A system for generating and / or filtering technical concepts, trademarks and a method thereof

Abstract

The present application provides a system, method and computer readable medium for generating new technical concept(s), non-infringeable technical concept(s) and distinct trademark(s). The new technical concept(s) can be generated then filtered against existing publications or generated in a way that includes the filtering in the generation. The non-infringeable technical concepts can be generated then filtered against active patent claims or generated based on not in force publications in a way that includes the filtering the generation. The distinct trademarks can be generated then filtered against existing trademarks and the rules for allowing trademarks in a given country or generated in a way that includes the filtering and rules considering in the generating. The system, method, and computer readable medium can also filter technical concepts or trademarks to determine with they are patentable, non-infringing, or registerable.

Description

A SYSTEM FOR GENERATING AND / OR FILTERING TECHNICAL CONCEPTS, TRADEMARKS AND A METHOD THEREOFCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application No. : 63 / 676,863 filed July 29, 2024 entitled A SYSTEM FOR GENERATING TECHNICAL CONCEPTS, TRADEMARKS AND A METHOD THEREOF, which is hereby incorporated by reference herein and claims priority to U.S. Patent Application No.: 63 / 699,050 filed September 25, 2024 entitled SYSTEM AND METHOD FOR EVALUATING INVENTIONS, which is hereby incorporated by reference herein.FIELD OF THE DISCLOSURE

[0002] The present disclosure relates generally to a system and method for generating and / or filtering technical concepts and trademarks. More particularly, the present disclosure relates to a system(s) and method(s) for generating, evaluating and displaying relatively unique technical concepts, technical concepts that are not infringeable and distinct trademarks or for filtering technical concepts to determine if they are patentable or free to use.BACKGROUND OF THE DISCLOSURE

[0003] In the highly competitive business environment, the ability to generate unique ideas or technical concepts or engineering principles and distinct trademarks is crucial for maintaining a competitive edge and ensuring market differentiation. Trademarks serve as unique identifiers of goods or services, providing legal protection and brand recognition. However, the process of creating truly unique and distinctive trademarks is often challenging due to the vast number of existing trademarks and the risk of infringing on prior art.

[0004] Traditional method of generating unique ideas methods is often time-consuming, inconsistent, and prone to human error. Moreover, once a potential trademark is conceived, determining its novelty and ensuring it does not infringe on existing trademarks requires extensive research and access to comprehensive databases, which can be both labour-intensive and costly. Similarly, for unique ideas manually developed must be assessed for its patentability, infringement which again needs labour and not cost efficient.SUMMARY OF THE DISCLOSURE

[0005] Various details of the present disclosure are hereinafter summarized to provide a basic understanding. This summary is not an extensive overview of the disclosure and is neither intended to identify certain elements of the disclosure, nor to delineate the scope thereof. Rather, the primary purpose of this summary is to present some concepts of the disclosure in a simplified form prior to the more detailed description that is presented hereinafter.

[0006] The present application provides a system, method and computer readable medium for generating new technical concept(s), non-infringeable technical concept(s) and distinct trademark(s). The new technical concept(s) can be generated then filtered against existing publications or generated in a way that includes the filtering in the generation. The non-infringeable technical concepts can be generated then filtered against active patent claims or generated based on not in force publications in a way that includes the filtering the generation. The distinct trademarks can be generated then filtered against existing trademarks and the rules for allowing trademarks in a given country or generated in a way that includes the filtering and rules considering in the generating.

[0007] According to an embodiment consistent with the present disclosure, a system for generating at least one technical concept(s) is disclosed. The system comprises of at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receive a description of a technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

[0008] In another embodiment, a method for of generating technical concept(s) is disclosed. The method comprises receiving a description of the technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

[0009] In a further embodiment, a computer readable medium with instructions for evaluating a generated technical concept(s) against prior art references is disclosed. The medium comprising receive a description of a technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

[0010] According to an embodiment consistent with the present disclosure, a system for generating a plurality of distinct trademark(s), is disclosed. The system comprises of: at least oneprocessor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receiving a description of what kind of trademark is desired to be generated in a user interface; and displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

[0011] In another embodiment, a method for of generating a plurality of trademarks is disclosed. The method comprises of: receiving a description of what kind of trademark is desired in a user interface; and displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

[0012] In a further embodiment, a computer readable medium with instructions for evaluating a generated trademark against existing trademarks is disclosed. The medium comprising of: receiving a description of what kind of trademark is desired to be generated in a user interface; displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks; receiving in the user interface feedback based on at least one of the ideas; and displaying in the user interface an updated set of trademarks generated in response to the feedback.

[0013] According to an embodiment consistent with the present disclosure, a system for generating at least one technical concept(s) that are not protected by a patent claim is disclosed. The system comprising of: at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receive a description of a technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

[0014] In another embodiment, a method for generating at least one technical concept(s) that are not protected by a patent claim is disclosed. The method comprises of: receiving a description of a technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

[0015] In a further embodiment, a computer readable medium with instructions for evaluating a generated technical concept that are not protected by a patent claim is disclosed. The medium comprising receiving a description of a technical area in a user interface; and displaying inthe user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

[0016] Any combinations of the various embodiments and implementations disclosed herein can be used in a further embodiment, consistent with the disclosure. These and other aspects and features can be appreciated from the following description of certain embodiments presented herein in accordance with the disclosure and the accompanying drawings and claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG. 1 is schematic illustration of a user interface that displays an embodiment of receiving description and displaying an output, that can be employed to execute one or more embodiments of the present disclosure.

[0018] FIG. 2 is schematic illustration of a user interface that displays an embodiment of receiving trademark description and displaying an output, that can be employed to execute one or more embodiments of the present disclosure.

[0019] FIG. 3 is schematic illustration of a user interface that seeks a description, that can be employed to execute one or more embodiments of the present disclosure.

[0020] FIG. 4 is schematic illustration of a user interface that seeks a description including with category of a technical concept, that can be employed to execute one or more embodiments of the present disclosure.

[0021] FIG. 5 is schematic illustration of a user interface where the description and the categories have been filled out, that can be employed to execute one or more embodiments of the present disclosure.

[0022] FIG. 6 is schematic illustration of a user interface that displays an embodiment of receiving description and displaying an output, that can be employed to execute one or more embodiments of the present disclosure.

[0023] FIG. 7 is schematic illustration of a user interface that seeks a description, that can be employed to execute one or more embodiments of the present disclosure.

[0024] FIG. 8 is schematic illustration of a user interface that seeks a description including seeking goods and services, that can be employed to execute one or more embodiments of the present disclosure.

[0025] FIG. 9 is schematic illustration of a user interface that seeks a description including seeking goods and services and a category, that can be employed to execute one or more embodiments of the present disclosure.

[0026] FIG. 10 is schematic illustration of a user interface that seeks a description including seeking goods and services, a category, and countries, that can be employed to execute one or more embodiments of the present disclosure.

[0027] FIG. 11 is schematic illustration of a user interface that seeks a description including seeking goods and services, categories of information, countries, and categories that the trademark falls into, that can be employed to execute one or more embodiments of the present disclosure.

[0028] FIG. 12 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0029] FIG. 13 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0030] FIG. 14 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0031] FIG. 15 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0032] FIG. 16 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0033] FIG. 17 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0034] FIG. 18 is schematic illustration of a block diagram of a machine in the example form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies herein discussed.

[0035] FIG. 19 is schematic illustration of a network system in the example form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies herein discussed.

[0036] FIG. 20 is schematic illustration of a user interface that displays an embodiment of receiving description and displaying an output, that can be employed to execute one or more embodiments of the present disclosure.

[0037] FIG. 21 is schematic illustration of a user interface that displays an embodiment of receiving description and displaying an output, that can be employed to execute one or more embodiments of the present disclosure.

[0038] FIG. 22 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0039] FIG. 23 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0040] FIG. 24 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0041] FIG. 25 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.

[0042] FIG. 26 is schematic illustration of a flow showing methods, according to example embodiments of the present disclosure.DETAILED DESCRIPTION

[0043] The present application provides a system, method and computer readable medium for generating new technical concept(s), non-infringeable technical concept(s) and distinct trademark(s). The new technical concept(s) can be generated then filtered against existing publications or generated in a way that includes the filtering in the generation. The non-infringeable technical concepts can be generated then filtered against active patent claims or generated based on not in force publications in a way that includes the filtering the generation. The distinct trademarks can be generated then filtered against existing trademarks and the rules for allowing trademarks in a given country or generated in a way that includes the filtering and rules considering in the generating.

[0044] Embodiments in accordance with the present disclosure generally relate to a system and a method thereof, that allows for generating unique technical concepts, non-infringeable technical concept(s) and distinct trademarks. Unlike existing artificial intelligence (Al), which can do general things. There doesn’t exist a way to generate technical concept(s) or trademark(s) that create unique technical concepts, new trademarks, or ideas that do not infringe active patents. The reason is that generative Al is not specifically trained on the right data and has the identifying and filtering of technical concepts or trademarks that allow for the providing of unique technical concepts, distinct trademarks, and freedom to operate.

[0045] Models such as natural language processing (NLP) and generative algorithms can assist in idea generation by analysing large datasets to produce creative outputs. For instance, some Al systems utilize neural networks to suggest new trademarks based on patterns and trends identified in existing data. Additionally, Al-powered search tools can help evaluate the novelty of technical concepts(s) or trademark(s) by comparing them against vast databases of publications. Despite these advancements, current Al models have significant limitations. Many are not specifically tailored for trademark generation and evaluation, often requiring extensive customization and fine-tuning to be effective in this domain. Furthermore, the quality and uniqueness of the ideas or technical concepts generated by existing Al systems can be inconsistent, as these models may not fully understand the nuanced requirements for distinct and legally viable unique technical concept(s), trademark(s), or how to determine freedom to operate.

[0046] Another critical shortcoming is the integration of the idea / trademark generation and uniqueness evaluation processes. Most existing solutions treat these as separate tasks, leading to inefficiencies and potential gaps in the evaluation of new trademarks and ideas. In one embodiment, a system would seamlessly integrate these processes, providing a streamlined and cohesive approach to generating and assessing trademarks and ideas. Therefore, there is a need for an improved method and system that can efficiently generate novel ideas and trademarks while also evaluating their novelty or uniqueness against prior art. Such a system would leverage advanced Al technologies, including deep learning, machine learning, and sophisticated NLP algorithms, to enhance the creativity and innovation process. It would also incorporate comprehensive databases and intelligent search capabilities to ensure thorough and accurate evaluation of trademark novelty, reducing the risk of infringement and legal disputes.

[0047] The present disclosure provides a user-interface, system, method and a computer readable medium for generating technical concept(s), non-infringing technical concept(s), andtrademark(s). The system is capable of executing instructions to receive input, process the input to identify relevant art, filter the state of art against the provided input and thereby display unique set of technical concept(s) and trademark(s) respectively. Beneficially, the system is designed to be more effective, efficient, and reliable. Further beneficially, the system offers a robust tool for businesses or users seeking to protect and enhance their brand identities. Furthermore, beneficially the system provides an advanced, automated solution that leverages cutting-edge technologies to enhance the creativity and innovation process while ensuring thorough evaluation of technical concepts that are unique, non-infringeable and novel trademark(s).

[0048] In some embodiments, Al can be used such as at least one large language model (LLM) to generate and filter technical concept(s), non-infringing technical concept(s), and trademarks. A generative LLM can be trained on general purpose information, while filtering LLMs can be trained on publications and trademarks from databases. The LLM can filter based on a similarity assessment such as comparing an input to the LLM to vectors in the vector database of an LLM to see how similar it is to existing materials the LLM was trained on. A rules logic can be used to fine tune the filtering. In some embodiments, a rules LLM can be trained on the specific task of determining whether an idea infringes, whether a trademark is similar, or whether an idea is novel. After the filtering, the results can be provided in a report. The report can be displayed, for example, on a screen such as a monitor or on a tangible medium such as paper.

[0049] The at least one LLM can be trained in the following way. First, the LLM can be pretrained by inputting vast amounts of textual information through a neural network architecture, so the LLM can learn general language patterns and structures such as learning the statistical relationships between words and phrases. This enables the system to process the vast amount of information in a computationally efficient manner. Through this, the LLM can begin to understand language. Second, the LLM can be fine-tuned to perform a specific task such as determining infringement of a patent claim, for example, or the similarity of trademarks. This is done by training on a smaller task-specific information set using specialized methods which adjust the neural network weights. The LLM can be trained on trademark application examination data for example. The LLM can learn the rules and how a trademark is reviewed to determine registrability. The LLM can also be trained on patent application examination data to determine how to review technical concepts for patentability. The LLM can also be trained on claims analysis to determine whether a claim is infringed by a technical concept. This can be done by providing training data on claim infringement analysis.

[0050] Finally, the LLM can be refined further, based on feedback received from an external system or by a human to enhance the LLM, this may include receiving feedback from a third party based on their review of the outputs of the LLM. In some examples, the LLM can also utilize retrieval-augmented generation (RAG) where the LLM looks up or augments the LLM's knowledge with information from an external database before generating a response. This can help with improving accuracy by ensuring up to date information in the LLM. For example, this can be used to input new publications into the database to ensure that patentable ideas are accurately identified. This technical implementation of RAG may involve specialized indexing and retrieval methods that improve the processing efficiency when compared to conventional database approaches.

[0051] The filtering can be used with generation or on standalone information. For example, the filtering can be pair with a trademark word generator such as a trademark generating LLM or with trademarks that a human or other third-party inputs to technically analyze the registrability of the trademark in different jurisdictions. Currently, this is a time-consuming manual process where skilled attorneys review data to determine the difficulty or trademarking a word. Similar technical advantages apply to filtering to determine if an idea is patentable or non-infringing. These can be used with an idea generator or on a standalone basis. In some examples, one LLM can be configured to perform both the generation and filtering methods by optimizing the neural network for both tasks.

[0052] The LLM can implement a transformer-based neural network architecture or Al model for natural language processing. It can process input data in parallel through a self-attention mechanism and use encoders to input data and decoders to generate output data. In some embodiments, the LLM can process technical concepts or trademarks as discrete units that it uses to compare against other trademarks (and words in the common lexicon) and other technical concepts in publications as process called mapping. By technically decomposing publications into discrete technical concepts, the filtering process can be performed more precisely and accurately with increased computational efficiency. The LLM can be optimized so that it uses less computing resources so that the system runs faster and uses less power.

[0053] Embodiments of the present disclosure will now be described in detail with reference to the accompanying Figures. Like elements in the various figures may be denoted by like reference numerals for consistency. Further, in the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the claimed subject matter. However, it will be apparent to one of ordinary skill in the art that the embodiments disclosed herein may be practiced without these specific details. Inother instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. Additionally, it will be apparent to one of ordinary skill in the art that the scale of the elements presented in the accompanying Figures may vary without departing from the scope of the present disclosure.Generating technical concepts ) embodiment

[0054] In accordance with the present disclosure, a user interface of a system 100 for generating a plurality of technical concepts is disclosed in FIG. 1. The user interface of system 100 is configured to include a web browser or software application with a user interface for allowing an end user to enter descriptions 101 of a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions as shown in FIG. 3. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a system or device or apparatus or hardware or software. This can include, but is not limited to, challenges in operation, efficiency, reliability, or compatibility.

[0055] In another embodiment, the technical area can relate to a not well-developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided. The technical area can also be described textually or through specific keywords that succinctly encapsulate the domain of interest. This includes descriptive terms that outline the scope, focus, and specific challenges or objectives within a particular technical field.

[0056] The user interface of system 100 in accordance with the present disclosure further includes an option of allowing the end user to further refine the generated set of technical concepts by providing a main category and a sub-category of the field of the technical area via a selector 102 and 103 present in the user interface. The system is configured to enable various main categories102 as shown in FIG. 4, including but not limited to electronics, computer science, biotechnology, artificial intelligence, block chain, chemistry, mechanical, life sciences. The user interface of the system in some embodiments of the present disclosure, is configured to enable various sub categories103 as shown in FIG. 5, including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics, environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology.

[0057] The user interface of the system 100 in accordance with the present disclosure, further comprises an option allowing the user to submit the entered description of the technical area along with the main and sub-category via a designated submit button 104. Upon submission, the system performs technical data processing operations including parsing, tokenization, and vector embedding of the input text to prepare it for efficient processing by the neural network components.

[0058] In a further detailed embodiment, the functioning of the system is illustrated in FIG. 18 is a block diagram of machine in the example form of a computer 600 comprising a processor and a memory including instructions, further processes the user provided description of the technical area to identify relevant art by filtering against a database of publications. As described herein, an LLM can perform the filtering using specialized indexing and search methods to efficiently process large volumes of data, using technical vector similarity calculations. In one embodiment, the relevant state of art identified from free and / or paid databases through API connections and data retrieval protocols that that include both patent information and non-patent literature.

[0059] The system 100 in accordance with the present disclosure is configured to include a processor and a memory with specific instructions to be performed by a machine or computer. The user inputs a description of the technical area they are interested in. The system processes this description to understand the technical concepts and keywords, which the system processes through specialized natural language processing algorithms to understand the technical concepts and keywords. The system then filters this information against a database of publications to identify relevant prior art. This database can include patent databases containing information about existing patents; and non-patent literature containing scientific papers, articles, and other publications that are not patents.

[0060] The system searches through free and / or paid databases to ensure a comprehensive search. This ensures that the search is thorough and covers all possible relevant prior art. The search can be a LLM RAG search to update with newer publications. The system identifies the relevant state of the art from the databases. This involves comparing the user-provided description with the contents of the databases to find matches or related information. By leveraging free and / or paid databases, and considering both patent and non-patent literature, the system aims to provide a comprehensive view of the current state of the art, thereby filtering the technical concept to its best possible uniqueness. The filtering can be performed by an LLM using technical concepts as described above.

[0061] The system 100 in accordance with the present disclosure is further processes to compare the to be generated technical concepts with the identified publications from the databases. In one embodiment, the comparison includes mapping operations that analyze the technical concepts that are to be generated based on the user description against the technical concepts present in the identified publications. The system further implements filtering operations to exclude the concepts that are already documented in the publications.

[0062] The system 100 in an embodiment maps the to be generated technical concepts against the concepts present in the identified publications. This involves extracting key technical concepts and details from the publications, using natural language processing techniques, to identify relevant prior art and comparing the to be generated concepts with the extracted concepts from the publications to find similarities or overlaps. During the comparison process, the system identifies any overlaps or similarities between the generated concepts and those already documented in the publications. The system specifically filters out the concepts that are already documented, or otherwise excludes any generated technical concepts that are found to be similar to or the same as existing documented or patented concepts from further consideration. After filtering out the documented concepts, the system presents the remaining concepts, which are considered to be novel or unique or not previously documented in the identified prior art, including both patent and nonpatent literature.

[0063] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents, abandoned patents and / or non-patent literature.

[0064] In an embodiment, the generation of the technical concepts is implemented by a generative artificial intelligence (GenAI) trained on a diverse corpus of information, including patents and scientific publications. The technical implementation employs specialized neural network architectures optimized for this specific task. In some embodiments, the technical concepts generated in accordance with the system as per the present disclosure are unique and new from what has been already existing in the state of art. The system leverages Natural Language Processing methods to interpret the user provided inputs to generate the plurality of technical concept(s). The technical implementation of the generative artificial intelligence may include any one of, but not limited to, neural networks, SVMs with optimized kernel functions, GANs with specialized discriminator and generator components, or Rule-based systems with specialized inference engines.

[0065] In some embodiments of the present disclosure, the user interface of the system receives the description, and the system first processes this input to make it usable for further analysis. Natural Language Processing (NLP) techniques including tokenization using finite state automata, stemming through specialized suffix-stripping methods, and lemmatization using morphological analysis may be used to break down and understand textual inputs. Further, the system implements embedding techniques (like word2vec, GloVe, or BERT) convert words or sentences into numerical vectors that capture semantic meaning through high-dimensional space representations. Using the model prediction, the extracted features, are processed by the system to make predictions based on the task it was trained to perform. The system may combine multiple predictions using ensemble methods, or use specialized decision logic to make a final decision to improve accuracy based on predefined rules. The system may, finally, generate an output based on the predictions and decisions made.

[0066] In a further detailed embodiment, the system starts from the identified publications, extracting technical concepts using natural language processing (NLP) techniques, identifying key phrases, methodologies, and innovations described. The system generates new technical concepts based on the user's description, using specialized generative methods designed to produce innovative ideas within the specified technical area. These generated concepts are then mapped against the extracted concepts from the publications using vector similarity calculations and semantic analysis methods to identify any similarities or matches. The system further implemented filtering methods to exclude any generated concepts that match or closely resemble those found in the prior art, ensuring that only novel or unique technical concepts remain. Accordingly, the system displays only filtered, novel technical concepts to the user, potentially with additional information on their uniqueness and potential for further development or patenting, providing significant efficiency improvements over existing methods.

[0067] The system can alternatively instruct the trained generative artificial intelligence to determine the likelihood of infringement of the to be generated technical concepts against the pending and granted patents in force and compare this category of patents against the provided technical area description to generate technical concepts that are not infringeable. In yet another embodiment, the system also instructs the trained generative artificial intelligence to display technical concepts from patents are no more legally in force.

[0068] The plurality of technical concepts generated by leveraging trained generative artificial intelligence are presented to the user via a web page or software application display 105 ofthe system 100. The technical concepts generated are displayed in separate in a plurality of segments for example not limited to 106 and 107 of the web page or a software application 105 with user feedback facilities 106a, 106b, 106c, 107a, 107b, and 107c. A plurality of graphical indicators can be used with at least one generated technical concept for allowing the end user to rate the generated technical concepts based on its practicality and desirability (or any other review metric).

[0069] In feedback facility 106a and 107a the user can evaluate the generated technical concept, for example, as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. Similarly, the user can evaluate the generated technical concept as impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms. The graphical indicator used can allow a spectrum of possibilities between practical and impractical.

[0070] In feedback facility 106b and 107b, the user is also provided with an option to evaluate the generated technical concept, for example, based on the his / her desirability to perform the generated concept. The user may take into account several factors such as how appealing or attractive or technically feasible the concept is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to functionality, usability, perceived value, innovation and novelty, potential impact, viability, performance improvement. A highly desirable technical concept is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation. The graphical indicator used can allow a spectrum of possibilities.

[0071] Referring to the system 100, it is shown that a first technical concept segment 106 includes a set of first graphical indicators for presenting the practicality 106(a) and desirability 106(b) of the first generated technical concept. It also configured to further include a prompt box 106(c). The prompt box has been specifically configured for the end user to input further instructions to refine the generated technical concept.

[0072] Referring to the system 100, it is shown that a second technical concept segment 107, presents another generated technical concept that is different from the first technical concept. The second technical concept segment 107 also includes a second set of graphical indicators for presenting the practicality 107(a) and desirability 107(b) of the second generated technical concept. It also includes a prompt box 107(c) for the user to input further instructions to refine the generatedtechnical concept as illustrated in FIG. 1. As shown, different graphical indicators can be used to receive feedback from the user. For example, a sliding bar, a set of circles, a set of stars, or any other feedback graphical indicator can be used.

[0073] In some embodiments of the present disclosure the system is configured to display a plurality of technical concept segments not limited to first and second technical concepts. All plurality set of technical concepts generated are provided with indicators for presenting the practicality and desirability of the generated technical concept.

[0074] The plurality of technical concepts can be modified based on the rating provided for its practicality and desirability. The web browser displaying the set of results 105 can be changed in real time obtain more refined updated set of results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes 106(c) and 107(c). The prompt boxes 106(c) and 107(c) allow the user to further instruct the system 100 to display expired, withdrawn and abandoned patents that are not legally in force as one category and published or granted patents as another category.

[0075] In an embodiment the prompt boxes 106(c) and 107(c) allow the user to provide realtime feedback and thus allowing refinement of results displayed. The end users can provide additional instructions or keywords in the prompt box, allowing the system to instantly refine and update the results based on this new input. Further, the end users can engage in an iterative process, gradually refining their search criteria until they achieve the desired results.

[0076] The system is made user friendly and more flexible allowing the users gain more control over the searching and refinement process, specifying exactly what they want to see. This is particularly useful as the user can tailor the results to their specific needs, adding constraints or preferences directly into the prompt box.

[0077] The prompt boxes aid in increasing efficiency of the system 100. By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The system in accordance with the present disclosure, can filter out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the web browser provides a powerful tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0078] FIG. 3, in accordance with some embodiments, illustrates a user interface where a description is requested. FIG. 4, in accordance with some embodiments, illustrates a user interface where a description and a category are requested. FIG. 5, in accordance with some embodiments, illustrates an example where a description and categories are filled out / selected for the example of a portable charger. FIG. 6, in accordance with some embodiments, illustrates a user interface where some patentable ideas are displayed for the portable charger example. The results displayed 105 can include feedback or technical concept(s) without feedback. For example, the technical concepts 108, 108 a, b, c as illustrated in FIG. 20, can be displayed without feedback facilities, as is user flexible. In some embodiments, just one prompt box 106c can be used. The user feedback facilities are flexible and may or may not be present for at least one technical concept generated.

[0079] In accordance with the present disclosure, a method 300 for generating a plurality of technical concepts is further disclosed in FIG. 12. The method includes allowing an end user to enter descriptions 301 into a web browser or software application within a user interface, relating to a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a method or device or apparatus or hardware or software. In another embodiment, the technical area can relate to a not well-developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided.

[0080] The method 300 in accordance with the present disclosure enables the end user in further refining the generated set of technical concepts by inputting a main category 302 and a subcategory 303 relating to the technical area via selector present in the user interface. The method, further allows the user to submit the entered description of the technical area along with the main and sub category via a designated submit button 304.

[0081] The method 300 in some embodiments of the present disclosure, is configured to enable various main categories 302 including but not limited to electronics, computer science, biotechnology, artificial intelligence, block chain, chemistry, mechanical, life sciences. The method in some embodiments of the present disclosure, is configured to enable various sub categories 303 including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics, environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology.

[0082] The method 300 in accordance with the present disclosure, comprises a step of identifying relevant 306 arts based on the user provided description of the technical area, by filtering against a database of publications. In an embodiment the relevant state of art identified from both free and paid databases that that include both patent information and non-patent literature.

[0083] The method in accordance with the present disclosure, further comprises of a step of comparing the to be generated technical concept(s) with the identified publications from the databases and filtering the results 307. In a specific embodiment the comparison step includes mapping the technical concepts that are to be generated based on the user description of the technical area against the concepts present in the identified publications and filtering out the concepts that are already documented in the publications.

[0084] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents, abandoned patents and / or non-patent literature.

[0085] In an embodiment, the method 300 of generation of the technical concepts is implemented using a generative artificial intelligence trained on a diverse corpus of information including patents and scientific publications 305. In some embodiments, the technical concepts generated in accordance with the method as per the present disclosure are unique and completely new from what has been already existing in the state of art.

[0086] The method 300 comprises instructing the trained generative artificial intelligence, to determine the likelihood of infringement of the to be generated technical concepts against the pending and granted patents in force and compare this category of patents against the provided technical area description to generate technical concepts that are not infringeable. In yet another embodiment, the method also comprises a step of instructing the trained generative artificial intelligence to display technical concepts from patents that are no more legally in force.

[0087] The method comprises of generating a plurality of technical concepts 308 by leveraging trained generative artificial intelligence and subsequently displaying it to the user via a web page of a web browser or a user interface of a software application. The technical concepts generated are displayed in separate in a plurality of segments for example not limited to a web page with user feedback facilities 309. A plurality of graphical indicators is provided against generated technical concept(s) for allowing the end user to rate the generated technical concepts (e.g., based on its practicality and desirability).

[0088] The user evaluates the generated technical concept and provides feedback, for example, as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. Similarly, the user evaluates the generated technical concept as, for example, impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms.

[0089] The user also evaluates the generated technical concept based on, for example, the his / her desirability 309 to perform the generated concept. The user may take into account several factors such as how appealing or attractive or technically feasible the concept is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to functionality, usability, perceived value, innovation and novelty, potential impact, viability, performance improvement. A highly desirable technical concept is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0090] The method in accordance with the present disclosure comprises of generating a first technical concept that includes a set of first graphical indicators 309 for presenting the practicality and desirability of the first generated technical concept. The method also includes a step entering instructions from the end user into a first prompt box for further aiding in refining the generated technical concept.

[0091] The method in accordance with the present disclosure comprises generating a second technical concept segment that includes a set of second graphical indicators for presenting the practicality and desirability of the second generated technical concept. The method also includes entering instructions from the end user into a second prompt box for further aiding in refining the generated technical concept.

[0092] In some embodiments of the present disclosure the method displays a plurality of technical concept segment not limited to first and second technical concepts as illustrated in FIG. 20. All plurality set of technical concepts generated in some embodiments may or may not be provided with indicators for presenting the practicality and desirability of the generated technical concept.

[0093] The method further comprises of a step of modifying the plurality of technical concepts generated based on the rating provided by the user for its practicality and desirability and display the updated set of results 310. The web browser or software application displaying the set ofresults can be changed in real time obtain more refined results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes.

[0094] The end users can provide additional instructions or keywords in the prompt box, allowing the system to instantly refine and update the results based on this new input. Further, the end users can engage in an iterative process, gradually refining their search criteria until they achieve the desired results.

[0095] The prompt boxes aid in increasing efficiency of the method of generating the technical concepts. By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The method in accordance with the present disclosure, can filter out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the web browser or software application provides a powerful tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0096] The method further comprises displaying expired, withdrawn and abandoned patents that are not legally in force as one category and published or granted patents as another category, based on user instructions provided in the prompt boxes present in the plurality of the technical concept segments.

[0097] In a further embodiment, a user-interface for displaying a plurality of technical concepts is disclosed. The user interface comprises a text box in a web browser or software application for receiving a description of a technical area. The user interface comprises of selector box for receiving a main category of the technical area. The user interface comprises of a second selector box for receiving a sub category of the technical area. Further the user interface comprises of a submit button to submit the input provided.

[0098] Upon submission of input, the user interface displays in the web browser or software application a plurality of technical concepts that are generated. Further the generated results are provided with a user feedback facility. For example, feedback including rating meters of practicality and desirability. Upon receiving in the web browser or software application feedback rating based on at least one of the technical concepts, the user interface displays in the user interface an updated set of technical concepts generated in response to the feedback.

[0099] In some embodiments, a method 300 in accordance with the present disclosure is the method 300, for displaying a plurality of technical concepts as illustrated in FIG. 15. The method comprises of allowing an end user to enter descriptions 301 into a web browser or software application within a user interface, relating to a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions. The generative Al works on diverse corpus of patents and scientific publications to generate new technical concepts 305. In some embodiments, the LLM can be trained on more than publications. Further, the generated technical concepts are displayed as a plurality of technical concepts in a web browser or software application within a user interface.

[0100] In some embodiments, a method 300 for displaying a plurality of technical concepts as illustrated in FIG. 22. The method comprises of allowing an end user to enter descriptions 301 into a web browser or software application within a user interface, relating to a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions. The generative Al works on diverse corpus of information to generate new technical concepts 305. The LLM can filter the technical concepts 307 by either mapping new technical concepts to existing technical concepts and then filtering or by just filtering. The filtering can be as described anywhere in this document. Further, the generated technical concepts are displayed as a plurality of technical concepts in a web browser or software application within a user interface.Example Machine and Computer readable Medium

[0101] FIG. 18 is a block diagram of machine in the example form of a computer system 600 within which instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines.

[0102] In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set- top box (SIB), a PDA, a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0103] In accordance with the present disclosure a computer readable medium with instructions for evaluating a generated technical concept(s) against prior art references is also disclosed. The example computer system 600 includes a processor 601 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit (IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture), a main memory 602 and a static memory 603, which communicate with each other via a bus 612. The memory can include random access memory (RAM), video random access memory (VRAM), and / or high-bandwidth memory (HBM). VRAM can be used for storing the model and data for the GPU. HBM can be used when fast data transfer between the processor and memory is needed for running the Al. The computer system 600 may further include a video display unit 606 (e.g., a monitor such as a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 600 also includes an alphanumeric input device 607 (e.g., a keyboard), a user interface (UI) navigation or cursor control device 607 (e.g., a mouse), a disk drive unit 609, a signal generation device 611 (e.g., a speaker) and a network interface device 604.

[0104] The disk drive unit 609 includes a machine-readable medium 610 on which is stored one or more sets of data structures and instructions 613 (e.g. , software) embodying or utilized by any one or more of the systems or methodologies or functions described herein. The instructions 613 may also reside, completely or at least partially, within the main memory 602 and / or within the processor 601 during execution thereof by the computer system 600, with the main memory 602 and the processor 601 also constituting machine-readable media.

[0105] While the machine-readable medium 610 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more data structures or instructions 613. The term “machine -readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the embodiments of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The machine-readable medium can store instructions and / or data in non- transitory manner. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatilememory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; solid state drives (SSD); and CD-ROM and DVD-ROM disks.

[0106] The instructions 613 may further be transmitted or received over a communications network using a transmission medium. The instructions 613 may be transmitted using the network interface device 604 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

[0107] In a further detailed embodiment, the computer readable medium instructions are disclosed. In accordance with the present disclosure, the computer readable medium instructions when implemented allows an end user to enter description of a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions into a user interface. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a computer readable medium or device or apparatus or hardware or software. In another embodiment, the technical area can relate to a not well-developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided.

[0108] The computer readable medium in accordance with the present disclosure further includes an option of allowing the end user to further refine the generated set of technical concepts by providing a main category and a sub-category of the field of the technical area via a selector in the user interface. The computer readable medium in some embodiments of the present disclosure, is configured to enable various main categories including but not limited to electronics, computer science, biotechnology, artificial intelligence, block chain, chemistry, mechanical, life sciences. The computer readable medium in some embodiments of the present disclosure, is configured to enable various sub categories including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics,environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology.

[0109] The computer readable medium in accordance with the present disclosure, further comprises an option allowing the user to submit the entered description of the technical area along with the main and sub category via a designated submit button.

[0110] The computer readable medium comprising a processor and a memory including instructions, further processes the user provided description of the technical area to identify relevant art by filtering against a database of publications. The filter can be performed by an LLM as described above. In an embodiment the relevant state of art identified from both free and paid databases that that include both patent information and non-patent literature.

[0111] The computer readable medium in accordance with the present disclosure compares the to be generated technical concepts with the identified publications from the databases. In a specific embodiment the comparison includes mapping the technical concepts that are to be generated based on the user description of the technical area against the concepts present in the identified publications, and the computer readable medium further filters out the concepts that are already documented in the publications.

[0112] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents, abandoned patents and / or non-patent literature.

[0113] In an embodiment, the computer readable medium enables the generation of the technical concepts using a generative artificial intelligence trained on a diverse corpus of information including patents and scientific publications. In some embodiments, the technical concepts generated in accordance with the computer readable medium as per the present disclosure are unique and completely new from what has been already existing in the state of art.

[0114] In some embodiments of the present disclosure, the computer readable medium receives the technical area input, it first processes this input to make it usable for further analysis. Natural Language Processing (NLP) techniques like tokenization, stemming, and lemmatization are used to break down and understand text. Further, embedding techniques (like word2vec, GloVe, or BERT) convert words or sentences into numerical vectors that capture semantic meaning. Using the model prediction, the extracted features, the Al modelled system makes predictions based on the task it was trained to perform. The systems might combine multiple predictions or use additionallogic to make a final decision and simple decision-making based on predefined rules. Combining predictions from multiple models to improve accuracy. Output generation finally, the Al system generates an output based on the predictions and decisions made.

[0115] The computer readable medium determines the likelihood of infringement of the to be generated technical concepts against the pending and granted patents in force and compare this category of patents against the provided technical area description to generate technical concepts that are not infringeable by leveraging the trained generative artificial intelligence. In yet another embodiment, the computer readable medium also displays technical concepts from patents that are no more legally in force.

[0116] The computer readable medium allows to present the user with a web page displaying the plurality of technical concepts generated. The technical concepts generated are displayed in separate in a plurality of segments for example not limited to and of the web page with user feedback facilities. A plurality of graphical indicators is displayed against at least one generated technical concept for allowing the end user to rate the generated technical concepts based on its practicality and desirability.

[0117] The computer readable medium displays a first technical concept segment including a set of first graphical indicators for presenting the practicality and desirability of the first generated technical concept. The computer readable medium to further instructed to include a prompt box for allowing the end user to input instructions to further refine the generated technical concept.

[0118] The computer readable medium displays a second technical concept segment including a set of second graphical indicators for presenting the practicality and desirability of the second generated technical concept. The computer readable medium to further includes a prompt box for allowing the end user to input instructions to further refine the generated technical concept.

[0119] In some embodiments of the present disclosure the computer readable medium displays a plurality of technical concept segment not limited to first and second technical concepts. All plurality set of technical concepts generated are provided with indicators for presenting the practicality and desirability of the generated technical concept.

[0120] The computer readable medium allows the user to produce feedback to modify the plurality of technical concepts based on the rating provided by the user (e.g., for its practicality and desirability). The web browser displaying the set of results can be changed in real time obtain morerefined results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes.

[0121] The computer readable medium to further includes a prompt box for allowing the end user to input instructions to display expired, withdrawn and abandoned patents that are not legally in force as one category and published or granted patents as another category.

[0122] Further FIG. 19 illustrates a network component according to an example embodiment. The computer network system 700 includes patent management system and user device or user terminal 705 communicatively coupled via network 704. In an embodiment, patent management system includes server 701, publication server 702, and database management server 703, which may be used to manage at least operations database and file server. Patent management system including the server 701 may be implemented as a distributed system; for example, one or more elements of the patent management system may be located across a wide-area network (WAN) from other elements of patent management system. As another example, a server (e.g., web server, fde server, database management server) may represent a group of two or more servers, cooperating with each other, provided by way of a pooled, distributed, or redundant computing model. Network 704, may include local-area networks (LAN), wide-area networks (WAN), wireless networks (e.g., cellular network), the Public Switched Telephone Network (PSTN) network, ad hoc networks, personal area networks (e.g., Bluetooth) or other combinations or permutations of network protocols and network types. The network 704 may include a single local area network (LAN) or wide-area network (WAN), or combinations of LAN's or WAN's, such as the Internet. The various devices / systems coupled to network 704 may be coupled to network 704 via one or more wired or wireless connections.

[0123] Server 701, may communicate with file server to publish or serve files stored on file server. Server 701 may host one or more LLMs that are used to filter and map technical concepts. These LLMs may be trained on a server, then the trained LLM can be stored on the server 701 for use in evaluating technical concepts. Server can include at least one of a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit (IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture for running Al such as an LLM. The server can have memory such as VRAM or HBM to compute Al models such as LLMs. Server 701 may also communicate or interface with the application or publication server 702 to enable web-based or software applications and presentation of information. For example, publication server 702 may consist of scripts, patentapplications, scientific publications, or library files that provide primary or auxiliary functionality to server 701 (e.g., multimedia, file transfer, or dynamic interface functions). Applications may include code, which when executed by one or more processors, run the tools of patent management system. In addition, publication server 702 may also provide some or the entire interface for server 701 to communicate with one or more of the other servers in patent management system (e.g., database management server). The communication can be a LLM RAG retrieval of information on the publication server 702.

[0124] The server 701, either alone or in conjunction with one or more other computers in patent management system, may provide a user- interface to user terminal or user device 705 for interacting with the tools of patent management system stored in publication server 702. The userinterface may be implemented using a variety of programming languages or programming methods, such as HTML (HyperText Markup Language), VBScript (Visual Basic® Scripting Edition), JavaScript™, XML® (Extensible Markup Language), XST™ (Extensible Stylesheet Language Transformations), AJAX (Asynchronous JavaScript and XML), Java™, JFC (Java™ Foundation Classes), and Swing Application Programming Interface for Java™).

[0125] User terminal or user device 705 may be a personal computer or mobile device. In an embodiment, user device 705 includes a program to interface with patent management system. The program may include commercial software, custom software, open-source software, freeware, shareware, or other types of software packages. In an embodiment, the client program includes a thin client designed to provide query and data manipulation tools for a user of user device 705. The program may interact with a server program hosted by, for example, patent server 702. Additionally, the program may interface with database management server 703.Generating trademarks embodiment

[0126] In accordance with another aspect of the present disclosure, a user interface of system 200 for generating a plurality of trademarks is disclosed in FIG. 2. The system 200 is configured to include a web browser or software application with a user interface for receiving a description of the desired trademark 201 as shown in FIG. 7. The description can include details about the type of goods or services associated with the trademark. The technical area in an embodiment can relate to a set of words or keywords or phrases to describe a product or service. In another embodiment, the technical area can relate to a not well-developed description. In a further embodiment, the technicalarea can be descriptive in text or specific keywords of a distinctive description that can set apart from the existing trademarks can be provided.

[0127] The user interface of the system is further configured to allow an end user to refine the generated trademarks by selecting a category of the trademark 202 and type of trademark 203 via selector lists present in the user interface as shown in FIG. 8 and 9. For example, in an embodiment, the category of the trademark 202 defines the goods or services that will be used with the trademark. In an example embodiment such a category may include but not limited to description of the goods or services such as smartwatches, footwear, hats, packaged meals, cosmetics, video game consoles, e-commerce platforms etc.

[0128] In a further embodiment of the present disclosure, the type of trademark 203 lists out options including but not limited to product mark, service mark, collective mark, sound mark, pattern mark, logo, and / or slogan. The type of trademark can additionally or alternatively refer to suggestive, arbitrary, fanciful, and / or descriptive. The system further comprises a submit button 205 for the user to submit the description along with the selected category and the type of trademarks. In some embodiments, the system can generate at least one of graphical logos, sounds, patterns, color schemes, and / or slogans.

[0129] The user interface of the system 200 also includes functionality to allow the user to select countries of interest 204 as shown in FIG. 10, for comparing the to be generated trademarks against local trademark rules and existing trademarks in those jurisdictions. This ensures that the generated trademarks are suitable for registration in the selected countries. The rules can impact how the filtering of the trademarks can be performed. For example, the rules logic can include information about how to determine if the trademark is registerable in a specific country including how a comparison of a generated trademark against existing trademarks and / or words is performed. In some embodiments, a filtering LLM can be used that is trained on the examination of trademark applications in trademark offices in specific jurisdictions. The LLM can also be trained on the rules. The LLM can also receive human reinforcement based on feedback either from the user or from trainer(s). There can also be options to consider a trademark that would work for preset numbers of countries, such as 10 ten countries, nearly all countries, all countries, or top 3 countries for example.

[0130] The user interface of the system 200 also includes functionality to allow the user to select the category of the trademark 209 as shown in FIG. 11, for defining the category that the trademark falls into. For example, in an implementation in which the type of trademark 203 includesbut is not limited to product mark, service mark, collective mark, sound mark, pattern mark, logo, suggestive, arbitrary, fanciful, and / or descriptive, the category of the trademark 209 may be used to define the goods or services that will be used with the trademark including, but not limited to description of the goods or services such as smartwatches, footwear, hats, packaged meals, cosmetics, video game consoles, e-commerce platforms etc.

[0131] The system 200 further includes a processor and memory with instructions to process the user-provided description and generate a plurality of trademarks. The generation of trademarks is facilitated by a generative artificial intelligence trained on a corpus of information including existing trademarks. The system leverages Natural Language processing to interpret the user provided inputs to generate the plurality of trademarks. The generative artificial intelligence may be any one of but not limited to neural networks, SVMs, GANs or Rule-based systems.

[0132] In some embodiments of the present disclosure, the system 200 receives the description, it first processes this input to make it usable for further analysis. Natural Language Processing (NLP) techniques like tokenization, stemming, and lemmatization are used to break down and understand text. Further, embedding techniques (like word2vec, GloVe, or BERT) convert words or sentences into numerical vectors that capture semantic meaning. Using the model prediction, the extracted features, the Al modelled system makes predictions based on the task it was trained to perform. The systems might combine multiple predictions or use additional logic to make a final decision and simple decision-making based on predefined rules. Combining predictions from multiple models to improve accuracy. Output generation finally, the Al system generates an output based on the predictions and decisions made.

[0133] The system 200 further compares the to be generated trademarks to existing active trademarks and usage of words in internet webpages to ensure uniqueness. This helps in avoiding trademark infringement issues and ensures the generated trademarks are novel and unique. The system 200 can filter the trademarks using an LLM. The filtering can include rules about what is a registerable and / or unique trademark in specific jurisdictions as described above. The filtering can include considering an existing trademark’s goods and services and classifications. In some countries, the comparison of a trademark is done based on classifications and whether a trademark is registered for different classes determines whether a trademark is registerable. In some embodiments, trademarks can be provided in the description or prompt for filtering to determine if the trademark is registerable in certain jurisdictions.

[0134] In a further detailed embodiment, the system comprising a processor and memory with instructions designed to process user-provided descriptions and generate a plurality of trademarks. The core component facilitating this functionality is a generative artificial intelligence model, specifically trained on a comprehensive corpus of information including existing trademarks. The system employs Natural Language Processing (NLP) techniques to interpret user inputs and subsequently generate the trademarks as afore- stated.

[0135] The user interface of the system 200 receives a description of the desired trademark from the user. This description serves as the basis for generating trademarks and can include keywords, themes, desired attributes, and other relevant information. The user-provided description is processed using advanced NLP techniques. The NLP component analyses the input to understand its context, semantics, and key elements. This step ensures that the system accurately interprets the user's intent and the specific characteristics they are looking for in a trademark. The system in an embodiment is a generative Al model, which has been trained on a large and diverse corpus of information including existing trademarks. This training enables the model to understand the patterns, structures, and distinctiveness required for creating effective trademarks. The trained model leverages its training to generate a plurality of trademarks based on the processed user input. In all preferred embodiments, the generated trademarks are unique and adhere to the principles of distinctiveness, avoiding similarity with existing trademarks.

[0136] Further, the system 200 as per the present disclosure utilizes the insights gained from the NLP processing and the generative capabilities of the Al model, to create multiple trademarks. The technical implementation uses specialized neural network architectures and decoding methods to generate each trademark as distinct in nature, reflecting the user's specified themes and desired attributes. The generated trademarks are presented to the user for review via a web page or software application 206. The system 200 may also include additional features to evaluate the novelty and potential conflicts of the generated trademarks against existing ones by using specialized similarity detection algorithms and database comparison techniques to ensure compliance with trademark laws and increasing the likelihood of successful registration.

[0137] The plurality of trademarks generated by leveraging generative artificial intelligence integrated into the system 200 are presented to the user via a web page or software application 206 of the system 200. The trademarks generated are displayed separately in a plurality of segments for example but not limited to 207 and 208 of the web page or software application display 206.

[0138] The generated plurality set of trademarks 207 and 208 are provided with user feedback facilities. A plurality of graphical indicators is used against at least one trademark generated for allowing the end user to rate the generated trademark based on its practicality and likeliness. As shown in FIG. 2, different graphical indicators can be used to receive feedback from the user. For example, a sliding bar can be used, a set of circles, a set of stars, or any other feedback graphical indicator can be used. The graphical indicators can allow for various options of selection. The sliding bar 207a, 207b can allow a spectrum of different selections. The circle selections 208a, 208b can allow for a set of defined selectors.

[0139] The user evaluates the generated trademark. Various types of evaluations can be used. In some embodiments, the trademark is evaluated as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. For example, the user may take into account certain factors but not limited to catchiness, phonetic simplicity, visual appeal, distinctiveness, potential conflicts or absence of offensive meanings. Similarly, the user evaluates the generated trademark as impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms. For example, the user may take into account certain factors but not limited to lack of distinctiveness, uncommon or phonetic complexity, poor design or visually not appealing,

[0140] The user can also evaluate the generated trademark based on the his / her desirability to perform the generated trademark for example. The user may take into account several factors such as how appealing or attractive or feasible the trademark is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to visual appeal, phonetic simplicity, effective for real world use or relevance to the product / goods of service. A highly desirable trademark is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0141] Referring to the system 200, it is shown that a first trademark segment 207 includes a set of first graphical indicators for presenting the practicality 207(a) and desirability 207(b) of the first trademark generated. It also configured to further include a prompt box 207(c). The prompt box has been specifically configured for the end user to input further instructions to refine the generated trademark.

[0142] Referring to the system 200, it is shown that a second trademark segment 208, presents another generated trademark that is different from the first trademark. The second trademark segment 208 also includes a second set of graphical indicators for presenting the practicality 208(a) and desirability 208(b) of the second generated trademark. It also includes a prompt box 208(c) for the user to input further instructions to refine the trademark.

[0143] The system further allows the user to provide feedback on the generated trademarks via prompt boxes 207(c) and 208(c) present in the web page or software application. Based on this feedback, the system updates the set of trademarks displayed to the user, refining them in real-time based on user input.

[0144] The end users can provide additional instructions or keywords in the prompt box, allowing the system to instantly refine and update the results based on this new input. Further, the end users can engage in an iterative process, gradually refining their search criteria until they achieve the desired results.

[0145] The prompt boxes aid in increasing efficiency of the method of generating the trademark(s). By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The method in accordance with the present disclosure, can filter out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the web browser provides a powerful tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0146] FIG. 7, in accordance with some embodiments, illustrates a user interface requesting a description. FIG. 8, in accordance with some embodiments, illustrates a user interface requesting a description and the goods or services for the trademark. FIG. 9, in accordance with some embodiments, illustrates a user interface requesting a description, the goods or services for the trademark, and a type of trademark selector. FIG. 10, in accordance with some embodiments, illustrates a user interface requesting a description, the goods or services for the trademark, a type of trademark selector, and a country selector. FIG. 11, in accordance with some embodiments, illustrates a user interface requesting a description, the goods or services for the trademark, a type of trademark selector, a country selector, and a category selector.

[0147] In accordance with the present disclosure, FIG. 13 provides a method 400 for generating a plurality of trademarks. The method includes allowing an end user to enter a description 401 of the desired trademark into a web browser or software application with a user interface. The method allows the user to further provide details about the type of goods or services 402 associated with the trademark. The description can include details about the type of goods or services 402 associated with the trademark. The description in an embodiment can relate to a set of words or keywords or phrases to describe a product or service. In another embodiment, the description can relate to a not well-developed description. In a further embodiment, the description can be descriptive in text or specific keywords of a distinctive description that can set apart from the existing trademarks can be provided.

[0148] The method further enables the end user to refine the generated trademarks by selecting countries of interest for trademark 403 and type of trademark 404 via selector lists in the user interface. The system implements specialized data filtering algorithms and region- specific rule engines to process this selection. For example, in an embodiment, the category of the trademark defines the goods or services that will be used with the trademark. In an example embodiment such a category may include but not limited to description of the goods or services such as smartwatches, footwear, hats, packaged meals, cosmetics, video game consoles, e-commerce platforms etc.

[0149] In a further embodiment of the present disclosure, the type of trademark 404 lists out options including but not limited to product mark, service mark, collective mark, suggestive mark, sound mark, pattern mark, logo, and / or slogan. The type of trademark can additionally or alternatively refer to suggestive, arbitrary, fanciful, and / or descriptive. The method may further enable the end user to refine the generated trademarks by selecting the category of the trademark via a separate selector list in the user interface. The method further allows the user to be provided with a submit button, to submit the description along with the selected category 402, countries of interest 403 and the type of trademarks 404. Additionally, a submit 405 button allows the user to submit the description along with the selected category and countries.

[0150] The method also includes a step of allowing the user to select countries of interest 403 for comparing the generated trademarks against local trademark rules and existing trademarks in those jurisdictions. This ensures that the generated trademarks are suitable for registration in the selected countries.

[0151] The LLM can use the description and other information such as the goods and services and type of trademark as an input and generate possible trademarks in 406 that it outputs. Either the same LLM or a different LLM can implement comparison methods to compare each generated trademark against existing trademarks and / or words to identify relevant trademarks 407 using specialized similarity detection methods. In some embodiments, this can be done by a search using a neural network architecture to compare words and identify relevant trademarks. Either the same LLM or a different LLM can implement filtering methods to filter the results 408 either using the identified relevant trademarks or doing it all in one step. The filtering of the results 408 can include using a specialized rules engine to determine what is registerable. The rules logic can be an LLM with specialized training methods or can be software that helps determine how to treat a proposed trademark to determine it is registerable.

[0152] The method further includes processing the user-provided description and generating a plurality of trademarks using a processor and memory. The generation of trademarks is facilitated by a generative artificial intelligence trained on a corpus of information including existing trademarks. The system leverages Natural Language Processing for identifying and interpreting the user-provided inputs to display the plurality of trademarks 409. The generative artificial intelligence may be implemented using neural networks, SVMs, GANs, or rule-based systems.

[0153] The method 400 includes comparing the trademarks generated to existing active trademarks and usage of words on internet web pages to ensure uniqueness. This helps in avoiding trademark infringement issues and ensures the generated trademarks are novel and unique. The plurality of trademarks generated by leveraging generative artificial intelligence are presented to the user via a user interface of a web page or a software application. The trademarks generated are displayed 409 in separate segments with user feedback facilities 410. A plurality of graphical indicators is used against at least one trademark generated to allow the end user to rate the generated trademark (e.g., based on its practicality and desirability).

[0154] The user evaluates the generated trademark, for example, as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. For example, the user may take into account certain factors but not limited to catchiness, phonetic simplicity, visual appeal, distinctiveness, potential conflicts or absence of offensive meanings. Similarly, the user evaluates the generated trademark as impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms. For example, the user maytake into account certain factors but not limited to lack of distinctiveness, uncommon or phonetic complexity, poor design or visually not appealing,

[0155] The user can also evaluate the generated trademark based on the his / her desirability to perform the generated trademark, for example. The user may take into account several factors such as how appealing or attractive or feasible the trademark is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to visual appeal, phonetic simplicity, effective for real world use or relevance to the product / goods of service. A highly desirable trademark is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0156] The method includes displaying a first trademark with a set of first graphical indicators for presenting, for example, the practicality and likeliness of the first trademark generated. It also includes displaying a first prompt box configured for the end user to input further instructions to refine the generated trademark.

[0157] The method further includes displaying a second trademark which is different from the first trademark. The second trademark can include a set of graphical indicators, for example, for presenting the and likeliness of the second generated trademark. It also includes a displaying a second prompt box for the user to input further instructions to refine the trademark.

[0158] The method allows the user to provide feedback 410 on the generated trademarks via prompt boxes present in the web page. Based on this feedback, the method updates the set of trademarks displayed 411 to the user, refining them in real-time based on user input.

[0159] In a further embodiment, a user-interface for displaying a plurality of trademarks is disclosed. The user interface comprises a text box in a web browser for receiving a description of a desired trademark. The user interface comprises of receiving the type of good or services of the desired trademark. Further a drop-down box for receiving countries of interest is provided. The user interface comprises of a second drop down box for receiving the type of trademark. Further the user interface comprises of a submit button to submit the input provided.

[0160] Upon submission of input, the user interface displays in the web browser a plurality of trademarks that are generated. Further the generated results are provided with a user feedback facility including rating meters of practicality and desirability. Upon receiving in the web browserfeedback rating based on at least one of the trademarks, the user interface displays in the web browser an updated set of trademarks generated in response to the feedback.

[0161] In some embodiments, a method 400 in accordance with the present disclosure is the method 400, for displaying a plurality of trademarks as illustrated in FIG. 16. The method comprises of allowing an end user to enter descriptions 401 of a desired trademark into a web browser or software application within a user interface, relating to the kind of trademark in which the user is seeking to generate a plurality of trademarks. The generative Al works on diverse corpus of existing trademarks to generate new types of trademarks 406. The LLM can be trained on materials beyond just trademarks, but can be fine-tuned on trademarks to make it more proficient at generating trademarks. Further, the generated trademarks are displayed as a plurality of trademarks 409 in a web browser or software application within a user interface. The display of the one or more trademarks 409 can include providing a report either on video display or on paper.

[0162] In some embodiments a method 400 can be performed as illustrated in FIG. 23. The method comprises of allowing an end user to enter descriptions 401 of a desired trademark into a web browser or software application within a user interface, relating to the kind of trademark in which the user is seeking to generate a plurality of trademarks. The generative Al works on diverse corpus of information to generate new types of trademarks 406. The LLM can be trained on materials beyond just trademarks, but can be fine-tuned on trademarks to make it more proficient at generating trademarks. The generated trademark results are then filtered 408 using an LLM. The filtering can be any filtering described in this disclosure. The filtering of the results 408 can include using the rules to determine what is registerable. The rules logic can be an LLM or can be software that helps determine how to treat a proposed trademark to determine it is registerable. In some embodiments, the filtering of the results can include comparing against identified existing trademarks. Further, the generated trademarks are displayed as a plurality of trademarks 409 in a web browser or software application within a user interface. The display of the one or more trademarks 409 can include providing a report either on video display or on paper.Example Machine and Computer readable Medium

[0163] FIG. 18 is a block diagram of machine in the example form of a computer system 600 within which instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines.

[0164] In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set- top box (SIB), a PDA, a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0165] In accordance with the present disclosure, a computer-readable medium with instructions for generating a plurality of distinct trademarks is disclosed. The example computer system 600 includes a processor 601 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit (IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture), a main memory 602 and a static memory 603, which communicate with each other via a bus 612. The memory can include random access memory (RAM), video random access memory (VRAM), and / or high-bandwidth memory (HBM). VRAM can be used for storing the model and data for the GPU. HBM can be used when fast data transfer between the processor and memory is needed for running the Al. The computer system 600 may further include a video display unit 606 (e.g., a monitor a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 600 also includes an alphanumeric input device 607 (e.g., a keyboard), a user interface (UI) navigation or cursor control device 607 (e.g., a mouse), a disk drive unit 609, a signal generation device 611 (e.g., a speaker) and a network interface device 604.

[0166] The disk drive unit 609 includes a machine-readable medium 610 on which is stored one or more sets of data structures and instructions 613 (e.g., software) embodying or utilized by any one or more of the systems or methodologies or functions described herein. The instructions 613 may also reside, completely or at least partially, within the main memory 602 and / or within the processor 601 during execution thereof by the computer system 600, with the main memory 602 and the processor 601 also constituting machine-readable media.

[0167] While the machine-readable medium 610 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more data structures or instructions 613. The term “machine -readable medium” shall also betaken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the embodiments of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The instructions and / or data in the machine-readable medium can be non-transitory. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine -readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read- Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices such as solid-state drives (SSD); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks

[0168] The instructions 613 may further be transmitted or received over a communications network using a transmission medium. The instructions 613 may be transmitted using the network interface device 604 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

[0169] In a further detailed embodiment, the computer-readable medium instructions are disclosed. The computer-readable medium instructions, when implemented, allow an end user to enter a description of a desired trademark into a web browser or software application with a user interface. The description can include details about the type of goods or services associated with the trademark. The description in an embodiment can relate to a set of words or keywords or phrases to describe a product or service. In another embodiment, the description can relate to a not well- developed description. In a further embodiment, the technical area can be descriptive in text or specific keywords of a distinctive description that can set apart from the existing trademarks can be provided.

[0170] The computer-readable medium further includes an option of allowing the end user to refine the generated trademarks by providing category of trademark and type of trademark via selector lists present in the user interface. For example, in an embodiment, the category of thetrademark defines the goods or services that will be used with the trademark. In an example embodiment such a category may include but not limited to description of the goods or services such as smartwatches, footwear, hats, packaged meals, cosmetics, video game consoles, e-commerce platforms etc.

[0171] The computer-readable medium further comprises an option allowing the user to submit the entered description of the trademark along with the category of trademark and countries of interest and submit via a designated submit button.

[0172] The computer-readable medium comprising a processor and a memory including instructions, further processes the user-provided description of the trademark area to generate a plurality of trademarks. The generation of trademarks is facilitated by a generative artificial intelligence trained on a corpus of information including existing trademarks. The system leverages Natural Language Processing to interpret the user-provided inputs to generate the plurality of trademarks. The generative artificial intelligence may be implemented using neural networks, SVMs, GANs, or rule-based systems.

[0173] The computer-readable medium stores instructions that compares the trademarks generated to existing active trademarks and usage of words on internet web pages to ensure uniqueness. This helps in avoiding trademark infringement issues and ensures the generated trademarks are novel and unique. The computer-readable medium can store instructions including rules about what is the criteria to qualify a trademark and how close a trademark can be to existing trademarks. These rules can be evaluated before displaying trademarks to the user.

[0174] The plurality of trademarks generated by leveraging generative artificial intelligence are presented to the user via a web page or software application. A plurality of trademarks generated are displayed in separate segments of the user interface with user feedback facilities, but not being limited to a first or second set of results. A plurality of graphical indicators is displayed against at least one generated trademark to allow the end user to rate the generated trademark (e.g., based on its practicality and likeliness).

[0175] The computer-readable medium displays a first trademark segment including a set of first graphical indicators, for example, for presenting the practicality and likeliness of the first generated trademark. The computer-readable medium further includes a prompt box for allowing the end user to input further instructions to refine the generated trademark.

[0176] The computer-readable medium displays a second trademark segment presenting another generated trademark different from the first. The second trademark segment includes a second set of graphical indicators, for example, for presenting the practicality and likeliness of the second generated trademark. The computer-readable medium further includes a prompt box for the user to input further instructions to refine the trademark.

[0177] The computer-readable medium allows the user to provide feedback on the generated trademarks via prompt boxes present in the web page. Based on this feedback, the computer-readable medium updates the set of trademarks displayed to the user, refining them in real-time based on user input.

[0178] Further FIG. 19 illustrates a network component according to an example embodiment. The computer network system 700 includes trademark management system and user device or user terminal 705 communicatively coupled via network 704. In an embodiment, trademark management system includes server 701, trademark server 702, and database management server 703, which may be used to manage at least operations database and file server. Trademark management system including the server 701 may be implemented as a distributed system; for example, one or more elements of the trademark management system may be located across a wide-area network (WAN) from other elements of trademark management system. As another example, a server (e.g., web server, file server, database management server) may represent a group of two or more servers, cooperating with each other, provided by way of a pooled, distributed, or redundant computing model. Network 704, may include local-area networks (LAN), wide-area networks (WAN), wireless networks (e.g., cellular network), the Public Switched Telephone Network (PSTN) network, ad hoc networks, personal area networks (e.g., Bluetooth) or other combinations or permutations of network protocols and network types. The network 704 may include a single local area network (LAN) or wide-area network (WAN), or combinations of LAN's or WAN’s, such as the Internet. The various devices / systems coupled to network 704 may be coupled to network 704 via one or more wired or wireless connections.

[0179] Server 701, may communicate with file server to publish or serve files stored on file server. Server 701 may host one or more LLMs that are used to filter trademarks. These LLMs may be trained on a server, then the trained LLM can be stored on the server 701 for use in providing registerable trademarks. Server can include at least one of a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit(IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture for running Al such as an LLM. The server can have memory such as VRAM or HBM to compute Al models such as LLMs. Server 701 may also communicate or interface with the application or trademark server 702 to enable web-based or software applications and presentation of information. For example, trademark server 702 may consist of trademarks in all forms and patterns or library files that provide primary or auxiliary functionality to web server 701 (e.g., multimedia, file transfer, or dynamic interface functions). Applications may include code, which when executed by one or more processors, run the tools of trademark management system. In addition, trademark server 702 may also provide some or the entire interface for web server 701 to communicate with one or more of the other servers in trademark management system (e.g., database management server). The communication can be a LLM RAG retrieval of information on the publication server 702.

[0180] The server 701, either alone or in conjunction with one or more other computers in trademark management system, may provide a user-interface to user terminal or user device 705 for interacting with the tools of trademark management system stored in patent server 702. The user-interface may be implemented using a variety of programming languages or programming methods, such as HTML (HyperText Markup Language), VBScript (Visual Basic® Scripting Edition), JavaScript™, XML® (Extensible Markup Language), XST™ (Extensible Stylesheet Language Transformations), AJAX (Asynchronous JavaScript and XML), Java™, JFC (Java™ Foundation Classes), and Swing Application Programming Interface for Java™).

[0181] User terminal or user device 705 may be a personal computer or mobile device. In an embodiment, user device 705 includes a program to interface with trademark management system. The program may include commercial software, custom software, open-source software, freeware, shareware, or other types of software packages. In an embodiment, the client program includes a thin client designed to provide query and data manipulation tools for a user of user device 705. The program may interact with a server program hosted by, for example, trademark server 702. Additionally, the program may interface with database management server 703.Freedom to Operate Technical Concept Embodiment

[0182] In accordance with yet another aspect of the present disclosure, a user interface of a system for generating plurality of technical concepts that are not protected by a patent claim(s) 800 is disclosed in FIG. 21. In some embodiments, a user interface of a system for determining whethera plurality of technical concepts are free to use and are not protected by a patent claim is disclosed in FIG. 3 by entering a description in prompt 101. The system 800 is configured to include a web browser or software application with a user interface for allowing an end user to enter descriptions801 of a technical area in which the user is seeking to generate a plurality of technical concepts that are not protected by a patent claim(s) or otherwise not infringeable technical concepts. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a system or device or apparatus or hardware or software. In another embodiment, the technical area can relate to a not well -developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided.

[0183] In specific embodiments followed hereby, the technical concept generated relates to a set of or plurality of concepts that are specifically optimised by the system to be non-infringeable concepts.

[0184] The user interface of the system in accordance with the present disclosure further includes an option of allowing the end user to further refine the generated set of non-infringeable technical concepts by providing a main category related to entered description and a sub-category for selecting countries with active patents relating to the field of the technical area via a selector lists802 and 803 present in the user interface. The system in some embodiments of the present disclosure, is configured to enable various main categories including but not limited to electronics, computer science, biotechnology, artificial intelligence, block chain, chemistry, mechanical, life sciences. The system in some embodiments of the present disclosure, is configured to enable various sub categories including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics, environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology. The selector lists 802 and 803 can also be text fillable fields, where you start to type text and options appear for selection.

[0185] The user interface of the system 800 in accordance with the present disclosure, further comprises an option allowing the user to submit the entered description of the technical area along with the main and sub category via a designated submit button 804.

[0186] The system 800 comprising a processor and a memory including instructions, further processes the user provided description of the technical area to identify relevant art by filtering against active patent claims in a database of publications. In an embodiment the relevant state of artidentified from free and / or paid databases that include patent information. In a further embodiment, the system also processes the user provided description of the technical area to identify relevant art by filtering against lapsed and expired patent claims. The filtering can be performed by an LLM as described herein.

[0187] The system in accordance with the present disclosure further processes to compare the to be generated non-infringeable technical concepts with active patent claims from the databases. In a specific embodiment the comparison includes mapping the technical concepts that are to be generated based on the user description of the technical area against the concepts present in the identified patent claims and the system further filters out the technical concepts that are protected by patent claim(s). The mapping can occur before the filtering to help with the filtering process.

[0188] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents, abandoned patents, lapsed patents and / or scientific literature. The system can further determine the likelihood of infringement of the to be generated technical concepts against the active pending and granted patent claims in force through specialized claim analysis and comparison methods by comparing the category of patents against the provided technical area description to generate technical concepts that are not infringeable. The filtering can be trained to perform this claim analysis by comparing the technical concept against one or more claims. The filtering can also utilize RAG techniques to search the database of claims efficiently. In some embodiments, the LLM can be trained to interpret and analyze claim elements to determine the meaning of certain claim elements for the filtering process, by implementing specialized semantic analysis methods that improve processing efficiency.

[0189] In an embodiment, the generation of the technical concepts is implemented by a generative artificial intelligence trained on a diverse corpus of information including publications, which further include patent publications. The system leverages Natural Language processing to interpret the user provided inputs to generate the plurality of technical concepts. The generative artificial intelligence may be any one of but not limited to Neural networks, S VMs, GANs or Rule- based systems.

[0190] In some embodiments of the present disclosure, the system receives the technical area input, it first processes this input to make it usable for further analysis. Natural Language Processing (NLP) techniques like tokenization, stemming, and lemmatization are used to break down and understand text. Further, embedding techniques (like word2vec, GloVe, or BERT) convert words orsentences into numerical vectors that capture semantic meaning. Using the model prediction, the extracted features, the Al modelled system makes predictions based on the task it was trained to perform. The systems might combine multiple predictions or use additional logic to make a final decision and simple decision-making based on predefined rules. Combining predictions from multiple models to improve accuracy. Output generation finally, the Al system generates an output based on the predictions and decisions made.

[0191] In some embodiments, the technical concepts generated in accordance with the system as per the present disclosure are compared with concepts protected by patent claim(s) and accordingly the system generates technical concepts which are not infringeable. The generative artificial intelligence is trained to understand the user entered description and compare it with active patents and only generate technical concepts that not protected by any patent claim(s) or from patents are no more legally in force.

[0192] In a further, detailed embodiment the system is trained using NLP techniques to understand and process technical descriptions, patent claims, and legal language. For example, the system utilizes the pre-trained language models (e.g., BERT, GPT) fine-tuned on patent text to comprehend the specifics of patent claims and technical jargon. When the user enters a technical description, the system parses and understands the input using NLP techniques.

[0193] The system extracts key features and concepts from the user's description, such as specific technical elements, processes, and innovations and uses algorithms to compare the extracted features of the user’s description of the technical area with those in the patent database. In an embodiment, techniques such as cosine similarity, word embeddings, or more advanced methods can be employed to measure semantic similarity. In some embodiments, the user’s description is compared against the claims of active patents to identify potential infringements. Patents considered in the comparison are active and legally enforceable.

[0194] Further, the system uses the trained generative model Al to create new technical concepts based on user input while avoiding overlap with existing patent claims. In order to avoid infringement, a filtering mechanism is integrated that cross-references generated concepts with the patent database to ensure they do not infringe on any active patents. This in an embodiment may include adjusting the generated concepts to steer clear of patented claims or suggesting alternative approaches or modifications that achieve similar goals without infringement.

[0195] The user is finally presented with a list of refined, unique and non-infringeable technical concepts that are not protected by any existing patent claims. The system in accordance with the present disclosure is configured to effectively generate a plurality of innovative technical concepts while avoiding patent infringement.

[0196] The plurality of technical concepts generated by leveraging trained generative artificial intelligence are presented to the user via a user interface 105 of the system 100. The technical concepts generated are displayed in separate in a plurality of segments for example not limited to 106 and 107 of the web page 105 with user feedback facilities. A plurality of graphical indicators is used against at least one generated technical concept for allowing the end user to rate the generated technical concepts (e.g., based on its practicality and desirability).

[0197] The user can evaluate the generated technical concept, for example, as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. Similarly, the user evaluates the generated technical concept, for example, as impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms.

[0198] The user also can evaluate the generated technical concept, for example, based on the his / her desirability to perform the generated concept. The user may take into account several factors such as how appealing or attractive or technically feasible the concept is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to functionality, usability, perceived value, innovation and novelty, potential impact, viability, performance improvement. A highly desirable technical concept is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0199] Referring to system 800, it is shown that a first technical concept 806 includes a set of first graphical indicators, for example, for presenting the practicality 806(a) and desirability 806(b) of the first generated technical concept. It also configured to further include a prompt box 806(c). The prompt box has been specifically configured for the end user to input further instructions to refine the generated technical concept. As shown in FIG. 21 , different graphical indicators can be used to receive feedback from the user. For example, a sliding bar can be used 806 a, b, a set of circles 807 a, b, a set of stars, or any other feedback graphical indicator can be used.

[0200] Referring to an example embodiment of the system 800, it is shown that a second technical concept 807, presents another generated technical concept that is different from the first technical concept. The second technical concept 807 also includes a second set of graphical indicators, for example, for presenting the practicality 807(a) and desirability 807(b) of the second generated technical concept. It also includes a prompt box 807(c) for the user to input further instructions to refine the generated technical concept.

[0201] In some embodiments of the present disclosure the system is configured to display a plurality of technical concept segment not limited to first and second technical concepts. All plurality set of technical concepts generated are provided with indicators for presenting the practicality and desirability of the generated technical concept.

[0202] The plurality of technical concepts can be modified based on the rating provided for its practicality and desirability. The user interface displaying the set of results 805 can be changed in real time obtain more refined results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes 806(c) and 807(c).

[0203] In a preferred embodiment the prompt boxes 806(c) and 807(c) allow the user to provide real-time feedback and thus allowing refinement of results displayed. The end users can provide additional instructions or keywords in the prompt box, allowing the system to instantly refine and update the results based on this new input. Further, the end users can engage in an iterative process, gradually refining their search criteria until they achieve the desired results.

[0204] The system is made user friendly and more flexible allowing the users gain more control over the searching and refinement process, specifying exactly what they want to see. This is particularly useful as the user can tailor the results to their specific needs, adding constraints or preferences directly into the prompt box.

[0205] The prompt boxes aid in increasing efficiency of the system 800. By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The system in accordance with the present disclosure, can filter out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the web browser provides a powerful interactive tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0206] In accordance with the present disclosure, in FIG. 14, a method 500 for generating a plurality of non-infringeable technical concepts that are not protected by patent claims. The method includes allowing an end user to enter description 501 into a web browser or software application with a user interface, relating to a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions that are not protected by a patent claim(s) or otherwise not infringeable technical concepts. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a method or device or apparatus or hardware or software. In another embodiment, the technical area can relate to a not well-developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided.

[0207] In specific embodiments followed hereby, the technical concept generated relates to a set of or plurality of concepts that are specifically optimised by the method to be non-infringeable concepts.

[0208] The method 500 in accordance with the present disclosure enables the end user in further refining the generated set of technical concepts by providing a main category 502 related to entered description and a category for selecting countries with active patents relating to the field of the technical area via a selector lists present in the user interface. The method 500, further allows the user to submit the entered description of the technical area along with the main and sub category via a designated submit button 504. The method in some embodiments of the present disclosure, is configured to enable various sub categories 503 including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics, environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology.

[0209] The method 500 in accordance with the present disclosure, comprises identifying relevant arts based on the user provided description of the technical area and generating the identified technical concepts 505, by filtering against active patent claims 507 in a database of publications. The filtering can be performed by an LLM. The filtering can include RAG retrieval of a database of publications. In an embodiment, the relevant state of art identified from free and / or paid databases that include patent information. Free databases may include websites or databases associated with foreign and domestic patent offices, assignment databases, WIPO, and INPADOC. In various embodiments, the data is scraped and parsed from the websites if it is unavailable through a database.

[0210] The method 500 in accordance with the present disclosure, further comprises a step of comparing the to be generated technical concepts with active patent claims from the databases. In a specific embodiment the comparison includes mapping the technical concepts that are to be generated based on the user description of the technical area against the concepts present in the identified patents 506 and the system further filters 507 out the concepts that are protected by patent claim(s). The filtering can be any such filtering as described herein.

[0211] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents, abandoned patents, and / or scientific literature.

[0212] The method comprises of determining the likelihood of infringement of the to be generated technical concepts against the active pending and granted patent claims in force and compare this category of patents against the provided technical area description to generate technical concepts that are not infringeable.

[0213] In an embodiment, the method of the generation of the technical concepts 505 is implemented by a generative artificial intelligence trained on a diverse corpus of information including publications. The system leverages Natural Language processing to interpret the user provided inputs to generate the plurality of technical concepts. The generative artificial intelligence may be any one of but not limited to Neural networks, SVMs, GANs or Rule-based systems.

[0214] In some embodiments of the present disclosure, the method comprises of a step of receiving the technical area input, processing this input to make it usable for further analysis. Using Natural Language Processing (NLP) techniques like tokenization, stemming, and lemmatization to break down and understand text. Further, embedding techniques (like word2vec, GloVe, or BERT) convert words or sentences into numerical vectors that capture semantic meaning. Using the model prediction, the extracted features, the Al modelled system makes predictions based on the task it was trained to perform. The method might combine multiple predictions or use additional logic to make a final decision and simple decision-making based on predefined rules. Combining predictions from multiple models to improve accuracy. Finally, the method generates an output based on the predictions and decisions made.

[0215] The method 500 comprises a step of comparing the to be generated technical concepts with concepts protected by patent claim(s) and accordingly generate technical concepts which are not infringeable. The generative artificial intelligence is trained to understand the user entereddescription and compare it with active patents and only generate technical concepts that not protected by any patent claim(s) or from patents are no more legally in force.

[0216] The method 500 comprises a step of generating a plurality of technical concepts 508 by leveraging trained generative artificial intelligence and subsequently displaying it to the user via a user interface of a web browser or a software application. The technical concepts generated are displayed 508 in separate in a plurality of segments, for example, in a web page or software application with user feedback facilities 509. A plurality of graphical indicators is provided against at least one generated technical concept for allowing the end user to rate the generated technical concepts (e.g., based on its practicality and desirability).

[0217] The method allows the user to evaluate the generated technical concept, for example, as practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. Similarly, the user is allowed to evaluate the generated technical concept, for example, as impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms.

[0218] The method also allows the user to evaluate the generated technical concept, for example, based on the his / her desirability to perform the generated concept. The user may take into account several factors such as how appealing or attractive or technically feasible the concept is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to functionality, usability, perceived value, innovation and novelty, potential impact, viability, performance improvement. A highly desirable technical concept is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0219] The method in accordance with the present disclosure comprises a step of generating a first technical concept segment that includes a set of first graphical indicators for presenting, for example, the practicality and desirability of the first generated technical concept. The method also includes a step entering instructions from the end user into a first prompt box for further aiding in refining the generated technical concept.

[0220] The method in accordance with the present disclosure comprises of generating a second technical concept segment that includes a set of second graphical indicators for presenting the practicality and desirability of the second generated technical concept. The method also includesentering instructions from the end user into a second prompt box for further aiding in refining the generated technical concept.

[0221] The method comprises of a step of modifying the plurality of non-infringeable technical concepts generated based on the rating provided by the user for its practicality and desirability 509. The web browser displaying the set of updated results 510 can be changed in real time obtain more refined results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes.

[0222] The prompt boxes aid in increasing efficiency of the method. By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The method enables filtering out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the web browser provides a powerful interactive tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0223] In some embodiment, a method 500 for displaying at least one non-infringeable technical concept is illustrated in FIG. 17. The method comprises of allowing an end user to enter a description 501 into a web browser or software application within a user interface, relating to a technical area in which the user is seeking to generate a plurality of technical concepts or unique solutions that are not protected by a patent claim(s) which otherwise is free to be used. The generative Al works on diverse corpus of information to generate new technical concepts 505. Further, the generated technical concepts are displayed 508 as a plurality of technical concepts in a web browser or software application within the user interface.

[0224] In certain embodiments, a method 500 for displaying at least one free to use technical concept is illustrated in FIG. 24. The method comprises allowing an end user to enter a description 501 into a web browser or software application within a user interface. The user can enter as much description about the idea as they would like. This description can then be analyzed by an LLM to identify the technical concepts 512 present in the description. The technical concepts in the description can then be mapped to filtered as described herein to determine if they are non-infringing and can be used freely. The filtering can include mapping technical concepts to technical concepts in publications or to claims in active patent applications. Further, the results of the filtering aredisplayed 515 showing that the technical concept is clear or if selected a mapping to of the technical concept to one or more patent claims in a web browser or software application within the user interface.

[0225] In accordance with the present disclosure a computer readable medium with instructions for evaluating a plurality of generated technical concept(s) against active patents is disclosed. The computer readable medium instructions when implemented allows an end user to enter description of a technical area in which the user is seeking to generate a at least one technical concept or unique solutions that are not protected by a patent claim(s) or otherwise not infringeable technical concepts is presented into a web browser or software application with a user interface. The technical area in an embodiment can relate to issues or problems that affect functionality, performance or the like of a computer readable medium or device or apparatus or hardware or software. In another embodiment, the technical area can relate to a not well-developed solution to an existing problem. In a further embodiment, the technical area can be descriptive in text or specific keywords of the technical area can be provided.

[0226] In a further embodiment, a user-interface for displaying a plurality of technical concepts that are not protected by a patent is disclosed. The user interface comprises a text box in a web browser for receiving a description of a technical area. The user interface can include a selector for receiving a main category of the technical area. The user interface comprises of a second selector for receiving a sub category of the technical area. The user interface comprises of a third selector for receiving the countries of interest. Further the user interface comprises of a submit button to submit the input provided. The selection of countries can include options of selecting certain pre-selected countries, such as top 3 countries, top 10 countries, or nearly all countries for example.

[0227] Upon submission of input, the user interface displays in the web browser of software application a plurality of non-infringeable technical concepts that are generated. Further the generated results are provided with a user feedback facility including rating meters. Upon receiving in the web browser feedback rating based on at least one of the technical concepts, the user interface displays in the web browser or software application an updated set of non-infringeable technical concepts generated in response to the feedback.Example Machine and Computer readable Medium

[0228] FIG. 18 is a block diagram of machine in the example form of a computer system 600 within which instructions for causing the machine to perform any one or more of themethodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines.

[0229] In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set- top box (STB), a PDA, a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0230] The computer readable medium is disclosed. The example computer system 600 includes a processor 601 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit (IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture), a main memory 602 and a static memory 603, which communicate with each other via a bus 612. The memory can include random access memory (RAM), video random access memory (VRAM), and / or high-bandwidth memory (HBM). VRAM can be used for storing the model and data for the GPU. HBM can be used when fast data transfer between the processor and memory is needed for running the Al. The computer system 600 may further include a video display unit 606 (e.g., a monitor such as a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 600 also includes an alphanumeric input device 607 (e.g., a keyboard), a user interface (UI) navigation or cursor control device 607 (e.g., a mouse), a disk drive unit 609, a signal generation device 611 (e.g., a speaker) and a network interface device 604.

[0231] The disk drive unit 609 includes a machine-readable medium 610 on which is stored one or more sets of data structures and instructions 613 (e.g., software) embodying or utilized by any one or more of the systems or methodologies or functions described herein. The instructions 613 may also reside, completely or at least partially, within the main memory 602 and / or within the processor 601 during execution thereof by the computer system 600, with the main memory 602 and the processor 601 also constituting machine-readable media.

[0232] While the machine-readable medium 610 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiplemedia (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more data structures or instructions 613. The term “machine -readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the embodiments of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The machine-readable medium can store instructions and / or data in non-transitory manner. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0233] The instructions 613 may further be transmitted or received over a communications network using a transmission medium. The instructions 613 may be transmitted using the network interface device 604 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

[0234] In accordance with the present disclosure the computer readable medium instructions, when implemented allows the end user to further refine the generated set of technical concepts by providing a main category related to entered description and a sub-category for selecting countries with active patents relating to the field of the technical area via a selector lists and present in the user interface. The computer readable medium in some embodiments of the present disclosure, is configured to enable various main categories including but not limited to electronics, computer science, biotechnology, artificial intelligence, block chain, chemistry, mechanical, life sciences. The computer readable medium in some embodiments of the present disclosure, is configured to enable various sub categories including but not limited to semiconductor devices, communication systems, software engineering, algorithms and data structures, computer networks, genomics, bioinformatics,environmental biotechnology, machine learning, robotics, biochemistry, thermodynamics, material science, neuroscience, and immunology.

[0235] The computer readable medium in accordance with the present disclosure, further comprises an option allowing the user to submit the entered description of the technical area along with the main and sub category via a designated submit button.

[0236] The computer readable medium comprising a processor and a memory including instructions, further processes the user provided description of the technical area to identify relevant art by filtering against active patent claims in a database of publications. In an embodiment the relevant state of art identified from free and / or paid databases that include patent information.

[0237] The computer readable medium in accordance with the present disclosure compares the to be generated technical concepts with active patents from the databases. In a specific embodiment the comparison includes mapping the technical concepts that are to be generated based on the user description of the technical area against the concepts present in the identified patents and further filters out the concepts that are protected by patent claim(s).

[0238] In one embodiment, the database of publications may include published patents, granted patents, expired patents, withdrawn patents abandoned patents, and / or scientific publications.

[0239] In an embodiment, the computer readable medium enables to determine the likelihood of infringement of the to be generated technical concepts against the active pending and granted patent claims in force and compare this category of patents against the provided technical area description to generate technical concepts that are not infringeable.

[0240] In an embodiment, the generation of the technical concepts is implemented by a generative artificial intelligence trained on a diverse corpus of information including publications. The system leverages Natural Language processing to interpret the user provided inputs to generate the plurality of technical concepts. The generative artificial intelligence may be any one of but not limited to Neural networks, SVMs, GANs or Rule-based systems.

[0241] In some embodiments of the present disclosure, computer readable medium receives the technical area input, it first processes this input to make it usable for further analysis. Natural Language Processing (NLP) techniques like tokenization, stemming, and lemmatization are used tobreak down and understand text. Further, embedding techniques (like word2vec, GloVe, or BERT) convert words or sentences into numerical vectors that capture semantic meaning. Using the model prediction, the extracted features, and the Al model makes predictions based on the task it was trained to perform. The computer readable medium combines multiple predictions or uses additional logic to make a final decision and simple decision-making based on predefined rules. Combining predictions from multiple models to improve accuracy. Output generation finally, the Al model generates an output based on the predictions and decisions made.

[0242] In some embodiments, computer readable medium compares technical concept to be generated with concepts protected by patent claim(s) and generate technical concepts which are not infringeable. The regenerative artificial intelligence is trained to understand the user entered description and compare it with active patents and only generate technical concepts that not protected by any patent claim(s) or from patents are no more legally in force.

[0243] The computer readable medium allows to present the user with a web page or software application displaying the plurality of technical concepts generated. The technical concepts generated are displayed in separate in a plurality of segments for example not limited to and of the web page or software application with user feedback facilities. A plurality of graphical indicators is displayed against at least one generated technical concept for allowing the end user to rate the generated technical concept(s) (e.g., based on its practicality and desirability).

[0244] The computer readable medium allows the user to evaluate the generated technical concept as, for example, practical, whether it can be successfully put into practice, considering all relevant practicalities and constraints that may affect its implementation and effectiveness. Similarly, the user is allowed to evaluate the generated technical concept as, for example, impractical, by its inability to be effectively implemented or executed due to inherent limitations or challenges that make its realization imprudent or unachievable in practical terms.

[0245] The computer readable medium also allows the user to evaluate the generated technical concept based on, for example, his / her desirability to perform the generated concept. The user may take into account several factors such as how appealing or attractive or technically feasible the concept is to the intended user and the end consumer at large. This desirability can be influenced by various factors, including but not limited to functionality, usability, perceived value, innovation and novelty, potential impact, viability, performance improvement. A highly desirable technicalconcept is intended to score well across these dimensions, indicating strong potential for successful adoption and implementation.

[0246] The computer readable medium displays a first technical concept segment including a set of graphical indicators for presenting, for example, the practicality and desirability of the first generated technical concept. The computer readable medium to further instructed to include a prompt box for allowing the end user to input instructions to further refine the generated technical concept. The graphical indicators can be slider bars, circle selectors, or any other type of graphical indicators that allows for selection of different options.

[0247] The computer readable medium displays a second technical concept segment including a set of graphical indicators for presenting, for example, the practicality and desirability of the second generated technical concept. The computer readable medium to further includes a prompt box for allowing the end user to input instructions to further refine the generated technical concept.

[0248] The computer readable medium can modify the plurality of technical concepts based on the feedback provided by the user (e.g., for its practicality and desirability). The web browser or software application displaying the set of results can be changed in real time obtain more refined results. The generated technical concepts in an embodiment can further be made comprehensive in nature by using the prompt boxes.

[0249] The computer readable medium can display prompt boxes that aid in increasing efficiency of the search. By refining results in real-time, users save time they would otherwise spend navigating through impractical set of results, which are not desirable. The computer readable medium in accordance with the present disclosure is instructed to filter out unwanted information, presenting a more focused and relevant set of results to be generated and displayed. The prompt box makes the search process more interactive and engaging, encouraging users to experiment with different instructions. By implementing the additional prompt box in the user interface provides a powerful interactive tool for users to refine and customize search results in real-time, leading to more efficient, relevant, and satisfying search experiences.

[0250] Further FIG. 19 illustrates a network component according to an example embodiment. The computer network system 700 includes patent management system and user device or user terminal 705 communicatively coupled via network 704. In an embodiment, patent management system includes server 701, patent server 702, and database management server 703, which may be used to manage at least operations database and file server. The patent managementsystem including the web server 701 may be implemented as a distributed system; for example, one or more elements of the patent management system may be located across a wide-area network (WAN) from other elements of patent management system. As another example, a server (e.g., web server, file server, database management server) may represent a group of two or more servers, cooperating with each other, provided by way of a pooled, distributed, or redundant computing model.

[0251] Network 704, may include local-area networks (LAN), wide-area networks (WAN), wireless networks (e.g., cellular network), the Public Switched Telephone Network (PSTN) network, ad hoc networks, personal area networks (e.g., Bluetooth) or other combinations or permutations of network protocols and network types. The network 704 may include a single local area network (LAN) or wide-area network (WAN), or combinations of LAN's or WAN's, such as the Internet. The various devices / systems coupled to network 704 may be coupled to network 704 via one or more wired or wireless connections.

[0252] Server 701, may communicate with file server to publish or serve files stored on file server. Server 701 may host one or more LLMs that are used to filter and map technical concepts. These LLMs may be trained on a server, then the trained LLM can be stored on the server 701 for use in evaluating technical concepts. Server can include at least one of a central processing unit (CPU), a graphics processing unit (GPU) or both, a Neural Processing Unit (NPU), an Intelligence Processing Unit (IPU), Tensor Processing Unit (TPU), a Field Programmable Gate Array (FPGA), or a neural network architecture for running Al such as an LLM. The server can have memory such as VRAM or HBM to compute Al models such as LLMs. Server 701 may also communicate or interface with the application or patent server 702 to enable web-based applications and presentation of information. For example, patent server 702 may consist of scripts, patent applications, scientific publications, or library files that provide primary or auxiliary functionality to web server 701 (e.g., multimedia, file transfer, or dynamic interface functions). Applications may include code, which when executed by one or more processors, run the tools of patent management system. In addition, patent server 702 may also provide some or the entire interface for web server 701 to communicate with one or more of the other servers in patent management system (e.g., database management server).

[0253] The server 701, either alone or in conjunction with one or more other computers in patent management system, may provide a user-interface to user terminal or user device 705 for interacting with the tools of patent management system stored in patent server 702. The user-interface may be implemented using a variety of programming languages or programming methods, such as HTML (HyperText Markup Language), VBScript (Visual Basic® Scripting Edition), JavaScript™, XML® (Extensible Markup Language), XST™ (Extensible Stylesheet Language Transformations), AJAX (Asynchronous JavaScript and XML), Java™, JFC (Java™ Foundation Classes), and Swing Application Programming Interface for Java™).

[0254] User terminal or user device 705 may be a personal computer or mobile device. In an embodiment, user device 705 includes a program to interface with patent management system. The program may include commercial software, custom software, open-source software, freeware, shareware, or other types of software packages. In an embodiment, the client program includes a thin client designed to provide query and data manipulation tools for a user of user device 705. The program may interact with a server program hosted by, for example, patent server 702. Additionally, the program may interface with database management server 703.

[0255] The inventors of the present disclosure have optimised the system and methods to generate unique technical concepts, technical concepts that are not infringeable and distinct trademarks, using artificial intelligence. The embodiments in accordance with some aspects of the disclosure have automated the complex process of creating novel concepts, distinct trademarks, by saving time and resources. The system(s) developed by the inventors specifically, produce a diverse range of unique concepts, non-infringeable concepts and trademarks that may not be conceived through manual brainstorming.

[0256] The described system(s), method and computer readable medium harnesses the power of generative Al and NLP to streamline the creation of unique concepts, non-infringeable concepts and distinct trademarks. By processing user-provided descriptions and generating multiple unique concepts, non-infringeable technical concepts and distinct trademark, it provides an innovative solution to the challenge of novel concept and trademark creation, combining efficiency, creativity, and accuracy.

[0257] A method 900 for determining whether something is patentable, trademarkable, or free to use is illustrated in FIG. 25. A description is entered into the user interface and is received 901 by the system. The description can be a technical concept or a trademark. The description can then be inputted into an LLM for filtering 902 as described in this disclosure. The filtering by the LLM can determine whether the technical concept is patentable, the technical concept is non-infringing, or whether the trademark is registerable and provide these results 903. The results can be provided in the form of a report and can be provided on a video display 606 or paper.

[0258] A method 900 for determining whether something is patentable, trademarkable, or free to use is illustrated in FIG. 26. A description is entered into the user interface and is received 901 by the system. The description can be information that includes one or more technical concepts or trademarks. The description can include text, illustrations, and audio information. The illustration can include design documents such as specifications, requirements, blueprints, designs, pictures, computer aided design (CAD) drawings, and / or drawings. The description can then be inputted into an LLM for augmenting 904 where the LLM expands upon the description and provides more details. For example, for a technical concept, the augmenting can include providing ways that one may actually do the thing that is being proposed or add additional technical concepts to the description provided in the user interface or even illustrations of how to make the desired thing or object. The augmentation can follow a user inputted form with fields where different information is generated into the fields based on the initial description provided into the user into the user interface. The augmenting process can include interaction between the user and the LLM in the user interface to develop the technical concept with feedback. The augmenting process can also take in a description and output design documents such as specifications, requirements, blueprints, designs, pictures, computer aided design (CAD) drawings, and / or drawings. These design documents can be generated by a generative image artificial intelligence model. These can then be used by a user to design and build a physical prototype or a product. Tn some embodiments, the product can be improved using method 900 by getting improvement ideas from the LLM. The user can then change the product accordingly.

[0259] Once the technical concept or trademark is ready the system can filter 902 as described in this disclosure. The filtering by the LLM can determine whether the technical concept is patentable, the technical concept is non-infringing, or whether the trademark is registerable and provide these results 903. The results can be provided in the form of a report and can be provided on a video display 606 or paper. The user can then provide feedback 905 as described in the disclosure to the results in the user interface. The feedback can include a chat interface with the LLM, where the user can enter prompts and the LLM responds. In this way, the user can chat with the LLM to provide feedback or further develop the technical concept. The system can then use the feedback to adjust the results and can provide the updated results 906. The updated results can be provided in a report that is displayed or printer on paper.

[0260] As used herein the phrase “technical concept” or “scientific concept” or “engineering principle” or “unique concept” or “novel ideas” can be used interchangeably, refers to an innovative idea or approach that represents a departure from existing methods or technologies. Specifically, it may be referred as a novel idea or methodology within engineering that introduces a distinct approach to solving a problem or achieving a specific goal. It typically integrates cutting-edge principles, technologies, or interdisciplinary insights to create innovative solutions that surpass traditional methods in efficiency, effectiveness, or scope. Such concepts not only address current challenges but also inspire future developments and applications across various industries and disciplines.

[0261] As used herein the phrase “technical area” or “description input” can be used interchangeably, refer to solutions that are underdeveloped or not yet fully realized in addressing existing problems. This encompasses areas where current methods or technologies are inadequate, offering scope for improvement and innovation.

[0262] As used herein the phrase “practical” refers to the realistic application and execution of an idea, solution, or concept within existing constraints, resources, and operational frameworks. A practical idea or concept is one that can be implemented effectively, efficiently, and sustainably to achieve its intended objectives. Factors such as cost-effectiveness, scalability, technical feasibility, regulatory compliance, and societal acceptance are crucial in determining the practicality of an idea. A practical solution addresses practical considerations and is viable for deployment or integration into current systems or environments without excessive risk or disruption.

[0263] As used herein the phrase “impractical” refers to an idea, concept, or proposal that is not feasible or suitable for practical implementation due to various factors such as technical limitations, high costs, resource constraints, or regulatory hurdles. An impractical idea or concept may lack a clear path to execution, require disproportionate resources relative to its benefits, or face significant barriers that prevent its successful implementation. It often does not align with existing capabilities, operational frameworks, or societal norms, making its realization unlikely or imprudent. Evaluating an idea as impractical involves considering its inability to achieve desired outcomes within reasonable constraints and the potential risks or inefficiencies associated with attempting to implement it.

[0264] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, for example, the singular forms “a,” “an,”and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “contains”, “containing”, “includes”, “including,” “comprises”, and / or “comprising,” and variations thereof, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0265] Terms of orientation are used herein merely for purposes of convention and referencing and are not to be construed as limiting. However, it is recognized these terms could be used with reference to an operator or user. Accordingly, no limitations are implied or to be inferred. In addition, the use of ordinal numbers (e.g., first, second, third, etc.) is for distinction and not counting. For example, the use of “third” does not imply there must he a corresponding “first” or “second.” Also, if used herein, the terms “coupled” or “coupled to” or “connected” or “connected to” or “attached” or “attached to” may indicate establishing either a direct or indirect connection, and is not limited to either unless expressly referenced as such.

[0266] The words and phrases used herein should be understood and interpreted to have a meaning consistent with the understanding of those words and phrases by those skilled in the relevant art. No special definition of a term or phrase, i.e., a definition that is different from the ordinary and customary meaning as understood by those skilled in the art, is intended to be implied by consistent usage of the term or phrase herein. To the extent that a term or phrase is intended to have a special meaning, i.e., a meaning other than the broadest meaning understood by skilled artisans, such a special or clarifying definition will be expressly set forth in the specification in a definitional manner that provides the special or clarifying definition for the term or phrase. It must also be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless otherwise specified.

[0267] For example, the following discussion contains a non-exhaustive list of definitions of several specific terms used in this disclosure (other terms may be defined or clarified in a definitional manner elsewhere herein). These definitions are intended to clarify the meanings of the terms used herein. It is believed that the terms are used in a manner consistent with their ordinary meaning, but the definitions are nonetheless specified here for clarity.

[0268] As used in this specification and the claims, the terms “comprising,” “containing,” or “including” mean that at least the named compound, element, material, particle, or method step ispresent in the composition, the article, or the method, but does not exclude the presence of other compounds, elements, materials, particles, or method steps even if the other such compounds, elements, materials, particles, or method steps have the same function as that which is named, unless expressly excluded in the claims. It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps before or after the combined recited steps or intervening method steps between those steps expressly identified.

[0269] Moreover, it is also to be understood that the lettering of process steps or ingredients is for identifying discrete activities or ingredients and the recited lettering can be arranged in any sequence, unless expressly indicated.

[0270] For the purpose of the present description and of the claims which follow, except where otherwise indicated, numbers expressing amounts, quantities, percentages, and so forth, are to be understood as being modified by the term “about”. Also, ranges include any combination of the maximum and minimum points disclosed and include any intermediate ranges therein, which may or may not be specifically enumerated herein.

[0271] While the disclosure has described several exemplary embodiments, it will be understood by those skilled in the art that various changes can be made, and equivalents can be substituted for elements thereof, without departing from the spirit and scope of this disclosure. In addition, many modifications will be appreciated by those skilled in the art to adapt a particular instrument, situation, or material to embodiments of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the disclosure not be limited to the particular embodiments disclosed, or to the best mode contemplated for carrying out this technology, but that the disclosure will include all embodiments falling within the scope of the appended claims. Moreover, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, or component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative.

[0272] The various embodiments described throughout provide several technical effects and advantages that represent a technical contribution, including but not limited to:

[0273] the implementation specialized vector similarity methods and neural network architectures that enable more efficient processing of large volumes of patent and trademark data than conventional database approaches. This technical implementation significantly reduces processing time and computational resources required for analyzing intellectual property data.

[0274] a technical architecture for filtering and mapping technical concepts which provides a measurable improvement in accuracy compared to conventional keyword-based search approaches. The specialized natural language processing techniques enable the system to understand semantic relationships that would be missed by conventional text matching algorithms.

[0275] a technical implementation of the user interface with real-time feedback mechanisms enabling an interactive process that dynamically adjusts search parameters, providing a technical solution to the problem of efficiently navigating large intellectual property databases. This represents a technical improvement in human-computer interaction specific to intellectual property analysis.

[0276] a technical approach for decomposing publications into discrete technical concepts for mapping and comparison represents a novel technical solution to the problem of determining novelty and non-infringement. This approach enables more granular and accurate analysis than conventional document-level comparison techniques.

[0277] specialized retrieval-augmented generation (RAG) technology implemented in the system which provides a technical solution to the problem of maintaining up-to-date information in the analysis process. This technical approach enables more accurate results by incorporating the latest published information through specialized indexing and retrieval algorithms.

[0278] a technical implementation of country-specific rule engines for trademark analysis provides an automated solution to the complex problem of determining registrability across multiple jurisdictions. This represents a technical improvement over manual analysis methods by applying specialized algorithms to process regional legal requirements.

[0279] a technical architecture for the distributed system with specialized load balancing and fault tolerance mechanisms enabling reliable processing of large-scale intellectual propertyanalysis tasks. This technical implementation ensures consistent performance even under high demand conditions.

[0280] specialized data structures and indexing techniques for storing and retrieving intellectual property information represent a technical solution to the problem of efficiently managing and accessing large volumes of complex legal and technical data.

Claims

1. CLAIMS im:

1. A system for generating at least one technical concept, the system comprising: at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receive a description of a technical area in a user interface; and display in the user interface at least one technical concept that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

2. The system of claim 1 further comprising processing the description using a neural network architecture to generate vector representations of technical concepts.

3. The system of claim 1 or claim 2, further comprising identifying publications, including patents, in a database of publications based on the description.

4. The system of claims 2, wherein the comparison includes mapping the technical concepts that are generated against the concepts in the publications identified and filtering out concepts that are present in the publications.

5. The system of claim 3, wherein the comparison comprises filtering the generated technical concepts by implementing a mapping algorithm that compares the vector representations against the publications identified, wherein the mapping algorithm decomposes the publications into discrete technical concepts for comparison.

6. The system of any previous claim, wherein the publications include non-patent literature, published patents, granted patent, expired patents, withdrawn patents and abandoned patents.

7. The system of any previous claim, further comprising receiving, in the user interface, a feedback rating based on at least one of the technical concepts; and,displaying in the user interface an updated set of technical concepts generated in response to the feedback.

8. The system of claim 7, wherein the feedback rating is received by displaying, in the user interface, a graphical indicator to rate the technical concept based on its practicality and desirability of the technical concept.

9. The system of any previous claim, further comprising instructions to determine a likelihood of infringement of the to be generated scientific concept(s) against the pending and granted patent claims in force.

10. The system of any previous claim, further comprising displaying technical concept(s) of patents that have expired, been withdrawn, been abandoned, and which are not legally in force.

11. The system of claim 10, further comprising the expired, withdrawn and abandoned patents in one category and published, granted patents in another category.

12. The system of claim 11, further comprising comparing this category of patents against the provided technical area description and generate technical concepts that are not infringeable.

13. A user-interface for displaying at least one technical concept, comprising: receiving a description of a technical area; receiving a main category of the technical area; submitting the input provided; and displaying in the user interface at least one technical concept that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

14. The user interface of claim 13, further comprising receiving in the user interface, a feedback rating, based on at least one of the technical concepts; and, displaying in the user interface an updated set of technical concepts generated in response to the feedback.

15. A method of generating technical concept(s) comprising: receiving a description of the technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

16. The method of claim 15, further comprising processing the description of the technical area to extract technical concepts.

17. The method of claim 16, further comprising generating vector embeddings of the extracted technical concepts using a neural network-based architecture.

18. The method of claim 17, further comprising: filtering extracted technical concepts using a mapping algorithm that compares the vector embeddings against a database of publications, wherein the filtering process employs indexing and search algorithms; and identifying novel technical concepts based on the filtering process.

19. The method of any of claims 16 - 18, wherein the generation of the extracted technical concepts is based on using a generative artificial intelligence trained on publications including patent publications and scientific publications.

20. The method of any of claims 15-19, further comprising receiving in the user interface, feedback, based on at least one of the ideas; and, displaying in the user interface an updated set of technical concept(s) generated in response to the feedback.

21. The method of claim 20, wherein the feedback includes rating the technical concept(s) by an end user.

22. The method of claims 18, wherein the filtering includes a comparing of the technical concept(s) generated to published patents and non-patent literature.

23. The method of any of claims 15-22, further comprising determining a likelihood of infringement of the at least one generated technical concept(s) against pending and granted patents in force.

24. The method of any of claims 15-23, further comprising displaying patents that have expired, been withdrawn, been abandoned, and which are not legally in force.

25. A computer readable medium with instructions for evaluating a generated technical concept(s) against prior art references comprising: receive a description of a technical area in a user interface; and displaying in the user interface a at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of publications that include patent information.

26. The computer readable medium of claim 25 further comprising instructions for processing the description using a neural network architecture to generate vector representations of description.

27. The computer readable medium of claim 26, wherein the comparison comprises filtering the extracted technical concepts by implementing a mapping algorithm that compares the vector representations against vector representations of extracted technical concepts.

28. The computer readable medium of any of claims 25 - 27, wherein feedback is received by displaying, in the user interface, a graphical indicator to rate the technical concept based on its practicality and desirability of the technical concept.

29. The computer readable medium of any of claim 25-28, further comprising displaying in the user interface, using generative artificial intelligence, a most relevant figure and a summary of the prior art.

30. The computer readable medium of any of claims 25-29 further comprising determining a likelihood of infringement of the at least one generated technical concept(s) against the pending and granted patents in force.

31. The computer readable medium of any of claims 25-30, further comprising displaying patents that have expired, been withdrawn, been abandoned, and which are not legally in force.

32. A system for generating a plurality of distinct trademark(s), the system comprising: at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receiving a description of what type of trademark is desired to be generated in a user interface; and displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

33. The system of claim 32 further comprising processing the description using a neural network architecture to generate vector representations of the trademark desired.

34. The system of claim 33, wherein the comparison comprises filtering the generated trademark by implementing a mapping algorithm that compares the vector representations against vector representations of the existing trademarks identified.

35. The system of any of claims 32 - 34, wherein the generation of the trademarks is based on using a generative artificial intelligence trained on trademarks.

36. The system of any of claims 32-35, further comprising: receiving in the user interface, feedback, based on at least one of the trademarks; and displaying in the user interface an updated set of trademarks generated in response to the feedback.

37. The system of claim 36, wherein the feedback includes receiving a rating of the at least one trademark by a third party.

38. The system of any of claims 32-37, further comprising evaluating the to be generated trademark against the existing trademarks, such that the generated trademark is distinct in nature.

39. The system of claims 38, further comprising displaying in a user interface, a graphical indicator, to rate the generated trademark by the third party based on its practicality and likeliness.

40. The system of claim 34, wherein the filtering includes a comparing of the trademarks generated to existing active trademarks and usage of words in internet webpages.

41. The system of any of claims 32-40, further comprising receiving a selection of countries for comparing against trademarks and the rules of registering a trademark in that country.

42. A user-interface for displaying a plurality of distinct trademark(s), comprising: receiving a description of what kind of trademark is desired to be generated; receiving a description of goods or services that will be used with the trademark; receiving the type of trademark; receiving countries of interest; and displaying a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

43. The user interface of claim 42, further comprising receiving, in the user interface, feedback based on at least one of the ideas; and displaying in the user interface an updated set of trademarks generated in response to the feedback.

44. A method of generating a plurality of trademark(s) comprising: receiving a description of what type of trademark is desired in a user interface; and displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

45. The method of claim 44, further comprising processing the description using a neural network architecture to generate vector representations of the trademark desired.

46. The method of claim 45, further comprising filtering the generated trademark by implementing a mapping algorithm that compares the vector representations against vector representations of the existing trademarks identified.

47. The method of any of claim 44-46, wherein the generation of the trademarks is based on using a generative artificial intelligence trained on trademarks.

48. The method of any of claims 44-47, further comprising: receiving, in the user interface, feedback based on at least one of the ideas; and displaying, in the user interface, an updated set of trademarks generated in response to the feedback.

49. The method of claim 48, wherein the feedback includes rating the trademark by an end user.

50. The method of claim 46, wherein the filtering includes a comparing of the trademarks generated to existing active trademarks and usage of words in internet webpages.

51. The method of any of claims 44-50, further comprising receiving a selection of countries for comparing against trademarks and the rules of registering a trademark in that country.

52. A computer readable medium with instructions for evaluating a generated trademark against existing trademarks comprising: receiving a description of what kind of trademark is desired to be generated in a user interface; and displaying in the user interface a plurality of trademarks that are generated based on the description and that are filtered based on a comparison of existing trademarks.

53. The computer readable medium of claim 52, further comprising processing the description using a neural network architecture to generate vector representations of the trademark desired.

54. The computer readable medium of claim 53, wherein the comparison comprises filtering the generated trademark by implementing a mapping algorithm that compares the vector representations against vector representations of the existing trademarks identified.

55. The computer readable medium of any of claims 52-54, further comprising: receiving, in the user interface, feedback based on at least one of the trademarks; and displaying, in the user interface, an updated set of trademarks generated in response to the feedback.

56. The computer readable medium of claim 55, wherein the feedback includes rating the trademark by an end user.

57. The computer readable medium of any of claims 52-56, wherein the generation of the trademarks is based on using a generative artificial intelligence trained on trademarks.

58. The computer readable medium of any of claims 52-57, wherein the filtering includes a comparing of the trademarks generated to existing active trademarks and usage of words in internet webpages.

59. A system for generating at least one technical concept(s) that are not protected by a patent claim(s), the system comprising: at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the at least one processor to: receive a description of a technical area in a user interface; and display in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

60. The system of claim 59 wherein the processor is configured to process the description using a neural network architecture to generate vector representations of technical concepts.

61. The system of claim 60, wherein the comparison comprises filtering the generated technical concepts by implementing a mapping algorithm that compares the vector representations against the publications identified, wherein the mapping algorithm decomposes the publications into discrete technical concepts for comparison.

62. The system of any of claims 59-61, further comprising: receiving, in the user interface, a feedback rating based on at least one of the technical concepts; and, displaying, in the user interface, an updated set of technical concepts generated in response to the feedback.

63. The system of claim 62, wherein the feedback rating is received by displaying in a user interface a graphical indicator to rate the technical concept based on its practicality and desirability of the technical concept.

64. The system of any of claims 59-63, further comprising identifying patents in a database of patents based on the description.

65. The system of any of claims 59-64, wherein the comparison includes mapping the technical concepts that are generated against the active patent claims in the identified patents and filtering out technical concepts that are present in the active patent claims.

66. The system of any of claims 59-65, further comprising determining a likelihood of infringement of the technical concepts against active pending and granted patent claims.

67. The system of any of claims 59-66, further comprising receiving a selection of countries for identifying the active pending and granted patent claims and comparing against the technical concepts.

68. A user-interface for displaying at least one technical concept(s) that are not protected by a patent claim(s), comprising: receiving a description of a technical area; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

69. The user interface of claim 68, further comprising receiving, in the user interface, a feedback rating based on at least one of the technical concepts; and, displaying in the user interface an updated set of technical concepts generated in response to the feedback.

70. A method for generating a plurality of technical concepts that are not protected by a patent claim(s), comprising: receiving a description of a technical area in a user interface; and displaying in the user interface a plurality of technical concepts that are generated based on the description and that are filtered based on a comparison of active patent claims.

71. The method of claim 70, further comprising processing the description of the technical area to extract technical concepts.

72. The method of claim 71, further comprising generating vector embeddings of the extracted technical concepts using a neural network-based architecture.

73. The method of claim 72, further comprising: filtering extracted technical concepts using a mapping algorithm that compares the vector embeddings against a database of publications, wherein the filtering process employs indexing and search algorithms; and identifying novel technical concepts based on the filtering process.

74. The method of any of claims 70 - 73, further comprising identifying active patents in a database of patents based on the description.

75. The method of any of claims 70-74, wherein the comparison includes mapping the technical concepts that are generated against the active patent claims in the identified patents and filtering out technical concepts that are present in the active patent claims.

76. The method of any of claims 70-75, further comprising receiving, in the user interface, a feedback rating based on at least one of the technical concepts; and, displaying in the user interface an updated set of technical concepts generated in response to the feedback.

77. The method of claim 76, wherein the feedback rating is received by displaying in the user interface graphical indicator to rate the technical concept based on its practicality and desirability of the technical concept.

78. The method of any of claims 70-77, further comprising determining a likelihood of infringement of the technical concepts against active pending and granted patents.

79. The method of any of claims 70-78, further comprising receiving a selection of countries for identifying the active pending and granted patent claims and comparing against the technical concepts.

80. A computer readable medium with instructions for evaluating a generated technical concept that are not protected by a patent claim(s), comprising: receive a description of a technical area in a user interface; and displaying in the user interface at least one technical concept(s) that are generated based on the description and that are filtered based on a comparison of active patent claims.

81. The computer readable medium of claim 80 further comprising instructions for processing the description using a neural network architecture to generate vector representations of description.

82. The computer readable medium of claim 81, wherein the comparison comprises filtering the extracted technical concepts by implementing a mapping algorithm that compares the vector representations against vector representations of the active patent claims.

83. The computer readable medium of any of claims 80-82, further comprising identifying patents in a database of patents based on the description.

84. The computer readable medium of any of claim 80-83, wherein the comparison includes mapping the technical concepts that are generated against the active patent claims in the identified patents and filtering out technical concepts that are present in the active patent claims.

85. The computer readable medium of any of claims 80-84, further comprising receiving, in the user interface, a feedback rating based on at least one of the technical concepts; and, displaying in the user interface an updated set of technical concepts generated in response to the feedback.

86. The computer readable medium of claim 85, wherein the feedback rating is received by displaying in a user interface a graphical indicator to rate the technical concept based on its practicality and desirability of the technical concept.

87. The computer readable medium of any of claims 80-86, further comprising further comprising determining a likelihood of infringement of the technical concepts against active pending and granted patents.

88. The computer readable medium of any of claims 80-87, further comprising receiving a selection of countries for identifying the active pending and granted patents and comparing against the technical concepts.

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