Patent Publication Number: US-2023141023-A1

Title: Systems and methods for a customized search platform

Description:
CROSS REFERENCE(S) 
     The instant application is a nonprovisional of and claim priority under 35 U.S.C. 119 to U.S. provisional application No. 63/277,091, filed Nov. 8, 2021, which is hereby expressly incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The embodiments relate generally to search engines, and more specifically to systems and methods for a customized search platform. 
     BACKGROUND 
     Search engines allow a user to provide a search query and return search results in response. Search sites such as Google.com, Bing.com, and/or the like usually provide a list of search results to a user from all sorts of data sources. For example, these existing search engines usually crawl web data to collect search results that are relevant to a search query. However, a user has little control or transparency on how or where the search engines conduct their search and what kind of search results they are going to get. 
     Therefore, there is a need for a customized search platform that provides users both control and transparency with regard to the searches they perform. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a simplified diagram illustrating a data flow between entities during a search, according to one embodiment described herein. 
         FIG.  2    is a simplified diagram illustrating a computing device implementing the search described in  FIG.  1   , according to one embodiment described herein. 
         FIG.  3    is a simplified block diagram of a networked system suitable for implementing the search framework described in  FIGS.  1 - 2    and other embodiments described herein. 
         FIG.  4 A  is a simplified block diagram of a networked system suitable for implementing the customized search platform framework described in  FIGS.  1 - 3    and other embodiments described herein. 
         FIG.  4 B  is a simplified block diagram of the ranker shown in  FIG.  4 A , as described with respect to  FIG.  4 A . 
         FIG.  4 C  is a simplified block diagram of the parser shown in  FIG.  4 A , as described with respect to  FIG.  4 A . 
         FIG.  5    is an example logic flow diagram illustrating a method of search based on the framework shown in  FIGS.  1 - 4 A , according to some embodiments described herein. 
         FIG.  6    is an example logic flow diagram illustrating a method of customized search based on the framework shown in  FIGS.  1 - 4 A , according to some embodiments described herein. 
         FIG.  7    is a simplified block diagram of an example search interface implementing the customized search platform framework described in  FIGS.  5 - 6    and other embodiments described herein. 
         FIGS.  8 A- 8 K  are exemplary search interfaces implementing the customized search platform framework described in  FIGS.  5 - 6    and other embodiments described herein. 
     
    
    
     Embodiments of the disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the disclosure and not for purposes of limiting the same. 
     DETAILED DESCRIPTION 
     As used herein, the term “network” may comprise any hardware or software-based framework that includes any artificial intelligence network or system, neural network or system and/or any training or learning models implemented thereon or therewith. 
     As used herein, the term “module” may comprise hardware or software-based framework that performs one or more functions. In some embodiments, the module may be implemented on one or more neural networks. 
     The present application generally relates to search engines, and more specifically to systems and methods for a customized search platform. 
     Search engines allow a user to provide a search query and return search results in response. Search sites such as Google.com, Bing.com, and/or the like usually adopts a centralized structure that provides a list of search results to a user from all sorts of data sources. For example, these existing search engines usually crawl web data to collect search results that are relevant to a search query. However, a user has little control or transparency on how or where the search engines conduct their search. In addition, a user has little control on how their personal or private information is collected or used by generic search engines. For example, users may often want to engage with dedicated databases for specific searches. For example, human resource staff may use background check websites to search for potential new hires. For another example, legal professionals may search a juris database such as LexisNexis for case law. However, these dedicated databases are often scattered and difficult for a layperson to use, e.g., requiring a certain level of professional knowledge to enter the most effective search strings. 
     For example, when a user searches for “U.S. patent Ser. No. 12/345,678,” a search engine such as Google or Bing may likely provide a list of search results such as Internet articles that mention the patent number “12345678.” If the user is indeed looking for the actual patent document for “U.S. patent Ser. No. 12/345,678,” preferably from an authorized data source such as the U.S. patent office database, going through all the search results can be cumbersome and inefficient for the user. User search experience is thus unsatisfactory in this type of search service. 
     In view of the need for improved user search experience, embodiments described herein provide systems and methods for a customized search platform. Specifically, the search system includes a web-based or mobile application platform that provides customized search experience for an individual user to have control over searches conducted at user preferred data sources. In one embodiment, the search system may, in response to a search query, determine one or more prioritized data sources based on the characteristics of the search query. For example, if the search query relates to a person&#39;s name such as “Richard Socher,” data sources such as social media (e.g., Facebook LinkedIn, Twitter, etc.), news media (e.g., Silicon Valley press, etc.), and/or the like, may be prioritized. For another example, if the search query relates to an abstract item such as “QRNN,” common knowledge data sources (e.g., Wikipedia, etc.), academic data sources (e.g., arXiv, Stanford course materials, etc.), discussion sources (e.g., Quora, Reddit, etc.) may be prioritized. 
     In addition, a user may select or deselect data sources for his or her own searches, and the search platform may conduct a search query submitted by the user only at, or at least prioritize data sources that the user is interested in, and/or exclude data sources the user has disapproved. 
     In one embodiment, a user may actively select data sources he or she is interested in via a user account management page with the search platform. For example, the user may actively select Wikipedia, Reddit, Arxiv.org, and/or the like, to be the preferred data sources. As a result, if the user search for “QRNN,” search results grouped per user selected data sources, e.g., Wikipedia, Reddit, Arxiv.org, may be presented to the user via a search user interface. The user can click on the icon of a data source, e.g., Reddit, and see a list of search results such as discussion threads relating to “QRNN” that are provided specifically from the data source “Reddit.” For another example, if the user clicks on the icon of “Wikipedia,” the Wikipedia page for “QRNN” may be provided. 
     In another embodiment, the search system may monitor user preference during user interaction with search results. For example, if the user actively elects “dislike” a displayed search result from a particular data source, or rarely interacts with search results from a particular data source, the search system may deprioritize search results from this particular data source. In the above example, if the user chooses to dislike or unselect search results from the data source “Reddit,” the Reddit icon may be removed from the user interface presenting search results. 
     In this way, by focusing on prioritized data sources based on characteristics of the search query itself and further prioritizing and filtering data sources per user preferences, the search system largely reduces computational complexity and improves search efficiency. Also, by allowing the user to have transparency and control over search data sources, user experience is largely improved. 
     OVERVIEW 
       FIG.  1    is a simplified diagram illustrating a data flow between entities implementing the processes described in  FIGS.  2 - 7   , according to one embodiment described herein. A user interacts with user device  110 , which in turn interacts with a search server  100  through input queries  112  provided by the user. Computing device  100  interacts with various data sources  103   a - n  (collectively referred to as  103 ). For example, the data sources  103   a - n  may be any number of available databases, webpages, servers, blogs, content providers, cloud servers, and/or the like. As described in further detail below with reference to  FIGS.  5 - 6   , search server  100  utilizes a parser  134  and ranker  132  to identify data sources  103  relevant to the input query  112 , obtains search results from data sources  103 , ranks  120  the search results, and presents the search results to the user via user device  110  to display sets of search results in user-engageable elements. 
     Common data sources  103   a - n  may include Wikipedia, Reddit, Twitter, Instagram (and/or social media), and/or the like. However, for different input queries  112 , the search system may intelligently recommend what kind of data sources  103  may be most relevant to the specific search query. For example, when a user types in a search for “Quasi convolutional neural network,” the search system may preliminarily determine (or categorize via a classifier) that the search term relates to a technical topic. Therefore, suitable data sources  103  may be identified as a knowledge base such as “Wikipedia,” a discussion forum on which users may discuss technical topics such as “Reddit,” an archive of scientific manuscript such as “arXiv” and/or the like may most likely be relevant or interesting to the user based on the input query  112 . The search system may then recommend these data sources  103  to the user. 
     In a different example, if the user performs a search with the input query  112  “Richard Socher,” which the search system may categorize it as the name of a person, the search system may rank the proposed data sources to include data sources  103  that are more relevant to people, such as “Instagram,” “LinkedIn,” “Google Scholar,” and/or the like. 
     In another embodiment, as described in further detail below with reference to  FIG.  7   , the user can interact with the search results via user device  110  through user-engageable elements. In this way, the computing device  100  can refine search results to better tailor results to a user&#39;s desires and preferences by allowing a user to customize their preferred data sources  103 . A user may choose to submit whether they prefer or do not prefer a particular data source by clicking on a “thumbs up” or “thumbs down” icon. Based on user submitted preferences, the search system may re-arrange and re-prioritize data sources. For example, if the user has elected “thumbs up” for “LinkedIn” but “thumbs down” for “Instagram,” when the user searches for a person&#39;s name such as “Richard Socher,” The search system may prioritize data sources such as LinkedIn but may de-prioritize data source such as “Instagram.” 
     Computer and Networked Environment 
       FIG.  2    is a simplified diagram illustrating a computing device  200  implementing the customized search server  100  described in  FIG.  1   , according to one embodiment described herein. As shown in  FIG.  2   , computing device  200  includes a processor  210  coupled to memory  220 . Operation of computing device  100  is controlled by processor  210 . And although computing device  200  is shown with only one processor  210 , it is understood that processor  210  may be representative of one or more central processing units, multi-core processors, microprocessors, microcontrollers, digital signal processors, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs) and/or the like in computing device  200 . 
     Computing device  200  may be implemented as a stand-alone subsystem, as a board added to a computing device, and/or as a virtual machine. In various embodiments, the communication device may comprise a personal computing device (e.g., smart phone, a computing tablet, a personal computer, laptop, a wearable computing device such as glasses or a watch, Bluetooth device, key FOB, badge, etc.) capable of communicating with the network. The service provider may utilize a network computing device (e.g., a network server) capable of communicating with the network. It should be appreciated that each of the devices utilized by users and service providers may be implemented as computer system  200  in a manner as follows. 
     Memory  220  may be used to store software executed by computing device  200  and/or one or more data structures used during operation of computing device  200 . Memory  220  may include one or more types of machine-readable media. Some common forms of machine-readable media may include floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read. 
     Processor  210  and/or memory  220  may be arranged in any suitable physical arrangement. In some embodiments, processor  210  and/or memory  220  may be implemented on a same board, in a same package (e.g., system-in-package), on a same chip (e.g., system-on-chip), and/or the like. In some embodiments, processor  210  and/or memory  220  may include distributed, virtualized, and/or containerized computing resources. Consistent with such embodiments, processor  210  and/or memory  220  may be located in one or more data centers and/or cloud computing facilities. 
     In some examples, memory  220  may include non-transitory, tangible, machine readable media that includes executable code that when run by one or more processors (e.g., processor  210 ) may cause the one or more processors to perform the methods described in further detail herein. For example, as shown, memory  220  includes instructions for search platform module  230  that may be used to implement and/or emulate the systems and models, and/or to implement any of the methods described further herein. A search platform module  230  may receive input  240  such as an input search query (e.g., a word, sentence, or other input provided by a user to perform a search) via the data interface  215  and generate an output  250  which may be one or more user-engageable elements presenting the search results according to different data sources. For instance, if input data including a name, such as “Richard Socher,” is provided, output data may include user-engageable elements showing results from “Twitter,” “Facebook,” “Instagram,” “TikTok,” or other social media websites. If input data including a food, such as “pumpkin pie,” is provided, output data may include user-engageable elements showing results from “All Recipes,” “Food Network,” or other food-related websites. If input data related to coding, such as an error in python, is provided, output data may include user-engageable elements showing results from “StackOverflow,” “Reddit,” or other webpages or blogs oriented towards coding assistance. 
     The data interface  215  may comprise a communication interface, a user interface (such as a voice input interface, a graphical user interface, and/or the like). For example, the computing device  200  may receive the input  240  (such as a search query) from a networked database via a communication interface. Or the computing device  200  may receive the input  240 , such as search queries, from a user via the user interface. 
     In some embodiments, the search platform module  230  is configured to parse inputs, categorize the inputs, and rank results. The search platform module  230  may further include a parser submodule  231 , a categorization submodule  232 , a ranker submodule  233  (e.g., similar to ranker  412  and parser  414  in  FIG.  4 A ), and a search submodule  234 . In one embodiment, the search platform module  230  and its submodules  231 - 234  may be implemented by hardware, software and/or a combination thereof. 
     In some embodiments, the search system adopts a search platform module  230  to generate and filter search results from all different data sources. For example, the search platform may include a ranker  233  and a parser  231  as shown in  FIGS.  1 - 4 A , to ingest user query, user contextual information, and other contextual information to coordinate which data sources are relevant, which corresponding data source application programming interfaces (APIs) should be contacted, how to parse the user query for each Search APP API, and ultimately the final ranked order of the data source results. 
     Some examples of computing devices, such as computing device  200  may include non-transitory, tangible, machine readable media that include executable code that when run by one or more processors (e.g., processor  210 ) may cause the one or more processors to perform the processes of method. Some common forms of machine-readable media that may include the processes of method are, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read. 
       FIG.  3    is a simplified block diagram of a networked system suitable for implementing the customized search platform framework described in  FIGS.  5 - 6    and other embodiments described herein. In one embodiment, block diagram  300  shows a system including the user device  310  which may be operated by user  340 , data sources  345   a  and  354   b - 345   n , platform  330 , and other forms of devices, servers, and/or software components that operate to perform various methodologies in accordance with the described embodiments. Exemplary devices and servers may include device, stand-alone, and enterprise-class servers which may be similar to the computing device  200  described in  FIG.  2   , operating an OS such as a MICROSOFT® OS, a UNIX® OS, a LINUX® OS, or other suitable device and/or server-based OS. It can be appreciated that the devices and/or servers illustrated in  FIG.  3    may be deployed in other ways and that the operations performed, and/or the services provided by such devices and/or servers may be combined or separated for a given embodiment and may be performed by a greater number or fewer number of devices and/or servers. One or more devices and/or servers may be operated and/or maintained by the same or different entities. 
     The user device  310 , data sources  345   a  and  354   b - 345   n , and the platform  330  may communicate with each other over a network  360 . User device  310  may be utilized by a user  340  (e.g., a driver, a system admin, etc.) to access the various features available for user device  310 , which may include processes and/or applications associated with the server  330  to receive an output data anomaly report. 
     User device  310 , data sources  345   a  and  354   b - 345   n , and the platform  330  may each include one or more processors, memories, and other appropriate components for executing instructions such as program code and/or data stored on one or more computer readable mediums to implement the various applications, data, and steps described herein. For example, such instructions may be stored in one or more computer readable media such as memories or data storage devices internal and/or external to various components of system  300 , and/or accessible over network  360 . 
     User device  310  may be implemented as a communication device that may utilize appropriate hardware and software configured for wired and/or wireless communication with data source  345  and/or the platform  330 . For example, in one embodiment, user device  310  may be implemented as an autonomous driving vehicle, a personal computer (PC), a smart phone, laptop/tablet computer, wristwatch with appropriate computer hardware resources, eyeglasses with appropriate computer hardware (e.g., GOOGLE GLASS®), other type of wearable computing device, implantable communication devices, and/or other types of computing devices capable of transmitting and/or receiving data, such as an IPAD® from APPLE®. Although only one communication device is shown, a plurality of communication devices may function similarly. 
     User device  310  of  FIG.  3    contains a user interface (UI) application  312 , and/or other applications  316 , which may correspond to executable processes, procedures, and/or applications with associated hardware. For example, the user device  310  may receive search results in the form of user-engageable elements from the platform  330  and display the message via the UI application  312 . In other embodiments, user device  310  may include additional or different modules having specialized hardware and/or software as required. 
     In various embodiments, user device  310  includes other applications  316  as may be desired in particular embodiments to provide features to user device  310 . For example, other applications  316  may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate APIs over network  360 , or other types of applications. Other applications  316  may also include communication applications, such as email, texting, voice, social networking, and IM applications that allow a user to send and receive emails, calls, texts, and other notifications through network  360 . For example, the other application  316  may be an email or instant messaging application that receives a prediction result message from the server  330 . Other applications  316  may include device interfaces and other display modules that may receive input and/or output information. For example, other applications  316  may contain software programs for asset management, executable by a processor, including a graphical user interface (GUI) configured to provide an interface to the user  340  to view and interact with user-engageable elements displaying search results. 
     User device  310  may further include database  318  stored in a transitory and/or non-transitory memory of user device  310 , which may store various applications and data and be utilized during execution of various modules of user device  310 . Database  318  may store user profile relating to the user  340 , predictions previously viewed or saved by the user  340 , historical data received from the server  330 , and/or the like. In some embodiments, database  318  may be local to user device  310 . However, in other embodiments, database  318  may be external to user device  310  and accessible by user device  310 , including cloud storage systems and/or databases that are accessible over network  360 . 
     User device  310  includes at least one network interface component  319  adapted to communicate with data sources  345   a  and  354   b - 345   n  and/or the server  330 . In various embodiments, network interface component  319  may include a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency, infrared, Bluetooth, and near field communication devices. 
     Data sources  345   a  and  354   b - 345   n  may correspond to a server that hosts one or more of the search applications  303   a - n  (or collectively referred to as  303 ) to provide search results including webpages, posts, or other online content hosted by data sources  345   a  and  354   b - 345   n  to the server  330 . The search application  303  may be implemented by one or more relational database, distributed databases, cloud databases, and/or the like. Search application  303  may be configured by platform  330 , by data source  345 , or by some other party. In some embodiments, 
     In one embodiment, the platform  330  may allow various data sources  345   a  and  354   b - 345   n  to partner with the platform  330  as a new data source. The search system provides an Application programming interface (API) for each data sources  345   a  and  354   b - 345   n  to plug into the service the search system. For example, the California Bar Association may register with the search system as a data source. In this way, the data source “California Bar Association” may appear amongst the available data source list on the search system. A user may select or deselect California Bar Association as a preferred data source for their search. In similar manners, additional data sources  345  may partner with the platform  330  to provide additional data sources for the search such that the user can understand where the search results are gathered. 
     The data source  345   a - n  (collectively referred to as  345 ) includes at least one network interface component  326  adapted to communicate with user device  310  and/or the server  330 . In various embodiments, network interface component  326  may include a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency, infrared, Bluetooth, and near field communication devices. For example, in one implementation, the data source  345  may send asset information from the search application  303 , via the network interface  326 , to the server  330 . 
     The platform  330  may be housed with the search platform module  230  and its submodules described in  FIG.  2   . In some implementations, platform  330  may receive data from search application  303  and/or network interface  326  at the data source  345  via the network  360  to generate user-engageable elements displaying search results. The generated user-engageable elements may also be sent to the user device  310  for review by the user  340  via the network  360 . 
     The database  332  may be stored in a transitory and/or non-transitory memory of the server  330 . In one implementation, the database  332  may store data obtained from the data vendor server  345 . In one implementation, the database  332  may store parameters of the search platform model  230 . In one implementation, the database  3232  may store user input queries, user profile information, search application information, search API information, or other information related to a search being performed or a search previously performed. 
     In some embodiments, database  332  may be local to the platform  330 . However, in other embodiments, database  332  may be external to the platform  330  and accessible by the platform  330 , including cloud storage systems and/or databases that are accessible over network  360 . 
     The platform  330  includes at least one network interface component  333  adapted to communicate with user device  310  and/or data sources  345   a  and  354   b - 345   n  over network  360 . In various embodiments, network interface component 333  may comprise a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency (RF), and infrared (IR) communication devices. 
     Network  360  may be implemented as a single network or a combination of multiple networks. For example, in various embodiments, network  360  may include the Internet or one or more intranets, landline networks, wireless networks, and/or other appropriate types of networks. Thus, network  360  may correspond to small scale communication networks, such as a private or local area network, or a larger scale network, such as a wide area network or the Internet, accessible by the various components of system  300 . 
     Example Architecture 
       FIG.  4 A  is a simplified block diagram of a networked system suitable for implementing the customized search platform framework described in  FIGS.  1 - 3    and other embodiments described herein. 
     Platform  410  (which is similar to  230  in  FIG.  2  or  330    in  FIG.  3   ) receives input data from a user. This input data may include one or more of a user query  402 , user context  404 , and other context  406 . User query  402  may include a word, multiple words, a sentence, or any other type of search query provided by a user performing a search using platform  410 . For example, the user query  402  may be a search term such as “quasi recurrent neural network,” “Richard Socher,” etc. User context  404  may include inputs representative of the user, including a user ID, user preferences, user click logs, or other information collected or provided by the user, user selected preferred data sources, user past activities “like” or “dislike” a search result or a search source, and/or the like. Other context  406  may include inputs representative of other useful input information, including information about world events, searches that have been conducted around the same time, searches that have been conducted around the same area, searches that increased in volume over a period of time, or other potential contextual information that may assist platform  410  in providing appropriate search results to the user. 
     User query  402  may be converted, via platform  410 , to a representative string q=(q 1 , . . . , q r )where each q is a single token in the string tokenized by some tokenization strategy. User context  404  and other context  406  may similarly be converted, via platform  410 , to representative strings u=(u 1 , . . . , u m ) (e.g., user context  404 ) and c=(c 1 , . . . , c p ) (e.g., other context  406 ). A permutation of these inputs are concatenated into a single input sentence, e.g., the combined input sequence s=[TASK, q, SEP, u, SEP, c]. This combined input sequence is the entirety of representative string q, followed by representative string u, followed by representative string c, with special reserved tokens (TASK and SEP) used to inform the system of where one sequence ends and another begins. 
     The single input sentence is then provided to parser  414  (e.g., similar to parser submodule  231  in  FIG.  2   ) and ranker  412  (e.g., similar to ranker submodule  233  in  FIG.  2   ). Each of the ranker  412  and the parser  414  may be built on a neural network. 
     Specifically, ranker  414  determines and ranks a list of search apps  420 - 420   n  to conduct the search. In some embodiments, each search applications  420   a - 420   n  corresponds to a particular data sources  103   a - n  in  FIG.  1   , or one of  345   a - 345   n , as shown in  FIG.  3   . For example, search app  420   a  corresponds to a search application that is configured to search within the database of Amazon.com; search app  420   b  corresponds to a search application that is configured to search within the database of Facebook.com, and/or the like. Ranker  414  uses the input sequence including user query  402 , user context  404 , and other context  406  to score the plurality of search apps  420   a - 420   n , by running the input sequence through a neural network model once for each search app  420   a - 420   n , as described in further detail below with respect to  FIG.  4 B . In this way, ranker  414  ranks a list of search apps corresponding to a list of data sources to conduct the user query  402 . For example, for a user query  402  search for “affordable instant pot”, search app  420   a  corresponding to Amazon.com may be prioritized over search app  420   b  corresponding to Facebook.com. 
     After the ranker  412  determines and ranks a list of search apps  420   a - n  for a particular user query  402 , the parser  414  determines a respective specific search input for each search app API  422   a - n  that corresponds to each search apps  420   a - n , respectively. Parser  414  may use the input sequence including user query  402 , user context  404 , and other context  406  to determine which tokens in a user query correspond to which inputs of search app APIs  422   a - 422   n , as described further below with respect to  FIG.  4 C . 
     For example, Ranker  412  utilizes the combined input sequence s for each search application  420   a - 420   n , where s is additionally concatenated with a representation of the search application  420  such that s a     i   =[s, search application  420   i ]. 
     Both ranker  412  and parser  414  make use of a variant of Transformer, where a sequence containing n tokens is embedded as a sequence of n vectors in    d . Each vector is the sum of a learned token embedding and a sinusoidal positional embedding. The sequence of vectors is stacked into a matrix X 0 ∈   n×d  and processed by l attention layers. The ith layer consists of two blocks, each preserving model dimension d. The first block uses multi-head attention with k heads. 
     
       
         
           
             
               
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     The second block uses a feedforward network with ReLU activation that projects inputs to an inner dimension f. This operation is parameterized by U∈    f×d  and V∈   f×d : 
         FF ( X )=max(0, XU ) V    
     Each block precedes core functionality with layer normalization and follows it with a residual connection. Together, they yield X i+1 : 
       Block1:   X   =LayerNorm( X   i )  H   i =MultiHead(   X     i )+   X     i    
       Block2:   H     i =LayerNorm( H   i )  X   i+1   =FF (   H     i )+   H     i    
     The final output of the Transformer for a single input sequence x is then X l . For example, the ranker  412  may pass an output matrix after l layers of attentions, which represents ranking information of the search apps  420   a - n , to the parser  414 . The parser  414  may output the matrix X l  comprising search inputs for the search apps  420   a - n , which are sent to the search app APIs  422   a - n , respectively. 
     The returned search results via the search app APIs  422   a - 422   n  are then sorted according to the ranking generated by the ranker  412  and presented in ranked order  430 . For example, result  431   a  corresponds to a group of search results from highest ranked search app  420   a , and result  431   n  corresponds to search results from the lowest ranked search apps  420   n . The results  431   a - 431   n  are then presented to the user via a graphical user interface or some other type of user output device. For example, search results  431   a - 431   n  may grouped and presented in the form of a list of user-engageable elements, each displaying an icon representing each respective search app (data source). Upon user selection of the icon, a list of search results from the respective search app may be presented to the user. Example UI diagrams can be found in  FIGS.  8 A- 8 K . 
     Therefore, by employing a neural-network based ranker  412  and parser  414 , the search platform may intelligently predict which data sources are likely to be prioritized for a particular user based on the user query  402  and user context  404 . For example, a user may directly configure a preference or dislike of a data source (e.g., see  FIG.  8 H,  8 J ). The search platform may thus include or exclude search apps corresponding to the preferred or disliked data sources in the search (e.g., as filtered by the ranker  412 ) accordingly. For another example, when a user dislikes a search post from Twitter.com, the search platform may not exclude Twitter.com from future search. However, if the user constantly dislikes search results from Twitter.com (e.g., more than a pre-defined times per day, per week, per percentage of total searches, etc.), the search platform may more likely deprioritize or exclude Twitter.com from future search. The neural model of the ranker  412  may be trained predict whether Twitter.com should be excluded or de-prioritized based on past user behaviors. 
     For example, the ranker  412  and the parser  414  may each be trained individually. A training input may comprise similar data such as the user query  402 , user context  404  and other context  406 . The ranker  412  is to generate a training output of a ranking of search apps (data sources), which are compared with the actual ground-truth ranking paired with the training input. A cross-entropy loss may be computed to update the ranker  412  via backpropagation. The parser  414  may be trained in a similar manner. For another example, the ranker  412  and the parser  414  may be trained jointly end-to-end. 
     These embodiments describe a system and method for a customized search platform that provides users control and transparency in their searches. In some instances, a user may prefer complete privacy when it comes to their searches and/or internet browsing. In such instances, a user may opt to remain in a “private mode” during searching. In other instances, a user may prefer results that are tailored to the user&#39;s preferences or interests. In such instances, a user may opt to instead enter a “personal mode.” 
     A user may opt to remain in a “private mode” during searching. In “private mode,” the computer system does not store queries on its servers, record clicks or any other interactions with the search engine, and disables all apps that require IP address or location, in order to protect user privacy. In a “private mode” embodiment, a user is able to perform searches with control over how the search engine collects and uses information about the user. Thus, in a “private mode” embodiment, platform  410  may collect input from a user query  402  and other context  406 , but may not have access to user context  404  in performing each search. In addition, user query  402  will not be retained by platform  410  beyond the need to perform an immediate search using the user query  402 . 
     In “personal mode,” a user can instead further customize their experience during searching to retain control of the searching while enjoying results tailored specifically to the user. A user can optionally create a user profile to retain and store preferences. In a “personal mode” embodiment, a user is able to control their own search experience through information they provide to the search system. For instance, a user can select preferred data sources, and can modify the order in which applications appear in response to a search query. User interactions may optionally be collected and used to provide better tailored results for the user in the future, but “personal mode” ensures that the user retains control of their search experience instead of SEO professionals or advertisers. Thus, in a “personal mode” embodiment, platform  410  may collect input including user query  402 , user context  404 , and other context  406 . The user context  404  collected and utilized may be controlled by the user. 
       FIG.  4 B  is a simplified block diagram of ranker  412 , as described with respect to  FIG.  4 A . Ranker  412  uses user query  402 , user context  404 , and other context  406  to score each search app  420   a - 420   n . Accordingly, the ranker  412  is run once for each search app  420  being ranked. 
     Ranker  412  decides a ranked order  430  over the set of search apps  420  Si. Given the input sequence s a     i    as described above with reference to  FIG.  4 A , ranker  412  runs a Transformer over the input sequence to get 
     
       
         
           
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     for each search app  420 . Each input sequence s a     i    is reduced to a single vector via a pooling mechanism (e.g., mean, maximum, minimum, convolutional, etc.) and multiplied by shared W b  ∈   1×d  to get a score for that search app  420 . 
     
       
         
           
             
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     This process is repeated for each search app  420   a - 420   n . The ranked order  430  is then determined by sorting these scores. 
       FIG.  4 C  is a simplified block diagram of parser  414 , as described with respect to  FIG.  4 A . Parser  414  tags each input of x with a marker of whether it corresponds to an input of a search app API  422 . Parser  414  operates on sequence x as described above, and computes the final output of the underlying Transformer architecture X 1 . Instead of pooling and computing scores for each search app  420   a - 420   n , parser  414  applies the following equation: 
       SlotScores ai ( x )=LayerNorm( X   l ) W   a     i      
     In the above equation, SlotScores ai  determine for each toke in the input sequence x whether that toke corresponds to a particular input (or slot here to disambiguate from its own input) in the ith search app API  420 , e.g., departure location, destination, etc. Each search app  420  has its own corresponding parameters W a     i    with entries corresponding to each slot that needs to be tagged for computing scores over its slots. 
     Example Workflows 
       FIG.  5    is an example logic flow diagram illustrating a method of search based on the framework shown in  FIGS.  1 - 4 C , according to some embodiments described herein. One or more of the processes of method  500  may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors may cause the one or more processors to perform one or more of the processes. In some embodiments, method  500  corresponds to the operation of the search platform module  230  (e.g.,  FIGS.  1 - 4 A ) that performs searches based on user inputs and provides user-engageable elements containing search results. 
     At step  502 , an input query is received via a data interface. As shown in  FIG.  4 A , according to some embodiments, this input query may include one or more of user query  402 , user context  404 , or other context  406 . In some embodiments, this input query is provided by a user through user interaction with the search system, such as by entering a search query into the search system. 
     In some embodiments, an input sequence may be generated by concatenating the input query (e.g., “Richard Socher,” “Quasi Recurrent Neural Network,” etc.) and a user context associated with a user who initiated the input query. The user context may include any combination of user profile information (e.g., user ID, user gender, user age, user location, zip code, device information, mobile application usage information, and/or the like), user configured preferences or dislikes of one or more data sources (e.g., as shown in  FIG.  8 H,  8 J ), and user past activities approving or disapproving a search result from a specific data source. In some embodiments, the input sequence further comprises supplemental context comprising contextual information from one or more data sources pertinent to the input query. This supplemental context may include indications of “popular search,” “top search,” “others are also searching . . . ,” “hashtags,” and other trending topics. The supplemental context may take into consideration global trends, local trends, news topics, or other relevant information based on current popularity. The supplemental context may also take into consideration searches of similar users, searches over a recent time period, searches from a geographic area, or other contextual information that may be pertinent to the input query. In some embodiments, a search input may be generated by a parsing neural model based on the input sequence. 
     At step  504 , the search system determines a first data source and a second data source that are relevant to the input query based at least in part on characteristics of a potential search object from the input query. As shown in  FIG.  4 A , according to some embodiments, this determination of relevant data sources is performed using the input query, including user query  402 , user context  404 , or other context  406 . In some embodiments, this determination of relevant data sources is based at least in part on characteristics of a potential search object from the input query. For instance, if the input query includes a name, such as “Richard Socher,” relevant data sources may include “Twitter,” “Facebook,” “Instagram,” “TikTok,” or other social media websites. If the input query includes a food, such as “pumpkin pie,” relevant data sources may include “All Recipes,” “Food Network,” or other food-related websites. If the input query is related to coding, such as an error in python, relevant data sources may include “StackOverflow,” “Reddit,” or other webpages or blogs oriented towards coding assistance. 
     In some embodiments, the determination of a first data source and a second data source that are relevant to the input query comprises generating an input sequence by concatenating the input query and a user context associated with a user who initiated the input query. A ranking neural model (e.g., see  412  in  FIG.  4 A ) may then generate a relevance score for each data source based on the input sequence, and using the relevance score, determine whether each data source is relevant based on whether the respective relevance scores are greater than a threshold. Each data source may be ranked based on the respective relevance scores. In some embodiments, an indication may be generated by a parsing neural model for each data source. 
     At step  506 , the search system may examine and filter the determined data sources based on stored user preferences of data sources, and generate/transmit search inputs customized for each data sources. In one implementation, if the user has previously selected a particular data source as a preferred data source, the search system may include and prioritize this particular data source. In one implementation, if the user has previously deselected or disapproved a particular data source, the search system may exclude this particular data source even if this data source may be determined as relevant at step  504 . 
     In one implementation, the search system may universally apply user preferred data sources in the search. For example, if “Wikipedia” has been selected by the user as a preferred data source, the search system may always place a group of search results from “Wikipedia” in the search for the user. 
     In another implementation, the search system may categorize user preferred data sources based on its type. For example, if “LinkedIn” has been selected by the user as a preferred data source, the search system may store “LinkedIn” as a preferred data source for a specific type of query (e.g., relating to human names), and may not prioritize searches at “Linkedin” when the query is irrelevant to a person&#39;s name (such as “high performance instant pot”). 
     In alternative implementations, at step  506 , the search system may transmit, via search application programming interfaces (APIs  422   a - n  in  FIG.  4 A ), a first search input and a second search input to respective first and second data sources. The search inputs may be customized from the input query for each data source. Additional context may be received from each search app API relating to the input query from the data sources, and the additional context may be used to determine which portion of the input query corresponds to each search app API. For example, for a search app API with data source “Linkedin.com,” the search input may be customized as “Posts mentioning Richard Socher,” “pages mentioning Richard Socher,” users named “Richard Socher,” stories mentioning “Richard Socher,” hashtags, when the user input query is “Richard Socher.” 
     At step  508 , the search system obtains and/or generates a first set of search results from the first data source and a second set of search results from the second data source. These search results correspond to results  431   a - 431   n  as shown in  FIG.  4 A . Each set of search results is obtained and/or generated for a different search app  420   a - 420   n , where each set of search results is ranked in a ranked order  430  as determined by ranker  412  and parser  414 . 
     At step  510 , the search system presents, via a user interface, a first user-engageable panel containing the first set of search results with a first indication of the first data source and a second user-engageable panel containing the second set of search results with a second indication of the second data source. These search results may be presented in user-engageable elements  700 , as shown in  FIG.  7    and  FIGS.  8 A- 8 K . Example UI diagrams can be found in  FIGS.  8 A- 8 K . Each set of search results generated at step  508  may be displayed in an additional user-engageable element at step  510 , such that each set of search results  431   a - 431   n  (as shown in  FIG.  4 A ) are generated and displayed to the user in corresponding user-engageable elements. In some embodiments, each user-engageable panel is presented in a ranked order according to the ranking determined in step  504 . 
       FIG.  6    is an example logic flow diagram illustrating a method of customized search based on the framework shown in  FIGS.  1 - 4 C , according to some embodiments described herein. One or more of the processes of method  600  may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors may cause the one or more processors to perform one or more of the processes. In some embodiments, method  600  corresponds to the operation of the search platform module  230  (e.g.,  FIGS.  1 - 4 A ) that performs searches based on user inputs and provides user-engageable elements containing search results. 
     At step  602 , a set of one or more ranked results are presented to a user via user-engageable elements. These results may be determined, for example, as discussed above for  FIG.  5   . In some embodiments, each search app  420   a - 420   n  (e.g.,  FIG.  4 A ) that is ranked and parsed by platform  410  will be sorted and presented to the user via a ranked order. Each search app  420   a - 420   n  will have a corresponding user-engageable element that allows the user to interact with search results, the search app  420 , 
     At step  604 , a user interacts with one or more of the search apps  420   a - 420   n  displaying results via the user-engageable elements, such as approval element  704  and disapproval element  706  shown in  FIG.  7   . For instance, in a search where the input query includes a food, such as “pumpkin pie,” relevant data sources may include “All Recipes,” “Food Network,” or other food-related websites. The user may interact with the “All Recipes” approval element  704  in user-engageable element  700  to indicate a preference for the “All Recipes” source. The search system may then use this information to update user preferences to use the “All Recipes” source for other food-based input queries. The user may also interact with the “Food Network” disapproval element  706  in user-engageable element  700  to indicate a negative preference for the “Food Network” source. The search system may then use this information to update user preferences to avoid the “Food Network” source for other food-based input queries. 
     In some embodiments, when a user selection of an additional data source is received, a new search input is customized from the input query to the respective data source and transmitted via a search app API integrated at the server of the additional data source. A set of search results from the data source may be received and presented via a user-engageable panel displaying the search results, as discussed further above and below. 
     At steps  606 / 616 , the user&#39;s preferences are updated based on the inputs provided. These user preferences may be included as user context  404  (e.g.,  FIG.  4 A ) to better tailor search results for the user. Following the example above, at step  606 , this may involve updating the user&#39;s preferences to reflect an increased desire to see the “All Recipes” source. Meanwhile, at step  616 , this may involve updating the user&#39;s preferences to reflect a decreased desire to see the “Food Network” source. 
     In some implementations, a user may directly configure a preference or dislike of a data source (e.g., see  FIG.  8 H,  8 J ). The search platform may thus include or exclude search apps corresponding to the preferred or disliked data sources in the search (e.g., as filtered by the ranker  412 ) accordingly. In some implementations, the search platform may apply rule-based criteria to filter data sources based on user past activities with respect to search results from a particular data source. For example, when a user dislikes a search post from Twitter.com, the search platform may not exclude Twitter.com from future search. However, if the user constantly dislikes search results from Twitter.com (e.g., more than a pre-defined times per day, per week, per percentage of total searches, etc.), the search platform may deprioritize or exclude Twitter.com from future search. 
     At steps  608 / 618 , the search results are updated based on the user&#39;s interactions with the system. Following the example above, at step  608 , this may involve increasing the ranked order  430  position for the result  431  corresponding to the “All Recipes” source (e.g.,  FIG.  4 A ). At step  616 , this may involve decreasing the ranked order  430  position for the result corresponding to the “Food Network” source, or alternatively, may result in the removal entirely of the “Food Network” source from the results  431   a - 431   n  displayed to the user. 
     At steps  610 / 620 , the updated results are presented to the user. These results may be presented to the user in ranked order  430 , where each result  431   a - 431   n  is shown in a user-engageable element  700  as discussed in further detail below for  FIG.  7   . 
     In some embodiments, the user may instead interact with additional user-engageable elements to provide an indication regarding search apps  420  which are not included within the ranked order of search apps presented to the user. In such instances, the search system will update user preferences as discussed above for step  606 . The search results may be updated in step  608  to increase the rank of the search app  420  or to add the search app  420  to the search results, if it was not already there, based on the input from the user-engageable element. 
       FIG.  7    is a simplified block diagram of an example search interface implementing the customized search platform framework described in  FIGS.  5 - 6    and other embodiments described herein.  FIG.  7    depicts user-engageable element  700 , and one or more user-engageable elements  700  may be presented to a user via a user interface, where each user-engageable element contains sets of search results. 
     User-engageable element  700  may include source information  710  providing information to users with important or useful information on what source is providing the results  702   a - 702   n  shown in user-engageable element  700 . For instance, this may be a website name to indicate that results  702   a - 702   n  are shown from a specific website related to the search input query. User-engageable element  700  corresponds to a single search app  420  as depicted in  FIG.  4 A , where the first user-engageable element  700  shown to a user is the first result in ranked order  430  (e.g., result  431   a ), and each subsequently shown user-engageable element  700  is the next result  431  in ranked order  430 . 
     Each user-engageable element  700  may provide one or more results  702   a - 702   n , which include web pages, social media posts, blog posts, recipes, videos, code segments, or other content relevant to a search query. Each result  702  may be user-engageable, and may allow the user to visit the web page, interact with the social media post directly, comment on blog posts, read and save recipes, watch videos, copy code, provide feedback, or otherwise interact with the content in each result  702 . Each user-engageable element  700  may also provide users to indicate their preference for the data source, using approval element  704  and disapproval element  706 , as discussed in further detail for  FIG.  6    above. 
       FIGS.  8 A- 8 K  are exemplary search interfaces  800   a - 800   k  implementing the customized search platform framework described in  FIGS.  5 - 6    and other embodiments described herein. 
     As seen in  FIG.  8 A , a customized search platform framework is utilized to allow users the option to perform a search and select data sources of the search. A user may search for a query, such as “quasi convolutional neural network” and the customized search platform may determine that according to the characteristics of this user query (e.g., a name of a scientific term, etc.), the data source “arXiv” provides the most relevant results and prominently display the data source to the user along with a list of search results that are provided specifically from “arXiv.” The search results from “Arxiv.org” are presented in a form of a slidable horizontal panel such that the user may engage with the panel to “slide” and view a list of results in panel. 
     As seen in  FIG.  8 B , for the same search query “quasi convolutional neural network,” the customized search platform may determine that “Reddit” is another relevant data source (e.g., search app) but may be ranked lower than “Arxiv.org.” Thus, search results from “Reddit” may be presented in another slidable horizontal panel that is displayed below the panel for “Arxiv.org,” such that the user may engage with the panel to “slide” and view a list of results in panel. 
     As seen in  FIG.  8 C , when the search query is “Richard Socher,” the customized search platform may determine that according to the characteristics of this user query (e.g., a name of a human being, etc.), the data source from social media, such as “Twitter” provides the most relevant results and prominently display the data source to the user along with a list of search results that are provided specifically from “Twitter.” The search results from “Twitter” are presented in a form of a slidable horizontal panel such that the user may engage with the panel to “slide” and view a list of Tweets from the user “Richard Socher” or mentioning “Richard Socher” in panel. 
     In another example as seen in  FIG.  8 D , if the user enters the query “Boeuf Bourguignon,” the customized search platform may preliminarily determine that this search term relates to a food item, a dish, and/or a related term, and thus may recommend search results from a data source such as “Recipes.com” or “FoodNetwork.” 
     In another example as seen in FIG. BE, if the user enters the query “Asian Boss Girl,” the customized search platform may preliminarily determine that this search item is a trendy topic and may refer to the social media sensation. Therefore, the customized search platform may recommend search results from social media sources, such as “Instagram,” “Pinterest,” “YouTube,” and/or other social media webpages. 
     In another example as seen in  FIG.  8 F , if the user enters the query “Squid Game,” the customized search platform may preliminarily determine that the search term relates to a media item. Therefore, the customized search platform may recommend search results from data sources providing media or details of the media item. 
     In another example as seen in  FIG.  8 G , if the user enters the query “wheels on the bus,” the customized search platform may preliminarily determine that while the search term may be related to a media item, unlike the previous example of “Squid Game,” the query “wheels on the bus” refers to an old-fashioned nursery. Therefore, the customized search platform may recommend search results from sources providing content aimed at the nursery rhyme, such as videos from “YouTube,” “TikTok,” or some other video source. 
     As seen in  FIG.  8 H , the customized search platform allows a user to customize their preferred data sources. A user may choose to submit whether they prefer or do not prefer a particular data source by clicking on an icon to show that preference or approval, such as a “thumbs up” or “thumbs down” icon. Based on the user submitted preferences, the customized search platform may rearrange and reprioritize search results. For example, if a user has elected “thumbs up” (e.g., approval) for “LinkedIn” but “thumbs down” (e.g., disapproval) for “Instagram,” when a user searches for a person&#39;s name such as “Richard Socher,” the customized search platform may prioritize results of “Richard Socher” from “LinkedIn” but may deprioritize the results from “Instagram.” 
       FIGS.  8 I- 8 K  show an example of a search being performed on the customized search platform before and after preferences are configured by a user. In  FIG.  8 I , a search is shown where a user initially enters a search query of “Jo Malone red roses,” where the customized search platform may return any search results from different data sources, e.g., various shopping sites such as Nordstrom.com, Walmart.com, Macys.com, etc. 
       FIG.  8 J  shows one embodiment of how a user may configure their source preferences. In this example, the user has configured their source preferences by electing “Amazon” and “Instagram” as the preferred sources when the customized search platform performs searches. 
     In  FIG.  8 K , the search of  FIG.  8 I  is repeated after the source preferences have been changed as seen in  FIG.  8   ). After the user configuration of source preferences, the customized search platform may preliminarily determine that the search term “Jo Malone red rose” relates to a product related to shopping. Therefore, the customized search platform may prioritize search results from the shopping site “Amazon” as per the user&#39;s preferences. 
     This description and the accompanying drawings that illustrate inventive aspects, embodiments, implementations, or applications should not be taken as limiting. Various mechanical, compositional, structural, electrical, and operational changes may be made without departing from the spirit and scope of this description and the claims. In some instances, well-known circuits, structures, or techniques have not been shown or described in detail in order not to obscure the embodiments of this disclosure. Like numbers in two or more figures represent the same or similar elements. 
     In this description, specific details are set forth describing some embodiments consistent with the present disclosure. Numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent, however, to one skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are meant to be illustrative but not limiting. One skilled in the art may realize other elements that, although not specifically described here, are within the scope and the spirit of this disclosure. In addition, to avoid unnecessary repetition, one or more features shown and described in association with one embodiment may be incorporated into other embodiments unless specifically described otherwise or if the one or more features would make an embodiment non-functional. 
     Although illustrative embodiments have been shown and described, a wide range of modification, change and substitution is contemplated in the foregoing disclosure and in some instances, some features of the embodiments may be employed without a corresponding use of other features. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. Thus, the scope of the invention should be limited only by the following claims, and it is appropriate that the claims be construed broadly and, in a manner, consistent with the scope of the embodiments disclosed herein.