Abstract:
An artificial intelligence assistant (“chatbot”) operates within a multi-tenant database and allows users to interact with the underlying structured database through a natural language interface without using a standard structured query language or database interface. Users may interact with the chatbot via a chatroom and perform database queries using natural language expressions in the same manner as asking a person to perform the tasks. In addition, the chatbot may check user permissions and security parameters to determine if the user is permitted to access or alter data within the multi-tenant database.

Description:
[0001]    The present application claims priority to U.S. Provisional Patent Application Ser. No. 62/366,749 filed on Jul. 26, 2016, entitled: CHATBOT FOR INTERFACING WITH MULTI-TENANT DATABASES which is incorporated by reference in its entirety. 
     
    
     COPYRIGHT NOTICE 
       [0002]    A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
       TECHNICAL FIELD 
       [0003]    The technology relates to a natural language platform for a database system. 
       BACKGROUND 
       [0004]    A user may not know how to interface with a database system. For example, the user may be non-technical, have an incomplete or missing database view, or be unfamiliar with the database schema. Even technical users may be reluctant to access databases when information needs to be accessed quickly, the user is unsure of the exact database syntax, or the information for basing a database query is incorrect. 
         [0005]    The database user may make assumptions based upon requested information, leading to undesired information or wasting time in back-and-forth information gathering. For example, a software project manager may want to know how many open bugs are left for a next software release. The project manager may access a dashboard that shows all open bugs but may not know how to filter the results to only display open bugs for a next software release. 
         [0006]    In another example, a user is tasked with updating the phone number for a customer record in a company database. Unaware of the exact database schema, the user may update a home phone number instead of a mobile phone number or work phone number. In yet another example, an account executive asks an assistant how many orders were completed last year. An hour later the assistant presents all placed orders, but the executive specifically wanted orders that were placed and fulfilled. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    The included drawings are for illustrative purposes and serve to provide examples of possible structures and operations for the disclosed inventive systems, apparatus, methods and computer-readable storage media. These drawings in no way limit any changes in form and detail that may be made by one skilled in the art without departing from the spirit and scope of the disclosed implementations. 
           [0008]      FIG. 1A  shows a block diagram of an example environment in which an on-demand database service can be used according to some implementations. 
           [0009]      FIG. 1B  shows a block diagram of example implementations of elements of  FIG. 1A  and example interconnections between these elements according to some implementations. 
           [0010]      FIG. 2  shows an example natural language processing platform (chatbot) used in a multi-tenant database. 
           [0011]      FIG. 3  shows how the chatbot operates within the multi-tenant database. 
           [0012]      FIG. 4  shows an example process for the chatbot. 
           [0013]      FIG. 5  shows an example process for operating a chatbot within a chat group. 
           [0014]      FIG. 6  shows an example chatbot database platform. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    Examples of systems, apparatus, computer-readable storage media, and methods according to the disclosed implementations are described in this section. These examples are being provided solely to add context and aid in the understanding of the disclosed implementations. It will thus be apparent to one skilled in the art that the disclosed implementations may be practiced without some or all of the specific details provided. In other instances, certain process or method operations, also referred to herein as “blocks.” have not been described in detail in order to avoid unnecessarily obscuring the disclosed implementations. Other implementations and applications also are possible, and as such, the following examples should not be taken as definitive or limiting either in scope or setting. 
         [0016]    In the following detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific implementations. Although these disclosed implementations are described in sufficient detail to enable one skilled in the art to practice the implementations, it is to be understood that these examples are not limiting, such that other implementations may be used and changes may be made to the disclosed implementations without departing from their spirit and scope. For example, the blocks of the methods shown and described herein are not necessarily performed in the order indicated in some other implementations. Additionally, in some other implementations, the disclosed methods may include more or fewer blocks than are described. As another example, some blocks described herein as separate blocks may be combined in some other implementations. Conversely, what may be described herein as a single block may be implemented in multiple blocks in some other implementations. Additionally, the conjunction “or” is intended herein in the inclusive sense where appropriate unless otherwise indicated; that is, the phrase “A, B or C” is intended to include the possibilities of “A,” “B,” “C,” “A and B,” “B and C,” “A and C” and “A, B and C.” 
         [0017]    Some implementations described and referenced herein are directed to systems, apparatus, computer-implemented methods and computer-readable storage media for identifying articles helpful in resolving user queries. 
         [0018]    In some implementations, the users described herein are users (or “members”) of an interactive online “enterprise social network,” also referred to herein as an “enterprise social networking system,” an “enterprise collaborative network,” or more simply as an “enterprise network.” Such online enterprise networks are increasingly becoming a common way to facilitate communication among people, any of whom can be recognized as enterprise users. One example of an online enterprise social network is Chatter®, provided by salesforce.com, inc. of San Francisco, Calif. salesforce.com, inc. is a provider of enterprise social networking services, customer relationship management (CRM) services and other database management services, any of which can be accessed and used in conjunction with the techniques disclosed herein in some implementations. These various services can be provided in a cloud computing environment as described herein, for example, in the context of a multi-tenant database system. Some of the described techniques or processes can be implemented without having to install software locally, that is, on computing devices of users interacting with services available through the cloud. While the disclosed implementations may be described with reference to Chatter® and more generally to enterprise social networking, those of ordinary skill in the art should understand that the disclosed techniques are neither limited to Chatter® nor to any other services and systems provided by salesforce.com, inc. and can be implemented in the context of various other database systems such as cloud-based systems that are not part of a multi-tenant database system or which do not provide enterprise social networking services. 
       I. Example System Overview 
       [0019]      FIG. 1A  shows a block diagram of an example of an environment  10  in which an on-demand database service can be used in accordance with some implementations. The environment  10  includes user systems  12 , a network  14 , a database system  16  (also referred to herein as a “cloud-based system”), a processor system  17 , an application platform  18 , a network interface  20 , tenant database  22  for storing tenant data  23 , system database  24  for storing system data  25 , program code  26  for implementing various functions of the system  16 , and process space  28  for executing database system processes and tenant-specific processes, such as running applications as part of an application hosting service. In some other implementations, environment  10  may not have all of these components or systems, or may have other components or systems instead of, or in addition to, those listed above. 
         [0020]    In some implementations, the environment  10  is an environment in which an on-demand database service exists. An on-demand database service, such as that which can be implemented using the system  16 , is a service that is made available to users outside of the enterprise(s) that own, maintain or provide access to the system  16 . As described above, such users generally do not need to be concerned with building or maintaining the system  16 . Instead, resources provided by the system  16  may be available for such users&#39; use when the users need services provided by the system  16 . that is, on the demand of the users. Some on-demand database services can store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers or tenants. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows of data such as feed items for a potentially much greater number of customers. A database image can include one or more database objects. A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s). 
         [0021]    Application platform  18  can be a framework that allows the applications of system  16  to execute, such as the hardware or software infrastructure of the system  16 . In some implementations, the application platform  18  enables the creation, management and execution of one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems  12 , or third party application developers accessing the on-demand database service via user systems  12 . 
         [0022]    In some implementations, the system  16  implements a web-based customer relationship management (CRM) system. For example, in some such implementations, the system  16  includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, renderable web pages and documents and other information to and from user systems  12  and to store to, and retrieve from, a database system related data, objects, and Web page content. In some MTS implementations, data for multiple tenants may be stored in the same physical database object in tenant database  22 . In some such implementations, tenant data is arranged in the storage medium(s) of tenant database  22  so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant&#39;s data, unless such data is expressly shared. The system  16  also implements applications other than, or in addition to, a CRM application. For example, the system  16  can provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third party developer) applications, which may or may not include CRM, may be supported by the application platform  18 . The application platform  18  manages the creation and storage of the applications into one or more database objects and the execution of the applications in one or more virtual machines in the process space of the system  16 . 
         [0023]    According to some implementations, each system  16  is configured to provide web pages, forms, applications, data and media content to user (client) systems  12  to support the access by user systems  12  as tenants of system  16 . As such, system  16  provides security mechanisms to keep each tenant&#39;s data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (for example, in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (for example, one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application (for example, OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as part of a single database, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and can include a distributed database or storage network and associated processing intelligence. 
         [0024]    The network  14  can be or include any network or combination of networks of systems or devices that communicate with one another. For example, the network  14  can be or include any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, cellular network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. The network  14  can include a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” (with a capital “1”). The Internet will be used in many of the examples herein. However, it should be understood that the networks that the disclosed implementations can use are not so limited, although TCP/IP is a frequently implemented protocol. 
         [0025]    The user systems  12  can communicate with system  16  using TCP/IP and, at a higher network level, other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, each user system  12  can include an HTTP client commonly referred to as a “web browser” or simply a “browser” for sending and receiving HTTP signals to and from an HTTP server of the system  16 . Such an HTTP server can be implemented as the sole network interface  20  between the system  16  and the network  14 , but other techniques can be used in addition to or instead of these techniques. In some implementations, the network interface  20  between the system  16  and the network  14  includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a number of servers. In MTS implementations, each of the servers can have access to the MTS data: however, other alternative configurations may be used instead. 
         [0026]    The user systems  12  can be implemented as any computing device(s) or other data processing apparatus or systems usable by users to access the database system  16 . For example, any of user systems  12  can be a desktop computer, a work station, a laptop computer, a tablet computer, a handheld computing device, a mobile cellular phone (for example, a “smartphone”). or any other Wi-Fi-enabled device, wireless access protocol (WAP)-enabled device, or other computing device capable of interfacing directly or indirectly to the Internet or other network. The terms “user system” and “computing device” are used interchangeably herein with one another and with the term “computer.” As described above, each user system  12  typically executes an HTTP client, for example, a web browsing (or simply “browsing”) program. such as a web browser based on the WebKit platform, Microsoft&#39;s Internet Explorer browser, Apple&#39;s Safari, Google&#39;s Chrome, Opera&#39;s browser, or Mozilla&#39;s Firefox browser, or the like, allowing a user (for example, a subscriber of on-demand services provided by the system  16 ) of the user system  12  to access, process and view information, pages and applications available to it from the system  16  over the network  14 . 
         [0027]    Each user system  12  also typically includes one or more user input devices, such as a keyboard, a mouse, a trackball, a touch pad, a touch screen, a pen or stylus or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (for example, a monitor screen, liquid crystal display (LCD), light-emitting diode (LED) display, among other possibilities) of the user system  12  in conjunction with pages, forms, applications and other information provided by the system  16  or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system  16 , and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like. 
         [0028]    The users of user systems  12  may differ in their respective capacities, and the capacity of a particular user system  12  can be entirely determined by permissions (permission levels) for the current user of such user system. For example, where a salesperson is using a particular user system  12  to interact with the system  16 . that user system can have the capacities allotted to the salesperson. However, while an administrator is using that user system  12  to interact with the system  16 , that user system can have the capacities allotted to that administrator. Where a hierarchical role model is used, users at one permission level can have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users generally will have different capabilities with regard to accessing and modifying application and database information, depending on the users&#39; respective security or permission levels (also referred to as “authorizations”). 
         [0029]    According to some implementations, each user system  12  and some or all of its components are operator-configurable using applications, such as a browser, including computer code executed using a central processing unit (CPU) such as an Intel Pentium® processor or the like. Similarly, the system  16  (and additional instances of an MTS, where more than one is present) and all of its components can be operator-configurable using application(s) including computer code to run using the processor system  17 , which may be implemented to include a CPU, which may include an Intel Pentium processor or the like, or multiple CPUs. 
         [0030]    The system  16  includes tangible computer-readable media having non-transitory instructions stored thereon/in that are executable by or used to program a server or other computing system (or collection of such servers or computing systems) to perform some of the implementation of processes described herein. For example, computer program code  26  can implement instructions for operating and configuring the system  16  to intercommunicate and to process web pages, applications and other data and media content as described herein. In some implementations, the computer code  26  can be downloadable and stored on a hard disk, but the entire program code, or portions thereof, also can be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disks (DVD), compact disks (CD), microdrives, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any other type of computer-readable medium or device suitable for storing instructions or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, for example, over the Internet, or from another server, as is well known, or transmitted over any other existing network connection as is well known (for example, extranet, VPN, LAN, etc.) using any communication medium and protocols (for example, TCP/IP, HTTP, HTTPS, Ethernet. etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a server or other computing system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (Java™ is a trademark of Sun Microsystems, Inc.). 
         [0031]      FIG. 1B  shows a block diagram of example implementations of elements of  FIG. 1A  and example interconnections between these elements according to some implementations. That is,  FIG. 1B  also illustrates environment  10 , but  FIG. 1B , various elements of the system  16  and various interconnections between such elements are shown with more specificity according to some more specific implementations. Additionally, in  FIG. 1B , the user system  12  includes a processor system  12 A, a memory system  12 B, an input system  12 C, and an output system  12 D. The processor system  12 A can include any suitable combination of one or more processors. The memory system  12 B can include any suitable combination of one or more memory devices. The input system  12 C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs. scanners, cameras, or interfaces to networks. The output system  12 D can include any suitable combination of output devices, such as one or more display devices, printers, or interfaces to networks. 
         [0032]    In  FIG. 1B , the network interface  20  is implemented as a set of HTTP application servers  100   1 - 100   N . Each application server  100 , also referred to herein as an “app server”, is configured to communicate with tenant database  22  and the tenant data  23  therein, as well as system database  24  and the system data  25  therein, to serve requests received from the user systems  12 . The tenant data  23  can be divided into individual tenant storage spaces  40 . which can be physically or logically arranged or divided. Within each tenant storage space  40 , user storage  42  and application metadata  44  can similarly be allocated for each user. For example, a copy of a user&#39;s most recently used (MRU) items can be stored to user storage  42 . Similarly, a copy of MRU items for an entire organization that is a tenant can be stored to tenant storage space  40 . 
         [0033]    The process space  28  includes system process space  102 , individual tenant process spaces  48  and a tenant management process space  46 . The application platform  18  includes an application setup mechanism  38  that supports application developers&#39; creation and management of applications. Such applications and others can be saved as metadata into tenant database  22  by save routines  36  for execution by subscribers as one or more tenant process spaces  48  managed by tenant management process  46 , for example. Invocations to such applications can be coded using PL/SOQL  34 , which provides a programming language style interface extension to API  32 . A detailed description of some PL/SOQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, issued on Jun. 1, 2010, and hereby incorporated by reference in its entirety and for all purposes. Invocations to applications can be detected by one or more system processes, which manage retrieving application metadata  44  for the subscriber making the invocation and executing the metadata as an application in a virtual machine. 
         [0034]    The system  16  of  FIG. 1B  also includes a user interface (UI)  30  and an application programming interface (API)  32  to system  16  resident processes to users or developers at user systems  12 . In some other implementations, the environment  10  may not have the same elements as those listed above or may have other elements instead of, or in addition to, those listed above. 
         [0035]    Each application server  100  can be communicably coupled with tenant database  22  and system database  24 , for example, having access to tenant data  23  and system data  25 , respectively, via a different network connection. For example, one application server  100   1  can be coupled via the network  14  (for example, the Internet), another application server  100   N-1  can be coupled via a direct network link, and another application server  100   N  can be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are examples of typical protocols that can be used for communicating between application servers  100  and the system  16 . However, it will be apparent to one skilled in the art that other transport protocols can be used to optimize the system  16  depending on the network interconnections used. 
         [0036]    In some implementations, each application server  100  is configured to handle requests for any user associated with any organization that is a tenant of the system  16 . Because it can be desirable to be able to add and remove application servers  100  from the server pool at any time and for various reasons, in some implementations there is no server affinity for a user or organization to a specific application server  100 . In some such implementations, an interface system implementing a load balancing function (for example, an F5 Big-IP load balancer) is communicably coupled between the application servers  100  and the user systems  12  to distribute requests to the application servers  100 . In one implementation, the load balancer uses a least-connections algorithm to route user requests to the application servers  100 . Other examples of load balancing algorithms, such as round robin and observed-response-time, also can be used. For example, in some instances, three consecutive requests from the same user could hit three different application servers  100 , and three requests from different users could hit the same application server  100 . In this manner, by way of example, system  16  can be a multi-tenant system in which system  16  handles storage of, and access to, different objects, data and applications across disparate users and organizations. 
         [0037]    In one example storage use case, one tenant can be a company that employs a sales force where each salesperson uses system  16  to manage aspects of their sales. A user can maintain contact data, leads data. customer follow-up data, performance data. goals and progress data, etc., all applicable to that user&#39;s personal sales process (for example, in tenant database  22 ). In an example of a MTS arrangement, because all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system  12  having little more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, when a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates regarding that customer while waiting for the customer to arrive in the lobby. 
         [0038]    While each user&#39;s data can be stored separately from other users&#39; data regardless of the employers of each user, some data can be organization-wide data shared or accessible by several users or all of the users for a given organization that is a tenant. Thus, there can be some data structures managed by system  16  that are allocated at the tenant level while other data structures can be managed at the user level. Because an MTS can support multiple tenants including possible competitors, the MTS can have security protocols that keep data. applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that can be implemented in the MTS. In addition to user-specific data and tenant-specific data, the system  16  also can maintain system level data usable by multiple tenants or other data. Such system level data can include industry reports, news, postings, and the like that are sharable among tenants. 
         [0039]    In some implementations, the user systems  12  (which also can be client systems) communicate with the application servers  100  to request and update system-level and tenant-level data from the system  16 . Such requests and updates can involve sending one or more queries to tenant database  22  or system database  24 . The system  16  (for example, an application server  100  in the system  16 ) can automatically generate one or more SQL statements (for example, one or more SQL queries) designed to access the desired information. System database  24  can generate query plans to access the requested data from the database. The term “query plan” generally refers to one or more operations used to access information in a database system. 
         [0040]    Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined or customizable categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or element of a table can contain an instance of data for each category defined by the fields. For example, a CRM database can include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table can describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some MTS implementations, standard entity tables can be provided for use by all tenants. For CRM database applications, such standard entities can include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. As used herein, the term “entity” also may be used interchangeably with “object” and “table.” 
         [0041]    In some MTS implementations, tenants are allowed to create and store custom objects, or may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman et al., issued on Aug. 17, 2010, and hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers. 
       II. Natural Language Platform for Database System 
       [0042]    A natural language processing platform is alternatively referred to as a chatbot. The chatbot accesses a multi-tenant database and uses metadata to identify datasets and fields within the multi-tenant database. The chatbot is a software program that receives a natural language user query, via direct messages or group chats, and may exist as a separate entity on a chat client. The chatbot may use natural language processing (NLP) to convert the natural language user query into a structured database query or action for accessing structured data in the multi-tenant database. The chatbot may generate a natural language response that includes results of the structured database query. If the natural language user query is insufficient for constructing a database query or action, the chatbot may ask the chatbot user for more information until a valid database query or action is determined. 
         [0043]      FIG. 2  shows an example natural language processing platform  110  operating as a chatbot within a database system  105 . One or more user systems  12 A- 12 C as described above may access a chatbot interface  106 . Chatbot interface  106  may be any user interface operating on user systems  12  or on database system  105  that receives natural language search queries  104 . Chatbot interface  106  may include software that operates in conjunction with a user interface used for accessing multi-tenant database  16  or accessing other external data  111 . For example, chatbot interface  106  may include Facebook® messenger, Flake, Apple® Imessage, or any other software where a user chats or sends text messages. In one example, chatbot interface  106  may receive and send text or audio data forming natural language query  104 . 
         [0044]    Chatbot  110  converts natural language query  104  into a structured database query  108  for accessing structured data in multi-tenant database  16 . Chatbot  110  also may access external data  111  based on query  104 . For example, chatbot  110  may access a website  111  and provide additional information related to query  104 . By converting natural language queries  104  into structured database queries  108 , chatbot  110  enable users with little database knowledge to more easily and quickly locate structured data and information in multi-tenant database  16  and external data  111 . 
         [0045]    It should be understood that natural language queries  104  and the structured database queries  108  based on natural language queries  104  may include any type of query that reads, modifies, and/or writes data from or into multi-tenant database  16 . 
         [0046]    In one example, chatbot  110  operates behind a firewall  107  within the same servers as database system  105  and performs searches directly on multi-tenant database  16 . By operating behind firewall  107 , chatbot  110  may directly access user permissions and associated organization data in multi-tenant database  16  without going through application programming interfaces (APIs). 
         [0047]    Users on user systems  12 A- 12 C may have associated login addresses  102 A- 102 C, respectively. Chatbot  110  may identify datasets within database  16  associated with the user login address  102  and generate structured database queries  108  that access the identified datasets. Thus, chatbot  108  not only converts natural language queries  104  into structured database queries  108  but also scopes the structured database queries to the datasets in multi-tenant database  16  associated with the user organization. 
         [0048]      FIG. 3  shows an example of how the chatbot operates within the multi-tenant database. In one example, a customer relationship management (CRM) application may store accounts, contacts, custom records, leads, opportunities, dashboards, and other reports in database  16 . A user interface  119  on user system  12  may display a list of the records and other objects  120  residing on database  16 . Of course any type of application may store any type of data, dataset, record, object, field, etc. in database  16 . Objects  120  may be stored as structured data in database  16 , such as in tables  140  that include columns  142 . In one example, data in database  16  is accessed using a structured query language (SQL). 
         [0049]    A user may enter natural language query  104  into user interface  119 . For example, a user John Smith may enter the natural language query “How many accounts are open?” User system  12  sends natural language query  104  to chatbot  110  in database system  16 . Chatbot  110  may use a user id, domain (@company.com), or internet address (IP address)  102  received from the user to identify an associated tenant or organization  136  in database  16 . 
         [0050]    Tenant  136  may include a group of users who share common access with specific privileges to a software instance. The multi-tenant architecture of database  16  provides tenant  136  with a dedicated share of the software instance typically including one or more of tenant specific data, user management, tenant-specific functionality, configuration, customizations, non-functional properties, associated applications, etc. Multi-tenancy may contrast with multi-instance architectures, where separate software instances operate on behalf of different tenants. In one example, tenant  136  may be associated with an organization, such as company A. 
         [0051]    Chatbot  110  uses address  102  to associate the user with a particular tenant  136  and organization. Database system  16  identifies user permissions  138 , tables, records, objects, metadata. or any other datasets associated with tenant  136 . For example, the datasets for a particular tenant  136  may have a same organization identifier. 
         [0052]    Chatbot  110  converts natural language query  104  into a structured database query  108  based on permissions  138 , datasets  140 , and metadata  132  associated with the tenant/organization  136 . Chatbot  110  receives data and/or information from database  16  responsive to structured database query  108  and converts the data into a natural language response  124 . For example, chatbot  110  may generate the following transcript:
       John Smith: How many accounts are open?   Chatbot: Five accounts are open.       
 
         [0055]    Chatbot  110  may ask questions to clarify the database search. For example, the user may be a project manager (PM) that directs the following natural language query to chatbot  110  to produce the following transcript:
       PM: Can you tell me how many open bugs are left?   Chatbot: Yes, would you like to know the count for all open bugs?   PM: No, just the ones for this release   Chatbot: There are 23 bugs with open status for release 8.0       
 
         [0060]    Chatbot  110  also may identify user permissions and only display database query results when the user has sufficient authorization. For example, chatbot  110  may identify permissions  138  associated with user address  102  and only display results when permissions  138  provide sufficient authorization. An example of this interaction may produce the following transcript:
       John: Can you tell me what the top 3 accounts are, based on volume?   Chatbot: I&#39;m sorry I cannot do that, John. You do not have the authority to view that information.       
 
         [0063]    Chatbot  110  also may clarify a natural language query and either read data from multi-tenant database  16 , modify data in multi-tenant database  16 , or write data to multi-tenant database  16  based on natural language query  104 . For example, chatbot  110  may not be able to identify a field in table  140  that corresponds with the word “phone” used in natural language query  104 . However, chatbot  110  may identify other similar fields  142 A and  142 B in table  140 , such as “home_phone” “work_phone”, and “mobile_phone.” This interaction may produce the following transcript:
       John: Can you update the phone number for Rick Jones to (555) 555-5555?   Chatbot: Would you like me to update Home Phone, Work Phone, or Mobile Phone?   User: Work phone   Chatbot: Done.       
 
         [0068]    Chatbot  110  may have a personality to engage the user. If further information is needed, chatbot  110  may request it politely instead of curtly. Chatbot  110  may also provide suggestions based on a context of the query.
       User: Who is our contact on the Acme account?   Chatbot: That would be Bill Johnson. Would you like his contact information?       
 
         [0071]    If there is a problem accessing multi-tenant database  16 , chatbot  110  may inform the user data is currently unavailable and include a description of the error.
       User: How many crashes were reported for 7.0?   Chatbot: I&#39;m sorry, that data is currently unavailable due to network issues. Please ask me again later       
 
         [0074]    As mentioned above, chatbot  110  may associate natural language queries  104  with specific tenants or organizations  136  within multi-tenant database  16 . The subset of data chatbot  110  queries then may depend on the user and associated organization interacting with chatbot  110 . For example,
       Query 1:   John Smith@company_A.com: How many accounts are open?   Chatbot: Right now, there are 40 Accounts that are open.   Query 2:   User_2 @company_B.com: How many accounts are open?   Chatbot: I could not find data for Accounts. Did you mean Customers?       
 
         [0081]    Different datasets may exist in database  16  for different organizations  136 . 
         [0082]    For example, organization  136  associated with company_A may include an accounts table  140  that chatbot  110  accesses pursuant to query 1. However, a second organization  136  associated with company_B and user_2 in query 2 may not have an account table. Accordingly, chatbot  110  sends a message back to user_2 indicating no Accounts exist for the associated organization  136 . 
         [0083]    Chatbot  110  may consolidate data from database  16  with data from other databases.
       RealEstateAgent: Show me the price of the property located at 123 Fig St in Walnut Creek, Calif.   Chatbot: The price of that property is $500,000. However, MLS shows that the status is SOLD.       
 
         [0086]    Chatbot  110  may perform separate API calls to external databases, such as a website and combine the external data with any data retrieved from database  16 . 
         [0087]    Chatbot  110  may initiate some API calls based on previous queries requesting similar data. For example, chatbot  110  may frequently receive queries for the price of property followed by requests for the property sale status. Chatbot  110  may automatically initiate a separate multiple listing service (MLS) website search in response to the property price query and supply results of the MLS search in combination with the price data obtained from database  16 . 
         [0088]    Chatbot  110  may operate within group chat applications. If participants of the chat session have insufficient permissions, chatbot  110  may only send search results directly to the authorized search requester.
       Query 1:   Sales person: Who is the Account Executive for the Acme Account?   Chatbot: The Account Executive is Jerry Jones.   Query 2:   Manager: What is the Sales Volume for that account?   Chatbot: I will tell you in a direct message.       
 
         [0095]    In the first example, a sales person and a manager may communicate via a group chat application. The manager may have higher permissions  138  than the sales person. During the group chat session, the sales person may send query 1 to chatbot  110 . The sales person and the manager both may have sufficient permissions  138  to view the account executive information for the Acme account. Accordingly, chatbot  110  may send the account executive name to both the sales person and the manager. 
         [0096]    In the second example, only the manager may have sufficient permissions  138  to view the sales volume data for the Acme account. Accordingly, chatbot  110  may only send the sales volume data to the manager via a private direct message. 
         [0097]    In another example, neither the sales person nor the manager may have sufficient permissions  138  to view requested data. Chatbot  110  then may send a natural language response to both the sales person and the manager indicating insufficient privileges for viewing the requested data.
       Chatbot  110  may generate numerical responses:   User: What percentage of open bugs are on 7.0?   Chatbot: 34.5% of Open bugs belong to Version 7.0
 
Chatbot  110  may identify a math operator such as “percentage” in a portion of natural language query  104 . Chatbot  110  then may generate structured query  108  that returns a mathematical result that identifies the number of open bugs associated with the 7.0 software release. Alternatively, chatbot  110  may perform the mathematical operation that divides the number of open bugs in the 7.0 software release by the total number of bugs in the 7.0 software release.
       
 
       Metadata 
       [0101]    Chatbot  110  may use metadata  132  to resolve ambiguities in natural language query  104 . It should be understood that metadata  132  is distinguished from other metadata referred to above in  FIGS. 1A and 1B  and used by multi-tenant database  16  for different tenants  136 . 
         [0102]    Chatbot  110  may parse different portions of query  104  to first identify associated datasets. For example, user John Smith may send the following natural language query  104 .
       John Smith@company_A: How many accounts are open?       
 
         [0104]    Chatbot  110  may identify the user and associated company associated with query  104  as described above to identify the associated datasets in database  16 . For example, chatbot  110  may scope the data in multi-tenant database  16  belonging to company A and accessible by John Smith. In this example, chatbot  110  identifies account table  140  as part of the dataset associated with company A that John Smith has access permissions. 
         [0105]    Chatbot  110  then may parse query  104  into three different phrases: 
         [0106]    1. How many 
         [0107]    2. accounts 
         [0108]    3. are open? 
         [0109]    Chatbot  110  may recognize the phrase “How many” as a record count. Chatbot  110  may recognize the second phrase “accounts” as a dataset or table  140  in database  16  to filter data upon. By using a natural language processor to recognize the singular form of words, and a simple matching algorithm. chatbot  110  may match the second phrase with account_table  140 . 
         [0110]    Chatbot  110  may identify the third phrase “are open” as a predicate for second phrase “accounts.” Chatbot  110  may identify a table-predicate field relationship in metadata  132 A between account table  140  and account category field  142 C. Metadata  132 A may direct chatbot  110  to filter the account_category in account table  140  per the “open” term in the third phrase. For instance, in terms of a SQL database. chatbot  110  may construct the following SQL command  108  from natural language query  104 :
       SELECT count(*) FROM account_table WHERE account_category=“open”       
 
         [0112]    Chatbot  110  may receive an integer response of 5 from database  16  in response to structured query  108 . Chatbot  110  may convert the integer response into natural language response  124  with the answer “Five accounts are open.” and send the answer to John Smith at user system  12 . 
         [0113]    In another example, a user may enter the natural language query:
       How many leads are active?
 
Chatbot  110  again identifies keywords in a first phrase “How many” as associated with a record count. Chatbot  110  recognizes second phrase “leads” as a dataset in database  16  to filter data upon. Chatbot  110  identifies a third phrase “are active” as a predicate to the second “leads” phrase. Chatbot  110  determines from a second entry in metadata  132 A that a predicate phrase associated with the leads table is mapped to an active status column. Accordingly, chatbot  110  generates a structured database query that filters the data in the leads table (not shown) upon “active” for “active status.”
   SELECT count(*) FROM leads_table WHERE active_status=“active”       
 
         [0116]    As also mentioned above, a user may enter an ambiguous query such as:
       What is the Acme phone number?
 
Chatbot  110  may identify a work phone  142 A and a mobile phone  142 B for the Acme account in table  140  but no generic phone number. Chatbot  110  may query the user for more specific information regarding the phrase “phone number.” Alternatively, metadata  132 B may map different query keywords to a specific table columns. For example, metadata  132 B may map the generic query phrase “phone number” to “work phone” in table  140 . Accordingly, chatbot  110  may generate the response:
   The Acme Work Phone number is 111-1234.       
 
         [0119]    Metadata  132  may map any natural language query phrase to any structured data or a structured query operation. For example, metadata  132 B may include a natural language keyword “percentage” that directs chatbot  110  to divide the value associated with a second phrase in the query by the value associated with a third phrase in the query. 
         [0120]      FIG. 4  shows an example chatbot process for generating structured database queries. In operation  200 A, metadata is created and stored in the multi-tenant database. The metadata may associate different natural language phrases with datasets or fields associated with a particular organization in the multi-tenant database. As described above, the metadata also may associate different keywords and/or natural language sentence structures, such as predicates, with different tables, columns, datasets, or SQL functions. 
         [0121]    Operation  200 B configures the chatbot with full access to the multi-tenant database. For example, the chatbot may operate behind the database firewall and perform preliminary and primary structured database queries. The chatbot may confirm the natural language queries include sufficient information for conversation into structure database queries and confirm user authorization to view the results. 
         [0122]    Operation  200 C uses a natural language processor to convert the natural language query into a structured database query. For example, the chatbot may parse the natural language query to identify keywords and sentence structures for different phrases within the query. 
         [0123]    Based on the natural language processing, the chatbot in operation  200 D determines if the user query contains sufficient information to construct a structured database query. For example, the chatbot may perform a preliminary search to identify datasets in the multi-tenant database corresponding with the user query. If no matching datasets are found in operation  200 D, the chatbot in operation  200 E may ask the user for additional information. For example, the chatbot may send a natural language response notifying the user the requested data cannot be found. In another example, the chatbot may find several datasets that closely match the user query and ask the user if one of the identified datasets is correct. 
         [0124]    The chatbot may update the user query with the additional user information in operation  200 C. If the additional information is sufficient, the chatbot in operation  200 D submits an updated structured database query to the multi-tenant database in operation  200 F. The chatbot then may convert the query results into a natural language response and send the converted response to the user. 
         [0125]      FIG. 5  shows a process for using the chatbot in a chat group. In operation  250 A, the chatbot receives a query from a group chat session. For example, during a group chat session, one of the participants may submit a natural language query via a messaging service asking the chatbot for sales data on a particular customer. In operation  250 B, the chatbot converts the natural language query into a structured database query and submits the structured query to the multi-tenant database system. 
         [0126]    The chatbot in operation  250 C determines if the participant issuing the query has sufficient authorization to view the query results. Either during a preliminary search, or after receiving final search results, the chatbot may compare user permissions with security levels assigned to the query content. For example, some content may be assigned a security level that allows viewing and/or editing by a sales person or a sales manager. Other content may be assigned a higher security level that only allows viewing and/or editing by the sales manager. If the requesting participant has insufficient permissions, the chatbot in operation  250 D sends a notification of the insufficient authorization to view the requested data. 
         [0127]    If the requesting participant has sufficient permissions in operation  250 C, the chatbot in operation  250 E determines if the other participants have sufficient permissions to view the search results. For example, the chatbot compares the permissions for all of the other chat group participants with the authorization levels assigned to the search results. If the other participants have insufficient authorization, the chatbot in operation  250 F may only send the search results to the requesting participant. For example, the chatbot may send the search results in a private email or private chat message to the requesting participant. If all permissions are sufficient, the chatbot in operation  250 G displays search results to all chat group participants. 
         [0128]      FIG. 6  shows an example chatbot platform. In one example chatbot  110  may operate on application server  126  as described above in  FIG. 3 . Chatbot  110  may include a natural language processor  304  that parses natural language text commonly used in person-to-person communications. Processor  304  may identify a grammatical sentence structure in natural language query  104  and identify keywords and a context for specific phrases within query  104  based on the sentence structure. Natural language processors  304  are known to those skilled in the art and are therefore not described in further detail. As mentioned above, the natural language processor  304  also may convert audio signals into text and then process the converted text. 
         [0129]    A validator  302  may receive the parsed data from natural language processor  304  and identify datasets in database  16  associated with query  104 . Validator  302  also may determine if the user has sufficient permissions to view the identified data. If datasets cannot be identified in database  16  for query  104 , validator  302  may send a request  303  back to user system  102  for additional information. A formatter  306  may convert request  303  into a natural language format for displaying on user system  12 . 
         [0130]    Validator  302  may access metadata  132  to resolve ambiguities in query  104 . For example, validator  302  may use metadata  132  to associate predicate phrases in query  104  with table columns. In another example, validator  302  may use metadata  132  to associate particular query keywords in query  104  with table columns or logical operations. 
         [0131]    Query processor  300  submits structured database query or action  108  to multi-tenant database  16  and formatter  306  may receive back database results  301 . Formatter  306  then may convert query results  301  into a natural language response  310  that user system  12  displays in web browser  308 . 
         [0132]    The specific details of the specific aspects of implementations disclosed herein may be combined in any suitable manner without departing from the spirit and scope of the disclosed implementations. However, other implementations may be directed to specific implementations relating to each individual aspect, or specific combinations of these individual aspects. Additionally, while the disclosed examples are often described herein with reference to an implementation in which an on-demand database service environment is implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the present implementations are not limited to multi-tenant databases or deployment on application servers. Implementations may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM and the like without departing from the scope of the implementations claimed. 
         [0133]    It should also be understood that some of the disclosed implementations can be embodied in the form of various types of hardware, software, firmware, or combinations thereof, including in the form of control logic, and using such hardware or software in a modular or integrated manner. Other ways or methods are possible using hardware and a combination of hardware and software. Additionally, any of the software components or functions described in this application can be implemented as software code to be executed by one or more processors using any suitable computer language such as, for example, Java, C++ or Perl using, for example, existing or object-oriented techniques. The software code can be stored as a computer- or processor-executable instructions or commands on a physical non-transitory computer-readable medium. Examples of suitable media include random access memory (RAM), read only memory (ROM), magnetic media such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like, or any combination of such storage or transmission devices. Computer-readable media encoded with the software/program code may be packaged with a compatible device or provided separately from other devices (for example, via Internet download). Any such computer-readable medium may reside on or within a single computing device or an entire computer system, and may be among other computer-readable media within a system or network. A computer system, or other computing device, may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user. 
         [0134]    While some implementations have been described herein, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present application should not be limited by any of the implementations described herein, but should be defined only in accordance with the following and later-submitted claims and their equivalents.