TW202127364A - System and method for innovation, creativity, and learning as a service - Google Patents

Abstract

A system and method for self-learning in innovation, creativity, and learning to facilitate users' task performances. The system is configured to better enable the construction of knowledge frameworks in relation to the tasks, determine a relevant goal; suggest a question based on the knowledge framework; and enable the user to input an answer and an insight. The system may be further configured to with a user interface that provides an emergent labelling tool and/or a knowledge visualization tool. The system is configured to provide a software as a service to enable creation of better knowledge frameworks, which in turn can be used to accelerate organizational wide self-learning for improved task performance.

Description

Systems and methods for innovation, creativity and learning as a service

The present disclosure generally relates to the field of artificial intelligence epistemological systems and methods, especially systems and methods for providing innovation, creativity, and learning as a service.

From an epistemological perspective, if people (including individuals and teams of individuals) have a better knowledge framework, they can potentially improve their task performance. However, in reality, many people may need to acquire an associated knowledge framework before starting a task for the first time. Some other people may get the concept of the related knowledge framework from their own experience, but they are still constantly looking for and constructing a better framework based on the context to improve their task performance. In view of the complexity of this nature of knowledge work, according to the acquisition context and the construction of the improved knowledge framework, certain requirements for learning, innovation and creativity of relevant personnel are met.

In one aspect, the present disclosure describes a system for self-learning in innovation and creativity based on information derived from one or more source objects to improve user task performance. The system includes: a non-transitory computer-readable storage medium containing instructions; and a system server coupled to the storage medium. The system is configured to execute instructions for the method, the instructions including: providing a user interface configured to receive user input; and constructing a knowledge framework related to the task. The knowledge framework is defined by a plurality of historical first attribute tags, and each of the plurality of historical first attribute tags is associated with at least one corresponding historical context element. The method further includes providing an automatic suggestion based on the context element and the knowledge frame, the automatic suggestion including one or more candidate attribute tags generated by the system in response to the selected context element at the user interface, wherein the one or more Each of the candidate attribute tags corresponds to one of the following: a goal associated with the task, a question related to the task, an answer to the question, and an insight different from the answer.

Optionally, providing automatic suggestions includes: in response to the selected context element in the user interface, determining a first attribute tag, the first attribute tag is associated with the context element, thereby defining the first attribute tag with respect to the knowledge framework, The context element is based on one or more source objects; provides one or more candidate attribute tags as automatic suggestions; and in response to the automatic suggestions received as user input, enables the user to input one or more other attribute tags so that The context element is associated with the label. The label is defined by at least a first attribute label, a second attribute label, a third attribute label, and a fourth attribute label, where the first attribute label corresponds to a target associated with the task, and the second attribute label corresponds to a problem related to the task, The third attribute label corresponds to the answer to the question, and the fourth attribute label corresponds to an opinion different from the answer. The system may be configured such that the automatic suggestion includes candidate attribute tags corresponding to the second attribute tag and the third attribute tag. The system may be configured such that the automatic suggestion includes candidate attribute tags corresponding to the first attribute tag, the second attribute tag, and the third attribute tag. The system may be configured such that the automatic suggestion includes candidate attribute tags corresponding to the second attribute tag, the third attribute tag, and the fourth attribute tag. The system may be configured such that the automatic suggestion includes candidate attribute tags corresponding to the first attribute tag, the second attribute tag, the third attribute tag, and the fourth attribute tag. The system may be configured such that the automatic suggestion includes candidate attribute tags, the candidate attribute tags corresponding to any combination of one or more of the following: a first attribute tag, a second attribute tag, a third attribute tag, and a fourth attribute tag.

Optionally, the system further includes: providing a user interface with an emerging tag tool configured to receive user input in response to the selected context element, wherein the user input includes an attribute tag, and the attribute tag therein It is one of the following: a first attribute label, a second attribute label, a third attribute label, a fourth attribute label and/or one or more custom labels.

Optionally, providing automatic suggestions for candidate second attribute tags includes: identifying historical first attribute tags from a plurality of first historical attribute tags based on the similarity between each associated context element; and using historical language The historical second attribute tag associated with the context element is used as the candidate second attribute tag for automatic suggestion. Optionally, the knowledge framework includes historical first attribute tags received as user input from at least the first user, and automatic suggestions therein are provided to the second user.

Optionally, the automatic suggestion for candidate second attribute tags is based on one or both of the following: historical second attribute tags and generative second attribute tags. The historical second attribute tag is associated with a historical context element having a similarity with the context element, and the historical context element is associated with a historical first attribute tag that is part of the knowledge framework. The generative second attribute is generated by a method that includes: obtaining an output from a natural language processing module based on the context element as the input; and using a generative adversarial network to iteratively compare the output with the historical second attribute tag, where Select the historical second attribute label based on the first attribute label and the knowledge frame. Optionally, the knowledge framework includes historical first attribute tags received as user input from at least the first user, and automatic suggestions therein are provided to the second user.

Optionally, the system is further configured to generate a label based on a weighted set of the trained machine learning model, which is relative to the model assigned to any one of the historical first attribute label, the historical second attribute label, and the historical third attribute label For each weight of, the larger weight is assigned to the model of the historical fourth attribute label.

Optionally, the emerging label tool further includes: a manual interactive panel configured to receive manual user input; and an automatic suggestion interactive panel configured to provide automatic suggestions.

Optionally, the user interface further includes a knowledge visualization tool configured to provide one or more automatic suggestions, wherein in response to a new source object provided by one or more social media channel subscriptions linked to the task, Provide one or more automatic suggestions.

According to another aspect, the present disclosure includes a method for deploying software as a service according to any of the embodiments described above.

It will be readily understood that, in addition to the described example embodiments, the components of the embodiments as generally described and illustrated in the drawings herein may be arranged and designed in various different configurations. As such, the following description of the example embodiments shown in the accompanying drawings is not intended to limit the scope of the claimed embodiments, but is merely representative of the example embodiments.

In the present disclosure, reference to "one embodiment", "another embodiment" or "an embodiment" (or the like) means that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment . As such, the phrases "in one embodiment" or "in an embodiment" appearing in various places throughout the specification do not necessarily all refer to the same embodiment.

In addition, in one or more embodiments, the described features, structures, or characteristics can be combined in any suitable manner. In the following description, many specific details are provided to provide a thorough understanding of the embodiments. However, those skilled in the relevant art will realize that various embodiments may be practiced without one or more specific details, or using other methods, components, materials, and the like. In other cases, for clarity, some or all known structures, materials, and operations will not be shown or described in detail.

Only for the purpose of helping understanding, embodiments of the system and method for providing self-learning in innovation, creativity, and learning as a service will be described below with reference to the working environment. It can be understood that the embodiments of the present disclosure can also be applied to various different application scenarios, such as learning, work, personal and/or social occasions. In an example scenario, a first user (eg, designer) is assigned to create a design for product packaging for a new product. The new product is a new innovation, so the first user does not have prior knowledge or experience in product-related packaging requirements. The second user in the company is assigned to execute a digital marketing campaign for the new product. The second user is a newbie in the digital marketing process. The second user also has no prior knowledge or experience on the subject of digital marketing or new products. This scenario is an example of a clean case, where the learner lacks a relevant cognitive system. Understandably, individuals and teams need a lot of learning, innovation and creativity to enable companies to launch digital marketing activities and launch new products on the market. As such, there is an urgent need to improve traditional methods of acquiring knowledge associated with tasks.

According to one aspect, the system 100 according to an embodiment of the present disclosure provides a tool 200 for creating a better knowledge framework 300, which in turn can be used to accelerate learning and improve task performance. The system 100 is configured such that it can provide services for promoting self-learning and/or improving task performance.

According to one embodiment, as shown in FIG. 1, the system 100 for creativity, innovation, and learning as a service can be implemented by a system server 110 having at least one processor configured to execute storage in a computer readable Instructions on the storage medium 112. The system 100 is configured to be accessible by a user through a user device 130 (such as a mobile phone, a tablet computer, a laptop computer, or a computing or smart device) and via a network 120 to provide services. The system can be configured to store data in a database 140, such as in a client database. The user can query a third-party information source 150, such as a website, during the user's learning process. For the avoidance of doubt, the user's access to such third-party resources mentioned in this article refers to authorized access to such resources.

FIG. 2 shows an example of a user interface 200 provided by the system 100. The user interface 200 may be in the form of a software application configured for use on a computing device. The user interface 200 may be in the form of a plug-in, an accessory, or a utility program. The user interface can be provided as part of an online platform, cloud-based application, downloadable program, etc. The user interface 200 may be configured to be suitable for use with a browser, an e-book reader, or other applications, such as social media applications, learning platforms, news applications, and so on. These possible forms are provided as examples only.

The user interface 200 includes an emerging label tool 210. In this embodiment, the emerging label tool 210 provides a text box 220 for the user to input the label 300. When the first user reads information about product packaging online (in this example, using a web browser), the emerging tagging tool is configured to enable the first user to display the source object 240 (in this example, a web article) Select (such as by highlighting) one or more contextual elements 230 (in this example, the phrase "you are planning an idea for your ideal customer rather than for yourself"). The emerging label tool 210 is shown here as a pop-up menu, but it can also be in the form of plug-ins, attachments, or utilities.

The source object can correspond to e-books, PDF documents, videos, audio records, multimedia files, websites, blogs, video blogs (vlog), applications, social media publishing, or any electronic entity. The source object can be accessed through electronic means of presenting, storing, or conveying information in a form that the user can read, hear, or perceive in other ways. The user interface 200 can be operated with other applications, so that the user can access the source object or the information therein without infringing on the rights permitted by the copyright owner of the electronic file. For example, the user interface 200 is configured so that users can access electronic files through generally permitted places, such as Internet browsers, e-book readers, e-library member accounts, social media applications, and so on. In this way, the source object may be a source object that resides locally in the user's computing device 130, or may be a source object that resides in another part of the network 120. The network 120 can be accessed via wireless, wired, or another form of connection. Examples of the network 120 include a local area network, a wide area network, an extranet, an intranet, and the like. The source need not be an electronic file. For example, the source may be an identifiable printed publication. In some embodiments, the user interface 200 may provide a reference tool so that the user can identify non-electronic resources, such as by referring to the International Standard Book Number (ISBN) or journal articles.

In response to the selection of the context element 230, the system 100 is configured to associate the context element with at least one tag 300/310. The system is configured to receive, suggest and/or generate at least one tag 300/310. The system is similarly capable of receiving input from a second user. For example, the second user can use the emerging label tool 210 to create contextual elements related to a digital marketing campaign, where each contextual element 230 corresponds to at least one tag 300 / 310 associated.

Each tag 300 can be defined according to one or more attribute tags 310. In this document, the term "tag" (or "tags") can refer to an attribute tag, or the term can refer to a tag bounded by one or more attribute tags. The system 100 is configured to store the tags 300/310 as a collection of tags in the database 140, where each tag 300 includes one or more attribute tags 310. The system 100 is configured such that each attribute tag 310 can be associated with a corresponding context element 230.

In this example, the tag 300 can be defined according to one or more of the following attribute tags 310: target attribute tag 312 (also referred to as the first attribute tag), question attribute tag 314 (also referred to as the second attribute tag), The answer attribute label 316 (also referred to as the third attribute label) and the insight attribute label 318 (also referred to as the fourth attribute label). For example, regarding the selected context element 230, the first user can use the emerging label tool 210 to input a label 300, where the label 300 is defined according to the following attribute labels 310: Label = (Goals, Questions, Answers, Insights) Goal = preliminary. design Question=Who is my ideal customer? Answer = Australian International School Insight = Koala

The emerging tagging tool 210 can be further configured so that the user can tag one or more contextual elements 230 with one or more tags 300, where the tags include an answer attribute tag 316. Each answer attribute tag 316 points to or corresponds to an answer. The answer attribute label 316 corresponds to the specific question attribute label 314. The system 100 is configured to enable the user (learner) to conceive and design an answer attribute label 316 and to form a correlation between the answer label and the selected context element 230. Each answer attribute label 316 and corresponding question attribute label 314 is related to at least one context element 230.

The emerging tagging tool 210 can be further configured so that the user can tag one or more contextual elements 230 with one or more insight attribute tags 318. Each insight attribute tag point 318 points to or corresponds to an insight. Insights can be described as cognitive events when users experience "Aha!" or "Eureka!" moments. The user who inputs or selects the insight attribute tag 318 is a learner, that is, a person who is undergoing a learning process. The system 100 is configured to enable the user (learner) to conceive and design the insight attribute label 318 and input or select the insight attribute label 318 to the selected context element 230. In one aspect, the insight attribute tag 318 can be used to capture creativity or innovative ideas. In one aspect, the insight attribute tag 318 "koala" is associated with a pair of tags, where the pair of tags includes the question attribute tag 314 labeled "Who is my ideal customer?" and the answer attribute tag 316 "Australian International School". In another aspect, the insight attribute tag 318 is associated with at least one first attribute tag and at least one other tag. In this example, the target attribute tag 312 is configured as the first attribute tag 410. The problem attribute tag is the at least one other tag 310.

As shown in FIG. 3, the system 100 is configured such that the frame 400 of the nodes 410 can be organized based on one of various types of attribute tags 310. In this document, this tag is also referred to as the first attribute tag 312. The first attribute tag 312 is configured to form a node 410 in the hierarchical frame 400 based on the relationship between the respective frame tags 410. In this example, the target attribute tag 312 is configured as the first attribute tag 410. The frame 400 may be defined as a network of nodes 410, where each node corresponds to the first attribute label 312. Each target attribute tag 312 can be configured to carry information about its relative position in the frame 400. The framework 400 may be organized stepwise or iteratively. Based on the degree of association of the first attribute tag/node 311/410, a relationship can be formed between the first attribute tag/node 311/410. The system is configured to enable the system to construct and/or modify the framework 400 in response to adding the label 300 to the collection of labels and as a result of forming a relationship between the various framework labels/nodes 311/410. The relationship between the various frame tags/nodes 311/410 can be used to represent the user's knowledge frame 400. In this example, the knowledge framework 400 based on the label input by the first user can be used to help the first user accelerate his learning, with the purpose of designing the packaging of a new product.

At the same time, when the second user searches for tips on planning digital marketing activities, he can identify different contextual elements 230 based on different source objects 240 and associate them with different tags 300. For example, when the second user reads information about consumer behavior analysis used in marketing, he may have questions about the relevance of consumer behavior analysis to his specific task, which is the launch of a new product. Digital marketing activities. The system 100 enables the second user to input such questions in the form of a tag 300, wherein the tag 300 has at least one question attribute tag 314 associated with a context element 230, and wherein the context element is based on the reading material of the second user. In this case, the second user can start with several tags, and each tag has only one attribute tag 310, for example: Question=When will the marketing campaign be launched? Question=Is social media associated with this product? Question=Who is the target market?

Each context element selected by the second user will be associated with a tag that includes one or more blank attribute tags, for example: Label = (blank, when to launch the marketing campaign, blank, blank)

The system 100 is configured so that the user can continue to modify the existing label 300. For example, the second user can retrieve the questions about contextual elements that he previously entered into the system. The second user can further modify the label 300 so that the label 300 is now defined by more attribute labels 310, for example: Question=When will the marketing campaign be launched? Answer = holiday

As shown in this example, the question (input for the question attribute tag 314) is posed by the first user and the second user themselves. In other words, when the user is in the role of a learner (a user who is going through a learning process to acquire knowledge), the question input is provided by the user. Each question attribute label 314 input, suggestion, or generated is a label corresponding to or associated with the corresponding context element 230.

When the second user continues to learn the topic for a period of time, he can continue to add new tags to his tag collection. The system 100 is configured to store a second set of tags originating from the second user, so that the second user can have his own personal knowledge frame. The knowledge framework 400 may be used to represent the current state of personal knowledge associated with the second user. The system 100 can also be configured such that both the first user and the second user can contribute to a shared tag set of tags. The resulting knowledge framework 400 can be used to represent the current state of the collective knowledge of the user team.

According to one aspect, the system 100 is configured to provide services to different clients. A customer (such as a company) may have multiple employees who use the system 100 as learners. The customer can appoint one of its employees as a coordinator or administrator. Coordinators or administrators can also be learners. An administrator is one or more people who handle the management aspects of learning (for example, tracking attendance, providing access to resources, etc.). A coordinator is one or more people who design the course outline and/or deliver the teaching content. The user interface 200 is configured to not highlight the role of the coordinator/administrator unless necessary. For example, it is optional for the coordinator to provide learners with a predetermined syllabus. In this way, the system 100 is beneficially suitable for real life or continuous learning. In real life or continuous learning, learners may be required to start without prior knowledge and without prior syllabus or framework to help learners learn systematically. To achieve the goal. Learners may also be required to "learn on the job" without the guidance of a tutor or manual. In an embodiment, users (including learners) can customize or set other custom attribute tags 310 and/or frame tags 410, such as “important”, “attention” and so on. In other words, although organizations in the education and training industry are not excluded as users of the services provided by the system 100, in fact, the system 100 is suitable for a wider variety of application scenarios.

According to another aspect, the system 100 enables learners to create their own dynamic "textbook" or information resource library 150. In the example of the first user, the first user can crawl the image of product packaging for similar products on the Internet. The first user can visit the website and recognize the associated criteria as the first context element. The emerging tag tool 210 enables the first user to identify the context element 230 by identifying its location, such as a URL (URL: Uniform Resource Locator) and/or other identifiers. The emerging tagging tool 210 may enable the first user to use the snapshot tool to identify contextual elements. The emerging tagging tool 210 can be further configured to enable the user to copy a part of the resource as a context element 230, where the copyable part can be controlled or predetermined according to the content permitted or permitted by the copyright holder subscribing to the system The maximum size.

According to another aspect, the first user can create a new question attribute label 314 and apply the question attribute label 314 to the identified/selected context element 230. The system 100 stores the problem attribute tags 314 in the database 140 as part of the tag set. If after using the system 100, the first user has developed a database 140 composed of a plurality of question attribute tags 314, the first user can then select the question attribute tag 314 from the tag set, and add the selected question attribute tag 314 is related to another contextual element 230. According to one aspect, since the user must think in the form of a question, the user is "forced" to think as a learner, that is, a knowledge seeker. By becoming the person who formulates the question (rather than the person who focuses on the answers or multiple pieces of information), the learner is more sensitive to their own knowledge gaps. In addition, the system 100 realizes that more learning can be gained from questions rather than answers. The system 100 is configured to extract a puzzle from a problem, and when it is assembled with other puzzles, at least a part of the knowledge frame 400 is generated.

The above-mentioned aspects are different from the traditional learning system. In the traditional learning system, the coordinator (with prior knowledge of the subject) designs the syllabus (multiple knowledge to be taught, the order in which multiple knowledge is taught, and the number of lessons to be taught). Learners of individual knowledge are absorbed or acquired), while the system 100 according to this embodiment provides learning that is more consistent with the spirit of self-directed, self-paced, and self-realized learning. The system 100 is configured to enable the learner to drive his/her own learning instead of overly forcing the learner with the information selected by the coordinator. Specifically, the system 100 is configured to implement the dynamic evolution of the knowledge framework 400. Such a knowledge framework 400 will be configured according to at least one target group or task of the learner. Such a knowledge framework 400 is configured to help learners perform tasks or improve the effect of performing tasks. According to this embodiment, the system 100 is configured such that the knowledge framework 400 is based on the framework tag 410 associated with the question attribute tag 314.

The system 100 is configured to construct a knowledge framework 400 based on other tags 300/310, such as answer attribute tags, insight attribute tags, and target attribute tags. The other tags 300/310 include those defined by any one or more of these attribute tags. Label. The knowledge framework 400 can also be based on customized tags 300/310, that is, the type of tags customized by the user. The knowledge framework 400 may be based on other attribute tags 310 in addition to the aforementioned attribute tags, such as visual-related attribute tags, purpose-related attribute tags, and the like. The tag 300 may be defined by one or more attribute tags 310 correspondingly. The tag 300 can be composed of any number of attribute tags 310.

According to another aspect of the present disclosure, an artificial intelligence/machine learning algorithm 520 is used to implement the system and method. FIG. 4 schematically illustrates another aspect of the system 100. The system 100 is also configured with a machine learning module 500. The system is configured with one or more databases 140, and the one or more databases 140 store the context elements 230 and related knowledge framework 400 associated with each tag 300/310 in one or more tag sets. The machine learning algorithm 520 is configured to utilize data stored in the database 140. One example is a natural language processing (NLP: Natural Language Processing) engine that is configured to extract data from the identified/selected context elements 230 and the applied tags 300/310. As another example, the first user can also use a user interface (such as the interface shown in Figure 2) to mark the text/video/image/audio file (source) he thinks with two labels "efficient" or "effective" Important part. The meaning of context elements and tags can be extracted by a natural language processing (NLP: Natural Language Processing) engine and fed to the machine learning module 500 for supervised or even unsupervised learning, as shown in FIG. 5.

As shown in FIG. 5, the machine learning module 500 of the system 100 may be configured to include one or more sub-modules in supervised learning or unsupervised learning. The machine learning module 500 may be configured to include one or more sub-modules, wherein the sub-modules may be configured to perform one or more of the following in combination: artificial neural network model, deep learning algorithm, dimensionality reduction method, feature extraction method, embedding generation method , Ensemble method, case-based method, Bayesian method, clustering and other suitable methods. The machine learning module may include sub-modules configured to implement one or more artificial neural network models. Examples of artificial neural network models include but are not limited to: back propagation method, Hopfield network method, self-organizing mapping method and learning vector quantization method. The machine learning module may include sub-modules configured to implement one or more deep learning algorithms. Examples of deep learning algorithms include, but are not limited to: transformers, long short-term memory (LSTM: Long short-term memory), autoencoders and decoders, generative adversarial networks, deep belief network methods, convolutional neural networks, recurrent neural networks Network, stacked autoencoder, etc. The machine learning module may include sub-modules configured to perform one or more dimensionality reduction methods. Examples of dimensionality reduction methods include, but are not limited to: principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection tracking, etc. The machine learning module may include sub-modules configured to perform one or more feature extraction methods. The machine learning module may include sub-modules configured to implement one or more embedded generative methods. Examples of feature extraction methods and/or embedded generation methods include, but are not limited to: continuous bag of words (CBOW), skip-gram methods, etc. The machine learning module may include sub-modules configured to implement one or more integrated methods. Examples of ensemble methods include, but are not limited to: enhancement, stacked generalization, gradient enhancement machine method, random forest method, etc. The machine learning module may include sub-modules configured to perform one or more instance-based methods. Examples of instance-based methods include, but are not limited to: k nearest neighbors, self-organizing maps, and so on. The machine learning module may include sub-modules configured to perform one or more Bayesian methods. Examples of Bayesian methods include but are not limited to: Naive Bayes, Bayesian belief networks, etc. The machine learning module may include sub-modules configured to implement one or more clustering methods. Examples of clustering methods include, but are not limited to: k-means clustering, expectation maximization, and so on.

The machine learning module in Figure 5 can be configured to learn from contextual elements and tags to modify or enrich the knowledge framework. Specifically, the system 100 may be configured to suggest associated tags 300/310 related to various aspects of the frame 400 being acquired or constructed. The system 100 can be configured to suggest various types of tags (automatic suggestions). Examples of two types of automatic suggestions (historical and generative) will be described below.

Referring to FIG. 6, at this time, based on the input from the first user, the database 140 has accumulated the tag set and the knowledge framework 400 has been generated. The system 100 is configured to automatically suggest candidate tags and/or candidate attribute tags. The candidate tags and/or candidate attribute tags may be presented to the user through the emerging tag tool 210 or the user interface 200. The presentation of the auto-suggestions can be selected in response to contextual elements. In this article, the terms "candidate tag" and "candidate attribute tag" can be used interchangeably based on the understanding of context, and refer to tags/attribute tags automatically suggested by the system to users. If the user accepts the automatic suggestion, the candidate tag or the candidate attribute tag is received as user input and used as the corresponding tag/attribute tag.

According to an embodiment, the emerging tag tool 600 is configured to suggest a candidate tag 610 (or one or more candidate attribute tags 610). The candidate tags 610 suggested by the system 100 may include historical tags 300/310 retrieved from the database 140. The candidate tag 610 may be selected from historical tags 300/310 used in the same cognitive item, or the candidate tag 610 may be a historical tag 300/310 in another existing or earlier item. The system 100 may be configured to select the historical tag 300/310 based on the similarity between the context element 630 selected by the second user and the historical context element 230 (FIG. 3). The historical context element 230 may include a context element used by the first user to associate with at least one tag. The historical context element 230 may also refer to context elements stored in the database. The historical context element 230 can also refer to a context element linked to the knowledge framework 400. In this example, the second user selection includes the contextual element that "digital marketing starts with describing your ideal customer". In response, the system 100 determines that the context element 630 has a certain degree of similarity to the historical context element "collecting design ideas for ideal customers..." (FIG. 3). Based on the historical tags 300/310 associated with the historical context element 230, the system 100 presents the following content as candidate tags 610: Goal = preliminary. design Question=Who is my ideal customer? Answer = Australian International School Insight = Koala

In this way, the second user can benefit from the learning of the first user. The emerging tag tool configured with the automatic suggestion 600 may be configured to allow the second user to confirm the use of the candidate tag 610. The emerging label tool 600 may also be configured to allow a second user to modify or replace the candidate label 610. Regarding similar contextual elements, the second user may have different tags that are more relevant to the nature of his role or task. The system 100 is configured to capture these changes, so that the resulting knowledge frame 400 is further enriched.

7, according to another embodiment, in response to the second user selecting the context element 630 "digital marketing starts with describing your ideal customer" and providing the target attribute tag "focus", the system 100 is configured to notify the second user of this It may be related to the existing node 410 of the knowledge framework 400. The knowledge frame 400 may be a knowledge frame shared by the first user and the second user because they are in the same team or performing the same or related tasks. If the second user selects similar contextual elements, the system 100 may suggest historical target attribute tags and question attribute tags. The system 100 may be configured to provide such automatic suggestions based on the machine learning module 500 as described above. Based on the knowledge framework 400, the system 100 may suggest to the second user that the historical target attribute label "preliminary. Design" may be related to the target attribute label "focus". The system 100 is configured to allow the second user to change the target attribute label from "focus" to "preliminary. design. focus" 612. The attribute tag 612 is also configured as a frame tag 410, and a new tag can be added to the associated frame 400. The system 100 actually helps the second user quickly understand how the newly acquired knowledge is related to the earlier acquired knowledge, and enables the second user to quickly continue to acquire other knowledge, especially in the knowledge framework 400 that is closely related to this tag 612 Knowledge.

In response to the contextual elements being highlighted, the system 100 may also be configured to suggest tags that are not found in the database. FIG. 8 shows another embodiment of the emerging label tool 800, which provides a first interactive panel 810 for manual user input of labels, a second interactive panel 820 for automatic suggestions based on historical labels, and a generation engine-based The third interactive panel 830 that is automatically suggested. In response to the selected context element, the user can use the text box 815 to enter the label 300/310. The first interactive panel 810 is configured to enable the user to input different types of tags 300/310, such as target attribute tags 811, question attribute tags 812, answer attribute tags 813, insight attribute tags 814, and/or combinations thereof. In some embodiments, the emerging label tool 800 includes a manual input interactive panel 810 for manual user input of labels, and an automatic suggestion interactive panel 820/830 for providing automatic suggestions. Examples of different methods of providing automatic suggestions are described below.

The second interactive panel 820 is configured to present automatic suggestions based on the selected contextual element. The automatic suggestion may include a target attribute tag 821, a question attribute tag 822, an answer attribute tag 823, an opinion attribute tag 824, and/or a combination thereof. The emerging label tool 800 is configured to enable users to adopt automatic suggestions, reject suggestions, or modify automatic suggestions for each attribute label 821/822/823/824. The emerging label tool 800 is also configured to allow the user to manually enter the label when the system does not provide automatic suggestions for any or all attribute labels. When the user accepts the automatic suggestion, the accepted automatic suggestion is regarded as the user's input to the system. When the user modifies the automatic suggestion, the modified automatic suggestion is regarded as the user's input to the system.

The third interactive panel 830 is configured to present automatic suggestions generated by the generation engine provided by the machine learning module 500. In the present disclosure, the generation engine may include a label generator based on a weighted set of trained machine models that are obtained by applying machine learning to the aforementioned historical attributes; or, differently, use Generative Adversarial Networks (Generative Adversarial Adversarial Networks). Networks). The generation engine that uses the generative confrontation network will be described here as an example. In some embodiments, in response to the second user selecting a contextual element, such as "digital marketing starts with describing your ideal customer", the system generates candidate generative questions and presents them to the user. The natural language processing/natural language processor (NLP) module in the machine learning module 500 may be configured such that the NLP module outputs possible problems based on the contextual elements as input. Based on the relative position of the target attribute label in the relevant knowledge frame, the generated question can be compared with the historical question, where the historical question is selected according to the knowledge frame based on its relevance. If the system determines the possible problem as a "false" problem, the result will be fed back to the generation engine. After iteratively performing this comparison and feedback, the system will present candidate questions that do not produce "false" results. In this example, the system provides candidate questions (candidate second attribute tags) as automatic suggestions. That is, the generative second attribute tag "Who are your loyal customers?" is provided to the second user as a candidate second attribute tag. When the system is configured in this way, the system will provide users with more "uncommon" label suggestions. This allows new ideas to emerge during the acquisition and construction of new knowledge frameworks. In this example, the generated question prompts the user to adopt a slightly different perspective and derive different answers from the suggestions based on historical tags. When considering related goals and questions together, the different answers "Sydney International School" can help users establish a cognitive connection with the new insight "wallaby".

The above is only an example of one of the automatic suggestions for an attribute tag. In some other embodiments, one or more tags/attribute tags (such as target attribute tags, question attribute tags, answer attribute tags, insights attribute tags, and any custom tags) can be determined by the system based on the selected context elements and/or An automatic suggestion of other tags/attribute tags. In this way, when the user accepts the suggestion provided by the system (whether historical or generated), since the process expands the knowledge framework 400, it can be said that an innovative type of service is produced. When a user accepts any other historical type or generation type of suggestion, it can be said that a creative type of service is produced, because it potentially opens up new services for users who are performing tasks but do not directly contribute to the modification of the knowledge framework itself. Perspective.

According to another embodiment, in the instance of a second user who has been assigned to perform a digital marketing campaign, the second user may use the system 100 to obtain knowledge about digital marketing. In the process of reading topics related to digital marketing, users can mark the types of problems that may be encountered in digital marketing. After a period of time, the system 100 can learn what is important and what is unimportant in the context of the topic, or what is more relevant and what is less relevant. In this way, after a period of time, and as the library 140/150 of sources (reference sources) represented by contextual elements associated with tags continues to grow, the system 100 can further play the role of a coordinator for the user. For example, from time to time, the system 100 can ask users questions such as "What do you know better about digital marketing?" or more general questions such as "What is the most important aspect?" Get an answer?", "Can you put it in the project phase now?" or "What issues do you think you will face?" etc.

In order to further help the organization learn and better modify the various knowledge frameworks being built, the system 100 can be configured to perform (at the back end) a process of putting together things that have been marked as "important", or such as "efficient" "Or "effective" any other meaningful tags. Such labels can also be weighted. In the foregoing example, the system 100 may be configured to use something that has been marked as “important” by the first user (for example, using sustainable packaging) to enrich or supplement the learning journey of the second user. Therefore, the second user can benefit from the knowledge framework 400 built by the first user, and more importantly, does not neglect the learning of important features for product marketing.

According to another aspect, the system is configured to present its "discovery results" to the user, such as through a graphical user interface format. Such "discovery results" can be configured to include areas that future learners will focus on from a previously constructed knowledge framework. FIG. 9 shows a schematic example of the knowledge visualization tool 900. "Important" or "associated" information can be presented to the user in a workbench environment that is configured to rearrange (select) 910 milestones, tasks, project phases, etc. relative to the timeline 930 or a series of milestones 940 Issues, questions, etc. 920. In this way, the system 100 can better help users transition from knowledge framework acquisition to applications by incorporating "important" information into the timeline. For example, the second user can use the user interface 200 provided by the system 100 to arrange the steps of the digital marketing campaign project. The system 100 can be configured to present links between questions and answers to users and link these to various associated steps/phases/milestones of the project. As such, the system is a "real-time" environment. In one embodiment, for example, a task is linked to one or more social media channel subscriptions. The system 100 may be configured to present automatic suggestions to the user whenever a new article is extracted from such subscription service. In other words, the user interface is configured such that the knowledge visualization tool can provide one or more automatic suggestions in response to new source objects provided by one or more social media channel subscriptions linked to the task.

If the third user (for example, in another region of the world) is also interested in learning digital marketing activities, the system 100 can accelerate the learning of the third user. Even if the third user is not informed of the question label designed by the second user, the system 100 is configured to help automate the identification of important contextual elements 230 and help automate the suggestions of question 314 and answer 316 to help learning. . For example, after a period of time, the system 100 may collect enough data to predict the types of problems 314 that are more likely to lead the user to insights. In this way, the system can "push" subsequent users to reach Insight 318 ("Aha!" moments) earlier or more frequently. After a period of time, the system 100 can obtain a collection of such "high-value" questions 314, which can be used to promote further creativity and learning.

The system 100 can be configured to provide analysis of emerging labeling processes. The system 100 can be configured to determine the number of tags 300/310 of each type. The system 100 can be configured to detect the time sequence of the tags 300/310 to help visualize the learning process. For example, in a digital marketing example, the system 100 can identify the time it takes for the user to obtain the insights 318. As an example, the second user spends 12 days to obtain insights 318, and insights 318 are the key to digital marketing is conversion. The system 100 can track the number of days it takes for others to reach similar insights. After a period of time, since the system 100 provides accelerated learning, with the aid of the system 100, subsequent learners can reach similar insights 318 in a shorter time. After a period of time, as more and more users use the system 100, the purpose is to shorten the time from the first learning to the insight 318.

The system 100 is configured to track learning outcomes. In other words, when a task is given, the system 100 can track the number of iterations required by the user to develop an appropriate knowledge framework 400. In this example, the learning result is a new knowledge framework 400, and the system 100 can measure the time required for such a framework to become an organization. In this example, this may take the form of providing a user interface 200 to a second user to plan marketing activities according to a timeline corresponding to the phases and sub-phases of the project. The system 100 can also be used to identify issues and problems that may be encountered in digital marketing, where the issues that may be encountered are based on things that dynamically evolve as the second user learns. When the second user is ready, the second user can go to the next step of executing the activity. The system 100 can continue to be used by a second user or another user to monitor digital marketing activities. The digital marketing campaign itself can serve as a source 240 of additional contextual elements 320 for further labeling, so that the knowledge framework 400 can be refined, improved, or otherwise updated. In this way, the emerging label tool 210 can continue to be used.

To help understanding, the following example describes how the system 100 can have a wider range of applications so that it can be applied to a group of users. The system 100 can be additionally configured so that multiple users can participate in a learning journey. Input from various members of the team can be color-coded or otherwise identified/associated with various members of the team. Team members can check the tags of other team members in the same team, and the user interface 200 can be configured to enable users to discuss without considering the source 240, the context element 230, and/or the tags 300/310. The user interface 200 can also be configured so that the user can answer questions 314 posed by other members of the team. The emerging tag tool 210 may be further configured to allow the user to link text/video/image/voice 240 to those questions 314.

The foregoing describes various embodiments of the system for self-learning in terms of innovation and creativity to improve the user's task performance based on information derived from one or more source objects. The system includes a non-transitory computer-readable storage medium containing instructions and a system server coupled to the storage medium and a user interface. The system is configured to execute instructions for the method (1000) as illustrated in FIG. 10. The method includes providing (1010) a user interface configured to receive user input. The method includes constructing (1020) a knowledge frame about the task, the knowledge frame being defined by a plurality of historical first attribute tags, each of the plurality of historical first attribute tags being associated with at least one corresponding historical context element . The method further includes providing (1030) automatic suggestions based on the context elements and the knowledge framework, the automatic suggestions including one or more candidate attribute tags generated by the system in response to the selected context elements at the user interface, wherein the one or Each of the plurality of candidate attribute tags corresponds to one of the following: a goal associated with the task, a question related to the task, an answer to the question, and insights different from the answer.

The system may be configured to execute instructions for the method (1100) as illustrated in FIG. 11. The method includes constructing (1020) a task-related knowledge frame, the knowledge frame being defined by a plurality of historical first attribute tags, each of the plurality of historical first attribute tags being related to at least one corresponding historical context element United. The method includes, in response to the selected context element at the user interface, determining (1130) a first attribute tag, the first attribute tag being associated with the context element so as to define the first attribute tag with respect to the knowledge framework. Context elements are based on one or more source objects. The method includes providing (1140) one or more candidate attribute tags as automatic suggestions based on the context element and the knowledge framework. And the method includes, in response to an automatic suggestion received as a user input, enabling (1150) the user to input one or more other attribute tags to associate the context element with the tag, the tag consisting of at least the first attribute tag, the first attribute tag, and the first attribute tag. The second attribute label, the third attribute label and the fourth attribute label are defined, where the first attribute label corresponds to the target associated with the task, the second attribute label corresponds to the task-related question, and the third attribute label corresponds to the answer to the question. And the fourth attribute tag corresponds to an opinion different from the answer. In this way, the system can be deployed in the form of software as a service.

In this way, the knowledge framework as described in this disclosure is very specific and contextual, can take many forms and can be continuously improved on it. In this disclosure, innovation, creativity, and learning as a service are regarded as a system and method that encourages people to improve these knowledge frameworks more creatively and innovatively, so that all aspects of organizational performance can enter a new level and so on. In this case, whether it is an individual or a team, it can be said that it has contributed to the development of learning or human potential. From an epistemological point of view, the system and method enable users to acquire the knowledge framework associated with any specific task more systematically and faster. In addition, the form of the acquired knowledge framework will undergo many changes and transformations, from a more abstract form to a more concrete form, until the learner is satisfied that the acquired knowledge framework is related to the context. Emerging tagging tools can enable better knowledge frameworks to be captured to facilitate future learning journeys. From an epistemological point of view, the data obtained in this way presents the historical trajectory of learning and can be used as a reference for future learners.

From another epistemological perspective, it is insights that drive innovation and creativity. Specifically, the above embodiments enable different users to benefit from the associated learning of other users, wherein the associated learning is automatically planned by the system. This is equivalent to a scenario where different users jointly "brainstorm" to find new innovations. This innovation may require the generation of a large number of new insights, but has the advantage of enabling different users to participate online from different locations at different times.

Unless expressly stated otherwise, the singular "a" and "an" used herein can be construed as including the plural "one or more".

The present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limiting. Many modifications and changes will be obvious to those of ordinary skill in the art. The example embodiments have been selected and described in order to explain the principles and practical applications, and to enable others of ordinary skill in the art to understand the disclosure of the various embodiments with various modifications, which are suitable for the specific intended use.

As such, although illustrative example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the description is not restrictive, and those skilled in the art can make various other changes and modifications therein without departing from the scope of the present disclosure .

100: System 110: system server 112: Computer readable storage medium 120: Network 130: Equipment 140: database 150: Information Resource Library 200: Tools/User Interface of Knowledge Framework 210: Emerging Label Tool 220: text box 230: Contextual Elements 240: source object/source 300: knowledge framework/label 310: label 312: target attribute label 314: problem attribute label 316: Answer attribute label 318: Insights attribute tag 400: Frame 410: frame label/node 500: Machine Learning Module 520: machine learning algorithm 600: Emerging Label Tool 610: Candidate label 612: Label 630: Contextual Elements 640: Source Object 714: label 800: Emerging Label Tool 810: The first interactive panel/manual input interactive panel 811: target attribute label 812: problem attribute label 813: Answer attribute label 814: Insights attribute tag 815: text box 820: The second interactive panel / automatic suggestion interactive panel 821: Target attribute tags included in automatic suggestions 822: Problem attribute tags included in automatic suggestions 823: Answer attribute tags included in automatic suggestions 824: Insight attribute tags included in automatic suggestions 830: The third interactive panel / automatic suggestion interactive panel 831: generative target attribute label 832: generative question attribute label 833: generative answer attribute label 834: Generative insights attribute tags 900: knowledge visualization tool 910: rearrangement (selection) 920: Milestones, tasks, project phases, issues, problems, etc. 930: Timeline 940: Milestone 1000: method 1010: Provide a user interface configured to receive user input 1020: Building a knowledge framework based on historical first attribute tags 1030: Provide automatic suggestions including one or more candidate attribute tags generated by the system 1100: Method 1130: Determine the first attribute tag associated with the context element 1140: Provide one or more candidate attribute tags as automatic suggestions based on contextual elements and knowledge frameworks 1150: Enable the input of one or more attribute tags to associate contextual elements with tags

Figure 1 is a schematic diagram of a system for innovation, creativity, and learning as a service.

Fig. 2 is a schematic diagram of a user interface with an emerging label tool according to an embodiment of the system.

Figure 3 shows the knowledge framework based on target attribute tags.

Fig. 4 is a schematic block diagram of a system according to an embodiment.

Fig. 5 is a schematic flow chart of the machine learning module of the system.

Fig. 6 is a schematic diagram of an emerging label tool according to another embodiment.

Fig. 7 is a schematic diagram of an emerging label tool according to another embodiment.

Fig. 8 is a schematic diagram of an emerging label tool according to another embodiment.

Figure 9 is a schematic diagram of the knowledge visualization tool provided by the system.

Fig. 10 is a schematic flowchart of a method according to an embodiment of the present disclosure.

Fig. 11 is a schematic flowchart of a method according to other embodiments.

100: System

110: system server

112: Computer readable storage medium

120: Network

130: Equipment

140: database

150: Information Resource Library

Claims (11)

A system for self-learning in terms of innovation and creativity to improve user performance on tasks based on information derived from one or more source objects, the system comprising: a non-transitory computer-readable storage medium containing instructions; And a system server coupled to the storage medium, the system being configured to execute instructions for a method, the method including: Providing a user interface configured to receive user input; Constructing a knowledge framework related to the task, the knowledge framework being defined by a plurality of historical first attribute tags, each of the plurality of historical first attribute tags being associated with at least one corresponding historical context element; and Provide an automatic suggestion based on the context element and the knowledge framework, the automatic suggestion including one or more candidate attribute tags generated by the system in response to the context element selected at the user interface, wherein Each of the one or more candidate attribute tags corresponds to one of the following: a goal associated with the task, a question related to the task, an answer to the question, and an opinion different from the answer. The system according to claim 1, further comprising: In response to the context element selected at the user interface, a first attribute tag is determined, and the first attribute tag is associated with the context element so that the first attribute tag is relative to the knowledge Frame definition, the context element is based on the one or more source objects; Providing the one or more candidate attribute tags as the automatic suggestion; and In response to the automatic suggestion received as a user input, the user is enabled to input one or more other attribute tags, so that the context element is associated with a tag, and the tag is composed of at least the first attribute tag and the tag. The second attribute tag, the third attribute tag, and the fourth attribute tag are defined, wherein the first attribute tag corresponds to the target associated with the task, and the second attribute tag corresponds to the task associated with the task. For related questions, the third attribute label corresponds to an answer to the question, and the fourth attribute label corresponds to an opinion different from the answer. The system according to claim 2, further comprising: providing a user interface with an emerging label tool configured to receive user input in response to the selected context element, wherein the user input includes The attribute tag, and the attribute tag therein is one of the following: the first attribute tag, the second attribute tag, the third attribute tag, the fourth attribute tag, and/or one or more custom tags . The system according to claim 3, wherein providing the automatic suggestion for the second attribute tag includes: Identifying historical first attribute tags from the plurality of first historical attribute tags based on the similarity between the respective associated context elements; and Use the historical second attribute tag associated with the historical context element as the candidate second attribute tag of the automatic suggestion. The system according to claim 4, wherein the knowledge framework includes historical first attribute tags received as user input from at least the first user, and the automatic suggestions therein are provided to the second user. The system according to claim 3, wherein the automatic suggestion for the candidate second attribute tag is based on one or both of the following: The historical second attribute tag associated with the historical context element has a similarity with the context element, and the historical context element is associated with the historical first attribute tag that is part of the knowledge framework; and Generating a second attribute tag, where the generating second attribute tag is generated by a method, and the method includes: Obtain an output from the natural language processing module based on the context element that is the input; and A generative confrontation network is used to iteratively compare the output with a historical second attribute label, wherein the historical second attribute label is selected based on the first attribute label and the knowledge framework. The system according to claim 6, wherein the knowledge framework includes historical first attribute tags received as user input from at least the first user, and the automatic suggestions therein are provided to the second user. The system according to claim 7, further comprising: A label is generated based on the weighted set of the trained machine learning model, which is relative to any one of the historical first attribute label, the historical second attribute label, and the historical third attribute label assigned to the weighted set For each weight of the model, a larger weight is assigned to the model related to the historical fourth attribute label. The system according to claim 7, wherein the emerging label tool further includes: A manual interaction panel configured to receive manual user input; and An automatic suggestion interactive panel that is configured to provide the automatic suggestion. The system according to claim 9, wherein the user interface further includes a knowledge visualization tool configured to provide one or more automatic suggestions, wherein in response to one or more social interactions linked to the task The new source object provided by the media channel subscription provides the one or more automatic suggestions. The system described in any one of claims 1 to 10 uses software as a method of service deployment.

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