Patent Publication Number: US-2023136956-A1

Title: Machine Learning System and Methods for Price List Determination From Free Text Data

Description:
RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent Application Ser. No. 63/273,874 filed on Oct. 29, 2021, the entire disclosure of which is hereby expressly incorporated by reference. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates generally to the field of machine learning. More specifically, the present disclosure relates to machine learning systems and methods for price list determination from free-form text data. 
     Related Art 
     In the insurance claims processing field, the ability to rapidly acquire information regarding an insurance claim is paramount. In particular, it is especially important to rapidly and accurately acquire information through the life cycle of an insurance claim, from first notice of loss (FNOL), collection of loss detail data, estimation of replacement items, and processing of payments to claim filers. Often, such information is manually captured by insurance adjusters, in a process that is time consuming and prone to errors. 
     There are currently computer-based insurance claims processing software applications utilized in the insurance industry. While such systems greatly assist with capturing and processing of relevant claims data, they require manual entry of claims data by users of such systems. Also, such systems require the user to manually parse claims data in order to determine one or more price lists for replacing lost equipment, materials, and objects. As a result, these systems are also susceptible to errors and require significant amounts of user time. This drawback is not limited to the insurance claims field, and indeed, many software systems which require manual data entry by users are subject to the same drawbacks as insurance claims processing software. 
     Accordingly, what would be desirable are machine learning systems and methods for price list determination from free-form text data, which addresses the foregoing and other needs. 
     SUMMARY 
     The present disclosure relates to machine learning systems and methods for price list determination from free-form text data. The system obtains a text description of an item, such as an item that is the subject of an insurance loss claim. The system processes the text description using a first machine learning model to identify an item being described by the text description. The system then processes the text description using a second machine learning model to identify one or more candidate matching items from a database. The system then automatically populates one or more user interface screens of a claims processing software application using the output of the first machine learning model and the output of the second machine learning model. The system electronically processes an insurance claim by the claims processing software application using the information automatically populated into the user interface, thereby greatly increasing the speed and accuracy by which insurance claims data can be processed by the claims processing software application. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing features of the invention will be apparent from the following Detailed Description of the Invention, taken in connection with the accompanying drawings, in which: 
         FIG.  1    is a diagram illustrating the system of the present disclosure; 
         FIG.  2    is a flowchart illustrating steps in accordance with the present disclosure; 
         FIGS.  3 - 10    are screenshots illustrating various user interface screens generated by the system; and 
         FIG.  11    is a flowchart illustrating, in greater detail, processing steps carried out by the system of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to machine learning systems and methods for price list determination from free-form text data, as described in detail below in connection with  FIGS.  1 - 11   . 
       FIG.  1    is a diagram illustrating the system of the present disclosure, indicated generally at  10 . The system  10  includes a processor  12  that executes system code (e.g., firmware or software)  16  that provides the specific functions disclosed herein. In particular, the system code  16  includes a data collection engine  18  which collects free-form text data from one or more data sources, such as a database  14  in communication with the system code  16 , an item classification engine  20  which processes the text description obtained by the engine  18  using a first machine learning model to classify an item being described by the text description, an item matching engine  22  which processes the text description obtained by the engine  18  using a second machine learning model to identify candidate matching items from a database, and a user interface population engine  24  which processes outputs generated by the engines  20 ,  22  and automatically populates one or more user interface screens of an insurance claims processing software application based on the output of the engines  20 ,  22 . 
     The processor  12  could comprise one or more of a personal computer, a server, a smart cellular telephone, a tablet computer, an embedded computing system, a cloud computing service/platform, or any other suitable processor. Additionally, the processor  12  could comprise a customized hardware device such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other suitable hardware device. The system code  16  could communicate with the database  14  over a network connection (e.g., over a local area network (LAN), wide area network (WAN), a wireless network connection, the Internet, etc.). Optionally, the database  14  could be stored on the processor  12 . The database  14  stores insurance claims processing information. The system code  16  could be programmed in any suitable high- or low-level programming languages including, but not limited to, C, C++, C#, Java, Python, or any other suitable programming language. 
       FIG.  2    is a flowchart illustrating steps in accordance with the present disclosure, indicated generally at  50 . The processing steps  50  are carried out by the system code  16  of  FIG.  1    and its associated software engines  18 - 22 . In step  52 , the system obtains a text description of an item from a suitable data source, such as the database  14  of  FIG.  1    or from direct text entry by a user in a user interface screen of an insurance claims processing software application, such as the XACTIMATE insurance claims processing software application. The text description can be a free-form text description of an item which does not require any particular text formatting. In step  54 , the system processes the text description using a first machine learning model to classify an item being described by the text description. For example, if the free text description is a string of text describing an insurance loss claim relating to a stolen backpack, the first machine learning model processes the free text description to classify the item being described by the text as a backpack. In this step, the system could assign one or more categories and/or sub-categories for the item, which could be tailored for usage with an insurance claims processing software application, such as the XACTIMATE insurance claims processing software application. Advantageously, such automatic classification by machine learning greatly increases the speed and accuracy with which data can be obtained and processed by insurance claims processing software applications. 
     In step  56 , the system processes the text description using a second machine learning model to identify candidate matching items from a database, such as a pricing database that stores a large amount of information relating to replacement items typically involved in insurance claims. For example, if the item described in the free text is classified in step  54  by the first machine learning model as a backpack, the second machine learning model in step  56  could identify one or more replacement backpacks of suitable quality and cost range. In step  58 , the system automatically populates one or more user interface screens of the claims processing software (e.g., one or more screens of the XACTIMATE claims processing software) using the outputs of the first and second machine learning models. Advantageously, by automatically populating the user interface screens of the claims processing software, the system greatly increases the speed and accuracy with which the claims processing software can access and process pricing information in connection with claims processing. Finally, in step  60 , the claims processing software application processes an insurance claim using the information automatically populated into the user interface by the system. 
       FIGS.  3 - 10    are screenshots of various user interface screens generated by the system, illustrating operation of the system. As can be seen in  FIG.  3   , the user interface screen  70  includes a plurality of fields of information relating to an insurance claim to be processed. Such information includes, but is not limited to, grouping codes, item descriptions, cat/sel descriptions, category information, unit prices, and other information. As can be seen in  FIG.  4    (which is a zoomed in view of  FIG.  3   ), an artificial intelligence-driven price list screen is displayed to the user, and includes pricing information automatically generated by the system using the machine learning models described in connection with  FIGS.  1 - 2   . The screen also provides the user with an indication of the confidence level of the artificial intelligence recommendation, the ability to automatically approve certain recommended items generated by the artificial intelligence features of the system of the present disclosure, and the ability to set price thresholds for such approvals. 
       FIG.  5    illustrates a user interface screen  90  which allows the user to enter free-text data describing an item. Such free-text data can include an item description, a reported cost, years during which the item was produced/sold, and additional helper text that can assist processing by the first and second machine learning models described herein. The system can automatically recommend specific types of text such as descriptions, reported prices, ages, conditions, quantities, coverage, original vendor information, category information, selector information, and grouping information. 
       FIG.  6    is a screenshot illustrating price list generation by the system of the present disclosure. When the free text information is entered by the user as illustrated in  FIG.  5    discussed above, the first and second machine learning models process the free-form text data to identify a product category and to identify one or more matching items from a pricing database. As can be seen in  FIG.  6   , the screen  100  displays the results of the machine learning models, which display a list of replacement items (in this case, replacement backpacks) as well as pricing information for the replacement items. By clicking on the “Compare” button, the user can be taken to a screen that shows a particular item, the reported item&#39;s details, and other information to allow for a side-by-side comparison of the items and to add the most correct item. As can be appreciated, the system allows for a rapid generation of pricing list information from free-text information using machine learning models. 
       FIG.  7    is screenshot  110  illustrating selection by the user of a desired replacement item from the pricing list of  FIG.  6   . Detailed information about the item is included, such as a description of the item which takes the year and depreciation into account to calculate a total loss value for the product, and other information. As can be seen in  FIG.  8   , the system can also generate a screen  120  which allows the user to perform the aforementioned comparison of items in the price list. Comparisons can be performed across brands, sizes, materials, features, prices, and other parameters. 
       FIG.  9    includes screenshots of user interface screens  130 - 134  which allow for processing and claims payments after the pricing list information is automatically populated by the system and selected by the user. Using the screens  130 - 132 , the user can enter inventory payment information, and in the screen  134 , the user can advance payment to an insurance claimant (e.g., by check).  FIG.  10    illustrates a screen  140  which allows the user to track payments and their processing statuses. 
       FIG.  11    is a flowchart illustrating, in greater detail, processing steps carried out by the system of the present disclosure, indicated generally at  150 . The processing steps illustrated in  FIG.  11    comprise an item-matching deep neural network (DNN) model. The item-matching DNN model was built using PyTorch and makes use of FastText (and BERT) embeddings for handling text. FastText embeddings could be used alone, if desired, since they are less computationally intensive and are therefore faster. 
     Essentially, the DNN model takes in the information of the item in the claim and the information of the items from the database that could potentially be the correct match. The potential correct matches are fetched from the database of items and presorted to a reasonable degree by an existing search API. After the model is fed this information, it outputs a number between 0 and 1 for each of the items returned by the search API. This number is an estimated probability that the item from the search API is the correct item for the filed claim. Because the estimated probability measures the level of confidence that a given item is the correct match, all the potential matches can be sorted in descending order using the estimated probabilities. If the estimated probabilities (and therefore the sorted list of matched items) are perfect, it can be expected that the correct item ranks first and appears in the first location of the sorted list. 
     Generally, one can assess the relative improvement in the sorting of two lists by computing the normalized discounted cumulative gain (NDCG). However, in the current case, only the location of the correct item (positive matched item) is important, and the relative locations of all the negative items (that should not be selected, and which make up 99 of 100 shortlisted items) is of little interest. As an alternative to NDCG, one could also compare two lists of the items, obtained from two independent sorting methods, by looking at the median location of the correct item (the median location of the item of interest) in the sorted lists. 
     Overall, the DNN model performed well and reduced the median position of the correct result from 10 down to 2. By making it easy to locate the correct item, this item-matching model and pipeline has the potential to reduce the time required for claims processing by a factor of 5 or by 500%. 
     Although the foregoing description of the invention is in connection with determination of price lists, it is to be understood that the invention can determine information other than lists, such as pricing data and other types of data. 
     Having thus described the present disclosure in detail, it is to be understood that the foregoing description is not intended to limit the spirit or scope thereof. What is desired to be protected by Letters Patent is set forth in the following claims.