data into the appropriate fields of the one or more blank matter intake forms306. In some embodiments, the field data extractor402extracts data from the input data302by searching its contents for text matching an intake field's predefined formula, then imports that data into the matter type's associated intake form. Each field of the blank matter intake form306has a data formula defined for data extraction (for example, a Contract matter type has the intake form field “contract value”. This field's data formula defines the appropriate contents to be of the formats “$xxx,xxx {x=numeric variable}”, “xx,xxx$”, “yyyyyy-yyyyyyyy {y=free text variable} dollars”, etc. Text in the input data302matching those criteria is assigned a data type label for extraction).

In other embodiments, the field data extractor402comprises a large language model that is operable to receive the input data302and the one or more blank matter intake forms306or a list of the fields of the one or more blank matter intake forms306. The system provides a prompt to the large language model for it to extract the fields from the one or more blank matter intake forms306, and to find field data within the input data302that matches the fields. In embodiments where only the fields are provided as input to the large language model rather than the blank matter intake forms306themselves, the prompt may be configured to ask the large language model to find the field data within the input data302that matches the provided fields. The output of the large language model may be field-field data pairs. The field data extractor402may further include a script that takes the field-field data pairs and populates the fields of the one or more blank matter intake forms306with the field data extracted from the input data302.

The end result is one or more populated matter intake forms116. These forms can be used for downstream purposes, such as, without limitation, providing legal representation, performing medical care based on the data in the matter intake forms116, creating a work (e.g., a website, an advertisement, an artwork), generating software code (e.g., generating a software program per requirements of the matter intake form), building a structure (e.g., building a house or renovating a building based on requirements of the matter intake form).

As a specific example, once a matter intake form is completed, the system then submits that completed matter intake form as a matter within the workflow tool for review and assignment. A legal matter may be any inquiry or dispute regarding the rights or obligations of a party in relation to an agreement. In embodiments, legal matter management may refer to a set of activities to be completed a company's legal department. In other embodiments, the system may be used to generate matter forms for any business group and is not limited to legal matters. For example, the system or method may extract data from an email, classify the data for a matter and populate a matter intake form.

In some embodiments, the matter intake forms are computer readable and may be automatically transmitted to a machine to autonomously perform an automated physical task, such as an autonomous vehicle that autonomously navigates an environment, or a three-dimensional printer that fabricates an article automatically per the field data of the matter intake form.

Referring now toFIG.5, a method for completing one or more matter intake forms is illustrated. At block502, input data related to a new matter is received by the system. The input data may be generated by any method, such as natural speech, use of a chat bot, imported electronic files, and free text. Next, at block504a matter is classified using the input data and a matter classifier of a matter intake form generator114. From the classified matter, one or more relevant matter intake forms are selected at block506. These forms may be selected from a repository of forms stored in a data storage device, for example.

At block508field data is extracted from the input data. The field data is data that is relevant to fields of the one or more matter intake forms that are selected at block506. In the legal context, field data may include client name, adverse party NAISC codes, amount at controversy, and the like. A field data extractor is configured to extract the field data from the input that is provided by the user. Next, at block510the selected one or more matter intake forms are populated with the field data that was extracted at block508. Now the system has created populated matter intake forms that are ready for downstream processes, such as transmitting the one or more matter intake forms to desired persons/entities at block512.

Embodiments of the present disclosure may be implemented by a computing device, and may be embodied as computer-readable instructions stored on a non-transitory memory device. Referring now toFIG.6, an example system for automatically generating matter intake forms as a computing device602is schematically illustrated. The example computing device602provides a system for automatically generating matter intake forms, and/or a non-transitory computer usable medium having computer readable program code for automatically generating matter intake forms embodied as hardware, software, and/or firmware, according to embodiments shown and described herein. While in some embodiments, the computing device602may be configured as a general purpose computer with the requisite hardware, software, and/or firmware, in some embodiments, the computing device602may be configured as a special purpose computer designed specifically for performing the functionality described herein. It should be understood that the software, hardware, and/or firmware components depicted inFIG.6may also be provided in other computing devices external to the computing device602(e.g., data storage devices, remote server computing devices, and the like).

As also illustrated inFIG.6, the computing device602(or other additional computing devices) may include a processor616, input/output hardware618, network interface hardware620, a data storage component622(which may store matter data624(e.g., data relating to matter classifications, previous matter data, and the like), form data626(e.g., data relating to the various forms), and any other data628for performing the functionalities described herein), and a non-transitory memory component604. The memory component604may be configured as volatile and/or nonvolatile computer readable medium and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components.

Additionally, the memory component604may be configured to store operating logic606, classifier608for automatically classifying input data of a new matter into a matter classification, data extraction logic610for extracting field data from the input data, and form fill logic612for filling one or more matter forms with the extracted field data as described herein (each of which may be embodied as computer readable program code, firmware, or hardware, as an example). It should be understood that the data storage component622may reside local to and/or remote from the computing device602, and may be configured to store one or more pieces of data for access by the computing device602and/or other components.

A local interface614is also included inFIG.6and may be implemented as a bus or other interface to facilitate communication among the components of the computing device602.

The processor616may include any processing component configured to receive and execute computer readable code instructions (such as from the data storage component622and/or memory component604). The input/output hardware618may include virtual reality headset, graphics display device, keyboard, mouse, printer, camera, microphone, speaker, touch-screen, and/or other device for receiving, sending, and/or presenting data. The network interface hardware620may include any wired or wireless networking hardware, such as a modem, LAN port, wireless fidelity (Wi-Fi) card, WiMax card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices

Included in the memory component604may be the operating logic606, classifier logic608, data extraction logic610, and form fill logic612. The operating logic606may include an operating system and/or other software for managing components of the computing device602. Similarly, the classifier logic608may reside in the memory component604and may be configured to automatically classify the matter of input data into a matter classification. The data extraction logic610also may reside in the memory component604and may be configured to automatically extract relevant field data from the input data based on the matter classification. The form fill logic612includes logic to automatically fill one or more matter intake forms with the extracted field data.

The components illustrated inFIG.6are merely exemplary and are not intended to limit the scope of this disclosure. More specifically, while the components inFIG.6are illustrated as residing within the computing device602, this is a non-limiting example. In some embodiments, one or more of the components may reside external to the computing device602.

It should now be understood that embodiments of the present disclosure are directed to systems and methods for automatically classifying matters (e.g., legal matters) into a matter classification, and automatically filling one or more matter intake forms with relevant data extracted from input data describing a new matter. The input data can take a variety of forms, such as electronic documents (e.g., emails), spoken language, free text, and interactions with a chat bot. Field data is automatically extracted from the input data by a model, such as a large language model, and used to populated the one or more matter intake forms. The process saves time and resources to produce the matter intake forms over traditional methods.