Patent Publication Number: US-2023133690-A1

Title: Processing forms using artificial intelligence models

Description:
FIELD OF TECHNOLOGY 
     The present disclosure relates generally to database systems and data processing, and more specifically to processing forms using artificial intelligence models. 
     BACKGROUND 
     A cloud platform (i.e., a computing platform for cloud computing) may be employed by many users to store, manage, and process data using a shared network of remote servers. Users may develop applications on the cloud platform to handle the storage, management, and processing of data. In some cases, the cloud platform may utilize a multi-tenant database system. Users may access the cloud platform using various user devices (e.g., desktop computers, laptops, smartphones, tablets, or other computing systems, etc.). 
     In one example, the cloud platform may support customer relationship management (CRM) solutions. This may include support for sales, service, marketing, community, analytics, applications, and the Internet of Things. A user may utilize the cloud platform to help manage contacts of the user. For example, managing contacts of the user may include analyzing data, storing and preparing communications, and tracking opportunities and sales. 
     Systems may use or otherwise support fillable forms having fields for input data and a variety of formats (e.g., order forms, invoices, etc.). A user may use the cloud platform to query for and extract meaningful information from a fillable form. In some systems, the form may have a specific template and the user may be limited to querying using specific terms or query formats. However, in cases with no predefined templates for reference, it is challenging to automatically extract information of interest from forms. Thus, techniques for extracting information from forms having different formats may be improved. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example of a form processing at a server system that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  2    illustrates an example of a computing system that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  3    illustrates an example of a process flow that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  4    illustrates an example of an input document that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  5    illustrates an example of a process flow that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  6    shows a block diagram of an apparatus that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  7    shows a block diagram of a processing component that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIG.  8    shows a diagram of a system including a device that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
         FIGS.  9  through  11    show flowcharts illustrating methods that support processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     An organization may store information and data for users (e.g., customers, organizations, etc.) such as data and metadata for exchanges, opportunities, orders, invoices, deals, assets, customer information, and the like. Some data storage and processing systems may receive or store data using forms with fillable fields, and some systems may support a variety of formats of fillable forms (e.g., order forms, invoices, etc.). Forms can be classified into at least two categories in terms of their layout flexibility: fixed forms and non-fixed forms. A fixed form may be defined as a form that has limited structure variations in terms of layouts, texts, and visual appearance. For example, a driver license may be considered a fixed form, since each state has a limited number of designs and formats for its driver license. A non-fixed form may be defined as a form that has a non-fixed structure or is otherwise flexible in terms of its layout and content. For example, an invoice may be considered a non-fixed form as each vendor may have their own design of invoices with different layouts, texts, and visual appearances. Systems may be configured to automatically ingest forms and extract information from them such that the information can be queried, stored, or otherwise processed. However, such systems may require a template or other reference to guide the system in understanding which fields correspond to which types of information. Such systems are thereby limited in their utility if there are no predefined templates for reference or if the system is tasked with ingesting non-fixed forms with varying formats. Therefore, it is challenging to automatically extract information of interest from non-fixed forms using current systems. 
     Techniques of the present disclosure provide for an automatic system to extract information of interest from fixed forms and non-fixed forms, thus improving the processing efficiency of documents having different formats. The techniques described herein provide for a method of extracting key-value pairs from arbitrary non-fixed forms based on specified requests (e.g., queries) by users. The system, which may include a database system, one or more application servers, a cloud platform, or any combination of computing devices and architectures as described herein, may use an artificial intelligence model (e.g., a machine learned model) applicable to arbitrary types of fixed forms and non-fixed forms. Users may use the artificial intelligence model to extract key-value pairs of interest or infer the value for arbitrary keys specified by users. To retrieve a value corresponding to an input phrase, the system may use an artificial intelligence model in conjunction with an image text extractor (e.g., an optical character recognition model). 
     The system may receive a user input including an input document (such as a form, a set of forms, etc.) and an input key phrase (e.g., a query). The input document may include a set of input text fields. Upon receiving the input form, the system may extract, using an optical character recognition model (or similar image or text processing model), a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. For instance, the system may process the input form (e.g., input as an image or text file) to detect and recognize the words [w 1 , w 2 , . . . , wM] and their locations in [b 1 , b 2 , . . . , bM] in the image. The system may then input the words (in the form of character strings or groups of characters), their corresponding locations, and the input key phrase into an artificial intelligence model (e.g., transformer-based model). The artificial intelligence model may be an example of a machine learned model that is trained to compute a probability, for each character string of the set of character strings, whether that a character string corresponds to the value for the key-value pair corresponding to the input key phrase. In some examples, the system may then generate a probability of each word being a value corresponding to the requested key. In examples where the value includes multiple words, the system may group potential value words into phrases based on the output of the artificial intelligence model and the spatial arrangement of the words. The system may identify the value for the key-value pair corresponding to the input key phrase based on inputting the extracted set of character strings and the set of two-dimensional locations into the machine learned model. In some examples, the system may output the matching value phrase for the input key phrase. 
     Aspects of the disclosure are initially described in the context of an environment supporting an on-demand database service. Aspects of the disclosure are further described with respect to a general system diagram that shows computing components and data flows that support processing forms using artificial intelligence models, a block diagram illustrating a user interface, and a process flow diagram illustrating various process and dataflows that support the techniques herein. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts that relate to processing forms using artificial intelligence models. 
       FIG.  1    illustrates an example of a system  100  for cloud computing that supports processing forms using artificial intelligence models in accordance with various aspects of the present disclosure. The system  100  includes cloud clients  105 , contacts  110 , cloud platform  115 , and data center  120 . Cloud platform  115  may be an example of a public or private cloud network. A cloud client  105  may access cloud platform  115  over network connection  135 . The network may implement transfer control protocol and internet protocol (TCP/IP), such as the Internet, or may implement other network protocols. A cloud client  105  may be an example of a user device, such as a server (e.g., cloud client  105 - a ), a smartphone (e.g., cloud client  105 - b ), or a laptop (e.g., cloud client  105 - c ). In other examples, a cloud client  105  may be a desktop computer, a tablet, a sensor, or another computing device or system capable of generating, analyzing, transmitting, or receiving communications. In some examples, a cloud client  105  may be operated by a user that is part of a business, an enterprise, a non-profit, a startup, or any other organization type. 
     A cloud client  105  may interact with multiple contacts  110 . The interactions  130  may include communications, opportunities, purchases, sales, or any other interaction between a cloud client  105  and a contact  110 . Data may be associated with the interactions  130 . A cloud client  105  may access cloud platform  115  to store, manage, and process the data associated with the interactions  130 . In some cases, the cloud client  105  may have an associated security or permission level. A cloud client  105  may have access to some applications, data, and database information within cloud platform  115  based on the associated security or permission level, and may not have access to others. 
     Contacts  110  may interact with the cloud client  105  in person or via phone, email, web, text messages, mail, or any other appropriate form of interaction (e.g., interactions  130 - a,    130 - b,    130 - c,  and  130 - d ). The interaction  130  may be a business-to-business (B2B) interaction or a business-to-consumer (B2C) interaction. A contact  110  may also be referred to as a customer, a potential customer, a lead, a client, or some other suitable terminology. In some cases, the contact  110  may be an example of a user device, such as a server (e.g., contact  110 - a ), a laptop (e.g., contact  110 - b ), a smartphone (e.g., contact  110 - c ), or a sensor (e.g., contact  110 - d ). In other cases, the contact  110  may be another computing system. In some cases, the contact  110  may be operated by a user or group of users. The user or group of users may be associated with a business, a manufacturer, or any other appropriate organization. 
     Cloud platform  115  may offer an on-demand database service to the cloud client  105 . In some cases, cloud platform  115  may be an example of a multi-tenant database system. In this case, cloud platform  115  may serve multiple cloud clients  105  with a single instance of software. However, other types of systems may be implemented, including—but not limited to—client-server systems, mobile device systems, and mobile network systems. In some cases, cloud platform  115  may support CRM solutions. This may include support for sales, service, marketing, community, analytics, applications, and the Internet of Things. Cloud platform  115  may receive data associated with contact interactions  130  from the cloud client  105  over network connection  135 , and may store and analyze the data. In some cases, cloud platform  115  may receive data directly from an interaction  130  between a contact  110  and the cloud client  105 . In some cases, the cloud client  105  may develop applications to run on cloud platform  115 . Cloud platform  115  may be implemented using remote servers. In some cases, the remote servers may be located at one or more data centers  120 . 
     Data center  120  may include multiple servers. The multiple servers may be used for data storage, management, and processing. Data center  120  may receive data from cloud platform  115  via connection  140 , or directly from the cloud client  105  or an interaction  130  between a contact  110  and the cloud client  105 . Data center  120  may utilize multiple redundancies for security purposes. In some cases, the data stored at data center  120  may be backed up by copies of the data at a different data center (not pictured). 
     Subsystem  125  may include cloud clients  105 , cloud platform  115 , and data center  120 . In some cases, data processing may occur at any of the components of subsystem  125 , or at a combination of these components. In some cases, servers may perform the data processing. The servers may be a cloud client  105  or located at data center  120 . 
     The data center  120  may be an example of a multi-tenant system that supports data storage, retrieval, data analytics, and the like for various tenants, such as the cloud clients  105 . As such, each cloud client  105  may be provided with a database instance in the datacenter  120 , and each database instance may store various datasets that are associated with the particular cloud client  105 . More particularly, each cloud client  105  may have a specific set of datasets that are unique for the cloud client  105 . The cloud platform and datacenter  120  may support a system that processes a set of datasets for a particular cloud client  105 . In some examples, the cloud platform and datacenter  120  support a system that receives an input document and an input key phrase from a particular cloud client  105  and generates a value for the input key phrase based on a machine learned model. In some examples, the input key phrase may be received as a natural language query. As such, the value corresponding to the input key phrase is based on a set of character strings (words or phrases) and a set of two-dimensional locations of the set of character strings on a layout of the input document. That is, the value determination in response to inputting a form may support customer specific analytics by capturing contexts or meanings that are unique to a form type and the cloud client  105 . 
     Forms are common in daily business workflows. A large amount of human effort is needed to process the massive number of form-like documents. Developing an automatic system to extract information of interest from forms may improve the processing efficiency. As described above, a fixed form may be defined as a form that has limited structure variations in terms of layouts, texts, and visual appearance. For example, a driver&#39;s license may be considered as a fixed form, since each state has one design (or a limited number of designs) of its driver license. The fixed structure of these forms may be used to utilize some predefined templates to extract information of interest. On the other hand, a non-fixed form may be defined as a form that has non-fixed structures. For example, an invoice may be considered as a non-fixed form given the fact that each vendor will have their own design of invoices with different layouts, texts, and visual appearances. Since there are no predefined templates for reference, it may be challenging to extract information of interest from non-fixed forms. 
     Some techniques may be utilized to extract information in the form of key-value pairs from non-fixed forms. In some examples, a system may extract all the key-value pairs from the form without considering the interest of users. After that, the users may manually select information from the redundant results. For instance, a system may receive a form as an input, and may identify a mapping between keys and values included in the form. However, there is no way for a user to query a value for a particular key included in the form. In this case, the user may receive a one to one mapping of keys and values, and may have to manually sort through the mapping in order to identify the requested key and determine their corresponding value. In another example, a system may extract key-value pairs of predefined field categories on invoices. However, such techniques for information extraction may work for a pre-fixed set of fields and may be specially designed for invoices and may not be used for other types of non-fixed forms. In some examples, a system may extract values for customized keys. However, such an extraction technique may depend on more information by users (such as the specific key&#39;s data type) and may not be able to handle virtual keys and it is limited for key variations. In some examples, virtual keys may be defined as keys that are not included in a document and key variation may occur when an input key is not an exact match with any key included in a document. Thus, the system configured to extract values for customized keys from a document may not be able to handle an input key that is not included in the document. 
     As described herein, the datacenter  120  and cloud platform  115  may support processing forms using artificial intelligence models using a key text as the input and can handle virtual keys and key variations. For instance, the datacenter  120  and cloud platform  115  may support receiving a query for an input key associated with a document, where the input key is not included in the document. Additionally or alternatively, the datacenter  120  and cloud platform  115  may support receiving a query for an input key associated with a document, where the input key is not an exact match with any key included in the document. In some examples, a system may receive an input document including a set of input text fields and an input key phrase querying a value for a key-value pair that corresponds to one or more of the set of input text fields. The system may use a machine learned model to determine a value for a key-value pair corresponding to the input key phrase. In some examples, the system may input the input key phrase into the machine learned model. The system may then identify a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. The system may then output the identified value corresponding to the input key phrase. 
     It should be appreciated by a person skilled in the art that one or more aspects of the disclosure may be implemented in a system  100  to additionally or alternatively solve other problems than those described herein. Furthermore, aspects of the disclosure may provide technical improvements to “conventional” systems or processes as described herein. However, the description and appended drawings only include example technical improvements resulting from implementing aspects of the disclosure, and accordingly do not represent all of the technical improvements provided within the scope of the claims. 
       FIG.  2    illustrates an example of a computing system  200  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The computing system  200  includes a user device  205  and a server  210 . The user device  205  may be an example of a device associated with a cloud client  105  or contact  110  of  FIG.  1   . The server  210  may be examples of aspects of the cloud platform  115  and the datacenter  120  of  FIG.  1   . For example, the server  210  may represent various devices and components (e.g., application servers, databases, cloud storage, etc.) that support an analytical data system as described herein. The server  210  may support a multi-tenant database system, which may manage various datasets  225  that are associated with specific tenants (e.g., cloud clients  105 ). In some examples, the datasets  225  may include a set of forms related to the tenant. In some examples, the server  210  may be configured to support a single organization or tenant instead of being configured as a multi-tenant system. The server  210  may also support data retrieval in response to input  215  (e.g., queries) received from user devices, such as user device  205 . For example, the server  210  may support retrieving a value from a form based on receiving an input key. The data (e.g., value to a corresponding input key) retrieved in response to an input  215  may be surfaced to a user at the user device  205 . 
     As described, the server  210  may manage various datasets  225  including forms having different formats. The datasets  225  may be associated with specific tenants in the example of a multi-tenant system. For example, a datastore may store a set of datasets  225  that are associated with the tenant corresponding to user device  205 . A dataset of the set of datasets  225  may include a fillable form or multiple forms. As depicted herein, the computing system  200  may support a variety of formats of fillable forms including fixed forms and non-fixed forms (e.g., order forms, invoices, etc.). Some computing systems may not be able to automatically extract information from forms having different formats. To support automatic extraction of information from forms, a data preprocessor  230  may identify fields from the forms in a dataset  225 . The datasets  225  may store training data including an indication of one or more fields of a form (e.g., a key) corresponding to a related field (e.g., a value) according to relationships between the fields. The training data may be forwarded to the training function  245 . According to one or more aspects, the training function  245  may utilize a set of forms (having different formats) to train a machine learned model. In some examples, the training function  245  may receive a set of training forms (e.g., input documents) from the dataset  225  and may extract a set of key-value pairs from the set of training forms or input files (stored in dataset  225 ). For instance, the training function  245  may train a model to identify a value corresponding to a key in an input document. The training function  245  may utilize labeled data in the set of training forms to identify a value for a corresponding key. 
     The training function  245  may train the machine learned model  235  (or some other machine learned model) based on inputting a set of input file formats into a transformer-based model. The transformer-based model is described in further detail with reference to  FIG.  3   . For each word in the set of training forms or input files, an annotation l may indicate whether the word w i  ∈ {0, 1} is a part of the value phrase corresponding to an input key phrase. During the training operation, the training function  245  may train the machine learned model  235  to calculate a binary cross entropy loss between a predicted probability and a ground-truth label using the following equation: 
     
       
         
           
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     In the equation, l w     i    is defined as an annotation for a word w i  and key-phrase is a key for which the value is calculated. The training function  245  may send the trained model to machine learned model  235 . Accordingly, the machine learned model  235  may be trained to identify a value for a particular key included in an input document. 
     According to one or more aspects of the present disclosure, the forms may be associated with at least one of reports, report types, data objects, data sets, or a combination thereof. In some examples, the computing system  200  may support analytics to extract meaningful information from different types of forms. According to aspects described herein, the data preprocessor  230  may receive one or more inputs  215  (e.g., queries such as natural language queries or database queries). The one or more inputs  215  may also include an input document including a set of input text fields. A user using the user device  205  may upload an input form including a set of input text fields. The input form may be of a fixed format or a non-fixed format. In some instances, each input document may include a set of fields. The data preprocessor  230  may receive the input  215  and may convert the input document into an input understandable by the data preprocessor  230 . 
     In some examples, the data preprocessor  230  may receive an input key phrase  215 - a  in addition to the input document. The input key phrase  215 - a  may query a value for a key-value pair that corresponds to one or more of the set of input text fields (e.g., the set of input text fields included in the input document received at the data preprocessor  230 ). In some examples, the data preprocessor  230  may extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The data preprocessor  230  may then input the extracted set of character strings and the set of two-dimensional locations into a machine learned model (at the machine learned model  235 ) that is trained (using the training function  245 ) to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase  215 - a.  As depicted herein, prior to receiving the input  215 , the training function  245  may train a machine learned model based on inputting a set of input file formats into the machine learned model. For instance, the training function  245  may receive a set of training forms including labeled data identifying keys and corresponding values for the keys. Based on the labeled data, the training function  245  may train the machine learned model  245  to identify a value corresponding to a key in an arbitrary document. As one method of identification, the training function  245  may train the machine learned model  245  to compute a probability of multiple potential words or phrases being a value corresponding to a key. The training function  245  may further train the machine learned model  245  to rank the computed probabilities to identify a value for a requested key. 
     In some examples, the data preprocessor  230  may input the input key phrase  215 - a  into the machine learned model. The machine learned model  235  (as trained by the training function  245 ) may identify a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase  215 - a.  In some instances, the data postprocessor  240  may rank the set of probabilities based on a value of each probability in the set of probabilities. The data postprocessor  240  may then identify the value for the key-value pair corresponding to the input key phrase  215 - a.  In some examples, identifying the value for the key-value pair corresponding to the input key phrase  215 - a  may be based on ranking the set of probabilities. For instance, the data postprocessor  240  may identify the value corresponding to the input key phrase  215 - a  as the value having a highest computed probability. For example, the machine learned model  235  may rank probabilities of a value for the key-value pair corresponding to the input key phrase  215 - a  and the data postprocessor  240  may identify a result (e.g., having a top ranked probability). Upon identifying the value corresponding to the requested input key phrase  215 - a,  the data postprocessor  240  may transmit the identified value (in results  220 ) to the user device  205 . As such, the result  220  including the identified value corresponding to the input key phrase  215 - a  may be returned to the user. The concepts and techniques described with reference to  FIG.  2    are further described with respect to the following figures. 
       FIG.  3    illustrates an example of a process flow  300  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The process flow diagram  300  may be implemented in conjunction with a user device and a server (e.g., an application server or a combination of computing devices as described herein). The user device may be an example of the user device  205  as described with respect to  FIG.  2   , and the server may be an example of the server  210  as described with respect to  FIG.  2   . Although one user device is depicted in the example of  FIG.  3   , it may be understood that the process flow  300  may be implemented using multiple user devices. The server may represent a set of computing components, data storage components, and the like, that support a database system as described herein. In some examples, the database system may be configured as a multi-tenant database system as described herein. 
     In some examples, the operations illustrated in the process flow  300  may be performed by hardware (e.g., including circuitry, processing blocks, logic components, and other components), code (e.g., software or firmware) executed by a processor, or any combination thereof. Alternative examples of the following may be implemented, where some steps are performed in a different order than described or are not performed at all. In some cases, steps may include additional features not mentioned below, or further steps may be added. 
     The aspects depicted herein provide a method for extracting key-value pairs from arbitrary non-fixed forms based on requests from users. The process flow  300  may be applicable to arbitrary types of non-fixed forms and users may be flexible to extract key-value pairs of interest. As described with reference to  FIG.  3   , the process flow  300  utilizes a transformer-based machine learned model (e.g., machine learned model  235 ) to infer the value for arbitrary keys specified by users. In some examples, the transformer-based machine learned model may be a deep learning model that adopts the mechanism of differentially weighing significance of different portions of an input data. Transformer-based machine learned models may be used in the field of natural language processing. While the process flow  300  is described with reference to a transformer-based machine learned model, it is to be understood that other machine learned models may be used for extracting key-value pairs from arbitrary non-fixed forms based on requests from users. 
     At  305 , a user may provide an input document including a set of input text fields. The input document may be stored in a database, server, cloud storage, or any other form of data storage as described with reference to  FIGS.  1  and  2   . The input document may be an example of a fixed form or a non-fixed form. The form may include a set of text fields and a set of values corresponding to the text fields. The text fields and values may be considered as key-value pairs such that a field may be considered a key and the text, numbers, or data in that field may be considered the value corresponding to the key. A specific example of a form with key-value pairs is provided with reference to  FIG.  4   . 
     At  310 , the user may input an input key phrase querying a value for a key-value pair that corresponds to one or more of the set of input text fields. For example, the user may upload a form and may query a value corresponding to a key included in the form. In some instances, a user may upload a form F including keys K 1 , K 2  and K 3 . Each key in the form may have a value associated with it. In one example, the form may include value V 1  corresponding to key K 1 , value V 2  corresponding to key K 2  and value V 3  corresponding to key K 3 . The user may query the value for input key phrase K 2 . In some examples, the form may have already been input or uploaded (e.g., previously, or by another user, etc.), and the user at  310  may provide the input key phrase for a querying operation. 
     At  315 , an optical character recognition model may extract from the input document a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. With reference to the prior example form, the optical character recognition model may extract the words K 1 , K 2 , K 3 , V 1 , V 2 , and V 3 . Although each value is depicted as a single word, it is to be understood that a single value phrase may include multiple words. In the case where a single value phrase includes multiple words, the optical character recognition model may identify each word separately. The optical character recognition model may process the input document (e.g., an image of a form) to detect and recognize optical character recognition words [w 1 , w 2 , . . . , wM] and their corresponding locations [b 1 , b 2 , . . . , bM] in the input document. In some examples, the locations may be x-y coordinates in the input document. Referring to form F, the optical character recognition model may detect and recognize optical character recognition words [K 1 , K 2 , K 3 , V 1 , V 2 , V 3 ] and their corresponding locations [x 1 y 1 , x 2 y 2 , x 3 y 3 , x 4 y 4 , x 5 y 5 , x 6 y 6 ]. Although an optical character recognition model is provide as an example, it is to be understood that any model capable of extracting text, values, or information from an image or text file may be used at  315 . 
     At  320 , the set of character strings and the set of two-dimensional locations of the set of character strings may be input into a machine learned model such as a transformer-based model. As described herein, the transformer-based model may be an example of a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. That is, the machine learned model may compute a probability for each character string, where the probability indicates how likely that character string is the value that corresponds to the key in the form that corresponds to the key phrase input by the user. 
     The transformer-based model may receive the words generated by the optical character recognition along with their corresponding locations and the requested key phrase as inputs and may generate the probability of each word being the value corresponding to the requested key or key phrase. In some examples, a server or other computing device executing the transformer-based model may tokenize the input key phrase into words, [kw 1 , kw 2 , . . . , kwN], where k may be a constant. In some examples, [kw 1 , kw 2 , . . . , kwN] and [w 1 +b 1 , w 2 +b 2 , . . . , wN+bN] may be inputted to the transformer-based model. As depicted herein, w 1  included in the optical character recognition words [w 1 , w 2 , . . . , wM] and b 1  included in their corresponding locations [b 1 , b 2 , . . . , bM] correspond to the word generated with the optical character recognition model and its locations in the input document. 
     At  325 , a server or other computing device may use the transformer-based model to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. In some examples, the input key phrase querying a value for a key-value pair may be associated with location information. In such cases where the input phrase has no location information, the transformer-based model may use a dummy location [0,0,0,0] for each key word to fit the transformer&#39;s input parameter. The transformer-based model may first generate a feature representation for each input key word and each optical character recognized word. In some examples, the feature representation may use a set of techniques that allows a system to perform feature detection or classification from raw data. The feature representation may include an array with a certain length (N) that represents the words (each input key word and each optical character recognized word) in a way that the transformer-based model can process. In the example of form F, the transformer-based model may generate feature representation for the input key word and the optical character recognition words [K 1 , K 2 , K 3 , V 1 , V 2 , V 3 ] may be [fkw 1 ′, fkw 2 ′, fkw 3 ′, fkw 4 ′, fkw 5 ′, fkw 6 ′]. 
     In some examples, a server or other computing device may generate a first set of feature representations for a set of keywords included in the set of input text fields and a second set of feature representations for the extracted set of character strings. For example, the server may generate the optical character recognized word&#39;s feature representation as [fkw 1 ′, fkw 2 ′, . . . , fkwN′]. The optical character recognized word&#39;s representation [fw 1 ′, fkw 2 ′, . . . , fkwN′] may further be projected by a fully connected layer leading to [fw 1 , fw 2 , . . . , fwM], where M is a total number of optical character recognition words in the input document (e.g., form F). For instance, the transformer-based model may project the feature representation array [fkw 1 ′, fkw 2 ′, . . . , fkwN′] to a fully connected layer generating an array [fw 1 , fw 2 , . . . , fwM]. 
     In some examples, the server may generate a unified feature representation for the input key phrase on the first set of feature representations and the second set of feature representations In some instances, the server may generate a unified representation f key-phrase  for the input key phrase by first averaging the features of all the key words and then projecting the averaged representation to another space by a fully connected layer. In some examples, the fully connected layer may be a normalization layer that receives an array as an input and provides a normalized term as an output. In the example depicted herein, the fully connected layer may receive an array including a normalized representation of the keywords and may generate a unified term (or representation) for the keywords in the input document. In some instances, the fully connected layers may be examples of encoder layers of the transformer-based model. 
     The transformer-based model, as part of an inference procedure, may then determine a probability of each extracted word being the value for the input key phrase. For example, the transformer-based model may identify a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. In some examples, the transformer-based model may apply a dot product between the unified feature representation for the input key phrase and each feature representation of the second set of feature representations. In some examples, the probability that the character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase is computed based on applying the dot product. 
     In some examples, the transformer-based model may obtain a matching score between the input key phrase and each of the extracted word. The matching score may be obtained by applying dot product between the representation of key phrase and each of the extracted word&#39;s feature representation. The transformer-based model may obtain the matching probability by applying the sigmoid function to the matching score using the following equation: 
         p (value| w   i , key-phrase, document)=sigmoid( f    key-phrase   ·f   w     i   ) 
     In the equation, the probability that a word w i  is a value corresponding to the input key phrase in the document is given by a sigmoid function to a dot product between the unified representation f key-phrase  and the feature representation word w i  (shown as f w     i   ). 
     In some examples, the transformer-based model may identify a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. In the example of form F, the transformer-based model may determine that the value V 1  has a probability p 1  of being the value corresponding to the input key phrase K 2 . Similarly, the transformer-based model may determine the value V 2  has a probability p 2  of being the value corresponding to the input key phrase K 2  and that the value V 3  has a probability p 3  of being the value corresponding to the input key phrase K 2 . The transformer-based model may rank the set of probabilities based on a value of each probability in the set of probabilities. For example, the set of probabilities may be ranked in decreasing order, such that character strings having the highest probability of being a match are listed first followed by character strings have lower probabilities. In the example of form F, the transformer-based model may rank the probabilities as p 2 , p 1 , and p 3 . Such a ranking may indicate that the value V 2  has the highest probability of being a value of the input key phrase K 2 . 
     In some examples, the transformer-based model may determine that the input key phrase does not match a key that corresponds to one or more of the set of input text fields. In the example of form F, the input key phrase may include a key K 5  which does not match the keys K 1 , K 2  and K 3  included in the document. In such cases, the transformer-based model may generate or otherwise identify a dummy key corresponding to the input key phrase based on determining that the input key phrase does not match the key. In some examples, the transformer-based model may identify an approximate match between the input key phrase (K 5 ) and the keys (K 1 , K 2  and K 3 ) included in the document. For instance, the transformer-based model may match with K 2 . In such cases, the transformer-based model may return V 2  as the value for the input key phrase K 5 . In some examples, identifying the value for the key-value pair is based on identifying the dummy key. The transformer-based model may also determine that the input key phrase is associated with an empty value field. In some examples, the server may identify a dummy value corresponding to the input key phrase based on determining that the input key phrase is associated with the empty value field. In some cases, the identified value corresponding to the input key phrase may include the dummy value. 
     At  335 , the server may perform a post processing operation to identify the value for the key-value pair corresponding to the input key phrase based on inputting the extracted set of character strings and the set of two-dimensional locations into the transformed-based model. For example, the server may identify the value for the key-value pair corresponding to the input key phrase in accordance with the probabilities ranked by the transformer-based model. In some instances, identifying the value for the key-value pair corresponding to the input key phrase is based on ranking the set of probabilities. 
     In some examples, the server may receive the probabilities of each value and may generate a value phrase proposal. Values from the input document may contain multiple words. For example, the value V 2  in form F may include words v 21 , v 22 , and v 23  (e.g., the value for the key K 2  may include several words and/or numbers), and as the post processing, the server may generate proposals by grouping nearby extracted words if their horizontal and/or vertical distance is within some threshold. For instance, if the words v 21 , v 22 , and v 23  are within a threshold vicinity of each other, then the server groups them as a single value phrase. Additionally or alternatively, the server may generate a probability of each proposal being included in the value for the input key phrase. For example, the server may determine whether the probability of each proposal is the maximum of the extracted words&#39; probabilities within this group. In the example of form F, the server may determine a combined probability of words v 21 , v 22 , and v 23 . If the probability of words v 21 , v 22 , and v 23  is higher than the probability of remaining groups, the server may then generate the value for the input key phrase (e.g., pick the proposal with the highest probability of this value). If the probability is lower than a threshold, the server may refrain from responding. Thus, the server may group one or more character strings into a value phrase based on an output of the machine learned model and the set of two-dimensional locations of the set of character strings. As depicted herein, identifying the value for the key-value pair may be based on grouping the one or more character strings. 
     At  340 , the server may transmit the identified value corresponding to the input key phrase. For example, the server may determine a value for the key-value pair corresponding to the input key phrase (received at  310 ) and may transmit the value to the user device that input the input key phrase. 
       FIG.  4    illustrates an example of an input document  400  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. 
     A user may submit the input document  400  to a system or application via a user interface. In some examples, the input document  400  may be input into the system by another user or already stored in a database or similar storage system. The input document  400  may be a form having a fixed format or a non-fixed format. A user may submit an input key phrase querying a value for a key-value pair. The user may be associated with a tenant of a multi-tenant database which has been using the cloud platform for data management. There may be several data stores of data and metadata associated with the tenant which may be used to train a machine learning model. The trained machine learning model may be used to process the query and generate a response. 
     The input document  400  may include multiple key-value pairs. For example, the input document  400  may include a key “bill to” and a corresponding value “John Smith 2 Court Square New York, N.Y. 12210.” As another example, the input document  400  includes a key  404  (“invoice #”) and a corresponding value  406  (“US-001”). As depicted with reference to  FIG.  4   , the input document  400  may include a value without any key associated with it. For instance, the value  402  include “East Repair Inc. 1912 Harvest Lane New York, N.Y. 12210.” However, the input document  400  does not include a key corresponding to the value  402 . 
     Once the user submits the input key phrase, the user interface receiving the input key phrase may send the input key phrase to a database server or some similar computing device or architecture running a machine learning model component. In some examples, the user interface may send a natural language query to a database server with a machine learning model component. For example, the natural language query may be processed by the database server (e.g., device  210  described with reference to  FIG.  2   ) and the database server may identify the input key phrase which may correspond to the natural language query. In some examples, the server may extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document  400 . For example, the server may extract the words or phrases “bill,” “to,” “ship,” “to,” “invoice #,” “US-001” and so on. The server may generate an array of words corresponding to the input document  400  and an array or their corresponding locations. For example, a reference location may be established for the input document  400  (e.g., the lower left-hand corner), and the locations (e.g., with respect to an X, Y coordinate system or any other coordinate system) of the words “bill”, “to,” “ship,” “to,” etc. may be determined with respect to the reference location. The server may then determine the value corresponding to the input key phrase based on one or more probability values computed by a machine learned model, as described in more detail with reference to  FIG.  3   . 
     In the example of  FIG.  4   , the user may submit an input key phrase as “invoice #.” In this example, the user is querying the value in the form associated with the field “invoice #.” Using the optical character recognition model and the machine learned model, as described herein, the server may determine that the value  406  “US-001” is the corresponding value to the input key phrase. In some examples, the input key phrase may not match any key in the input document  400 . For instance, instead of inputting “invoice #” the user may input “invoice number.” The server may determine that the phrase “invoice number” is a closest match to the key  404  “invoice #.” The technique for matching the input key phrase to a key included in the input document  400  is described in further details in  FIG.  3   . Upon matching, the server may return the value  406  “US-001” as the value of the input key phrase “invoice number.” 
     In some examples, the server may determine that the input key phrase is associated with an empty value field. The server may identify a dummy value corresponding to the input key phrase based on determining that the input key phrase is associated with the empty value field. In such instances, the server may return the dummy value in response to an input key phrase that is associated with an empty value. 
       FIG.  5    illustrates an example of a process flow  500  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The process flow diagram  500  includes a user device  505  and a server  510 . The user device  505  may be an example of the user device  205  as described with respect to  FIG.  2   , and the server  510  may be an example of the server  210  as described with respect to  FIG.  2   . Although one user device  505  is depicted in the example of  FIG.  5   , it may be understood that the process flow  500  may include multiple user devices  505 . The server may represent a set of computing components, data storage components, and the like, as described herein, and the processing may occur across on or more multiple devices. In some examples, the server  510  may support a multi-tenant database system as described herein. The process illustrated in  FIG.  5    may be performed for various tenants of the multiple tenant system. 
     In some examples, the operations illustrated in the process flow  500  may be performed by hardware (e.g., including circuitry, processing blocks, logic components, and other components), code (e.g., software or firmware) executed by a processor, or any combination thereof. Alternative examples of the following may be implemented, where some steps are performed in a different order than described or are not performed at all. In some cases, steps may include additional features not mentioned below, or further steps may be added. 
     At  515 , the server  510  may receive an input document including a set of input text fields. In some examples, the input document may include a fixed form, a non-fixed form, or both. The server  510  may receive the input document through an upload or submission process via a user interface. 
     At  520 , the server  510  may receive an input key phrase querying a value for a key-value pair that corresponds to one or more of the set of input text fields. The server  510  may receive the input key phrase via the user interface. The user interface used to submit the input document may be the same or different than the user interface used to submit the input key phrase. 
     At  525 , the server  510  may extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. At  530 , the server  510  may compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. In some examples, the server  510  may input the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute the probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. In some cases, the server  510  may input the input key phrase into the machine learned model. The server  510  may then identify a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. 
     At  535 , the server  510  may rank the set of probabilities based on a value of each probability in the set of probabilities. At  540 , the server  510  may identify the value for the key-value pair corresponding to the input key phrase based on inputting the extracted set of character strings and the set of two-dimensional locations into the machine learned model. In some examples, identifying the value for the key-value pair corresponding to the input key phrase may be based on ranking the set of probabilities. Additionally or alternatively, the server  510  may group one or more character strings into a value phrase based on an output of the machine learned model and the set of two-dimensional locations of the set of character strings. In some examples, identifying the value for the key-value pair is based on grouping the one or more character strings. At  545 , the server  510  may transmit the identified value corresponding to the input key phrase to the user device  505 . 
       FIG.  6    shows a block diagram  600  of a device  605  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The device  605  may include an input module  610 , an output module  615 , and a processing component  620 . The device  605  may also include a processor. Each of these components may be in communication with one another (e.g., via one or more buses). 
     The input module  610  may manage input signals for the device  605 . For example, the input module  610  may identify input signals based on an interaction with a modem, a keyboard, a mouse, a touchscreen, or a similar device. These input signals may be associated with user input or processing at other components or devices. In some cases, the input module  610  may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system to handle input signals. The input module  610  may send aspects of these input signals to other components of the device  605  for processing. For example, the input module  610  may transmit input signals to the processing component  620  to support processing forms using artificial intelligence models. In some cases, the input module  610  may be a component of an I/O controller  810  as described with reference to  FIG.  8   . 
     The output module  615  may manage output signals for the device  605 . For example, the output module  615  may receive signals from other components of the device  605 , such as the processing component  620 , and may transmit these signals to other components or devices. In some examples, the output module  615  may transmit output signals for display in a user interface, for storage in a database or data store, for further processing at a server or server cluster, or for any other processes at any number of devices or systems. In some cases, the output module  615  may be a component of an I/O controller  810  as described with reference to  FIG.  8   . 
     For example, the processing component  620  may include a document input component  625 , a key phrase component  630 , an extraction component  635 , a probability component  640 , a value identification component  645 , a value transmission component  650 , or any combination thereof. In some examples, the processing component  620 , or various components thereof, may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the input module  610 , the output module  615 , or both. For example, the processing component  620  may receive information from the input module  610 , send information to the output module  615 , or be integrated in combination with the input module  610 , the output module  615 , or both to receive information, transmit information, or perform various other operations as described herein. 
     The processing component  620  may support form processing at a server in accordance with examples as disclosed herein. The document input component  625  may be configured as or otherwise support a means for receiving an input document including a plurality of input text fields. The key phrase component  630  may be configured as or otherwise support a means for receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The extraction component  635  may be configured as or otherwise support a means for extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The probability component  640  may be configured as or otherwise support a means for inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The value identification component  645  may be configured as or otherwise support a means for identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The value transmission component  650  may be configured as or otherwise support a means for transmitting the identified value corresponding to the input key phrase. 
       FIG.  7    shows a block diagram  700  of a processing component  720  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The processing component  720  may be an example of aspects of a processing component or a processing component  620 , or both, as described herein. The processing component  720 , or various components thereof, may be an example of means for performing various aspects of processing forms using artificial intelligence models as described herein. For example, the processing component  720  may include a document input component  725 , a key phrase component  730 , an extraction component  735 , a probability component  740 , a value identification component  745 , a value transmission component  750 , a grouping component  755 , a matching component  760 , a feature representation component  765 , a training component  770 , or any combination thereof. Each of these components may communicate, directly or indirectly, with one another (e.g., via one or more buses). 
     The processing component  720  may support form processing at a server in accordance with examples as disclosed herein. The document input component  725  may be configured as or otherwise support a means for receiving an input document including a plurality of input text fields. The key phrase component  730  may be configured as or otherwise support a means for receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The extraction component  735  may be configured as or otherwise support a means for extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The probability component  740  may be configured as or otherwise support a means for inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The value identification component  745  may be configured as or otherwise support a means for identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The value transmission component  750  may be configured as or otherwise support a means for transmitting the identified value corresponding to the input key phrase. 
     In some examples, the key phrase component  730  may be configured as or otherwise support a means for inputting the input key phrase into the machine learned model. In some examples, the probability component  740  may be configured as or otherwise support a means for identifying a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. 
     In some examples, the probability component  740  may be configured as or otherwise support a means for ranking the set of probabilities based at least in part on a value of each probability in the set of probabilities, wherein identifying the value for the key-value pair corresponding to the input key phrase is based at least in part on ranking the set of probabilities. 
     In some examples, the grouping component  755  may be configured as or otherwise support a means for grouping one or more character strings into a value phrase based at least in part on an output of the machine learned model and the set of two-dimensional locations of the set of character strings, wherein identifying the value for the key-value pair is based at least in part on grouping the one or more character strings. 
     In some examples, the matching component  760  may be configured as or otherwise support a means for determining that the input key phrase does not match a key that corresponds to one or more of the plurality of input text fields. In some examples, the value identification component  745  may be configured as or otherwise support a means for identifying a dummy key corresponding to the input key phrase based at least in part on determining that the input key phrase does not match the key, wherein identifying the value for the key-value pair is based at least in part on identifying the dummy key. 
     In some examples, the value identification component  745  may be configured as or otherwise support a means for determining that the input key phrase is associated with an empty value field. In some examples, the value identification component  745  may be configured as or otherwise support a means for identifying a dummy value corresponding to the input key phrase based at least in part on determining that the input key phrase is associated with the empty value field, wherein the identified value corresponding to the input key phrase comprises the dummy value. 
     In some examples, the feature representation component  765  may be configured as or otherwise support a means for generating a first set of feature representations for a set of keywords included in the plurality of input text fields and a second set of feature representations for the extracted set of character strings. In some examples, the feature representation component  765  may be configured as or otherwise support a means for generating an unified feature representation for the input key phrase based at least in part on the first set of feature representations and the second set of feature representations, wherein identifying the value for the key-value pair corresponding to the input key phrase is based at least in part on the unified feature representation. 
     In some examples, the feature representation component  765  may be configured as or otherwise support a means for applying a dot product between the unified feature representation for the input key phrase and each feature representation of the second set of feature representations, wherein the probability that the character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase is computed based at least in part on applying the dot product. 
     In some examples, the training component  770  may be configured as or otherwise support a means for training the machine learned model based at least in part on inputting a plurality of input file formats into the machine learned model. In some examples, the input document comprises a fixed form, a non-fixed form, or both. In some examples, the machine learned model comprises a transformer-based machine learned model. 
       FIG.  8    shows a diagram of a system  800  including a device  805  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The device  805  may be an example of or include the components of a device  605  as described herein. The device  805  may include components for bi-directional data communications including components for transmitting and receiving communications, such as a processing component  820 , an I/O controller  810 , a database controller  815 , a memory  825 , a processor  830 , and a database  835 . These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus  840 ). 
     The I/O controller  810  may manage input signals  845  and output signals  850  for the device  805 . The I/O controller  810  may also manage peripherals not integrated into the device  805 . In some cases, the I/O controller  810  may represent a physical connection or port to an external peripheral. In some cases, the I/O controller  810  may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. In other cases, the I/O controller  810  may represent or interact with a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the I/O controller  810  may be implemented as part of a processor  830 . In some examples, a user may interact with the device  805  via the I/O controller  810  or via hardware components controlled by the I/O controller  810 . 
     The database controller  815  may manage data storage and processing in a database  835 . In some cases, a user may interact with the database controller  815 . In other cases, the database controller  815  may operate automatically without user interaction. The database  835  may be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database. 
     Memory  825  may include random-access memory (RAM) and ROM. The memory  825  may store computer-readable, computer-executable software including instructions that, when executed, cause the processor  830  to perform various functions described herein. In some cases, the memory  825  may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices. 
     The processor  830  may include an intelligent hardware device, (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor  830  may be configured to operate a memory array using a memory controller. In other cases, a memory controller may be integrated into the processor  830 . The processor  830  may be configured to execute computer-readable instructions stored in a memory  825  to perform various functions (e.g., functions or tasks supporting processing forms using artificial intelligence models). 
     The processing component  820  may support form processing at a server in accordance with examples as disclosed herein. For example, the processing component  820  may be configured as or otherwise support a means for receiving an input document including a plurality of input text fields. The processing component  820  may be configured as or otherwise support a means for receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The processing component  820  may be configured as or otherwise support a means for extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The processing component  820  may be configured as or otherwise support a means for inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The processing component  820  may be configured as or otherwise support a means for identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The processing component  820  may be configured as or otherwise support a means for transmitting the identified value corresponding to the input key phrase. 
     By including or configuring the processing component  820  in accordance with examples as described herein, the device  805  may support techniques for handling different types of forms, dummy values, and keys without values, and improved user experience related to processing documents without a predefined template. 
       FIG.  9    shows a flowchart illustrating a method  900  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The operations of the method  900  may be implemented by an application server or its components as described herein. For example, the operations of the method  900  may be performed by an application server as described with reference to  FIGS.  1  through  8   . In some examples, an application server may execute a set of instructions to control the functional elements of the application server to perform the described functions. Additionally or alternatively, the application server may perform aspects of the described functions using special-purpose hardware. 
     At  905 , the method may include receiving an input document including a plurality of input text fields. The operations of  905  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  905  may be performed by a document input component  725  as described with reference to  FIG.  7   . 
     At  910 , the method may include receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The operations of  910  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  910  may be performed by a key phrase component  730  as described with reference to  FIG.  7   . 
     At  915 , the method may include extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The operations of  915  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  915  may be performed by an extraction component  735  as described with reference to  FIG.  7   . 
     At  920 , the method may include inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The operations of  920  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  920  may be performed by a probability component  740  as described with reference to  FIG.  7   . 
     At  925 , the method may include identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The operations of  925  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  925  may be performed by a value identification component  745  as described with reference to  FIG.  7   . 
     At  930 , the method may include transmitting the identified value corresponding to the input key phrase. The operations of  930  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  930  may be performed by a value transmission component  750  as described with reference to  FIG.  7   . 
       FIG.  10    shows a flowchart illustrating a method  1000  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The operations of the method  1000  may be implemented by an application server or its components as described herein. For example, the operations of the method  1000  may be performed by an application server as described with reference to  FIGS.  1  through  8   . In some examples, an application server may execute a set of instructions to control the functional elements of the application server to perform the described functions. Additionally or alternatively, the application server may perform aspects of the described functions using special-purpose hardware. 
     At  1005 , the method may include receiving an input document including a plurality of input text fields. The operations of  1005  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1005  may be performed by a document input component  725  as described with reference to  FIG.  7   . 
     At  1010 , the method may include receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The operations of  1010  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1010  may be performed by a key phrase component  730  as described with reference to  FIG.  7   . 
     At  1015 , the method may include extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The operations of  1015  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1015  may be performed by an extraction component  735  as described with reference to  FIG.  7   . 
     At  1020 , the method may include inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The operations of  1020  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1020  may be performed by a probability component  740  as described with reference to  FIG.  7   . 
     At  1025 , the method may include inputting the input key phrase into the machine learned model. The operations of  1025  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1025  may be performed by a key phrase component  730  as described with reference to  FIG.  7   . 
     At  1030 , the method may include identifying a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. The operations of  1030  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1030  may be performed by a probability component  740  as described with reference to  FIG.  7   . 
     At  1035 , the method may include ranking the set of probabilities based at least in part on a value of each probability in the set of probabilities, wherein identifying the value for the key-value pair corresponding to the input key phrase is based at least in part on ranking the set of probabilities. The operations of  1035  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1035  may be performed by a probability component  740  as described with reference to  FIG.  7   . 
     At  1040 , the method may include identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The operations of  1040  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1040  may be performed by a value identification component  745  as described with reference to  FIG.  7   . 
     At  1045 , the method may include transmitting the identified value corresponding to the input key phrase. The operations of  1045  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1045  may be performed by a value transmission component  750  as described with reference to  FIG.  7   . 
       FIG.  11    shows a flowchart illustrating a method  1100  that supports processing forms using artificial intelligence models in accordance with aspects of the present disclosure. The operations of the method  1100  may be implemented by an application server or its components as described herein. For example, the operations of the method  1100  may be performed by an application server as described with reference to  FIGS.  1  through  8   . In some examples, an application server may execute a set of instructions to control the functional elements of the application server to perform the described functions. Additionally or alternatively, the application server may perform aspects of the described functions using special-purpose hardware. 
     At  1105 , the method may include receiving an input document including a plurality of input text fields. The operations of  1105  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1105  may be performed by a document input component  725  as described with reference to  FIG.  7   . 
     At  1110 , the method may include receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields. The operations of  1110  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1110  may be performed by a key phrase component  730  as described with reference to  FIG.  7   . 
     At  1115 , the method may include extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. The operations of  1115  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1115  may be performed by an extraction component  735  as described with reference to  FIG.  7   . 
     At  1120 , the method may include inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase. The operations of  1120  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1120  may be performed by a probability component  740  as described with reference to  FIG.  7   . 
     At  1125 , the method may include determining that the input key phrase does not match a key that corresponds to one or more of the plurality of input text fields. The operations of  1125  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1125  may be performed by a matching component  760  as described with reference to  FIG.  7   . 
     At  1130 , the method may include identifying a dummy key corresponding to the input key phrase based at least in part on determining that the input key phrase does not match the key, wherein identifying the value for the key-value pair is based at least in part on identifying the dummy key. The operations of  1130  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1130  may be performed by a value identification component  745  as described with reference to  FIG.  7   . 
     At  1135 , the method may include identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting. The operations of  1135  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1135  may be performed by a value identification component  745  as described with reference to  FIG.  7   . 
     At  1140 , the method may include transmitting the identified value corresponding to the input key phrase. The operations of  1140  may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of  1140  may be performed by a value transmission component  750  as described with reference to  FIG.  7   . 
     A method for form processing at a server is described. The method may include receiving an input document including a plurality of input text fields, receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields, extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document, inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase, identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting, and transmitting the identified value corresponding to the input key phrase. 
     An apparatus for form processing at a server is described. The apparatus may include a processor, memory coupled with the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to receive an input document including a plurality of input text fields, receive an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields, extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document, input the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase, identify the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting, and transmit the identified value corresponding to the input key phrase. 
     Another apparatus for form processing at a server is described. The apparatus may include means for receiving an input document including a plurality of input text fields, means for receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields, means for extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document, means for inputting the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase, means for identifying the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting, and means for transmitting the identified value corresponding to the input key phrase. 
     A non-transitory computer-readable medium storing code for form processing at a server is described. The code may include instructions executable by a processor to receive an input document including a plurality of input text fields, receive an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields, extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document, input the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase, identify the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting, and transmit the identified value corresponding to the input key phrase. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for inputting the input key phrase into the machine learned model and identifying a set of probabilities for the set of character strings being the value for the key-value pair corresponding to the input key phrase. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for ranking the set of probabilities based at least in part on a value of each probability in the set of probabilities, wherein identifying the value for the key-value pair corresponding to the input key phrase may be based at least in part on ranking the set of probabilities. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for grouping one or more character strings into a value phrase based at least in part on an output of the machine learned model and the set of two-dimensional locations of the set of character strings, wherein identifying the value for the key-value pair may be based at least in part on grouping the one or more character strings. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining that the input key phrase does not match a key that corresponds to one or more of the plurality of input text fields and identifying a dummy key corresponding to the input key phrase based at least in part on determining that the input key phrase does not match the key, wherein identifying the value for the key-value pair may be based at least in part on identifying the dummy key. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining that the input key phrase may be associated with an empty value field and identifying a dummy value corresponding to the input key phrase based at least in part on determining that the input key phrase may be associated with the empty value field, wherein the identified value corresponding to the input key phrase comprises the dummy value. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for generating a first set of feature representations for a set of keywords included in the plurality of input text fields and a second set of feature representations for the extracted set of character strings and generating an unified feature representation for the input key phrase based at least in part on the first set of feature representations and the second set of feature representations, wherein identifying the value for the key-value pair corresponding to the input key phrase may be based at least in part on the unified feature representation. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for applying a dot product between the unified feature representation for the input key phrase and each feature representation of the second set of feature representations, wherein the probability that the character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase may be computed based at least in part on applying the dot product. 
     Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for training the machine learned model based at least in part on inputting a plurality of input file formats into the machine learned model. 
     In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the input document comprises a fixed form, a non-fixed form, or both. In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the machine learned model comprises a transformer-based machine learned model. 
     It should be noted that the methods described above describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Furthermore, aspects from two or more of the methods may be combined. 
     The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “exemplary” used herein means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples. 
     In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If just the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label. 
     Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration). 
     The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as used herein, including in the claims, “or” as used in a list of items (for example, a list of items prefaced by a phrase such as “at least one of” or “one or more of”) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an exemplary step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on.” 
     Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media can comprise RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media. 
     The description herein is provided to enable a person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein, but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.