Systems and methods for enhanced search, content, and advertisement delivery

Abstract

The present invention provides a search engine system generally comprising: a user interface in a computing environment for receiving at least a first field from a user; and a content display module to automatically generate expected search results in the computing environment based on the at least first field. The content display module is to automatically display search results including an arrangement of a plurality of visual content elements based on expected search results. A content display module is used to direct a user to content at one of the network and the local destination, associated with a visual content element selectable by the user through the user interface.

Description

[0001] Cross - reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 357,171, filed Jun. 30, 2016, which is incorporated herein by reference in its entirety. Field of the Invention

[0003] This application relates to the field of enhancing Internet searches, and more particularly to improvements in the discovery of digital content and the delivery of advertising content in Internet searches. Background Art

[0004] Internet search has become a ubiquitous tool for discovering content and interacting with content, but many search platforms, especially those on mobile devices, do not provide a good customer experience, resulting in often giving up searching before obtaining the desired results. In many cases, when a user of a mobile device is searching for desired results based on content, poor design, difficult and inconvenient query input mechanisms, ineffective presentation of search results, and long execution times between query input and presentation of search results can all be part of the many reasons why applications and search engines cannot reach users of mobile devices. Summary of the Invention

[0005] In an embodiment, a search engine system generally includes: a user interface in a computing environment for receiving at least a first field from a user; and a content display module for automatically generating expected search results in the computing environment based on at least the first field. The content display module is for automatically displaying search results, which includes an arrangement of a plurality of visual content elements based on the expected search results. The content display module is for guiding the user to content at one of a network and a local destination, which is associated with one of the visual content elements selectable by the user through the user interface.

[0006] In an embodiment, the user interface is for receiving at least a second field from the user. In an embodiment, the content display module is for updating the plurality of visual content elements based on expected search results generated according to a combination of at least the first field and the second field.

[0007] In an embodiment, the user interface is for receiving at least a second field from the user and for determining a run - time between receiving at least one of the first field and the second field. In an embodiment, the content display module is for updating the plurality of visual content elements based on expected search results generated according to a combination of at least the first field and the second field.

[0008] In an embodiment, the system includes a timing module for monitoring: the running time between receiving at least a first field and a second field from a user; and the user's navigation path, which is used as an indication of the quality of an expected search result generated based on a combination of at least the first field and the second field.

[0009] In an embodiment, a content display module is configured to automatically display a plurality of visual content elements, including visual, tactile, and interactive components. In an embodiment, the content display module is connected to a machine learning module, which is configured to improve at least one of the selection, arrangement, and presentation of the visual, tactile, and interactive components of the visual content elements, and the components provide a guided path to one of a website and a native application. In an embodiment, the improvement of at least one of the selection, arrangement, and presentation is based on a metric related to the number of visual content elements that a user accesses to achieve a desired search result.

[0010] In an embodiment, the content display module is connected to a machine learning module, which is configured to improve at least one of the selection, arrangement, and presentation of the visual, tactile, and interactive components of the visual content elements, and the components are used to guide a user to the content of one of a website and a native application, which is associated with one of the visual content elements selectable by the user through a user interface. In an embodiment, the improvement is based on a metric related to the time required for the user to obtain a desired result and / or based on a metric related to at least one of the number of visual content elements accessed by the user before obtaining the desired result and the path used by the user to access a series of visual content elements before obtaining the desired result.

[0011] In an embodiment, the content display module is connected to a machine learning module, which is configured to improve at least one of the selection, arrangement, and presentation of the visual, tactile, and interactive components of the visual content elements, and the components are used to guide a user to the content of one of a website and a native application, which is associated with one of the visual content elements selectable by the user through a user interface. In an embodiment, the improvement of at least one of the selection, arrangement, and presentation of the visual, tactile, and interactive components of the visual content elements includes at least one of the following: updating the placement of the visual content elements in a row, updating the adjustment of the size of the visual content elements, updating the number of rows for displaying the visual content elements, and updating the size of the rows for displaying the visual content elements.

[0012] In an embodiment, the system includes an advertiser bidding engine for associating an advertisement bid with advertisement content on a user interface based on at least an expected search result.

[0013] In an embodiment, the user interface includes a timing module that monitors at least a first field and at least one of a previous search query and a previously visited site, the previously visited site being from search results that are generated as expected search results based on at least one of the first field, the previous search query, and the previously visited site. In an embodiment, the timing module is used to interpret the running time of a search and navigation process as an indication of the quality of search results.

[0014] In an embodiment, the content display module is used to automatically generate a set of variants of visual content elements that represent content that can be alternatively presented from one of a website and a local application from available image, video, data, and metadata content. In an embodiment, the content display module is used to rank the visual content elements for display within the user interface based on a combination of factors including: keyword field input, user intent, and user interaction tracking.

[0015] In an embodiment, the content display module includes a user intent ontology engine for automatically adding and / or removing links between members of a set of keywords and keyword fields and members of the user intent ontology, for example, based on tracking the results of a user's interaction with visual content elements, the visual content elements being at least one generated, selected, configured, and presented based on a combination understanding of at least one keyword, a first field, a second field, and an intent.

[0016] In an embodiment, the user interface is used to detect signals from a user's computer device to automatically infer user intent during idle time associated with the user's computer device. In an embodiment, the user interface is used to present visual content elements to engage the user during idle time.

[0017] In an embodiment, the system includes a keyword user intent ontology engine for linking members of a set of keyword fields and keywords with members of the user intent ontology.

[0018] In an embodiment, the content display module is connected to a machine learning module that is used to optimize the market, where sponsors can bid for the right to place at least one visual content element on a user's computer device based on user intent and the type of the user's computer device.

[0019] In an embodiment, the machine learning module is used to automatically determine at least one of a minimum price and the right to bid based on the degree of determination of the relationship between a visual content element and a type of user intent, the type of user intent being based on tracking the results of users with a suitable intent type when engaging with advertising content having similar characteristics to the visual content element. In an embodiment, the system includes a user interaction tracking module for tracking a user's interaction with visual content elements.

[0020] In an embodiment, the user interaction tracking module is used to provide feedback to optimize at least one of discovery, generation, presentation, and arrangement of visual content elements. In an embodiment, the user interaction tracking module is used to track the behavior of the user, which includes tracking at least one of tapping, swiping, force tapping, and dwell time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features, and advantages of the devices, systems, and methods described herein will become apparent from the following detailed description of specific embodiments shown in the accompanying drawings. The drawings are not necessarily to scale, but rather focus on illustrating the principles of the devices, systems, and methods described herein. Many embodiments of the present disclosure are illustrated by way of example and not limited to the figures of the accompanying drawings, in which:

[0022] Figure 1 is a schematic diagram of the technology stack and computing environment of a search engine system connected to a user interface and an advertiser bidding infrastructure according to various embodiments of the present disclosure;

[0023] Figure 2 is of a search engine system according to various embodiments of the present disclosure Figure 1 schematic diagram of a high-level system architecture;

[0024] Figure 3 is a schematic diagram of an exemplary search engine system for performing search operations according to various embodiments of the present disclosure;

[0025] Figure 4 is a flowchart of a method for describing various embodiments of the present disclosure;

[0026] Figure 5 is a schematic diagram of a predetermined arrangement of visual content elements related to search results regarding movie theaters and movie information according to various embodiments of the present disclosure;

[0027] Figure 6 Similar to Figure 5 and shows a predetermined arrangement of visual content elements related to search results regarding live TV shows;

[0028] Figure 7 Similar to Figure 5 and shows a predetermined arrangement of visual content elements related to search results regarding actors;

[0029] Figure 8 Similar to Figure 5 and shows a predetermined arrangement of visual content elements related to search results regarding artists; and

[0030] Figure 9 is a flowchart of a method for analyzing field bidding according to various embodiments. Detailed implementation manners

[0031] Embodiments will be described more fully hereinafter with reference to the accompanying drawings, which show exemplary embodiments. However, the present invention may be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these embodiments are provided to convey the scope of the present disclosure to those skilled in the art.

[0032] All documents mentioned herein are incorporated herein by reference in their entirety. Items mentioned in the singular should be understood to include the plural, and vice versa, unless otherwise expressly stated or clearly apparent from the context. Grammatical connections are intended to express any and all combinations of transitions and connections of the phrases, sentences, words, etc. joined together, unless otherwise stated or clearly apparent from the context. Thus, the term "or" should generally be understood to mean "and / or" and the like.

[0033] The ranges of values recited herein are not intended to be limiting, but rather any and all values falling within the range are individually incorporated herein, unless otherwise stated herein, and each individual value within the range is incorporated into this specification as if it were individually recited herein. Those skilled in the art should understand that words such as "about" and "approximate" attached to a numerical value should be construed as a deviation in order to achieve the intended purpose satisfactorily. The values and / or ranges of values provided herein are merely exemplary and do not constitute a limitation on the scope of the described embodiments. The use of any and all examples or exemplary language ("such as", "for example", etc.) provided herein is merely for better illustrating the embodiments and does not limit the scope of the embodiments or the claims. No language in the specification should be construed as indicating that any non-stated element is necessary for the practice of the present invention.

[0034] In the following description, it should be understood that terms such as "first", "second", "top", "bottom", "upward", "downward", etc. are for convenience and should not be construed as restrictive terms, unless the contrary is specifically stated.

[0035] Figure 1 A typical technology stack and computing environment 100 for a search engine system 102 according to various embodiments of the present disclosure are shown. The search engine system 102 may include a search engine module 104, which is connected to a computer device 108 and a data communication network 110. The search engine module 104 may include or be integrated with the following: a content display module 120, a content ranking module 122, an auto-suggest generator 124, a content database 128, an incremental timing module 130, a utility database 132, an auto-visual element variant generator 134, a machine learning module 138, a keyword user intent ontology engine 140, and an advertising market 142. The advertising market 142 may include an advertising auction engine 144.

[0036] The user computing device 108 may include a user interface 150, which may take the form of a website, a native operating system application, a mobile application, etc. The user interface 150 may allow the user 160 ( Figure 2 ) to start entering a search string to generate the expected search results 162 ( Figure 2 ). Thus, the user interface 150 may present the search results 224 from the data communication network 110. The user interface 150 may include a search query module 180, a search results module 182, a user interaction tracking module 184, and an idle time detector 188.

[0037] The user interface 150 may be connected to or integrated with the search engine module 104. The user interface 150 may be connected to the search engine module 104 via the data communication network 110. The user interface 150 may send a query field 200, search result improvement data 202, a selected action 204, and an idle time notification 208 to the search engine module 104. Refer to Figure 2 , the query field 200 ( Figure 1 ) may include a first field 210, a second field 212, and additional fields 214, such as a third field, a fourth field, etc. Refer to Figure 1 , the search engine module 104 may send auto-suggestions 220, which may include expected search results 160, expected search queries 222, and search results 224. The search results 224 may include search result improvement data 202. The auto-suggestions 220 may include additional query fields 200, which may be used to complete the search term (by adding it to the field from the user), thereby improving one or more of the expected search queries 222. The auto-suggestions 220 may also include additional query fields 200, which may be used to complete the search term (by adding it to the field from the user), thereby providing one or more of the expected search results 162, such that the auto-suggestions 222 may complete the query and provide the expected search results 162.

[0038] The search engine system 102 may also include an advertiser user interface 250 and an advertiser bidding engine 252. The advertiser user interface 250 and the advertiser bidding engine 252 may be connected to each other and together connected to the search engine module 104.

[0039] Figure 2 A schematic high-level system architecture of the search engine system 102 according to various embodiments of the present disclosure is shown. Refer to Figure 2 And continuing to refer to Figure 1, the search engine system 102 may include a search engine module 104, which may include a search index 260, which is used to interact with users and advertisers, such as user 160 and advertisers 262, 264. The search engine system 102 may generate search results 224 for user 160, which may include content from websites, advertisement content 268, 270 provided by advertisers 262, 264, and publishers 272, 274. For user 160, the search engine module 130 may include a website or a mobile software application, or be integrated with one of them. The mobile software application includes both hardware and software for providing the systems and methods disclosed herein. The user interface 150 may be used to enable user 160 to input a search query or fields therein, observe autocomplete suggestions 220, and interact with search results 224. The advertiser user interface 250 may enable advertisers 262, 264 to bid on predetermined keywords and fields that may match, the bidding being based on or derived from query fields 200 from user 160, including expected search results 162, first field 210, second field 212, and additional field 214. Thus, a successful bid may result in the integration of selected advertisement content 268, 270 into search results 224. Users 160, 280, 282 and advertisers 262, 264 may communicate with the search engine 130 via a data communication network 110, which is deployed in one or more cloud network facilities 290, which may include or be integrated with the data communication network 110.

[0040] The search results 224 may be arranged and displayed in a visual content element 300. The search engine module 104 may include a content display module 120 to automatically organize and display the content in the visual content element 300. The content display module 120 may automatically generate the visual content element 300 from available image, video, data, and metadata content about a website or application. The content display module 120 may also generate variants of the visual, tactile, and interactive components of the visual content element 300, which may represent alternative presentations of the content.

[0041] As Figure 2 shown, the search engine module 104 may include a machine learning module 152. The machine learning module 152 may use machine learning to improve at least one of the selection, configuration, and presentation of the visual, tactile, or interactive components of the visual content element 300, and these components may provide a guided path for a website or application. The guided path includes guiding user 160 to the content at a network destination, which is related to one of the visual content elements 300 that user 160 can select via the user interface 122. The improvement to the automatically improved elements may involve: the selection of the visual content element 300, the placement of the visual content elements 300 in a row, the adjustment of the size of the visual content element 300, the number of rows of the visual content element 300, the size of the row for displaying the visual content element 300, and other factors.

[0042] The machine learning module 152 can learn to use metrics to improve at least one of the selection, configuration, and presentation of the visual content element 300. The metrics can relate to the number of visual content elements 300 that the user 160 can engage with before obtaining the desired result path to an internal (on-device) destination, a network destination, a local system destination, etc. The metrics can relate to the time required for the user 160 to obtain the desired result. Automatically improving the improvement and selection of the visual content element 300 may involve: the selection of the visual content element 300, the placement of the visual content elements 300 in a row, the adjustment of the size of the visual content element 300, the number of rows for displaying the visual content element 300, the size of the row containing the visual content element 300, and other factors.

[0043] Figure 3 Shown are the search engine 130 and an exemplary user interface 150 during a keyword search operation. The user 160 can be connected to the user computer device 108, which can generally be a personal computer, a cellular phone, or other mobile computing device. In the case of a mobile phone, the user interface 150 can include a browser or a software application that includes a query window 302 for entering keywords or the initial letters of keywords, herein referred to as "fields". The keyword or the field of the keyword can be sent to the search engine 130, and the relevant content is sent back to the user 160 and displayed. The content includes possible auto-suggestions 304, a list of regular search results 308, which includes hyperlinks to websites, other World Wide Web content, internal (on-device) destinations, website destinations, local system destinations, etc. The content can also include structured results 310, where the regular search results 308 can be synthesized into a meaningful format or image that can be directly utilized and processed by the user 160 without having to click through to the target page represented by the hyperlink in the search result list. An "auto-suggestion" can be a prediction of the entire search query, which can itself count as an expected search query. The "auto-suggestion" also updates as letters are added to the field.

[0044] As Figure 3 shown, the field can be the text string "RE" typed by the user. The auto-suggestions include the following words:

[0045] "REDDIT"

[0046] "RESTAURANTS"

[0047] "RED LOBSTER," and

[0048] "REALTOR."

[0049] For example, the structured result 310 can include a graphic or a map 312 that shows the location of the restaurants closest to the user 160 at that time.

[0050] exist Figure 3 On the right is an exemplary search engine module 104 according to the present disclosure. The search engine module 104 includes a content display module 120 for generating relevant search results 302 from one or more sources and combining these search results 302. The search engine module 104 also includes a content ranking module 122, which uses the relevance information and the completion time described in more detail below to determine the best ranking of the results. The search engine module 104 also includes an automatic suggestion generator 124, which is used to select multiple popular queries matching the keywords or keyword fields entered by the user in real time. The search engine module 104 also includes a content database 128 with search guidelines, which includes content and pointers to content that can be used for search results.

[0051] According to various embodiments of the present disclosure, the search engine 104 of the present disclosure further includes an incremental timing module 130, which is used to calculate the "completion time", that is, the running time between sending the search result 224 and the completion of the action of the user 160 triggering the search. That is, if the query is interpreted as a question, the incremental time should be the running time between the question and the answer. In an embodiment, the time required to answer the user's question should be considered as a measure of the practicality of the search result selected by the user. In this example, the shorter the incremental time, the greater the practicality of the search result. The practicality of the search result 224 is recorded in the utility database 132 and used to generate automatic suggestions 222 and expected search results 162, select search result content, and rank the selected content. The selected content can be ranked by the content ranking module 122. The content ranking module 122 can use a ranking algorithm to rank the content. The content ranking algorithm can use a combination of keyword field input, user intent, and user interaction tracking (such as tracking user tapping, sliding, hard tapping and dwell time, etc.) to rank the visual, tactile, and interactive content elements presented to the user.

[0052] refer to Figure 1, according to embodiments of the present disclosure, determination of user intent may include idle intent detection. In an embodiment, the search engine module 104 may use the idle time detector 188 to detect idle intent. In this example, the idle time detector 188 may detect a signal from the user computing device 108 to infer the intent of the user 160 to utilize the idle time using the user computing device 108. The idle time detector 188 may utilize the background to intuitively understand the intent of the user 160. After detecting the idle intent, the search engine module 104 may present visual content elements 300 for engaging the user during idle time, such as popular news and entertainment content (as compared to providing information to handle non-idle intents, such as the intent to find a restaurant, etc.). In an embodiment, after detecting the idle intent, the presentation of the visual content elements 300 may include providing a guiding intent to the user to search among a set of information, etc. This may include providing information that the user may wish to discover, making the user aware that the information is available for searching.

[0053] In an embodiment, the idle time detector 188 may include an idle interface and an idle button. In this example, when the user intends to spend idle time using the mobile device, the idle interface and the idle button are available for the user. In an embodiment, the idle time detector 188 may detect the idle intent and, after such detection, may present the idle interface or the idle button. In an embodiment, the idle time detector 188 may generate an idle time reminder when detecting the idle time. In an embodiment, the idle interface may be displayed on the lock screen of the device and when the user interacts with the idle interface, such as by "swiping left" on the phone lock screen, it is activated.

[0054] In an embodiment, the user intent may also include or be associated with a user intent ontology. The user intent ontology may be formed by the keyword user intent ontology engine 140, which may link a set of keywords and members of the keyword fields to members of the user intent ontology. In an embodiment, the user intent ontology is searchable. Searching the user intent ontology generates a set of possible intents and the associated probabilities of each intent occurring. In an embodiment, the user intent ontology may be used in the form of a classifier for the intensity of a specific intent based on text input from one or more fields of the user 160.

[0055] In an embodiment, the keyword user intent ontology engine 154 may automatically add or remove links between members of a set of keywords and keyword fields to members of the user intent ontology. These links may be based on the tracking results of different users 160 interacting with the visual content elements 300, where the visual content elements 300 are at least one of generated, selected, arranged, and presented based on the user's combined understanding of keywords or keyword fields and intents.

[0056] In an embodiment, keyword fields and keywords can be automatically linked to or removed from the user intent ontology by the user intent ontology engine 140. Automatically adding and removing links between a set of keyword and keyword field members and user intent ontology members can include automatically removing intents, such as by collapsing user intents and discarding unused or unverified user intents for a given keyword or field (where verification occurs by tracking the results of inferring a given user intent based on the keyword or field, such as tracking user engagement with content and ads generated based on the inferred or linked user intent). This can optimize and thus omit useless intents for the fields of user 120, a set of users 120, or all users, such as by only maintaining links that improve the interaction rate, or by maintaining the user intent ontology and identifying true positives based on feedback from user interaction with content (such as visual content elements as described herein) that reflects the user intent.

[0057] In an embodiment, automatically adding and removing links between a set of keyword and keyword field members to user intent ontology members can improve current partitioning methods, such as determining whether to collapse two highly related user intents, decomposing a single intent into multiple intents, and the like.

[0058] In an embodiment, automatically adding and removing links between a set of keyword and keyword field members to user intent ontology members can allow the search engine system 102 to include keyword and intent markets, where keywords with intent probabilities are linked for bidding on keyword-intent combinations.

[0059] In an embodiment, visual content elements may be presented that include visual content including text content that may be suitable for a given inferred intent, such as an intent inferred from a keyword field. In this example, the field or keyword may be linked to a user intent ontology, which in turn may be linked to various content types (i.e., visual images, photos, videos, and text) associated with the visual content element 300, which may take the form of a visual card, ribbon, box, etc., such that an appropriate set of visual content elements 300 can be presented that reflects the possible intent of the user when the user begins to enter one or more query fields 200 (i.e., the first field 210, the second field 212, and the available additional field 214). In an embodiment, the keyword and intent marketplace may include automatically generating digital content, such as the visual cards mentioned above or other visual content elements that contain content from a selected data source; including automatically pulling data from available sources and presenting the data in various ways, such as using images of different sizes, different image configurations, different fonts, different font sizes, etc. In an embodiment, the keyword and intent marketplace may also include automatically presenting visual content based on understanding the user's intent (such as any variations included in the user intent ontology), and adjusting the presentation of the data by changing and selecting different presentations based on tracking results.

[0060] Figure 4 An exemplary method of performing a search according to an embodiment of the present application is shown. First, a user inputs a keyword or a field of keywords into a mobile search application of another search tool. In an embodiment, the fields (i.e., the first field 210, the second field 212, and the additional field 214 as needed) may be sent to and received (410) at a search engine module 104 that processes them. Then, the search engine module 104 may identify one or more complete keyword phrases based on the keyword or field provided by the user, based on popular keyword phrases provided by past users. In an embodiment, these popular keyword phrases may be presented to the user as Figure 3 autosuggestions in a drop-down menu 304 at 420. After that, the search engine module 104 may provide a list of search results 224 based on the keyword manually entered by the user or the selection autosuggested at 430.

[0061] In an embodiment, the search engine module 104 may use run time and navigation path information as an indicator of search result quality. In an embodiment, the search engine module 104 may include a timer or idle time detector 188 to measure run time. The timer may monitor the fields of a search query and at least one previous search query, as well as previously visited sites, and generate search results 224 through such monitoring. In an embodiment, the search results 224 may be a prediction of the entire search query but derived from at least one of the search query fields, previous search queries, and previously visited sites, where the timer may interpret the run time of the search and navigation process as an indication of the quality of the search results 224.

[0062] When the search results 224 are transmitted to the user (or the user selects a specific search result), the incremental timing module 130 may start a timer at 440. The timer may be used to measure the run time until a transaction is completed and the navigation path of the transaction, i.e., when and by what actions the user obtains an answer, completes a task, or obtains the results of the initial search that prompted the search. The search engine module 104 may monitor user activity at 450 to obtain evidence that the user has obtained the desired results. When the search engine module 104 determines at 460 that the user has taken a specific action or selected a specific function indicating task completion, the incremental timing module 130 may stop at 470 and record the run time in the utility database 132 at 480. In an embodiment, this run time may be associated with the selected search result and may represent a measure of the utility of the search result for the query. In an embodiment, thereafter, the search engine module 104 may determine the relevance and ranking of subsequent search results 224, at least in part, based on the utility measure of the current search results. That is, in response to the current search, later search results 224 may be ranked higher or lower at 490 relative to other search results 224, thereby providing a mechanism to send only the most relevant search results 224, resulting in the shortest time to complete the task.

[0063] The following are examples of how various embodiments may be implemented. The user enters the field "u" in the search area 302 of the current search engine interface. In response, the search engine module 104 may record a first time Tl and may send the following phrases as autocomplete suggestions:

[0064] "U2"

[0065] "uses"

[0066] "UNITED AIRLINES"

[0067] "UNIVISION"

[0068] Near the auto - suggestions, information about the first auto - suggestion (i.e., U2) is displayed immediately adjacent to or below the auto - built list. If and when the user clicks on the U2 link (e.g., the U2 SPOTIFY link), the search engine module 104 can cause the SPOTIFY TM app to launch including U2 TM channel's internal SPOTIFY TM page. If and when the user sees the U2 TM channel and clicks the "play" button, the search engine module 104 can consider the user task completed and can record the second time T2. The difference between T2 and T1 can represent the running time to complete the user request.

[0069] The search engine module 104 can track the running time from the initial input in the "u" field (here the first field 210) until the user takes an action on the U2 TM SPOTIFY TM page and the user's navigation path. In an embodiment, then the total running time required to complete is used to weight the user's path u -> U2 -> SPOTIFY. Additionally, the running time required to complete can also be used to weight the same path for the group to which the user belongs and other user context information, where the group includes (e.g.) ANDROID TM users currently located on the east coast of the United States. By weighting this path, in an embodiment, the search engine module 104 can change the probability of this path being selected and / or presented to the user 160 relative to other paths that can be presented to the user 160. That is, if the running time is short, the relevance of the above - mentioned path can be increased, or if the running time is long, the relevance of the path can be decreased.

[0070] Figures 5 to 8 An exemplary search result page for specialized content that describes a predetermined arrangement of visual content elements 300. For example, if a user searches for movies currently showing in theaters, the most useful search results 224 can be retrieved from the search index of the content database 128, and these results can be used to populate the shown template. In this template, the search engine module 104 can generate a search result page with one of the predetermined arrangements, which includes multiple rows, and each row includes a specific information category or classification.

[0071] In Figure 5In an example, the user may be searching for the movie "The Martian", which produces results as shown in visual content element 300, including: the first row of a list of general information and links 510, 511, 512, the second row of a list of movie theaters 520, 521 where the movie is playing, the third row of a list of actors 530, 531 in the movie, and the fourth row of news reports 540, 541 about the movie. In an embodiment, the general information may include (for example) the name and reviews of the director 520, a link to the movie trailer 521, and a link to Wikipedia TM 512 or a link to an online encyclopedia such as Wikipedia that provides more comprehensive information

[0072] In Figure 6 an embodiment, the user is searching for a TV program, which produces results as shown in visual content element 300, including: the first row of a list of general information and links 610, 611, 612, the second row of a list of links to watch the TV program 620, movies 621, the third row of a list of actors 630, 631 in the TV program, and the fourth row of news reports 640, 641. In an embodiment, the general information may include (for example) the name and reviews of the director 620, a link to the movie trailer 621, and a link to Wikipedia TM 612 or a link to an online encyclopedia such as Wikipedia that provides more comprehensive information

[0073] In Figure 7 an example, the user is searching for a specific actor, which produces results as shown in visual content element 300, including: the first row of general information and links 710, 711, the second row of a list of links to news reports 720 about the actor and the actor's Twitter TM 721 or other SMS messages, the third row of a list of movies or TV shows 730, 731 in which the actor appears, and the fourth row of actor photos 740, 741. In an embodiment, the general information may include (for example) the age and birthplace of the actor 720, a link to Wikipedia TM 711 or a link to an online encyclopedia such as Wikipedia that provides more comprehensive information

[0074] In Figure 8 an example, the user is searching for a specific music artist, which may produce search results 224 as shown in visual content element 300, including the first row of general information and links 810, 811, 812, news 820 about the music artist or a Twitter post from the artist TMThe second line of the list of links to the other SMS message 821, the third line of the links to the songs or music videos 830, 831 of the music artist, and the fourth line of the music artist photos 840, 841. In an embodiment, the general information may include (for example) the age and birthplace 820 of the music artist, Wikipedia TM A link to 811 or other online encyclopedias that find more comprehensive information, and a link to one or more music sites 812 where the music artist performs.

[0075] In an embodiment, the search engine module 104 can be used to receive bids from advertisers for complete keywords and keyword fields. For each specific keyword, the total number of fields available for auction is equal to the number of letters in the keyword, in the field, or in other phrases (or single words) to be searched. That is, an advertiser can bid on one or more fields, where the difference between these fields is as little as one letter. For example, the search engine module 104 can initiate (for example) 17 different auctions for the phrase "Chineserestaurant", with each field starting with "c," "ch," "chi," "chin," "chine," etc. being auctioned. Although the search engine module 104 can support 17 different auctions, some of these auctions will be selected and some will not be selected based on cost-effectiveness considerations. Specifically, by analyzing the probability of a user clicking on an advertisement and the above incremental time, an advertiser can choose to bid on a small set of fields that optimize the return on investment. In an embodiment, only the highest-performing fields are selected and the advertisements are incorporated into the search results 224.

[0076] Figure 9 Shown is a flowchart of the process for identifying keyword fields for which an advertisement promotion is initiated and advertisements are sent in response to these fields. First, at 910, an advertiser sends to the search engine module 104 the keyword that the advertiser is interested in and the maximum price per click (PPC) or price per thousand impressions (PPM) or price per user action (PPA), such as the price that the advertiser is willing to pay for a click-through for downloading an application or launching an application. Thus, the received keyword can then be parsed or broken down into multiple fields at 920, where the number of fields is approximately equal to the number of letters in the keyword.

[0077] In an embodiment, the search engine module 104 comments on each field and the potential set of existing keywords sharing this field at 930, and evaluates the likelihood that the field corresponds to the end-user intent and the advertiser's keyword or field. The relevance score can be expressed as the percentage of the time when the user means the advertiser's keyword or field when the user simply types a specific field (e.g., 3.4% as opposed to 96.6% for other cases). At 940, the relevance score of each field is compared with the keywords and other ads sharing this field, and the content of this advertiser is evaluated (scored) relative to the content of other advertisers having the same field.

[0078] The machine learning module 152 previously mentioned in this application can also optimize the market, where advertisers can bid on the right to place content elements based on the type of intent of the users of the device where the content elements will be placed. Based on the degree of determination of the relationship between the content and the intent, machine learning is used to set at least one of the minimum price and the bidding right, and the intent is based on the results of tracking users with a suitable intent type when joining the content element with content having similar characteristics.

[0079] Next, the search engine module 104 can determine the minimum bid required for each field at 950 by setting the minimum bid required to "resolve" the relevance conflict (if any), where the relevance conflict comes from the corresponding relevance score at 930 and the scoring at 940, including the current bids for this field from other potential advertisers. Additionally, under the assumed required bid, the search engine module 104 calculates the probability of payable user events (such as impressions, clicks, purchases, downloads, or other actions) from the advertiser at 960.

[0080] After that, the search engine module 104 compares the cost per event calculated at 960 with the price the advertiser is willing to spend at 910. For example, if there are fields with excessive costs due to the highest price setting (PPC, PPM, or PPA), the search engine module 104 can close the bidding for these fields and initiate the promotion of the remaining fields at 990, where the remaining fields have a PPC, PPM, or PPA equal to or lower than that of the original keyword.

[0081] For example, if the advertiser hopes that the average price per click is $2.50 and the keyword is 'games', then the search engine module 104 calculates the PPC bid for this exact keyword (e.g., $2.35 for "games"), and the bids for each substring of "games" that meets this maximum PPC are as follows:

[0082] "games": $2.35

[0083] "game": $2.15

[0084] "gam": $1.45

[0085] "ga": $1.00

[0086] "g": $0.45

[0087] However, the search engine will also determine the minimum required PPC for the keyword "games" and each of its fields based on a machine learning model that predicts user behavior when presenting content after the user types in the following fields:

[0088] Minimum PPC for "games" $1.50

[0089] Minimum PPC for "game" $1.55

[0090] Minimum PPC for "gam" $1.60

[0091] Minimum PPC for "ga" $4.75

[0092] Minimum PPC for "g" $17.50

[0093] In an embodiment, the first two fields of the keyword "games" are both below the maximum PPC required by advertisers for that field and above the minimum PPC enforced by the search engine, where the "gam" field would not be cost-effective and would not meet the advertiser's goals, and its bid volume may be set to $0, and the same is true for the subsequent fields "ga" and "g".

[0094] Detailed embodiments of the present invention are disclosed herein; however, it should be understood that the various embodiments disclosed are only examples of the present disclosure, which may be embodied in various forms. Accordingly, the specific structural and functional details disclosed herein should not be construed as limiting, but should only be construed as the basis for the appended claims and as a basis for teaching those skilled in the art to employ the present invention in various ways in almost any appropriate detailed structure.

[0095] The term "a" as used herein is defined as one or more than one. The term "another" as used herein is defined as at least a second or more. The terms "comprising" and / or "having" as used herein are defined as including (i.e., an open-ended transition).

[0096] Although only a few embodiments of the present invention have been given and described, it will be apparent to those skilled in the art that many changes and modifications can be made thereto without departing from the spirit and scope of the present invention as described in the following claims. All patent applications and patents, including foreign and domestic, and all other publications cited herein are hereby incorporated by reference in their entirety to the maximum extent permitted by law.

[0097] The methods and systems described herein can be deployed, in whole or in part, by a machine that executes computer software, program code, and / or instructions on a processor. The present disclosure can be implemented as a method on a machine, or as part of or in relation to a system or apparatus of a machine, or as a computer program product embodied in a computer-readable medium executed on one or more machines. In an embodiment, the processor can be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. The processor can be any type of computing or processing device capable of executing program instructions, code, binary instructions, etc. The processor can be or can include a signal processor, digital processor, embedded processor, microprocessor, or any variant such as a coprocessor (mathematical coprocessor, image coprocessor, communication coprocessor, etc.) and processors that can directly or indirectly contribute to the execution of program code or program instructions stored thereon. In addition, the processor is capable of executing multiple programs, threads, and code. Threads can be executed simultaneously to enhance the performance of the processor and facilitate the simultaneous operation of applications. As an implementation, the methods, program code, program instructions, etc. described herein can be implemented in one or more threads. Threads can spawn other threads with specified priorities associated therewith; the processor can execute these threads based on priorities or other orders provided in the program code. The processor or any machine utilizing it can include non-transitory memory that stores the methods, code, instructions, and programs described herein and elsewhere. The processor can access a non-transitory storage medium through an interface, which can store the methods, code, and instructions described herein and elsewhere. The storage medium related to the processor can include, but is not limited to, one or more of CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, etc., which is used to store methods, programs, code, program instructions, or other types of instructions that can be executed by a computing or processing device.

[0098] The processor can include one or more cores that can enhance the speed and performance of a multi-processor. In an embodiment, the processor can be a dual-core processor, quad-core processor, other chip-level multi-processor, and similar processors that combine two or more independent cores (referred to as chips).

[0099] The methods and systems described herein may be deployed, in part or in whole, by a machine that executes computer software on a server, client, firewall, gateway, hub, router, or other such computer and / or network hardware. The software program may be associated with a server, which includes a file server, print server, domain server, Internet server, intranet server, cloud server, and other variations, such as secondary servers, host servers, distributed servers, etc. The server may include one or more memories, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices via wired or wireless media, etc. The methods, programs, or code described herein and elsewhere may be executed by the server. In addition, other devices required to execute the methods described in this application may be considered part of the infrastructure associated with the server.

[0100] The server may provide an interface to other devices, including but not limited to clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, etc. In addition, such coupling and / or connection may facilitate the remote execution of programs over a network. The networking of some or all of these devices may facilitate the parallel processing of programs or methods in one or more locations without departing from the scope of the present invention. In addition, any device connected to the server via an interface may include at least one storage medium capable of storing methods, programs, code, and / or instructions. A central repository may provide program instructions for execution on different devices. In this embodiment, a remote repository may be used as a storage medium for program code, instructions, and programs.

[0101] The software program may be associated with a client, which may include a file client, print client, domain client, Internet client, intranet client, and other variations, such as secondary clients, host clients, distributed clients, etc. The client may include one or more memories, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices via wired or wireless media, etc. The methods, programs, or code described herein and elsewhere may be executed by the client. In addition, other devices required to execute the methods described in this application may be considered part of the infrastructure associated with the client.

[0102] The client can provide an interface to other devices, which include but are not limited to servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, etc. In addition, such coupling and / or connection can facilitate the remote execution of programs over a network. The networking of some or all of these devices can facilitate the parallel processing of programs or methods at one or more locations without departing from the scope of the present invention. In addition, any device connected to the client through the interface can include at least one storage medium capable of storing methods, programs, code, and / or instructions. A central repository can provide program instructions for execution on different devices. In this embodiment, a remote repository can be used as a storage medium for program code, instructions, and programs.

[0103] The methods and systems described herein can be deployed in part or in whole through a network infrastructure. The network infrastructure can include elements such as the following: computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active or passive devices, modules, and / or components known in the art. Among other components, the computing and / or non-computing devices associated with the network infrastructure can include storage media such as flash memory, buffers, stacks, RAM, ROM, etc. The programs, methods, program code, and instructions described herein and elsewhere can be executed through one or more network infrastructure elements. The methods and systems described herein can be applicable to any kind of private, group, or hybrid cloud computing network or cloud computing environment, including networks or environments involving features of software as a service (SaaS), platform as a service (PaaS), and / or infrastructure as a service (IaaS).

[0104] The methods, program code, and instructions described herein and elsewhere can be implemented on a cellular network having multiple cells. The cellular network can be either a frequency division multiple access (FDMA) network or a code division multiple access (CDMA) network. The cellular network can include mobile devices, cell sites, base stations, repeaters, antennas, towers, etc. The cellular network can be a GSM, GPRS, 3G, EVDO, mesh, or other network type.

[0105] The methods, program codes, and instructions described herein and elsewhere can be implemented on a mobile device via the mobile device. The mobile device can include a navigation device, a cellular phone, a mobile phone, a mobile personal digital assistant, a laptop computer, a palmtop computer, a netbook, a pager, an e-book reader, a music player, etc. In addition to other components, these devices can also include storage media, such as flash memory, buffers, RAM, ROM, and one or more computing devices. The computing devices associated with the mobile device are capable of executing the program codes, methods, and instructions stored thereon. Alternatively, the mobile device can be used to cooperate with other devices to execute instructions. The mobile device can communicate with a base station, which is connected to a server and is used to execute program codes. The mobile device can communicate on a peer-to-peer network, a grid network, or other communication networks. The program codes can be stored on a storage medium associated with the server and executed by a computing device embedded in the server. The base station can include a computing device and a storage medium. The storage device can store program codes and instructions, which can be executed by the computing device associated with the base station.

[0106] Computer software, program code, and / or instructions can be stored and / or accessed on a machine-readable medium, which can include: computer components, devices, and recording media that retain digital data for calculating certain intervals of time; semiconductor memories known as random access memories (RAM); mass storage devices typically used for more permanent storage, such as optical disks, magnetic storage in the form of hard disks, tapes, drums, cards, and other types; processor registers, cache memories, volatile memories, non-volatile memories; optical memories, such as CDs, DVDs; removable media, such as flash memory (e.g., USB sticks or keys), floppy disks, tapes, paper tapes, punched cards, standalone RAM disks, Zip drives, removable mass storage, offline, etc.; other computer memories, such as dynamic memories, static memories, read / write memories, volatile memories, read-only, random access, sequential access, location-addressable, file-addressable, content-addressable, network-attached memories, storage area networks, barcodes, magnetic ink, etc.

[0107] The methods and systems described herein can transform physical and / or intangible items from one state to another. The methods and systems described herein can also transform data representing physical and / or intangible items from one state to another.

[0108] The elements described and depicted herein, including the flowcharts and background diagrams throughout the figures, imply logical boundaries between the elements. However, in accordance with software or hardware engineering practices, the elements and their functions may be implemented on a machine as an overall software structure, independent software modules, or modules that employ external routing, code, services, etc., or combinations thereof, via a computer-executable medium having a processor capable of executing program instructions stored thereon, and all such implementations are within the scope of the present disclosure. Examples of such machines may include, but are not limited to: personal digital assistants, laptop computers, personal computers, mobile phones, other handheld computing devices, medical devices, wired or wireless communication devices, transducers, chips, calculators, satellites, desktop personal computers, e-books, gadgets, electronic devices, devices with artificial intelligence, computing devices, networked devices, servers, routers, etc. Additionally, the elements or any other logical components shown in the flowcharts and block diagrams may be implemented on a machine capable of executing program instructions. Thus, while the above figures and descriptions set forth the functional aspects of the disclosed system, it should be inferred from these descriptions that there is no specific software configuration for implementing these functional aspects, unless explicitly stated or clearly visible in the context. Similarly, it should be understood that the various steps identified and described above may be altered, and the order of the steps may be adapted to a particular application of the technology disclosed herein. All such variations and modifications are intended to fall within the scope of the present disclosure. Accordingly, the order of the various steps shown and / or described should not be construed as requiring that the steps be performed in a particular order, unless required by a particular application or explicitly stated or clearly visible in the context.

[0109] The above methods and / or programs, and the steps associated therewith, may be implemented in hardware, software, or a combination of hardware and software suitable for a particular application. The hardware may include general-purpose computers and / or special-purpose computing devices, or particular aspects or components of a particular computing device. The program may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices, as well as internal and / or external memories. Alternatively, the program may be implemented in a special-purpose integrated circuit, programmable gate array, programmable array logic, or any other device or combination of devices suitable for processing electronic signals. It should be further understood that one or more programs may be implemented as computer-executable code capable of being executed on a machine-readable medium.

[0110] The computer-executable code may be generated using structured programming languages such as C, object-oriented programming languages such as C++, or any other high-level or low-level programming language (including assembly language, hardware description languages, and database programming languages and techniques), which may be stored, compiled, or interpreted to run on the above devices, as well as different combinations of processors, processor architectures, or different combinations of hardware and software, or any other machine capable of executing program instructions.

[0111] Accordingly, on the one hand, the above-described methods and combinations thereof may be embodied in computer-executable code that, when executed on one or more computing devices, performs the steps thereof. On the other hand, these methods may be embodied in a system that performs the steps thereof and may be distributed in various ways across devices, or all functionality may be integrated into a dedicated stand-alone device or other hardware. On the other hand, components that perform the steps associated with the above programs may include any of the foregoing hardware and / or software. All such arrangements and combinations are intended to fall within the scope of the present disclosure.

[0112] Although the present invention has been disclosed and described in detail in connection with the preferred embodiments shown, various modifications and improvements will be apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure should not be limited by the foregoing examples, but should be construed in the broadest sense permitted by law.

[0113] In the context of describing the present disclosure (particularly in the context of the following claims), the use of the terms "a," "an," and "the" and similar reference terms should be construed to cover both the singular and the plural unless otherwise indicated herein or the context clearly dictates the contrary. The terms "comprising," "having," "including," and "containing" should be construed as open-ended terms (i.e., meaning "including, but not limited to") unless otherwise noted. The ranges of values recited herein are merely used as a shorthand method of individually referring to each individual value falling within the range, unless otherwise indicated herein, and each individual value is incorporated into the specification as if it were individually recited herein. All of the methods described herein may be performed in any suitable order unless otherwise indicated herein or clearly contradicted by the context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein is merely for the purpose of better illustrating the present disclosure and does not pose a limitation on the scope of the present disclosure unless otherwise stated. No language in the specification should be construed as indicating any non-recited element as essential to the practice of the invention.

[0114] Although the foregoing written description of the invention enables one of ordinary skill in the art to make and use what is currently considered to be the best mode thereof, one of ordinary skill in the art will understand and appreciate that there are changes, combinations, and equivalents to the specific embodiments, methods, and examples described herein. Accordingly, the present disclosure is not limited to the foregoing embodiments, methods, and examples, but all embodiments and methods are within the scope and spirit of the present disclosure.

[0115] No element in a claim that does not expressly recite a "means" for performing a specified function or a "step" for performing a specified function shall be construed to cover the corresponding structure, material, or acts specified in 35 U.S.C. § 112(f). In particular, any use of the word "step" in a claim is not intended to invoke the provisions of 35 U.S.C. § 112(f).

[0116] Those skilled in the art will appreciate that numerous design configurations are possible to enjoy the advantages of the present invention's system. Accordingly, assuming that the embodiments of the present invention have a wide variety of configurations and arrangements, the scope of the present invention is affected by the scope of the claims below and not limited by the above-described embodiments.

Claims

1. A search engine system, comprising: A user input section configured to receive a search request from a user; A user intention section that infers the user's intention; A content optimization section that uses machine learning to determine the degree of relationship between the search content and the user's intention; And A result generation section that automatically generates digital content by extracting data from the search content and automatically presents the digital content based on the user's intention.

2. The search engine system according to claim 1, wherein The result generation section automatically generates the digital content as at least one of a visual card, a ribbon, or a cartridge.

3. The search engine system according to claim 1, wherein, The result generation section generates the digital content to fill a template in a predetermined arrangement based on a specific information category or classification.

4. The search engine system according to claim 1, wherein, The user intention section infers the user's intention based on context and idle time.

5. The search engine system according to claim 1, wherein, The result generation section assigns weights to paths based on user context information for presentation to the user.

6. The search engine system according to claim 5, wherein, The result generation section assigns the weights to the paths based on the group to which the user belongs, as at least a part of the user context information.

7. The search engine system according to claim 5, wherein, The result generation section assigns weights to the paths based on the time elapsed by the group to which the user belongs.

8. The search engine system according to claim 1, wherein The result generation section adjusts the representation of the search content by changing and selecting different representations based on the tracking results.

9. A computerized method for implementing a search engine, the computerized method comprising: Receiving a search request from a user; Inferring the user's intention; Using machine learning to determine the degree of relationship between the search content and the user's intention; And Automatically generating digital content by: Extracting data from the search content; and Automatically presenting the digital content based on the user's intention.

10. The computerized method according to claim 9, wherein, Automatically presenting the digital content further includes presenting the digital content as at least one of a visual card, a ribbon, or a cartridge.

11. The computerized method according to claim 9, wherein, Generating digital content further includes filling a template in a predetermined arrangement based on a specific information category or classification.

12. The computerized method according to claim 9, wherein, Inferring the user's intention is also based on context and idle time.

13. The computerized method according to claim 9, wherein, Automatically presenting the digital content further includes assigning weights to paths based on user context information for presentation to the user.

14. The computerized method according to claim 13, wherein, Assigning the weights to the paths is also based on the group to which the user belongs, as at least a part of the user context information.

15. The computerized method according to claim 13, wherein, Assigning weights to the paths is also based on the time elapsed by the group to which the user belongs.

16. The computerized method according to claim 9, wherein The automatically generating digital content further includes adjusting the representation of the search content by changing and selecting different representations based on the tracking results.

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