Method for processing interaction of browser tab page

Abstract

The disclosure provides an interaction processing method of a browser tab, and relates to the technical field of computers, and the method comprises the following steps: in response to a grouping trigger instruction for a plurality of opened tabs in a browser, obtaining tab information of the opened tabs; performing semantic analysis and clustering grouping on the tab information to obtain a grouping result, and optimizing the grouping result to determine grouping suggestion information; grouping a plurality of opened tabs of the browser according to the grouping suggestion information to determine a target group, and displaying the plurality of opened tabs through the target group. The disclosure can improve the tab grouping efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a method for interactive processing of browser tabs. Background Technology

[0002] A browser can include multiple tabs. Currently, most browsers only offer basic tab grouping functionality, requiring users to manually select and group tabs, which is cumbersome and inefficient. Some browsers attempt fixed grouping or simple categorization, but these methods have limitations and low accuracy. Summary of the Invention

[0003] The purpose of this disclosure is to provide an interactive processing method for browser tabs, thereby overcoming, at least to some extent, the problem of low efficiency in browser tab grouping caused by the limitations and defects of related technologies.

[0004] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.

[0005] According to one aspect of this disclosure, a method for handling the interaction of browser tabs is provided, comprising: In response to a grouped trigger command for multiple open tabs in a browser, the tab information of the open tabs is obtained; Semantic analysis and clustering are performed on the tab information to obtain grouping results, and the grouping results are optimized to determine grouping suggestion information; Based on the grouping suggestion information, the multiple open tabs of the browser are grouped to determine the target group, and the multiple open tabs are displayed through the target group.

[0006] In one exemplary embodiment of this disclosure, the group triggering command is triggered by hovering over the browser sidebar, by right-clicking the tab menu, or by using a group shortcut key.

[0007] In one exemplary embodiment of this disclosure, displaying multiple opened tabs through the target group includes: The open tabs in the same target group will be merged and displayed in the group display area with the same display attributes; In response to an operation to expand the grouped display area, the multiple open tabs in the grouped display area are expanded and displayed; In response to a collapse display operation on the grouped display area, at least a portion of the open tabs in the grouped display area are hidden.

[0008] In one exemplary embodiment of this disclosure, the method further includes: In response to a confirmation request for the target group, the opened tab is displayed according to the target group; In response to an adjustment operation on the target group, the adjustment group corresponding to the adjustment operation is determined, and in response to a confirmation operation on the adjustment group, the opened tab is displayed according to the adjustment group; the adjustment operation includes a move operation and / or a rename operation.

[0009] In one exemplary embodiment of this disclosure, the tab information includes one or more of the following: page content, webpage domain name, webpage title keywords, and access time.

[0010] In one exemplary embodiment of this disclosure, the grouping result includes a group title name, and the process of determining the group title name includes: Based on the title keywords of the opened tabs within the group, generate the group title name in the grouping results.

[0011] In one exemplary embodiment of this disclosure, the step of performing semantic analysis and clustering grouping on the tab information to obtain grouping results includes: The tab information and the first prompt word are input into the first model to determine the semantic feature vector; The initial grouping results are determined by clustering the semantic feature vectors using a clustering algorithm. The initial grouping results are corrected to determine the final grouping result.

[0012] In one exemplary embodiment of this disclosure, the step of correcting the initial grouping result and determining the grouping result includes: Based on the first model, cluster analysis is performed on the individual grouping results in the initial grouping results to determine the first auxiliary grouping results; The boundary grouping results in the first auxiliary grouping results are assigned to specific groups to determine the second auxiliary grouping results; The initial grouping result is corrected based on the first auxiliary grouping result and the second auxiliary grouping result to obtain the grouping result.

[0013] In one exemplary embodiment of this disclosure, optimizing the grouping results to determine grouping suggestion information includes: If the grouping results do not meet the preset grouping requirements, the grouping results and the second prompt word are input into the second model for reclassification, so as to adjust the group title name or the group to which the opened tab belongs in the grouping results and determine the grouping suggestion information.

[0014] In one exemplary embodiment of this disclosure, the method further includes: In response to the undo operation on the target group, stop displaying the opened tabs according to the target group.

[0015] In some embodiments of this disclosure, the technical solutions, on the one hand, respond to a grouping trigger command for multiple open tabs in a browser, perform semantic analysis and clustering grouping on the tab information to obtain grouping results, and optimize the output grouping results to obtain grouping suggestion information output by the model. Then, based on the grouping suggestion information, the browser tabs are grouped to determine the target group, and the tabs are displayed according to the target group. This avoids users manually selecting tabs and grouping, reduces operation steps, improves the convenience of tab grouping, and also improves the efficiency of tab grouping. On the other hand, grouping suggestion information can be determined based on the tab information of open tabs, increasing the dimension of intelligently determining grouping suggestion information, reducing user operation steps, improving the efficiency and accuracy of tab organization, improving the accuracy of grouping suggestion information, enhancing intelligence and practical value, and improving reliability.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0018] Figure 1 The diagram illustrates an interactive processing method for a browser tab according to an embodiment of the present disclosure.

[0019] Figure 2 This diagram illustrates the interface diagram of the tab grouping function triggered according to an embodiment of the present disclosure.

[0020] Figure 3 The diagram illustrates a tab grouping display interface in an embodiment of this disclosure.

[0021] Figure 4 The illustration shows a schematic diagram of the tab grouping process according to an embodiment of the present disclosure.

[0022] Figure 5 This is a schematic block diagram illustrating an interactive processing system for browser tabs according to an embodiment of the present disclosure. Detailed Implementation

[0023] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0024] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0025] To address the technical problems in related technologies, this disclosure provides an interactive processing method for browser tabs, referencing... Figure 1 As shown, the main steps include: Step S110: In response to a grouping trigger command for multiple open tabs in the browser, obtain tab information of the open tabs; Step S120: Perform semantic analysis and clustering on the tab information to obtain grouping results, and optimize the grouping results to determine grouping suggestion information; Step S130: Group the multiple open tabs of the browser according to the grouping suggestion information to determine the target group, and display the multiple open tabs through the target group.

[0026] Next, with reference to the accompanying drawings, each step of the browser tab interaction processing method in the embodiments of this disclosure will be described in detail.

[0027] Step S110: In response to a group trigger command for multiple open tabs in the browser, obtain tab information of the open tabs.

[0028] In some embodiments of this disclosure, the target user can be any user, and the browser can be any type of browser, and the browser can include multiple tabs. A tab refers to an independent, switchable webpage display area in a browser window. Multiple tabs can coexist in the same browser window, loading and running different webpages without interfering with each other.

[0029] Grouping trigger commands are used to quickly group multiple open tabs. These commands can be triggered via hovering in the browser sidebar, via the tab's right-click menu, or via grouping shortcuts, or other methods; no specific limitation is made here. A hovering operation refers to moving the computer mouse pointer to a specified location and keeping it stationary, without clicking or dragging. In some embodiments, detecting a hovering operation in the browser sidebar, a triggering operation via the tab's right-click menu, or a triggering operation via a grouping shortcut can all be considered as detecting a grouping trigger command. Grouping shortcuts can be, for example, grouping controls or tab grouping controls provided at a preset location in the browser. First, let's describe the grouping controls. Grouping controls can display multiple tab display controls, such as tab grouping controls, tab group opening or expanding controls, independent window creation controls, incognito window creation controls, etc. Tab grouping controls are used to quickly group multiple tabs. The preset location can be around multiple tabs; for example, it can be displayed on either side of the first tab or the last tab; no specific limitation is made here.

[0030] If a hover operation by a target user on a grouping control is detected, a control area can be displayed around the grouping control. Furthermore, if a trigger operation on the tab grouping control within the control area is detected, a group trigger command can be executed to activate the tab grouping function. The trigger operation can be a click operation or any other operation that can cause the control to be triggered; no specific limitation is made here.

[0031] In other embodiments, if a click operation by the target user on the grouping control is detected, it can be considered a trigger operation on the tab grouping control, and a group trigger command can be directly triggered. Alternatively, the tab grouping control can be displayed at a preset location as a grouping shortcut; when a click operation on the tab grouping control is detected, a group trigger command is triggered.

[0032] In response to this group trigger command, the system automatically retrieves tab information for all currently open tabs in the browser. Open tabs refer to all currently open tabs, and tab information includes one or more of the following: page content, webpage domain, webpage title keywords, and access time. Page content can be the text information such as the body, title, and keywords of the webpage loaded by the tab. The webpage domain and webpage title keywords can be used to determine the website type. Website types can be, for example, academic, e-commerce, tool, social media, etc. For instance, the URL (Uniform Resource Locator) domain of the webpage loaded by the tab might identify it as a shopping website, while the title keywords might identify it as a work document. Access time can include one or more of the following: tab opening time, closing time, dwell time, and last access time.

[0033] The tab information collection module actively scans and monitors data related to open tabs in real time to collect tab information from the browser. For example, the module locates the core content of the webpage from the collected webpage source code, extracts keywords from this core content to obtain key content for the tab. This extracted key content is input into a semantic classification model, which outputs a semantic website type through semantic analysis. The domain name website type is determined based on the domain name matching result, and the website type is determined based on both the domain name website type and the semantic website type. The website type can include one type or two types, such as "tools + office". The tab information collection module uses timestamps and timers to accurately track various dimensions of access time, such as directly obtaining the creation or closing timestamp of the browser tab object to determine the opening and closing times. When the browser window is in the foreground and the tab is currently selected, a second-level timer is started, and the accumulated time is the dwell time.

[0034] refer to Figure 2 As shown, a grouping control 201 can be provided around the location of the tab, and the grouping control 201 may include a tab grouping control 202. Detecting a trigger operation on the tab grouping control 202 within the control area allows for the automatic acquisition of tab information of open tabs in the browser in response to a grouping trigger command.

[0035] Step S120: Perform semantic analysis and clustering on the tab information to obtain grouping results, and optimize the grouping results to determine grouping suggestion information.

[0036] In this embodiment of the disclosure, the grouping results include group title information and the tabs contained in each group represented by the group title information. The grouping suggestion information also includes group title information and the tabs contained in each group. The grouping results and the grouping suggestion information may be the same or may have some differences, depending on the actual needs.

[0037] In some embodiments, for each group, the group title name in the grouping results can be generated based on the title keywords of the opened tabs within the group. Title keywords can be title keywords with a frequency greater than a frequency threshold. In some embodiments, the title keywords used to determine the group title name can also include content keywords with a frequency greater than a frequency threshold. Specifically, the title keywords can be directly used as the group title name, or the title keywords can be processed by at least one of the following operations: concatenation, summarization, synonym replacement, transformation, or adding prefixes, suffixes, or preset identifiers. The processed text is then used as the group title name; no specific limitation is made here. The added prefixes, suffixes, and preset identifiers can be determined according to actual needs, for example, they can be related to dates or users; no specific limitation is made here. In addition, at least some keywords and prompt words from the title keywords can be input into the model so that the model can modify them according to the prompt words. The modified keywords are then used as the group title name; no specific limitation is made here. Prompt words can be user preference prompt words or style prompt words, etc., specifically determined according to actual needs. In this embodiment of the disclosure, the group title name is generated by using the title keywords of the opened tabs within the group, which can improve the matching between the group title name and the actual scenario.

[0038] For example, grouping suggestion information can be determined through an intelligent grouping algorithm module. This intelligent grouping algorithm module may include a clustering module and an optimization module. The clustering module determines the grouping results using a first model, and the optimization module further optimizes and adjusts the grouping results determined by the first model using a second model, outputting the final grouping suggestion information for the opened tabs.

[0039] First, the clustering process based on the first model will be explained. The first model can be a large language model. When historical usage data exists for the target user, grouping suggestions can be determined by combining historical usage data and tab information. When historical usage data does not exist, grouping suggestions are determined based on tab information. Historical usage data can be used to describe the target user's usage preferences when using browser tabs, and is used to describe the target user's browsing behavior. Historical usage data can include one or more of the following: operation behavior, behavior object, behavior time, behavior frequency, and operation relationship between different tabs; operation behavior can be, for example, at least one of clicking, dwelling, favorites, and input; operation relationship between different tabs can be, for example, the target user can associate tab A and tab B together, but tab A and tab C cannot be associated.

[0040] For example, historical usage data and tab information can be concatenated to obtain concatenated information. This concatenated information, along with a first prompt word, is input into a first model to determine a semantic feature vector. The semantic feature vector is then used to perform clustering calculations to determine the grouping results. The first prompt word can be used to specify the requirements for clustering and grouping the tab information and historical usage data.

[0041] After inputting the concatenated information and the first prompt word into the first model, the model can decompose them into text tokens, mapping each text token to a unique integer ID. The word embedding module in the embedding layer maps the integer ID of each text token to a fixed-dimensional initial semantic vector. The positional encoding module in the word embedding layer performs positional encoding on the initial semantic vector to obtain a token vector sequence. In the encoder part, query, key, and value matrices are calculated for the token vector sequence. An attention weight matrix is ​​obtained by performing matrix dot product, scaling, and softmax operations through a multi-head attention mechanism layer. Weighted summation and multi-head parallel computation generate the multi-head attention output. Further, the multi-head attention output is processed through residual connections and a feedforward neural network, and then combined with residual connections and layer normalization to obtain intermediate features. These intermediate features are then pooled through a pooling layer to extract semantic feature vectors. The semantic feature vectors contain global contextual semantic information. Extracting semantic feature vectors from historical usage data and tab information using the first model improves the accuracy and comprehensiveness of feature extraction.

[0042] After the first model outputs semantic feature vectors, a clustering algorithm can be selected based on the semantic feature vectors. The clustering algorithm here can be an unsupervised clustering algorithm, such as the K-Means++ algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), or hierarchical clustering.

[0043] When using the K-Means++ clustering algorithm, the following steps are taken: First, a sample vector is randomly selected from the sample set as the first initial cluster center. The minimum distance from all samples to the initial cluster center is calculated. The square of the minimum distances is used as the probability that the sample will be selected as the next center. The next cluster center is selected from the sample set according to this probability distribution. This process is repeated iteratively until K centers are selected. Next, for each sample in the sample set, the cosine distance from it to the K cluster centers is calculated. The sample is assigned to the cluster corresponding to the nearest cluster center, forming K initial clusters. For each initial cluster, the mean vector of all sample vectors within that cluster is calculated and used as the new cluster center. Finally, the update difference of all cluster centers is calculated. The iteration terminates when the difference is less than or equal to the convergence threshold or when the maximum number of iterations is reached. The final K clusters are output, representing the initial grouping result obtained by the clustering algorithm. The initial grouping result may include the group title name, the tabs contained in the group corresponding to the group title name, and may also retain the target user's historical usage data without modifying it.

[0044] The value of K in the clustering algorithm can be determined based on the first model. For example, the user's initial prompt, the page content of the tab, and the description of historical behavior data are summarized to generate reference text; the reference text and the prompt are then input into the first model to obtain the K value. The prompt is used to indicate the grouping requirements based on the sample semantics. Of course, the K value can also be determined using the silhouette coefficient method or other algorithms; no specific limitation is made here.

[0045] In addition, the initial grouping results can be revised to determine the final grouping results. Specifically, cluster analysis can be performed on individual grouping results in the initial grouping results, and / or on boundary grouping results in the first auxiliary grouping results.

[0046] For example, for the individual grouping results represented by the outlier grouping results in the clustering calculation, the semantic analysis results of the individual grouping results are analyzed by the first model. When the similarity between the semantic analysis results and other groups is greater than the similarity threshold, the individual grouping results can be merged into other groups to determine the first auxiliary grouping results.

[0047] For the boundary grouping results in the first auxiliary grouping results, the boundary grouping results are assigned to specific groups by the first model or by grouping determination time to determine the second auxiliary grouping results. The specific groups can be determined according to actual needs.

[0048] For example, for boundary grouping results where the semantic feature vector in the first auxiliary grouping result lies at the boundary between two groups, the semantic feature vector is semantically analyzed using the first model, and the specific group to which it belongs is determined based on the semantic analysis results. Alternatively, based on the grouping determination time of multiple groups assigned by the boundary grouping results during the clustering process, the group with the shortest grouping determination time is determined as the specific group.

[0049] Furthermore, the initial grouping results can be updated and corrected based on the first and second auxiliary grouping results to obtain the final grouping results. For each grouping result, the first model can input the center vector corresponding to the grouping result into the embedding back mapping module of the first model to generate the corresponding semantic description text; extract the first prompt words, tab website types, and historical usage behavior types of all users within the group corresponding to the grouping result, perform frequency statistics and semantic aggregation, and obtain the high-frequency information aggregation results of the grouping results, such as high-frequency semantic elements, high-frequency behavioral attributes, and high-frequency website types. Based on the grouping semantic description text and the high-frequency information aggregation results, combined with the semantic requirements of the business scenario, the first model generates a group title name and a group core tag for each grouping result, so that the group title name and tab are used as the grouping results.

[0050] When no historical usage data exists, the tab information and the first prompt word are input into the first model to determine the semantic feature vector, and clustering calculation is performed on the semantic feature vector to determine the grouping results. The first prompt word can be used to specify the requirements for clustering and grouping the tab information. After the first model outputs the semantic feature vector, a clustering algorithm can be selected based on the semantic feature vector, such as the K-Means++ algorithm. One sample vector is randomly selected from the sample set as the first initial cluster center; the minimum distance from all samples to the initial cluster center is calculated: the square of the minimum distances of all samples is used as the probability that the sample will be selected as the next center, and the next cluster center is selected from the sample set according to this probability distribution; this iteration is repeated until K centers are selected. For each sample in the sample set, the cosine distance from it to the K cluster centers is calculated, and the sample is assigned to the cluster corresponding to the nearest cluster center, forming K preliminary clusters; for each preliminary cluster, the mean vector of all sample vectors within that preliminary cluster is calculated as the new cluster center. Calculate the update difference of all cluster centers. Terminate the iteration when the difference is less than or equal to the convergence threshold or when the maximum number of iterations is reached. Output the final K clusters, which represent the initial grouping result obtained by the clustering algorithm. The initial grouping result may include the group title name and the tabs contained in the group corresponding to that title name.

[0051] Cluster analysis is performed on the individual grouping results in the initial grouping results to determine the first auxiliary grouping results, and / or cluster analysis is performed on the boundary grouping results in the first auxiliary grouping results to determine the second auxiliary grouping results. The initial grouping results are then updated and corrected based on the first and second auxiliary grouping results to obtain the final grouping results.

[0052] After determining the grouping results, the results can be evaluated based on a third model and third prompt words to determine whether they meet the preset grouping requirements. The third prompt words indicate the evaluation requirements for the grouping results, which may include evaluation dimensions, the weight value of each dimension, and the conditions for meeting the grouping requirements. Evaluation dimensions can be determined according to actual needs, and may include at least one of reasonableness, scenario matching, and completeness. Conditions for meeting the preset grouping requirements may include a score greater than a score threshold, which can be determined according to actual needs. Preset grouping requirements may also include the number of groups reaching a quantity threshold, the similarity of tags within a group reaching a similarity threshold, etc.

[0053] The third model breaks down the third prompt word into multiple dimensions, assigns a score to each dimension based on its scoring criteria, and then weights and sums the scores of each dimension with their respective weight values ​​to obtain the grouping result score. Further, the grouping result score is used to determine whether it meets the preset grouping requirements.

[0054] In some embodiments, if the grouping results do not meet the preset grouping requirements, the grouping results and the second prompt word are input into the second model for reclassification to adjust the grouping results and determine grouping suggestion information. Adjusting the grouping results here refers to adjusting the group title names in the grouping results. In addition, the group to which the tab belongs can also be adjusted. To reduce computational load, the granularity of the second prompt word can be greater than that of the first prompt word. That is, the first prompt word is more complex, and the second prompt word is simpler.

[0055] The second prompt word and grouping results are input into the second model for word embedding to obtain a word embedding vector. Position encoding is added to the word embedding vector to obtain a position encoding vector. The word embedding vector and the position encoding vector are added to obtain the standardized input feature vector. The standardized input feature vector is split into a query vector, a key vector, and a value vector. Masked self-attention is computed in parallel using multiple attention heads. Each attention head captures the basic semantic association between the second prompt word and the grouping results, and between the group title name and the core data features within the group. The results of the multi-attention head computation are concatenated and fused through a linear layer to obtain the basic self-attention feature vector. The basic self-attention feature vector is then subjected to residual connections and layer normalization to obtain a normalized basic feature vector. The normalized basic feature vector is then nonlinearly mapped through a feedforward neural network to obtain a feedforward mapped feature vector. The feedforward mapped feature vector is then subjected to residual connections and layer normalization. The intermediate features are determined; for the intermediate features and the autoregressive mask vector, mask self-attention calculation is performed to focus on the mapping from semantic features to the generated sequence, and to determine the word order, core keyword priority and length limit of the optimized group title name, so as to obtain the generative self-attention feature vector; the generative self-attention feature vector is processed through residual connection, layer normalization, feedforward neural network, linear layer and softmax layer to calculate the probability distribution of each text word, and the text word with the highest probability is selected as the generated character until the generated character meets the second prompt word optimization requirements, so as to adjust the group title name in the grouping result and obtain the optimized group title name.

[0056] If adjustments to the tabs in the grouping results are needed, the second prompt word, the grouping results, and the feature information of all tabs in the grouping results can be input into the second model. The output will be the adjusted group identifier or group title name of the tab. The second prompt word may include the rule requirements for grouping adjustment. The specific process of adjusting and optimizing the group to which a tab belongs through the second model is similar to the process of adjusting the tab name, and will not be repeated here.

[0057] Adjusting the group titles or the groups to which tabs belong in the grouping results can output grouping suggestions. These suggestions can include group titles and tabs.

[0058] Step S130: Group the multiple open tabs of the browser according to the grouping suggestion information to determine the target group, and display the multiple open tabs according to the target group.

[0059] In this embodiment of the disclosure, after obtaining the grouping suggestion information, multiple open tabs can be categorized and organized according to the output grouping suggestion information to determine the target group, and tabs can be displayed according to the target group. Furthermore, a preview page can be provided.

[0060] For example, on the preview page, multiple group display areas can be provided at the original display position of the tabs, depending on the number of target groups. Each group display area can include the group title name of the target group and one or more open tabs belonging to that target group. Multiple open tabs belonging to the target group need to be displayed together. For example, open tabs of the same target group can be displayed together in group display areas with the same display attributes; the same display attributes can be, for example, the same color, the same shape, or the same style, etc. For example, tabs belonging to the same target group can be aggregated and displayed in the group display area corresponding to the same color block. For tabs of the same target group, in response to the expand display operation of the group display area, multiple open tabs in the group display area are expanded and displayed, that is, all open tabs are displayed; in response to the collapse trigger operation of the group display area, at least some of the open tabs in the group display area are hidden. The display operation can be triggered by clicking the group display area, and the collapse display operation can be triggered by clicking the group display area again when the tab is expanded. In addition, it can also be triggered by the expand control or collapse control of the group display area. Expanding tabs within the same target group facilitates viewing detailed information; collapsing tabs saves display space and avoids clutter. It's important to note that during expansion and collapse, the group title can be displayed in a preset position within the group display area. This preset position can be at the top, the header, or any suitable location; no specific limitation is made here. The group title display area is independent of the main group display area and can be located above it. The group title can be displayed independently and can be triggered separately for modification or editing. When collapsing is triggered, only the group title can be displayed, or only a portion of the tabs can be shown. The display order of different groups can be determined according to user preference.

[0061] In some embodiments, in response to a confirmation operation on a target group, an open tab can be displayed based on the target group. For example, a confirmation control can be provided within or around the group display area. A click on the confirmation control is considered a confirmation operation, and the target group recommended by the first and second models can be directly used as the grouping criterion to display the open tab. After clicking the confirmation operation, the open tab is displayed in the same way as the preview page. For example, see [reference needed]. Figure 3 As shown, multiple group display areas can be provided. The group title name of the target group can be displayed in the group display area 301. For example, the group title name can be "Learning". The group display area can also include multiple open tabs with the group title name "Learning".

[0062] If the identified target group does not meet the preset grouping requirements, an adjustment operation on the target group can be initiated, and the corresponding adjustment group can be determined. Adjustment operations include move operations and / or rename operations. Move operations can be drag operations. For example, if a drag operation on an open tab in the group display area is detected, the group to which the open tab belongs within the target group can be adjusted, enabling tab movement across groups. If a click operation on a rename control located around the group display area is detected, or if a click operation on a group title name is detected, the group title name of the target group can be adjusted, thus obtaining the adjustment group. If a click operation on an adjustment control located around the group display area is detected, or if a group trigger command is triggered again, the group title name and tabs can be adjusted to obtain the adjustment group.

[0063] Furthermore, in response to confirmation of an adjustment group, the system can display open tabs based on the adjustment group. Additionally, if no adjustment action is received within a preset time after receiving the adjustment group, the system can automatically display open tabs based on the adjustment group. Relating the adjustment action to the corresponding adjustment group reflects user feedback on the grouping suggestion, thus improving accuracy.

[0064] In addition, it can respond to undo operations on the target group, stopping the display of open tabs in the target group format. Undo can include global undo and partial undo. Specifically, after displaying open tabs according to the target group, a global undo control can be displayed around the tab group control, around the group display area, or at any other location. If a click on the global undo control is detected, it is considered a global undo operation, stopping the display of all open tabs in the target group format, thus achieving global undo. For each target group, a partial undo control can also be provided within the group display area of ​​each target group. If a click on the partial undo control is detected, it is considered a partial undo operation, determining the group display area where the partial undo control is located and the target group it corresponds to. Therefore, it can undo the display of open tabs in that group display area according to the target group, while maintaining the display format of open tabs in other groups, thus achieving partial undo.

[0065] When the number of open tabs in a target group is greater than or equal to a threshold, a regrouping function can be provided for that target group. For example, tab grouping controls can be displayed around the grouping display area of ​​the target group. When a click on the tab grouping controls is detected, the tab information of the open tabs in the target group is input into a first model and combined with a first prompt to determine the regrouping result. The regrouping result is then input into a second model for optimization to determine regrouping suggestion information. Upon receiving confirmation of the regrouping suggestion information, the regrouping suggestion information is confirmed as regrouping. The open tabs in the target group are then grouped according to the regrouping, and the open tabs are displayed through the regrouping. Based on this, the grouping display area of ​​the target group may include group titles. Clicking a group title name provides multiple regrouping titles. Open tabs belonging to the same regrouping title name can be displayed together within the grouping display area of ​​that regrouping title name.

[0066] It should be added that the target group or subgrouping can be dynamically updated based on user data to ensure the accuracy of the target grouping and subgrouping. Furthermore, the target grouping or subgrouping can be collapsed or hidden.

[0067] In this embodiment, a first model performs semantic analysis and clustering on the target user's historical usage data and collected tab information to obtain grouping results. A second model then optimizes the grouping results to determine suggested grouping information. The intelligent identification of tab grouping suggestions improves grouping accuracy and efficiency, reduces user manual organization time, achieves efficient and personalized tab management, enhances the browser user experience, and adapts to diverse user needs.

[0068] Figure 4 The diagram illustrates the tab interaction process. (See reference) Figure 4 As shown, after the target user opens the browser, they click the tab grouping control to trigger the smart grouping function. In response to the grouping trigger command for the opened tabs, the tab information of the opened tabs is collected.

[0069] Furthermore, feature processing is performed on tab information and target user historical usage data, along with semantic analysis and clustering to output grouping suggestions. Based on these suggestions, corresponding score rankings, sorting, and classifications are generated to determine the target group. When a confirmation action is detected for the target group, the tab grouping results are displayed according to the target group. When an adjustment action is detected for the target group, the adjusted group is determined, and when a confirmation action is detected for the adjusted group, the grouping results of the opened tabs are displayed according to the adjusted group.

[0070] When displaying open tabs via target groups, they are presented as group containers. Target groups support operations such as collapsing, renaming, dragging and adjusting across groups, and quick switching, facilitating management. As the same user triggers the page multiple times, the resulting groups increasingly match the user's preferences. Furthermore, frequently visited tabs of a target user can be displayed as a separate target group for quick access.

[0071] In some embodiments, tab grouping can still be completed even when the network is interrupted or the network signal is weak; tabs that have not finished loading can also be grouped; tabs with the same domain name but different content can be divided into different groups; and different tabs with similar page content in the tab information can be divided into the same group.

[0072] In this embodiment, grouping is based on tab information such as page content, semantically rather than solely by domain name, window, or time. Addressing the problem of cluttered and difficult-to-find tabs in browsers in related technologies, an intelligent algorithm automatically identifies tab information such as page content, website type, and access time to determine tab grouping suggestions. This enables rapid tab grouping and convenient access in the browser, improving the user experience. The addition of an intelligent grouping algorithm, which automatically groups tabs based on content, source, and usage frequency, significantly reduces user steps, improves organization efficiency and personalization accuracy, and enhances the accuracy and user-friendliness of tab grouping, resulting in higher intelligence and practical value.

[0073] This disclosure also provides an interactive processing system for browser tabs, referencing... Figure 5 As shown, the system 500 mainly includes the following modules: The tab information collection module 510 is used to obtain tab information of open tabs in response to group triggering instructions for multiple open tabs in the browser. The intelligent grouping algorithm module 520 is used to perform semantic analysis and clustering grouping on the tab information to obtain grouping results, and to optimize the grouping results to determine grouping suggestion information; The group display module 530 is used to group multiple open tabs of the browser according to the group suggestion information to determine a target group, and display multiple open tabs through the target group.

[0074] This can be achieved by providing a grouping shortcut represented by a grouping control around the location of the tab in the browser. This grouping control can include tab grouping controls. Upon detecting a trigger action on the tab grouping control, the browser can automatically retrieve information about the tabs currently open. Alternatively, the grouping trigger can be triggered via hovering over the browser sidebar or via the right-click menu of a tab. The tab information can include one or more of the following: the content of the open tabs, the webpage domain name, the webpage title keywords, and the access time.

[0075] The first model performs semantic analysis and clustering on the tab information to obtain grouping results. A second model further optimizes and adjusts the grouping results determined by the first model, outputting final grouping suggestions for the opened tabs. These grouping suggestions are then used as target groups, and multiple opened tabs are displayed based on these target groups.

[0076] The technical solutions in this disclosure improve the efficiency and accuracy of tab grouping in a browser.

[0077] Exemplary embodiments of this disclosure also provide an electronic device. This electronic device can be the aforementioned terminal device or server. Generally, the electronic device may include a processor and a memory, the memory storing executable instructions of the processor, and the processor configured to execute the aforementioned browser tab interaction processing method by executing the executable instructions. Furthermore, the electronic device may also include a display for displaying a user interface.

[0078] The electronic device is described below as an example in the form of a general-purpose computing device. This electronic device is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0079] The components of an electronic device may include, but are not limited to: at least one processing unit, at least one storage unit, a bus connecting different system components (including storage units and processing units), and a display unit.

[0080] The storage unit stores program code that can be executed by the processing unit to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure. For example, the processing unit may perform the following steps.

[0081] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0082] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0083] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0084] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0085] It should be noted that some embodiments of this disclosure also provide a computer program product, which includes a computer program that implements the above-described method when executed by a processor.

[0086] In one implementation, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0087] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0088] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0089] Computer programs can be carried or transmitted via signals such as electrical, magnetic, optical, electromagnetic, and infrared rays. Electronic devices can convert signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to be executed by the processor of the electronic device) the method steps of various exemplary embodiments of this disclosure.

[0090] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0091] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0092] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0093] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0094] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for interactive processing of browser tabs, characterized in that, include: In response to a grouped trigger command for multiple open tabs in a browser, the tab information of the open tabs is obtained; Semantic analysis and clustering are performed on the tab information to obtain grouping results, and the grouping results are optimized to determine grouping suggestion information; Based on the grouping suggestion information, the multiple open tabs of the browser are grouped to determine the target group, and the multiple open tabs are displayed through the target group.

2. The browser tab interaction processing method according to claim 1, characterized in that, The group trigger command is triggered by hovering over the browser sidebar, by right-clicking the tab menu, or by using the group shortcut key.

3. The browser tab interaction processing method according to claim 1, characterized in that, The step of displaying multiple opened tabs through the target group includes: The open tabs in the same target group will be merged and displayed in the group display area with the same display attributes; In response to an operation to expand the grouped display area, the multiple open tabs in the grouped display area are expanded and displayed; In response to a collapse display operation on the grouped display area, at least a portion of the open tabs in the grouped display area are hidden.

4. The browser tab interaction processing method according to claim 1, characterized in that, The method further includes: In response to a confirmation request for the target group, the opened tab is displayed according to the target group; In response to an adjustment operation on the target group, the adjustment group corresponding to the adjustment operation is determined, and in response to a confirmation operation on the adjustment group, the opened tab is displayed according to the adjustment group; the adjustment operation includes a move operation and / or a rename operation.

5. The browser tab interaction processing method according to claim 1, characterized in that, The tab information includes one or more of the following: page content, webpage domain name, webpage title keywords, and access time.

6. The browser tab interaction processing method according to claim 1, characterized in that, The grouping results include group title names, and the process of determining the group title names includes: Based on the title keywords of the opened tabs within the group, generate the group title name in the grouping results.

7. The browser tab interaction processing method according to claim 1, characterized in that, The process of performing semantic analysis and clustering grouping on the tab information to obtain grouping results includes: The tab information and the first prompt word are input into the first model to determine the semantic feature vector; The initial grouping results are determined by clustering the semantic feature vectors using a clustering algorithm. The initial grouping results are corrected to determine the final grouping result.

8. The browser tab interaction processing method according to claim 7, characterized in that, The step of correcting the initial grouping result and determining the grouping result includes: Based on the first model, cluster analysis is performed on the individual grouping results in the initial grouping results to determine the first auxiliary grouping results; The boundary grouping results in the first auxiliary grouping results are assigned to specific groups to determine the second auxiliary grouping results; The initial grouping result is corrected based on the first auxiliary grouping result and the second auxiliary grouping result to obtain the grouping result.

9. The browser tab interaction processing method according to claim 1, characterized in that, The step of optimizing the grouping results to determine grouping suggestion information includes: If the grouping results do not meet the preset grouping requirements, the grouping results and the second prompt word are input into the second model for reclassification, so as to adjust the group title name or the group to which the opened tab belongs in the grouping results and determine the grouping suggestion information.

10. The browser tab interaction processing method according to claim 1, characterized in that, The method further includes: In response to the undo operation on the target group, stop displaying the opened tabs according to the target group.

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